Search Results (3,020)

Optimizing healthcare resources in neurodegenerative diseases requires balancing diagnostic performance with cost constraints. We introduce AHN-BudgetNet—a tiered, cost-aware assessment framework for Parkinson’s disease motor severity prediction—evaluated on 1387 simulated PPMI subjects via patient-level GroupKFold validation. Our analysis tested seven tier combinations encompassing demographic, self-reported, and clinical features. The baseline (T0) yields AUC = 0.65 (95% CI [0.629, 0.681]) at no cost. Self-assessments (T1) alone achieved an AUC = 0.69 (95% CI [0.643, 0.733]) at USD 75, with an efficiency of 1.07. The combined T0 + T1 set reached AUC = 0.75 (95% CI [0.729, 0.772]) at USD 75, with efficiency 1.43. T2 alone obtained AUC = 0.53 (95% CI [0.517, 0.542]) at USD 300 and efficiency 0.07. The full T0+T1+T2 set achieved the highest performance—AUC = 0.76 (95% CI [0.735, 0.774])—at USD 375, with efficiency 0.54, reflecting diminishing returns beyond T1. High-cost tiers (T3/T4) could not be empirically validated due to over 88% missing data, emphasizing the value of accessible assessments. Gaussian Mixture on Tier 0 features yielded a silhouette score of 0.54, compared to 0.53 for K-means, confirming that patient-reported outcomes can support clinical stratification. Our results underpin evidence-based resource allocation: budgets USD ≤75 prioritize T1, while budgets USD ≤375 justify a comprehensive assessment. This confirms that structured tier prioritization supports robust, resource-efficient diagnosis in resource-limited clinical environments. Full article

Wheeled bipedal robots (WBRS) combine the terrain adaptability potential of legged robots with the motion efficiency of wheeled robots, but the terrain adaptability is affected by the control system. Aiming at the defect that the traditional modeling ignores the dynamic changes in head angle and center of mass height, this paper proposes a method of integrated dynamic modeling and hierarchical control. The posture balance optimizes the system performance index through the linear quadratic regulator (LQR) to control the in-wheel motor, and the state feedback matrix is designed to suppress the tipping caused by external interference. At the same time, the changes in head angle and center of mass height are included in the balance control variables. The center of mass height changes are fed back through the proportional differential (PD) control and virtual model control (VMC) algorithm to control the joint motor. Simulation experiments are carried out on multiple platforms to verify that the proposed method effectively improves the control robustness of the traditional wheeled bipedal robot through geometric-dynamic coupling modeling and LQR-PD hybrid control, providing a new method of complex terrain adaptive control. Full article

The rapid growth in the number of motor vehicles has exacerbated traffic congestion. The occurrence of congestion not only poses significant challenges for traffic management authorities but also severely impacts residents’ travel and daily routines. Against this backdrop, predicting traffic flow can provide crucial insights for anticipating changing traffic patterns. Therefore, this paper proposes a novel hybrid deep learning architecture (CNN-LSTM-GRU) for highway traffic flow prediction that integrates spatiotemporal and meteorological dimensions. Our approach constructs a multidimensional feature matrix encompassing temporal sequences, spatial correlations, and weather conditions. Convolutional Neural Networks (CNN) are employed to capture spatial patterns, while Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks jointly model temporal dependencies. Through systematic hyperparameter tuning and step-length optimization, we validate the model using real-world traffic data from a provincial highway network. The experimental evaluation analyzes the following two critical dimensions: (1) holiday vs. non-holiday traffic patterns, and (2) the impact of weather data integration. Comparative analysis reveals that our hybrid model demonstrates superior prediction accuracy over standalone LSTM, GRU, and their CNN-based counterparts (CNN-LSTM, CNN-GRU). Full article

Background: Stiff-knee gait commonly involves rectus femoris spasticity in patients with central nervous system lesions. Diagnostic nerve blocks aid in predicting treatment outcomes; however, current techniques may overlook multiple nerve branches that innervate the rectus femoris muscle, potentially resulting in an incomplete assessment of treatment outcomes. Methods: We present an ultrasound-guided approach that we currently use in our practice, using anatomical landmarks, including the femoral artery, the sartorius muscle, and the rectus femoris’ characteristic “J-shaped” internal tendon. The technique employs an “elevator” scanning method to identify all motor nerve branches (typically 2–3) entering the proximal third of the rectus femoris muscle. Each branch is blocked using an in-plane needle approach with 1–2 mL of 2% lidocaine. Results: The technique enables the visualization of hyperechoic nerve branches entering the rectus femoris muscle from medial to lateral, sometimes accompanied by small vascular branches that are identifiable with a Doppler ultrasound. Optimal ultrasound settings include probes >8 MHz, appropriate focus positioning, and dynamic range < 60 dB. The multi-branch approach produces rapid-onset motor weakness (5–10 min). Conclusions: This comprehensive multi-branch rectus femoris nerve block technique may enhance diagnostic accuracy for spasticity assessment, potentially leading to more informed treatment selection for stiff-knee gait. Full article

The development and implementation of scientifically substantiated solutions for the improvement and modernization of electromechanical devices, systems, and complexes, including electric drives, is an urgent theoretical and applied task for energetics, industry, transport, and other key areas, both in global and national contexts. The aim of this paper is to identify a rational model of an induction motor that balances computational simplicity and control system performance based on predictive approaches while ensuring maximum energy efficiency and reference tracking during the operation in dynamic modes. Five main mathematical models of an induction machine with different levels of detail have been selected. Three predictive control models have been implemented using GRAMPC (v 2.2), Matlab MPC Toolbox (v 24.1), and fmincon (R2024a) (from Matlab Optimization Toolbox). It has been established that in the dynamic mode of operation, the equivalent induction motor circuit with parameters $R_{fe} =const$, $L_{\mu }=f\left(I_{1d}\right)$, and $T_F=f\left({\omega }_{Rm}\right)$ is the most appropriate in terms of the following criteria: accuracy of control action generation, computation speed, and calculation of energy consumption. Full article

Population aging intensifies the demand for rehabilitation services, which are already suffering from staff shortages. In response to this challenge, the implementation of new technologies in physiotherapy is needed. For such a task, rehabilitation exoskeletons can be used. While designing such tools, their functionality and safety must be ensured. Therefore, simulations of their strength and kinematics must meet set criteria. This paper aims to present a methodology for simulating the dynamics of rehabilitation exoskeletons during activities of daily living and determining the reactions in the construction’s joints, as well as the required driving torques. The methodology is applied to the SmartEx-Home exoskeleton. Two versions of a multibody model were developed in the Matlab/Simulink environment—a rigid-only version and one with deformable components. The kinematic chain of construction was reflected with the driven rotational joints and modeled passive sliding open bearings. The simulation outputs include the driving torques and joint reaction forces and the torques for various input trajectories registered using IMU sensors on human participants. The results obtained in the investigation show that in general, to mobilize shoulder flexion/extension or abduction/adduction, around 30 Nm of torque is required in such a lightweight exoskeleton. For elbow flexion/extension, around 10 Nm of torque is needed. All of the reactions are presented in tables for all of the characteristic points on the passive and active joints, as well as the attachments of the extremities. This methodology provides realistic load estimations and can be universally used for similar structures. The presented numerical results can be used as the basis for a strength analysis and motor or force sensor selection. They will be directly implemented for the process of mass minimization of the SmartEx-Home exoskeleton based on computational optimization. Full article

Duct fan motors must provide high torque within limited space to maintain airflow while requiring low vibration characteristics to minimize fluid resistance caused by fan oscillation. Axial Flux Permanent Magnet Motor (AFPM) offers higher torque performance than Radial Flux Permanent Magnet Motor (RFPM) due to their large radial and short axial dimensions. In particular, the coreless AFPM structure enables superior low-vibration performance. Conventional AFPM typically employs a core-type stator, which presents manufacturing difficulties. In core-type AFPM, applying a multi-stator configuration linearly increases winding takt time in proportion to the number of stators. Conversely, a Printed Circuit Board (PCB) stator AFPM significantly reduces stator production time, making it favorable for implementing multi-stator topologies. The use of multi-stator structures enables various topological configurations depending on (1) stator placement, (2) magnetization pattern of permanent magnets, and (3) rotor arrangement—each offering specific advantages. This study evaluates and analyzes the performance of different topologies based on efficient arrangements of magnets and stators, aiming to identify the optimal structure for duct fan applications. The validity of the proposed approach and design was verified through three-dimensional finite element analysis (FEA). Full article

This study proposes a multi-via structure as a loss-reduction design technique to mitigate current crowding in a slotless axial flux permanent magnet motor (AFPM) equipped with printed circuit board (PCB) stators. The PCB stator enables high current density operation through parallel copper-foil stacking and supports an ultra-compact structural configuration. However, current concentration in the via regions can increase copper loss and phase resistance. In this work, the via position and diameter were defined as design variables to perform a sensitivity analysis of current distribution and phase resistance variation. The effects of current density dispersion and the potential for copper loss reduction were evaluated using three-dimensional finite-element analysis (FEA). The results confirm that adopting a multi-via structure improves current path uniformity and reduces electrical losses, thereby enhancing overall efficiency. Furthermore, the analysis shows that excessive via enlargement or overuse does not necessarily yield optimal results and, in certain cases, may lead to localized current peaks. These findings demonstrate that the multi-via structure is an effective and appropriate design strategy for PCB stators and highlight the importance of optimized via placement tailored to each stator configuration. Full article

To significantly reduce fuel consumption and improve fuel economy in hybrid tractor under complex working conditions, an energy—saving strategy based on working condition prediction and Deep Deterministic Policy Gradient and Fuzzy control (DDPG-Fuzzy) was proposed. Firstly, a hybrid tractor system dynamics model containing diesel, motor, and power battery was established. Secondly, a working condition prediction model for plowing velocity and resistance was constructed based on the adaptive cubic exponential smoothing method. Finally, a two-layer control architecture was designed. The upper layer adopted the DDPG algorithm, which takes demand torque, equivalent fuel consumption, and the State of Charge (SOC) as state inputs to optimize energy consumption by generating the diesel benchmark torque through the policy network. The lower layer introduced a fuzzy control compensation mechanism that calculates the torque correction based on the plowing velocity error and the plowing resistance deviation to adjust the power allocation. In light of on this, an energy—saving strategy for hybrid tractor based on working condition prediction and DDPG-Fuzzy control was proposed. Under a standard 140 s plowing cycle, the results showed that the working condition prediction model achieved mean prediction accuracies of 97% for plowing velocity and 96.8% for plowing resistance. Under plowing conditions, the proposed strategy reduced the equivalent fuel consumption by 9.7% compared to the power-following strategy, and reduced SOC by 4.4% while maintaining it within a reasonable range. By coordinating the operation of the diesel and motor within high-efficiency regions, this approach enhances fuel economy under complex working conditions. Full article

Background/Objective: The global acceleration of population aging presents profound challenges to the physical, psychological, and social well-being of older adults. As traditional exercise programs face limitations in accessibility, personalization, and sustained social support, there is a critical need for innovative, inclusive, and community-integrated digital movement solutions. This study aimed to develop and evaluate Movement Poomasi, a hybrid digital healthcare application designed to promote physical activity, improve digital accessibility, and strengthen social connectedness among older adults. Methods: From March 2023 to November 2023, Movement Poomasi was developed through an iterative user-centered design process involving domain experts in physical therapy and sports psychology. In this study, the term UI/UX—short for user interface and user experience—refers to the overall design and interaction framework of the application, encompassing visual layout, navigation flow, accessibility features, and user engagement optimization tailored to older adults’ sensory, cognitive, and motor characteristics. The application integrates adaptive exercise modules, senior-optimized UI/UX, voice-assisted navigation, and peer-interaction features to enable both home-based and in-person movement engagement. A two-phase usability validation was conducted. A 4-week pilot test with 15 older adults assessed the prototype, followed by a formal 6-week study with 50 participants (≥65 years), stratified by digital literacy and activity background. Quantitative metrics—movement completion rates, session duration, and engagement with social features—were analyzed alongside semi-structured interviews. Statistical analysis included ANOVA and regression to examine usability and engagement outcomes. The application has continued iterative testing and refinement until May 2025, and it is scheduled for re-launch under the name Wello! in August 2025. Results: Post-implementation UI refinements significantly increased navigation success rates (from 68% to 87%, p = 0.042). ANOVA revealed that movement selection and peer-interaction tasks posed greater cognitive load (p < 0.01). A strong positive correlation was found between digital literacy and task performance (r = 0.68, p < 0.05). Weekly participation increased by 38%, with 81% of participants reporting enhanced social connectedness through group challenges and hybrid peer-led meetups. Despite high satisfaction scores (mean 4.6 ± 0.4), usability challenges remained among low-literacy users, indicating the need for further interface simplification. Conclusions: The findings underscore the potential of hybrid digital platforms tailored to older adults’ physical, cognitive, and social needs. Movement Poomasi demonstrates scalable feasibility and contributes to reducing the digital divide while fostering active aging. Future directions include AI-assisted onboarding, adaptive tutorials, and expanded integration with community care ecosystems to enhance long-term engagement and inclusivity. Full article

Axial flux motors, characterized by compact axial dimensions and high torque density, are well-suited for space-constrained applications such as in-wheel drives and flying vehicles. However, conventional axial flux permanent magnet synchronous motors (AFPMSMs) face challenges such as high-temperature demagnetization, reduced efficiency at high speeds, and elevated manufacturing costs. Electrically excited synchronous motors (EESMs) offer a promising alternative, providing high-temperature reliability and superior high-speed capability while maintaining high torque density. In this paper, a novel composite-cooled axial flux electrically excited synchronous motor (AFEESM) is proposed. From an electromagnetic design perspective, the effects of key parameters such as shaft-to-outer-diameter ratio, inner-to-outer-diameter ratio, slot depth, and yoke thickness on output performance are systematically investigated, and a dedicated design procedure is established. Through multi-objective optimization, the motor’s torque output is increased by 19.6%. Comparative simulations are conducted to evaluate differences in torque density, efficiency, and cost between the proposed AFEESM, a conventional radial flux EESM, and an AFPMSM. To address the cooling requirements of double-sided windings on both the stator and rotor, a dual-channel composite cooling structure is developed, integrating internal–external double-loop water cooling for the stator and axial through-hole air cooling for the rotor, reducing the peak temperature by over 36%. Finally, a prototype is manufactured, and no-load characteristics and load efficiency validate the effectiveness of the electromagnetic design and the structural reliability of the motor. Full article

The relationship between sleep and epilepsy involves complex interactions between thalamocortical circuits, circadian mechanisms, and sleep architecture that fundamentally influence seizure susceptibility and cognitive outcomes. Epileptic activity disrupts essential sleep oscillations, particularly sleep spindles generated by thalamic circuits. Thalamic epileptic spikes actively compete with physiological sleep spindles, impairing memory consolidation and contributing to cognitive dysfunction in epileptic encephalopathy. This disruption explains why patients with epilepsy often experience learning difficulties despite adequate seizure control. Sleep stages show differential seizure susceptibility. REM sleep provides robust protection through enhanced GABAergic inhibition and motor neuron suppression, while non-REM sleep, particularly slow-wave sleep, increases seizure risk. These observations reveal fundamental mechanisms of seizure control within normal brain physiology. Circadian clock genes (BMAL1, CLOCK, PER, CRY) play crucial roles in seizure modulation. Dysregulation of these molecular timekeepers creates permissive conditions for seizure generation while being simultaneously disrupted by epileptic activity, establishing a bidirectional relationship. These mechanistic insights are driving chronobiological therapeutic approaches, including precisely timed antiseizure medications, sleep optimization strategies, and orexin/hypocretin system interventions. This understanding enables a paradigm shift from simple seizure suppression toward targeted restoration of physiological brain rhythms, promising transformative epilepsy management through sleep-informed precision medicine. Full article

Background: Soft tissue sarcomas (STSs) are rare and heterogeneous malignancies requiring a multidisciplinary approach to diagnosis and treatment. Advances in surgical techniques, chemotherapy, and radiotherapy have improved survival rates but often result in significant functional impairments, particularly in patients undergoing limb-sparing procedures. Rehabilitation is crucial for restoring mobility and independence, with recent studies emphasizing the importance of personalized rehabilitation protocols tailored to specific surgical interventions. Quantitative assessments, such as 3D motion capture and surface electromyography, provide objective insights into gait performance and motor function, enabling more precise rehabilitation strategies to optimize recovery. Methods: This study evaluated gait performance in 21 patients with lower-limb impairment following limb-sparing surgery for STS. Patients underwent two instrumented gait assessments using marker-based 3D motion capture and surface electromyography to measure spatiotemporal gait parameters, joint kinematics, and muscle activity. Independence in the activity of daily living was assessed with the modified Barthel Index in both timepoints. Results: Following rehabilitation, patients demonstrated significant improvements in functional independence, as reflected by an increase in the modified Barthel Index (p < 0.001). Gait analysis revealed increased walking speed, stride length, cadence, and improved joint range of motion at the hip, knee, and ankle, though electromyographic analysis showed no statistically significant differences in muscle activation patterns or co-contraction indices. Conclusions: These findings underscore the importance of a rehabilitation programs personalized on gait strategies. A deeper understanding of motor adaptations based on sarcoma location and surgical approach could further refine rehabilitation protocols, ultimately enhancing patient outcomes and quality of life. Full article

Line-Start Permanent Magnet Synchronous Motors (LSPMSMs) offer a hybrid solution that combines the high efficiency of permanent magnet motors with the self-starting capability of induction machines. This review examines their key performance characteristics, historical development, and design approaches. Advantages such as high efficiency, improved power factor, and operational stability are discussed alongside challenges like limited critical inertia and synchronization issues. Design enhancements through rotor topology optimization and cage resistance adjustment are also explored. Finally, market trends and economic considerations are evaluated, highlighting the strong potential of LSPMSMs in energy-efficient motor applications. Full article

This study aimed to determine optimal forming conditions by comparing the compaction behavior and microstructural characteristics of two Fe-based Soft Magnetic Composite (SMC) powders, Somaloy 700HR 5P and Somaloy 130i 5P. A full factorial design was employed with powder type, compaction temperature, and punch speed as variables. Finite element modeling (FEM) using experimentally derived properties predicted density and stress distributions in toroidal geometries. 700HR 5P exhibited higher stress under most conditions, while both powders showed similar axial density gradients. Experimental results validated the simulations. SEM analysis revealed that 130i 5P had fewer microvoids and clearer particle boundaries. As revealed by TEM-EDS analyses, after heat treatment, both powders exhibited a tendency for the insulation layers to become more uniform and continuous. The insulation layer of 700HR 5P was relatively thicker but retained some pores, whereas that of 130i 5P was thinner yet exhibited smoother and more continuous coverage. XRD analysis indicated that both powders retained an α-Fe solid solution. These results demonstrate that powder properties, composition, and insulation stability significantly influence compaction and microstructural evolution. This work systematically compares the formability and insulation stability of two commercial Somaloy powders and elucidates process–structure–property relationships through an application-oriented evaluation integrating experimental design, FEM, and microstructural characterization, providing practical insights for optimal process design. Full article