Search Results (3,289)

Traditional path planning methods primarily optimize distance or time, without fully considering the impact of slope gradients in park road networks, variations in vehicle load capacity, and braking energy recovery characteristics on the energy consumption of pure electric commercial vehicles. To address these issues, this paper proposes a globally optimized path planning method based on energy consumption minimization. The proposed method introduces a multi-factor coupled energy consumption model for pure electric commercial vehicles, integrating slope gradients, load capacity, motor efficiency, and energy recovery. Using this vehicle energy consumption model and the park road network topology map, an energy consumption topology map representing energy consumption between any two nodes is constructed. An energy-optimized improved ant colony optimization algorithm (E-IACO) is proposed. By introducing an exponential energy consumption heuristic factor and an enhanced pheromone update mechanism, it prioritizes energy-saving path exploration, thereby effectively identifying the optimal energy consumption path within the constructed energy consumption topology map. Simulation results demonstrate that in typical three-dimensional industrial park scenarios, the proposed energy-optimized path planning method achieves maximum reductions of 10.57% and 4.90% compared to the A* algorithm and ant colony optimization (ACO), respectively, with average reductions of 5.14% and 1.97%. It exhibits excellent stability and effectiveness across varying load capacities. This research provides a reliable theoretical framework and technical support for reducing logistics operational costs in industrial parks and enhancing the economic efficiency of pure electric commercial vehicles. Full article

Efficient resource allocation is a central challenge in multi-tenant cloud, fog, and edge environments, where heterogeneous tenants compete for shared resources under dynamic and uncertain workloads. Static or purely heuristic methods often fail to capture strategic tenant behavior, whereas many existing game-theoretic approaches overlook stochastic demand variability, fairness, or scalability. This paper proposes a Dynamic and Stochastic Game-Theoretic Allocation (DSGTA) model that jointly models non-cooperative tenant interactions, repeated strategy adaptation, and random workload fluctuations. The framework combines a Nash-like dynamic equilibrium, achieved via a lightweight best-response update rule, with an approximate Shapley-value-based fairness mechanism that remains tractable for large tenant populations. The model is evaluated on synthetic scenarios, with a trace-driven setup built from the Google 2019 Cluster dataset, and a scalability study is conducted with up to $K=500$ heterogeneous tenants. Using a consistent set of core metrics (tenant utility, resource cost, fairness index, and SLA satisfaction rate), DSGTA is compared against a static game-theoretic allocation (SGTA) and a dynamic pricing-based allocation (DPBA). The results, supported by statistical significance tests, show that DSGTA achieves higher utility, lower average cost, improved fairness and competitive utilization across diverse strategy profiles and stochastic conditions, thereby demonstrating its practical relevance for scalable, fair, and economically efficient resource allocation in realistic multi-tenant cloud environments. Full article

Biometric authentication on smartphones has advanced rapidly in recent years, with face recognition becoming the dominant modality due to its convenience and easy integration with modern mobile hardware. However, despite these developments, smartphone-based facial recognition systems remain vulnerable to a broad spectrum of attacks. This survey provides an updated and comprehensive examination of the evolving attack landscape and corresponding defense mechanisms, incorporating recent advances up to 2025. A key contribution of this work is a structured taxonomy of attack types targeting smartphone facial recognition systems, encompassing (i) 2D and 3D presentation attacks; (ii) digital attacks; and (iii) dynamic attack patterns that exploit acquisition conditions. We analyze how these increasingly realistic and condition-dependent attacks challenge the robustness and generalization capabilities of modern face anti-spoofing (FAS) systems. On the defense side, the paper reviews recent progress in liveness detection, deep-learning- and transformer-based approaches, quality-aware and domain-generalizable models, and emerging unified frameworks capable of handling both physical and digital spoofing. Hardware-assisted methods and multi-modal techniques are also examined, with specific attention to their applicability in mobile environments. Furthermore, we provide a systematic overview of commonly used datasets, evaluation metrics, and cross-domain testing protocols, identifying limitations related to demographic bias, dataset variability, and controlled laboratory conditions. Finally, the survey outlines key research challenges and future directions, including the need for mobile-efficient anti-spoofing models, standardized in-the-wild evaluation protocols, and defenses robust to unseen and AI-generated spoof types. Collectively, this work offers an integrated view of current trends and emerging paradigms in smartphone-based face anti-spoofing, supporting the development of more secure and resilient biometric authentication systems. Full article

In this paper, an event-triggered optimal control strategy is proposed for three-phase grid-connected voltage source inverters (VSIs) based on the voltage-modulated direct power control (VM-DPC) principle. The optimization control problem of VSIs is addressed in the framework of nonzero sum (NZS) games to ensure mutual cooperation between active power and reactive power. To achieve optimal performance, the power components are driven to track their desired references while minimizing the individual performance index function. Accurate tracking of active and reactive powers not only stabilizes the grid but also guarantees compliant renewable integration. An adaptive dynamic programming (ADP) approach is adopted, where the critic neural network (NN) approximates the value function and provides optimal control policies. Moreover, an event-triggered mechanism with a dead-zone operation is incorporated to reduce redundant updates, thereby saving computational and communication resources. The stability of the closed-loop system and a strictly positive minimum inter-event interval are guaranteed. Simulation results verify that the proposed method achieves accurate power tracking, improved dynamic performance, and efficient implementation. Full article

This paper provides a comprehensive review of the dynamic tensile behavior of concrete, focusing on its implications for seismic-resistant and impact-prone structures such as dams. The present work distinguishes itself in the following ways: providing the first comprehensive synthesis explicitly focused on large-aggregate dam concrete behavior across the seismic strain rate range (${10}^{-4}$ to ${10}^{-2}$ s${}^{-1}$), which is critical yet underrepresented in the existing literature; integrating recent experimental and numerical advances regarding moisture effects, load history, and cyclic loading—factors that are essential for dam safety assessments; and critically evaluating current design guidelines for concrete dams against state-of-the-art research to identify gaps between engineering practice and scientific evidence. Through the extensive synthesis of experimental data, numerical simulations, and existing guidelines, the study examines key factors influencing dynamic tensile strength, including strain rate effects, crack evolution, testing techniques, and material variables such as moisture content, load history, and aggregate size. Experimental results from spall tests, split Hopkinson pressure bar configurations, and cyclic loading protocols are analyzed, revealing dynamic increase factors ranging from 1.1 to over 12, depending on the strain rates, saturation levels, and preloading conditions. The roles of inertial effects, free water (via the Stefan effect), and microstructural heterogeneity in enhancing or diminishing tensile performance are critically evaluated. Numerical models, including finite element, discrete element, and peridynamic approaches, are discussed for their ability to simulate crack propagation, inertia-dominated responses, and moisture interactions. The review identifies and analyzes current design guidelines. Key conclusions emphasize the necessity of integrating moisture content, load history, and mesoscale heterogeneity into dynamic constitutive models, alongside standardized testing protocols to bridge gaps between laboratory data and real-world applications. The findings advocate for updated engineering guidelines that reflect recent advances in rate-dependent fracture mechanics and multi-scale modeling, ensuring safer and more resilient concrete infrastructure under extreme dynamic loads. Full article

In order to solve the problems of slow convergence speed, insufficient accuracy, and easily falling into the local optimum of the traditional whale optimization algorithm (WOA) in mobile robot path planning, an improved whale optimization algorithm (IWOA) combined with the bird navigation mechanism was proposed. Specific improvement measures include using logical chaos mapping to initialize the population to enhance the randomness and diversity of the initial solution, designing a nonlinear convergence factor to prevent the algorithm from prematurely entering the shrinking surround phase and extending the global search time, introducing an adaptive spiral shape constant to dynamically adjust the search range to balance exploration and development capabilities, optimizing the individual update strategy in combination with the bird navigation mechanism, and optimizing the algorithm through companion position information, thereby improving the stability and convergence speed of the algorithm. Path planning simulations were performed on 30 × 30 and 50 × 50 grid maps. The results show that compared with WOA, MSWOA, and GA, in the 30 × 30 map, the path length of IWOA is shortened by 3.23%, 7.16%, and 6.49%, respectively; in the 50 × 50 map, the path length is shortened by 4.88%, 4.53%, and 28.37%, respectively. This study shows that IWOA has significant advantages in the accuracy and efficiency of path planning, which verifies its feasibility and superiority. Full article

The environmental crisis precipitated by climate change has accelerated the urgency of urban green and low-carbon transformation. In 2024, China’s Action Plan for the National Standardization Development Outline (2024–2025) stipulated requirements for continuously improving the standard system for carbon peaking and carbon neutrality in public institutions. As key venues for knowledge innovation and energy consumption, the low-carbon transformation of higher education institutions holds significant importance for China’s achievement of its dual carbon goals. However, China lacks a systematic evaluation framework specifically designed for university campus carbon emissions. Existing green campus assessment standards often suffer from inadequate indicator adaptability, a lack of update mechanisms, and limited coverage. The STARS sustainability assessment system, widely adopted in North America, offers valuable reference points for establishing campus carbon emissions evaluation frameworks due to its features of indicator adaptability, dynamic update mechanisms, and comprehensive evaluation dimensions. This paper conducts an exploratory comparative case study of Princeton University (USA) and Tianjin University (China)—two leading research-intensive institutions—within the STARS 2.2 framework. It systematically analyses their divergent approaches to carbon management and evaluation, not as representatives of their respective continents, but as exemplars of how advanced universities operationalize low-carbon transitions. Based on this analysis and a review of domestic Chinese standards, it proposes a development pathway for China’s university campus carbon emissions evaluation system: (1) Establish a differentiated indicator system combining ‘universal fundamentals with discipline-specific types’ to enhance adaptability to campus characteristics; (2) Establish a mechanism for periodic version updates to the evaluation standard itself, ensuring alignment with evolving national carbon goals and technological advancements; (3) Develop a comprehensive and transparent carbon accounting framework that integrates direct emissions, purchased energy, and indirect sources. This research provides theoretical foundations and methodological support for institutional development and practical optimization in carbon emissions evaluation within Chinese higher education institutions. Full article

In response to the challenges posed by the complex evolution of risks and the static nature of traditional assessment methods during the construction of long-span prestressed concrete (PC) continuous rigid-frame bridges, this study proposes a risk assessment framework that integrates visual perception with dynamic probabilistic reasoning. By combining an improved YOLOv8 model with the Decision-making Trial and Evaluation Laboratory–InterpretiveStructure Modeling (DEMATEL–ISM) algorithm, the framework achieves intelligent identification of risk elements and causal structure modelling. On this basis, a dynamic Bayesian network (DBN) is constructed, incorporating a sliding window and forgetting factor mechanism to enable adaptive updating of conditional probability tables. Using the Tongshun River Bridge as a case study, at the identification layer, we refine onsite targets into 14 risk elements (F1–F14). For visualization, these are aggregated into four categories—“Bridge, Person, Machine, Environment”—to enhance readability. In the methodology layer, leveraging causal a priori information provided by DEMATEL–ISM, risk elements are mapped to scenario probabilities, enabling scenario-level risk assessment and grading. This establishes a traceable closed-loop process from “elements” to “scenarios.” The results demonstrate that the proposed approach effectively identifies key risk chains within the “human–machine–environment–bridge” system, revealing phase-specific peaks in human-related risks and cumulative increases in structural and environmental risks. The particle filter and Monte Carlo prediction outputs generate short-term risk evolution curves with confidence intervals, facilitating the quantitative classification of risk levels. Overall, this vision-guided dynamic risk assessment method significantly enhances the real-time responsiveness, interpretability, and foresight of bridge construction safety management and provides a promising pathway for proactive risk control in complex engineering environments. Full article

The Internet of Things (IoT) has established an exceptional ecosystem of interconnected devices where a vast multitude of heterogeneous devices can communicate, collect, and share data for enhanced decision-making processes. To effectively analyze this immense volume of data, researchers have deployed advanced machine learning algorithms and deep neural networks. However, these approaches typically rely on centralized data storage for training, which raises significant privacy concerns. Federated Learning (FL) addresses this issue by allowing devices to train local models on their own data and share only model updates. Despite this advantage, FL remains vulnerable to several security threats, including model poisoning, data manipulation, and Byzantine attacks. Therefore, robust and scalable authentication mechanisms are essential to ensure secure participation in FL environments. This study provides a comprehensive survey of authentication in FL. We examine the authentication process, discuss the associated key challenges, and analyze architectural considerations relevant to securing FL deployments. Existing authentication schemes are reviewed and evaluated in terms of their effectiveness, limitations, and practicality. To provide deeper insight, we classify these schemes along two dimensions as follows: their underlying enabling technologies, such as blockchain, cryptography, and AI-based methods, and the system contexts in which FL operates. Furthermore, we analyze the datasets and experimental environments used in current research, identify open research challenges, and highlight future research directions. To the best of our knowledge, this study presents the first structured and comprehensive analysis of authentication mechanisms in FL, offering a foundational reference for advancing secure and trustworthy federated learning systems. Full article

Post-Traumatic Stress Disorder (PTSD) is consistently linked to multidimensional working memory (WM) impairments, encompassing deficits in sustained attention, verbal and visuospatial processing, and executive control, with inhibitory dysfunction emerging as a key feature. This scoping review synthesizes evidence from 39 studies examining neurobiological mechanisms, trauma-related factors, genetic and hormonal influences, gender differences, and task-specific variability. Findings indicated that PTSD is associated with altered activation and connectivity in the prefrontal cortex, hippocampus, and related neural networks, often resulting in compensatory but inefficient recruitment patterns. Emotional distraction and comorbidities such as depression, alcohol use, and traumatic brain injury can exacerbate cognitive deficits. Performance impairments are evident across both emotional and neutral WM tasks, with visuospatial and updating processes being particularly vulnerable. Risk factors include chronic trauma exposure, older age, APOE ε4 allele, and the BDNF Val66Met (rs6265) polymorphism, while modulators such as oxytocin, cortisol, and physical activity show potential cognitive benefits under specific conditions. Methodological heterogeneity and limited longitudinal data restrict generalizability. These findings underscore the importance of early screening, targeted cognitive interventions, and inclusion of underrepresented populations to refine prevention and treatment strategies for PTSD-related WM deficits. Full article

Background/Objectives: Parkinson’s disease (PD) and Dementia with Lewy Bodies (DLB) are neurodegenerative disorders characterized by the accumulation of misfolded alpha-synuclein protein in the brain. Mutations in the glucocerebrosidase 1 (GBA1) gene have been identified as a significant genetic risk factor for both PD and DLB. GBA1 encodes for the lysosomal enzyme glucocerebrosidase, which is responsible for the breakdown of glucosylceramide (GC). Deficiencies in glucocerebrosidase activity lead to the accumulation of glucosylceramide within lysosomes, contributing to lysosomal dysfunction and impaired protein degradation. The aim of this narrative review is to update the underlying mechanisms by which GBA1 mutations contribute to the pathogenesis of PD and DLB. Methods: A comprehensive literature search was conducted across four major electronic databases (PubMed, Web of Science (Core Collection), Scopus, and Embase) from inception to 8 November 2025. The initial search identified approximately 1650 articles in total, with the number of hits from each database being as follows: PubMed (~450), Web of Science (~380), Scopus (~520), and Embase (~300). Results: The mechanism by which mutations in the GBA1 gene contribute to PD involves both loss-of- function and gain-of-function pathways, which are not mutually exclusive. Typically, GBA1 mutations lead to a loss of function by reducing the activity of the GCase enzyme, impairing the autophagy- lysosomal pathway and leading to α-synuclein accumulation. However, some mutant forms (GBA1L444P) of the GCase enzyme can also acquire a toxic gain of function, contributing to α-synuclein aggregation through mechanisms like endoplasmic reticulum stress and misfolding. While Venglustat effectively reduced GC levels, a key marker associated with GBA1-PD, the lack of clinical improvement led to the discontinuation of its development for this indication. Conclusions: GBA1-mediated lysosomal and lipid dysregulation represents a key pathogenic axis in PD and DLB. Understanding these mechanisms provides crucial insight into disease progression and highlights emerging therapeutic strategies—such as pharmacological chaperones, substrate reduction therapies, and gene-targeted approaches—aimed at restoring GCase function and lysosomal homeostasis to slow or prevent neurodegeneration. Full article

Reinforcement learning (RL) has achieved remarkable success in complex decision-making tasks; however, its application to structured combinatorial optimization problems in operations research (OR) and smart manufacturing remains challenging due to high-dimensional state spaces, inefficient exploration, and unstable training dynamics. In this work, we propose Information Bottleneck-Enhanced Reinforcement Learning (IBE), a novel framework that integrates information-theoretic regularization into attention-based RL architectures to enhance both representation learning and exploration efficiency. IBE introduces two complementary objectives: (1) a state representation bottleneck, which drives the encoder to extract compact and task-relevant representations from high-dimensional sensory or operational data by minimizing redundant information; (2) a policy bottleneck, which regularizes policy optimization through an information-based exploration bonus derived from the mutual information between states and actions. Together, these mechanisms promote more robust representations, smoother policy updates, and more effective exploration in large, structured decision spaces. We evaluate IBE on representative routing and scheduling problems that commonly arise in logistics and sensor-driven manufacturing systems. Experimental results show that IBE consistently outperforms strong RL baselines, including PPO, REINFORCE, AM, and NeuOpt in both performance and stability. Comprehensive ablation studies further confirm the complementary effects of the two bottleneck components. Overall, IBE provides a principled and generalizable framework for improving RL performance in combinatorial optimization and real-world industrial decision-making under Industry 4.0 environments. Full article

This review systematically explores the potential of the active inference framework in illuminating the cognitive mechanisms of decision-making in repeated games. Repeated games, characterized by multi-round interactions and social uncertainty, closely resemble real-world social scenarios in which the decision-making process involves interconnected cognitive components such as inference, policy selection, and learning. Unlike traditional reinforcement learning models, active inference, grounded in the principle of free energy minimization, unifies perception, learning, planning, and action within a single generative model. Belief updating occurs by minimizing variational free energy, while the exploration–exploitation dilemma is balanced by minimizing expected free energy. Based on partially observable Markov decision processes, the framework naturally incorporates social uncertainty, and its hierarchical structure allows for simulating mentalizing processes, providing a unified account of social decision-making. Future research can further validate its effectiveness through model simulations and behavioral fitting. Full article

Installation in sand is sensitive to its evolving pore structure, yet design models rarely update permeability for real-time fabric changes. This study tracks the stress-dependent pore size distribution of coarse sand under laterally constrained compression using high-resolution X-ray nano-CT. Scans taken at six axial stress levels show that the distribution shifts toward smaller radii while keeping its log-normal shape. A single shifting factor, defined as the current median radius normalized by the initial value, captures this translation. The factor decays with axial stress according to a power law, and the exponent as well as the reference pressure are calibrated from void ratio data. The resulting closed-form expression links mean effective stress to pore radius statistics without extra fitting once the compressibility constants are known. This quantitative relation between effective stress and pore size distribution has great potential to be embedded into coupled hydro-mechanical solvers, enabling engineers to refresh hydraulic permeability at every computation step, improving predictions of excess pore pressure and soil resistance during suction anchor penetration for floating wind foundations. Full article

With the continuous development of semiconductor manufacturing technology and information technology, the sizes of wafer chips are becoming smaller and the variety is increasing, which has put forward high requirements for wafer chip precision manufacturing and packaging workshops. On the one hand, the market demand for multiple varieties and small batches will increase the difficulty of scheduling. On the other hand, the complex manufacturing environment brings various types of dynamic events that will disrupt production plans. Accordingly, this work researches the wafer chip precision packaging workshop rescheduling problem under events of machine breakdown, emergency order inserting and original order modification. Firstly, the mathematical model for the addressed problem is established, and the rolling horizon technology is adopted to deal with multiple dynamic events. Then, a hybrid algorithm combining an improved firefly optimization framework and variable neighborhood search strategy is proposed. The population evolution mechanism is designed based on the location-updating law of fireflies in nature. The variable neighborhood search is applied for avoiding local optima and sufficiently exploring in the neighborhood. At last, the test results of comparative experiments and engineering cases indicate that the proposed method is effective and stable and is superior to the current advanced algorithms. Full article