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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: 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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

This paper aims to investigate the differences between conference papers and journal papers in the field of computer science based on Bayesian network. This paper investigated the differences between conference papers and journal papers in the field of computer science based on Bayesian network, a knowledge-representative framework that can model relationships among all variables in the network. We defined the variables required for Bayesian networks modeling, calculated the values of each variable based Aminer dataset (a literature data set in the field of computer science), learned the Bayesian network and derived some findings based on network inference. The study found that conferences are more attractive to senior scholars, the academic impact of conference papers is slightly higher than journal papers, and it is uncertain whether conference papers are more innovative than journal papers. The study was limited to the field of computer science and employed Aminer dataset as the sample. Further studies involving more diverse datasets and different fields could provide a more complete picture of the matter. By demonstrating that Bayesian networks can effectively analyze issues in Scientometrics, the study offers valuable insights that may enhance researchers' understanding of the differences between journal and conference in computer science. Academic conferences play a crucial role in facilitating scholarly exchange and knowledge dissemination within the field of computer science. Several studies have been conducted to examine the distinctions between conference papers and journal papers in terms of various factors, such as authors, citations, h-index and others. Those studies were carried out from different (independent) perspectives, lacking a systematic examination of the connections and interactions between multiple perspectives. This paper supplements this deficiency based on Bayesian network modeling. [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]

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]

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]

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]

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 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]

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]

The study intends to presents a Bibliometric analysis of articles presented at the Nigerian Library Association's (NLA's) National Conferences/ AGMs, 2019-2021, it will find out the distribution of articles that were presented in NLA's conferences from 2019-2021, the degree of collaboration among the authors, and collaborative index of articles presented at NLA's National conferences from the year 2019-2021. Data were collected from the NLA's journals. To achieve the purpose of the study, three objectives guided the study. The study adopted descriptive survey design. The data were analyzed using frequency table and simple percentage. The findings of the study reveal that 29 articles were presented of which 17 of the number is multiple authors, and that of single authors is 12. The degree of collaboration of articles presented at NLA's National conferences 2019 to 2021 is 0.59, this indicates that degree of collaboration was positive; this reveals that NLA is presenting high quality articles. And the collaborative index of articles presented at NLA's conference in the year 2019 is 0.25 and in 2021 is 0.54. This indicates that the collaboration among the authors is positive. This indicates that NLA journals are publishing collaborative articles of high quality. The study recommended that measures should be taken to ensure that the invitations for papers reach every nooks and crannies of Nigeria; this can increase the number of contributors. More authors should be motivated to produce collaborative articles. More authors need orientation. [ABSTRACT FROM AUTHOR]

An institutional repository (IR) is a digital collection that archives and provides access to an institution's scholarly and creative output, such as a university, research organization, or museum. The purpose of an IR is to collect, preserve, and share the intellectual work of an institution, making it accessible to a wider audience. An IR typically includes research articles, conference papers, theses and dissertations, preprints, reports, datasets, multimedia, and other scholarly and creative works produced by faculty, staff, and students. An institutional profile in Google Scholar can function as an institutional repository. While it may not have all the features and functionalities of a traditional institutional repository, it can still provide a platform for institutions to share and showcase their research output. [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]

The aim of this review was to analyze and summarize the most common adverse events (AEs) and complications after magnetic resonance-guided focused ultrasound (MRgFUS) therapy in uterine fibroids (UFs) and to establish the risk factors of their occurrence. We searched for original research studies evaluating MRgFUS therapy in UFs with outcomes containing AEs and/or complications in different databases (PubMed/MEDLINE, SCOPUS, COCHRANE) until March 2022. Reviews, editorials, opinions or letters, case studies, conference papers and abstracts were excluded from the analysis. The systematic literature search identified 446 articles, 43 of which were analyzed. According to available evidence, the overall incidence of serious complications in MRgFUS therapy is relatively low. No AEs/complications were reported in 11 out of 43 analyzed studies. The mean occurrence of all AEs in the analyzed material was 24.67%. The most commonly described AEs included pain, skin burns, urinary tract infections and sciatic neuropraxia. Major AEs, such as skin ulcerations or deep vein thrombosis, occurred in 0.41% of cases in the analyzed material. MRgFUS seems to be safe in UF therapy. The occurrence of AEs, especially major ones, is relatively low in comparison with other methods. The new devices and more experience of their users seem to reduce AE rate. The lack of unification in AE reporting and missing data are the main issues in this area. More prospective, randomized studies with unified reporting and long follow-up are needed to determine the safety in a long-term perspective. [ABSTRACT FROM AUTHOR]

Many problems in imaging and low-level vision can be formulated as nonconvex variational problems. A promising class of approaches to tackle such problems are convex relaxation methods, which consider a lifting of the energy functional to a higher-dimensional space. However, they come with increased memory requirements due to the lifting. The present paper is an extended version of the earlier conference paper by Ye et al. (in: DAGM German conference on pattern recognition (GCPR), 2021) which combined two recent approaches to make lifting more scalable: product-space relaxation and sublabel-accurate discretization. Furthermore, it is shown that a simple cutting-plane method can be used to solve the resulting semi-infinite optimization problem. This journal version extends the previous conference work with additional experiments, a more detailed outline of the complete algorithm and a user-friendly introduction to functional lifting methods. [ABSTRACT FROM AUTHOR]

Handwritten signatures are widely used for identity authorization. However, verifying handwritten signatures is cumbersome in practice due to the dependency on extra drawing tools such as a digitizer, and because the false acceptance of a forged signature can cause damage to property. Therefore, exploring a way to balance the security and user experiment of handwritten signatures is critical. In this paper, we propose a handheld signature verification scheme called SilentSign, which leverages acoustic sensors (i.e., microphone and speaker) in mobile devices. Compared to the previous online signature verification system, it provides handy and safe paper-based signature verification services. The prime notion is to utilize the acoustic signals that are bounced back via a pen tip to depict a user's signing pattern. We designed the signal modulation stratagem carefully to guarantee high performance, developed a distance measurement algorithm based on phase shift, and trained a verification model. In comparison with the traditional signature verification scheme, SilentSign allows users to sign more conveniently as well as invisibly. To evaluate SilentSign in various settings, we conducted comprehensive experiments with 35 participants. Our results reveal that SilentSign can attain 98.2 % AUC and 1.25 % EER. We note that a shorter conference version of this paper was presented in Percom (2019). Our initial conference paper did not finish the complete experiment. This manuscript has been revised and provided additional experiments to the conference proceedings; for example, by including System Robustness, Computational Overhead, etc. [ABSTRACT FROM AUTHOR]

It is challenging to design complex structures and calculate their dimensions only on the basis of theory. This issue was resolved efficiently by the FEM. Using the applicable test, the mechanical properties of the materials used in the damper are estimated, effectively guiding the design of the device and boosting its vibration-reducing performance. A large-load unmanned aerial vehicle (UAV) airborne radar is the subject of this research article. According to particular use case, vibration dampers for the radar-mounting platform are designed using passive vibration isolation technology to reduce aircraft vibration transmission to the radar. A conference paper has already presented prior research on the same topic. Earlier research confirmed the vibration-isolating efficacy of the T-type rubber isolator, which can greatly improve the operational conditions of the airborne radar. In contrast, earlier studies relied more on experimental validation and did not use numerical simulations. This research's primary objective is the development of a T-type rubber isolator, whose vibration reduction performance is proven using FEM and experiments. Utilizing a suitable simulation analysis method to investigate the vibration reduction of an unmanned aerial vehicle's (UAV) airborne component is a crucial technical task. By comparing multiple material models, a more precise model for characterizing rubber was developed. [ABSTRACT FROM AUTHOR]

The nature of construction and formulation of the multi-objective functions in the flexible job shop (FJS) provides great insights about the study of the technique and the mechanism in scheduling helping to prioritize efforts for optimizing the manufacturing execution performance, which draw the focus of the researchers at home and abroad. From the perspective of the model, the solution method and the objective function, this paper reviews and analyzes the related academic papers and conferences articles during past few years. As a result, the construction forms of the multi-objective function and the scheduling scenarios show strongly positive correlation with the Makespan, the delivery tardiness, the energy consumption or the cost. Consequently, facing with the characteristic in a general way, the detailed construction analysis on the function for the flexible job shop scheduling problem (FJSP) is critical reviewed, and a total 6 types with 49 kinds classification are discussed. Obviously, the main goals are to summarize, analyze, discuss, and synthesize the existing achievements, the current research status, and the ongoing studies on the FJSP functions construction, and additionally to give useful insight into future research. Combined with the development trend of the intelligent manufacturing and the operations research, arguing for the formulation of the multi-obj. function, this review is expected to contribute to the future research of FJSP. [ABSTRACT FROM AUTHOR]

Biodiesel can be produced from vegetable oils, animal fats, frying oils, and from microorganism-synthesized oils. These sources render biodiesel an easily biodegradable fuel. The aim of this work was to perform an advanced bibliometric analysis of primary studies relating to biodiesel production worldwide by identifying the key countries and regions that have shown a strong engagement in this area, and by understanding the dynamics of their collaboration and research outputs. Additionally, an assessment of the main primary feedstocks employed in this research was carried out, along with an analysis of the current and future trends that are expected to define new paths and methodologies to be used in the manufacture of biodegradable and renewable fuels. A total of 4586 academic outputs were selected, including peer-reviewed research articles, conference papers, and literature reviews related to biodiesel production, in the time period spanning from 2010 to 2021. Articles that focused on feedstocks for the production of biodiesel were also included, with a search that returned 330 papers. Lastly, 60 articles relating to biodiesel production via sewage were specifically included to allow for an analysis of this source as a promising feedstock in the future of the biofuel market. Via the geocoding and the document analyses performed, we concluded that China, Malaysia, and India are the largest writers of articles in this area, revealing a great interest in biofuels in Asia. Additionally, it was noted that environmental concerns have caused authors to conduct research on feedstocks that can address the sustainability challenges in the production of biodiesel. [ABSTRACT FROM AUTHOR]

The pathophysiology of chronic rhinosinusitis (CRS) is multifactorial and not entirely clear. The objective of the review was to examine the current state of knowledge concerning the role of exosomes in CRS. For this systematic review, we searched PubMed/MEDLINE, Scopus, CENTRAL, and Web of Science databases for studies published until 7 August 2022. Only original research articles describing studies published in English were included. Reviews, book chapters, case studies, conference papers, and opinions were excluded. The quality of the evidence was assessed with the modified Office and Health Assessment and Translation (OHAT) Risk of Bias Rating Tool for Human and Animal Studies. Of 250 records identified, 17 were eligible, all of which had a low to moderate risk of overall bias. Presented findings indicate that exosomal biomarkers, including proteins and microRNA, act as promising biomarkers in the diagnostics and prognosis of CRS patients and, in addition, may contribute to finding novel therapeutic targets. Exosomes reflecting tissue proteomes are excellent, highly available material for studying proteomic alterations noninvasively. The first steps have already been taken, but more advanced research on nasal exosomes is needed, which might open a wider door for individualized medicine in CRS. [ABSTRACT FROM AUTHOR]

With the prevalence of hardware accelerators as an integral part of the modern systems on chip (SoCs), the ability to model accelerators quickly and accurately within the system in which it operates is critical. This paper presents gem5-SALAMv2 as a novel system architecture for LLVM-based modeling and simulation of custom hardware accelerators integrated into the gem5 framework. It overcomes the inherent limitations of state-of-the-art trace-based pre-register-transfer level (RTL) simulators by offering a truly "execute-in-execute" LLVM-based model. It enables scalable modeling of multiple dynamically interacting accelerators with full-system simulation support. To create long-term sustainable expansion compatible with the gem5 system framework, gem5-SALAM offers a general-purpose and modular communication interface and memory hierarchy integrated into the gem5 ecosystem, streamlining designing and modeling accelerators for new and emerging applications. gem5-SALAMv2 expands upon the framework established in gem5-SALAMv1 with improved LLVM-based elaboration and simulation, improved and more extensible system integration, and new automations to simplify rapid prototyping and design space exploration. 1 1 Conference Paper Extension: This work extends the work presented in gem5-SALAM: A System Architecture for LLVM-based Accelerator Modeling from MICRO 2020 (Rogers et al., 2020). This work expands on the aforementioned work by revamping the gem5-SALAM internals to provide more robust and extensible simulations, introducing new automation tools for expanding and simplifying design space exploration, and demonstrates the new capabilities of gem5-SALAMv2 by exploring multiple configurations of simple neural network architectures. Validation on the MachSuite (Reagen et al., 2014) benchmarks presents a timing estimation error of less than 1% against the Vivado High-Level Synthesis (HLS) tool. Results also show less than a 4% area and power estimation error against Synopsys Design Compiler. Additionally, system validation against implementations on an Ultrascale+ ZCU102 shows an average end-to-end timing error of less than 2%. Lastly, we demonstrate the upgraded capabilities of gem5-SALAMv2 by exploring accelerator platforms for two deep neural networks, LeNet5 and MobileNetv2. In these explorations, we demonstrate how gem5-SALAMv2 can simulate such systems and guide architectural optimizations for these types of accelerator-rich architectures. 2 2 The most up-to-date version of gem5-SALAMv2 is available at https://github.com/TeCSAR-UNCC/gem5-SALAM.. [ABSTRACT FROM AUTHOR]

Particle swarm optimization algorithms (PSOA) is a metaheuristic algorithm used to optimize computational problems using candidate solutions or particles based on selected quality measures. Despite the extensive research published, studies that critically examine its recent scientific developments and research impact are lacking. Therefore, the publication trends and research landscape on PSOA research were examined. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and bibliometric analysis techniques were applied to identify and analyze the published documents indexed in Scopus from 2001 to 2021. The published documents on PSOA increased from 8 to 1,717 (21,362.50%) due to the growing applications of PSOA in solving computational problems. "Conference papers" is the most common document type, whereas the most prolific researcher on PSOA is Andries P. Engelbrecht (South Africa). The most active affiliation (Ministry of Education) and funding organization (National Natural Science Foundation) are based in China. The research landscape on PSOA revealed high levels of publications, citations, and collaborations among the top authors, institutions, and countries worldwide. Keywords co-occurrence analysis revealed that "particle swarm optimization (PSO)" occurred more frequently than others. The findings of the study could provide researchers and policymakers with insights into the prospects and challenges of PSOA research relative to similar algorithms in the literature. [ABSTRACT FROM AUTHOR]

We try to assess the validity of assumptions taken when deriving drift velocity. We give simple formulas for characteristics of the spatiotemporal correlation function of the observed diffraction pattern for the frozen flow and the more general Briggs model. Using Low-Frequency Array (LOFAR) Cassiopeia intensity observation, we compare the experimental velocity scaling factor with a theoretical one to show that both models do not follow observations. We also give a qualitative comparison of our drift velocity estimates with SuperDARN convection maps. The article is essentially an extended version of the conference paper: "Determining ionospheric drift and anisotropy of irregularities from LOFAR core measurements", Signal Processing Symposium 2021 (SPSympo 2021). [ABSTRACT FROM AUTHOR]

The technology for transcranial magnetic stimulation (TMS) has significantly changed over the years, with important improvements in the signal generators, the coils, the positioning systems, and the software for modeling, optimization, and therapy planning. In this systematic literature review (SLR), the evolution of each component of TMS technology is presented and analyzed to assess the limitations to overcome. This SLR was carried out following the PRISMA 2020 statement. Published articles of TMS were searched for in four databases (Web of Science, PubMed, Scopus, IEEE). Conference papers and other reviews were excluded. Records were filtered using terms about TMS technology with a semi-automatic software; articles that did not present new technology developments were excluded manually. After this screening, 101 records were included, with 19 articles proposing new stimulator designs (18.8%), 46 presenting or adapting coils (45.5%), 18 proposing systems for coil placement (17.8%), and 43 implementing algorithms for coil optimization (42.6%). The articles were blindly classified by the authors to reduce the risk of bias. However, our results could have been influenced by our research interests, which would affect conclusions for applications in psychiatric and neurological diseases. Our analysis indicates that more emphasis should be placed on optimizing the current technology with a special focus on the experimental validation of models. With this review, we expect to establish the base for future TMS technological developments. [ABSTRACT FROM AUTHOR]

The article discusses the importance of writing a well-crafted abstract for research papers and the significance of abstracts in the field of anaesthesia. It emphasizes that abstracts are often the only part of the research available to readers and can determine whether readers will explore the full paper. The article provides guidelines for writing abstracts, including the structure, content, and formatting. It also highlights common errors to avoid in abstracts, such as omitting important details and using inappropriate keywords. The article concludes by stating that the abstract supplement of the Indian Journal of Anaesthesia provides a platform for both experienced and novice authors to showcase their research. [Extracted from the article]

Definition: This entry presents the theoretical fundamentals, the mathematical formulation, and the numerical solution for the problem of desiccation cracks in clayey soils. The formulation uses two stress state variables (total stress and suction) and results in a non-symmetric and nonlinear system of transient partial differential equations. A release node algorithm technique is proposed to simulate cracking, and the strategy to implement it in the hydromechanical framework is explained in detail. This general framework was validated with experimental results, and several numerical examples were published at international conferences and in journal papers. [ABSTRACT FROM AUTHOR]

The aim of this paper was to present the development of the journal Kinesiology - International Journal of Fundamental and Applied Kinesiology over the five decades of its publication through the analysis of the articles using bibliometric indicators. The sample consisted of 1083 articles published over the period of 50 years, from the 1st volume in 1971 until the 52nd volume in 2020. The analysis of the articles revealed that the most common articles were original scientific papers, while the least represented articles were categorized as conference papers. From 1971 to 1996, most articles were written in the Croatian language. However, in the period 1996-1999, the journal was published both in Croatian and English and since 2000 onwards exclusively in English. The cited references in the last decade of the journal's publication were analysed in order to point out the patterns of scientific communication in kinesiology and to determine the recognizability of the published papers at the international level. Since 2006, greater visibility of Kinesiology - International Journal of Fundamental and Applied Kinesiology has been enabled by providing access to full texts of publications through the Portal of Scientific Journals of Croatia - Hrčak, whereas the articles published until the end of 2005 were retrogradely digitized. [ABSTRACT FROM AUTHOR]

Why R? Turkey 2022 was a non-profit conference that aimed to bring Turkish R users together and encourage them to attend the R conferences. The targeted audience of the conference consisted of, data scientists, data analysts, and all R users from academia and industry. The three-day conference, which consisted of several events such as workshops, regular talks, lightning talks, short tutorials, and panels, was free of charge and fully online. This article describes the challenges and benefits, as well as providing an overview of the conference's content and participants' profile. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is a scheme in which several consumers work collectively to unravel machine learning (ML) problems, with a dominant collector synchronizing the procedure. This decision correspondingly enables the training data to be distributed, guaranteeing that the individual device's data are secluded. The paper systematically reviewed the available literature using the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) guiding principle. The study presents a systematic review of appliable ML approaches for FL, reviews the categorization of FL, discusses the FL application areas, presents the relationship between FL and Blockchain Technology (BT), and discusses some existing literature that has used FL and ML approaches. The study also examined applicable machine learning models for federated learning. The inclusion measures were (i) published between 2017 and 2021, (ii) written in English, (iii) published in a peer-reviewed scientific journal, and (iv) Preprint published papers. Unpublished studies, thesis and dissertation studies, (ii) conference papers, (iii) not in English, and (iv) did not use artificial intelligence models and blockchain technology were all removed from the review. In total, 84 eligible papers were finally examined in this study. Finally, in recent years, the amount of research on ML using FL has increased. Accuracy equivalent to standard feature-based techniques has been attained, and ensembles of many algorithms may yield even better results. We discovered that the best results were obtained from the hybrid design of an ML ensemble employing expert features. However, some additional difficulties and issues need to be overcome, such as efficiency, complexity, and smaller datasets. In addition, novel FL applications should be investigated from the standpoint of the datasets and methodologies. [ABSTRACT FROM AUTHOR]

A comprehensive study of the Earth System and its different environments requires understanding of multi-dimensional data acquired with a multitude of different sensors or produced by various models. Here we present a component-wise scalable web-based framework for simultaneous visualisation of multiple data sources. It helps contextualise mixed observation and simulation data in time and space. This work is an extended version of the conference paper (Buck et al., 2021). [Display omitted] • Open-source (EUPL) hybrid application for realtime visualisation of 4D geoscientific data. • Split into native server and web client to utilize the strengths of both platforms. • Desktop builds are released for Windows, Linux, and MacOS. • Viewer used on expedition cruises to plan underwater exploration missions • Presentation and data validation capabilities used by GLODAP [ABSTRACT FROM AUTHOR]

This paper conducted a comparative analysis of the conference publications published by Chinese scholars from 2006 to 2019. The main results are as follows. First, the conference papers are unevenly distributed among the 31 provincial administrative regions. The annual number of conference papers published by scholars from different regions fluctuated during the study period. Second, there are different disciplinary distributions of China Proceedings of Conferences Full‐text Database (CPCD) papers and Conference Proceedings Citation Index (CPCI) papers, and the CPCI publications have a relatively centralized disciplinary distribution. Third, it is noteworthy that there are more CPCD papers than CPCI papers. With the introduction of the new science and technology evaluation standards of the Ministry of Science and Technology, there should be more conference papers in the future. [ABSTRACT FROM AUTHOR]

Generative artificial intelligence (GAI) is a rapidly growing field with a wide range of applications. In this paper, a thorough examination of the research landscape in GAI is presented, encompassing a comprehensive overview of the prevailing themes and topics within the field. The study analyzes a corpus of 1319 records from Scopus spanning from 1985 to 2023 and comprises journal articles, books, book chapters, conference papers, and selected working papers. The analysis revealed seven distinct clusters of topics in GAI research: image processing and content analysis, content generation, emerging use cases, engineering, cognitive inference and planning, data privacy and security, and Generative Pre-Trained Transformer (GPT) academic applications. The paper discusses the findings of the analysis and identifies some of the key challenges and opportunities in GAI research. The paper concludes by calling for further research in GAI, particularly in the areas of explainability, robustness, cross-modal and multi-modal generation, and interactive co-creation. The paper also highlights the importance of addressing the challenges of data privacy and security in GAI and responsible use of GAI. [ABSTRACT FROM AUTHOR]