Citations Of Papers Research Articles

Perception and Adaptation Strategies of Smallholder Farmers to Drought Risk: A Scientometric Analysis

Droughts are a worldwide issue that affects ecosystems’ economies and cultures; therefore, its perception and adaptation strategies among smallholder farmers are crucial for the mitigation of drought risk, and for sustainable food production. We used the bibliometric method to analyze 121 publications from the Scopus database to better understand the existing situation and trends in the field of drought risk. During the years under consideration, the field saw a significant increase in publication output, with an annual growth rate of roughly 68.14 percent. On a national level, the United States scored first with the most publications and the most academic influence, with the majority of top papers citations coming from USA-connected universities and research centers. The top five most frequently used keywords and keyword-plus were, drought, adaptation, agriculture, smallholder farmers, and climate change. Some of the adaptation strategies adopted by smallholder farmers, which could be used by many nations to deal with drought events, include: rainwater harvesting, diversification of income sources, planting of short-season cash crops to enhance cash flow, use of drought-tolerance herds, etc. This research offers a plan to navigate the intellectual dilemma in drought risk research and offers guidance for researchers in all continents, particularly the Africans and the Europeans, in further studies in this area, as the agricultural sector contributes significantly to the economy of many nations.

Exploring the relationships between altmetric counts and citations of papers in different academic fields based on co-occurrence analysis

Exploring the relationships between altmetric counts and citations of papers in different academic fields based on co-occurrence analysis

A Machine Learning Model to Predict Citation Counts of Scientific Papers in Otology Field.

One of the most widely used measures of scientific impact is the number of citations. However, due to its heavy-tailed distribution, citations are fundamentally difficult to predict but can be improved. This study was aimed at investigating the factors and parts influencing the citation number of a scientific paper in the otology field. Therefore, this work proposes a new solution that utilizes machine learning and natural language processing to process English text and provides a paper citation as the predicted results. Different algorithms are implemented in this solution, such as linear regression, boosted decision tree, decision forest, and neural networks. The application of neural network regression revealed that papers' abstracts have more influence on the citation numbers of otological articles. This new solution has been developed in visual programming using Microsoft Azure machine learning at the back end and Programming Without Coding Technology at the front end. We recommend using machine learning models to improve the abstracts of research articles to get more citations.

Use of Academic Social Networking Sites by Academic Community at National Institute of Fashion Technology Centers in India


 Academic social networking sites (ASNS) are trending in academics and play a significant role in disseminating knowledge. The study aims to explore the usage of ASNS by the faculty members and researchers of NIFT Centers across India. An online questionnaire method used to collect the primary data from 17 NIFT Centers. According to the results of the data analysis, the majority of respondents were aware of and utilised academic social networking sites (ASNS) in NIFT Centers. Google Scholar achieved the highest level of awareness and usage (34.98 %), followed by ResearchGate (27.09 %), Linkedin (18.23 %), and Academia.edu (17.24 %). The findings indicate that faculty members and researchers use ASNS primarily to access freely available millions of research papers, slightly less for communicating in a new way with researchers, and very rarely for increasing citations of research papers. Finally, the results reveal a positive attitude towards using famous academic social networking sites. NIFT should organise workshops and seminars on ASNS platforms to raise awareness and visibility of their research.

Emerging Research Trends in Marketing: A Review of Australasian Marketing Journal

This article reviews papers published in the Australasian Marketing Journal (AMJ) from 2015 to 2022 (Issue 2). About 276 papers were considered in our analysis. Adopting text mining, we reveal the key terms associated with research published in AMJ over the last 6 years. We employ a topic modeling procedure to find 15 topics that have been featured in the Journal and to depict the trends in topics over time. For example, in 2021 and 2022, there has been a big increase in “Digital,” “Artificial Intelligence,” “Sustainability,” “Online Engagement,” and “Purchase Intention” topics, in line with the AMJ’s desire to capture the current industry and academic trends. A citation analysis shows the growth of articles and citations of papers published in AMJ and reveals some of the most cited papers. It is demonstrated that methodological articles are more likely to receive a high number of citations. We conclude by suggesting emerging topics and future research directions.

Comparative study of scaling parameters and research output of selected highly- and moderately-cited individual authors

The real data of cumulative citations ln of selected nth paper of individual N papers published by some highly- and moderately-cited individual authors are analyzed to compare Hirsch and Hirsch-type indices h, h1, hf and hm, and citation radii R and Rf from consideration of: (1) the number An of coauthors of the paper, (2) the normalization of citations ln and cumulative fraction lnf of citation of the nth paper by mean and median citations of the citations ln of all Nc cited papers, and (3) the determination of effective rank neff of the lnf citations. Analysis of the ln(n), lnf(n) and lnf(neff) data was also carried out by using a Langmuir-type function l = l0[1-aKn/(1+Kn)], where l denotes the citations ln and lnf of all cited Nc papers arranged in the decreasing order, a is an effectiveness parameter, K is the so-called Langmuir constant, n denotes the rank n or neff of citations and l0 is the value of l when n or neff approaches zero. For a comparison of the publication output of different authors it was found that the hm index is more consistent than other indices, and it can be normalized to account for the publication career of different authors. However, Langmuir-type function is not adequate for comparison of the publication output of different authors because it describes the rank-order distribution patterns satisfactorily in terms of two parameters. To compare the publication output of different authors independent of their career length t, it is suggested to use scaling parameters h/t, hf/t and hm/t.

RETRACTED: Heat transfer analysis of a water/lithium-bromide vapor generator system used for battery thermal development

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/locate/withdrawalpolicy).Please note that an Expression of Concern has previously been published to alert readers to an ongoing investigation into this paper. This retraction supersedes the Expression of Concern.This article has been retracted at the request of the Editor-in-Chief.In investigating concerns brought up regarding the article, the editor reached out to the authors for an explanation.The authors failed to provide a satisfactory explanation to the above points.Furthermore, the acceptance of this article was partly based upon the positive advice of a reviewer report from a reviewer who was closely linked to the Guest Editor, Masoud Afrand.The Editor noticed a significant increase of citations of papers authored by one of the Guest Editors between the original submission and the revised version. In summary, 0 papers by Nader Karimi were cited in the original version of the paper. This increased to 8 papers in the revised version. Post-publication, the Editor also discovered that the email addresses used by the authors during the submission process were associated with multiple researcher accounts. The Editor has determined that the authorship of the paper cannot be relied upon.Overall, the Editor has determined that the paper should be retracted.

Quantifying the Risk to Health Care Workers of Cough as an Aerosol Generating Event in an Ambulance Setting: A Research Report.

United Kingdom Health Security Agency (UKHSA) guidance related to mask use for health care workers in a non-aerosol generating procedure (AGP) setting has remained as Level 2 water repellent paper mask (surgical mask) only. Energetic respiratory events, such as coughing, can generate vast numbers of droplets and aerosols. Coughing, considered to be a non-AGP event, frequently occurs in the relatively small, confined space of an ambulance (∼25 m3). The report seeks to explore whether existing research can provide an indication of the risk to ambulance staff, via aerosol transmission, of an acute respiratory infection (ARI) during a coughing event within the clinical setting of an ambulance. International bibliographic databases were searched (CINAHL Plus, SCOPUS, PubMed, and CENTRAL) using appropriate search strings and a combination of relevant medical subject headings with appropriate truncation. Methodological filters were not applied. Papers without an English language abstract were excluded from the review. Grey literature was sought by searching specialist databases OpenGrey and GreyNet, as well as key organizations' websites. The initial search identified 2,405 articles. Following screening, along with forward and backward citation of key papers identified within the literature search, 36 papers were deemed eligible for the scoping review. Attempts to replicate a clinical environment to investigate the risk of transmission of airborne viruses to health care workers during a coughing event provided evidence for the generation of respirable aerosol particles and thus potential transmission of pathogens. In cases of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), potential to infect versus true airborne transmission is a debate that continues, but there is general consensus that a large variation of cough characteristics and aerosol generation amongst individuals exists. Studies widely endorsed face masks as a source control device, but there were conflicting views about the impact of mask leakage. Further research is required to provide clarity of the risk to health care workers when caring for a coughing patient in the confined clinical ambulance setting and to provide an evidence base to assist in the determination of appropriate respiratory protective equipment (RPE).

Translational framework for implementation evaluation and research: Protocol for a qualitative systematic review of studies informed by Normalization Process Theory (NPT) [version 1; peer review: 2 approved

BackgroundNormalization Process Theory (NPT) identifies mechanisms that have been demonstrated to play an important role in implementation processes. It is now widely used to inform feasibility, process evaluation, and implementation studies in healthcare and other areas of work. This qualitative synthesis of NPT studies aims to better understand how NPT explains observed and reported implementation processes, and to explore the ways in which its constructs explain the implementability, enacting and sustainment of complex healthcare interventions.MethodsWe will systematically search Scopus, PubMed and Web of Science databases and use the Google Scholar search engine for citations of key papers in which NPT was developed. This will identify English language peer-reviewed articles in scientific journals reporting (a) primary qualitative or mixed methods studies; or, (b) qualitative or mixed methods evidence syntheses in which NPT was the primary analytic framework. Studies may be conducted in any healthcare setting, published between June 2006 and 31 December 2021. We will perform a qualitative synthesis of included studies using two parallel methods: (i) directed content analysis based on an already developed coding manual; and (ii) unsupervised textual analysis using Leximancer® topic modelling software.OtherWe will disseminate results of the review using peer reviewed publications, conference and seminar presentations, and social media (Facebook and Twitter) channels. The primary source of funding is the National Institute for Health Research ARC North Thames. No human subjects or personal data are involved and no ethical issues are anticipated.

Video Relay Interpretation and Overcoming Barriers in Health Care for Deaf Users: Scoping Review.

BackgroundPersons who are deaf are more likely to avoid health care providers than those who can hear, partially because of the lack of means of communication with these providers and the dearth of available interpreters. The use of video remote interpretation, namely the video camera on an electronic device, to connect deaf patients and health providers has rapidly expanded owing to its flexibility and advantageous cost compared with in-person sign language interpretation. Thus, we need to learn more about how this technology could effectively engage with and respond to the priorities of its users.ObjectiveWe aimed to identify existing evidence regarding the use of video remote interpretation (VRI) in health care settings and to assess whether VRI technology can enable deaf users to overcome barriers to interpretation and improve communication outcomes between them and health care personnel.MethodsWe conducted a search in 7 medical research databases (including MEDLINE, Web of Science, Embase, and Google Scholar) from 2006 including bibliographies and citations of relevant papers. The searches included articles in English, Spanish, and French. The eligibility criteria for study selection included original articles on the use of VRI for deaf or hard of hearing (DHH) sign language users for, or within, health care.ResultsFrom the original 176 articles identified, 120 were eliminated after reading the article title and abstract, and 41 articles were excluded after they were fully read. In total, 15 articles were included in this study: 4 studies were literature reviews, 4 were surveys, 3 were qualitative studies, and 1 was a mixed methods study that combined qualitative and quantitative data, 1 brief communication, 1 quality improvement report, and 1 secondary analysis. In this scoping review, we identified a knowledge gap regarding the quality of interpretation and training in sign language interpretation for health care. It also shows that this area is underresearched, and evidence is scant. All evidence came from high-income countries, which is particularly problematic given that most DHH persons live in low- and middle-income countries.ConclusionsFurthering our understanding of the use of VRI technology is pertinent and relevant. The available literature shows that VRI may enable deaf users to overcome interpretation barriers and can potentially improve communication outcomes between them and health personnel within health care services. For VRI to be acceptable, sign language users require a VRI system supported by devices with large screens and a reliable internet connection, as well as qualified interpreters trained on medical interpretation.

Predicting the impact of no. of authors on no. of citations of research publications based on neural networks

Any work's citations are regarded as a key characteristic that leads to its appraisal and study. Citations are one of the most important indicators of a research publication's quality. Citations can have a favorable or bad impact on any piece of work or publication depending on a variety of circumstances, including author skill, publication venue, research topic, and so on. The goal of this study is to see how the number of co-authors affects the number of citations in research papers. There will be a correlation analysis between the number of co-authors and the number of citations for research articles, and we will observe how the number of co-authors affects the number of citations for publications. Citation data is gathered from databases such as DBLP, ACM, MAG (Microsoft Academic Graph), and others. There are 629,814 papers and 632,752 citations in the initial version. We use two methods to examine the impact of co-author count on the number of citations in a research paper: (i) Pearson’s correlation coefficient (PCC), and (ii) multiple regression (MR). To test the impact of co-author count on citation count of research publications, we calculate Pearson’s correlation coefficient (ra) between the two variables number of authors (NA) and citation count (CC). We also calculate Pearson’s correlation coefficient between the citation count (CC) and the most effective variables to compare between the impact of the number of authors and the impact of the other factors such as (i) rc between number of countries (NC) and citation count (CC). (ii) rv between venue category (VC) and citation count (CC). (iii) ry between Year_From (YF) and citation count (CC). Empirical evidence shows that co-authored publications achieve higher visibility and impact. To predict the number of citations from the previously mentioned factors (NA, NC, VC, and YF), we use multiple linear regression (MLR). The goal of multiple linear regression (MLR) is to model the linear relationship between the explanatory (independent) variables and response (dependent) variables. The higher R-square, the tight relationship exists between dependent variables and independent variables. It is observed that the R-square decreases in the case of removing NA which means that the NA is the most influential factor (the relation between NA and CC is the most powerful relation). The main originality of this paper is to introduce an effective prediction module (EPM) which uses probabilistic neural network (PNN) to predict the number of citations from the most effective factors (NA, NC, VC, and YF).

Analysis of publications by authors of Ukrainian institutes in Scopus‐delisted titles

AbstractIn Ukraine, Scopus data are used to evaluate academics. Existing shortcomings in the Ukrainian evaluation system allow them to publish in titles that have been delisted from Scopus, and continue to use those papers as credible research output for evaluation. The purpose of this study was to analyse the publishing activity of Ukrainian institutions in Scopus‐delisted titles (as of September 2021) in different fields between 2011 and 2020 and to attempt to appreciate how common this practice is among Ukrainian authors. Scopus was sourced to collect bibliographic and citations‐related data, while SciVal was used to analyse these data. The findings suggest that for 17 Ukrainian institutions, papers from titles that have been delisted from Scopus still play an important part of the publication achievement of their employees. In particular, in the field of economics, econometrics and finance, 46.92% of Ukrainian papers were published in a title that was excluded from Scopus. Moreover, the analysis indicated that in two Ukrainian institutions, the level of citation of such papers significantly exceeds the average number of citations to Scopus‐indexed papers in the same year and in the same field. Given that bibliometric indicators are also used for research assessment in other Eastern European countries, the results of this paper are applicable to a wider geographic context.

The Extent of Gender Gap in Citations in Ophthalmology Literature.

PurposeTo investigate the severity and causes of gender imbalance in the counts of ophthalmology citations.MethodsThe PubMed database was searched to identify cited papers that were published in four journals (Prog Retin Eye Res, Ophthalmology, JAMA Ophthalmol, and Invest Ophthalmol Vis Sci) between August 2015 and July 2020, and those that referenced these cited papers by 2021 July (i.e., citing papers). The gender category of a given paper is defined by the gender of the first and last author (MM, FM, MF, and FF; M means male and F means female). A generalized additive model to predict the expected proportion was fitted. The difference between the observed proportion and expected proportion of citations of a paper’s gender category was the primary outcome.ResultsThe proportion of female-led (MF and FF) papers slightly increased from 27% in 2015 to 30% in 2020. MM, FM, MF, and FF papers were cited as −9.3, −1.5, 13.0, and 23.9% more than expected, respectively. MM papers cited 13.9% more male-led (MM and FM) papers than female-led papers, and FF papers cited 33.5% fewer male-led papers than female-led papers. The difference between the observed proportion and expected proportion of MM citing papers within male-led and female-led cited papers grew at a rate of 0.13 and 0.67% per year.ConclusionThe high frequency of citations of female-led papers might narrow the gender gap in the citation count within ophthalmology. These findings show that papers by female-led are less common, so the gender gap might still exist even with their high citation count.

Global scientific production, international cooperation, and knowledge evolution of public administration

AbstractPublic administration is a discipline with considerable history, and is also a diverse, interdisciplinary field in social science. To analyze its evolution, discover the present research foci, and predict future development trends, this study applied scientometrics visualization technology to evaluate over 72,000 scientific articles from the 1920s to 2020s. This research referred to the SSCI and JCR databases to gather scientific data of the discipline and the journals' impact factor. Consequently, paper citations, cited journals, journal co‐citations, author co‐citations, authoritative papers, top countries, productive institutes, average references, and research collaboration trends were analyzed on the bases of the published literature. This study found top productive journals in the discipline, discovered productive countries and institutes, present the research foci, and predicted future development trends. Through this study, scientific production, international cooperation, and knowledge evolution mode of public administration research offers a clear knowledge map of the public administration discipline.

Keywords-Driven Paper Recommendation Based on Mobile Edge Computing Environment Framework

In the cloud computing era, a paper recommender system is usually deployed on the cloud server and return recommendation results to readers directly. However, considering the paper recommender system, processing tremendous paper citation data on the cloud cannot provide fine-grained personalized and real-time recommendations for each reader because these recommended papers from the cloud are far from readers and probably not correlated strongly with each other for helping each reader research further and deeper in the interested field. Recently, the edge-cloud collaboration-based recommender system has been used for releasing parts of the cloud computing task to the edge and provides the recommendation near the client. Based on the edge computing recommender system, a keywords-driven and weight-aware paper recommendation approach is presented, namely, LP-PRk+ w (link prediction-paper recommendation), to enable intelligent, personalized, and efficient paper recommendation services in the mobile edge computing environment. Specifically, the whole paper recommendation process mainly covers two parts: optimizing the existing paper citation graph via introducing a weighted similarity (i.e., building a weighted paper correlation graph) and then recommending a set of correlated papers according to the weighted paper correlation graph and the users’ query keywords. Experiments on a real-world paper correlation dataset, Hep-Th, show the capability of our proposal for improving the paper recommendation performance and its superiority against other related solutions.

A bibliometric analysis of the Journal of Musculoskeletal Surgery and Research

Objectives: The Journal of Musculoskeletal Surgery and Research (JMSR) is a relatively new publication focusing on research related to musculoskeletal surgery and related research. We aimed to analyze its bibliometrics to help the concerned community and JMSR’s audience, authors, and editorial team get to know the journal more in-depth. Methods: We performed a manual search of the JMSR’s publications for assessing various bibliometric parameters, including the type of published documents and their citation frequencies, authors with most papers, authors affiliations (national vs. international), and frequency distribution of keywords from the Journal’s inception in the mid-2017 through 2021. We used the Google scholar database and analyzed the data using Microsoft Excel VOS viewer software. Results: Two hundred and twenty-nine documents have been published from mid-2017 through 2021 in JMSR by 540 authors (458 males and 82 females), having a trend of the annual increment (R2 0.3684). There were 97 original articles published to date with a mean of 5.14 ± 4.20 articles in each issue. By the end of 2021, JMSR has received 192 citations from 197 citable papers out of 229 published documents from 2017 through 2021, with an average of 38.40 cites/year and 0.97 cites/paper, and an h–index of 6. We found 2.4 ± 1.5 (Range 1–6) institutions per article, with 110 (55.7%) being local institutes, whereas 85 (44.3%) were international institutes. The number of authors per paper ratio was 4.3 ± 1.7. Conclusion: JMSR is providing quality scholarship publications in orthopedics and musculoskeletal research, which could provide global insight pertaining to the field, to its viewers, authors, and editorial team.

Predicting information diffusion via deep temporal convolutional networks

Information cascade diffusion is ubiquitous in modern social medias and other fields, such as viral marketing, paper citation dynamics, and public opinion communication. However, the existing deep learning-based methods to model and predict the growth of information cascades pay much attention to the nodes in the cascades and ignore the overall propagation structure of the cascades. Simultaneously, they are usually of high complexity and low computational efficiency. In this paper, we propose a novel deep learning framework for information cascade predictor, named CasTCN, which can effectively capture the structure dynamic of the information cascades. Firstly, we design a dynamic mapping mechanism, which can represent the overall structure of information cascades an its dynamic evolution. Secondly, we extract the higher-level representation of cascade network through deep temporal convolutional network. Finally, the prediction module is applied to predict the growth scale of information cascades. Since CasTCN is a graph-level method, it has relatively fewer model parameters, so the model training time is also less than other baseline methods. Our experiments on two real-world datasets show that CasTCN can achieve better performance than other baseline methods on both effectiveness and efficiency. Compared with the state-of-the-art methods, the prediction error is reduced by 8.58%, 7.13% and 6.89% respectively on 3 subdatasets of Weibo, and 15.00%, 11.02% and 9.12% on 3 subdatasets of APS respectively. Also, the training time is much more less than baselines.

The Italian contribution to the marketing literature

The purpose of this study is to describe the contribution of Italian scholars to the international literature of marketing, and to identify the intellectual structure of Italian scholars’ production, by mapping the co-authorships network and the main researched themes. Results highlight that Italian marketing scholars significantly intensified their productivity in the last ten years from different viewpoints. There has been a growing process of internationalization, a grownup of articles published by multiple co-authors in journals with higher impact factor, and a better performance in terms of citations of Italian papers compared to the international average. These results are all positive signals of a dynamic and developing community.

Citation versus altmetrics of papers published in PLOS ONE: A comparative analysis of six countries

Citation versus altmetrics of papers published in PLOS ONE: A comparative analysis of six countries

Research artifacts and citations in computer systems papers.

Research in computer systems often involves the engineering, implementation, and measurement of complex systems software and data. The availability of these artifacts is critical to the reproducibility and replicability of the research results, because system software often embodies numerous implicit assumptions and parameters that are not fully documented in the research article itself. Artifact availability has also been previously associated with higher paper impact, as measured by citations counts. And yet, the sharing of research artifacts is still not as common as warranted by its importance. The primary goal of this study is to provide an exploratory statistical analysis of the artifact-sharing rates and associated factors in the research field of computer systems. To this end, we explore a cross-sectional dataset of papers from 56 contemporaneous systems conferences. In addition to extensive data on the conferences, papers, and authors, this analyze dataset includes data on the release, ongoing availability, badging, and locations of research artifacts. We combine this manually curated dataset with citation counts to evaluate the relationships between different artifact properties and citation metrics. Additionally, we revisit previous observations from other fields on the relationships between artifact properties and various other characteristics of papers, authors, and venue and apply them to this field. The overall rate of artifact sharing we find in this dataset is approximately 30%, although it varies significantly with paper, author, and conference factors, and it is closer to 43% for conferences that actively evaluated artifact sharing. Approximately 20% of all shared artifacts are no longer accessible four years after publications, predominately when hosted on personal and academic websites. Our main finding is that papers with shared artifacts averaged approximately 75% more citations than papers with none. Even after controlling for numerous confounding covariates, the release of an artifact appears to increase the citations of a systems paper by some 34%. This metric is further boosted by the open availability of the paper’s text.