Search Results (371)

Maglev trains are susceptible to various types of operational challenges, including track irregularities, load variations, and actuator faults. It is evident that these factors can compromise suspension performance and even pose a serious risk to operational safety. This paper proposes a prescribed performance event-triggered fault-tolerant control method for the electromagnetic suspension system of a maglev train subject to multiple constraints. A projection-based adaptive extended state observer is designed to estimate the unknown gain caused by actuator faults and load variations, as well as the external disturbance. In light of the disparity in upper and lower safety margins inherent to the suspension gap error, arising from track irregularities, an asymmetric prescribed performance function and an error transformation are devised to ensure that the gap tracking error perpetually complies with the asymmetric prescribed performance constraint. In addressing the issue of rapid variations in the suspension gap, the vertical velocity is also constrained through the implementation of prescribed performance, resulting in a joint constraint framework that encompasses both the gap tracking error and the vertical motion. A dynamic event-triggered mechanism has been incorporated into the backstepping design with a view to reducing unnecessary control updates under limited communication resources, while Zeno behavior has been excluded from the closed-loop system. Within this framework, a dynamic gain adjustment mechanism with an explicitly bounded rate of variation is further developed to achieve smoother gain adaptation. The uniform ultimate boundedness of all closed-loop signals is demonstrated through Lyapunov stability analysis under the prescribed multiple constraints. The efficacy of the proposed method is demonstrated through comparative simulation results. Full article

Effective thermal management is a cornerstone of safe, long-life lithium-ion battery operation, especially under high-rate charge–discharge and dynamic driving conditions. Conventional active cooling technologies face inherent trade-offs between heat dissipation efficiency, system complexity, and temperature uniformity, while phase change materials (PCMs) provide a promising passive alternative by absorbing latent heat during phase transition to buffer temperature spikes, improve temperature uniformity, and delay thermal runaway propagation. This paper presents a comprehensive review of recent advances in PCM-based lithium-ion battery thermal management, systematically covering the full scope from fundamental battery heat generation mechanisms to material synthesis optimization and hybrid system integration. At the material level, we analyze state-of-the-art strategies to address the intrinsic drawbacks of organic PCMs—low thermal conductivity, mismatched phase transition temperatures, and high flammability—including the construction of carbon/metal conductive skeletons, compositional tuning of phase change behavior, and flame-retardant modifications. These approaches have yielded composite PCMs with significantly improved heat transport capability and fire safety, while preserving high latent heat storage capacity. At the system level, we evaluate the thermal performance of pure passive PCM configurations, which excel at peak temperature suppression and inter-cell temperature uniformity, as well as hybrid designs that combine PCMs with air or liquid cooling to resolve heat accumulation issues and maintain stable performance under prolonged, demanding operating cycles. Despite these advances, key challenges remain: balancing high thermal conductivity with high latent heat capacity, developing climate-adaptable phase transition temperatures, and integrating multiple functionalities without compromising core thermal storage properties. Looking forward, future research directions include multifunctional integrated composites, smart adaptive PCMs, cost-effective scalable manufacturing, and precision structural engineering. This review also summarizes quantified performance trade-offs and provides actionable design guidelines for both material development and system-level integration. Full article

Visible light communication (VLC) has emerged as a transformative optical wireless technology for sixth-generation (6G) networks, offering license-free spectrum access, inherent electromagnetic-interference immunity, high spatial confinement, and the unique ability to combine high-speed wireless connectivity with solid-state lighting infrastructure. However, the transition from conventional VLC links to practical 6G optical wireless systems requires far more than advanced modulation and signal processing. Future VLC performance will be strongly determined by the co-design of photonic front-ends, including high-speed transmitters, spectrally engineered emitters, reconfigurable optical interfaces, intelligent receivers, and energy-autonomous detection units. This article provides a comprehensive, device-centered review of photonic hardware and artificial intelligence (AI) enablers for next-generation 6G VLC systems. Particular attention is given to micro-LEDs, laser diodes, color-conversion materials, including perovskite quantum dots, advanced photodetectors, imaging receivers, wavelength-shifting fiber receivers, solar-cell-based receivers, optical reconfigurable intelligent surfaces (RISs), metasurfaces, beam-steering components, and optical wireless power transfer. This review discusses how AI can support inverse photonic design, transmitter and receiver calibration, nonlinear impairment mitigation, channel-aware beam control, and energy-aware resource management. Unlike broader VLC surveys that mainly emphasize network architecture, this article provides a device-centered perspective on AI-enabled photonic integration for 6G VLC, supported by a comprehensive survey of recent experimental demonstrations. Key challenges related to bandwidth, optical efficiency, receiver field of view, mobility, safety, standardization, and practical deployment are summarized, followed by a research roadmap for 2025–2032. Full article

Upper-limb motor dysfunction resulting from neurological disorders severely limits patients’ activities of daily living and social participation. Pneumatic upper-limb rehabilitation robots have emerged as a promising intervention owing to their inherent compliance, lightweight design, and high power-to-weight ratio, which facilitate safe, repetitive, and home-based training. Despite these advantages, extensive clinical translation remains hindered by challenges including actuator hysteresis, nonlinear dynamics, limited accuracy in intention recognition, and inconsistent clinical evaluation metrics. This review systematically examines recent advancements in pneumatic upper-limb rehabilitation robots across four critical dimensions: structural design, human–robot interaction, control strategies, and clinical translation. We comparatively analyze rigid exoskeletons, soft wearable devices, and rigid–soft hybrid configurations based on output capability, motion accuracy, comfort, and clinical applicability. The findings suggest that while rigid systems offer high precision and soft systems maximize safety, rigid–soft hybrid architectures represent a critical developmental trend for balancing motion accuracy with interaction compliance. Furthermore, the review evaluates multimodal sensing techniques (e.g., EMG, EEG, and IMUs) for motion intention decoding and training state monitoring, alongside conventional, adaptive, and artificial intelligence-driven control methods aimed at compensating for pneumatic nonlinearity and improving real-time response. Current clinical evidence indicates that these systems effectively enhance upper-limb function and muscle strength, particularly in post-stroke rehabilitation; however, existing trials are frequently constrained by small sample sizes, short interventions, and heterogeneous protocols. Future research must prioritize rigid–soft hybrid architectures, robust multimodal sensor fusion, digital twin-assisted assessment, adaptive intelligent control, and standardized home-based rehabilitation platforms. Ultimately, this comprehensive review provides a concise reference for the design optimization and clinical deployment of next-generation pneumatic rehabilitation systems. Full article

Background/Objectives: Healthcare delivery systems are inherently complex, and efforts to improve quality and safety often fail to achieve sustained, system-wide impact because of misalignment between work system design, care processes, and clinical practice. This article presents a practical, theory-informed framework that integrates human factors and systems thinking to guide the design and implementation of clinical pathways for reliable care delivery and system-level transformation. Methods: A conceptual framework was developed through synthesis of literature from human factors engineering, sociotechnical systems theory, and healthcare quality improvement. The framework organizes healthcare delivery into three interconnected domains—the work system, care processes, and outcomes—while positioning clinical pathways as the operational mechanism linking system design to care execution. Pediatric immunization was used as an illustrative case example. Results: The framework illustrates how misalignment in system design may contribute to variability in care processes and outcomes. Clinical pathways integrated into routine workflows and supported by human factors strategies—including decision-support heuristics, workflow simulation, and structured debriefing—may promote more reliable care delivery. Sustained implementation across teams and clinical settings is expected to reduce missed opportunities, improve consistency of care, and support progression toward system-level transformation. Conclusions: Clinical pathways can serve as practical tools for translating system design principles into reliable healthcare delivery when implemented using human factors and systems-based approaches. This framework provides healthcare leaders and quality improvement teams with a structured approach for designing, implementing, and scaling pathways to support safer, more reliable, and more consistent care delivery. Full article

In low-Earth-orbit (LEO) satellite constellation remote sensing for surface maritime target detection, the inherent characteristics of discontinuous detection epochs, non-uniform temporal intervals, and clutter contamination invariably cause conventional data association algorithms to suffer from validation gate degradation, covariance divergence, and combinatorial explosion. To circumvent these limitations, this paper proposes a multi-target, time-adaptive fast association method tailored for discontinuous sparse observations. Within the joint probabilistic data association (JPDA) framework, the proposed method analyzes the mismatch between the Kalman filter prediction covariance and the actual error under discontinuous observations. A time-interval adaptive gating mechanism maintains the gate detection probability across arbitrary revisit intervals. Secondly, to resolve the massive connected cluster problem triggered by the densification of the validation matrix, a progressive clustering strategy inspired by simulated annealing is designed, which recursively decomposes the global, exponentially scaling association graph into independent subgraphs of manageable sizes. Building upon this, a depth-first search (DFS) heap pruning technique is integrated with the Hungarian hard assignment algorithm as a safety-degradation mechanism to safeguard numerical robustness in extreme scenarios. Comparative experiments demonstrate that the proposed method significantly enhances both tracking accuracy and track completeness across various constellation coverage characteristics and maritime clutter intensities. Furthermore, its execution efficiency satisfies real-time simulation requirements, effectively supporting engineering application for surface maritime target detection. Full article

Food packaging plays an essential role in maintaining food quality, safety, and shelf life during storage, transportation, and retail distribution. However, conventional petroleum-derived plastics raise increasing environmental concerns, while many biopolymer-based packaging materials remain limited by insufficient mechanical strength, poor water resistance, limited barrier properties, and unstable active functions. Natural extract-based multifunctional gels provide a promising strategy for sustainable food packaging due to their inherent biocompatibility, structural versatility, and synergistic interplay between active extracts and gel networks, which can immobilize bioactive compounds, regulate mass transfer, and integrate preservation and monitoring functions within one material platform. This review summarizes recent advances in natural extract-based multifunctional gels for food packaging, with emphasis on polysaccharide-based, protein-based, lipid-based, and other naturally derived gel systems. Representative material formats include hydrogels, aerogels, emulsion gels, oleogels, and gel-derived films or coatings, while typical components include chitosan, alginate, pectin, starch, proteins, natural waxes, lignin, anthocyanins, curcumin, polyphenols, and essential oils. Their gelation mechanisms, structural features, preparation strategies, and functional applications are discussed, including antioxidant activity, antimicrobial preservation, barrier enhancement, controlled release, freshness monitoring, and biodegradability. Furthermore, the potential interactions between natural extracts and gel matrices are also discussed, including hydrogen bonding, electrostatic interactions, ionic crosslinking, dynamic covalent bonding, hydrophobic association, and lipid crystallization. The main merits of these systems include renewability, biodegradability, tunable network interactions, active-agent protection, and integration of preservation and monitoring functions, whereas persistent challenges include moisture sensitivity, mechanical instability, variable extract composition, migration safety, and scale-up cost. This review provides guidance for designing natural extract-based gel packaging materials with improved functionality, sustainability, and practical applicability. Full article

Fracture in concrete is inherently a multi-stage process, yet traditional three-stage frameworks do not explicitly distinguish between micro-crack development and macro-crack propagation, particularly under varying ligament length conditions. The influence of ligament length (notch-to-depth ratios of 0.0, 0.2, 0.3, 0.4, and 0.5) on the crack propagation characteristics in notched concrete beams under three-point bending is investigated. Three-dimensional digital image correlation (3D DIC) was employed to monitor full-field displacement and strain, enabling the evaluation of key fracture parameters including horizontal displacement, crack mouth opening displacement (CMOD), horizontal strain, fracture process zone (FPZ) length, macro-crack length, and total fracture zone length. A high-magnification industrial camera (100×) was simultaneously used for real-time observation of the notch tip. Based on the evolution of these parameters, the fracture process was divided into four distinct stages: linear elastic stage, micro-crack initiation and propagation stage, macro-crack initiation and propagation stage, and complete failure stage. The industrial camera observations confirmed macro-crack initiation at approximately 60% of the post-peak load, validating the proposed four-stage division. Quantitative results show that increasing the notch depth ratio from 0.0 to 0.5 reduces the peak load by approximately 30–40% and decreases the nominal stress proportionally. The FPZ was found to be fully developed at the 60% post-peak load threshold, after which it diminished as macro-crack propagation dominated. Aggregate bridging, crack deflection, and crack branching were consistently identified as the primary toughening mechanisms governing the ligament effect. The crack propagation mechanisms in the four stages are controlled by the combined effects of front free boundary effect, stress concentration effect, ligament effect, and back free boundary effect. These findings provide a refined understanding of concrete fracture that can inform the safety assessment and design of concrete bending members in infrastructure construction. Full article

Automatic Identification System (AIS)-based vessel trajectory prediction is essential for maritime traffic management and navigation safety. Existing deep learning methods typically model vessel motion within a unified temporal representation space, which may entangle long-term navigation trends with local maneuvering behaviors. However, vessel trajectories inherently exhibit heterogeneous dynamics, including steady route evolution and non-stationary maneuver perturbations. To address this issue, this paper proposes MD-EDTCNFormer, a motion-decoupled dual-stream framework for vessel trajectory prediction. A Global Navigation Dynamics Encoder is designed to capture dominant route-level temporal evolution from raw AIS sequences, while a Residual Maneuver Dynamics Encoder explicitly models maneuver-related local perturbations through state transition residual representations. In addition, a state-adaptive motion aggregation mechanism is introduced to dynamically balance global navigation dependencies and local maneuver-aware dynamics under different navigation states. Depthwise separable temporal convolution and efficient channel attention are further integrated to suppress redundant temporal-channel coupling and emphasize dynamically dominant motion cues. Experiments on a real-world AIS dataset from the Zhoushan coastal area demonstrate the effectiveness of the proposed framework under coastal traffic conditions, and show improvements in prediction accuracy and trajectory stability compared with representative baseline methods. Full article

This study assesses the intrinsic hazards of an energy-integrated gas oil hydrocracking process from a sustainability-oriented process safety perspective. Hydrocracking units are essential in modern refineries for upgrading heavy gas oil fractions into higher-value fuels; however, they operate under severe conditions involving high temperatures, elevated pressures, hydrogen-rich environments, complex process structures, and large inventories of hazardous substances. In this context, improving energy efficiency through heat integration must be evaluated together with its implications for inherent safety and sustainable process design. The Inherent Safety Index (ISI) methodology was applied at the conceptual design stage to quantify the intrinsic risk level of the process and identify the main contributors to chemical and process-related hazards. The results yielded a total ISI value of 46, composed of a chemical safety index of 26 and a process safety index of 20, indicating a high intrinsic hazard level. The most significant contributors were toxic exposure (I_TOX = 6), inventory magnitude (I_I = 5), and process structure (I_ST = 5), while the large ISBL inventory of 2838.3 t, together with operating conditions reaching 456.4 °C and 166.8 bar, substantially increased the inherent risk of the system. Although energy integration contributes to improved thermal performance, the results indicate that it does not significantly reduce the intrinsic hazard level. Sensitivity analysis showed that optimization of operating temperature and pressure could reduce the ISI from 46 to approximately 43. These findings demonstrate that the intrinsic risk of energy-integrated hydrocracking systems is primarily governed by operating severity, hazardous material inventories, and toxicity, highlighting the importance of incorporating inherent safety principles during the conceptual design stage to achieve safer, more resilient, and more sustainable refinery operations. Full article

The accurate prediction of hydrodynamic characteristics and structural responses in underwater fishing gear is critical for optimizing design, ensuring operational safety, and minimizing environmental impact. To overcome the computational costs and scalability limitations of traditional physical modeling, this study evaluates three machine learning algorithms such as Random Forest (RF), Light Gradient Boosting Machine (LightGBM), and Support Vector Machine with a Radial Basis Function kernel (SVM-RBF) to predict the hydrodynamic coefficients and structural responses of tuna longline components, including mainlines and branch lines. Models were trained and validated using a comprehensive flume tank dataset encompassing six gear configurations tested across varying flow velocities and lead-line weights. Results demonstrate that optimal model selection is inherently task dependent. For hydrodynamic coefficients, LightGBM achieved superior predictive accuracy for branch-line drag (whole-dataset R² = 0.8315), while both LightGBM and SVM-RBF excelled in lift prediction. Conversely, structural responses (sinking depth and x-displacement) proved inherently more difficult to model deterministically due to high-frequency transient dynamics and stochastic variability. While LightGBM provided balanced generalization for sinking depth, SVM-RBF exhibited severe overfitting for x-displacement. In contrast, RF maintained the most conservative and consistent performance across structural targets, effectively mitigating the memorization of dynamic noise observed in the more complex algorithms. Beyond predictive modeling, feature importance analysis identified flow velocity, lead-line weight, material stiffness, and geometric parameters as dominant physical drivers, validating the physical plausibility of the models. Crucially, the integration of experimental and ML analyses revealed that a polylactic acid (PLA)-integrated midsection configuration consistently yielded the lowest and most stable drag force (0.004–0.13 N at 0.49 m/s), representing a 30–60% reduction compared to conventional nylon lines. Furthermore, the study uncovered novel physical phenomena, including velocity-independent deformation stability, progressive transient sinking kinetics, and tension-induced load redistribution. These findings establish machine learning as a reliable, scalable surrogate for longline gear design, advocating for thin-diameter, biodegradable PLA-integrated lines to enhance hydrodynamic efficiency and mitigate marine plastic pollution, while underscoring the necessity of task-specific algorithm selection for robust engineering applications. Full article

Driven by the low-carbon transformation in the construction industry, Moso bamboo has emerged as a promising green material. However, a critical gap exists in current structural design theories: they predominantly rely on homogeneous assumptions, failing to capture the inherent spatial variability and coupled strength–stiffness degradation of bamboo. Experimental results reveal a distinct longitudinal gradient, where the top section (5–8 m) exhibits approximately 15% higher average tensile strength and 12% higher average elastic modulus compared to the bottom section (1–3 m). This oversight severely compromises the accuracy of structural reliability evaluations. To address this, this study pioneers a high-precision digital representation method by developing a novel three-dimensional (3D) anisotropic bivariate coupled random field model. Experimental and stochastic finite element simulations demonstrate that the model accurately replicates spatial variations, maintaining relative errors for primary statistical indicators strictly below 1% while robustly capturing the bivariate coupling characteristics. Crucially, by integrating non-parametric probability box (P-box) theory, the macroscopic tensile resistance of full-scale bamboo members is rigorously bounded within a definitive statistical interval. Furthermore, the extracted Interval Skewness Ratio generally remains greater than 1.0 (averaging 1.38), providing robust quantitative proof of an asymmetric structural degradation governed by the brittle weakest-link failure mechanism. By effectively eliminating non-physical sample generation associated with traditional univariate models, this research makes a significant contribution to the field, providing a rigorous digital twin framework and theoretical foundation for the advanced non-probabilistic safety design of modern bamboo structures. Full article

The assembly quality of scaffolding systems directly governs the safety of personnel on construction sites. According to construction safety statistics, scaffolding-related accidents account for approximately 30–40% of construction fatalities globally, with geometric assembly deviations being a contributing factor in over 60% of scaffold collapse incidents. Traditional scaffolding inspections rely heavily on manual measurements, which are inherently inefficient, hazardous, and difficult to scale comprehensively. This study presents an automated evaluation framework that integrates computer vision with structural mechanics simulations. First, an object detection model based on the SegFormer encoder architecture is developed to precisely identify scaffolding standards, ledgers, and couplers against complex site backgrounds. Its hierarchical Transformer encoder and global self-attention mechanism enable the model to capture long-range topological relationships, achieving a mean Average Precision (mAP@0.5) of 95.2% on a custom dataset with an inference speed of 45 FPS per 640 × 640 image patch. For complete high-resolution frame processing including tiling and geometric extraction, the end-to-end pipeline requires approximately 8–12 s per frame. Second, a simplified Hough transform with a restricted parameter domain is introduced. Integrated with a dual-track image processing workflow, this algorithm performs sub-pixel centerline fitting to automatically extract critical geometric parameters, including lift height and bay width, maintaining a relative measurement error within 3.5% compared to manual ground truth. Finally, a parameterized finite element model is established. An automated mapping middleware dynamically injects the extracted as-built parameters into the simulation environment. Comparative simulation analysis indicates that a 14.7% deviation in standard lift height, coupled with an initial tilt defect of 1/150, precipitates a 22.4% reduction in the predicted structural stability factor, illustrating the framework’s capability for assessing relative capacity degradation between design intent and as-built conditions. This framework establishes a robust, closed-loop pipeline spanning visual perception and structural safety assessment, indicating potential for automated construction site safety management. Full article

Background: Mental health services face significant challenges in providing equitable care to ethnic minority and migrant populations. Despite the right to healthcare, disparities in service use among minority communities reflect not only practical barriers but also deeper issues of cultural compatibility between patients and health systems. Aim: This study aimed to provide a systematic overview of the main difficulties encountered by ethnic minority and migrant people when seeking psychological support from mental health services, with a specific focus on linguistic and communicative barriers and organisational and economic barriers. Methods: A scoping review was conducted using the SCOPUS database, searching for peer-reviewed studies focused on the European context. Studies were included if they addressed ethnic minorities’ experiences with mental health services from a user perspective and included primary research of any design. Studies focused exclusively on staff perspectives or not specifically addressing ethnic minorities’ help-seeking were excluded. Twenty studies met the inclusion criteria. A qualitative narrative synthesis was adopted, following PRISMA-ScR guidelines. Results: People with an ethnic or migrant background face specific and compounded barriers when seeking mental health support. Two main categories were identified: linguistic and communicative difficulties, including language distance, limited interpreter availability, and the gap between Western biomedical models and cultural frameworks of distress; and organisational and economic obstacles, including poor knowledge of available services, socioeconomic disadvantage, stigma, and institutional distrust. Discussion: These barriers are deeply structural and cannot be addressed through awareness campaigns alone. Increased access to services is not inherently beneficial unless accompanied by a fundamental transformation ensuring that care is culturally appropriate, safe, and genuinely responsive to minority communities’ needs. Distrust of mental health services may in part reflect a historically grounded and legitimate response to institutions whose practices have not always served the interests of minority groups. Conclusions: Reducing disparities in mental health care requires multi-level intervention, including inclusive policies, training of culturally competent professionals, and a critical rethinking of the models underpinning mental healthcare care. Future research should attend not only to the quantity of service use among minority populations but to the quality, cultural legitimacy, and safety of the care provided. Full article

Objectives: Coronavirus disease 2019 (COVID-19) poses a significant threat to kidney transplant recipients (KTRs) due to chronic immunosuppression. While Azvudine has demonstrated antiviral efficacy, its long-term impact on allograft function in KTRs remains unknown. This single-arm retrospective study provides a comprehensive evaluation of the outcomes. Methods: This single-center, retrospective study consecutively enrolled 20 KTRs diagnosed with COVID-19 during the Omicron surge (December 2022–January 2023), all treated with an Azvudine-centered regimen. Clinical data, treatment responses, and serial renal function parameters were analyzed. Long-term follow-up extended to a median of 39 months. Results: The median time to COVID-19 nucleic acid negativity was 13.22 days. Renal function improved significantly during treatment: estimated glomerular filtration rate increased by a mean of 27.29 mL/min/1.73 m², and serum creatinine decreased by 57.72 µmol/L (both p < 0.01). Common complications included electrolyte imbalances and co-infections (50% of patients). Crucially, at a median follow-up of 39 months, 75% (15/20) of patients maintained normal allograft function. No Azvudine-related adverse events or drug interactions with immunosuppressants were observed. Conclusions: While acknowledging the inherent limitations of a non-comparative design, this study provides preliminary evidence suggesting that an Azvudine-centered regimen may be associated with promising efficacy, a favorable safety profile without interference with immunosuppressive therapy, and—most importantly—durable renal allograft survival in KTRs with COVID-19. Our data suggest that this regimen could contribute to mitigating the long-term risk of allograft dysfunction, potentially bringing outcomes closer to the expected baseline for stable recipients. Coupled with its low risk of drug–drug interactions, potential immunomodulatory benefits, and superior cost-effectiveness, Azvudine could be considered a valuable therapeutic option for this high-risk population in the post-pandemic era, although these findings warrant cautious interpretation. Controlled trials are ultimately needed to confirm these observations. Full article