Search Results (2,273)

This study quantifies the influence of soil–structure interaction (SSI) on key parameters of performance-based seismic design (PBSD) for steel moment-resisting frames. Specifically, PBSD is extended as a methodology in which explicit structural performance levels, such as immediate occupancy, damage limitation, life safety, and collapse prevention, serve as the basis for sizing and detailing structural members under specified seismic hazard levels, instead of traditional force-based design. The PBSD framework is further developed to incorporate SSI by adopting a beam on a nonlinear Winkler foundation model. This model captures the nonlinear soil response and its interaction with the structure, enabling a more realistic design framework within a performance-based context. To evaluate and quantify the influence of SSI in the PBSD method, an extensive parametric study is performed using 100 far-field ground motions, categorized into four groups (25 records each) corresponding to EC8 soil types A, B, C, and D. Nonlinear time history analyses reveal consistent trends across the examined frames. When SSI is neglected, the fundamental natural period (T) is systematically underestimated by approximately up to 3.5% on EC8 soil type C and up to 15% on soil type D. As a result, the base shear and the mean values of maximum interstorey drift ratios (IDRs) are overestimated compared to cases accounting for soil flexibility, with the largest drift discrepancies observed in frames with eight or more storeys on soil D. The analyses further reveal that softer soils (e.g., Soil D) lead to significantly higher q values, particularly for moderate-to-long period structures, whereas stiffer soils (e.g., Soil B) cause only minor deviations, remaining close to fixed-base values. A complementary machine learning module, trained on the same dataset, is employed to predict base shear, maximum IDR, and the behavior factor q. It successfully reproduces the deterministic SSI trends, achieving coefficients of determination (R²) ranging from 0.986 to 0.992 for maximum IDR, 0.947 to 0.948 for base shear, and 0.944 to 0.952 for q. Feature importance analysis highlights beam and column ductility, soil class, and performance level as the most influential predictors of structural response. Full article

The aim of this experiment was to determine whether reticulorumen temperature (ReT), rumination, activity or pH captured by a rumen sensor bolus system (smaXtec animal care GmbH, Graz, Austria) can be used as an early indicator of heat load (HL) and to assess how its daily patterns are influenced by diurnal effects. Physiological and behavioral data from 70 male feedlot cattle (Uckermärker, Hereford, Simmentaler) housed in a closed barn were investigated using the calculated temperature-humidity index (THI) from remote HOBO Onset climate sensors over a period of 210 days. Using time series analysis and seasonal ARIMA modeling, it was found that ReT followed the same patterns throughout days with a THI < 74 as well as days under heat load conditions. Time series and correlation analyses were also performed for the rumen pH, rumination index and activity index. The collective mean ReT over the winter days assessed (n = 14,971) was 39.48 °C, with a minimum mean of 38.31 °C and a maximum mean of 40.69 °C. In comparison, the collective mean ReT over the summer days assessed (n = 14,030) was 39.53 °C, with a minimum mean of 38.39 °C and a maximum mean of 42.02 °C. Pearson’s correlation did not reveal a relationship between THI and ReT (r = −0.06; p < 0.001) and only minimally for rumination (r = −0.11; p < 0.001). Rumination clearly decreased with increasing ambient temperature in comparison to days with a THI < 74. A long-term effect is also visible when the monthly mean rumination from all bulls tends to decrease slightly from February to May and then increases beginning in June. The mean pH values decreased throughout the summer months. Nevertheless, the comparison between daily fluctuations in pH values under HL failed to yield significant deviations from those captured on days of winter. The Pearson correlation for rumen pH showed a weak negative linear relationship with THI (r = −0.3; p < 0.001). The monthly means of the motion activity index could also not verify that HL led to increasing activity (Pearson correlation for motion activity and THI: r = 0.04; p < 0.001). The heat load had no visible short-term effects on the ReT or rumen pH, but rumination and peak motion activity were reduced on days with high ambient temperatures. Full article

Wearable auditory feedback systems have demonstrated potential to support gait rehabilitation, yet user experience and engagement remain underexplored. This feasibility study investigated the usability and perceptions of an IMU-based (WT901BLECL, WitMotion) sonification system designed to deliver real-time gait feedback. Twenty healthy participants walked on a treadmill at two speeds under three conditions: no feedback, discrete bass tones, and continuous whoosh tones. The proposed system, with an IMU sensor embedded in a flexible garment, combined real-time gait analysis with auditory cues. Participants reported high levels of comfort, with most (90%) indicating that they had a positive overall experience. Discrete bass tones enhanced awareness of specific gait phases, particularly heel strike and initial contact, whereas continuous whoosh sounds extended awareness to the trunk and hips but were occasionally perceived as distracting. Motivation effects were mixed, and no significant correlations were found between subjective ratings and biomechanical measures, reflecting individual variability in auditory cue interpretation. These results emphasized the role of sound modality in influencing gait perception and highlighted the importance of user-centered design in wearable rehabilitation technologies. The study provides foundational evidence for refining personalized auditory feedback systems and supports future investigations with clinical populations, such as stroke survivors and individuals with Parkinson’s Disease. Full article

Accidental opioid overdose and Sudden Unexpected Death in Epilepsy (SUDEP) represent major forms of preventable mortality, often involving sudden-onset catastrophic events that could be survivable with rapid detection and intervention. The current physiological monitoring technologies are potentially applicable, but face challenges, including complex setups, poor patient compliance, high costs, and uncertainty about community-based use. Paradoxically, simple clinical observation in supervised injection facilities has proven highly effective, suggesting observable changes in central body motion may be sufficient to detect life-threatening events. We describe a novel wearable biosensor for continuous central body motion monitoring, offering a potential early warning system for life-threatening events. The biosensor incorporates a low-power, triaxial MEMS accelerometer within a discreet, chest-worn device, enabling long-term monitoring with minimal user burden. Two system architectures are described: stored data for retrospective analysis/research, and an in-development system for real-time overdose detection and response. Early user research highlights the importance of accuracy, discretion, and trust for adoption among people who use opioids. The initial clinical data collection, including the OD-SEEN study, demonstrates feasibility for capturing motion data during real-world opioid use. This technology represents a promising advancement in non-invasive monitoring, with potential to improve the outcomes for at-risk populations with multiple health conditions. Full article

The high operational velocities of high-speed trains present constraints on their onboard track intrusion detection systems for real-time capture and analysis, encompassing limited computational resources and motion image blurring. This emphasizes the critical necessity of track perimeter intrusion monitoring systems. Consequently, an intelligent monitoring system employing trackside cameras is constructed, integrating weakly supervised video anomaly detection and unsupervised foreground segmentation, which offers a solution for monitoring foreign objects on high-speed train tracks. To address the challenges of complex dataset annotation and unidentified target detection, weakly supervised learning detection is proposed to track foreign object intrusions based on video. The pretraining of Xception3D and the integration of multiple attention mechanisms have markedly enhanced the feature extraction capabilities. The Top-K sample selection alongside the amplitude score/feature loss function effectively discriminates abnormal from normal samples, incorporating time-smoothing constraints to ensure detection consistency across consecutive frames. Once abnormal video frames are identified, a multiscale variational autoencoder is proposed for the positioning of foreign objects. A downsampling/upsampling module is optimized to increase feature extraction efficiency. The pixel-level background weight distribution loss function is engineered to jointly balance background authenticity and noise resistance. Ultimately, the experimental results indicate that the video anomaly detection model achieved an AUC of 0.99 on the track anomaly detection dataset and processes 2 s video segments in 0.41 s. The proposed foreground segmentation algorithm achieved an F1 score of 0.9030 in the track anomaly dataset and 0.8375 on CDnet2014, with 91 Frames per Second, confirming its efficacy. Full article

Background: This study employed a markerless motion capture system to quantify the external game load of elite 3 × 3 basketball players and evaluated its association with game performance. Methods: Twenty-four female 3 × 3 basketball games from the 2024 Paris Olympic Games were analyzed, involving 32 players from eight national teams. A markerless motion capture system was used to collect six categories of external load metrics during games, and 22 types of technical statistics were gathered to determine performance. Collected data were standardized according to live game time (min⁻¹). Repeated-measures correlation analysis was applied to examine the relationships between external load and performance, while mixed-effects models were used to compare external load differences between better- and worse-performing groups (classified by Player Value). Results: The correlations between external load and performance indicators were trivial to small. Accelerations (ACC) were significantly associated with the greatest number of performance indicators (e.g., points, rebounds, 1-point made, key assists), while rebounds were significantly correlated with the largest number of external load metrics (e.g., total distance, low-intensity active distance, high-intensity active distance); however, all correlations remained at the small level (r = 0.16–0.24). No significant differences in external load were observed between players of differing performance groups (p > 0.05). Conclusions: In elite 3 × 3 basketball, external load reflects players’ involvement and effort rather than serving as a primary determinant of game performance. This study provides new empirical evidence on the characteristics of 3 × 3 basketball, suggesting that coaches and strength and conditioning practitioners should adopt a comprehensive perspective when evaluating performance, with external load being more suitable for training regulation and fatigue monitoring. Full article

The rapid growth of Internet of Medical Things (IoMT) devices offers promising avenues for real-time, personalized healthcare while also introducing critical challenges related to data privacy, device heterogeneity, and deployment scalability. This paper presents E-DMFL (Enhanced Distributed Multimodal Federated Learning), an Enhanced Distributed Multimodal Federated Learning framework designed to address these issues. Our approach combines systems analysis principles with intelligent model design, integrating PyTorch-based modular orchestration and TensorFlow-style data pipelines to enable multimodal edge-based training. E-DMFL incorporates gated attention fusion, differential privacy, Shapley-value-based modality selection, and peer-to-peer communication to facilitate secure and adaptive learning in non-IID environments. We evaluate the framework using the EarSAVAS dataset, which includes synchronized audio and motion signals from ear-worn sensors. E-DMFL achieves a test accuracy of 92.0% in just six communication rounds. The framework also supports energy-efficient and real-time deployment through quantization-aware training and battery-aware scheduling. These results demonstrate the potential of combining systems-level design with federated learning (FL) innovations to support practical, privacy-aware IoMT applications. Full article

Maritime Autonomous Surface Ships (MASSs) raise safety and regulatory challenges that extend beyond technical reliability. This study builds on a published system-theoretic process analysis (STPA) of degraded operations that identified 92 loss scenarios. These scenarios were reformulated into a two-round Delphi survey with 20 experts from academic, industry, seafaring, and regulatory backgrounds. Panelists rated each scenario on severity, likelihood, and detectability. To avoid rank reversal, common in the Risk Priority Number, an adjusted index was applied. Initial concordance was low (Kendall’s W = 0.07), reflecting diverse perspectives. After feedback, Round 2 reached substantial agreement (W = 0.693, χ² = 3265.42, df = 91, p < 0.001) and produced a stable Top 10. High-priority items involved propulsion and machinery, communication links, sensing, integrated control, and human–machine interaction. These risks are further exacerbated by oceanographic conditions, such as strong currents, wave-induced motions, and biofouling, which can impair propulsion efficiency and sensor accuracy. This highlights the importance of environmental resilience in MASS safety. These clusters were translated into five action bundles that addressed fallback procedures, link assurance, sensor fusion, control chain verification, and alarm governance. The findings show that Remote Operator competence and oversight are central to MASS safety. At the same time, MASSs rely on artificial intelligence systems that can fail in degraded states, for example, through reduced explainability in decision making, vulnerabilities in sensor fusion, or adversarial conditions such as fog-obscured cameras. Recognizing these AI-specific challenges highlights the need for both human oversight and resilient algorithmic design. They support explicit inclusion of Remote Operators in the STCW convention, along with watchkeeping and fatigue rules for Remote Operation Centers. This study provides a consensus-based baseline for regulatory debate, while future work should extend these insights through quantitative system modeling. Full article

Electrocardiograms (ECGs) are critical for cardiovascular disease diagnosis, but their accuracy is often compromised by electrode motion (EM) artifacts—large, nonstationary distortions caused by patient movement and electrode-skin interface shifts. These artifacts overlap in frequency with genuine cardiac signals, rendering traditional filtering methods ineffective and increasing the risk of false alarms and misdiagnosis, particularly in wearable and ambulatory ECG applications. To address this, we propose the Feature-Shuffle Multi-Head Attention Autoencoder (FMHA-AE), a novel architecture integrating multi-head self-attention (MHSA) and a feature-shuffle mechanism to enhance ECG denoising. MHSA captures long-range temporal and spatial dependencies, while feature shuffling improves representation robustness and generalization. Experimental results show that FMHA-AE achieves an average signal-to-noise ratio (SNR) improvement of 25.34 dB and a percentage root mean square difference (PRD) of 10.29%, outperforming conventional wavelet-based and deep learning baselines. These results confirm the model’s ability to retain critical ECG morphology while effectively removing noise. FMHA-AE demonstrates strong potential for real-time ECG monitoring in mobile and clinical environments. This work contributes an efficient deep learning approach for noise-robust ECG analysis, supporting accurate cardiovascular assessment under motion-prone conditions. Full article

This paper presents a stability analysis of single-channel, slow-rolling, Semi-Automatic Command to Line of Sight (SACLOS) missiles using a comparison of the Routh–Hurwitz and the Frank–Wall stability criteria and a nonlinear analysis. Beginning with a six-degree-of-freedom (6-DOF) model in the Resal frame, a linearized model for the commanded motion is developed. This linearized model, which features complex coefficients due to the coupling of longitudinal channels in rolling missiles, is used to define the structural scheme of the commanded object and its flight quality parameters. The guidance kinematic relations, guidance device equations, and actuator relations, incorporating a switching function specific to slow-rolling, single-channel missiles, are also defined and linearized within the Resal frame to construct a comprehensive structural diagram of the SACLOS missile. From this, the characteristic polynomial with complex coefficients is derived and analyzed by comparing the Routh–Hurwitz and the Frank–Wall stability criteria. This analysis determines a stability domain for the guidance gain and establishes a minimum limit for the guidance time. The stability domain defined through the linear model is then validated using a nonlinear model in the body frame. Full article

This paper addresses the challenges of inadequate trajectory tracking accuracy and limited parameter adaptability encountered by hip joints in lower-limb exoskeletons operating across multi-terrain environments. To mitigate these issues, we propose a hybrid control strategy that synergistically combines the slime mould algorithm (SMA) with fuzzy PID control, thereby improving the trajectory tracking performance in such diverse conditions. Initially, we established a dynamic model of the hip joint in the sagittal plane utilizing the Lagrange method, which elucidates the underlying motion mechanisms involved. Subsequently, we designed a fuzzy PID controller that facilitates dynamic parameter adjustment. The integration of the slime mould algorithm (SMA) allows for the optimization of both the quantization factor and the proportional factor of the fuzzy PID controller, culminating in the development of a hybrid control framework that significantly enhances parameter adaptability. Ultimately, we performed a comparative analysis of this hybrid control strategy against conventional PID, fuzzy PID, and PSO-fuzzy PID controls through MATLABR2023b/Simulink simulations as well as experimental tests across a range of multi-terrain scenarios including flat ground, inclines, and stair climbing. The results indicate that in comparison to PID, fuzzy PID, and PSO-fuzzy PID controls, our proposed strategy significantly reduced the adjustment time by 15.06% to 61.9% and minimized the maximum error by 39.44% to 72.81% across various terrains including flat ground, slope navigation, and stair climbing scenarios. Additionally, it lowered the steady-state error ranges by an impressive 50.67% to 90.75%. This enhancement markedly improved the system’s response speed, tracking accuracy, and stability, thereby offering a robust solution for the practical application of lower-limb exoskeletons. Full article

The convergence of Industrial Internet of Things (IIoT) and digital twin technology offers new paradigms for process automation and control. This paper presents an integrated IIoT and digital twin framework for intelligent control of a gas–liquid separation unit with interacting flow, pressure, and differential pressure loops. A comprehensive dynamic model of the three-loop separator process is developed, linearized, and validated. Classical stability analyses using the Routh–Hurwitz criterion and Nyquist plots are employed to ensure stability of the control system. Decentralized multi-loop proportional–integral–derivative (PID) controllers are designed and optimized using the Integral Absolute Error (IAE) performance index. A digital twin of the separator is implemented to run in parallel with the physical process, synchronized via a Kalman filter to real-time sensor data for state estimation and anomaly detection. The digital twin also incorporates structured singular value ($\mu$) analysis to assess robust stability under model uncertainties. The system architecture is realized with low-cost hardware (Arduino Mega 2560, MicroMotion Coriolis flowmeter, pneumatic control valves, DAC104S085 digital-to-analog converter, and ENC28J60 Ethernet module) and software tools (Proteus VSM 8.4 for simulation, VB.Net 2022 version based human–machine interface, and ML.Net 2022 version for predictive analytics). Experimental results demonstrate improved control performance with reduced overshoot and faster settling times, confirming the effectiveness of the IIoT–digital twin integration in handling loop interactions and disturbances. The discussion includes a comparative analysis with conventional control and outlines how advanced strategies such as model predictive control (MPC) can further augment the proposed approach. This work provides a practical pathway for applying IIoT and digital twins to industrial process control, with implications for enhanced autonomy, reliability, and efficiency in oil and gas operations. Full article

The transition toward Industry 4.0 and the emerging concept of Industry 5.0 demand intelligent tools that integrate efficiency, adaptability, and human-centered design. This paper presents a Computer Vision-based framework for automated motion classification in Methods-Time Measurement 2 (MTM-2), with the aim of supporting industrial time studies and ergonomic risk assessment. The system uses a Convolutional Neural Network (CNN) for pose estimation and derives angular kinematic features of key joints to characterize upper limb movements. A two-stage experimental design was conducted: first, three lightweight classifiers—K-Nearest Neighbors (KNN), Support Vector Machines (SVMs), and a Shallow Neural Network (SNN)—were compared, with KNN demonstrating the best trade-off between accuracy and efficiency; second, KNN was tested under noisy conditions to assess robustness. The results show near-perfect accuracy (≈100%) on 8919 motion instances, with an average inference time of 1 microsecond per sample, reducing the analysis time compared to manual transcription. Beyond efficiency, the framework addresses ergonomic risks such as wrist hyperextension, offering a scalable and cost-effective solution for Small and Medium-sized Enterprises. It also facilitates integration with Manufacturing Execution Systems and Digital Twins, and is therefore aligned with Industry 5.0 goals. Full article

This study proposes a Personalized Deep Learning-Based Sleep Recommendation System Using Lifelog Data (PDSRS-LD). Traditional sleep research primarily relies on bio signals such as EEG and ECG recorded during sleep but often fails to sufficiently reflect the influence of daily activities on sleep quality. To address this limitation, we collect lifelog data such as stress levels, fatigue, and sleep satisfaction via wearable devices and use them to construct individual user profiles. Subsequently, real sleep data obtained from an AI-powered motion bed are incorporated for secondary training to enhance recommendation performance. PDSRS-LD considers comprehensive user data, including gender, age, and physical activity, to analyze the relationships among sleep quality, stress, and fatigue. Based on this analysis, the system provides personalized sleep improvement strategies. Experimental results demonstrate that the proposed system outperforms existing models in terms of F1 score and Average Precision (mAP). These results suggest that PDSRS-LD is effective for real-time, user-centric sleep management and holds significant potential for integration into future smart healthcare systems. Full article

Accurately predicting the muscle strength of key muscle–tendon units during human motion is vital for understanding movement mechanisms, optimizing exercise training, evaluating rehabilitation progress, and advancing prosthetic control technologies. Traditional prediction methods often suffer from low accuracy and high computational complexity. To address these challenges, this study employs independent component analysis (ICA) to predict the muscle strength of tendon units in primary moving parts of the human body. The proposed method had the highest accuracy in localization, at 98% when the sample size was 20. When the sample size was 100, the proposed method had the shortest localization time, with a localization time of 0.025 s. The accuracy of muscle strength prediction based on backpropagation neural network for key muscle–tendon units in human motion was the highest, with an accuracy of 99% when the sample size was 100. The method can effectively optimize the accuracy and efficiency of muscle strength prediction for key muscle–tendon units in human motion and reduce computational complexity. Full article