Search Results (1,466)

Introduction: Pathogenic mutations in leucine-rich repeat protein kinase-2 (LRRK2), particularly G2019S, constitute the most common cause of autosomal dominant PD. Methods: Mouse models encoding human mutant alpha-synuclein (SNCA A53T) and LRRK2 G2019S were treated with a brain-penetrant kinase inhibitor (BK40196). Behavior, nigrostriatal and mesolimbic dopamine (DA) pathways were examined. Results: Mice harboring LRRK2 G2019S do not show age-dependent motor symptoms, but mice encoding SNCA A53T display motor deficits, while both strains exhibit anxiety-like behavior and BK40196 improves motor and behavioral defects. BK40196, a multi-kinase inhibitor of Abelson (Abl), Discoidin domain receptor (DDR)-1, c-KIT and FYN, alters microglial morphology and alpha-synuclein levels in SNCA A53T mice and improves DA neurotransmission, primarily via the nigrostriatal system. BK40196 inhibits brain LRRK2 G2019S (IC₅₀ of 89nM) and does not affect phosphorylated or total peripheral LRRK2 levels (lungs, kidneys, liver, etc.). LRRK2 G2019S mice treated with BK40196 exhibit distinct increases in DA in mesolimbic neurons such as the nucleus accumbens (NAcc), suggesting differential mechanisms of DA neurotransmission in mutant alpha-synuclein and LRRK2 models of PD. Conclusions: LRRK2 G2019S may primarily involve mesolimbic pathways leading to nonmotor symptoms independent of the motor and behavioral manifestations associated with alpha-synuclein via the nigrostriatal system. BK40196 may provide a comprehensive and synergistic therapeutic approach that addresses multiple mechanisms to reduce the pathologies related to LRRK2 G2019S and/or SNCA in PD. The multiple pathologies of PD necessitate a holistic approach that simultaneously targets inflammation and autophagy and LRRK2 inhibition. Full article

This study presents a systematic topological progression and multi-objective optimization of a Permanent Magnet-assisted Non-overlapping Winding Line-Start Synchronous Reluctance Motor (PMaNWLS-SynRM) for industrial applications. To explicitly highlight the core contribution, the research establishes a rigorous comparative framework evaluating the transition from a conventional 4-pole/36-slot distributed winding (DW) to a 2 × 12-slot non-overlapping winding (NW) architecture. Baseline results demonstrate that the NW configuration shortens end-turns, successfully reducing total electromagnetic losses from 417 W to 349 W and improving steady-state efficiency from 93.7% to 95.1%. To overcome the inherent starting limitations of pure synchronous reluctance machines, an aluminum squirrel-cage is integrated to enable robust direct-on-line (DOL) synchronization, while NdFeB permanent magnets are embedded within the rotor flux barriers to mitigate asynchronous spatial harmonics and elevate torque density. Finite element analysis (FEA) confirms this magnetic assistance raises the average synchronous torque to 65.8 Nm while suppressing absolute torque ripple to 1.37 Nm. Finally, an evolutionary genetic algorithm is deployed across 440 iterative configurations to resolve geometric multi-physics conflicts. The finalized optimized design achieves a 13.2 kW output power at 1800 rpm, maximizing average torque to 70.12 Nm and strictly dampening absolute torque ripple to an industry-acceptable 1.04 Nm. Operating with an aggregated total loss of 1382 W, the optimized PMaNWLS-SynRM yields a 90.5% operational efficiency, definitively validating its suitability as an ultra-premium IE4/IE5 alternative to conventional induction motors. Full article

Falls are a leading cause of paediatric injuries, yet school-based prevention relies heavily on subjective observation rather than objective biomechanical assessment. This paper introduces the Safe Fall framework, integrating a judo-inspired educational programme with an occlusion-robust computer vision pipeline to quantify safe falling strategies. We analysed video recordings of 285 schoolchildren using a multi-stage architecture combining YOLOv8 for detection, SAM 2 for segmentation, and MMPose for skeletal tracking. The intervention yielded significant improvements in 60% of kinematic metrics ($p<0.05$), most notably a $+61.4\%$ increase in descent rate and expanded rolling ranges, indicating a shift from hazardous “freezing” behaviours to controlled energy dissipation. Unsupervised clustering confirmed a migration of students towards safe motor profiles, while a Random Forest classifier achieved an accuracy of 98.3% and an AUC of 0.998 in distinguishing fall quality. These findings demonstrate that integrating pedagogical training with automated vision modelling provides a scalable and evidence-based approach for reducing injury risk in real-world school environments. Full article

Electroencephalogram (EEG) signals serve as the core information carrier for brain–computer interfaces (BCIs); however, their highly non-stationary nature, extremely low signal-to-noise ratio, and significant inter-individual variability pose considerable challenges for signal decoding. Existing deep learning methods struggle to strike a balance between multi-scale, fine-grained feature extraction and efficient long-range temporal modeling. To overcome this limitation, this study proposes a novel deep cascaded architecture, MSResBiMamba, which deeply integrates multi-scale spatiotemporal feature learning with cutting-edge long-sequence modeling techniques. The model first utilizes an enhanced multi-scale spatiotemporal convolutional network (MS-CNN) combined with a SE-channel attention mechanism to adaptively extract local multi-band features and dynamically suppress redundant artefacts. Subsequently, it innovatively introduces an enhanced bidirectional Mamba (Bi-Mamba) module to efficiently capture non-causal long-range temporal dependencies with linear computational complexity, whilst cascading multi-head self-attention mechanisms to establish global higher-order feature interactions. Extensive experiments on the BCI Competition IV-2a dataset demonstrate that MSResBiMamba achieves outstanding classification performance in multi-class motor imagery tasks, significantly outperforming traditional methods and existing state-of-the-art neural networks. Ablation studies and t-SNE visualisations further confirm the model’s robustness in feature decoupling and cross-subject applications, providing a high-precision, high-efficiency decoding solution for BCI systems. Full article

The link between neurodevelopment in infants exposed to maternal gestational diabetes mellitus (GDM) and fetal DNA methylation remains unexplored. We conducted this hypothesis-generating study to investigate the association between fetal DNA methylation and neurodevelopmental outcomes in children of mothers with GDM. We carried out a prospective, observational pilot cohort study comparing infants exposed to maternal GDM with an unexposed control group. Umbilical cord blood DNA methylation was assessed using targeted methylome sequencing covering 3.34 million CpG sites. Infant neurodevelopment was evaluated at age two years using the Bayley-III Scales. Bioinformatics processing identified differentially methylated regions (DMRs), followed by multiple enrichment analyses of DMR-associated genes and partial correlation analyses. Multi-dimensional enrichment analysis of the 1053 identified DMR-associated genes revealed a significant convergence of pathways related to neurogenesis, synaptic components, and axonal guidance. Infants born to mothers with GDM exhibited lower scores in cognitive, language, and motor domains, which were associated with identifiable DNA methylation signatures at birth. Significant correlations were observed in genes essential for brain scaffolding and synaptic circuitry, most notably WNT4, the PCDHG alpha/beta clusters, and PALM. Additionally, methylation patterns in FOXF2 and CHFR suggest a potential impact on blood–brain barrier integrity, while associations with FSTL3 and H6PD highlight a systemic metabolic ‘cross-talk’ influencing neurodevelopment. Although these pilot findings are hypothesis-generating and require further functional validation, this study provides pioneering evidence that neurodevelopmental alterations in the offspring of mothers with GDM are potentially associated with intrauterine epigenetic modifications detectable at birth. Full article

Precise monitoring of forearm muscle groups is crucial for decoding motor intentions in human–machine interfaces (HMIs) and rehabilitation. However, traditional surface electromyography (sEMG) electrodes face significant challenges in densely packed muscle regions with large skin deformations, leading to severe signal crosstalk and unstable contact. Here, we report a flexible, low-cost 16-channel copper electrode array system designed for the high-density monitoring of multiple forearm muscle activities. Through a facile fabrication process, rigid copper is transformed into a conformable sensing interface. The optimized serpentine interconnects endow the array with excellent stretchability and effectively isolate motion-induced stress, ensuring high-quality signal acquisition under complex deformations. The high-density 2 × 8 array enables the spatiotemporal mapping of distributed flexor and extensor muscle groups. Integrated with a customized wireless data acquisition system, the array successfully demonstrates real-time, multi-channel sEMG monitoring of various hand movements (e.g., fist clenching, wrist flexion/extension), clearly revealing specific muscle activation patterns. This low-cost, high-performance flexible sensor array provides a highly promising tool for complex gesture decoding, electromyographic imaging, and next-generation wearable HMIs. Full article

Individual differences and long calibration time present significant challenges to the practical implementation of brain–computer interfaces (BCIs). Domain adaptation technology can help mitigate these challenges by leveraging knowledge from existing subjects. Although domain adaptation methods have achieved progress in BCIs, there remains a need for further exploration in class structure and cross-domain dispersion. In this paper, we propose a novel framework, multi-constrained transfer learning with selective pseudo-label update (MCTLP). First, Euclidean alignment is applied to reduce inter-subject variability at the data level. Then, multi-constrained feature alignment (MCFA) is introduced, which iteratively constructs a kernel mapping space and then determines an optimized subspace to align both marginal and conditional distributions at the feature level under class structure and dispersion constraints. Moreover, in this iterative process of feature alignment, a selective pseudo-label update method is proposed to update the pseudo-labels of only the target samples with high classification confidence to realize more reliable conditional distribution alignment. Two benchmark datasets were used to verify the presented MCTLP. The results showed that MCTLP outperformed other existing methods, demonstrating its strong ability for cross-subject transfer. Full article

Active magnetic levitation bearings incorporate backup bearings that support the rotor during a breakdown, allowing it to maintain its circular movement despite the loss of magnetic force. This safeguards both the stator of the magnetic levitation bearing and the motor stator from harm. Research reveals that ball bearings are susceptible to failure mechanisms, including raceway wear and scoring. The principal cause is the unregulated motion of the rolling parts, which are divided by the cage, once wear manifests, resulting in raceway lag. This leads to significant contact deformation between the rolling elements and the raceway, along with prolonged cumulative impacts between the rolling elements and the cage. Cage-free bearings prevent collisions between the cage and rolling elements; yet, the orbital motion of the rolling elements in these bearings demonstrates a level of independence and randomness relative to traditional caged ball bearings. This presents considerable obstacles to attaining standard orbital motion in cage-free ball bearings. Despite advancements in technology that have largely elucidated the non-linear motion dynamics of ball bearings, several critical hurdles in behavioral characterization persist. This work presents a thorough review of the non-linear motion behavior of ball bearings and the methodologies for their multi-body dynamic characterization. This report proposes future research topics to improve the design of high-performance bearings and augment their reliability. Full article

Background/Objectives: Parkinson’s disease (PD) is a progressive neurodegenerative disorder that poses a substantial threat to global human health. Yam (Dioscorea opposita Thunb.) is a traditional medicinal and edible plant that has long been used in Asia, Africa, and the Caribbean. Its major bioactive components, such as dioscin and polysaccharides, have been reported to exhibit neuroprotective effects; however, the impact of dietary yam on PD progression remains to be elucidated. Therefore, we sought to evaluate its neuroprotective potential and the underlying mechanisms in 1-methyl-4-phenyl-1,2,3,6 tetrahydropyridine (MPTP)-induced PD mice. Methods: Mice received six-week dietary yam supplementation. Behavioral, histological, and neurochemical analyses were performed to assess motor function, dopaminergic neuron integrity, and dopamine levels. Gut microbiota and metabolic profiles were analyzed using 16S rRNA gene sequencing and non-targeted metabolomics. Transcriptomic sequencing and Western blot analysis of the substantia nigra pars compacta (SNc) were conducted to investigate molecular mechanisms, and integrative multi-omics analysis was applied to explore microbiota–metabolite–host interactions. Results: Yam supplementation improved motor function, preserved nigrostriatal dopaminergic neurons, and restored striatal dopamine levels in PD mice. Notably, yam was associated with the maintenance of intestinal homeostasis by strengthening barrier integrity and enriching beneficial taxa, including Ileibacterium, Lachnospiraceae NK4A136 group, and Blautia. Consistently, yam also elevated neuroprotective purines and amino acids, including inosine, xanthine, and succinic acid. At the molecular level, yam treatment modulated mitochondrial oxidative phosphorylation by increasing PGC-1α and COX7c expression, and reduced inflammasome-related neuroinflammatory signaling. Integrative modeling showed significant associations between yam-modulated genes and PD-related indices with microbiota and metabolites. Conclusion: These findings suggest that yam may represent a potential dietary strategy for alleviating PD-related neurodegeneration by modulating the microbiota–gut–brain axis. Full article

Motor neuron diseases (MNDs) encompass a clinically heterogeneous group of neurodegenerative conditions with varying impact on dexterity, mobility, decision making, respiratory and bulbar function. While consensus best-practice recommendations exist for genetic screening, diagnostic work-up, pharmacological and respiratory management, disease-specific facets of driving safety, assessment approaches and intervention strategies to support patients for safe driving have not been comprehensively reviewed. MNDs have unique, phenotype-specific clinical features, and therefore require a careful, thorough and systematic approach to evaluate driving safety. While MNDs are primarily associated with progressive motor impairment, extrapyramidal, cerebellar, cognitive, behavioural, and respiratory manifestations of the disease also affect driving safety and necessitate comprehensive driving assessments and individualised strategies to enable patients to continue to drive. The majority of existing papers focus on amyotrophic lateral sclerosis, and low-incidence MND phenotypes, such as PLS, SBMA, PPS, are glaringly understudied from a driving safety perspective despite the relatively slower progression of these conditions. Beyond the review of specific aspects of driving in MNDs, the main objective of this review paper is to raise awareness of non-motor aspects of MNDs with regard to driving safety and to explore viable strategies to support patients to maintain their independence. Despite the considerable differences in driving regulations around the globe, there are core, disease-specific aspects of MND which are universal. The careful consideration of these clinical factors, comprehensive domain-by-domain assessments, and the implementation of practical, individualised adaptations may enable patients to continue driving safely, maintain their independence and enhance their quality of life. In this paper, we propose a systematic, multidomain driving safety assessment scheme for MND, and outline viable intervention strategies to enhance driving safety. Full article

Varroa destructor (Bee mite) and Deformed Wing Virus are primary causes of honeybee colony collapse. This study developed an automated AI-based vision inspection system for detecting bee mites and deformed bees using the YOLO algorithm. The system integrates an RGB camera, a beecomb rotation motor, and an image transmission module to enable automated dual-sided image acquisition of the beecomb. The image characteristics of normal bees, bee mites, and deformed bees were analyzed, and YOLO-based object detection models were developed to classify them. Six YOLO models—based on YOLOv8 and YOLOv11 architectures across three model sizes (nano, small, and large)—were evaluated on 405 test images (6441 objects). The proposed system reduced the inspection time from 240 s required for manual method to 20 s per beecomb, achieving 12-fold efficiency improvement. Comparative analysis showed model-task specialization: YOLOv8l excelled in detecting small bee mites (F1: 92.5%, mAP[0.5]: 92.1%), while YOLOv11s achieved the highest performance for morphologically diverse deformed bees (F1: 95.1%). Error analysis indicated that detection performance was influenced by morphological characteristics. Deformed bee detection errors correlated with overlap in wing-to-body ratio: DB Type II exhibited 18.6% miss rate, while DB Type III achieved perfect detection. In bee mite detection, a sensitivity–specificity trade-off was observed: YOLOv11l had the lowest false negatives (2.5%) but highest false positives, while YOLOv8l demonstrated superior discrimination. These results demonstrate the practical potential of the proposed system for field deployment in apiaries, supporting early pest diagnosis and improved colony health management. The model-task specialization framework provides guidance for architecture selection based on object characteristics. Future work will focus on multi-location validation and real-time monitoring integration. Full article

Legged robots are widely used for walking, running, jumping, and landing on the ground. As mission terrains become increasingly complex, legged robots with greater adaptability are required. However, limited research attention has been paid to enhancing their impact resistance and obstacle-surmounting capabilities. Due to the limitations of motor manufacturing and material, it is more difficult to improve the impact resistance of the motor than to design proper leg lengths. Considering rigid multi-link medium- and large-sized legged robots, we optimize leg lengths to minimize the impact torque on leg joints. An optimal leg-length combination that maximizes obstacle-surmounting capability for medium- and large-size multi-link legged robots is conducted. This research provides a concrete design basis for leg-length optimization in medium- and large-sized multi-link legged robots with the aim of improving impact resistance and obstacle surmounting. Full article

Telerehabilitation—the remote delivery of rehabilitation services—is undergoing a paradigm shift with the convergence of immersive virtual reality (VR) and wearable biosensor technologies. This perspective article outlines a vision for home-based motor and cognitive rehabilitation that is engaging, personalized, and data-driven. We describe how immersive VR environments (for example, simulations of home settings or supermarkets) coupled with wearable sensors can address current challenges in rehabilitation by increasing patient motivation, enabling real-time biofeedback, and supporting remote clinician supervision. Gamification mechanisms and rich sensory feedback in VR are highlighted as key strategies to enhance user engagement and adherence to therapy. We discuss conceptual innovations such as multi-sensor data integration, dynamic difficulty adaptation, and AI-driven personalization of exercises, derived from recent research and our development experience, and consider their potential benefits for patients with neuro-cognitive-motor impairments (e.g., stroke, Parkinson’s disease, and multiple sclerosis). Implementation scenarios for home-based therapy are presented, emphasizing scalability, standardized digital metrics for monitoring progress, and seamless involvement of clinicians via telehealth platforms. We also critically examine the current limitations of VR and telehealth rehabilitation and how an integrative model could overcome these barriers. More specifically, this perspective defines the engineering requirements of a closed-loop VR-based telerehabilitation framework, including multimodal data synchronization, calibration, signal-quality management, interpretable adaptive control, digital biomarker validation, and practical strategies to improve accessibility, privacy, and scalability in home-based neurological rehabilitation. Full article

In this study, a novel direct torque control (DTC) strategy is proposed to mitigate the torque ripple issue inherent in switched reluctance motors (SRMs), which is caused by the double salient pole configuration and the pulse power supply mode. The strategy is based on the prediction and optimization of a long-time-domain model. Central to this method is the development of a multi-step predictive optimization framework. By incorporating hysteresis control, the conventional approach of minimizing instantaneous error in predictive control is shifted towards minimizing tracking error over an extended time frame. A dual-objective evaluation function is also introduced, which simultaneously optimizes both torque smoothness and switching frequency, ensuring their collaborative enhancement. To validate the proposed method, a 6/4-pole SRM simulation model was implemented using MATLAB/Simulink 2024B, and comparisons were made with traditional methods. The results demonstrate that this strategy significantly reduces torque pulsation and lowers the system’s switching frequency, even under varying operational conditions such as different rotational speeds and sudden load variations. Consequently, this approach not only guarantees improved dynamic performance but also enhances the motor’s efficiency and stability. Full article

In the aviation sector, there is a growing demand for high-specific-power electrical machines to realize More Electric Aircraft (MEA). The goals for these machines were set by the National Aeronautics and Space Administration (NASA) as 1 $\mathrm{M}$$\mathrm{W}$ power, >13 kW kg⁻¹ of power density, and efficiency >96%. To address these requirements, this paper proposes an electromagnetic design of a high-speed, power-dense, 1 $\mathrm{M}$$\mathrm{W}$ radial-flux Permanent Magnet Synchronous Machine (PMSM) for aerospace propulsion applications that achieves NASA targets. Achieving high-specific-power objectives necessitates geometry optimization that simultaneously minimizes motor mass while maximizing output power. This paper presents a faster optimization algorithm that hybridizes Genetic Algorithm and Artificial Neural Network (ANN)-based surrogate modeling to optimize the motor for multi-objective goals. The proposed framework employs a multi-objective approach targeting maximum torque output and efficiency within a minimum motor mass. This approach, using an ANN-based surrogate, significantly reduces optimization time by saving 95% of the time compared to FEM simulations. The optimized 1 $\mathrm{M}$$\mathrm{W}$ motor attains 98% efficiency and an active power density of 24.87 kW kg⁻¹. The various stages of the optimization are presented in detail and a comparison of the time saving using the proposed algorithm is outlined. To demonstrate the feasibility of design, a detailed electromagnetic analysis, stator thermal analysis with a jet impingement design, and magnet demagnetization risk analysis were also presented. Full article