Search Results (188)

With the advancement of urbanization and motorization, urban traffic conditions increasingly affect both travel efficiency and system stability, yet existing studies based on high-frequency OD data mainly focus on single aspects such as congestion patterns or travel time variability, lacking a unified analytical framework that jointly captures time-varying travel impedance, reliability, and anomaly risks under comparable conditions, especially in cross-city contexts. This study constructs a standardized analytical framework with a novel integration based on a “city × weekday × 5 min interval” structure, using high-frequency navigation OD data from eight major cities in China over four consecutive weeks, totaling approximately 560,000 valid samples. Travel Time per Unit Distance (TTUD) is employed as the core metric, and a distance-stratified weighting approach is adopted to improve cross-city comparability. Reliability is characterized by variability, dispersion, and tail risk, and anomalous events are identified using a dynamic baseline. The results reveal clear intra-week temporal regularity and significant inter-city heterogeneity, with weekday evening peaks generally lasting longer than those on weekends, reflecting sustained commuting pressure and slower dissipation of travel demand. A total of 249 anomaly events are detected, with higher frequency and persistence on weekdays, highlighting the increased vulnerability of traffic systems during peak commuting periods and indicating that commuting periods are more prone to sustained deviations due to higher system load and demand instability. Overall, the proposed framework provides a unified and comparable basis for cross-city traffic performance evaluation and supports practical applications such as peak-period traffic management, congestion mitigation, and traffic risk monitoring. Full article

This study addresses the premature failure of electric motor bearings caused by inverter-induced parasitic currents. We propose a novel segmented end shield design utilizing 24 carbon fiber-reinforced polymer (CFRP) lamellae to provide both structural support and galvanic isolation. The “main working” of the design relies on a segmented architecture where the lamellae are adhesively bonded between a central bearing housing and an outer mounting flange, creating a high-impedance path that interrupts circulating currents. Experimental validation focused on both mechanical stability and dielectric performance. Results indicate that the assembly maintains rotor positional integrity under nominal loads while providing an insulation resistance > 1 GΩ at 1 kV and a structural capacitance of 2.47 nF. These parameters effectively mitigate low-frequency circulating currents. Data analysis, derived from the mean values of repeated test cycles, confirms that the composite architecture serves as a viable, mechanically robust alternative to conventional metallic end shields. Full article

Wide-bandgap power devices, particularly silicon carbide (SiC) MOSFETs, have seen widespread adoption in electric vehicle (EV) motor drive systems due to their superior switching characteristics, including high switching speeds and high switching frequencies. However, these advantages exacerbate motor terminal overvoltage, with peaks reaching twice the inverter output voltage, causing insulation breakdown in windings and bearing electro-corrosion, which shorten motor lifespan. Traditional overvoltage prediction methods, such as distributed parameter models or detailed ladder network approaches, require extensive system parameters and involve high computational loads, while simplified models lack generality. To address these issues, this paper proposes a simplified prediction method based on a lumped ladder network model combined with frequency scanning. The approach uses impedance analysis to identify anti-resonance frequencies, enabling direct estimation of overvoltage amplitudes without prior knowledge of cable or motor specifics. Experimental validation on a SiC-based drive system demonstrates prediction errors below 10% and a reduction in computational time compared to conventional methods. Full article

Background/Objectives: To evaluate the utility of phase angle (PhA) derived from bioelectrical impedance analysis as a screening indicator for pre-frailty in community-dwelling older adults. Methods: This cross-sectional study included 171 participants (36 men and 135 women) in Japan in 2023. PhA at 50 kHz was measured using bioelectrical impedance analysis and evaluated as a potential screening indicator for pre-frailty. Assessments included body composition, physical function tests (maximum walking speed, Timed Up and Go (TUG), grip strength, knee extension strength, and one-leg stance time with eyes open), cognitive function (MoCA-J), and the Motor Fitness Scale (MFS), a questionnaire assessing physical function, along with the Kihon Checklist (KCL). Frailty status was defined using KCL scores (4–7: pre-frailty; ≥8: frailty), and participants were classified into robust and pre-frail/frail groups. Results: PhA was significantly correlated with physical function measures, including grip strength (r = 0.54, p < 0.01), MFS (r = 0.36, p < 0.01), maximum walking speed (r = 0.20, p < 0.05), knee extension strength (r = 0.16, p < 0.05), and TUG (r = −0.17, p < 0.05). In women, logistic regression analysis showed that PhA was independently associated with pre-frailty (age-adjusted odds ratio: 2.38; 95% CI: 1.08–5.23; p < 0.05). ROC analysis yielded an area under the curve of 0.65 (95% CI: 0.56–0.74), indicating modest discriminative ability. Age-adjusted cutoff values of PhA were 4.19° and 4.74°, corresponding to points prioritizing sensitivity and specificity, respectively. Conclusions: PhA is associated with physical function and may serve as a simple, non-invasive indicator for identifying pre-frailty in community settings. However, given its modest discriminative ability, PhA alone may not be sufficient as a standalone screening tool and should be used in combination with other clinical indicators for clinical application. Full article

Introduction: Malnutrition and sarcopenia are commonly observed in African patients with Parkinson’s disease (PD); however, limited data exist regarding the nutritional status and body composition of PD patients in sub-Saharan Africa. This study aims to describe the clinical, nutritional, and anthropometric characteristics of PD patients from Sogakope, in the Volta Region of Ghana. Methods: A total of 20 PD patients were recruited. All participants underwent comprehensive clinical and nutritional assessments. Motor symptoms were evaluated with the Unified Parkinson’s Disease Rating Scale (UPDRS). Dealing with non-motor symptoms, constipation was diagnosed according to the Roma III Criteria while dysphagia was assessed using the Swallowing Disturbance Questionnaire. The presence and impact of sialorrhea was determined using the Sialorrhea Clinical Scale. Nutritional assessment was performed with the Mini Nutritional Assessment short form (MNA-sf). Body composition parameters were measured using Bioelectrical Impedance Analysis (BIA), and muscle strength was evaluated with the Handgrip Strength Test. Correlations were assessed by Pearson or Spearman correlation analysis, as appropriate. Partial correlation analysis controlling for significant clinical variables was also performed. Results: Daily caloric intake was significantly lower compared to Western populations and was associated with a reduced body mass index (BMI) and body fat percentage. Constipation (80%) and sarcopenia (45%) were highly prevalent, whereas dysphagia was reported in only 15% of participants. Over 75% of patients were at risk of malnutrition. A significant inverse correlation was found between thigh circumference and disease duration (r = −0.517; p = 0.02). Additionally, protein intake (g/kg/day) was inversely correlated with motor symptom severity, as measured by the UPDRS Part III in the ON state (r = −0.544; p = 0.02). Conclusions: This study demonstrates a high prevalence of nutritional deficiencies, sarcopenia, and altered body composition in Ghanaian PD patients. These nutritional impairments are significantly associated with disease duration and motor symptom severity. The findings highlight the urgent need for early nutritional screening and intervention as part of a multidisciplinary approach to Parkinson’s disease management in resource-limited settings. Full article

Background: The decoding of motor imagery electroencephalography (MI-EEG) is constrained by core issues including low signal-to-noise ratio (SNR) and cross-session as well as cross-subject domain shift, which seriously impedes the practical deployment of brain–computer interfaces (BCIs). Methods: To address these challenges, this paper proposes a novel end-to-end MI-EEG decoding method named BARN-DA. Two innovative modules, Band-Aware Channel Attention (BACA) and Multi-Scale Kernel Perception (MSKP), are designed: one enhances discriminative channel features by modeling channel information fused with frequency band feature representation, and the other captures complex data correlations via multi-scale parallel convolutions to improve the discriminability of the network’s feature extraction. Subsequently, the features are mapped onto the Riemannian manifold. For the source and target domain features residing on this manifold, a Riemannian Maximum Mean Discrepancy (R-MMD) loss is designed based on the log-Euclidean metric. This approach enables the effective embedding of Symmetric Positive Definite (SPD) matrices into the Reproducing Kernel Hilbert Space (RKHS), thereby reducing cross-domain discrepancies. Results: Experimental results on four public datasets demonstrate that the BARN-DA method achieves average cross-session classification accuracies of 84.65% ± 8.97% (BCIC IV 2a), 89.19% ± 7.69% (BCIC IV 2b), and 61.76% ± 12.68% (SHU), as well as average cross-subject classification accuracies of 65.49% ± 11.64% (BCIC IV 2a), 78.78% ± 8.44% (BCIC IV 2b), and 78.14% ± 14.41% (BCIC III 4a). Compared with state-of-the-art methods, BARN-DA obtains higher classification accuracy and stronger cross-session and cross-subject generalization ability. Conclusions: These results confirm that BARN-DA effectively alleviates low SNR and domain shift problems in MI-EEG decoding, providing an efficient technical solution for practical BCI systems. Full article

Unbalanced supply voltage (USV) represents a critical power quality challenge in industrial environments, significantly degrading the performance, efficiency, and operational lifespan of three-phase induction motors. Accurate real-time estimation of sequence impedances ($Z_a,Z_b,Z_c$) and detection of the Negative Voltage Factor (NVF) are essential for effective condition monitoring and preventive maintenance strategies. While existing machine learning methods have demonstrated promising accuracy, they often rely on manual feature engineering, lack hierarchical representation learning, and treat impedance estimation and fault detection as isolated tasks. This paper proposes a unified Deep Multi-Task Learning framework that leverages Residual Multilayer Perceptron (ResMLP) architectures for feature-based learning and Temporal Convolutional Networks (TCNs) for end-to-end raw signal learning. Our contributions include: (1) introduction of a Multi-Head ResMLP architecture that jointly optimizes phase impedance and fault detection, achieving superior NVF accuracy (MAE = 0.0007) and a fault detection F1-score of 0.8831; (2) investigation of raw-voltage TCN models for voltage-only diagnostics, with analysis of the trade-offs between end-to-end learning and feature-based approaches; (3) extensive ablation studies demonstrating the impact of network depth, data augmentation, and training protocols on model generalization; and (4) deployment of PyTorch (v2.0.1)-based models suitable for embedded systems with real-time inference capabilities (2.3 ms per prediction). Experimental validation on a 1.1 kW three-phase motor dataset under diverse load conditions (0–10 Nm) and USV magnitudes (5–15 V) confirms the robustness and practical applicability of the proposed approach for industrial fault diagnosis and condition monitoring systems. Full article

Background: In traditional karate, performance effectiveness is determined, among other factors, by the speed of stimulus processing and the precision of the motor response. Body composition may indirectly modulate these abilities; however, data on karate athletes are limited. Methods: The study included 27 men—active members of the Polish national team in traditional karate (18–30 years; training experience ≥ 5 years; black belt). Body composition was assessed using segmental bioelectrical impedance analysis (InBody 770), and psychomotor abilities were measured with the TEST2DRIVE system: SIRT (simple reaction), CHORT (choice reaction), HECTOR (simple reaction), and SPANT (spatial anticipation). Results: The psychomotor profile showed the longest reaction times in CHORT and the shortest in SIRT. Associations with body composition were selective: in SIRT, the median simple reaction time demonstrated a moderate positive relationship with lean-mass-related parameters, with no associations for motor time. No significant correlations with body composition were found in CHORT or HECTOR. In SPANT, significant associations concerned motor time only, which was positively related to selected indices of adiposity and fat distribution, whereas choice reaction time and accuracy were independent of body composition. Conclusion: In traditional karate athletes, body composition is not an unambiguous predictor of psychomotor performance, and its relevance depends on task characteristics. The findings suggest that potential effects of somatic parameters are expressed mainly in selected execution components; therefore, assessments of competitive readiness should combine body composition monitoring with tests that differentiate the reaction phase from the motor phase. Full article

Spontaneous intracerebral hemorrhage (ICH) is associated with substantial mortality and morbidity. Current management paradigms rely heavily on the rapid interpretation of neuroimaging and clinical data, yet are frequently constrained by limitations in processing speed, diagnostic accuracy, and prognostic precision. Artificial intelligence (AI), specifically machine learning (ML) and deep learning (DL), offers transformative potential to circumvent these challenges across the entire continuum of ICH care. This comprehensive review synthesizes the rapidly evolving landscape of AI applications in ICH management. Through a systematic evaluation of recent literature, we examine studies focused on the development, validation, or critical appraisal of AI-driven technologies for ICH care. Our analysis encompasses automated neuroimaging, computer-assisted surgical navigation, brain–computer interfaces (BCIs), prognostic modeling, and fundamental research into disease mechanisms. AI has demonstrated performance comparable to that of clinical experts in automating hematoma segmentation, predicting complications such as hematoma expansion, and refining surgical planning via augmented reality. Furthermore, BCIs present innovative therapeutic avenues for motor rehabilitation. However, the translation of these technological advances into routine clinical practice is impeded by substantial challenges, including data heterogeneity, model opacity (“black-box” issues), workflow integration barriers, regulatory ambiguities, and ethical concerns surrounding accountability and algorithmic bias. The integration of AI into ICH care signifies a paradigm shift from standardized treatment protocols toward dynamic, precision medicine. Realizing this vision necessitates interdisciplinary collaboration to engineer robust, generalizable, and interpretable AI systems. Key priorities include the establishment of large-scale multimodal data repositories, the advancement of explainable AI (XAI) frameworks, the execution of rigorous prospective clinical trials to validate efficacy, and the implementation of adaptive regulatory and ethical guidelines. By systematically addressing these barriers, AI can evolve from a mere analytical tool into an indispensable clinical partner, ultimately optimizing patient outcomes. Full article

This paper presents a bench-top occupational power-assist robot aimed at reducing biomechanical effort during repetitive material handling. The prototype adopts a SCARA-like structure with three degrees of freedom and provides assistance on the vertical (z) axis through a three-phase brushless DC (BLDC) motor driven in field-oriented control with inner-loop current regulation. The user interacts with the robot through a single handle-mounted load cell. The measured interaction force is converted, via a calibration-based mapping, into a motor current reference that enforces a prescribed force-sharing ratio. In this way, the drive’s embedded current loop acts as the low-level torque regulator, and the system can share gravitational and inertial loads without additional environment force sensing or explicit high-level impedance/admittance dynamics. A coupled electro-mechanical model is derived and used to select the assistance gain and to verify feasibility in simulation. A pilot experimental campaign with eight participants and two payloads (0.5 kg and 1.5 kg) was carried out on sinusoidal and random tracking tasks. With assistance enabled, the operator contribution was reduced to about 15% of the total load, and the mean bicep brachii EMG amplitude decreased by about 60%, while tracking accuracy was generally preserved and often improved. Full article

Aimed at mitigating DC bus voltage fluctuations in electrolytic capacitor-less (ECL) motor drive systems caused by insufficient damping, conventional model predictive control (MPC) offers a fast dynamic response but fails to enhance the inherent damping or fully suppress such voltage variations. To address this limitation, this paper proposes a model predictive control strategy with adaptive virtual resistance adjustment (AVR-MPC). First, a virtual resistance loop is embedded into the active power decoupling circuit to reshape the system impedance and improve the damping characteristics at the model level. Subsequently, the state equations incorporating the virtual resistance are derived using small-signal modeling, and a Lyapunov function is constructed to determine its stable operating range. Based on this analysis, a dynamic relationship between the virtual resistance and the predicted current deviation is established, enabling adaptive tuning of the virtual resistance in response to the current deviation, thereby enhancing system stability under transient conditions. Finally, experimental results validate the effectiveness of the proposed control strategy. Full article

The reliability and efficiency of induction motors in Industry 4.0 environments critically depend on advanced fault detection systems capable of real-time monitoring and diagnosis. This paper presents a novel deep learning approach combining convolutional neural networks (CNNs) and long short-term memory (LSTM) networks for automated detection and classification of inter-turn short-circuit faults in three-phase induction motors. Our methodology processes three-phase current signals through a sophisticated CNN-LSTM architecture that extracts both spatial and temporal fault patterns. The proposed system classifies seven distinct motor conditions: healthy operation, three levels of high-impedance faults (HI-1 to HI-3), and three levels of low-impedance faults (LI-1 to LI-3). Experimental validation demonstrates exceptional performance, with the CNN-LSTM model achieving 97.2% accuracy, significantly outperforming traditional machine learning approaches, including SVM (66.3%), Random Forest (67.4%), and KNN (78.1%). The system provides real-time fault classification with inference times under 3 ms, making it suitable for continuous monitoring in smart manufacturing environments. Full article

Wide-bandgap transistors have short voltage rise and fall times, thus leading to overvoltage at the end of the cable connecting the inverter and the motor. In this paper, the overvoltage reduction possibilities have been investigated analytically, experimentally, and based on a simulation model. High-frequency models of the motor and the cable have been created based on impedance measurements. Different solutions for overvoltage reduction have been compared and an improved combined filter for the inverter with high switching frequency has been proposed. The overvoltage that was initially 80 percent has been reduced to below 10 percent by applying the filtering solution. Full article

With the rapid advancement of intelligent technologies in electric vehicles, various control technologies and algorithms are emerging. Most existing research, however, is limited to simulations of single modules such as suspension, braking, and battery management, lacking comprehensive modeling and simulation for the entire vehicle system, which impedes the integrated development and verification of advanced intelligent technologies. Therefore, this article focuses on the vehicle control system of electric vehicles. It first analyzes the overall scheme and clarifies the core functions of system operation control, fault detection, and storage. Subsequently, a data acquisition simulation platform for the vehicle control system is established based on MATLAB/Simulink, creating simulation modules for accelerator pedal, braking pedal, key position, and gear signal, forming a complete vehicle simulation platform. For the established simulation platform, specific electric vehicle model parameters are set, and under the QC/T759 urban driving conditions, simulations of the electric vehicle’s operation are conducted to obtain relevant signals such as vehicle speed, accelerator pedal, and braking pedal, verifying the feasibility of the vehicle control system. Finally, a hardware platform for the entire vehicle power system is built, and based on the PCAN-Explorer5 software, the connection and debugging of the vehicle controller, battery management system, and motor control unit are achieved to obtain the status parameters of each system and debug the vehicle control system, laying the foundation for the actual operation of the pure electric SUV. Through the simulation of the electric vehicle’s control system, the R&D cycle is greatly shortened, development costs are reduced, and a foundation is established for the actual vehicle debugging of electric vehicles. Full article

Background and Objectives: Long COVID-19 (LC) is a multifaceted condition characterized by persistent fatigue and impaired health-related quality of life (HRQoL). Exercise intolerance and post-exertional symptom exacerbation (PESE) pose challenges for rehabilitation. This study aimed to evaluate the effects of a 12-week core-focused plank exercise program on fatigue and HRQoL in women with LC, using validated patient-reported measures. Materials and Methods: A pilot quasi-experimental design was implemented, with non-randomized group allocation. Thirty-nine women with LC were recruited from the Madrid Long COVID Association. Participants were assigned to either an intervention group (n = 20), which completed a supervised plank-based motor control program, or a control group (n = 19), which maintained usual activity. Fatigue was assessed using the Modified Fatigue Impact Scale (MFIS), and HRQoL was measured using the EQ-5D-5L and EQ Visual Analog Scale (EQ-VAS). Body composition was evaluated via bioelectrical impedance analysis. Results: The intervention group showed significant reductions after intervention in the MFIS total scores compared to the control group, particularly in the physical (21.26 ± 6.76 vs. 25.21 ± 6.06; p < 0.001) and psychosocial domains (4.51 ± 0.41 vs. 5.21 ± 0.38; p < 0.001), without triggering PESE. EQ-VAS scores improved significantly (63.94 ± 15.33 vs. 46.31 ± 14.74; p = 0.034). No significant changes were found in body composition parameters, suggesting that benefits were driven by neuromuscular adaptations rather than morphological changes. Conclusions: A core-focused, non-aerobic exercise program effectively reduced fatigue and improved perceived health status in women with LC. These findings support the use of motor control-based interventions as a safe and feasible strategy for LC rehabilitation, particularly in populations vulnerable to PESE, suggesting clinical applicability for the rehabilitation of women with LC. Further randomized trials are warranted to confirm these results and explore long-term outcomes. Full article