Search Results (334,293)

The exponential growth of genomic and proteomic data has made computational protein–protein interaction (PPI) prediction indispensable, driving the need for a comprehensive and method-aware evaluation of supervised learning approaches. PPIs are fundamental to understanding cellular processes and disease mechanisms, yet experimental identification remains slow, costly, and difficult to scale. This survey systematically investigates ten supervised learning models—Extreme Learning Machine (ELM), Convolutional Neural Networks (CNNs), Graph Neural Networks (GNNs), Deep Neural Networks (DNNs), Naïve Bayes, Probabilistic Decision Tree, Support Vector Machine (SVM), Least Squares SVM (LS-SVM), K-Nearest Neighbor (KNN), and Weighted K-Nearest Neighbor (WKNN)—through a tri-layered framework that integrates Comparative Quantitative Analysis, Comparative Observational Analysis, and Experimental Evaluations. Beyond conventional accuracy summaries, this work provides critical commentary tied to real-world use, analyzing where techniques succeed or fail in practice—for instance, when instance-based methods bottleneck during inference, when kernel choices influence SVM variance, or when deep architectures trade accuracy for computational cost. The survey also offers concrete deployment guidance, such as calibration insights for WKNN versus KNN under varying feature noise or dataset curation quality, delivering operational perspectives that typical surveys omit. Comparative Quantitative Analysis consolidates metrics such as accuracy, F1-score, and computational time from the existing literature, while Comparative Observational Analysis evaluates interpretability, scalability, dataset suitability, and efficiency. Complementing these, Experimental Evaluations conducted by the authors empirically validate model performance on benchmark datasets. Together, these layers provide a unified and evidence-backed perspective on algorithmic strengths, weaknesses, and practical applicability. Findings show that GNNs and DNNs achieve the highest predictive accuracy due to their ability to capture structural and topological relationships, whereas ELM and Naïve Bayes offer superior efficiency. SVM and LS-SVM maintain robust stability under noisy conditions, and CNNs are well-suited for sequence-based prediction tasks. By combining empirical validation, critical insights, and deployment-focused recommendations, this survey delivers decision-grade guidance that bridges theoretical understanding with real-world implementation, thus clarifying the trade-offs among accuracy, efficiency, and scalability in PPI detection research. Full article

A series of Hf co-doped uranium-doped lithium niobate (LN:U,Hf) crystals with a diameter of one inch were grown by the modified Bridgman method. XPS analysis showed that U ions coexist in mixed valence states of U⁴⁺, U⁵⁺, and U⁶⁺. At 442 nm, LN:U,Hf_1.0 exhibited a fast photorefractive response of 0.32 s together with a high saturation diffraction efficiency of 82.01%. With increasing Hf concentration, the optical damage resistance was significantly enhanced, and LN:U,Hf_5.0 achieved an optical damage threshold of 2.8 × 10⁵ W/cm². Two-beam coupling experiments indicated that electrons are the dominant charge carriers and diffusion is the main transport mechanism. It demonstrates that co-doping Hf⁴⁺ provides an effective route to simultaneously enhance photorefractive response and optical damage resistance in LN:U, offering potential for high-power and fast-response photonic devices. Full article

Organizations ought to demonstrate a responsibility for conditions that reduce moral stress and enhance moral resilience for their employees. No literature to date has explored how Emergency Management Personnel (EMP) experience both moral stress and distress [(di)stress], building up to stigma during health crises, given their role in emergency management operations. This study draws from a primary study of EMP, including frontline and first responders and those in leadership, who reported structural stigma during the COVID-19 pandemic. Our research question was, In what ways did structural stigma shape the moral landscape of emergency management practice during COVID-19? This qualitative study draws on the paradigm of critical realism to conduct thematic analysis. Interviews and focus groups were collected in 2024 from a total of 23 participants in the Greater Toronto Area, Canada. Participants represented EMP across emergency and public service sectors. System-level stressors revealed disruptions or “cracks” from an overwhelmed public services system. In sum, systemic “cracks” gave rise to organizational mechanisms designed to compensate for system failures, inadvertently propagating structural stigma. At times these mechanisms generated moral distress and/or resilience, through simultaneously expanding and limiting EMP’s responsibility and agency. The authors suggest that EMP build their leadership capacity to enhance skills of structural competency. Full article

In persistent patrol and online task discovery in environments with obstacles, unmanned aerial vehicle (UAV) swarms are constrained by limited battery capacity and frequent recharging disrupts patrol continuity. In comparison, unmanned ground vehicle (UGV) fleets have higher endurance and payload capacity and can serve as mobile charging platforms while executing ground-service tasks. In such collaborative scenarios, UAVs patrol along a coverage path and discover tasks online, whereas UGVs execute discovered ground tasks and provide mobile charging support. To cope with rendezvous uncertainty due to obstacle-induced detours and inefficient usage of UGV time during charging, this study proposes an energy-constrained UAV-UGV coordination framework based on adaptive anticipatory rendezvous and time-window scheduling. In particular, the adaptive anticipatory rendezvous module handles anticipatory rendezvous planning, while the time-window scheduling module models the post-rendezvous charging stage as a schedulable time window for opportunistic ground-task insertion. Simulations demonstrate that the proposed framework consistently reduces system energy consumption, completion time, and the number of emergency landings compared with three representative baselines. Moreover, a UAV-UGV prototype with AprilTag-based visual landing and post-landing mechanical correction is developed to validate the engineering feasibility of the key closed-loop process. Full article

This research investigates the formation of an amorphous phase in a non-equiatomic aluminum-based alloy, Al₈₂Fe₁₂Cu₂Nb₂Co₂, synthesized via mechanical alloying. By utilizing minor additions of Nb, Co, and Cu, structural stability and “chemical complexity” effects are achieved in a matrix dominated by a single element (82% Al). Thermodynamic analysis reveals that a moderately negative mixing enthalpy (ΔHₘᵢₓ = −6.89 kJ/mol) and elevated configurational entropy (ΔSₘᵢₓ = 5.420 J/mol·K) are the primary thermodynamic drivers of amorphization, supplemented by a transitional-regime atomic size mismatch (δ = 4.82%). The evolution of the structure, morphology, and magnetic properties of mechanically alloyed amorphous Al₈₂Fe₁₂Cu₂Nb₂Co₂ as a function of milling time was systematically investigated using X-ray diffraction, scanning electron microscopy, Fourier-transform infrared spectroscopy, and a vibrating sample magnetometer. Full article

Many existing preference-based multi-objective evolutionary algorithms compress decision-maker preferences into a single point or direction, which limits their ability to specify desired objective ranges. This paper proposes Dual-Layer SPEA2 with Preference Differential Evolution (DLSPEA2-PDE), which guides populations toward hyper-rectangular preference regions of interest through three mechanisms: virtual boundary reference-point guidance, dual-layer fitness assignment, and region-aware differential evolution. The algorithm is validated on ZDT and DTLZ benchmark suites under single-ROI, multi-ROI, and infeasible-ROI scenarios, and comparisons with diverse types of preference-based MOEAs confirm its robustness and coverage uniformity under diverse ROI configurations. To demonstrate practical utility, the algorithm is applied to a multi-stage emergency resource allocation model that accounts for pre-disaster prediction uncertainty and employs conditional value-at-risk to control tail risk, capturing how prediction biases propagate and amplify through inter-stage coupling. On this problem, DLSPEA2-PDE significantly outperforms SPEA2 across all indicators, and runs substantially faster than T-NSGA-II at comparable solution quality. Full article

This study addresses the problem of robust video-based tracking of laboratory rats in open-field and Y-maze experiments under challenging acquisition conditions, including non-uniform illumination, low contrast, and heterogeneous recording setups. Existing approaches based on classical image processing or deep learning often fail to maintain stable localization under such conditions or require large, annotated datasets. We propose a hybrid tracking framework that combines an improved motion–appearance voting mechanism with consistency-constrained optimization for open-field experiments, together with a comparative deep learning-based detection strategy for Y-maze analysis. The proposed method introduces (i) adaptive dual-threshold motion extraction, (ii) directionally constrained temporal validation, and (iii) a robustness-driven fusion of motion and appearance cues. Experimental results demonstrate that the proposed approach achieves reliable tracking with a maximum localization error below 10 pixels under severe illumination variations. In the Y-maze scenario, a comparative evaluation of multiple detectors (YOLOv5, YOLOv9, YOLO12, Faster R-CNN) highlights the trade-off between accuracy and inference time, with YOLOv9 providing the best balance. The main contribution consists of enabling robust behavioral quantification in low-quality experimental conditions using limited training data, bridging the gap between classical tracking robustness and deep learning flexibility. Full article

Varistors are critical overvoltage protection components in modern power electronic systems. They effectively absorb and dissipate surge energy to ensure the safe and stable operation of electrical equipment. However, surface defects can lead to substandard performance or even trigger equipment failure, compromising overall system stability. Therefore, high-precision surface defect detection is essential for quality assurance. To address these challenges, we propose a lightweight model termed Defect-Aware and Edge-Reconstruction Enhanced YOLO (DAER-YOLO) for efficient varistor inspection. First, we construct a C3k2-based defect-aware enhancement module (C3k2-iEMA). This module tackles the difficulty of extracting features from small or morphologically complex defects. By integrating multi-scale feature extraction, an attention mechanism, and efficient nonlinear mapping, it strengthens the perception of defect details. Second, to enhance the reconstruction capability for edge damage and small-object defects, we introduce the Efficient Up-Convolution Block (EUCB). This block improves multi-level feature fusion and generates clearer enhanced feature maps. Based on these improvements, DAER-YOLO outperforms the YOLOv11n baseline on a custom varistor dataset, with mAP@50 and mAP@50:95 increasing by 1.6% and 2.3%, respectively. Experimental results demonstrate that the model effectively improves detection accuracy while exhibiting significant potential for real-time industrial applications. Full article

Acid Orange 3 (AO3) is a widely used azo dye in leather, paper, and textile dyeing. Untreated direct discharge into water bodies severely threatens human health and aquatic ecosystems, yet efficient degradation remains challenging for conventional technologies. In this work, RuO₂/CeO₂ heterostructure was synthesized and immobilized on a Ti substrate via controlled hydrothermal and annealing treatments, yielding RuO₂/CeO₂@Ti electrode. The electrode showed electrocatalytic activity for the oxygen evolution reaction (OER) over a wide pH range. Under optimized conditions (47 mA/cm², pH 6, 0.25 M NaCl), 150 mg/L AO3 was degraded by 95.89% within 180 min. The degradation mechanism was elucidated by GC-MS and DFT (density functional theory) calculations. The degradation process was dominated by indirect oxidation, sequentially involving azo bond cleavage, heterocyclic ring opening, desulfurization, denitrification, benzene ring cleavage, and mineralization of small molecules into H₂O and CO₂. Full article

While hydroxypropyl methylcellulose (HPMC) is a promising plant-based alternative to gelatin, its industrial application is limited by poor mechanical properties and high production costs. In this study, high-performance HPMC-based hard capsules were developed using an HPMC/gellan gum/ι-carrageenan ternary system. The formulation and preparation process were optimized via single-factor experiments, response surface methodology, and low-field nuclear magnetic resonance analysis. Scanning electron microscopy was applied to characterize the microstructural evolution during disintegration. The optimized capsules exhibited rapid disintegration within 15 min across four pH media and satisfied the requirements of the Chinese Pharmacopoeia (2025). Drug dissolution profiles using cefradine and ranitidine hydrochloride showed over 85% cumulative release within 30 min, with similarity factors higher than 50 relative to commercial gelatin capsules under the tested conditions. This work provides a feasible and low-cost strategy for the industrial production of plant-based capsules and promotes the high-value utilization of polysaccharide-based capsule materials. Full article

Gravelly soil is widely used in western China but suffers from poor gradation, low water stability, and weak freeze–thaw resistance. Traditional cementitious stabilizers involve high energy and carbon emissions. To address these issues, a novel, eco-friendly gravelly soil stabilizer was prepared from waste foamed concrete (WFC) via crushing, ball milling, and high-temperature calcination. This study systematically evaluated stabilization performance and mechanisms. Results indicate that the WFC stabilizer significantly enhances soil properties. At the optimal 30% dosage, the 28-day unconfined compressive strength (UCS) reached 6.5 MPa (a 333% increase), and water stability was significantly improved. Under freeze–thaw conditions, the 30% dosage yielded a mere 2% mass loss after five cycles, with the UCS reaching 9.56 MPa (a 437% increase). Microstructural analyses (XRD, SEM) revealed that hydration generates calcium silicate hydrate (C-S-H) gel and katoite (Ca₃Al₂(SiO₄)_3−x(OH)_4x). These products effectively fill soil pores and the spaces of the particles, optimizing the microstructure. This study provides a sustainable pathway for WFC recycling and offers a relatively lower energy consumption, low-carbon and high-performance stabilizer for reinforcing gravelly soil subgrades in cold regions. Full article

Multi-unmanned aerial vehicle (UAV) platforms integrate radio-frequency (RF) sensing, datalinks, and onboard embedded compute; adversarial electronic warfare (EW) degrades these subsystems through jamming and forces decentralized control policies to act on fragmented observations—a setting aligned with intelligent electronic systems and autonomous robotics in contested spectrum. Cooperative swarms then face two compounding failure modes: loss of coherent situational awareness, and reward-driven passive survival that suppresses mission completion. Memory-based multi-agent reinforcement learning (MARL) partially addresses the first but tends to reinforce the second; dense intent shaping addresses the second but becomes unreliable when observations are incomplete. We propose CIDA (Cognitive–Intent Dual-Stream Architecture), a reinforcement learning framework that decouples belief reconstruction from tactical intent at the representation level while coupling them through a unified actor–critic update. The cognitive stream encodes a 64-step observation history with a pre-normalized Transformer to reconstruct threat belief; the intent stream supplies a hierarchical potential field (reconnaissance, threat-weighted engagement, and approach incentives). A steady-state training mechanism (dynamic reward scaling and adaptive gradient clipping) stabilizes Transformer-based on-policy learning under non-stationary multi-agent dynamics. In a complex terrain scenario with SAM, AAA, and jammer assets, CIDA reaches 96.15% task success versus 12.21% (memoryless PPO) and 25.28% (MAPPO+RNN), with ablations showing nonlinear coupling and emergent tactics such as jammer bypass and weak-sector traversal. Results are robust to a four-fold sweep of the intent-shaping weight (above 90% success). Full article

Background: Open banking (OB) is rapidly transforming financial ecosystems by enabling controlled data sharing among multiple actors through application programming interfaces (APIs). While this transformation promises innovation and competition, it also introduces complex security challenges that extend beyond purely technical considerations. Despite growing attention in academic and professional domains, existing reviews provide limited integration of security concerns with global adoption patterns and cross regional variation. Methods: This systematic review analyses empirical and conceptual research on security in OB published between 1999 and 2025, capturing early digital banking studies that later informed the development of OB. The literature is structured into three distinct phases: foundational digital banking developments, regulatory formalisation of OB frameworks, and post-implementation expansion of OB ecosystems. A comprehensive search was conducted across major academic databases and scholarly portals, complemented by relevant regulatory and policy sources. Following duplicate removal, title and abstract screening, full-text eligibility assessment, and methodological quality appraisal, 117 studies were retained for qualitative synthesis. Results: The findings reveal recurring security challenges arising from the interaction between technological infrastructures, regulatory frameworks, and user behaviour within OB ecosystems. Technical safeguards such as APIs, strong customer authentication, and encryption are necessary but insufficient when they are misaligned with regulatory implementation and user behaviour. Behavioural factors, including trust, consent understanding, and security-related decision making, play a central role in shaping ecosystem resilience. Based on this synthesis, the study develops a tri-dimensional security framework integrating technological, regulatory, and behavioural dimensions. The bibliometric analysis of 117 studies reveals that technological security dominates the literature (58%), followed by regulatory governance (44%) and behavioural dimensions (42%). However, only 17.9% of studies integrate all three dimensions simultaneously. APIs and authentication mechanisms represent the most frequent technological terms, while PSD2 and GDPR dominate regulatory discourse. Trust and decision-making are the most recurrent behavioural constructs. The relatively low proportion of fully integrated studies confirms a structural fragmentation within OB security research, thereby empirically justifying the proposed tri-dimensional framework. Chronologically, early studies (1999–2015) predominantly focused on technical security mechanisms and regulatory compliance, whereas more recent research (2020–2025) increasingly highlights the interplay between regulatory frameworks and user behaviour, suggesting a shift towards a more holistic understanding of security within OB adoption. Conclusions: This systematic review concludes that integrating technological, regulatory, and behavioural perspectives advances a more comprehensive understanding of security in OB ecosystems. The proposed tri-dimensional security framework provides a structured foundation for future research and supports policy-relevant and practice-oriented security design. Full article

Driven by the rapid proliferation of generative artificial intelligence, the computing power industry is undergoing a paradigm shift from traditional linear supply chains toward complex, interdependent innovation ecosystems. This study investigates the evolutionary dynamics of the computing power ecosystem, specifically examining the strategic interplay between antitrust regulation and vertical integration. We construct a tripartite evolutionary game framework involving the government regulators, leading computing power incumbents, and downstream AI innovators. By deriving evolutionarily stable strategies, we analyze the underlying mechanisms of system transitions and employ numerical simulations to explore key parametric sensitivities. The theoretical analysis suggests that the evolution of the AI computing power innovation ecosystem manifests distinct stage-based progressions and threshold-driven bifurcation characteristics—potentially transitioning from an initial efficiency-based state of “natural monopoly and passive dependence” during the industry’s emergence, through transitionary states such as the “comfort zone trap” or “regulatory stalemate” during the expansion phase, and ultimately converging toward a mature configuration of “co-opetition and endogenous growth.” The model suggests that downstream AI firms may benefit from advancing vertical integration, achieving hardware–software co-optimization through self-developed domain-specific architectures, The analysis further implies that the leading computing power firm could strengthen its ecological niche by opening its underlying interfaces and software stacks to maintain its ecological niche as the industry cornerstone in integrated form. For the government, it is necessary to establish precise dynamic intervention and orderly exit mechanisms. Full article

Blockchain systems generate massive volumes of transactional data, yet most existing analytical approaches rely on query-based retrieval mechanisms that treat transactions as isolated records. In this paper, a trajectory-based framework for blockchain analysis is introduced where user activity is modeled as temporally ordered behavioral patterns. Four types of blockchain trajectories are formally defined: miner reward trajectories, sender value-and-fee trajectories, receiver value trajectories, and sender–receiver interaction trajectories. Unlike traditional query frameworks, trajectories are treated as first-class analytical objects, explicitly constructed and returned as outputs, thereby enabling structured temporal reasoning over blockchain behavior. To demonstrate the practicality of the approach, the proposed trajectory functions are implemented in Python 3.12 and experiments are conducted using real data from the Ethereum blockchain. Compared with conventional query-based approaches that return isolated transactions, the experimental results show that the proposed trajectory-based framework enables a more systematic identification of temporal behavioral patterns, including persistent miner dominance, recurrent zero-value interactions, sender–receiver role reversals and sender dominance by sending the highest values across several periods. The results show that trajectory-based modeling provides a systematic lens for uncovering temporal and structural regularities that are not readily observable through conventional query techniques. This work establishes a formal foundation for behavioral blockchain analytics and opens new research directions in centralization measurement, predictive modeling, and trajectory similarity analysis. Full article