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The industrial shift from Industry 4.0 to Industry 5.0 has transformed organizational thinking, moving the focus from purely technological implementation to a more human-centered approach. The current study has focused on the Industry 5.0 technological capabilities to bring into circular economy practices aligned with sustainable development goals, aiming to enhance sustainable performance. Moreover, the resource-based theory has grounded the development of the comprehensive framework on Industry 5.0 technological capabilities (artificial intelligence capabilities, big data analytical capabilities, Internet of Things capabilities, machine learning capabilities, and blockchain technology capabilities) and circular economy practices (eco-design, management system, and investment recovery) to achieve sustainable performance (environmental performance, social performance, and economic performance). Data have been collected from 179 respondents from the Chinese manufacturing industry. Additionally, data have been analyzed using the structural equation modeling technique. The results showed that Industry 5.0 technological capabilities directly affect sustainable performance. Moreover, circular economy practices played a dual, moderating, and mediating role between Industry 5.0 technological capabilities and sustainable performance. The current study has contributed to filling a gap in the literature on Industry 5.0 capabilities, especially in the circular economy and sustainable performance perspective. The practical contribution recommended is that if organizations focused on their Industry 5.0 technological capabilities, it would boost circular economy practices and sustainable performance to achieve sustainable development goals. [ABSTRACT FROM AUTHOR]

Inland waterways, characterized by their complex, narrow paths, see significantly higher traffic volumes compared to maritime routes, increasing the regulatory demands on traffic management. Predictive modeling of ship traffic flows, utilizing real AIS historical data, enhances route and docking planning for ships and port managers. This approach boosts transportation efficiency and safety in inland waterway navigation. Nevertheless, AIS data are flawed, marred by noise, disjointed paths, anomalies, and inconsistent timing between points. This study introduces a data processing technique to refine AIS data, encompassing segmentation, outlier elimination, missing point interpolation, and uniform interval resampling, aiming to enhance trajectory analysis reliability. Utilizing this refined data processing approach on ship trajectory data yields independent, complete motion profiles with uniform timing. Leveraging the Transformer model, denoted TRFM, this research integrates processed AIS data from the Yangtze River to create a predictive dataset, validating the efficacy of our prediction methodology. A comparative analysis with advanced models such as LSTM and its variants demonstrates TRFM's superior accuracy, showcasing lower errors in multiple metrics. TRFM's alignment with actual trajectories underscores its potential for enhancing navigational planning. This validation not only underscores the method's precision in forecasting ship movements but also its utility in risk management and decision-making, contributing significantly to the advancement in maritime traffic safety and efficiency. [ABSTRACT FROM AUTHOR]

A comprehensive and high-quality lexicon plays a crucial role in traditional text classification approaches. It improves the utilization of linguistic knowledge. Although it is helpful for this task, the lexicon has received little attention in current neural network models. First, obtaining a high-quality lexicon is not easy. Second, an effective automated lexicon extraction method is lacking, and most lexicons are handcrafted, which is very inefficient for big data. Finally, there is no effective way to use a lexicon in a neural network. To address these limitations, we propose a pre-attention mechanism for text classification in this study, which can learn the attention values of various words based on their effects on classification tasks. Words with different attention values can form a domain lexicon. Experiments on three publicly available and authoritative benchmark text classification tasks show that our models obtain competitive results compared with state-of-the-art models. For the same dataset, when we use the pre-attention mechanism to obtain attention values, followed by different neural networks, words with high attention values have a high degree of coincidence, which proves the versatility and portability of the pre-attention mechanism. We can obtain stable lexicons using attention values, which is an inspiring method of information extraction. [ABSTRACT FROM AUTHOR]

Background: Accurately identifying effective biomarkers and translating them into clinical practice have significant implications for improving clinical outcomes in hepatocellular carcinoma (HCC). In this study, our objective is to explore appropriate methods to improve the accuracy of biomarker identification and investigate their clinical value. Methods: Concentrating on the N6-methyladenosine (m6A) modification regulators, we utilized dozens of multi-omics HCC datasets to analyze the expression patterns and genetic features of m6A regulators. Through the integration of big data analysis with function experiments, we have redefined the biological roles of m6A regulators in HCC. Based on the key regulators, we constructed m6A risk models and explored their clinical value in estimating prognosis and guiding personalized therapy for HCC. Results: Most m6A regulators exhibit abnormal expression in HCC, and their expression is influenced by copy number variations (CNV) and DNA methylation. Large-scale data analysis has revealed the biological roles of many key m6A regulators, and these findings are well consistent with experimental results. The m6A risk models offer significant prognostic value. Moreover, they assist in reassessing the therapeutic potential of drugs such as sorafenib, gemcitabine, CTLA4 and PD1 blockers in HCC. Conclusions: Our findings suggest that the mutual validation of big data analysis and functional experiments may facilitate the precise identification and definition of biomarkers, and our m6A risk models may have the potential to guide personalized chemotherapy, targeted treatment, and immunotherapy decisions in HCC. [ABSTRACT FROM AUTHOR]

With the rise of digital technologies, a political‐economic configuration recognised as “platform capitalism” has raised concerns over monopolistic tendencies, lack of accountability, expanded rentiership, workers’ precarity, and more. Existing analyses, however, show a distinctly urban bias—centring on housing, transportation, retail, and gig labour—and have yet to engage with the agrarian dimensions of this phenomenon despite considerable potential impacts on the future of farming. Here we begin the process of theorising agrarian platform capitalism, offering a typology of platforms in the agri‐food sector, and bringing together critiques of platform capitalism with the distinctive features of agrarian political economy. Our analysis identifies four prominent characteristics of agrarian platform capitalism which largely corroborate existing critiques albeit with some distinctive contours. As in other sectors, platforms intensify rentiership regarding both real estate and digital assets. Agricultural platforms also display a familiar tendency to thrive in spaces of regulatory retreat and are in some cases even endorsed by regulatory agencies, highlighting the potential for public–private platformisation. Some agricultural platform companies deploy populist rhetoric beyond established tropes of consumer welfare, latching onto farmers’ deep frustrations with the highly concentrated agribusiness sector. Efforts to reign in agrarian platform power may be further constrained by legitimising discourses of hunger relief and sustainability. [ABSTRACT FROM AUTHOR]

Introduction: The development of science and technology has driven rapid changes in the social environment, especially the rise of the big data environment, which has greatly increased the speed at which people obtain information. However, in the process of big data processing, the allocation of information resources is often unreasonable, leading to a decrease in efficiency. Therefore, optimizing task scheduling algorithms has become an urgent problem to be solved. Methods: The study optimized task scheduling algorithms using artificial intelligence (AI) methods. A task scheduling algorithm optimization model was designed using support vector machine (SVM) and K-nearest neighbor (KNN) combined with fuzzy comprehensive evaluation. In this process, the performance differences of different nodes were considered to improve the rationality of resource allocation. Results and Discussion: By comparing the task processing time before and after optimization with the total cost, the results showed that the optimized model significantly reduced task processing time and total cost. The maximum reduction in task processing time is 2935 milliseconds. In addition, the analysis of query time before and after optimization shows that the query time of the optimized model has also been reduced. The experimental results demonstrate that the proposed optimization model is practical in handling task scheduling problems and provides an effective solution for resource management in big data environments. This research not only improves the efficiency of task processing, but also provides new ideas for optimizing future scheduling algorithms. [ABSTRACT FROM AUTHOR]

ABSTRACT The application of big data in education sparks debates on its effects. Researchers explore its impact on learning outcomes, with divergent views on neural networks' potential and negative consequences. This study assesses big data's influence on basic literacy skills in music education for 5th graders. Using clustering and k‐density analyses, scholars monitored changes over 5 months at [Music College of Jimei University] Elementary School. Results show big data technologies helped maintain motivation and improved academic success. Musical performance and the Sounddraw app garnered the most interest and effectiveness. These results indicate that the use of big data technologies is not only effective in increasing engagement but also in enhancing the cognitive understanding of musical concepts. This adaptability is crucial in music education, where students’ individual learning paces and creative inclinations vary widely. Moreover, by leveraging data‐driven insights, educators can track progress more precisely, identifying areas where students excel and where they may need additional support. Future research should delve into the psycho‐emotional impact of regular big data use in music learning. This study underscores the practical application of modern educational programs to meet evolving needs and advocates interactive learning to mitigate traditional methods’ drawbacks. [ABSTRACT FROM AUTHOR]

Background: Having a primary care provider is associated with better care experiences and lower care costs. In 2021, INSPIRE-PHC released Primary Care Data Reports - publicly available summaries of administrative billing data about how populations in each of Ontario's 60 health teams use primary care services. Given the characterization of Canadian primary care systems as 'in crisis', publicly available data about primary care at the regional level presented a significant opportunity for knowledge mobilization. An understandable resource could ground the public conversation about primary care access in data. Recognizing the role that lived experience plays in ensuring the public understands research findings, a partnership between patient advisors, Ontario Health Team representatives, researchers, and trainees was established to co-produce public-facing infographics based on primary care data. Methods: Evidence-based guidelines for public health infographic creation and elements of transformative action research guided a six-meeting process to engage up to 14 patient advisors, three Ontario Health Team staff and two primary care trainees. Patient advisors were affiliated with a provincial patient-oriented primary health care research group or a Hamilton-based Ontario Health Team. Ninety-minute meetings were conducted virtually, and notes were shared with attendees to ensure they accurately reflected the conversation. Two consultations with Ontario Health Team-affiliated primary care providers provided direction and ensured project outputs aligned with local priorities. Results: Project partners shared feedback on draft infographics, audience identification, priority elements from Primary Care Data Reports to include in the infographics, and aesthetic features (e.g., headings, colour scheme, charts). Project partners felt the most important metrics to convey to the public were those that simultaneously reinforced the benefits of primary care on individual health outcomes and health system costs. Conclusions: Patient engagement in research is becoming widespread, but co-developing knowledge products with patient and health system partners is less common. Our approach to engaging patients prevented both oversimplification and unnecessary complexity in a public-facing visual about attachment to primary care. Plain English summary: Primary care is the first point of contact for patients accessing the health care system. Primary care providers (i.e., family doctors and nurse practitioners – working individually or in teams of nurses, dieticians, pharmacists, social workers and others) – help patients and families manage health concerns over time by working to prevent, diagnose, and treat illnesses and link patients with specialist care when needed. Having a primary care provider is associated with better healthcare experiences and lower costs of care. In 2021, the Primary Care Data Reports were released to the public. The reports contain administrative data about how patients engage with primary care in each of the 58 Ontario Health Teams. Patient advisors, Ontario Health Team representatives, researchers, and trainees created an infographic to share this data in a way that can be understood by everyone. Infographics use images to help people process information faster, which can make health-related topics easier to understand. By understanding the value of primary care, people can participate in conversations about how to ensure primary care is accessible to everyone. Engaging patients is becoming common in research, but collaborating with patient advisors and health system partners to create visual summaries is less common. We had six virtual meetings that were 90 min each with up to 14 patient advisors, Ontario Health Team representatives, researchers and trainees. Patient advisors helped decide who the audience would be, which information from the Primary Care Data Reports to include, and how the infographic would look. Patient advisors felt it was most important to highlight the benefits patients can get from primary care, and the impact primary care can have to reduce health system costs. We consulted with primary care clinicians to ensure the infographic reflected local priorities. Working with patient advisors and Ontario Health Team representatives helped to create an infographic about Primary Care Data Reports data that is easy to understand by the public. [ABSTRACT FROM AUTHOR]

The exposome encompasses the full range of environmental exposures throughout a person's lifetime and plays an important role in cardiovascular health. Interactions with the social, natural, and built components of the exposome significantly impact cardiovascular disease prevalence and mortality. Robust data analytics, including machine learning and geospatial analysis, have advanced our understanding of how these factors converge to influence cardiovascular disease risk. The integration of multiomics platforms and advanced computational approaches enhances our ability to characterize the exposome, leading to targeted public health interventions and innovative risk reduction strategies aimed at improving cardiovascular health globally. These multiomics platforms that integrate factors such as genomics, epigenomics, clinical data, social factors, environmental factors, and wearable technology will characterize the exposome in greater detail concerning cardiovascular health. In this review, we aimed to elucidate the components of the exposome and discuss recent literature regarding their relationship to cardiovascular health. [ABSTRACT FROM AUTHOR]

Educational Data Mining (EDM) applies advanced data mining techniques to analyse data from educational settings, traditionally aimed at improving student performance. However, EDM's potential extends to enhancing administrative functions in educational organisations. This systematisation of knowledge (SoK) explores the use of EDM in organisational administration, examining peer-reviewed and non-peer-reviewed studies to provide a comprehensive understanding of its impact. This review highlights how EDM can revolutionise decision-making processes, supporting data-driven strategies that enhance administrative efficiency. It outlines key data mining techniques used in tasks like resource allocation, staff evaluation, and institutional planning. Challenges related to EDM implementation, such as data privacy, system integration, and the need for specialised skills, are also discussed. While EDM offers benefits like increased efficiency and informed decision-making, this review notes potential risks, including over-reliance on data and misinterpretation. The role of EDM in developing robust administrative frameworks that align with organisational goals is also explored. This study provides a critical overview of the existing literature and identifies areas for future research, offering insights to optimise educational administration through effective EDM use and highlighting its growing significance in shaping the future of educational organisations. [ABSTRACT FROM AUTHOR]

With the exponential growth of data across diverse fields, applying conventional statistical methods directly to large-scale datasets has become computationally infeasible. To overcome this challenge, subsampling algorithms are widely used to perform statistical analyses on smaller, more manageable subsets of the data. The effectiveness of these methods depends on their ability to identify and select data points that improve the estimation efficiency according to some optimality criteria. While much of the existing research has focused on subsampling techniques for independent data, there is considerable potential for developing methods tailored to dependent data, particularly in time-dependent contexts. In this study, we extend subsampling techniques to irregularly spaced time series data which are modeled by irregularly spaced autoregressive models. We present frameworks for various subsampling approaches, including optimal subsampling under A-optimality, information-based optimal subdata selection, and sequential thinning on streaming data. These methods use A-optimality or D-optimality criteria to assess the usefulness of each data point and prioritize the inclusion of the most informative ones. We then assess the performance of these subsampling methods using numerical simulations, providing insights into their suitability and effectiveness for handling irregularly spaced long time series. Numerical results show that our algorithms have promising performance. Their estimation efficiency can be ten times as high as that of the uniform sampling estimator. They also significantly reduce the computational time and can be up to forty times faster than the full-data estimator. [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]

There is growing interest in effectively using artificial-intelligence-based data analysis. Unfortunately, analyzing large-scale data incurs excessive computation costs. While approximate methods are commonly used to reduce computation costs, they cannot yield exact results; they sacrifice accuracy to improve efficiency. This article introduces representative methods of pruning unnecessary computations for fast and accurate data analysis. [ABSTRACT FROM AUTHOR]

Through the sports group teaching of school students, this paper uses the evaluation method to construct the ability evaluation model, collects the changes of language ability, and tests the students' understanding ability, expression ability, action language, and group cooperation. Following the teaching for three months, the average value of the group which processes experimental activities has increased to 0.55, and the average value of the control group has increased to about 0.4. In terms of teamwork, the experiment should lead the control group by 35 percentage points. It is found that physical education teachers encounter some problems when using new media teaching, such as lack of teacherâ student interaction, blind pursuit of teaching progress, blind use of courseware, etc. This study has improved the understanding of furthermost students who are pursuing physical education in college on new media teaching, and is conducive to the sustainable development of physical education in the new media environment. There is a guarantee of big data analytics capability that it can investigate the data and can turn it into useful information for a variety of purposes. [ABSTRACT FROM AUTHOR]

The traditional clustering algorithms are not appropriate for large real‐world datasets or big data, which is attributable to computational expensiveness and scalability issues. As a solution, the last decade's research headed towards distributed clustering using the MapReduce framework. This study conducts a bibliometric review to assess, establish, and measure the patterns and trends of the MapReduce‐based partitioning, hierarchical, and density clustering algorithms over the past decade (2013–2023). A digital text‐mining‐based comprehensive search technique with multiple field‐specific keywords, inclusion measures, and exclusion criteria is employed to obtain the research landscape from the Scopus database. The Scopus‐obtained data is analyzed using the VOSViewer software tool and coded using the R statistical analysis tool. The analysis identifies the numbers of scholarly articles, diversities of article sources, their impact and growth patterns, details of most influential authors and co‐authors, most cited articles, most contributing affiliations and countries, and their collaborations, use of different keywords and their impact, and so forth. The study further explores the articles and reports the methodologies employed for designing MapReduce‐based counterparts of traditional partitioning, hierarchical, and density clustering algorithms and their optimizations and hybridizations. Finally, the study lists the main research challenges encountered in the past decade for MapReduce‐based partitioning, hierarchical, and density clustering. It suggests possible areas for future research to contribute further in this field. [ABSTRACT FROM AUTHOR]

Government statistical offices worldwide are under pressure to produce statistics rapidly and for more detailed geographies, to compete with unofficial estimates available from web-based big data sources or from private companies. Commonly suggested sources of improved health information are electronic health records and medical claims data. These data sources are collectively known as real-world data (RWD) because they are generated from routine health care processes, and they are available for millions of patients. It is clear that RWD can provide estimates that are more timely and less expensive to produce—but a key question is whether or not they are very accurate. To test this, we took advantage of a unique health data source that includes a full range of sociodemographic variables and compared estimates using all of those potential weighting variables versus estimates derived when only age and sex are available for weighting (as is common with most RWD sources). We show that not accounting for other variables can produce misleading and quite inaccurate health estimates. [ABSTRACT FROM AUTHOR]

Data makes the world go round. Now more than ever, routine police work depends on the collection and analysis of digital information. Law enforcement agencies possess vast sums of intel on who we are, where we go, and what we do. The proliferation of digital technology has transformed federal criminal procedure—from how police investigate crimes to how prosecutors prove them at trial. Courts and commentators have written much about the first part, but less so about the second. Together, they represent two sides of the same problem: constitutional doctrine lagging behind new technology, leading to suboptimal constraints on law enforcement conduct. This Note explores the effects of digital technology on the nature and scope of federal prosecutors’ disclosure obligations under Brady v. Maryland. As police pass along more data to prosecutors—usually terabytes at a time—prosecutors face the difficult task of sifting through mountains of evidence to determine what is exculpatory or otherwise favorable to the defense. Often, prosecutors turn over their entire case file, knowing full well that defense counsel will fare no better. This state of affairs puts our adversarial system on shaky ground. This Note urges district courts to exercise greater oversight of the discovery process, requiring prosecutors to take reasonable precautions so exculpatory evidence comes to light. [ABSTRACT FROM AUTHOR]

Artificial neural network models for the task of detection are used in many fields and find various applications. Models of this kind require adequate computational resources and thus require adequate energy expenditure. The increase in the number of parameters, the complexity of architectures, and the need to process large data sets significantly increase energy consumption, which is becoming a key sustainability challenge. Optimization of computing and the development of energy-efficient hardware technologies are essential to reduce the energy footprint of these models. This article examines the effect of the type of model, as well as its parameters, on energy consumption during inference. For this purpose, sensors built into the graphics card were used, and software was developed to measure the energy demand of the graphics card for different architectures of YOLO models (v8, v9, v10), as well as for different batch and model sizes. This study showed that the increase in energy demand is not linearly dependent on batch size. After a certain level of batch size, the energy demand begins to decrease. This dependence does not occur only for n/t size models. Optimum utilization of computing power due to the number of processed images for the studied models occurs at the maximum studied batch size. In addition, tests were conducted on an embedded device. [ABSTRACT FROM AUTHOR]

Remote sensing image (RSI) segmentation is an effective method to interpret remote sensing information and an important means of remote sensing data information processing. Traditional RSI segmentation methods have some problems such as poor segmentation accuracy and low similarity difference measurement. Therefore, we propose a multi-scale segmentation (MSS) method for remote sensing big data image. First, the segmentation scale of RSI is divided, and the quantitative value of histogram band is used to calculate the similarity index between different objects; Second, the parameters in the same spot are improved based on the maximum area method to determine the shape factor of RSI; Finally, the object closure model is established to clarify the region conversion cost, and the RSI is dynamically segmented based on Multi-scale convolutional neural networks; The MSS algorithm of RSI is designed, and the MSS method of RSI is obtained. The results show that the maximum similarity difference measure of the proposed method is 0.648, and the similarity difference measure always remains the largest. The maximum recall of RSI is 0.954, and the highest recall is 0.988, indicating that the RSI segmentation accuracy of the proposed method is good. [ABSTRACT FROM AUTHOR]

Big data analysis of human behavior can provide the basis and support for the application of various scenarios. Using sensors for human behavior analysis is an effective means of identification method, which is very valuable for research. To address the problems of low recognition accuracy, low recognition efficiency of traditional human behavior recognition (HBR) algorithms in complex scenes, in this paper, we propose an HBR algorithm for Mobile Big data analytics in wireless sensor network using improved transfer learning. First, different wireless sensors are fused to obtain human behavior mobile big data, and then by analyzing the importance of human behavior features (HBF), the dynamic change parameters of HBF extraction threshold are calculated. Second, combined with the dynamic change parameters of threshold, the HBF of complex scenes are extracted. Finally, the best classification function of human behavior in complex scenes is obtained by using the classification function of HBF in complex scenes. Human behavior in complex scenes is classified according to the HBF in the feature set. The HBR algorithm is designed by using the improved transfer learning network to realize the recognition of human behavior in complex scenes. The results show that the proposed algorithm can accurately recognize up to 22 HBF points, and can control the HBR time within 2 s. The human behavior false recognition rate of miscellaneous scenes is less than 10%. The recognition speed is above 10/s, and the recall rate can reach more than 98%, which improves the HBR ability of complex scenes. [ABSTRACT FROM AUTHOR]

A growing number of publicly available human functional magnetic resonance imaging (fMRI) data sets use a 'deep' sampling approach, where many hours of data are acquired from a few individuals. We highlight an emerging approach within deep fMRI, which we refer to as 'intensive' fMRI: the creation of large-scale, publicly shared data sets that extensively sample cognitive phenomena to support the investigation of brain function at the single voxel level. We discuss key principles of intensive fMRI, its benefits and challenges, and practical considerations in creating intensive fMRI data sets. We discuss how intensive fMRI can advance scientific discovery in systems neuroscience, machine learning, and artificial intelligence. The rise of large, publicly shared functional magnetic resonance imaging (fMRI) data sets in human neuroscience has focused on acquiring either a few hours of data on many individuals ('wide' fMRI) or many hours of data on a few individuals ('deep' fMRI). In this opinion article, we highlight an emerging approach within deep fMRI, which we refer to as 'intensive' fMRI: one that strives for extensive sampling of cognitive phenomena to support computational modeling and detailed investigation of brain function at the single voxel level. We discuss the fundamental principles, trade-offs, and practical considerations of intensive fMRI. We also emphasize that intensive fMRI does not simply mean collecting more data: it requires careful design of experiments to enable a rich hypothesis space, optimizing data quality, and strategically curating public resources to maximize community impact. [ABSTRACT FROM AUTHOR]

Many extreme-scale applications require the movement of large quantities of data to, from, and among leadership computing facilities, as well as other scientific facilities and the home institutions of facility users. These applications, particularly when leadership computing facilities are involved, can touch upon edge cases (e.g., terabyte files) that had not been a focus of previous Globus optimization work, which had emphasized rather the movement of many smaller (megabyte to gigabyte) files. We report here on how automated client-driven chunking can be used to accelerate both the movement of large files and the integrity checking operations that have proven to be essential for large data transfers. We present detailed performance studies that provide insights into the benefits of these modifications in a range of file transfer scenarios. [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]

For securing the network, intrusion detection systems are frequently used in wireless sensor networks to fight against insider attacks by adopting the appropriate trust-based methods. Still, the sensors could create an enormous amount of data that reduces the efficacy of trust computation in the big data era. This paper aims to introduce a new attack detection system under the big data perspective. Originally, the input data is fed the preprocessing phase, in which the normalization process takes place. Further, the MapReduce framework is used to handle the bulk data by reducing it. The preprocessed data is subjected to extract the features, where it extracts the raw features, statistical features, and higher-order statistical features. Here, the feature selection is done by chi-square ranking and info-gain ranking. Further, these features are provided as the input to the classification phase, where multilayer perception (MLP) is used for detecting the presence of an attack. For making the classification more precise, weights of MLP are optimally tuned by proposed slap swarm updated sparrow optimized algorithm which integrated the concept of sparrow search algorithm and salp swarm algorithm. Finally, the performance of presented scheme is calculated to existing approaches under different metrics. [ABSTRACT FROM AUTHOR]

The rapid progress in fields such as data mining and machine learning, as well as the explosive growth of sports big data, have posed new challenges to the research of sports big data. Most of the available sports data mining techniques concentrates on extracting and constructing effective features for basic sports data, which cannot be achieved simply by using data statistics. Especially in the targeted mining of sports data, traditional mining techniques still have shortcomings such as low classification accuracy and insufficient refinement. In order to solve the problem of low accuracy in traditional mining methods, the study combines the random forest algorithm with the artificial raindrop algorithm, and adopts a sports data mining method based on feature selection to achieve effective analysis of sports big data. This study is based on the evaluation method of motion effects using random forests, and uses feature extraction algorithms to study the motion effect impacts. It uses the information gain index to rank the importance of features and accurately gain the degree of influence of exercise on various indicators of the human body. Through simulation verification, the algorithm proposed by the research institute performs the best in accuracy and FI scores on the training and testing sets, with accuracies of 0.849 ± 0.021 and 0.819 ± 0.022, respectively, and F1 scores of 0.837 ± 0.020 and 0.864 ± 0.021, respectively. This indicates that the algorithm proposed by the research institute has high classification accuracy and performance proves that the Random Forest-based feature selection algorithm established in this study is superior to the existing traditional feature extraction and extraction methods in terms of both performance and accuracy. The proposal of this data analysis method has achieved accurate and efficient utilization of sports big data, which is of great significance for the development of the sports education industry. [ABSTRACT FROM AUTHOR]

Horticultural products such as fruits, vegetables, and tea offer a range of important nutrients such as protein, carbohydrates, vitamins and lipids. However, the present yield and quality do not meet the requirements of the rapid population growth associated with global climate change, the decline in horticultural practitioners, poor automation, and epidemic diseases such as COVID-19. In this context, smart horticulture is expected to greatly improve the land output rates, resource-use efficiency, and productivity, all of which should facilitate the sustainable development of the horticulture industry. Emerging technologies, such as artificial intelligence, big data, the Internet of Things, and cloud computing, play an important role. This paper reviews past developments and current challenges, offering future perspectives for horticultural chain management. We expect that the horticulture industry would benefit from integration with smart technologies. This requires the use of novel solutions to build a new advanced system encompassing smart breeding, smart cultivation, smart transportation, and smart sales. Finally, a new development approach combining precise perception, smart operation, and smart control should be instituted in the horticulture industry. Within 30 years, we expect that the industry will embrace mechanical, automatic, and informational production to transform into a smart industry. [ABSTRACT FROM AUTHOR]

Purpose: The current study queries the American Academy of Ophthalmology (AAO) Intelligent Research in Sight (IRIS) registry for data on the epidemiology, work-up, and management patterns of autoimmune orbital inflammation. Methods: Analysis and description of patient data from the IRIS registry between 2013 and 2019 reviewing patients with autoimmune or idiopathic orbital inflammation with filters based on International Classification of Disease (ICD) and Current Procedural Terminology (CPT) codes. Patients with thyroid eye disease, orbital cellulitis, and orbital abscess were excluded. Main Outcome Measures: Demographic descriptions included gender, age, geographic region, and treatment. Sub-analysis was performed by assessing rates of imaging, biopsy, lab work-up, and diagnostic categories. Results: In a final cohort of 20,584 patients, the mean age of onset of orbital inflammation was 51.7 years; 67% female; and 63% Caucasian, 21% unknown, 12% Black, 2.6% Asian, and 1.5% other. Only 49 had imaging, 78 had laboratory work-up, and 1,411 had biopsy codes. Treatment results showed 166 patients receiving antibiotics, 224 patients receiving steroids, and 35 patients receiving both. Conclusions: This study assessed the epidemiology, diagnostic patterns, and treatment patterns for orbital inflammation through the AAO IRIS registry. Practise patterns suggest a relatively low overall rate of imaging and laboratory studies compared to biopsies, although this certainly under-represents the actual number of imaging and laboratory studies and exemplifies the inherent imprecision of using a large database. However, the methodology of this study provides a framework of approaching the IRIS registry for oculoplastic research. [ABSTRACT FROM AUTHOR]

Although research has investigated the drivers of pro-environmental intentions, little research has adopted big data to understand the socio-demographic profile of customers who share electronic word-of-mouth (i.e., online reviews) about tourism services' environmental practices (i.e., green eWOM). Our methodology includes a large sample of customers with different demographic characteristics and trip motivations, who shared green eWOM about different product types (496,813 hotel reviews, 129,455 airline reviews, and 22,373 reviews of tourist attractions). This sampling enables us to get an accurate picture of the customer segments who are sharing green eWOM. We used text analytics for data extraction (i.e., environmentally-related semantics), ANOVA tests, and ordinary least squares (OLS) regression for data analysis. Findings reveal that Gen Z Asian male customers traveling for business and staying at hotel chains are more likely to discuss sustainability practices in their reviews. The longitudinal analysis shows a steady increase in green eWOM for hotels and consumers are more often sharing green eWOM for tourist attractions, rather than hotels or airlines. Interestingly, green eWOM is associated with 5-star reviews and country of origin influences green eWOM of various travel and tourism services. [ABSTRACT FROM AUTHOR]

This study provides a methodological contribution in the analysis of tourists' sustainable behaviour. Traditional methods encounter challenges in addressing the attitude-behaviour gap, prompting the need for new approaches. Using diverse big data sources, the paper introduces a novel methodology for analysing real behaviour patterns, extending Social Network Analysis techniques. The aim is to identify sustainable behaviours that ease overcrowding, distribute visitor flows, and optimize economic diversification. The methodology is valuable for local authorities in monitoring tourist behaviours, aiding in informed decision-making for a more sustainable approach to tourism. [ABSTRACT FROM AUTHOR]

This study proposes that social-mediated leadership communication is a dynamic influence process, involving visible leader communications, follower emotional responses, and subsequent leader actions. These interactions form a feedback loop, where follower reactions influence leaders' strategic communication. We tested this framework by analyzing U.S. political leaders' engagement with the Green New Deal during the 2020 Presidential campaign. Using Facebook posts from congressional politicians and data on follower reactions, we applied longitudinal network models, natural language processing, and multilevel event history models to examine factors behind leaders' decisions to co-share content and discuss specific policy topics. Our findings support the dynamic influence framework, showing that follower reactions, especially those indicating support and boosting message virality, are most likely to prompt shifts in leaders' communication strategies. [ABSTRACT FROM AUTHOR]

Computational Medicine encompasses the application of Statistical Machine Learning and Artificial Intelligence methods on several traditional medical approaches, including biochemical testing which is extremely valuable both for early disease prognosis and long-term individual monitoring, as it can provide important information about a person's health status. However, using Statistical Machine Learning and Artificial Intelligence algorithms to analyze biochemical test data from Electronic Health Records requires several preparatory steps, such as data manipulation and standardization. This study presents a novel approach for utilizing Electronic Health Records from large, real-world databases to develop predictive precision medicine models by exploiting Artificial Intelligence. Furthermore, to demonstrate the effectiveness of this approach, we compare the performance of various traditional Statistical Machine Learning and Deep Learning algorithms in predicting individuals' future biochemical test outcomes. Specifically, using data from a large real-world database, we exploit a longitudinal format of the data in order to predict the future values of 15 biochemical tests and identify individuals at high risk. The proposed approach and the extensive model comparison contribute to the personalized approach that modern medicine aims to achieve. [ABSTRACT FROM AUTHOR]

Anastomotic leakage in visceral surgery is associated with a large number of known and also unknown or even unmeasurable parameters. Furthermore, the associations between the individual factors are intertwined and complex. According to current data a preoperative prediction is not reliably possible and should be distinguished from intraoperative or postoperative prediction models. Most studies on this topic do not exceed an area under the curve (AUC) of 0.70. A thorough understanding of statistics and prediction models is necessary to correctly interpret the published works. Due to the relatively low incidence rate of anastomotic leakage from a statistical point of view, large datasets are required for adequate prediction. Multimodal data and complex algorithms can potentially handle big data more accurately and improve predictability; however, these models have so far not been applied in the clinical routine. [ABSTRACT FROM AUTHOR]

Introduction: The evaluation of the performance of new methods, expected to provide cheaper and faster results than existing (reference) methods in the health field, is based on comparing the results obtained with this new method to those obtained with the existing method. The primary aim of this study is to examine the correlational and absolute agreement between measurement methods in clinical studies using Bland–Altman analysis and methodological (Ordinary Least Squares, Weighted Ordinary Least Squares, Deming, Weighted Deming, Passing–Bablok, Theil-Sen, and Passing–Bablok for Large Data Sets.) methods, and the secondary aim is to compare the accuracy and precision of Hadlock (I–V) formulas used for fetal weight estimation. Materials and methods: The study was conducted on singleton pregnancies examined in the Prenatal Diagnosis and Treatment Unit of the Department of Obstetrics and Gynecology at Inonu University Faculty of Medicine and who gave birth in the Obstetrics Unit between 01.01.2020 and 01.09.2023, whose gestational ages were confirmed by first-trimester ultrasonography. Estimated fetal weights were calculated using Hadlock (I–V) formulas, and the agreement of these weights with birth weight was evaluated with Bland–Altman method. Results: The comparison of estimated fetal weights calculated using Hadlock formulas with birth weight was analyzed using Bland–Altman analysis, ICC, and CCC values along with regression analyses. According to the mean difference values obtained by Bland–Altman analysis, the estimated fetal birth weights obtained by the Hadlock IV formula were most consistent with the actual birth weights. Conclusions: The estimated fetal weights obtained using the Hadlock IV formula resulted in the closest measurements to the birth weight. This study showcases the efficacy of a new web-based software, Method Comparison Software (MCS), which can be utilized for evaluating the agreement between different methods in clinical measurements. [ABSTRACT FROM AUTHOR]

Urban traffic problems are one of the major challenges in the development of smart cities. To effectively alleviate traffic congestion and improve urban traffic management, a distributed big data analytics platform based on the MapReduce computing framework is investigated to process urban traffic data containing millions of traffic flow, speed and timestamp records. The study proposes an improved Long Short-Term Memory (LSTM) neural network algorithm and describes the network structure, training process and parameter tuning in detail. The effectiveness of the improved algorithm was verified through comparative analysis with the traditional ARIMA, linear regression and support vector regression algorithms. The results showed that the improved LSTM algorithm achieved an accuracy of 97.62% in traffic flow prediction, which was significantly better than other algorithms. The results demonstrate that the method can provide reliable technical support and decision-making basis for traffic management in smart cities. [ABSTRACT FROM AUTHOR]

The so-called strength of weak ties (SWT) theory asserts that weak relationships are more instrumental than strong ones in job searching and sharing. Yet, some posit the opposite: job opportunities tend to be shared through strong ties in some business areas. Nevertheless, little empirical research has been conducted to substantiate the hypotheses and unravel the rationales behind them. This paper attempts to contribute empirical evidence to this management field by focusing on the construction waste hauling business in Hong Kong. Four null hypotheses about the relationships between haulers' tie strength and job opportunity sharing (defined as waste hauling order sharing) are proposed, and then a big data set containing 11.7 million waste hauling records is analyzed to test the hypotheses. The analysis shows that, in general, the stronger the tie strength of two haulers, the higher the quantity of job opportunities they share. Among all ties a hauler owns, the greater the proportion of strong ties, the higher the quantity and quality (e.g., shorter distance and less underloading) of job opportunities will be shared. However, these positive effects will be diminished when the strength or proportion exceeds a certain point. These empirical findings not only highlight the strength of strong ties, providing an empirical supplement for the long-standing SWT theory, but also exemplify an exploration of applying SWT theory in the construction management field. Moreover, this study provides practical implications for construction waste hauling businesses to improve efficiency and the public sector to pursue social optimality in construction waste management. [ABSTRACT FROM AUTHOR]

Data science is booming with big data, and machine learning provides better predictive analyses. However, conventional statistical models can effortlessly interpret the effect estimates, and the prediction models are generally in a closed form. Therefore, this research integrates the logistic regression model's core with the Random Forest structure to create a blended novel machine learning method, the Random Logistic Machine (RLM). In this way, the new approach preserves the statistical and machine learning advantages. Computer simulation studies examined the predictive ability of RLM, random forest, and Logistic Regression under various scenarios. The results showed that the RLM delivers a comparable performance to Random Forests and Logistic Regression. An application to the Breast Cancer Wisconsin (Diagnostic) Data Set also demonstrates the superior performance of the new approach. [ABSTRACT FROM AUTHOR]

In real-world scenarios, datasets often lack full supervision due to the high cost associated with acquiring decision labels. Completing datasets by filling in missing labels is essential for preserving the valuable feature information of individual samples. Furthermore, in the era of big data, datasets tend to exhibit high dimensionality, which adds complexity to subsequent data processing. In this study, a new semi-supervised feature selection technique is introduced. Firstly, a fully supervised dataset is created by utilizing a local density decision-labeling algorithm to fill in missing decision labels within the semi-supervised dataset. Next, a fuzzy dependency-based feature selection approach is presented to find and keep the most pertinent characteristics for the finished datasets. Finally, the effectiveness and reliability of our proposed method are validated through a series of rigorous experiments. [ABSTRACT FROM AUTHOR]

Systems for graph processing are a key enabler for insights from large-scale graphs that are critical to many new advanced technologies such as Artificial Intelligence, Internet of Things, and blockchain. In this study, we benchmark another two widely utilized graph processing systems, Apache Spark GraphX and Apache Fink, concerning the key performance criterion by means of response time, scalability, and computational complexity. We demonstrate our results which show the capability of each system for real-world graph applications, and hence, providing a quantitative understanding to select the system for our purpose. GraphX's strength was in processing batch in-memory workloads typical of blockchain and machine learning model optimization, while Flink excelled in processing stream data, which is timely and important to the IoT world. These performance characteristics emphasize how the capabilities of graph processing systems can match the requirements for the performance of different emerging technology applications. Our findings ultimately inform practitioners about system efficiencies and limitations, but also the recent advances in hardware accelerators and algorithmic improvements aimed at shaping the new graph processing frontier in diverse technology domains. [ABSTRACT FROM AUTHOR]

Copyright of Cyber Security & Data Governance is the property of Editorial Office of Information Technology & Network Security and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents a comparative study on license plate detection and recognition algorithms in unconstrained environments, which include varying illuminations, nonstandard plate templates, and different English language fonts. A prime objective of this study is to assess how well these models handle such challenges. These problems are common in developing countries like Pakistan where diverse license plates, styles, and abrupt changes in illuminations make license plates detection and recognition a challenging task. To analyze the license plate detection problem Faster-RCNN and end-to-end (E2E) methods are implemented. For the license plate recognition task, deep neural network and the CA-CenterNet-based methods are compared. Detailed simulations were performed on authors' own collected dataset of Pakistani license plates, which contains substantially different multi-styled license plates. Our study concludes that, for the task of license plate detection, Faster-RCNN yields a detection accuracy of 98.35%, while the E2E method delivers 98.48% accuracy. Both detection algorithms yielded a mean detection accuracy of 98.41%. For license plate recognition task, the DNN-based method yielded a recognition accuracy of 98.90%, while the CA-CenterNet-based method delivered a high accuracy of 98.96%. In addition, a detailed computational complexity comparison on various image resolutions revealed that E2E and the CA-CenterNet are more efficient than their counterparts during detection and recognition tasks, respectively. [ABSTRACT FROM AUTHOR]

Laser-induced breakdown spectroscopy allows fast and versatile elemental analysis, standing as a promising technique for a wide range of applications both at the research and industry levels. Yet, its high operation speed comes with a high throughput of data, which introduces some challenges at the level of the data processing domain, mainly due to the large computational load and data volume. In this work, we analyze and discuss opportunities of distributed computing paradigms and resources to address some of these challenges, covering most of the procedures usually employed in typical applications. We infer the possible impact of such computing resources by presenting some metrics of simple processing prototypes running in state-of-the-art computing facilities. Our results allow us to conclude that, while underexplored so far, these computing resources may allow for the development of tools for timely research and analysis in demanding applications and introduce novel solutions toward a more agile working environment. [ABSTRACT FROM AUTHOR]

Copyright of Railway Construction Technology is the property of Railway Construction Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Smart Rail Transit is the property of Smart Rail Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study measures residential segregation and investigates its association with travel mobility by using mobile phone data from Shenzhen, China. It considers residential segregation in terms of income level and migrant group; and travel mobility in terms of travel frequency, activity space, and travel distance. Unlike previous research on residential segregation and travel mobility, our research uses mobile phone data to produce empirical evidence. The extent of segregation between different migrant and income‐level groups is measured using the location quotient and Getis‐Ord index. This enables us to develop a linear regression model with which to investigate the associations of residential segregation with travel mobility. The study results show that the segregation of middle‐ and low‐income groups and migrants from Southwestern China and Jiangxi is negatively associated with travel mobility among those in the suburbs; meanwhile, for groups segregated in the city center, there is a positive association with travel mobility. These findings suggest that residential segregation is especially adverse for the travel mobility of disadvantaged groups and those living in the suburbs. Accordingly, the paper presents policy recommendations that would enhance travel mobility by alleviating the problems associated with the residential segregation of socially disadvantaged groups living in the suburbs. [ABSTRACT FROM AUTHOR]

The escalation of conflict in Ukraine has triggered the largest refugee crisis in Europe since WWII. As of early April 2024, over 5.9 million people have fled Ukraine. Large‐scale efforts have been made to identify the major receiving countries. However, less is known about the subnational areas within host countries where refugees have migrated. Identifying these areas is key for the appropriate allocation of humanitarian aid. By combining digital Facebook API data and traditional data from Eurostat, this paper aims to identify and characterise potential settlement areas of Ukrainians across the main destination countries in Europe. We identify high concentrations of Ukrainians in urban areas with a preexisting diaspora and tight labour market conditions across southern, northern‐west and central Poland and the city of Prague in the Czech Republic. We also find potential settlements in key urban agglomerations with a moderate diaspora and high levels of unemployment in Spain. Only in Romania, refugees seem to have settled in rural areas which show a moderate diaspora but low levels of unemployment. Potential settlement areas in Germany, Italy and the United Kingdom are spread across the country. Surprisingly, we do not identify potential settlement areas in bordering regions with Ukraine within neighbouring countries, suggesting that refugees may have used them as transit points. Our findings point out that different packages of humanitarian assistance may be needed according to the number of refugees and the characteristics of settlement areas. [ABSTRACT FROM AUTHOR]

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We extend the Fama–MacBeth regression framework for cross-sectional return prediction to incorporate big data and machine learning. Our extension involves a three-step procedure for generating return forecasts based on Fama–MacBeth regressions with regularization and predictor selection as well as forecast combination and encompassing. As a by-product, it provides estimates of characteristic payoffs. We also develop three performance measures for assessing cross-sectional return forecasts, including a generalization of the popular time-series out-of-sample R 2 statistic to the cross section. Applying our extension to over 200 firm characteristics, our cross-sectional return forecasts significantly improve out-of-sample predictive accuracy and provide substantial economic value to investors. Overall, our results suggest that a relatively large number of characteristics matter for determining cross-sectional expected returns. Our new method is straightforward to implement and interpret, and it performs well in our application. [ABSTRACT FROM AUTHOR]

Purpose: Regardless of the diverse research on big data analytics (BDA) across different supply chains, little attention has been paid to exploit this information across service supply chains. The healthcare supply chains, where supply chain operations consume the second highest expenditures, have not completely attained the potential gains from data analytics. So, this paper explores the challenges of BDA at various levels of healthcare supply chains. Design/methodology/approach: Drawing on the resource-based view (RBV), this research explores the various challenges of big data at organizational and operational level of different nodes in healthcare supply chains. To demonstrate the links among supply chain nodes, the authors have used a supplier-input-process-output-customer (SIPOC) chart to list healthcare suppliers, inputs (such as employees) supplied and used by the main healthcare processes, outputs (products and services) of these processes, and customers (patients and community). Findings: Using thematic analysis, the authors were able to identify numerous challenges and commonalities among these challenges for the case of healthcare supply chains across United Arab Emirates (UAE). An applicable exploration on organizational (Socio-technical) and operational challenges to BDA can enable healthcare managers to acclimate efficient and effective strategies. Research limitations/implications: The identified common socio-technical and operational challenges could be verified, and their impacts on the sustainable performance of various supply chains should be explored using formal research methods. Practical implications: This research advances the body of literature on BDA in healthcare supply chains in that (1) it presents a structured approach for exploring the challenges from various stakeholders of healthcare chain; (2) it presents the most common challenges of big data across the chain and finally (3) it uses the context of UAE where government is focusing on medical tourism in the coming years. Originality/value: Originality of this work stems from the fact that most of the previous academic research in this area has focused on technology perspectives, a clear understanding of the managerial and strategic implications and challenges of big data is still missing in the literature. [ABSTRACT FROM AUTHOR]