Search Results (2,245)

Background: Visual impairment significantly affects balance and motor skills in children, often leading to postural instability and locomotor difficulties, thereby affecting lifestyle and general health. The aim of this study was to assess balance level and fundamental motor skills in the locomotion of youth with regard to their level of visual impairment. Methods: The pilot study included 25 physically active young people with visual impairments, divided into three groups (B1, B2, B3) based on the severity of impairment. Balance was assessed using the AMTI AccuSway platform, both with eyes open and eyes closed. Locomotor skills were evaluated using the TGMD-3. Statistical analysis involved Kruskal–Wallis, ANOVA, and correlation tests. Results: Significant differences in balance were found between the B1 and B2 groups, with poorer balance in individuals with more severe visual impairments, particularly in static conditions. No significant differences in locomotor skills were observed between groups. However, girls performed better in balance tests, particularly with eyes closed. A positive correlation was found between balance and locomotor skills in the B2 and B3 groups. Conclusions: Visual impairment negatively impacts balance, particularly in individuals with more severe impairments. However, no significant differences were found in locomotor skills between the groups. Regular physical activity supports motor development. Targeted interventions are necessary to improve balance and locomotor skills, especially in children with more severe visual impairments. Full article

In complex indoor and outdoor scenarios, traditional GPS-based ranging technology faces limitations in availability due to signal occlusion and user privacy issues. Wireless signal ranging technology based on 5G base stations has emerged as a potential alternative. However, existing methods are limited by low efficiency in constructing static signal databases, poor environmental adaptability, and high resource overhead, restricting their practical application. This paper proposes a 5G wireless signal ranging framework that integrates mobile edge computing (MEC) and crowdsourced intelligence to systematically address the aforementioned issues. This study designs a progressive solution by (1) building a crowdsourced data collection network, using mobile terminals equipped with GPS technology to automatically collect device signal features, replacing inefficient manual drive tests; (2) developing a progressive signal update algorithm that integrates real-time crowdsourced data and historical signals to optimize the signal fingerprint database in dynamic environments; (3) establishing an edge service architecture to offload signal matching and trajectory estimation tasks to MEC nodes, using lightweight computing engines to reduce the load on the core network. Experimental results demonstrate a mean positioning error of 5 m, with 95% of devices achieving errors within 10 m, as well as building and floor prediction error rates of 0.5% and 1%, respectively. The proposed framework outperforms traditional static methods by 3× in ranging accuracy while maintaining computational efficiency, achieving significant improvements in environmental adaptability and service scalability. Full article

For the guide vane adjusting mechanism, precision represents the primary design requirement. Meanwhile, due to the presence of aerodynamic loads under actual operating conditions, stagnation forces emerge that affect the mechanism motion characteristics, including the response speed and precision. This paper establishes kinematic and static analysis models of the guide vane adjusting mechanism through analytical modeling methods, investigates analytical approaches for mechanism adjustment precision and stagnation force, and conducts error and sensitivity analyses of the mechanism parameters based on these analytical models. Building upon this foundation, an optimization design method integrating adjustment precision and force transmission performance is proposed using a multi-objective genetic algorithm. Optimizing the critical design parameters, such as the mechanism dimensions and positions, can enhance both the adjustment precision and force transmission performance. Through case studies, significant reductions in motion precision errors and the peak stagnation force and maximum differences in stagnation force were achieved, validating the feasibility of this optimization design approach. Full article

Phishing remains a persistent cybersecurity threat, often bypassing traditional detection methods due to evolving attack techniques. This study presents a Reinforcement Learning (RL)-based phishing detection framework, leveraging a Deep Q-Network (DQN) to enhance detection accuracy, reduce false positives, and improve classification performance. The model was trained and evaluated using a real-world dataset comprising 5000 emails (2500 phishing and 2500 benign) and externally validated against a synthetic phishing dataset of 1000 samples simulating unseen attacks. It achieved a 95% accuracy, 96% precision, 94% recall, and a 2% false positive rate on the real-world dataset and a 93% accuracy, 94% precision, and a 4% false positive rate on the synthetic dataset. Area Under the Curve (AUC) analysis yielded a score of 0.92, confirming excellent classification separability and alignment with the model’s high accuracy and low false positive rate. This work contributes to scalable, real-world phishing defense by addressing the limitations of static detection systems and improving detection reliability. Full article

Breast cancer remains the most common malignancy in women, yet, many patients fail to achieve full remission despite significant advancements. This is largely due to tumour heterogeneity and the limitations of current experimental models in accurately replicating the complexity of in vivo tumour environment. In this study, we present a compartmentalised alginate hydrogel platform as an innovative in vitro tool for three-dimensional breast cancer cell culture. To mimic the heterogeneity of tumour tissues, we developed a core–shell structure (3.5% alginate core and 2% alginate shell) that mimic the stiffer, denser internal tumour matrix. The human triple-negative breast cancer cell line (MDA-MB-231) was embedded in core–shell alginate gels to assess viability, proliferation and hypoxic activity. Over one week, good cells proliferation and viability was observed, especially in the softer shell. Interestingly, cells within the stiffer core were more positive to hypoxic marker expression (HIF-1α) than those embedded in the shell, confirming the presence of a hypoxic niche, as observed in vivo. When cultured in the MIVO^® milli fluidic organ-on-chip resembling the physiological fluid flow conditions, cancer cells viability became comparable between core and shell hydrogel area, emphasising the importance of the fluid flow in nutrients diffusion within three-dimensional matrixes. Cisplatin chemotherapy treatment further highlighted these differences: under static conditions, cancer cell death was prominent in the softer shell, whereas cells in the stiffer core remained resistant to cisplatin. Conversely, drug diffusion was more homogeneous in the core–shell structured treated in the organ-on-chip, leading to a uniform reduction in cell viability. These findings suggest that integrating a compartmentalised core–shell cell laden alginate model with the millifluidic organ on chip offers a more physiologically relevant experimental approach to deepening cancer cell behaviour and drug response. Full article

The free-form single-layer reticulated shell structure has the characteristics of complex shape, a high degree of static indeterminacy, and difficult node positioning in the construction process, and the nodal deviations that may occur in the construction stage have a significant impact on the reliability performance of the structure. In order to evaluate the influence of the nodal deviation on the reliability performance of the structure in the process of shape optimization, this paper takes the free-form surface of the rectangular plane as the initial structure. Shape optimization is carried out with the objective function of minimizing the strain energy under the uniform vertical load, and the influence of the nodal deviation on the reliability performance of the optimized structure is performed by analyzing changes in the structural response’s probability density function (PDF). The elastic modulus, yield strength, and nodal deviation of the material were selected as the basic random variables, and the PDF of the structural response was calculated using the probability density evolution method. In the case of considering and ignoring the nodal deviation, respectively, the PDF of the maximum displacement response of the structure under the same iteration step is calculated and compared. The results indicate that compared with the initial structure, the reliability performance of the optimized structure is significantly less sensitive to node deviations. Full article

The Risley prism system offers advantages such as compact structure and excellent dynamic performance, making it suitable for installation on static and motion platforms for target acquisition, aiming, and tracking. This paper presents a strapdown line-of-sight (LOS) stabilization method for the Risley prism system on motion platforms. The method establishes the coordinate transformation between the Risley prism and the motion platform. Real-time platform attitude angles from an inertial measurement unit (IMU) are used to compute the direction cosine matrix, which, combined with the coordinate transformation, determines the target’s actual guided position in the Risley prism’s coordinate. The Risley prism’s rotational angles are then calculated based on the target’s actual guided position to ensure LOS stability and capture the target. After LOS stabilization, an image-based closed-loop tracking cascade control system that integrates a Risley prism and a fast steering mirror with a single image detector (IBCLTCR-F), is used to enable fast and high-precision target tracking. Experimental results demonstrate that the proposed method achieves disturbance rejection of −32.8 dB, −28.8 dB, and −17.3 dB for platform disturbances at 0.05 Hz, 0.2 Hz, and 0.5 Hz, respectively. Furthermore, compared to the Risley prism system, the IBCLTCR-F system improves the dynamic response capability of target tracking in the nonlinear region by a factor of 10 and reduces the tracking error by 70%. Full article

To study the response of a flexible offshore floating photovoltaic (FPV) array under waves and a current, a numerical model is established using OrcaFlex. The effects of different waves and currents, as well as their coupled effects on the motion response of the offshore PFV array and the tension in the connectors and moorings under different static tensions, are investigated. Differences are illustrated between the responses of the buoys at different positions and under different moorings under the wave. With the relaxed moorings, the surge response of the buoy facing the wave increased by 159.3% compared with the buoy facing away from the wave. The current causes the overall drift of the array, which greatly influences the buoys facing the current. The mooring tension facing the wave restricts the motion of the buoys under the same direction as the wave and current, which shows that the trend of the buoys’ responses with the wave decreases with the increase in the current velocity, as the pitch reduces to 76.9% under relaxed moorings. There is a significant difference between the results obtained by the superposition summation wave and current loads and the ones of the combined wave–current. With the increase in the wave–current angle, the response is increased by 348.2% as the constraint of the moorings and the connectors is weakened. Full article

Bridges are critical nodes in transportation networks, and the evaluation of their service performance is of vital importance. Rapid assessment techniques based on the theory of influence lines have become a significant research topic. This study proposes a rapid testing method for the influence lines of beam-type bridges, with the synchronous monitoring of dynamic vehicle positions and a wireless network of multiple sensors. Field testing on a 30 m span T-beam bridge revealed that the measured vertical displacement during slow continuous driving corresponded with the static load test data within a deviation of ±6%, with the entire testing process completed in only 5 min, demonstrating efficiency and minimal traffic interference. Based on the measured influence lines, rapid bridge bearing capacity assessments and finite element model updating were researched. A case study of a simply supported T-beam bridge composed of prefabricated prestressed concrete showed that the calculated values using the proposed rapid assessment method deviated from traditional load test values between −5.68% and 4.69%, indicating a small error margin. After applying this method to the model updating of a (25 + 45 + 25) m continuous beam bridge on a highway, the inversion errors of the concrete elastic modulus and prestress were 1.40% and 1.20%, respectively, confirming the reliability of the precision. The rapid testing method for influence lines can be applied to bridge inspection, evaluation, and model updating. Full article

Relational governance, as a flexible and informal mechanism, plays a critical role in addressing the challenges of construction projects in the VUCA environment. However, existing research has rarely examined relational governance mechanisms from a resilience perspective. Furthermore, current studies on organizational resilience often overlook static characteristics, leaving the interplay between static and dynamic resilience underexplored. To address this gap, this study aims to explore how relational governance influences project performance through organizational resilience, with a focus on the mediating roles of static resilience and dynamic resilience. Empirical data were collected through 270 construction professionals, with questionnaire design refined through expert interviews and a pilot study to ensure validity and reliability. Partial least squares structural equation modeling (PLS-SEM) was employed to assess relationships. The findings indicate that relational governance positively influences addressing opportunistic behaviors of construction participants and improving project performance. Mutual trust and timely commitment among project participants in relational governance significantly impact both the static characteristics and dynamic capabilities of construction projects. Additionally, organizational resilience partially mediates the relationship between relational governance and project performance. This study advances the understanding of the mechanisms linking relational governance and project performance from a resilience perspective and provides actionable insights for fostering efficient governance practices. Full article

This paper presents an enhanced bipolar control strategy for 400 Hz three-phase inverters in aviation ground power supplies, with a focus on maintaining symmetry in power output under unbalanced load conditions. The strategy integrates Linear Active Disturbance Rejection Control (LADRC) for robust positive sequence voltage regulation, Proportional Integral with repetitive control (PI + RC) for harmonic suppression in positive sequence currents, and a Quasi-Proportional Resonance (QPR) controller for negative sequence components in the static coordinate system. By doing so, it simplifies negative sequence control and combines PI + RC to improve the dynamic response and eliminate periodic errors. In the context of symmetry, the proposed strategy effectively reduces the total harmonic distortion (THD) and the three-phase current imbalance degree. Simulation results show significant improvements: under balanced loads, THD is reduced by 41.5% (from 1.95% to 1.14%) compared to traditional PI control; under single-phase and three-phase unbalanced loads, THD decreases by 52.7% (2.56% to 1.21%) and 48.1% (2.39% to 1.24%), respectively. The system’s settling time during load transients is shortened by over 30%, and the three-phase current imbalance degree is reduced by 60–70%, which validates the strategy’s effectiveness in enhancing power quality and system stability, thus restoring and maintaining the symmetry of the power output. Full article

Characterizing imaging performance requires a multidisciplinary approach that evaluates various interconnected parameters, including dosage optimization and dynamic accuracy. Radiation dose and dynamic accuracy are challenged by patient motion that results in poor image quality. These challenges are more prevalent in the brain/cardiac pediatric patient imaging, as they relate to excess radiation dose that may be associated with various complications. Scanning vulnerable pediatric patients ought to eliminate anesthesia due to critical risks associated in some cases with intracranial hemorrhages, brain strokes, and congenital heart disease. Some pediatric imaging, however, requires prolonged scanning under anesthesia. It can often be a laborious, suboptimal process, with limited field of view and considerable dose. High dynamic accuracy is also necessary to diagnose tissue’s dynamic behavior beyond its static structural morphology. This study presents several performance characterization experiments from a new robotic multimodal imaging system using specially designed calibration methods at different system configurations. Additional musculoskeletal imaging and imaging from a pediatric brain stroke patient without anesthesia are presented for comparisons. The findings suggest that the system’s large dynamically controlled gantry enables scanning at full patient movement and with important improvements in scan times, accuracy, radiation dose, and the ability to image brain structures without anesthesia. This could position the system as a potential transformative tool in the pediatric interventional imaging landscape. Full article

Smart guidance systems in museums and galleries are now essential for delivering quality user experiences. Visually impaired visitors face significant barriers when navigating galleries due to existing smart guidance systems’ dependence on visual cues like QR codes, manual numbering, or static beacon positioning. These traditional methods often fail to provide adaptive navigation and meaningful content delivery tailored to their needs. In this paper, we propose a novel Mobile-AI-based Smart Docent System that seamlessly integrates real-time navigation and depth of guide services to enrich gallery experiences for visually impaired users. Our system leverages camera-based on-device processing and adaptive BLE-based localization to ensure accurate path guidance and real-time obstacle avoidance. An on-device object detection model reduces delays from large visual data processing, while BLE beacons, fixed across the gallery, dynamically update location IDs for better accuracy. The system further refines positioning by analyzing movement history and direction to minimize navigation errors. By intelligently modulating audio content based on user movement—whether passing by, approaching for more details, or leaving mid-description—the system offers personalized, context-sensitive interpretations while eliminating unnecessary audio clutter. Experimental validation conducted in an authentic gallery environment yielded empirical evidence of user satisfaction, affirming the efficacy of our methodological approach in facilitating enhanced navigational experiences for visually impaired individuals. These findings substantiate the system’s capacity to enable more autonomous, secure, and enriched cultural engagement for visually impaired individuals within complex indoor environments. Full article

Aiming to solve the difficult problem of the miniaturization of servo valves, this paper designs a subminiature two-dimensional (2D) electro-hydraulic servo valve, which realizes the integration of the pilot stage and the power stage and significantly improves the work-to-weight ratio. Meanwhile, a high-power-density brushless DC motor (BLDC) is adopted as the electro-mechanical converter to further reduce the volume and mass. Firstly, the structure and working principle of the two-dimensional (2D) servo valve are described, and the mathematical model of the electro-mechanical converter is established. Aiming at the special working condition of the electro-mechanical converter with high-frequency oscillation at a small turning angle, this paper designs a position–current double closed-loop PID control algorithm based on the framework of the vector control algorithm (FOC). At the same time, the current feedforward compensation technique is included to cope with the high-frequency nonlinear disturbance problem of the electro-mechanical converter. The stability conditions of the electro-mechanical converter and the main valve were established based on the Routh–Hurwitz criterion, and the effects of the control algorithm of the electro-mechanical converter and the main parameters of the main valve on the stability of the system were analyzed. The dynamic and static characteristics of the 2D valve were simulated and analyzed by establishing a joint simulation model in Matlab/Simulink and AMESim. The prototype was fabricated, and the experimental bench was built; the size of the experimental prototype was 31.7 mm × 29.3 mm × 31 mm, and its mass was 73 g. Under a system pressure of 7 MPa, the flow rate of this valve was 5 L/min; the hysteresis loop of the spool-displacement input–output curve was 4.8%, and the linearity was 2.54%, which indicated that it had the ability of high-precision control and that it was suitable for the precision fluid system. The step response time was 7.5 ms, with no overshoot; the frequency response amplitude bandwidth was about 90 Hz (−3 dB); the phase bandwidth was about 95 Hz (−90°); and the dynamic characterization experiment showed that it had a fast response characteristic, which can satisfy the demand of high-frequency and high-dynamic working conditions. Full article

Financial fraud detection is an important field in financial technology, and strong and effective machine learning (ML) models are needed to detect fraudulent transactions with high accuracy and reliability. Conventional fraud detection models, like probabilistic, instance-based, and tree-based models, tend to have high error rates, class imbalance problems, and poor adaptability to changing fraud patterns. These issues call for sophisticated methods that improve predictive accuracy while being computationally efficient. To overcome these limitations, this research introduces the Voted Perceptron (VP) model, which utilizes an iterative learning process to dynamically adapt decision boundaries based on misclassified examples. In contrast to traditional models with static decision rules, the VP model constantly updates its weight parameters, thus providing better fraud detection abilities. The evaluation compares VP with state-of-the-art machine learning models, such as Average One Dependency Estimator (A1DE), K-nearest Neighbor (KNN), Naïve Bayes (NB), Random Tree (RT), and Functional Tree (FT), by using important performance metrics, like Mean Absolute Error (MAE), Root Mean Square Error (RMSE), True Positive Rate (TPR), recall, and accuracy. Experimental results show that VP outperforms its rivals significantly, yielding better fraud detection performance with low error rates and high recall. Furthermore, an ablation study confirms the influence of essential VP model elements on general classification performance. These results demonstrate VP to be an extremely effective model for detecting financial fraud, with enhanced flexibility towards evolving fraud patterns, and confirm the necessity for intelligent fraud detection mechanisms within financial organizations. Full article