Search Results (207)

Muscle–joint flexibility is defined as the ability of a muscle to stretch in a controlled manner, allowing a wide range of movement at the joints. While numerous methodologies exist for improving flexibility, few studies have investigated the role of athletes’ perceptual processes and awareness related to their own body and movement control during such training. In this pilot study, we explored how two different training protocols—static and dynamic stretching (control group, CON) and multi-joint resistance training (experimental group, EXP)—influence both flexibility and psychophysical awareness, understood as a multidimensional construct involving perceived flexibility improvements, self-assessed control over exercise execution, and cognitive-emotional responses such as engagement, motivation, and satisfaction during physical effort. The study involved 24 male amateur track-and-field athletes (mean age 23 ± 2.5 years), randomized into two equal groups. Over 12 weeks, both groups trained three times per week. Flexibility was assessed using the Sit and Reach Test at three time points (pre-, mid-, and post-intervention). A 2 × 3 mixed ANOVA revealed a significant group × time interaction (F = 20.17, p < 0.001), with the EXP group showing greater improvements than the CON group. In the EXP group, Sit and Reach scores increased from pre = 28.55 cm (SD = 4.91) to mid = 29.39 cm (SD = 4.67) and post = 29.48 cm (SD = 4.91), with a significant difference between pre and post (p = 0.01; d = 0.35). The CON group showed minimal changes, with scores of pre = 28.66 cm (SD = 4.92), mid = 28.76 cm (SD = 5.03), and post = 28.84 cm (SD = 5.10), and no significant difference between pre and post (p = 0.20; d = 0.04). Psychophysical awareness was assessed using a custom questionnaire structured on a 5-point Likert scale, with items addressing perception of flexibility, motor control, and exercise-related bodily sensations. The questionnaire showed excellent internal consistency (Cronbach’s α = 0.92). Within the EXP group, psychophysical awareness increased significantly (from 3.50 to 4.17; p = 0.01; d = 0.38), while no significant change occurred in the CON group (p = 0.16). Post-hoc power analysis confirmed small to moderate effect sizes within the EXP group, although between-group differences lacked sufficient statistical power. These results suggest that resistance training may improve flexibility and concurrently enhance athletes’ psychophysical self-awareness more effectively than traditional stretching. Such findings offer practical implications for coaches seeking to optimize flexibility training by integrating alternative methods that promote both physical and perceptual adaptations. Full article

In hydropower tunnel systems, unlined pressurized tunnels in competent rock are commonly used for cost-effective construction. Incorporating pressurized sand traps at the downstream end of these tunnels can increase plant capacity and improve energy efficiency. The present work focuses on optimizing the performance of existing pressurized sand traps. Hydraulic scale models were developed and tested at the Hydraulic Laboratory of NTNU, Within the 960 MW Tonstad Hydropower Plant in southern Norway as a case study. This study compares 1:1 velocity/sediment scaling with Froude scaling through physical experiments, analyzing velocity profiles via Particle Image Velocimetry (PIV) and sediment trap efficiency. Results show that Froude scaling, combined with geometric sediment scaling, provides superior accuracy in trap efficiency scaling across varying factors. However, in many practical hydropower applications, the large scaling factor required for laboratory models results in very small model sediments, leading to cohesion limitations. In such cases, Froude scaling may not be feasible. The 1:1 scaling method provides a conservative alternative. Hence, for practical applications, 1:1 scaling may be more cost-effective and sufficient for designing pressurized sand traps. This study emphasizes the importance of accounting for unscaled parameters and flow phenomena in hydraulic model design. Full article

The widespread use of WiFi Internet-of-Things (IoT) devices has rendered them valuable tools for detecting information about the physical environment. Recent studies have demonstrated that WiFi Channel State Information (CSI) can detect physical events like movement, occupancy increases, and gestures. This paper empirically investigates the conditions under which WiFi sensing technology remains effective for keystroke detection. To achieve this timely goal of assessing whether it can raise any privacy concerns, experiments are conducted using commodity hardware to predict the accuracy of WiFi CSI in detecting keys pressed on a keyboard. Our novel results show that, in an ideal setting with a robotic arm, the position of a specific key can be predicted with $99\%$ accuracy using a simple machine learning classifier. Furthermore, human finger localisation over a key and actual key-press recognition is also successfully achieved, with $94\%$ and $89\%$ reduced accuracy values, respectively. Moreover, our detailed investigation reveals that to ensure high accuracy, the gap distance between each test object must be substantial, while the size of the test group should be limited. Finally, we show WiFi sensing technology has limitations in small-scale gesture recognition for generic settings where proper device positioning is crucial. Specifically, detecting keyed words achieves an overall accuracy of $94\%$ for the forefinger and $87\%$ for multiple fingers when only the right hand is used. Accuracy drops to $56\%$ when using both hands. We conclude WiFi sensing is effective in controlled indoor environments, but it has limitations due to the device location and the limited granularity of sensing objects. Full article

Antioxidant gel foams are promising materials for coal mine fire prevention due to their unique physicochemical properties. To address the limitations of conventional suppression methods under high-temperature conditions, this study investigates a newly developed antioxidant gel foam and its mechanism in inhibiting coal spontaneous combustion. A novel antioxidant gel foam was formulated by incorporating TBHQ and modified montmorillonite into a sodium alginate-based gel system. This formulation enhances the thermal stability, water retention, and free radical scavenging capacity of the gel. This study uniquely combines multi-scale experimental methods to evaluate the performance of this material in coal fire suppression. Multi-scale experiments, including FTIR, leakage air testing, programmed temperature rise, and small-scale fire extinction, were conducted to evaluate its performance. Experimental results indicate that the antioxidant gel foam exhibits excellent thermal stability in the temperature range of 200–500 °C. Its relatively high decomposition temperature enables it to effectively resist structural damage in high-temperature environments. During thermal decomposition, the gel releases only a small amount of gas, while maintaining the integrity of its internal micro-porous structure. This characteristic significantly delays the kinetics of coal oxidation reactions. Further research revealed that the spontaneous combustion ignition temperature of coal samples treated with the gel was significantly higher, and the oxygen consumption rate during spontaneous combustion was significantly reduced, indicating that the gel not only effectively suppressed the acceleration of the combustion reaction but also significantly reduced the release of harmful gases such as HCl. Scanning electron microscope analysis confirmed that the gel maintained a good physical structure under high temperatures, forming an effective oxygen barrier, which further enhanced the suppression of coal spontaneous combustion. These findings provide important theoretical and practical guidance for the application of antioxidant gel foams in coal mine fire prevention and control, confirming that this material has great potential in coal mine fire safety, offering a new technological approach to improve coal mine safety. Full article

This article presents a novel off-cloud anchor sharing framework designed to enable seamless device interoperability for Mixed Reality (MR) multi-user and multi-platform applications. The proposed framework enables local storage and synchronization of spatial anchors, offering a robust and autonomous alternative for real-time collaborative experiences. Such anchors are digital reference points tied to specific positions in the physical world that allow virtual content in MR applications to remain accurately aligned to the real environment, thus being an essential tool for building collaborative MR experiences. This anchor synchronization system takes advantage of the use of local anchor storage to optimize the sharing process and to exchange the anchors only when necessary. The framework integrates Unity, Mirror and Mixed Reality Toolkit (MRTK) to support seamless interoperability between Microsoft HoloLens 2 devices and desktop computers, with the addition of external IoT interaction. As a proof of concept, a collaborative multiplayer game was developed to illustrate the multi-platform and anchor sharing capabilities of the proposed system. The experiments were performed in Local Area Network (LAN) and Wide Area Network (WAN) environments, and they highlight the importance of efficient anchor management in large-scale MR environments and demonstrate the effectiveness of the system in handling anchor transmission across varying levels of spatial complexity. Specifically, the obtained results show that the developed framework is able to obtain anchor transmission times that start around 12.7 s for the tested LAN/WAN networks and for small anchor setups, and to roughly 86.02–87.18 s for complex physical scenarios where room-sized anchors are required. Full article

Advancements in semiconductor packaging toward higher integration and interconnect density have increased the risk of structural defects—such as missing solder balls, pad delamination, and bridging—that can disrupt thermal conduction paths, leading to localized overheating and potential chip failure. To address the limitations of traditional non-destructive testing methods in detecting micron-scale defects, this study introduces a multimodal detection approach combining finite-element thermal simulation, infrared thermography, and the YOLO11 deep learning network. A comprehensive 3D finite-element model of a ball grid array (BGA) package was developed to analyze the impact of typical defects on both steady-state and transient thermal distributions, providing a solid physical foundation for modeling defect-induced thermal characteristics. An infrared thermal imaging platform was established to capture real thermal images, which were then compared with simulation results to verify physical consistency. An integrated dataset of simulated and infrared images was constructed to enhance the robustness of the detection model. Leveraging the YOLO11 network’s capabilities in end-to-end training, dataset small-object detection, and rapid inference, the system achieved accurate and rapid localization of defect regions. Experimental results show a mean average precision (mAP) of 99.5% at an intersection over union (IoU) threshold of 0.5 and an inference speed of 556 frames per second on the simulation dataset. Training with the hybrid dataset improved detection accuracy on real images from 41.7% to 91.7%, significantly outperforming models trained on a single data source. Furthermore, the maximum temperature discrepancy between simulation and experimental measurements was less than 5%, validating the reliability of the proposed method. This research offers a high-precision, real-time solution for semiconductor packaging defect detection, with substantial potential for industrial application. Full article

Additive Manufacturing (AM), as a revolutionary breakthrough in advanced manufacturing paradigms, leverages its unique layer-by-layer construction advantage to exhibit significant technological superiority in the fabrication of complex structural components for aerospace, biomedical, and other fields. However, when addressing industrial-grade precision manufacturing requirements, key challenges such as the multi-scale characteristics of surface damage precursors, interference from background noise, and the scarcity of high-quality training samples severely constrain the intelligent transformation of AM quality monitoring systems. This study proposes an innovative microscopic polarization YOLOv11-LSF intelligent inspection framework, which establishes an automated non-destructive testing methodology for AM device micro-nano damage precursors through triple technological innovations, effectively breaking through existing technical bottlenecks. Firstly, a multi-scale perception module is constructed based on the Large Separable Kernel Attention mechanism, significantly enhancing the network’s feature detection capability in complex industrial scenarios. Secondly, the cross-level local network VoV-GSCSP module is designed utilizing GSConv and a one-time aggregation method, resulting in a Slim-neck architecture that significantly reduces model complexity without compromising accuracy. Thirdly, an innovative simulation strategy incorporating physical features for damage precursors is proposed, constructing a virtual and real integrated training sample library and breaking away from traditional deep learning reliance on large-scale labeled data. Experimental results demonstrate that compared to the baseline model, the accuracy (P) of the YOLOv11-LSF model is increased by 1.6%, recall (R) by 1.6%, mAP50 by 1.5%, and mAP50-95 by 2.8%. The model hits an impressive detection accuracy of 99% for porosity-related micro-nano damage precursors and remains at 94% for cracks. Its unique small sample adaptation capability and robustness under complex conditions provide a reliable technical solution for industrial-grade AM quality monitoring. This research advances smart manufacturing quality innovation and enables cross-scale micro-nano damage inspection in advanced manufacturing. Full article

We present a two-scale physics-informed neural network (TSPINN) algorithm to address structural parameter inversion problems involving small parameters. The algorithm’s core mechanism directly embeds small parameters into the neural network architecture. By constructing a two-scale neural network architecture, this approach enables the simultaneous analysis of structural dynamic responses and local parameter perturbation effects, which effectively addresses challenges posed by high-frequency oscillations and parameter sensitivity. Numerical experiments demonstrate that TSPINNs significantly improve prediction accuracy and convergence speed compared to conventional physics-informed neural networks (PINNs) and maintain robustness in high-stiffness scenarios. The T-shaped tower shaking table test results confirm that the model’s identification errors for stiffness reduction coefficients and mass parameters remain below 10% under lower noisy conditions, demonstrating high precision and strong generalization capability for multi-damage scenarios and random load excitations. Full article

Parkinson’s disease (PD) is the second-most common neurodegenerative disease. With more than 20,000 new diagnosed cases each year, PD affects millions of individuals worldwide and is most prevalent in the elderly population. The current clinical methods for the diagnosis and severity assessment of PD rely on the visual and physical examination of subjects and identifying key disease motor signs and symptoms such as bradykinesia, rigidity, tremor, and postural instability. In the present study, we developed a method for the diagnosis and severity assessment of PD using Gramian Angular Fields (GAFs) in combination with deep Convolutional Neural Networks (CNNs). Our model was applied to PD gait signals captured using pressure sensors embedded into insoles. Our results indicated an accuracy of 98.6%, a true positive rate (TPR) of 99.2%, and a true negative rate (TNR) of 98.5%, showcasing superior classification performance for PD diagnosis compared to the methods used in recent studies in the literature. The estimation of disease severity scores using gait signals showed a high accuracy for the Hoehn and Yahr score as well as the Timed Up and Go (TUG) test score (R² > 0.8), while we achieved a lower prediction performance for the Unified Parkinson’s Disease Rating Scale (UPDRS) and its motor component (UPDRSM) scores (R² < 0.2). These results were achieved using gait signals recorded in time windows as small as 10 s, which may pave the way for shorter, more accessible assessment tools for diagnosis and severity assessment of PD. Full article

In recent years, the twin-barge float-over method has been widely used in offshore installations. This paper conducts numerical simulation and experimental research on the twin-barge float-over installation of offshore wind turbines (TBFOI-OWTs), focusing primarily on seakeeping performance, and also explores the influence of the gap distance on the hydrodynamic behavior of TBFOI-OWTs. Model tests are conducted in the ocean basin at Tsinghua Shenzhen International Graduate School. A physical model with a scale ratio of 1:50 is designed and fabricated, comprising two barges, a truss carriage frame, two small wind turbines, and a spread catenary mooring system. A series of model tests, including free decay tests, regular wave tests, and random wave tests, are carried out to investigate the hydrodynamics of TBFOI-OWTs. The experimental results and the numerical results are in good agreement, thereby validating the accuracy of the numerical simulation method. The motion RAOs of TBFOI-OWTs are small, demonstrating their good seakeeping performance. Compared with the regular wave situation, the surge and sway motions in random waves have greater ranges and amplitudes. This reveals that the mooring analysis cannot depend on regular waves only, and more importantly, that the random nature of realistic waves is less favorable for float-over installations. The responses in random waves are primarily controlled by motions’ natural frequencies and incident wave frequency. It is also revealed that the distance between two barges has a significant influence on the motion RAOs in beam seas. Within a certain range of incident wave periods (10.00 s < T < 15.00 s), increasing the gap distance reduces the sway RAO and roll RAO due to the energy dissipated by the damping pool of the barge gap. For installation safety within an operating window, it is meaningful but challenging to have accurate predictions of the forthcoming motions. For this, this study employs the Whale Optimization Algorithm (WOA) to optimize the Long Short-Term Memory (LSTM) neural network. Both the stepwise iterative model and the direct multi-step model of LSTM achieve a high accuracy of predicted heave motions. This study, to some extent, affirms the feasibility of float-over installation in the offshore wind power industry and provides a useful scheme for short-term predictions of motions. Full article

In order to improve the perception accuracy of the apple tree fruit recognition model and to reduce the model size, a lightweight apple target recognition method based on an improved YOLOv5s artificial intelligence algorithm was proposed, and relevant experiments were designed. The Depthwise Separable Convolution (DWConv) module has many advantages: (1) It has high computational efficiency, reducing the number of parameters and calculations in the model; (2) It makes the model lightweight and easy to deploy in hardware; (3) DWConv can be combined with other modules to enhance the multi-scale feature extraction capability of the detection network and improve the ability to capture multi-scale information; (4) It balances the detection accuracy and speed of the model; (5) DWConv can flexibly adapt to different network structures. Because of its efficient computing modes, lightweight design, and flexible structural adaptation, the DWConv module has significant advantages in multi-scale feature extraction, real-time performance improvement, and small-object detection. Therefore, this method improves the original YOLOv5s network architecture by replacing the embedded Depthwise Separable Convolution in its Backbone network, which reduces the size and parameter count of the model while ensuring detection accuracy. The experimental results show that for the test-set images, the proposed improved model has an average recognition accuracy of 92.3% for apple targets, a recognition time of 0.033 s for a single image, and a model volume of 11.1 MB. Compared with the original YOLOv5s model, the average recognition accuracy was increased by 0.8%, the recognition speed was increased by 23.3%, and the model volume was compressed by 20.7%, effectively achieving lightweight improvement of the apple detection model and improving the accuracy and speed of detection. The detection algorithm proposed in the study can be extended to the intelligent measurement of apple biological and physical characteristics, including for size measurement, shape analysis, and color analysis. The proposed method can improve the intelligence level of orchard management and horticultural technology, reduce labor costs, assist precision agriculture technology, and promote the transformation of the horticultural industry toward sustainable development. Full article

Background: Disadvantaged children often enter kindergarten with delays in fundamental motor skill (FMS) competence, which is critical for future physical activity engagement. The Summer Success—Successful Kinesthetic Instruction for Preschoolers (SS-SKIP) program was designed to address these developmental gaps, with a short, intensive intervention. This pilot study evaluated the impact of a 4-week SS-SKIP program on FMS, perceived motor competence (PMC), and executive function (EF). Methods: Twenty-one preschool children (mean age = 62.62 ± 4.61 months) from disadvantaged communities participated in an intensive, month-long (240 min) program. FMS were assessed using the Test of Gross Motor Development-2 (TGMD-2), PMC was evaluated using the Pictorial Scale for Perceived Competence, and EF was measured via the Head–Toes–Knees–Shoulders (HTKS), Go/No-Go, and Sorting cards tests. Standing long jump was measured in meters. A pretest–post-test design assessed program impact using 2 Gender X 2 Session MANOVAs/ANOVAs on dependent variables. Results: Analysis of differences in baseline measures of FMS competence and EF by Gender and Session revealed no significant main effects of Gender, Session, or their interaction across measures (all p > 0.05). Repeated measures ANOVAs by Gender revealed a significant main effect for Time for locomotor standard scores (p < 0.001), object control standard scores (p < 0.001), and HTKS scores (p < 0.001), indicating improvement from pretest to post-test. By contrast, jump distance, PMC, Go/No-Go and Card Sorting scores were non-significant (p > 0.05). Conclusions: A short, intense SS-SKIP FMS intervention significantly enhanced FMS and improved HTKS performance. This pilot study was limited by the lack of a control group and small N. These findings underscore the potential of short, targeted interventions in addressing early motor delays in disadvantaged preschoolers, warranting further investigation into their long-term impacts. Full article

Cyber-physical systems need more intelligent decision-making methods. To address this issue with respect to incomplete process models and inefficient scheduling, we have previously proposed a new method called Petri-nets-adaptive ant colony optimization (PN-AACO). This method targets small-scale job shops with shared resource limits. These shops require symmetric job designs for resource sharing but have asymmetric job processing times. PN-AACO uses Petri net symmetry at edge nodes but faces a problem. Its marking–transition pheromone index mechanism causes state space explosion from Petri nets. This leads to a decrease in the computational speed of the algorithm in the face of an increase in scale or state, which results in a longer overall manufacturing process time that impacts productivity. Thus, we propose the improved PN-AACO (iPN-AACO). The improved method uses transition–transition pheromone recording to control pheromone amounts. It also adds pheromone-based initial selection and best-known-paths-based probability rules. Tests show this approach speeds up computations up to 92% in more-states models while keeping scheduling effective. Full article

The article presents the experimental and computational investigations on carbon-reinforced concrete (CRC) slabs with hollow-core cross-sections. Designed for use in building construction, they combine the benefits of lightweight construction, resource efficiency, and precise prefabrication. Three geometrically identical elements were manufactured and tested until failure in four-point bending tests. The slabs demonstrated a high load capacity of around 50 kNm, together with high ductility due to a deformation of more than 80 mm before failure. The load-deflection curves recorded could be reproduced very well with the analytical-physical calculation model created for both the non-cracked and cracked slab states. The strengths and stiffnesses of the materials used for input were derived from small-scale, accompanying material tests. As a result, the calculation model was ultimately used to design the carbon-reinforced ceilings of the CRC technology demonstration house CUBE, which was finished in 2022 in Dresden, East Germany. Full article

Oil sands tailings consist of a combination of sand, fine particles, water, and residual unextracted bitumen in varying ratios. The management of these mine waste tailings is largely influenced by their consolidation behavior. Large strain consolidation testing, such as the multi-step large strain consolidation (MLSC) test, is commonly used to determine consolidation properties but requires considerable time. A benchtop centrifuge (BTC) apparatus was proposed to derive the consolidation parameters of the following three clay-rich oil sands tailings slurries: two samples of high-plasticity fluid fine tailings (FFT) and one of low-plasticity FFT. Comparison with the MLSC tests illustrates that the BTC-derived compressibility data closely matched the MLSC test’s compressibility curve within the BTC stress range. However, the hydraulic conductivity from the BTC test was an order of magnitude higher than that from the MLSC test. The consistency of the BTC method and the validation of scaling laws were confirmed through modeling-of-models tests, showing a consistent average void ratio regardless of the specimen height or gravity scale. The influence of the small radius of the BTC was found to be minimal. The limitations of the BTC in the physical modeling of the consolidation behavior are discussed and their impact on the interpretation of the observed consolidation behavior is addressed. Overall, the BTC test provides a rapid method to gain insight on high-water-content slurries’ large strain consolidation behavior. Full article