Search Results (657)

FPGA-based reconfigurable systems play a vital role in many critical domains by virtue of their unique advantages. They can effectively adapt to dynamically changing application scenarios, while featuring high parallelism and low power consumption. As a result, they have been widely adopted in key sectors such as aerospace, nuclear industry, and weapon equipment, where high performance and stability are of utmost importance. However, these systems face significant challenges. The continuous and drastic reduction in chip process size has led to increasingly complex and delicate internal circuit structures and physical characteristics. Meanwhile, the operating environments are often harsh and unpredictable. Additionally, the adoption of untrusted third-party foundries to reduce development costs further compounds these issues. Collectively, these factors make such systems highly susceptible to reliability threats, including environmental radiation, aging degradation, and malicious hardware attacks. These problems severely impact the stable operation and functionality of the systems. Therefore, ensuring the highly reliable operation of reconfigurable systems has become a critical issue that urgently needs to be addressed. There is a pressing need to summarize their technical characteristics, research status, and development trends comprehensively and in depth. In response, this paper conducts relevant research. By systematically reviewing 183 domestic and international research papers published between 2012 and 2024, it first provides a detailed analysis of the root causes of reliability issues in reconfigurable systems, thoroughly exploring their underlying mechanisms. Second, it focuses on the key technologies for achieving high reliability, encompassing four types of fault-tolerant design technologies, three types of aging mitigation technologies, and two types of hardware attack defense technologies. The paper comprehensively summarizes relevant research findings and the latest advancements in this field, offering a wealth of references for related research. Finally, it conducts a detailed comparative analysis and summary of the research hotspots in the field of high-reliability reconfigurable systems. It objectively evaluates the achievements and shortcomings of current research efforts and delves into the development trends of key technologies for high-reliability reconfigurable systems, providing clear directions for future research and practical applications. Full article

Autonomous driving perception systems are vulnerable to physical adversarial attacks. Existing defenses largely adopt loosely coupled architectures where visual and kinematic cues are processed in isolation, thus failing to exploit physical spatiotemporal consistency as a structural prior and often struggling to balance adversarial robustness, transferability, accuracy, and efficiency under realistic attacks. We propose a physics-aware trajectory–appearance consistency defense that detects and corrects spatiotemporal inconsistencies by tightly coupling visual semantics with physical dynamics. The module combines a dual-stream spatiotemporal encoder with endogenous feature orchestration and a frequency-domain kinematic embedding, turning tracking artifacts that are usually discarded as noise into discriminative cues. These inconsistencies are quantified by a Trajectory–Appearance Mutual Exclusion (TAME) energy, which supports a physics-aware switching rule to override flawed visual predictions. Operating on detector backbone features, outputs, and tracking states, the defense can be attached as a plug-in module behind diverse object detectors. Experiments on nuScenes, KITTI, and BDD100K show that the proposed defense substantially improves robustness against diverse categories of attacks: on nuScenes, it improves Correction Accuracy (CA) from 86.5% to 92.1% while reducing the computational overhead from 42 ms to 19 ms. Furthermore, the proposed defense maintains over 71.0% CA when transferred to unseen detectors and sustaining 72.4% CA under adaptive attackers. Full article

Curling presents a challenging continuous-control problem in which shot outcomes depend on long-horizon interactions between complex physical dynamics, strategic intent, and opponent responses. Despite recent progress in applying reinforcement learning (RL) to games and sports, curling lacks a unified environment that jointly supports stable, rule-consistent simulation, structured state abstraction, and scalable agent training. To address this gap, we introduce a comprehensive learning framework for curling AI, consisting of a full-sized simulation environment, a task-aligned Markov decision process (MDP) formulation, and a two-phase training strategy designed for stable long-horizon optimization. First, we propose a novel MDP formulation that incorporates stone configuration, game context, and dynamic scoring factors, enabling an RL agent to reason simultaneously about physical feasibility and strategic desirability. Second, we present a two-phase curriculum learning procedure that significantly improves sample efficiency: Phase 1 trains the agent to master delivery mechanics by rewarding accurate placement around the tee line, while Phase 2 transitions to strategic learning with score-based rewards that encourage offensive and defensive planning. This staged training stabilizes policy learning and reduces the difficulty of direct exploration in the full curling action space. We integrate this MDP and training procedure into a unified Curling RL Framework, built upon a custom simulator designed for stability, reproducibility, and efficient RL training and a self-play mechanism tailored for strategic decision-making. Agent policies are optimized using Soft Actor–Critic (SAC), an entropy-regularized off-policy algorithm designed for continuous control. As a case study, we compare the learned agent’s shot patterns with elite match records from the men’s division of the Le Gruyère AOP European Curling Championships 2023, using 6512 extracted shot images. Experimental results demonstrate that the proposed framework learns diverse, human-like curling shots and outperforms ablated variants across both learning curves and head-to-head evaluations. Beyond curling, our framework provides a principled template for developing RL agents in physics-driven, strategy-intensive sports environments. Full article

Modern battlefield operations require wearable technologies to operate reliably under harsh physical, environmental, and security conditions. This review looks at today and tomorrow’s potential for ready field-grade wearables embedded with biometric authentication systems. It details physiological, kinematic, and multimodal sensor platforms built to withstand rugged, high-stress environments, and reviews biometric modalities like ECG, PPG, EEG, gait, and voice for continuous or on-demand identity confirmation. Accuracy, latency, energy efficiency, and tolerance to motion artifacts, environmental extremes, and physiological variability are critical performance drivers. Security threats, such as spoofing and data tapping, and techniques for template protection, liveness assurance, and protected on-device processing also come under review. Emerging trends in low-power edge AI, multimodal integration, adaptive learning from field experience, and privacy-preserving analytics in terms of defense readiness, and ongoing challenges, such as gear interoperability, long-term stability of templates, and common stress-testing protocols, are assessed. In conclusion, an R&D plan to lead the development of rugged, trustworthy, and operationally validated wearable authentication systems for the current and future militaries is proposed. Full article

Balancing convergence accuracy and population diversity remains a fundamental challenge in multi-objective optimization, particularly for complex and constrained engineering problems. To address this issue, this paper proposes a novel Multi-Objective Crested Porcupine Optimizer (MOCPO), inspired by the hierarchical defensive behaviors of crested porcupines. The proposed algorithm integrates four biologically motivated defense strategies—vision, hearing, scent diffusion, and physical attack—into a unified optimization framework, where global exploration and local exploitation are dynamically coordinated. To effectively extend the original optimizer to multi-objective scenarios, MOCPO incorporates a reference-point guided external archiving mechanism to preserve a well-distributed set of non-dominated solutions, along with an environmental selection strategy that adaptively partitions the objective space and enhances solution quality. Furthermore, a multi-level leadership mechanism based on Euclidean distance is introduced to provide region-specific guidance, enabling precise and uniform coverage of the Pareto front. The performance of MOCPO is comprehensively evaluated on 18 benchmark problems from the WFG and CF test suites. Experimental results demonstrate that MOCPO consistently outperforms several state-of-the-art multi-objective algorithms, including MOPSO and NSGA-III, in terms of IGD, GD, HV, and Spread metrics, achieving the best overall ranking in Friedman statistical tests. Notably, the proposed algorithm exhibits strong robustness on discontinuous, multimodal, and constrained Pareto fronts. In addition, MOCPO is applied to UAV path planning in four complex terrain scenarios constructed from real digital elevation data. The results show that MOCPO generates shorter, smoother, and more stable flight paths while effectively balancing route length, threat avoidance, flight altitude, and trajectory smoothness. These findings confirm the effectiveness, robustness, and practical applicability of MOCPO for solving complex real-world multi-objective optimization problems. Full article

Plant viruses cause substantial yield and quality losses worldwide, and their rapid evolution can erode deployed host resistance. This review synthesizes current knowledge of antiviral resistance and tolerance mechanisms, using barley yellow dwarf virus (BYDV) in cereals as an illustrative case study. We first summarize key layers of plant antiviral immunity, including pre-formed physical and chemical barriers, dominant and recessive resistance genes, RNA silencing, hormone-regulated defense signaling, and degradation pathways such as the ubiquitin–proteasome system and selective autophagy. We then discuss how these mechanisms are exploited in breeding and biotechnology, covering conventional introgression, marker-assisted selection, QTL mapping and pyramiding, induced variation (mutation breeding and TILLING/ecoTILLING), transgenic strategies (pathogen-derived resistance and plantibodies), RNA interference-based approaches, and CRISPR-enabled editing of susceptibility factors. Finally, we highlight emerging nano-enabled tools and propose integrated strategies that combine genetic resistance with surveillance and vector management to improve durability under climate change and ongoing viral diversification. Full article

Achieving high reliability remains the critical challenge for pulsed power supplies (PPS), whose core components are susceptible to severe degradation and catastrophic failure due to long-term operation under electrical, thermal and magnetic stresses, particularly those associated with high voltage and high current. This reliability challenge fundamentally limits the widespread deployment of PPSs in defense and industrial applications. This article provides a comprehensive and systematic review of the reliability challenges and recent technological progress concerning PPSs, focusing on three hierarchical levels: component, system integration, and extreme operating environments. The review investigates the underlying failure mechanisms, degradation characteristics, and structural optimization of key components, such as energy storage capacitors and power switches. Furthermore, it elaborates on advanced system-level techniques, including novel thermal management topologies, jitter control methods for multi-module synchronization, and electromagnetic interference (EMI) source suppression and coupling path optimization. The primary conclusion is that achieving long-term, high-frequency operation depends on multi-physics field modeling and robust, integrated design approaches at all three levels. In summary, this review outlines important research directions for future advancements and offers technical guidance to help speed up the development of next-generation PPS systems characterized by high power density, frequent repetition, and outstanding reliability. Full article

Ectomycorrhizal (ECM) symbiosis is a fundamental mutualism crucial for forest eco-system health. Its establishment is governed by sophisticated molecular dialogue preceding physical colonization. This review synthesizes this pre-colonization crosstalk, beginning with reciprocal signal exchange where root exudates trigger fungal growth, and fungal lipochitooligosaccharides activate host symbiotic programming, often via the common symbiosis pathway. Successful colonization requires fungi to navigate plant immunity. They employ effectors, notably mycorrhiza-induced small secreted proteins (MiSSPs), to suppress defenses, e.g., by stabilizing jasmonate signaling repressors or inhibiting apoplastic proteases, establishing a localized “mycorrhiza-induced resistance.” Concurrent structural adaptations, including fungal hydrophobins, expansins, and cell wall-modifying enzymes like chitin deacetylase, facilitate adhesion and apoplastic penetration. While this sequential model integrates immune suppression with structural remodeling, current understanding is predominantly derived from a limited set of model systems. Significant knowledge gaps persist regarding species-specific determinants in non-model fungi and hosts, the influence of environmental variability and microbiome interactions, and methodological challenges in capturing early signaling in situ. This review’s main contributions are: providing a synthesized sequential model of molecular crosstalk; elucidating the dual fungal strategy of simultaneous immune suppression and structural remodeling; and identifying crucial knowledge gaps regarding non-model systems and species-specific determinants, establishing a research roadmap with implications for forest management and ecosystem sustainability. Full article

This study presents a validated framework to quantify regional sea-level risk on U.S. coasts by (i) extracting trends and seasonality from satellite altimetry (ADT, GMSL), (ii) learning regional dynamical regimes via PCA-embedded KMeans on gridded ADT time series, and (iii) coupling these regimes with socioeconomic exposure (population, income, ocean-sector employment/GDP) and wetland submersion scoring. Relative to linear and ARIMA/SARIMA baselines, a sinusoid+trend fit and an LSTM forecaster reduce out-of-sample error (MAE/RMSE) across the North Atlantic, North Pacific, and Gulf of Mexico. The clustering separates high-variability coastal segments, and an interpretable submersion score integrates elevation quantiles and land cover to produce ranked adaptation priorities. Overall, the framework converts heterogeneous physical signals into decision-ready coastal risk tiers to support targeted defenses, zoning, and conservation planning. Full article

The digital transformation of electric power systems into smart grids has significantly expanded the cybersecurity risk landscape of the energy sector. While advanced sensing, communication, automation, and data-driven control improve efficiency, flexibility, and renewable energy integration, they also introduce complex cyber–physical interdependencies and new vulnerabilities across interconnected technical and organisational domains. This study adopts a scoping review methodology in accordance with PRISMA-ScR to systematically analyse smart grid cybersecurity from an architecture-aware and resilience-oriented perspective. Peer-reviewed scientific literature and authoritative institutional sources are synthesised to examine modern smart grid architectures, key security challenges, major cyberthreats, and documented real-world cyber incidents affecting energy infrastructure up to 2025. The review systematically links architectural characteristics such as field devices, communication networks, software platforms, data pipelines, and externally operated services to specific threat mechanisms and observed attack patterns, illustrating how cyber risk propagates across interconnected grid components. The findings show that cybersecurity challenges in smart grids arise not only from technical vulnerabilities but also from architectural dependencies, software supply chains, operational constraints, and cross-sector coupling. Based on the analysis of historical incidents and emerging research, the study identifies key defensive strategies, including zero-trust architectures, advanced monitoring and anomaly detection, secure software lifecycle management, digital twins for cyber–physical testing, and cyber-resilient grid design. The review concludes that cybersecurity in smart grids should be treated as a systemic and persistent condition, requiring resilience-oriented approaches that prioritise detection, containment, recovery, and safe operation under adverse conditions. Full article

Wireless Sensor Networks (WSNs) operate under strict energy constraints and are therefore highly vulnerable to radio interference, particularly jamming attacks that directly affect communication availability and network lifetime. Although jamming and anti-jamming mechanisms have been extensively studied, energy is frequently treated as a secondary metric, and analyses are often conducted in partial isolation from system assumptions, protocol behavior, and deployment context. This fragmentation limits the interpretability and comparability of reported results. This article presents a systematic literature review (SLR) covering the period from 2004 to 2024, with a specific focus on energy-aware jamming and mitigation strategies in IEEE 802.15.4-based WSNs. To ensure transparency and reproducibility, the literature selection and refinement process is formalized through a mathematical search-and-filtering model. From an initial corpus of 482 publications retrieved from Scopus, 62 peer-reviewed studies were selected and analyzed across multiple dimensions, including jamming modality, affected protocol layers, energy consumption patterns, evaluation assumptions, and deployment scenarios. The review reveals consistent energy trends among constant, random, and reactive jamming strategies, as well as significant variability in the energy overhead introduced by defensive mechanisms at the physical (PHY), Medium Access Control (MAC), and network layers. It further identifies persistent methodological challenges, such as heterogeneous energy metrics, incomplete characterization of jamming intensity, and the limited use of real-hardware testbeds. To address these gaps, the paper introduces an energy-centric taxonomy that explicitly accounts for attacker–defender energy asymmetry, cross-layer interactions, and recurring experimental assumptions, and proposes a minimal set of standardized energy-related performance metrics suitable for IEEE 802.15.4 environments. By synthesizing energy behaviors, trade-offs, and application-specific implications, this review provides a structured foundation for the design and evaluation of resilient, energy-proportional WSNs operating under availability-oriented adversarial interference. Full article

This study provides a systematic overview of Natural Language Processing (NLP)-based frameworks for Cyber Threat Intelligence (CTI) and the early prediction of cyberattacks in Industry 4.0. As digital transformation accelerates through the integration of IoT, SCADA, and cyber-physical systems, manufacturing environments face an expanding and complex cyber threat landscape. Following the PRISMA 2020 systematic review protocol, 80 peer-reviewed studies published between 2015 and 2025 were analyzed across IEEE Xplore, Scopus, and Web of Science to identify methods that employ NLP for CTI extraction, reasoning, and predictive modelling. The review finds that transformer-based architectures, knowledge graph reasoning, and social media mining are increasingly used to convert unstructured data into actionable intelligence, thereby enabling earlier detection and forecasting of cyber threats. Large Language Models (LLMs) demonstrate strong potential for anticipating attack sequences, while domain-specific models enhance industrial relevance. Persistent challenges include data scarcity, domain adaptation, explainability, and real-time scalability in operational-technology environments. The review concludes that NLP is reshaping Industry 4.0 cybersecurity from reactive defense toward predictive, adaptive, and intelligence-driven protection, and it highlights the need for interpretable, domain-specific, and resource-efficient frameworks to secure Industry 4.0 ecosystems. Full article

Background: Evidence on how physical and technical factors distinguish U-18 basketball levels is limited, yet these determinants may aid talent identification and development. This study examined differences in anthropometric, physical performance, and technical characteristics between high-level (HL; n = 38) and low-level (LL; n = 35) U-18 male basketball players and explored relationships between technical skills and key physical attributes across all participants. Methods: Participants were evaluated across anthropometry, physical performance, and basketball-specific technical skills. Statistical analyses assessed between-group differences and correlations, with significance set at p ≤ 0.05. Results: Compared to LL players, HL players exhibited significantly superior physical attributes, including greater height (Cohen’s d = 0.67) and arm-span (d = 0.65), reduced body fat (d = −0.58), and advanced performance metrics (10 m-speed running (d = −0.78), 20 m-speed running (d = −0.93), flexibility (d = 1.26), counter-movement jump height (d = 1.27), intermittent endurance (d = 1.18)). Technical proficiency in tasks such as 10 m- and 20 m-speed dribbling, maneuver dribbling and defensive sliding was also significantly faster in the HL group (d = −0.96, d = −1.05, d = −1.87, and d = −1.14, respectively). Several anthropometric and performance variables were strongly correlated with technical skills, indicating their relevance for distinguishing competitive levels. Conclusions: These findings underscore the interplay of physical, technical, and performance factors in high-level youth basketball. Coaches may use this information to guide targeted training strategies that support talent identification, player development, and competitive success. Full article

Plants secrete a heterogenous population of membrane-enclosed extracellular vesicles that harbour an incredible diversity of molecular cargo. It is the complexity of the molecular cargo encapsulated by plant extracellular vesicles (PEVs) which facilitates the fundamental role PEVs play in mediating communication and signalling. PEV molecular cargo is composed of a diverse mixture of lipids, metabolites, proteins, and nucleic acids. Among the nucleic acids, the microRNA (miRNA) class of small regulatory RNA can be viewed as one of the most biologically relevant. Plant miRNAs regulate the expression of genes essential for all aspects of development as well as to control the gene expression changes required to drive the adaptive and defensive responses of plants to environmental stress and pathogen attack. Furthermore, recent research has shown that specific miRNA cohorts are selectively packaged into PEVs as part of the molecular-level response of a plant to its growth environment. For example, PEVs are loaded with a specific miRNA population for their targeted delivery to sites of pathogen infection in the host plant, or for cross-kingdom delivery of host-plant-encoded miRNAs to the pathogen itself. Here we outline PEV physical properties, compare PEV biogenesis pathways, detail the composition of PEV molecular cargo, and go on to provide detailed commentary on the role of PEV-delivered miRNAs in plant development, environmental stress adaptation, and pathogen defence. We conclude this article with a proposal for the potential future use of PEVs and their miRNA cargo in agriculture and aquaculture. Full article

The rapid convergence of artificial intelligence (AI), cloud computing, and 5G communication has positioned extended reality (XR) as a core technology bridging the physical and virtual worlds. Encompassing virtual reality (VR), augmented reality (AR), and mixed reality (MR), XR has demonstrated transformative potential across sectors such as healthcare, industry, education, and defense. However, the compact architecture and limited computational capabilities of XR devices render conventional cryptographic authentication schemes inefficient, while the real-time transmission of biometric and positional data introduces significant privacy and security vulnerabilities. To overcome these challenges, this study introduces PXRA (PUF-based XR authentication), a lightweight and secure authentication and key distribution protocol optimized for cloud-assisted XR environments. PXRA utilizes a physically unclonable function (PUF) for device-level hardware authentication and offloads elliptic curve cryptography (ECC) operations to the cloud to enhance computational efficiency. Authenticated encryption with associated data (AEAD) ensures message confidentiality and integrity, while formal verification through ProVerif confirms the protocol’s robustness under the Dolev–Yao adversary model. Experimental results demonstrate that PXRA reduces device-side computational overhead by restricting XR terminals to lightweight PUF and hash functions, achieving an average authentication latency below 15 ms sufficient for real-time XR performance. Formal analysis verifies PXRA’s resistance to replay, impersonation, and key compromise attacks, while preserving user anonymity and session unlinkability. These findings establish the feasibility of integrating hardware-based PUF authentication with cloud-assisted cryptographic computation to enable secure, scalable, and real-time XR systems. The proposed framework lays a foundation for future XR applications in telemedicine, remote collaboration, and immersive education, where both performance and privacy preservation are paramount. Our contribution lies in a hybrid PUF–cloud ECC architecture, context-bound AEAD for session-splicing resistance, and a noise-resilient BCH-based fuzzy extractor supporting up to 15% BER. Full article