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Halide perovskites are promising light-absorbing materials for high-efficiency solar cells, while the crystalline phase of halide perovskites may influence the device's efficiency and stability. In this work, we investigated the thermally driven phase transition of perovskite (CsPbIxBr3−x), which was confirmed by electron diffraction and high-resolution transmission electron microscopy results. CsPbIxBr3−x transitioned from δ phase to α phase when heated, and the γ phase was obtained when the sample was cooled down. The γ phase was stable as long as it was isolated from humidity and air. A template matching-based data analysis method enabled visualization of the thermally driven phase evolution of perovskite during heating. We also proposed a possible atomic movement in the process of phase transition based on our in situ heating experimental data. The results presented here may improve our understanding of the thermally driven phase transition of perovskite as well as provide a protocol for big-data analysis of in situ experiments. [ABSTRACT FROM AUTHOR]

The escalating frequency and sophistication of cyber threats pose significant challenges to traditional intrusion detection methods. Signature-based misuse detection, hybrid detection, and anomaly detection, while effective in isolation, often struggle to keep pace with the ever-evolving tactics employed by attackers. This research stems from the pressing need to develop intrusion detection models that offer improved accuracy, adaptability, and robustness in the face of rapidly changing attack vectors. Our study leverages a comprehensive set of experiments conducted on diverse datasets, including N-BaIoT, NSL-KDD, and CICIDS2017. The primary focus is on the evaluation and comparison of machine learning algorithms such as Random Forest, XGBoost, and decision trees. Notably, our research culminates in the development of a hybrid intrusion detection model that combines the strengths of these algorithms. The results of our experiments indicate that the hybrid model, particularly when combining Random Forest and XGBoost, exhibits exceptional accuracy. This approach outperforms individual algorithms, achieving an accuracy rate of 97% in certain cases. We attribute this success to the ensemble learning approach, which capitalizes on the consensus of diverse classifiers. In conclusion, our research not only demonstrates the effectiveness of ensemble learning in enhancing intrusion detection but also underscores the importance of continuous adaptation in the face of evolving threats. By leveraging network traffic data, our hybrid model offers a promising avenue for bolstering intrusion classification in Big Data environments. [ABSTRACT FROM AUTHOR]

The visual dimension of cities has been a fundamental subject in urban studies since the pioneering work of late-nineteenth- to mid-twentieth-century scholars such as Camillo Sitte, Kevin Lynch, Rudolf Arnheim, and Jane Jacobs. Several decades later, big data and artificial intelligence (AI) are revolutionizing how people move, sense, and interact with cities. This article reviews the literature on the appearance and function of cities to illustrate how visual information has been used to understand them. A conceptual framework, urban visual intelligence, is introduced to systematically elaborate on how new image data sources and AI techniques are reshaping the way researchers perceive and measure cities, enabling the study of the physical environment and its interactions with the socioeconomic environment at various scales. The article argues that these new approaches would allow researchers to revisit the classic urban theories and themes and potentially help cities create environments that align with human behaviors and aspirations in today's AI-driven and data-centric era. [ABSTRACT FROM AUTHOR]

• The impact of digital economy on green total factor productivity in China is investigated. • The evidence on digital economy from a policy perspective is further supplied. • The ordinary least squares model and fixed effects model are used to research simultaneously. • Technological innovation, industrial structure optimization and resource misallocation as the transmission channels are considered in mechanism test. Digital economy has become a major driver of the green transformation of the economy. The "National Big Data Comprehensive Pilot Zone" policy is taken as a quasi-natural experiment. The Difference-in-Differences model is employed to explore the impact of digital economy on green total factor productivity, and the heterogeneity of this impact is further explored. The mechanism test model is applied to investigate its indirect transmission mechanism. It is found that digital economy significantly enhances green total factor productivity. Further, the impact is larger and more significant in the mid-western, non-resource-based, and large cities, whereas it is smaller or insignificant in the eastern, resource-based, and small and medium-sized cities. Results of mechanism analysis show that digital economy enhances green total factor productivity through the transmission channels of technological innovation, industrial structure optimization, and resource misallocation. Policy recommendations are provided on the basis of these results to establish the environment and digital economy that are mutually beneficial. [ABSTRACT FROM AUTHOR]

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

Surveillance of human subjects is how data-intensive companies obtain much of their data, yet surveillance increasingly meets with social and regulatory resistance. Data-intensive companies are thus seeking other ways to meet their data needs. This article explores one of these: the creation of synthetic data, or data produced artificially as an alternative to real-world data. I show that capital is already heavily invested in synthetic data. I argue that its appeal goes beyond circumventing surveillance to accord with a structural tendency within capitalism toward the autonomization of the circuit of capital. By severing data from human subjectivity, synthetic data contributes to the automation of the production of automation technologies like machine learning. A shift from surveillance to synthesis, I argue, has epistemological, ontological, and political economic consequences for a society increasingly structured around data-intensive capital. [ABSTRACT FROM AUTHOR]

As video game production is becoming increasingly data-driven, player surveillance shapes the everyday realities of users and developers. Remote online tracking and the resulting optimization and governance of in-game activity subscribe to the Big Data methodology as a way of accounting for entire player populations. By design, player surveillance serves the interests of developers and publishers, who have exclusive access to this proprietary data. Yet, discursively, these parties attempt to present surveillance as a mutually beneficial endeavor aimed at improving video games. A part of this strategy is the video game industry's selective information disclosure, which I explore empirically on the example of telemetry infographics. Based on a thematic analysis of 200 infographics from 127 games, I show how publicly disseminated infographics contribute to the normalization of player surveillance by presenting it as a source of harmless trivia to be collected and shared by fans and the specialized press. [ABSTRACT FROM AUTHOR]

Nowadays, computational intelligence-assisted autonomous assessment of education quality has become a more and more general concern in the area of smart education management. As education quality assessment is a complicated process with multiple heterogeneous factors, it remains challenging to make effective assessment using simple information modality and criteria. To deal with this issue, this paper introduces multiscale deep learning, and proposes a novel data-driven autonomous assessment framework for education quality. In particular, four aspects of heterogeneous indicators are selected as the basic indexes and a dilated convolutional neural network structure is formulated to perform multiscale feature extraction. Then, the structural equation is adopted to make multivariate characterization and output the final assessment results. At last, some simulations are carried out on realistic education operation data to evaluate the efficiency of the proposed autonomous assessment framework. Two aspects of findings can be deduced from the results. For one thing, multisource fusion of multiple indicators well makes sense to autonomous education assessment. For another, multiscale deep learning can provide some beneficial promotion to assessment efficiency. [ABSTRACT FROM AUTHOR]

In recent times, MapReduce has become a popular tool for handling big data. At the same time, uncertainty is related to arbitrariness, fuzziness, ambiguity, irregularity and incomplete knowledge. In RS theory, the uncertainty behaviour of the data in the dataset of interest is managed by using upper and lower approximate sets and classification accuracy. The RS model is integrated with data clustering technique for optimal outcomes. With this motivation, this study designs an Optimal Clustering with RS‐Based Rule Generation Model (OC‐RSRGM) for data classification on MapReduce environment. The OC‐RSRGM technique aims to generate an optimal set of rules using RST for the data classification process and it involves a two‐stage process namely Optimal Fuzzy c‐Means Clustering (OFCM) and RSRGM‐based rule generation with classification. The OFCM technique is derived to eradicate the local optimal problem of the FCM (Fuzzy c‐Means) model using Barnacles Mating Optimizer (BMO). It provides the decision‐makers with all the information needed to design appropriate mechanisms to support their decision‐making activities. The Hadoop MapReduce tool is used to handle big data. The proposed method combines an FCM, BMO, RS theory to accomplish effective decision‐making. The OC‐RSRGM technique can be employed to continuous value dataset where data point does not offer any class details and it might be uncertain. To validate the performance of OC‐RSRGM technique, a detailed experimental analysis is carried out to highlights the betterment of OC‐RSRGM technique. The proposed OC‐RSRGM technique has obtained an effective outcome with the CT of 5.43 s. [ABSTRACT FROM AUTHOR]

The rise in human mobility data in recent years provides a new frontier to understanding the spatiotemporal dynamics of mobility to urban parks. These human mobility data offer profound merit in the study of how accessible urban parks are through real-time analytics of individual travel patterns and locations. This study systematically reviewed 46 peer-reviewed publications from the Web of Science and Academic Search Complete databases relevant to human mobility data and urban park accessibility measures. The study aims to provide a comprehensive and quantitative evaluation of the utilization of human mobility data in the field of urban park accessibility research. This objective is pursued through a systematic examination of existing literature within this research domain. We summarize the types of mobility datasets and the modelling tasks adopted. Meanwhile, we grouped the methodological/thematic frameworks from empirical use of human mobility data in park accessibility into seven categories: 1) inequality and inequity level, 2) users' perception and exposure level, 3) frequency and variations in park visitations, 4) park service area or effective service radius, 5) happiness and sentiment level, 6) travel mode choice and trip assignment, and 7) park characteristics. Additionally, we highlighted the obstacles in integrating human mobility data into park accessibility research and engaged in a discussion surrounding prominent ethical dilemmas related to the utilization of big data. [ABSTRACT FROM AUTHOR]

The peculiar nonlinear optical responses of two-dimensional (2D) materials endow them highly attractive for photonics applications. This review systematically discusses the performance of pulsed lasers employing 2D materials as their modulation device. First, it is crucial to categorize 2D materials that can act as saturable absorbers, encompassing a range of substances such as Graphene, Black Phosphorus, Topological Insulators, MXene, and even heterojunction. Furthermore, a thorough investigation is conducted to analyze the performance indexes to study the nonlinear characteristics of 2D materials in the pulse generation. Especially, the focus is on the 1.5 μ m band ultrafast lasers utilizing 2D materials. This paper also provides a comprehensive analysis of the obstacles and prospects in 2D materials preparation, and their utilization in the pulse laser systems. [ABSTRACT FROM AUTHOR]

Recent advances in open neuroimaging data are enhancing our comprehension of neuropsychiatric disorders. By pooling images from various cohorts, statistical power has increased, enabling the detection of subtle abnormalities and robust associations, and fostering new research methods. Global collaborations in imaging have furthered our knowledge of the neurobiological foundations of brain disorders and aided in imaging-based prediction for more targeted treatment. Large-scale magnetic resonance imaging initiatives are driving innovation in analytics and supporting generalizable psychiatric studies. We also emphasize the significant role of big data in understanding neural mechanisms and in the early identification and precise treatment of neuropsychiatric disorders. However, challenges such as data harmonization across different sites, privacy protection, and effective data sharing must be addressed. With proper governance and open science practices, we conclude with a projection of how large-scale imaging resources and collaborations could revolutionize diagnosis, treatment selection, and outcome prediction, contributing to optimal brain health. [ABSTRACT FROM AUTHOR]

Online social media platforms have emerged as integral channels for facilitating social interactions, with celebrities utilizing these platforms to engage with their fan base and cultivate a substantial following. The group of engaged fans, commonly referred to as “active fans”, represents individuals who actively communicate with celebrities and actively participate in discussions pertaining to the celebrities’ endeavors. For celebrities, the task of retaining and augmenting the count of active fans holds immense significance, as it significantly amplifies their social impact and commercial value. Here, we construct dynamic weighted active fan networks by leveraging data from 2021 on Sina Weibo, which stands as China’s largest social media platform. Through a comparative analysis encompassing the network’s structure, the growth rate and the duration of active fans, we delve into the influence wielded by six distinct thematic categories, endorsement, variety, public welfare, sports, music and national affairs. This analysis covers a cohort of nine celebrities spanning five diverse domains, including actors, singers, online influencers, anchors and athletes. The growth trajectory and life cycle exhibited by celebrity fans exhibit notable variations, both within and across the aforementioned themes. These dynamics are further influenced by the inherent structural attributes of the personal fan network belonging to each celebrity. Employing the K-Shape time series clustering algorithm, we have undertaken an in-depth exploration of outburst growth patterns observed in active fans and determined the optimal value of the number of clusters to be k=4 through comparative analysis. Our findings underscore that the themes of endorsement and public welfare exhibit all four growth patterns, namely Double-Peak, Oscillatory, Single-Peak and Continuous Growth Patterns. In contrast, when considering all themes collectively, they collectively demonstrate a single-peaked decaying growth pattern the insights gleaned from this study not only serve as a valuable reference and guide for celebrities across diverse domains who aspire to bolster and augment their social influence but also contribute to the burgeoning fan economy. Moreover, this research introduces novel perspectives and insights for scrutinizing patterns of fan growth and their corresponding dynamics. [ABSTRACT FROM AUTHOR]

Aim Materials and Methods Results Conclusions This prospective cohort study investigated the association between periodontal diseases (PDs) and all‐cause and cause‐specific mortality.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.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).Severe PD is associated with all‐cause and cause‐specific mortality among US adults after multivariable adjustment. [ABSTRACT FROM AUTHOR]

The layout, scale and spatial form of urban employment centers are important guidelines for the rational layout of public service facilities such as urban transportation, medical care, and education. In this paper, we use Internet cell phone positioning data to identify the workplace and residence of users in the Beijing city area and obtain commuting data of the employed to measure the employment center system in Beijing. Firstly, the employment density distribution is generated using the data of the working places of the employed persons, and the employment centers are identified based on the employment density of Beijing. Then, we use the business registration data of employment centers to measure the industrial diversity within the employment centers by using the ecological Shannon Wiener Diversity Index, and combine the commuting links between employment centers and places of residence to measure the energy level of each employment center, analyze the hinterland and sphere of influence of each center, and finally using the industrial diversity index of employment centers and the average commuting time of employed persons, combined with the K-Means clustering algorithm, to classify the employment centers in Beijing. The employment center identification and classification method based on big data constructed in this study can help solve the limitations of the previous employment center system research in terms of center identification and commuting linkage measurement due to large spatial units and lack of commuting data to a certain extent. The study can provide reference for the regular understanding and technical analysis of employment centers and provide help for the employment multi-center system in Beijing in terms of quantifying the employment spatial structure, guiding the construction of multi-center system, and adjusting the land use rules. [ABSTRACT FROM AUTHOR]

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

The COVID-19 pandemic has triggered a global public health crisis, affecting hundreds of countries. With the increasing number of infected cases, developing automated COVID-19 identification tools based on CT images can effectively assist clinical diagnosis and reduce the tedious workload of image interpretation. To expand the dataset for machine learning methods, it is necessary to aggregate cases from different medical systems to learn robust and generalizable models. This paper proposes a novel deep learning joint framework that can effectively handle heterogeneous datasets with distribution discrepancies for accurate COVID-19 identification. We address the cross-site domain shift by redesigning the COVID-Net's network architecture and learning strategy, and independent feature normalization in latent space to improve prediction accuracy and learning efficiency. Additionally, we propose using a contrastive training objective to enhance the domain invariance of semantic embeddings and boost classification performance on each dataset. We develop and evaluate our method with two large-scale public COVID-19 diagnosis datasets containing CT images. Extensive experiments show that our method consistently improves the performance both datasets, outperforming the original COVID-Net trained on each dataset by 13.27% and 15.15% in AUC respectively, also exceeding existing state-of-the-art multi-site learning methods. [ABSTRACT FROM AUTHOR]

Cell states were traditionally defined by how they looked, where they were located, and what functions they performed. In this post-genomic era, the field is largely focused on a molecular view of cell state. Moving forward, we anticipate that the observables used to define cell states will evolve again as single-cell imaging and analytics are advancing at a breakneck pace via the collection of large-scale, systematic cell image datasets and the application of quantitative image-based data science methods. This is, therefore, a key moment in the arc of cell biological research to develop approaches that integrate the spatiotemporal observables of the physical structure and organization of the cell with molecular observables toward the concept of a holistic cell state. In this perspective, we propose a conceptual framework for holistic cell states and state transitions that is data-driven, practical, and useful to enable integrative analyses and modeling across many data types. Technological advances have brought "big data" to the cellular level. Rafelski and Theriot share their vision of defining a cell's state in a holistic way that reflects all molecular, organizational, functional, and environmental observables and spatiotemporal dynamics. The proposed view aims to provide a conceptual framework for quantitative modeling. [ABSTRACT FROM AUTHOR]

In the digital age, firms should continually innovate and adapt to remain competitive and enhance performance. Innovation and adaptation require firms to take a holistic approach to their corporate structuring to ensure efficiency and effectiveness to stay competitive. This study examines how corporate restructuring impacts firm performance in Vietnam. We then investigate the moderating role of digital transformation in the corporate restructuring–firm performance nexus. We use content analysis, with a focus on particular terms, including "digitalization," "big data," "cloud computing," "blockchain," and "information technology" for 11 years, from 2011 to 2021. The frequency index from these keywords is developed to proxy the digital transformation for the Vietnamese listed firms. A final sample includes 118 Vietnamese listed firms with sufficient data for the analysis using the generalized method of moments (GMM) approach. The results indicate that corporate restructuring, including financial, portfolio, and operational restructuring, has a negative effect on firm performance in Vietnam. Digital transformation also negatively affects firm performance. However, corporate restructuring implemented in conjunction with digital transformation improves the performance of Vietnamese listed firms. These findings largely remain unchanged across various robustness analyses. [ABSTRACT FROM AUTHOR]

The planning of human resources and the management of enterprises consider the organization's size, the amount of effort put into operations, and the level of productivity. Inefficient allocation of resources in organizations due to skill-task misalignment lowers production and operational efficiency. This study addresses organizations' poor resource allocation and use, which reduces productivity and the efficiency of operations, and inefficiency may adversely impact company production and finances. This research aims to develop and assess a Placement-Assisted Resource Management Scheme (PRMS) to improve resource allocation and usage and businesses' operational efficiency and productivity. PRMS uses expertise, business requirements, and processes that are driven by data to match resources with activities that align with their capabilities and require them to perform promptly. The proposed system PRMS outperforms existing approaches on various performance metrics at two distinct levels of operations and operating levels, with a success rate of 0.9328% and 0.9302%, minimal swapping ratios of 12.052% and 11.658%, smaller resource mitigation ratios of 4.098% and 4.815%, mean decision times of 5.414s and 4.976s, and data analysis counts of 6387 and 6335 Success and data analysis increase by 9.98% and 8.2%, respectively, with the proposed strategy. This technique cuts the switching ratio, resource mitigation, and decision time by 6.52%, 13.84%, and 8.49%. The study concluded that PRMS is a solid, productivity-focused corporate improvement method that optimizes the allocation of resources and meets business needs. [ABSTRACT FROM AUTHOR]

into account when analyzing stroke-related deficits.P30: This study explores the relationship between sleep duration and cognitive function in older adults. The researchers analyzed data from the National Health and Aging Trends Study and found that both short and long sleep durations were associated with poorer cognitive function. The findings suggest that maintaining an optimal sleep duration is important for cognitive health in older adults.P31: This study investigates the impact of neighborhood characteristics on physical activity levels among older adults. The researchers used data from the Health and Retirement Study and found that neighborhood walkability, safety, and social cohesion were positively associated with physical activity. The findings highlight the importance of creating age-friendly neighborhoods that promote physical activity and support healthy aging.P32: This study examines the relationship between social media use and mental health outcomes among college students. The researchers surveyed college students and found that higher levels of social media use were associated with increased symptoms of depression and anxiety. The findings suggest that promoting healthy social media use and providing mental health support for college students is crucial.P33: This study explores the impact of mindfulness-based interventions on stress and well-being in healthcare professionals. The researchers conducted a systematic review and meta-analysis of randomized controlled trials and found that mindfulness-based interventions were effective in reducing stress and improving well-being among healthcare professionals. The findings suggest that incorporating mindfulness practices into healthcare settings can benefit both healthcare professionals and patient outcomes.P34: This study investigates the relationship between physical activity and mental health outcomes in adolescents. The researchers analyzed data from the National [Extracted from the article]

Inspections of concrete bridges across the United States represent a significant commitment of resources, given their biannual mandate for many structures. With a notable number of aging bridges, there is an imperative need to enhance the efficiency of these inspections. This study harnessed the power of computer vision to streamline the inspection process. Our experiment examined the efficacy of a state-of-the-art Visual Transformer (ViT) model combined with distinct image enhancement detector algorithms. We benchmarked against a deep learning Convolutional Neural Network (CNN) model. These models were applied to over 20,000 high-quality images from the Concrete Images for Classification dataset. Traditional crack detection methods often fall short due to their heavy reliance on time and resources. This research pioneers bridge inspection by integrating ViT with diverse image enhancement detectors, significantly improving concrete crack detection accuracy. Notably, a custom-built CNN achieves over 99% accuracy with substantially lower training time than ViT, making it an efficient solution for enhancing safety and resource conservation in infrastructure management. These advancements enhance safety by enabling reliable detection and timely maintenance, but they also align with Industry 4.0 objectives, automating manual inspections, reducing costs, and advancing technological integration in public infrastructure management. [ABSTRACT FROM AUTHOR]

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

Effectively and efficiently retrieving images from remote-sensing databases is a critical challenge in the realm of remote-sensing big data. Utilizing hand-drawn sketches as retrieval inputs offers intuitive and user-friendly advantages, yet the potential of multi-level feature integration from sketches remains underexplored, leading to suboptimal retrieval performance. To address this gap, our study introduces a novel zero-shot, sketch-based retrieval method for remote-sensing images, leveraging multi-level feature extraction, self-attention-guided tokenization and filtering, and cross-modality attention update. This approach employs only vision information and does not require semantic knowledge concerning the sketch and image. It starts by employing multi-level self-attention guided feature extraction to tokenize the query sketches, as well as self-attention feature extraction to tokenize the candidate images. It then employs cross-attention mechanisms to establish token correspondence between these two modalities, facilitating the computation of sketch-to-image similarity. Our method significantly outperforms existing sketch-based remote-sensing image retrieval techniques, as evidenced by tests on multiple datasets. Notably, it also exhibits robust zero-shot learning capabilities in handling unseen categories and strong domain adaptation capabilities in handling unseen novel remote-sensing data. The method's scalability can be further enhanced by the pre-calculation of retrieval tokens for all candidate images in a database. This research underscores the significant potential of multi-level, attention-guided tokenization in cross-modal remote-sensing image retrieval. For broader accessibility and research facilitation, we have made the code and dataset used in this study publicly available online. [ABSTRACT FROM AUTHOR]

Physical education and training are essential ways to improve the physical quality of the nation, and China has incorporated “building a healthy China” and “fitness for all” into its national development strategy, integrating a strong sports nation into the Chinese dream. The study of digital recording and automated training in sports is of profound value. Motion capture technology can digitally record the training process in a digital physical education training system. At the same time, accurate modeling and calculation can analyze the training effects and give appropriate guidance and feedback. This study develops a new and improved hierarchical K-means algorithm by combining the known classification algorithm K-means with a hierarchical algorithm. The performance of the old and new algorithms are compared and then applied to physical education training data to produce clustering results and analysis to reduce the model, which is used to reduce the number of parameters in the model and improve the recognition speed. The experimental results demonstrate that the relevant models proposed in this study achieve an average accuracy of 91.27% and 92.26%, respectively, which is better than a single network model and can effectively use big data for health event detection. The empirical results show that the improved model algorithm outperforms the single network model and can detect health events using big data. [ABSTRACT FROM AUTHOR]

Background Objective Method Method The disease related to the heart is serious and can lead to death. Precise heart disease prediction is imperative for the effective treatment of cardiac patients. This can be attained by machine learning (ML) techniques using healthcare data. Several models on the basis of ML predict and identify disease in the heart, but this model cannot manage a huge database because of the deficiency of the smart model. This paper provides an optimized SpinalNet with a MapReduce model to categorize heart disease.The objective is to design a big data approach for heart disease classification using the proposed Jellyfish Search Flow Regime Optimization (JSFRO)‐based SpinalNet.The binary image conversion is applied on Electrocardiogram (ECG) images for converting the image to binary image. MapReduce model is adapted, in which the mappers execute feature extraction and the reducer performs heart disease classification. In the mapper phase, the features like statistical features, shape features and temporal features are extracted and in reducer, the SpinalNet with JSFRO is considered. Here, the training of SpinalNet is done with JSFRO, which is produced by the unification of Jellyfish Search Optimization (JSO) and Flow Regime Optimization (FRO).The JSFRO‐based SpinalNet offered effectual performance with the finest accuracy of 90.8%, sensitivity of 95.2% and specificity of 93.6%. [ABSTRACT FROM AUTHOR]

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

Summary: Global assessments of mycorrhizal symbiosis present large sampling gaps in rich biodiversity regions. Filling these gaps is necessary to build large‐scale, unbiased mycorrhizal databases to obtain reliable analyses and prevent misleading generalizations. Underrepresented regions in mycorrhizal research are mainly in Africa, Asia, and South America. Despite the high biodiversity and endemism in these regions, many groups of organisms remain understudied, especially mycorrhizal fungi. In this Viewpoint, we emphasize the importance of inclusive and collaborative continental efforts in integrating perspectives for comprehensive trait database development and propose a conceptual framework that can help build large mycorrhizal databases in underrepresented regions. Based on the four Vs of big data (volume, variety, veracity, and velocity), we identify the main challenges of constructing a large mycorrhizal dataset and propose solutions for each challenge. We share our collaborative methodology, which involves employing open calls and working groups to engage all mycorrhizal researchers in the region to build a South American Mycorrhizal Database. By fostering interdisciplinary collaborations and embracing a continental‐scale approach, we can create robust mycorrhizal trait databases that provide valuable insights into the evolution, ecology, and functioning of mycorrhizal associations, reducing the geographical biases that are so common in large‐scale ecological studies. [ABSTRACT FROM AUTHOR]

The credit risk assessment acts as an important part in daily affairs for financial institutions. But in the era of big data, the growing business volume makes it an urgent demand to develop digital ways of credit risk assessment. Currently, the machine learning is universally employed to establish various data-driven models for this purpose. However, machine learning models generally suffer from limited ability of feature representation and robustness, and cannot deal with more complex financial security scenarios. To deal with this issue, this work introduces ensemble learning to construct a stronger credit risk prediction model via integration of several basic machine learning models. Thus, an ensemble learning-enhanced smart prediction model for financial credit risk is proposed in this paper. Three classification-based machine learning models (support vector machine, artificial neural network and radial basis function) are selected as the basic classifiers, and "voting" strategy is utilized to integrate them into a novel strong classifier. A real-world financial credit dataset released by a Chinese commercial bank was selected as the experimental scenario. The obtained results show that the proposal has better prediction accuracy compared with basic machine learning models without ensemble learning. [ABSTRACT FROM AUTHOR]

Objective: Hospitals and healthcare providers should assess and compare the quality of care given to patients and based on this improve the care. In the Netherlands, hospitals provide data to national quality registries, which in return provide annual quality indicators. However, this process is time-consuming, resource intensive and risks patient privacy and confidentiality. In this paper, we presented a multicentric 'Proof of Principle' study for federated calculation of quality indicators in patients with colorectal cancer. The findings suggest that the proposed approach is highly time-efficient and consume significantly lesser resources. Materials and methods: Two quality indicators are calculated in an efficient and privacy presevering federated manner, by i) applying the Findable Accessible Interoperable and Reusable (FAIR) data principles and ii) using the Personal Health Train (PHT) infrastructure. Instead of sharing data to a centralized registry, PHT enables analysis by sending algorithms and sharing only insights from the data. Results: ETL process extracted data from the Electronic Health Record systems of the hospitals, converted them to FAIR data and hosted in RDF endpoints within each hospital. Finally, quality indicators from each center are calculated using PHT and the mean result along with the individual results plotted. Discussion and conclusion: PHT and FAIR data principles can efficiently calculate quality indicators in a privacy-preserving federated approach and the work can be scaled up both nationally and internationally. Despite this, application of the methodology was largely hampered by ELSI issues. However, the lessons learned from this study can provide other hospitals and researchers to adapt to the process easily and take effective measures in building quality of care infrastructures. [ABSTRACT FROM AUTHOR]

PURPOSE: We present and validate a rule-based algorithm for the detection of moderate to severe liver-related immune-related adverse events (irAEs) in a real-world patient cohort. The algorithm can be applied to studies of irAEs in large data sets. METHODS: We developed a set of criteria to define hepatic irAEs. The criteria include: the temporality of elevated laboratory measurements in the first 2-14 weeks of immune checkpoint inhibitor (ICI) treatment, steroid intervention within 2 weeks of the onset of elevated laboratory measurements, and intervention with a duration of at least 2 weeks. These criteria are based on the kinetics of patients who experienced moderate to severe hepatotoxicity (Common Terminology Criteria for Adverse Events grades 2-4). We applied these criteria to a retrospective cohort of 682 patients diagnosed with hepatocellular carcinoma and treated with ICI. All patients were required to have baseline laboratory measurements before and after the initiation of ICI. RESULTS: A set of 63 equally sampled patients were reviewed by two blinded, clinical adjudicators. Disagreements were reviewed and consensus was taken to be the ground truth. Of these, 25 patients with irAEs were identified, 16 were determined to be hepatic irAEs, 36 patients were nonadverse events, and two patients were of indeterminant status. Reviewers agreed in 44 of 63 patients, including 19 patients with irAEs (0.70 concordance, Fleiss' kappa: 0.43). By comparison, the algorithm achieved a sensitivity and specificity of identifying hepatic irAEs of 0.63 and 0.81, respectively, with a test efficiency (percent correctly classified) of 0.78 and outcome-weighted F1 score of 0.74. CONCLUSION: The algorithm achieves greater concordance with the ground truth than either individual clinical adjudicator for the detection of irAEs. Three (sometimes 4) rules to identify hepatic immune-related adverse events in the setting of ICI. [ABSTRACT FROM AUTHOR]

Smartphone location data provide the most direct field disaster distribution data with low cost and high coverage. The large-scale continuous sampling of mobile device location data provides a new way to estimate the distribution of disasters with high temporal–spatial resolution. On September 5, 2022, a magnitude 6.8 earthquake struck Luding County, Sichuan Province, China. We quantitatively analyzed the Ms 6.8 earthquake from both temporal and geographic dimensions by combining 1,806,100 smartphone location records and 4,856 spatial grid locations collected through communication big data with the smartphone data under 24-hour continuous positioning. In this study, the deviation of multidimensional mobile terminal location data is estimated, and a methodology to estimate the distribution of out-of-service communication base stations in the disaster area by excluding micro error data users is explored. Finally, the mathematical relationship between the seismic intensity and the corresponding out-of-service rate of communication base stations is established, which provides a new technical concept and means for the rapid assessment of post-earthquake disaster distribution. [ABSTRACT FROM AUTHOR]

PURPOSE: The emergence of large real-world clinical databases and tools to mine electronic medical records has allowed for an unprecedented look at large data sets with clinical and epidemiologic correlates. In clinical cancer genetics, real-world databases allow for the investigation of prevalence and effectiveness of prevention strategies and targeted treatments and for the identification of barriers to better outcomes. However, real-world data sets have inherent biases and problems (eg, selection bias, incomplete data, measurement error) that may hamper adequate analysis and affect statistical power. METHODS: Here, we leverage a real-world clinical data set from a large health network for patients with breast cancer tested for variants in BRCA1 and BRCA2 (N = 12,423). We conducted data cleaning and harmonization, cross-referenced with publicly available databases, performed variant reassessment and functional assays, and used functional data to inform a variant's clinical significance applying American College of Medical Geneticists and the Association of Molecular Pathology guidelines. RESULTS: In the cohort, White and Black patients were over-represented, whereas non-White Hispanic and Asian patients were under-represented. Incorrect or missing variant designations were the most significant contributor to data loss. While manual curation corrected many incorrect designations, a sizable fraction of patient carriers remained with incorrect or missing variant designations. Despite the large number of patients with clinical significance not reported, original reported clinical significance assessments were accurate. Reassessment of variants in which clinical significance was not reported led to a marked improvement in data quality. CONCLUSION: We identify the most common issues with BRCA1 and BRCA2 testing data entry and suggest approaches to minimize data loss and keep interpretation of clinical significance of variants up to date. We identify the most common issues with testing data entry and suggest approaches to minimize data loss and keep interpretation of clinical significance of variants up to date. [ABSTRACT FROM AUTHOR]

Background: Irregular time series (ITS) are common in healthcare as patient data is recorded in an electronic health record (EHR) system as per clinical guidelines/requirements but not for research and depends on a patient's health status. Due to irregularity, it is challenging to develop machine learning techniques to uncover vast intelligence hidden in EHR big data, without losing performance on downstream patient outcome prediction tasks. Methods: In this paper, we propose Perceiver, a cross-attention-based transformer variant that is computationally efficient and can handle long sequences of time series in healthcare. We further develop continuous patient state attention models, using Perceiver and transformer to deal with ITS in EHR. The continuous patient state models utilise neural ordinary differential equations to learn patient health dynamics, i.e., patient health trajectory from observed irregular time steps, which enables them to sample patient state at any time. Results: The proposed models' performance on in-hospital mortality prediction task on PhysioNet-2012 challenge and MIMIC-III datasets is examined. Perceiver model either outperforms or performs at par with baselines, and reduces computations by about nine times when compared to the transformer model, with no significant loss of performance. Experiments to examine irregularity in healthcare reveal that continuous patient state models outperform baselines. Moreover, the predictive uncertainty of the model is used to refer extremely uncertain cases to clinicians, which enhances the model's performance. Code is publicly available and verified at https://codeocean.com/capsule/4587224. Conclusions: Perceiver presents a computationally efficient potential alternative for processing long sequences of time series in healthcare, and the continuous patient state attention models outperform the traditional and advanced techniques to handle irregularity in the time series. Moreover, the predictive uncertainty of the model helps in the development of transparent and trustworthy systems, which can be utilised as per the availability of clinicians. [ABSTRACT FROM AUTHOR]

Clubroot caused by Plasmodiophora brassicae is a devastating disease of Cruciferous crops. Developing cultivars with clubroot resistance (CR) is the most effective control measure. For the two major Brassica vegetable species B. rapa and B. oleracea, several commercial cultivars with unclear CR pedigrees have been intensively used as CR donors in breeding. However, the continuous occurrence of CR-breaking makes the CR pedigree underlying these cultivars one of the breeders' most urgent concerns. The complex intraspecific diversity of these two major Brassica vegetables has also limited the applicability of CR markers in different breeding programs. Here we first traced the pedigree underlying two kinds of CR that have been widely applied in breeding by linkage and introgression analyses based on public resequencing data. In B. rapa, a major locus CRzi8 underlying the CR of the commercial CR donor 'DegaoCR117' was identified. CRzi8 was further shown to have been introgressed from turnip (B. rapa ssp. rapifera) and that it carried a potential functional allele of Crr1a. The turnip introgression carried CRbc, sharing the same coding sequence with the CRb that was also identified from chromosome C07 of B. oleracea CR cultivars with different morphotypes. Within natural populations, variation analysis of linkage intervals of CRzi8, PbBa8.1, CRb, and CRbc yielded easily resolved InDel markers (> 20 bp) for these fundamental CR genes. The specificity of these markers was tested in diverse cultivars panels, and each exhibited high reliability in breeding. Our research demonstrates the value of the practice of applying resequencing big data to solve urgent concerns in breeding programs. [ABSTRACT FROM AUTHOR]

Global climate change has exacerbated alterations in urban thermal environments, significantly impacting the daily lives and health of city residents. Measuring and understanding urban land surface temperatures (LST) and their influencing factors is important in addressing global climate change and enhancing the well-being of residents. However, due to limitations in data precision and analytical methods, existing studies often overlook the microscale examination closely related to residents' daily lives, and lack a deep exploration of the spatial heterogeneity of the influencing factors. This leads to these results being ineffective in guiding the planning and construction of cities. Taking Shenzhen as a case study, our study investigates the effects of various microscale build environment characteristics of LST using street view images and machine learning. A convolutional neural network model adopting the SegNet architecture is used to perform semantic segmentation on street view images, extracting features of the microscale urban-built environment. The LST is inverted through the Google Earth Engine (GEE) platform. By using Multiscale Geographically Weighted Regression (MGWR) models, our study reveals the comprehensive impact of the urban-built environment on LST and its significant spatial heterogeneity. The findings indicate that the proportions of sky, roads, and buildings are positively correlated with LST, while trees have a significant cooling effect. Although earth and water can reduce LST, their overall contribution is minimal due to limitations in their area and distribution patterns. This study not only reveals the key factors affecting urban LST at the microscale but also emphasizes the necessity of considering the spatial heterogeneity of these factors' impacts. This suggests the need for targeted strategies for different areas to effectively improve the urban thermal environment and achieve sustainable urban development. [ABSTRACT FROM AUTHOR]

The increasing availability of longitudinal data (repeated numerical observations of the same units at different times) requires the development of flexible techniques to automatically detect errors in such data. Besides standard types of errors, which can be treated with generic error correction techniques, large longitudinal datasets may present specific problems not easily traceable by the generic techniques. In particular, after applying those generic techniques, time series in the data may contain trends, natural fluctuations and possible surviving errors. To study the data evolution, one main issue is distinguishing those elusive errors from the rest, which should be kept as they are and not flattened or altered. This work responds to this need by identifying some types of elusive errors and by proposing a statistical-mathematical approach to capture their complexity that can be applied after the above generic techniques. The proposed approach is based on a system of indicators and works at the formal level by studying the differences between consecutive values of data series and the symmetries and asymmetries of these differences. It operates regardless of the specific meaning of the data and is thus applicable in a variety of contexts. We implement this approach in a relevant database of European Higher Education institutions (ETER) by analyzing two key variables: "Total academic staff" and "Total number of enrolled students", which are two of the most important variables, often used in empirical analysis as a proxy for size, and are considered by policymakers at the European level. The results are very promising. [ABSTRACT FROM AUTHOR]

For years, educational practitioners and decision-makers have encouraged teachers to implement a balanced literacy approach in class. However, the effects of balanced literacy on the reading comprehension of students from different backgrounds have not been carefully examined in the current literature. Using the large data sets from the 2016 International Reading Literacy Study (PIRLS), this study examines the effects of balanced literacy on the performance in getting direct information and interpreting skills of native-English speakers and English Language Learners (ELLs). This study indicates that teacher-directed activities benefited 4th-grade native- English speakers and ELLs' reading outcomes in getting direct information and interpreting skills. However, the other components of balanced literacy, including student-centered instructions and independent learning activities, could not contribute to 4th-grade students' reading performance in getting direct information and interpreting skills. [ABSTRACT FROM AUTHOR]

Purpose: Hip and knee arthroplasty aims to reduce joint pain and increase functional mobility in patients with osteoarthritis; however, the degree to which arthroplasty is associated with higher physical activity is unclear. The current study sought to assess the association of hip and knee arthroplasty with objectively measured physical activity. Methods: This cross-sectional study analyzed wrist-worn accelerometer data collected in 2013-2016 from UK Biobank participants (aged 43-78 yr). Multivariable linear regression was performed to assess step count, cadence, overall acceleration, and activity behaviors between nonarthritic controls, end-stage arthritic, and postoperative cohorts, controlling for demographic and behavioral confounders. From a cohort of 94,707 participants with valid accelerometer wear time and complete self-reported data, electronic health records were used to identify 3506 participants having undergone primary or revision hip or knee arthroplasty and 68,389 nonarthritic controls. Results: End-stage hip or knee arthritis was associated with taking 1129 fewer steps per day (95% confidence interval (CI), 811-1447; P < 0.001) and having 5.8 fewer minutes per day (95% CI, 3.0-8.7; P < 0.001) of moderate-to-vigorous activity compared with nonarthritic controls. Unilateral primary hip and knee arthroplasties were associated with 877 (95% CI, 284-1471; P = 0.004) and 893 (95% CI, 232-1554; P = 0.008) more steps than end-stage osteoarthritic participants, respectively. Postoperative unilateral hip arthroplasty participants demonstrated levels of moderate-to-vigorous physical activity and daily step count equivalent to nonarthritic controls. No difference in physical activity was observed between any cohorts in terms of overall acceleration, or time spent in daily light activity, sedentary behavior, or sleep. Conclusions: Hip and knee arthroplasties are associated with higher levels of physical activity compared with participants with end-stage arthritis. Unilateral hip arthroplasty patients, in particular, demonstrate equivalence to nonarthritic peers at more than 1 yr after surgery. [ABSTRACT FROM AUTHOR]

The paper examines the special aspects of using Big Data technology in education. The population was made up of 356 third-year university students. To study Big Data technology, a questionnaire was used where respondents rated: cloud technology; apps; Massive Open Online Courses (MOOCs) and digital learning platforms. The study suggested that the education sector is ambitiously applying Big Data technology, both online and offline. All surveyed respondents use apps in Big Data learning and analysis: 73.03% use Moodle, 67.13% use Zoom, 65.17% use Quizlet, 50.84% use Skype, and 35.11% use Slack. MOOCs in education are used by 75% of respondents. Digital learning platforms are used by all respondents. All students use cloud technology. When dealing with Big Data technologies, students preferred apps (8.9 ± 1.33) instead of the cloud (6.9 ± 0.11). Students believe that the important factors for using Big Data in the learning process include: quality of information (85.96%); interest (77.81%); instructor's support (66.85%). The research findings make it possible to integrate Big Data technology into the learning process, thus improving learning outcomes and providing greater speed in processing reliable and meaningful data. [ABSTRACT FROM AUTHOR]

In this review article, we explore the transformative impact of deep learning (DL) on structural bioinformatics, emphasizing its pivotal role in a scientific revolution driven by extensive data, accessible toolkits and robust computing resources. As big data continue to advance, DL is poised to become an integral component in healthcare and biology, revolutionizing analytical processes. Our comprehensive review provides detailed insights into DL, featuring specific demonstrations of its notable applications in bioinformatics. We address challenges tailored for DL, spotlight recent successes in structural bioinformatics and present a clear exposition of DL—from basic shallow neural networks to advanced models such as convolution, recurrent, artificial and transformer neural networks. This paper discusses the emerging use of DL for understanding biomolecular structures, anticipating ongoing developments and applications in the realm of structural bioinformatics. [ABSTRACT FROM AUTHOR]

The need for speeding up data analytics increases inevitably due to the need for extracting valuable information from social media, data generated by smart devices with sensors, patterns of people's communications over the web, items viewed and bought by global-scale customers, cloud applications, etc., all of which take part in the "Big Data." Such kind of interaction data is very well represented as sparse graphs to enable the graph analytics, which requires efficient underlying kernels. The breadth-first search (BFS)-based traversal is a commonly used kernel in graph algorithms such as the betweenness centrality algorithm for centrality analysis. In this work, we focus on parallel BFS operations and propose hypergraph-based combinatorial models that aim at reducing cache misses and hence exploiting data locality during the parallel BFS operations. Our models are based on finding new vertex visit orders so that locality in accessing the data associated with vertices is exploited. Experiments on graphs arising in a wide range of applications show that our proposed models achieve on average 9% performance improvement in the CPU-based Ligra data analytics framework. [ABSTRACT FROM AUTHOR]

With the advent of big data science, the field of geoscience has undergone a paradigm shift toward data-driven scientific discovery. However, the abundance of geoscience data distributed across multiple sources poses significant challenges to researchers in terms of data compilation, which includes data collection, collation, and database construction. To streamline the data compilation process, we present GeoKnowledgeFusion, a publicly accessible platform for the fusion of text, visual, and tabular knowledge extracted from the geoscience literature. GeoKnowledgeFusion leverages a powerful network of models that provide a joint multimodal understanding of text, image, and tabular data, enabling researchers to efficiently curate and continuously update their databases. To demonstrate the practical applications of GeoKnowledgeFusion, we present two scenarios: the compilation of Sm-Nd isotope data for constructing a domain-specific database and geographic analysis, and the data extraction process for debris flow disasters. The data compilation process for these use cases encompasses various tasks, including PDF pre-processing, target element recognition, human-in-the-loop annotation, and joint multimodal knowledge understanding. The findings consistently reveal patterns that align with manually compiled data, thus affirming the credibility and dependability of our automated data processing tool. To date, GeoKnowledgeFusion has supported forty geoscience research teams within the program by processing over 40,000 documents uploaded by geoscientists. [ABSTRACT FROM AUTHOR]