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ABSTRACT Class imbalance is a critical challenge in big data analytics, often leading to biased predictive models. This imbalance can lead to biased models that perform well on the majority class but poorly on the minority class. Many machine learning models tend to be biased towards the majority class because they aim to minimise overall error, often leading to poor performance on the minority class. This paper presents the balanced ensemble neural network, a novel solution to effectively address class imbalance in big data. Balanced ensemble neural network combines the robust capabilities of neural networks with the power of ensemble learning, incorporating class balancing strategies to ensure fair representation of minority classes. The methodology involves integrating multiple neural networks, each trained on balanced subsets of data using techniques like Synthetic Minority Over‐sampling Technique and Random Undersampling. This integration aims to leverage the strengths of individual networks while reducing their inherent biases. Our extensive experiments across various datasets reveal that BENN achieves an AUC‐ROC score of 0.94, surpassing other models such as random forest (0.88), support vector (0.84) and single neural net (0.80). It was also observed that BENN's performance is better compared to traditional neural network models and standard ensemble methods in key metrics like accuracy, precision, recall, F1‐score and AUC‐ROC. The results specifically highlight BENN's effectiveness in accurately classifying instances of minority classes, a notable challenge in many existing models. These findings underscore BENN's potential as a substantial advancement in handling class imbalance within big data environments, offering a promising direction for future research and application in machine learning. [ABSTRACT FROM AUTHOR]

Text classification is the process of labelling a given set of text documents with predefined classes or categories. Existing Arabic text classifiers are either applying classic Machine Learning algorithms such as k‐NN and SVM or using modern deep learning techniques. The former are assessed using small text collections and their accuracy is still subject to improvement while the latter are efficient in classifying big data collections and show limited effectiveness in classifying small corpora with a large number of categories. This paper proposes a new approach to Arabic text classification to treat small and large data collections while improving the classification rates of existing classifiers. We first demonstrate the ability of analogical proportions (AP) (statements of the form 'x is to y as z is to t'), which have recently been shown to be effective in classifying 'structured' data, to classify 'unstructured' text documents requiring preprocessing. We design an analogical model to express the relationship between text documents and their real categories. Next, based on this principle, we develop two new analogical Arabic text classifiers. These rely on the idea that the category of a new document can be predicted from the categories of three others, in the training set, in case the four documents build together a 'valid' analogical proportion on all or on a large number of components extracted from each of them. The two proposed classifiers (denoted AATC1 and AATC2) differ mainly in terms of the keywords extracted for classification. To evaluate the proposed classifiers, we perform an extensive experimental study using five benchmark Arabic text collections with small or large sizes, namely ANT (Arabic News Texts) v2.1 and v1.1, BBC‐Arabic, CNN‐Arabic and AlKhaleej‐2004. We also compare analogical classifiers with both classical ML‐based and Deep Learning‐based classifiers. Results show that AATC2 has the best average accuracy (78.78%) over all other classifiers and the best average precision (0.77) ranked first followed by AATC1 (0.73), NB (0.73) and SVM (0.72) for the ANT corpus v2.1. Besides, AATC1 shows the best average precisions (0.88) and (0.92), respectively for the BBC‐Arabic corpus and AlKhaleej‐2004, and the best average accuracy (85.64%) for CNN‐Arabic over all other classifiers. Results demonstrate the utility of analogical proportions for text classification. In particular, the proposed analogical classifiers are shown to significantly outperform a number of existing Arabic classifiers, and in many cases, compare favourably to the robust SVM classifier. [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. Research Highlights: ‐Recent machine learning and deep learning methods provide innovative tools for the study of primate vocal communication.‐Applied to massive acoustic recordings, they offer new perspectives for better identifying and studying primate vocal productions.‐We provide a comprehensive review of established approaches and latest advances in this rapidly evolving field. [ABSTRACT FROM AUTHOR]

Ground‐based phenological observation data are the most accurate phenological monitoring data currently available. Making effective use of available information on social media to retrieve phenological data is of considerable value in alleviating the lack of phenological data in regions with missing observation sites. In this study, a logistic curve fitting method was developed to extract phenological data on specific species from social media data. After verifying the relationship between the site observation data and the temperature, timing data for two typical phenological phenomena in China, namely cherry blossom flowering in spring and ginkgo leaf coloration in autumn were reconstructed and published. The data availability is from 2010 to 2019 in 176 cities and 2009 to 2018 in 155 cities. This dataset is an effective supplement for existing phenological data, and this method also provides a reference for obtaining phenological data for specific species. [ABSTRACT FROM AUTHOR]

The metaverse is rapidly reshaping our understanding of education, yet identifying the public's beliefs, emotions and sentiments towards its adoption in education remains largely uncharted empirically. Grounded in the Technology Acceptance Model (TAM) and Digital Diffusion Theory (DOI), this paper aims to fill this gap using a big‐data approach and machine learning to scrape comments made by social media users on recent popular posts or videos related to adopting the metaverse in education from three prominent social media platforms. The cleaning process narrowed down 11,024 comments to 4277, then analysed them using thematic, emotion and sentiment analysis techniques. The thematic analysis revealed that adopting the metaverse in education evokes a complex range of public beliefs: (1) innovative learning methods; (2) accessibility and inclusion; (3) concerns about quality and effectiveness; (4) technological challenges and the digital divide; (5) the future of work and skills; and (6) privacy and security concerns. Integrating these themes with emotion and sentiment analyses reveals a landscape of a significant portion of neutral sentiments that corroborates enthusiasm attenuated by caution. This careful consideration stresses the urgent need for a balanced approach to adopting the metaverse in education to ensure that resulting educational advancements benefit all learners equitably. As one of the first studies to offer a multidimensional view of the public's beliefs about metaverse education using big data, this research not only contributes to TAM and DOI but also provides critical insights that could inform policy, enhance educational practices and guide future scholarship in this emerging field. [ABSTRACT FROM AUTHOR]

Background Study Design and Methods Results Discussion Since the beginning of 2024, several European countries reported unusually high numbers of Human parvovirus B19 (B19V) infections. An increase in B19V incidence rate might have implications for blood products for direct transfusion, however, large data sets for analysis of this outbreak are missing.B19V nucleic acid testing (NAT) of plasma donations collected between June 2018 and May 2024 from mainly Central European countries (n = 9.6 million) and the United States (n = 70.7 million) was done to the individual donation level.In Central Europe, there was a marked increase in B19V incidence from November 2023 onwards, which peaked in April 2024 with a 33‐fold higher than average B19V incidence versus before the COVID‐19 pandemic. In the United States, a similar trend was seen, with a yet still 6‐fold lower increase than in Europe at the same time. The largest increase in B19V positivity was seen in the youngest plasma donor cohort.A B19V infection gap during the COVID‐19 pandemic is likely the basis for the rebound outbreak in 2023/2024, particularly in Europe. B19V NAT of millions of plasma donations provides for large scale numbers to solidify available epidemiology insight, and to support adequate risk assessments. Based on the situation it may be prudent to consider B19V NAT for blood components specifically directed towards transfusion to higher risk recipients, or alternatively, preselecting B19V seropositive individuals or advanced age donors at higher likelihood of seropositivity and thus lower risk of virus transmission. [ABSTRACT FROM AUTHOR]

A growing trend in bioethics highlights the importance of using big data science methods to advance normative insight. This has been called the "digital turn" in bioethics by Salloch and Ursin. Automated data processing can, for example, detect significant patterns of correlation that have escaped the attention of human scholars. Although we agree that such technological innovations could bolster existing methods in empirical bioethics (EB), we argue that it should not be conceptualized as a new turn but rather as a revivification, and possibly an amplification of entrenched debates in EB. We begin by highlighting some convergences between EB and digital bioethics that Salloch and Ursin seem to categorize as fundamental differences and end up with elaborating on some risks related to the integration of empirical findings with normative (philosophical) analysis in the digitalization trend. [ABSTRACT FROM AUTHOR]

In this paper, we develop an approach for analyzing the increasingly important strand of research that deals with automated systems of governing. Such systems, which figure prominently in public policy and regulation, are designed to utilize the rapid advancement in computer technology, like artificial intelligence, with the purpose of governing something or someone. Drawing on a large sample of articles we present a comprehensive analysis of scholarly works where these systems are studied as political, rather than neutral, instruments of governing. We find that the current state of the art articulates the politics of automated systems of governing in three ways. Namely, as part of ontological, epistemological and ideological questions. We conclude that future research should investigate the complex forms of marketization nested in these systems, that it should move from theoretical examples to detailed empirical studies and that political science should get more involved with the issue. [ABSTRACT FROM AUTHOR]

Introduction: An ideal orthodontic treatment involves qualitative and quantitative measurements of dental and skeletal components to evaluate patients' discrepancies, such as facial, occlusal, and functional characteristics. Deciding between orthodontics and orthognathic surgery remains challenging, especially in borderline patients. Advances in technology are aiding clinical decisions in orthodontics. The increasing availability of data and the era of big data enable the use of artificial intelligence to guide clinicians' diagnoses. This study aims to test the capacity of different machine learning (ML) models to predict whether orthognathic surgery or orthodontics treatment is required, using soft and hard tissue cephalometric values. Methods: A total of 920 lateral radiographs from patients previously treated with either conventional orthodontics or in combination with orthognathic surgery were used, comprising n = 558 Class II and n = 362 Class III patients, respectively. Thirty‐two measures were obtained from each cephalogram at the initial appointment. The subjects were randomly divided into training (n = 552), validation (n = 183), and test (n = 185) datasets, both as an entire sample and divided into Class II and Class III sub‐groups. The extracted data were evaluated using 10 machine learning models and by a four‐expert panel consisting of orthodontists (n = 2) and surgeons (n = 2). Results: The combined prediction of 10 models showed top‐ranked performance in the testing dataset for accuracy, F1‐score, and AUC (entire sample: 0.707, 0.706, 0.791; Class II: 0.759, 0.758, 0.824; Class III: 0.822, 0.807, 0.89). Conclusions: The proposed combined 10 ML approach model accurately predicted the need for orthognathic surgery, showing better performance in Class III patients. [ABSTRACT FROM AUTHOR]

Teaching middle school students with learning disabilities strategies for writing essays that incorporate information from source texts helps prepare them for academic success. The purpose of this pilot study was to investigate the effects of self‐regulated strategy development (SRSD) writing instruction using technology tools implemented by special education teachers on the source‐based argumentative writing performance of middle school students with learning disabilities. In this quasi‐experimental, pretest–posttest design study, three middle school special education teachers engaged in online Practice‐Based Professional Development. The teachers then implemented SRSD writing instruction to teach source‐based argumentative writing with technology tools. Implementation of the SRSD writing instruction by special education teachers positively impacted the number of argumentative elements, quality, number of transitions and length of students' source‐based argumentative essays. Teachers implemented the intervention with high fidelity and quality and felt that the intervention supported their students' writing. Teachers and students were positive about the SRSD writing instruction. This study adds to the evidence base that SRSD is an effective writing intervention for middle school students with learning disabilities. [ABSTRACT FROM AUTHOR]

Augmentative and alternative communication (AAC) refers to methods and tools that facilitate communication for individuals with limited functional speech. This study aims to present and analyse the research status of AAC interventions for children with special needs in mainland China using a bibliometric analysis approach. The researcher conducted a literature search in the China National Knowledge Infrastructure (CNKI) database to identify relevant studies. Based on the literature search, 80 articles met the inclusion criteria. The research literature spans from 2007 to 2022, with a notable increase in publications after 2015. The majority of studies focus on children with autism, while other disabilities, such as intellectual disabilities and cerebral palsy, receive less attention. AAC interventions primarily utilize low‐technology communication aids, with limited use of high‐technology tools. Research on AAC interventions for children with special needs in mainland China has grown recently. However, it still falls short of meeting the potential demand to promote the application and development of AAC. More diverse research is needed, including various disabilities and higher‐technology AAC tools. The effectiveness of AAC in impacting users and their communication partners will increase their quality of life and social interactions. [ABSTRACT FROM AUTHOR]

How might interpretivist qualitative researchers tackle large data sets consisting of millions or even billions of words? Corpus‐assisted discourse studies (CADS) is the approach we explore here. Specifically designed for the analysis of voluminous textual data, it offers a recognized empirical approach for making sense of such data. But it does so within an epistemology that understands language to be central in shaping our understanding of the world around us, so that CADS can assist researchers in revealing the social dynamics of the text – including the ideology and power that is latent in many such corpora. Bringing together the training of applied linguists and a management scholar, we discuss the background to CADS and its differences from text‐mining approaches such as topic modelling, which have been more widely used in management studies to date. Focusing on the needs of people who are new to the approach, we then offer a worked example to show CADS' potential in exploring a management‐related corpus. Our paper concludes with a discussion of the strengths and weaknesses of the approach and its potential for future discursively orientated management research – especially in the context of the rise of 'big data'. [ABSTRACT FROM AUTHOR]

This study aims to analyze the Industry 4.0 (I4.0) technologies as motivators to circular economy (CE) in the Brazilian foodtech sector via most relevant stakeholders. The study focuses on specific startups in the foodtech sector in the Brazilian context, offering a unique perspective on a relevant niche. It explores how I4.0 technologies are fundamental in the practices adopted by foodtechs, indicating an innovative approach aligned with technological trends. About the methodology, it was carried out through interviews with nine managers of foodtechs. Other sources of data collect have been used, such as reports, presentations, folders, products catalogs, consultations in companies sites, Instagram, Facebook, Linkedin, and videos on the Companies YouTube. The results show that: (i) the I4.0 technologies are fundamental in the practices adopted by the foodtechs; (ii) the CE being internalized, supported by I4.0; (iii) CE is made possible by gathering synergy and co‐participation from stakeholders which are strategic and relevant to foodtechs; (iv) innovation factor is present in the adoption of I4.0, in the operationalization of the practices of CE and in the production of synergy among the stakeholders relevant to foodtechs; (v) the creation of new markets is also a consequence of the adoption of I4.0, the adoption of CE and the articulation of stakeholders in the context analyzed; (vi) virtualize and regenerate are the most strengthened strategies at the sphere of the analyzed foodtechs; (vii) IoT, Big Data and integration of ubiquitous and intelligent components in supply chains are the highlighted artifacts in the making of CE practices at foodtechs levels; (viii) foodtechs of food producers have shown a greater synergy and articulation of stakeholders to the success of CE supported by I4.0. The contribution of the study is associated to the legitimation of I4.0 technologies as motivators to the adoption of CE and the engagement of stakeholders to successful circular business models. The study used a qualitative approach and collected data through interviews with nine startups in Brazil. Thematic analysis was employed to analyze the data. The limitations of the study include the small sample size and the focus on startups in one country. Future research should aim to explore the integration of I4.0 and the CE in larger samples, across multiple countries, and involving different types of organizations. Additionally, the study highlights the need for government support for startups in the form of funding, incentives, and regulations, as well as the importance of education and training programs to develop skills related to I4.0 and the CE. Overall, the research objectives are to understand the opportunities and challenges of I4.0 and CE integration in startups and to provide insights for policymakers and practitioners on how to foster sustainable business practices. The specificity of your study in exploring the relationship between the CE and I4.0 technologies in Brazilian foodtechs is a unique contribution. Many studies may focus on broader industries or international contexts, while yours delves into a specific, relevant niche. [ABSTRACT FROM AUTHOR]

Aims: Heart failure (HF) is a clinical syndrome with no definitive diagnostic tests. HF registries are often based on manual reviews of medical records of hospitalized HF patients identified using International Classification of Diseases (ICD) codes. However, most HF patients are not hospitalized, and manual review of big electronic health record (EHR) data is not practical. The US Department of Veterans Affairs (VA) has the largest integrated healthcare system in the nation, and an estimated 1.5 million patients have ICD codes for HF (HF ICD‐code universe) in their VA EHR. The objective of our study was to develop artificial intelligence (AI) models to phenotype HF in these patients. Methods and results: The model development cohort (n = 20 000: training, 16 000; validation 2000; testing, 2000) included 10 000 patients with HF and 10 000 without HF who were matched by age, sex, race, inpatient/outpatient status, hospital, and encounter date (within 60 days). HF status was ascertained by manual chart reviews in VA's External Peer Review Program for HF (EPRP‐HF) and non‐HF status was ascertained by the absence of ICD codes for HF in VA EHR. Two clinicians annotated 1000 random snippets with HF‐related keywords and labelled 436 as HF, which was then used to train and test a natural language processing (NLP) model to classify HF (positive predictive value or PPV, 0.81; sensitivity, 0.77). A machine learning (ML) model using linear support vector machine architecture was trained and tested to classify HF using EPRP‐HF as cases (PPV, 0.86; sensitivity, 0.86). From the 'HF ICD‐code universe', we randomly selected 200 patients (gold standard cohort) and two clinicians manually adjudicated HF (gold standard HF) in 145 of those patients by chart reviews. We calculated NLP, ML, and NLP + ML scores and used weighted F scores to derive their optimal threshold values for HF classification, which resulted in PPVs of 0.83, 0.77, and 0.85 and sensitivities of 0.86, 0.88, and 0.83, respectively. HF patients classified by the NLP + ML model were characteristically and prognostically similar to those with gold standard HF. All three models performed better than ICD code approaches: one principal hospital discharge diagnosis code for HF (PPV, 0.97; sensitivity, 0.21) or two primary outpatient encounter diagnosis codes for HF (PPV, 0.88; sensitivity, 0.54). Conclusions: These findings suggest that NLP and ML models are efficient AI tools to phenotype HF in big EHR data to create contemporary HF registries for clinical studies of effectiveness, quality improvement, and hypothesis generation. [ABSTRACT FROM AUTHOR]

Research on weather and climate extremes has become integral to climate science due to their increasing societal relevance and impacts in the context of anthropogenic climate change. In this perspective we examine recent changes and evolving paradigms in the study of extreme events, emphasizing the increasingly interdisciplinary nature of research and their societal implications. We discuss the importance of understanding the physical basis of extreme events and its linkages to climate impacts, highlighting the need for collaboration across multiple disciplines. Furthermore, we explore the challenge of big climate data analysis and the application of novel statistical methods, such as machine learning, in enhancing our understanding of extreme events. Additionally, we address the engagement with different stakeholder groups and the evolving landscape of climate services and private‐sector involvement. We conclude with reflections on the risks and opportunities for early career researchers in navigating these interdisciplinary and societal demands, stressing the importance of meaningful scientific engagement, and removing barriers to inclusivity and collaboration in climate research. [ABSTRACT FROM AUTHOR]

Inflammations have been linked to tumours, suggesting a potential association between NLRP1 and cancer. Nevertheless, a systematic assessment of NLRP1's role across various cancer types currently absent. A comprehensive bioinformatic analysis was conducted to determine whether NLRP1 exhibits prognostic relevance linked to immune metabolism across various cancers. The study leveraged data from the TCGA and GTEx databases to explore the clinical significance, metabolic features, and immunological characteristics of NLRP1, employing various tools such as R, GEPIA, STRING and TISIDB. NLRP1 exhibited differential expression patterns across various cancers, with elevated expression correlating with a more favourable prognosis in lung adenocarcinoma (LUAD) and pancreatic adenocarcinoma (PAAD). Downregulation of NLRP1 reduced tumour metabolic activity in LUAD. Moreover, the mutational signature of NLRP1 was linked to a favourable prognosis. Interestingly, high NLRP1 expression inversely correlated with tumour stemness while positively correlating with tumour immune infiltration in various cancers including LUAD and PAAD. Through extensive big data analysis, we delved into the role of NLRP1 across various tumour types, constructing a comprehensive role map of its involvement in pan‐cancer scenarios. Our findings highlight the potential of NLRP1 as a promising therapeutic target specifically in LUAD and PAAD. [ABSTRACT FROM AUTHOR]

This article presents opportunities for enriching anthropological knowledge and methods with machine learning and data analysis. Different examples show how quantitative methods empower anthropologists and how computational methods supplement ethnography, from sensor data and interview transcripts to designing technology solutions and automatically labeling cultural heritage. Conversely, the authors discuss the benefits of qualitative approaches in contemporary anthropological research and show how to transition from data analysis to ethnography and vice versa. Finally, the article pinpoints aspects in which each method can fail individually. It discusses why a combination of the two approaches, called circular mixed methods, minimizes the chance of failure and maximizes insights from the data. [ABSTRACT FROM AUTHOR]

Big data and associated approaches to analyse it are on the rise, especially in healthcare settings. This growth is also seen with unique applications in the field of dermatology. While big data offer a plethora of opportunity for improving our current understanding of disease and ability to deliver care, as with any technology innovation, the potential pitfalls should be addressed. In this piece, we highlight opportunities and challenges associated with big data in dermatology. Opportunities include large and novel data sources that may offer a wealth of information, automated detection, classification and diagnostics and improved public health monitoring. Challenges include data quality, issues of interpretability and disparities within artificial intelligence (AI) training data sets. Clinicians and researchers in the field should be aware of these developments within the field of big data to understand how best it may be used toward improving patient care and health outcomes, particularly in the field of dermatology. [ABSTRACT FROM AUTHOR]

Operating a lithium‐ion battery (LIB) in a wide temperature range is essential for ensuring a stable electricity supply amidst fluctuating temperatures caused by climate or terrain changes. Electrolyte plays a pivotal role in determining the temperature durability of batteries. However, specialized electrolytes designed for either low or high temperatures typically possess distinct features. Therefore, wide‐temperature electrolytes (WTEs) are necessary as they encompass a combination of diverse properties, which complicates the clear instruction of WTE design. Here we represent an artificial intelligence (Al)‐assisted workflow of WTE design through stepwise parameterizations and calculations. Linear mono‐nitriles are identified as ideal wide‐liquidus‐range solvents that can "softly" solvate lithium ions by weak interactions. In addition, the explainable modules revealed the halogenoid similarity of cyanide as fluorine on the electrolyte properties (e.g. boiling point and dielectric constant). With the further introduction of an ether bond, 3‐methoxypropionitrile (MPN) has been eventually determined as a main electrolyte solvent, enabling the battery operation from −60 to 120 °C. Particularly, a LiCoO2/Li cell using the proposed WTE can realize stable cycling with capacity retention reaching 72.3 % after 50 cycles under a high temperature of 100 °C. [ABSTRACT FROM AUTHOR]

Protected areas (PAs) are an essential tool for conservation amid the global biodiversity crisis. Optimizing PAs to represent species at risk of extinction is crucial. Vertebrate representation in PAs is assessed using species distribution databases from the International Union for Conservation of Nature (IUCN) and the Global Biodiversity Information Facility (GBIF). Evaluating and addressing discrepancies and biases in these data sources are vital for effective conservation strategies. Our objective was to gain insights into the potential constraints (e.g., differences and biases) of these global repositories to objectively depict the diversity of threatened vertebrates in the global system of PAs. We assessed differences in species richness (SR) of threatened vertebrates as reported by IUCN and GBIF in PAs globally and then compared how biased this information was with reports from independent sources for a subset of PAs. Both databases showed substantial differences in SR in PAs (t = −62.35, p ≤ 0.001), but differences varied among regions and vertebrate groups. When these results were compared with data from independent assessments, IUCN overestimated SR by 575% on average and GBIF underestimated SR by 63% on average, again with variable results among regions and groups. Our results indicate the need to improve analyses of the representativeness of threatened vertebrates in PAs such that robust and unbiased assessments of PA effectiveness can be conducted. The scientific community and decision makers should consider these regional and taxonomic disparities when using IUCN and GBIF distributional data sources in PA assessment. Overall, supplementing information in these databases could lead to more robust and reliable analyses. Additional efforts to acquire more comprehensive and unbiased data on species distributions to support conservation decisions are clearly needed. [ABSTRACT FROM AUTHOR]

Differences between urban, suburban, and rural voting outcomes in U.S. presidential elections is a common area of both academic research and news media analysis. Most scrutiny and mapping of the geography of the presidential vote is done at the county scale, with counties described as either urban, suburban, or rural. Small towns are often assumed to mirror surrounding rural areas, with little research examining election results and historic patterns in these places. This is understandable: Accurate spatial data at a fine scale for national elections is difficult and, in some states, impossible, to obtain. Research presented here (in states where precinct‐level data is readily available) indicates that while most small towns indeed voted Republican in the 2020 U.S. election, a closer inspection indicates that they are more Democratic‐leaning than surrounding rural areas. This research indicates the value of studying these fine‐scale data as well as the challenges faced in acquiring these data and resulting lack of research and visualization of these differences. [ABSTRACT FROM AUTHOR]

The process of locating and identifying missing persons presents a complex challenge that hinges on the collection and comparison of diverse data sets. This commentary offers an overview of some of the difficulties and considerations associated with data management in the context of large‐scale missing person identifications. Such complexities include the uniqueness of each disaster event, the response time to the event, the variable quality and quantity of data, and the involvement of numerous stakeholders, all of which contribute to the intricacies of data management. In addition, the paramount considerations of privacy and ethical standards further compound these challenges, especially when dealing with sensitive information such as genetic data. This commentary describes the integrated Data Management System (iDMS) developed by the International Commission on Missing Persons (ICMP) as one example of a comprehensive, freely available solution for data collection, storage, protection, and analysis in missing person cases. The various advantages of the system are discussed, including the system's interoperability among the diverse array of stakeholders involved. While the iDMS streamlines data management processes and therefore represents a significant advancement in the field of missing person identification, it is concluded that the pending issue extends beyond the software tools to encompass the lack of political will among stakeholders to collaborate there remains a pressing need for all stakeholders involved in the identification process to commit to a mechanism that facilitates compatibility and interoperability if different tools are used in disaster victim identification (DVI) scenarios. [ABSTRACT FROM AUTHOR]

The development of hard X‐ray nanoprobe techniques has given rise to a number of experimental methods, like nano‐XAS, nano‐XRD, nano‐XRF, ptychography and tomography. Each method has its own unique data processing algorithms. With the increase in data acquisition rate, the large amount of generated data is now a big challenge to these algorithms. In this work, an intuitive, user‐friendly software system is introduced to integrate and manage these algorithms; by taking advantage of the loosely coupled, component‐based design approach of the system, the data processing speed of the imaging algorithm is enhanced through optimization of the parallelism efficiency. This study provides meaningful solutions to tackle complexity challenges faced in synchrotron data processing. [ABSTRACT FROM AUTHOR]

Purpose: A number of algorithms have been developed to calculate screening intervals for diabetic retinopathy on the basis of individual risk factors. However, these approaches have not considered proliferative diabetic retinopathy (PDR) and diabetic macular oedema (DME) as separate end points and death as competing risk. Methods: A multi‐state survival model with death as competing risk was used to predict the screening interval for diabetic retinopathy based on information about all 2446 patients from a well‐defined population who had started treatment for either PDR or DME during 25 years. The performance of the model was tested on the existing database and at seven screening sites on patients who had not developed a treatment requiring condition. Results: Testing on the existing database showed that at a risk level of 2% the algorithm could predict a screening interval with a success rate higher than 90% and a 1.75 times average prolongation of the screening interval without failing to detect the development of verified PDR og DME. The model was limited to a diabetes duration shorter than 40 years and depended on knowledge of relevant risk factors. At the other participating screening sites the algorithm predicted shorter intervals than the screener. Conclusions: Algorithms for individualised screening for diabetic retinopathy can prolong screening intervals without losing patients who develop a vision threatening condition. The calculation of screening intervals requires access to relevant risk factors and should be developed on large data sets that reflect the population in which the algorithm should be used. [ABSTRACT FROM AUTHOR]

Developing walkable urban environments is crucial for enhancing urban liveability and sustainability. To evaluate the chrono‐urbanism status, this study combines conventional census and mapping data with social media check‐in big data. A composite index is proposed, which assesses the accessibility of essential urban functions under the 5/10/15‐minute chrono‐thresholds. This index incorporates five categories of urban functions (public transit, living, education, health care and entertainment) in Macau. Utilising check‐in information from Sina Weibo, the level of attraction for different urban functions is integrated into the calculation. The findings highlight significant disparities in chrono‐urbanism status, with regions inhabited by lower socio‐economic status residents being more vulnerable than the affluent institutional and commercial centre of Macau. Leveraging social media big data, this study provides valuable insights to urban planners and policy‐makers, enabling them to understand the overall chrono‐urbanism status and target interventions for developing a more sustainable and liveable urban environment in Macau. [ABSTRACT FROM AUTHOR]

Aim: This prospective cohort study investigated the association between periodontal diseases (PDs) and all‐cause and cause‐specific mortality. Materials and Methods: We utilized adult participants recruited from six National Health and Nutrition Examination Survey cycles (1999–2014) and linked mortality data from the National Death Index up to December 2019. Baseline clinical periodontal examinations were performed by trained and calibrated examiners. All‐cause and cause‐specific mortality was modelled through multivariable Cox proportional hazards and Fine–Gray models to account for competing risks. All models were adjusted for demographic and lifestyle variables, clinical measurements and comorbidities. Results: Overall, 15,030 participants were included, with a median length of follow‐up of 9 years. Risk of all‐cause mortality was 22% greater in people with PD than the control group (adjusted hazard ratio [HR]: 1.22, 95% confidence interval [CI]: 1.12–1.31). Risks of mortality by cardiovascular diseases (CVD), respiratory disease and diabetes were highest in participants with severe PD (CVD—sub‐distribution HR [SHR]: 1.38, 95% CI: 1.16–1.64; respiratory—SHR: 1.62, 95% CI: 1.07–2.45; diabetes—SHR: 1.68, 95% CI: 1.12–2.53). Conclusions: Severe PD is associated with all‐cause and cause‐specific mortality among US adults after multivariable adjustment. [ABSTRACT FROM AUTHOR]

Online retail platforms have increasingly utilized big data technologies to gather demand information, which is then shared with upstream manufacturers employing various selling modes, including a hybrid format that encompasses both direct and indirect selling. Previous studies have suggested that platforms should refrain from sharing demand information with manufacturers engaged in indirect selling. In this study, we present a game‐theoretic model to examine the factors influencing the online platform's decision to share information with an indirect selling manufacturer and under what conditions. Our initial analysis, considering exogenous selling formats in the base model, reveals that the platform's information sharing behavior is primarily influenced by selling format structures, commission fee rates, and competition intensity. The platform always has an incentive to share information with direct selling manufacturers; however, under a hybrid selling format, information sharing with indirect selling manufacturers may occur, particularly when both the commission fee rate and competition intensity are relatively high. We extend our investigation to explore the platform's optimal format‐dependent information sharing behavior, accounting for manufacturers' endogenous selling format decisions, and demonstrate the robustness of our main findings from the base model. Overall, our research offers valuable insights and guidelines to assist online platforms in making informed decisions about their information sharing practices. [ABSTRACT FROM AUTHOR]

Global changes currently roiling the contemporary world of work present a significant challenge for the sociology of work. The lifestyles of many people are becoming more dynamic and uncertain. As work itself starts to lose its familiar outlines, the employment paradigm that formed during the industrial period seems increasingly inapplicable. Such transformations give rise to criticism of existing concepts and the emergence of new discourses that simultaneously expand and complicate an understanding of the prospects for further development. In this article, we attempt to uncover problems that limit the possibility of generalizing scientific understanding in the field of sociology of work. Particular attention is paid to the integration of Russian experience into the global agenda. The review highlights the importance of clarifying the blurred conceptual framework of the sociology of work, enhancing the distinction between work and life, and identifying the possibilities and limitations of using big data for empirical analysis. In conclusion, we emphasize the need to counter the fragmentation of the sociology of work and its "dissolution" into other areas of knowledge by providing a strong theoretical background while maintaining the comparability of measurements. Addressing these gaps is essential for a more accurate reflection of modern labor relations. [ABSTRACT FROM AUTHOR]

Background Objectives Methods Results Conclusions The broad histomorphological spectrum of melanocytic pathologies requires large data sets to develop accurate and generalisable deep learning (DL)‐based diagnostic pathology classifiers. Weakly supervised DL promotes utilisation of larger training data sets compared to fully supervised (patch annotation) approaches.To evaluate weakly supervised DL image classifiers for discriminating melanomas from naevi on haematoxylin and eosin (H&E)‐stained pathology slides.A representative H&E slide for 260 naevi and 260 melanomas from mucocutaneous sites at one tertiary institution was digitized. Clinicopathological features were recorded for each case including thickness and histological subtype. Whole‐slide or whole‐tissue section labels were applied. The ground truth was established by consensus diagnosis from two pathologists. Multiple‐instance learning models, Trans‐MIL, CLAM and DTFD‐MIL were evaluated at 10×, 20× and 40× magnifications using stratified fivefold Monte Carlo cross‐validation, with 80/10/10 splits for training/validation/test groups, to predict melanoma from naevus. Heatmaps were generated to understand model performance.Naevi cases were younger (median age: 51 years; melanoma median age: 71.5 years), with more balanced sex distribution (males: 48.8%, melanoma male subgroup: 64.2%). The most frequent histological subtypes of naevi and melanomas were dysplastic compound (n = 99, 38.1%) and superficial spreading (n = 124, 47.7%), respectively. Average AUC (±1 SD) for Trans‐MIL, CLAM and DTFD‐MIL across test groups were 0.9952 ± 0.006, 0.9925 ± 0.0052 and 0.9708 ± 0.0328, at 20× magnification, respectively. Performance of the models varied according to the magnification used. Heatmaps from the two best performing models, Trans‐MIL and CLAM, generally indicated attention on appropriate tissue regions for interpretation.Weakly supervised DL on pathological slides of common mucocutaneous melanocytic tumours provides highly accurate diagnostic value for discrimination of melanomas and naevi. External validation and further assessment on less frequently occurring histologic subtypes and borderline cases using this method is required. [ABSTRACT FROM AUTHOR]

With the proliferation of cancer research based on large databases, misalignment of research questions and data set capabilities is inevitable. Nationally maintained databases are appealing to cancer researchers because of the ease of access to large amounts of patient data available for analysis and risk estimation. Data sets that are commonly used in cancer research include the National Cancer Database, the SEER (Surveillance, Epidemiology, and End Results) program of the National Cancer Institute, the SEER–Medicare database, the American College of Surgeons National Surgical Quality Improvement Program, and the Healthcare Cost and Utilization Project databases, among others. Each data set has pros and cons with respect to variable availability and the ability to analyze cancer‐specific outcomes. It is critical for researchers to understand the strengths and limitations of each database. Changing variable definitions, the length of postoperative data collection, and the availability of patient‐reported outcomes or social determinants of health data are examples of factors that researchers must consider when selecting a data set for research purposes. For the current review, the authors summarized the advantages and disadvantages of various national data sets for cohort studies in cancer populations. [ABSTRACT FROM AUTHOR]

Deep learning‐based models have become ubiquitous across a wide range of applications, including computer vision, natural language processing, and robotics. Despite their efficacy, one of the significant challenges associated with deep neural network (DNN) models is the potential risk of copyright leakage due to the inherent vulnerability of the entire model architecture and the communication burden of the large models during publishing. So far, it is still challenging for us to safeguard the intellectual property rights of these DNN models while reducing the communication time during model publishing. To this end, this paper introduces a novel approach using knowledge distillation techniques aimed at training a surrogate model to stand in for the original DNN model. To be specific, a knowledge distillation generative adversarial network (KDGAN) model is proposed to train a student model capable of achieving remarkable performance levels while simultaneously safeguarding the copyright integrity of the original large teacher model and improving communication efficiency during model publishing. Herein, comprehensive experiments are conducted to showcase the efficacy of model copyright protection, communication‐efficient model publishing, and the superiority of the proposed KDGAN model over other copyright protection mechanisms. [ABSTRACT FROM AUTHOR]

An efficient calculation tool for rigorously optimizing the instrument parameters of matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry (MALDI‐TOF MS) is demonstrated in this work. Optimization of MALDI‐TOF MS has been an experience‐based, time‐consuming process without a rigorous theoretical principle. Based on the comprehensive calculation model reported previously, we developed a software tool, MAXTOF, to rapidly determine instrument parameters providing ultimate mass resolving power (MRP). The software conducts big data analysis of most instrument parameters to predict the MRP, including instrument dimensions, voltages, mass‐to‐charge ratio of ions, and ion extraction delay. It can complete the calculation in seconds, providing parameters that serve as the starting point for subsequent optimization that greatly accelerates the optimization process. Experimental validation using a commercial instrument demonstrates MAXTOF's efficacy in predicting optimal parameters and enhancing MRP. The predicted ion extraction delays for bradykinin fragment, P14R polypeptide, and insulin chain B ions were within 5% of the values observed through long‐term optimization. The observed MRP of those ions ranged from 2800 to 4500. Results show that the method and software are highly advantageous for fast and precise measurements. [ABSTRACT FROM AUTHOR]

One of the basic scientific problems concerning geographic information science is how to rapidly organize, query, and compute spatiotemporal big data. The spatiotemporal discrete global grid system (DGGS) provides a homogenized discrete structure for processing multiscale and multitype spatiotemporal data. To date, most research in spatiotemporal DGGS has focused on spatial discretization while neglecting temporal discretization. Here, we propose a general modeling scheme for spatiotemporal DGGS with emphasis on encoding and operating multiscale time grids. We subdivide continuous time into multiscale temporal grids, which are then encoded as integers. Moreover, we designed integer code operations, including hierarchical traversal, neighborhood finding, and temporal relationship calculations. Compared to the multiscale time segment integer coding (MTSIC) approach, the proposed method resulted in 22% higher encoding efficiency, 10.92 times faster decoding, 2.81 times better parent code finding efficiency, 41% improved efficiency, 100% accuracy in finding children codes (compared to less than 100% with MTSIC), and a 62% enhancement in temporal relationship calculation efficiency. The application of querying spatiotemporal trajectory data validates the feasibility and practicality of substituting conventional string‐based time and floating‐point location coordinates with spatiotemporal integer codes to query data. The time encoding and operation methods proposed here indicate high efficiency, superior accuracy, and broad application prospects. [ABSTRACT FROM AUTHOR]

Our knowledge of biodiversity hinges on sufficient data, reliable methods, and realistic models. Without an accurate assessment of species distributions, we cannot effectively target and stem biodiversity loss. Species range maps are the foundation of such efforts, but countless studies have failed to account for the most basic assumptions of reliable species mapping practices, undermining the credibility of their results and potentially misleading and hindering conservation and management efforts. Here, we use examples from the recent literature and broader conservation community to highlight the substantial shortfalls in current practices and their consequences for both analyses and conservation management. We detail how different decisions on data filtering impact the outcomes of analysis and provide practical recommendations and steps for more reliable analysis, whilst understanding the limits of what available data will reliably allow and what methods are most appropriate. Whilst perfect analyses are not possible for many taxa given limited data, and biases, ensuring we use data within reasonable limits and understanding inherent assumptions is crucial to ensure appropriate use. By embracing and enacting such best practices, we can ensure both the accuracy and improved comparability of biodiversity analyses going forward, ultimately enhancing our ability to use data to facilitate our protection of the natural world. [ABSTRACT FROM AUTHOR]

Nearly a third of the world's population suffers from pollen‐induced respiratory allergies—and the number is rising. For those affected, the situation is becoming more stressful every year with climate change, rising atmospheric CO2 levels, and urbanization. These phenomena form a complex interface between human health and global change, yet the critical information needed to unravel it remains fragmented. A holistic big data approach, with close collaboration between Earth system scientists and health experts, is urgently needed to achieve meaningful insights that advance our understanding and management of increasing pollen‐induced respiratory allergies under global change. [ABSTRACT FROM AUTHOR]

Using an automated novel approach we conduct a reproducible systematic survey of electromagnetic ion cyclotron wave activity detected by Van Allen Probe B during the time period 2013 January 1–2019 July 15. We identify approximately 500 hr of EMIC wave activity, an occurrence rate of ∼ 0.85%. Accounting for satellite dwell time, we find that EMIC waves preferentially occur on the dayside, between 9 and 15 magnetic local time. This is true for both the H+ and He+wavebands. Higher amplitude waves are found at higher values of L shell, while weaker waves occur at low L. The highest amplitudes are concentrated at high L near dawn and dusk. It is also found that EMIC wave occurrence is enhanced during periods of strong geomagnetic activity, with an occurrence rate of 2.7%. During storm times, waves preferentially occur in the afternoon and early evening sectors. The full list of electromagnetic ion cyclotron wave detection times and their properties is made publicly available to the community. This provides a reference catalog for comparison with other magnetospheric phenomena and other wave databases. Plain Language Summary: Electromagnetic Ion Cyclotron (EMIC) waves are found throughout Earth's magnetosphere and the solar system. These waves in Earth's magnetosphere interact with the ring current and radiation belt population and push these particles into our atmosphere. Thus, it is useful to know when and where these waves occur. In this paper, we present a new approach toward identifying these waves in large data sets. Using this new approach, we identified 500 hr of EMIC waves from the Van Allen Probe B data set between Jan 2013–July 2019. Our catalog of events follows similar statistics found for EMIC waves by others, validating our methodology. Like others, we found that the waves occur more frequently on the dayside of the Earth. Higher amplitude waves were found at greater distances from the Earth, close to the edge of the magnetosphere. It was also found that the wave activity was greater during periods of geomagnetic activity than during quiet conditions. And in the interest of open science, we have made both the detection code and the list of waves available to the public. Key Points: We conduct a survey of electromagnetic ion cyclotron waves detected by Van Allen Probe B between 2013 January 1–2019 July 15The overall EMIC wave occurrence rate was ∼0.85% with a dayside preference. Stronger waves are found at higher L shells and during stormsThe full list of EMIC wave detection times and their properties is made publicly available to the community, along with the methodology [ABSTRACT FROM AUTHOR]

Economics has long shunned qualitative research methods, such as interviews, focus groups and observational studies, in preference for quantitative methods, such as empirical analysis of secondary data and field experiments. Moreover, recent years, with advances in econometric theory, have seen a notable increase in the size and quality of data sets that are needed to publish quantitative research in leading journals. This has the effect of significantly increasing the 'entry costs' for researchers interested in studying economic development. Crucially, it also limits the topics that can be studied. In particular, it focuses attention on issues and countries where large data sets exist or can be collected. In this paper, we argue that by putting too much weight on internal validity, economics has adopted a too narrow definition of 'rigour' and would benefit from embracing qualitative and mixed methods research. To illustrate our point, we pay particular attention to the informal economy. The informal economy is understudied by economists because of the lack of available quantitative data. We show that this fundamentally impacts our understanding of the wider economy. Qualitative research can enrich our understanding by providing a more complete and nuanced view of the economy. [ABSTRACT FROM AUTHOR]

The introduction of thousands of wind turbines along the North American Atlantic continental shelf over the next decade will constitute the largest regional change in marine substrates since the retreat of the Laurentide Ice Sheet over 14,000 years ago. Here, two large data sets, SMAST drop camera survey (242,949 samples, 2003 to 2019) and the US Geological Survey databases (27,784 samples, 1966 to 2011), are combined to derive sea floor surficial substrate probability maps for the Northeastern US continental shelf from Virginia Beach to the Gulf of Maine to 300 m depth (218,571 km2). Geostatistical models were used to estimate the probability of five geologic and one biogenic substrate types being present at a 250 m resolution, and the proportional contribution of each substrate type to the seabed composition at a 500 m resolution. By providing the first synoptic maps depicting the probability of a particular substrate or combination of substrates occurring at any location on the Northeastern US continental shelf, including planned wind energy sites, we aim to (1) provide insights regarding how substrates in the areas selected for wind energy development compare with other locations, (2) motivate the development of a priori expectations for ecosystem changes to inform monitoring and research efforts going forward, and (3) to provide a baseline characterization of the Northeastern US continental shelf surficial substrates to support robust examination of the future changes observed in areas impacted by wind energy installations. [ABSTRACT FROM AUTHOR]

Informed, wise, land management has been a challenge since a long time, but has gained extra importance through the need for responding to climate change effects. The European Green Deal is one example of a concertation effort in a world where data availability, sharing options, and just‐in‐time exploitation are not entirely solved. The LandSupport service responds to this challenge through a combination of a Spatial Decision Support System on top of a datacube management and analytics engine. In this paper, we specifically focus on this datacube engine and how it can contribute to a better decision making through an ordered multidimensional data management paired with strictly standards‐compliant interfaces. Further, the approach described shows a remedy against the current fragmentation of services: through a location‐transparent federation a single common pool of information is established, including distributed fusion allowing an unlimited mix‐and‐match of data regardless of their location. [ABSTRACT FROM AUTHOR]

We describe the development and validation of an autonomous monitoring station that identifies and records the movement of social insects into and out of the colony. The hardware consists of an illuminated channel and a fixed camera to capture the wasps' activities. An ad hoc post‐processing software was developed to identify the direction of movement and caste of the recorded individuals. Validation results indicate that the model can detect with high levels of accuracy the presence of workers, drones and gynes, whereas direction of movement is accurate only for workers and drones, but not for gynes. Further development of the software and hardware should enable higher levels of accuracy, especially in terms of the direction of movement of reproductive individuals. This innovative tool holds immense potential for advancing ecological and behavioural research by providing researchers with rapid and easily accessible data. Understanding the activity patterns of individual wasps within the colony can yield valuable insights into factors influencing their growth, foraging patterns and the behaviour of reproductive individuals. Ultimately, this information can be incorporated into effective management plans for controlling harmful social insect populations in both ecological and productive systems. [ABSTRACT FROM AUTHOR]

Motivation Purpose Approach and methods Findings Policy implications Academicians and policy‐makers grapple with monitoring the economic impact of crises such as COVID‐19 when survey data are scarce.We show how a set of publicly available real‐time indicators—nitrogen dioxide emissions, night‐time lights, mobile phone mobility tracking, internet searches, and food prices—tracked changes in GDP across multiple countries in 2020.We first describe the extent to which real‐time indicators captured the COVID‐19 crisis. We employ linear models, selected using the least absolute shrinkage and selection operator (LASSO), to examine the capacity of these indicators to track GDP growth during the crisis.Google Mobility and staple food prices both sharply declined in March and April 2020, followed by a rapid recovery returning to baseline levels by July and August 2020. Mobility and staple food prices experienced a milder decrease in low‐income countries.Nitrogen dioxide and night‐time lights showed a similar pattern, with the steepest fall followed by a swift recovery in lower middle‐income countries.In April and May, Google search terms reflecting economic distress and religiosity spiked in some regions but not in others.Linear models explain about half of the variation in annual GDP growth in 68 countries. In a smaller subset of higher‐income countries, real‐time indicators explain about 65% of the variation in quarterly GDP growth.Real‐time indicators offer several advantages in crisis monitoring, being readily available, cost‐effective, and not requiring face‐to‐face interactions, which are particularly valuable during a pandemic. [ABSTRACT FROM AUTHOR]

Various neural network architectures are often used to forecast movements in financial markets. Most research in quantitative analytics in finance uses interval financial data as this reduces the raw tick big data, but the averaging can lose key behaviour patterns. This work presents an alternative novel method to reduce raw tick data whilst retaining important information for training, as demonstrated with intraday trading using the EURO/USD currency pair. This time series reduction method focuses on short periods preceding significant movements in financial features and allows the most popular neural network architectures to be applied using less powerful but more readily available computer resources. It is shown that the proposed data preprocessing method for machine learning and other AI‐techniques successfully reduced the size of the selected dataset covering a three‐year period (2018–2021) by 275 times. [ABSTRACT FROM AUTHOR]

Designing and developing sustainable circular supply chain networks for electric vehicle (EV) lithium‐ion battery recycling and production requires complex environmental sustainability and economic viability assessment. EVs use a lot of data for battery management and delivering optimum performance, and the Internet of Things (IoT) plays a major role in managing this data. This study develops a bi‐objective mixed‐integer linear programming model for designing a sustainable circular supply chain to manage the manufacturing, remanufacturing, and distribution of EV lithium‐ion batteries under uncertainty using the IoT and big data. The proposed model simultaneously minimizes total costs and CO2 emissions and uses IoT to improve network performance and create a traceable and secure environment. A fuzzy multi‐objective method solves the bi‐objective optimization model under uncertainty, and a simulation algorithm examines the effectiveness of the proposed model through simulated problems. [ABSTRACT FROM AUTHOR]

Background: Usage of electronic gaming machines (EGMs) and on‐line gambling is strongly associated with gambling‐related harm. Player‐tracking systems can monitor a gambler's activity across multiple sessions and/or operators, providing a clearer picture of the person's risk of harm with respect to these gambling formats and enabling harm reduction efforts. The Finnish and Norwegian state monopolies have player‐tracking systems in place, while the United Kingdom is implementing an operator‐led system called 'single customer view' for on‐line gambling, and Australian states are proposing similar 'player cards' for land‐based EGMs. Argument: Player tracking can advance harm reduction efforts in three ways. First, player tracking improves our understanding of gambling‐related harm by providing data on how the population gambles, which can potentially be linked with operator, government and/or prevalence data sets. Secondly, player tracking can be used to implement harm reduction measures such as expenditure limits, self‐exclusion and age verification. Thirdly, player tracking can provide a platform to evaluate harm reduction measures via gold‐standard field trials. These potential benefits need to be weighed against various practical and ethical issues. Conclusions: The potential benefits of player‐tracking systems would be maximized via systems administered independently of the gambling industry and implemented universally across all gambling in a given jurisdiction. [ABSTRACT FROM AUTHOR]

Objectives: The present study was conducted to evaluate the reproducibility of Lekholm and Zarb classification system (L&Z) for bone quality assessment of edentulous alveolar ridges and to investigate the potential of a data‐driven approach for bone quality classification. Materials and Methods: Twenty‐six expert clinicians were asked to classify 110 CBCT cross‐sections according to L&Z classification (T0). The same evaluation was repeated after one month with the images put in a different order (T1). Intra‐ and inter‐examiner agreement analyses were performed using Cohen's kappa coefficient (CK) and Fleiss' kappa coefficient (FK), respectively. Additionally, radiomic features extraction was performed from 3D edentulous ridge blocks derived from the same 110 CBCTs, and unsupervised clustering using 3 different clustering methods was used to identify patterns in the obtained data. Results: Intra‐examiner agreement between T0 and T1 was weak (CK 0.515). Inter‐examiner agreement at both time points was minimal (FK at T0: 0.273; FK at T1: 0.243). The three different unsupervised clustering methods based on radiomic features aggregated the 110 CBCTs in three groups in the same way. Conclusions: The results showed low agreement among clinicians when using L&Z classification, indicating that the system may not be as reliable as previously thought. The present study suggests the possible application of a reproducible data‐driven approach based on radiomics for the classification of edentulous alveolar ridges, with potential implications for improving clinical outcomes. Further research is needed to determine the clinical significance of these findings and to develop more standardized and accurate methods for assessing bone quality of edentulous alveolar ridges. [ABSTRACT FROM AUTHOR]

Systematical investigation of deep mantle structure beneath the Pamir Plateau, western Tian Shan and their surroundings is of great significance to understand dynamics of continental collision, intracontinental orogenesis and deformation in response to the Indo‐Eurasian collision. In this research, we imaged the mantle transition zone (MTZ) structure beneath these regions using 42,560 P‐wave receiver functions obtained from 352 seismic stations and 6,173 teleseismic events. Our results reveal significant 15–20 km depression of the 410‐km discontinuity (d410) mainly beneath the southern Kazakh Shield, which is consistent with the low‐velocity anomaly in tomographic models and thus attributed to the mantle upwelling from the MTZ, providing evidence for the fossil Tian Shan plume responsible for the Late Cretaceous‐Paleocene basaltic magmatism (74–52 Ma) at the western Tian Shan. Considering that the d410 is slightly depressed by ∼8 km beneath the western Tian Shan, deep subduction of the Tarim lithosphere is likely excluded and its subhorizontal indentation into the Tian Shan is preferred. As a result, segments of thickened Tian Shan lithosphere delaminated and accumulated near the 660‐km discontinuity (d660), which induce small‐scale upwelling across the d410 there. The d410 is depressed by ∼10–15 km beneath Tarim, which is interpreted to be caused by the mantle upwelling originating from beneath the d410. The d660 below the central Hindu Kush is extremely depressed by 25–30 km, providing direct evidence for the deep subduction of Indian lithosphere into the bottom of the MTZ and suggesting different mechanisms for continental collision between the Hindu Kush and Pamir Plateau. Plain Language Summary: Intensive tectonic activities are ongoing in the Pamir Plateau, western Tian Shan and their surroundings, whereas the deep mantle structure and related dynamical processes are poorly constrained by independent seismological observations. Here we mapped the detailed mantle transition zone (MTZ) structure utilizing large data sets of seismic data by imaging the 410‐km (d410) and 660‐km (d660) discontinuities. Our results show prominent depression of d410 (15–20 km) below the southern Kazakh Shield in agreement with the tomography‐derived low‐velocity anomalies, which we interpret as mantle upwelling from the MTZ likely associated with the fossil Tian Shan plume and the eruption of basaltic magmatism (74–52 Ma) in the western Tian Shan. Furthermore, the subhorizontal indentation of Tarim lithosphere into western Tian Shan is supported by slightly depressed d410 (∼8 km) there, followed by the delamination of thickened Tian Shan lithosphere into the bottom of the MTZ. Furthermore, mantle upwelling from the MTZ beneath the Tarim Basin is indicated by the depressed d410 there (∼10–15 km). While the Indian slab underthrusts northward beneath the Pamir Plateau, it subducts deep into the bottom of the MTZ beneath the central Hindu Kush. Key Points: The mantle transition zone structure beneath the Pamir Plateau and western Tian Shan and adjacent regions are imaged with great detailThe 15–20 km depression of the 410‐km discontinuity beneath southern Kazakh Shield is likely associated with the fossil Tian Shan plumeSignificant depression of the 660‐km discontinuity (25–30 km) suggests deep subduction of the Indian slab beneath the central Hindu Kush [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]

In the field of hot rolling big data, the presence of different steel types, specifications, and data heterogeneity poses significant challenges to the accuracy and stability of using single machine learning regression technology for prediction. Therefore, this study proposes a hot‐rolled strip crown prediction method that combines data clustering and fusion modeling. First, this article introduces a relevant mechanism for designing cluster strategies. The optimal clustering strategy is determined through comparative experiments using rolling process parameters, strip size, and main material components as the clustering features. Subsequently, the K‐Means++ algorithm is used to effectively cluster the training and testing datasets based on this strategy, generating multiple clusters for both datasets. Finally, this study establishes seven different training models to match the most suitable regression prediction model for each cluster, and matching between each cluster and the model is determined through rigorous testing. The evaluation of the fusion model shows an R2 value of 0.829 and a root mean square error value of 3.974. The experimental results show that the proposed method outperforms traditional methods in solving the challenges of multiclass classification and data heterogeneity, providing strong data support for the intelligent control of the hot‐rolled strip crown in the future. [ABSTRACT FROM AUTHOR]