JCR Impact Factor Research Articles

Advanced procedures and scheduling for aircraft assembly processes: A systematic review approach

The target of the study is to conduct a systematic review of the scientific literature published over the last 20 years on advanced procedures and scheduling of aircraft assembly processes. The bibliography analysed includes the 25 most cited references during this period, published in English, open access and fee-paying, applicable to the aeronautical field, and published in aeronautical, mechanical, or manufacturing engineering journals, and with a Q1 (80%) and Q2 (20%) classification by JCR impact factor. The final selection of articles has a total number of 806 citations and a total average of 32.24 citations/article. Forty percent of the selected articles are focused on the optimisation of the assembly process planning, 28% are aimed at improving the assembly process by developing the machining process of the individual components that constitute the assembly, and 20% of the studies address their research to the automation of the process by implementing robots. In addition, 16% of the selected articles include the proposal of new algorithms and process models, and 12% involve the development of new ad hoc software, with the integration of the developed software into existing software commonly used in the aeronautical sector, such as CATIA by Dassault Systèmes. On the other hand, 52% of the works include within the study the analysis of a case on an industrial scale in the aeronautical sector such as engines, fuselages, aircraft wings and tails, satellites, and final assembly lines. Finally, regarding the origin of the studies, China is the country with the highest percentage of publications on this topic, accounting for 40% of the selected articles, followed by the United Kingdom in second place with 16% and Canada in third place with 12%.

Artificial intelligence in COVID-19 evidence syntheses was underutilized, but impactful: a methodological study

ObjectivesA rapidly developing scenario like a pandemic requires the prompt production of high-quality systematic reviews, which can be automated using artificial intelligence (AI) techniques. We evaluated the application of AI tools in COVID-19 evidence syntheses. Study DesignAfter prospective registration of the review protocol, we automated the download of all open-access COVID-19 systematic reviews in the COVID-19 Living Overview of Evidence database, indexed them for AI-related keywords, and located those that used AI tools. We compared their journals’ JCR Impact Factor, citations per month, screening workloads, completion times (from pre-registration to preprint or submission to a journal) and AMSTAR-2 methodology assessments (maximum score 13 points) with a set of publication date matched control reviews without AI. ResultsOf the 3,999 COVID-19 reviews, 28 (0.7%, 95% CI 0.47–1.03%) made use of AI. On average, compared to controls (n = 64), AI reviews were published in journals with higher Impact Factors (median 8.9 vs. 3.5, P < 0.001), and screened more abstracts per author (302.2 vs. 140.3, P = 0.009) and per included study (189.0 vs. 365.8, P < 0.001) while inspecting less full texts per author (5.3 vs. 14.0, P = 0.005). No differences were found in citation counts (0.5 vs. 0.6, P = 0.600), inspected full texts per included study (3.8 vs. 3.4, P = 0.481), completion times (74.0 vs. 123.0, P = 0.205) or AMSTAR-2 (7.5 vs. 6.3, P = 0.119). ConclusionAI was an underutilized tool in COVID-19 systematic reviews. Its usage, compared to reviews without AI, was associated with more efficient screening of literature and higher publication impact. There is scope for the application of AI in automating systematic reviews.

Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature review.

When put into practice in the real world, predictive maintenance presents a set of challenges for fault detection and prognosis that are often overlooked in studies validated with data from controlled experiments, or numeric simulations. For this reason, this study aims to review the recent advancements in mechanical fault diagnosis and fault prognosis in the manufacturing industry using machine learning methods. For this systematic review, we searched Web of Science, ACM Digital Library, Science Direct, Wiley Online Library, and IEEE Xplore between January 2015 and October 2021. Full-length studies that employed machine learning algorithms to perform mechanical fault detection or fault prognosis in manufacturing equipment and presented empirical results obtained from industrial case-studies were included, except for studies not written in English or published in sources other than peer-reviewed journals with JCR Impact Factor, conference proceedings and book chapters/sections. Of 4549 records, 44 primary studies were selected. In 37 of those studies, fault diagnosis and prognosis were performed using artificial neural networks (n = 12), decision tree methods (n = 11), hybrid models (n = 8), or latent variable models (n = 6), with one of the studies employing two different types of techniques independently. The remaining studies employed a variety of machine learning techniques, ranging from rule-based models to partition-based algorithms, and only two studies approached the problem using online learning methods. The main advantages of these algorithms include high performance, the ability to uncover complex nonlinear relationships and computational efficiency, while the most important limitation is the reduction in model performance in the presence of concept drift. This review shows that, although the number of studies performed in the manufacturing industry has been increasing in recent years, additional research is necessary to address the challenges presented by real-world scenarios.

Selecting the appropriate leading journal in Hospitality and Tourism research: a guide based on the topic-journal fit and the JCR impact factor

Selecting the appropriate academic journal is a priority issue for researchers in the process of publishing a manuscript. If researchers could quantify the research topic in terms of its fit with the journal requirements before the submission of a paper, then the assessment of journal suitability could be much easier. Basing any decision on journal impact factors alone might obviously result in a mismatch, eventual rejection and a consequent loss of time. Taking the twelve leading Tourism and Hospitality journals as a reference, the main research topics mentioned in the abstracts of 20,381 articles are identified, using the Latent Dirichlet Allocation algorithm and other text-mining techniques running the R programming language. Subsequently, a quantitative measure of the fit of the research topics in each journal is offered according to their frequency of occurrence. The results suggested that the importance of the topic-journal fit with respect to the impact factor depended on the variance of the fits among the journals. Finally, a guide of the most suitable journals for the topics is presented, based on the JCR impact factor and the fit of the topic. Some recommendations are likewise offered on the use of this methodology and its limitations.

Decentralizing science: Towards an interoperable open peer review ecosystem using blockchain

Scientific publication and its Peer Review system strongly rely on a few major industry players controlling most journals (e.g. Elsevier), databases (e.g. Scopus) and metrics (e.g. JCR Impact Factor), while keeping most articles behind paywalls. Critics to such system include concerns about fairness, quality, performance, cost, unpaid labor, transparency, and accuracy of the evaluation process. The Open Access movement has tried to provide free access to the published research articles, but most of the aforementioned issues remain. In such context, decentralized technologies such as blockchain offer an opportunity to experiment with new models for scientific production and dissemination relying on a decentralized infrastructure, aiming to tackle multiple of the current system shortcomings. This paper makes a proposal for an interoperable decentralized system for an open peer review ecosystem, relying on emerging distributed technologies such as blockchain and IPFS. Such system, named “Decentralized Science” (DecSci), aims to enable a decentralized reviewer reputation system, which relies on an Open Access by-design infrastructure, together with transparent governance processes. Two prototypes have been implemented: a proof-of-concept prototype to validate DecSci’s technological feasibility, and a Minimum Viable Product (MVP) prototype co-designed with journal editors. In addition, three evaluations have been carried out: an exploratory survey to assess interest on the issues tackled; two sets of interviews to confirm both the main problems for editors and to validate the MVP prototype; and a cost analysis of the main operations, both execution cost and actual price. Additionally, the paper discusses the multiple interoperability challenges such proposal faces, including an architecture to tackle them. This work finishes with a review of some of the open challenges that this ambitious proposal may face.

Is There a Need for a New Journal Metric? Correlations between Jcr Impact Factor Metrics and the Journal Citation Indicator - Jci

Is There a Need for a New Journal Metric? Correlations between Jcr Impact Factor Metrics and the Journal Citation Indicator - Jci

Editorial Note for Journal of Surgery [Jurnalul de chirurgie

I am pleased to mention that during the year 2019, all issues of volume 15 were published online well within the time and the print issues were also brought out and dispatched within 30 days of publishing the issue online. Journal of Surgery [Jurnalul de chirurgie] during this year also brought out 4th Global Meeting on Plastic, Aesthetic and Reconstructive Surgery, which consisted of ~11 abstracts. The JCR Impact factor of JOS for the year 2018 was 1.91, while the current SJR index dropped to 0.256. The H-index of the journal, however, increased to 50 and JOS ranked first among all journals in the field of Surgical Sciences published in India, 2nd in Asia and 48th in the world. During the calendar year 2019, JOS received a total of 62 papers, out of which 36 articles (58%) were rejected in the preliminary screening due to plagiarism or being out of the format and peer review process. During 2019 around 14 articles were subjected for publication after they are accepted in the peer review process. In the 3 issues of Volume 15 published during the year 2019, a total of 14 articles were published (at an average of 8 articles per issue) of which, articles were published from authors all around the world. A total of 110 research scientists from all over the world reviewed the 14 articles published in volume 15. Average publication lag time of an article was further reduced to 2-3 weeks. During the calendar year 2019, a total of 32 Editors, 15 Reviewers joined the board of JOS and contributed their invaluable services towards contribution as well as publication of articles. I take this opportunity to acknowledge the contribution of Eugen Tarcoveanu and Radu Moldovanu during the final editing of articles published and the support rendered by the editorial assistant, Isabelle Gomez in bringing out issues of JOS in time. I would also like to express my gratitude to all the authors, reviewers, the publisher, the advisory and the editorial board of JOS, the office bearers for their support in bringing out yet another volume of JOS and look forward to their unrelenting support to bring out the Volume 16 of JOS in scheduled time.

The Veterinary Quarterly now ranked a top 10 journal in the Veterinary Science 2018 JCR

Dear Reader,It is our pleasure to announce that the Veterinary Quarterly has seen its 2018 JCR Impact Factor increase from 1.492 to 2.340. As a consequence, it has also moved up in the rankings, an...

Should We Use JCR Impact Factor or We Can Have Our Own Relative Importance Metrics?

Should We Use JCR Impact Factor or We Can Have Our Own Relative Importance Metrics?

Writing for Publication in Business Administration: Comparing Insights from Brazilian and American Scholars

This paper seeks to compare the process of bibliographical production of Brazilian professors who lecture in postgraduate programs in the field of Business Studies with that of their North-American counterparts. In-depth interviews were conducted with professors, lectures in Brazil or US, who have presented articles for publication to international journals with JCR impact factor greater than 1. Keeping in mind the particularities of each nation's educational system, the results call for the organization of the process of bibliographical production in Brazil, so as to reduce the existing gap between the research conditions prevailing in Brazil and the US.

A proposal for a novel impact factor as an alternative to the JCR impact factor

One disadvantage of the JCR impact factor, the most commonly used assessment tool for ranking and evaluating scientific journals, is its inability in distinguishing among different shapes of citation distribution curves, leading to unfair evaluation of journals in some cases. This paper aims to put forward an alternative impact factor (IF′) that can properly reflect citation distributions. The two impact factors are linearly and positively correlated, and have roughly the same order of magnitude. Because of the ability of IF′ in distinguishing among different shapes of citation distribution curves, IF′ may properly reflect the academic performance of a scientific journal in a way that is different from the JCR impact factor with some unique features that reward journals with highly cited papers. Therefore, it is suggested that IF′ could be used to complement the JCR impact factor.

An annual JCR impact factor calculation based on Bayesian credibility formulas

Our aim deals with appraising the annual impact calculation of journals belonging to the JCR, in terms of the expected citation (with or without selfcitations) by published paper in a range of k-years. A Bayesian approach to the problem, should reflect not only the current prestige of a journal, but also taking into account its recent trajectory. In this wide context, credibility theory becomes an adequate mechanism deciding whether journal's impact factor calculation to be more or less plausible. Under prior belief that journal quality is determined by its impact factor, we model the citation-quality process by choosing a conjugated family of the exponential class in order to obtain a net impact credibility formula. Proposed weighting schema produces the effect of smoothing out any sudden increases or decreases in the year-by-year impact factor.

Making an Impact for Children

This issue of Child Abuse Review opens with some importantnews for our readers. We have just heard from the Institute forScientific Information Journal Citation Reports that Child AbuseReview has been awarded an impact factor of 1.463. The impactfactor is ‘a measure of the frequency with which the ‘averagearticle’ in a journal has been cited in a particular year or period.The annual JCR impact factor is a ratio between citations andrecent citable items published. Thus, the impact factor of a journalis calculated by dividing the number of current year citations tothe source items published in that journal during the previoustwo years’ (Thomson Reuters, 2011). This is a brilliant rankingfor a first award, which places Child Abuse Review sixth out of39 in the social work category and 14/40 in the family studiescategory. Nicky Stanley, who is coming to the end of her termof office as Co-Editor at the end of the year, must take much ofthe credit for tenaciously campaigning over many years for thejournal’s formal recognition, along with Alison Gridley our Wiley-Blackwell Publishing Editor who has vigorously pushed throughour claim. Thanks also go to the editorial team, all our authors,all those who help us with peer review, and of course all our Editor-ial Board members and BASPCAN representatives whose supporthas been invaluable. This is a very exciting time indeed to beinvolved with Child Abuse Review. Please spread the good newsamongst your colleagues and other professional contacts.

Comparing impact factors from two different citation databases: The case of Computer Science

Journal impact factors continue to play an important role in research output assessment, in spite of the criticisms and debates around them. The impact factor rankings provided in the Journal Citation Reports (JCR™) database by Thompson Reuters have enjoyed a position of monopoly for many years. But this has recently changed with the availability of the Scopus™ database and its associated journal ranking published in the Scimago Journal Rank (SJR) Web page, as the former provides a citation database with similar inclusion criteria to those used in the JCR and the latter and openly accessible impact factor-based ranking. The availability of alternatives to the JCR impact factor listings using a different citation database raises the question of the extent to which the two rankings can be considered equally valid for research evaluation purposes. This paper reports the results of a contrast of both listings in Computer Science-related topics. It attempts to answer the validity question by comparing the impact factors of journals ranked in both listings and their relative position. The results show that impact factors for journals included in both rankings are strongly correlated, with SJR impact factors in general slightly higher, confirming previous studies related to other disciplines. Nonetheless, the consideration of tercile and quartile position of journal yields some divergences for journals appearing in both rankings that need to be accounted for in research evaluation procedures.

Impact factor and the Australian Occupational Therapy Journal: A notable achievement

Journal quality metrics have been around since the 1960s (Garfield, 2006). Currently the primary journal quality metrics used by universities, research review agencies and research funding bodies are those published by the Thomson Reuters ISI (a private, for-profit company) and reported in the Journal Citation Reports® (JCR). They are based on citation data for journals included in the Science Citation Index/(Expanded) (SCI/(E)) and the Social Science Citation Index (SSCI), which can collectively be searched through the Thomson Reuters ISI Web of Knowledge database. One key journal quality metric that is calculated in the JCR is the Impact Factor (IF), a measure of the frequency with which the average article in a journal has been cited in a particular year. It is calculated by dividing the number of citations in the current year to articles published in the previous two years by the total number of articles published in the previous two years. Another quality metric often reported is the journal ranking of a publication on the JCR database. Three years ago, the AOTJ editorial board with assistance from staff from Wiley-Blackwell prepared an application for submission to Thomson Reuters ISI Web of Knowledge for the AOTJ to be included on the database, to receive an IF and journal ranking. Two years ago, the AOTJ submission was accepted. Now for the first time, the AOTJ has an IF of 0.438, an impressive achievement indeed. Currently there are only three other occupational therapy journals in the JCR Reports for 2009. The 2009 IFs for these three journals were: American Journal of Occupational Therapy (AJOT), 1.419; Occupational Therapy Journal of Research (OTJR), 0.358; and Hong Kong Journal of Occupational Therapy (HKJOT), 0.462. The 2009 SJR journal rankings for AOTJ, HKJOT, AJOT and OTJR were 31/36, 30/36, 17/55 and 50/55, respectively. IFs are increasingly being used in academic, research and grant funding arenas as a quality indicator of individual staff members and as an eligibility criteria for promotion, pay increases, grant funding access eligibility and receiving resources within higher education contexts (Saha, Saint & Christakis, 2003). For example, the Australian Government/Australian Research Council (2010) has initiated a programme referred to as the Excellence in Research for Australia (ERA) initiative which will assess ‘research quality within Australia’s higher education institutions using a combination of indicators and expert review by committees comprising experienced, internationally-recognised experts’ (retrieved on 2 August 2010 from http://www.arc.gov.au/era/default.htm). One of the quantitative indicators that will be used to assess universities’ research quality, including those that have occupational therapy education programs, will be the JCR IF and journal ranking. How universities perform in the ERA exercise will have significant implications for the allocation of scarce research funding resources. The ERA has also developed its own journal ranking system where publications from all fields of study (e.g. psychology, physics, law and engineering) are ranked as A, B or C based on their quality indicators such as prestige, quality and rigor. Currently, no occupational therapy journals have been given the ranking of A by ERA, AOTJ and AJOT are ranked level B and other occupational therapy journals are ranked as a level C (such as CJOT, BJOT, SJOT and OTI). IF and other journal quality metrics are points of reference for occupational therapists (and others) who publish and hopefully this will encourage prospective authors to submit high-calibre manuscripts to AOTJ that are relevant to the discipline. In order for the AOTJ to remain competitive (nationally and internationally) and relevant to its readership and the authors who publish in it, achieving an IF is an outstanding accomplishment. The AOTJ editorial board in collaboration with Wiley-Blackwell will continue to work on constructive ways to improve the journal’s IF and journal ranking when it is next calculated and published. This will assist to ensure that the AOTJ continues to be the high-quality, useful, recognised refereed publication it is.

A reference-based Hirschian similarity measure for journals

Hirsch’s concept of h-index was used to define a similarity measure for journals. The h-similarity is easy to calculate from the publicly available data of the Journal Citation Reports, and allows for plausible interpretation. On the basis of h-similarity, a relative eminence indicator of journals was determined: the ratio of the JCR impact factor to the weighted average of that of similar journals. This standardization allows journals from disciplines with lower average citation level (mathematics, engineering, etc.) to get into the top lists.

GIScience Journals Ranking and Evaluation: An International Delphi Study

AbstractResearchers’ fame in most scientific fields is closely linked to their publishing capacity, both in terms of quantity and quality. In GIScience, as in other fields, this situation demands that the researcher evaluate and to be very familiar with the scientific journals in which they could publish. Some specialized journals (e.g. Journal of Citation Reports or JCR) are devoted to ranking these reviews according to various methods and criteria. Compared to other scientific communities, GIScience is relatively new and constantly evolving. Therefore, the journals of this field do not benefit from any real formal ranking yet. The objective of this paper is to present the process and results of a study aimed at addressing this gap. More specifically, the challenge is to elaborate an importance ranking of the scientific journals in the field of GIScience. To do so, both a qualitative (Delphi study carried out with 40 international experts) and a quantitative (JCR impact factor) approach has been implemented. This triangulation method leads to an early global ranking of the journals of this field.

Rank‐normalized impact factor: A way to compare journal performance across subject categories

AbstractIt is well known that uninformed science administrators often use ISI's journal impact factors without taking into account the inherent citation characteristics of individual scientific disciplines. A rank normalized impact factor (rnlF) is proposed which involves use of order statistics for the complete set of journals within each JCR category. We believe the normalization procedure provides reliable and easily interpretable values. For any journal j, its rnlF is designated as rnlF1and equals (K–R1+ 1)/K, where R1 is the descending rank of journal j in its JCR category and K is the number of journals in the category. Note: JCR impact factor listings are published in descending order. The proposed rnlF is compared with normalized impact factors proposed by earlier authors. The efficacy of the rnlF is illustrated in the cases of seven highly‐cited scientists, one each from seven different fields.

A Deficiency in the Algorithm for Calculating the Impact Factor of Scholarly Journals: The Journal Impact Factor

The Journal Citation Reports ® (JCR), published by the Institute for Scientific Information, annually ranks more than 6,500 science and social science journals, by calculating their impact factors (and many other useful measures) from the citations they receive from journals monitored by ISI in the Science Citation Index (SCI), Social Science Citation Index (SSCI), and Arts & Humanities Citation Index (A&HCI) databases that process about 25 million citations annually. As Eugene Garfield, the founder of ISI, the inventor of the journal impact factor, and the deservedly most cited information scientist defines it: “[t]he annual JCR impact factor is a ratio between citations and recent citable items published. Thus, the impact factor of a journal is calculated by dividing the number of current year citations to the source items published in that journal during the previous two years, by calculating the ratio between the number of citations received and the number of citable articles published” (Garfield, 1994 [emphases added]). For example, the impact factor of Cortex was determined by the algorithm shown in Figure 1 in the 1999 edition of JCR (the most current as of this writing).

Towards appropriate indicators of journal impact

This paper reviews a range of studies conducted by the authors on indicators reflecting scholarly journal impact. A critical examination of the journal impact data in theJournal Citation Reports (JCR), published by the Institute for Scientific Information (ISI) has shown that the JCR impact factor is inaccurate and biased towards journals revealing a rapid maturing or decline in impact. In addition, it was found that the JCR cited half life is an inappropriate measure of decline of journal impact. More appropriate impact measures of scholarly journals are proposed. A new classification system is explored, describing both maturing and decline of journal impact as measured through citations. Suggestions for future research are made, analysing in more detail the distribution of citations among papers in a journal.