Search Results (8,397)

Serving students with special educational needs (SENs) involves recognising that their learning is closely linked to their emotional needs. Self-esteem and socio-emotional well-being play a key role in their motivation and adaptation to school. In this context, physical activity-based interventions at school emerge as a possible way to strengthen their self-esteem and socio-emotional well-being. The aim of this study was to analyse the effects of a web-based active break programme on self-esteem in students aged 6 to 10 years with SENs and on socio-emotional well-being in the subgroup of first–second-grade students. A pre-specified sub-analysis was conducted of a multicentre randomised controlled trial with a sample of 161 students with special educational needs (7.8 ± 1.1 years, 32% girls), divided into a control group (85 students) and an experimental group (76 students). A programme of video-guided active breaks was implemented in the classroom, applied twice a day, five days a week for 12 weeks, via a web platform. Self-esteem was assessed using the School Self-Esteem Test (SSET), and socio-emotional well-being was assessed using the Self-Report of Socio-Emotional Well-Being (SRSEWB). A significant Time × Group interaction was observed for self-esteem, F_{(1, 157)} = 5.43, p = 0.021, η²_p = 0.033, but no statistically significant effects were detected for socio-emotional well-being. These findings suggest that active break interventions may help strengthen self-esteem in students with SENs. Future research should examine the temporal stability of these improvements, determine the optimal intervention duration required to generate sustained changes, and evaluate longer-term socio-emotional outcomes. Full article

Carbon-Fiber-Reinforced Polyetheretherketone (CFR-PEEK) instrumentation is increasingly preferred in spinal oncology for its physical properties, minimizing imaging artifacts and facilitating precise postoperative radiotherapy planning and tumor surveillance. However, a significant technical limitation exists: the current unavailability of dedicated CFR-PEEK pedicle screws for the cervical spine. The smallest available implants are designed for thoracic use (minimum diameter 4.5 mm, minimum length 25 mm), posing substantial risks of neurovascular injury when applied to smaller cervical pedicles. We present a technical note/feasibility report illustrated by a single case of robot-assisted placement of thoracic CFR-PEEK screws in the cervical spine for the treatment of a C7 Giant Cell Tumor. Following neoadjuvant therapy with Denosumab, a single-stage, two-step circumferential resection and reconstruction was performed. The anterior step was complicated by an iatrogenic injury to the highly adherent left vertebral artery (VA), which was successfully repaired. Consequently, the posterior step required maximal precision to preserve the sole remaining intact VA on the right side. Given the anatomical mismatch between the 4.5 mm thoracic screws and the narrow cervical pedicles (measuring as narrow as 3.2 mm on the critical right side), robotic navigation (ExcelsiusGPS^®) was utilized to plan and execute safe trajectories. Specifically, on the side of the intact VA, a small, controlled medial cortical violation was planned to avoid lateral vascular compromise. The procedure resulted in rigid, artifact-free stabilization with no immediate neurological sequelae. This single-case experience suggests that robotic guidance may facilitate adaptation of thoracic CFR-PEEK instrumentation to the cervical spine in selected oncologic scenarios; reproducibility, costs, and long-term outcomes remain uncertain. Full article

In situ tunnel expansion provides a cost-effective and environmentally sustainable alternative to new tunnel construction. However, staged widening disturbs the lining–rock system, triggering complex, non-monotonic stability responses. This study integrates physical model tests and FLAC3D simulations to investigate the mechanical evolution of a limestone tunnel widened by the Center Diaphragm (CD) method. Seven cross-sections (S1–S7) were fabricated and tested under uniaxial compression with digital image correlation. Results show that the peak load decreases from 385.73 kN in the lined baseline (S1) to 184.14 kN at the first unilateral cut (S3), a 49% reduction, but recovers to 262.28 kN at the left-half closure (S4) before dropping to 128.16 kN at the upper-right excavation (S5). The final relined stage (S7) regains 200.69 kN, a 40% improvement over the unlined enlarged state (S6). Numerical analyses confirm this non-monotonic trajectory in terms of the peak plastic-zone fraction. It reaches at 86.32% in S3, decreases to 74.03% in S4, and rises to 76.43% in S5. The fractions further reach 88.51% in S6 and 87.70% in S7, reflecting the enlarged span and redistributed yielding. Targeted bolting at weak stages S3 and S5 reduced plastic-zone fraction by 14.73 and 4.75 percentage points, and reduced crown settlement by 68% and 41%, respectively. These findings challenge the conventional monotonic degradation assumption, identify S3 and S5 as critical weak links, and validate selective reinforcement for enhancing stability during tunnel expansion. Full article

Freeze–thaw cycles frequently cause damage to canal slopes in cold regions, which has become a potential adverse factor leading to slope failure. This study investigates the coupled thermo-hydro-mechanical (THM) behavior and stability evolution of canal slopes under freeze–thaw cycle conditions through integrated physical model tests and numerical simulations. The evolution processes of temperature distribution, maximum frozen depth, unfrozen water content, deformation, and safety factor of canal slopes were evaluated. The results showed that both the maximum frozen depth and deformation increased continuously within a reasonable service life of 20 years. The maximum deformation concentrated in the middle of the slope, and the maximum unfrozen water content on the slope surface decreased by 0.06. The stability of a canal slope is subject to the dual influences of service time and seasonal variations. Overall, the safety factor decreases with the increase in service time. The safety factor is influenced by the degree of slope freezing. Compared to November, the safety factor in March of the following year increases by 0.15. As slope failure initiates at the slope toe, necessary engineering measures must be implemented at the slope toe in the design of canals to maintain slope stability. This research provides data support for frost damage mitigation and stability assessment of canals in cold regions. Full article

Robotic manipulators were initially introduced to replace repetitive human labor and have since evolved to perform complex tasks in dynamic environments. In such systems, imitation learning and reinforcement learning models capable of real-time trajectory generation are widely applied. Among these approaches, imitation learning enables rapid training when high-quality datasets are available. However, it suffers from high costs associated with collecting expert demonstration data and significant performance variability depending on data quality. Recently, learning approaches utilizing large-scale datasets have been explored, but they often struggle to guarantee reliable performance in tasks requiring precise control and incur substantial computational costs for model construction, limiting their applicability as a general-purpose learning strategy. To address these limitations, this paper proposes an imitation learning framework that integrates sampling-based motion planning with a hybrid data curation strategy. The proposed method employs a sampling-based planner (e.g., RRT*) to generate diverse physically feasible trajectories, thereby reducing the cost of acquiring expert demonstration data. The generated trajectories are then curated through clustering-based grouping and rule-based filtering to select high-quality training samples from large-scale datasets. The proposed framework automatically generates physically feasible trajectories while selecting high-quality data from large trajectory pools, thereby improving training stability and reducing data-related costs. Experimental results demonstrate that the proposed method achieves an average success rate of 79.1% (95% CI: 74.3–83.2%) and produces trajectories with shorter trajectories, lower final distances, and reduced joint movements compared to conventional filtering methods. Full article

Fatigue and reduced energy availability significantly affect athletic performance, and nutritional strategies to maintain carbohydrate and electrolyte levels are critical for delaying fatigue and preserving endurance. This study aimed to evaluate the effects of carbohydrate and electrolyte (CHO-E) supplementation on sports performance in physically active individuals. A systematic review and meta-analysis of 26 studies, including randomised and observational designs, was conducted. Four separate analyses examined the impact of CHO-E supplementation on performance outcomes, metabolic biomarkers, blood mineral concentrations, and additional performance descriptors. The meta-analysis showed that CHO-E supplementation significantly increased time to exhaustion (Standard Mean Difference (SMD) 0.60; 95% confidence interval (CI): 0.17, 1.02; p = 0.006), blood glucose levels (SMD 0.82; 95% CI: 0.45, 1.19; p < 0.001), and blood sodium levels (SMD 0.22; 95% CI: 0.07, 0.36; p = 0.004) compared to placebo, while no significant effect was observed for time to finish (SMD −0.07; 95% CI: −0.28, 0.13; p = 0.49). These findings indicate that CHO-E supplementation during moderate-to-high intensity exercise can enhance performance by extending endurance and supporting metabolic and electrolyte balance. Overall, the results support the targeted use of CHO-E supplementation to maintain energy availability and physiological stability during prolonged physical activity, providing evidence-based guidance for athletes and practitioners. Full article

This study presents a systematic comparative benchmark between two distinct paradigms for solving nonlinear partial differential equations (PDEs): the data-driven Physics-Informed Neural Networks (PINNs) and the analytical Homotopy Analysis Method (HAM). We apply both methods to a unified family of canonical PDEs, the Burgers, Huxley, Fisher, Burgers–Huxley, and Burgers–Fisher equations, under identical problem setups, domain discretization, and validation metrics. PINNs incorporate physical laws directly into neural network training by minimizing a loss function that enforces PDE residuals, yielding physically consistent solutions even for strongly nonlinear problems. HAM provides approximate analytical solutions using a unified framework, and the same initial guess, auxiliary linear operator, and auxiliary function across all equations despite their distinct nonlinearities. The controlled, consistent application of both methods enables a fair, reproducible comparison across this equation family. The results provide a quantitative performance map under identical conditions, delineating when PINNs (high accuracy, long-term stability, and generalization capability) are preferable, versus when HAM (computational speed, short-term analytic approximation, and lower memory footprint) offers advantages. While the finite radius of convergence of the truncated HAM series is theoretically expected, our controlled comparison quantifies for the first time how this degradation varies across equation types, revealing that the choice between methods depends on specific problem requirements including error tolerance, available computational resources, and temporal horizon. The novelty lies not in solving each equation individually, but in deriving a performance taxonomy that systematically connects equation features (shocks, stiffness, and reaction–diffusion coupling) to optimal solver choice—providing previously unavailable, evidence-based guidance for the scientific computing community. This study establishes the first rigorous, controlled comparative benchmark between analytic and data-driven PDE solvers across a spectrum of nonlinearities, providing a reproducible baseline for future hybrid scientific machine learning solvers. Full article

Background: Motor decline associated with ageing compromises mobility, postural control and the ability, thereby increasing risk among older adults. Biomechanical characterization of movement, particularly using non-linear methods, offers a process-oriented approach capable of detecting subtle changes in motor coordination. The MIND&GAIT programme has previously demonstrated benefits in physical function in frail older individuals; however, its potential to improve motor coordination parameters that underpin fall risk remains insufficiently explored. Objectives: To analyse the impact of the MIND&GAIT program on motor coordination during sit-to-stand (STS) and walking tasks, two daily activities strongly associated with fall risk, using advanced non-linear and biomechanical metrics in institutionalized older adults. Methods: Fourteen institutionalized older adults (82.21 ± 7.14 years) participated. Three-dimensional acceleration and angular velocity were recorded using inertial sensors. Motor variability and predictability were quantified using the multivariate Lyapunov exponent (LyEM) and multivariate incremental entropy (MIE). STS (30 s) and walking-in-place (2 min) tasks were assessed pre- and post-intervention following a three-month, thrice-weekly programme. Results: Although no statistically significant differences emerged (ps > 0.05), trends were observed suggesting increases in LyEM during STS and in both MIE and LyEM during walking were found post-intervention. These exploratory findings may indicate enhanced motor complexity, stability and adaptability, features associated with reduced fall vulnerability. Conclusions: Despite the absence of statistical significance, the biomechanical trends observed suggest improvements in motor coordination patterns relevant to fall risk reduction in institutionalized older adults following the MIND&GAIT programme. These findings highlight the potential of structured exercise-based interventions for promoting safer movement behaviors in frail populations. Full article

As a critical actuator in aero-engine control systems, the health condition of the Fuel Metering Unit (FMU) directly influences flight safety and maintenance efficiency, making the precise prediction of its degradation process a core task in the engine’s Prognostic and Health Management (PHM). This paper presents a novel inverted stabilized LSTM-Transformer (isLTransformer) approach for predicting the health state of aero-engine FMUs, addressing the limitations of existing methods in modeling long-sequence multivariate data. Firstly, a Composite Health Indicator (CHI) is constructed through semi-supervised learning (SSL), which fuses multi-sensor monitoring data to quantitatively characterize the degradation trend of the FMU throughout its operational lifecycle. Secondly, the proposed isLTransformer model is designed by replacing the feedforward network in traditional iTransformer with a stabilized LSTM module, which maintains the self-attention mechanism’s capability to explicitly model dynamic correlations between multiple variables while enhancing the ability to capture nonlinear degradation within individual variables. A physical FMU test bench is designed for the real-world PHM degradation experiments, and the collected dataset was used to demonstrate the effectiveness of the proposed method. Evaluation metrics, including Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), are employed to assess the prediction accuracy. The proposed method demonstrates high monotonicity and trend consistency in CHI construction. Compared to the inverted Transformer (iTransformer) and iTransformer- Bi-directional Long Short-Term Memory (BiLSTM), the proposed isLTransformer framework demonstrates significantly reduced prediction errors, validating its superiority in multivariate long-sequence prediction tasks and effectiveness for aero-engine FMU health prediction. Full article

A coupled thermo-mechanical probabilistic model for porosity prediction in robotic Cold Metal Transfer (CMT) welding is proposed and experimentally validated under industrial conditions. Unlike conventional energy-based approaches, the formulation explicitly incorporates fixture-induced geometric deviations through the effective stick-out relation ${SO}_0.$ A dimensionless fixture influence factor, $X_f$, is introduced to quantify mechanical–process interaction. A fractional factorial design followed by a reduced DOE-enabled separation of thermal and mechanical effects. Logistic regression integrating process energy descriptors and $X_f$ achieved strong predictive capability (AUC = 0.91; 95% CI: 0.87–0.94). The fixture influence factor exhibited the highest standardized effect (OR = 3.74), while a 1 mm increase in effective stick-out doubled porosity probability (OR = 2.10), demonstrating the dominance of mechanical coupling within the evaluated operating window. Industrial implementation confirmed model relevance: geometric stabilization reduced rework from 11.58% to 4.76% and increased OEE from 79.58% to 87%. The results establish fixture mechanics as a primary control variable for weld robustness and provide a physically grounded framework for predictive quality optimization in robotic CMT systems. Full article

Single memristive nanowire networks have emerged as a promising pathway for energy-efficient neuromorphic computing, owing to their intrinsic nonlinearity, high dimensionality, fading memory and volatile switching dynamics relevant to physical reservoir computing. While prior works focused on oxide- or silver-based network systems, these approaches face trade-offs between operating voltage, cost, stability, and scalability. This work presents a proof-of-concept demonstration of stochastic polyvinylpyrrolidone (PVP)-coated nickel nanowire networks as low-cost and scalable memristive platforms, exhibiting low-voltage resistive switching (1–2 V). The electrical characterization reveals predominantly volatile resistive switching combined with nonvolatile behavior, consistent with a filamentary conduction mechanism at nanowire junctions. The switching dynamics are governed by the polymer coating thickness, with an intermediate PVP concentration (Ni@PVP = 1:25) showing optimal performance, with a resistance ratio of ~200, stable retention over 1 h, and a reproducible endurance of over 45 cycles. These results establish Ni@PVP nanowire networks as promising memristive platforms for neuromorphic hardware applications and physical reservoir computing, with relevant properties such as fading memory and nonlinear dynamics. Full article

Urban playgrounds are vital public spaces that support children’s play, social interaction, and well-being. However, many playgrounds in socially disadvantaged or aging urban areas experience physical deterioration, limited play diversity, and declining use. Although corporate social responsibility (CSR) initiatives have increasingly supported playground regeneration, many projects continue to emphasize short-term physical improvements rather than participatory processes and social value creation. This study conceptualizes CSR-supported, child-participatory playground regeneration as a social value creation process and examines how CSR enables process continuity through a structured six-stage participatory approach spanning planning, design, construction, and post-opening use. Two cases were selected from the “Save the Playground” program in Seoul, Korea: Saerok Children’s Park in a stable residential neighborhood and Mukjeong Children’s Park in a high-mobility, multicultural commercial district. Using a qualitative multiple-case study design, the study triangulates workshop outputs, observational records, facilitator field notes, and official program documents through thematic and cross-case analyses. The findings indicate that CSR support primarily ensured process continuity and facilitated multi-actor coordination across project stages. By securing implementation continuity and stabilizing governance arrangements, CSR support allowed participatory outputs to be iteratively translated into design development and post-opening evaluation. Post-opening outcomes differed by urban context; nevertheless, both cases showed social value creation through strengthened place attachment, responsibility-oriented use, and inclusive mixed-group play. This study advances a cross-case analytical framework linking urban context, participatory mechanisms, and post-opening social value outcomes, contributing to a more context-sensitive understanding of CSR-supported participatory design processes and their implications for sustainable urban public space development. Full article

Optical reflective markers are widely used in precision medicine, computer-assisted surgery, and robotic interventions. Nevertheless, intraoperative tracking still faces challenges such as sensor saturation, Point Spread Function (PSF) blooming, and flat-top artifacts, which affect localization precision and stability. Traditional deep learning detectors perform well in general object recognition but are limited in handling saturated infrared reflective markers due to their neglect of optical physics and inability to separate signal from blooming interference. This paper presents a physics-prior-guided network integrating a Brightness-Prior-Enhanced Spatial Attention (BPESA) mechanism for high-precision sub-pixel marker localization under saturation conditions. The method achieves a Root Mean Square (RMS) error of 0.52 pixels (approximately 0.11 mm) on a dataset of 8000 binocular images and reduces the localization error by approximately 54.4% compared with the baseline YOLOv8 model, while maintaining an inference speed of 134.6 FPS. The results demonstrate that optical blooming interference can be effectively mitigated by a learnable physics-prior branch, providing accurate marker coordinates that form a foundation for potential downstream tracking or navigation tasks. Full article

Fiber-reinforced polymer (FRP) composites used in marine environments suffer progressive degradation due to hydrothermal aging, which undermines their structural, physical and morphological integrity. In this study, a novel polyester-based nano-gelcoat reinforced with hexagonal boron nitride (h-BN) nanoparticles was developed as an advanced FRP composite coating for marine applications. Glass fiber/epoxy laminates coated with h-BN/polyester nano-gelcoat were subjected to accelerated hydrothermal aging (immersion in 80 °C artificial seawater for 90 days). Mechanical (tensile/flexural tests), viscoelastic (creep and stress relaxation), thermal (DSC/TGA), and morphological (optical microscopy/SEM) analyses were performed on aged and unaged samples. The h-BN-enhanced nano-gelcoat increased the composite’s resistance to hydrothermal aging. In particular, the optimally doped nano-gelcoat (~1 wt% h-BN) retained the highest tensile and flexural strength and modulus, reducing the property losses seen in the unreinforced system by about half (flexural strength 531.29 MPa vs. 1070.52 MPa for the uncoated laminate). Thermal analysis indicated elevated decomposition onset temperatures and higher char yields with h-BN, confirming improved thermal stability. Morphological observations revealed well-dispersed h-BN at 1 wt% with minimal microcracking, whereas higher filler loadings led to agglomeration. Additionally, a TOPSIS-based multi-criteria decision-making (MCDM) analysis was performed across mechanical, viscoelastic, and thermal metrics, which identified the 1 wt% h-BN coating as the most balanced formulation after hydrothermal aging. Full article

Under high-altitude, low-Reynolds-number conditions, flow instability in confined dual-fan configurations severely limits the propulsion and thermal management efficiency of heavier-than-air aircraft. This study establishes a high-fidelity 3D transient numerical model using curvature-corrected shear stress transport (SST) turbulence modeling, integrated with proper orthogonal decomposition (POD), dynamic mode decomposition (DMD), and nonlinear stability analysis to investigate rotational direction control mechanisms. Results indicate that co-rotating configurations trigger intense low-frequency pulsations and significant flow skewness due to wall-adhesion effects. Conversely, the counter-rotating layout reconstructs vortex topology by forming a strong interaction shear layer, which enhances local momentum exchange and suppresses large-scale coherent structures. While counter-rotation exhibits a higher initial growth rate, its significantly enhanced nonlinear aerodynamic damping forces the flow into a low-amplitude quasi-steady state, reducing inlet non-uniformity by 74% and increasing mass flow by 5.19%. These findings clarify the physical mechanisms of vortex interference in regulating stability and provide critical design insights for optimizing compact propulsion systems in heavier-than-air high-altitude platforms, such as long-endurance UAVs. Full article