Search Results (3,807)

Objective: To analyze the relationship between weekly accumulated external load and pre-match neuromuscular performance assessed through the countermovement jump (CMJ), in under-21 (U21) football players across 10 competitive microcycles. Methods: Sixteen U21 football players (age: 18.9 ± 0.42 years; height: 180 ± 6.3 cm; body mass: 78.5 ± 8.5 kg) from a Chilean professional club were monitored over 10 consecutive weeks. In each microcycle, the relationship between changes in neuromuscular performance estimated from CMJ-derived variables and two components of external load was analyzed: (1) weekly accumulated external load and (2) the acute–chronic workload ratio (ACWR). External load variables included total distance (TD), high-speed running distance (HSR), accelerations (ACC), decelerations (DC), and PlayerLoad (PL). CMJ variables included jump height (JH), modified reactive strength index (RSI-mod), and peak eccentric velocity (PEV). Performance changes were calculated as the percentage change (Δ%) between MD + 2 (start of the microcycle) and MD − 1 (pre-match). Pearson or Spearman correlation coefficients were applied depending on data distribution. Results: Significant negative associations were observed between weekly accumulated external load and changes in CMJ performance. Reductions in JH were associated with TD, HSR, ACC, and PL. Similar patterns were found for RSI-mod, while PEV showed a particularly strong association with ACC. Additionally, ACWR demonstrated significant negative relationships with CMJ changes, especially for HSR, ACC, and PL. Conclusions: Higher weekly accumulated external loads and elevated ACWR, particularly in high-intensity metrics such as high-speed running and accelerations, are associated with impaired pre-match neuromuscular performance. Consequently, monitoring CMJ-derived variables alongside external load data is recommended to manage fatigue and optimize match readiness in young football players. Full article

This study presents a novel methodology integrating high-resolution X-ray computed tomography, digital volume correlation and statistical visualisation mapping, to perform microscale observations and correlate delamination fracture mechanisms in heterogeneous materials. To demonstrate the utility of this integrated approach, it is applied to study the damage behaviour of aerospace carbon/epoxy composite laminates with an open hole, subjected to quasi-static tension and fatigue at a load ratio of 1:10. The study also explores the applicability of a Paris law type relationship to determine effective macroscopic fatigue delamination resistance in the load-bearing plies. The X-ray imaging for both load cases revealed extensive formation of delaminated fracture surfaces surrounding both glass fibre interlacing weaves and entrained voids within them, acting as preferential sites for localised strain hot spots. It is demonstrated that local energy dissipation is governed by the recurring weave pattern and topological order, which can help explain the typical damage state in quasi-static behaviour, establishing a direct link between microstructural features and macrostructural material response. Full article

Ball-end copy-milling is widely used for finishing complex components, yet its influence on surface integrity is generally overlooked and remains insufficiently addressed. Milling often generates tensile residual stresses at the machined surface, which are detrimental to fatigue performance and commonly require costly postprocessing, particularly in fatigue-critical parts such as turbine blades. In this context, the present study evaluates the capability of Prestress-Assisted Machining under uniform bending loads to improve the surface integrity of ball-end copy-milled Aluminium 5083 workpieces. Experimental tests were conducted on slender specimens with different thicknesses and curvature radii while maintaining constant cutting conditions. After machining and unclamping, surface residual stresses were measured by X-ray diffraction, and the effects of prestressing on geometry, cutting forces and surface roughness were also assessed. The results demonstrate that this method markedly increases compressive residual stresses in the prestressing direction, from approximately 30 MPa to about 180 MPa, and that this variation can be accurately described by subtracting the elastic prestressing stress from the residual stresses obtained without external loads applied. Moreover, no relevant adverse effects were observed in cutting forces or roughness, and corrected toolpaths allowed a uniform slot depth. These findings identify bending-based Prestress-Assisted Machining as an effective and predictable strategy for improving surface integrity in ball-end copy-milling and extend its applicability beyond previously reported pocket and slot milling operations. Full article

Background: Anterior cruciate ligament (ACL) injuries are increasing in young athletes and many are related to non-contact, spontaneous mechanical fatigue-related ruptures. The objective of this narrative review is to identify and synthesize the anatomical, histological, physiological, and biomechanical basis of extracellular matrix (ECM) factors that contribute to ACL injuries and suggest ways to decrease their occurrence. Methods: The primary investigator searched PubMed, Web of Science, and Google Scholar database titles and abstracts using search phrases with Boolean operators: “anterior cruciate ligament” OR “ACL”, OR “cranial cruciate ligament” AND “disease”; “anterior cruciate ligament” OR “ACL”, OR “cranial cruciate ligament” AND “spontaneous rupture” OR “non-contact injury”; and “anterior cruciate ligament” OR ACL, OR cranial cruciate ligament” AND “crosslink”, “collagen” OR “extracellular matrix”; and “anterior cruciate ligament” OR “ACL”, OR “cranial cruciate ligament” AND “microtrauma”, OR “sudden” OR “fatigue failure”. The primary investigator and a sports orthopedic surgeon reviewed titles and abstracts of diverse evidence sources. From these identified sources, the study team performed full text reviews, selected contributing articles, performed Strength of Recommendation Taxonomy (SORT) grading, and synthesized the following themes: A Hostile Environment, ACL Strain, and Poor Nutrient Delivery; Accumulative ACL Microtrauma and Mechanical Failure; The ACL Differs From Other Ligaments; Collagen, the ECM, and ACL Mechanobiology; Crimps and ACL ECM Stretch; Crosslinks Improve ECM Mechanical Properties; The Delicate Collagen Synthesis and Degradation Balance; Exercise Training and the ACL; Can Nutraceuticals Help Restore the Balance?; Training Induced ACL Hypoxia; Estrogen and the Female Athlete; Counting Pitches or Counting Collagen Fiber Ruptures; and Restoring A Positive Anabolic–Catabolic Collagen Balance. Results: Regular exercise training within a physiologically safe loading range is vital to ACL ECM health. However, low or moderate evidence suggested that poor blood supply, slow metabolism, and a hypoxic environment may unbalance anabolic and catabolic homeostasis. Active rest and recovery concepts that prevent youth baseball shoulder and elbow injuries may help prevent non-contact ACL injuries. Conclusions: More prescriptive active rest and recovery intervals and neuromuscular control training may restore the anabolic–catabolic balance that increases mature crosslink density and improves ACL ECM strength. Confirmatory studies are needed to better establish therapeutic intervention mode(s), timing, dosage, and frequency optimization. Full article

This study addresses, within the framework of fracture mechanics, the structural analysis of the DEMO (demonstration power plant) divertor—a key component in fusion reactors—subjected to particularly severe loading conditions. A global model of the divertor was developed using Finite Element Method (FEM) analysis through the software ANSYS Workbench 2024, including all structural subcomponents. Thermal and internal pressure load cases were considered. The FEM analysis enabled the identification of critical areas prone to stress concentration. Based on the global results, a submodeling technique was applied to analyze locally critical components with higher resolution. On these submodels, a Linear Elastic Fracture Mechanics (LEFM) analysis was performed using the FRANC3D (v 8.6.2) software. Static semi-elliptical cracks were introduced in various configurations, and the stress intensity factor was evaluated to assess their criticality. Subsequently, an incremental crack growth analysis was conducted to simulate crack propagation based on the local stress field, also accounting for directional variations. Finally, a lifetime analysis was carried out using Paris’ law, estimating the fatigue cycles for an arbitrary crack propagation under the given loading conditions. The entire procedure was repeated for each subcomponent and loading condition, resulting in a broad and detailed understanding of the fracture response of the system. This approach provides crucial insights for the design, inspection, and long-term maintenance of the divertor. Full article

The development of supersonic aircraft presents significant challenges in ensuring safety during early design stages, particularly for fuel tank systems exposed to extreme thermal and structural loads. Conventional document-based zonal safety analysis methods are limited in their ability to capture dynamic interactions between spatial subsystem configurations and functional system behavior during early conceptual design, leading to delayed hazard identification. This study proposes an integrated framework combining computer-aided design (CAD) and model-based systems engineering (MBSE) to support early-stage zonal hazard analysis. The framework links spatial subsystem modelling with functional system architecture to enable iterative hazard identification and mitigation. Applied to the SA-24 Phoenix conceptual supersonic aircraft, the approach identifies critical risks, including fuel vaporization, over-pressurization, and structural fatigue, and evaluates mitigation strategies such as thermal insulation and redundant venting. Functional hazard analysis and fault tree analysis are used to assess failure scenarios and ensure compliance with EASA CS-25 requirements. Results indicate an estimated reduction of up to 40% in risk priority number (RPN) values for key thermal hazard pathways and a 25% reduction in conceptual design iteration time compared with conventional approaches. The findings demonstrate that CAD–MBSE integration offers a scalable and efficient methodology for early hazard identification, contributing to safer and more reliable supersonic aircraft design. Full article

Explosive ordnance (EO), including AXO (abandoned explosive ordnance), IEDs (improvised explosives devices), and UXO (unexploded ordnance), are widely recognised for their blast and fragmentation hazards, but they also represent a persistent and under-addressed source of occupational chemical exposure for explosive ordnance disposal (EOD) personnel. EOD core activities liberate mixed metals and energetic chemicals, resulting in exposures that are multi-route (inhalation of dusts and fumes, dermal loading amplified by sweat and glove occlusion, and ingestion via hand-to-mouth transfer during eating, drinking, or smoking) and multi-temporal (repeated low-dose background plus task-driven spikes), as well as chemically complex. Clinically, this can present as syndromic overlap across acute and chronic domains, with symptoms that are easily misattributed to heat stress, dehydration, infection, or fatigue. Acute effects of concern include neurotoxic presentations (headache, dizziness, confusion, tremor, and seizure), respiratory and mucosal irritation following dust or fume events, gastrointestinal symptoms, and patterns suggestive of acute hepatic or renal stress, particularly when high-intensity tasks occur in hot environments that compound physiologic strain. Chronic outcomes relevant to repeatedly exposed EOD personnel include renal function decline, neurocognitive effects that can degrade operational decision making and safety, persistent haematologic abnormalities, and endocrine disruption signals, with long-latency risks requiring cautious interpretation given sparse longitudinal data and confounding co-exposures. This review synthesises the current evidence base through an EOD lens and translates it into pragmatic clinical and programmatic actions: task-based exposure characterisation; tiered biomonitoring and medical surveillance aligned to operational tempo; incident-triggered assessment pathways after high-residue events; and prevention strategies that work under field constraints, including contamination control zones, hygiene enforcement, glove and respiratory protection optimisation, tool and vehicle decontamination, and measures to prevent secondary transfer and take-home exposure. The central takeaway is practical: EOD programs can reduce morbidity and improve readiness by treating explosive ordnance as a chemical mixture exposure problem, adopting mixture-aware clinical triage, and embedding surveillance and controls that match how EOD work is actually performed. Full article

As floating offshore wind turbines (FOWTs) scale towards 10 MW+ capacities, suppressing wave-induced rotational resonance becomes critical for system survivability. This study introduces an optimized, highly symmetrical four-float semi-submersible platform, explicitly tailored to support the DTU 10 MW wind turbine and paired with an orthogonal four-point mooring system. Using three-dimensional linear potential flow theory via ANSYS AQWA, comprehensive frequency- and time-domain hydrodynamic evaluations were conducted. To address the inherent limitations of inviscid potential flow assumptions, an empirical added-damping method was implemented. Quantitative results demonstrate a drastic reduction in motion responses: the peak Response Amplitude Operator (RAO) for heave decreased by 68.6% (from 1.945 m/m to 0.610 m/m). Most notably, the peak RAOs for the critical rotational degrees of freedom—pitch and roll—were reduced by over 92% (from 2.080 °/m and 2.216 °/m to ~0.168 °/m, respectively). Ultimately, compared to traditional asymmetric three-float concepts, this novel symmetric omnidirectional layout provides a more uniform restoring stiffness. The resulting suppression of pitch and roll resonance results in a profound reduction in tower-base bending moments and gyroscopic loads, thereby significantly enhancing the dynamic stability, safety margins, and fatigue life of the 10 MW FOWT under extreme survival sea states. Full article

Shot peening is a critical surface treatment for improving the fatigue resistance of aircraft propeller blades operating under complex cyclic loads. While accurate coverage evaluation is essential for quality assurance, its development is severely hindered by a fundamental bottleneck: the extreme scarcity of annotated datasets in this niche aerospace domain, where data collection is costly and low-frequency, as each acquisition requires the actual peening of high-value components. Consequently, existing practices are restricted to subjective manual inspection or conventional segmentation methods that lack robustness under complex textures. To bridge this gap, this study develops an integrated automated surface evaluation framework, termed DT-ZSAM (Dual-Threshold Zero-shot Assessment Model), which circumvents the data-dependency bottleneck by leveraging the zero-shot capabilities of the Segment Anything Model (SAM) within a custom-designed prompt-generation pipeline. To ensure end-to-end automation without manual intervention, the framework identifies candidate regions via a dual-threshold scheme in grayscale and brightness domains and extracts representative prompt points through density-based analysis refined by DBSCAN clustering. Experimental results demonstrate that the proposed framework achieves precise segmentation without requiring any pixel-level annotated training data. Notably, the proposed framework yielded a coverage rate of 30.57%, aligning closely with the expert visual consensus (25–35%), whereas the standard commercial instrument (TCV-2A) significantly overestimated the coverage at 62.33% due to its sensitivity to surface textures and fixed calibration logic. This framework provides a robust and pragmatic solution for high-stakes industrial quality control, offering a reliable path for automating inspection in domains where large-scale data acquisition is practically unfeasible. Full article

Geometric discontinuities in aero-engine turbine blades generate multiple stress concentrations along the airfoil, rendering life prediction exceptionally challenging. While conventional skeletal point method (SPM) offers reasonable accuracy in predicting creep-fatigue-interaction (CFI) life for simple structural specimens, they prove inadequate for geometries with poor symmetry. This study introduces a novel two-dimensional skeletal point method (2D SPM) to analyze stress evolution characteristics, identify representative stresses, and predict CFI life in complex structures. Leveraging the film-cooling hole (FCH) features of a representative turbine blade, three perforated plate specimens were designed, manufactured, and subjected to CFI testing. Failure analysis confirmed crack initiation at hole-edge stress concentration zones, followed by inward propagation. Specimen fracture surfaces exhibited predominantly ductile dimpling features, with multi-origin fatigue characteristics observed only near hole-edges, collectively indicating creep-damage-dominated failure mechanisms. Five life prediction methodologies were comparatively evaluated. The results demonstrate that the 2D-SPM achieved the highest accuracy (all predictions within twofold scatter bands), followed by the conventional SPM (also within twofold scatter bands). The nominal stress method showed moderate accuracy (within fivefold scatter bands), while both hot point method and TCD methods proved unsuitable for creep-fatigue scenarios with significant stress evolution. Full article

The prediction of metal fatigue life has evolved from classical empirical approaches to advanced, data-driven computational models. However, traditional methods struggle with large data scatter, complex variable-amplitude loading, and the cost of experimental testing. These limitations are particularly pronounced in additively manufactured (AM) components, which exhibit random porosity and are highly sensitive to process parameters. This review integrates classical fatigue mechanics with modern data-driven methodologies. It evaluates fatigue-life prediction for metallic alloys, welded assemblies, and AM materials. We review classical prediction tools, machine learning (ML) algorithms, deep learning architectures, and physics-informed neural networks (PINNs). ML models capture nonlinear degradation patterns but suffer from limited interpretability (“black-box” behavior) and are unable to extrapolate from small datasets. Embedding governing physical laws into PINNs helps mitigate these limitations. This approach enhances physical consistency, reduces training-data requirements, and strengthens extrapolation capability. In additively manufactured metals, defect location is often a more critical predictor of fatigue failure than defect size or morphology. To address data scarcity, we highlight the use of generative adversarial networks and transfer learning. Integrated models, combined with real-time structural health monitoring data, enable accurate, dynamic digital twins and preemptive fatigue prognosis. Full article

Gingival recession is commonly linked to alveolar bone dehiscence, inflammatory burden, traumatic brushing, or excessive orthodontic forces. However, recession is also observed in some patients despite apparently mild or “biologically acceptable” loading, particularly in thin periodontal phenotypes. Here, we propose the Gingival Creep Failure Theory, a hypothesis-driven conceptual framework in which gingival soft tissues undergo time-dependent viscoelastic deformation (creep) under sustained or repetitive tensile microstrain. Over time, accumulated deformation and microstructural fatigue may reduce recoil capacity and shift the gingival margin apically once tissue-level tolerance is exceeded. Gingival connective tissue is modeled as a fiber-reinforced, fluid-rich viscoelastic composite whose response depends on collagen architecture, cross-linking, proteoglycan-mediated hydration, and vascular support. In thin phenotypes characterized by reduced connective tissue volume and altered extracellular matrix (ECM) organization, creep progression is hypothesized to accelerate, lowering the threshold at which fatigue-related microdamage translates into clinically detectable marginal migration. Evidence from collagenous connective tissue biomechanics supports the plausibility that sub-failure sustained or cyclic loading can produce cumulative deformation and incomplete recovery; however, direct creep–fatigue data for human gingiva remain limited, underscoring the need for targeted validation studies. This hypothesis integrates soft tissue mechanics with periodontal phenotype biology and orthodontic loading patterns and proposes creep and microstructural fatigue as plausible time-dependent contributors to gingival recession in susceptible phenotypes. Because direct in vivo gingival strain and creep–fatigue measurements remain limited, the model should be interpreted as hypothesis-generating and in need of targeted clinical and experimental validation. Full article

Background: Wrist pain is frequently reported among housewives and linked to repetitive household tasks, yet the drivers of pain-related disability remain unclear. Beyond physical load, psychosocial factors such as catastrophizing, mood symptoms, and self-efficacy may shape severity and functional impact. Purpose: To evaluate the severity of wrist pain and wrist-related disability in Turkish housewives and to identify the psychosocial and symptom-related factors associated with these outcomes. Methods: This cross-sectional study was conducted with 92 Turkish housewives reporting wrist pain ≥ 3/10 on the Numeric Pain Rating Scale (NPRS) and who have been married for at least 1 year and performing at least 1 h of housework, via Google Forms. Fatigue and wrist pain were measured using the Numeric Rating Scale (NRS), as well as the Patient-Rated Wrist Evaluation–Turkish version (PRWE-T), Pain Self-Efficacy Questionnaire (PSEQ), Patient Health Questionnaire-4 (PHQ-4), and Pain Catastrophizing Scale (PCS). Sociodemographic data were also collected. Associations were analyzed with Spearman’s correlation, and simple linear regression identified factors explaining wrist pain severity and disability. Results: PRWE-T total scores showed strong positive correlations with pain catastrophizing (p < 0.001), PHQ-4 depression (p < 0.001), and PHQ-4 anxiety (p < 0.001), while correlating negatively with pain self-efficacy (p < 0.05). PCS was also strongly correlated with PHQ-4 anxiety (p < 0.001) and PHQ-4 total (p < 0.001), but negatively with PSEQ (p < 0.001). Multivariable regression analyses have shown that PCS and fatigue may be predictory of wrist pain and disability. Additional factors included fatigue severity (p = 0.002), PHQ-4 depression (p < 0.001), and PHQ-4 anxiety (p = 0.001). Conclusions: These findings highlight the multidimensional nature of wrist symptoms in this population. Full article

Owing to the intricate mechanical coupling characteristics and the considerable difficulty in extracting synergistic spatio-temporal features from high-dimensional sensor data under fluctuating alternating loads, this study proposes a robust anomaly detection framework that combines Normalized Mutual Information (NMI) and Spatio-Temporal Graph Neural Networks (STGNN). First, NMI is utilized to quantify the nonlinear physical coupling intensity among multi-source sensors, thereby filtering out weakly correlated noise and reconstructing the spatial topological structure of the coupler system. Subsequently, a deep learning architecture incorporating Graph Convolutional Networks (GCN), Gated Recurrent Units (GRU), and one-dimensional convolutional residual connections is developed to capture the dynamic evolutionary characteristics of equipment states across both spatial interactions and temporal sequences. Finally, based on the model’s health-state predictions, a moving average algorithm is introduced to smooth the residual sequences, and an anomaly early-warning baseline is established in conjunction with the 3σ criterion. Experimental validation conducted using field service data from heavy-haul trains demonstrates that, compared to conventional serial CNN and Long Short-Term Memory (LSTM) models, the proposed method exhibits superior fitting performance and robustness against noise, effectively reducing the false alarm rate within normal working intervals. In a real-world case study, the method successfully identified variations in spatial linkage features induced by local damage and triggered timely alerts. Notably, the spatial alarm nodes were highly consistent with the fatigue crack initiation sites identified through on-site magnetic particle inspection. This study provides a viable data-driven analytical framework for the condition monitoring and anomaly identification of critical load-bearing components in heavy-haul trains. Full article

Prestressed concrete railway sleepers are key structural components that determine the safety, durability, and serviceability of modern railway infrastructure. This study presents a comprehensive investigation of the design, testing, and acceptance of prestressed concrete sleepers in accordance with EN 13230, with particular reference to the requirements applied on the Polish railway network. The analysis integrates normative provisions, analytical calculations, finite element modeling, and experimental verification, including static, dynamic, and fatigue load tests. Special attention is given to the k_t coefficient, which accounts for prestress losses, fatigue degradation, and the development of concrete strength throughout the service life. This coefficient plays a critical role in the acceptance criteria for sleepers during mandatory product testing. The influence of concrete age on the variability of k_t is examined, showing that the highest variability occurs within the first 180 days of curing. Full-scale laboratory tests performed on PS-94 sleepers confirm compliance with standard requirements regarding cracking loads, crack width limits, and ultimate load capacity under both exceptional and fatigue loading conditions. Numerical simulations provide additional insight into stress and displacement distributions in critical cross-sections, supporting the experimental findings. The results indicate that most of prestressing force losses occur during the early service period. This observation supports the application of age-dependent acceptance criteria, which may improve conformity assessment procedures for prestressed concrete railway sleepers in contemporary railway engineering practice. Full article