Search Results (1,510)

Peritoneal adhesions are a common complication following abdominal surgery. Their formation is associated with factors such as inflammatory responses, imbalance in the fibrinolytic system, and proliferation of fibrous tissue. Currently available anti-adhesion products on the market are primarily physical barrier membranes, which have limited functionality, can only serve as passive barriers, and have suboptimal handling and residence times. To address this issue, this study utilized 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide hydrochloride/N-hydroxysuccinimide (EDC/NHS) chemical crosslinking to create polyglutamic acid-polylysine (PGA-PL) hydrogels and tested their anti-adhesion and reparative effects in a cecum–abdominal wall adhesion model in Sprague–Dawley (SD) rats. Experimental results demonstrated that treatment with PGA-PL hydrogels significantly reduced the occurrence of adhesions, effectively decreased inflammatory cell infiltration, neovascularization, and collagen deposition, while exhibiting excellent biocompatibility. Additionally, it markedly reduced the expression of pro-inflammatory factors, transforming growth factor-β1 (TGF-β1), and α-smooth muscle actin (α-SMA) in both serum and local tissue, increased the levels of plasminogen activators, and promoted the proliferation of peritoneal mesothelial cells. PGA-PL hydrogel is a safe and effective anti-adhesion material; its efficacy not only relies on serving as a physical barrier but also actively modulates local inflammatory responses, fibrotic processes, and fibrinolytic balance, while promoting mesothelial repair. Full article

Edible packaging is commonly assessed through good manufacturing practice (GMP), hazard analysis and critical control point (HACCP) systems, food-contact-material (FCM) compliance, and microbial risk assessment (MRA). These approaches respectively control production hygiene, process hazards, constituent migration, and pathogen exposure, but none routinely requires an integrated demonstration that an externally handled package remains suitable for intentional ingestion at the end of its lifecycle. This critical narrative review therefore proposes Edible Packaging Hygiene (EPH) as a conceptual, not yet validated, framework that adds a point-of-consumption endpoint and links the surface history of the edible article with microbial, chemical, allergenic, and physical exposure. Direct evidence on commercially relevant edible-package surfaces is limited; consequently, the review distinguishes direct edible-packaging evidence from indirect low-moisture-food, conventional-packaging, and hand-transfer evidence, regulatory requirements, and author-proposed controls. EPH combines process stabilisation, prevention of post-process contamination, moisture and package-integrity control, realistic handling verification, and chemical assessment of both migration and ingestion of the residual package. It also frames the need for secondary protection as a product-specific, testable design hypothesis rather than a universal disadvantage. Priority research comprises finished-surface challenge and transfer studies, gastrointestinal transformation of nanoforms, realistic retail and consumer simulations, and integrated safety-lifecycle assessment. The principal contribution is the operational integration of established food-safety principles around hygienic integrity of an intentionally consumed surface at the point of consumption. Full article

Background/Objectives: This study examined associations of mental health and health-related quality of life (HRQOL) with suicidal ideation among older Korean adults, and whether an HRQOL difference persisted after adjustment for depressive symptoms. Methods: The 2022 Korea Community Health Survey included 79,441 adults aged ≥65 years. The outcome was a past-year report of thoughts of wanting to die, which may include passive death wishes. Complex-samples logistic models used categorical PHQ-8, self-rated health, and stress (n = 78,838). An exploratory design-based linear analysis estimated adjusted EQ-5D differences (n = 79,033). Results: The weighted outcome prevalence was 9.9% (95% CI 9.6–10.2). Standardized prevalences across the PHQ-8 categories were 6.5%, 13.6%, 18.7%, and 23.9%; the highest versus lowest category difference was 17.4 percentage points (95% CI 14.3–20.5). Stress and EQ-5D anxiety/depression were positively associated with the outcome, whereas happiness was inversely associated (aOR 0.770, 95% CI 0.751–0.790). The adjusted EQ-5D mean difference for respondents with versus without the outcome was −0.0531 (95% CI −0.0589 to −0.0473). The primary mental-health associations persisted with expanded covariates; some physical-domain estimates depended on specification or variance handling. Conclusions: Depressive symptoms, stress, anxiety/depression, and happiness were associated with the outcome, and an HRQOL difference persisted after depressive-symptom adjustment. These mutually adjusted, cross-sectional findings concern a single item that may include passive death wishes and do not establish causal effects or a clinical diagnosis. Full article

IoT deployments handle credential hygiene reactively: cloned, replayed, or stale credentials are typically discovered only after misuse, and revocation state is often propagated through centralized lists whose integrity cannot be independently verified. This article introduces the Yang–Baxter IoT Consistency Gateway (YB-IoT-CG), a Zero-Trust admission-control framework that pushes an algebraic layer of credential screening to the edge and anchors security evidence on a modeled permissioned-ledger architecture. YB-IoT-CG operates after conventional secret-based authentication and Yang–Baxter equality is used as an execution-consistency invariant rather than as proof of device identity or message authenticity. Each authenticated message is hashed into a digest and reduced to an algebraic transcript that is verified over two Yang–Baxter traversal paths. Beyond equality checking, chain-proximity metrics inspired by time–memory trade-off analysis, nonce and timestamp freshness heuristics, and contextual risk scoring identify credentials that should be proactively challenged or revoked before telemetry is admitted. The proactive risk component is deterministic and policy-based rather than a learned predictive model. Decisions are batched into Merkle trees whose roots are represented through a permissioned-ledger model, credential revocation is propagated through a modeled on-chain registry, and device identities are bound to W3C Decentralized Identifiers (DIDs) with verifiable credentials. This design provides tamper-evident audit support while keeping ledger interaction off the packet decision path. Validation is based on a controlled Python 3.13.7 packet-level simulation and a parameterized ledger model, not on a physical IoT or live Hyperledger Fabric deployment. Across 60 seeded simulation runs, the complete post-authentication screening pipeline obtained a median incremental decision time of 7.8 $\mathsf{\mu }$s, 99.4% aggregate decision accuracy for the modeled attack classes, a 0.43% false rejection rate, and a 31.4 ms amortized ledger service-time equivalent per decision for a batch size of 64. The 99.4% value is a property of the composed freshness/context/registry/YB policy and is not a YB-only detection rate; the YB-specific positive guarantee is limited to the isolated fault class of Proposition 7. These results provide simulation-based evidence supporting a lightweight, explainable, auditable, and proactive approach to credential hygiene at the edge. Full article

Background/Objectives: Musculoskeletal disorders of the lower back remain one of the leading causes of work-related health problems in occupations involving manual material handling. Passive industrial exoskeletons have gained increasing attention as a workplace-oriented assistance technology to reduce physical strain during lifting, carrying, and forward-bending tasks. This pilot study investigates the effect of a passive back-support exoskeleton on spinal posture during a simulated palletizing task. Manual palletizing remains relevant in manufacturing and distribution environments, despite increasing automation, because flexible, variable, and economically feasible work processes are still required. Methods: Five participants performed repeated palletizing cycles under three conditions: wearing an activated exoskeleton, wearing a deactivated exoskeleton, and without an exoskeleton. Spinal posture was captured using an optical motion-capture system with reflective markers placed along the spine. Marker-defined dorsal segment angles were calculated, normalized to an individual upright reference posture, and analyzed for deviations during distinct task phases. Results: The results indicate a tendency toward reduced spinal flexion when the exoskeleton was activated, particularly in the thoracic and lumbar regions. In contrast, larger deviations from the physiological reference posture were observed when the exoskeleton was deactivated or not worn. Inter-individual differences related to body height and prior ergonomic training were identified. Conclusions: Although the limited sample size does not allow definitive conclusions, the findings suggest that passive back-support exoskeletons can contribute to improved spinal posture during manual palletizing. The study provides a quantitative methodological framework for future large-scale investigations and supports the role of motion capture as an objective assessment tool in ergonomic exoskeleton research. Full article

Deterministic propagation prediction faces a fundamental trade-off: the image method is exact but single-mechanism, while the shooting and bouncing ray method is flexible yet suffers from path omissions due to discrete sampling. This paper proposes the hybrid forward–backward ray tube propagation model (HFB-RTPM), which decouples the exhaustive forward construction of a ray tube tree from the backward path screening at the receiver, handling reflection, refraction, diffraction, and diffuse scattering within a unified framework. A quasi-3D extension and a dual-lobe diffuse scattering model are further introduced. Simulations show 99.8% reflection path completeness (RMSE 0.05 dB) and 100% diffraction completeness under all conditions. With diffuse scattering, the mmWave NLoS RMSE drops from 23.9/16.2 dB to 6.9/6.8 dB at 28/73 GHz, with a single model configuration valid across both frequency bands. In field measurements at 2.3–5.9 GHz across dense urban blocks, the simulation-to-measurement RMSE ranges from 5.6 to 8.5 dB. For large-scale coverage, HFB-RTPM is two orders of magnitude faster than the image method. The proposed method achieves accuracy, efficiency, and physical completeness for deterministic multipath prediction in 6G wireless networks. Full article

In distribution network and microgrids, energy storage (ES) systems possess four-quadrant operational capabilities, making them inherently high-quality resources for reactive power (RP) regulation. However, existing research has primarily focused on optimizing the active power of ES to achieve economic objectives, while the potential for RP and voltage control has not been fully explored. Meanwhile, traditional RP optimization methods have inherent limitations in terms of real-time performance, addressing uncertainty, and handling nonlinear problems. To address these challenges, this paper proposes a data-driven adaptive RP and voltage control method for distribution network with ES using deep reinforcement learning (DRL)–Soft Actor–Critic (SAC) algorithm. First of all, this method models the grid’s RP and voltage control problem as a sequential decision-making process, with the core being the construction of a control agent that integrates grid operational states with a deep neural network. Through continuous interaction with the environment, this agent autonomously learns and dynamically adapts to the random fluctuations in photovoltaic (PV) output and load without relying on precise physical models. Secondly, this paper sets minimizing network losses, voltage deviations, and the operational costs of RP equipment in ES as comprehensive optimization objectives, translating them into a reward function within the DRL-SAC framework. Leveraging the powerful nonlinear mapping capabilities and extremely fast forward computation speed of deep neural networks, the strategy achieves a data-driven approximation of the optimal RP control strategy in complex grid environments. Finally, the superiority of the strategy is comprehensively verified on the modified IEEE 33-bus system under three typical operating conditions (daytime fluctuation, extreme weather, sudden load change). The results show that the voltage qualification rate is increased to 99.1% and the network loss is reduced by 33.7%, providing an engineering-feasible solution for ES systems to participate in distribution network RP and voltage regulation. Full article

The rapidly changing digitalization of the energy sector, fueled by smart grids, the Industrial Internet of Things (IIoT), Advanced Metering Infrastructure (AMI), and Supervisory Control and Data Acquisition (SCADA) systems, is leading to a great and symmetrical improvement in workflow and the ability to have real-time insights. In parallel, the massive adoption of low-power and resource-constrained devices that are still connected makes cybersecurity threats more serious and thus necessitates lightweight authentication mechanisms to have safe, secure, and symmetrical communicative. Selecting the right authentication method involves a challenging multi-criteria decision-making (MCDM) process where various factors such as security aspects, computation power needed, communication capabilities that can be delivered, and deployment-related aspects are considered together, along with inherent uncertainties in expert evaluations. This paper proposes a Hesitant Fuzzy (HF)-based hybrid method that combines the Analytic Network Process (ANP) with the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) for the evaluation of lightweight authentication systems in the energy domain. This symmetrical HF-ANP is used for the modeling of the interrelations between major evaluation criteria such as security strength, computation efficiency, communication effectiveness, and deployment scalability. It can also handle the experts’ hesitant and uncertain preferences. The calculated weighting factors are then input to the HF-TOPSIS method to rank five different lightweight authentication protocols: Hash-Based Authentication, Elliptic Curve Cryptography (ECC)-Based Lightweight Authentication, Physical Unclonable Function (PUF)-Based Authentication, Blockchain-Assisted Lightweight Authentication, and Certificate-less Lightweight Authentication. Furthermore, sensitivity analysis and comparison analysis are conducted to verify the strength, symmetry, consistency, and reliability of the proposed framework. Our results indicate that the integrated HF-ANP and TOPSIS procedure provides a symmetrical, comprehensive, and systematic decision-making tool to appraise lightweight authentication techniques amid uncertainties. The proposed method will be very beneficial for different types of energy industrial players, system designers, and security experts to pick secure, efficient, and scalable methods of authentication to protect the critical energy facilities against the new generation of cyber threats. Full article

Surface electromyography (sEMG) signals acquired with low-cost sensors tend to exhibit variable degradation that can compromise the reliability of myoelectric control systems outside controlled conditions. This work presents an adaptive processing pipeline for sEMG signals composed of three chained stages: a motor-intent classifier based on dilated temporal convolutional networks, whose function is to determine whether a signal window contains muscle activity associated with a voluntary gesture; a dual-output quality assessor that estimates a continuous score and a binary acceptability label, aimed at deciding whether the signal can be used directly or requires intervention; and a convolutional autoencoder that recovers the morphology of degraded windows before they are used in prosthetic control. The decision policy for reconstruction operates on two independent thresholds and classifies each window into one of three states: signal discard, direct acceptance, or active restoration. The models within the proposed architecture are trained on the public NinaPro DB1, DB3, and DB10 datasets and validated without recalibration on signals recorded from two participants with transradial amputation over three to four weekly sessions. The results show that the pipeline correctly handles signal profiles with opposing characteristics. Inference latency remained below 5 ms at the 50th percentile across all scenarios, consistent with real-time operation. All inference was executed on a host computer; a prototype using an Arduino UNO R4 WiFi as a peripheral interface was built to display the pipeline’s decisions on physical hardware and to confirm that the serial-communication link does not introduce additional latency, not to perform on-board inference. A downstream evaluation further showed that, while restoration improved signal-level fidelity metrics, it did not translate into improved motor-intent classification accuracy relative to using the degraded signal directly, a limitation discussed explicitly in this work. Full article

Agricultural mobile platforms provide the physical foundation for sensing, field operations, and material handling in smart farming, yet their design is strongly constrained by crop geometry, terrain conditions, task-specific payloads, energy demand, and operational reliability. This review examines agricultural mobile platforms from four complementary perspectives: design and operational requirements, platform classification, powertrain and mobility technologies, and agricultural applications. Wheeled, tracked, legged and wheel-legged, and rail-guided or constrained-motion platforms are compared in terms of mobility characteristics and suitable operating environments. Power sources, drive systems, steering mechanisms, mobility control, and platform–implement integration are further discussed, with particular attention to electrification, distributed drive, dynamic loads, and coordinated power allocation. Representative applications include crop monitoring and phenotyping, precision crop management, weeding and harvesting, and transportation and multi-purpose operations. Current challenges arise from the strong coupling among terrain adaptability, payload, energy capacity, autonomy, and long-term reliability, as well as limited interoperability between platforms and implements. Future development should emphasize task-oriented reconfigurable platforms, standardized mechanical and electrical interfaces, task-level energy management, and mobility control that accounts for real-time platform and terrain conditions. Full article

Asian elephants (Elephas maximus) face substantial thermoregulatory constraints because of their large body size, low relative surface area, sparse hair, and lack of functional sweat glands. Reliable body temperature measurement is essential for assessing thermal status and evaluating welfare in both wild and managed populations, but conventional rectal thermometry requires close physical contact, animal training, and repeated manual handling, making high-frequency, continuous, large-scale monitoring impractical. This study developed a non-invasive framework for automatically detecting the outer ear and extracting auricular surface temperature from infrared thermograms. Rectal temperature, regional surface temperatures, ambient temperature, and relative humidity were measured synchronously in eight semi-captive Asian elephants, yielding 425 matched observations. The associations between rectal temperature and the surface temperatures of three anatomical regions (head, outer ear, torso and limbs) were analyzed using repeated-measures correlation accounting for the non-independence of repeated measurements. Mean outer-ear temperature showed the strongest within-individual association with rectal temperature (r_rm = 0.395, p < 0.001), identifying the outer ear as the optimal thermal window for subsequent automated monitoring. Eight lightweight YOLO models—YOLOv5n, YOLOv5s, YOLOv8n, YOLOv8s, YOLO11n, YOLO11s, YOLO26n, and YOLO26s—were trained on 2178 annotated infrared images and evaluated on an independent 194-image test set from extra elephants. Model performance was assessed using detection metrics, inference speed, Bland–Altman agreement, Taylor diagram statistics, and a weighted multi-criteria score with Monte Carlo sensitivity analysis. YOLO11n achieved the best overall performance, with an mAP50 of 0.933 and an inference speed of 164 frames per second. The proposed framework provides an efficient method for automated auricular temperature monitoring and has potential applications in elephant welfare management and remote physiological surveillance. Full article

This paper studies a coupled system of viscoelastic wave equations with frictional damping, cross-tempered fractional damping, viscoelastic memory, and logarithmic source nonlinearities. The fractional damping acts across the two components, so that the fractional feedback in each equation is generated by the velocity of the other component. To handle the memory and fractional terms, we introduce suitable history and diffusive variables and reformulate the problem as an evolution system in an extended energy space. Under appropriate assumptions on the relaxation kernels, fractional parameters, and nonlinear exponent, we establish the local well-posedness of mild and strong solutions using semigroup theory. We then use a potential-well argument to prove global existence for initial data in the stable set. Under an additional decay condition on the relaxation kernels, an appropriate Lyapunov functional is constructed to establish the exponential decay of the energy. Finally, numerical simulations based on a finite-difference scheme and a physics-informed neural network (PINN) are used to illustrate the predicted decay behavior. Full article

Background: Alcohol-associated liver disease (ALD) involves ethanol-related metabolic injury, inflammation, gut–liver dysfunction, and progressive loss of skeletal muscle and functional reserve. Exercise could influence several of these pathways, but ALD-specific evidence remains limited. We therefore evaluated exercise as an adjunct, not a substitute for established care, across the ALD spectrum. Methods: We conducted a structured narrative review of PubMed/MEDLINE, Google Scholar, and publisher records updated through 20 August 2026. Evidence was classified as direct human ALD/MetALD evidence, direct preclinical alcohol-injury evidence, indirect clinical evidence from MASLD or mixed-etiology cirrhosis, or mechanistic evidence from broader exercise biology. Results: Direct human evidence is predominantly observational. Leisure-time physical activity is associated with lower liver mortality across drinking patterns and with lower odds of at-risk advanced fibrosis in MetALD/ALD. These associations do not establish the efficacy of structured exercise training. Preclinical alcohol-injury models support redox- and Nrf2-mediated hepatoprotection, whereas proposed effects on AMPK-SIRT1-PGC-1α signaling, mitochondrial quality, gut microbial metabolites, myokines, and ammonia handling remain largely extrapolated. Metabolomic studies identify disturbances in redox balance, lipid intermediates, acylcarnitines, bile acids, and amino acid and tryptophan metabolism. These alterations provide candidate endpoints for future trials. Myostatin and decorin are associated with ALD severity, but their responsiveness to exercise is unknown. We therefore propose a stage-specific research and practice framework with explicit safety considerations. Conclusions: Exercise is a promising but unproven multisystem adjunct for ALD. It should complement abstinence support, alcohol use disorder care, nutrition, and standard medical management. ALD-specific trials should integrate metabolomics, liver outcomes, functional measures, sex, alcohol exposure, disease stage, and adverse events. Full article

Underground coal-mine equipment operates under broadband noise, high dust, humidity, and methane. Acoustic sensing is uniquely suited to this environment: it captures vibration, friction, and airflow signatures without physical contact, incurs low sensor-deployment cost, responds at millisecond speed, and remains effective in low-light, high-dust conditions where optical and vibration alternatives fail. Acoustic fault perception is therefore critically important in underground coal-mine operations. Three unresolved challenges remain: (i) motor whine, material-collision impacts, and ventilation-fan roar compound into a low-SNR soundscape where conventional models lose noise robustness; (ii) acoustic signatures vary widely across equipment types and fault-development stages; and (iii) existing supervised classifiers, trained on imbalanced data, exhibit limited generalization and output only binary judgments, lacking the semantic descriptions that maintenance crews actually need. To address all three, we propose a domain-adaptive audio LLM coupling a BEATs encoder (frozen during Stage II, adapted via Low-Rank Adaptation (LoRA) during Stage I), a Querying Transformer (Q-Former) alignment layer with Dynamic Acoustic Token Compression (DATC), a LLaMA-3.1-8B decoder adapted via LoRA, and Constrained Decoding for Structured Fault Description (CD-SFD) enforcing a three-slot output of fault type, danger level, and handling recommendation. DATC allocates query budget by signal energy to suppress noise-dominated frames; CD-SFD is a state-machine decoder that guarantees the three-slot schema. We release CMEASD: 1200 recordings comprising 24 physical machines (4 per equipment type, 12/6/6 machine-disjoint split). Under machine-disjoint evaluation, the model reaches Macro Accuracy 84.6 ± 1.5% and Macro F1 82.9 ± 1.6%, outperforming the strongest discriminative baseline (PANNs-Transformer, 81.7%) by +2.9 pp and SALMONN-LoRA by +2.5 pp. Ablations attribute +2.4/+1.4/+0.9 pp to DATC, CD-SFD, and the domain prompt. A 30-day mine trial achieves 7.0 s end-to-end latency with 21/30 days of stable, zero-false-shutdown operation. Full article

The inspection of conventional door locks in public buildings is a repetitive security task commonly performed manually at predefined times. This paper presents the development and experimental validation of a non-invasive, low-complexity robotic system designed for autonomous door lock-state verification. The core contribution is a novel physical-interaction method integrated into an indoor omnidirectional mobile robot that infers the lock state of a lever-type door handle without infrastructure modifications. The system executes a three-stage operational workflow: 2D LiDAR-based global positioning in front of target doors, depth-camera-based local realignment of the robot and the door handle, and physical actuation coupled with state inference via kinematic feedback. By depressing the handle during a controlled forward motion, forward displacement identifies an unlocked door, whereas motion resistance signals a locked state. The system was evaluated in a real facility across 18 target doors during eight complete inspection missions. Out of 144 verification attempts, the system achieved a 97.9% success rate; LiDAR global positioning enabled immediate handle actuation in 96 cases (66.7%), while depth-camera realignment successfully corrected 45 handle misalignments. These results validate the reliability and low-complexity of physical-feedback inference for routine autonomous facility security. Full article