Search Results (687)

Exercise-induced fatigue can degrade athletic performance and increase injury risk, yet traditional fatigue assessments often rely on subjective measures. This study proposes an objective fatigue recognition approach using high-fidelity motion capture data and deep learning. This study induced both cognitive and physical fatigue in 50 male participants through a dual task (mental challenge followed by intense exercise) and collected three-dimensional lower-limb joint kinematics and kinetics during vertical jumps. A bidirectional Gate Recurrent Unit (GRU) with an attention mechanism (BiGRU + Attention) was trained to classify pre- vs. post-fatigue states. Five-fold cross-validation was employed for within-sample evaluation, and attention weight analysis provided insight into key fatigue-related movement phases. The BiGRU + Attention model achieved superior performance with 92% classification accuracy and an Area Under Curve (AUC) of 96%, significantly outperforming the single-layer GRU baseline (85% accuracy, AUC 92%). It also exhibited higher recall and fewer missed detections of fatigue. The attention mechanism highlighted critical moments (end of countermovement and landing) associated with fatigue-induced biomechanical changes, enhancing model interpretability. This study collects spatial data and biomechanical data during movement, and uses a bidirectional Gate Recurrent Unit (GRU) model with an attention mechanism to distinguish between non-fatigue states and fatigue states involving both physical and psychological aspects, which holds certain pioneering significance in the field of fatigue state identification. This study lays the foundation for real-time fatigue monitoring systems in sports and rehabilitation, enabling timely interventions to prevent performance decline and injury. Full article

The Qinghai Lake Basin (QLB), as a key component of the ecological security barrier on the Tibetan Plateau, is crucial for regional sustainable development due to the stability of its alpine agro-pastoral ecosystems. This study aims to systematically analyze the spatiotemporal evolution patterns and underlying driving mechanisms of eco-environmental quality (EEQ) in the QLB from 2001 to 2022. Based on Google Earth Engine (GEE) and long-term MODIS data, we constructed a Remote Sensing Ecological Index (RSEI) model to evaluate the EEQ dynamics. Geodetector (GD) was applied to quantitatively identify key driving factors and their interactions. The findings reveal: (1) The mean RSEI value increased from 0.46 in 2001 to 0.51 in 2022, showing a fluctuating improvement trend with significant transitions toward higher ecological quality grades; (2) spatially, a distinct “high-north-south, low-center” pattern emerged, with excellent-grade areas (4.77%) concentrated in alpine meadows and poor-grade areas (5.10%) mainly in bare rock regions; (3) 47.81% of the region experienced ecological improvement, whereas 16.34% showed degradation, predominantly above 3827 m elevation; and (4) GD analysis indicated natural factors dominated EEQ differentiation, with temperature (q = 0.340) and elevation (q = 0.332) being primary drivers. The interaction between temperature and precipitation (q = 0.593) exerted decisive control on ecological pattern evolution. This study provides an efficient monitoring framework and a spatially explicit governance paradigm for maintaining differentiated management and ecosystem stability in alpine agro-pastoral regions. Full article

Conducted against the background of a highway project in Zhuji, Zhejiang Province, this study investigates the deterioration behavior of tuffaceous sandstone under the combined action of acid rain and wet–dry cycles. Laboratory experiments were carried out to explore its mechanical properties and damage evolution mechanisms. Standard specimens prepared from field rock samples were subjected to wet–dry cycles using an acidic solution with pH ≈ 5.0. By integrating uniaxial compression, Brazilian splitting, ultrasonic wave monitoring, and acoustic emission techniques, a systematic analysis was carried out to evaluate the degradation of mechanical parameters, the evolution of wave velocity, and the underlying damage and failure mechanisms. The results indicate the following: (1) With the increase in the number of acidic dry–wet cycles, the compressive and tensile strengths of tuffaceous sandstone decrease significantly; the deterioration rate first decreases and then increases, with 150 cycles identified as the critical threshold for strength deterioration, beyond which the material enters a stage of rapid degradation. (2) The evolution of ultrasonic wave velocity shows a significant negative correlation with strength deterioration, and the attenuation rate of wave velocity exhibits a consistent trend with the number of cycles as that of strength deterioration. (3) Acoustic emission RA-AF analysis reveals that tensile cracks in tuffaceous sandstone gradually decrease while shear cracks slowly increase, with cracks primarily developing along the weakly cemented tuffaceous areas. (4) This study established fitting formulas for the deterioration of compressive and tensile strengths with the number of cycles, as well as a damage calculation formula based on changes in wave velocity. (5) This study provides practical support for mitigating natural disasters, such as slope instability, induced by this type of combined weathering. Full article

The development and function of the heart are governed by a highly coordinated network of regulatory mechanisms, among which miRNAs play a central role. These small, non-coding molecules modulate gene expression predominantly through mRNA degradation. This narrative review aims to summarize current knowledge about biogenesis, its impact on heart development and function, and its clinical implications in pediatric cardiology. We discuss how specific miRNAs contribute to shaping the normal heart and influencing the pathogenesis of congenital malformations. Furthermore, we review disease-specific miRNA signatures identified in the most common congenital heart defects and some acquired diseases, including hypoplastic left heart syndrome (HLHS), tetralogy of Fallot (TOF), bicuspid aortic valve (BAV), septation defects, cardiomyopathies, arrhythmias, and myocarditis. Many studies indicate that circulating and tissue miRNAs can become non-invasive biomarkers for early diagnosis and disease monitoring. Experimental data suggest their potential use in treatment despite many delivery and safety challenges. However, further research is necessary to fully exploit the potential of miRNAs and effectively translate these findings into clinical practice in pediatric cardiology. Full article

Underground granaries offer natural insulation for long-term grain storage, yet spatial heterogeneity in temperature and humidity can drive condensation and degrade grain quality. To address this issue, mechanical ventilation is commonly employed, yet evidence remains limited on whether pretreating the inlet air before ventilation can further reduce the risk of condensation. In order to bridge this gap, a custom-designed small-scale underground granary was employed, in which temperature and relative humidity of the grain pile, surrounding soil, and ambient air were monitored at 28 sampling points. The effectiveness of mechanical ventilation and ventilation pretreatment in reducing condensation was also assessed. Results demonstrated that during static storage, the granary was minimally affected by external conditions. Yet, a high temperature and humidity area developed at the top of the grain pile over the 24-day period of static storage. Under mechanical ventilation, local relative humidity decreased but grain temperature still responded to ambient conditions. In contrast, ventilation pretreatment stabilized inlet air, lowered peak grain temperature by 1 °C, and improved relative humidity reduction from 6% to 12%. This produced a more uniform temperature–humidity profile and markedly reduced condensation risk. Full article

Biopolymers have emerged as a transformative class of materials that reconcile high-performance functionality with environmental stewardship. Their inherent capacity for controlled degradation and biocompatibility has driven rapid advancements across electronics, sensing, actuation, and healthcare. In flexible electronics, these polymers serve as substrates, dielectrics, and conductive composites that enable transient devices, reducing electronic waste without compromising electrical performance. Within sensing and actuation, biodegradable polymer matrices facilitate the development of fully resorbable biosensors and soft actuators. These systems harness tailored degradation kinetics to achieve temporal control over signal transduction and mechanical response, unlocking applications in in vivo monitoring and on-demand drug delivery. In healthcare, biodegradable polymers underpin novel approaches in tissue engineering, wound healing, and bioresorbable implants. Their tunable chemical architectures and processing versatility allow for precise regulation of mechanical properties, degradation rates, and therapeutic payloads, fostering seamless integration with biological environments. The convergence of these emerging applications underscores the pivotal role of biodegradable polymers in advancing sustainable technology and personalized medicine. Continued interdisciplinary research into polymer design, processing strategies, and integration techniques will accelerate commercialization and broaden the impact of these lower eCO₂ value materials across diverse sectors. This perspective article comments on the innovation in these sectors that go beyond the applications of biodegradable materials in packaging applications. Full article

This review provides a comprehensive synthesis of the coupled effect of temperature and solar radiation on photovoltaic (PV) module performance and lifespan. Although numerous investigations have examined these stressors in themselves, this research addresses their interrelationship and evaluates the way climatic conditions affect short-term performance fluctuation and long-term degradation mechanisms. The assessment consolidates outcomes from model strategies, laboratory tests, and field monitoring studies. Through the presentation of these findings in a narrative form, the paper identifies recurring difficulties in terms of the absence of shared assessment metrics and the low level of standardisation of long-term test regimes. Second, it underlines the importance of predictive modelling and live monitoring as important management tools for coupled stressors. Finally, the review points out research gaps and underscores future research avenues, including ongoing work towards the development of a coupling index, a composite measure being piloted in individual studies, and advancements in materials technology, predictive methodology, and durability testing. Full article

To elucidate the fatigue damage evolution of solar road panels under long-term loading and enhance their structural durability, this study develops a particle-based discrete element model and simulates fatigue responses under different structural configurations and loading rates. A strength degradation index was established by introducing peak stress and terminal stress, enabling quantitative evaluation of strength deterioration. Combined with fracture evolution, the dominant mesoscopic damage mechanisms were revealed. The results indicate that structural configuration strongly influences fatigue performance, with square panels showing the best resistance due to geometric symmetry and stable boundary constraints. Loading rate regulates damage evolution: lower rates promote structural coordination but may delay cumulative failure, while higher rates suppress overall deformation yet increase localized fracture risk. Based on these findings, a nonlinear predictive model of the strength degradation rate was constructed (R² = 0.935), offering reliable support for structural life prediction and design optimization. Finally, fatigue-resistant design strategies are proposed, including optimal structural configuration, controlled loading rates, bonding enhancement, and integration of online monitoring—providing both theoretical and technical guidance for high-performance, long-lifespan solar road systems. Full article

Abnormal shutdown detection in oilfield pumping units presents significant challenges, including degraded image quality under low-light conditions, difficulty in detecting small or obscured targets, and limited capabilities for dynamic state perception. Previous approaches, such as traditional visual inspection and conventional image processing, often struggle with these limitations. To address these challenges, this study proposes an intelligent method integrating multi-scale feature enhancement and low-light image optimization. Specifically, a lightweight low-light enhancement framework is developed based on the Zero-DCE algorithm, improving the deep curve estimation network (DCE-Net) and non-reference loss functions through training on oilfield multi-exposure datasets. This significantly enhances brightness and detail retention in complex lighting conditions. The DAFE-Net detection model incorporates a four-level feature pyramid (P3–P6), channel-spatial attention mechanisms (CBAM), and Focal-EIoU loss to improve localization of small/occluded targets. Inter-frame difference algorithms further analyze motion states for robust “pump-off” determination. Experimental results on 5000 annotated images show the DAFE-Net achieves 93.9% mAP@50%, 96.5% recall, and 35 ms inference time, outperforming YOLOv11 and Faster R-CNN. Field tests confirm 93.9% accuracy under extreme conditions (e.g., strong illumination fluctuations and dust occlusion), demonstrating the method’s effectiveness in enabling intelligent monitoring across seven operational areas in the Changqing Oilfield while offering a scalable solution for real-time dynamic anomaly detection in industrial equipment monitoring. Full article

Environmentally assisted cracking (EAC) can be an aggressive degradation mechanism for materials in safety-critical applications across a variety of industries, particularly when combined with cyclic mechanical loading. Corrosion fatigue, a prominent form of EAC, often affects tubular components such as piping, heat exchangers, and boiler tubes in chemical, refining, and power generation industries. This study presents the design and validation of a low-cost, automated test system for evaluating EAC under controlled laboratory conditions. The system integrates electromechanical loading, force measurement, and closed-loop control of temperature and pH. Crack growth is monitored using a compliance-based method calibrated using finite element analysis. Environmental control loops were validated for stability and responsiveness. Performance was demonstrated through tests on carbon steel specimens in acidic chloride solution and polymethylmethacrylate (PMMA) specimens in xylene solvents. The system demonstrated accurate load control, environmental stability, and sensitivity to crack extension. The test system also enabled detection of crack closure behavior in carbon steel specimens resulting from corrosion product buildup during immersion in acidic chloride solution. Additionally, the system effectively distinguished varying impacts of environmental severity in PMMA testing (100% xylene vs. 50% xylene–50% ethanol), confirming its suitability for comparative studies. This test platform enables efficient, repeatable evaluation of EAC fatigue performance across a range of materials and environments. Full article

This study investigates the dynamic performance and degradation behavior of corrugated cardboard used as protective packaging for home appliances subjected to random vibrations during transportation. Simulated vibration tests were conducted on fully packaged refrigerators to assess the mechanical response of cardboard and expanded polystyrene (EPS) supports under prolonged vibration excitation. Relaxation tests were performed to characterize time-dependent stress decay in the absence of vibration, while cantilever beam experiments quantified dynamic stiffness degradation during vibration exposure. The vibration-induced damage was evaluated by monitoring the decrease in support stiffness over time, revealing a distinct exponential reduction that correlated with increasing excitation levels. Statistical load count analyses, based on auto-spectral methods and Basquin’s power law, were used to model fatigue behavior and predict service life. The findings demonstrated that corrugated cardboard exhibited comparable performance to EPS in maintaining support stiffness while offering the advantage of environmental sustainability. These results provide quantitative evidence supporting the use of cardboard as an effective and eco-friendly alternative to polymer-based packaging materials, contributing to the development of optimized packaging solutions with enhanced vibration durability. Full article

In recent years, the synergistic effect among production, maintenance, and quality control within manufacturing systems has garnered increasing attention in academic and industrial circles. In high-quality production settings, the real-time identification of minute process deviations holds significant importance for ensuring product quality. Traditional approaches, such as routine quality inspections or Shewhart control charts, exhibit limitations in sensitivity and response speed, rendering them inadequate for meeting the stringent requirements of high-precision quality control. To address this issue, this paper presents an integrated framework that seamlessly integrates stochastic process modeling, dynamic optimization, and quality monitoring. In the realm of quality monitoring, an exponentially weighted moving average (EWMA) control chart is employed to monitor the production process. The statistic derived from this chart forms a Markov process, enabling it to more acutely detect minor shifts in the process mean. Regarding maintenance strategies, a state-dependent preventive maintenance (PM) and corrective maintenance (CM) mechanism is introduced. Specifically, preventive maintenance is initiated when the system is in a statistically controlled state and the inventory level falls below a predefined threshold. Conversely, corrective maintenance is triggered when the EWMA control chart generates an out-of-control (OOC) signal. To facilitate continuous production during maintenance activities, an inventory buffer mechanism is incorporated into the model. Building upon this foundation, a joint optimization model is formulated, with system states, including equipment degradation state, inventory level, and quality state, serving as decision variables and the minimization of the expected total cost (ETC) per unit time as the objective. This problem is formalized as a constrained dynamic optimization problem and is solved using the genetic algorithm (GA). Finally, through a case study of the production process of vibroseis equipment, the superiority of the proposed model in terms of cost savings and system performance enhancement is empirically verified. Full article

Background: The incorporation of biosensors into orthopedic implants, particularly tibial components in total knee arthroplasty (TKA), marks a new era in personalized joint replacement. These smart systems aim to provide real-time physiological and mechanical data, enabling dynamic postoperative monitoring and enhanced surgical precision. Objective: This narrative review synthesizes the current landscape of electrochemical biosensor-embedded tibial implants in TKA, exploring technical mechanisms, clinical applications, challenges, and future directions for translation into clinical practice. Methods: A comprehensive literature review was conducted across PubMed and Google Scholar. Articles were thematically categorized into technology design, integration strategies, preclinical and clinical evidence, regulatory frameworks, ethical considerations, and strategic recommendations. Findings were synthesized narratively and organized to support forward-looking system design. Results: Smart tibial implants have demonstrated feasibility in both bench and early clinical settings. Key advances include pressure-sensing intraoperative tools, inertial measurement units for remote gait tracking, and chemical biosensors for infection surveillance. However, the field remains limited by biological encapsulation, signal degradation, regulatory uncertainty, and data privacy challenges. Interdisciplinary design, standardized testing, translational funding, and ethical oversight are essential to scaling these innovations. Conclusions: Biosensor-enabled tibial components represent a promising convergence of orthopedics, electronics, and data science. By addressing the technological, biological, regulatory, and ethical gaps outlined herein, this field can transition from prototype to widespread clinical reality—offering new precision in arthroplasty care. Full article

Background/Objectives: Diabetic retinopathy is a leading cause of blindness in diabetic patients, with disease susceptibility influenced by both genetic and environmental factors. This study aimed to identify novel genetic variants associated with DR and evaluate interactions between polygenic risk scores (PRS) and lifestyle factors in a Korean diabetic cohort. Methods: After excluding subjects with non-diabetic retinopathy eye diseases (n = 2519), we analyzed data from 50,361 non-diabetic controls, 4873 diabetic participants without retinopathy (DM-NR), and 165 with diabetic retinopathy (DM-DR). We conducted genome-wide association studies comparing DM-NR and DM-DR groups, performed generalized multifactor dimensionality reduction (GMDR) analysis for epistatic interactions, developed unweighted PRS models, and examined PRS–lifestyle interactions using two-way analysis of covariance. Results: DM-DR prevalence showed strong associations with metabolic syndrome and its components. Five novel genetic variants were identified: ABCA4_rs17110929, MMP2-AS1_rs2576531, FOXP1_rs557869288, MRPS33_rs1533933, and DRD2_rs4936270. A significant three-way epistatic interaction among the first three variants was discovered through GMDR analysis. High-PRS individuals (scores 5–6) showed a 49-fold higher odds ratio of DM-DR compared to low-PRS individuals (scores 0–2; p < 0.0001). MAGMA analysis revealed enrichment in pathways related to protein degradation, vascular function, and neuronal signaling, with predominant upregulation in brain tissues. Significant PRS × lifestyle interactions were identified for fruit intake, coffee consumption, alcohol intake, eating duration, and physical activity, with lifestyle factors modifying genetic risk effects (all p < 0.003). Conclusions: These findings identify novel genetic variants and epistatic interactions in DM-DR pathogenesis, supporting the use of PRS-based risk stratification for intensive monitoring and personalized lifestyle interventions. The discovery of brain tissue-enriched pathways suggests DM-DR shares mechanisms with neurodegenerative diseases, expanding therapeutic targets beyond traditional vascular approaches. Full article

Urban rivers, as crucial components of ecosystems, serve multiple functions, including flood control, drainage, and landscape services. However, with the acceleration of urbanization, factors such as industrial wastewater discharge, domestic sewage leakage, and surface runoff pollution have led to increasingly severe degradation of water quality in urban rivers. Unmanned aerial vehicle (UAV) remote sensing technology, with its sub-meter spatial resolution and operational flexibility, demonstrates significant advantages in the detailed monitoring of complex urban water systems. This study proposes an Ensemble Self-Attention Enhanced Mixture Density Network (ESA-MDN), which integrate an ensemble learning framework with a mixture density network and incorporates a self-attention mechanism for feature enhancement. This approach better captures the nonlinear relationships between water quality parameters and remote sensing features, achieving high-precision modeling of water quality parameter distributions. The resulting spatiotemporal distribution maps provide valuable support for pollution source identification and management decision making. The model successfully retrieved five water quality parameters, Chl-a, TSS, COD, TP, and DO, and validation metrics such as R², RMSE, MAE, MSE, MAPE, bias, and slope were utilized. Key metrics for the ESA-MDN test set were as follows: Chl-a (R² = 0.98, RMSE = 0.31), TSS (R² = 0.93, RMSE = 0.27), COD (R² = 0.93, RMSE = 0.39), TP (R² = 0.99, RMSE = 0.02), and DO (R² = 0.88, RMSE = 0.1). The results indicated that ESA-MDN can effectively extract water quality parameters from multispectral remote sensing data, with the generated spatiotemporal water quality distribution maps providing crucial support for pollution source identification and emergency response decision making. Full article