Search Results (2,428)

Real-time feedback is increasingly valued in sports training, yet in curling it is commonly delivered through off-stone displays or post hoc review, forcing disruptive gaze shifts while athletes track a moving and continuously rotating stone, especially during collaborative sweeping. To address this gap, we present TabletStone, a stone-mounted tablet interface that provides in situ, glanceable feedback with role-aware layouts and rotation-robust visualization. TabletStone is implemented as a lightweight, UDP-driven endpoint that renders upstream training signals on the stone while adapting the UI to throwers and sweepers. To preserve readability under rotation, we formalize an absolute-position fixation strategy based on an on-device yaw estimate and counter-rotation transforms. We evaluate TabletStone through an initial controlled user study with six experienced curlers performing sweeping while reading on-stone values under two conditions (baseline and absolute-position fixation). The study showed higher subjective readability together with improved accuracy and recall for absolute-position fixation, while precision remained high in both conditions; missed readouts remained the dominant failure mode under workload. Overall, these results support the feasibility and potential usefulness of combining role-aware UI/UX with rotation-aware stabilization for on-object feedback in curling training, while broader training effects remain to be validated. Full article

To mitigate local congestion and address the adaptability limitations of traditional static routing under dynamic traffic, this paper proposes an end-to-end routing method based on a Graph Attention Network (GAT), termed Congestion-Aware Graph Attention Routing (CA-GAR). To alleviate the issue of local optima in traditional heuristic iterative optimization, we design a dynamic link cost optimization algorithm with multi-start parallel exploration. This algorithm employs a ”penalty–reselection–reward” closed-loop feedback mechanism, performing global searches from multiple random initial states to generate a high-quality, empirically near-optimal cost matrix as supervised labels. Building on this, CA-GAR leverages a multi-head attention mechanism to adaptively aggregate high-order topological features of nodes and edges, and incorporates a staged hierarchical hyperparameter optimization strategy to map real-time network states to link costs. Simulation results demonstrate that CA-GAR outperforms traditional static routing under light, medium, and heavy loads. Under high-load burst conditions, the method exhibits effective congestion avoidance capability, reducing end-to-end delay by approximately 50% and lowering the packet loss rate to as low as 2%. Compared with QLRA, CA-GAR shows promising performance in multi-path traffic splitting and possesses robust fast rerouting capabilities during node failures, thereby achieving intelligent traffic distribution and global load balancing. Full article

Prefabricated steel structures have been increasingly adopted in modern construction due to their high efficiency, sustainability, and industrialized production. However, their construction quality and efficiency are often compromised by accumulated geometric deviations during fabrication, transportation, assembly, and welding, while traditional construction control and welding processes remain highly dependent on manual measurements and empirical operations. To address these challenges, this study proposes a digital construction framework for prefabricated steel structures, integrating high-precision three-dimensional (3D) laser scanning, Building Information Modeling (BIM), and intelligent welding technologies. First, high-precision 3D laser scanning is employed to capture the as-built geometric information of prefabricated steel components, generating dense point cloud data for construction-stage deviation detection and quantitative comparison with BIM-based design models. Based on deviation analysis, a digital construction control strategy is established to support real-time feedback, error compensation, and assembly adjustment. An engineering case study involving a complex prefabricated steel structure is conducted to validate the proposed framework. The results demonstrate that the integrated digital construction and intelligent welding approach significantly improves assembly accuracy, weld positioning precision, and construction efficiency, while reducing manual intervention and error accumulation. Overall, this study contributes to the body of knowledge by proposing a unified closed-loop digital construction paradigm that integrates geometric perception, deviation-driven decision-making, and intelligent welding execution, thereby bridging the gap between construction control and robotic fabrication in prefabricated steel structures. Full article

Virtual reality (VR) interfaces increasingly rely on interaction within peripersonal space. However, the conditions under which interaction performance in virtual environments can approximate those of comparable real-world tasks remain underexplored, particularly for hierarchical menus requiring precise sequential input. This study investigated how the presence or absence of tactile feedback influences movement time and selection accuracy during hierarchical menu interaction in peripersonal space across different task difficulty levels. Twelve participants performed a three-level hierarchical selection task on a 4 × 3 menu in two controlled experiments with a stereoscopic 3D TV. Two interaction conditions were tested: a surface-based condition, with the menu attached to the physical screen, and a mid-air condition, with the menu positioned 35 cm and 45 cm in front of participants. Selections were confirmed using a handheld remote. Results showed no statistically significant difference in movement time and selection accuracy between the virtual and real environments when screen-surface targets provided tactile feedback, but performance declined for mid-air targets without tactile references, particularly under higher task difficulty levels. These findings suggest that tactile feedback, coordinated visual target placement, and users’ familiarity with touchscreen-like interaction jointly act as key factors for designing effective, immersive, and user-friendly VR menu systems in peripersonal space. Full article

Extreme hydrological events fundamentally alter sediment transport dynamics across grain, reach, and watershed scales, rendering classical equilibrium-based transport formulations inadequate. This review synthesizes recent advances in multiscale sediment transport modeling under highly unsteady and high-magnitude forcing conditions. At the grain scale, particle-resolved simulations demonstrate that sediment entrainment is governed by turbulence intermittency and transient force exceedance rather than mean bed shear stress thresholds, particularly when the hydrograph rise timescale (T_h) becomes comparable to particle response times (T_p). At the reach scale, non-equilibrium transport emerges when the unsteadiness ratio T_h/T_a∼O(1), where T_a is the sediment adaptation timescale representing the time required for sediment flux to adjust toward transport capacity. Under these conditions, pronounced hysteresis between discharge and sediment flux is observed, requiring relaxation-based transport formulations instead of instantaneous equilibrium laws. At the watershed scale, the sediment delivery ratio (SDR), defined as the ratio of sediment yield at the basin outlet to total hillslope erosion, becomes highly time-dependent. Extreme precipitation events can activate hillslope-channel connectivity, increasing SDR by orders of magnitude relative to baseline conditions. A unified dimensionless scaling framework is presented based on mobility intensity (θ/θ_c, where θ is the Shields parameter and θ_c is its critical value for incipient motion), unsteadiness ratio (T_h/T_a), and morphodynamic coupling (T_f/T_m, where T_f is the hydraulic advection timescale and T_m is the morphodynamic adjustment timescale). This framework enables classification of sediment transport regimes ranging from quasi-equilibrium to cascade-dominated states. The synthesis demonstrates that predictive uncertainty increases nonlinearly across scales due to timescale compression, threshold activation, and feedback between flow hydraulics and evolving morphology. Recent developments in hybrid physics-AI approaches show promise in improving predictive capability by enabling dynamic transport closures, surrogate modeling of computationally expensive microscale processes, and data assimilation for real-time forecasting. However, these approaches remain limited by extrapolation uncertainty and the need to enforce physical constraints. Overall, this review concludes that regime-aware multiscale coupling, combined with uncertainty quantification and adaptive modeling strategies, is essential for robust sediment hazard prediction and climate-resilient infrastructure design under intensifying hydrological extremes. Full article

Background: Gastric cancer survivors frequently encounter a “care gap” after discharge because of complex postgastrectomy syndromes. We evaluated “WECARE,” a bidirectional digital health platform designed to provide real-time symptom monitoring and multidisciplinary support. The primary goal of this study was to assess the efficacy of the platform by measuring the change in the Korean Quality of Life Questionnaire for Gastric Cancer Survivors (KOQUSS-40) total score over a six-month recovery period. Methods: This nationwide, multicenter pilot randomized controlled trial was conducted by the Korean Quality of Life in Stomach Cancer Patients Study Group (KOQUSS) across nine tertiary centers in Korea. A total of 88 patients who underwent curative gastrectomy were enrolled. Following an initial optimization phase involving 22 patients, the remaining 66 patients were randomized at a 1:1 ratio to the WECARE group or the control group. The WECARE group used a platform integrating the KOQUSS-40 algorithm for structured symptom reporting, real-time feedback on nutrition and exercise, and educational content on meal planning, symptom coping, and recovery. Assessments were performed at baseline and at 1, 3, and 6 months after discharge. Results: The WECARE group showed high feasibility and acceptability, with an adherence rate of 86.7% and an 82% satisfaction rate. At 6 months, the KOQUSS-40 total score (primary endpoint) did not differ significantly between the WECARE and control groups (85.3 ± 1.6 vs. 83.8 ± 1.6, p = 0.603). However, the WECARE group showed a numerically favorable recovery trajectory from the acute postoperative phase. Subgroup analysis revealed a positive trend in reflux symptom management in the WECARE group (p = 0.0856). In addition, more than 77% of users reported that the platform improved their self-management capabilities. Conclusions: The WECARE platform is a feasible and acceptable digital intervention for gastric cancer survivors. Although the primary endpoint was not significantly different, the favorable recovery trajectory, high adherence, and patient engagement support further evaluation in larger studies with longer follow-up and broader healthcare settings. Full article

To address the actuator fault problem faced by underactuated surface vessels (USVs), this study develops an active fault-tolerant control scheme based on finite-time output feedback. First, a finite-time neural terminal homogeneous state observer with a portional-integral structure is established. High-precision pose reconstruction enables finite-time synchronous reconstruction of unmeasured states. This allows unknown nonlinearities to be explicitly expressed online and incorporated into the compensation channel, significantly reducing the sensitivity of modeling errors to control performance. A neural damping mechanism is used to structurally reconstruct uncertain dynamics and loss-of-effectiveness (LOE) fault factors within the system, thereby constructing an online approximator to achieve real-time identification and compensation of composite uncertainties. This integrates the unknown nonlinearities and fault effects of the original system into an online-updatable estimation channel. Adopting a backstepping-based design methodology, a finite-time hybrid event-triggered control (ETC) architecture is further constructed. By introducing an event-triggered update mechanism at the control layer, the real-time continuous control signal is transformed into a discrete update. Based on Lyapunov stability theory, a comprehensive analysis is carried out to verify the stability of the proposed control scheme. Numerical simulations are finally carried out to validate the effectiveness of the scheme. Simulation results show that the tracking error is reduced by about $93\%$ and $60\%$ compared to the comparison scheme. Full article

The aviation industry operates in a security-sensitive environment where customer feedback may contain not only expressions of satisfaction or dissatisfaction but also threatening or violent language with potential security implications. While conventional sentiment analysis effectively captures customer opinions, it remains insufficient for identifying security-relevant linguistic cues that could signal risks requiring proactive intervention. This study addresses this gap by introducing a security-aware ambient intelligence framework for detecting violent language in airline customer reviews. This framework supports intelligent internet-based monitoring systems and real-time threat detection. We present the first annotated dataset of airline reviews specifically labeled for violent and threatening content, derived from 3629 reviews and balanced through manual resampling to achieve equal representation across positive, neutral, negative, and violent classes. The proposed framework employs VADER-based sentiment analysis for initial polarity estimation, combined with a validated annotation process to identify violent or threat-related content, followed by comprehensive feature engineering combining TF-IDF (2000 features) with text statistics and sentiment scores. We systematically evaluate individual classifiers (Random Forest, Decision Tree, SVM, Naive Bayes) against ensemble methods (Voting, Stacking, Boosting) using accuracy, precision, recall, F1-score, and ROC AUC metrics. Results demonstrate that Stacking achieves the highest raw performance (98.57% accuracy, F1-macro 0.9856), while Naive Bayes offers an optimal balance between effectiveness and computational efficiency (81.79% accuracy, F1-macro 0.8172, training time 0.03 s). This is the first dataset and framework designed for security-aware analysis of airline reviews. The selected Naive Bayes model achieves per-class F1-scores of 0.9978 for neutral, 0.7814 for negative, 0.7482 for violent, and 0.7415 for positive reviews, with a macro-average ROC AUC of 0.7123. The framework is deployed with serialized components enabling real-time prediction, supporting both single-review analysis and batch processing for integration into airline security monitoring systems. This work establishes a foundation for security-aware natural language processing in critical infrastructure contexts, bridging the gap between conventional sentiment analysis and proactive threat detection. Full article

This paper presents the design, implementation, and experimental validation of a real-time embedded photovoltaic (PV) emulator based on the two-diode model, using a dSPACE DS1103 platform for hardware validation. The proposed system aims to accurately reproduce the electrical behavior of PV modules under varying environmental conditions, including irradiance and temperature variations. The emulator architecture combines a lookup-table-based modelling approach with a programmable DC power source, enabling deterministic real-time execution and efficient implementation. A multi-level control structure is employed, integrating inner-loop regulation, model-based reference generation, and feedback control to ensure accurate tracking of the PV current–voltage (I–V) characteristics. Experimental results demonstrate that the emulator achieves high accuracy, with an approximation error of approximately 1.2% under standard operating conditions. The system exhibits stable dynamic behavior characterized by a time constant of approximately 0.5 s, with performance maintained across different sampling intervals and load conditions. Additional simulations confirm that the two-diode model preserves high accuracy over a temperature range of 15–60 °C, with deviations below 2%. The results highlight that the two-diode model provides an optimal trade-off between modelling accuracy and computational complexity for real-time embedded applications. The proposed emulator offers a flexible and reliable platform for laboratory validation of photovoltaic behavior and provides the foundation for future testing of maximum power point tracking (MPPT) algorithms, power electronic converters, and embedded control strategies under controlled conditions. Full article

To address trajectory-tracking degradation of unmanned surface vehicles (USVs) in constrained waters caused by model uncertainty, strong environmental disturbances, and actuator limitations, this paper proposes a robust disturbance-observer-based optimization model predictive control method. First, a nonlinear tracking error model is established for a 3-DOF USV by incorporating environmental loads, parametric perturbations, and unmodeled dynamics into the kinematic and dynamic equations. Based on this model, a prediction model suitable for model predictive control is derived through linearization and discretization. Then, to estimate complex unknown disturbances online, a robust disturbance observer integrating a radial basis function neural network (RBFNN) with an adaptive sliding-mode mechanism is developed, enabling real-time approximation and compensation of lumped disturbances in the surge and yaw channels. Furthermore, to overcome actuator saturation caused by the direct superposition of feedforward compensation and feedback control in conventional composite strategies, a dynamic constraint reconstruction mechanism is introduced. By feeding the observer-generated compensation signal back into the MPC optimizer, the feasible control region is updated online so that the total control input satisfies both magnitude and rate constraints of the propulsion system. Theoretical analysis based on Lyapunov theory proves the uniform ultimate boundedness of the observation errors and neural-network weight estimation errors, while input-to-state stability theory is employed to establish closed-loop stability. Comparative simulations under sinusoidal trajectories, time-varying curvature paths, and large-maneuver turning conditions demonstrate that the proposed method significantly improves tracking accuracy, disturbance rejection capability, and control feasibility under severe disturbances and parameter mismatch. Full article

Seismic-induced torsional response remains a significant barrier to achieving resilient and sustainable building foundations, as traditional passive isolation systems often fail to regulate rotational motion effectively. This study examines an adaptive gyroscopic feedback-based foundation control system designed to provide automated torsional seismic mitigation. The proposed system integrates real-time angular velocity sensing using MEMS gyroscopes, Kalman filter state estimation, and an adaptive Linear Quadratic Regulator to modulate damping in response to changing ground motion. A single-degree-of-freedom torsional foundation model was developed and evaluated in GNU Octave 8.4.0/MATLAB R2024a Simulink using the recorded El Centro 1940 NS earthquake input. The adaptive controller achieved notable improvements, reducing total vibration energy by 69%, peak angular displacement by 47.6%, and RMS angular velocity by 39.5% relative to the uncontrolled case, while keeping control energy below 19% of the seismic input. These results demonstrate that gyroscopic feedback enhances damping, limits torsional resonance, and stabilises foundation behaviour under actual earthquake excitation. The system’s low energy requirement, compatibility with embedded hardware, and automated response characteristics underscore its potential for integration into sustainable and intelligent foundation designs. While results are demonstrated using the El Centro 1940 record as a benchmark, broader generalisation will be established through multi-record suites and uncertainty quantification in future work. The study highlights a feasible pathway for advancing automated seismic protection in buildings through active, sensor-driven torsional control. Full article

Abnormal gait associated with neuromuscular and musculoskeletal disorders represents a growing clinical burden, particularly in aging populations. This study presents a modular, low-cost Smart Rehabilitation Walker (SRW) that integrates multimodal sensing and real-time haptic feedback to enable simultaneous gait monitoring and corrective intervention in both clinical and home environments. The system combines force-sensing resistors for bilateral load symmetry assessment, inertial measurement units for fall detection, and surface electromyography (sEMG) for neuromuscular activity monitoring within a closed-loop assistive feedback architecture. A 15-day pilot study involving ten individuals with rheumatoid arthritis and clinically observed neurological gait abnormalities demonstrated measurable improvements in gait biomechanics. The Force Symmetry Index (FSI), calculated using the Robinson symmetry metric, decreased from an average of 0.9691 to 0.2019, corresponding to a 79.26% average reduction in inter-limb load asymmetry. Concurrently, sEMG measurements showed a substantial increase in neuromuscular activation (ΔEMG = 4.28), with statistical analysis confirming a significant improvement across participants (paired t-test: t(9) = 13.58, p < 0.001). To model rehabilitation trajectories, a nonlinear predictive framework based on Gaussian Process Regression achieved high predictive accuracy (R² ≈ 0.9, with a mean RMSE of 0.0385), while providing uncertainty-aware trend estimation. Validation using an independent amyotrophic lateral sclerosis gait dataset further demonstrated the transferability of the analytical pipeline. These results highlight the potential of sensor-enabled assistive walkers as scalable platforms for quantitative gait rehabilitation, adaptive feedback, and long-term mobility monitoring. Full article

Inefficient urban traffic management remains a critical challenge, as conventional signal controllers—built on fixed timing plans—cannot cope with the dynamic nature of modern city traffic. This study addresses this limitation by developing a decentralized MARL-based framework capable of coordinating five interconnected intersections as a unified traffic cell. Central to the proposed solution is the Strategic Anti-Blocking Phase Adjustment (SAPA) module, which enables intersections to autonomously modify phase durations in response to real-time traffic conditions. The framework is designed to handle heterogeneous demand patterns, with particular emphasis on arterial corridors connecting urban centers to peripheral zones. Integration of a Visible Light Communication (VLC) network allows continuous monitoring of key variables, including vehicle kinematics and pedestrian activity, feeding the agents with rich environmental feedback. Experimental evaluation confirms the effectiveness of the approach: the SAPA-augmented DQN achieves roughly 33% shorter vehicle queues and a ~70% reduction in pedestrian waiting counts relative to a standard DQN baseline. Remarkably, these gains bring the value-based method to a performance level comparable to MAPPO, a considerably more complex multi-agent policy optimization algorithm, establishing SAPA as an efficient and scalable enhancement for intelligent urban traffic control. Full article

Autonomous traversal flight in unknown 3D environments remains challenging due to mapping bottlenecks and computational latency. Inspired by pigeons navigating cluttered forests through instantaneous visual perception rather than constructing global metric maps, this paper presents a pigeon-inspired depth-reasoning-driven decision framework for agile quadrotor traversal in unmapped spaces without explicit map construction. To ensure feasibility, we leverage a robust state estimation backbone enhanced by deep-learning-based feature matching, providing stable pose feedback under aggressive maneuvers. The core contribution is a pigeon-inspired depth-reasoning framework that translates raw sensory depth data into a hybrid optimization framework, integrating both hard safety constraints and soft geometric smoothness constraints, directly emulating the three avian mechanisms: gap selection via instantaneous depth gradients, path selection that minimizes posture changes, and a safety field driven by the looming effect. By bypassing time-consuming mapping and spatial discretization processes, the framework significantly reduces perception-to-control latency. Finally, validated via simulations and real-world experiments on a resource-constrained quadrotor platform, our map-less approach achieves superior decision frequencies and comparable safety margins to those of state-of-the-art map-based planners. This framework offers a practical, high-frequency solution for autonomous flight where computational resources and environmental knowledge are strictly limited. Full article

Energy efficiency improvement represents a central strategic objective of the European Union (EU), essential for mitigating climate change and facilitating the transition toward a sustainable energy system. In 2023, renewable energy sources generated approximately 46% of the electricity produced in the EU, becoming the dominant component of the regional energy mix. This progress has been supported by coherent public policies, dedicated investment programs, and regulatory mechanisms aimed at accelerating the adoption of sustainable technologies. However, the existing literature highlights a research gap regarding the relationship between the dynamics of the European energy transition, the operational challenges generated by the rapid increase in the share of renewable energy sources, and the potential for energy savings in the residential sector through non-technological interventions. This paper analyzes the structural transformations of the European energy mix, the limitations of energy systems in the context of accelerated renewable energy integration, and the role of behavioral interventions in supporting the stability of the energy system. The study examines the dynamics of residential energy consumption, behavioral determinants of energy use, and the effectiveness of instruments such as information campaigns, real-time feedback, dynamic pricing, and demand response programs. The results indicate that these interventions can reduce peak loads, increase consumption flexibility, and alleviate pressure on energy networks under conditions of variable renewable energy generation. The integration of energy storage systems and the implementation of low-cost behavioral measures can act as complementary instruments for maintaining the dynamic stability of the energy system and for achieving the EU’s sustainability and climate neutrality objectives. Full article