Author: John Muschelli and Clarke Iakovakis
Date: 2025-07-30

# Gathering Multiple Subject Queries with Large Numbers of Authors

#### John Muschelli and Clarke Iakovakis

#### 2025-07-30

In `rscopus` we try to use the Scopus API to present queries about authors and affiliations. Here we will use an example from Clarke Iakovakis.

## Setup

First, let’s load in the packages we’ll need.

    library("rscopus")
    library("dplyr")
    library("tidyr")

Next, we need to see if we have an API key available. See the API key vignette for more information and how to set the keys up. We will use the `have_api_key()` functionality.

    have_api_key()
    #> [1] TRUE

We also need to make sure you are authorized to search Scopus with `is_elsevier_authorized`. If you have an API key, but `is_elsevier_authorized()` is `FALSE`, then it’s likely due to the fact that you’re not on the necessary IP that the key needs:

    authorized = is_elsevier_authorized()
    #> HTTP specified is: https://api.elsevier.com/authenticate

## Creating a query

Here we will create a query of a specific affiliation, subject area, publication year, and type of access (OA = open access). Let’s look at the different types of subject areas:

    rscopus::subject_areas()
    #>  [1] "AGRI" "ARTS" "BIOC" "BUSI" "CENG" "CHEM" "COMP" "DECI" "DENT" "EART"
    #> [11] "ECON" "ENER" "ENGI" "ENVI" "HEAL" "IMMU" "MATE" "MATH" "MEDI" "NEUR"
    #> [21] "NURS" "PHAR" "PHYS" "PSYC" "SOCI" "VETE" "MULT"

These categories are helpful because to search all the documents it’d be too big of a call. We may also get rate limited. We can search each separately, store the information, save them, merge them, and then run our results.

The author of this example was analyzing data from OSU (Oklahoma State University), and uses the affiliation ID from that institution (`60006514`). If you know the institution name, but not the ID, you can use `process_affiliation_name` to retrieve it. Here we make the queries for each subject area:

    # create the query
    Query <- paste0("AF-ID(60006514) AND SUBJAREA(",
                    subject_areas(),
                    ") AND PUBYEAR = 2018 AND ACCESSTYPE(OA)")

## Running the query

Let’s pull the first subject area information. Note, the count may depend on your API key limits. We also are asking for a complete view, rather than the standard view. The `max_count` is set to \(20000\), so this may not be enough for your query and you need to adjust.

    if (authorized) {
      make_query = function(subj_area) {
        paste0("AF-ID(60006514) AND SUBJAREA(",
               subj_area,
               ") AND PUBYEAR = 2018 AND ACCESSTYPE(OA)")
      }
      i = 3
      subj_area = subject_areas()[i]
      print(subj_area)
      completeArticle <- scopus_search(
        query = make_query(subj_area),
        view = "COMPLETE",
        count = 200)
      print(names(completeArticle))
      total_results = completeArticle$total_results
      total_results = as.numeric(total_results)
    } else {
      total_results = 0
    }
    #> [1] "BIOC"
    #> Warning in scopus_search(query = make_query(subj_area), view = "COMPLETE", :
    #> STANDARD view can have a max count of 200 and COMPLETE 25
    #> The query list is:
    #> list(query = "AF-ID(60006514) AND SUBJAREA(BIOC) AND PUBYEAR = 2018 AND ACCESSTYPE(OA)",
    #>     count = 25, start = 0, view = "COMPLETE")
    #> $query
    #> [1] "AF-ID(60006514) AND SUBJAREA(BIOC) AND PUBYEAR = 2018 AND ACCESSTYPE(OA)"
    #>
    #> $count
    #> [1] 25
    #>
    #> $start
    #> [1] 0
    #>
    #> $view
    #> [1] "COMPLETE"
    #>
    #> Response [https://api.elsevier.com/content/search/scopus?query=AF-ID%2860006514%29%20AND%20SUBJAREA%28BIOC%29%20AND%20PUBYEAR%20%3D%202018%20AND%20ACCESSTYPE%28OA%29&count=25&start=0&view=COMPLETE]
    #>   Date: 2025-07-30 23:00
    #>   Status: 200
    #>   Content-Type: application/json;charset=UTF-8
    #>   Size: 161 kB
    #> Total Entries are 91
    #> 4 runs need to be sent with current count
    #>   |                                                                              |                                                                      |   0%  |                                                                              |===================================                                   |  50%  |                                                                              |======================================================================| 100%
    #> Number of Output Entries are 91
    #> [1] "entries"        "total_results"  "get_statements"

Here we see the total results of the query. This can be useful if the `total_results = 0` or they are greater than the max count specified (not all records in Scopus are returned).

## Getting the content

The `gen_entries_to_df` function is an attempt at turning the parsed JSON to something more manageable from the API output. You may want to go over the list elements `get_statements` in the output of `completeArticle`. The original content can be extracted using `httr::content()` and the `"type"` can be specified, such as `"text"` and then `jsonlite::toJSON` can be used explicitly on the JSON output. Alternatively, any arguments to `jsonlite::toJSON` can be passed directly into `httr::content()`, such as `flatten` or `simplifyDataFrame`.

These are all alternative options, but we will use `rscopous::gen_entries_to_df`. The output is a list of `data.frame`s after we pass in the `entries` elements from the list.

    if (authorized) {

      # areas = subject_areas()[12:13]
      areas = c("ENER", "ENGI")
      names(areas) = areas
      results = purrr::map(
        areas,
        function(subj_area) {
          print(subj_area)
          completeArticle <- scopus_search(
            query = make_query(subj_area),
            view = "COMPLETE",
            count = 200,
            verbose = FALSE)
          return(completeArticle)
        })
      entries = purrr::map(results, function(x) {
        x$entries
      })
      total_results = purrr::map_dbl(results, function(x) {
        as.numeric(x$total_results)
      })
      total_results = sum(total_results, na.rm = TRUE)

      df = purrr::map(entries, gen_entries_to_df)
      MainEntry = purrr::map_df(df, function(x) {
        x$df
      }, .id = "subj_area")

      ddf = MainEntry %>%
                   filter(as.numeric(`author-count.$`) > 99)
      if ("message" %in% colnames(ddf)) {
        ddf =  ddf %>%
                   select(message, `author-count.$`)
        print(head(ddf))
      }

      MainEntry = MainEntry %>%
        mutate(
          scopus_id = sub("SCOPUS_ID:", "", `dc:identifier`),
          entry_number = as.numeric(entry_number),
          doi = `prism:doi`)
      #################################
      # remove duplicated entries
      #################################
      MainEntry = MainEntry %>%
        filter(!duplicated(scopus_id))

      Authors = purrr::map_df(df, function(x) {
        x$author
      }, .id = "subj_area")
      Authors$`afid.@_fa` = NULL

      Affiliation = purrr::map_df(df, function(x) {
        x$affiliation
      }, .id = "subj_area")
      Affiliation$`@_fa` = NULL

      # keep only these non-duplicated records
      MainEntry_id = MainEntry %>%
        select(entry_number, subj_area)
      Authors = Authors %>%
        mutate(entry_number = as.numeric(entry_number))

      Affiliation = Affiliation %>%
        mutate(entry_number = as.numeric(entry_number))

      Authors = left_join(MainEntry_id, Authors)
      Affiliation = left_join(MainEntry_id, Affiliation)

      # first filter to get only OSU authors
      osuauth <- Authors %>%
        filter(`afid.$` == "60006514")

    }
    #> [1] "ENER"
    #> Warning in scopus_search(query = make_query(subj_area), view = "COMPLETE", :
    #> STANDARD view can have a max count of 200 and COMPLETE 25
    #> [1] "ENGI"
    #> Warning in scopus_search(query = make_query(subj_area), view = "COMPLETE", :
    #> STANDARD view can have a max count of 200 and COMPLETE 25
    #> Joining with `by = join_by(entry_number, subj_area)`
    #> Joining with `by = join_by(entry_number, subj_area)`

At the end of the day, we have the author-level information for each paper. The `entry_number` will join these `data.frame`s if necessary. The `df` element has the paper-level information in this example, the `author` `data.frame` has author information, including affiliations. There can be multiple affiliations, even within institution, such as multiple department affiliations within an institution affiliation. The `affiliation` information relates to the affiliations and can be merged with the author information.

## Funding agencies

Here we look at the funding agencies listed on all the papers. This can show us if there is a pattern in the funding sponsor and the open-access publications. Overall, though, we would like to see the funding of all the papers if a specific funder requires open access. This checking allows libraries and researchers ensure they are following the guidelines of the funding agency.

    if (total_results > 0) {
      cn = colnames(MainEntry)
      cn[grep("fund", tolower(cn))]

      tail(sort(table(MainEntry$`fund-sponsor`)))

      funderPoland <- filter(
        MainEntry,
        `fund-sponsor` == "Ministerstwo Nauki i Szkolnictwa Wyższego" )
      dim(funderPoland)

      osuFunders <- MainEntry %>%
        group_by(`fund-sponsor`) %>%
        tally() %>%
        arrange(desc(n))
      osuFunders
    }
    #> # A tibble: 22 × 2
    #>    `fund-sponsor`                                    n
    #>    <chr>                                         <int>
    #>  1 <NA>                                             20
    #>  2 National Science Foundation                      19
    #>  3 Agence Nationale de la Recherche                  7
    #>  4 U.S. Department of Agriculture                    4
    #>  5 National Natural Science Foundation of China      3
    #>  6 Natural Science Foundation of Fujian Province     2
    #>  7 Oklahoma State University                         2
    #>  8 U.S. Department of Energy                         2
    #>  9 University of Oklahoma Health Sciences Center     2
    #> 10 American Association of Petroleum Geologists      1
    #> # ℹ 12 more rows

## Number of author limits

In the Scopus API, if there are \(> 100\) authors on a paper, it only will retrieve the first 100 authors. For those cases, we must use the `abstract_retrieval` to get all the author-level information. Here we make this information into an integer so that we can filter the rows we need to run based on author count.

    if (total_results > 0) {

      # if there are 100+ authors, you have to use the abstract_retrieval function to get the full author data
      # coerce to integer first
      MainEntry <- MainEntry %>%
        mutate(`author-count.$` = as.integer(`author-count.$`))
      run_multi = any(MainEntry$`author-count.$` > 99)
      print(run_multi)
    }
    #> [1] TRUE

In this case, we see there are some articles with author counts \(> 99\) and we must get all author information for those.

## More than 100 authors

Now, the `abstract_retrieval` function can take a number of identifiers for papers, such as DOI, PubMed ID, and Scopus ID. Here we will use the Scopus ID, as it is given for all results, but we could also use DOI.

    if (total_results > 0) {
      if (run_multi) {

        MainEntry_99auth <- MainEntry %>%
          filter(`author-count.$` > 99)

        MainEntry_99auth_id = MainEntry_99auth %>%
          select(entry_number, subj_area)
        auth99 = left_join(MainEntry_99auth_id, Authors)
        affil99 = left_join(MainEntry_99auth_id, Affiliation)

        missing_table = MainEntry %>%
          ungroup() %>%
          mutate_at( vars(scopus_id, doi), .funs = is.na) %>%
          summarize_at(.vars = vars(scopus_id, doi), .funs = sum)
        print(missing_table)
      }
    }
    #> Joining with `by = join_by(entry_number, subj_area)`
    #> Joining with `by = join_by(entry_number, subj_area)`
    #>   scopus_id doi
    #> 1         0   0

## Get all the authors

Here we will go through each Scopus ID and get the article information. We will create an affiliation `data.frame` and an author `data.frame`. The non-relevant columns will be deleted, such as `entry_number` since it refers to a different set of elements from a list now. The column names will be harmonized with the `Authors` and `Affiliation` data sets. The respective data is removed from the `Authors` and `Affiliation` data set and joined with the new data with the richer information.

    if (total_results > 0) {
      # ids = MainEntry_99auth$scopus_id[1:3]
      ids = MainEntry_99auth$scopus_id
      names(ids) = ids
      big_list = purrr::map(
        ids,
        abstract_retrieval,
        identifier = "scopus_id",
        verbose = FALSE)

      all_affil_df = purrr::map_df(
        big_list, function(x) {
          d = gen_entries_to_df(
            x$content$`abstracts-retrieval-response`$affiliation)
          d$df
        }, .id = "scopus_id")

      all_df = purrr::map_df(
        big_list, function(x) {
          d = gen_entries_to_df(
            x$content$`abstracts-retrieval-response`$authors$author)
          d$df
        }, .id = "scopus_id")

      ##########################
      # Remove prefix ce: for harmonization
      ##########################
      no_ce = function(x) {
        sub("^ce:", "", x)
      }
      all_df = all_df %>%
        rename_all(.funs = no_ce) %>%
        rename(authid = "@auid",
               `afid.$` = `affiliation.@id`,
               authname = "indexed-name")

      all_df$entry_number = NULL
      all_affil_df$entry_number = NULL

      author_table = all_df %>%
        group_by(scopus_id) %>%
        distinct(authid) %>%
        tally()
      head(author_table)
      stopifnot(all(ids %in% author_table$scopus_id))
      # harmonizing with MainEntry
      author_table = author_table %>%
        rename(`author-count.$` = n)
      MainEntry_99auth$`author-count.$` = NULL
      MainEntry_99auth = left_join(MainEntry_99auth, author_table)

      #######################
      # Harmonized
      #######################
      all_df = MainEntry_99auth %>%
        select(entry_number, subj_area, scopus_id) %>%
        left_join(all_df)
      print(setdiff(colnames(Authors), colnames(all_df)))

      # grab only relevant columns
      all_df = all_df[, intersect(colnames(Authors), colnames(all_df))]

      # remove the old entries
      Authors = anti_join(Authors, MainEntry_99auth_id)
      # put the new data in
      Authors = full_join(Authors, all_df)

      #######################
      # Harmonized
      #######################
      all_affil_df = all_affil_df %>%
        rename(`affiliation-url` = "@href",
               afid = "@id")
      all_affil_df = MainEntry_99auth %>%
        select(entry_number, subj_area, scopus_id) %>%
        left_join(all_affil_df)
      setdiff(colnames(Affiliation), colnames(all_affil_df))

      # remove the old entries
      Affiliation = anti_join(Affiliation, MainEntry_99auth_id)
      # put the new data in
      Affiliation = full_join(Affiliation, all_affil_df)

      MainEntry = anti_join(MainEntry, MainEntry_99auth_id)
      MainEntry = full_join(MainEntry, MainEntry_99auth)
      MainEntry
    }
    #> Joining with `by = join_by(scopus_id)`
    #> Joining with `by = join_by(scopus_id)`
    #> [1] "orcid"
    #> Joining with `by = join_by(entry_number, subj_area)`
    #> Joining with `by = join_by(entry_number, subj_area, `@_fa`, `@seq`,
    #> `author-url`, authid, authname, surname, `given-name`, initials, `afid.$`)`
    #> Joining with `by = join_by(scopus_id)`
    #> Joining with `by = join_by(entry_number, subj_area)`
    #> Joining with `by = join_by(entry_number, subj_area, `affiliation-url`, afid,
    #> affilname, `affiliation-city`, `affiliation-country`)`
    #> Joining with `by = join_by(subj_area, entry_number)`
    #> Joining with `by = join_by(subj_area, `@_fa`, `prism:url`, `dc:identifier`,
    #> eid, `dc:title`, `dc:creator`, `prism:publicationName`, `prism:issn`,
    #> `prism:volume`, `prism:pageRange`, `prism:coverDate`, `prism:coverDisplayDate`,
    #> `prism:doi`, pii, `dc:description`, `citedby-count`, `prism:aggregationType`,
    #> subtype, subtypeDescription, `author-count.@limit`, `author-count.$`,
    #> authkeywords, `source-id`, `fund-acr`, `fund-sponsor`, openaccess,
    #> openaccessFlag, `freetoread.value.$`, `freetoreadLabel.value.$`, entry_number,
    #> `fund-no`, `prism:eIssn`, `prism:issueIdentifier`, `article-number`,
    #> `pubmed-id`, scopus_id, doi)`
    #>    subj_area @_fa
    #> 1       ENER true
    #> 2       ENER true
    #> 3       ENER true
    #> 4       ENER true
    #> 5       ENER true
    #> 6       ENER true
    #> 7       ENER true
    #> 8       ENER true
    #> 9       ENER true
    #> 10      ENER true
    #> 11      ENER true
    #> 12      ENER true
    #> 13      ENGI true
    #> 14      ENGI true
    #> 15      ENGI true
    #> 16      ENGI true
    #> 17      ENGI true
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    #> 72      ENGI true
    #> 73      ENGI true
    #> 74      ENGI true
    #>                                                          prism:url
    #> 1  https://api.elsevier.com/content/abstract/scopus_id/85054821560
    #> 2  https://api.elsevier.com/content/abstract/scopus_id/85056753255
    #> 3  https://api.elsevier.com/content/abstract/scopus_id/85060137733
    #> 4  https://api.elsevier.com/content/abstract/scopus_id/85054934481
    #> 5  https://api.elsevier.com/content/abstract/scopus_id/85048160156
    #> 6  https://api.elsevier.com/content/abstract/scopus_id/85040701043
    #> 7  https://api.elsevier.com/content/abstract/scopus_id/85053793633
    #> 8  https://api.elsevier.com/content/abstract/scopus_id/85049632118
    #> 9  https://api.elsevier.com/content/abstract/scopus_id/85059745853
    #> 10 https://api.elsevier.com/content/abstract/scopus_id/85046139712
    #> 11 https://api.elsevier.com/content/abstract/scopus_id/85041520105
    #> 12 https://api.elsevier.com/content/abstract/scopus_id/85053780575
    #> 13 https://api.elsevier.com/content/abstract/scopus_id/85063715171
    #> 14 https://api.elsevier.com/content/abstract/scopus_id/85063718545
    #> 15 https://api.elsevier.com/content/abstract/scopus_id/85057052295
    #> 16 https://api.elsevier.com/content/abstract/scopus_id/85051825814
    #> 17 https://api.elsevier.com/content/abstract/scopus_id/85056089746
    #> 18 https://api.elsevier.com/content/abstract/scopus_id/85050665801
    #> 19 https://api.elsevier.com/content/abstract/scopus_id/85056287867
    #> 20 https://api.elsevier.com/content/abstract/scopus_id/85050823996
    #> 21 https://api.elsevier.com/content/abstract/scopus_id/85057319364
    #> 22 https://api.elsevier.com/content/abstract/scopus_id/85055096320
    #> 23 https://api.elsevier.com/content/abstract/scopus_id/85049698280
    #> 24 https://api.elsevier.com/content/abstract/scopus_id/85050523660
    #> 25 https://api.elsevier.com/content/abstract/scopus_id/85056375754
    #> 26 https://api.elsevier.com/content/abstract/scopus_id/85052576568
    #> 27 https://api.elsevier.com/content/abstract/scopus_id/85047410314
    #> 28 https://api.elsevier.com/content/abstract/scopus_id/85063716967
    #> 29 https://api.elsevier.com/content/abstract/scopus_id/85063714351
    #> 30 https://api.elsevier.com/content/abstract/scopus_id/85052139961
    #> 31 https://api.elsevier.com/content/abstract/scopus_id/85044841208
    #> 32 https://api.elsevier.com/content/abstract/scopus_id/85042200128
    #> 33 https://api.elsevier.com/content/abstract/scopus_id/85052674663
    #> 34 https://api.elsevier.com/content/abstract/scopus_id/85046114031
    #> 35 https://api.elsevier.com/content/abstract/scopus_id/85046730287
    #> 36 https://api.elsevier.com/content/abstract/scopus_id/85045062626
    #> 37 https://api.elsevier.com/content/abstract/scopus_id/85028416529
    #> 38 https://api.elsevier.com/content/abstract/scopus_id/85049784220
    #> 39 https://api.elsevier.com/content/abstract/scopus_id/85044925112
    #> 40 https://api.elsevier.com/content/abstract/scopus_id/85063715868
    #> 41 https://api.elsevier.com/content/abstract/scopus_id/85047822635
    #> 42 https://api.elsevier.com/content/abstract/scopus_id/85045322623
    #> 43 https://api.elsevier.com/content/abstract/scopus_id/85047253959
    #> 44 https://api.elsevier.com/content/abstract/scopus_id/85047078107
    #> 45 https://api.elsevier.com/content/abstract/scopus_id/85042416266
    #> 46 https://api.elsevier.com/content/abstract/scopus_id/85047460209
    #> 47 https://api.elsevier.com/content/abstract/scopus_id/85038222030
    #> 48 https://api.elsevier.com/content/abstract/scopus_id/85042903915
    #> 49 https://api.elsevier.com/content/abstract/scopus_id/85061673268
    #> 50 https://api.elsevier.com/content/abstract/scopus_id/85030104528
    #> 51 https://api.elsevier.com/content/abstract/scopus_id/85042599129
    #> 52 https://api.elsevier.com/content/abstract/scopus_id/85042182555
    #> 53 https://api.elsevier.com/content/abstract/scopus_id/85041049557
    #> 54 https://api.elsevier.com/content/abstract/scopus_id/85067228101
    #> 55 https://api.elsevier.com/content/abstract/scopus_id/85116316808
    #> 56 https://api.elsevier.com/content/abstract/scopus_id/85015208658
    #> 57 https://api.elsevier.com/content/abstract/scopus_id/85052436742
    #> 58 https://api.elsevier.com/content/abstract/scopus_id/85045133536
    #> 59 https://api.elsevier.com/content/abstract/scopus_id/85041232528
    #> 60 https://api.elsevier.com/content/abstract/scopus_id/85053216367
    #> 61 https://api.elsevier.com/content/abstract/scopus_id/85049648832
    #> 62 https://api.elsevier.com/content/abstract/scopus_id/85046696880
    #> 63 https://api.elsevier.com/content/abstract/scopus_id/85042279762
    #> 64 https://api.elsevier.com/content/abstract/scopus_id/85062247946
    #> 65 https://api.elsevier.com/content/abstract/scopus_id/85035786507
    #> 66 https://api.elsevier.com/content/abstract/scopus_id/85058860285
    #> 67 https://api.elsevier.com/content/abstract/scopus_id/85057881096
    #> 68 https://api.elsevier.com/content/abstract/scopus_id/85058844591
    #> 69 https://api.elsevier.com/content/abstract/scopus_id/85056083831
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    #>                                                                                                                                                                                    dc:title
    #> 1                                                           Determinants of public golf course visitation and willingness to pay for turfgrass enhancement: A case study from Oklahoma, USA
    #> 2                                                                                    Analysis of the stress field in the DeSoto canyon Salt Basin for ensuring safe offshore carbon storage
    #> 3                                                                        Prospects for the sustainability of social-ecological systems (SES) on the Mongolian plateau: Five critical issues
    #> 4                                                                                               Dryland belt of Northern Eurasia: Contemporary environmental changes and their consequences
    #> 5                                                                                                Biochar enhanced ethanol and butanol production by Clostridium carboxidivorans from syngas
    #> 6                        Biomass production of herbaceous energy crops in the United States: field trial results and yield potential maps from the multiyear regional feedstock partnership
    #> 7                                                                                                   Geomorphic identification of physical habitat features in a large, altered river system
    #> 8                   Impact of drought on chemical composition and sugar yields from dilute-acid pretreatment and enzymatic hydrolysis of Miscanthus, a tall fescue mixture, and switchgrass
    #> 9                                                                                 Study of bubble size, void fraction, and mass transport in a bubble column under high amplitude vibration
    #> 10                                                                         Understanding Infrastructure Resiliency in Chennai, India Using Twitter's Geotags and Texts: A Preliminary Study
    #> 11                                                                               Comparing social media data and survey data in assessing the attractiveness of Beijing Olympic Forest Park
    #> 12                                                                          Climate-informed environmental inflows to revive a drying lake facing meteorological and anthropogenic droughts
    #> 13                                                                                                                          Extreme learning machines as encoders for sparse reconstruction
    #> 14                                                                                                   A hybrid approach for model order reduction of barotropic quasi-geostrophic turbulence
    #> 15                                                                                                                         Real-time detection of distracted driving based on deep learning
    #> 16                                                                            Effects of capsule on surface diffuse reflectance spectroscopy of the subcapsular parenchyma of a solid organ
    #> 17                                                                                   Equipment power consumption and load factor profiles for buildings’ energy simulation (ASHRAE 1742-RP)
    #> 18                                                                 Near–fiber effects of UV irradiation on the fiber–matrix interphase: A combined experimental and numerical investigation
    #> 19                                                                              Performance assessment of five different soil moisture sensors under irrigated field conditions in Oklahoma
    #> 20                                                                                                       Stress induced dissolution and time-dependent deformation of portland cement paste
    #> 21                                                                                                    A Cloud-Based Secure and Privacy-Preserving Clustering Analysis of Infectious Disease
    #> 22                                       Epifaunal foraminifera in an infaunal world: Insights into the influence of heterogeneity on the benthic ecology of oxygen-poor, deep-sea habitats
    #> 23                                                                                                                  A Human-Robot Collaborative System for Robust Three-Dimensional Mapping
    #> 24                                                                                           Investigations on surface microstructure in high-speed milling of Zr-based bulk metallic glass
    #> 25                                                                                                            Locomotion of a cylindrical rolling robot with a shape changing outer surface
    #> 26                                                                                                                                        Low-rate characterization of a mechanical inerter
    #> 27                                                                                                                            Influence of cobalt doping on residual stress in ZnO nanorods
    #> 28                                                          A hybrid analytics paradigm combining physics-based modeling and data-driven modeling to accelerate incompressible flow solvers
    #> 29                                                                     Flow structure and force generation on flapping wings at low Reynolds numbers relevant to the flight of tiny insects
    #> 30                                                                                                    Automatic groove measurement and evaluation with high resolution laser profiling data
    #> 31                                                                                                              Design and Validation of a Recirculating, High-Reynolds Number Water Tunnel
    #> 32                                                                                         Joint spectrum sensing and resource allocation in multi-band-multi-user cognitive radio networks
    #> 33                                                                                                  Perspective review on solid-organ transplant: Needs in point-of-care optical biomarkers
    #> 34                                                                                                                     Kinetics of isochronal crystallization in a Fe-based amorphous alloy
    #> 35          In vivo percutaneous reflectance spectroscopy of fatty liver development in rats suggests that the elevation of the scattering power is an early indicator of hepatic steatosis
    #> 36                                                                                                             Micromechanical models for the stiffness and strength of UHMWPE macrofibrils
    #> 37                                                                                                             Cardiorespiratory Model-Based Data-Driven Approach for Sleep Apnea Detection
    #> 38                                                                          Thinking about the Contradictions of Space Use of Square Dance in Chinese Cold Cities through Newspaper Reports
    #> 39                                         Predicting the performance of radiant technologies in attics: Reducing the discrepancies between attic specific and whole-building energy models
    #> 40                                                                                    Leaky flow through simplified physical models of bristled wings of tiny insects during clap and fling
    #> 41                                                                                                 Anisotropic Plasmonic Response of Black Phosphorus Nanostrips in Terahertz Metamaterials
    #> 42                                                                                Remote sensing and GIS techniques for assessing irrigation performance: Case study in southern California
    #> 43                                                                                                                    Lane marking detection and reconstruction with line-scan imaging data
    #> 44                                                                         Convolutional neural network-based embarrassing situation detection under camera for social robot in smart homes
    #> 45                                                                                                                   Special issue on ‘1st International Conference on Rail Transportation’
    #> 46                                                                                     Using statistical and machine learning methods to evaluate the prognostic accuracy of SIRS and qSOFA
    #> 47                                                                           Pressure controlled micro-viscous deformation assisted spark plasma sintering of Fe-based bulk amorphous alloy
    #> 48                                                                                                           Fly ash particle characterization for predicting concrete compressive strength
    #> 49                                                                                                                     Characterization of bubble size distributions within a bubble column
    #> 50                                                                                                                       Model-Based Reinforcement Learning in Differential Graphical Games
    #> 51                                                                                                                                     RiSH: A robot-integrated smart home for elderly care
    #> 52                                                                                                               Pulsed terahertz imaging of breast cancer in freshly excised murine tumors
    #> 53                                                                      Passive infrared (PIR)-based indoor position tracking for smart homes using accessibility maps and a-star algorithm
    #> 54                                                                                                                     Introduction to the inaugural issue of Journal of Business Analytics
    #> 55                                                                             Exploring Commercial Counter-UAS Operations: A Case Study of the 2017 Dominican Republic Festival Presidente
    #> 56                                                                                                                     Wavelet based macrotexture analysis for pavement friction prediction
    #> 57                                                                                                                                     Cooperative Aerial Load Transport with Force Control
    #> 58                                                                                            Impacts of sample size on calculation of pavement texture indicators with 1mm 3D surface data
    #> 59                                          Flexible nine-channel photodetector probe facilitated intraspinal multisite transcutaneous photobiomodulation therapy dosimetry in cadaver dogs
    #> 60                                                                                                                           Development of a Ground-Based Peanut Canopy Phenotyping System
    #> 61                                                                                                    An in silico investigation of a lobe-specific targeted pulmonary drug delivery method
    #> 62                                                                                                Structure Preserving Finite Differences in Polar Coordinates for Heat and Wave Equations.
    #> 63                                                                                                                                New signals for vector-like down-type quark in U(1) of E6
    #> 64                           The synthesis of nano-sized zsm-5 zeolite by dry gel conversion method and investigating the effects of experimental parameters by taguchi experimental design
    #> 65                                                                                                      Automated assembly skill acquisition and implementation through human demonstration
    #> 66                                                      Search for Higgs boson pair production in the γγWW∗ channel using pp collision data recorded at √s = 13 TeV with the ATLAS detector
    #> 67                                                                                                                         Operation and performance of the ATLAS Tile Calorimeter in Run 1
    #> 68                                                        Measurement of the azimuthal anisotropy of charged particles produced in √sNN = 5.02 TeV Pb+Pb collisions with the ATLAS detector
    #> 69 Erratum to: Measurement of the W-boson mass in pp collisions at √s=7 TeV with the ATLAS detector (The European Physical Journal C, (2018), 78, 2, (110), 10.1140/epjc/s10052-017-5475-4)
    #> 70                                                      Prompt and non-prompt J/ ψ and ψ(2 S) suppression at high transverse momentum in 5.02TeV Pb+Pb collisions with the ATLAS experiment
    #> 71                                                           Measurement of differential cross-sections of a single top quark produced in association with a W boson at √s=13TeV with ATLAS
    #> 72                                                                                                      Measurement of the W-boson mass in pp collisions at √s=7TeV with the ATLAS detector
    #> 73                                         Search for the direct production of charginos and neutralinos in final states with tau leptons in √s=13TeV pp collisions with the ATLAS detector
    #> 74                                           Search for electroweak production of supersymmetric particles in final states with two or three leptons at √s = 13 TeV with the ATLAS detector
    #>                dc:creator
    #> 1                Joshi O.
    #> 2                 Meng J.
    #> 3                 Chen J.
    #> 4             Groisman P.
    #> 5                  Sun X.
    #> 6                Lee D.K.
    #> 7            Guertault L.
    #> 8               Hoover A.
    #> 9         Mohagheghian S.
    #> 10             Chong W.K.
    #> 11                Wang Z.
    #> 12             Alborzi A.
    #> 13 Abdullah Al Mamun S.M.
    #> 14    Mashfiqur Rahman S.
    #> 15                Tran D.
    #> 16                Piao D.
    #> 17          Omer Sarfraz
    #> 18              Babu L.K.
    #> 19               Datta S.
    #> 20            Moradian M.
    #> 21                 Liu J.
    #> 22        Venturelli R.A.
    #> 23                  Du J.
    #> 24            Maroju N.K.
    #> 25           Puopolo M.G.
    #> 26        Madhamshetty K.
    #> 27              Kaphle A.
    #> 28    Mashfiqur Rahman S.
    #> 29    Santhanakrishnan A.
    #> 30                  Li L.
    #> 31            Elbing B.R.
    #> 32                Wang X.
    #> 33                Piao D.
    #> 34                Paul T.
    #> 35                Piao D.
    #> 36                Dong H.
    #> 37               Gutta S.
    #> 38            Xiaobing L.
    #> 39         Fontanini A.D.
    #> 40            Kasoju V.T.
    #> 41                  Fo Q.
    #> 42          Taghvaeian S.
    #> 43                  Li L.
    #> 44                Yang G.
    #> 45                Zhai W.
    #> 46               Gupta A.
    #> 47                Paul T.
    #> 48                 Kim T.
    #> 49        Mohagheghian S.
    #> 50        Kamalapurkar R.
    #> 51                Do H.M.
    #> 52              Bowman T.
    #> 53                Yang D.
    #> 54                 Ram S.
    #> 55           Wallace R.J.
    #> 56                Yang G.
    #> 57               Thapa S.
    #> 58                  Li L.
    #> 59                Piao D.
    #> 60                Yuan H.
    #> 61                Feng Y.
    #> 62           Trenchant V.
    #> 63                 Das K.
    #> 64      Mohammadparast F.
    #> 65                  Gu Y.
    #> 66              Aaboud M.
    #> 67              Aaboud M.
    #> 68              Aaboud M.
    #> 69              Aaboud M.
    #> 70              Aaboud M.
    #> 71              Aaboud M.
    #> 72              Aaboud M.
    #> 73              Aaboud M.
    #> 74              Aaboud M.
    #>                                                                        prism:publicationName
    #> 1                                                              Journal of Cleaner Production
    #> 2                                            International Journal of Greenhouse Gas Control
    #> 3                                                             Environmental Research Letters
    #> 4                                                             Environmental Research Letters
    #> 5                                                                     Bioresource Technology
    #> 6                                                                              Gcb Bioenergy
    #> 7                                                                     E3s Web of Conferences
    #> 8                                                               Frontiers in Energy Research
    #> 9                                                                            Chemengineering
    #> 10                                                                               Engineering
    #> 11                                                                Sustainability Switzerland
    #> 12                                                            Environmental Research Letters
    #> 13                                                                                    Fluids
    #> 14                                                                                    Fluids
    #> 15                                                         Iet Intelligent Transport Systems
    #> 16                                                              Journal of Biomedical Optics
    #> 17                                          Science and Technology for the Built Environment
    #> 18                                                                      Materials and Design
    #> 19                                                                       Sensors Switzerland
    #> 20                                                                      Materials and Design
    #> 21                   Proceedings 2018 2nd IEEE Symposium on Privacy Aware Computing Pac 2018
    #> 22                                                               Frontiers in Marine Science
    #> 23                                                    IEEE ASME Transactions on Mechatronics
    #> 24                                                        Journal of Manufacturing Processes
    #> 25                                                                                  Robotics
    #> 26                                                                                  Machines
    #> 27                                             Materials Science in Semiconductor Processing
    #> 28                                                                                    Fluids
    #> 29                                                                                    Fluids
    #> 30                                                                       Sensors Switzerland
    #> 31                                    Journal of Fluids Engineering Transactions of the ASME
    #> 32                                                       IEEE Transactions on Communications
    #> 33                                                              Journal of Biomedical Optics
    #> 34                                                           Journal of Alloys and Compounds
    #> 35                                             Journal of Innovative Optical Health Sciences
    #> 36                                            Journal of the Mechanics and Physics of Solids
    #> 37                                         IEEE Journal of Biomedical and Health Informatics
    #> 38                                   Iop Conference Series Materials Science and Engineering
    #> 39                                                                      Energy and Buildings
    #> 40                                                                                    Fluids
    #> 41                                                                    IEEE Photonics Journal
    #> 42                                            Journal of Irrigation and Drainage Engineering
    #> 43                                                                       Sensors Switzerland
    #> 44                                                                       Sensors Switzerland
    #> 45                                              International Journal of Rail Transportation
    #> 46                                                           Healthcare Informatics Research
    #> 47                                                           Journal of Alloys and Compounds
    #> 48                                                       Construction and Building Materials
    #> 49                                                                                    Fluids
    #> 50                                           IEEE Transactions on Control of Network Systems
    #> 51                                                           Robotics and Autonomous Systems
    #> 52                                                              Journal of Biomedical Optics
    #> 53                                                                       Sensors Switzerland
    #> 54                                                             Journal of Business Analytics
    #> 55                               International Journal of Aviation Aeronautics and Aerospace
    #> 56                                                         Ksce Journal of Civil Engineering
    #> 57                                                                                      <NA>
    #> 58                                         Periodica Polytechnica Transportation Engineering
    #> 59                                                              Journal of Biomedical Optics
    #> 60                                                                                      <NA>
    #> 61 Frontiers in Biomedical Devices Biomed 2018 Design of Medical Devices Conference Dmd 2018
    #> 62                                                                                      <NA>
    #> 63                                                               European Physical Journal C
    #> 64                                                       Journal of Experimental Nanoscience
    #> 65                                                           Robotics and Autonomous Systems
    #> 66                                                               European Physical Journal C
    #> 67                                                               European Physical Journal C
    #> 68                                                               European Physical Journal C
    #> 69                                                               European Physical Journal C
    #> 70                                                               European Physical Journal C
    #> 71                                                               European Physical Journal C
    #> 72                                                               European Physical Journal C
    #> 73                                                               European Physical Journal C
    #> 74                                                               European Physical Journal C
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    #> 74   14346044           78            <NA>      2018-01-01
    #>    prism:coverDisplayDate                          prism:doi               pii
    #> 1        20 December 2018      10.1016/j.jclepro.2018.09.125 S0959652618328488
    #> 2           December 2018        10.1016/j.ijggc.2018.11.006 S1750583618304481
    #> 3           December 2018           10.1088/1748-9326/aaf27b              <NA>
    #> 4        15 November 2018           10.1088/1748-9326/aae43c              <NA>
    #> 5            October 2018     10.1016/j.biortech.2018.05.106 S0960852418307740
    #> 6            October 2018                 10.1111/gcbb.12493              <NA>
    #> 7        5 September 2018        10.1051/e3sconf/20184002031              <NA>
    #> 8            19 June 2018           10.3389/fenrg.2018.00054              <NA>
    #> 9               June 2018     10.3390/chemengineering2020016              <NA>
    #> 10             April 2018          10.1016/j.eng.2018.03.010 S2095809918303047
    #> 11        1 February 2018                 10.3390/su10020382              <NA>
    #> 12                   2018           10.1088/1748-9326/aad246              <NA>
    #> 13          December 2018              10.3390/fluids3040088              <NA>
    #> 14          December 2018              10.3390/fluids3040086              <NA>
    #> 15        1 December 2018          10.1049/iet-its.2018.5172              <NA>
    #> 16        1 December 2018         10.1117/1.JBO.23.12.121602              <NA>
    #> 17       26 November 2018      10.1080/23744731.2018.1483148              <NA>
    #> 18        5 November 2018       10.1016/j.matdes.2018.07.050 S0264127518305811
    #> 19        5 November 2018                  10.3390/s18113786              <NA>
    #> 20        5 November 2018       10.1016/j.matdes.2018.07.060 S0264127518306002
    #> 21        26 October 2018             10.1109/PAC.2018.00017              <NA>
    #> 22        17 October 2018           10.3389/fmars.2018.00344              <NA>
    #> 23           October 2018         10.1109/TMECH.2018.2854544              <NA>
    #> 24           October 2018       10.1016/j.jmapro.2018.07.020 S1526612518310016
    #> 25      10 September 2018            10.3390/robotics7030052              <NA>
    #> 26       1 September 2018            10.3390/machines6030032              <NA>
    #> 27         September 2018         10.1016/j.mssp.2018.05.019 S1369800118304074
    #> 28         September 2018              10.3390/fluids3030050              <NA>
    #> 29         September 2018              10.3390/fluids3030045              <NA>
    #> 30         17 August 2018                  10.3390/s18082713              <NA>
    #> 31          1 August 2018                  10.1115/1.4039509              <NA>
    #> 32            August 2018         10.1109/TCOMM.2018.2807432              <NA>
    #> 33          1 August 2018          10.1117/1.JBO.23.8.080601              <NA>
    #> 34           15 July 2018      10.1016/j.jallcom.2018.04.133 S0925838818314348
    #> 35            1 July 2018          10.1142/S1793545818500190              <NA>
    #> 36              July 2018         10.1016/j.jmps.2018.03.015 S0022509617309791
    #> 37              July 2018          10.1109/JBHI.2017.2740120              <NA>
    #> 38           19 June 2018     10.1088/1757-899X/371/1/012040              <NA>
    #> 39           15 June 2018      10.1016/j.enbuild.2018.03.054 S0378778817337544
    #> 40              June 2018              10.3390/fluids3020044              <NA>
    #> 41              June 2018         10.1109/JPHOT.2018.2842059              <NA>
    #> 42            1 June 2018 10.1061/(ASCE)IR.1943-4774.0001306              <NA>
    #> 43            20 May 2018                  10.3390/s18051635              <NA>
    #> 44            12 May 2018                  10.3390/s18051530              <NA>
    #> 45           3 April 2018      10.1080/23248378.2018.1439742              <NA>
    #> 46             April 2018          10.4258/hir.2018.24.2.139              <NA>
    #> 47          25 March 2018      10.1016/j.jallcom.2017.12.147 S092583881734344X
    #> 48          20 March 2018  10.1016/j.conbuildmat.2018.01.059 S095006181830059X
    #> 49             March 2018              10.3390/fluids3010013              <NA>
    #> 50             March 2018          10.1109/TCNS.2016.2617622              <NA>
    #> 51             March 2018        10.1016/j.robot.2017.12.008 S0921889017300477
    #> 52        1 February 2018          10.1117/1.JBO.23.2.026004              <NA>
    #> 53          February 2018                  10.3390/s18020332              <NA>
    #> 54         2 January 2018      10.1080/2573234X.2018.1507527              <NA>
    #> 55                   2018           10.15394/ijaaa.2018.1224              <NA>
    #> 56         1 January 2018          10.1007/s12205-017-1165-x              <NA>
    #> 57         1 January 2018       10.1016/j.ifacol.2018.07.085 S2405896318308280
    #> 58                   2018                  10.3311/PPtr.9587              <NA>
    #> 59         1 January 2018          10.1117/1.JBO.23.1.010503              <NA>
    #> 60         1 January 2018       10.1016/j.ifacol.2018.08.081 S2405896318311972
    #> 61                   2018               10.1115/DMD2018-6928              <NA>
    #> 62         1 January 2018       10.1016/j.ifacol.2018.03.096 S2405896318301009
    #> 63           January 2018     10.1140/epjc/s10052-017-5495-0              <NA>
    #> 64         1 January 2018      10.1080/17458080.2018.1453172              <NA>
    #> 65           January 2018        10.1016/j.robot.2017.10.002 S0921889016303888
    #> 66        1 December 2018     10.1140/epjc/s10052-018-6457-x              <NA>
    #> 67        1 December 2018     10.1140/epjc/s10052-018-6374-z              <NA>
    #> 68        1 December 2018     10.1140/epjc/s10052-018-6468-7              <NA>
    #> 69        1 November 2018     10.1140/epjc/s10052-018-6354-3              <NA>
    #> 70       1 September 2018     10.1140/epjc/s10052-018-6219-9              <NA>
    #> 71           1 March 2018     10.1140/epjc/s10052-018-5649-8              <NA>
    #> 72        1 February 2018     10.1140/epjc/s10052-017-5475-4              <NA>
    #> 73        1 February 2018     10.1140/epjc/s10052-018-5583-9              <NA>
    #> 74                   2018     10.1140/epjc/s10052-018-6423-7              <NA>
    #>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         dc:description
    #> 1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   With increasing competition and rising operational costs, golf course managers are focusing on innovative approaches that can help attract and retain visitors. Therefore, understanding economic rationale for landscape improvement can help managers to prioritize operational budget. The purpose of this study was to identify the determinants of golfers’ repeated visitation to public golf courses and understand economic rationale for aesthetic quality improvement in public golf facilities. We surveyed golf course visitors at Lakeside Memorial Golf Course, a municipal facility in Stillwater, Oklahoma. Study results suggest that golfers’ frequency to revisit was positively influenced by prestige of the facility and course design. The willingness to pay (WTP) estimates, which accounted for respondent's maximum propensity to pay above already paid green fees, for winter turfgrass enhancements were about two times higher than their estimated costs. Study results suggested landscape improvement strategies and provided several recommendations to golf course management.
    #> 2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Offshore geologic CO<inf>2</inf> storage offers an attractive option to reduce greenhouse gas emissions. Vast CO<inf>2</inf> storage capacity exists in Cretaceous-Neogene sandstone in the DeSoto Canyon Salt Basin. Understanding the stress and pressure regimes in the basin can help evaluate the geomechanical integrity of the formations, thus minimizing the risk of CO<inf>2</inf> migrating out of the storage complex. Borehole breakouts were identified using four-arm dipmeter logs. Elongation of the breakouts is aligned with the minimum horizontal compressive stress (Sh<inf>min</inf>), which tends to be oriented northeast-southwest. Vertical reservoir stresses are influenced by rock and fluid density. Lithostatic and hydrostatic stress each have a power-law relationship to depth. The average lithostatic stress (Sv) gradient is ∼21.4 kPa/m. Hydrostatic pressure gradient increases with brine density to a maximum of ∼12.2 kPa/m. Geometric mean of the Sh<inf>min</inf>-depth values correspond to an effective Sh<inf>min</inf> - effective Sv quotient of ∼0.5. Injection pressure can be maintained safely below the estimated effective minimum horizontal stress, thereby reducing the risk of cross-formational flow. Future study should focus on further constraint of geomechanical properties, reservoir integrity, and seal integrity.
    #> 3                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            The Mongolian Plateau hosts two different governments: the Mongolian People's Republic and the Inner Mongolia Autonomous Region, a provincial-level government of the People's Republic of China. The divergence between these governments has widened in the past century, mostly due to a series of institutional changes that generated different socioeconomic and demographic trajectories. Due to its high latitude and altitude, the Plateau has been highly sensitive to the rapid changes in global and regional climates that have altered the spatial and temporal distributions of energy and water. Based on a recent workshop to synthesize findings on the sustainability of the Plateau amidst socioeconomic and environmental change, we identify five critical issues facing the social-ecological systems (SES): (1) divergent and uncertain changes in social and ecological characteristics; (2) declining prevalence of nomadism; (3) consequences of rapid urbanization in transitional economies; (4) the unsustainability of large-scale afforestation efforts in the semi-arid and arid areas of Inner Mongolia; and (5) the role of institutional changes in shaping the SES on the Plateau. We emphasize that lessons learned in Inner Mongolia are valuable, but may not always apply to Mongolia. National land management policies and regulations have long-term effects on the sustainability of SES; climate change adaptation policies and practices must be tuned to local conditions and should be central to decision-making on natural resource management and socioeconomic development pathways.
    #> 4                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         The dryland belt (DLB) in Northern Eurasia is the largest contiguous dryland on Earth. During the last century, changes here have included land use change (e.g. expansion of croplands and cities), resource extraction (e.g. coal, ores, oil, and gas), rapid institutional shifts (e.g. collapse of the Soviet Union), climatic changes, and natural disturbances (e.g. wildfires, floods, and dust storms). These factors intertwine, overlap, and sometimes mitigate, but can sometimes feedback upon each other to exacerbate their synergistic and cumulative effects. Thus, it is important to properly document each of these external and internal factors and to characterize the structural relationships among them in order to develop better approaches to alleviating negative consequences of these regional environmental changes. This paper addresses the climatic changes observed over the DLB in recent decades and outlines possible links of these changes (both impacts and feedback) with other external and internal factors of contemporary regional environmental changes and human activities within the DLB.
    #> 5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Biochar has functional groups, pH buffering capacity and cation exchange capacity (CEC) that can be beneficial in syngas fermentation. This study examined the properties of biochar made from switchgrass (SGBC), forage sorghum (FSBC), red cedar (RCBC) and poultry litter (PLBC), and their effects on ethanol and butanol production from syngas using Clostridium carboxidivorans. Experiments were performed in 250 mL bottle reactors with a 50 mL working volume at 37 °C fed syngas containing CO:H<inf>2</inf>:CO<inf>2</inf> (40:30:30 by volume). Results showed that PLBC and SGBC enhanced ethanol production by 90% and 73%, respectively, and butanol production by fourfold compared to standard yeast extract medium without biochar (control). CO and H<inf>2</inf> utilization in PLBC and SGBC media increased compared to control. PLBC had the highest pH buffering capacity, CEC and total amount of cations compared with SGBC, FSBC and RCBC, which could have contributed to its highest enhancement of ethanol and butanol production.
    #> 6                                                                                                                                                                                                                                                                                                                                                                         Current knowledge of yield potential and best agronomic management practices for perennial bioenergy grasses is primarily derived from small-scale and short-term studies, yet these studies inform policy at the national scale. In an effort to learn more about how bioenergy grasses perform across multiple locations and years, the U.S. Department of Energy (US DOE)/Sun Grant Initiative Regional Feedstock Partnership was initiated in 2008. The objectives of the Feedstock Partnership were to (1) provide a wide range of information for feedstock selection (species choice) and management practice options for a variety of regions and (2) develop national maps of potential feedstock yield for each of the herbaceous species evaluated. The Feedstock Partnership expands our previous understanding of the bioenergy potential of switchgrass, Miscanthus, sorghum, energycane, and prairie mixtures on Conservation Reserve Program land by conducting long-term, replicated trials of each species at diverse environments in the U.S. Trials were initiated between 2008 and 2010 and completed between 2012 and 2015 depending on species. Field-scale plots were utilized for switchgrass and Conservation Reserve Program trials to use traditional agricultural machinery. This is important as we know that the smaller scale studies often overestimated yield potential of some of these species. Insufficient vegetative propagules of energycane and Miscanthus prohibited farm-scale trials of these species. The Feedstock Partnership studies also confirmed that environmental differences across years and across sites had a large impact on biomass production. Nitrogen application had variable effects across feedstocks, but some nitrogen fertilizer generally had a positive effect. National yield potential maps were developed using PRISM-ELM for each species in the Feedstock Partnership. This manuscript, with the accompanying supplemental data, will be useful in making decisions about feedstock selection as well as agronomic practices across a wide region of the country.
    #> 7                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    Altered flow regimes in streams can significantly affect ecosystems and disturb ecological processes, leading to species loss and extinction. Many river management projects use stream classification and habitat assessment approaches to design practical solutions to reverse or mitigate adverse effects of flow regime alteration on stream systems. The objective of this study was to develop a methodology to provide a primary identification of physical habitats in an 80-km long segment of the Canadian River in central Oklahoma. The methodology relied on basic geomorphic variables describing the stream and its floodplain that were derived from aerial imagery and Lidar data using Geographic Information Systems. Geostatistical tests were implemented to delineate habitat units. This approach based on high resolution data and did not require in-site inspection provided a relatively refined habitat delineation, consistent with visual observations. Future efforts will focus on validation via field surveys and coupling with hydro-sedimentary modeling to provide a tool for environmental flow decisions.
    #> 8                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Environmental factors like drought impact the quality of biomass entering a bioconversion process. Drought often reduces the sugar content in lignocellulosic biomass, which could have economic impacts, particularly when compounded with losses in dry biomass yield; however, the effects on conversion efficiency are not completely understood. This study investigated how drought may impact biomass composition and sugar yields from dilute-acid pretreatment and enzymatic hydrolysis of Miscanthus, a tall fescue mixture, and switchgrass from Nebraska, Missouri, and Oklahoma, respectively, grown as part of Regional Feedstock Partnership field trials. Samples were grown and harvested in 2010 during non-drought conditions and in 2012 during extreme drought conditions. Non-structural glucose and proline were significantly greater in 2012 compared with 2010 for Miscanthus, which suggests drought stress occurred. Structural glucan and xylan were significantly decreased in 2012 for Miscanthus; however, reactivity and sugar yields from dilute-acid pretreatment and enzymatic hydrolysis were significantly greater in 2012 compared with 2010, suggesting that although structural sugars may decrease during drought conditions, sugar yields and reactivity may increase. For the tall fescue mixture, proline was greater, and structural sugars were lower in 2012, indicating drought stress, but minimal differences were observed in the conversion experiments. Few differences were observed for switchgrass composition and reactivity between years. The observed patterns are likely because of site-specific climatic conditions combined with the tolerance each species may have to drought. As drought occurrence and severity have increased, it is necessary to understand drought impacts to mitigate risks to future bioenergy industry growth.
    #> 9                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Vertical vibration is known to cause bubble breakup, clustering and retardation in gas-liquid systems. In a bubble column, vibration increases the mass transfer ratio by increasing the residence time and phase interfacial area through introducing kinetic buoyancy force (Bjerknes effect) and bubble breakup. Previous studies have explored the effect of vibration frequency (f), but minimal effort has focused on the effect of amplitude (A) on mass transfer intensification. Thus, the current work experimentally examines bubble size, void fraction, and mass transfer in a bubble column under relatively high amplitude vibration (1.5 mm < A <9.5 mm) over a frequency range of 7.5–22.5 Hz. Results of the present work were compared with past studies. The maximum stable bubble size under vibration was scaled using Hinze theory for breakage. Results of this work indicate that vibration frequency exhibits local maxima in both mass transfer and void fraction. Moreover, an optimum amplitude that is independent of vibration frequency was found for mass transfer enhancements. Finally, this work suggests physics-based models to predict void fraction and mass transfer in a vibrating bubble column.
    #> 10                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Geotagging is the process of labeling data and information with geographical identification metadata, and text mining refers to the process of deriving information from text through data analytics. Geotagging and text mining are used to mine rich sources of social media data, such as video, website, text, and Quick Response (QR) code. They have been frequently used to model consumer behaviors and market trends. This study uses both techniques to understand the resilience of infrastructure in Chennai, India using data mined from the 2015 flood. This paper presents a conceptual study on the potential use of social media (Twitter in this case) to better understand infrastructure resiliency. Using feature-extraction techniques, the research team extracted Twitter data from tweets generated by the Chennai population during the flood. First, this study shows that these techniques are useful in identifying locations, defects, and failure intensities of infrastructure using the location metadata from geotags, words containing the locations, and the frequencies of tweets from each location. However, more efforts are needed to better utilize the texts generated from the tweets, including a better understanding of the cultural contexts of the words used in the tweets, the contexts of the words used to describe the incidents, and the least frequently used words.
    #> 11                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             Together with the emerging popularity of big data in numerous studies, increasing theoretical discussions of the challenges and limitations of such data sources exist. However, there is a clear research gap in the empirical comparison studies on different data sources. The goal of this paper is to use "attractiveness" as a medium to examine the similarity and differences of Social media data (SMD) and survey data in academic research, based on a case study of the Beijing Olympic Forest Park, in Beijing, China. SMD was extracted from two social media platforms and two surveys were conducted to assess the attractiveness of various locations and landscape elements. Data collection, keyword extraction and keyword prioritization were used and compared in the data gathering and analysis process. The findings revealed that SMD and survey data share many similarities. Both data sources confirm that natural ambience is more appreciated than cultural elements, particularly the naturalness of the park. Spaces of practical utility are more appreciated than facilities designed to have cultural meanings and iconic significance. Despite perceived similarities, this study concludes that SMD exhibits exaggerated and aggregated bias. This resulted from the intrinsic character of SMD as volunteered and unstructured data selected through an emotional process rather than from a rational synthesis. Exciting events were reported more often than daily experiences. Reflecting upon the strength and weakness of SMD and survey data, this study would recommend a combined landscape assessment process, which first utilizes SMD to build up an assessment framework, then applies conventional surveys for supplementary and detailed information. This would ultimately result in comprehensive understanding.
    #> 12                                                                                                                                                                                                                                                                                                                                                                                                                                                                        The rapid shrinkage of Lake Urmia, one of the world's largest saline lakes located in northwestern Iran, is a tragic wake-up call to revisit the principles of water resources management based on the socio-economic and environmental dimensions of sustainable development. The overarching goal of this paper is to set a framework for deriving dynamic, climate-informed environmental inflows for drying lakes considering both meteorological/climatic and anthropogenic conditions. We report on the compounding effects of meteorological drought and unsustainable water resource management that contributed to Lake Urmia's contemporary environmental catastrophe. Using rich datasets of hydrologic attributes, water demands and withdrawals, as well as water management infrastructure (i.e. reservoir capacity and operating policies), we provide a quantitative assessment of the basin's water resources, demonstrating that Lake Urmia reached a tipping point in the early 2000s. The lake level failed to rebound to its designated ecological threshold (1274 m above sea level) during a relatively normal hydro-period immediately after the drought of record (1998-2002). The collapse was caused by a marked overshoot of the basin's hydrologic capacity due to growing anthropogenic drought in the face of extreme climatological stressors. We offer a dynamic environmental inflow plan for different climate conditions (dry, wet and near normal), combined with three representative water withdrawal scenarios. Assuming effective implementation of the proposed 40% reduction in the current water withdrawals, the required environmental inflows range from 2900 million cubic meters per year (mcm yr<sup>−1</sup>) during dry conditions to 5400 mcm yr<sup>−1</sup> during wet periods with the average being 4100 mcm yr<sup>−1</sup>. Finally, for different environmental inflow scenarios, we estimate the expected recovery time for re-establishing the ecological level of Lake Urmia.
    #> 13                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    Reconstruction of fine-scale information from sparse data is often needed in practical fluid dynamics where the sensors are typically sparse and yet, one may need to learn the underlying flow structures or inform predictions through assimilation into data-driven models. Given that sparse reconstruction is inherently an ill-posed problem, the most successful approaches encode the physics into an underlying sparse basis space that spans the manifold to generate well-posedness. To achieve this, one commonly uses a generic orthogonal Fourier basis or a data specific proper orthogonal decomposition (POD) basis to reconstruct from sparse sensor information at chosen locations. Such a reconstruction problem is well-posed as long as the sensor locations are incoherent and can sample the key physical mechanisms. The resulting inverse problem is easily solved using l <inf>2</inf> minimization or if necessary, sparsity promoting l <inf>1</inf> minimization. Given the proliferation of machine learning and the need for robust reconstruction frameworks in the face of dynamically evolving flows, we explore in this study the suitability of non-orthogonal basis obtained from extreme learning machine (ELM) auto-encoders for sparse reconstruction. In particular, we assess the interplay between sensor quantity and sensor placement in a given system dimension for accurate reconstruction of canonical fluid flows in comparison to POD-based reconstruction.
    #> 14                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           We put forth a robust reduced-order modeling approach for near real-time prediction of mesoscale flows. In our hybrid-modeling framework, we combine physics-based projection methods with neural network closures to account for truncated modes. We introduce a weighting parameter between the Galerkin projection and extreme learning machine models and explore its effectiveness, accuracy and generalizability. To illustrate the success of the proposed modeling paradigm, we predict both the mean flow pattern and the time series response of a single-layer quasi-geostrophic ocean model, which is a simplified prototype for wind-driven general circulation models. We demonstrate that our approach yields significant improvements over both the standard Galerkin projection and fully non-intrusive neural network methods with a negligible computational overhead.
    #> 15                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Driver distraction is a leading factor in car crashes. With a goal to reduce traffic accidents and improve transportation safety, this study proposes a driver distraction detection system which identifies various types of distractions through a camera observing the driver. An assisted driving testbed is developed for the purpose of creating realistic driving experiences and validating the distraction detection algorithms. The authors collected a dataset which consists of images of the drivers in both normal and distracted driving postures. Four deep convolutional neural networks including VGG-16, AlexNet, GoogleNet, and residual network are implemented and evaluated on an embedded graphic processing unit platform. In addition, they developed a conversational warning system that alerts the driver in real-time when he/she does not focus on the driving task. Experimental results show that the proposed approach outperforms the baseline one which has only 256 neurons in the fully-connected layers. Furthermore, the results indicate that the GoogleNet is the best model out of the four for distraction detection in the driving simulator testbed.
    #> 16                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 We hypothesize that the capsular optical properties and thickness combined affect how accurate the diffuse reflectance on the surface of a capsular solid organ represents that on the subcapsular parenchyma. Monte Carlo simulations on two-layer geometries evaluated how a thin superficial layer with the thickness from 10 to 1000μm affected the surface diffuse reflectance over a source-detector separation spanning 0.01 to 10 mm. The simulations represented the superficial layer presenting various contrasts concerning refractive index, anisotropy factor, absorption coefficient, and reduced scattering coefficient, versus those of the subsurface main medium. An analytical approach modeled the effects of the superficial layer of various thicknesses and optical properties on diffuse reflectance. Diffuse reflectance spectroscopy was performed ex vivo on 10 fresh human livers and 9 fresh human kidneys using a surface probe with a 3-mm source-detector separation. The difference of the device-specific diffuse reflectance on the organ between with the capsule and without the capsule has significantly greater spectral variation in the kidney than in the liver. The significantly greater spectral deviation of surface diffuse reflectance between with and without the capsule in the kidney than in the liver was analytically accountable by considering the much thicker capsule of the kidney than of the liver.
    #> 17                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    In most office buildings, plug loads are one of the main contributors to the building’s overall energy consumption and therefore need to be considered in load calculations. The heat gains from different office equipment deviate from their peak values due to both user and equipment behavior. The variation in the heat gains of different office equipment can be presented by average weekday and weekend power consumption profiles. These power consumption profiles serve as an input to the energy simulation programs for the purpose of load calculation. In this article, weekday and weekend power consumption profiles are developed for different office equipment. In addition, load factor profiles are developed for some typical office spaces and are compared against the peak load factors listed in the ASHRAE Handbook of Fundamentals (2017). Recommendations for the minimum number of testing days necessary to obtain the equipment peak heat gain are also made.
    #> 18                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Characterization of the interphase region in carbon fiber reinforced polymer (CFRP) is challenging because of the length scale involved. The interpretation of measured load-displacement curves using indentation is affected by the lack of analytical solutions that account for the fiber constraint effect. A combination of AFM (Atomic Force Microscopy) based indentation and FE (Finite Element) simulations showed a gradient in the elastic modulus of the interphase evaluated along a radial line from the fiber. 3D FEA (Finite Element Analysis) indicated that fiber constraint effect is significant in the region less than 40 nm away from the fiber. Nonetheless, the apparent rise in elastic modulus due to fiber constraint is limited when compared to the gradient in the elastic modulus of the interphase. Additionally, this technique is used to demonstrate that UV irradiation causes a rapid decrease in the modulus of the region near the fiber due to photocatalytic degradation of carbon fiber but subsequently increases due to high cross-linking. Whereas, the modulus of the matrix at 8 mm away from the fiber decreased by 32% after 24 h of UV irradiation. This indicates that the response of epoxy to UV irradiation is influenced by the proximity to the reinforcement.
    #> 19                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Meeting the ever-increasing global food, feed, and fiber demands while conserving the quantity and quality of limited agricultural water resources and maintaining the sustainability of irrigated agriculture requires optimizing irrigation management using advanced technologies such as soil moisture sensors. In this study, the performance of five different soil moisture sensors was evaluated for their accuracy in two irrigated cropping systems, one each in central and southwest Oklahoma, with variable levels of soil salinity and clay content. With factory calibrations, three of the sensors had sufficient accuracies at the site with lower levels of salinity and clay, while none of them performed satisfactorily at the site with higher levels of salinity and clay. The study also investigated the performance of different approaches (laboratory, sensor-based, and the Rosetta model) to determine soil moisture thresholds required for irrigation scheduling, i.e., field capacity (FC) and wilting point (WP). The estimated FC and WP by the Rosetta model were closest to the laboratory-measured data using undisturbed soil cores, regardless of the type and number of input parameters used in the Rosetta model. The sensor-based method of ranking the readings resulted in overestimation of FC and WP. Finally, soil moisture depletion, a critical parameter in effective irrigation scheduling, was calculated by combining sensor readings and FC estimates. Ranking-based FC resulted in overestimation of soil moisture depletion, even for accurate sensors at tsites with lower levels of salinity and clay.
    #> 20                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          While the stress-induced dissolution of various minerals has gained attention as an important time-dependent deformation mechanism, this has only sparingly been investigated in portland cement systems. In this paper, X-ray Computed Tomography (XCT) is used to make direct observations of the microstructural evolution in cement paste samples under different levels of stress during their first 60 h of hydration. Stiffness and creep measurements are also made while imaging the changes in the microstructure. The results show that stress applied at ages between 24 h and 60 h caused an increase in stiffness, increase in early age creep, and a statistically higher amount of dissolution of individual particles near the point of load application.
    #> 21                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              The early detection of where and when fatal infectious diseases outbreak is of critical importance to the public health. To effectively detect, analyze and then intervene the spread of diseases, people's health status along with their location information should be timely collected. However, the conventional practices are via surveys or field health workers, which are highly costly and pose serious privacy threats to participants. In this paper, we for the first time propose to exploit the ubiquitous cloud services to collect users' multi-dimensional data in a secure and privacy-preserving manner and to enable the analysis of infectious disease. Specifically, we target at the spatial clustering analysis using Kulldorf scan statistic and propose a key-oblivious inner product encryption (KOIPE) mechanism to ensure that the untrusted entity only obtains the statistic instead of individual's data. Furthermore, we design an anonymous and sybil-resilient approach to protect the data collection process from double registration attacks and meanwhile preserve participant's privacy against untrusted cloud servers. A rigorous and comprehensive security analysis is given to validate our design, and we also conduct extensive simulations based on real-life datasets to demonstrate the performance of our scheme in terms of communication and computing overhead.
    #> 22 A reduction in dissolved oxygen availability in marine habitats is among the predicted consequences of increasing global temperatures. An understanding of past oxygenation is critical for predictions of future changes in the extent and distribution of oxygen minimum zones (OMZs). Benthic foraminifera have been used to assess changes in paleo-oxygenation, and according to prevailing thought, oxygen-poor marine benthic habitats are dominated by sediment-dwelling infaunal foraminifera, while more oxygenated environments are populated with more epifaunal taxa. However, in this study we found elevated densities of epifaunal taxa in oxygen-poor habitats. A series of 16 multicores were taken on depth transects (360-3000 m) across an OMZ in the Southern California Bight to investigate the ecology of living (rose bengal stained) benthic foraminifera. Dissolved oxygen concentrations in bottom water at sampling sites varied from 21 to 162 μmol/l. Sampling focused on bathymetric highs in an effort to collect seafloor surface materials with coarse sediments in areas not typically targeted for sampling. Mean grain size varied from about 131 (gravelly sand) to about 830 μm (coarse sand with fine gravel). Vertical distribution patterns (0-2 cm) were consistent with those of conspecifics reported elsewhere, and reconfirm that Cibicidoides wuellerstorfi and Hanzawaia nipponica have a living preference at or near the sediment-water interface. As expected, assemblages were dominated by infaunal taxa, such as Uvigerina and Bolivina, traditionally associated with the supersaturated, unconsolidated mud, characteristic of OMZ habitats, suggesting that these taxa are not sensitive to substrate type. However, despite dysoxic conditions (21-28 μmol/l), epifaunal taxa comprised as much as 36% of the stained population at the five sites with the coarsest mean grain size, while other measured environmental parameters remained relatively constant. We suggest that these epifaunal taxa, including C. wuellerstorfi, prefer habitats with coarse grains that allow them to remain at or above the sediment-water interface. These results suggest that seafloor habitat heterogeneity contributes to the distribution of benthic foraminifera, including in low-oxygen environments. We submit that paleo-oxygenation methods that use epifaunal indicator taxa need to reconsider the dissolved oxygen requirements of epifaunal taxa.
    #> 23                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   Map building is a fundamental problem in many robotic applications. Currently, most robots still lack sufficient high-level intelligence to achieve robust, efficient, and complete mapping of real world environments. In this paper, we develop a human-robot collaborative three-dimensional (3-D) mapping system based on a mobile robot platform equipped with a rotating RGB-D camera. This system introduces the robot's capability of quantitatively evaluating the mapping performance with human remote guidance. In this way, the robot is able to proactively cooperate with the human operator in real time for improved mapping performance. First, a Bayesian framework is proposed that fuses robot motion and visual features for regular 3-D mapping. Second, a binary hypothesis testing problem is formulated to evaluate the accuracy of camera pose estimation. When the estimated pose has small errors, the camera configuration is saved as a safe camera pose (SCP). When the estimated pose has large errors, a self-recovery mechanism is introduced allowing the robot to trace back to the last saved SCP. The proposed system is tested in different scenarios. The experimental results show that the system can run in real time and improve the accuracy and robustness of the mapping process.
    #> 24                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 Bulk metallic glasses (BMGs) are a relatively new group of metallic alloys with amorphous microstructure. BMGs have been increasingly used in industrial applications due to their high strength and high corrosion resistance. The microstructural evolution and the surface generation mechanisms in machining of BMGs and crystalline metal alloys are substantially different. This paper presents the investigations on surface microstructure and progressive tool wear in high-speed milling of Zr-based BMG. The crystallization of originally amorphous BMG material caused by the milling process was examined. The microstructure properties of BMG at amorphous and crystalline phases were determined. The effect of coolant on the progressive tool wear and the constituent element changes of BMG was investigated. The results provide new and comprehensive information on surface microstructure property in response to high-speed milling of Zr-BMG, and can guide the process planning in achieving desired surface property in milling of Zr-BMG components.
    #> 25                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    A cylindrical rolling robot is developed that generates roll torque by changing the shape of its flexible, elliptical outer surface whenever one of four elliptical axes rotates past an inclination called trigger angle. The robot is equipped with a sensing/control system by which it measures angular position and angular velocity, and computes error with respect to a desired step angular velocity profile. When shape change is triggered, the newly assumed shape of the outer surface is determined according to the computed error. A series of trial rolls is conducted using various trigger angles, and energy consumed by the actuation motor per unit roll distance is measured. Results show that, for each of three desired velocity profiles investigated, there exists a range of trigger angles that results in relatively low energy consumption per unit roll distance, and when the robot operates within this optimal trigger angle range, it undergoes minimal actuation burdening and inadvertent braking, both of which are inherent to the mechanics of rolling robots that use shape change to generate roll torque. A mathematical model of motion is developed and applied in a simulation program that can be used to predict and further understand behavior of the robot.
    #> 26                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               In this study, improved analytical models, numerical parametric explorations, and experimental characterization are presented for a mechanical inerter to bring out dependencies for dynamic mass amplification under low rates ( < 5 Hz) of excitation. Two common realizations of the inerter-the ball-screw and the rack-and-pinion versions-are considered. Theoretical models incorporating component inertias and sizing were developed for both versions. The dependence of the specific inertance on key design parameters is explored through simulations. Based on these simulations, a prototype rack-and-pinion inerter delivering a specific inertance above 90 was designed, fabricated, and tested under low-rate displacement and acceleration-controlled excitations. The measured specific inertance was found to display an exponential decline with an increase in excitation frequency for both cases. Deviations from predictions are attributable to the frequency dependence of internal stiffness and damping in the fabricated prototype. Using a phase-matching procedure for a representative lumped model, the internal stiffness and damping in the prototype were estimated. Examination of the phase spectra reveals an influence of the excitation frequency on the internal stiffness, damping, and consequently specific inertance. Further, based on the results of this study, design perspectives for such mechanical inerters, which are seeing increasing use in several low-frequency applications, are also presented. It is envisioned that this approach can be utilized to subsume the specific nonlinear characteristics of individual inerters into a simple yet unsimplistic model that can be used to more efficiently and accurately predict the behavior of multi-element, inerter-based systems that employ them.
    #> 27                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Cobalt-doped (0%, 5%, 10%, 15%, and 20%) zinc oxide (ZnO) nanorods were deposited on silicon wafers by a chemical bath deposition technique. Variations in energy band gap and stress due to cobalt doping were analyzed by X-ray diffraction techniques, optical measurements and modeled by density functional theory calculations. Also, the direct residual stress in ZnO nanorods was investigated by measuring the difference in curvature across the doped ZnO thin films deposited on a silicon substrate using the Bow Optic wafer stress measurement system. The stress in doped ZnO films was found to be of compressive in nature. The residual stress in doped ZnO thin films was found in the range of 0.0427–1.0174 GPa. The residual stress was also found to scale with the cobalt doping concentration in ZnO thin film. Also, the crystalline structure of various cobalt doped ZnO nanorods films was confirmed by X-ray diffraction analysis to be of the wurtzite structure. The bandgap of cobalt-doped ZnO was red-shifted from 3.30 eV to 3.21 eV as the cobalt concentration in ZnO nanostructures varied from 0% to 20%. A linear relationship between stress and bandgap shifting in cobalt-doped ZnO nanorods was obtained based on x-ray diffraction and direct stress measurements.
    #> 28                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Numerical solution of the incompressible Navier-Stokes equations poses a significant computational challenge due to the solenoidal velocity field constraint. In most computational modeling frameworks, this divergence-free constraint requires the solution of a Poisson equation at every step of the underlying time integration algorithm, which constitutes the major component of the computational expense. In this study, we propose a hybrid analytics procedure combining a data-driven approach with a physics-based simulation technique to accelerate the computation of incompressible flows. In our approach, proper orthogonal basis functions are generated to be used in solving the Poisson equation in a reduced order space. Since the time integration of the advection-diffusion equation part of the physics-based model is computationally inexpensive in a typical incompressible flow solver, it is retained in the full order space to represent the dynamics more accurately. Encoder and decoder interface conditions are provided by incorporating the elliptic constraint along with the data exchange between the full order and reduced order spaces. We investigate the feasibility of the proposed method by solving the Taylor-Green vortex decaying problem, and it is found that a remarkable speed-up can be achieved while retaining a similar accuracy with respect to the full order model.
    #> 29                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        In contrast to larger species, little is known about the flight of the smallest flying insects, such as thrips and fairyflies. These tiny animals range from 300 to 1000 microns in length and fly at Reynolds numbers ranging from about 4 to 60. Previous work with numerical and physical models have shown that the aerodynamics of these diminutive insects is significantly different from that of larger animals, but most of these studies have relied on two-dimensional approximations. There can, however, be significant differences between two- and three-dimensional flows, as has been found for larger insects. To better understand the flight of the smallest insects, we have performed a systematic study of the forces and flow structures around a three-dimensional revolving elliptical wing. We used both a dynamically scaled physical model and a three-dimensional computational model at Reynolds numbers ranging from 1 to 130 and angles of attacks ranging from 0 <sup>◦</sup> to 90 <sup>◦</sup> . The results of the physical and computational models were in good agreement and showed that dimensionless drag, aerodynamic efficiency, and spanwise flow all decrease with decreasing Reynolds number. In addition, both the leading and trailing edge vortices remain attached to the wing over the scales relevant to the smallest flying insects. Overall, these observations suggest that there are drastic differences in the aerodynamics of flight at the scale of the smallest flying animals.
    #> 30                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             Grooving is widely used to improve airport runway pavement skid resistance during wet weather. However, runway grooves deteriorate over time due to the combined effects of traffic loading, climate, and weather, which brings about a potential safety risk at the time of the aircraft takeoff and landing. Accordingly, periodic measurement and evaluation of groove performance are critical for runways to maintain adequate skid resistance. Nevertheless, such evaluation is difficult to implement due to the lack of sufficient technologies to identify shallow or worn grooves and slab joints. This paper proposes a new strategy to automatically identify airport runway grooves and slab joints using high resolution laser profiling data. First, K-means clustering based filter and moving window traversal algorithm are developed to locate the deepest point of the potential dips (including noises, true grooves, and slab joints). Subsequently the improved moving average filter and traversal algorithms are used to determine the left and right endpoint positions of each identified dip. Finally, the modified heuristic method is used to separate out slab joints from the identified dips, and then the polynomial support vector machine is introduced to distinguish out noises from the candidate grooves (including noises and true grooves), so that PCC slab-based runway safety evaluation can be performed. The performance of the proposed strategy is compared with that of the other two methods, and findings indicate that the new method is more powerful in runway groove and joint identification, with the F-measure score of 0.98. This study would be beneficial in airport runway groove safety evaluation and the subsequent maintenance and rehabilitation of airport runway.
    #> 31                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    Commercial water tunnels typically generate a momentum thickness based Reynolds number (Re) 1/41000, which is slightly above the laminar to turbulent transition. The current work compiles the literature on the design of high-Reynolds number facilities and uses it to design a high-Reynolds number recirculating water tunnel that spans the range between commercial water tunnels and the largest in the world. The final design has a 1.1 m long test-section with a 152 mm square cross section that can reach speed of 10 m/s, which corresponds to Re =15,000. Flow conditioning via a tandem configuration of honeycombs and settling-chambers combined with an 8.5:1 area contraction resulted in an average test-section inlet turbulence level &lt;0.3% and negligible mean shear in the test-section core. The developing boundary layer on the test-section walls conform to a canonical zero-pressure-gradient (ZPG) flat-plate turbulent boundary layer (TBL) with the outer variable scaled profile matching a 1/7th power-law fit, inner variable scaled velocity profiles matching the log-law and a shape factor of 1.3.
    #> 32                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        In this paper, the joint spectrum sensing and resource allocation problem is investigated in a multi-band-multi-user cognitive radio (CR) network. Assuming imperfect spectrum sensing information, our goal is to jointly optimize the sensing threshold and power allocation strategy such that the average total throughput of secondary users (SUs) is maximized. In Addition, the power of SUs is constrained to keep the interference introduced to primary users under certain limit, which gives rise to a nonconvex mixed integer non-linear programming (MINLP) optimization problem. Our contribution in this paper is threefold. First, it is illustrated that the dimension of the nonconvex MINLP problem can be significantly reduced, which helps to re-formulate the optimization problem without resorting to integer variables. Second, it is demonstrated that the simplified formulation admits the canonical form of a monotonic optimization, and an \\epsilon -optimal solution can be achieved using the polyblock outer approximation algorithm. Third, a practical low-complexity spectrum sensing and resource allocation algorithm is developed to reduce the computational cost. Finally, the effectiveness of proposed algorithms is verified by simulations.
    #> 33                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               Solid-organ transplant is one of the most complex areas of modern medicine involving surgery. There are challenging opportunities in solid-organ transplant, specifically regarding the deficiencies in pathology workflow or gaps in pathology support, which may await alleviations or even de novo solutions, by means of point-of-care, or point-of-procedure optical biomarkers. Focusing the discussions of pathology workflow on donor liver assessment, we analyze the undermet need for intraoperative, real-time, and nondestructive assessment of the donor injuries (such as fibrosis, steatosis, and necrosis) that are the most significant predictors of post-transplant viability. We also identify an unmet need for real-time and nondestructive characterization of ischemia or irreversible injuries to the donor liver, earlier than appearing on morphological histology examined with light microscopy. Point-of-procedure laparoscopic optical biomarkers of liver injuries and tissue ischemia may also facilitate post-transplant management that is currently difficult for or devoid of pathological consultation due to lack of tools. The potential and pitfalls of point-of-procedure optical biomarkers for liver assessment are exemplified in breadth for steatosis. The more general and overarching challenges of point-of-procedure optical biomarkers for liver transplant pathology, including the shielding effect of the liver capsule that was quantitated only recently, are projected. The technological and presentational benchmarks that a candidate technology of point-of-procedure optical biomarkers for transplant pathology must demonstrate to motivate clinical translation are also foreseen.
    #> 34                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                In this paper, the kinetics of isochronal differential scanning calorimetry of Fe<inf>48</inf>Cr<inf>15</inf>Mo<inf>14</inf>Y<inf>2</inf>C<inf>15</inf>B<inf>6</inf> amorphous alloy was analyzed to understand the mechanism of crystallization. The crystallization process constituted of overlapping exothermic transformations occurring from 900 to 1100 K with activation energies from 369 to 437 kJmol<sup>−1</sup>. Analysis of the mechanism of crystallization under the theoretical framework of the Johnson-Mehl-Avrami-Kolmogorov model revealed that these transformations occurred by three-dimensional growth with either a decreasing nucleation rate or from nuclei that have already been formed. A comparison of the kinetic parameters of nucleation and growth of this alloy with that of Fe<inf>50</inf>Cr<inf>15</inf>Mo<inf>14</inf>C<inf>15</inf>B<inf>6</inf> amorphous alloy showed the effect of yttrium on the increase of thermal stability and impediment of atomic diffusivity leading to decreased nucleation rate in the alloy.
    #> 35                                                                                                                                                                                                                                                                                                                                     This study assessed whether there was a scattering spectral marker quantifiable by reflectance measurements that could indicate early development of hepatic steatosis in rats for potential applications to pre-procurement organ evaluation. Sixteen rats were fed a methionine-choline-deficient (MCD) diet and eight rats were fed a normal diet. Direct assessment of the liver parenchyma of rats in vivo was performed by percutaneous reflectance spectroscopy using a single fiber probe at the beginning of diet-intake and arbitrary post-diet-intake times up to 11 weeks to render longitudinal comparison. Histological sampling of the liver over the duration of diet administration was performed on two MCD-diet treated rats and one control rat euthanized after reflectance spectroscopy measurement. The images of hematoxylin/eosin-stained liver specimens were analyzed morphometrically to evaluate the lipid size changes associated with the level of steatosis. The MCD-diet-treated group (n=16) had mild steatosis in seven rats, moderate in three rats, severe in six rats, and no other significant pathology. No control rats (n=8) developed hepatic steatosis. Among the parameters retrieved from per-SfS, only the scattering power (can be either positive or negative) appeared to be statistically different between MCD-treated and control livers. The scattering power for the 16 MCD-diet-treated livers at the time of euthanasia and presenting various levels of steatosis was 0.33±0.21, in comparison to 0.036±0.25 of the eight control livers (p=0.0189). When evaluated at days 12 and 13 combined, the scattering power of the 16 MCD-diet-treated livers was 0.32±0.17, in comparison to 0.10±0.11 of the eight control livers (p=0.0017). All of four MCD-treated livers harvested at days 12 and 13 presented mild steatosis with sub-micron size lipid droplets, even though none of the MCD-treated livers were sonographically remarkable for fatty changes. The elevation of the scattering power may be a valuable marker indicating early hepatic steatosis before the steatosis is sonographically detectable.
    #> 36                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Ultrahigh molecular weight polyethylene (UHMWPE) fibers have a complex hierarchical structure that at the micron-scale is composed of oriented chain crystals, lamellar crystals, and amorphous domains organized into macrofibrils. We developed a computational micromechanical modeling study of the effects of the morphological structure and constituent material properties on the deformation mechanisms, stiffness and strength of the UHMWPE macrofibrils. Specifically, we developed four representative volume elements, which differed in the arrangement and orientation of the lamellar crystals, to describe the various macrofibrillar microstructures observed in recent experiments. The stiffness and strength of the crystals were determined from molecular dynamic simulations of a pure PE crystal. A finite deformation crystal plasticity model was used to describe the crystals and an isotropic viscoplastic model was used for the amorphous phase. The results show that yielding in UHMWPE macrofibrils under axial tension is dominated by the slip in the oriented crystals, while yielding under transverse compression and shear is dominated by slips in both the oriented and lamellar crystals. The results also show that the axial modulus and strength are mainly determined by the volume fraction of the oriented crystals and are insensitive to the arrangements of the lamellar crystals when the modulus of the amorphous phase is significantly smaller than that of the crystals. In contrast, the arrangement and size of the lamellar crystals have a significant effect on the stiffness and strength under transverse compression and shear. These findings can provide a guide for new materials and processing design to improve the properties of UHMWPE fibers by controlling the macrofibrillar morphologies.
    #> 37                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    Obstructive sleep apnea (OSA) is a chronic sleep disorder affecting millions of people worldwide. Individuals with OSA are rarely aware of the condition and are often left untreated, which can lead to some serious health problems. Nowadays, several low-cost wearable health sensors are available that can be used to conveniently and noninvasively collect a wide range of physiological signals. In this paper, we propose a new framework for OSA detection in which we combine the wearable sensor measurement signals with the mathematical models of the cardiorespiratory system. Vector-valued Gaussian processes (GPs) are adopted to model the physiological variations among different individuals. The GP covariance is constructed using the sum of separable kernel functions, and the GP hyperparameters are estimated by maximizing the marginal likelihood function. A likelihood ratio test is proposed to detect OSA using the widely available heart rate and peripheral oxygen saturation (SpO<inf>2</inf>) measurement signals. We conduct experiments on both synthetic and real data to show the effectiveness of the proposed OSA detection framework compared to purely data-driven approaches.
    #> 38                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       The square dance is one of the most popular new physical activities in China in recent years, and has become the hotspot of Chinese garden research. This study attempts to interpret the space use contradictions of square dance from the angle of newspaper report, and then make up the deficiency of the planning designer's understanding of square dance space. We collected 749 news reports in 8 years of four authoritative newspapers in Harbin, and probed into the square use contradictions of usability, accessibility and climate environment of square dance. We hope to provide the content basis for future social science research in the field of planning and design, and provide the direction for technical research.
    #> 39                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                The use of radiant technology in attics aims to reduce the radiation component of heat transfer between the attic floor and roof decks, gables, and eaves. Recently, it has been shown that EnergyPlus underestimates the savings using radiant technologies in attic spaces. The aim of this study is to understand why EnergyPlus underestimates the performance of radiant technologies and provide a solution strategy that works within the current capabilities of EnergyPlus. The analysis uses three attic energy models as a baseline for comparison for EnergyPlus. Potential reasons for the discrepancies between the attic specific energy models and EnergyPlus are isolated and individually tested. A solution strategy is proposed using the Energy Management System (EMS) capabilities within EnergyPlus. This solution strategy produces similar results to the other attic specific energy models. This paper shows that the current capabilities of EnergyPlus are sufficient to simulate radiant technologies in attics. The methodology showcased in this paper serves as a guide for engineers and researchers who would like to predict the performance radiant technology in attics using the whole building energy software, EnergyPlus.
    #> 40                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        In contrast to larger flight-capable insects such as hawk moths and fruit flies, miniature flying insects such as thrips show the obligatory use of wing-wing interaction via “clap and fling” during the end of upstroke and start of downstroke. Although fling can augment lift generated during flapping flight at chord-based Reynolds number (Re) of 10 or lower, large drag forces are necessary to clap and fling the wings. In this context, bristles observed in the wings of most tiny insects have been shown to lower drag force generated in clap and fling. However, the fluid dynamic mechanism underlying drag reduction by bristled wings and the impact of bristles on lift generated via clap and fling remain unclear. We used a dynamically scaled robotic model to examine the forces and flow structures generated during clap and fling of: three bristled wing pairs with varying inter-bristle spacing, and a geometrically equivalent solid wing pair. In contrast to the solid wing pair, reverse flow through the gaps between the bristles was observed throughout clap and fling, resulting in: (a) drag reduction; and (b) weaker and diffuse leading edge vortices that lowered lift. Shear layers were formed around the bristles when interacting bristled wing pairs underwent clap and fling motion. These shear layers lowered leakiness of flow through the bristles and minimized loss of lift in bristled wings. Compared to the solid wing, peak drag coefficients were reduced by 50-90% in bristled wings. In contrast, peak lift coefficients of bristled wings were only reduced by 35-60% from those of the solid wing. Our results suggest that the bristled wings can provide unique aerodynamic benefits via increasing lift to drag ratio during clap and fling for Re between 5 and 15.
    #> 41                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           Two-dimensional black phosphorus (BP) recently emerged as an outstanding material for optoelectronics and nanophotonics applications. In contrast to graphene, BP has a sufficiently large electronic bandgap and its high carrier mobility allows for efficient free-carrier absorption in the infrared and terahertz regimes. Here, we present a reflective structure to enhance the response of nanostructured monolayer BP at terahertz frequencies and investigate localized surface plasmon resonances in BP nanostrip arrays. Anisotropic absorption is observed in the proposed BP metamaterials due to the puckered crystal structure of the monolayer BP, and further investigations show that the plasmonic resonances are strongly depending on the geometric parameters of the nanostrips and the coupling between the adjacent nanostrips. We expect that the monolayer BP is an outstanding candidate of highly anisotropic plasmonic material for ultrascaled optoelectronic integration.
    #> 42                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          This paper presents the potential of remotely sensed data in addressing spatially distributed irrigation equity, adequacy, and sustainability. The surface energy balance algorithm for land (SEBAL) was implemented to map actual evapotranspiration (ET) over an irrigation district in southern California. Potential ET was also mapped based on the Priestley-Taylor method, modified to account for the effect of horizontally transported energy on enhancing/suppressing ET. Remotely sensed products were integrated with ground-based data in a Geographical Information System (GIS) environment to quantify several irrigation and drainage performance indicators. The amongand within-field coefficients of variation of actual ET were comparable to previous studies, suggesting that water consumption was uniform across the irrigation district. The relative ET was high, indicating that irrigation supply was adequate. The extensive network of open drains was also found to be functioning at an optimal level according to the results of two performance indicators based on the magnitude and uniformity of groundwater depth.
    #> 43                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           Lane marking detection and localization are crucial for autonomous driving and lane-based pavement surveys. Numerous studies have been done to detect and locate lane markings with the purpose of advanced driver assistance systems, in which image data are usually captured by vision-based cameras. However, a limited number of studies have been done to identify lane markings using high-resolution laser images for road condition evaluation. In this study, the laser images are acquired with a digital highway data vehicle (DHDV). Subsequently, a novel methodology is presented for the automated lane marking identification and reconstruction, and is implemented in four phases: (1) binarization of the laser images with a new threshold method (multi-box segmentation based threshold method); (2) determination of candidate lane markings with closing operations and a marching square algorithm; (3) identification of true lane marking by eliminating false positives (FPs) using a linear support vector machine method; and (4) reconstruction of the damaged and dash lane marking segments to form a continuous lane marking based on the geometry features such as adjacent lane marking location and lane width. Finally, a case study is given to validate effects of the novel methodology. The findings indicate the new strategy is robust in image binarization and lane marking localization. This study would be beneficial in road lane-based pavement condition evaluation such as lane-based rutting measurement and crack classification.
    #> 44                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         Recent research has shown that the ubiquitous use of cameras and voice monitoring equipment in a home environment can raise privacy concerns and affect human mental health. This can be a major obstacle to the deployment of smart home systems for elderly or disabled care. This study uses a social robot to detect embarrassing situations. Firstly, we designed an improved neural network structure based on the You Only Look Once (YOLO) model to obtain feature information. By focusing on reducing area redundancy and computation time, we proposed a bounding-box merging algorithm based on region proposal networks (B-RPN), to merge the areas that have similar features and determine the borders of the bounding box. Thereafter, we designed a feature extraction algorithm based on our improved YOLO and B-RPN, called F-YOLO, for our training datasets, and then proposed a real-time object detection algorithm based on F-YOLO (RODA-FY). We implemented RODA-FY and compared models on our MAT social robot. Secondly, we considered six types of situations in smart homes, and developed training and validation datasets, containing 2580 and 360 images, respectively. Meanwhile, we designed three types of experiments with four types of test datasets composed of 960 sample images. Thirdly, we analyzed how a different number of training iterations affects our prediction estimation, and then we explored the relationship between recognition accuracy and learning rates. Our results show that our proposed privacy detection system can recognize designed situations in the smart home with an acceptable recognition accuracy of 94.48%. Finally, we compared the results among RODA-FY, Inception V3, and YOLO, which indicate that our proposed RODA-FY outperforms the other comparison models in recognition accuracy.
    #> 45                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                <NA>
    #> 46                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     Objectives: The objective of this study was to compare the performance of two popularly used early sepsis diagnostic criteria, systemic inflammatory response syndrome (SIRS) and quick Sepsis-related Organ Failure Assessment (qSOFA), using statistical and machine learning approaches. Methods: This retrospective study examined patient visits in Emergency Department (ED) with sepsis related diagnosis. The outcome was 28-day in-hospital mortality. Using odds ratio (OR) and modeling methods (decision tree [DT], multivariate logistic regression [LR], and naïve Bayes [NB]), the relationships between diagnostic criteria and mortality were examined. Results: Of 132,704 eligible patient visits, 14% died within 28 days of ED admission. The association of qSOFA ≥2 with mortality (OR = 3.06; 95% confidence interval [CI], 2.96–3.17) greater than the association of SIRS ≥2 with mortality (OR = 1.22; 95% CI, 1.18–1.26). The area under the ROC curve for qSOFA (AUROC = 0.70) was significantly greater than for SIRS (AUROC = 0.63). For qSOFA, the sensitivity and specificity were DT = 0.39, LR = 0.64, NB = 0.62 and DT = 0.89, LR = 0.63, NB = 0.66, respectively. For SIRS, the sensitivity and specificity were DT = 0.46, LR = 0.62, NB = 0.62 and DT = 0.70, LR = 0.59, NB = 0.58, respectively. Conclusions: The evidences suggest that qSOFA is a better diagnostic criteria than SIRS. The low sensitivity of qSOFA can be improved by carefully selecting the threshold to translate the predicted probabilities into labels. These findings can guide healthcare providers in selecting risk-stratification measures for patients presenting to an ED with sepsis.
    #> 47                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               In this paper, the theoretical framework of viscous flow deformation is presented as a model to investigate the inherent role of applied pressure in the densification of Fe-based amorphous alloy powder during spark plasma sintering. The proposed model revealed that the evolution of the structural geometry of the powder compact resulted in an amplification of the applied pressure to a larger contact pressure. The resulting pressure controlled compressive viscous flow deformation of the particles exhibited the contribution of increased applied pressure towards enhancement of densification and thus confirmed the validity of the present model for analyzing pressure-assisted sintering of amorphous alloy powder. The application of this theoretical model correctly predicted the trend of increasing final density of the compacts with pressure while further improvement on its accuracy can be attained upon establishment of the relative contribution of mass flow and deformation of powder particles towards their consolidation.
    #> 48                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       A new classification approach is presented that uses individual fly ash particle measurements to provide improved and more in-depth information about the properties of the concrete. The technique uses machine guided X-ray microanalysis to measure the compositions of 2000 randomly selected particles from 20 different fly ashes. This paper details the methods used to acquire the particle composition data and the derivation of the representative groups of the fly ash particles or classification groups. This method is named the Particle Model. To investigate the utility of these groups, 12 fly ashes were used at a 20% mass replacement of the cement in a series of concrete mixtures, which were tested for compressive strength at various times over 180 d of curing. Seven of the nine particle compositions identified were found to influence the compressive strength of the concrete with a linear model R-squared value of 0.99. The Particle Model showed a statistically significant improvement over the Class C or F classification from ASTM C618 and EN450. This work aims to establish the Particle Model and show that the classification shows promise to be used as a method to predict the physical properties of concretes that contain fly ash.
    #> 49                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       The current study experimentally examines bubble size distribution (BSD) within a bubble column and the associated characteristic length scales. Air was injected into a column of water via a single injection tube. The column diameter (63–102 mm), injection tube diameter (0.8–1.6 mm) and superficial gas velocity (1.4–55 mm/s) were varied. Large samples (up to 54,000 bubbles) of bubble sizes measured via 2D imaging were used to produce probability density functions (PDFs). The PDFs were used to identify an alternative length scale termed the most frequent bubble size (d <inf>mf</inf> ) and defined as the peak in the PDF. This length scale as well as the traditional Sauter mean diameter were used to assess the sensitivity of the BSD to gas injection rate, injector tube diameter, injection tube angle and column diameter. The d <inf>mf</inf> was relatively insensitive to most variation, which indicates these bubbles are produced by the turbulent wakes. In addition, the current work examines higher order statistics (standard deviation, skewness and kurtosis) and notes that there is evidence in support of using these statistics to quantify the influence of specific parameters on the flow-field as well as a potential indicator of regime transitions.
    #> 50                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             This paper seeks to combine differential game theory with the actor-critic-identifier architecture to determine forward-in-time, approximate optimal controllers for formation tracking in multiagent systems, where the agents have uncertain heterogeneous nonlinear dynamics. A continuous control strategy is proposed, using communication feedback from extended neighbors on a communication topology that has a spanning tree. A model-based reinforcement learning technique is developed to cooperatively control a group of agents to track a trajectory in a desired formation. Simulation results are presented to demonstrate the performance of the developed technique.
    #> 51                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             This article presents the development of a robot-integrated smart home (RiSH) which can be used for research in assistive technologies for elderly care. The RiSH integrates a home service robot, a home sensor network, a body sensor network, a mobile device, cloud servers, and remote caregivers. A layered architecture is proposed to guide the design and implementation of the RiSH software. Basic service functions are developed to allow the RiSH to recognize human body activity using an inertial measurement unit (IMU) and the home service robot to perceive the environment through audio signals. Based on these functions, we developed two low-level applications: (1) particle filter-based human localization and tracking using wearable motion sensors and distributed binary sensors; (2) Dynamic Bayesian Network-based human activity recognition using microphones and distributed binary sensors. Both applications extend the robot's perception beyond its onboard sensors. Utilizing the low-level applications, a high-level application is realized that detects and responds to human falls. We conducted experiments in our RiSH testbed to evaluate auditory perception services, human body activity recognition, human position tracking, sound-based human activity monitoring, and fall detection and rescue. Twelve human subjects were asked to conduct daily activities in the testbed and mimic falls. All data of their movement, body activities, and sound events were collected by the robot. Human trajectories were estimated with a root mean square error of less than 0.2 m. The robot was able to recognize 37 human activities through sound events with an average accuracy of 88% and detect falling sounds with an accuracy of 80% at the frame level. The experiments show the operation of the various components in the RiSH and the capabilities of the home service robot in monitoring and assisting the resident.
    #> 52                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  This paper investigates terahertz (THz) imaging and classification of freshly excised murine xenograft breast cancer tumors. These tumors are grown via injection of E0771 breast adenocarcinoma cells into the flank of mice maintained on high-fat diet. Within 1 h of excision, the tumor and adjacent tissues are imaged using a pulsed THz system in the reflection mode. The THz images are classified using a statistical Bayesian mixture model with unsupervised and supervised approaches. Correlation with digitized pathology images is conducted using classification images assigned by a modal class decision rule. The corresponding receiver operating characteristic curves are obtained based on the classification results. A total of 13 tumor samples obtained from 9 tumors are investigated. The results show good correlation of THz images with pathology results in all samples of cancer and fat tissues. For tumor samples of cancer, fat, and muscle tissues, THz images show reasonable correlation with pathology where the primary challenge lies in the overlapping dielectric properties of cancer and muscle tissues. The use of a supervised regression approach shows improvement in the classification images although not consistently in all tissue regions. Advancing THz imaging of breast tumors from mice and the development of accurate statistical models will ultimately progress the technique for the assessment of human breast tumor margins.
    #> 53                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             Indoor occupants’ positions are significant for smart home service systems, which usually consist of robot service(s), appliance control and other intelligent applications. In this paper, an innovative localization method is proposed for tracking humans’ position in indoor environments based on passive infrared (PIR) sensors using an accessibility map and an A-star algorithm, aiming at providing intelligent services. First the accessibility map reflecting the visiting habits of the occupants is established through the integral training with indoor environments and other prior knowledge. Then the PIR sensors, which placement depends on the training results in the accessibility map, get the rough location information. For more precise positioning, the A-start algorithm is used to refine the localization, fused with the accessibility map and the PIR sensor data. Experiments were conducted in a mock apartment testbed. The ground truth data was obtained from an Opti-track system. The results demonstrate that the proposed method is able to track persons in a smart home environment and provide a solution for home robot localization.
    #> 54                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                <NA>
    #> 55                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                <NA>
    #> 56                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                Pavement friction and texture characteristics are important aspects of road safety. Despite extensive studies conducted in the past decades, knowledge gaps still remain in understanding the relationship between pavement macrotexture and surface skid resistance. This paper implements discrete wavelet transform to decompose pavement surface macrotexture profile data into multi-scale characteristics and investigate their suitability for pavement friction prediction. Pavement macrotexture and friction data were both collected within the wheel-path from six High Friction Surface Treatment sites in Oklahoma using a high-speed profiler and a Grip Tester. The collected macrotexture profiles are decomposed into multiple wavelengths, and the total and relative energy components are calculated as indicators to represent macrotexture characteristics at various wavelengths. Correlation analysis is performed to examine the contribution of the energy indicators on pavement friction. The macrotexture energy within wavelengths from 0.97 mm to 3.86 mm contributes positively to pavement friction while that within wavelengths from 15.44 mm to 61.77 mm shows negative impacts. Subsequently, pavement friction prediction model is developed using multivariate linear regressive analysis incorporating the macrotexture energy indicators. Comparisons between predicted and monitored friction data demonstrates the robustness of the proposed friction prediction model.
    #> 57                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   We consider a group of aerial manipulators (AM) collaboratively transporting a flexible payload. Each AM is a combination of an Unmanned Aerial Vehicle (UAV) with a two-degree-of-freedom robotic manipulator (RM) attached to it. Contact forces between the agents (AMs) and the payload are modeled as the gradient of nonlinear potentials that describe the deformation of the payload. We develop an adaptive decentralized control law for transporting a payload with an unknown mass without explicit communication between the agents. The algorithm guarantees that all the agents converge to a desired velocity and the contact forces are regulated. The sum of the estimates of the unknown mass from all the agents converge to the true mass. Using the inverse kinematics of the AM, we implement the developed algorithm at the kinematic level for the AMs and demonstrate its effectiveness in simulations.
    #> 58                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           The emerging 1mm resolution 3D data collection technology is capable of covering the entire pavement surface, and provides more data sets than traditional line-of-sight data collection systems. As a result, quantifying the impact of sample size including sample width and sample length on the calculation of pavement texture indicators is becoming possible. In this study, 1mm 3D texture data are collected and processed at seven test sites using the PaveVision3D Ultra system. Analysis of Variance (ANOVA) test and linear regression models are developed to investigate various sample length and width on the calculation of three widely used texture indicators: Mean Profile Depth (MPD), Mean Texture Depth (MTD) and Power Spectra Density (PSD). Since the current ASTM standards and other procedures cannot be directly applied to 3D surface for production due to a lack of definitions, the results from this research are beneficial in the process to standardize texture indicators' computations with 1mm 3D surface data of pavements.
    #> 59                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Noninvasive photobiomodulation therapy (PBMT) of spinal cord disease remains speculative due to the lack of evidence for whether photobiomodulatory irradiances can be transcutaneously delivered to the spinal cord under a clinically acceptable PBMT surface irradiation protocol. We developed a flexible nine-channel photodetection probe for deployment within the spinal canal of a cadaver dog after hemilaminectomy to measure transcutaneously transmitted PBMT irradiance at nine sites over an eight-cm spinal canal length. The probe was built upon a 6.325-mm tubular stem, to the surface of which nine photodiodes were epoxied at approximately 1 cm apart. The photodiode has a form factor of 4.80 mm×2.10 mm×1.15 mm (length×width×height). Each photodiode was individually calibrated to deliver 1 V per 7.58 μW/cm<sup>2</sup> continuous irradiance at 850 nm. The outputs of eight photodiodes were logged concurrently using a data acquisition module interfacing eight channels of differential analog signals, while the output of the ninth photodiode was measured by a precision multimeter. This flexible probe rendered simultaneous intraspinal (nine-site) measurements of transcutaneous PBMT irradiations at 980 nm in a pilot cadaver dog model. At a surface continuous irradiance of 3.14 W/cm<sup>2</sup> applied off-contact between L1 and L2, intraspinal irradiances picked up by nine photodiodes had a maximum of 327.48 μW/cm<sup>2</sup> without the skin and 5.68 μW/cm2 with the skin.
    #> 60                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                Phenotypic information of peanut canopy, including height, width, shape, and density are important in the selection of the best cultivars of peanuts. However, current methods to acquire these data are mainly by manual measurements or qualitative scorings. These methods are laborious, time-consuming, and subjective. In this study, a ground-based peanut canopy phenotypic system was developed to improve the efficiency and accuracy of the data collection on peanut canopy architecture. The system was on a ground-based, remote controlled cart with a sensor suite of two RGB cameras, a thermal camera, a laser scanner and an RTK GPS. Software programs was developed to control the system and collect, store, and analyze the data. This system was tested in the peanut growth season of 2017. The result showed that the system was able to complete the data collection at least four times faster than previous manual collection. The data collected were with a much higher resolution, thus could be used to acquire detailed features of peanut canopy.
    #> 61                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                <NA>
    #> 62                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       This paper proposes a finite difference spatial discretization that preserves the geometrical structure, i.e. the Dirac structure, underlying 2D heat and wave equations in cylindrical coordinates. These equations are shown to rely on Dirac structures for a particular set of boundary conditions. The discretization is completed with time integration based on Stormer-Verlet method.
    #> 63                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           We consider the pair production of vector-like down-type quarks in an E<inf>6</inf> motivated model, where each of the produced down-type vector-like quark decays into an ordinary Standard Model light quark and a singlet scalar. Both the vector-like quark and the singlet scalar appear naturally in the E<inf>6</inf> model with masses at the TeV scale with a favorable choice of symmetry breaking pattern. We focus on the non-standard decay of the vector-like quark and the new scalar which decays to two photons or two gluons. We analyze the signal for the vector-like quark production in the 2γ + ≥ 2 j channel and show how the scalar and vector-like quark masses can be determined at the Large Hadron Collider.
    #> 64                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      In this study ZSM-5 Nano particles with high crystallinity were synthesized using a dry gel conversion technique. An L9 orthogonal array of the Taguchi method was applied to investigate the effect of synthesis parameters, such as crystallization time, gel drying temperature, molar composition of template (TPAOH) and water content in the crystallization stage on crystallinity and particle size of the ZSM-5 catalyst. The products were characterized by X-ray diffraction (XRD) and scanning electron microscopy (SEM). Beside short crystallization time, the particle sizes were considerably smaller in comparison with those prepared using the hydrothermal method. The results showed that the particle size and crystallinity increased with increasing water content and crystallization time. The effects of gel drying temperature and molar composition of template were found to be more complex, however. Comparing to hydrothermal method, ZSM-5 samples synthesized with the dry gel conversion exhibited higher selectivity to gasoline than other hydrocarbons.
    #> 65                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   Acquiring robot assembly skills through human demonstration is an important research problem and can be used to quickly program robots in future manufacturing industries. To teach robots complex assembly skills, the robots should be able to recognize the objects (parts and tools) involved, the actions applied, and the effect of the actions on the parts. It is non-trivial to recognize the subtle assembly actions. To estimate the effect of the actions on the assembly part is also challenging due to the small part sizes. In this paper, using a RGB-D camera, we build a Portable Assembly Demonstration (PAD) system which can automatically recognize the objects (parts/tools) involved, the actions conducted and the assembly states characterizing the spatial relationship among the parts. The experiment results proved that this PAD system can generate a high level assembly script with decent accuracy in object and action recognition as well as assembly state estimation. The assembly script is successfully implemented on a Baxter robot.
    #> 66                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              Searches for non-resonant and resonant Higgs boson pair production are performed in the γγWW<sup>∗</sup> channel with the final state of γγℓνjj using 36.1 fb <sup>- 1</sup> of proton–proton collision data recorded at a centre-of-mass energy of s=13 TeV by the ATLAS detector at the Large Hadron Collider. No significant deviation from the Standard Model prediction is observed. A 95% confidence-level observed upper limit of 7.7 pb is set on the cross section for non-resonant production, while the expected limit is 5.4 pb. A search for a narrow-width resonance X decaying to a pair of Standard Model Higgs bosons HH is performed with the same set of data, and the observed upper limits on σ(pp→ X) × B(X→ HH) range between 40.0 and 6.1 pb for masses of the resonance between 260 and 500 GeV, while the expected limits range between 17.6 and 4.4 pb. When deriving the limits above, the Standard Model branching ratios of the H→ γγ and H→ WW<sup>∗</sup> are assumed.
    #> 67                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           The Tile Calorimeter is the hadron calorimeter covering the central region of the ATLAS experiment at the Large Hadron Collider. Approximately 10,000 photomultipliers collect light from scintillating tiles acting as the active material sandwiched between slabs of steel absorber. This paper gives an overview of the calorimeter’s performance during the years 2008–2012 using cosmic-ray muon events and proton–proton collision data at centre-of-mass energies of 7 and 8 TeV with a total integrated luminosity of nearly 30 fb<sup>- 1</sup>. The signal reconstruction methods, calibration systems as well as the detector operation status are presented. The energy and time calibration methods performed excellently, resulting in good stability of the calorimeter response under varying conditions during the LHC Run 1. Finally, the Tile Calorimeter response to isolated muons and hadrons as well as to jets from proton–proton collisions is presented. The results demonstrate excellent performance in accord with specifications mentioned in the Technical Design Report.
    #> 68                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    Measurements of the azimuthal anisotropy in lead–lead collisions at sNN = 5.02 TeV are presented using a data sample corresponding to 0.49 nb <sup>- 1</sup> integrated luminosity collected by the ATLAS experiment at the LHC in 2015. The recorded minimum-bias sample is enhanced by triggers for “ultra-central” collisions, providing an opportunity to perform detailed study of flow harmonics in the regime where the initial state is dominated by fluctuations. The anisotropy of the charged-particle azimuthal angle distributions is characterized by the Fourier coefficients, v<inf>2</inf>–v<inf>7</inf>, which are measured using the two-particle correlation, scalar-product and event-plane methods. The goal of the paper is to provide measurements of the differential as well as integrated flow harmonics v<inf>n</inf> over wide ranges of the transverse momentum, 0.5 < p<inf>T</inf>< 60 GeV, the pseudorapidity, | η| < 2.5, and the collision centrality 0–80%. Results from different methods are compared and discussed in the context of previous and recent measurements in Pb+Pb collisions at sNN = 2.76 TeV and 5.02 TeV. In particular, the shape of the p<inf>T</inf>dependence of elliptic or triangular flow harmonics is observed to be very similar at different centralities after scaling the v<inf>n</inf> and p<inf>T</inf>values by constant factors over the centrality interval 0–60% and the p<inf>T</inf>range 0.5 < p<inf>T</inf>< 5 GeV.
    #> 69                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    It has been found that Figure 30 shows the 68% and 99% confidence-level contours for the W boson and top quark mass measurements, instead of the 68% and 95% confidence-level contours, as stated in the legend.
    #> 70                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      A measurement of J/ ψ and ψ(2 S) production is presented. It is based on a data sample from Pb+Pb collisions at sNN=5.02TeV and pp collisions at s=5.02TeV recorded by the ATLAS detector at the LHC in 2015, corresponding to an integrated luminosity of 0.42nb-1 and 25pb-1 in Pb+Pb and pp, respectively. The measurements of per-event yields, nuclear modification factors, and non-prompt fractions are performed in the dimuon decay channel for 9<pTμμ<40 GeV in dimuon transverse momentum, and - 2 < y<inf>μ</inf> <inf>μ</inf>< 2 in rapidity. Strong suppression is found in Pb+Pb collisions for both prompt and non-prompt J/ ψ, increasing with event centrality. The suppression of prompt ψ(2 S) is observed to be stronger than that of J/ ψ, while the suppression of non-prompt ψ(2 S) is equal to that of the non-prompt J/ ψ within uncertainties, consistent with the expectation that both arise from b-quarks propagating through the medium. Despite prompt and non-prompt J/ ψ arising from different mechanisms, the dependence of their nuclear modification factors on centrality is found to be quite similar.
    #> 71                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         The differential cross-section for the production of a W boson in association with a top quark is measured for several particle-level observables. The measurements are performed using 36.1fb-1 of pp collision data collected with the ATLAS detector at the LHC in 2015 and 2016. Differential cross-sections are measured in a fiducial phase space defined by the presence of two charged leptons and exactly one jet matched to a b-hadron, and are normalised with the fiducial cross-section. Results are found to be in good agreement with predictions from several Monte Carlo event generators.
    #> 72                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           A measurement of the mass of the W boson is presented based on proton–proton collision data recorded in 2011 at a centre-of-mass energy of 7 TeV with the ATLAS detector at the LHC, and corresponding to 4.6fb-1 of integrated luminosity. The selected data sample consists of 7.8 × 10 <sup>6</sup> candidates in the W→ μν channel and 5.9 × 10 <sup>6</sup> candidates in the W→ eν channel. The W-boson mass is obtained from template fits to the reconstructed distributions of the charged lepton transverse momentum and of the W boson transverse mass in the electron and muon decay channels, yielding (Formula presented.) where the first uncertainty is statistical, the second corresponds to the experimental systematic uncertainty, and the third to the physics-modelling systematic uncertainty. A measurement of the mass difference between the W<sup>+</sup> and W<sup>-</sup> bosons yields mW+-mW-=-29±28 MeV.
    #> 73                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  A search for the direct production of charginos and neutralinos in final states with at least two hadronically decaying tau leptons is presented. The analysis uses a dataset of pp collisions corresponding to an integrated luminosity of 36.1 fb<sup>- 1</sup>, recorded with the ATLAS detector at the Large Hadron Collider at a centre-of-mass energy of 13 TeV. No significant deviation from the expected Standard Model background is observed. Limits are derived in scenarios of [InlineMediaObject not available: see fulltext.] pair production and of [InlineMediaObject not available: see fulltext.] and [InlineMediaObject not available: see fulltext.] production in simplified models where the neutralinos and charginos decay solely via intermediate left-handed staus and tau sneutrinos, and the mass of the τ~ <inf>L</inf> state is set to be halfway between the masses of the [InlineMediaObject not available: see fulltext.] and the [InlineMediaObject not available: see fulltext.]. Chargino masses up to 630 GeV are excluded at 95% confidence level in the scenario of direct production of [InlineMediaObject not available: see fulltext.] for a massless [InlineMediaObject not available: see fulltext.]. Common [InlineMediaObject not available: see fulltext.] and [InlineMediaObject not available: see fulltext.] masses up to 760 GeV are excluded in the case of production of [InlineMediaObject not available: see fulltext.] and [InlineMediaObject not available: see fulltext.] assuming a massless [InlineMediaObject not available: see fulltext.]. Exclusion limits for additional benchmark scenarios with large and small mass-splitting between the [InlineMediaObject not available: see fulltext.] and the [InlineMediaObject not available: see fulltext.] are also studied by varying the τ~ <inf>L</inf> mass between the masses of the [InlineMediaObject not available: see fulltext.] and the [InlineMediaObject not available: see fulltext.].
    #> 74                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    A search for the electroweak production of charginos, neutralinos and sleptons decaying into final states involving two or three electrons or muons is presented. The analysis is based on 36.1 fb<sup>−1</sup> of √s = 13 TeV proton– proton collisions recorded by the ATLAS detector at the Large Hadron Collider. Several scenarios based on simplified models are considered. These include the associated production of the next-to-lightest neutralino and the lightest chargino, followed by their decays into final states with leptons and the lightest neutralino via either sleptons or Standard Model gauge bosons; direct production of chargino pairs, which in turn decay into leptons and the lightest neutralino via intermediate sleptons; and slepton pair production, where each slepton decays directly into the lightest neutralino and a lepton. No significant deviations from the Standard Model expectation are observed and stringent limits at 95% confidence level are placed on the masses of relevant supersymmetric particles in each of these scenarios. For a massless lightest neutralino, masses up to 580 GeV are excluded for the associated production of the next-to-lightest neutralino and the lightest chargino, assuming gauge-boson mediated decays, whereas for slepton-pair production masses up to 500 GeV are excluded assuming three generations of mass-degenerate sleptons.
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    #>                                                                                                                                                   authkeywords
    #> 1                                                                                       Regression model | Turfgrass | Willingness to pay | Winter overseeding
    #> 2                                                      East-central Gulf of Mexico | Geomechanics | In-situ stress field | Leakage risk | Offshore CO2 storage
    #> 3                                             global change | institutional change | land use | mongolian plateau | social-ecological systems | sustainability
    #> 4                                                climatic and environmental changes | impact and feedbacks of human activity on environment | Northern Eurasia
    #> 5                                                                        Biobutanol | Biochar | Bioethanol | Clostridium carboxidivorans | Syngas fermentation
    #> 6                                             bioenergy | biomass | Conservation Reserve Program | energycane | feedstock | Miscanthus | sorghum | switchgrass
    #> 7                                                                                                                                                         <NA>
    #> 8                                                                      Composition | Conversion performance | Drought | Miscanthus | Switchgrass | Tall fescue
    #> 9                                                                                      Bubble column | Bubble size | Mass transfer | Vibration | Void fraction
    #> 10                                                                                                                Engineering design | Flooding | Social media
    #> 11                                                 Beijing Olympic Forest Park | Landscape attractiveness | Research methods | Social media data | Survey data
    #> 12 Anthropogenic drought | Change | Climate variability | Environmental inflow requirement | Lake Urmia | Restoration | Sustainable water resources management
    #> 13          Compressive sensing | Extreme learning machines | Proper orthogonal decomposition | Sensors | Singular value decomposition | Sparse reconstruction
    #> 14                          Extreme learning machine | Galerkin projection | Hybrid modeling | Proper orthogonal decomposition | Quasi-geostrophic ocean model
    #> 15                                                                                                                                                        <NA>
    #> 16                capsule | diffuse optical spectroscopy | diffuse reflectance spectroscopy | functional near-infrared spectroscopy | light-tissue interaction
    #> 17                                                                                                                                                        <NA>
    #> 18                                                                  AFM indentation | Carbon fiber composites | Fiber–constraint | Interphase | UV irradiation
    #> 19                                                                       Irrigation management | Salinity | Soil moisture depletion | Volumetric water content
    #> 20                                                               Cement Hydration | Creep | Dissolution | Microstructure evolution | X-ray Computed Tomography
    #> 21                Clustering analysis | Group signature | Identity-based encryption | Kulldorf scan statistic | Public health | Secure multi party computation
    #> 22                                                                              Benthic foraminifera | Epifauna | Infauna | Oxygen | Southern California Bight
    #> 23                                                                              Bayesian fusion | human-robot collaboration | three-dimensional indoor mapping
    #> 24                                                                                                  Bulk metallic glass | Crystallization | High-speed milling
    #> 25               Actuation burden | Cylindrical | Economic locomotion | Elliptical | Inadvertent braking | Robot | Rolling | Shape changing | Velocity control
    #> 26                                                                                              Inerter | Low-rate | Parametric designDynamic characterization
    #> 27                                                                       Band gap | Cobalt doping | Density functional theory | Residual stress | ZnO nanorods
    #> 28               Data-driven modeling | Hybrid analytics | Navier-Stokes equations | Physics-based modeling | Poisson solver | Proper orthogonal decomposition
    #> 29                                                          Aerodynamics | Flow visualization | Immersed boundary method | Insect flight | Low Reynolds number
    #> 30                     Airport runway | Groove dimension | K-means clustering | Naïve bayes classifier | Point laser | Profiling data | Support Vector Machine
    #> 31                                                                                                                                                        <NA>
    #> 32                                                                   Cognitive radio | MINLP | monotonic optimization | resource allocation | spectrum sensing
    #> 33                                                    fibrosis | optical biomarker | optical spectroscopy | point-of-care | solid-organ transplant | steatosis
    #> 34                                                                          Differential scanning calorimetry | Metallic glass | Non-isothermal transformation
    #> 35                                                                                     diffuse reflectance spectroscopy | Hepatic steatosis | liver transplant
    #> 36                                                                                                             Micromechanical | Stiffness | Strength | UHMWPE
    #> 37                         Cardiorespiratory system mathematical model | Gaussian process state-space model | multimodal sensor fusion | sleep apnea detection
    #> 38                                                                                                                                                        <NA>
    #> 39                                                                                                                                                        <NA>
    #> 40                                                                  Biorobotics | Bristled wings | Clap and fling | Flapping flight | Leakiness | Tiny insects
    #> 41                                                                                                             Black phosphorus | surface plasmon | terahertz.
    #> 42                                                                                         Evapotranspiration | Groundwater | Landsat | Performance indicators
    #> 43                  Image binarization | Lane marking detection | Lane marking reconstruction | Laser sensor | Line scan camera | Support vector machine (SVM)
    #> 44                                                                               Convolutional neural networks | Privacy detection | Smart home | Social robot
    #> 45                                                                                                                                                        <NA>
    #> 46                                Artificial intelligence | Medical informatics | Sepsis | Severity of illness index | Systemic inflammatory response syndrome
    #> 47                                                                 Bulk amorphous alloy | Contact pressure | Spark plasma sintering | Viscous flow deformation
    #> 48                                                                                   B. EDX | C. Compressive Strength | D. Fly Ash | E. Concrete | E. Modeling
    #> 49                                        Bubble column | Bubble size distribution | Kurtosis | Probability density function | Sauter mean diameter | Skewness
    #> 50                                                      Dynamic programming | graphical games | learning systems | networked control systems | optimal control
    #> 51                                                                                       Assistive technology | Elderly care | Home service robot | Smart home
    #> 52                                                   biomedical optics | breast cancer | medical imaging | mice | morphing. | statistical modeling | terahertz
    #> 53                                                                 A-start algorithm | Accessibility map | Indoor position tracking | Pir sensors | Smart home
    #> 54                                                                                                                                                        <NA>
    #> 55                                                                                                                                                        <NA>
    #> 56                                                                        multivariate analysis | pavement friction | pavement macrotexture | wavelet analysis
    #> 57                                                             aerial manipulators | Cooperative control | force control | load transport | multi-agent system
    #> 58                    3D Data | Mean Profile Depth (MPD) | Mean Texture Depth (MTD) | Pass filter | Pavement texture indicators | Power Spectral Density (PSD)
    #> 59                                                                       dosimetry | low-level light therapy | photobiomodulation therapy | spinal cord injury
    #> 60                                                                                                 Image processing and analysis | Laser scanner | Phenotyping
    #> 61                                                                                                                                                        <NA>
    #> 62                                           Distributed port-Hamiltonian systems | finite difference method | heat equation | staggered grids | wave equation
    #> 63                                                                                                                                                        <NA>
    #> 64                                                                                              Dry gel conversion | MTG process | Nano-sized particle | ZSM-5
    #> 65                                                             Action recognition | Assembly state estimation | Learning by demonstration | Object recognition
    #> 66                                                                                                                                                        <NA>
    #> 67                                                                                                                                                        <NA>
    #> 68                                                                                                                                                        <NA>
    #> 69                                                                                                                                                        <NA>
    #> 70                                                                                                                                                        <NA>
    #> 71                                                                                                                                                        <NA>
    #> 72                                                                                                                                                        <NA>
    #> 73                                                                                                                                                        <NA>
    #> 74                                                                                                                                                        <NA>
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    #>                                                                     fund-sponsor
    #> 1                                                 U.S. Department of Agriculture
    #> 2                                   American Association of Petroleum Geologists
    #> 3                         National Key Research and Development Program of China
    #> 4                                                    National Science Foundation
    #> 5                                                 U.S. Department of Agriculture
    #> 6                                                  South Dakota State University
    #> 7                                                                           <NA>
    #> 8                               Office of Energy Efficiency and Renewable Energy
    #> 9                                                      U.S. Department of Energy
    #> 10                                                                          <NA>
    #> 11                                  National Natural Science Foundation of China
    #> 12                                                                          <NA>
    #> 13                                                                          <NA>
    #> 14                                                                          <NA>
    #> 15                                                   National Science Foundation
    #> 16                                 University of Oklahoma Health Sciences Center
    #> 17                                                                          <NA>
    #> 18                                                   National Science Foundation
    #> 19                                                U.S. Department of Agriculture
    #> 20                                                   National Science Foundation
    #> 21                                                   National Science Foundation
    #> 22                                                       University of Minnesota
    #> 23                                                   National Science Foundation
    #> 24                                                   National Science Foundation
    #> 25                                                     Oklahoma State University
    #> 26                                                                          <NA>
    #> 27                                                   National Science Foundation
    #> 28                                                                          <NA>
    #> 29                                                   National Science Foundation
    #> 30                                 Natural Science Foundation of Fujian Province
    #> 31                                                   National Science Foundation
    #> 32                                                   National Science Foundation
    #> 33                                 University of Oklahoma Health Sciences Center
    #> 34                                                   National Science Foundation
    #> 35                                                               Kerr Foundation
    #> 36                                                      Army Research Laboratory
    #> 37                                                                          <NA>
    #> 38                                                                          <NA>
    #> 39                                                     U.S. Department of Energy
    #> 40                                                   National Science Foundation
    #> 41                                  National Natural Science Foundation of China
    #> 42                                                                          <NA>
    #> 43                                 Natural Science Foundation of Fujian Province
    #> 44                 Conselho Nacional de Desenvolvimento Científico e Tecnológico
    #> 45                                    University of Illinois at Urbana-Champaign
    #> 46                                                     Oklahoma State University
    #> 47                                                   National Science Foundation
    #> 48                                                   National Science Foundation
    #> 49                                                                          <NA>
    #> 50                                                   National Science Foundation
    #> 51                                                   National Science Foundation
    #> 52                                                   National Science Foundation
    #> 53                                  National Natural Science Foundation of China
    #> 54                                                                          <NA>
    #> 55                                                                          <NA>
    #> 56                                                                          <NA>
    #> 57                                                                          <NA>
    #> 58                                                                          <NA>
    #> 59                                                                          <NA>
    #> 60                                                U.S. Department of Agriculture
    #> 61                                                 National Institutes of Health
    #> 62                                                                          <NA>
    #> 63                              Department of Atomic Energy, Government of India
    #> 64                                                                          <NA>
    #> 65                                                   National Science Foundation
    #> 66                                              Agence Nationale de la Recherche
    #> 67                                              Agence Nationale de la Recherche
    #> 68                                              Agence Nationale de la Recherche
    #> 69                                     Science and Technology Facilities Council
    #> 70 Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS)
    #> 71                                              Agence Nationale de la Recherche
    #> 72                                              Agence Nationale de la Recherche
    #> 73                                              Agence Nationale de la Recherche
    #> 74                                              Agence Nationale de la Recherche
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    #> 69          1           TRUE                all         All Open Access
    #> 70          1           TRUE                all         All Open Access
    #> 71          1           TRUE                all         All Open Access
    #> 72          1           TRUE                all         All Open Access
    #> 73          1           TRUE                all         All Open Access
    #> 74          1           TRUE                all         All Open Access
    #>    entry_number                      fund-no prism:eIssn prism:issueIdentifier
    #> 1             1                         <NA>        <NA>                  <NA>
    #> 2             2               DE-FOA-0001246        <NA>                  <NA>
    #> 3             3                      1313761    17489326                    12
    #> 4             4                     41701227    17489326                    11
    #> 5             5            DOTS59-07-G-00053    18732976                  <NA>
    #> 6             6            DE-FC36-05GO85041    17571707                    10
    #> 7             7                         <NA>    22671242                  <NA>
    #> 8             8                         <NA>    2296598X                   JUN
    #> 9             9                 DE-NA0003525    23057084                     2
    #> 10           10                         <NA>        <NA>                     2
    #> 11           11                     41625003    20711050                     2
    #> 12           12                         <NA>    17489326                     8
    #> 13            2                         <NA>    23115521                     4
    #> 14            4                         <NA>    23115521                     4
    #> 15            5             CISE/IIS/1427345        <NA>                    10
    #> 16            6                         <NA>    15602281                    12
    #> 17           10                         <NA>    2374474X                    10
    #> 18           11                      1649481    18734197                  <NA>
    #> 19           12                58-3070-5-007        <NA>                    11
    #> 20           13                      1300024    18734197                  <NA>
    #> 21           15                  IIS-1722791        <NA>                  <NA>
    #> 22           16                 OCE 10-60992    22967745                   OCT
    #> 23           17 CISE/IIS 1231671/IIS 1427345        <NA>                     5
    #> 24           18               CMMI – 1553815        <NA>                  <NA>
    #> 25           19                         <NA>    22186581                     3
    #> 26           20                         <NA>    20751702                     3
    #> 27           21                   NNX13AN01A        <NA>                  <NA>
    #> 28           22                         <NA>    23115521                     3
    #> 29           23                      0217229    23115521                     3
    #> 30           25                     51608123        <NA>                     8
    #> 31           26                      1604978    1528901X                     8
    #> 32           27                      1547447        <NA>                     8
    #> 33           28                         <NA>    15602281                     8
    #> 34           29                 CMMI-1462602        <NA>                  <NA>
    #> 35           30                         <NA>    17937205                     4
    #> 36           31             W911NF-12-2-0022        <NA>                  <NA>
    #> 37           32                         <NA>    21682208                     4
    #> 38           33                         <NA>    1757899X                     1
    #> 39           34                         <NA>        <NA>                  <NA>
    #> 40           36                      1512071    23115521                     2
    #> 41           37                  61420106006        <NA>                     3
    #> 42           38                         <NA>        <NA>                     6
    #> 43           39                     51608123        <NA>                     5
    #> 44           40                QKHRZ[2015]13        <NA>                     5
    #> 45           41                         <NA>    23248386                     2
    #> 46           42                         <NA>    2093369X                     2
    #> 47           44                 CMMI-1462602        <NA>                  <NA>
    #> 48           45                      10.1.24        <NA>                  <NA>
    #> 49           46                         <NA>    23115521                     1
    #> 50           47                      1217908        <NA>                     1
    #> 51           48        KQTD20140630154026047        <NA>                  <NA>
    #> 52           50                      1408007    15602281                     2
    #> 53           51                     51607029        <NA>                     2
    #> 54           54                         <NA>    25732358                     1
    #> 55           55                         <NA>    23746793                     2
    #> 56           56                         <NA>    19763808                     1
    #> 57           57                         <NA>    24058963                    12
    #> 58           58                         <NA>    15873811                     1
    #> 59           59                         <NA>    15602281                     1
    #> 60           60             2017-67013-26193    24058963                    17
    #> 61           61                         <NA>        <NA>                  <NA>
    #> 62           62                         <NA>    24058963                     2
    #> 63           63                DE-SC-0016013    14346052                     1
    #> 64           64                         <NA>    17458099                     1
    #> 65           65 CISE/IIS 1231671/IIS 1427345        <NA>                  <NA>
    #> 66            7                 ST/L006162/1    14346052                    12
    #> 67            8                       GRIDPP    14346052                    12
    #> 68            9                 ST/L003449/1    14346052                    12
    #> 69           14                 ST/N000234/1    14346052                    11
    #> 70           24                 ST/N000447/1    14346052                     9
    #> 71           49                       GRIDPP    14346052                     3
    #> 72           52                 PP/E000452/1    14346052                     2
    #> 73           53                 ST/H001158/2    14346052                     2
    #> 74           66                       GRIDPP    14346052                    12
    #>    article-number pubmed-id   scopus_id                                doi
    #> 1            <NA>      <NA> 85054821560      10.1016/j.jclepro.2018.09.125
    #> 2            <NA>      <NA> 85056753255        10.1016/j.ijggc.2018.11.006
    #> 3          123004      <NA> 85060137733           10.1088/1748-9326/aaf27b
    #> 4          115008      <NA> 85054934481           10.1088/1748-9326/aae43c
    #> 5            <NA>  29886351 85048160156     10.1016/j.biortech.2018.05.106
    #> 6            <NA>      <NA> 85040701043                 10.1111/gcbb.12493
    #> 7           02031      <NA> 85053793633        10.1051/e3sconf/20184002031
    #> 8              54      <NA> 85049632118           10.3389/fenrg.2018.00054
    #> 9              16      <NA> 85059745853     10.3390/chemengineering2020016
    #> 10           <NA>      <NA> 85046139712          10.1016/j.eng.2018.03.010
    #> 11            382      <NA> 85041520105                 10.3390/su10020382
    #> 12         084010      <NA> 85053780575           10.1088/1748-9326/aad246
    #> 13             88      <NA> 85063715171              10.3390/fluids3040088
    #> 14             86      <NA> 85063718545              10.3390/fluids3040086
    #> 15           <NA>      <NA> 85057052295          10.1049/iet-its.2018.5172
    #> 16         121602  30054997 85051825814         10.1117/1.JBO.23.12.121602
    #> 17           <NA>      <NA> 85056089746      10.1080/23744731.2018.1483148
    #> 18           <NA>      <NA> 85050665801       10.1016/j.matdes.2018.07.050
    #> 19           3786  30400674 85056287867                  10.3390/s18113786
    #> 20           <NA>      <NA> 85050823996       10.1016/j.matdes.2018.07.060
    #> 21        8511835      <NA> 85057319364             10.1109/PAC.2018.00017
    #> 22            344      <NA> 85055096320           10.3389/fmars.2018.00344
    #> 23        8408776      <NA> 85049698280         10.1109/TMECH.2018.2854544
    #> 24           <NA>      <NA> 85050523660       10.1016/j.jmapro.2018.07.020
    #> 25             52      <NA> 85056375754            10.3390/robotics7030052
    #> 26             32      <NA> 85052576568            10.3390/machines6030032
    #> 27           <NA>      <NA> 85047410314         10.1016/j.mssp.2018.05.019
    #> 28             50      <NA> 85063716967              10.3390/fluids3030050
    #> 29             45      <NA> 85063714351              10.3390/fluids3030045
    #> 30           2713  30126164 85052139961                  10.3390/s18082713
    #> 31         081102      <NA> 85044841208                  10.1115/1.4039509
    #> 32        8294277      <NA> 85042200128         10.1109/TCOMM.2018.2807432
    #> 33         080601  30160078 85052674663          10.1117/1.JBO.23.8.080601
    #> 34           <NA>      <NA> 85046114031      10.1016/j.jallcom.2018.04.133
    #> 35        1850019      <NA> 85046730287          10.1142/S1793545818500190
    #> 36           <NA>      <NA> 85045062626         10.1016/j.jmps.2018.03.015
    #> 37           <NA>  28816683 85028416529          10.1109/JBHI.2017.2740120
    #> 38         012040      <NA> 85049784220     10.1088/1757-899X/371/1/012040
    #> 39           <NA>      <NA> 85044925112      10.1016/j.enbuild.2018.03.054
    #> 40             44      <NA> 85063715868              10.3390/fluids3020044
    #> 41        4500709      <NA> 85047822635         10.1109/JPHOT.2018.2842059
    #> 42       05018002      <NA> 85045322623 10.1061/(ASCE)IR.1943-4774.0001306
    #> 43           1635  29783789 85047253959                  10.3390/s18051635
    #> 44           1530  29757211 85047078107                  10.3390/s18051530
    #> 45           <NA>      <NA> 85042416266      10.1080/23248378.2018.1439742
    #> 46           <NA>      <NA> 85047460209          10.4258/hir.2018.24.2.139
    #> 47           <NA>      <NA> 85038222030      10.1016/j.jallcom.2017.12.147
    #> 48           <NA>      <NA> 85042903915  10.1016/j.conbuildmat.2018.01.059
    #> 49        3010013      <NA> 85061673268              10.3390/fluids3010013
    #> 50           <NA>      <NA> 85030104528          10.1109/TCNS.2016.2617622
    #> 51           <NA>      <NA> 85042599129        10.1016/j.robot.2017.12.008
    #> 52         026004  29446263 85042182555          10.1117/1.JBO.23.2.026004
    #> 53            332  29364188 85041049557                  10.3390/s18020332
    #> 54           <NA>      <NA> 85067228101      10.1080/2573234X.2018.1507527
    #> 55           <NA>      <NA> 85116316808           10.15394/ijaaa.2018.1224
    #> 56           <NA>      <NA> 85015208658          10.1007/s12205-017-1165-x
    #> 57           <NA>      <NA> 85052436742       10.1016/j.ifacol.2018.07.085
    #> 58           <NA>      <NA> 85045133536                  10.3311/PPtr.9587
    #> 59         010503  29363291 85041232528          10.1117/1.JBO.23.1.010503
    #> 60           <NA>      <NA> 85053216367       10.1016/j.ifacol.2018.08.081
    #> 61           <NA>      <NA> 85049648832               10.1115/DMD2018-6928
    #> 62           <NA>      <NA> 85046696880       10.1016/j.ifacol.2018.03.096
    #> 63             35      <NA> 85042279762     10.1140/epjc/s10052-017-5495-0
    #> 64           <NA>      <NA> 85062247946      10.1080/17458080.2018.1453172
    #> 65           <NA>      <NA> 85035786507        10.1016/j.robot.2017.10.002
    #> 66           1007      <NA> 85058860285     10.1140/epjc/s10052-018-6457-x
    #> 67            987      <NA> 85057881096     10.1140/epjc/s10052-018-6374-z
    #> 68            997      <NA> 85058844591     10.1140/epjc/s10052-018-6468-7
    #> 69            898      <NA> 85056083831     10.1140/epjc/s10052-018-6354-3
    #> 70            762      <NA> 85053672925     10.1140/epjc/s10052-018-6219-9
    #> 71            186      <NA> 85043280928     10.1140/epjc/s10052-018-5649-8
    #> 72            110      <NA> 85041818739     10.1140/epjc/s10052-017-5475-4
    #> 73            154      <NA> 85042530255     10.1140/epjc/s10052-018-5583-9
    #> 74            995      <NA> 85063567068     10.1140/epjc/s10052-018-6423-7

# Conclusion

The Scopus API has limits for different searches and calls. Using a combination of APIs, we can gather all the information on authors that we would like. This gives us a full picture of the authors and co-authorship at a specific institution in specific scenarios, such as the open access publications from 2018.