Search Results (1,118)

Background and Objectives: Evidence on motor performance in children with autism spectrum disorder (ASD) is scarce and inconsistent. The association of motor impairments with autism severity and intelligence remains insufficiently studied. We aimed to examine motor performance parameters in children with ASD compared with typically developing (TD) peers. Materials and Methods: In this cross-sectional study, a convenience sample of 26 children with ASD, aged 4–10 years, was recruited from specialized centers in KSA, alongside 27 age- and sex-matched TD children. For the ASD group, severity (Childhood Autism Rating Scale, CARS-2) and intelligence quotient (Stanford–Binet Intelligence Scale, SB5) were extracted from medical records. CARS-2 score was utilized to categorize children with ASD into two groups (mild-to-moderate and severe groups). All study children were assessed for gross and fine motor skills using the Movement Assessment Battery for Children-2 (MABC-2), balance, muscle strength, endurance, and flexibility. Results: ASD groups recorded significantly lower scores in all MABC-2 component areas when compared to the TD group (p < 0.001). Aiming and catching percentile was significantly lower in the severe ASD group compared to the mild-to-moderate group (p = 0.05). Furthermore, children with ASD exhibited increased hypermobility, predominantly at the elbow joints, reduced grip strength, shorter distance in the modified 6 min walk test, and lower standing long-jump performance (p < 0.001) when compared to TD group; however, no significant difference was recorded between the ASD groups. Spearman correlation revealed that aiming and catching was negatively correlated with autism severity (CARS-2) (r = −0.38, p = 0.05) and positively with IQ (r = 0.51, p = 0.03). Aiming and catching was positively correlated with grip strength (r = 0.55, p = 0.003), endurance (r = 0.58, p = 0.002), and jump distance (r = 0.44, p = 0.03), while balance was positively correlated with grip strength (r = 0.44, p = 0.02). Conclusions: Children with ASD exhibit significant impairments in gross and fine motor performance compared with TD peers, accompanied by hypermobility, reduced strength, and diminished endurance. Notably, aiming and catching ability correlated with both IQ and autism severity as well as specific motor parameters, suggesting its potential as a clinical marker of motor–cognitive interaction in ASD. Full article

Biomimetic quadruped robots, inspired by the musculoskeletal systems of animals, employ pneumatic artificial muscles (PAMs) as compliant actuators to achieve flexible, efficient, and adaptive locomotion. This study focuses on a pneumatic artificial muscle (PAM)-driven biomimetic leg joints system. First, its kinematic and dynamic models are established. Next, to address the challenges posed by the strong nonlinearities and complex time-varying uncertainties inherent in PAMs, an adaptive robust control algorithm is proposed by employing the Udwadia controller. Rigorous theoretical analysis of the adaptive robust control algorithm is verified via the Lyapunov stability method. Finally, numerical simulations and hardware experiments are conducted on the PAM-driven biomimetic leg joints system under desired trajectories, where the adaptive robust control algorithm is systematically compared with three conventional control algorithm to evaluate its control performance. The experimental results show that the proposed controller achieves a maximum tracking error of within 0.05 rad for the hip joint and within 0.1 rad, highlighting its strong potential for practical deployment in real-world environments. Full article

This work presents the fabrication and a systematic evaluation of an ionic polymer-metal composite (IPMC) sensor, focusing on its potential for human motion monitoring and human–computer interaction. The sensor was fabricated via a solution casting and electroless plating process, and its morphology characterized using scanning electron microscopy. The sensing performance was comprehensively assessed, revealing high sensitivity (1.059 mV/N) in the low-pressure region, a fast response time (~50 ms), and reliable stability over prolonged cyclic testing. Furthermore, the sensor can respond to both the magnitude and rate of applied mechanical stimuli. To explore its application potential, the IPMC was tested in scenarios ranging from input pattern recognition—including distinguishing mouse-click patterns, handwritten letters, and binary-encoded presses—to human motion monitoring, where it effectively captured and differentiated signals from facial expressions, swallowing, breathing, and joint movements. The results suggest that the developed IPMC sensor is a promising candidate for applications in wearable health monitoring and flexible interactive systems. Full article

Metal-composite joints, leveraging the high specific strength/stiffness and superior fatigue resistance of carbon fiber reinforced polymers (CFRP) alongside metallic materials’ excellent toughness and formability, have become prevalent in aerospace structures. Fastener flexibility serves as a critical parameter governing load distribution prediction and fatigue life assessment, where accurate quantification directly impacts structural reliability. Current approaches face limitations: experimental methods require extended testing cycles, numerical simulations exhibit computational inefficiency, and conventional machine learning (ML) models suffer from “black-box” characteristics that obscure mechanical principle alignment, hindering aerospace implementation. This study proposes an integrated framework combining numerical simulation with explainable ML for fastener flexibility analysis. Initially, finite element modeling (FEM) constructs a dataset encompassing geometric features, material properties, and flexibility values. Subsequently, a random forest (RF) prediction model is developed with five-fold cross-validation and residual analysis ensuring accuracy. SHapley Additive exPlanations (SHAP) methodology then quantifies input features’ marginal contributions to flexibility predictions, with results interpreted in conjunction with theoretical flexibility formulas to elucidate key parameter influence mechanisms. The approach achieves 0.99 R2 accuracy and 0.11 s computation time while resolving explainability challenges, identifying fastener diameter-to-plate thickness ratio as the dominant driver with negligible temperature/preload effects, thereby providing a validated efficient solution for aerospace joint optimization. Full article

Flexible sensors have emerged as critical interfaces for information exchange between soft biological tissues and machines. Here, we present a dual-mode stretchable sensor system capable of synchronous strain and electromyography (EMG) signal detection, integrated with wireless WIFI transmission for real-time joint movement monitoring. The system consists of two key components: (1) A multi-channel gel electrode array for high-fidelity EMG signal acquisition from target muscle groups, and (2) a novel capacitive strain sensor made of stretchable micro-cracked gold film based on Styrene Ethylene Butylene Styrene (SEBS) that exhibits exceptional performance, including >80% stretchability, >4000-cycle durability, and fast response time (<100 ms). The strain sensor demonstrates position-independent measurement accuracy, enabling robust joint angle detection regardless of placement variations. Through synchronized mechanical deformation and electrophysiological monitoring, this platform provides comprehensive movement quantification, with data visualization interfaces compatible with mobile and desktop applications. The proposed technology establishes a generalizable framework for multimodal biosensing in human motion analysis, robotics, and human–machine interaction systems. Full article

In the field of earthquake-resistant design, there is an increasing emphasis on evaluating buildings as integrated systems rather than as assemblies of independent components. Hybrid wall systems based on structural steel and reinforced concrete offer a promising alternative to existing approaches by combining the stiffness and toughness of concrete with the ductility and flexibility of steel, which enhances resilience and seismic performance. The objective of this scientific study is to obtain preliminary analytical estimates of the earthquake response of a prototype hybrid steel RC coupled wall building that is being developed as part of a joint research program between the National Center for Research on Earthquake Engineering (NCREE) and New Zealand’s Centre for Earthquake Resilience (QuakeCoRE). Nonlinear response history analyses were carried out on the prototype building, using scaled ground motions and nonlinear hinge properties assigned to the primary lateral force resisting elements to replicate the expected inelastic behavior of the hybrid system. The results were used to evaluate story drift demands, deformation patterns, coupling beam behavior, and buckling restrained brace behavior, providing a system-level perspective on the expected earthquake performance of the proposed hybrid wall system. To deepen the current experimental understanding of the seismic behavior of the proposed hybrid structural system, a large-scale shaking table test is planned at NCREE as the next stage of this collaborative research. Full article

In a mother–child underwater bio-inspired robotic system, the equivalent lever arm between the master and slave inertial navigation systems (INSs) varies with launcher attitude changes and structural flexure. This time-varying lever arm introduces hard-to-model systematic velocity errors that degrade the accuracy and filter convergence of velocity difference-based transfer alignment. Traditional rigid body compensation relies on precise, constant lever-arm parameters and fails when booms, launch tubes, or flexible manipulators undergo appreciable deformation or reconfiguration. To address this, we augment a “velocity–attitude joint matching and innovation-based adaptive Kalman filter (AKF)” framework with an attention-based Long Short-Term Memory (LSTM) feed-forward module. Using only a short, real-time Inertial Measurement Unit (IMU) sequence from the slave INS, the module predicts and compensates the velocity bias induced by the lever arm. Numerical simulations of an underwater bio-inspired robot deployment scenario show that, under typical maneuvers (acceleration, turning, fin-flapping, and S-curve), the proposed method reduces the root-mean-square (RMS) misalignment angle error from about 14.5′ to 5.2′ and the RMS installation error angle from 8.8′ to 3.0′—average reductions of about 64% and 66%, respectively—substantially improving the robustness and practical applicability of transfer alignment under time-varying lever arms and flexible disturbances. Full article

Action quality assessment automates the evaluation of human movement proficiency, which is vital for applications like sports training and rehabilitation, where objective feedback enhances patient outcomes. Action quality assessment processes motion capture data to generate quality scores for action execution. In rehabilitation exercises, joints typically work synergistically in functional groups. However, existing methods struggle to accurately model the collaborative relationships between joints. Fixed joint grouping is not flexible enough, while fully adaptive grouping lacks the guidance of prior knowledge. In this paper, based on rehabilitation theory in clinical medicine, we propose a dynamic, motion-aware grouping strategy. A two-stream architecture independently processes joint position and orientation information. Fused features are adaptively clustered into 6 functional groups by a joint motion energy-driven learnable mask generator, and intra-group temporal modeling and inter-group spatial projection are achieved through two-stage attention interaction. Our method achieves competitive results and obtains the best scores on most exercises of KIMORE, while remaining comparable on UI-PRMD. Experimental results using the KIMORE dataset show that the model outperforms current methods by reducing the mean absolute deviation by 26.5%. Ablation studies validate the necessity of dynamic grouping and the two-stream design. The core design principles of this study can be extended to fine-grained action-understanding tasks such as surgical operation assessment and motor skill quantification. Full article

Driven by regulatory and environmental demands, composite structures must combine high structural performance, recyclability, and resource efficiency. Here, an investigation on the structural adhesive bonding of glass-fibre-reinforced thermoplastic Elium© composite laminates is undertaken. Substrates are manufactured using vacuum infusion. Evaluation is performed on the following three commercial two-component adhesives cured at RT: an epoxy (EP), a polyurethane (PU), and an acrylate system (AC). Based on Dynamic Mechanical Analysis, the glass transition temperatures of the EP, PU, and AC adhesives are 56.5, 102.9, and 111.9 °C, respectively. The AC adhesive exhibits the highest shear strength and displacement at failure, reflecting a superior load-bearing capacity. Fractographic analysis further supports these findings: AC joints show a mixed substrate/cohesive failure mode, while EP samples fail exclusively by adhesion failure and PU samples predominantly by a mixture of special cohesion, adhesion and substrate failure. Regarding processing, the EP samples show the highest pot life, followed by PU and then AC. Nonetheless, the pot life of the AC adhesive does not limit its range of application.. The results highlight the advantages of adhesive bonding of Elium© in enabling lightweight and more circular composites. RT-cured adhesives eliminate the need for drilling and energy-intensive thermal curing, allowing design flexibility and reductions in CO₂ footprint within composite production. Full article

Buildings account for nearly 32% of global energy consumption and serve as key demand-side flexibility resources in power systems with high renewable penetration. However, their utilization is constrained by the lack of an integrated framework that can jointly quantify energy-adjustable margin (BAM) and response duration (RD) under realistic operational and thermal comfort constraints. This study presents a coupled data–physical simulation framework integrating a Particle Swarm Optimization–Long Short-Term Memory–Random Forest (PSO-LSTM-RF) hybrid load forecasting model with EnergyPlus(24.1.0)-based building simulation. The PSO-LSTM-RF model achieves high-accuracy short-term load prediction, with an average R² of 0.985 and mean absolute percentage errors of 1.92–5.75%. Predicted load profiles are mapped to physically consistent baseline and demand-response scenarios using a similar-day matching mechanism, enabling joint quantification of BAM and RD under explicit thermal comfort constraints. Case studies on offices, shopping malls, and hotels reveal significant heterogeneity: hotels exhibit the largest BAM (up to 579.27 kWh) and longest RD (up to 135 min), shopping malls maintain stable high flexibility, and offices show moderate BAM with minimal operational disruption. The framework establishes a closed-loop link between data-driven prediction and physics-based simulation, providing interpretable flexibility indicators to support demand-response planning, virtual power plant aggregation, and coordinated optimization of source–grid–load interactions. Full article

The accurate prediction of crack initiation and propagation is essential for assessing the structural integrity of mechanically joined components and other complex assemblies. To overcome the limitations of existing finite element tools, a modular Python framework has been developed to automate three-dimensional crack growth simulations. The program combines geometric reconstruction, adaptive remeshing, and the numerical evaluation of fracture mechanics parameters within a single, fully automated workflow. The framework builds on open-source components and remains solver-independent, enabling straightforward integration with commercial or research finite element codes. A dedicated sequence of modules performs all required steps, from mesh separation and crack insertion to local submodeling, stress and displacement mapping, and iterative crack-front update, without manual interaction. The methodology was verified using a mini-compact tension (Mini-CT) specimen as a benchmark case. The numerical results demonstrate the accurate reproduction of stress intensity factors and energy release rates while achieving high computational efficiency through localized refinement. The developed approach provides a robust basis for crack growth simulations of geometrically complex or residual stress-affected structures. Its high degree of automation and flexibility makes it particularly suited for analyzing cracks in clinched and riveted joints, supporting the predictive design and durability assessment of joined lightweight structures. Full article

This paper designs a hardware-implementable joint denoising and demosaicing acceleration system. Firstly, a lightweight network architecture with multi-scale feature extraction based on partial convolution is proposed at the algorithm level. The partial convolution scheme can reduce the redundancy of filters and feature maps, thereby reducing memory accesses, and achieve excellent visual effects with a smaller model complexity. In addition, multi-scale extraction can expand the receptive field while reducing model parameters. Then, we apply separable convolution and partial convolution to reduce the parameters of the model. Compared with the standard convolutional solution, the parameters and MACs are reduced by 83.38% and 77.71%, respectively. Moreover, different networks bring different memory access and complex computing methods; thus, we introduce a unified and flexibly configurable hardware acceleration processing platform and implement it on the Xilinx Zynq UltraScale + FPGA board. Finally, compared with the state-of-the-art neural network solution on the Kodak24 set, the peak signal-to-noise ratio and the structural similarity index measure are approximately improved by 2.36dB and 0.0806, respectively, and the computing efficiency is improved by 2.09×. Furthermore, the hardware architecture supports multi-parallelism and can adapt to the different edge-embedded scenarios. Overall, the image processing task solution proposed in this paper has positive advantages in the joint denoising and demosaicing system. Full article

This study assessed the effect of slicer-made interlocking joints on 3D printed sandwich panels manufactured through fused filament fabrication (FFF) in terms of flexural properties and energy absorption. Composites were prepared with thermoplastic polyurethane (TPU) as the core material and polyamide (PA), polylactic acid (PLA), polyethylene terephthalate (PET) as skin materials for each of the three composites, respectively. In order to assess the implications of internal geometry, 3D printing was done on five infill topologies (Cross-3D, Grid, Gyroid, Line and Honeycomb) at 20% density. All samples had 20% core density and underwent three point bending testing for flexural testing. It was noted that the Grid and Gyroid cores had the best performance in terms of maximum load capacity based on stretch-dominated behavior while Cross-3D and Honeycomb had lower strengths but stable moments during the bending process. Since Cross-3D topology offered the lowest deflection, it was selected for further experiments with slicer added interlocks at the face–core interface. This study revealed the most notable improvements as gains of up to 15% in peak load, 48% in maximum deflection, and 51% in energy absorption compared with the non-interlocked designs. The PET/TPU interlocked demonstrated the best performance in terms of the energy absorption (2.45 J/mm³) and peak load (272.6 N). In contrast, the PA/TPU interlocked exhibited the best flexibility and ductility with a mid-span deformation of 21.34 mm. These results confirm that slicer-generated interlocking interfaces lead to better load capacity and energy dissipation, providing a lightweight, damage-tolerant design approach for additively manufactured sandwich beams. Full article

This paper investigates the behavior of PM-type polyurethane flexible joints connecting structural components. Although flexible polyurethanes are known for their energy dissipation capacity and ability to accommodate large deformations—particularly under seismic actions—research addressing their performance under shear loading remains limited. The primary objective of this work was to characterize these joints under varying levels of normal stress, identify failure modes, and estimate key mechanical parameters. Nine masonry triplet specimens, composed of concrete units and PM-type polyurethane, were subjected to shear testing using a procedure adapted from EN 1052-3. Tests were carried out at three precompression levels: 0.2, 0.6, and 1.0 N/mm². Tensile tests were further performed to calibrate material models. The results showed that increasing precompression led to higher ultimate shear loads. All specimens failed due to shear failure at the unit–flexible joint interface, with no damage observed in the masonry units. Based on linear regression following EN 1052-3, the initial shear strength was determined to be 0.729 N/mm², corresponding to a friction coefficient of 0.14. Full article