Search Results (690)

This paper presented an optimization and material enhancement framework for improving the efficiency of a 1 HP Permanent Split Capacitor (PSC) motor toward IE2/IE3 efficiency classes. The proposed approach integrated Design of Experiments (DOE) using the Taguchi method with loss-based analysis to investigate the influence of key design parameters, including stator stack height, capacitor value, and silicon steel grade on PSC motor efficiency. Taguchi L8 and L9 orthogonal arrays were applied to evaluate parameter interactions and identify dominant factors affecting motor performance. To enhance predictive capability, a Response Surface Methodology (RSM) model was developed based on experimental data to establish the relationship between design variables and motor efficiency within the investigated operating range. The resulting efficiency map was used to identify high-efficiency operating regions and support practical PSC motor design evaluation. Experimental validation under multi-load operating conditions confirmed that the optimized motor achieved an efficiency improvement from 76.1% to 80.4% (4.6% absolute increase), with less than 2% deviation between simulation and experimental results. The optimized motor also demonstrated improved operating behavior across varying speed and load conditions while maintaining practical operating stability. The proposed framework provided a practical and simplified approach for PSC motor efficiency improvement under the investigated operating conditions and offers an alternative to computationally intensive optimization approaches for industrial motor applications. Full article

Background: Non-small-cell lung cancer (NSCLC) research has transitioned from tumor-intrinsic mechanisms to immune-driven therapeutic innovation; however, the translation of mechanistic insights into clinically actionable strategies remains incompletely defined. Methods: A total of 1213 records were retrieved from the Web of Science Core Collection (SCI-Expanded) (2009–2025). After data cleaning and duplicate removal (n = 13), 1200 publications were analyzed. Bibliometrix (R) and CiteSpace were used for performance analysis, keyword mapping, thematic evolution, and co-citation clustering. Results: Annual scientific production increased markedly after 2018, paralleling the expansion of immunotherapy. Keyword co-occurrence identified three major thematic domains (>40 high-frequency keywords) linking molecular mechanisms, biomarkers, and therapeutic strategies. Thematic mapping highlighted immunotherapy, tumor microenvironment, and PD-1 as dominant motor themes, while resistance-related pathways formed a central mechanistic–translational axis. Thematic evolution demonstrated a shift from EGFR-targeted therapy to epithelial–mesenchymal transition (EMT)-centered resistance and subsequently to immune-dominant strategies. Co-citation clustering produced robust structures (Q ≈ 0.62; silhouette ≈ 0.84), identifying EMT as a persistent mechanistic hub. Pilot ELISA validation confirmed significant increases in TGF-β1 (~2.05-fold), IL-6 (~2.59-fold), CCL2 (~2.10-fold), and PD-L1 (~2.56-fold) under EMT-inducing conditions (p < 0.01). Conclusions: This integrative approach provides a quantitative and experimentally supported framework linking mechanistic insights to therapeutic innovation in NSCLC. Full article

Conventional model predictive control (MPC) is highly vulnerable to motor parameter variations. Meanwhile, existing parameter-based MPC schemes are often constrained by the accuracy of model reconstruction. To overcome these limitations, this article proposes a model-free predictive control (MFPC) strategy based on a fuzzy approximation method for a permanent magnet synchronous motor (PMSM). Leveraging the exceptional nonlinear mapping capability of fuzzy approximation, the proposed strategy approximates the autoregressive term within a structurally simple first-order autoregressive model with exogenous input (ARX). This significantly enhances model reconstruction accuracy. Furthermore, discrete-time Lyapunov stability analysis rigorously demonstrates that the estimation errors of the internal states under the proposed control scheme are uniformly ultimately bounded (UUB). Finally, experimental results reveal that the proposed MFPC strategy achieves superior steady-state current quality while ensuring excellent dynamic performance, effectively validating the advantages of the proposed method. Full article

Robotic assistive devices, such as exoskeletons, are increasingly employed in walking rehabilitation. Therefore, the measurement of both movement kinematics and cognitive workload is important to understand this human–robot interaction in real-world contexts. To address this need this study presents the validation of a framework integrating inertial motion capture (Xsens) and eye-tracking sensor (Pupil Neon) within a Mixed Reality (Meta Quest 3) architecture. We developed an overground dual-task paradigm in which holographic numbers appear in the user’s peripheral vision. This setup actively stimulates visuospatial attention while quantifying kinematic and cognitive output. To validate the framework, the protocol has been tested on 30 healthy subjects across repeated exoskeleton training sessions. Statistical analyses revealed that the Coefficient of Multiple Correlation (CMC) and Spectral Arc Length (SPARC), calculated on the shank angular velocity, together with the Step Length Variability, exhibited significant time effects (p < 0.01), mapping the transition toward automated gait. Concurrently, pupillometric data demonstrated a measurable reduction in neurocognitive demand; specifically, the Task-Evoked Pupillary Response (TEPR) decreased significantly across progressive training sessions (p < 0.05). With this work, we validated a measurement protocol that aims to provide a novel methodology for objectively evaluating motor and cognitive adaptation in wearable assistive devices. Full article

Background: Autosomal recessive intellectual developmental disorder-13 (MRT13; OMIM #613192) is a rare neurodevelopmental disorder caused by pathogenic variants in TRAPPC9. Most reported variants are single-nucleotide variants (SNVs), small insertions/deletions, or copy number variants (CNVs), whereas complex structural variants (SVs) remain poorly characterized. Objectives: This study sought to review the clinical and molecular spectrum of TRAPPC9-related disorder, harmonize reported variants, explore genotype–phenotype correlations, and expand the mutational spectrum by reporting a novel patient with a cryptic SV. Methods: We report a novel patient whose diagnostic workup included array-CGH, whole-exome sequencing, karyotyping, and optical genome mapping. Additionally, a systematic literature search was primarily conducted in PubMed/MEDLINE from 2009 to January 2026, with Embase, Web of Science, Google Scholar, Orphanet, OMIM, and ClinVar used as supplementary sources. Patients carrying pathogenic/likely pathogenic TRAPPC9 variants were included. Clinical and molecular data were extracted and descriptively summarized. Genotype–phenotype correlations were explored. Reported variants were re-annotated using MANE Select reference transcripts. Results: The reported patient showed biallelic TRAPPC9 disruption due to two independently inherited structural variants: a maternal ~35 kb intragenic deletion involving exons 10–12, identified by 400K array-CGH, and a paternal balanced translocation t(4;8) disrupting TRAPPC9 within intron 8, characterized by trio-OGM and paired-end whole-genome sequencing (PE-WGS). Thirty-one studies reporting 75 previously published patients were included in the literature review; together with the novel patient described here, the final cohort comprised 76 patients. Intellectual disability was present in 100% of cases, followed by brain MRI abnormalities (95.9%), microcephaly (82.3%), motor delay (71.4%), dysmorphic features (69.8%), obesity (52.8%), behavioral abnormalities/autism spectrum disorder (49.2%/43.8%), and epilepsy (15.9%). Most patients (84.2%) harbored homozygous variants. Thirty-two distinct sequence variants were identified, predominantly loss-of-function. CNVs were identified in 13.2% of patients. No genotype–phenotype correlations were identified. Conclusions: The systematic review provides an updated and harmonized overview of the clinical and molecular spectrum of TRAPPC9-related disorder, supporting the presence of a recognizable phenotype and confirming the predominance of loss-of-function variants. Our case further highlights the contribution of cryptic structural variants to the mutational spectrum of TRAPPC9 and the diagnostic value of advanced genomic approaches. Full article

Ensuring the cleanliness of precision components is critical in Hard Disk Drive (HDD) manufacturing, where microscopic dust contamination on the Voice Coil Motor Assembly (VCMA) can lead to positioning errors, unstable head movement, and long-term reliability failures. However, automated inspection of such contamination remains challenging because dust particles are extremely small, visually irregular, and often appear under complex microscopic backgrounds. This study presents an explainable hybrid deep learning framework for microscopic dust inspection by integrating object detection for precise localization and image classification for defect confirmation. Three YOLO architectures, namely YOLOv5, YOLOv8, and YOLOv11, were comparatively evaluated for dust detection, while three convolutional neural network (CNN) models, ResNet50, EfficientNetB0, and MobileNetV2, were implemented using transfer learning with frozen feature extraction layers for Good (G) and Not Good (NG) image-level classification. The experimental dataset consisted of annotated microscopic VCMA images, with data augmentation applied to the training subset to mitigate limited sample size and class imbalance. Experimental results showed that YOLOv8 achieved the strongest overall aggregate detection performance, whereas YOLOv5 was selected as the preferred detector for subsequent hybrid integration because it produced fewer false positives under reflective and textured microscopic backgrounds. YOLOv11 exhibited lower detection performance in the present setting, likely due to its architectural characteristics being less suited to the limited-data and high-background-complexity conditions of this study. In the present experimental setting, YOLOv5 achieved mAP@0.5 = 0.62, precision = 0.75, and recall = 0.69. For image-level classification, EfficientNetB0 achieved the highest classification accuracy of 93.10%, with F1-score = 0.932 and AUC = 0.986. In addition, Grad-CAM visualizations demonstrated that EfficientNetB0 consistently focused on physically meaningful dust-contaminated regions, thereby enhancing the interpretability of the classification results. Overall, the proposed hybrid framework integrating YOLOv5-based localization with EfficientNetB0-based defect confirmation showed promising potential for improving inspection reliability, false-alarm control, and explainability in automated VCMA quality inspection. These findings support the feasibility of explainable deep learning for microscopic defect inspection in HDD manufacturing and suggest its potential applicability to other precision manufacturing environments. Full article

Neodymium-based permanent magnets are strategic materials for the global energy transition, supporting technologies such as wind turbines and electric motors. However, the concentration of supply and the environmental impacts of rare-earth mining raise concerns about supply-chain security and technological dependence. This study analyzed the international scientific output on neodymium within the energy transition framework, identifying temporal trends, research areas, influential authors, and the geographic distribution of knowledge. The PROKNOW-C method was applied to the Scopus database, covering the period from 2004 to 2024. After filtering and standardization steps, 1384 documents were analyzed using VOSviewer to map networks of co-authorship, co-citation, and keyword co-occurrence. The results indicated a growth in publications after 2015, coinciding with intensifying debates on sustainability and technological innovation. These publications are concentrated around three main themes: energy efficiency in motors and technological advancements in wind turbine generators, circular economy strategies, and the development of alternative materials. Scientific output is led by China, the United States, and Japan. Ultimately, this mapping reveals that while the electromechanical application of neodymium is a consolidated field, there is an urgent need to foster research and public policies focused on recycling technologies to mitigate supply-chain vulnerabilities and ensure the material security of the global energy transition. Full article

The continuous rotating valve plate piston pump (CRVPPP) can efficiently drive actuators such as hydraulic cylinders or hydraulic motors to generate excitation motion. This CRVPPP-driven excitation system can avoid the throttling losses associated with servo-valve-controlled excitation systems. However, this excitation system exhibits an asymmetric excitation phenomenon during actual operation. Through theoretical analysis and experimental research on the mechanical characteristics of the valve plate pair in the CRVPPP, it was found that the asymmetric excitation originates from the annular grooves of the fixed valve plate alternating between oil suction and discharge states. This alternation subjects the rotating valve plate to an overturning moment, which in turn causes a periodic variation in the end-face clearance of the valve plate. Targeting the asymmetric and nonlinear leakage characteristics of the CRVPPP, an adaptive neural network module was established based on the Amesim-Matlab/Simulink co-simulation framework. This module incorporates the mapping from the rotational speeds of the rotating valve plate and cylinder block to the equivalent leakage opening of the distribution grooves. By training with experimental data, the CRVPPP- driven excitation system model was formulated. Experimental results show that the established model achieves a correlation coefficient of 0.99786 on the training set, indicating its excellent fitting accuracy. Furthermore, the mean squared error on the test set is within 0.04 mm², demonstrating the model’s good generalization ability. It can reproduce the dynamic characteristics of the CRVPPP-driven excitation system with high precision, thereby laying a solid modeling foundation for the characteristic analysis, structural optimization, and high-precision control of such excitation systems. Full article

Magnetorheological (MR) dampers enable semi-active control in prosthetic knees by providing rapidly adjustable resistance with low mechanical complexity. This paper evaluates three torque level control methodologies for a transfemoral prosthetic leg incorporating an MR damper: a model-based feedforward strategy, an adaptive inverse-dynamics controller, and a robust inverse-dynamics controller. A Lagrange-based planar leg model with explicit force-to-torque mapping is formulated, and a reference knee trajectory is estimated from measurable gait variables using a cubic polynomial model whose order is selected through least-squares RMSE analysis. Each controller is assessed using knee-angle tracking accuracy and control effort to capture the practical trade-off between motion quality and energy demand. Results demonstrated that the adaptive inverse-dynamics controller has the smallest tracking error but requires the highest effort, whereas the robust inverse-dynamics approach realizes approximately the same tracking performance with reduced effort, thereby suggesting the best accuracy–effort compromise in the present work. Results, likewise, examined actuator feasibility by considering the MR damper as the primary dissipative element and the DC motor as a supplemental active actuator required when damping alone cannot satisfy the commanded knee torque. Full article

Parkinson’s disease (PD) is the second most prevalent neurodegenerative disorder worldwide, and its early diagnosis remains a major challenge due to reliance on subjective clinical assessments. This study proposes a bio-inspired computational framework for automatic PD detection that draws explicit architectural inspiration from two biological systems: the hierarchical tonotopic organization of the human auditory cortex, which motivates the design of a 1D Convolutional Neural Network (CNN) for vocal biomarker analysis, and the basal ganglia–cerebellar motor control circuit, which motivates the selection and design of features extracted from Archimedean spiral drawing tasks. Unlike previous studies that apply standard machine learning techniques without grounding architectural choices in biological mechanisms, the proposed framework establishes a direct mapping between neural processing pathways and model design decisions. A Support Vector Machine (SVM) classifier evaluated on the Kaggle vocal dataset achieved 87% test accuracy with no overfitting, outperforming AdaBoost, Random Forest, KNN, XGBoost, and Decision Trees in terms of generalization. The 1D CNN applied to UCI spiral drawing data achieved 85% test accuracy, with overfitting behavior addressed through architectural regularization strategies including early stopping. A conceptual multimodal fusion architecture integrating both modalities is proposed as a direction for future experimental validation; it was not implemented or experimentally validated within the present study. The primary novelty of the framework resides in this explicit biomimetic grounding, which distinguishes it from existing performance-driven approaches. Results confirm that biologically grounded computational models constitute promising objective decision-support tools for early PD diagnosis. Full article

Introduction: Subcortical motor mapping thresholds are routinely used during glioblastoma resection to reduce the risk of permanent neurological injury, but their association with survival is not well established. We evaluated whether the final subcortical motor mapping threshold recorded at the end of resection is associated with overall survival (OS) in patients undergoing mapping-guided resection of motor-eloquent glioblastoma, including exploratory evaluation by residual fluorescence adjacent to the motor pathway. Methods: We performed a retrospective single-center cohort study of consecutive adults with newly diagnosed IDH-wild-type glioblastoma (2018–2024) who underwent motor mapping-guided resection with a documented final subcortical stimulation threshold and received adjuvant oncological therapy. The prespecified exposure was stimulation threshold ≤ 5 mA versus >5 mA. OS was analyzed using Kaplan–Meier estimates and Cox regression. To reduce overfitting, the primary adjusted Cox model included stimulation threshold, age, and temozolomide exposure; a fully adjusted model including age, sex, extent of resection, radiotherapy regimen, and temozolomide was established for sensitivity analysis. Because proportional hazards assumptions were not fully satisfied, restricted mean survival time (RMST) differences were also estimated at prespecified horizons. Results: Among 36 patients, stimulation threshold ≤ 5 mA was associated with shorter OS compared with >5 mA (log-rank p = 0.001). In the primary adjusted Cox model, stimulation threshold > 5 mA remained associated with lower mortality risk (HR 0.35, 95% CI 0.15–0.82, p = 0.016); results were directionally consistent in the fully adjusted model (HR 0.24, 95% CI 0.089–0.643, p = 0.0046). RMST analyses favored the >5 mA group at 12, 18, and 24 months. In exploratory analyses, the association appeared most evident in patients without residual fluorescence adjacent to the motor pathway. Conclusions: Lower final subcortical stimulation thresholds were associated with shorter overall survival after mapping-guided resection of motor-eloquent glioblastoma. These findings suggest that the final intraoperative stimulation threshold was associated with overall survival in adjusted exploratory models and may provide prognostic information in addition to its established role in surgical safety; however, prospective validation in larger cohorts is warranted. Full article

Introduction: Navigated transcranial magnetic stimulation (nTMS) is an advanced, noninvasive method for stimulation-based functional brain mapping. Its main clinical value in neurosurgery lies in preoperative identification of eloquent cortical areas and the integration of functional information into neuronavigation-based surgical planning. State of the Art: This narrative review with a structured literature search summarizes the historical and technical foundations of TMS/nTMS, but primarily focuses on neurosurgical applications, including motor and language mapping, comparison with functional MRI and direct cortical stimulation, safety considerations, and practical limitations. Broader neurological and therapeutic applications are discussed as contextual extensions rather than as a comprehensive disease-specific review. Clinical Implications: Current evidence is strongest for preoperative motor mapping in patients with tumors located in or near the motor–eloquent cortex. Language mapping, neurological diagnostics, and therapeutic repetitive TMS (rTMS) applications remain more heterogeneous and require careful interpretation according to the level of evidence, protocol standardization, and patient selection. Future Directions: Further multicenter studies, standardized mapping protocols, integration with advanced imaging and tractography, and health-system implementation strategies are needed to define the optimal role of nTMS in personalized neurosurgical and neurological care. Full article

Background: Reaction time (RT) is a fundamental measure of information processing speed in cognitive neuroscience and is influenced by both structural and functional brain properties. While prior studies have independently linked white matter microstructure and EEG alpha oscillations to cognitive performance, their joint contribution to distinct aspects of RT remains unclear. This study aims to investigate whether multimodal data can dissociate neural systems underlying cognitive and motor components of processing speed. Methods: We analyzed diffusion tensor imaging, resting-state individual EEG alpha peak frequency (IAF), demographic variables, and behavioral RT measures from a GO/NO-GO paradigm in 24 healthy adults from the Cuban Human Brain Mapping Project. Behavioral metrics included the mean, standard deviation and skewness of reaction times for simple and complex tasks. Sparse multiple canonical correlation analysis was applied to identify multivariate associations across modalities. Results: Two significant latent dimensions were identified. The first dimension linked bilateral fronto-temporal association tracts (SLF, IFOF, UNC) with complex RT performance, reflecting higher-order cognitive processing. The second dimension associated motor and interhemispheric tracts (CGC, CST, ILF, forceps major and minor) with intra-individual asymmetric variability (skewness) across tasks, indicating a motor-execution consistency system. IAF did not significantly contribute to either dimension. Sex showed strong associations with both components. Conclusions: Distinct white matter networks were associated with separable cognitive and motor aspects of processing speed, while resting-state alpha frequency did not show stable contributions with behavioral variability in this sample. IAF showed minimal contribution within the identified sparse multivariate dimensions. These findings highlight the importance of multimodal and multivariate approaches for understanding and potentially disentangling complex brain–behavior relationships. Full article

Fabric defect detection is one of the most significant challenges in the textile industry due to its critical role in quality assessment and management. Conventional single-stage detectors often capture small-scale and low-contrast defects inadequately. On the other hand, high-accuracy two-stage methods suffer from excessive computational complexity. This study proposes an improved Single Shot MultiBox Detector (SSD) model by replacing the feature map layer with the Bidirectional Feature Pyramid Network (BiFPN) from EfficientDet. Also, Bayesian optimization is utilized to systematically tune the related hyperparameters, which improves convergence stability and detection performance without manual intervention. Performance evaluation involves a trade-off between mean average precision at IoU 0.5 (mAP50) and execution time or frames per second (FPS), given that fabric defect detection requires the rotation of fabric motors or rollers. Experiments on a fabric defect dataset demonstrate that the proposed SSD-BiFPN framework outperforms baseline SSD models when it comes to precision, recall, and mean average precision, particularly for small and irregular defects. Additionally, the proposed architecture demonstrates satisfactory real-time performance when implemented on an NVIDIA Jetson Nano platform, highlighting its appropriateness for edge-based industrial inspection scenarios. Full article

Auditory neurofeedback training (NFT) based on brain–computer interfaces (BCIs) has recently entered the precision motor domain as a task-embedded neural state regulation paradigm. Compared to traditional standalone NFT approaches (e.g., relaxation or attention training designed to enhance general cognitive abilities), task-embedded paradigms integrate feedback directly into the motor task execution process. However, this design inevitably creates a dual-task scenario, and the effects of such a scenario on neural activity and behavioral performance have received limited systematic investigation in the existing literature. This study designed and implemented a closed-loop BCI system employing five-level heartbeat sound feedback and used this system as a research platform to examine the immediate neural mechanism changes and potential dual-task interference effects induced by single-session auditory NFT in moderately skilled shooters. The system maps real-time EEG features onto graded auditory signals varying in playback rate and volume intensity, incorporating a dynamic threshold adjustment mechanism. Twenty-two moderately skilled shooters completed three within-subject conditions (no-sound baseline, SMR enhancement, and theta suppression) in a single session with 32-channel EEG and behavioral data recorded simultaneously. Analyses employed whole-brain cluster-based permutation tests, cross-frequency coupling analysis, and functional connectivity analysis. Cluster-based permutation tests revealed that theta feedback induced a significant frontal 4–7 Hz suppression cluster (cluster p = 0.004), whereas SMR feedback did not produce significant 12–15 Hz enhancement at the group level. Theta feedback elicited cross-frequency spillover as follows: sensorimotor SMR power decreased significantly in theta responders (d = −0.69), with frontal theta and sensorimotor SMR changes positively correlated (r = 0.67, p < 0.001). Functional connectivity analysis using debiased weighted phase lag index (dwPLI) further demonstrated significant theta-band network reorganization (cluster p = 0.034). At the neural level, clear modulation effects were observed, but shooting ring values did not improve significantly under feedback conditions, and aiming time was significantly prolonged—a behavioral pattern consistent with potential dual-task interference from task-embedded auditory feedback. Single-session auditory NFT can act on the prefrontal cognitive control network and induce cross-frequency network reorganization, but the feedback channel itself constitutes a parallel task that may limit the short-term transfer of induced neural states to behavioral performance. This study examined the neural mechanisms of task-embedded auditory NFT and reported the dual-task costs that have been less characterized in prior “task + feedback” research, providing design considerations and preliminary mechanistic evidence for future development of auditory NFT in precision motor skill training. Full article