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In an era overwhelmed by academic big data, students grapple with identifying academic papers that resonate with their learning objectives and research interests, due to the sheer volume and complexity of available information. This study addresses the challenge by proposing a novel academic paper recommendation system designed to enhance personalized learning through the nuanced understanding of academic social networks. Utilizing the theory of social homogeneity, the research first constructs a sophisticated academic social network, capturing high-order social relationships, such as co-authorship and advisor–advisee connections, through hypergraph modeling and advanced network representation learning techniques. The methodology encompasses the development and integration of a hypergraph convolutional neural network and a contrastive learning framework to accurately model and recommend academic papers, focusing on aligning with students' unique preferences and reducing reliance on sparse interaction data. The findings, validated across multiple real-world datasets, demonstrate a significant improvement in recommendation accuracy, particularly in addressing the cold-start problem and effectively mapping advisor–advisee relationships. The study concludes that leveraging complex academic social networks can substantially enhance the personalization and precision of academic paper recommendations, offering a promising avenue for addressing the challenges of academic information overload and fostering more effective personalized learning environments. [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]

Background: The advent of Personalized Medicine (PM) holds significant promise in revolutionizing healthcare by tailoring treatments to individual patients based on their data. However, its successful implementation requires the seamless integration of innovative technologies and presents formidable challenges in terms of sustainability. To tackle these challenges head-on, the International Consortium for Personalized Medicine (ICPerMed) was established, and the IC2PerMed project, as part of this consortium, seeks to foster collaboration between the European Union (EU) and China in the field of Personalized Medicine. Based on the results collected by the project, the objective of this study is to discern the key priorities for the implementation of Personalised Medicine concerning Information and Communication Technologies (ICT) and Big Data and digital solutions, with a particular emphasis on data management and protection. Methods: A Delphi survey was conducted to gather expert's consensus on the main priorities for actions on Information and Communication Technologies (ICT) and Big Data and digital solutions in the field of Personalized Medicine. Results: The survey identified seven priorities in the area of Big Data and digital solutions, including data interoperability, standards, security measures, and international partnerships. Additionally, twelve priorities were identified for the innovation-to-market process, emphasizing cost-effectiveness, need assessment, and value definition in resource allocation. Conclusions: The effective implementation of new technologies in Personalized Medicine research and practice is essential for the advancement of healthcare systems in both the European and Chinese contexts. The identified priorities play a pivotal role in promoting the sustainability of health systems and driving innovation in the implementation of Personalized Medicine. Addressing challenges related to data interoperability, standards, security, international collaboration, cost-effectiveness, and value assessment is of utmost importance in order to propel the progress of Personalized Medicine in healthcare systems. [ABSTRACT FROM AUTHOR]

Traces of mercury ions in environmental water can harm humans and animals. Paper-based visual detection methods have been widely developed for the rapid detection of mercury ions; however, existing methods are not sensitive enough to be used in real environments. Here, we developed a novel, simple and effective visual fluorescent sensing paper-based chip for the ultrasensitive detection of mercury ions in environmental water. CdTe-quantum-dots-modified silica nanospheres were firmly absorbed by and anchored to the fiber interspaces on the paper's surface to effectively avoid the unevenness caused by liquid evaporation. The fluorescence of quantum dots emitted at 525 nm can be selectively and efficiently quenched with mercury ions, and the ultrasensitive visual fluorescence sensing results attained using this principle can be captured using a smartphone camera. This method has a detection limit of 2.83 µg/L and a fast response time (90 s). We successfully achieved the trace spiking detection of seawater (from three regions), lake water, river water and tap water with recoveries in the range of 96.8–105.4% using this method. This method is effective, low-cost, user-friendly and has good prospects for commercial application. Additionally, the work is expected to be utilized in the automated big data collection of large numbers of environmental samples. [ABSTRACT FROM AUTHOR]

Conjecture about the weak replicability in social sciences has made scholars eager to quantify the scale and scope of replication failure for a discipline. Yet small-scale manual replication methods alone are ill-suited to deal with this big data problem. Here, we conduct a discipline-wide replication census in science. Our sample (N= 14,126 papers) covers nearly all papers published in the six top-tier Psychology journals over the past 20 y. Using a validated machine learning model that estimates a paper's likelihood of replication, we found evidence that both supports and refutes speculations drawn from a relatively small sample of manual replications. First, we find that a single overall replication rate of Psychology poorly captures the varying degree of replicability among subfields. Second, we find that replication rates are strongly correlated with research methods in all subfields. Experiments replicate at a significantly lower rate than do non-experimental studies. Third, we find that authors' cumulative publication number and citation impact are positively related to the likelihood of replication, while other proxies of research quality and rigor, such as an author's university prestige and a paper's citations, are unrelated to replicability. Finally, contrary to the ideal that media attention should cover replicable research, we find that media attention is positively related to the likelihood of replication failure. Our assessments of the scale and scope of replicability are important next steps toward broadly resolving issues of replicability. [ABSTRACT FROM AUTHOR]

With the prevalence of artificial intelligence technologies, big data has been utilized to higher extent in many cross-domain fields. This paper concentrates on the digital assessment of vocational education level in some specific areas, and proposes a graph neural network-based assessment model for this purpose. Assume that all vocational colleges inside a specific region are with a social graph, in which each college is a node and the relations among them are the edges. The graph neural network (GNN) model is formulated to capture global structured features of all the nodes together. The GNN is then employed for the sequential modeling pattern, and the evolving characteristics of all the colleges can be captured. Some experiments are also conducted to evaluate the performance of the proposed GNN-VEL. It is compared with two typical forecasting methods under evaluation of two metrics. The results show that it performs better than other two methods. [ABSTRACT FROM AUTHOR]

Background: The clinical effects of smartphone‐based interventions for bipolar disorder (BD) have yet to be established. Objectives: To examine the efficacy of smartphone‐based interventions in BD and how the included studies reported user‐engagement indicators. Methods: We conducted a systematic search on January 24, 2022, in PubMed, Scopus, Embase, APA PsycINFO, and Web of Science. We used random‐effects meta‐analysis to calculate the standardized difference (Hedges' g) in pre‐post change scores between smartphone intervention and control conditions. The study was pre‐registered with PROSPERO (CRD42021226668). Results: The literature search identified 6034 studies. Thirteen articles fulfilled the selection criteria. We included seven RCTs and performed meta‐analyses comparing the pre‐post change in depressive and (hypo)manic symptom severity, functioning, quality of life, and perceived stress between smartphone interventions and control conditions. There was significant heterogeneity among studies and no meta‐analysis reached statistical significance. Results were also inconclusive regarding affective relapses and psychiatric readmissions. All studies reported positive user‐engagement indicators. Conclusion: We did not find evidence to support that smartphone interventions may reduce the severity of depressive or manic symptoms in BD. The high heterogeneity of studies supports the need for expert consensus to establish ideally how studies should be designed and the use of more sensitive outcomes, such as affective relapses and psychiatric hospitalizations, as well as the quantification of mood instability. The ISBD Big Data Task Force provides preliminary recommendations to reduce the heterogeneity and achieve more valid evidence in the field. [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]

Objectives: The International Society for Bipolar Disorders Big Data Task Force assembled leading researchers in the field of bipolar disorder (BD), machine learning, and big data with extensive experience to evaluate the rationale of machine learning and big data analytics strategies for BD. Method: A task force was convened to examine and integrate findings from the scientific literature related to machine learning and big data based studies to clarify terminology and to describe challenges and potential applications in the field of BD. We also systematically searched PubMed, Embase, and Web of Science for articles published up to January 2019 that used machine learning in BD. Results: The results suggested that big data analytics has the potential to provide risk calculators to aid in treatment decisions and predict clinical prognosis, including suicidality, for individual patients. This approach can advance diagnosis by enabling discovery of more relevant data‐driven phenotypes, as well as by predicting transition to the disorder in high‐risk unaffected subjects. We also discuss the most frequent challenges that big data analytics applications can face, such as heterogeneity, lack of external validation and replication of some studies, cost and non‐stationary distribution of the data, and lack of appropriate funding. Conclusion: Machine learning‐based studies, including atheoretical data‐driven big data approaches, provide an opportunity to more accurately detect those who are at risk, parse‐relevant phenotypes as well as inform treatment selection and prognosis. However, several methodological challenges need to be addressed in order to translate research findings to clinical settings. [ABSTRACT FROM AUTHOR]

As an important branch of machine learning, deep learning (DL) models with multiple hidden layer structures have the ability to extract highly representative features from the input. At present, fault detection and diagnosis (FDD) and health monitoring solutions developed based on DL models have received extensive attention in academia and industry along with the rapid improvement of computing power. Therefore, this paper focuses on a comprehensive review of DL model–based FDD and health monitoring schemes in view of common problems of industrial systems. First, brief theoretical backgrounds of basic DL models are introduced. Then, related publications are discussed about the development of DL and graphical models in the industrial context. Afterwards, public data sets are summarized, which are associated with several research papers. More importantly, suggestions on DL model–based diagnosis and health monitoring solutions and future developments are given. Our work will have a positive impact on the selection and design of FDD solutions based on DL and graphical models in the future. [ABSTRACT FROM AUTHOR]

This paper offers a situated analysis of infrastructures of surveillance and control in Rotterdam through Latour & Hermant’s concept of the oligopticon. Oligopticons are networked devices and their supporting infrastructures that render the city in extremely narrow but very clear representations, in this case as a city of waste hotspots. In Rotterdam, public management is designing data-hungry oligopticons that map, control and intervene in the illegal disposal of waste and litter. An analysis of two vignettes from the author’s ethnographic fieldwork finds that these oligopticons do more than simply make visible and control these practices of waste disposal: they instead engage in punitive politics that frames those who illegally dispose of waste as exceptions to a normalizing way of life, fighting not waste but difference. Through ethnographic comparison, this paper aims to evoke thought, imagination and reflection in readers regarding the consequences of infrastructures of surveillance and control and the intimate knowledge that may escape these infrastructures. [ABSTRACT FROM AUTHOR]

This paper considers the quantitative comparison of several alternative models to perform deconvolution in situations where there is no ground truth data available. With applications to very large data sets in mind, we focus on linear deconvolution models based on a Wiener filter. Although comparatively simple, such models are widely prevalent in large scale setting such as high-resolution image restoration because they provide an excellent trade-off between accuracy and computational effort. However, in order to deliver accurate solutions, the models need to be properly calibrated in order to capture the covariance structure of the unknown quantity of interest and of the measurement error. This calibration often requires onerous controlled experiments and extensive expert supervision, as well as regular recalibration procedures. This paper adopts an unsupervised Bayesian statistical approach to model assessment that allows comparing alternative models by using only the observed data, without the need for ground truth data or controlled experiments. Accordingly, the models are quantitatively compared based on their posterior probabilities given the data, which are derived from the marginal likelihoods or evidences of the models. The computation of these evidences is highly non-trivial and this paper consider three different strategies to address this difficulty—a Chib approach, Laplace approximations, and a truncated harmonic expectation—all of which efficiently implemented by using a Gibbs sampling algorithm specialised for this class of models. In addition to enabling unsupervised model selection, the output of the Gibbs sampler can also be used to automatically estimate unknown model parameters such as the variance of the measurement error and the power of the unknown quantity of interest. The proposed strategies are demonstrated on a range of image deconvolution problems, where they are used to compare different modelling choices for the instrument's point spread function and covariance matrices for the unknown image and for the measurement error. [ABSTRACT FROM AUTHOR]

With the advent of big data, the swift advancement of diverse algorithmic technologies has enhanced the transaction efficiency of the e-commerce business. Nevertheless, it is crucial to acknowledge that e-commerce platforms might employ algorithmic technology to enforce differential pricing for various consumers with the aim of maximizing profits, thus infringing upon the lawful rights and interests of consumers. This paper focuses on the algorithmic price discrimination commonly observed on e-commerce platforms. To effectively regulate this behavior, the paper utilizes evolutionary game theory (EGT) to analyze the strategies employed by e-commerce platforms, consumers, and market regulators to achieve stability. This research employs a real-life situation and utilizes parametric simulation to visualize and analyze the process and outcomes of the three-party evolutionary game. The results demonstrate the credibility and feasibility of the article's findings. Based on our research, we have reached the following findings: During the process of evolution, the strategic decisions made by the game participants from the three parties will mutually impact each other, and various elements exert varying degrees of influence on the strategic choices made by the game participants from each party. Collaborative governance can enable consumers and market regulators to regulate the discriminatory pricing behavior of e-commerce platforms effectively. This article offers valuable insights into the governance of violations in the e-commerce sector based on robust data and model research. [ABSTRACT FROM AUTHOR]

This paper offers a counter-map of automation in social services decision-making in Australia. It aims to amplify alternative discourses that are often obscured by power inequalities and disadvantage. Redden (2005) has used counter-mapping to frame an analysis of big data in government in Canada, contrasting with 'dominant outward facing government discourses about big data applications' to focus on how data practices are both socially shaped and shaping. This paper reports on a counter-mapping project undertaken in Australia using a mixed methods approach incorporating document analysis, interviews and web scraping to amplify divergent discourses about automated decision-making. It demonstrates that when the focus of analysis moves beyond dominant discourses of neoliberal efficiency, cost cutting, accuracy and industriousness, alternative discourses of service users' experiences of automated decision-making as oppressive, harmful, punitive and inhuman(e) can be located. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive review of the use of computational bioacoustics as well as signal and speech processing techniques in the analysis of primate vocal communication. We explore the potential implications of machine learning and deep learning methods, from the use of simple supervised algorithms to more recent self‐supervised models, for processing and analyzing large data sets obtained within the emergence of passive acoustic monitoring approaches. In addition, we discuss the importance of automated primate vocalization analysis in tackling essential questions on animal communication and highlighting the role of comparative linguistics in bioacoustic research. We also examine the challenges associated with data collection and annotation and provide insights into potential solutions. Overall, this review paper runs through a set of common or innovative perspectives and applications of machine learning for primate vocal communication analysis and outlines opportunities for future research in this rapidly developing field. [ABSTRACT FROM AUTHOR]

The rapid progression of emerging technologies like the Internet of Things (IoT) and Big Data analytics for manufacturing has driven innovation across various industries worldwide. Production data are utilized to construct a model for quality evaluation and analysis applicable to components processed by machine tools, ensuring process quality for critical components and final product quality for the machine tools. Machine tool parts often encompass several quality characteristics concurrently, categorized into three types: smaller-the-better, larger-the-better, and nominal-the-better. In this paper, an evaluation index for the nominal-the-better quality characteristic was segmented into two single-sided Six Sigma quality indexes. Furthermore, the process quality of the entire component product was assessed by n single-sided Six Sigma quality indexes. According to numerous studies, machine tool manufacturers conventionally base their decisions on small sample sizes (n), considering timeliness and costs. However, this often leads to inconsistent evaluation results due to significant sampling errors. Therefore, this paper established fuzzy testing rules using the confidence intervals of the q single-sided Six Sigma quality indices, serving as the fuzzy quality evaluation model for components of machine tools. [ABSTRACT FROM AUTHOR]

The interaction between technology and healthcare has a long history. However, recent years have witnessed the rapid growth and adoption of the Internet of Things (IoT) paradigm, the advent of miniature wearable biosensors, and research advances in big data techniques for effective manipulation of large, multiscale, multimodal, distributed, and heterogeneous data sets. These advances have generated new opportunities for personalized precision eHealth and mHealth services. IoT heralds a paradigm shift in the healthcare horizon by providing many advantages, including availability and accessibility, ability to personalize and tailor content, and cost-effective delivery. Although IoT eHealth has vastly expanded the possibilities to fulfill a number of existing healthcare needs, many challenges must still be addressed in order to develop consistent, suitable, safe, flexible, and power-efficient systems that are suitable fit for medical needs. To enable this transformation, it is necessary for a large number of significant technological advancements in the hardware and software communities to come together. This keynote paper addresses all these important aspects of novel IoT technologies for smart healthcare-wearable sensors, body area sensors, advanced pervasive healthcare systems, and big data analytics. It identifies new perspectives and highlights compelling research issues and challenges, such as scalability, interoperability, device-network-human interfaces, and security, with various case studies. In addition, with the help of examples, we show how knowledge from CAD areas, such as large scale analysis and optimization techniques can be applied to the important problems of eHealth. [ABSTRACT FROM AUTHOR]

Abstract Background Our current understanding of the pathophysiology and management of sepsis is associated with a lack of progress in clinical trials, which partly reflects insufficient appreciation of the heterogeneity of this syndrome. Consequently, more patient-specific approaches to treatment should be explored. Aims To summarize the current evidence on precision medicine in sepsis, with an emphasis on translation from theory to clinical practice. A secondary objective is to develop a framework enclosing recommendations on management and priorities for further research. Sources A global search strategy was performed in the MEDLINE database through the PubMed search engine (last search December 2017). No restrictions of study design, time, or language were imposed. Content The focus of this Position Paper is on the interplay between therapies, pathogens, and the host. Regarding the pathogen, microbiologic diagnostic approaches (such as blood cultures (BCs) and rapid diagnostic tests (RDTs)) are discussed, as well as targeted antibiotic treatment. Other topics include the disruption of host immune system and the use of biomarkers in sepsis management, patient stratification, and future clinical trial design. Lastly, personalized antibiotic treatment and stewardship are addressed (Fig. 1). Implications A road map provides recommendations and future perspectives. RDTs and identifying drug-response phenotypes are clear challenges. The next step will be the implementation of precision medicine to sepsis management, based on theranostic methodology. This highly individualized approach will be essential for the design of novel clinical trials and improvement of care pathways. [ABSTRACT FROM AUTHOR]

This paper examines the work practices involved in making data legible to machines and machine output legible to humans. The study is based on ethnographic research of a team of art experts at DNArt – a data classification system that features a growing database of art images, a classification scheme, a similarity matching algorithm, and a website that together serve as a consumer judgment device in an emerging online market for art. I analyze interactions from meeting observations, interviews, documentation, and online interaction data to show how non-technical art experts explain and repair sociotechnical breakdowns – when their expectations for similarity between art images and artists differ from the similarity relations produced by the algorithm. By repairing breakdowns, the art experts construct the algorithm anew, as a legitimate revealer of similarity in art. In doing so, the team's repair work is folded back into the black box of the algorithm, rendering it invisible and unacknowledged, sometimes even by the experts themselves. [ABSTRACT FROM AUTHOR]

I thank the discussors for their critique of the above paper. In geology there are rarely any absolute models but working hypotheses, which will be modified with new data. The paper under discussion, Charrach (Carbonates Evaporites 33:727–766, 2018a), presents a very new large data set, which is multi-disciplinary and integrated the most recent research. The critique by Möller et al. (Carbonates Evaporites 10.1007/s13146-019-00486-3, 2019) and their paper of 2018 (Int J Earth Sci 107:2409–2431, 2018), presents a computer simulation, which must be validated with field evidence and recent research in the area, and not based on assumptions, which have turned into facts over time. The discussors may be unfamiliar with the geological research that has been published over the last 20 years in the areas under consideration. Furthermore, the research on marine–non-marine evaporite formations is hardly considered in their discussion, yet much has been published. [ABSTRACT FROM AUTHOR]

From the moment a man took ‘the king’s shilling’ and was sworn to serve as a soldier in the nineteenth-century British Army, his life proceeded as a file as well as a fighting man. Disorder and desertion drove the utilitarian purposes of discipline and tracking, while constant pressure to account for expenditure in lives and money added further impetus to the copious industry of military record-keeping. Individuals were enumerated, named, appraised and allocated pay. Such archives produce a disorderly silence where men are present but without voice. Carefully archived and always public, military files have a continuing currency through the post-army lives of soldiers into the twenty-first century for descendants and historians. Tracking the life of ‘files’ over time, the paper reflects on the shifting forms of knowledge produced. In particular, it notes the tensions between the densely written form of the files in a population of rank and file soldiers who were partially literate; the highly detailed individuation of the files within a heavily conformist institution, and the modernity of post-1850s record-keeping in an institution bound by tradition. It ends with a reflection on the limitations and opportunities presented by digital access to this substantial archive of imperial-colonial conflict. Abbreviations: AJCP: Australian Joint Copying Project TNA: The National Archives, London WO: War Office [ABSTRACT FROM AUTHOR]

The objective of this study is to analyze and foresee potential outliers in pulp and handsheet properties for larger data sets. The method is divided into two parts comprising a generalized Extreme Studentized Deviate (ESD) procedure for laboratory data followed by an analysis of the findings using a multivariable model based on internal variables (i. e. process variables like consistency and fiber residence time inside the refiner) as predictors. The process data used in this has been obtained from CD-82 refiners and from a laboratory test program perspective, the test series were extensive. In the procedure more than 290 samples were analyzed to get a stable outlier detection. Note, this set was obtained from pulp at one specific operating condition. When comparing such "secured data sets" with process data it is shown that an extended procedure must be performed to get data sets which cover different operating points. Here 100 pulp samples at different process conditions were analyzed. It is shown that only about 60 percent of all tensile index measurements were accepted in the procedure which indicates the need to oversample when performing extensive trials to get reliable pulp and handsheet properties in TMP and CTMP processes.

With the advancement of society and the improvement of living standards, the significance of students' physical health has become increasingly prominent. However, currently, the assessment and analysis of students' physical health heavily depend on conventional statistical methods. Even with the application of data mining-related methodologies for analysis and evaluation, the exploitation and utilization of physical health big data remain relatively restricted. In this paper, an improved Canopy-K-means algorithm based on principal component analysis (PCA) is used to construct and analyze a portrait of students' physical fitness and health. The method combines data dimensionality reduction techniques and cluster analysis techniques, and its combined performance is the best compared to other algorithms in the ablation experiments for both male and female student data groups. In this paper, the algorithm was used to process the grouping of physical fitness test data of male and female students, realize the construction and analysis of students' physical fitness and health portrait, and give the exercise prescription for different categories of students. In this paper, the physical health test data of students of Yunnan Agricultural University in 2020–2022 were collected to carry out experiments, and the results found that there are differences in physical health status among students of different genders, grades, and majors in this university, and the physical health status of the students of Classes 2018 and 2019 is generally deficient; on different majors, the students of the Faculty of Agricultural Sciences are slightly superior to the Faculty of Science and Technology, and the students of the Faculty of Science and Technology are slightly superior to the students of the Faculty of Humanities and Social Sciences. Our study offers novel methods and ideas for the assessment and analysis of students' physical health, holding significant implications for schools and related departments in formulating scientific and effective physical education policies and health promotion strategies. [ABSTRACT FROM AUTHOR]

Simple Summary: Artificial intelligence has become essential for aiding in different knowledge domains by improving knowledge extraction from raw data and process automation. In dairy production, artificial intelligence offers promising applications in detecting and managing bovine mastitis, the most critical disease affecting the mammary gland in dairy cows, impacting milk production and profitability in dairy farms. This research evaluated the evolution of artificial intelligence applications in bovine mastitis between 2011 and 2021 using the Scopus database and the frequency of terms cited in titles, abstracts, and keywords. We selected the 62 papers that were the most relevant according to their citation index. Our results pointed out that the terms "machine learning" and "mastitis" were the most cited, with a significant increase between 2018 and 2021. There was an increase in artificial intelligence applications for bovine mastitis per country, showing applications primarily aimed at improving the current mastitis detection systems. The most cited model was artificial neural networks. We concluded that using artificial intelligence in bovine mastitis was related to mastitis detection as a vital tool to prevent this disease, considering its major impacts on dairy production and economic return. Mastitis, an important disease in dairy cows, causes significant losses in herd profitability. Accurate diagnosis is crucial for adequate control. Studies using artificial intelligence (AI) models to classify, identify, predict, and diagnose mastitis show promise in improving mastitis control. This bibliometric review aimed to evaluate AI and bovine mastitis terms in the most relevant Scopus-indexed papers from 2011 to 2021. Sixty-two documents were analyzed, revealing key terms, prominent researchers, relevant publications, main themes, and keyword clusters. "Mastitis" and "machine learning" were the most cited terms, with an increasing trend from 2018 to 2021. Other terms, such as "sensors" and "mastitis detection", also emerged. The United States was the most cited country and presented the largest collaboration network. Publications on mastitis and AI models notably increased from 2016 to 2021, indicating growing interest. However, few studies utilized AI for bovine mastitis detection, primarily employing artificial neural network models. This suggests a clear potential for further research in this area. [ABSTRACT FROM AUTHOR]

This paper aims to examine the effects of digital technologies on academic knowledge production in the field of urban research. It provides a comprehensive overview of the evolution of technology-based urban research literature, delineates the knowledge structure, and investigates prevalent trends. The study employs bibliometric analysis to analyze bibliographical and textual data extracted from scientific documents, which allows for the discovery of the existing epistemological structure of the discipline or research domain. The research was conducted by conducting a comprehensive and up-to-date review of the literature available in the Web of Science Core Collection regarding how concepts such as big data, artificial intelligence (AI), and the Internet of Things (IoT) are discussed in academic papers in the field of urban studies. A total of 2055 academic papers that met the established criteria were identified and analyzed using bibliometric analysis software called Bibliometrix. The results of the research aid in examining the integration of technological advancements into urban research, and reveal the temporal, spatial, and disciplinary distribution of scientific articles, as well as their reflections on new research areas and both frequently studied and yet unexplored topics. [ABSTRACT FROM AUTHOR]

Remote sensing technology in the new era gradually need a new scientific paradigm driven by big data and knowledge. However, in the current research, the use of existing knowledge is slightly insufficient. As for the whole research process, there is a lack of guidance based on the field knowledge. In view of the above problems in the field of remote sensing in agriculture, it is of great significance to organize knowledge based on concept graph and develop knowledge-driven big data analysis theory and methods. This paper focuses on the construction and application of agricultural remote sensing concept graph. Firstly, it explores the core contents, research hotspots and development trends of the current agricultural remote sensing field through reference visualization and analysis methods such as keyword frequency statistics, keyword co-occurrence, word detection with stronger citation burst and so on. Then, this paper summarizes the comprehensive ontology of agricultural remote sensing based on the results of keyword clustering. Thus, the schema layer of concept graph is constructed from top to bottom. Under the guidance of the schema layer, the data layer of the concept graph is artificially filled from the bottom up with different triads stored in Neo4j. In terms of application, retrieval, query and recommendation of the proposed concept graph for agricultural remote sensing tasks is a typical application case. Based on the results of the recommendation, it could guide the process of information extraction such as cultivated land parcel delineation and crop type identification at the parcel scale. The study of concept graph helps to summarize and generalize the relevant knowledge in the field of agricultural remote sensing. Relevant research provide effective solutions for automated and intelligent agricultural information extraction and application. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive review of tree-based models and their application in condition assessment and prediction of water, wastewater, and sewer pipe failures. Tree-based models have gained significant attention in recent years due to their effectiveness in capturing complex relationships between parameters of systems and their ability in handling large data sets. This study explores a range of tree-based models, including decision trees and ensemble trees utilizing bagging, boosting, and stacking strategies. The paper thoroughly examines the strengths and limitations of these models, specifically in the context of assessing the pipes' condition and predicting their failures. In most cases, tree-based algorithms outperformed other prevalent models. Random forest was found to be the most frequently used approach in this field. Moreover, the models successfully predicted the failures when augmented with a richer failure data set. Finally, it was identified that existing evaluation metrics might not be necessarily suitable for assessing the prediction models in the water and sewer networks. [ABSTRACT FROM AUTHOR]

Traditional methods have the limitations of low accuracy and inconvenient operation in analyzing students’ abnormal behavior. Hence, a more intuitive, flexible, and user-friendly visualization tool is needed to help better understand students’ behavior data. In this study a visual analysis and interactive interface of students’ abnormal behavior based on a clustering algorithm were examined and designed. Firstly, this paper discusses the development of traditional methods for analyzing students’ abnormal behavior and visualization technology and discusses its limitations. Then, the K-means clustering algorithm is selected as the solution to find potential abnormal patterns and groups from students’ behaviors. By collecting a large number of students’ behavior data and preprocessing them to extract relevant features, a K-means clustering algorithm is applied to cluster the data and obtain the clustering results of students’ abnormal behaviors. To visually display the clustering results and help users analyze students’ abnormal behaviors, a visual analysis method and an interactive interface are designed to present the clustering results to users. The interactive functions are provided, such as screening, zooming in and out, and correlation analysis, to support users’ in-depth exploration and analysis of data. Finally, the experimental evaluation is carried out, and the effectiveness and practicability of the proposed method are verified by using big data to obtain real student behavior data. The experimental results show that this method can accurately detect and visualize students’ abnormal behaviors and provide intuitive analysis results. This paper makes full use of the advantages of big data to understand students’ behavior patterns more comprehensively and provides a new solution for students’ management and behavior analysis in the field of education. Future research can further expand and improve this method to adapt to more complex students’ behavior data and needs. [ABSTRACT FROM AUTHOR]

Summary: Where the response variable in a big dataset is consistent with the variable of interest for small area estimation, the big data by itself can provide the estimates for small areas. These estimates are often subject to the coverage and measurement error bias inherited from the big data. However, if a probability survey of the same variable of interest is available, the survey data can be used as a training dataset to develop an algorithm to impute for the data missed by the big data and adjust for measurement errors. In this paper, we outline a methodology for such imputations based on an k‐nearest neighbours (kNN) algorithm calibrated to an asymptotically design‐unbiased estimate of the national total, and illustrate the use of a training dataset to estimate the imputation bias and the "fixed‐k asymptotic" bootstrap to estimate the variance of the small area hybrid estimator. We illustrate the methodology of this paper using a public‐use dataset and use it to compare the accuracy and precision of our hybrid estimator with the Fay–Harriot (FH) estimator. Finally, we also examine numerically the accuracy and precision of the FH estimator when the auxiliary variables used in the linking models are subject to undercoverage errors. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence (AI) can radically change almost every aspect of the human experience. In the medical field, there are numerous applications of AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, this fact being supported by the exponential increase in the number of publications in which the algorithms play an important role in data analysis, pattern discovery, identification of anomalies, and therapeutic decision making. Furthermore, with technological development, there have appeared new models of machine learning (ML) and deep learning (DP) that are capable of exploring various applications of AI in cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional cardiology, and many others. In this sense, the present article aims to provide a general vision of the current state of AI use in cardiology. Results: We identified and included a subset of 200 papers directly relevant to the current research covering a wide range of applications. Thus, this paper presents AI applications in cardiovascular imaging, arithmology, clinical or emergency cardiology, cardiovascular prevention, and interventional procedures in a summarized manner. Recent studies from the highly scientific literature demonstrate the feasibility and advantages of using AI in different branches of cardiology. Conclusions: The integration of AI in cardiology offers promising perspectives for increasing accuracy by decreasing the error rate and increasing efficiency in cardiovascular practice. From predicting the risk of sudden death or the ability to respond to cardiac resynchronization therapy to the diagnosis of pulmonary embolism or the early detection of valvular diseases, AI algorithms have shown their potential to mitigate human error and provide feasible solutions. At the same time, limits imposed by the small samples studied are highlighted alongside the challenges presented by ethical implementation; these relate to legal implications regarding responsibility and decision making processes, ensuring patient confidentiality and data security. All these constitute future research directions that will allow the integration of AI in the progress of cardiology. [ABSTRACT FROM AUTHOR]

What constitutes good intelligence is best understood by practitioners but has not been explored through empirical analysis and in the context of a digital age. This paper presents the first research inside all the agencies that form the Australian National Intelligence Community exploring how they are impacted by big data. Intelligence is often opaque to outsiders, yet understanding the characteristics of good intelligence is important to societies that rely on intelligence agencies for national security. This paper reflects the previously unheard perspectives of members of the agencies that form the Australian National Intelligence Community – where there is a significant empirical gap. Semi-structured interviews with 47 participants explored the impact of big data on intelligence and decision-making in Australia. This paper finds that intelligence must meet the following characteristics, many established in historical literature, in order to be considered good intelligence; (i) timely, (ii) purposeful, (iii) actionable, (iv) accurate, (v) provides value-add for an intended audience, and, (vi) is unbiased. This article explores and unpacks each of these characteristics of good intelligence and finds they remain critical in a big data era. [ABSTRACT FROM AUTHOR]

The paper reports findings from a study that examining how cross-platform social media analysis can help to map the digital discourse on sustainable mobility and sustainable transport, and enhance the understanding of sociotechnical low-carbon transport transitions. Using the hashtag search queries #sustainabletransport and #sustainablemobility, 33,121 Tweets (2013–2021) and 8,089 Instagram images including captions (2017/2018–2021) were scraped using the Python modules Twint and Instaloader. Quantitative text and sentiment analyses were applied to the Tweets and image captions. Additionally, an automated machine learning-based image analysis of the Instagram images was conducted using object detection via OpenCV. Synthesized results formed the base for a cross-platform analysis inspired by Rogers' method comprising hot topics/key themes, user mentions, sentiment polarity, and co-hashtags. Notably, electromobility emerged as a prominent theme, particularly on Instagram, while #sustainabletransport was closely associated with active travel, notably bicycling, and #sustainablemobility showcased a dominance of electromobility discourse. The study demonstrates the investigative potentials of cross-platform social media analysis studies to enhance the understanding of sociotechnical low-carbon transport transitions. Drawing on key results, the paper suggests an adapted version of the Geelsean Multi-Level Perspective on Sociotechnical Transitions. [ABSTRACT FROM AUTHOR]

Developing and executing large-scale data analysis applications in parallel and distributed environments can be a complex and time-consuming task. Developers often find themselves diverted from their application logic to handle technical details about the underlying runtime and related issues. To simplify this process, ParSoDA, a Java library, has been proposed to facilitate the development of parallel data mining applications executed on HPC systems. It simplifies the process by providing built-in scalability mechanisms relying on the Hadoop and Spark frameworks. This paper presents ParSoDA-Py, the Python version of the ParSoDA library, which allows for further support of commonly used runtimes and libraries for big data analysis. After a complete library redesign, ParSoDA can be now easily integrated with other Python-based distributed runtimes for HPC systems, such as COMPSs and Apache Spark, and with the large ecosystem of Python-based data processing libraries. The paper discusses the adaptation process, which takes into consideration the new technical requirements, and evaluates both usability and scalability through some case study applications. [ABSTRACT FROM AUTHOR]

Water constitutes an indispensable resource crucial for the sustenance of humanity, as it plays an integral role in various sectors such as agriculture, industrial processes, and domestic consumption. Even though water covers 71% of the global land surface, governments have been grappling with the challenge of ensuring the provision of safe water for domestic use. A contributing factor to this situation is the persistent contamination of available water sources rendering them unfit for human consumption. A common contaminant, pesticides are not frequently tested for despite their serious effects on biodiversity. Pesticide determination in water quality assessment is a challenging task because the procedures involved in the extraction and detection are complex. This reduces their popularity in many monitoring campaigns despite their harmful effects. If the existing methods of pesticide analysis are adapted by leveraging new technologies, then information concerning their presence in water ecosystems can be exposed. Furthermore, beyond the advantages conferred by the integration of wireless sensor networks (WSNs), the Internet of Things (IoT), Machine Learning (ML), and big data analytics, a notable outcome is the attainment of a heightened degree of granularity in the information of water ecosystems. This paper discusses methods of pesticide detection in water, emphasizing the possible use of electrochemical sensors, biosensors, and paper-based sensors in wireless sensing. It also explores the application of WSNs in water, the IoT, computing models, ML, and big data analytics, and their potential for integration as technologies useful for pesticide monitoring in water. [ABSTRACT FROM AUTHOR]

For solving the storage and indexing problem of big data, this paper proposes a big data storage algorithm and an indexing algorithm, respectively. Simultaneously, the data classification and segmentation algorithm is given, and then, the classified data are segmented and sliced based on features of data. Moreover, the calculation of the service queue length on data slicing is given to achieve fast storage of slices in the data server. A data reconstruction method is given to index each slice of the original data and implement the data reconstruction according to the given reconstruction method. At the same time, an evaluation method for data reconstruction is also given to achieve the minimum difference between the reconstructed data and the original data. Simulation results show that the accurate rate of the data reconstruction method proposed in this paper is higher than 93%. In addition, compared with the existing data reconstruction methods, it is found that it not only has higher reconstruction accurate rate, faster reconstruction process, stronger anti-interference ability, but also lower information loss. These researches in this paper provide the powerful methods for protecting important documents, reducing losses, reducing waste of resources, and improving the convenience, speed, and safety in work, life, and so on. [ABSTRACT FROM AUTHOR]

Despite advances in myocardial reperfusion therapies, acute myocardial ischaemia/reperfusion injury and consequent ischaemic heart failure represent the number one cause of morbidity and mortality in industrialized societies. Although different therapeutic interventions have been shown beneficial in preclinical settings, an effective cardio-protective or regenerative therapy has yet to be successfully introduced in the clinical arena. Given the complex pathophysiology of the ischaemic heart, large scale, unbiased, global approaches capable of identifying multiple branches of the signalling networks activated in the ischaemic/reperfused heart might be more successful in the search for novel diagnostic or therapeutic targets. High-throughput techniques allow high-resolution, genome-wide investigation of genetic variants, epigenetic modifications, and associated gene expression profiles. Platforms such as proteomics and metabolomics (not described here in detail) also offer simultaneous readouts of hundreds of proteins and metabolites. Isolated omics analyses usually provide Big Data requiring large data storage, advanced computational resources and complex bioinformatics tools. The possibility of integrating different omics approaches gives new hope to better understand the molecular circuitry activated by myocardial ischaemia, putting it in the context of the human 'diseasome'. Since modifications of cardiac gene expression have been consistently linked to pathophysiology of the ischaemic heart, the integration of epigenomic and transcriptomic data seems a promising approach to identify crucial disease networks. Thus, the scope of this Position Paper will be to highlight potentials and limitations of these approaches, and to provide recommendations to optimize the search for novel diagnostic or therapeutic targets for acute ischaemia/reperfusion injury and ischaemic heart failure in the post-genomic era. [ABSTRACT FROM AUTHOR]

Recognizing actions from still images has been popularly studied recently. In this paper, we model an action class as a flexible number of spatial configurations of body parts by proposing a new spatial sum–product network (SPN). First, we discover a set of parts in image collections via unsupervised learning. Then, our new spatial SPN is applied to model the spatial relationship and also the high-order correlations of parts. To learn robust networks, we further develop a hierarchical spatial SPN method, which models pairwise spatial relationship between parts inside subimages and models the correlation of subimages via extra layers of SPN. Our method is shown to be effective on two benchmark data sets. [ABSTRACT FROM PUBLISHER]

Psychological variables remain a much under-investigated sub-category of individual differences (IDs) compared with cognitive ones. The present paper aims to gain a better understanding of the psychological effects of bilingualism by investigating national identity (NI), a socio-psychological construct, based on big data, that has rarely been examined. Drawing upon the 2015 Chinese Social Survey (CSS), which utilised a nationally representative sample (N = 10242), we employed a 'more refined' version of hierarchical regression analysis on the influence of foreign-language (FL)-based bilingualism and other sociobiographical variables on NI. Out of the 18 initial independent variables, satisfaction with life (1.7%–2.2%) and age (1.2%–1.4%) emerged as important predictors for NI as their minimum effect size value (ΔR2, see the range in brackets) exceeded the 'typical' benchmark (1%); in contrast, the influence respectively from FL mastery (.006%–.040%) and FL use (.000%–.004%) was negligible. In other words, our key finding is that a person's FL-based bilingualism had little to do with his/her NI. Implications for China's plan to reform FL (e.g. English) learning are discussed, and future research directions are also proposed. [ABSTRACT FROM AUTHOR]

With the booming of Big Data and the Internet of Things, various urban networks have been built based on intercity flow data, and how to combine them to learn a more comprehensive understanding of mega-city regions is becoming more and more indispensable. In this paper, we designed a graph-based multi-view clustering method based on graph learning to explore the mega-city region structures from multi-source data. An example of clustering analysis consists of the people flow network, cargo flow network, and information flow network, covering 88 cities from Beijing, Tianjin, Hebei Province, Shandong Province, Henan Province, Jiangsu Province, Anhui Province, Shanghai, and Zhejiang Province in China is used to illustrate the applicability of the idea in super mega-city region scale studies. Utilizing the proposed clustering method, a unified network representation is calculated, and 5 mega-city regions, Beijing-Tianjin-Hebei Cluster, Henan Cluster, Shandong Cluster, Shanghai-Jiangsu-Anhui Cluster, and Zhejiang Cluster, are detected based on intercity flow networks. City-to-city flows, including Luan-Taizhou, Lianyungang-Chuzhou, and Xuzhou-Bengbu of the people network, Shanghai-Hangzhou, Suzhou-Shanghai, and Shanghai-Ningbo of the cargo network, Shanghai-Hangzhou, Bozhou-Jinhua, and Huaibei-Bozhou of the information network, are suggested to be further enhanced to facilitate the ongoing nationwide constructions of urban agglomerations in China. The multi-view clustering method proved to be a helpful calculation framework for mega-city region analysis, which would also be considered as a substantial foundation for further urban explorations with more advanced graph learning techniques. [ABSTRACT FROM AUTHOR]

AbstractSubsampling is an effective approach to address computational challenges associated with massive datasets. However, existing subsampling methods do not consider model uncertainty. In this paper, we investigate the subsampling technique for the Akaike information criterion (AIC) and extend the subsampling method to the smoothed AIC model-averaging framework in the context of generalized linear models. By correcting the asymptotic bias of the maximized subsample objective function used to approximate the Kullback–Leibler divergence, we derive the form of the AIC based on the subsample. We then provide a subsampling strategy for the smoothed AIC model-averaging estimator and study the corresponding asymptotic properties of the loss and the resulting estimator. A practically implementable algorithm is developed, and its performance is evaluated through numerical experiments on both real and simulated datasets. [ABSTRACT FROM AUTHOR]

The importance of technology and managerial risk management in banks has increased due to the financial crisis. Banks are the most affected since there are so many of them with poor financial standing. Due to this problem, an unstable and inefficient financial system causes economic stagnation in both the banking sector and overall economy. Data envelopment analysis (DEA) has been used to examine decision-making units (DMUs) performance to enhance efficiency. Currently, with the rapid growth of big data, adding more DMUs will likely require a large amount of memory and CPU time on the computer system, which will be the biggest challenge. As a result, machine learning (ML) approaches have been used to analyze financial institution performance, but many of them have variances in predictions or model stability, making measuring bank efficiency extremely difficult. For this, ensemble learning is commonly used to evaluate the performance of financial institutions in this context. This paper presents a robust super learner ensemble technique for assessing bank efficiency, with four machine learning models serving as base learners. These models are the support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF), and AdaBoost classifier (ADA) which represent the base learners and their results utilized to train the meta-learner. The super learner (SL) approach is an extension of the stacking technique, which generates an ensemble based on cross-validation. One important benefit of this cross-validation theory-based technique is that it can overcome the overfitting issue that plagues most other ensemble approaches. When SL and base learners were compared for their forecasting abilities using different statistical standards, the results showed that the SL is superior to the base learners, where different variable combinations were used. The SL had accuracy (ACC) of 0.8636–0.9545 and F1-score (F1) of 0.9143–0.9714, while the basic learners had ACC of 0.5909–0.8182 and F1 of 0.6897–0.9143. So, SL is highly recommended for improving the accuracy of financial data forecasts, even with limited financial data. [ABSTRACT FROM AUTHOR]

As Global Navigation Satellite Systems (GNSS) infrastructure expands and more satellites are launched, the number of signals and measurements available to users increases, resulting in a corresponding rise in data generation rates. GNSS receivers with the ability to process triple frequency measurements and multi-constellation reception, have been complemented with the massive deployment of connected, GNSS-enabled devices such as smartphones, wearables, and IoT sensors, triggering the growth of a highly dispersed cloud of GNSS data. This trend observed in other domains presents both challenges and opportunities for applications of GNSS technology. The scientific exploitation of GNSS data involves developing new concepts through the analysis of the aforementioned sources of information. Traditionally, the extraction of insights from GNSS data has relied on searching and downloading datasets from multiple repositories. These datasets provided by numerous organisations are scattered worldwide, containing gems of information difficult to track and discover. This approach hinders research productivity, as scientists are forced to shift their focus from data exploration, analysis, modelling, visualization, and realisation of conclusions to a long list of repetitive and manual tasks of little added value. The GNSS Science Support Centre (GSSC) hosts ESA's innovative Digital Platform for GNSS, GSSC Now, which integrates a large GNSS repository with cutting-edge features for data exploration and analysis. The paper presents key functionalities of GSSC Now and highlights its value through case studies of real-world GNSS applications. This platform expands established computational limits, placing the data source side-by-side with cloud computing capabilities. The change of paradigm provides scientists with seamless execution of Machine Learning models, interactive algorithms and visualisation tools on a vast repository of GNSS data, removing barriers that hinder research productivity. [ABSTRACT FROM AUTHOR]

AbstractWe propose investigating optimal subsampling for functional regression with massive datasets based on the mode value, which is referred to as functional quasi-mode regression, to reduce data volume and alleviate computational burden. Utilizing data-adaptive weights derived from regression residuals, the suggested regression offers enhanced robustness against non-normal errors compared to traditional least squares or maximum likelihood estimation methods. To estimate the model, we employ B-spline basis functions to approximate the functional coefficient and include a penalty term in the objective function for enforcing smoothness in the resulting estimator. We adopt a computationally efficient mode-expectation-maximization algorithm, augmented by a Gaussian kernel, for numerical estimation. Under mild regularity conditions, we derive the asymptotic distributions of both full data and subsample quasi-mode estimators. The optimal subsampling probabilities by minimizing the asymptotic variance-covariance matrix under A- and L-optimality criteria are identified. These optimal probabilities rely on the full data estimate, prompting the development of a two-step algorithm to approximate the optimal subsampling procedure. The resultant algorithm is processing-efficient and can significantly reduce computational time compared to the full data approach. We also establish the asymptotic normality of the quasi-mode estimator obtained through this two-step algorithm. To assess finite sample performance, we conduct Monte Carlo simulations and analyze air quality data, showcasing the effectiveness of the developed estimator. Supplemental materials for this paper are available online. [ABSTRACT FROM AUTHOR]

This narrative review explores the potential, complexities, and consequences of using artificial intelligence (AI) to screen large government-held facial image databases for the early detection of rare genetic diseases. Government-held facial image databases, combined with the power of artificial intelligence, offer the potential to revolutionize the early diagnosis of rare genetic diseases. AI-powered phenotyping, as exemplified by the Face2Gene app, enables highly accurate genetic assessments from simple photographs. This and similar breakthrough technologies raise significant privacy and ethical concerns about potential government overreach augmented with the power of AI. This paper explores the concept, methods, and legal complexities of AI-based phenotyping within the EU. It highlights the transformative potential of such tools for public health while emphasizing the critical need to balance innovation with the protection of individual privacy and ethical boundaries. This comprehensive overview underscores the urgent need to develop robust safeguards around individual rights while responsibly utilizing AI's potential for improved healthcare outcomes, including within a forensic context. Furthermore, the intersection of AI and sensitive genetic data necessitates proactive cybersecurity measures. Current and future developments must focus on securing AI models against attacks, ensuring data integrity, and safeguarding the privacy of individuals within this technological landscape. [ABSTRACT FROM AUTHOR]

Satellite service systems transfer data from satellite providers to the big data industry, which includes data traders and data analytics companies. This system needs to provide access to numerous users whose specific identities are unknown. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) allows unidentified users with the proper attributes to decrypt data, providing fine-grained access control of data. However, traditional CP-ABE does not protect access policies. Access policies are uploaded to the cloud, stored, and downloaded in plain text, making them vulnerable to privacy breaches. When the access policy is completely hidden, users need to use their own attributes to try matching one by one, which is an inefficient process. In order to efficiently hide the access policy fully, this paper introduces a new efficient and expressive Fully Policy-Hidden Ciphertext-Policy Attribute-Based Encryption scheme (CP-ABE-FPH), which integrates the 2-way handshake O-PSI method with the ROBDD method. The integration offers advantages: (1) High efficiency and high expressiveness. The access policy using ROBDD is highly expressive but computationally intensive due to its recursive nature. This shortcoming is overcome in CP-ABE-FPH using the proposed O-PSI method, and the access policy is matched quickly and secretly. (2) High flexibility. The decryption process does not require the owner or the Key Generation Center (KGC) to be online, and system attributes can be added at any time. Security analysis shows that the access policy is fully hidden. Efficiency analysis and simulation results show that the proposed scheme is highly efficient in decryption compared with existing schemes. [ABSTRACT FROM AUTHOR]

BACKGROUND: Visualization of sports has a lot of potential for future development in data sports because of how quickly things are changing and how much sports depend on data. Presently, conventional systems fail to accurately address sports persons' dynamic health data change with less error rate. Further, those systems are unable to distinguish players' health data and their visualization in a precise manner. An excellent starting point for building fitness solutions based on computer vision technology is the data visualization technology that arose in the age of big data analytics. OBJECTIVE: This research presents a Big Data Analytic assisted Computer Vision Model (BD-CVM) for effective sports persons healthcare data management with improved accuracy and precision. METHODS: The fitness and health of professional athletes are analyzed using information from a publicly available sports visualization dataset. Machine learning-assisted computer vision dynamic algorithm has been used for an effective image featuring and classification by categorizing sports videos through temporal and geographical data. RESULTS: The significance of big data's great potential in screening data during a sporting event can be reasonably analyzed and processed effectively with less error rate. The proposed BD-CVM utilized an error analysis module which can be embedded in the design further to ensure the accuracy requirements in the data processing from sports videos. CONCLUSION: The research findings of this paper demonstrate that the strategy presented here can potentially improve accuracy and precision and optimize mean square error in sports data classification and visualization. [ABSTRACT FROM AUTHOR]