Search Results (21,040)

This study investigated associations between substitutions and match running performance (MRP) under the new five-substitution rule, utilising running data from the 2022 FIFA World Cup involving all 32 participating men’s national teams, comprising elite professional football players at the highest international competitive level. A paired sample t-test compared running performance among entire match players (EMP), replaced players (RP), and substitute players (SP) per team per match. A linear mixed model (LMM) was used to analyse the association between substitutions and MRP while also considering match-related factors associated with MRP as covariates and controlling for team variations. The main finding was that substitute players exhibit superior running performance compared to RP and EMP. Running metrics related to match outcomes indicate that more substitutions are associated with increases in total running distance and the number of sprints. This study highlights the importance of substitutions on team running performance under the new rules in modern elite football. Coaches can optimise their substitution strategies and physical training according to the new rules to meet the high-intensity demands of the game. Full article

This study examines the link between deep approaches to learning (DAL) and undergraduate senior students’ employability skills and perceived satisfaction in STEM fields in the United States. DAL, comprising higher-order (HO) and reflective/integrated (RI) learning constructs, enhances the understanding of real-world applications and promotes reflective thinking about individual ideas in broader contexts. HO activities focus on analyzing, synthesizing, and applying new information in practical scenarios such as internships, classroom discussions, and presentations. RI activities involve integrating existing knowledge with new ideas. The efficacy of DAL in improving student outcomes including employability and satisfaction skills was investigated using Structural Equation Modeling (SEM), which included a Confirmatory Factor Analysis (CFA) to measure observed variables associated with the four latent factors (HO, RI, student satisfaction, and employability skills), followed by structural analysis to explore the relationship between these latent factors. Data from 14,292 senior students surveyed by the National Study of Student Engagement (NSSE) in 2018 were analyzed. The results indicated a significant positive effect of DAL on students’ satisfaction and perceived employability skills, underscoring its importance in higher education for STEM students. These findings can guide higher education institutions (HEIs) in focusing on DAL activities for meaningful learning outcomes and enhanced critical thinking. Full article

The fixation time of alleles is a fundamental concept in population genetics, traditionally studied using the Wright–Fisher model and classical coalescent theory. However, these models often assume homogeneous environments and equal reproductive success among individuals, limiting their applicability to real-world populations where environmental heterogeneity plays a significant role. In this paper, we introduce a new forward-time model for estimating fixation time that incorporates environmental heterogeneity through the use of fractional calculus. By introducing a fractional parameter α, we capture the effects of heterogeneous environments on offspring production. To solve the resulting fractional differential equations, we develop a novel spectral method based on Eta-based functions, which are well-suited for approximating solutions to complex, high-variation systems. The proposed method reduces the problem to an optimization framework via the operational matrix of fractional derivatives. We demonstrate the effectiveness and accuracy of this approach through numerical examples and show that it consistently captures fixation dynamics across various scenarios. This work offers a robust and flexible framework for modeling evolutionary processes in heterogeneous environments. Full article

Skywells are crucial for climate regulation in traditional Chinese dwelling architecture, exhibiting significant variations across climatic regions. This study focuses on humid–hot China, using Huangshan, to explore skywell parameters’ impact on thermal comfort and energy efficiency. Field research on 24 buildings in the World Heritage Site Xidi, Hong Villages, and Chinese Historical Pingshan Village, combined with Grasshopper’s Ladybug tool, established a parametric model. Using orthogonal design, performance simulation, and Python-based machine learning, six morphological parameters were analyzed: width-to-length ratio, height-to-width ratio, orientation, hall depth, wing width, and shading width. After NSGA-II multi-objective optimization, the summer Percentage of Time Comfortable (P_TC) increased by 5.3%, 38.14 h; the Universal Thermal Climate Index (UTCI) relatively improved by 2%; energy consumption decreased by 8.6%, 0.14 kWh/m²; and the useful daylight illuminance increased by 28%, 128.4 h. This confirms the climate adaptability of courtyard-style buildings in humid–hot China and identifies optimized skywell parameters within the study scope. Full article

The rapid surge of Artificial Internet-of-Things (AIoT) devices has outpaced the deployment of robust, privacy-preserving anomaly detection solutions suitable for resource-constrained edge environments. This paper presents a two-stage hybrid Federated Learning (FL) framework for IoT anomaly detection and classification, validated on the real-world N-BaIoT dataset. In the first stage, each device trains a generative Artificial Intelligence (AI) model on benign traffic only, and in the second stage a Histogram-based Gradient-Boosting (HGB) classifier labels flagged traffic. All models operate under a synchronous, collaborative FL architecture across nine commercial IoT devices, thus preserving data privacy and minimizing communication. Through both inter- and intra-benchmarking against state-of-the-art baselines, the Variational Autoencoder–HGB (VAE-HGB) pipeline emerges as the top performer, achieving an average end-to-end accuracy of 99.14% across all classes. These results demonstrate that reconstruction-driven generative AI models, when combined with federated averaging and efficient classification, deliver a highly scalable, accurate, and privacy-preserving solution for securing resource-constrained IoT environments. Full article

The discovery of Asgard archaea has reshaped our understanding of eukaryotic origins, supporting a two-domain tree of life in which eukaryotes emerged from Archaea. Building on this revised framework, we propose the Pre-prokaryotic Organismal Lifeforms Existing Today (POLET) hypothesis, which suggests that relic pre-prokaryotic life forms—termed POLETicians—may persist in deep, anoxic, energy-limited environments. These organisms could represent a living bridge to the RNA world and other origin-of-life models, utilizing racemic oligoribonucleotides and peptides, non-enzymatic catalysis, and mineral-assisted compartmentalization. POLETicians might instead rely on radical-based redox chemistry or radiolysis for energy and maintenance. These biomolecules may be racemic or noncanonical, eluding conventional detection. New detection methods are required to determine such life. We propose generalized nanopore sequencing of any linear polymer—including mirror RNAs, mirror DNAs, or any novel genetic material—as a potential strategy to overcome chirality bias in modern sequencing technologies. These approaches, combined with chiral mass spectrometry and stereoisomer-resolved analytics, may enable the detection of molecular signatures from non-phylogenetic primitive lineages. POLETicians challenge the assumption that all life must follow familiar biochemical constraints and offer a compelling extension to our search for both ancient and extant forms of life hidden within Earth’s most extreme environments. Full article

Background: Breast cancer is still the most common type of cancer worldwide. Advances and the global use of artificial intelligence (AI) have opened up new opportunities to improve diagnostic accuracy and optimize breast cancer screening. This review summarizes the findings from systematic reviews to assess the current situation of AI integration in mammography. Methods: A total of 28 systematic reviews were included and analyzed using a standardized narrative checklist to assess the impact of AI on mammography imaging. Bibliometric analysis and thematic synthesis were used to assess trends, evaluate the performance of AI in different modalities and identify challenges and opportunities for clinical implementation. Results and Discussion: AI technologies show an overall performance comparable to radiologists in terms of sensitivity and specificity, especially when integrated with human interpretation to detect breast cancer in mammography. However, most studies are retrospective, which raises concerns about their generalizability to real-world clinical settings. Key limitations include potential dataset bias—often stemming from the over-representation of specific imaging equipment or clinical environments—limited ethnic and demographic diversity, the lack of model explainability that hinders clinical trust, and an unclear or evolving legal and regulatory framework that complicates integration into standard practice. Conclusions: AI has the potential to transform mammography screening, but its integration into the real world requires prospective validation, ethical safeguards and robust regulatory oversight. Coordinated international efforts are essential to ensure that AI is used safely, fairly and effectively in breast cancer diagnostics. Full article

Point cloud segmentation underpins various applications in geospatial analysis, such as autonomous navigation, urban planning, and management. Kernel Point Convolution (KPConv) has become a de facto standard for such tasks, yet its fixed geometric kernel limits the modeling of fine-grained contextual relationships—particularly in heterogeneous, real-world point cloud data. In this paper, we introduce the adaptation of a Local Contextual Attention (LCA) mechanism for the KPConv network, with reweighting kernel coefficients based on local feature similarity in the spatial proximity domain. Crucially, our lightweight design preserves KPConv’s distance-based weighting while embedding adaptive context aggregation, improving boundary delineation and small-object recognition without incurring significant computational or memory overhead. Our comprehensive experiments validate the efficacy of the proposed LCA block across multiple challenging benchmarks. Specifically, our method significantly improves segmentation performance by achieving a 20% increase in mean Intersection over Union (mIoU) on the STPLS3D dataset. Furthermore, we observe a 16% enhancement in mean F1 score (mF1) on the Hessigheim3D benchmark and a notable 15% improvement in mIoU on the Toronto3D dataset. These performance gains place LCA-KPConv among the top-performing methods reported in these benchmark evaluations. Trained models, prediction results, and the code of LCA are available in a GitHub opensource repository. Full article

This paper proposes a new accelerated fixed-point algorithm based on a double-inertial extrapolation technique for solving structured variational inclusion and convex bilevel optimization problems. The underlying framework leverages fixed-point theory and operator splitting methods to address inclusion problems of the form $0\in (A+B)\left(x\right)$, where A is a cocoercive operator and B is a maximally monotone operator defined on a real Hilbert space. The algorithm incorporates two inertial terms and a relaxation step via a contractive mapping, resulting in improved convergence properties and numerical stability. Under mild conditions of step sizes and inertial parameters, we establish strong convergence of the proposed algorithm to a point in the solution set that satisfies a variational inequality with respect to a contractive mapping. Beyond theoretical development, we demonstrate the practical effectiveness of the proposed algorithm by applying it to data classification tasks using Deep Extreme Learning Machines (DELMs). In particular, the training processes of Two-Hidden-Layer ELM (TELM) models is reformulated as convex regularized optimization problems, enabling robust learning without requiring direct matrix inversions. Experimental results on benchmark and real-world medical datasets, including breast cancer and hypertension prediction, confirm the superior performance of our approach in terms of evaluation metrics and convergence. This work unifies and extends existing inertial-type forward–backward schemes, offering a versatile and theoretically grounded optimization tool for both fundamental research and practical applications in machine learning and data science. Full article

Artificial intelligence (AI) is increasingly investigated as a potential adjunct in the diagnosis and staging of lung cancer, particularly through integration with bronchoscopy and endobronchial ultrasound (EBUS). Deep learning models have been applied to modalities such as white-light imaging, autofluorescence bronchoscopy, and spectroscopy, with the aim of assisting lesion detection, standardizing interpretation, and reducing interobserver variability. AI has also been explored in EBUS for lymph node assessment and guidance of transbronchial needle aspiration (EBUS-TBNA), with preliminary studies suggesting possible improvements in diagnostic yield. However, current evidence remains largely confined to small, retrospective, single-center datasets, often reporting performance under idealized conditions. External validation is rare, reproducibility is undermined by a lack of data and code availability, and workflow integration into real-world bronchoscopy practice has not been demonstrated. As such, most systems should still be regarded as experimental. Translating AI into routine thoracic oncology will require large-scale, prospective, multicenter validation studies, greater data transparency, and careful evaluation of cost-effectiveness, regulatory approval, and clinical utility. Full article

Precise classification of unsound wheat grains is essential for crop yields and food security, yet most existing approaches rely on vision-only models that demand large labeled datasets, which is often impractical in real-world, data-scarce settings. To address this few-shot challenge, we propose UWGC, a novel vision-language framework designed for few-shot classification of unsound wheat grains. UWGC integrates two core modules: a fine-tuning module based on Adaptive Prior Refinement (APE) and a text prompt enhancement module that incorporates Advancing Textual Prompt (ATPrompt) and the multimodal model Qwen2.5-VL. The synergy between the two modules, leveraging cross-modal semantics, enhances generalization of UWGC in low-data regimes. It is offered in two variants: UWGC-F and UWGC-T, in order to accommodate different practical needs. Across few-shot settings on a public grain dataset, UWGC-F and UWGC-T consistently outperform existing vision-only and vision-language methods, highlighting their potential for unsound wheat grain classification in real-world agriculture. Full article

Deep learning models have demonstrated remarkable success in recognising tipping points and providing early warning signals. However, there has been limited exploration of their application to dynamical systems governed by coloured noise, which characterizes many real-world systems. In this study, we show that it is possible to leverage the normal forms of three primary types of bifurcations (fold, transcritical, and Hopf) to construct a training set that enables deep learning architectures to perform effectively. Furthermore, we showed that this approach could accommodate coloured noise by replacing white noise with red noise during the training process. To evaluate the classifier trained on red noise compared to one trained on white noise, we tested their performance on mathematical models using Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) scores. Our findings reveal that the deep learning architecture can be effectively trained on coloured noise inputs, as evidenced by high validation accuracy and minimal sensitivity to redness (ranging from 0.83 to 0.85). However, classifiers trained on white noise also demonstrate impressive performance in identifying tipping points in coloured time series. This is further supported by high AUC scores (ranging from 0.9 to 1) for both classifiers across different coloured stochastic time series. Full article

Background: Theintegration of artificial intelligence (AI) into clinical decision support systems (CDSSs) has significantly enhanced diagnostic precision, risk stratification, and treatment planning. AI models remain a barrier to clinical adoption, emphasizing the critical role of explainable AI (XAI). Methods: This systematic meta-analysis synthesizes findings from 62 peer-reviewed studies published between 2018 and 2025, examining the use of XAI methods within CDSSs across various clinical domains, including radiology, oncology, neurology, and critical care. Model-agnostic techniques such as visualization models like Gradient-weighted Class Activation Mapping (Grad-CAM) and attention mechanisms dominated in imaging and sequential data tasks. Results: However, there are still gaps in user-friendly evaluation, methodological transparency, and ethical issues, as seen by the absence of research that evaluated explanation fidelity, clinician trust, or usability in real-world settings. In order to enable responsible AI implementation in healthcare, our analysis emphasizes the necessity of longitudinal clinical validation, participatory system design, and uniform interpretability measures. Conclusions: This review offers a thorough analysis of the state of XAI practices in CDSSs today, identifies methodological and practical issues, and suggests a path forward for AI solutions that are open, moral, and clinically relevant. Full article

Digital Twins (DTs) have become central to intelligent asset management within Industry 4.0, enabling real-time monitoring, diagnostics, and predictive maintenance. However, implementing Prognostics and Health Management (PHM) strategies within DT frameworks remains a significant challenge, particularly in systems experiencing multi-component degradation (MCD). MCD occurs when several components degrade simultaneously or in interaction, complicating detection and isolation processes. Traditional data-driven fault detection models often require extensive historical degradation data, which is costly, time-consuming, or difficult to obtain in many real-world scenarios. This paper proposes a model-based, control-driven approach to MCD detection, which reduces the need for large training datasets by leveraging reference tracking performance in closed-loop control systems. We benchmark the accuracy of four control strategies—Proportional-Integral (PI), Linear Quadratic Regulator (LQR), Model Predictive Control (MPC), and a hybrid model—within a Digital Twin-enabled hydraulic system testbed comprising multiple components, including pumps, valves, nozzles, and filters. The control strategies are evaluated under various MCD scenarios for their ability to accurately detect and isolate degradation events. Simulation results indicate that the hybrid model consistently outperforms the individual control strategies, achieving an average accuracy of 95.76% under simultaneous pump and nozzle degradation scenarios. The LQR model also demonstrated strong predictive performance, especially in identifying degradation in components such as nozzles and pumps. Also, the sequence and interaction of faults were found to influence detection accuracy, highlighting how the complexities of fault sequences affect the performance of diagnostic strategies. This work contributes to PHM and DT research by introducing a scalable, data-efficient methodology for MCD detection that integrates seamlessly into existing DT architectures using containerized RESTful APIs. By shifting from data-dependent to model-informed diagnostics, the proposed approach enhances early fault detection capabilities and reduces deployment timelines for real-world DT-enabled PHM applications. Full article

This study investigates collaborative disaster response strategies involving the government and social organizations from a dynamic perspective, incorporating stochastic disturbances that influence emergency resource supply. To examine the strategic interactions among the participants, three stochastic differential game models are formulated under distinct scenarios: centralized decision making for collusive emergency response, decentralized emergency response without a cost-sharing contract, and decentralized emergency response with a cost-sharing contract. Under an infinite-horizon planning framework, the closed-form solutions for the optimal response efforts and the corresponding value functions are derived for all three scenarios and comparatively analyzed. The results indicate that compared with the purely decentralized scenario, introducing a cost-sharing mechanism achieves a Pareto improvement by optimizing both overall system efficiency and emergency supply availability. Although the centralized collusive model results in the highest expected level of emergency resource supply, it is also associated with the greatest uncertainty. Furthermore, a numerical simulation based on emergency resource allocation during the Wenchuan earthquake is conducted. The results show significant differences in resource availability and response performance under different response mechanisms. Centralized collaboration, together with a well-designed cost-sharing mechanism, can significantly enhance the robustness and efficiency of the overall system, offering important insights for optimizing real-world disaster response strategies. Full article