Search Results (31,103)

This study investigates the transient wheel–rail contact mechanics of welded joints in heavy-haul rails via a validated 3D finite element model, and analyzes the stick-slip behavior, dynamic response and elastoplastic characteristics in the base material zone, heat-affected zone and weld bead zone. Results show a distinct contact state transition from stick-slip in the base material to predominant slip within the welded zones, indicating higher wear susceptibility. Dynamic response analysis reveals the highest and lowest contact-point acceleration amplitudes in the base material and heat-affected zone, respectively, due to material heterogeneity. Plastic deformation consistently initiates at the rail surface, where stress and strain concentrate, establishing it as the primary site for damage nucleation. A systematic parametric study shows that plastic deformation can be effectively mitigated by increasing the yield strength and elastic modulus of the welded joint material, or reducing the wheelset velocity, unsprung mass and wheel–rail friction coefficient. In contrast, adjusting the primary suspension and fastener parameters exerts a negligible influence on plastic deformation control. These findings provide a mechanistic basis for optimizing the performance and maintenance of welded joints in heavy-haul rail operations. This study reveals the coupling law of multiple mechanisms among contact behavior, dynamic response and material failure during the damage initiation process of rail welded joints from the mechanistic perspective, which provides a theoretical basis for the structural optimization, condition assessment and maintenance of rail welded joints in heavy-haul railways. Full article

Osteoarthritis (OA) represents a degenerative joint disease which advances through cartilage breakdown, synovial inflammation, and subchondral bone transformation until it causes persistent pain and mobility loss. The scientific community lacks complete knowledge about OA disease mechanisms and post-operative healing processes despite arthroplasty surgery providing effective symptom relief. This study investigated plasma proteomic changes in OA patients before and after arthroplasty. The cohort included eight OA patients undergoing knee or hip arthroplasty and ten age-, sex-, and body mass index-matched healthy controls. Plasma proteins were analyzed using liquid chromatography–tandem mass spectrometry following enzymatic digestion and depletion of high-abundance components. The bioinformatic analysis together with quantitative methods showed that OA patients experienced changes in inflammatory pathways, extracellular matrix remodeling, immune system regulation and coagulation processes. A total of 93 proteins were differentially abundant in the pre-operative comparison. Among these, 63 proteins were consistently up-regulated and 23 were consistently down-regulated across both pre- and post-operative time points. In addition, 20 proteins exhibited post-operative-specific changes. These findings highlight both persistent disease-associated alterations and transient proteomic shifts linked to post-operative recovery. Overall, this study identifies candidate plasma proteomic signatures associated with OA and surgical intervention, providing exploratory insights into disease monitoring and potential personalized therapeutic strategies. Full article

Osteoarthritis (OA) is the main degenerative joint disease affecting nearly 7% of world population. OA is a multifactorial pathology due to environmental, inflammatory and genetic causes. Recently, the diet and consumption of specific foods have been associated to onset and progression of OA. Dietary patterns, macronutrients, micronutrients, and bioactive compounds can influence inflammatory pathways, oxidative stress, and cartilage metabolism. These effects are mediated not only by structural support but also through the modulation of gene expression and cellular signaling pathways. The emerging fields of nutrigenomics and nutrigenetics provide a mechanistic framework to explain interindividual variability in dietary responses. Nutrigenomics investigates how nutrients influence gene expression and molecular pathways involved in OA pathophysiology, whereas nutrigenetics examines how genetic polymorphisms affect nutrient metabolism, bioavailability, and biological efficacy. This narrative review critically examines current evidence on the interaction between diet, nutraceuticals, and common non-pathological genetic variants in OA. We discuss whether specific dietary patterns exert genotype-independent effects or require personalized approaches to optimize outcomes. By integrating genetic, metabolic, and nutritional perspectives, this review aims to clarify inconsistent findings in the literature and to outline the potential of precision nutrition as a complementary strategy for OA prevention and management. The integration of these approaches enables the development of personalized nutritional strategies tailored to an individual’s genetic background, metabolic profile, and comorbid conditions such as obesity, cardiovascular disease, and diabetes. Full article

Intelligent soft open points (SOPs) and energy storage systems (ESSs) are effective ways to absorb distributed new energy in the spatial and temporal dimensions, and play an important role in improving the new-energy-carrying capacity of distribution networks. Existing planning models for SOPs and ESSs in distribution networks are often nonlinear and non-convex, and are usually transformed into a mixed-integer second-order cone optimization (MISOCP) model. However, this transformation often needs stringent relaxation conditions, and the solution speed and convergence performance of the model are poor. These disadvantages make traditional MISOCP models unsuitable for optimal planning for complex hybrid networks. To overcome these limitations, a joint planning method for AC/DC hybrid networks based on an improved DC power flow (IDCPF) algorithm is proposed in this paper. The proposed method transforms the original nonlinear model into an approximate linear model, improving the solution speed and accuracy of the model. The effectiveness of the proposed method is validated through case studies on an improved AC/DC 43-node network, which demonstrates the accuracy and numerical stability of the planning model. Full article

(1) Achilles tendon rupture is one of the most severe lower-limb injuries, frequently occurring during movements involving maximal dorsiflexion with the knee at near-full extension. Preventive strategies are crucial, particularly for athletes engaged in high-risk sports such as basketball. (2) In this work, we examined the effect of a novel Achilles brace on Achilles tendon loading during concentric and eccentric mechanisms associated with tendon rupture. (3) Twenty-eight young basketball players performed tests under two conditions: with the adaptive brace and without it (control). Participants were divided into two groups (n = 14 in both). The first group assessed concentric Achilles loading by performing three plantar-flexor strength tests in three different joint configurations: maximal dorsiflexion with the knee flexed (FKF); injury mechanism position—full plantar flexion with the knee extended (FKE); and neutral ankle position with the knee extended (NKE). The number of maximal heel-raise repetitions performed before onset of fatigue was recorded. The second group assessed eccentric tendon loading by performing single-leg forced maximal-velocity dorsiflexion with the knee extended. In all tests, the time between maximal plantar flexion and maximal dorsiflexion, as well as the ankle range of motion, was analyzed using 2D video. Paired t-tests were used to compare braced and control conditions. In all tests, the ankle range of motion (ROM) did not differ significantly between brace and control conditions. Wearing the brace significantly improved plantar-flexor muscle strength only in the FKE test (31 ± 1.3 repetitions with brace vs. 21 ± 1.3 in control, p < 0.05). No significant differences were found for the FKF (27 ± 1.3 vs. 25 ± 1.3) or NKE (25 ± 1.3 vs. 24 ± 1.3) positions. During drop eccentric loading, wearing the brace resulted in a significantly slower transition time from plantar flexion to dorsiflexion (460 ± 60 ms with brace vs. 320 ± 30 ms in control, p < 0.001). (4) In brief, the novel Achilles brace was found to significantly reduces Achilles tendon load during both concentric and eccentric activities, but only in high-risk joint positions. These findings suggest that the brace provides mechanical protection, and may reduce the risk of Achilles tendon rupture, in athletes exposed to high tendon stress. Full article

Domain shift in multi-source MRI imaging data significantly degrades the performance of Alzheimer’s disease diagnostic models. This study aims to develop an effective unsupervised domain adaptation method to enhance diagnostic accuracy across different clinical datasets. We propose a Joint Domain and Category Dual Adaptation framework (JDC-DA) that integrates metric learning and adversarial learning. The method employs multi-scale feature aggregation to capture diverse lesion characteristics, generates dynamic prototype features through category clustering, and implements a novel metric learning approach that simultaneously aligns both domain-level and category-level feature distributions. Additionally, we introduce a classification certainty maximization strategy that establishes a dual adversarial mechanism between domain discriminator and classification discrepancy discriminator. The framework was evaluated on four public datasets (ADNI-1, ADNI-2, ADNI-3, AIBL) containing 1230 baseline sMRI scans for four classification tasks: AD vs. NC, MCI vs. NC, AD vs. MCI, and AD vs. MCI vs. NC. The proposed JDC-DA method achieved superior performance with accuracies of 92.16%, 83.56%, 81.96%, and 79.12% for the four classification tasks respectively, significantly outperforming existing state-of-the-art domain adaptation methods across all evaluation metrics. The JDC-DA framework effectively addresses domain shift challenges in Alzheimer’s disease diagnosis through its integrated approach to feature alignment and adversarial learning. The method demonstrates strong potential for clinical application in automated diagnosis systems, particularly for handling multi-center neuroimaging data with distribution discrepancies. Full article

To address the challenges of reduced delivery efficiency, complex routing decisions, and limited system robustness in cooperative transportation involving a single drone and an autonomous vehicle under dynamic weather conditions, this study investigates the optimization of drone–autonomous vehicle collaborative delivery in complex and uncertain environments. The objective is to improve task execution efficiency while enhancing the adaptability of the transportation system to dynamic disturbances. To this end, an optimization model is developed by incorporating weather variations, drone–vehicle coordination constraints, and the spatiotemporal characteristics of delivery tasks. Based on this model, a dedicated solution algorithm is proposed to achieve efficient joint optimization of route planning and task allocation in complex environments. Numerical results demonstrate that, for the same randomly generated instance, the drone–truck collaborative delivery strategy reduces the delivery time from 414.55 to 385.10 compared with the truck-only scheme, corresponding to an improvement of 7.1%, thereby confirming the effectiveness of the collaborative transportation strategy. Furthermore, when weather factors are taken into account and drone–truck cooperation is allowed, the proposed algorithm reduces the delivery time from 392.84, obtained by a conventional algorithm, to 338.39, yielding a performance improvement of 13.8%. These results verify the effectiveness and superiority of the proposed algorithm in dynamic weather environments. Overall, the proposed method significantly improves the efficiency of the cooperative transportation system and provides theoretical support and methodological guidance for drone–autonomous vehicle collaborative delivery in complex environments. Full article

Driver fatigue detection (DFD) in low-light nighttime driving environments is crucial for road safety, but it remains challenging due to degraded image quality and computational constraints. This paper proposes a real-time three-stage framework specifically designed for nighttime driver fatigue detection, integrating low-light image enhancement, joint face and facial landmark detection, and geometry-based fatigue judgment. In the initial stage, the framework utilizes the Zero-Reference Deep Curve Estimation (Zero-DCE) algorithm to improve the visual quality of input images under low-light conditions. Subsequently, a novel lightweight single-stage detector, You Only Look Once for Joint Face and Facial Landmark Detection (YOLOJFF), is introduced for efficient joint localization. Finally, fatigue judgment is performed in real-time by calculating the Eye Aspect Ratio (EAR) and Mouth Aspect Ratio (MAR) from the detected landmarks and using a sliding time window strategy. Experimental results demonstrate that the enhancement module significantly improves detection performance. The YOLOJFF model achieves a favorable balance, with 90.9% precision, 87.6% mean Average Precision (mAP), and 5.2 Normalized Mean Error (NME), while requiring only 3.7 million (M) parameters and running at 107.5 FPS. The proposed framework provides a robust and efficient solution for real-time DFD in nighttime scenarios. Full article

Background: Acute kidney injury (AKI) is a common and high-risk complication of hospitalization that frequently occurs in patients with chronic cardiometabolic disease. Although heart failure (HF) and diabetes mellitus (DM) are prevalent among hospitalized adults and may differentially modify AKI-associated outcomes, their joint impact on in-hospital risk profiles and cumulative burden remains incompletely characterized. Methods: We conducted a retrospective analysis of adult hospitalizations complicated by AKI using a nationally representative inpatient database. Hospitalizations were classified into four cardiorenal metabolic phenotypes: AKI alone, AKI with HF, AKI with DM, and AKI with both HF and DM. Primary outcomes included in-hospital mortality, dialysis initiation, and mechanical ventilation. Survey-weighted multivariable logistic regression models incorporating HF, DM, and their interaction were used to estimate adjusted associations and model-based predicted probabilities. Adjusted risks were visualized across outcomes, and a composite burden metric was constructed to summarize cumulative in-hospital adverse events. Results: AKI outcomes varied substantially across cardiorenal metabolic phenotypes. HF was consistently associated with higher adjusted mortality and mechanical ventilation risk, whereas DM alone was associated with lower adjusted mortality. A significant interaction between HF and DM was observed regarding dialysis initiation, with a disproportionately higher adjusted risk when both conditions coexisted. Integrated visualization across outcomes demonstrated distinct risk profiles by phenotype, with the combined HF and DM group exhibiting the highest cumulative burden of adverse in-hospital events. Conclusions: Among hospitalizations complicated by AKI, the underlying cardiorenal metabolic status is associated with marked heterogeneity in in-hospital outcomes. HF appears to be a dominant modifier of AKI-associated risk, while DM exerts outcome-specific effects and synergistically increases the risk of dialysis initiation when combined with HF. These findings highlight the importance of incorporating cardiometabolic context into AKI risk stratification approaches and underscore the value of multidimensional in-hospital assessments. Full article

This study investigates the common assumption that segmental inertial parameters remain constant during manual lifting using a model-based experimental approach. The primary objective was to evaluate the variability in these parameters and the subsequent effects on biomechanical calculations. The research was conducted with 20 participants (10 females and 10 males) who performed lifting tasks in the two-dimensional sagittal plane under three distinct load conditions: 2.5 kg, 5.0 kg, and 7.5 kg. Angular variations of the hand, arm, and leg joints were recorded using video-based image processing techniques. These kinematic data, integrated with anthropometric measurements, were incorporated into Newton–Euler-based equations of motion to determine joint reaction forces and net joint moments. During the initial forward dynamics stage, the solvability of the problem was tested using constant mass ratios from the established literature. In the following inverse dynamics stage, genetic algorithms were utilized to overcome solution diversity and identify the variable inertial parameters responsible for the observed motion. The results indicate that changes in segment moments of inertia reached 18–37%, leading to variations of 0–19% in net joint moments. These findings highlight the critical necessity of incorporating dynamic inertial parameters into accurate biomechanical moment calculations for manual materials handling. Full article

Background: Adolescent idiopathic scoliosis (AIS) is often associated with central nervous system disorders and deficits in sensorimotor function. While the Schroth method is a common clinical intervention, research evidence regarding its effectiveness in enhancing sensorimotor control remains limited. This study aimed to evaluate the impact of the Schroth method on sensorimotor control and quality of life (QoL) in AIS patients. Methods: Sixty female participants (mean age 13.4 years) with Cobb angles between 10° and 45° were divided into an intervention group (n = 30), receiving Schroth exercises and bracing for 10 weeks, and a control group (n = 30), receiving bracing alone. Outcome measures included static and dynamic balance, spine lateral flexion joint position sense (JPS), upper-limb functional proprioception, and the GR-BSSQ Brace questionnaire. Results: Statistical analysis using two-way mixed ANOVA revealed significant Group × Time interactions across several parameters. The Schroth group showed significant improvements in static and dynamic balance, with ellipse area reduction (p = 0.005) and reduced Fukuda test distance (p = 0.007), respectively. Significant enhancements were noted in spine lateral flexion JPS (Bilateral p = 0.008) and upper-limb proprioception (Bilateral p = 0.000). Furthermore, the intervention group reported a significant improvement in QoL scores compared to the control (p = 0.000). Conclusions: The findings demonstrate that the Schroth method was associated with enhanced sensorimotor control, supporting its use as a targeted approach to improve functional outcomes in individuals with AIS. These results highlight the clinical value of the method, beyond spinal curve correction. Full article

Stone-built cultural heritage sites face significant threats from weathering and environmental stress, leading to structural damage or even total collapse. Consequently, robust monitoring and conservation strategies are essential. This study introduces the Monument Rockfall Risk Assessment (MRRA), a heuristic prioritization framework designed for the rapid ranking of detachment risks in monumental contexts. The MRRA was tested on the Piazzale Michelangelo Ramps in Florence (Italy), which are prone to rockfall hazard due to the presence of unstable blocks made of Pietraforte sandstone. The methodology employs a qualitative-heuristic risk rating approach, considering factors such as joint characteristics, centre of gravity location, and estimated kinetic energy of falling blocks. Susceptibility, vulnerability, and elements at risk were evaluated for each unstable block to calculate a relative risk index, which was then aggregated to determine the overall risk of each coping. The methodology was applied to a recent rockfall event that occurred in 2020 and compared with expert judgement to evaluate the model’s performance in identifying criticalities. Since decisions on defence and restoration works depend on geomechanical, social, and economic factors, this study explores an approach to establish optimal risk rating thresholds for the MRRA methodology, balancing false and missed alarms. Full article

Background: Knee osteoarthritis (KOA) is one of the leading causes of chronic pain and long-term disability worldwide. Despite its high prevalence, KOA remains underrepresented in repetitive peripheral magnetic stimulation (rPMS) research. While total knee arthroplasty remains the definitive treatment, there is a growing need for non-invasive approaches to reduce symptoms in patients seeking conservative alternatives or awaiting surgery. Methods: Thirty patients with KOA underwent a non-invasive treatment program consisting of eight sessions of double-coil repetitive peripheral magnetic stimulation (rPMS) over three weeks. Outcome measures included pain intensity assessed by the Visual Analog Scale (VAS), functional ability evaluated by the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and the Timed Up and Go test (TUG), and joint mobility measured as knee flexion and extension. Clinical relevance was evaluated using the Minimal Clinically Important Difference (MCID), and subgroup analyses were performed according to Kellgren-Lawrence (KL) grade. Results: Double-coil rPMS was associated with statistically and clinically significant improvements in all outcomes. MCID responder rates exceeded 80% for VAS and TUG, exceeded 70% for WOMAC, and approached 50% for joint mobility outcomes. Subgroup analysis indicated that patients with lower KL grades experienced greater pain reduction, whereas those with higher grades showed greater functional gains. Conclusions: Double-coil rPMS provided preliminary evidence of potential clinical benefit as a non-invasive approach in patients with KOA. Given the single-arm pilot design, the findings should be interpreted cautiously and require confirmation in adequately powered randomized controlled trials with longer follow-up. Full article

Background/Objectives: Youths with cerebral palsy (CP) have reduced levels of physical activity (PA) due to motor impairments and functional difficulties. Few studies have observed its link with various factors and none in young adults with CP. This study aimed to investigate the relationships between PA and various factors in young adults with CP, such as gait function, endurance, participation, and personal/environmental influences. Methods: Participants over 15 years old with CP who underwent Clinical Gait Analysis (CGA), the 6 min walk test, and wore an actimeter (ActiGraph GT3X+) for seven days were included. PA was assessed by daily step count (NbSteps/day). Explanatory factors included the Gait Profile Score (GPS), walking speed, subjective walking perception, joint pain, fatigue, 6 min walk distance, SF-36 questionnaire scores, and lifestyle habits. Correlations, univariate, and multivariate regression models were used for the analysis. Results: Forty-seven CP patients (28 males, 19 females, mean age 23.6 years) were included, with 82% classified as GMFCS I and 18% as GMFCS II. The average NbSteps/day was 5685. Significant correlations were found between NbSteps/day and subjective perception, pain, GMFCS level, and walking speed. Multivariate regression identified walking speed and physiotherapy (PT) sessions as significant predictors of PA. Conclusions: PA in young adults with CP is linked to walking speed, GMFCS level, joint pain, fatigue, and PT. No differences have been observed between patient unilateral or bilateral CP. However, individual behaviors vary and are not fully explained by linear regression analysis. Full article

The evolution toward 6G Computing Power Networks (CPN) aims to deeply integrate multi-tier computing resources across Cloud, Edge, and end devices. However, the significant heterogeneity of computing resources, characterized by varying hardware architectures such as CPUs, GPUs, and NPUs, coupled with the time-varying network topology caused by terminal mobility, poses severe challenges to realizing efficient integrated scheduling that satisfies Quality of Service (QoS). To address spatiotemporal mismatches between task requirements and hardware architectures, this paper proposes an integrated scheduling method combining Discrete Time-Varying Graph (DTVG) construction with Multi-Agent Reinforcement Learning (MARL). Specifically, we model the dynamic interaction between mobile tasks and heterogeneous nodes as a DTVG to capture spatiotemporal evolution and employ a QMIX-based algorithm to enable collaborative decision-making among distributed agents. Simulation results demonstrate that the proposed approach effectively solves the joint optimization problem of heterogeneous resource matching and dynamic path planning, significantly outperforming traditional baselines in terms of resource utilization and average latency. This study confirms that incorporating graph-theoretic modeling with reinforcement learning offers a robust solution for the complex coupling of communication and computation in dynamic 6G networks. Full article