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Successful honors programs inspire and sustain a vibrant and committed faculty. This essay presents an established honors program which demonstrates, through varied faculty commitments over time, honors as a valuable asset in identifying, recruiting, supporting, and rewarding a strong, creative, loyal faculty that benefits the entire institution. Authors suggest multiple ways for establishing and nurturing the kinds of relationships that enhance both honors and its dedicated faculty. Leveraging honors for professional growth and pedagogical development, these include the design of interdisciplinary courses and special seminars, mentoring of student projects, engaging in study-travel ventures, winning distinguished awards, serving on important committees, earning academic grants, and presenting at conferences. As exemplified in its long and rich history, honors continues to be an incubating space for faculty development and student success.

It is essential to establish a multi-dimensional postgraduate quality evaluation system for student assessment and training. This study aimed to explore the construction of the multiindex and hierarchical comprehensive evaluation system for postgraduate training in science and engineering based on the Context, Input, Process, Product (CIPP) model using Analytic Hierarchy Process. It involved 756 postgraduates in physics and engineering who were randomly selected via the Internet. Data were collected from the questionnaire about postgraduates' basic information. After collection, Factor Analysis was used to verify the rationality of the design of second-level and third-level indicators, and adjust the corresponding weights. On this basis, Cluster Analysis was used to classify the training quality of the postgraduates based on their scores on academic ability, basic quality, and social ability indicators. The results revealed that the index system includes 4 first-level indicators,12 second-level indicators and 36 third-level indicators, and different weights being assigned to the indicators according to their influence on the training quality of postgraduates in science and engineering. This study also provides some reference for the quality of science and engineering postgraduate training in Chinese universities by proposing relevant measures, which could be interesting also for international audience.

The learning management system (LMS) is a crucial component of the e-learning transformation which is becoming more urgent amid the Coronavirus disease (COVID-19) outbreak. The issue of adopting LMS is even more decisive in developing countries, where lots of efforts have been put out to broaden educational opportunities. However, there has not yet been any comprehensive analysis of how LMS-related issues are examined in these countries. To address this gap, this study uses the bibliometric method to construct an overview of research on this topic. The results unveil the distribution of the literature, prominent actors, and dominant themes in the literature of LMS in developing countries. In summary, the topic is a robustly potential research matter. Future researchers can use this study as a starter when investigating relevant subjects.

This study analyzed the National Communication Association (NCA) members' perceptions regarding the effectiveness of their own and their peer presentations and the challenges they faced when preparing and giving conference presentations. Overall, participants (n = 187) feel fairly content with the effectiveness of NCA conference presentations and the value they have gained from them. The effectiveness of others' presentations has a significant association with members' perceived value of the conference presentations. The "lack of experience" and "lack of confidence" are key variables that heighten anxiety which can impact the effectiveness of presentations. "Process anxiety" was positively associated with presentation effectiveness whereas "performance anxiety" was negatively associated with presentation effectiveness. This needs assessment provides informed suggestions for making academic presentations more effective in the future.

The article is based on the lecture ‘Mathematics teacher training from the perspective of STEM – a particular case’ given by its author at the ATCM 2023 conference and is an extension of a related paper that was published in the conference proceedings. It further develops the idea that the task of teacher training schools is to create a suitable environment for the provision of education, impulses for the development of the necessary knowledge and skills, as well as to support these. It provides a detailed description of specific creative activities for future teachers, which develop their awareness of the applicability of mathematics in solving real problems and knowledge of its overlap with technology. It is based on historical problems and their context, as presented in the conference paper. Emphasis is placed on the use of computers, especially dynamic geometry software GeoGebra, to model the geometric nature of given problems and for their effective solution. [ABSTRACT FROM AUTHOR]

Background: Natural language processing, such as ChatGPT, demonstrates growing potential across numerous research scenarios, also raising interest in its applications in public health and epidemiology. Here, we applied a bibliometric analysis for a systematic assessment of the current literature related to the applications of ChatGPT in epidemiology and public health. Methods: A bibliometric analysis was conducted on the Biblioshiny web-app, by collecting original articles indexed in the Scopus database between 2010 and 2023. Results: On a total of 3431 original medical articles, "Article" and "Conference paper", mostly constituting the total of retrieved documents, highlighting that the term "ChatGPT" becomes an interesting topic from 2023. The annual publications escalated from 39 in 2010 to 719 in 2023, with an average annual growth rate of 25.1%. In terms of country production over time, the USA led with the highest overall production from 2010 to 2023. Concerning citations, the most frequently cited countries were the USA, UK, and China. Interestingly, Harvard Medical School emerges as the leading contributor, accounting for 18% of all articles among the top ten affiliations. Conclusions: Our study provides an overall examination of the existing research interest in ChatGPT's applications for public health by outlining pivotal themes and uncovering emerging trends. [ABSTRACT FROM AUTHOR]

Background: Iron deficiency anemia (IDA) is a prevalent hematological complication associated with gastrointestinal (GI) cancers due to an increased loss of iron and decreased iron absorption. The purpose of this systematic review is to evaluate the use of parenteral iron to treat IDA in patients with GI cancer. Methods: PubMed, Cochrane, EMBASE, CINHAL and Scopus were searched from January 1, 2010 to September 29, 2023 with no language restrictions. We excluded editorials, case reports, abstracts, conference papers, and poster presentations. Studies were included if they discussed IDA, GI neoplasms, use of iron supplementation (with or without erythropoietin-stimulating agents [ESAs]), defined anemia and had an adult patient population. We assessed the efficacy of parenteral iron in comparison to other iron supplementation methods when treating IDA in patients with GI cancer. The Cochrane Risk of Bias Tool 2 (RoB 2) and the Risk Of Bias In Non-randomized Studies of Interventions (ROBINS-I) assessment tools were used to assess the quality of the included studies. Moreover, the Cochrane Effective Practice and Organization data collection form was used to collect pertinent study information. Results: Our search yielded 3,969 studies across all databases. Twenty-one studies were included (6 randomized control trials; 15 non-randomized studies). Of the 15 studies evaluating hemoglobin (Hb) response, seven studies found an increase in Hb levels when patients were treated with IV iron. The 14 studies evaluating red blood cell (RBC) transfusion rates found conflicting differences in RBC transfusion needs when treated with IV iron. Studies analyzing health related outcomes typically found an increase in quality of life and decreased post-operative complications. Discussion: This review demonstrates improved outcomes of IDA in patients with GI cancer treated with IV iron instead of other iron supplementation methods. Timely diagnosis and appropriate IDA management can greatly improve quality of life in this patient population, especially if myelosuppressive chemotherapy is required. [ABSTRACT FROM AUTHOR]

This article responds to a conference call for papers that makes universalist assumptions about clause structures, assuming all languages in the world basically follow the same organizing principles in terms of clause structure, argument structure, and alignment. The article presents data from Tagalog to show how different a language can be from the assumed universal organizing principles to make the point that by imposing an Indo-European framework on non-Indo-European languages, we are overlooking the true diversity of language forms found in the world's languages. [ABSTRACT FROM AUTHOR]

The question of how agricultural knowledge and innovation systems (AKISs) can address the issue of sustainable management of natural resources (SMNR) is presented in this conference paper. This literature review, which collected published research from the Scopus electronic database, aimed to explore the value of AKISs in enhancing the sustainability of natural resources. Therefore, it examined and evaluated the roles of AKISs as either positive or negative overall. Moreover, it analyzed whether the use of AKISs supports the goal of creating a sustainable system that links agriculture with natural resources. Among its findings, this review presents the positive and negative outcomes of each element and potential future scenarios/suggestions if the current trends persist. [ABSTRACT FROM AUTHOR]

Background: Commercial electroencephalography (EEG) devices have become increasingly available over the last decade. These devices have been used in a wide variety of fields ranging from engineering to cognitive neuroscience. Purpose: The aim of this study was to chart peer-review articles that used consumer-grade EEG devices to collect neural data. We provide an overview of the research conducted with these relatively more affordable and user-friendly devices. We also inform future research by exploring the current and potential scope of consumer-grade EEG. Methods: We followed a five-stage methodological framework for a scoping review that included a systematic search using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. We searched the following online databases: PsycINFO, MEDLINE, Embase, Web of Science, and IEEE Xplore. We charted study data according to application (BCI, experimental research, validation, signal processing, and clinical) and location of use as indexed by the first author's country. Results: We identified 916 studies that used data recorded with consumer-grade EEG: 531 were reported in journal articles and 385 in conference papers. Emotiv devices were used most, followed by the NeuroSky MindWave, OpenBCI, interaXon Muse, and MyndPlay Mindband. The most common usage was for brain-computer interfaces, followed by experimental research, signal processing, validation, and clinical purposes. Conclusions: Consumer-grade EEG is a useful tool for neuroscientific research and will likely continue to be used well into the future. Our study provides a comprehensive review of their application, as well as future directions for researchers who plan to use these devices. [ABSTRACT FROM AUTHOR]

Background: The Mediterranean Sea is a region characterized by high seismic activity, with at least 200 tsunami events recorded from the fourth century to the present twenty-first century. Numerous studies have been conducted to understand past tsunami events, earthquake–tsunami generation, tsunami recurrence periods, tsunami vulnerability zones, and tsunami hazard mitigation strategies. Therefore, gaining insights into future trends and opportunities in Mediterranean Sea tsunami research is crucial for significantly contributing to all relevant aspects. This study aims to assess such trends and opportunities through a scientometric analysis of publications indexed by Web of Science from 2000 to 2023. Results: Based on a selection of 329 publications, including research articles, review articles, book chapters, and conference papers, published between 2000 and 2023, Italy has the highest number of publications and citations in this field. The number of publications has increased significantly, especially after the 2004 Indian Ocean, 2011 Tohoku, and 2018 Palu tsunamis. According to the keyword analysis, the terms "tsunami", "earthquake", "hazard", "wave", "Mediterranean", "coast", and "tectonic" were the most frequently used in these publications. Research themes consist of four classifications: motor themes, such as seismic hazard; specific but well-developed themes, like tsunamiite; emerging or disappearing themes, for example, climate change; and general or basic themes, such as equations and megaturbidite. The number of publications related to the motor theme classification continued to grow throughout 2000–2023. Topics from 2011–2023 are more complex compared to 2000–2010, characterized by the emergence of new keywords such as evacuation planning, risk reduction, risk mitigation, building vulnerability, coastal vulnerability, climate change, probabilistic tsunami hazard assessment (PTVA-3 and PTVA-4). However, topics that were popular in the 2000–2010 period (e.g., paleotsunami deposits, earthquake, and tsunami propagation analysis) also increased in 2011–2023. Conclusions: Research topics with high centrality and density such as seismic hazard will continue to develop and prospect. The cluster network of this topic includes seismoturbidites, sedimentary features, tsunami modeling, active faults, catalog, and historical earthquakes. [ABSTRACT FROM AUTHOR]

426 Membership Services426 Library Subscriptions426 Future FMA Annual Meetings427 Upcoming Conferences and Call for Papers427 FMA National Honor Society427 Collegiate Fellow Recipients427 Superior Faculty Advisors427 Superior Chapters427 FMA Placement Services427 Complaints, Compliments, SuggestionsGRAPHGRAPH [Extracted from the article]

Background: Practice-based research networks (PBRNs) have been recognized as essential laboratories and mechanisms for developing primary care research. This scoping review aims to examine and map the features and development trends of productivity, research categories, and methods in original primary care research conducted by global PBRNs between 1991 and 2023. Methods: We have assembled an interdisciplinary team that will undertake this scoping review, following the framework developed by Arksey and O'Malley. Targeted literature includes original primary care research conducted by PBRNs, published from January 1, 1991, to December 31, 2023. An integrated search strategy will gather publications from 3 electronic databases (PubMed, Web of Science, and Embase), 16 major primary health care journals, and 364 relevant organizations. Two experienced researchers will independently screen the titles, keywords, and abstracts of all references and extract data regarding eight key elements. Disagreements between the reviewers will be resolved through group discussions, moderated by a third reviewer. Articles to be included will (1) be conducted in the primary care context, (2) be led by PBRNs, (3) provide a full report of original research, and (4) be published in a peer-reviewed journal between the aforementioned dates in any language. Exclusions encompass reviews, letters, commentaries, case reports, and conference papers. Final data will be displayed using tables and charts according to different conceptual categories. Discussion: This scoping review is one of the initial attempts to delineate the development trends and features of primary care research conducted by PBRNs. This study will provide reference information for researchers in countries/regions that are building their research infrastructure and capacity in general practice, family medicine, and primary care. Systematic review registration: Registered in OSF on July 25, 2022 (https://osf.io/zgv9c). [ABSTRACT FROM AUTHOR]

This study employs a bibliometric approach to analyze the landscape of artificial intelligence (AI) components used in performance measurement. As organizations increasingly leverage AI for optimizing processes and decision-making, understanding the trends in AI components becomes imperative. The identified AI components are classified based on their roles in enhancing performance measurement, offering insights into the prevalent methodologies and emerging technologies. The bibliometric analysis encompasses a comprehensive review of scholarly articles, conference papers, and patents, systematically exploring the evolving field. In this research, the methodology involves data extraction from reputable academic databases and patent repositories, followed by applying bibliometric techniques to quantify and visualize key aspects. The findings of this study contribute to the existing knowledge by mapping the intellectual structure of AI components for performance measurement. [ABSTRACT FROM AUTHOR]

To stimulate the development and application of formal methods in industry, we need to promote research and development for the improvement of formal methods and tools for industrial applications, and we need to exchange experiences of the industrial usage of these methods and tools. This special issue of Software Tools for Technology Transfer presents various tools and experience reports that are targeting the use of formal methods in industry. The papers in this special issue are extended versions of selected conference papers from the proceedings of the 27th International Conference on Formal Methods for Industrial Critical Systems (FMICS 2022). [ABSTRACT FROM AUTHOR]

228 Membership Services228 Library Subscriptions228 Future FMA Annual Meetings229 Upcoming Conferences and Call for Papers229 FMA National Honor Society229 Collegiate Fellow Recipients229 Superior Faculty Advisors229 Superior Chapters229 FMA Placement Services229 Complaints, Compliments, SuggestionsGRAPHGRAPH [Extracted from the article]

This paper aims to investigate the differences between conference papers and journal papers in the field of computer science based on Bayesian network.

In the age of increasing process automation and data-driven decision-making, ensuring the reliability, transparency and usability of data is of paramount importance. In this context, the concept of "data observability" has aroused the interest of practitioners and there is also a lot of grey content on it. On the other hand, there is a lack of academic effort to define and build on the concept. This conference paper will therefore examine the importance of "data observability" in modern data ecosystems. The focus is on the definition and characterisation of the concept, the differentiation from other concepts (e.g. data quality, data monitoring, data discovery, data operations) and why this concept appears to be so important in an increasingly data-driven world. In addition, the concept of "data observability" is categorised in the dynamically developing research field of data governance. For this purpose, a multivocal literature review (MLR) was conducted, a form of systematic literature review (SLR) which, in addition to the published (formal) literature (e.g. journal and conference papers), also includes and brings together the grey literature (e.g. blog posts, videos and white papers). The results show that the concept of "data observability" has the potential to revolutionise the way companies manage, analyse and derive insights from their data, ultimately leading to more informed and confident decision-making. Nevertheless, there is still plenty of room for further research into the specific contribution to better data and therefore better business processes and decisions. [ABSTRACT FROM AUTHOR]

Introduction/purpose: Anomaly detection-based Network Intrusion Detection Systems (NIDSs) have emerged as a valuable tool, particularly in military fields, for protecting networks against cyberattacks, specifically focusing on Netflow data, to identify normal and abnormal patterns. This study investigates the effectiveness of anomaly-based machine learning (ML) and deep learning (DL) models in NIDSs using the publicly available NF-UQ-NIDS dataset, which utilizes Netflow data, with the aim of enhancing network protection. Methods: The authors Sarhan, M., Layeghy, S., Moustafa, N. and Portmann, M. in the conference paper Big Data Technologies and Applications, in 2021, involve a preprocessing step where 8 features are selected for the training phase out of the 12 available features. Notably, the IP source and destination addresses, as well as their associated ports, are specifically excluded. The novelty of this paper lies in the preprocessing of the excluded features and their inclusion in the training phase, employing various classification ML and DL algorithms such as ExtraTrees, ANN, simple CNN, and VGG16 for binary classification. Results: The performance of the classification models is evaluated using metrics such as accuracy, recall, etc., which provide a comprehensive analysis of the obtained results. The results show that the ExtraTrees ML model outperforms all other models when using our preprocessing features, achieving a classification accuracy of 99.09%, compared to 97.25% in the reference dataset. Conclusion: The study demonstrates the effectiveness of anomalybased ML and DL models in NIDSs using Netflow data. [ABSTRACT FROM AUTHOR]

Aim: We aim to assess the overall survival (OS) in patients with Anaplastic Astrocytoma (AA) undergoing gross total resection (GTR) as compared to partial resection (PR) subtotal resection (STR), or biopsy. Methods: This systematic review followed Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA) guidelines. An electronic search from PubMed/Medline was conducted from their inception to 26th April 2022. We included AA patients undergoing any surgical intervention resulting in GTR, PR, STR or biopsy. We did not include letters, case reports, abstracts, conference papers, reviews, and studies where full text was unavailable. We included only those articles which were published in English. Results: Five cohorts were used in this study. Two studies assessed OS in GTR, PR/STR and biopsy, while one study compared GTR and STR/biopsy. Another study was used to compare OS between GTR and local excision/STR, and another was used to assess the complications/benefits of these surgeries. Three studies showed a significant increase in OS in patients who underwent GTR compared to the other interventions, while one study showed a non-significant effect on OS (p= 0.249). Conclusion: Our study concluded a significant increase in OS when patients with AA had GTR instead of STR, PR or biopsy. Although these surgeries might carry some disadvantages, GTR allows a more positive effect on neurological status. Still, more studies need to be conducted to assess the efficiency of these surgeries. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) offers the advantage of producing complex parts more efficiently and in a lesser production cycle time as compared to conventional subtractive manufacturing processes. It also provides higher flexibility for diverse applications by facilitating the use of a variety of materials and different processing technologies. With the exceptional growth of computing capability, researchers are extensively using machine learning (ML) techniques to control the performance of every phase of AM processes, such as design, process parameters modeling, process monitoring and control, quality inspection, and validation. Also, ML methods have made it possible to develop cybermanufacturing for AM systems and thus revolutionized Industry 4.0. This paper presents the state-of-the-art applications of ML in solving numerous problems related to AM processes. We give an overview of the research trends in this domain through a systematic literature review of relevant journal articles and conference papers. We summarize recent development and existing challenges to point out the direction of future research scope. This paper can provide AM researchers and practitioners with the latest information consequential for further development. [ABSTRACT FROM AUTHOR]

The use of generative learning models in natural language processing (NLP) has significantly contributed to the advancement of natural language applications, such as sentimental analysis, topic modeling, text classification, chatbots, and spam filtering. With a large amount of text generated each day from different sources, such as web-pages, blogs, emails, social media, and articles, one of the most common tasks in NLP is the classification of a text corpus. This is important in many institutions for planning, decision-making, and creating archives of their projects. Many algorithms exist to automate text classification tasks but the most intriguing of them is that which also learns these tasks automatically. In this study, we present a new model to infer and learn from data using probabilistic logic and apply it to text classification. This model, called GenCo, is a multi-input single-output (MISO) learning model that uses a collaboration of partial classifications to generate the desired output. It provides a heterogeneity measure to explain its classification results and enables a reduction in the curse of dimensionality in text classification. Experiments with the model were carried out on the Twitter US Airline dataset, the Conference Paper dataset, and the SMS Spam dataset, outperforming baseline models with 98.40%, 89.90%, and 99.26% accuracy, respectively. [ABSTRACT FROM AUTHOR]

This paper analyses the conference papers published by double first‐class universities from mainland China between 2006 and 2019. The main findings are as follows: first, the number of conference papers from different double first‐class universities varied greatly, with the annual numbers of papers in the China Proceedings of Conferences Full‐text Database (CPCD) and in the Conference Proceedings Citation Index (CPCI) fluctuating during this period. Second, most of the top 10 disciplines of the papers in the CPCD and CPCI were in the natural sciences, with only two in the social sciences. The CPCD papers and CPCI papers had some overlap in their disciplinary distributions. Third, the CPCD and CPCI papers differed little in total number, with the number of CPCI papers being slightly higher than the number of CPCD papers. With the introduction of new regulations from the Ministry of Science and Technology (MST), there will likely be an increase in the number of Chinese‐language conference papers in the future. [ABSTRACT FROM AUTHOR]

Current literature dealing with the theory of sampling process flows is limited in scope, a situation addressed by papers presented at conferences by the author. This paper provides a summary of methods to quantify process sampling variance in a sound statistical manner. Two methods of calculation of the process sampling variance are presented here with models for robust estimation of the process covariance/variogram. The strategies for process sampling to acquire the data for process characterisation are described. The method for process characterisation can be based on special sampling campaigns, on the analysis of on-line measurement data, or on the analysis of historical data, which may be based on composite samples, such as shift samples. These scenarios are all dealt with as well as methods based on analysis of process dynamics. The latter analysis demonstrates that use of covariance functions drawn from geostatistical practice can be inappropriate for process analysis. The work is supported with plant data examples and quantitative analyses of process conditions. [ABSTRACT FROM AUTHOR]

This study aims to explore the SCImago Institutional Ranking (SIR) 2022 to gain insight into the profile of Sri Lankan universities. Additionally, the study aims to investigate whether there was consistency between Scopus profiles and SIR. Furthermore, the research delves into various factors that impact the research ranking of an academic institution as defined by SCImago, which goes beyond just the number of publications. The data for the study were retrieved from SIR (2022) and Scopus database (2022) and were systematically analyzed. The author chose the following options to extract the data for overall ranking, Universities as a sector, Sri Lanka as the country and 2022 as the year based on all subject areas. The study found that the number of Sri Lankan universities eligible for the SCImago ranking has gradually increased from 2013 to 2022. According to SIR, the University of Colombo is the top-ranked academic institution in Sri Lanka, followed by Rajarata University of Sri Lanka and the University of Jaffna. Out of the fourteen universities in Sri Lanka, eight were ranked by SIR, with six being in the Q1 (first cluster) and two in the Q2 (second cluster). Interestingly, the Rajarata University of Sri Lanka was ranked 1st in SIR 2022 for its research performance among Sri Lankan universities with their 105 publications indexed in Scopus. Analysis of data shows that there is no relationship between the Scopus profile and the SIR. It is also suggesting that having a high number of articles in the Scopus database does not necessarily guarantee a high rank in SIR. The SIR not only depends on the number of publications but also on other factors related to the quality of the publications. Therefore, universities cannot assume their SIR position by considering only the quantity of Scopus indexed publications. SIR mainly considers the quality of the publications to measure the institutional research performance. The SCImago ranking evaluates the institutional whole performance through three of its indicators, and research performance is measured only through Scopus indexed publication. Sri Lankan publications in local and international journals which are not indexed in Scopus and conference papers were not counted for research performance. The findings of this study will facilitate the institutions to compare their position with other institutions, standardize their research practices, improve the international collaboration to uphold the academic benchmark, regulate their research publications and promote their visibility and finally support government bodies and policymakers regarding fund allocations and strategic planning. [ABSTRACT FROM AUTHOR]

Moving objects databases (MODs) have been extensively studied due to their wide variety of applications including traffic management, tourist service and mobile commerce. However, queries in natural languages are still not supported in MODs. Since most users are not familiar with structured query languages, it is essentially important to bridge the gap between natural languages and the underlying MODs system commands. Motivated by this, we design a natural language interface for moving objects, named NALMO. In general, we use semantic parsing in combination with a location knowledge base and domain-specific rules to interpret natural language queries. We design a corpus of moving objects queries for model training, which is later used to determine the query type. Extracted entities from parsing are mapped through deterministic rules to perform query composition. NALMO is able to well translate moving objects queries into structured (executable) languages. We support five kinds of queries including time interval queries, range queries, nearest neighbor queries, trajectory similarity queries and join queries. We develop the system in a prototype system SECONDO and evaluate our approach using 280 natural language queries extracted from popular conference and journal papers in the domain of moving objects. Four volunteers give the system satisfaction and related suggestions through three rounds of independent tests. Experimental results show that (i) NALMO achieves accuracy and precision 96.8% and 81.1%, respectively, (ii) the average time cost of translating a query is 1.49s, and (iii) the average satisfaction is 95.5%. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Long-acting reversible contraception (LARC) is a feasible solution to reduce unintended pregnancy and abortion rates. Nonetheless, the worldwide prevalence of LARC remains low. The LARC uptake depends heavily on healthcare workers because every LARC insertion requires a clinician's visit, and they are a trusted source of contraceptive information. The study is aimed to explore the trend and distributions of global publications related to LARC and healthcare workers. MATERIALS AND METHODS: Articles related to LARC and healthcare workers were retrieved from the Scopus database. Eligible publications were limited to research articles, reviews, and conference papers published in the English language with no date restriction. The bibliometric data was analysed using Biblioshiny to obtain distributions in terms of the number of publications, journals, countries, institutions, citations, and keywords. RESULTS: A total of 681 eligible publications related to LARC and healthcare workers were retrieved from the Scopus database between 1967 to 2022. The number of publications was low for the first four decades but increased steadily from 2010 onwards. The leading country in this research area was the United States of America and the majority of the most productive institutions were from this country. Half of the frequently cited publications highlighted healthcare workers' bias, attitude, and practice on LARC provision to clients. CONCLUSION: This bibliometric analysis can guide scholars to understand the global overview of research about LARC and healthcare workers. It may be useful for health practitioners and researchers who are interested in exploring this research area. [ABSTRACT FROM AUTHOR]

Many researchers in the construction field have explored the utilization of augmented reality (AR) and its impact on the industry. Previous studies have shown potential uses for AR in the construction industry. However, a comprehensive critical review exploring the ways in which AR supports synchronized communication is still missing. This paper aims to fill this gap by examining trends identified in the literature and by analyzing both beneficial and challenging attributes. This work was performed by collecting numerous journal and conference papers, using keywords including "augmented reality", "construction", and "synchronous communication". The papers were then categorized based on the reported attributes that were indicated to be challenges or benefits. Throughout the analysis, several benefits were consistently reported, including training, visualization, instantly sharing information, decision making, and intuitive interaction. Similarly, several challenges were consistently reported, such as difficulty in manipulation, unfriendly interface, device discomfort, and sun brightness. Regarding other attributes, such as field of view, cost, safety hazards, and hands-free mode, researchers provided divergent reports regarding whether they were beneficial or detrimental to AR communication. These findings provide valuable guidance for future researchers and practitioners, enabling them to leverage AR for synchronized communication in ways that consistently offer value. [ABSTRACT FROM AUTHOR]

In this paper, a comprehensive statistics-based review of islanding detection methods (IDMs) in microgrids (MGs) is presented. Islanding detection is the situation of isolating the MG from the main grid whether programmed as a result of load managing purposes or un-programmed due to the occurrence of faults. Islanding detection is a vital issue in MG's analyses due to the prevention of subsequent protection problems in the power system. In other words, when the MG's operation mode changes, the current passing through the protective devices changes subsequently and the protection system should be able to adapt the new settings to the protective devices. So, IDMs are vital for electrical engineers to overcome the abovementioned protection issue. This review paper surveys the existing literature in IDMs by concentration on total publications, type of publications (journal, conference paper, or book), five authors with the highest number of publications (including the affiliations), and five most published sources. Also, the five most cited publications and state-of-the-art IDMs are investigated in detail, utilizing some known and novel categorizations. This paper will be useful for the MG's researchers to know the most desirable IDMs, especially in recent years, and provides an insightful overview for future studies. [ABSTRACT FROM AUTHOR]

Background: For thousands of years till nowadays, Nigella sativa (NS) has served as a common spice and food preservative. Its seed extracts, seed oil, and essential oil in traditional medicine have been used to remedy many ailments such as headaches, fever, gastric complaints, and even rheumatism. In addition, the antibacterial, virucidal, fungicidal, and antiparasitic properties of NS are well known. However, studies on the possible immunomodulatory effects of black cumin are relatively scarce. This article discusses in vitro and in vivo research supporting the immunomodulatory role of NS. Methods: The review is based on articles, books, and conference papers printed until September 2022, found in the Web of Science, PubMed, Wiley Online Library, and Google Scholar databases. Results: Experimental findings were reported concerning the ability of NS to modulate inflammation and immune responses or cytotoxic activity. Conclusions: All results suggest that NS can potentially be employed in developing effective therapeutic agents for regulating immune reactions. [ABSTRACT FROM AUTHOR]

Some states in the U.S. have traditionally received less federal research funding than other states. The National Science Foundation (NSF) created a program in 1979, called the Experimental Program to Stimulate Competitive Research (EPSCoR) to enhance the research competitiveness in such states. While the geographic disparity in federal research funding is well known, the overall impact of federal funding on the research performance of EPSCoR and non-EPSCoR has not been previously studied. In the current study, we compared the combined research productivity of Ph.D. granting institutions in EPSCoR versus the non-EPSCoR states to better understand the scientific impact of federal investments in sponsored research across all states. The research outputs we measured included journal articles, books, conference papers, patents, and citation count in academic literature. Unsurprisingly, results indicated that the non-EPSCoR states received significantly more federal research funding than their EPSCoR counterparts, which correlated with a higher number of faculty members in the non-EPSCoR versus EPSCoR states. Also, in the overall research productivity expressed on a per capita, the non-EPSCoR states fared better than EPSCoR states. However, when the research output was measured based on per $1M investment of federal research funding, EPSCoR states performed significantly better than the non-EPSCoR states in many research productivity indicators, with the notable exception of patents. Together, this study found preliminary evidence that EPSCoR states achieved a high degree of research productivity despite receiving significantly fewer federal research dollars. The limitations and next steps of this study are also discussed. [ABSTRACT FROM AUTHOR]

Diabetes Mellitus (DM) causes global exhaustion, consumes economic resources, and has several risk factors. The bibliometric studies re-evaluate the research efforts on this illness using mathematical and statistical tools to indicate current research and future trends. This study examines KSA's DM research during 2010-2021. Data were acquired from Scopus and analyzed using VOSviewer and MS Excel. Several characteristics were examined to measure the quantity and quality of KSA-related DM articles. In total, 1,919 journal and conference papers were published. DM research included researchers from multidisciplinary sectors. Thirty-seven percent of them have ten or more scientific publications. Al-Daghri, N.M. (King Saud University) leads the pack. In total, 757 (39.44%) research projects got funding from 159 sources within and outside KSA. Memish, Z.A. is the most cited author. The Saudi Medical Journal has the most citations (1214). Al-Daghri, N.M. (KSU) collaborates the most. One hundred forty-one nations aided KSA's diabetes research. Egypt's High Institute of Public Health has the most scientific collaboration with KSA. Authors' and all Keywords analyses indicated a rich knowledge structure. Diabetes Care Journal has the most cocitations with 2,220 and a total link strength of 19,283, followed by The New England Journal of Medicine. The study results will be helpful to stakeholders to understand better the trends and performance of diabetes-related regional research, which will be beneficial. [ABSTRACT FROM AUTHOR]

Aims: This article aims to perform a Systematic Literature Review (SLR) to better understand the structures of different methods, techniques, models, methodologies, and technologies related to provenance data management in health information systems (HISs). The SLR developed here seeks to answer the questions that contribute to describing the results. Method: An SLR was performed on six databases using a search string. The backward and forward snowballing technique was also used. Eligible studies were all articles in English that presented on the use of different methods, techniques, models, methodologies, and technologies related to provenance data management in HISs. The quality of the included articles was assessed to obtain a better connection to the topic studied. Results: Of the 239 studies retrieved, 14 met the inclusion criteria described in this SLR. In order to complement the retrieved studies, 3 studies were included using the backward and forward snowballing technique, totaling 17 studies dedicated to the construction of this research. Most of the selected studies were published as conference papers, which is common when involving computer science in HISs. There was a more frequent use of data provenance models from the PROV family in different HISs combined with different technologies, among which blockchain and middleware stand out. Despite the advantages found, the lack of technological structure, data interoperability problems, and the technical unpreparedness of working professionals are still challenges encountered in the management of provenance data in HISs. Conclusion: It was possible to conclude the existence of different methods, techniques, models, and combined technologies, which are presented in the proposal of a taxonomy that provides researchers with a new understanding about the management of provenance data in HISs. [ABSTRACT FROM AUTHOR]

In this paper, a systematic literature review is presented, through a survey of the main digital databases, regarding modelling methods for Short-Term Load Forecasting (STLF) for hourly electricity demand for residential electricity and to realize the performance evolution and impact of Artificial Intelligence (AI) in STLF. With these specific objectives, a conceptual framework on the subject was developed, along with a systematic review of the literature based on scientific publications with high impact and a bibliometric study directed towards the scientific production of AI and STLF. The review of research articles over a 10-year period, which took place between 2012 and 2022, used the Preferred Reporting Items for Systematic and Meta-Analyses (PRISMA) method. This research resulted in more than 300 articles, available in four databases: Web of Science, IEEE Xplore, Scopus, and Science Direct. The research was organized around three central themes, which were defined through the following keywords: STLF, Electricity, and Residential, along with their corresponding synonyms. In total, 334 research articles were analyzed, and the year of publication, journal, author, geography by continent and country, and the area of application were identified. Of the 335 documents found in the initial research and after applying the inclusion/exclusion criteria, which allowed delimiting the subject addressed in the topics of interest for analysis, 38 (thirty-eight) documents were in English (26 journal articles and 12 conference papers). The results point to a diversity of modelling techniques and associated algorithms. The corresponding performance was measured with different metrics and, therefore, cannot be compared directly. Hence, it is desirable to have a unified dataset, together with a set of benchmarks with well-defined metrics for a clear comparison of all the modelling techniques and the corresponding algorithms. [ABSTRACT FROM AUTHOR]

Big Data research has rapidly developed in the last 10 years, making it an interesting topic to investigate to understand the trends and developments of Big Data in Education. This study aims to analyze source documents and state contributions, and visualize research trends on the subject of Big Data in education during the last 10 years and identify potential research topics related to Big Data in Education in the future. This study uses a literature review and Meta-Analyses (PRISMA) method associated with bibliometric analysis using the Scopus and VOSviewer databases, through which 1,076 documents were obtained. The results of the bibliometric analysis show that Big Data research in education has increased significantly in the last 10 years. However, in the last year, it has decreased, this is the next challenge, and an opportunity for future research. The most common types of documents are conference papers, the source of most documents is conference proceedings, and the country that contributes the most is China. English is the most widely spoken language, and the authors with the highest contribution are Daniel, B. K. Further research related to Big Data can be used and implemented in the business world for Big Data analysis in education. Big Data can also be integrated with Science, technology, engineering, and mathematics (STEM) which can be a further research opportunity that can be applied to education because analyzing Big Data requires STEM learning skills. Thus, this research recommends finding updates in the study of Big Data in education by integrating STEM in education because analyzing Big Data again requires STEM learning expertise. This study has limitations in using only one database, namely Scopus, to obtain research data. Therefore, it is recommended that Big Data in Education research be conducted using other databases besides Scopus to obtain more extensive data. [ABSTRACT FROM AUTHOR]

Preserving customers' expectations and understanding factors affecting their purchasing decisions are crucial in designing effective marketing and advertising strategies. However, constantly and swiftly changing the customers' interests and consumption behaviors make it inevitable to utilize sophisticated tools and approaches based on advanced technologies. Among them, by measuring the customers' physiological and neural signals, studying the customers' cognitive and affective responses to marketing stimuli, neuromarketing provides deep insight into the customers' motivations, preferences, and decisions. Recently, the internet of everything (IoE) has brought many new opportunities to the industry and has attracted the attention of many researchers in recent years. The main objective of this paper is to address how the IoE would empower neuromarketing techniques. Hence, an in-depth understanding of current research issues as well as emerging trends would help meet this goal. In this paper, a comprehensive review has been done by reviewing numerous journal and conference papers from various academic databases (sciencedirect.com, IEEE Xplore, Springer, Elsevier, Wiley, ACM digital library) based on applying various filtrations on specific keywords to identify and categorize the IoE devices in the neuromarketing field. In particular, we discussed the importance and the applications of IoE gadgets and devices, especially wearable medical technologies in eight neuromarketing techniques. Finally, we described critical existing challenges and limitations in neuromarketing and IoE gadgets that researchers have dealt with in this field of research. [ABSTRACT FROM AUTHOR]

Ant-inspired metaheuristic algorithms known as ant colony optimization (ACO) offer an approach that has the ability to solve complex problems in both discrete and continuous domains. ACOs have gained significant attention in the field of water resources management, since many problems in this domain are non-linear, complex, challenging and also demand reliable solutions. The aim of this study is to critically review the applications of ACO algorithms specifically in the field of hydrology and hydrogeology, which include areas such as reservoir operations, water distribution systems, coastal aquifer management, long-term groundwater monitoring, hydraulic parameter estimation, and urban drainage and storm network design. Research articles, peer-reviewed journal papers and conference papers on ACO were critically analyzed to identify the arguments and research findings to delineate the scope for future research and to identify the drawbacks of ACO. Implementation of ACO variants is also discussed, as hybrid and modified ACO techniques prove to be more efficient over traditional ACO algorithms. These algorithms facilitate formulation of near-optimal solutions, and they also help improve cost efficiency. Although many studies are attempting to overcome the difficulties faced in the application of ACO, some parts of the mathematical analysis remain unsolved. It is also observed that despite its popularity, studies have not been successful in incorporating the uncertainty in ACOs and the problems of dimensionality, convergence and stability are yet to be resolved. Nevertheless, ACO is a potential area for further research as the studies on the applications of these techniques are few. [ABSTRACT FROM AUTHOR]

This article is a follow-up contribution that extends the conference paper (Nxumalo, in: Laarman, Sokolova (eds)Model Checking Software - 27th International Symposium, 2021) which presented the spotlight abstraction method to enable an efficient model checking of schedulability properties of real-time operating systems. Our approach is based on iterative spotlight abstraction whereby the real-time task queue is divided into a so-called spotlight and a shade. Initially, an abstract state space model is generated from the spotlight, which contains a small number of tasks that appear at the front of the queue and the shade contains the remaining tasks. The schedulability of the spotlight tasks is model checked, whereas the behavior of the shade is summarized, and the result is saved for later re-use. If this result is still inconclusive, more tasks are iteratively brought from the shade into the spotlight, with which the model checker can proceed. These steps are repeated until a decisive schedulability result is obtained. In this article, we enrich the encoding of real-time systems into timed automata by separating operations over clock variables from operations based on other variables such as Boolean and integers. We enforce to maintain a reasonable size of the spotlight model by applying counter abstraction to the tasks that were previously confirmed schedulable in the previous iterations. We compare our approach against Timestool and RTLib, an Uppaal template, in the analysis of the schedulability of real-time tasks for the First-In-First-Out and Shortest-Deadline-First scheduling policies. Empirical results show that our approach can handle more tasks than Timestool and RTLib. [ABSTRACT FROM AUTHOR]

This research focused on using RGB-D images and modifying an existing machine learning network architecture to generate predictions of the location of successfully grasped objects and to optimize the control system for state delays. A five-finger gripper designed to mimic the human palm was tested to demonstrate that it can perform more delicate missions than many two- or three-finger grippers. Experiments were conducted using the 6-DOF robot arm with the five-finger and two-finger grippers to perform at least 100 actual machine grasps, and compared to the results of other studies. Additionally, we investigated state time delays and proposed a control method for a robot manipulator. Many studies on time-delay systems have been conducted, but most focus on input and output delays. One reason for this emphasis is that input and output delays are the most commonly occurring delays in physical or electronic systems. An additional reason is that state delays increase the complexity of the overall control system. Finally, it was demonstrated that our network can perform as well as a deep network architecture with little training data and omitting steps, such as posture evaluation, and when combined with the hardware advantages of the five-finger gripper, it can produce an automated system with a gripping success rate of over 90%. This paper is an extended study of the conference paper. [ABSTRACT FROM AUTHOR]

This article is the result of a two-hour workshop given to new faculty attending the International Scientific School on Engineering Education for New Industrialization held in Kazan, Tatarstan from September 23–28, 2013. The goal of the workshop was to help faculty who are interested in publishing in English at conferences. The workshop had three parts: selecting the best venue, creating an interesting title and writing a good abstract.

When micro-expressions are mixed with normal or macro-expressions, it becomes increasingly challenging to spot them in long videos. Aiming at the specific time prior of micro-expressions (MEs), an ME spotting network called AEM-Net (adaptive enhanced ME detection network) is proposed. This paper is an extension of the conference paper presented at the Chinese Conference on Biometric Recognition (CCBR). The network improves spotting performance in the following five aspects. Firstly, a multi-stage channel feature extraction module is constructed to extract the features at different depths. Then, an attention spatial-temporal module is leveraged to obtain salient and discriminative micro-expression segments while suppressing the generation of excessively long or short suggestions. Thirdly, a ME-NMS (non-maximum suppression) network is developed to reduce redundancy and decision errors. Fourthly, a multi-scale feature fusion module is introduced to fuse up-sampling features of high-level maps and fine-grained information, which obtains meaningful information on feature distribution and contributes to a good representation of MEs. Finally, two spotting mechanisms named anchor-based and anchor free were integrated to get final spotting. Extensive experiments were conducted on prevalent CAS (ME) 2 and the SAMM-Long ME databases to evaluate the spotting performance. The results show that the AEM-Net achieves competitive performance, outperforming other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this paper, we present a new method for isolating real roots of a bivariate nonlinear system. It is a subdivision method based on analyzing the local geometrical properties of the given system. We propose the concept of opposite monotone system in a box and use it to determine the existence and the uniqueness of a simple real zero of the system in the box. We have implemented our method and the experiments show the effectivity and efficiency of our approach, especially for high degree sparse polynomial systems. This paper is an extended version of the ISSAC'19 conference paper (Cheng and Wen (2019)). [ABSTRACT FROM AUTHOR]

In data-parallel optimization of machine learning models, workers collaborate to improve their estimates of the model: more accurate gradients allow them to use larger learning rates and optimize faster. In the decentralized setting, in which workers communicate over a sparse graph, current theory fails to capture important aspects of real-world behavior. First, the 'spectral gap' of the communication graph is not predictive of its empirical performance in (deep) learning. Second, current theory does not explain that collaboration enables larger learning rates than training alone. In fact, it prescribes smaller learning rates, which further decrease as graphs become larger, failing to explain convergence dynamics in infinite graphs. This paper aims to paint an accurate picture of sparsely-connected distributed optimization. We quantify how the graph topology influences convergence in a quadratic toy problem and provide theoretical results for general smooth and (strongly) convex objectives. Our theory matches empirical observations in deep learning, and accurately describes the relative merits of different graph topologies. This paper is an extension of the conference paper by Vogels et al. (2022). Code: github.com/epfml/topology-in-decentralized-learning. [ABSTRACT FROM AUTHOR]