Search Results (22,017)

The rapid proliferation of Internet of Things (IoT) devices in cloud-based e-learning platforms has posed significant security risks, particularly in protecting learner information, authentication of devices, and safe communication in the highly heterogeneous learning settings. Current cryptographic solutions are largely based on classical public-key infrastructure (PKI) protocols such as RSA and ECC, which will become vulnerable with the advent of large-scale quantum computers capable of executing Shor’s algorithm. In addition, traditional perimeter-based security models are inadequate for handling the dynamics, scattered, and resource-limited characteristics of IoT-enabled educational systems. As a solution to these problems, this paper introduces ZeroTrustEdu, a scalable zero-trust cryptographic solution that combines lightweight post-quantum key management with adaptive trust scoring of cloud-connected IoT e-learning infrastructure. The proposed framework makes three fundamental contributions namely: (1) a hierarchical zero-trust security model with no implicit trust, operating across device, edge, and cloud layers; (2) a lightweight key distribution protocol based on the Module-Lattice Key Encapsulation Mechanism (ML-KEM) compliant with NIST FIPS 203 standards and (3) an adaptive behavioral trust scoring engine that dynamically adjusts device and user trust levels based on real-time interaction analytics. The architecture is evaluated using extensive NS-3 network simulations with up to 100,000 concurrent IoT nodes with formal security analysis under Chosen Plaintext Attack (CPA) and Chosen Ciphertext Attack (CCA) threat models. Comparative evaluation against RSA-2048, ECC-P256, and AES-256 baselines demonstrates that, ZeroTrustEdu delivers a 62% ± 3% (95% CI, 10 independent runs) reduction in ML-KEM encapsulation latency (12.8 ms for key encapsulation/decapsulation, contributing to a complete device authentication latency of 47.3 ms including ML-DSA signature operations), 45% reduced communication overheads, and 38% reduction in energy consumption on ARM Cortex-M4 constrained devices compared to RSA-2048 and achieves provable post-quantum security reducible to the hardness of the Module Learning With Errors (MLWE) problem. These findings demonstrate that the proposed architecture provides a viable, scalable, and quantum-resilient security solution for next-generation IoT-enabled e-learning environments. The cryptographic security of ZeroTrustEdu is guaranteed at the primitive level through NIST-standardized ML-KEM (FIPS 203) and ML-DSA (FIPS 204), with IND-CCA2 and EUF-CMA security formally proven in the respective standards; full protocol-level formal verification using automated theorem provers (ProVerif, Tamarin) is identified as valuable future work to rule out protocol-composition vulnerabilities beyond primitive-level guarantees. Full article

In the field of subgrade compaction quality assessment, accurate volume measurement of excavated pits is hindered by non-uniform point cloud distribution, environmental noise interference, and complex irregular boundary features. To address these challenges, this paper proposes a robust volume detection framework that integrates adaptive point cloud refinement and morphological discrimination. First, a pose normalization method employing RANSAC plane fitting and rigid body transformation corrects the spatial orientation of the raw point clouds. To balance data redundancy removal with feature preservation, a gradient adaptive simplification strategy based on local density feedback and K-nearest neighbor estimation is developed. Subsequently, a cross-sectional area calculation model utilizing piecewise-cubic polynomial fitting is proposed to mitigate boundary noise and accurately reconstruct irregular contours. Furthermore, a dynamic outlier removal mechanism based on the Median Absolute Deviation (MAD) and sliding windows is introduced to eliminate non-physical geometric fluctuations. Finally, the total volume is aggregated using a hybrid strategy of Simpson’s rule and a frustum compensation operator. Experimental results on simulated pits with typical topological defects demonstrate that the proposed algorithm outperforms traditional methods, achieving an average relative volume error of less than 0.8%. This approach significantly improves the robustness and precision of sensor-based automated subgrade compaction quality measurement. Full article

This systematic review examines how organisational resilience is conceptualised, enacted, and enabled in the Digital Age, characterised by Artificial Intelligence (AI), Generative AI, the Internet of Things (IoT), Big Data, and Robotics. Despite their transformative potential, these technologies are often treated as peripheral tools rather than core mechanisms in resilience architectures. Adopting a critical realist paradigm, we conducted a Systematic Literature Review (SLR) following the PRISMA 2020 protocol to review thirty (30) peer-reviewed empirical studies (2017–present). A pre-SLR conceptual framework, linking Business Intelligence and Responsiveness constructs, guided data extraction and synthesis. Building on this, we propose a conceptual framework and explanatory model grounded in the Context–Mechanism–Outcome logic. The model distinguishes generative mechanisms (real domain), organisational responses (actual domain), and observable indicators (empirical domain). The review identifies Collective Capability (CC), Adaptive Capability (AC) and Dynamic Capability (DC) mechanisms as key generative powers, with Digital Age enablers embedded within Adaptive Capability (AC) and Dynamic Capability (DC). Together, these mechanisms contribute to Systemic Preparedness (SP), Rapid Recovery (RR) and Generative Stability (GS), thereby supporting the emergence of Organisational Resilience (OR). This reconceptualises resilience as an emergent, non-linear outcome of mechanism interactions, offering a unified direction. Future research should prioritise longitudinal multi-case studies and quantitative testing of Context–Mechanism–Outcome configurations, supported by mixed-method designs to validate and refine the proposed framework. Full article

Accurate target speech mask estimation is the key to single-channel speech separation. Masks generated by conventional mask networks are easily corrupted by interfering speech and background noise, which degrades separation performance. To solve this problem, this paper proposes a Cross-Purification Mask Network (CPMN), which consists of three core modules: the Dynamic Context-Aware Mechanism (DCAM), Feature Cross-Complementation Mechanism (FCCM), and Adaptive Purification Mask Mechanism (APMM). The DCAM aggregates dynamic sliding window and long-term temporal features to capture long-range temporal dependencies of masks and enhance the localization accuracy of target speech. The FCCM fuses weighted mask features of interfering speakers to dynamically supplement missing information in target speech masks. The APMM combines adaptive filters and residual networks to output high-precision refined masks. The CPMN is embedded into three mainstream speech separation frameworks including Conv-TasNet, DPTNet, and TDANet, and extensive experiments are conducted on Libri2Mix, WHAM!, and WSJ0-2Mix datasets. The results show that the CPMN brings stable performance gains. After integration, TDANet achieves SI-SNRi of 17.4 dB (+0.5 dB) on Libri2Mix and 15.2 dB (+0.4 dB) on WHAM!. Meanwhile, Conv-TasNet and DPTNet obtain SI-SNR improvements of 0.3 dB (15.6 dB) and 0.4 dB (20.8 dB) on WSJ0-2Mix, respectively. Full article

Open-vocabulary semantic segmentation (OVSS) aims to achieve pixel-level object segmentation guided by arbitrary natural language descriptions. Although pre-trained vision–language models (VLMs) have significantly advanced the development of OVSS, their reliance on the Vision Transformer (ViT) architecture imposes a fundamental constraint on dense prediction. Specifically, the absence of hierarchical downsampling in ViT-based VLM results in single-scale representations that trade spatial localization for global semantics. To address these issues, this paper proposes a hierarchical boundary-constrained decoding network for OVSS, called CLIP-HBD. Our approach leverages VLM semantic priors to reconstruct multi-scale features and introduces a boundary-constrained decoding strategy to refine edge details. Specifically, CLIP-HBD leverages a ConvNeXt-based backbone alongside a hierarchical adaptation mechanism to fuse multi-layer VLM features, generating a comprehensive multi-scale representation. To address the issue of boundary inaccuracy, we perform explicit boundary prediction based on multi-scale representations, where the resulting boundary maps are subsequently transformed into structural constraints to steer the decoder’s focus toward boundary regions. By integrating structural constraints with hierarchical features, the decoding process effectively maintains semantic consistency and restores precise object boundaries. Extensive experiments demonstrate that CLIP-HBD achieves superior performance in both segmentation precision and boundary quality across multiple benchmarks. Full article

Against the global backdrop of carbon neutrality and the green transition of the construction sector, modern timber-framed buildings have emerged as a core enabler of sustainable construction. However, a systematic synthesis of research on indoor hygrothermal environments and thermal comfort in such buildings remains lacking, and the underlying coupling mechanisms—as well as pathways for performance optimization—are still insufficiently understood. To address these gaps, this study aims to systematically characterize and evaluate the performance features of indoor thermal and moisture environments in modern timber buildings, and to identify the key influencing factors and their underlying mechanisms. In accordance with the PRISMA 2020 guidelines for systematic reviews, this study identified and analyzed 203 high-quality peer-reviewed publications retrieved from three major academic databases, covering the period 2010–2025. Specifically, the literature search was conducted across the Web of Science, Scopus, and the China National Knowledge Infrastructure (CNKI), and visualization analysis was performed using VOSviewer 1.6.20 software. The results indicate that timber-framed buildings exhibit distinctive indoor hygrothermal characteristics: rapid temperature response, strong humidity buffering capacity, and superior thermal insulation performance compared with concrete structures, enabling indoor relative humidity to remain stably within the thermally comfortable range. Nevertheless, challenges persist, including summer overheating and elevated risks of mold growth under hot-humid conditions. Furthermore, the PMV model demonstrates significant predictive deviation for thermal comfort in timber-framed buildings; its application thus requires calibration incorporating both the hygrothermal properties of timber materials and occupants’ psychological adaptation. This study synthesizes the current state of research, identifies key influencing factors, and proposes climate-responsive optimization strategies to advance the development of robust thermal comfort models and support the low-energy, high-comfort design of timber-framed buildings. Full article

To address the uncertainty of the measurement noise covariance matrix in vehicle state estimation, this paper proposes a symmetry-aware extended Kalman filter optimized by a chaotic-gradient strategy. The symmetry-aware concept is introduced from the approximate mirror symmetry of vehicle lateral dynamics under left and right steering excitations. Under identical road adhesion and vehicle operating conditions, the yaw-rate and sideslip-angle responses should exhibit balanced statistical characteristics for positive and negative lateral motions. However, a fixed measurement noise covariance matrix may break this balance and lead to direction-dependent estimation bias or delayed convergence. To improve the statistical consistency of the estimation process, the proposed method adaptively tunes the measurement noise covariance matrix according to the innovation covariance mismatch. A chaotic search mechanism is first used to enhance global exploration, and a variable-step gradient method is then applied to refine the local optimal solution. Through the iterative combination of chaotic traversal and gradient-based refinement, the proposed observer improves the balance between model prediction and measurement correction under stochastic disturbances. The effectiveness of the proposed method is verified through CarSim and MATLAB/Simulink co-simulation. The results show that, compared with EKF, UKF, and AEKF benchmark observers, the proposed CG_EKF provides more accurate estimation of vehicle yaw rate and sideslip angle. Full article

In close-range monocular visual measurement and cooperative target pose estimation, conventional planar targets are constrained by viewpoint changes and are prone to perspective distortion. Although spherical targets provide omnidirectional observability, their PnP-based pose estimation may still suffer from large errors under limited fields of view and sparse feature observations. To address this issue, this paper proposes an integrated visual measurement framework covering both high-precision spherical target construction and robust pose estimation. First, a composite marker layout based on adaptively scaled latitude–longitude topology is designed. To suppress cumulative distortion caused by long-sequence multi-view rigid registration, a center-to-pole point-cloud stitching strategy is developed, and multiple observations are fused using geometric-consistency weighting to accurately reconstruct the feature-point coordinate system of the target. Second, a joint optimization method is proposed by combining feature-point reprojection error with a contour center consistency constraint. Specifically, the theoretical contour center is predicted from the analytical projection model of the sphere and constrained to agree with the observed contour center fitted from the image. In addition, an SQPnP-based sequential reinitialization mechanism is introduced to improve robustness under sparse-point observations. Simulation results demonstrate that the proposed method achieves higher accuracy and robustness under continuous pose changes, sparse feature points, and different noise levels, compared with EPnP, EPnP+LM, LM, and SQPnP, while real-image experiments further demonstrate its practical feasibility. Full article

Fusion-based hyperspectral image super-resolution (HSI-SR) on diffusion models exhibits promising performance in generating high-quality, realistic features. However, existing methods are confronted with two limitations: (1) static conditional guidance is discordant with the dynamic denoising process, and (2) modality conflicts are inadequately addressed by concatenation. To address these challenges, we propose a novel Modulated Diffusion Framework with Spatial–Spectral Disentangled Guidance (SSDG). Specifically, it introduces a Dynamic Modulated Residual Network (DMRN), which leverages a time-aware mechanism to dynamically adjust conditional feature injection, ensuring adaptive guidance throughout all denoising stages. Furthermore, we design a training-free SSDG strategy to explicitly decouple spatial and spectral guidance during sampling, allowing for flexible control over the fusion process to mitigate modality conflicts. Extensive experiments on three public datasets demonstrate that the proposed method achieves state-of-the-art performance, exhibiting superior robustness, particularly in challenging noisy scenarios. Full article

Soybean (Glycine max) is a vital food and oil crop in China, yet its yield and quality are severely threatened by piercing–sucking damage caused by Riptortus pedestris (Hemiptera: Alydidae) to soybean pods. Under global climate warming and expanded soybean cultivation, temperature has become a key environmental factor driving the spread of and aggravated damage caused by R. pedestris. We investigated the effects of temperature (32, 36, 40, 42, and 44 °C) and exposure duration (1–4 h) on the energy substances and antioxidant enzyme activities in adult R. pedestris. These two factors also had significant effects on the pest’s energy substances and antioxidant defense. Under short-term high-temperature stress, the water loss rate and fat, total sugar, and glycogen contents increased significantly, while protein content showed a fluctuating upward trend, with distinct sexual differences in these responses; the water loss and energy substance levels within the lethal high-temperature range, around 44 °C, were generally higher than those in the sublethal range (36–42 °C). R. pedestris showed physiological changes consistent with enhanced heat tolerance and adaptability, including water balance regulation, carbohydrate and lipid accumulation, and modulation of protein synthesis and degradation. In the sublethal high-temperature range, antioxidant enzyme activity patterns were altered, and SOD activity was increased; meanwhile, the MDA content also rose, and POD and CAT activities decreased. In the lethal high-temperature range, the overall antioxidant enzyme activities were lower than in the suitable temperature range, with the POD activities and MDA content still rising. These results suggest that the dynamic adjustment of antioxidant enzyme activities may contribute to alleviating oxidative damage and rapid adaptation to temperature-induced oxidative stress in R. pedestris. These findings indicate that R. pedestris possesses physiological plasticity to cope with sublethal heat stress through metabolic reallocation and antioxidant defense activation, but extreme temperatures cause severe physiological disruption. This study provides insights into the thermal biology and heat resistance mechanisms of this pest under climate warming scenarios. Full article

Silk fibroin (SF)–polyphenol systems have emerged as a versatile class of gels and hydrogels in which supramolecular interactions and dynamic crosslinking regulate network formation, responsiveness, and multifunctional performance. Polyphenols interact with SF through hydrogen bonding, hydrophobic interactions, π–π stacking, metal coordination, and covalent crosslinking, thereby modulating conformational transitions, gelation behavior, structural stability, and interfacial functionality. These interaction mechanisms enable the development of SF–polyphenol gel systems with tunable mechanical properties, wet adhesion, antioxidant activity, self-healing capability, and stimuli responsiveness. This review summarizes recent advances in SF–polyphenol gels and hydrogels, with particular emphasis on molecular interaction mechanisms, gelation and fabrication strategies, responsive behaviors, and structure–property relationships. Representative preparation approaches, including solution blending, electrospinning, impregnation–adsorption, enzymatic crosslinking, metal–phenolic coordination, and photo-initiated processing, are systematically discussed in relation to their effects on network architecture and functional output. The responsive behaviors of these systems under pH, redox, electrical, thermal, and optical stimuli are also analyzed from the perspective of dynamic gel networks and adaptive material design. Emerging applications of SF–polyphenol gels in bioadhesives, delivery platforms, flexible bioelectronics, wound-related materials, and sustainable functional systems are highlighted. Current limitations associated with polyphenol instability, formulation sensitivity, reproducibility, and scale-up are critically discussed, together with future opportunities for predictive design of gel-based natural polymer systems. This review provides a comprehensive framework for understanding SF–polyphenol gelation and for guiding the development of next-generation multifunctional gels and hydrogels. Full article

With the rapid development of global aquaculture, the frequent occurrence of fish diseases has had a serious impact on the efficiency of aquaculture and the ecological environment. Antimicrobial peptides, as a kind of natural immune active substance existing in organisms, participate in innate immunity and adaptive immunity. Due to their extensive antibacterial properties and low toxicity, they have gradually become a hot topic in scientific research. This article reviews the classification, tissue distribution, mechanism of action, extraction, and synthesis techniques of antimicrobial peptides (AMPs) derived from fish, as well as their applications in disease prevention in aquaculture, product preservation, and antibiotic substitution. Although antimicrobial peptides are expected to become alternatives to antibiotics, challenges such as environmental stability, production costs, and regulatory frameworks remain to be addressed. This article holds that antimicrobial peptides derived from fish are a feasible strategy for sustainable aquaculture. The future development direction lies in biotechnology-driven optimization, carrier innovation, and combined application with traditional antibiotics. Full article

Background: Peripheral muscle electrostimulation (PME), encompassing neuromuscular electrical stimulation (NMES) and functional electrical stimulation (FES), has been increasingly acknowledged as an effective adjunctive or complementary treatment to voluntary exercise in elderly cardiac patients who cannot perform sufficient amounts of exercise, for whom there is limited research on optimal protocols. Sarcopenia, defined as a progressive decrease in muscle mass, strength and function, affects approximately 34% of heart failure (HF) patients and considerably worsens their prognosis. The objective of this systematic review is to summarize current evidence on the theoretical mechanisms, physiological pathways, safety and efficacy of PME in older adults within a cardiac rehabilitation (CR) setting, with a specific emphasis on sarcopenia reversal. Methods: We performed a systematic review following the PRISMA 2020 guidelines. A systematic search was conducted on the PubMed, Embase, Cochrane Library, CINAHL and PEDro databases from inception until December 2025. We searched for randomized controlled trials (RCTs) and controlled clinical trials focusing on PME in patients with cardiac diseases aged 65 years or older. The main outcomes were physical function (assessed with the Short Physical Performance Battery [SPPB] and 6 min walk distance [6MWD]), muscle strength, muscle mass and safety. The Cochrane Risk of Bias tool was used to evaluate the quality of the studies. Results: Eight studies were included, with 387 participants and a mean age between 78 and 85 years. PME consistently improved lower-extremity muscle strength (MD: 5.2% body weight, 95% CI = 1.2–9.1, p = 0.013) along with SPPB scores, which ranged from +2.3 to +2.67 points (all p < 0.05). Home-based PME (NMES) achieved 100% adherence rates, and no cardiovascular adverse events were reported. The mechanisms by which PME is beneficial involve peripheral skeletal muscle adaptations without eliciting central hemodynamic stress, increased endothelial function, aerobic enzyme activity, protein anabolism stimulation or muscle proteolysis inhibition. No significant effects were observed on BNP levels, hospital readmissions or mortality. PME has been shown to attenuate the progression of sarcopenia through hypertrophy of type I and II muscle fibers, as well as mitochondrial biogenesis. Conclusions: PME is a safe, feasible adjunct to conventional CR in frail, elderly cardiac patients, particularly those with exercise intolerance and sarcopenia. It improves peripheral muscle function, physical performance, and muscle protein balance without cardiovascular stress. Larger multicenter trials are needed to establish optimal protocols and long-term clinical outcomes. Full article

Series arc faults on the DC side of photovoltaic (PV) systems are a critical hazard that can trigger system fires. Conventional contact-based detection methods suffer from cumbersome installation and high retrofit cost, whereas existing non-contact approaches mostly rely on megahertz-level high-frequency sampling and therefore require expensive radio-frequency instrumentation or high-performance computing platforms. As a result, it remains difficult to simultaneously achieve strong interference immunity and real-time performance on low-cost embedded devices with limited resources. To address this engineering paradox between high-frequency sampling and constrained computational capability, this paper proposes a fully embedded, non-contact arc fault detection system based on a 12–80 kHz low-frequency sub-band selection strategy. By exploiting the physical characteristic of broadband energy elevation induced by arc faults, the proposed strategy avoids dependence on high-bandwidth hardware. Guided by this strategy, a Moebius-topology coaxial shielded loop antenna is employed as the near-field sensor, while an ultra-simplified passive analog front end is constructed directly by using the on-chip programmable gain amplifier and analog-to-digital converter of the microcontroller unit, enabling efficient signal acquisition and fast Fourier transform processing within the target sub-band. To cope with complex background noise in the low-frequency range, an environment-adaptive baseline mechanism based on exponential moving average and exponential absolute deviation is developed for dynamic decoupling. In addition, a lightweight INT8-quantized multilayer perceptron is introduced as a nonlinear auxiliary module, thereby forming a robust hybrid decision architecture with complementary rule-based and artificial intelligence components. Experimental results show that, under the tested household, laboratory, and PV-site conditions, the proposed system achieved an overall detection rate of 97%, while the remaining 3% mainly corresponded to failed ignition or non-sustained arc attempts rather than persistent false triggering during normal monitoring. Full article

In fixed-view interaction scenarios of unmanned restaurants, face detection models face two core bottlenecks: the mismatch between training data distribution and real deployment scenarios, and the misalignment between model feature capacity allocation and business priority. To address these problems, this paper takes YOLOv8n (You Only Look Once version 8n) as the baseline, proposes a unified Scale-Aligned Capacity Allocation (SACA) theoretical framework, and constructs an end-to-end Scale Distribution Reconstruction Network (SDRNet) for lightweight face detection. First, we define the SACA loss with KL (Kullback-Leibler) divergence as the core optimization objective, which mathematically characterizes the matching degree between model capacity allocation and real scene face scale distribution. Second, a two-stage scene-aware scale distribution reconstruction strategy is designed based on the SACA framework, which derives the core face scale interval of the unmanned restaurant scene through a monocular imaging model, and constructs a scene-adaptive training dataset based on the public WIDER FACE benchmark, which is highly consistent with the real scale distribution of unmanned restaurant scenarios. Third, three scale-aligned lightweight modules, including LFEM (Lightweight Feature Extraction Module), LDown (Feature Segmentation and Sparse Optimization Module), and MSCH (Multi-Feature Shared Convolution Module), are proposed to realize the priority allocation of model capacity to core interaction scales, achieving collaborative optimization of data distribution and model structure. Fourth, a 2 × 2 controlled experiment is designed to separate the independent contributions of the data strategy and architectural improvements, and the robustness of the proposed model is verified on the standard WIDER FACE benchmark. Finally, a scale-specific validation mechanism is established to conduct fine-grained evaluation of the model’s detection performance on faces of different scales, avoiding the overall indicator masking the accuracy fluctuation of core scenarios. Experimental results show that the parameters of the proposed model are reduced to 1.76 M (a decrease of 41%), and the computational complexity is reduced to 5.5 GFLOPs (Giga Floating-point Operations Per Second) (a decrease of 32%). The mAP@0.5 (mean Average Precision) of the core medium-scale face reaches 0.684, with the performance loss controlled within 2% compared with the baseline. On the standard WIDER FACE benchmark, the model maintains competitive detection accuracy under extreme lightweight compression, which fully verifies its robustness. On the NVIDIA Jetson Orin NX embedded platform, the inference frame rate of TensorRT-FP16 reaches 79.9 FPS (Frames Per Second), which fully meets the real-time deployment requirements of resource-constrained unmanned restaurant scenarios. Full article