Search Results (2,501)

Resource-constrained IoT systems face a fundamental conflict between cryptographic security and energy efficiency, particularly in critical infrastructure monitoring requiring long-term autonomous operation. This study presents a hybrid framework integrating signal-adaptive compression with hardware-accelerated authenticated encryption to resolve this trade-off. The Dynamic Payload Compression with Selective Encryption framework classifies sensor data into three SNR regimes and applies adaptive compression strategies: 24.15-fold compression for low-SNR backgrounds, 1.77-fold for transitional states, and no compression for high-SNR leak detection events. Experimental validation using 2714 acoustic sensor samples demonstrates 5.91-fold average payload reduction with 100% detection accuracy. The integration with STM32L5 hardware AES acceleration reduces power–data correlation from 0.820 to 0.041, increasing differential power analysis attack complexity from 500 to over 221,000 required traces. Compression-induced timing variance provides additional side-channel masking, burying cryptographic signals beneath a 0.00009 signal-to-noise ratio. Projected on 19,200 mAh lithium thionyl chloride batteries, the system achieves 14-year operational lifetime under realistic duty cycles, exceeding industrial requirements for critical infrastructure protection while maintaining robust security against physical attacks. Full article

The integration of vertical greenery systems (VGSs) into existing reinforced concrete (RC) buildings raises questions regarding interface kinematics and the permanent displacement of soil-retaining elements under seismic excitation. This study experimentally investigates the residual displacement of façade-mounted living walls and rooftop planter pods anchored to a deficient RC frame under shake table excitation. A 1:3 scale reinforced concrete frame was tested in two distinct phases: initially as a deficient, unretrofitted structure (Phase A), and subsequently as a retrofitted system integrated with vertical greenery elements (Phase B). High-precision terrestrial laser scanning (TLS) was employed before and after successive seismic excitation stages to generate dense three-dimensional point clouds. Cloud-to-cloud comparison techniques were used to quantify global structural displacement and local kinematic behavior of greenery components, while results were validated against conventional displacement sensors. The RC frame exhibited millimeter-scale permanent displacements consistent with draw-wire measurements. In contrast, planter pods demonstrated configuration-dependent behavior, including up to 8 cm translational sliding and rotational responses reaching 13° under repeated excitation, whereas living wall panels remained stable. Notably, a 95% reduction in point cloud density reproduced global deformation patterns with an RMSE of 3.03 mm and quantified peak displacements with only ~2% deviation from full-resolution results. The findings demonstrate the capability of TLS-based monitoring to detect differential kinematic behavior of integrated VGSs, while highlighting the variability in performance of friction-based rooftop anchorage utilizing different robust planter pod fixing systems. Full article

The increasing deployment of collaborative robots in industrial manufacturing environments has enabled close human–robot collaboration, making rapid and reliable collision detection essential for worker safety. This paper presents a learning-based framework for real-time detection and classification of hard and soft collisions using stacked Gated Recurrent Unit (GRU) networks. A two-stage pipeline is introduced, in which collision detection and collision type classification are performed sequentially using separate models, and its performance is validated through extensive experiments on a collision dataset collected from a six-joint collaborative robot executing random point-to-point motions. Without requiring joint torque sensors, unmodeled joint friction is implicitly compensated through learning for both detection and classification. Compared to our previous work, the proposed method achieves improved detection performance, and its robustness is further demonstrated through systematic generalization experiments under simulated dynamic model uncertainties. In addition, the classification model accurately distinguishes between hard and soft collisions, providing a basis for differentiated post-collision reaction strategies. Overall, the proposed sensorless collision detection and classification framework provides a practical and cost-effective solution for real-world industrial human–robot collaboration. Full article

To systematically analyze the differences and underlying mechanisms of pantograph–catenary arc discharge characteristics under different operating conditions, this paper measures the complete transient waveforms of arc current, external electric field, and voltage between carriages under various operating conditions based on a unified experimental platform, using flexible current probes, electric field sensors, and active differential probes for synchronous acquisition. The research results reveal the quantitative correlation and physical mechanism between the mechanical parameters of the pantograph–catenary system and the electromagnetic transient responses under four typical conditions: fixed gap between the pantograph and catenary, pantograph raising, pantograph lowering, and pantograph–catenary separation vibration. These findings provide references for condition monitoring, fault warning, pantograph optimization design, and system-level electromagnetic compatibility evaluation of the pantograph–catenary system. Full article

Dragon fruit (Hylocereus spp.) is an emerging crop in the tropics and subtropics, but its production is increasingly threatened by diseases that reduce yield and profitability. Early diagnosis of these diseases is crucial for timely intervention, yet visual symptoms often appear only after significant infection has occurred. The study aims to evaluate how optical spectral reflectance can detect dragon fruit diseases and identify the most responsive spectral regions. In this study, six major dragon fruit stem diseases: Neoscytalidium stem canker, stem sunburn, anthracnose, Botryosphaeria stem canker, Bipolaris stem rot, and bacterial soft rot were characterized by the goal of identifying unique spectral signatures for early detection and differentiation of each disease. Seventy-two potted dragon fruit plants of three distinct species were grown under four organic vermicompost treatments (0, 5, 10, 20 tons/acre) in both open-field and high-tunnel conditions together, in a randomized complete block design. A handheld spectroradiometer (350–2500 nm) was used to collect reflectance from the diseased and healthy cladodes (stem segment). Various spectral vegetative indices were computed to identify disease-specific features. The results revealed distinct spectral features for each disease. Infected cladodes consistently exhibited higher reflectance especially in the visible region (400–700 nm) and the near-infrared region (900–2500 nm) of the spectrum than healthy cladodes. The Normalized Difference Vegetative Index (NDVI), Green Normalized Difference Vegetative Index (GNDVI), and Spectral Ratio (SR) spectral indices were significantly higher in healthy plants than in diseased ones, reflecting higher chlorophyll concentration and plant biomass. Conversely, the 1110/810 ratio was lower in healthy plants than in diseased plants, suggesting a more compact internal plant structure. Statistical analysis revealed highly significant differences (p < 0.00001) between healthy and diseased spectra in the Red, Green and NIR regions. Linear Discriminant Analysis(LDA) achieved the highest classification accuracy (OA = 0.642, κ = 0.488), though performance was limited for minority classes. These findings demonstrate that targeted spectral sensing can identify dragon fruit diseases before obvious symptoms emerge. By pinpointing disease-specific spectral indices, our study paves the way for early-warning tools such as targeted multispectral sensors or drone-based imaging that would enable growers to intervene sooner and limit losses. These results highlight the potential for development of UAV-based or portable spectral sensors for large-scale, near real-time disease monitoring in dragon fruit production. Full article

The convergence of Large Language Models (LLMs) and Internet of Things (IoT) systems has created a new class of intelligent applications across healthcare, industrial automation, smart cities, and connected homes. However, this integration introduces a complex and largely underexplored security landscape. LLMs deployed in IoT contexts face threats spanning both the AI and embedded systems domains, including prompt injection through sensor-driven inputs, model extraction from edge devices, data poisoning of IoT data streams, and privacy leakage through LLM-generated responses grounded in personal data. Simultaneously, LLMs are proving to be powerful tools for IoT security, with LLM-based intrusion detection systems achieving 95–99% accuracy on standard IoT datasets and LLM-driven threat intelligence outperforming traditional machine learning by significant margins. We systematically review 88 papers from IEEE, ACM, MDPI, and arXiv (2020–2025), providing: (1) a structured taxonomy of security threats targeting LLM-IoT systems, (2) a review of LLMs as security enablers for IoT, (3) an evaluation of privacy-preserving architectures including federated learning, differential privacy, homomorphic encryption, and trusted execution environments, (4) domain-specific security analysis across healthcare, industrial, smart home, smart grid, and vehicular IoT, and (5) a literature-based comparative analysis of LLM-based security systems. A central finding is the accuracy–efficiency–privacy trilemma: the model compression techniques needed to deploy LLMs on resource-constrained IoT devices can degrade security and even introduce new vulnerabilities. Our analysis provides researchers and practitioners with a structured understanding of both the risks and opportunities at the frontier of LLM-IoT security. Full article

This paper proposes a multi-strategy hybrid-enhanced Educational Competition Optimizer (MEECO) to improve the performance of swarm-based optimization algorithms in complex search environments. From the perspective of symmetry, population-based optimization algorithms inherently rely on the symmetric distribution and evolution of individuals in the search space, while the imbalance between exploration and exploitation often leads to symmetry breaking, resulting in premature convergence and loss of diversity. Unlike the standard ECO, which suffers from limited information exchange, premature convergence, and boundary stagnation, the proposed method integrates three complementary mechanisms: adaptive differential evolution, vertical crossover, and global-best-guided boundary handling. Specifically, the adaptive differential evolution strategy enhances global exploration and maintains population distribution symmetry through dynamic mutation, the vertical crossover mechanism improves inter-dimensional symmetry and information interaction, and the boundary-handling strategy restores symmetry by guiding infeasible solutions back to promising regions. These strategies jointly improve population diversity, exploration–exploitation balance, and convergence efficiency while preserving structural symmetry in the search process. Extensive experiments on CEC2017 and CEC2022 benchmark suites demonstrate that MEECO consistently achieves superior optimization accuracy, faster convergence speed, and stronger robustness compared with several state-of-the-art algorithms. Statistical analyses further confirm the significance and reliability of the improvements. In addition, the proposed method is applied to a wireless sensor network node deployment problem, where it significantly improves coverage rate and deployment uniformity. The results indicate that MEECO provides an effective, robust, and symmetry-preserving optimization framework for both benchmark problems and real-world engineering applications. Full article

Major depressive disorder (MDD) lacks reliable laboratory tests for diagnosis and treatment monitoring, underscoring the need for robust molecular readouts in blood. Beyond symptom-based classification, MDD can also be viewed as a condition involving impaired homeostatic regulation across stress-responsive, immune, metabolic, and neural systems. Consistent with this perspective, altered intron retention (IR) patterns have been observed in peripheral blood in depression-related and treatment-response contexts, supporting the translational relevance of this RNA-processing layer to mood disorders. A key observation underpinning this review is that IR can function as a reversible, intervention-responsive readout of physiological state. In a pre-symptomatic stress-like state in klotho mutant mice (a premature-aging model), widespread IR increases revert toward a healthy pattern upon treatment, suggesting that IR is embedded in a controllable homeostatic layer. Against the backdrop of limited cross-cohort transferability of differential gene expression (DGE) signatures, we propose that IR provides a mechanistically grounded biomarker layer because it reports regulated RNA processing states rather than context-fragile abundance endpoints. We operationalize IR as a post-transcriptional “throttle” on effective gene output, with increased IR/detained intron (DI) states acting as a reversible brake and decreased IR acting as an accelerator that increases translation-competent mRNA supply. Mechanistic exemplars across immune, metabolic, and neuronal systems (e.g., IFNG, OGT, MAT2A, neuronal activity-triggered intron excision, and intron detention-mediated stemness/differentiation switching in adult neural stem cells) show that defined inputs can switch IR/DI states to tune output kinetics. Integrating these findings, we propose an “Intron Retention Homeostat” (IR-Homeostat) model in which cells sense deviations from physiological set points and implement feedback control of gene output through switchable IR/DI regulation. This framework positions IR not only as a robust state readout for stratification, treatment response prediction, and pharmacodynamic profiling, but also as a tractable entry point to identify the molecular sensors and mediators that couple homeostatic signals to RNA processing control. Full article

Systemic lupus erythematosus (SLE) frequently presents joint pain and stiffness, yet clinicians lack an objective, rapid method to quantify joint inflammation at the point of care. We introduce the Lupus Optical Tomography Imaging System (LOTIS), a wearable near-infrared (NIR) device that performs real-time three-dimensional tomographic imaging of hemodynamic changes in finger joints. LOTIS was developed to address key limitations of our earlier Flexible Optical Imaging System (FOIS), including mechanical fragility, high noise levels, single-joint acquisition, and slow reconstruction times. The new system integrates modular, mechanically robust optical patches with on-sensor digitization and a computationally efficient, non-iterative multispectral reconstruction algorithm to produce frame-by-frame maps of hemoglobin concentration. In a preliminary study using a standardized venous-occlusion protocol, LOTIS differentiated SLE-affected joints from those of healthy controls. Diseased joints exhibited blunted and spatially diffuse hemodynamic responses, whereas healthy joints showed localized and robust changes. These results demonstrate that LOTIS provides an operator-independent, patient-friendly method for quantifying joint-specific hemodynamic changes in real time, offering strong potential as a clinical tool for objective assessment and longitudinal monitoring of lupus arthritis. Full article

Assistive mobility devices for small animals require reliable monitoring to ensure safe and comfortable operation without increasing system complexity or invasiveness. This study presents a low-cost monitoring platform that integrates a laser-induced graphene (LIG) contact-temperature sensor into a passive mobility device for small dogs, supported by a lightweight Internet of Things (IoT) architecture. The system combines contact temperature, ambient temperature, speed, and obstacle distance using an energy-aware acquisition strategy and prioritized wireless transmission for near-real-time monitoring. An unsupervised anomaly detection framework based on Isolation Forest identifies potentially unsafe operating conditions without labeled pathological data by leveraging absolute temperature and the differential feature $\Delta T$ between contact and ambient measurements. Experimental validation was conducted under controlled indoor conditions across six independent sessions with a small-breed dog, including static and dynamic phases to ensure repeatability. The system achieved packet delivery ratios of approximately 95%, with typical end-to-end latencies below 500 ms and worst-case delays below 850 ms. The proposed approach detected localized thermal deviations associated with friction or prolonged contact while remaining robust to normal activity- and environment-driven variations. These results demonstrate the feasibility of integrating LIG-based sensing and unsupervised analytics into assistive animal mobility platforms to enhance safety through continuous, non-invasive monitoring. Full article

Heart rate recovery (HRR) is an important indicator of cardiovascular autonomic function, yet evidence in women with breast cancer remains limited. This study aimed to analyze heart rate recovery during the first two minutes following a maximal exercise test and to examine its association with age, weekly physical activity, and oncological treatment duration using wearable technology. A cross-sectional design was applied in 22 women with breast cancer enrolled in an oncological exercise program. Participants performed a maximal treadmill test using the Modified Bruce Protocol, after which the mean heart rate was recorded across eight 15 s recovery intervals using a wearable chest-strap heart rate sensor integrated with an inertial device (WIMU PRO). Results showed a progressive and significant decrease in heart rate during recovery, with the first statistically significant pairwise difference emerging at 45–60 s post-exercise compared to the initial recovery interval (p < 0.05), within the context of a continuous HR decline. Regression analysis identified weekly physical activity hours (β = −0.281, p = 0.013) and oncological treatment duration (β = −0.245, p = 0.038) as significant predictors of mean heart rate recovery, explaining 4.8% of the variance, while age was not significantly associated (β = 0.049, p = 0.622). In conclusion, a differentiated recovery pattern emerged at approximately 45–60 s post-exercise, with weekly physical activity and oncological treatment duration as determinants. These findings support the use of wearable-based monitoring to inform individualized exercise prescription in women with breast cancer. Full article

Background: Exacerbations of chronic obstructive pulmonary disease (COPD) are a leading cause of morbidity, mortality, and healthcare burden worldwide. Early detection and timely intervention remain important challenges in COPD management, given the unpredictable nature of acute deterioration and limitations of traditional spirometry-based risk assessment. Methods: This narrative review synthesizes artificial intelligence (AI)-driven approaches for predicting and detecting chronic obstructive pulmonary disease (COPD) exacerbations across electronic health records, wearable sensors, imaging, environmental data, and patient-reported outcomes, emphasizing novel discoveries and emerging relationships rather than predictive performance. Results: Three major discoveries have been made. First, measurable physiological and behavioral deterioration may precede symptom recognition by approximately 7–14 days, thereby establishing a potential intervention window for anticipatory care. Second, machine learning (ML) models integrating pollutant exposure, medication adherence, and clinical characteristics have identified phenotypes with differential environmental sensitivity, including unexpected exposure–adherence interactions. Third, deep neural network analysis of full spirometry curves has revealed structural phenotypes beyond traditional Forced Expiratory Volume (FEV₁)-based measures and novel imaging biomarkers. The predictive performance ranges from the Area Under the Curve (AUC) 0.72–0.95, with a pooled meta-analytic AUC of approximately 0.77. Conclusions: AI has uncovered hidden patterns in the progression of COPD, supporting a shift from reactive to anticipatory management. Translation to routine care requires prospective validation, improved interpretability, workflow integration, and generalizability and equity. Full article

Surface electromyography (sEMG) provides a non-invasive measure of the neural drive transmitted from the central nervous system to muscles by capturing the spatiotemporal summation of motor unit action potentials at the skin surface, and is therefore widely used to study neuromuscular coordination during motor tasks. By reflecting neural drive transmitted from the central nervous system to peripheral muscles, sEMG provides valuable insights for investigating neuromuscular coordination during upper-limb motor tasks. Within the framework of modular motor control, muscle synergy analysis has been increasingly applied to characterize coordinated muscle activation patterns extracted from multi-channel sEMG recordings. In this study, sEMG signals were collected from twelve stroke patients and nine healthy subjects during robot-assisted upper-limb training, involving two movement trajectories (straight and rectangular) and multiple robot-assisted levels. Muscle synergies were extracted using non-negative matrix factorization (NMF). A synergy merging–splitting model, combined with a Functional Driving Ratio (FDR), was employed to characterize both the muscle synergy reorganization and the relative activation contributions of driving versus stabilizing muscle components in terms of motor control strategy. The results showed that healthy subjects maintained consistent muscle coordination patterns across different assistive levels, while making task-dependent adjustments to muscle activation to adapt to variations in movement trajectories. For stroke patients, higher functional status was correlated with more differentiated coordination patterns and relatively higher FDR values, suggesting greater reliance on task-relevant agonist muscles during movement execution. In contrast, lower-function patients exhibited less differentiated coordination patterns accompanied by reduced FDR values, indicating the increased involvement of stabilizing or antagonist muscles. This shift may reflect compensatory control strategies and the reduced efficiency of neuromuscular coordination during assisted upper-limb movements. These findings suggest that sEMG-based muscle synergy features and the FDR may provide quantitative, sensor-derived support for characterizing neuromuscular coordination during robot-assisted rehabilitation. Full article

High-precision detection of hazardous gases with low refractive indices ranging from 1.000 to 1.100, specifically including methane, carbon monoxide, and sulfur dioxide, is critical for industrial safety, yet conventional sensors often suffer from limited sensitivity and severe thermal cross-sensitivity. This work presents a Magneto-Optical Differential Photonic Crystals Sensor (MO-DPCS) utilizing indium antimonide (InSb) to address these constraints. Employing the Multi-Objective Dragonfly Algorithm (MODA), the system was inversely optimized to maximize magneto-optical polarization splitting while rigorously maintaining an ultra-high transmission efficiency. Crucially, an angular interrogation architecture operating under oblique incidence was established to maximize the magneto-optical non-reciprocity, where the detection was realized by fixing the terahertz source frequency and monitoring the precise angular displacements of the steep spectral edges. A differential detection technique was employed to utilize the non-reciprocal phase changes wherein Transverse Electric (TE) and Transverse Magnetic (TM) modes display contrasting kinematic characteristics in the presence of an external magnetic field. The findings indicate that with an adjusted magnetic field of 0.033 T, the MO-DPCS attains an exceptional differential sensitivity of 30.8°/RIU, much above the 0.8°/RIU seen in the unmagnetized condition. The differential approach efficiently eliminates common-mode thermal noise, minimizing temperature-induced drift to below 0.35° across a 1 K range. The suggested MO-DPCS offers a robust, self-referencing solution for stable and high-sensitivity gas sensing applications with a detection limit of 4.18 × 10⁻⁴ RIU. Full article

Accurate meteorological visibility estimation is critical to the safety and reliability of transportation and environmental monitoring systems. Despite the prevalence of deep learning, models often struggle with the non-linear visual degradation caused by varying atmospheric conditions and a scarcity of instrument-calibrated datasets. This study makes two primary contributions. First, we introduce the Hong Kong Chu Hai College Visibility Dataset (HKCHC-VD) comprising 11,148 high-resolution images paired with precise visibility measurements from a Biral SWS-100 sensor. Second, we propose a Range-Aware Attention Framework (RAT-Attn), an adaptive attention mechanism that translates classical range-specific atmospheric modeling into differentiable deep learning operations. This is a domain-specific architectural optimization that integrates a dual-backbone architecture (CNN and Vision Transformer) with a learnable threshold mechanism. This design enables the model to dynamically prioritize spatial and channel-wise features based on estimated visibility intervals, specifically targeting the non-linear visual degradation unique to fog and haze. Experimental results demonstrate that our proposed approach outperforms existing baselines, including VisNet and landmark ANN-based methods. The ResNet + ViT (spatial-threshold) variant achieves the most balanced performance, recording a Mean Squared Error (MSE) of 5.87 km², a Mean Absolute Error (MAE) of 1.65 km, and a classification accuracy of 87.07%. In critical low-visibility conditions (0 to 10 km), the framework reduces regression error by over 75% compared to the baselines. These results confirm that range-aware adaptive feature fusion is essential for robust meteorological estimation in real-world environments. Full article