Search Results (17,356)

A novel auxetic honeycomb (RSSHR) is developed by introducing the arc-shaped structure into the re-entrant star-shaped honeycomb (RSSH). Based on theoretical models and finite element methods, the dynamic crushing responses of RSSH and RSSHR plate (RSSH_P and RSSHR_P) structures are investigated to elucidate the dependence of plateau stress, negative Poisson’s ratio (NPR), deformed shape and specific energy absorption (SEA) on crushing velocity. The stress–strain curves of two types of structures are calculated to analyze configuration–mechanical property relationships. The results exhibit that the plateau stress and SEA of the RSSH_P and RSSHR_P structures increase as the crushing velocity increases. Owing to the stress-mitigating effect of the arc-shaped structure, the RSSHR_P structure exhibits a stronger NPR effect. And the SEA of the RSSHR_P structure is higher than that of the RSSH_P structure. In addition, it is also found that at low crushing velocity, the stress–strain curves of the two structures exhibit three distinct stages: the elastic stage (I), the stress plateau stage (II) and the densification stage (III). During the crushing process, there are three deformed shapes. They are the global deformed shape, the local deformed shape and the layer-by-layer deformed shape. Full article

Owing to the high pharmacological relevance and multidimensional quality attributes of Panax spp., accurate authentication and quality evaluation of Panax-derived herbal materials remain challenging within traditional Chinese medicine (TCM) quality control systems. Conventional approaches often face trade-offs among analysis speed and throughput, non-destructive measurement, and analytical accuracy, which can limit their suitability for modern, large-scale quality control. This review summarizes recent advances in vibrational and related analytical techniques—infrared (IR) and near-infrared (NIR) spectroscopy, Raman spectroscopy, terahertz (THz) spectroscopy, hyperspectral imaging (HSI), and nuclear magnetic resonance (NMR)—for authentication and quality evaluation of Panax materials. We compare the capabilities of each modality in supporting key tasks, including species authentication, geographical origin tracing, age/cultivation-stage discrimination, and quantitative assessment of major chemical markers, with emphasis on the underlying measurement principles. In general, NIR and HSI are well suited to rapid, high-throughput screening of bulk samples, whereas Raman and NMR provide higher chemical specificity for molecular and structural characterization. To mitigate limitations of single-modality analysis, this review discusses a methodological shift from conventional spectral fingerprinting and chemometric approaches toward model-driven, data-enabled sensing strategies for robust quality evaluation. Specifically, we highlight multimodal data fusion frameworks combined with interpretable machine-learning/deep-learning methods to build robust classification and regression models for quality assessment. This perspective aims to support standardized and scalable authentication and quality evaluation of Panax herbal materials and to facilitate the digitization of quality control workflows for Chinese herbal medicines. Full article

Maritime target detection under complex adverse weather conditions (e.g., fog, rain, and low light) is crucial for Unmanned Surface Vehicle (USV) navigation. However, achieving high detection accuracy and efficiency remains challenging due to coupled environmental interference and limited computing resources. In this paper, we propose W-MTD, a task-specific distillation framework designed for weather-robust and lightweight maritime target detection based on knowledge distillation. Building upon the Fine-grained Distribution Refinement (D-FINE) detection model, this method constructs a dual-path knowledge distillation framework tailored for maritime scenes. Through the synergistic optimization of feature similarity constraints and decoupled distillation, it facilitates multi-level knowledge transfer from a teacher model to a lightweight student model, mitigating feature degradation caused by model compression. A multi-scenario augmentation strategy is designed to balance convergence across different weather conditions. Experiments show that W-MTD’s student model improves detection accuracy by 7.0–13.9% under three adverse weather conditionscompared to the baseline teacher model trained solely on clear weather data while maintaining comparable clear-weather performance. With only 4 M parameters and 7 GFLOPs, the student model demonstrates favorable performance and efficiency compared to other real-time detectors, indicating its potential suitability for USV deployment. Full article

Urban environments face heightened seismic risks due to dense infrastructure and population concentration. Traditional seismic methods often face significant practical limitations in cities due to space constraints, traffic disruption, and acoustic noise, necessitating reliable alternative geophysical approaches for fault screening. This study evaluates the efficacy and practical utility of the opposing-coils transient electromagnetic method (OCTEM) as an effective alternative to conventional seismic techniques for detecting shallow-fault-like resistivity signatures under complex urban electromagnetic noise. By employing dual coaxial coils with opposing currents, the OCTEM suppresses primary-field interference, enabling high-resolution imaging of subsurface structures at depths of 0–200 m. A case study in Tiancheng Chengyuan, Cangzhou City, China, demonstrates the OCTEM’s capability to reliably delineate stratigraphic interfaces and resistivity anomalies under challenging electromagnetic background conditions. Field data exhibited a mean square relative error of 4.01%, validating its data quality and measurement stability. The survey successfully identified stratigraphic continuity and localized heterogeneity features within the investigation zone. These results establish the OCTEM as a robust and efficient tool for urban fault screening, particularly in environments where traditional high-resolution seismic methods are impractical or economically unfeasible. Full article

Background: Overweight women are at increased risk of metabolic dysfunction, muscle loss, and reduced physical function during middle age. Resistance training (RT), combined with a high-protein diet and omega-3 supplementation, may help mitigate these risks; however, their combined effects remain unclear. Objective: To examine whether omega-3 supplementation enhances the effects of RT combined with a high-protein diet on body composition, muscular performance, and selected biochemical markers in overweight women. Methods: Fifty-four overweight women (40–53 years) were randomly assigned to RT plus omega-3 supplementation with a high-protein diet (RO), RT plus placebo with a high-protein diet (RP), or a non-training control group (C). The RT intervention was performed three times per week for 8 weeks. Body composition, muscular performance, and circulating markers related to muscle metabolism and clinical safety were assessed before and after the intervention. Results: Forty-four participants completed the study. Both intervention groups demonstrated significant reductions in body mass and fat mass, alongside increases in skeletal muscle mass (SMM) and improvements in muscular strength, endurance, and power compared with the C group (p < 0.001). Markers related to muscle metabolism improved in both RT groups, with greater changes observed in the RO group. Clinical safety markers remained within normal ranges, with no between-group differences. Conclusions: Eight weeks of RT combined with a high-protein diet effectively improved body composition, muscle function, and anabolic signaling in overweight women. Short-term omega-3 supplementation selectively modulated biochemical markers but did not provide additional improvements in SMM, performance, or clinical safety markers, suggesting that its benefits may be limited without longer-term or higher-dose interventions. Full article

Background: Hypothermic oxygenated perfusion (HOPE) has emerged as a promising preservation strategy before liver transplantation, mitigating ischemia–reperfusion injury and improving graft function, especially in marginal grafts and donors after cardiac death. Methods: This is a prospective monocentric study; 34 HOPE-treated liver grafts were enrolled and analyzed through histopathology and RT-PCR to assess endothelial-related gene expression and its correlation with post-transplant outcome. The aim of the present study was to assess the relationship between the expression of genes related to vascular activation and homeostasis and post-transplant clinical characteristics. Results: Expression of SMA and TGF-β1 was significantly associated with arteriolar myointimal thickening of the graft (p = 0.007 and 0.068). Higher expression of SMA, ERG, and TGF-β1 was correlated with a shorter post-operative intensive care stay (p = 0.070, p = 0.010 and p = 0.029, respectively), particularly with post-transplant acute kidney injury. Conclusions: These findings highlight the role of endothelial activation and vascular homeostasis for an early recovery after liver transplantation, posing an important issue for healthcare systems as well, and suggesting molecular markers for graft assessment and risk stratification. Full article

Forecasting post-seismic landslide displacement is challenged by the difficulty in distinguishing short-term acceleration from creep and the risk of spatiotemporal leakage. To address this, an interpretable deep-learning framework is developed, integrating SBAS-InSAR time series with an Attention-enhanced Gated Recurrent Unit (Attention-GRU). Prior to modeling, a multi-stage preprocessing strategy, including empirical mode decomposition, is applied to mitigate noise and delineate active deformation zones. Unlike standard architectures, the model’s temporal attention mechanism adaptively amplifies critical precursory acceleration phases. Furthermore, a strict landslide-object-based partitioning strategy is employed to rigorously mitigate spatiotemporal leakage. The framework was evaluated in the Le’an Town landslide cluster using multi-source data. Targeting identified hazardous regions, the method achieved an R² of 0.93 and reduced MAPE by 42.7% relative to the SVR baseline. This reflects a location-specific predictive capability, within active zones rather than regional generalization. SHapley Additive exPlanations (SHAP) further confirmed the model captures physical relationships, such as sensitivity to 25–35° slopes and vegetation degradation. Ultimately, the proposed framework offers a transparent, physically interpretable tool for operational hazard mitigation. Full article

Background/Objectives: White-collar workers experience a unique dual burden of high psychological demands and prolonged static loading, creating a need to understand how physical resilience may mitigate these stressors. This study investigated the moderating role of specific muscle fitness components in the associations between work stress, burnout, and well-being among white-collar workers. To address the gap in task-specific physical resilience, we employed a cross-sectional design involving 321 full-time employees. Methods: Work stress (job control and demands), burnout, and well-being were assessed via structured questionnaires, while grip strength, abdominal endurance, and back muscle endurance were objectively measured. Results: Results indicated that the muscle fitness components were not directly associated with either burnout or well-being. However, the moderation model for burnout was significant (F = 15.837, p < 0.001; adjusted R² = 0.278), where back muscle endurance significantly moderated the association between psychological job demands and burnout (β = −0.121, p < 0.05), whereas no such moderating effect was observed for well-being. In contrast, no such moderating effect was observed for well-being, nor did grip strength or abdominal endurance exhibit significant buffering effects on either psychological outcome. Conclusions: These findings demonstrate the relevance of task-specific physical resources in sedentary environments, specifically that back endurance functions as a buffer against burnout but may be insufficient to directly enhance overall well-being. The results suggest that while integrating task-specific physical assessments is vital for burnout prevention, psychosocial organizational support remains essential for fostering comprehensive well-being. Full article

Background/Objectives: Alcohol-associated liver disease (ALD) is a major cause of chronic liver injury, in which disruption of the gut–liver axis plays a key pathogenic role. Probiotics have been proposed as a potential therapeutic strategy to mitigate alcohol-induced liver damage; however, the preclinical evidence has not been systematically synthesised. This systematic review aimed to evaluate and summarise the hepatoprotective effects of probiotic supplementation in experimental animal models of ALD. Methods: The review protocol was pre-registered in PROSPERO (CRD420250653666) and followed PRISMA guidelines. A systematic search was conducted across PubMed, EMBASE and AGRICOLA databases using relevant keywords from inception to 30 April 2025. We included preclinical randomised controlled trials evaluating single-strain probiotic interventions against placebo or untreated controls in animal models of ALD. Risk of bias was assessed using SYRCLE’s tool, and the certainty of evidence for critical outcomes was evaluated using the GRADE framework. A narrative synthesis was performed, as a quantitative meta-analysis was precluded by incomplete numerical outcome reporting. Results: From initial 628 records, 36 studies were included in the final synthesis. Probiotic supplementation consistently attenuated alcohol-induced liver injury, as evidenced by marked reductions in serum ALT and AST levels and improved liver histology. Mechanistically, probiotics restored gut barrier integrity, reduced systemic endotoxemia, and suppressed pro-inflammatory pathways. Furthermore, probiotic treatment effectively counteracted alcohol-induced gut dysbiosis by increasing microbial diversity and restoring taxonomic balance, notably by reversing the alcohol-induced expansion of Proteobacteria. Despite these consistent directional effects, the overall certainty of evidence for the critical outcomes was rated as very low. Conclusions: Although the preclinical literature suggests hepatoprotective effects of probiotics in experimental ALD, these findings should be interpreted with caution due to the very low certainty of evidence. The observed benefits are limited by methodological shortcomings, indirectness inherent to animal models, and incomplete outcome reporting. This review provides a structured preclinical framework to inform the design of future translational studies and well-controlled clinical trials evaluating probiotics as potential adjunctive therapies in human ALD. Full article

To mitigate the significant impact of system nonlinearities, time-varying parameters, and external load disturbances on the output force of hydraulic servo systems in active hydraulic suspensions for engineering vehicles, this study proposes a beetle swarm optimization (BSO)-optimized extended state observer (ESO)-based sliding mode control (SMC) strategy. A comprehensive mathematical model of the hydraulic servo system is established, and an ESO-based SMC controller is designed, taking into account the coupled effects of chamber pressure dynamics and external loads on the uncertain output force. The stability of the closed-loop system is rigorously analyzed and verified using Lyapunov stability theory. The effectiveness of the proposed control strategy is verified through both numerical simulations and experimental tests. For step inputs of 5000 N and 8000 N, overshoot is significantly reduced compared with the conventional proportional–integral–derivative control and the standard extended state observer-based sliding mode control, while the settling time is shortened by more than 65% in simulations and up to 75% in experiments. Under sinusoidal force excitations at frequencies of 0.5 Hz, 1 Hz, and 2 Hz, the maximum tracking error, mean error, and standard deviation of the tracking error are substantially reduced, with the maximum error reduction exceeding 90%. These results demonstrate that the proposed method achieves high-precision force tracking under external disturbances and pronounced system uncertainties, providing an effective solution for force control of hydraulic servo systems in active suspension applications for engineering vehicles. Full article

Grain quality inspection is crucial for seed stored, with image segmentation playing a key role in this process. However, existing methods face challenges such as high computational costs, expensive data annotation, and data privacy concerns, which hinder the acquisition of large-scale labeled datasets and limit model performance. To overcome these challenges, we propose a novel semi-supervised learning (SSL) paradigm for seed segmentation. Our approach integrates VMUNet and UNet into a unified framework, combining UNet’s capacity for fine-grained detail extraction with VMUNet’s strengths in global semantic model, enabling richer pixel-level feature representation. We introduce an orthogonal attention mechanism into the VMUNet encoder to model feature dependencies across channel, spatial, and scale dimensions, improving information fusion and feature enhancement. Additionally, a perturbation strategy is applied in the dual-branch decoder, combined with consistency regularization, to enhance robustness and generalization. This helps mitigate overfitting and reduces excessive reliance on boundary details during decoding. Experimental results on a corn seed dataset show that the proposed method achieves 91.2% accuracy with 100% labeled data and 91.9% with only 50% labeled data, outperforming fully supervised methods by 0.6%. These results demonstrate the method’s high segmentation performance and practical potential while maintaining data privacy. These results confirm that OAMamba provides an accurate, robust, and annotation-efficient solution for corn seed segmentation, showing strong potential for practical deployment in agricultural intelligent inspection systems. Full article

Background: Intimate partner violence (IPV) represents a major global public health concern with profound psychological and social consequences for women. This review synthesizes contemporary evidence (2020–2025) on IPV prevalence, mental health outcomes, and healthcare implications among female populations worldwide. Methods: 18 peer-reviewed studies, encompassing approximately 62,000 women across various countries, were analyzed for study design, sample characteristics, IPV prevalence, and associated outcomes. Results: IPV prevalence varied widely across studies, ranging from 15% in population-based antenatal samples to over 85% among incarcerated or trauma-exposed groups. Across studies reporting mental health outcomes, depression prevalence ranged from 20% to over 50%, while PTSD prevalence ranged from approximately 30% to 70%, depending on measurement tools and population characteristics. No pooled estimates were calculated. IPV survivors showed higher emergency department use (2.6-fold), inpatient admissions (2.2-fold), and healthcare costs (2.2-fold) compared with non-exposed women. Emerging interventions, such as digital safety programs, behavioral antenatal packages, and validated screening tools, demonstrated encouraging effectiveness. Conclusions: IPV remains widespread and linked to psychological distress and elevated healthcare burden. Integration of routine screening, trauma-informed mental health services, and multisectoral prevention frameworks is essential to mitigate its enduring impact on women’s health and well-being. Full article

Arc fault is the dominant cause of fire in photovoltaic (PV) systems, making its accurate identification crucial for PV fire prevention. This study investigates the influence of voltage (200, 300, and 400 V) and current (3, 5, 7, 9, and 11 A) on the DC series arc fault characteristics in PV systems obtained through experimental analysis. The results show that voltage variation has a negligible impact on arc fault behavior, while higher current levels substantially increase noise in the arc fault signals. To effectively mitigate noise, this paper proposes a denoising method that combines an improved moss growth optimization algorithm (IMGO) with improved complete ensemble empirical mode decomposition featuring adaptive noise (ICEEMDAN). It is found that the IMGO-ICEEMDAN denoising algorithm can effectively diminish noise in current signals, broaden characteristic frequency bands, and ameliorate arc feature discernibility. Subsequently, an integrated multi-scale spatiotemporal model is developed to extract arc fault features from the denoised signals. The model employs wide deep convolutional neural networks (WDCNNs) and bidirectional long short-term memory (BiLSTM) networks for parallel feature extraction, supplemented by a cross-attention (CA) module to optimize feature integration. The proposed WDCNN-BiLSTM-CA model ultimately achieves a detection accuracy of 99.89%, demonstrating superior detection performance over conventional methods, such as CNN-GRU and 1DCNN-LSTM models. This work provides a reliable framework for arc fault detection and fire risk reduction in PV systems. Full article

Background: Emergency department (ED) overcrowding strains clinicians and potentially compromises urgent care quality. Unscheduled return visits (URVs), also known as readmissions, contribute to this cycle, motivating tools that identify high-risk patients at discharge. Methods: This study performed a retrospective study using ED electronic health records (EHRs) from Kaohsiung Veterans General Hospital from January 2018 to December 2022 (n = 184,653). The model integrates structured variables, such as vital signs, medication and laboratory counts, and ICD-10–based comorbidity measures, with unstructured physician notes. Key physiologic measurements were transformed into binary form using clinical reference intervals, and random under-sampling addressed class imbalance. A multimodal, CNN was proposed and evaluated with an 8:2 train–test split and 10-fold Monte Carlo cross-validation. Results: The proposed model achieved a sensitivity of 0.717 (CI: [0.695, 0.738]), accuracy of 0.846 (CI: [0.842, 0.850]), and AUROC of 0.853. Binary transformation improved recall and AUROC relative to the original numeric representations. SHAP analysis showed that unstructured features dominated prediction, while structured variables added complementary value. In a small-scale pilot evaluation using the SHAP-enabled interface, participating physicians reported the system helped surface high-risk cohorts and reduced cognitive workload by consolidating relevant patient information for rapid cross-checking. Conclusions: An interpretable CNN-based clinical decision support system can predict ED revisit risk from multimodal EHR data and demonstrates practical usability in a real-world clinical setting, supporting targeted discharge planning and follow-up as a near-term approach to mitigate overcrowding. Full article

Lipid sources are essential components in modern swine nutrition, not only due to their high energy density but also because of their positive effects on palatability, feed efficiency, and micronutrient absorption. However, rising raw material costs have encouraged the use of oxidized fats and oils (OxFO) as a cost-effective alternative in pig diets. These lipids, degraded by thermal and handling factors, undergo chemical alterations that negatively affect digestibility, energy metabolism, and animal health. This review critically examines the current scientific evidence regarding the impact of oxidized fat consumption in swine production systems. The physiological and biochemical mechanisms by which lipid oxidation products impair mitochondrial β-oxidation, cellular oxidative balance, energy efficiency, and meat quality are discussed. Moreover, the practical consequences on productive performance, muscle oxidative stability, and the expression of inflammatory and antioxidant markers are explored. Findings suggest that although the use of oxidized fats may offer economic savings, their metabolic and productive repercussions can compromise profitability and sustainability. The need to define safe inclusion thresholds (when replacement is not feasible), standardize analytical methods to assess oxidation status, and consider nutritional alternatives to mitigate adverse effects is emphasized. Full article