Scientific Editors Research Articles

Introductory Word by the Scientific Editor of This Issue of the Journal

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A Data-Driven Optimization Framework for Static Rebalancing Operations in Bike Sharing Systems

Bike sharing systems have been widely deployed in urban cities for first- and last-mile transportation. However, because of the geographical and temporal imbalance of bike demand, bikes need to be reallocated system-wide among stations during the night to maintain a high service level while minimizing demand loss due to stockout or overcapacity. Two technical challenges remain in optimizing the static bike rebalancing operations. One challenge is to accurately predict bike pickup and dropoff demand at each station, considering demand substitution effects and subsequently determining the optimal rebalancing quantity for each station. The other is to efficiently optimize the routing of multiple rebalancing vehicles for large-scale bike sharing systems, considering outlier stations with rebalancing quantities exceeding vehicle capacity. To this end, we propose an end-to-end solution to tackle the aforesaid challenges. Specifically, we first develop deep learning-based predictors that capture the time dependencies of station-level demand, the impact of weather conditions, and the demand substitution effect by nearby stations. Based on the demand rate, a sequential simulation-based demand loss estimator is developed to find the optimal rebalancing quantities that lead to the minimum expected demand loss. Then, a mixed integer linear programming model is formulated to optimize the routing problem of rebalancing vehicles. To address the computational challenge, we propose a data-driven decomposition algorithm to support a multivehicle multivisit rebalancing strategy by decomposing the multivehicle routing problem into smaller and tractable single-vehicle routing problems, which can be solved in parallel. Finally, extensive numerical experiments using real-world data from New York City Citi Bike demonstrate the accuracy of the proposed bike demand predictors, the impact of demand substitution, and the efficiency of the data-driven optimization framework. History: Accepted by Ram Ramesh, Area Editor for Data Science & Machine Learning. Funding: This work was supported by the National Natural Science Foundation of China [Grant 72201222] and the Hong Kong Research Grants Council [Grants CityU 21500220 and CityU 11504322]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0182 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0182 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Security-Diplomacy Nexus: Nature of Bilateral Cooperation in Kenya - India Military Diplomatic Relations Since 1963

Bilateral and multilateral agreements have been one of the mechanisms through which growth and development have taken place. The newly created independent states of the Cold War period presented a major paradigm shift in international cooperation to include military diplomacy. Kenya and India being among these newly created states have had their cooperation where by the two countries have used their good diplomatic relations to bolster one another's military capabilities. The current study sought to put into perspective the conduct of bilateral relations between Kenya and India. The objective of this study was to examine the nature of bilateral cooperation in enhancing Kenya-India military-diplomatic relations since 1963. The study was anchored on power theory. The study adopted a descriptive research design. The study was carried out in Nairobi County, Kenya with a specific focus on the Ministry of Foreign Affairs, Defence Ministry, and Indian High Commission. The target population was 2500. The sample size for the study was determined using Yamane's (1967) formula to calculate the sample size, which was 250 Qualitative data obtained from interviews were analysed using narrative reports using thematic techniques. Quantitative data from the questionnaires was edited, coded, and entered into a computer spreadsheet in a standard format to allow descriptive statistics analysis. Statistical Packages for Social Sciences (SPSS) edition 24 and Microsoft Office Excel 2013 were used for the quantitative data. The findings of the study revealed that the nature of Kenya-India military diplomatic relations manifested in various forms including through High-profile visits by Kenyan and Indian officials to both states as a way of strengthening bilateral relations. Technological transfer was another way through which these two nations shaped their diplomatic relationship, particularly in information and communication technology (ICT). Additionally, Defense trade and trade agreements, Security Partnerships, Counterterrorism activities, Maritime Safety, and Peacekeeping missions were common areas of cooperation between Kenya and India. The study concludes that bilateral cooperation between Kenya and India has been instrumental in enhancing their military-diplomatic relations since 1963. The study recommends that, to better understand the nature of bilateral cooperation, Kenya and India should establish a permanent joint military commission to oversee and coordinate defense initiatives.

An Algorithm for Clustering with Confidence-Based Must-Link and Cannot-Link Constraints

We study here the semisupervised k-clustering problem where information is available on whether pairs of objects are in the same or different clusters. This information is available either with certainty or with a limited level of confidence. We introduce the pair-wise confidence constraints clustering (PCCC) algorithm, which iteratively assigns objects to clusters while accounting for the information provided on the pairs of objects. Our algorithm uses integer programming for the assignment of objects, which allows us to include relationships as hard constraints that are guaranteed to be satisfied or as soft constraints that can be violated subject to a penalty. This flexibility distinguishes our algorithm from the state of the art, in which all pair-wise constraints are considered hard or all are considered soft. We developed an enhanced multistart approach and a model-size reduction technique for the integer program that contribute to the effectiveness and efficiency of the algorithm. Unlike existing algorithms, our algorithm scales to large-scale instances with up to 60,000 objects, 100 clusters, and millions of cannot-link constraints (which are the most challenging constraints to incorporate). We compare the PCCC algorithm with state-of-the-art approaches in an extensive computational study. Even though the PCCC algorithm is more general than the state-of-the-art approaches in its applicability, it outperforms the state-of-the-art approaches on instances with all hard or all soft constraints in terms of both run time and various metrics of solution quality. The code of the PCCC algorithm is publicly available on GitHub. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Funding: The research of D. S. Hochbaum was supported by the AI Institute NSF Award [Grant 2112533]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0419 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0419 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Communicating about soil biodiversity: insights from science editorials and future recommendations

Given the urgency of the global soil degradation crisis, soil scientists must communicate the importance of soil as being part of nature and the critical need for conserving soil biodiversity. Drawing on a thematic analysis of editorials related to soil conservation and management, we analyzed common themes, key messages, and frames that authors used to advocate for change. Soil biodiversity was referred to in 8 of the 11 editorials, but it was less emphasized and discussed in less detail than messages centered around food production, water resources, and climate change. Editorials structured arguments around economic development, scientific and technical uncertainty, and morality and ethics. We believe future editorials should apply other persuasive frames including social progress, public accountability, and working-towards-compromise when advocating for soil biodiversity conservation. Soil ecologists can improve communications about soil biodiversity by (1) identifying an audience and selecting relevant key messages, (2) strategically applying a persuasive frame, and (3) amplifying diverse voices with a consistent message. We provide a guide for developing essays that encourages soil ecologists to shape discourse, advance policy priorities, and enable non-soil ecologists to communicate about soil biodiversity conservation.

Preface

The 7th International Conference on Computer Network, Electronic and Automation (ICCNEA 2024) was held in Xi’an Technological University from Sep. 20 to 22, 2024. The conference took place in the beautiful city of Xi’an, China, a city with a history of three thousand years. The conference aims to provide a high-level international forum for engineers and scholars to present their new advances and research results. The main topics of ICCNEA 2024 were: Networks and Computing, Computer Software and Theory, Control and Automation, Electronic Technology. In addition, the conference also invited six technical experts to give keynote speech. A total of 218 manuscripts were submitted to the conference. Three rounds of review were adopted, the first round was subject scope, scientific, technical and innovative review, and 132 articles were left after this round of review; the second round was double-expert blind review to strive for the originality, scientific and applied value of the articles, and 86 manuscripts were left in the second round. Finally, the third round was the process committee’s collective decision to accept the final articles. Finally, 79 excellent articles were selected. Beside the main venue, four sub-venues were set up to conduct technical exchanges in the four areas of manuscript solicitation. Each author presented their research hotspot. The conference was supported by State and Provincial Joint Engineering Lab. of Advanced Network, Monitoring and Control, China, Xi’an Technological University, China, Missouri Western State University, USA, Belarusian State University of Transport, Republic of Belarus, James Cook University, Australia, Chang’an University, Xi’an, China, Shaanxi Science and Technology Exchange Center, Xi’an, China, Base of Information of Vehicle-Infrastructure Sensing and ITS, China, Xi’an University of Architecture and Technology, Xi’an China. The organizers would like to express their gratitude and thank all the participants for their valuable contribution to the conference. This year’s conference is successfully over and we are looking forward to the next conference! List of Scientific committee and Editors are available in this Pdf.

Apresentação

When I was invited to be the scientific editor of this issue of Revista Internacional em Língua Portuguesa (RILP), dedicated to the theme ‘Environment and Blue Economy’, I was worried about the responsibility of reviewing articles on a subject that I have always found little understood in a world where pure economics is the centre of national and international policies.

Forecasting Urban Traffic States with Sparse Data Using Hankel Temporal Matrix Factorization

Forecasting urban traffic states is crucial to transportation network monitoring and management, playing an important role in the decision-making process. Despite the substantial progress that has been made in developing accurate, efficient, and reliable algorithms for traffic forecasting, most existing approaches fail to handle sparsity, high-dimensionality, and nonstationarity in traffic time series and seldom consider the temporal dependence between traffic states. To address these issues, this work presents a Hankel temporal matrix factorization (HTMF) model using the Hankel matrix in the lower dimensional spaces under a matrix factorization framework. In particular, we consider an alternating minimization scheme to optimize the factor matrices in matrix factorization and the Hankel matrix in the lower dimensional spaces simultaneously. To perform traffic state forecasting, we introduce two efficient estimation processes on real-time incremental data, including an online imputation (i.e., reconstruct missing values) and an online forecasting (i.e., estimate future data points). Through extensive experiments on the real-world Uber movement speed data set in Seattle, Washington, we empirically demonstrate the superior forecasting performance of HTMF over several baseline models and highlight the advantages of HTMF for addressing sparsity, nonstationarity, and short time series. History: Accepted by Ram Ramesh, Area Editor for Data Science & Machine Learning. Funding: This research was supported by the Institute for Data Valorisation, the Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation, the National Natural Science Foundation of China [Grants 12371456, 72101049, 72232001], the Sichuan Science and Technology Program [Grant 2024NSFJQ0038], and the Fundamental Research Funds for the Central Universities [Grant DUT23RC(3)045].

Medical Writing and Editing Internships: Preparing the Next Generation of Medical Communication Professionals

Conference Education Session Report Speakers Barbara Gastel, MD, MPH, ELS(H), Professor, Texas A&M University, College Station, TX Joan Affleck, MA, MBA, Associate Vice President, Head of Medical Writing, Merck & Co., Inc., Rahway, NJ Moderator Erica Goodoff, ELS(D), Senior Scientific Editor, The University of Texas MD Anderson Cancer Center, Houston, TX

Legal liability for plagiarism of scientific works: How do major publishers protect their content

The increase in the number of scientific publications and the spread of legal liability for plagiarism testify to the high priority and relevance of the issue of content protection for large publishing houses in today’s conditions. Thus, the purpose of the study was to establish the main mechanisms and approaches used by large publishers to combat plagiarism, namely in the legal context. To achieve this goal, the study used a number of methodological tools, i.e., functional and systematic approaches, the method of analysis and synthesis, the method of comparison, and the formal legal method. During the research, the concepts of “plagiarism”, “scientific work”, “content” were defined and their characteristics were described. In addition, various theoretical approaches to solving the problem of plagiarism in scientific works were considered. Based on this, the theoretical principles of copyright protection for scientific works were researched and substantiated. Also, during the research, the specifics of the application of modern legal methods of copyright protection in large publishing houses were revealed. As a result, the effectiveness of practical methods of combating plagiarism was identified and evaluated. It is also worth mentioning the development of practical recommendations for the authors of scientific works regarding the protection of their rights. The results obtained during the research can be used as methodological material for authors who wish to protect their scientific works from plagiarism, as well as for scientific editors, reviewers and other specialists who work with scientific texts

UPAYA PENGENALAN EDITORIAL DAN PENULISAN JURNAL ILMIAH PADA TENAGA PENDIDIK DI SMP NEGERI 2 KAMPAR KIRI HILIR KABUPATEN KAMPAR

Abstract: Partner situation analysis, teachers become professional educators, especially for those who have received teacher certification. Teachers are required to write scientific articles published in scientific journals so that their work can be read by various groups in need. Partner problems, teachers are still not familiar with scientific editors in the context of publishing scientific manuscripts, there is still a lack of teacher knowledge about how to submit scientific manuscripts to scientific journal editorials to be published in scientific journals. Submitting scientific manuscripts is the first step for teachers in order to publish their scientific work during teaching. So far, teachers still do not understand what scientific journals are and also how to submit scientific manuscripts to editors so that they can be published in scientific journals. The target output to be achieved in this activity is a form of change in the knowledge and ability of teachers as educators to submit scientific manuscripts to editors to be able to publish their scientific manuscripts to scientific journals. The solution provided in this activity is to provide training on submitting scientific manuscripts to editorial to be published in scientific journals. Based on the results of the service that was carried out on December 15, 2016, information was obtained that there was a change in teacher knowledge about what scientific journal publishers are, the usefulness of publishers, submitting manuscripts to publishers and how strategies for writing articles so that they can be published in scientific journals.

Elevating the Standards of Scientific Editing in Clinical Surgery: Learning from Editorial and Journal Role Models

Elevating the Standards of Scientific Editing in Clinical Surgery: Learning from Editorial and Journal Role Models

Report on the 2024 Council of Science Editors Annual Meeting

Report on the 2024 Council of Science Editors Annual Meeting

Ranking Model Averaging: Ranking Based on Model Averaging

Ranking problems are commonly encountered in practical applications, including order priority ranking, wine quality ranking, and piston slap noise performance ranking. The responses of these ranking applications are often considered as continuous responses, and there is uncertainty on which scoring function is used to model the responses. In this paper, we address the scoring function uncertainty of continuous response ranking problems by proposing a ranking model averaging (RMA) method. With a set of candidate models varied by scoring functions, RMA assigns weights for each model determined by a K-fold crossvalidation criterion based on pairwise loss. We provide two main theoretical properties for RMA. First, we prove that the averaging ranking predictions of RMA are asymptotically optimal in achieving the lowest possible ranking risk. Second, we provide a bound on the difference between the empirical RMA weights and theoretical optimal ones, and we show that RMA weights are consistent. Simulation results validate RMA superiority over competing methods in reducing ranking risk. Moreover, when applied to empirical examples—order priority, wine quality, and piston slap noise—RMA shows its effectiveness in building accurate ranking systems. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Funding: This research was supported by the Beijing Municipal Natural Science Foundation [Grant 1222002], the National Natural Science Foundation of China [Grants 12071457, 12201018, 12301364, 71925007, 72091212, and 72273120], National Quality Infrastructure [Grant 2022YFF0609903], the Chinese Academy of Sciences Project for Young Scientists in Basic Research [Grant YSBR-008], and the Natural Science Foundation of Anhui Province [Grant 2308085QA09]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://doi.org/10.1287/ijoc.2023.0257 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0257 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Towards a minimum data set for the female stress urinary incontinence surgical literature: a collaborative work.

There has been a need for an acceptable common minimum data set in the scientific literature as regards the surgical treatment of female stress urinary incontinence (SUI). Such a data set, if widely adopted, would improve the quality of the literature and allow objective comparisons between and across interventions. The surgical treatment of female stress urinary incontinence has evolved considerably over the past few decades. The corresponding body of literature has grown exponentially describing the outcomes of hundreds of studies of these novel interventions. However, historically, the literature in this space has been of uneven quality. In order to improve the reporting of clinical studies, and ultimately patient outcomes, a standard minimum data set for trial design and publications was created by a collaborative group formed from leading scientific societies. The consensus document created from this novel collaboration between members of SUFU (Society of Urodynamics, Female Pelvic Medicine & Urogenital Reconstruction), AUGS (American Urogynecologic Society), and ICS (International Continence Society) provides clear guidance for the structure of clinical studies and reporting of results in the peer-reviewed literature. This has substantial potential ramifications for scientific journals, journal editors, peer reviewers, investigators, regulatory agencies, industry, clinicians, and patients.

Assessing sex and gender equity in submission guidelines of radiology journals: A cross-sectional study

PurposeOur study aimed to determine the current percentage of gender and sex equity promoting (GSEP) radiology journals, defined as satisfying at least one criterion of the Sex and Gender Equity in Research (SAGER) checklist, published by the European Association of Science Editors (EASE). A secondary objective was to compare characteristics of GSEP and non-GSEP journals. MethodsA cross-sectional analysis between June 24 and July 3, 2023, was conducted. The author submission guidelines of radiology journals with a 2021 Journal Impact Factor (JIF) were assessed according to the SAGER checklist. GSEP journals were defined as satisfying one or more SAGER checklist criteria in their research instructions. Bibliometric data and journal information were collected from the Journal Citation Reports and National Library of Medicine catalogue. ResultsOnly 39.7 % (52) of 132 journals satisfied at least one SAGER checklist criterion. Median 2021 JIFs were higher in GSEP journals (4.62, IQR: 3.73 – 5.21) than non-GSEP journals (2.70, IQR: 2.32) (p = 0.00). Median 2021 Journal Citation Index (JCI) scores were higher in GSEP (0.64, 0.56 – 0.73) than non-GSEP journals (0.97, 0.83 – 1.10) (p = 0.00). Cited half-life was shorter for GSEP (5.40, 4.80 – 6.50) than non-GSEP journals (6.70, 5.70 – 7.40) (p = 0.05). Elsevier published 33 of 52 of GSEP journals. Conclusion60.3% of radiology journals with a 2021 JIF do not meet a single SAGER checklist criterion in their author submission guidelines. GSEP journals had higher impact and source metrics and a shorter cited half-life. Publishers may play a significant role in promoting endorsement of the SAGER checklist in the author submission guidelines of radiology journals.

Preface

Abstract The 27th International Conference on Building Materials, Products and Technologies (ICBMPT 2024) was held on 13th to 14th May 2024. This year, the conference took place in the beautiful town Mikulov in a region popular for vineyards and wine cellars with great wine. The main topics of ICBMPT 2024 were: Inorganic binders, Smart materials for 21st century, Sustainable building materials and circularity, Technology and test methods and Alternative materials. Also, several keynote speakers were attracted to contribute to our conference. The organizers from the Research Institute for Building Materials collected 15 interesting papers among research workplaces in the Czech Republic and abroad. The conference brings not only interesting presentations, but also it creates a platform for scientific discussions and proposals of new mutual projects. Besides the conference program, there was a possibility to take part in the specialized workshop Healthy Buildings with many interesting topics, among others biodeteriogen on façade and indoor, chemical and physical influences inside buildings, asbestos under the skin of buildings and radioactive substances in buildings. As side events, there was an option being skilled in the seminar on European grant opportunities and in the minisymposium on Mathematical simulations in research. The organizers would like to express their gratitude and thank all the participants for their valuable contribution to the conference. This year’s conference is successfully over and we are looking forward to the next conference! List of Scientific committee and Editors are available in this Pdf.

Peer review trends in six fisheries science journals

BackgroundAs the production of scientific manuscripts and journal options both increase, the peer review process remains at the center of quality control. Recent advances in understanding reviewer biases and behaviors along with electronic manuscript handling records have allowed unprecedented investigations into the peer review process.MethodsWe examined a sample of six journals within the field of fisheries science (and all published by the American Fisheries Society) specifically looking for changes in reviewer invitation rates, review time, patterns of reviewer agreements, and rejection rates relative to different forms of blinding.ResultsData from 6,606 manuscripts from 2011–2021 showed significant increases in reviewer invitations. Specifically, four journals showed statistically significant increases in reviewer invitations while two showed no change. Review times changed relatively little (± 2 weeks), and we found no concerning patterns in reviewer agreement. However, we documented a consistently higher rejection rate—around 20% higher—of double-blinded manuscripts when compared to single-blinded manuscripts.ConclusionsOur findings likely represent broader trends across fisheries science publications, and possibly extend to other life science disciplines. Because peer review remains a primary tool for scientific quality control, authors and editors are encouraged to understand the process and evaluate its performance at whatever level can help in the creation of trusted science. Minimally, our findings can help the six journals we investigated to better understand and improve their peer review processes.

A Fusion Pretrained Approach for Identifying the Cause of Sarcasm Remarks

Sarcastic remarks often appear in social media and e-commerce platforms to express almost exclusively negative emotions and opinions on certain instances, such as dissatisfaction with a purchased product or service. Thus, the detection of sarcasm allows merchants to timely resolve users’ complaints. However, detecting sarcastic remarks is difficult because of its common form of using counterfactual statements. The few studies that are dedicated to detecting sarcasm largely ignore what sparks these sarcastic remarks, which could be because of an empty promise of a merchant’s product description. This study formulates a novel problem of sarcasm cause detection that leverages domain information, dialogue context information, and sarcasm sentences by proposing a pretrained language model-based approach equipped with a novel hybrid multihead fusion-attention mechanism that combines self-attention, target-attention, and a feed-forward neural network. The domain information and the dialogue context information are then interactively fused to obtain the domain-specific dialogue context representation, and bidirectionally enhanced sarcasm-cause pair representations are generated for detecting sarcasm spark. Experimental results on real-world data sets demonstrate the efficacy of the proposed model. The findings of this study contribute to the literature on sarcasm cause detection and provide business value to relevant stakeholders and consumers. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Funding: This work was partially supported by the National Natural Science Foundation of China [Grants 72293575, 62071467, and 62141608] and the Research Grant Council of the Hong Kong Special Administrative Region, China [Grants 11500322 and 11500421]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0285 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0285 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Systematic review on Artificial Intelligence in the editorial management of scientific journals

INTRODUCTION: scientific journals play a crucial role in the dissemination and validation of scientific knowledge, and editorial management ranges from conceptualization to post-publication of content. Artificial intelligence (AI) has had a great impact on scientific communication and editorial management of scientific journals. AI can offer solutions and benefits for editorial management, but it also poses technical, economic, social and ethical challenges that should be considered and approached with caution and responsibility. OBJECTIVE: to describe the benefits and limitations of the use of AI in the editorial management of scientific journals through a systematic literature review. METHOD: a systematic literature review was conducted based on the PRISMA methodology. An information search was carried out in different bibliographic database systems, indexing systems and search engines, and inclusion and exclusion criteria were applied to the identified studies. RESULTS: the information search allowed retrieving a total of 2750 sources, of which 10 articles that met the stated criteria were included. Benefits such as the facilitation of writing, translation, review and editing tasks were identified, as well as limitations related to ethical issues, bias, errors and plagiarism generated by AI. CONCLUSIONS: While AI can streamline the production and analysis of information distribution, it also poses challenges in terms of reliability, ethics and authenticity of published content. It requires the critical involvement of human intelligence for proper exploitation.