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

Intro: The aim of the current study was to develop and describe a new measuring system for the orientation of a tooth in a digitalized cast of a jaw and provide new angular values for the rotation, torque and tip of maxillary and mandibular teeth. Methods: This retrospective cross‐sectional study involved the utilization of a sub‐group of extrinsic Euler‐angles to derive optimal norm values per tooth in three different planes of orientation ('rotation', 'torque' and 'tip') by evaluating the digital representations of the teeth derived from a database containing over 17,500 patients. The process involved the entry of the.stl files of the jaw pairs into a fully automated software system (Smyl:Ai, Ulm, Germany) whereupon jaw alignment, teeth segmentation, landmark identification and visual validation of input files was conducted prior to calculation of the norm values for the three different planes of orientation. Results: The digital scans in stereolithography (.STL)‐file format of the upper and lower dentitions of 1914 individuals with optimal occlusion were chosen and evaluated. New mean (standard deviation) angular values were determined for the rotation, torque and tip of maxillary and mandibular teeth. Conclusion: The findings facilitate the reappraisal of rotation, torque and tip values currently acceptable as ideal. They will inform anthropologists and dental researchers about occlusion and alignment in orthodontic and non‐orthodontic patients and provide baseline data for future studies. The methodology will also enable the evaluation of large numbers of data in relatively short timeframes. [ABSTRACT FROM AUTHOR]

Large phylogenies derived from publicly available genetic sequences are becoming a popular and indispensable tool in addressing core questions in ecology and evolution, as well as in tackling challenging conservation issues. Optimizing taxonomic coverage and data quality is essential for improving the precision and reliability of phylogenetic reconstructions and evolutionary inferences. Here we present PyNCBIminer, a user‐friendly software that automates the assembly of large DNA data sets from GenBank for phylogenetic reconstruction using the supermatrix method. PyNCBIminer uses the iterative BLAST procedure to retrieve genetic sequences accurately and efficiently from GenBank. The state‐of‐the‐art strategies also serve to improve taxa coverage and the quality of target DNA markers. PyNCBIminer is designed to efficiently handle large data sets, but it is also suitable for medium and small data sets. It is open source and freely available at GitHub (https://github.com/Xiaoting-Xu/PyNCBIminer) and Gitee (https://gitee.com/xiaotingxu/PyNCBIminer). Its utility and performance are demonstrated through the assembly of phylogenetic data sets encompassing several genetic markers of varying sizes for the angiosperm order Dipsacales. PyNCBIminer holds an advantage over similar programs in that it performs the majority of computations on the NCBI server, eliminating the need for users to build and maintain large local databases and reducing the demands on their computers. In addition, it integrates other commonly used phylogenetic analysis software, providing users from various backgrounds with convenient options for retrieving and assembling GenBank sequence data, along with flexible features that allow for user‐defined parameters and strategies. [ABSTRACT FROM AUTHOR]

How many factors are there? It is a critical question that researchers and practitioners deal with when estimating factor models. We proposed a new eigenvalue ratio criterion for the number of factors in static approximate factor models. It considers a pooled squared correlation matrix which is defined as a weighted combination of the main observed squared correlation matrices. Theoretical results are given to justify the expected good properties of the criterion, and a Monte Carlo study shows its good finite sample performance in different scenarios, depending on the idiosyncratic error structure and factor strength. We conclude comparing different criteria in a forecasting exercise with macroeconomic data. [ABSTRACT FROM AUTHOR]

Recent advances in wireless networking, big data technologies, namely Internet of Things (IoT) 5G networks, health care big data analytics, and other technologies in artificial intelligence (AI) and wearables, have supported the progression of intellectual disease diagnosis methods. Medical data covers all patient data such as pharmacy texts, electronic health reports (EHR), prescriptions, study data from medical journals, clinical photographs, and diagnostic reports. Big data is a renowned method in the healthcare sector, with beneficial datasets that are highly difficult, voluminous, and rapid for healthcare providers for interpreting and computing using prevailing tools. This study combines concepts like deep learning (DL) and big data analytics in medical field. This article develops a new healthcare data analytics using optimal machine learning model in big data environment (HDAOML‐BDE) technique. The presented HDAOML‐BDE technique mainly aims to examine the healthcare data for disease detection and classification in the big data environment. For handling big data, the HDAOML‐BDE technique uses Hadoop MapReduce environment. In addition, the HDAOML‐BDE technique uses manta ray foraging optimization‐based feature selection (MRFO‐FS) technique to reduce high dimensionality problems. Moreover, the HDAOML‐BDE method uses relevance vector machine (RVM) model for the healthcare data environment. Furthermore, the arithmetic optimization algorithm (AOA) is utilized for the parameter tuning of the RVM classifier. The simulation results of the HDAOML‐BDE technique are tested on a healthcare dataset, and the outcomes portray the improved performance of the HDAOML‐BDE strategy over recent approaches in different measures. [ABSTRACT FROM AUTHOR]

E‐commerce art design major is a new major that has developed rapidly in recent years. In the era of big data, teaching methods, teaching methods and teaching contents have changed accordingly. At the same time, with the popularization and application of big data, the demand direction of e‐commerce art design professional society for talents has also changed. Therewith, the model of teaching is also undergoing a transformation. The curriculum system of the e‐commerce art design major needs to integrate the thinking in data in response to the social demand. Therefore, starting with the concept and connotation of thinking in data, status analysis of the art design major of application‐oriented universities, and analysis of the application of the thinking in data in the education of the e‐commerce art design major, this paper explores how to apply the thinking in data in the teaching of the e‐commerce art design major of application‐oriented universities and optimize the talent training in this major, so as to provide new ideas for the development in the e‐commerce art design major. [ABSTRACT FROM AUTHOR]

At present, the mainstream mode of machine learning algorithms is the data‐driven method, which mainly relies on the self‐learning ability of deep neural networks and continuously evolving models in data‐driven training. However, the pure data‐driven method has some critical problems, such as high data collection cost, poor interpretability and easy to be be disturbed by noise. Although the knowledge‐driven method has high stability, it lacks self‐learning and evolution ability in the face of comprehensive and complex problems. In recent years, the convergence of data and domain knowledge has combined the advantages of both learning paradigms. One typical way is to embed domain knowledge into the data‐driven model to improve the interpretability of the model, and then use the self‐learning ability of the data‐driven model to explore knowledge, and continuously iterate the domain knowledge to form a closed loop. The data‐knowledge dual‐driven methods have brought transformative innovations in machine learning. This review first introduced the advantages and necessity of the data‐knowledge dual‐driven model in the field of artificial intelligence. Then, the applications of the data‐knowledge dual‐driven model in the smart marine field were introduced. Finally, the challenges and trends of the data‐knowledge dual‐driven artificial intelligence are anticipated. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid advancement of hardware and software algorithms, information technology has penetrated every aspect of our lives, creating waves of digital transformation in enterprises represented by four underlying technologies, namely: 'artificial intelligence, big data, cloud computing, and blockchain'. As a result, digital technology has emerged as the primary force behind industrial transformation and upgrading, as well as high‐quality development. Using the data of Shanghai and Shenzhen A‐share listed companies from 2007 to 2020, this paper empirically examines the impact of the digital transformation of listed companies on their stock price crash risk and its mechanism using a two‐way fixed effect model with time and individual effects. It can be seen from the research results that, overall, enterprise digital transformation can significantly reduce the risk of a stock price crash. Further analysis reveals that this risk‐inhibiting effect is more apparent for private enterprises and small and medium‐sized enterprises. In terms of impact mechanism, digital transformation can reduce the risk of a stock price crash by improving the quality of enterprise information internally, while the more analysts pay attention to an issue, the more obvious the role of digital transformation in reducing the risk of a stock price crash for enterprises externally. However, it is worth noting that if the stock price of an enterprise crashes, blindly implementing digital transformation may increase the crash risk. The paper illustrates the practical importance of digital technology from the perspective of enterprises in stabilizing the crash risk of stock prices and improving the performance of the capital market. Meanwhile, it contributes to theoretical research on the impact of digital transformation on microeconomic entities and provides practical knowledge and evidence for enterprises to accelerate digital transformation. [ABSTRACT FROM AUTHOR]

In this paper, a novel telescopic broad Bayesian learning (TBBL) is proposed for sequential learning. Conventional broad learning suffers from the singularity problem induced by the complexity explosion as data are accumulated. The proposed TBBL successfully overcomes the challenging issue and is feasible for sequential learning with big data streams. The learning network of TBBL is reconfigurable to adopt network augmentation and condensation. As time evolves, the learning network is augmented to incorporate the newly available data and additional network components. Meanwhile, the learning network is condensed to eliminate the network connections and components with insignificant contributions. Moreover, as a benefit of Bayesian inference, the uncertainty of the estimates can be quantified. To demonstrate the efficacy of the proposed TBBL, the performance on highly nonstationary piecewise time series and complex multivariate time series with 100 million data points are presented. Furthermore, an application for long‐term structural health monitoring is presented. [ABSTRACT FROM AUTHOR]

Follicular lymphoma (FL) is the second most common type of lymphoma (20% of all non‐Hodgkin lymphomas), derived from germinal centre (GC) B cells, and is characterised by its significant clinical, prognostic and biological heterogeneity, leading to complexity in management. Despite significant biological investigation and indisputable clinical progress since the advent of the immunotherapy era more than 20 years ago, much remains to be done to understand and cure this lymphoma. Today, FL is metaphorically a giant puzzle on the table with patches of sky, landscape and foliage clearly appearing. However, many of the remaining pieces are held by various stakeholders (e.g. clinicians, pathologists, researchers, drug developers) without global agreement on what the gaps are, or any clear blueprint on how to solve the puzzle of understanding the heterogeneity of this disease and create curative and tailored therapies. With the advent of new investigation and drug technologies, together with recent advances in our capacity to manage big data, the time seems ripe for a change of scale. More than ever, this will require collaboration between and within all stakeholders to overcome the current bottlenecks in the field. As for every investigator, we acknowledge that this first draft is necessarily biased, incomplete and some FL expert readers might recognise some remaining gaps not addressed. We hope they will reply to make this effort a collaborative one to assemble all the pieces in the most ideal fashion. As such, this review intends to be a first step and an interactive platform to a collaborative roadmap towards better understanding and care of FL. [ABSTRACT FROM AUTHOR]

This study evaluates the impact of Big Data Analytics (BDA) on firm sustainable performance (FSP). BDA is conceptualized as a dual construct comprising predictive and prescriptive analytics, while FSP is considered from a triple bottom line (TBL) perspective comprising the economic, social, and environmental lines of firm performance. The study relies exclusively on independent third‐party BDA and FSP data pertaining to 522 firms from the US S&P500 Index and the Canadian S&P500/TSX60 Index. The data is analyzed with ordinary least squares (OLS) regression, and the findings reveal, on aggregate, that BDA has a direct, positive, and significant effect on overall FSP. The results of the piecemeal analysis show that BDA is positively related to the economic, social, and environmental dimensions. Furthermore, our distinction between predictive and prescriptive analytics suggests that prescriptive analytics outperforms the FSP results obtained with predictive analytics moderately. The study insights provide strategic knowledge for firms seeking to leverage digitalization for enhanced corporate citizenship while boosting their digital capabilities. The impact of technology, especially Big Data, on sustainability, has gained traction in the literature, yet this is the first study to delve deeper into the detailed relationships between both constructs by deciphering and quantifying the impact of BDA components on the TBL. [ABSTRACT FROM AUTHOR]

Big birding events provide a valuable opportunity to develop datasets to supplement systematic bird monitoring. However, the contributions of these big datasets remain unclear. In this study, we examine two big birding events in Kerala, India (Great Backyard Bird Count and Onam Bird Count). Data submitted during these events between 2014 and 2022 were analysed to assess data quantity, species representation, spatial coverage and birder recruitment. The events contributed a disproportionately large amount of data (12% of all Kerala bird data) and effectively recruited volunteers (> 50% recruited in 2 months) into the citizen science programme. Although the data exhibited a spatial bias (missing 18% of Kerala), the reporting frequencies of common birds from these events were in agreement with the semi‐structured birding efforts conducted throughout the rest of the year. The simplicity of their protocols and reasonably high data quality make big birding events a useful component of citizen science. Promoting such events is useful for engaging citizens in bird monitoring and conservation, especially in densely populated and biodiverse countries such as India. [ABSTRACT FROM AUTHOR]

Cotton, cotton yarn, and other cotton products have frequent price volatility, increasing the difficulty for industry participants to develop rational business decision plans. To support cotton textile industry decision‐makers, we apply data mining methods to extract the main influencing factors affecting cotton yarn futures prices from big data and build a probabilistic forecasting model for cotton yarn price volatility with uncertainty assessment. Based on Explainable Artificial Intelligence (XAI) and data‐driven perspectives, we use the LassoNet algorithm to extract 18 features most relevant to the target variable from the massive data and visualize the importance values of the selected features to improve the reliability. Moreover, by combining conformal forecasting (CP) with quantile regression (QR), the uncertainty measure of the point estimation results of the long and short‐term memory (LSTM) model is applied to improve the application value of the model. Finally, SHAP (SHapley Additive exPlanations) is introduced to analyze the SHAP values of the input features on the output results and to explore in depth the interaction and mechanism of action between the input features and the target variables to improve the explainability of the model. Our model provides a "big data‐forecasting model‐decision support" decision paradigm for real‐world problems. [ABSTRACT FROM AUTHOR]

Objective: We aimed to examine trends in novel antiandrogen receptor signal inhibitor (ARSI) usage and medical costs by collecting real‐world big data included in The National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB) Open Data, covering most of the clinical practices throughout Japan. Methods: Usage data for outpatient prescriptions from 2016 to 2021 were extracted from the NDB Open Data. Among the 459,610 million tablets/capsules prescribed, prostate cancer‐specific agents (bicalutamide, estramustine phosphate, flutamide, abiraterone, enzalutamide, apalutamide, and darolutamide) were selected to investigate the trends of usage and medical costs. Results: In total, 764.8 billion medications were recorded. Among these, standard dose‐adjusted prescriptions for bicalutamide, abiraterone, enzalutamide, apalutamide, darolutamide, and other vintages (estramustine phosphate, flutamide) was 276, 14.2, 18.1, 2.19, 0.34, and 20.3 million, respectively. The usage of ARSI increased significantly from 6.1% in 2016 to 16% in 2021. The medical costs for prostate cancer‐specific agents increased significantly (1.8‐fold) from 2016 to 2021. Despite the limited usage of ARSIs, a majority of the medical costs had been spent on ARSIs. Medical costs associated with ARSIs increased significantly from 59% to 89% (p < 0.001). Conclusion: ARSI usage and medical costs associated with prostate cancer increased significantly from 2016 to 2021. Despite the limited use of ARSIs, a considerable proportion of the medical costs for prostate cancer‐specific agents had been spent on ARSIs. [ABSTRACT FROM AUTHOR]

Summary: Cloud Computing (CC) and big data are growing technology in the business. Big data is demonstrated in terms of volume, variety, and velocity. CC is employed for storing, processing, and accessing data. Many cryptographic techniques have been developed to enhance big data security in cloud computing. However, security and privacy are the primary concerns in protecting data, as it is highly sensitive. Yet, it faces the major problems of inefficient performance, increased time consumption, and lack of data confidentiality and integrity. To address this issue, proposed Extreme Learning with Projection Relational Algebraic Secured Data Transmission (ELPRA‐SDT) is introduced to secure data transactions from cloud users to cloud servers with enhanced data confidentiality and reduced time consumption for big cloud data. The proposed ELPRA‐SDT consists of two major processes namely registration and key generation. At first, the user's IP address is registered employing a transitive advanced set relation theory graph model in a cloud server (CS) for retrieving the numerous services. The CS generates private and public keys for each registered user's IP address using the Transitive Operational and Time Synchronized Random Winternitz Key generation model. After, the user sends a request to the CS for acquiring data. The CS validates the requested user based on security policy attributes. Second, the Projection Relational Algebraic Signcryption and Unsigncryption algorithm performs signature verification to ensure secure data access for protecting the data. Results of experiments carried out by using Coburg Intrusion Detection Data Sets‐001 dataset in Java. ELPRA‐SDT method is more efficient and more suitable for providing security and privacy to network traces in the Cloud. The result shows maximum performance with data confidentiality by 10% and data integrity by 13%. In addition, delay is reduced by 32%, and data delivery time and communication complexity is decreased by 28% and 24% to other existing methods. [ABSTRACT FROM AUTHOR]

ABSTRACT Aim Location Taxon Methods Results Conclusions Surveying for a species of concern ahead of proposed activities that alter its habitat is routine practice in conservation and management. Such surveys may accumulate large datasets that could further elucidate trends in abundance and distribution. However, the as‐needed surveying of proposed activities may impart a sample site selection bias on the data if used for another purpose. Management of a threatened, forest‐nesting seabird offered an example of this. Here we assessed how resource management planning and survey requirements can bias clearance monitoring survey data collected prior to proposed timber harvests, if those data are used for other purposes.Oregon and Washington, USA.Marbled Murrelet (Brachyramphus marmoratus).To assess how timber planning and other factors influenced marbled murrelet survey location selection, we used logistic regression models to examine habitat associations of marbled murrelet survey sites (n = 9178) encompassing proposed timber harvests, and the survey stations (n = 38,923) therein, across the murrelet's inland range in Washington and Oregon, USA between 1989 and 2021. We then simulated the effect this selective sampling might have on assessments of occupancy trends.Most habitat characteristics considered did influence where surveys were located, with distance to roads often being the strongest predictor of survey location. The strength of selection for each covariate changed over time, such that a habitat characteristic strongly influenced location selection in a year but was less influential in another year. The simulation analysis suggested that the non‐random selection of survey sites could profoundly bias assessments of occupancy trends.When using these clearance monitoring survey data– or any data–beyond their original purpose, careful consideration should be given to the scope of inference provided and analytical methods used, to ensure that observed trends are the product of biological processes and not biased by sampling artefacts. [ABSTRACT FROM AUTHOR]

The potential of passively generated big data sources in transport modelling is well‐recognised. However, assessing their accuracy and suitability for policymaking remains challenging due to the lack of ground‐truth (GT) data for validation. This study evaluates the accuracy of inferring human mobility patterns from global positioning system (GPS), call detail records (CDR), and global system for mobile communication (GSM) data. Using outputs from an agent‐based simulation platform (MATSim) as 'synthetic GT' (SGT), synthetic GPS, CDR, and GSM data were generated, considering their positional disturbances and conventional spatiotemporal resolutions. Mobility information, including activity location, departure time, and trajectory distance, derived from the synthetic data, was compared with SGT to evaluate the accuracy of passive trajectory data at both disaggregate and aggregate levels. The results indicated a higher accuracy of GPS data in identifying stay locations at high resolution. But, GSM data at a lower resolution effectively accounted for over 80% of the variability in stay locations. Comparisons of departure time distribution and travel distance revealed higher measurement errors in GSM and CDR data than in GPS data. The proposed simulation‐based accuracy assessment framework will aid transport planners select the most suitable data for specific analyses and understand the potential margin of error involved. [ABSTRACT FROM AUTHOR]

The Fourth Industrial Revolution based on digitalization, the development of AI, robotics, big data, and increasing automation is dredging up older debates on the end of human work. This article contributes to this debate arguing that these changing circumstances represent an opportunity to advance a renewed consideration of human work. By emphasizing its most distinctively human dimensions, including gratuitousness, relationality, and meaningfulness, we propose the articulation of a social model that recognizes relational goods as a specific contribution of human work in an attempt to overcome the state and the market's monopoly of public life built around the dichotomy between the private and the public. Such a social model is based on the concept of circular subsidiarity, which represents a departure from traditional notions of subsidiarity by promoting a dynamic and reciprocal interplay between three essential societal spheres: the state, the market, and organized civil society, all of which have their role to fulfill by providing security, efficiency, and relational goods. Ultimately, this article suggests that circular subsidiarity can lead to a more inclusive and equitable social model by acknowledging how relational goods not only humanize civil society but also sustain the functioning of both the market and the state. [ABSTRACT FROM AUTHOR]

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

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

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

Premise: Although previous studies have reported a positive correlation between leaf dry mass per unit area (LMA) and mean leaf thickness (LT), the LMA versus LT scaling relationship has not been determined due to limited sample sizes, despite its importance in estimating leaf bulk tissue density (mass per unit volume). Methods: This issue was addressed using between 174 and 185 leaves from each of nine phylogenetically diverse species to investigate the LMA vs. LT scaling relationship. For each leaf, lamina thickness was measured at 12 positions (avoiding midribs and major veins) to calculate LT, and LMA was measured based on leaf area and dry mass measurements. Reduced major axis regression protocols were used to determine the LMA vs. LT scaling exponent (i.e., the slope). Bootstrap percentile methods were used to calculate the 95% confidence intervals of slopes. Results: A statistically significant LMA vs. LT relationship was found for each species; seven of the nine scaling exponents were significantly greater than unity indicating that LMA (and thus leaf bulk tissue density) disproportionately increased with increasing LT. In addition, the conspecific variation in LMA exceeded the interspecific variation in LMA as a consequence of differences in LT. Conclusions: These results indicate that empirical measurements of LMA and LT can be used to accurately estimate leaf bulk tissue density, which provides insights into adaptive life‐history strategies, conspecific variation, and (with sufficiently large data sets) phylogenetic trends. [ABSTRACT FROM AUTHOR]

Insulator defect detection is crucial for the stable operation of power systems. It has become a mainstream research direction to realise insulator defect detection based on the combination of line images captured by UAVs and deep learning techniques. However, the existing high‐quality insulator defect detection models still face problems such as relying on massive‐labelled data and huge model parameters. Especially on resource‐constrained devices, it becomes a challenge to strike a balance between model lightweighting and performance. Although the knowledge distillation technique provides a solution for model lightweighting, the loss of information in the distillation process leads to the performance degradation of small models, which in turn creates a paradox between lightweighting and performance. Hence, an insulator defect detection method based on federated knowledge distillation is proposed. The method not only realises the lightweighting of the model, but also effectively improves the model performance by collaboratively training the model through the federated learning approach. Moreover, the asynchronous aggregation approach and model freshness mechanism designed in the method further enhance the training efficiency and collaborative effect. The experimental results show that the detection accuracy and efficiency of this paper's method on public datasets are significantly better than the benchmark algorithm. [ABSTRACT FROM AUTHOR]

In geostatistics, traditional spatial models often rely on the Gaussian process (GP) to fit stationary covariances to data. It is well known that this approach becomes computationally infeasible when dealing with large data volumes, necessitating the use of approximate methods. A powerful class of methods approximate the GP as a sum of basis functions with random coefficients. Although this technique offers computational efficiency, it does not inherently guarantee a stationary covariance. To mitigate this issue, the basis functions can be ‘normalized’ to maintain a constant marginal variance, avoiding unwanted artefacts and edge effects. This allows for the fitting of nearly stationary models to large, potentially non‐stationary datasets, providing a rigorous base to extend to more complex problems. Unfortunately, the process of normalizing these basis functions is computationally demanding. To address this, we introduce two fast and accurate algorithms to the normalization step, allowing for efficient prediction on fine grids. The practical value of these algorithms is showcased in the context of a spatial analysis on a large dataset, where significant computational speedups are achieved. While implementation and testing are done specifically within the LatticeKrig framework, these algorithms can be adapted to other basis function methods operating on regular grids. [ABSTRACT FROM AUTHOR]

The plain language movement waged a silent revolution in the last generation, passing nearly 800 laws nationwide with little public debate. The movement asserted that it could scientifically show that there is a widespread readability crisis in legal documents, particularly contracts, that are unreadable to most adults. This article presents the largest empirical analysis of these claims to date, utilizing a dataset of 2 million contracts spanning multiple decades and industries and applying machine learning techniques. The study challenges fundamental tenets of the plain language movement. Contrary to prevailing beliefs, consumer agreements have median reading scores almost indistinguishable from those of daily news articles. A critical evaluation further exposes that readability tools endorsed by the movement are shoddy and manipulable and can produce grade‐level differences of up to 4.6 years for identical texts. Moreover, the movement's core belief that Americans cannot read past the level of an eighth grader is exposed as an unsubstantiated myth. These findings fundamentally challenge the premises and effectiveness of one of the central consumer protection policies. These results call for a radical rethinking of legal access strategies, suggesting a shift from superficial readability metrics to addressing substantive issues in market dynamics and focusing on truly vulnerable populations. More broadly, this case study serves as a cautionary tale about the propagation of myths in legal scholarship and the potential for well‐intentioned reform movements to divert attention and resources from more effective interventions. [ABSTRACT FROM AUTHOR]

Currently, in the organizational research community, artificial intelligence (AI), machine learning (ML), and big data techniques are being vigorously explored as a set of modern‐day approaches contributing to a multidisciplinary science of people at work. This paper discusses more specifically how these sophisticated technologies, methods, and data might together advance the science of people at work through various routes, including improving theory and knowledge, construct measurements, and predicting real‐world outcomes. Inspired by the four articles in the current special issue highlighting several of these aspects in essential ways, we also share other possibilities for future organizational research. In addition, we indicate many key practical, ethical, and institutional challenges with research involving AI/ML and big data (i.e., data accessibility, methodological skill gaps, data transparency, privacy, reproducibility, generalizability, and interpretability). Taken together, the opportunities and challenges that lie ahead in the areas of AI and ML promise to reshape organizational research and practice in many exciting and impactful ways. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) methods have created insurmountable performance in prediction tasks for geoscientific problems yet are unable to derive process insights and answer specific scientific questions. The geoscience community faces a dilemma of reconciling process comprehension with high predictive accuracy. Here we introduce a deep process learning (DPL) approach empowering neural networks to deduce intrinsic processes from observable data, wherein the intuitive physics of geosystems is directly coupled within the deep learning (DL) architecture as structural prior. We aim to incorporate as raw common concepts as possible as macroscopic guidance: on the one hand, to reduce interference with DL's data adaptability. On the other hand, to allow the information flow of the model to converge along specific paths toward the target output, thus enabling the potential to gain process insights with limited supervision. Illustrating its application to precipitation‐runoff modeling across the USA, DPL yields an ensemble median Nash‐Sutcliffe efficiency of 0.758 and Kling‐Gupta efficiency of 0.778 with robust transferability, compared to 0.762 and 0.751 for the state‐of‐the‐art DL model. The good match between internal representations of DPL and independent data sets of snow water equivalent and evapotranspiration, along with its superior capability for catchment water budget closures, demonstrates proficient process mastery. The study also highlights beneficial synergies from large‐scale data collaboration, promoting the organic unity of process understanding and predictive performance. This work shows a promising avenue for learning processes from big data and will benefit geoscientific domains that remain concerned with process clarity in the era of AI. Plain Language Summary: Recent machine‐learning approaches, especially deep neural networks, have shown great skill in making predictions in geosciences. However, their black‐box nature of lacking physical process expression may result in predictions that disobey physical laws and are difficult to interpret. Here we show how this can be overcome by proposing deep process learning, which enables a unification of geophysical processes understanding with predictive performance. We illustratively develop six intuitive physics‐guided DL submodels based on different priori insights to predict daily streamflow in over 500 catchments across the conterminous United States. Our results show that the developed DPL models achieve predictive accuracy comparable to the state‐of‐the‐art long short‐term memory (LSTM) model. Moreover, these models can produce high‐precision outputs for entirely unobserved intermediate variables without resorting to any kinetic formulation, relying solely on supervised learning of basin outlet streamflow. The resulting enhanced water balance closure capability demonstrates the potential of DPL to improve the understanding of regional water availability. The results demonstrate that DPL is a versatile paradigm capable of providing AI solutions for re‐understanding physical systems in hydrology and other domains of Earth science directly from data. Key Points: Deep process learning (DPL) integrates geophysical intuition into neural networks for process inference from dataDeep process learning approach achieves integrated excellence of prediction accuracy and physical process understanding in runoff modelingFrom local to global patterns, intuitive physics‐guided AI models enhance target variable predictions due to higher geophysical credibility [ABSTRACT FROM AUTHOR]

The optimization of the total passenger travel time and total train energy consumption are critical factors in metro operation optimization. However, deriving an optimal train operation plan that incorporates both passenger travel time and total train energy consumption is a complex task because it should consider numerous variables representing the operational status of the urban railway, such as the number of boarding and alighting passengers, number of on‐board passengers in each train, and entire train operation status along the line. Moreover, owing to the fluctuating nature of passenger demand, which can change rapidly over time, its optimization becomes challenging. To address this challenge, this study develops a recurrent neural network‐based real‐time metro operation optimization model trained using data representing the moments when the trains departed from the stations. These data are derived and reconstructed from various simulated operation plans while searching for optimal daily metro timetable. Consequently, the proposed model derives the real‐time optimal operation strategies for trains departing from the next station within an average of 0.18 s. The result of metro operation simulations using proposed optimal operation strategies reveals a 7–14% improvement in efficiency compared to the current train operation strategies. [ABSTRACT FROM AUTHOR]

Any technological innovation will be accompanied by new challenges and risks, and the connected and automated vehicles (CAVs) are no exception. Among them, the argument that cooperating platoons may fall victim to cyberattacks through wireless communication has emerged as a significant issue. Therefore, this paper designs a communication topology anomaly response system (CTARS) to ensure platoon safety, which consists of a trigger module and a control module. The primary objective of the trigger module is to distinguish abnormal vehicle behavior based on time to collision (TTC) indicators, and the control module combines the model predictive control (MPC) and bidirectional long short‐term memory (Bi‐LSTM) to achieve accurate trajectory prediction of and optimal control, working in tandem with the trigger module. Subsequently, the real dataset HISTORIC is used to calibrate the multiple vehicle intelligent driver model (IDM) and train the trajectory prediction model. Furthermore, comparative simulations are conducted, encompassing various forms of cyberattacks, in order to examine the evolution characteristics of CAVs platoons (CAVP) and evaluate the performance of CTARS. The results demonstrate the remarkable effectiveness of CTARS in safeguarding the security of CAVP during cyberattacks, thus confirming its exceptional performance. [ABSTRACT FROM AUTHOR]

Small interfering RNA (siRNA) has revolutionised biomedical research and drug development through precise post‐transcriptional gene silencing technology. Despite its immense potential, siRNA therapy still faces technical challenges, such as delivery efficiency, targeting specificity, and molecular stability. To address these challenges and facilitate siRNA drug development, we have developed siRNAEfficacyDB, a comprehensive database that integrates experimentally validated siRNA efficacy data. This database contains 3544 siRNA records, covering 42 target genes and 5 cell lines. It provides detailed information on siRNA sequences, target genes, inhibition efficiencies, experimental techniques, cell lines, siRNA concentrations, and incubation times. siRNAEfficacyDB offers a user‐friendly web interface that makes it easy to query, browse and analyse data, enabling efficient access to siRNA‐related information. In summary, siRNAEfficacyDB provides a useful data foundation for siRNA drug design and optimisation, serving as a valuable resource for advancing computer‐aided siRNA design research and nucleic acid drug development. siRNAEfficacyDB is freely available at https://cellknowledge.com.cn/siRNAEfficacy for non‐commercial use. [ABSTRACT FROM AUTHOR]

Artificial Intelligence is playing a crucial role in healthcare by enhancing decision‐making and data analysis, particularly during the COVID‐19 pandemic. This virus affects individuals across all age groups, but its impact is more severe on the elderly and those with underlying health issues like chronic diseases. This study aimed to develop a machine learning model to improve the prediction of COVID‐19 in patients with acute respiratory symptoms. Data from 915 patients in two hospitals in Saudi Arabia were used, categorized into four groups based on chronic lung conditions and COVID‐19 status. Four supervised machine learning algorithms—Random Forest, Bagging classifier, Decision Tree, and Logistic Regression—were employed to predict COVID‐19. Feature selection identified 12 key variables for prediction, including CXR abnormalities, smoking status, and WBC count. The Random Forest model showed the highest accuracy at 99.07%, followed by Decision Tree, Bagging classifier, and Logistic Regression. The study concluded that machine learning algorithms, particularly Random Forest, can effectively predict and classify COVID‐19 cases, supporting the development of computer‐assisted diagnostic tools in healthcare. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Administrative Sciences (John Wiley & Sons, Inc.) is the property of Wiley-Blackwell 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 Canadian Journal of Administrative Sciences (John Wiley & Sons, Inc.) is the property of Wiley-Blackwell 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.)

The traditional manufacturing industry is facing the impact of Big Data, and all aspects of product research and development, process design, quality management, production and operation are urgently looking forward to the birth of innovative methods to cope with the challenges of big data in the industrial background. Although traditional manufacturing enterprises have initially established quality control information system, there are still many problems and limitations in the existing quality control system, which cannot meet the operation needs of manufacturing enterprises. This paper chooses the quality management of manufacturing enterprises as the research object, and uses advanced technologies such as computer, internet, big data and cloud computing to build a data system model of quality control of manufacturing enterprises. this paper optimizes the quality data processing process by establishing the quality monitoring model, and builds an intelligent quality supervision platform, and sets up the quality alarm rules for manufacturing enterprises. If the deviation between the actual value and the predicted value of the quality monitoring index is mapped between [0,1], the quality monitoring system can generate an outlier probability score. In this study, the traditional manual quality management is transformed into the management mode of the "internet + quality data" in order to realize the information, digital and intelligent quality management of manufacturing enterprises. This comprehensive research method combines modern digital technology and relevant theories of quality management to explore the optimization scheme of quality management of manufacturing enterprises, and also provides reference experience for the information construction of other similar enterprises. [ABSTRACT FROM AUTHOR]

ABSTRACT We are surrounded by overwhelming big data, which brings substantial advances but meanwhile poses many challenges. A very large portion of big data contains geospatial information and hence geospatial big data, which is crucial for decision making if being utilized strategically. Among others, volumes in size and high dimensions are two major challenges that prevent strategic decision making from geospatial big data. Interactive map‐based and geovisualization enabled web applications are intuitive and useful to construct knowledge. More importantly, such interactive web map applications are powerful to intuitively reveal insights from high‐dimensional geospatial big data for actionable decision making. We propose an interactive and data‐driven web mapping framework, named dciWebMapper, for visualizing and sensing high‐dimensional geospatial (big) data in an interactive and scalable manner. To demonstrate the wide applicability and usefulness of our framework, we have applied our dciWebMapper framework to three real‐world case studies and implemented three corresponding web map applications: iLit4GEE‐AI, iWURanking, and iTRELISmap. We expect and hope the three web maps demonstrated in different domains, from literature big data analysis to world university ranking to scholar mapping, will provide a good start and inspire researchers and practitioners in various domains to apply our dciWebMapper to solve and/or aid in solving impactful problems. [ABSTRACT FROM AUTHOR]

ABSTRACT Aim Location Time Period Major Taxa Studied Methods Results Main Conclusions As global change accelerates, accurate predictions of species distributions and biodiversity patterns are critical to limit biodiversity loss. Numerous studies have found that coarse‐grain species distribution models (SDMs) perform poorly relative to fine‐grain models because they mismatch environmental information with observations. However, it remains unclear how grain‐size biases vary in intensity across space and time, possibly generating inaccurate predictions for specific regions, seasons or species. For example, coarse‐grain biases may intensify in patchy, discontinuous landscapes. Such biases may accumulate to produce highly misleading estimates of continental and seasonal biodiversity patterns.United States and Canada.2004–2021.Birds (Aves).We fit presence‐absence SDMs characterising the summer and winter distributions of 572 bird species native to the US and Canada across five spatial grains from 1 to 50 km, using observations from the eBird citizen science initiative. We combined these predictions to generate seasonal biodiversity estimates across the US and Canada, which we validated using observations from 322 independent sites.We find that in both seasons, 1 km models more accurately predicted species presence, absence and richness at local sites. Coarse‐grain models (even at 3 km) consistently under‐predicted range area, potentially missing important habitat. This bias intensified during summer (83%–86% of species) when many birds have smaller ‘operational scales’ via localised home ranges while breeding. Biases were greatest in desert regions with patchier habitat and for range‐restricted and habitat‐specialist species. Predictions based on coarse‐grain models overpredicted avian diversity in the west and underpredicted it in the great plains, prairie pothole region and boreal zones.We demonstrate that coarse‐grain models can bias seasonal and continental estimates of biodiversity patterns across space and time and that grain‐related biases intensify during summer and in patchier landscapes, especially for range‐restricted and habitat specialist species at risk of population declines. [ABSTRACT FROM AUTHOR]

ABSTRACT Consumer Internet of Things (CIoT) has revolutionised agriculture by integrating intelligent computing, artificial intelligence and big data technologies in crop monitoring. This paper explores the application of intelligent computing and deep learning methodologies in crop monitoring within the CIoT framework. In CIoT‐based crop monitoring, a vision sensor collects real‐time data from crop leaf images. The image dataset is processed using state‐of‐the‐art deep learning models and intelligent computing algorithms. This integration enables the early detection of crop diseases by leveraging computer vision and deep learning. Intelligent computing systems provide accurate disease classification, real‐time alerts, and actionable recommendations for optimised crop management practises. This advanced system empowers farmers to make data‐driven decisions, such as irrigation optimization, targeted pesticide application and nutrient supplementation, to maximise crop productivity and minimise losses. A benchmark dataset of leaf images is used, and a deep learning based model is presented for classifying healthy and diseased leaves. Experimental results demonstrate an accuracy rate of 0.98, with detailed validation, including dataset size and model parameters. Key benefits of intelligent computing in CIoT‐based crop monitoring include enhanced resource efficiency, reduced environmental impact, and improved sustainability. The paper also addresses the challenges of implementing AI and big data technologies, such as data privacy, security, interoperability and resource management in agricultural settings. [ABSTRACT FROM AUTHOR]

Introduction: Although endoscopic balloon dilation (EBD) is a common therapeutic approach for managing strictures associated with Crohn's disease (CD), the clinical application and complication rates of EBD remain unclear. Methods: We collected admission data for patients who underwent EBD using a nationwide database. We compared EBD outcomes between ileal and colonic strictures, CD and ulcerative colitis, and CD and anastomotic strictures arising from cancer‐related surgery. Subsequently, propensity score matching was employed to facilitate comparisons between each group. Results: The median duration of hospital stay was 4 days. Endoscopic hemostasis and urgent surgery rates after EBD for CD‐related strictures were considerably low (0.035% and 0.11%, respectively). Most patients with CD underwent only one EBD procedure during a single admission. Although no significant differences in patient backgrounds and severe complications were observed between ileal and colonic stricture in CD, multiple EBD procedures were more commonly performed for ileal strictures than for colonic stricture. Moreover, EBD for ileal stricture was more frequently conducted in high‐volume centers than in low‐volume centers. Regarding severe complications after EBD, no significant differences were observed between CD‐related strictures and ulcerative colitis or anastomotic strictures related to cancer surgery. Conclusion: Our findings support the safe and effective use of EBD for both ileal and colonic strictures associated with CD. The clinical practice and safety outcomes of EBD for CD‐related strictures were comparable to those for strictures stemming from other etiologies. [ABSTRACT FROM AUTHOR]

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

ABSTRACT “Truth,” it turns out, is rather slippery. As geographers have become increasingly sensitized to issues of representation, they have delved into varying understandings of truth as a social construction. Most recently, the question of post‐truth has become significant. While cartographers have long discussed this issue, such as lying with maps and maps as propaganda, the GIS community has been slow to discuss it. This paper situates the emerging nexus between GIS and post‐truth within the evolving tradition of critical GIS. It calls for abandoning the correspondence theory of truth and replacing it with the pragmatist one. Next, it summarizes the contours of post‐truth as a social and epistemological phenomenon. Finally, it points to several areas in which post‐truth and GIS intersect, including the mapping of disinformation, location spoofing, big data, deepfake videos and remote sensing, and data quality. It concludes by calling for the GIS community to abandon unrealistic notions of objectivity and instead become more socially and politically self‐conscious. [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]

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]

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]

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]

The use of data and statistics along with computational systems heralded the beginning of a quantitative revolution in Geography. Use of simulation models (Cellular Automata and Agent‐Based Models) followed in the late 1990s, with ontology and epistemology of complexity theory and modelling being defined a little less than two decades ago. We are, however, entering a new era where sensors regularly collect and update large amounts of spatio‐temporal data. We define this 'Big Data' as geolocated data collected in sufficiently high volume (exceeding storage capacities of the largest personal hard drives currently available), that is updated at least daily, from a variety of sources in different formats, often without recourse to verification of its accuracy. We then identify the exponential growth in the use of complexity simulation models in the past two decades via an extensive literature review (broken down by application area), but also notice a recent slowdown. Further, a gap in the utilisation of Big Data by modellers to calibrate and validate their models is noted, which we attribute to data availability issues. We contend that Big Data can significantly boost simulation modelling, if certain constraints and issues are managed properly. [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]

Prolonged stress and the need for rapid uptake of information can have detrimental effects on memory and cognition, whereas meaningfulness of study material and motivation to learn can have positive effects. How do these opposing conditions impact workplace learning in essential frontline workers during a global pandemic? We analyzed learning data collected longitudinally since before the pandemic in over 85,000 essential frontline grocery workers and nonessential telecommunications workers via a learning management system that incorporates a spaced retrieval schedule, where items are retrieved following retention intervals of varying length. Findings indicate more rapid knowledge uptake in grocery workers (a) during than before the pandemic, (b) for COVID‐19‐related content than non‐COVID content, and (c) in the United States than in Canada. Longer‐term maintenance of training material was similar across groups. Evidence of enhanced workplace learning and retention supports efforts to integrate empirically based strategies from the behavioral sciences into learning‐based technologies. [ABSTRACT FROM AUTHOR]

Monitoring greenhouse gas (GHG) emissions is crucial for developing effective mitigation strategies. Recent advances in satellite remote‐sensing measurements allow us to track greenhouse gas emissions globally. This study assessed CO2 ${\text{CO}}_{2}$ emissions from various point or local sources, particularly power plants in India, using 8 years of concurrent high‐spatial resolution OCO‐2 satellite measurements. A Gaussian plume (GP) model was used to evaluate the power plant emissions reported in the Carbon Brief (CB) database. In total (39 cases), 42 different power plant CO2 ${\text{CO}}_{2}$ emissions were assessed, with 26 of them being assessed more than once. The estimated power plant CO2 ${\text{CO}}_{2}$ emissions were within ± $\pm $ 25% of the emissions reported in the CB database in 11 out of 39 cases and within ± $\pm $ 50% in 18 cases. To evaluate the EDGAR and ODIAC CO2 ${\text{CO}}_{2}$ emission inventories in terms of missing and highly underestimated sources, we estimated the cross‐sectional (CS) CO2 ${\text{CO}}_{2}$ emission flux for 45 cases. We identified the possible omission of power plant emissions in three cases for both inventories. Furthermore, we also showed 17 cases in which CO2 ${\text{CO}}_{2}$ emissions from unknown (non‐power plant) sources were highly underestimated in the EDGAR and ODIAC CO2 ${\text{CO}}_{2}$ emission inventories. Due to the simplicity of the employed approaches and their lower computational requirements compared to other methods, they can be applied to large data sets over extended time periods. This enables the acquisition of initial emission estimates for various sources, including those that are unknown and underestimated. Plain Language Summary: Tracking greenhouse gas emissions from large sources is crucial for combating climate change, and recent advances in space‐based remote sensing have enhanced our ability to monitor these emissions. In this study, we utilized high‐resolution data from the OCO‐2 satellite to assess CO2 ${\text{CO}}_{2}$ emissions from power plants in India over the past 8 years. We compared the satellite‐based CO2 ${\text{CO}}_{2}$ emission estimates with reported values from the Carbon Brief database. Our analysis indicated that the satellite‐based estimates were generally close to the reported values, though some discrepancies were observed. Additionally, we reviewed two CO2 ${\text{CO}}_{2}$ emission databases, EDGAR and ODIAC, to identify any missing or underestimated emissions. Our findings suggested that some power plant emissions might be absent from these databases and that emissions from other sources were often underestimated. Our methods are simpler and less resource‐intensive compared to other approaches, making them effective for analyzing large data sets over extended periods. This approach provides initial estimates of emissions, even for sources that are poorly reported, and supports efforts to enhance overall emission tracking and reporting. Key Points: CO2 emissions from power plants in India were assessed using 8 years of OCO‐2 satellite measurementsIdentified highly underestimated and missing emission sources within global emission inventories, suggesting the need for re‐evaluationSatellite‐based emission assessments show great promise and will improve with upcoming high‐resolution satellite missions [ABSTRACT FROM AUTHOR]