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Purpose: The purpose of this survey was to create a list of essential historical and contemporary readings for undergraduate and graduate students in the field of exercise physiology. Methods: Fifty-two exercise physiologists/sport scientists served as referees, and each nominated ∼25 papers for inclusion in the list. In total, 396 papers were nominated by the referees. This list was then sent back to the referees, with the instructions to nominate the "100 essential papers in sports and exercise physiology." Results: The referees cast 4722 votes. The 100 papers with the highest number of votes received 51% (2406) of the total number of votes. A total of 37 papers in the list of "100 essential papers" were published >50 years ago, and 63 papers were published since 1973. Conclusions: This list of essential studies will provide a perspective on contemporary studies, the "giant's shoulders" to enable young scholars to "see further" or to understand where they have "come from." This compilation is also meant to impress on students that, given the (lack of) technology available in the past, some of the early science required enormous intuitive leaps on the part of historical scientists. [ABSTRACT FROM AUTHOR]

Purpose: This narrative review explores papers published in peer-reviewed journals reporting research from a practice research module of a qualifying program to examine their potential contribution to knowledge in mental health social work. Methods: A narrative review was undertaken according to the PRISMA extension for scoping reviews of papers published by the first three cohorts of a practice research module. Results: Eleven papers were included in the review which found, for example, some deficits in practitioners' knowledge, confidence and understanding. The studies were modest and had some common limitations, but a high number of online views indicate that the papers have the potential to impact on practice. Conclusion: This review found that it is possible to use a practice research module of a qualifying program to train social workers to undertake and publish high-quality research which has the potential to influence policy or practice beyond a students' own learning. [ABSTRACT FROM AUTHOR]

The notable increase in retraction papers has attracted considerable attention from diverse stakeholders. Various sources are now offering information related to research integrity, including concerns voiced on social media, disclosed lists of paper mills, and retraction notices accessible through journal websites. However, despite the availability of such resources, there remains a lack of a unified platform to consolidate this information, thereby hindering efficient searching and cross-referencing. Thus, it is imperative to develop a comprehensive platform for retracted papers and related concerns. This article aims to introduce "Amend," a platform designed to integrate information on research integrity from diverse sources. The Amend platform consolidates concerns and lists of problematic articles sourced from social media platforms (e.g., PubPeer, For Better Science), retraction notices from journal websites, and citation databases (e.g., Web of Science, CrossRef). Moreover, Amend includes investigation and punishment announcements released by administrative agencies (e.g., NSFC, MOE, MOST, CAS). Each related paper is marked and can be traced back to its information source via a provided link. Furthermore, the Amend database incorporates various attributes of retracted articles, including citation topics, funding details, open access status, and more. The reasons for retraction are identified and classified as either academic misconduct or honest errors, with detailed subcategories provided for further clarity. Within the Amend platform, a total of 32,515 retracted papers indexed in SCI, SSCI, and ESCI between 1980 and 2023 were identified. Of these, 26,620 (81.87%) were associated with academic misconduct. The retraction rate stands at 6.64 per 10,000 articles. Notably, the retraction rate for non-gold open access articles significantly differs from that for gold open access articles, with this disparity progressively widening over the years. Furthermore, the reasons for retractions have shifted from traditional individual behaviors like falsification, fabrication, plagiarism, and duplication to more organized large-scale fraudulent practices, including Paper Mills, Fake Peer-review, and Artificial Intelligence Generated Content (AIGC). The Amend platform may not fully capture all retracted and concerning papers, thereby impacting its comprehensiveness. Additionally, inaccuracies in retraction notices may lead to errors in tagged reasons. Amend provides an integrated platform for stakeholders to enhance monitoring, analysis, and research on academic misconduct issues. Ultimately, the Amend database can contribute to upholding scientific integrity. This study introduces a globally integrated platform for retracted and concerning papers, along with a preliminary analysis of the evolutionary trends in retracted papers. [ABSTRACT FROM AUTHOR]

This Position Paper from the Academy of Nutrition Sciences is the third in a series which describe the nature of the scientific evidence and frameworks that underpin nutrition recommendations for health. This paper focuses on evidence which guides the application of dietary recommendations for individuals. In some situations, modified nutrient intake becomes essential to prevent deficiency, optimise development and health, or manage symptoms and disease progression. Disease and its treatment can also affect taste, appetite and ability to access and prepare foods, with associated financial impacts. Therefore, the practice of nutrition and dietetics must integrate and apply the sciences of food, nutrition, biology, physiology, behaviour, management, communication and society to achieve and maintain human health. Thus, there is huge complexity in delivering evidence-based nutrition interventions to individuals. This paper examines available frameworks for appraising the quality and certainty of nutrition research evidence, the development nutrition practice guidelines to support evidence implementation in practice and the influence of other sources of nutrition information and misinformation. The paper also considers major challenges in applying research evidence to an individual and suggests consensus recommendations to begin to address these challenges in the future. Our recommendations target three groups; those who deliver nutrition interventions to individuals, those funding, commissioning or undertaking research aimed at delivering evidence-based nutrition practice, and those disseminating nutritional information to individuals. [ABSTRACT FROM AUTHOR]

Big data refers to extremely large data generated at high volume, velocity, variety, and veracity. The nurse scientist is uniquely positioned to leverage big data to suggest novel hypotheses on patient care and the healthcare system. The purpose of this paper is to provide an introductory guide to understanding the use and capability of big data for nurse scientists. Herein, we discuss the practical, ethical, social, and educational implications of using big data in nursing research. Some practical challenges with the use of big data include data accessibility, data quality, missing data, variable data standards, fragmentation of health data, and software considerations. Opposing ethical positions arise with the use of big data, and arguments for and against the use of big data are underpinned by concerns about confidentiality, anonymity, and autonomy. The use of big data has health equity dimensions and addressing equity in data is an ethical imperative. There is a need to incorporate competencies needed to leverage big data for nursing research into advanced nursing educational curricula. Nursing science has a great opportunity to evolve and embrace the potential of big data. Nurse scientists should not be spectators but collaborators and drivers of policy change to better leverage and harness the potential of big data. [ABSTRACT FROM AUTHOR]

Value co‐creation as a research topic has been a critical proposition of interest to a wide range of disciplines. In recent years, the field of cultural heritage has also developed a range of information practices to promote different forms of value co‐creation. While the body of literature has begun to accumulate, we still lack a comprehensive understanding of conceptual foundations and how value co‐creation is implemented in cultural heritage information practices. This paper conducted a hermeneutic literature review to understand and conceptualize the core aspects of value co‐creation in various cultural heritage information works. In particular, this paper provides a research mapping of the genres of co‐creation in cultural heritage information practices, the actors, and the roles played by the stakeholders. Further, this review provides an overview of the dominant value co‐creation models in the cultural heritage field. It distills the drivers of value co‐creation and the core value consequences that arise from a process perspective. Finally, this paper puts forward a thematic, theoretical, and methodological agenda for future work based on these findings. The review contributes to the literature by proposing an integrated conceptual framework that summarizes the core elements of value co‐creation in cultural heritage information practices. The framework also serves as a call for action, providing a general understanding of the conceptual foundation for future research. [ABSTRACT FROM AUTHOR]

Open data as an integral part of the open science movement enhances the openness and sharing of scientific datasets. Nevertheless, the normative utilization of data journals, data papers, scientific datasets, and data citations necessitates further research. This study aims to investigate the citation practices associated with data papers and to explore the role of data papers in disseminating scientific datasets. Dataset accession numbers from NCBI databases were employed to analyze the prevalence of data citations for data papers from PubMed Central. A dataset citation practice identification rule was subsequently established. The findings indicate a consistent growth in the number of biomedical data journals published in recent years, with data papers gaining attention and recognition as both publications and data sources. Although the use of data papers as citation sources for data remains relatively rare, there has been a steady increase in data paper citations for data utilization through formal data citations. Furthermore, the increasing proportion of datasets reported in data papers that are employed for analytical purposes highlights the distinct value of data papers in facilitating the dissemination and reuse of datasets to support novel research. [ABSTRACT FROM AUTHOR]

Background: Medical text, as part of an electronic health record, is an essential information source in healthcare. Although natural language processing (NLP) techniques for medical text are developing fast, successful transfer into clinical practice has been rare. Especially the hospital domain offers great potential while facing several challenges including many documents per patient, multiple departments and complex interrelated processes. Methods: In this work, we survey relevant literature to identify and classify approaches which exploit NLP in the clinical context. Our contribution involves a systematic mapping of related research onto a prototypical patient journey in the hospital, along which medical documents are created, processed and consumed by hospital staff and patients themselves. Specifically, we reviewed which dataset types, dataset languages, model architectures and tasks are researched in current clinical NLP research. Additionally, we extract and analyze major obstacles during development and implementation. We discuss options to address them and argue for a focus on bias mitigation and model explainability. Results: While a patient's hospital journey produces a significant amount of structured and unstructured documents, certain steps and documents receive more research attention than others. Diagnosis, Admission and Discharge are clinical patient steps that are researched often across the surveyed paper. In contrast, our findings reveal significant under-researched areas such as Treatment, Billing, After Care, and Smart Home. Leveraging NLP in these stages can greatly enhance clinical decision-making and patient outcomes. Additionally, clinical NLP models are mostly based on radiology reports, discharge letters and admission notes, even though we have shown that many other documents are produced throughout the patient journey. There is a significant opportunity in analyzing a wider range of medical documents produced throughout the patient journey to improve the applicability and impact of NLP in healthcare. Conclusions: Our findings suggest that there is a significant opportunity to leverage NLP approaches to advance clinical decision-making systems, as there remains a considerable understudied potential for the analysis of patient journey data. [ABSTRACT FROM AUTHOR]

In recent years, the exploration of knowledge in large-scale human mobility has gained significant attention. In order to achieve a semantic understanding of human behavior and uncover patterns in large-scale human mobility, Named Entity Recognition (NER) is a crucial technology. The rapid advancements in IoT and CPS technologies have led to the collection of massive human mobility data from various sources. Therefore, there's a need for Cross-domain NER which can transfer entity information from the source domain to automatically identify and classify entities in different target domain texts. In the situation of the data-poor, how could we transfer human mobility knowledge over time and space is particularly significant, therefore this paper proposes an Adaptive Text Sequence Enhancement Module (at-SAM) to help the model enhance the association between entities in sentences in the data-poor target domains. This paper also proposes a Predicted Label-Guided Dual Sequence Aware Information Module (Dual-SAM) to improve the transferability of label information. Experiments were conducted in domains that contain hidden knowledge about human mobility, the results show that this method can transfer task knowledge between multiple different domains in the data-poor scenarios and achieve SOTA performance. [ABSTRACT FROM AUTHOR]

Objective: Despite the decrease in the number of foreign visitors and residents in Japan due to the coronavirus disease 2019, a resurgence is remarkable from 2022. However, Japan's medical support system for foreign patients, especially residents, is inadequate, with language barriers potentially causing health disparities. Comprehensive interpretation and translation services are challenging, but "plain Japanese" may be a viable alternative for foreign patients with basic Japanese language skills. This study explores the application and obstacles of plain Japanese in the medical sector. Methods: A literature review was performed across these databases: Web of Science, PubMed, Google Scholar, Scopus, CINAHL Plus, Springer Link and Ichushi-Web (Japanese medical literature). The search covered themes related to healthcare, care for foreign patients, and scholarly articles, and was conducted in July 2023. Results: The study incorporated five papers. Each paper emphasized the language barriers foreign residents in Japan face when accessing healthcare, highlighting the critical role and necessity of plain Japanese in medical environments. Most of the reports focused on the challenges of delivering medical care to foreign patients and the training of healthcare professionals in using plain Japanese for communication. Conclusion: The knowledge and application of plain Japanese among healthcare professionals are inadequate, and literature also remains scarce. With the increasing number of foreign residents in Japan, the establishment of a healthcare system that effectively uses plain Japanese is essential. However, plain Japanese may not be the optimal linguistic assistance in certain situations, thus it is imperative to encourage more research and reports on healthcare services using plain Japanese. [ABSTRACT FROM AUTHOR]

Accessible Summary: What is known on the subject?: Open Dialogue was developed in Finland in the 1980s by clinical psychologist, Jaakko Seikkula. It is a development of family therapy, recognises previous trauma and has proven to be very effective in situations of acute mental illness, and in particular psychosis.Trauma Informed Care is a practice based on the understanding of and responsiveness to the impact of trauma. When people have experienced trauma, they may have difficulties in their everyday life and experience negative physical health outcomes as well as the risk of developing mental ill health.Open Dialogue is aligned to mental health care which aims to be trauma‐informed, person‐centred and rights‐based. Examples exist of the use of both approaches for service delivery with limited evaluation. What the paper adds to existing knowledge?: To our knowledge, no formal evaluation has been made of the use of open dialogue as a Trauma Informed therapy approach to support individuals and their family networks.Although both approaches recognise the impact of trauma on individuals, no study has explored the effectiveness of this treatment combination for use by mental health nurses.This review is timely as it provides insights into contemporary services that are trauma informed and have used Open Dialogue to extend therapy work with individuals and their family/networks.This scoping review was able to determine whether recommendations for clinical practice and training in Open Dialogue with Trauma Informed Care approaches could be identified. What are the implications for practice?: This review provided a broad overview on the current types of trauma‐informed care services incorporating Open Dialogue approaches into their practice.The literature, though sparce, identifies that Trauma Informed Care recognises multiple origins for mental ill health. Open dialogue has an affinity with the common values of mental health nurses. As combined therapies, they are demonstrating usefulness in engaging families and people in their journey towards recovery.Rigid adherence to Open Dialogue focus and delivery as well as training practices could be revised to make them more open to what people and their families wish to discuss. The person with mental ill health and previous trauma should be able to direct the narrative. Trauma Informed Practice principles could be adapted to improve consumer satisfaction with Open Dialogue approaches. Introduction: A large proportion of people who access mental health services have a lived experienced of trauma and are more likely to have a history of complex trauma. Open Dialogue and Trauma Informed Care practices identify previous trauma as a factor related to later psychosis. This scoping review has identified similarities and contrasts in how an Open Dialogue and Trauma Informed Care approach have been combined to complement one another for clinical work with people presenting with psychosis and previous trauma. Aim: We aimed to answer the following research question in this scoping review: What is known of the combined use of Open Dialogue and Trauma Informed Care practice when working with consumers and their family networks? As such, the purpose of this paper was to explore the application to practice and identify if any training existed and been evaluated. Method: This scoping review was based on the Arksey and O'Malley's framework. A comprehensive search was performed across five electronic databases. Grey literature was also searched through Psyche Info and Google Scholar for books, Dissertation and Theses, alongside hand searching of the reference of the studies. Articles searched was from January 2013 to January 2023. Results: Five distinct themes were identified from the literature: (1) Linking open dialogue with trauma, (2) Response to treatment, (3) Empowerment and information sharing, (4) Interpretation by clinical services, (5) Staff training outcomes. Discussion: Some tentative recommendations for practice recognised the individuals' unique story and perspective, suggested that trauma is an important concept to assess. Services practising as Trauma Informed Services that have incorporated an Open Dialogue approach have mixed experiences. The use of Open Dialogue may have some benefits for family work and exploring consumer narratives while building a network of support. However, consumers identified similar frustrations with service delivery as with the family therapy literature. For example, it was difficult to bring family members together and difficult to discuss previous traumatic events in front of family. People experiencing training in Open Dialogue reported it taking a slow pace and not what they were familiar with. Implications for Practice: Open Dialogue can facilitate engagement of consumers and their family networks and greater recognition of the peer workforce to promote collaboration in therapy is needed. Future research should also focus on evaluating the effectiveness of such services and comparing their outcomes across regions. [ABSTRACT FROM AUTHOR]

Reviews have long been recognized as among the most important forms of scientific communication. The rapid growth of the primary literature has further increased the need for reviews to distill and interpret the literature. This review on Reviews and Reviewing: Approaches to Research Synthesis encompasses the evolution of the review literature, taxonomy of review literature, uses and users of reviews, the process of preparing reviews, assessment of review quality and impact, the impact of information technology on the preparation of reviews, and research opportunities for information science related to reviews and reviewing. In addition to providing a synthesis of prior research, this review seeks to identify gaps in the published research and to suggest possible future research directions. [ABSTRACT FROM AUTHOR]

Blockchain-based applications are becoming more and more widespread in business operations. In view of the shortcomings of existing enterprise blockchain evaluation methods, this paper proposes a multi-source heterogeneous blockchain data quality evaluation model for enterprise business activities, so as to achieve efficient evaluation of business activity information consistency, credibility and value. This paper proposes a multi-source heterogeneous blockchain data quality assessment method for enterprise business activities, aiming at the problems that most of the data in enterprise business activities come from different data sources, information representation is inconsistent, information ambiguity between the same block chain is serious, and it is difficult to evaluate the consistency, credibility and value of information. The method firstly proposes an entity information representation method based on the Representation learning for fusing entity category information (CEKGRL) model, which introduces the triad structure of related entities in blockchain, then associates them with enterprise business activity categories, and carries out similarity calculation through contextual information to achieve blockchain information consistency assessment. After that, a trustworthiness characterization method is proposed based on information sources, information comments, and information contents, to obtain the trustworthiness assessment of the business. Finally, based on the information trustworthiness characterization, a value assessment method is introduced to assess the total value of business activity information in the blockchain, and a blockchain quality assessment model is constructed. The experimental results show that the proposed model has great advantages over existing methods in assessing inter-block consistency, intra-block activity information trustworthiness and the value of blockchain. [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of information source identification in online social networks (OSNs). By analyzing the research situation of source identification problems and challenges (such as the randomness of the information dissemination process and complexity of the underlying network topology), this paper studies the problem of multiple source diffusion and proposes a source identification algorithm based on multi-attribute topological clustering (MaTC). The basic idea of the algorithm is to decompose the multi-source problems into a series of single-source problems by using clustering partitioning to improve accuracy and efficiency. Firstly, it estimates the number of source nodes, which is also the number of network partitions, then characterizes the combination of multiple attribute structures as an attribute index of topological clustering, performs an analysis of the distribution of real source nodes in each partition to evaluate the accuracy of the clustering partition, and finally uses Jordan centrality within each partition for single-source identification. Through comparative experiments, it is verified that the proposed MaTC algorithm is superior to the comparison algorithms in evaluating indicators. [ABSTRACT FROM AUTHOR]

This paper examines a comment by Priscillian (d. c. 385) in his Liber apologeticus that certain people erroneously applied to God the unusual Latin neologism, masculofemina. He contrasts their perspective with scriptural teaching about the Holy Spirit being poured out on both men and women. This raises two questions, namely, how Priscillian's comment relates to accusations he faced of encouraging dangerous intimacy between men and women and the source of his information about their teaching. This paper argues that the central thrust of Priscillian's teaching is around the notion that the spirit of prophecy was manifested in both sexes, but that he distinguished his teaching from that of Valentinian gnostics to defend his own orthodoxy. It argues that Priscillian acquired this teaching about God as masculofemina from the translation into Latin of the Aduersus haereses of Irenaeus of Lyons (d. c. 202). The term also occurs within the writing of Marius Victorinus (c. 359–61) in defense of Catholic Christianity. Priscillian drew on Irenaeus to defend the orthodoxy of his notion that the gift of prophecy was given to both men and women. [ABSTRACT FROM AUTHOR]

Organizing archaeological artefacts under a conceptual system is part and parcel of archaeological research. As an abundant material category, pottery artefacts classified in an effective typological model provide a rich source of information for the discipline. However, building a typological model from scratch, as well as maintaining it, often represents a challenge. To support archaeological research, automated methods are increasingly utilized in sustaining classification models. Yet, there is potential for advancement in creating, rethinking, and updating typological arrangements by means of digital, label-driven, or data-driven algorithmic approaches. In this paper, we take a step towards fulfilling this potential while highlighting the fuzziness involved in typological arrangements. We present a complete research pipeline of pottery form quantification, fuzzy-type description, and fuzzy-type definition which is in principle applicable to any typological model. The methodological pipeline is implemented, first, in rim segments to algorithmically construct polythetic rim descriptors; second, in complete profiles to algorithmically connect the global form with the attributed functional class; and third, in types to investigate within-class form variation and its chronological relevance. This paper provides tools to formalize the ambivalence of typological classification using fuzzy logic and revisit the theoretical model to investigate the vagueness of belonging to a class based on morphological aspects of pottery profiles. [ABSTRACT FROM AUTHOR]

Asset management is a process that deals with numerous types of data, including spatial and temporal data. Such an occurrence is attributed to the proliferation of information sources. However, the lack of a comprehensive asset data model that encompasses the management of both spatial and temporal data remains a challenge. Therefore, this paper proposes a graph-based spatio-temporal data model to integrate spatial and temporal information into asset management. In the spatial layer, we provide a graph-based method that uses topological containment and connectivity relationships to model the interior building space using data from 3D city models. In the temporal layer, we proposed the Aggregated Directly-Follows Multigraph (ADFM), a novel process model based on a directly-follows graph (DFG), to show the chronological flow of events in asset management by taking into consideration the repetitive nature of events in asset management. The integration of both layers allows spatial, temporal, and spatio-temporal queries to be made regarding information about events in asset management. This method offers a more straightforward query, which helps to eliminate duplicate and false query results when assessed and compared with a flattened graph event log. Finally, this paper provides information for the management of 3D spaces using a NoSQL graph database and the management of events and their temporal information through graph modelling. [ABSTRACT FROM AUTHOR]

Purpose: As businesses keep investing substantial resources in developing business analytics (BA) capabilities, it is unclear how the performance improvement transpires as BA affects performance in many different ways. This paper aims to analyze how BA capabilities affect firms' agility through resources like information quality and innovative capacity considering industry dynamism and the resulting impact on firm performance. Design/methodology/approach: This paper tested the research hypothesis using primary data collected from 192 companies operating in Bangladesh. The data were analyzed using partial least squares-based structural equation modeling. Findings: The results indicate that BA capabilities improve business resources like information quality and innovative capacity, which, in turn, significantly impact a firm's agility. This paper also found out that industry dynamism moderates the firms' agility and, ultimately, firms' performance. Practical implications: The contribution of this work provides insight regarding the role of business analytics capabilities in increasing organizational agility and performance under the moderating effects of industry dynamism. Originality/value: The present research is to the best of the authors' knowledge among the first studies considering a firm's agility to explore the impact of BA on a firm's performance in a dynamic environment. While previous researchers discussed resources like information quality and innovative capability, current research theoretically argues that these items are a leveraging point in a BA context to increase firm agility. [ABSTRACT FROM AUTHOR]

Background: The number of children who require palliative care has been estimated to be as high as 21 million globally. Delivering effective children's palliative care (CPC) services requires accurate population-level information on current and future CPC need, but quantifying need is hampered by challenges in defining the population in need, and by limited available data. The objective of this paper is to summarise how population-level CPC need is defined, and quantified, in the literature. Methods: Scoping review performed in line with Joanna Briggs Institute methodology for scoping reviews and PRISMA-ScR guidelines. Six online databases (CINAHL, Cochrane Library, EMBASE, Medline, PsycINFO, and Web of Science), and grey literature, were searched. Inclusion criteria: literature published in English; 2008–2023 (Oct); including children aged 0–19 years; focused on defining and/or quantifying population-level need for palliative care. Results: Three thousand five hundred seventy-eight titles and abstracts initially reviewed, of which, 176 full-text studies were assessed for eligibility. Overall, 51 met the inclusion criteria for this scoping review. No universal agreement identified on how CPC need was defined in population-level policy and planning discussions. In practice, four key definitions of CPC need were found to be commonly applied in quantifying population-level need: (1) ACT/RCPCH (Association for Children with Life-Threatening or Terminal Conditions and their Families, and the Royal College of Paediatrics and Child Health) groups; (2) The 'Directory' of Life-Limiting Conditions; (3) 'List of Life-Limiting Conditions'; and (4) 'Complex Chronic Conditions'. In most cases, variations in data availability drove the methods used to quantify population-level CPC need and only a small proportion of articles incorporated measures of complexity of CPC need. Conclusion: Overall, greater consistency in how CPC need is defined for policy and planning at a population-level is important, but with sufficient flexibility to allow for regional variations in epidemiology, demographics, and service availability. Improvements in routine data collection of a wide range of care complexity factors could facilitate estimation of population-level CPC need and ensure greater alignment with how need for CPC is defined at the individual-level in the clinical setting. [ABSTRACT FROM AUTHOR]

An informed search strategy based on random statistical analysis was developed for synchronous recognition of groundwater pollution source information and aquifer parameters. An informed search iterative course (ISIC) was accordingly designed, and each iteration included the determination of attempt point and state transition. In this paper, two improvement techniques were first adopted for choosing attempt points and judging state transition in ISIC to improve search efficiency and precision. The first improvement was that the variable radius free search method was applied to choosing the attempt point, and the size of the search radius was constantly adjusted in ISIC, taking the search ergodicity and efficiency into account. The second improvement technique was a Tsallis formula used for state transition judgment, and the controlled factor in the Tsallis formula was regulated continuously so that the search could consider ergodicity and efficiency simultaneously. Furthermore, frequent calls to the groundwater pollution numerical simulator to calculate the likelihood have inflicted a huge computational burden during ISIC. An effective way is to construct a substitute for emulating the simulator with a low calculating load. However, the mapping relation between the import and export of the numerical simulator was complex and had many variables. The precision of the substitute based on shallow learning is low sometimes. Therefore, we adopted the deep learning method and built an improved deep confidence network (DCN) substitute to emulate the highly nonlinear simulator. Finally, the synchronous recognition results for groundwater pollution source information and aquifer parameters were gained when ISIC ceased. The above-mentioned methods were verified in a case involving groundwater pollution. The consequence indicated that the ISIC with an improved DCN substitute can synchronously recognize groundwater pollution source information and aquifer parameters with a high degree of precision and efficiency. [ABSTRACT FROM AUTHOR]

Background: Mass media significantly contributes to the dissemination of scientific information, including health information on prevention, diagnosis, treatment, national programs, and government schemes. Aims and objectives: To identify the number of news articles related to health in 4 regional newspapers and to classify the news items in terms of type of disease/ health issue covered. Methodology: Four leading newspapers were reviewed to identify the health-related news, their importance in terms of location in the newspaper, and the type of health issue covered. These were further classified based on the level of evidence used. Results: We extracted 720 health-related articles; 411 in English and 309 in Kannada dailies. The English newspaper had maximum coverage on environment 95 (23.1%) and health advisories 60 (14.5%). The Kannada papers on the other hand were dominated by health advisories 65 (21%), followed by nutrition and fitness 48 (15.3%), and environment 40 (12.9%). After the articles were categorized according to their evidence, it was discovered that 92% of them were evidence-based, with 84% of those articles based on Grade-II evidence. Conclusion: Health news in newspapers varies, often focusing on environment and health advisories, and relies heavily on expert opinion rather than empirical data. [ABSTRACT FROM AUTHOR]

Background: Knowledge of changes in the domains of physical activity (PA) during the transition period from primary to secondary education and the factors associated with these changes, are prerequisites for the design of effective PA promotion strategies. Therefore, the first aim of this study was to systematically review changes in general, leisure-time, school, transport, work, and home PA across the transition from primary to secondary education. The second aim was to systematically review the individual, social, and physical environmental factors that were associated with these changes. Methods: Records published up until September 2023 were retrieved from five electronic databases. We included longitudinal and cross-sectional studies that investigated general or domain-specific PA from 2 years before to 2 years after the transition from primary to secondary education. Information on source, study characteristics, sample characteristics, PA, and factors were extracted from the papers included. We reported the direction of change in general and domain-specific PA and the direction of association of the factors with change in general and domain-specific PA. Results: Forty-eight papers were included in the study. The evidence on changes in PA and associated factors was greatest for general PA. A limited number of the studies investigated the separate domains of leisure-time, school, and transport. Most studies on general and school PA reported a decline in PA, but there were no consistent results for the domains of leisure-time and transport. With respect to the associated factors, evidence was predominantly found for individual factors and to a lesser degree for physical environmental and social environmental factors. None of the factors were consistently associated with changes in general or domain-specific PA during the school transition. Conclusions: For the design of targeted PA promotion strategies, further studies are warranted to explore changes in the specific domains of PA across the transition from primary to secondary education, especially in the domains of leisure-time, transport, home, and work PA. In addition, the interactions between factors at different socioecological levels to influence changes in PA need to be addressed more in the future. Trial registration: PROSPERO CRD42020190204. [ABSTRACT FROM AUTHOR]

The Europeana digital library features cultural heritage collections from over 3,000 European institutions described in 37 languages. However, most textual metadata describe the records in a single language, the data providers' language. Improving Europeana's multilingual accessibility presents challenges due to the unique characteristics of cultural heritage metadata, often expressed in short phrases and using in-domain terminology. This work presents the EuropeanaTranslate project's approach and results, aimed at translating Europeana metadata records from 23 EU languages into English. Machine Translation engines were trained on a cleaned selection of bilingual and synthetic data from Europeana, including multilingual vocabularies and relevant cultural heritage repositories. Automatic translations were evaluated through standard metrics and human assessments by linguists and domain cultural heritage experts. The results showed significant improvements when compared to the generic engines used before the in-domain training as well as the eTranslation service for most languages. The EuropeanaTranslate engines have translated over 29 million metadata records on Europeana.eu. Additionally, the MT engines and training datasets are publicly available via the European Language Grid Catalogue and the ELRC-SHARE repository. [ABSTRACT FROM AUTHOR]

This research aims to enlighten current and future research trends on political connections by providing complete bibliometric mapping through network studies on political connections. Utilizing Biblioshiny, the Bibliometrics R package, multi-perspective research publications on political connections from 2003 to the third quarter of 2023 were discovered and analyzed. This bibliometric analysis provides valuable insights regarding current and future publications on political connections. The most prolific contributors, the most used keywords, the most productive countries and sources, the most cited publications, and the most productive sources of information are network analysis data about co-occurrence networks. The discourse revolves around issue mapping data on political connection studies. The search method used to find related literature is the Scopus database. This research contributes valuable information to help other researchers map ongoing and future investigations into political connections. This bibliometric analysis of 294 documents is the first regarding political connections known to researchers. This evaluation emphasizes annual publication trends, authors, publications, countries, organizations, and most productive sources, determining potential future research objectives. The research on political connections was published in early 2003. No papers were released on this subject for 3 years after 2003 (2003–2005), which shows that the growth of this academic field slowed down for ten years. There were significant changes in 2011, and many more linked papers have been released. Authors from the USA and China significantly contributed to the initial scientific journal papers on their political connections. Hong Kong, China, and Australia are recognized as leading nations in scholarly contributions to studying political connections. Chinese scholars spearheaded the collaborative publication effort focused on political connections, with the USA and Australian experts following suit. Based on indexed terms, political connections, sustainable development, and financial performance are trending keywords. These results show possible directions for further research into political connections. [ABSTRACT FROM AUTHOR]

Background: The global issue of ecological resource scarcity, worsened by climate change, necessitates effective methods to promote resource conservation. One commonly used approach is presenting ecological resource scarcity information. However, the effectiveness of this method remains uncertain, particularly in an unpredictable world. This research aims to examine the role of perceived environmental unpredictability in moderating the impact of ecological resource scarcity information on pro-environmental behavior (PEB). Methods: We conducted three studies to test our hypothesis on moderation. Study 1 (N = 256) measured perceived general environmental unpredictability, perceived resource scarcity and daily PEB frequencies in a cross-sectional survey. Study 2 (N = 107) took it a step further by manipulating resource scarcity. Importantly, to increase ecological validity, Study 3 (N = 135) manipulated the information on both ecological resource scarcity and nature-related environmental unpredictability, and measured real water and paper consumption using a newly developed washing-hands paradigm. Results: In Study 1, we discovered that perceived resource scarcity positively predicted PEB, but only when individuals perceive the environment as less unpredictable (interaction effect: 95% CI = [-0.09, -0.01], ΔR2 = 0.018). Furthermore, by manipulating scarcity information, Study 2 revealed that only for individuals with lower levels of environmental unpredictability presenting ecological resource scarcity information could decrease forest resource consumption intention (interaction effect: 95%CI = [-0.025, -0.031], ΔR2 =.04). Moreover, Study 3 found that the negative effect of water resource scarcity information on actual water and (interaction effect: 95%CI = [3.037, 22.097], ηp2 =.050) paper saving behaviors (interaction effect: 95%CI = [0.021, 0.275], ηp2 =.040), as well as hypothetical forest resource consumption (interaction effect: 95%CI = [-0.053, 0.849], ηp2 =.023) emerged only for people who receiving weaker environmental unpredictability information. Conclusion: Across three studies, we provide evidence to support the moderation hypothesis that environmental unpredictability weakens the positive effect of ecological resource scarcity information on PEB, offering important theoretical and practical implications on the optimal use of resource scarcity to enhance PEB. [ABSTRACT FROM AUTHOR]

Background: The Internet is a widely utilized platform for accessing health-related information and pregnant women are no different in this regard. The aim of the present study was to review and describe the use of the internet as a source of information during pregnancy. Methods: This systematic review was conducted by searching electronic databases such as PubMed until May 2023. The author also conducted general Internet searches using combinations of the search terms in the Google search engine. The search terms were as follows: pregnancy, "pregnancy period", "pregnant women", internet, network, information, "information sources", "information resources", "information tools", and "information providers". The search was limited to English language papers without limitations regarding publishing dates. Quantitative studies were sought, that reported original research and described Internet use by pregnant women and women within one year postpartum. Results: Fifteen articles met the inclusion criteria and were included in the review. This systematic review found that women used the Internet as a source of information during pregnancy. The most often mentioned topics of interest were fetal development, stages of delivery and nutrition. Women generally find health information on the internet as reliable and useful. Some papers included in this review found that higher education is associated with increased Internet use and the pregnant women with a university degree used the Internet more than women with less than a high school education, and that nulliparous women were more likely to seek information than multiparous women. Conclusions: Most of the pregnant women did not discuss the information they have retrieved from the Internet with their health providers. Consequently, healthcare providers should be prepared to support women in effectively navigating online resources, as well as interpreting and integrating the retrieved knowledge. [ABSTRACT FROM AUTHOR]

Background: Advance care planning discussions are crucial in the management and support of individuals with life-limiting or life-threatening conditions. Few studies have examined best practices for advance care planning with adolescents and young adults. Aim: To identify core components of current guidelines, frameworks and tools for advance care planning discussions with adolescents and young adults with life-limiting or life-threatening conditions and their families. Design: A scoping review of the literature was conducted followed by a thematic analysis of the included papers. The scoping review was reported according to the Joanna Briggs Institute approach to the conduct of scoping reviews. Data sources: Five databases [Cochrane Central Register of Controlled Trials (CENTRAL), Cochrane Database of Systematic Reviews, PsycInfo, PubMed and Scopus] were searched for English-language papers published between inception until January 2023. Results: The search yielded 2976 papers, of which 9 met the inclusion criteria. Five main themes were identified: (i) utilisation of standardised documents and protocols; (ii) shared decision-making between the adolescents and young adults, their families and the healthcare team; (iii) the importance of open and honest communication with adolescents and young adults during advance care planning discussions; (iv) individualisation and flexibility in the advance care planning process and (v) timing of advance care planning initiation. Conclusions: Results highlight the importance of engaging adolescents and young adults in advance care planning and considering their unique needs when initiating and framing these discussions. Our findings can be used by healthcare professionals to inform advance care planning in this group. [ABSTRACT FROM AUTHOR]

The article explores the concept and importance of population health metrics, emphasizing the need for multidimensional and integrative measures to assess community health outcomes comprehensively. Topics discussed include the evolution and definitions of population health, the challenges of measuring health disparities and outcomes globally, and the role of innovative methodologies in improving population health assessment and equity.

The present paper makes a systematic review of 131 articles on research productivity basing upon a model developed by Denyer and Tranfield. The current review paper analyzed the quantitative and qualitative dimensions of the sample literature in the field of research productivity. The quantitative analysis incorporates the chronological distribution of articles, keyword wise distribution, form wise distribution, discipline wise distribution, publisher wise distribution, data sources used in the articles, most prolific journals of the publications and qualitative dimensions studied are top-cited articles and all the metrices used in these articles. The paper is based upon literature retrieved from Scopus bibliographic database and other online databases like Emerald, Taylor & Francis, Google Scholar, JSTOR, ProQuest, and EBSCO for full-text articles. It was revealed that there is significant growth in the number of articles during the fourth decade (2011–2020) whereas the highest numbers of citations (1530) have been received during the third decade from 2001 to 2010. Journal articles appeared to be the predominant source of information. A wide range of metrices found to be used in the sample literature to analyze the research productivity namely basic metrics, metrics dealing with growth, collaboration and metrics at institute level and author level metrics. It will serve as a reference tool to the scholars and practitioners in the field who will be acquainted with the publications on research productivity as well as various metrices, laws of bibliometrics, statistical tools, used in the articles. [ABSTRACT FROM AUTHOR]

Purpose: Although there is a growing body of work on immigrants' information behavior, little is known about the pre-arrival information experiences of immigrants who consult formal information sources such as immigration agents. Drawn from a larger study on the information behavior of immigrants, this paper mainly reports the semi-structured interview findings on the pre-arrival information experiences of Bangladeshi immigrants who used formal information sources with discussion on how that affected their post-arrival settlement into Canada. Design/methodology/approach: The study used a mixed method approach with semi-structured interviews (n = 60) and surveys (n = 205) with participants who arrived in Canada between the years of 1971 and 2017. Data were collected from May 2017 to February 2018. Findings: Although the overall scope of the original study is much larger, this paper features findings on the pre-arrival information experiences derived mainly from an analysis of interview data. This study provides insights into the pre-arrival information experiences of Bangladeshi immigrants consulting formal information sources such as immigration firms, individual immigration consultants and more formal government agencies. The author introduces a new concept of "information crafting" by exploring the negative consequences of selective information sharing by immigration consultants/agents in newcomers' settlements in Canada, primarily positive information about life in Canada, sometimes with exaggeration and falsification. The interview participants shared story after the story of the settlement challenges they faced after arriving in Canada and how the expectations they built through the information received from immigration consultants and government agencies did not match after arrival. This study emphasizes the importance of providing comprehensive information about life in Canada to potential newcomers so that they can make informed decisions even before they apply. Originality/value: The findings of this study have theoretical and practical implications for policy and research. This study provides insights into the complicated culturally situated pre-arrival information experiences of Bangladeshi immigrants. Moreover, the study findings encourage researchers in various disciplines, including psychology, migration studies and geography, to delve more deeply into newcomers' information experiences using an informational lens to examine the information newcomers receive from diverse sources and their effects on their post-arrival settlement in a new country. The study challenges the general assumptions that formal information sources are always reputable, useful, and comprehensive, and it provides some future directions for research that seeks to understand the culturally situated information behavior of diverse immigrant groups. [ABSTRACT FROM AUTHOR]

Current unsupervised domain adaptation methods for intelligent fault diagnosis focus on learning domain-invariant representations under covariate shift assumption. However, the covariate shift assumption is usually unsatisfied when each fault class in different domains consists of multiple modes with skewed proportions, which is common in industrial scenarios. Imbalanced data from multiple modes cause the presence of within-domain class imbalance and between-domain label distributional shift. This paper introduces a novel subpopulation shift that further considers the domain shift from a subpopulation perspective, i.e., that covariate shift and label distributional shift across domains are caused by shifts in the multiple modes. To address this issue, a label propagation-based unsupervised domain adaptation is proposed based on a realistic expansion assumption. We apply the theoretical analysis of the proposed method with a bi-level optimization strategy adapted from meta-learning. Using joint optimization of a teacher model and a student model, the label propagation-based model-agnostic meta-learning (LPMAML) not only propagates supervision information from the source to the target but also adjusts the teacher's strategy throughout the student's learning process. To alleviate the noise caused by label distributional shift, we integrate a sampling-based alignment method that aligns the empirical label distributions across the two domains into LPMAML. Experimental results on three bearing datasets show that the proposed method has impressive generalization ability under covariate, label distributional, and subpopulation shifts. The proposed method offers consistent improvements to unsupervised domain adaptation (UDA) methods. Compared with the vanilla UDA methods, the average diagnosis accuracies of the proposed method on the label distributional shift benchmark and subpopulation shift benchmark are improved by 8.21% and 7.63%, respectively. [ABSTRACT FROM AUTHOR]

Managing genetic disease using medically assisted reproductive technology is increasingly promoted as a feasible option, given revolutionary advances in genomics. Far less attention has been directed to the issue of whether there is equitable access to this option. Context and circumstance determine equitable access; however, reporting has drawn overwhelmingly from affluent Anglo-western populations in developed countries. The experiences of poorer, less educated subpopulations within affluent countries and populations in less developed countries are underreported. The ability of consumers to understand the opportunities and risks of medically assisted reproductive technology is likewise not well described in the literature despite significant technological complexity and evidence that genetic disease may be overrepresented within some disadvantaged population groups. Equity is achieved by identifying barriers and allocating appropriate resources to enable understanding and access. In the case of utilising medically assisted technology, social and power relationships, regulations, and the presumptions of authority figures and policymakers reduce equitable access. Physical or cultural marginalisation from mainstream health services may result in reduced access to genetic and prenatal testing, in-vitro fertilisation and genetic screening of embryos necessary for medically assisted reproduction. Cost and regulatory frameworks can likewise limit opportunities to engage with services. Moreover, the quality of the information provided to prospective users of the technology and how it is received governs understanding of prevention and inhibits adequately informed choice. Best practice care and adequately informed choice can only be achieved by conscientiously attending to these accessibility issues. Deep engagement with at-risk people and critical reflection on mainstream accepted standpoints is required. This paper outlines issues associated with engaging with medically assisted reproduction encountered by Aboriginal families living with Machado-Joseph Disease in some of the most remote areas of Australia. It is the right of these families to access such technologies regardless of where they live. Current barriers to access raise important questions for service providers with implications for practice as new technologies increasingly become part of standard medical care. [ABSTRACT FROM AUTHOR]

Localizing faults in a software is a tedious process. The manual approach is becoming impractical because of the large size and complexity of contemporary computer systems as well as their logs, which are often the primary source of information about the fault. Log-based Fault Localization (LBFL) is a popular method applied for this purpose. However, in real-world scenarios, this method is vulnerable to a large number of previously unseen log lines. In this paper, we propose a novel method that can guide programmers to the location of a fault by creating a hierarchy of log lines with the highest rank, selected by the traditional LBFL method. We use the intuition that the symptoms of faults are in the context of normal behavior, whereas suspicious log lines grouped together are from new or additional functionalities turned on during faulty execution. To obtain this context, we used unsupervised log sequence segmentation, which has been previously used to segment log sequences into meaningful segments. Experiments on real-life examples show that our method reduces the effort to find the most crucial logs by up to 64% compared with the traditional timestamp approach. We demonstrate that context is highly useful in advancing fault localization, showing the possibility of further speeding up the process. [ABSTRACT FROM AUTHOR]

Due to the lack of annotations for nighttime low-light images, object detection in low-light images has always been a challenging problem. Achieving high-precision results at night is also an issue. Additionally, we aim to use a single nighttime dataset to complete the knowledge distillation task while improving the detection accuracy of object detection models under nighttime low-light conditions and reducing the computational cost of the model, especially for small targets and objects contaminated by special nighttime lighting. This paper proposes a Nighttime Unsupervised Domain Adaptation Network (NUDN) based on knowledge distillation to address these issues. To improve the detection accuracy of nighttime images, high-confidence bounding box predictions from the teacher and region proposals from the student are first fused, allowing the teacher to perform better in subsequent training, thus generating a combination of high-confidence and low-confidence pseudo-labels. This combination of feature information is used to guide model training, enabling the model to extract feature information similar to that of source images in nighttime low-light images. Nighttime images and pseudo-labels undergo random size transformations before being used as input for the student, enhancing the model's generalization across different scales. To address the scarcity of nighttime datasets, we propose a nighttime-specific augmentation pipeline called LightImg. This pipeline enhances nighttime features, transforming them into daytime features and reducing issues such as backlighting, uneven illumination, and dim nighttime light, enabling cross-domain research using existing nighttime datasets. Our experimental results show that NUDN can significantly improve nighttime low-light object detection accuracy on the SHIFT and ExDark datasets. We conduct extensive experiments and ablation studies to demonstrate the effectiveness and efficiency of our work. [ABSTRACT FROM AUTHOR]

Private set intersection (PSI) has important application value, however, current quantum PSI protocols are either unsuitable for multi-party scenarios or inefficient. Recently, Imran (arXiv:2303.17196v3, 2023) proposed two quantum secure multi-party greatest common divisor (GCD) protocols that can be used for PSI, but with the downside of information leakage and resource consumption. In this paper, we propose a novel quantum secure multi-party GCD protocol that has higher security and lower complexity. To hide privacy, each party randomly selects a coefficient within a range determined by his input integer, and with the assistance of a semi-honest third party TP, all parties secretly calculate the linear combination of their inputs under these coefficients. Once enough linear combinations are collected, TP calculates the GCD of these combinations, which is equal to the GCD of all input integers. To verify the honesty of participants, a quantum zero-knowledge proof sub-protocol is designed. Analysis shows that our GCD protocol is correct and has security against malicious attacks. Moreover, its complexity is polynomial level and lower than Imran's. Furthermore, we demonstrate the scalability of our GCD protocol in private set operations, such as private set intersection, private set intersection cardinality, private multi-set intersection, etc. [ABSTRACT FROM AUTHOR]

The resilience and adaptability of complex networks is crucial in ensuring their functionality against disruptions. Particularly, maintaining network connectivity under various attack scenarios is a key aspect of such resilience. Network connectivity refers to the degree to which nodes within a network are interconnected and able to exchange information or resources. Its robustness reflects the ability of a network to maintain connectivity under various attacks. Such ability has profound physical significance, ensuring the stability and reliability of real-world systems. Currently, connectivity robustness assessments rely heavily on very time-consuming attack simulations. This paper introduces a graph neural network framework for network connectivity robustness learning (GCRL) to advance the study of network connectivity robustness. GCRL transforms initial degree distributions and network topology into informative embedding vectors, which are then processed by a robustness learning module mainly composed of multi-layer perceptron, achieving both high speed and precision. Our extensive experiments demonstrate the superior performance of GCRL obtained in less time compared to existing methods, especially in tough scenarios where test data distributions significantly differ from the training set. The framework also shows adaptability to networks of different sizes, making it a more generalized solution for complex network robustness learning. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has highlighted the need for reliable information and news sources. In 2020, Xinhua News Agency launched "Zhiyun," an epidemic reporting robot that can generate COVID-19 news reports based on visual data. This development raises key issues regarding the effectiveness of machine-generated news compared to traditional sources, especially news related to major public health events such as the pandemic. Using the Cognitive-Affective-Conative Model and ANCOVA method, this paper conducts experimental research to obtain data and studies the impact of machine-made news on the audience's attitude towards COVID-19 news. The analysis used a 2 × 2 factorial online experimental method to test the impact of two variables: "theme" and "news format." The research results indicate that the theme and news format significantly affect the audience's attitude towards epidemic news, and machine-generated video news received a more positive response than news written by human journalists. Based on the results of this study, it can be concluded that machine-generated news has great potential to provide accessible and reliable information during major public health events such as COVID-19. This study has significant implications for the news industry, indicating the possibility of increasing the use of machine news production in the future. [ABSTRACT FROM AUTHOR]

What is the relationship between counterinsurgency and institution-building? When do wartime institutions persist once conflict has ended? Classic theories examine how war spurs new institutions within the central state, while extensive research on rebel governance examines how insurgent actors forge new rules to garner civilian compliance and cement control. However, the legacies of armed conflict for state institutions in the theater of war remain relatively neglected. We theorize the process of local counterinsurgent institution-building and the drivers of institutional endurance following counterinsurgency. By analyzing two local counterinsurgent institutions in Nicaragua and a shadow case drawn from Indonesia, we find that while state leaders may generate new institutional arrangements to elicit information and garner resources, institutional persistence is driven by local reappropriation as communities pursue their own postwar governance and development goals. Overall, this paper contributes a new understanding for the divergent postwar paths of local institutions generated amid counterinsurgency. [ABSTRACT FROM AUTHOR]

Introduction. The volume and quality of online health information requires consumers to be discerning. Aim. This study aimed to explore consumer Internet use for health information, preferred format and what factors helped them to trust the source. Methods. A cross-sectional study was conducted in 2016–2017 with adults attending three cardiology outpatient clinic sites using a short paper-based survey. The survey included questions regarding online health information use and perceived trustworthiness with opportunities for free text responses. Survey data were summarised with key questions adjusted by age group, gender and ethnicity using logistic regression. Results. Of the 708 respondents (51% women, 66% aged 45–74 years, 16% Māori, 12% Pacific), 73% had sought health information online (64% in the previous 12 months), commonly for medication side effects, their health condition and self-help. Most (65%) were successful, although Pacific respondents reported a lower likelihood of search success compared to Europeans. Younger age groups were more concerned about information quality. Fact sheets (80%) were the most popular format and for all ethnic groups, followed by short videos (31%) and discussion groups (23%). Trusting online information required many strategies with 72% wanting health professionals to recommend websites. Discussion. Online health information seeking is a norm for consumers, with simple fact sheets being the preferred format to build knowledge and skills. With the rising tide of misinformation, health portal providers need to offer accurate and easy-to-read fact sheets in their suite of formats and health professionals need to support consumers guiding them to trusted websites. [ABSTRACT FROM AUTHOR]

The European Pressure Ulcer Advisory Panel, National Pressure Injury Advisory Panel, and Pan Pacific Pressure Injury Alliance collaboratively developed three editions of the International Clinical Practice Guidelines for preventing and treating pressure ulcers/injuries in 2009, 2014, and 2019. Dissemination and uptake of these guidelines are important to improve care. The aim of this work was to provide an updated and expanded citation analysis of the three published guidelines. Referencing formats, citation counts, and global distribution of citations of the 2009, 2014, and 2019 guidelines were searched in the citation database Scopus from 2009 to 2023. Duplicates were removed manually. The search identified 330 referencing formats with 2887 citations from 2009 to 2023 for the three guideline editions. Citations displayed geographical diversity, with citation peaks observed approximately 4 years after each edition's release and ongoing citations to the present. The International Clinical Practice Guidelines for preventing and treating pressure ulcers/injuries updated since 2009 were cited more than 2800 times in the scientific literature exceeding the thresholds of highly cited papers in the field of clinical medicine. This indicates successful dissemination worldwide. [ABSTRACT FROM AUTHOR]

Identifying key classes of a software system can help developers understand the system quickly, reduce the time for system maintenance, and prevent security risks caused by defects in key classes. So far, many approaches have been proposed to identify key classes from software systems. However, some approaches select too many key class candidates, making it inconvenient and difficult for developers to start understanding the system from these classes. For the other approaches, although the number of key class candidates is not large, their effectiveness needs to be further improved. To this end, in this paper, we propose a new model, named SAVM, to detect key classes by combining static analysis and a voting mechanism. First, we extract structural information from the source codes of a software system and construct a class coupling network (CCN) using this information. Then, we present the VRWD method that iteratively identifies important nodes in CCN based on a voting mechanism. Specifically, in each iteration, a node votes for its outgoing neighbors and in the meantime receives votes from its incoming neighbors. Afterward, the node that attains the highest voting score is elected as the important node in this turn. Finally, the corresponding classes of the selected important nodes are the key class candidates. The effectiveness of the proposed model and eight other baselines is evaluated in eight open-source Java projects. The experimental results show that although no method performs the best in all projects, according to the average ranking of the Friedman test, our method overall performs better compared to the baselines. In addition, this paper also proves through experiments that our approach can be applied to large-scale software projects. These indicate that our approach is a valuable technique for developers. [ABSTRACT FROM AUTHOR]

When the near-field and far-field information of a target is uncertain, it is necessary to choose a suitable localization method. The modified polar representation (MPR) method integrates the two scenarios and achieves a unified localization with direction of arrival (DOA) estimation in the far field and position estimation in the near field. Previous studies have only proposed solutions for stationary environments and have not considered the motion factor. Therefore, this paper proposes a new unified positioning algorithm using multi-sensor time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements without prior target source information. The method represents the position of the target source using MPR and describes the localization problem as a weighted least squares (WLS) problem with two constraints. We first obtain the initial estimates by WLS without considering the constraints and then investigate a two-step error correction method based on the constraints. The first step corrects the initial estimate using the Taylor series expansion technique, and the second step corrects the DOA estimate in the previous step using the direct error compensation technique based on the properties of the second constraint. Simulation experiments show that the method is effective for the unified positioning of moving targets and can achieve the Cramer–Rao lower bound (CRLB). [ABSTRACT FROM AUTHOR]

Acute kidney injury (AKI) presents significant challenges in pediatric care, often remaining underrecognized. This paper provides an overview of pediatric AKI, highlighting its epidemiology, pathophysiology, diagnosis, predisposing conditions, and treatment. AKI in children stems from diverse causes, including renal tubular damage, vasoconstriction, and inflammation. Diagnosis relies on traditional markers such as serum creatinine and urine output, alongside emerging biomarkers such as Cystatin C, NGAL, KIM-1, IL-18, TIMP-2 and IGFBP7, urinary calprotectin, URBP4, L-FABP, and clusterin. Various pediatric conditions predispose to AKI, including type 1 diabetes, pneumonia, bronchiolitis, appendicitis, gastroenteritis, COVID-19, multisystem inflammatory syndrome, sickle cell disease, and malignancies. Treatment entails supportive care with fluid management and, in severe cases, renal replacement therapy. Timely recognition and management are essential to mitigating adverse outcomes. Enhanced awareness and integration of novel biomarkers could improve pediatric AKI care, warranting further research for better diagnosis and management. [ABSTRACT FROM AUTHOR]

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Following the established triple helix concept, civil society has become an essential part of regional innovation policy models and the building of quadruple helix structures. Despite the growing literature on this topic, findings are still diverse and fragmented. This study aims to address the gap in the literature by systematically presenting the role of civil society as an element of the quadruple helix in regional innovation policy. It discusses the successes achieved, the contributing factors, the challenges faced, and solutions to overcome those challenges. A systematic literature review protocol was applied to identify the most relevant papers and a critical review of the main content of the articles was conducted according to the designed research questions. The main findings of this study revealed two key points to improve civil society engagement in local innovation policies: knowledge/information resources and communication/coordination mechanisms. Addressing these two key points is thought to enhance participation and transparency in the policy process and promote more robust innovation collaboration. [ABSTRACT FROM AUTHOR]

Background: The emergence of ChatGPT in the education literature represents a transformative phase in educational technology research, marked by a surge in publications driven by initial research interest in new topics and media hype. While these publications highlight ChatGPT's potential in education, concerns arise regarding their quality, methodology, and uniqueness. Objective: Our study employs unconventional methods by combining altmetrics and bibliometrics to explore ChatGPT in education comprehensively. Methods: Two scholarly databases, Web of Science and Altmetric, were adopted to retrieve publications with citations and those mentioned on social media, respectively. We used a search query, "ChatGPT," and set the publication date between November 30th, 2022, and August 31st, 2023. Both datasets were within the education‐related domains. Through a filtering process, we identified three publication categories: 49 papers with both altmetrics and citations, 60 with altmetrics only, and 66 with citations only. Descriptive statistical analysis was conducted on all three lists of papers, further dividing the entire collection into three distinct periods. All the selected papers underwent detailed coding regarding open access, paper types, subject domains, and learner levels. Furthermore, we analysed the keywords occurring and visualized clusters of the co‐occurring keywords. Results and Conclusions: An intriguing finding is the significant correlation between media/social media mentions and academic citations in ChatGPT in education papers, underscoring the transformative potential of ChatGPT and the urgency of its incorporation into practice. Our keyword analysis also reveals distinctions between the themes of the papers that received both mentions and citations and those that received only citations but no mentions. Additionally, we noticed a limitation that authors' choice of keywords might be influenced by individual subjective judgements, potentially skewing results in thematic analysis based solely on author‐assigned keywords such as keyword co‐occurrence analysis. Henceforth, we advocate for developing a standardized keyword taxonomy in the educational technology field and integrating Large Language Models to enhance keyword analysis in altmetric and bibliometric tools. This study reveals that ChatGPT in education literature is evolving from rapid publication to rigorous research. Lay Description: What is currently known about this topic?: ChatGPT in education has seen a surge in publications driven by media hype.Early publications tend to lack rigour and reiterate known advantages/disadvantages.Literature reviews on ChatGPT in education have limitations in scope and depth.Some studies have explored altmetrics and bibliometrics in other fields. What does this paper add?: Combines altmetrics and bibliometrics to analyse publications of ChatGPT in education.Addresses challenges in the discourse by offering unconventional analysis methods.Identifies publication trends and investigates the relationship between media attention and citations.Determines key themes in the literature through keyword co‐occurrence analysis. Implications for practice/or policy: Expectations of continued growth in ChatGPT literature but with evolving publication trends.Distinctive characteristics of ChatGPT in education challenge keyword analysis.Proposes the development of a unified keyword taxonomy for clarity in the field.Suggests enhancing altmetrics and bibliometrics tools using Large Language Models. [ABSTRACT FROM AUTHOR]

Background: In recent years, the integration of large language models (LLMs) into healthcare has emerged as a revolutionary approach to enhancing doctor–patient communication, particularly in the management of diseases such as prostate cancer. Methods: Our paper evaluated the effectiveness of three prominent LLMs—ChatGPT (3.5), Gemini (Pro), and Co-Pilot (the free version)—against the official Romanian Patient's Guide on prostate cancer. Employing a randomized and blinded method, our study engaged eight medical professionals to assess the responses of these models based on accuracy, timeliness, comprehensiveness, and user-friendliness. Results: The primary objective was to explore whether LLMs, when operating in Romanian, offer comparable or superior performance to the Patient's Guide, considering their potential to personalize communication and enhance the informational accessibility for patients. Results indicated that LLMs, particularly ChatGPT, generally provided more accurate and user-friendly information compared to the Guide. Conclusions: The findings suggest a significant potential for LLMs to enhance healthcare communication by providing accurate and accessible information. However, variability in performance across different models underscores the need for tailored implementation strategies. We highlight the importance of integrating LLMs with a nuanced understanding of their capabilities and limitations to optimize their use in clinical settings. [ABSTRACT FROM AUTHOR]

Background: Patient medical records are records relating to the patient's identity, diagnosis, action, and to the treatment that has been given to the patient. Thus, medical records must be protected from damage, but damage to medical records is still found in every hospital. Purpose: This study attempts to investigate the causes of damage to hospital medical record materials. Methods: this is literature review, and the research was obtained from google scholar and the portal garuda, using full text articles. The measured results were factors that cause damage to medical record documents. Results: The main factor were still officers with a low level of education, namely high school graduates. This has an impact on filing officers to be not disciplined in maintaining medical record documents, and the absence of implementation of training related to management and storage management in the filing section. The machine factor was that there was no medical record rack available in accordance with the number of medical record documents. The method factor was that there was no SOP for maintaining medical records. The material factor was the raw material for the map using thin paper. The media factor, namely the storage space, has not been avoided from the dangers of water, fire, and biological damage. Conclusion: Based on 5M factors, the factors that have the highest influence on damage to medical record documents are the elements of people, machine, and material. While the factor that causes the least damage to medical record documents is the method. [ABSTRACT FROM AUTHOR]

We examine whether shareholder votes in environmental and social (ES) proposals are informative about firms' ES risks. ES proposals are unique in that they nearly always fail. We examine whether mutual funds' support for these failed proposals contains information about the ES risks that firms face. Higher support in failed ES proposals predicts subsequent ES incidents and the effects of these incidents on shareholder value. Examining the detailed records of fund votes, we find that agency frictions between a group of shareholders contribute to proposal failure. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online [ABSTRACT FROM AUTHOR]