Search Results (1,572)

Among brain regions, the cerebellum (CBL) has traditionally been associated with motor control. However, increasing evidence from connectomics and functional imaging has expanded this view, revealing its involvement in a wide range of cognitive and integrative processes. Despite this emerging relevance, the CBL has received comparatively less attention in aging research, which has focused mainly on other central nervous system (CNS) regions such as the neocortex and hippocampus. This review synthesizes the current evidence on glial cell aging across the CNS, emphasizing how cerebellar circuits follow distinct trajectories in terms of cellular remodeling, transcriptional reprogramming, and structural vulnerability. Recent findings highlight that cerebellar astrocytes and microglia exhibit specific signatures related to aging compared to their cortical counterpart, including moderate reactivity, selective immune response, and spatial reorganization. Cerebellar white matter (WM) undergoes structural alteration, suggesting that oligodendroglial cells may undergo region-specific alterations, particularly within WM tracts, although these aspects remain underexplored. Despite the presence of glial remodeling, the CBL maintains a notable degree of structural and functional integrity during aging. This resilience may be the result of the CBL’s ability to maintain synaptic adaptability and homeostatic balance, supported by its highly organized and compartmentalized architecture. A better understanding of the dynamics of cerebellar glial cells in aging may provide new insight into the mechanisms of brain maintenance and identify potential biomarkers for healthy brain aging. Full article

Model-based sensorless control of interior permanent-magnet synchronous motors (IPMSMs) typically employs an estimation observer with embedded position information, followed by a position extraction process. Although a type-2 phase-locked loop (PLL) is widely adopted for position and speed extraction, it suffers from steady-state tracking errors under variable-speed operation, leading to torque bias in IPMSM torque control. To mitigate this issue, this paper first proposes an adaptive bandpass full-order observer in the stationary reference frame. Subsequently, a Kalman filter (KF)-based compensation strategy is introduced for the PLL to eliminate tracking errors while maintaining system stability. Experimental validation on a 300 kW platform confirms the effectiveness of the proposed sensorless torque control algorithm, demonstrating significant reductions in position error and torque fluctuations during acceleration and deceleration. Full article

The exponential growth of autonomous systems demands robust security mechanisms that can operate within the extreme constraints of real-time embedded environments. This paper introduces SMART DShot, a groundbreaking machine learning-enhanced framework that transforms the security landscape of unmanned aerial vehicle motor control systems through seamless integration of adaptive timing correction and real-time anomaly detection within Digital Shot (DShot) communication protocols. Our approach addresses critical vulnerabilities in Electronic Speed Controller (ESC) interfaces by deploying four synergistic algorithms—Kalman Filter Timing Correction (KFTC), Recursive Least Squares Timing Correction (RLSTC), Fuzzy Logic Timing Correction (FLTC), and Hybrid Adaptive Timing Correction (HATC)—each optimized for specific error characteristics and attack scenarios. Through comprehensive evaluation encompassing 32,000 Monte Carlo test iterations (500 per scenario × 16 scenarios × 4 algorithms) across 16 distinct operational scenarios and PolarFire SoC Field-Programmable Gate Array (FPGA) implementation, we demonstrate exceptional performance with 88.3% attack detection rate, only 2.3% false positive incidence, and substantial vulnerability mitigation reducing Common Vulnerability Scoring System (CVSS) severity from High (7.3) to Low (3.1). Hardware validation on PolarFire SoC confirms practical viability with minimal resource overhead (2.16% Look-Up Table utilization, 16.57 mW per channel) and deterministic sub-10 microsecond execution latency. The Hybrid Adaptive Timing Correction algorithm achieves 31.01% success rate (95% CI: [30.2%, 31.8%]), representing a 26.5% improvement over baseline approaches through intelligent meta-learning-based algorithm selection. Statistical validation using Analysis of Variance confirms significant performance differences (F(3,1996) = 30.30, p < 0.001) with large effect sizes (Cohen’s d up to 4.57), where 64.6% of algorithm comparisons showed large practical significance. SMART DShot establishes a paradigmatic shift from reactive to proactive embedded security, demonstrating that sophisticated artificial intelligence can operate effectively within microsecond-scale real-time constraints while providing comprehensive protection against timing manipulation, de-synchronization, burst interference, replay attacks, coordinated multi-channel attacks, and firmware-level compromises. This work provides essential foundations for trustworthy autonomous systems across critical domains including aerospace, automotive, industrial automation, and cyber–physical infrastructure. These results conclusively demonstrate that ML-enhanced motor control systems can achieve both superior security (88.3% attack detection rate with 2.3% false positives) and operational performance (31.01% timing correction success rate, 26.5% improvement over baseline) simultaneously, establishing SMART DShot as a practical, deployable solution for next-generation autonomous systems. Full article

Objectives: The increasing use of fetal MRI has increased the diagnosis of posterior fossa malformations, yet the long-term neurodevelopmental outcomes of affected fetuses remain unclear. This study aims to examine the long-term neurodevelopmental outcomes of fetuses with structural posterior fossa malformation diagnosed on fetal MRI. Methods: A historical cohort study was conducted at a single tertiary referral center, including fetuses diagnosed with structural posterior fossa malformations and apparently healthy fetuses who underwent fetal brain MRI between 2011 and 2019. Maternal, pregnancy, and newborn characteristics were compared between groups, alongside long-term neurodevelopmental outcomes using the Vineland Adaptive Behavior Scales II (VABS-II) questionnaire. This included an extensive assessment of malformation types, additional structural, genetic, or neurodevelopmental anomalies, and outcomes. Results: A total of 126 fetuses met the inclusion criteria, of which 70 were apparently healthy fetuses, and 56 had structural posterior fossa malformations. Among the latter, 18 pregnancies were terminated, 4 resulted in neonatal death, and 11 were lost to follow-up. No significant differences were found in the overall neurodevelopmental outcomes between fetuses with structural posterior fossa malformation (93.4 ± 19.0) and apparently healthy fetuses (99.8 ± 13.8). Motor skills scores were lower among fetuses with structural posterior fossa malformations (87.7 ± 16.5 vs. 99.3 ± 17.2, p = 0.01) but remained within the normal range. Conclusion: Fetuses with structural posterior fossa malformations may exhibit normal long-term neurodevelopmental outcomes if no additional anomalies are detected during thorough prenatal screening that includes proper sonographic, biochemical and genetic screening, as well as fetal MRI. Further research with larger cohorts and longer-term assessments is recommended to validate these findings and support clinical decision-making. Full article

Background and Objective: Temperature management is key for reliable operation of permanent magnet synchronous motors (PMSMs). The lumped-parameter thermal network (LPTN) is fast and interpretable but struggles with nonlinear behavior under high power density. We propose OLTEM, a physics-informed deep model that combines LPTN with a thermal neural network (TNN) to improve prediction accuracy while keeping physical meaning. Methods: OLTEM embeds LPTN into a recurrent state-space formulation and learns three parameter sets: thermal conductance, inverse thermal capacitance, and power loss. Two additions are introduced: (i) a state-conditioned squeeze-and-excitation (SC-SE) attention that adapts feature weights using the current temperature state, and (ii) an enhanced power-loss sub-network that uses a deep MLP with SC-SE and non-negativity constraints. The model is trained and evaluated on the public Electric Motor Temperature dataset (Paderborn University/Kaggle). Performance is measured by mean squared error (MSE) and maximum absolute error across permanent-magnet, stator-yoke, stator-tooth, and stator-winding temperatures. Results: OLTEM tracks fast thermal transients and yields lower MSE than both the baseline TNN and a CNN–RNN model for all four components. On a held-out generalization set, MSE remains below 4.0 °C² and the maximum absolute error is about 4.3–8.2 °C. Ablation shows that removing either SC-SE or the enhanced power-loss module degrades accuracy, confirming their complementary roles. Conclusions: By combining physics with learned attention and loss modeling, OLTEM improves PMSM temperature prediction while preserving interpretability. This approach can support motor thermal design and control; future work will study transfer to other machines and further reduce short-term errors during abrupt operating changes. Full article

Background: Congenital anosmia (CA) is a rare condition characterized by a lifelong inability to perceive odors, which significantly affects daily life and may be linked to broader neurodevelopmental alterations. This study aimed to investigate structural brain differences in patients with CA using MRI, focusing on gray matter (GM) and white matter (WM) changes and their implications for neurodevelopment. Methods: This retrospective study included 28 patients with CA and 28 age- and gender-matched healthy controls. Patients with CA were diagnosed at a single medical center between 1 January 2001 and 30 August 2024. Controls were randomly selected from an imaging database and had no history of olfactory dysfunction. Brain Magnetic Resonance Imaging (MRI)was analyzed using volumetric analysis in SPM12.GM and WM volumes were quantified across 11 anatomical brain regions based on theWFU_PickAtlas toolbox, including frontal, temporal, parietal, occipital, limbic, sub-lobar, cerebellum (anterior/posterior), midbrain, the pons, and the frontal–temporal junction. Left–right hemispheric comparisons were also conducted. Results: Patients with CA exhibited significantly smaller GM volumes compared to healthy controls (560.6 ± 114.7 cc vs. 693.7 ± 96.3 cc, p < 0.001) but larger WM volumes (554.2 ± 75.4 cc vs. 491.1 ± 79.7 cc, p = 0.015). Regionally, GM reductions were observed in the frontal (131.9 ± 33.7 cc vs. 173.7 ± 27.0 cc, p < 0.001), temporal (81.1 ± 18.4 cc vs. 96.5 ± 14.1 cc, p = 0.001), parietal (52.4 ± 15.2 cc vs. 77.2 ± 12.4 cc, p < 0.001), sub-lobar (57.8 ± 9.7 cc vs. 68.2 ± 10.2 cc, p = 0.001), occipital (39.1 ± 13.0 cc vs. 57.8 ± 8.9 cc, p < 0.001), and midbrain (2.0 ± 0.5 cc vs. 2.3 ± 0.4 cc, p = 0.006) regions. Meanwhile, WM increases were notable in the frontal(152.0 ± 19.9 cc vs. 139.2 ± 24.0 cc, p = 0.027), temporal (71.5 ± 11.5 cc vs. 60.8 ± 9.5 cc, p = 0.001), parietal (75.8 ± 12.4 cc vs. 61.9 ± 11.5 cc, p < 0.001), and occipital (58.7 ± 10.3 cc vs. 41.9 ± 7.9 cc, p < 0.001) lobes. A separate analysis of the left and right hemispheres revealed similar patterns of reduced GM and increased WM volumes in patients with CA across both sides. An exception was noted in the right cerebellum-posterior, where patients with CA showed significantly greater WM volume (5.625 ± 1.667 cc vs. 4.666 ± 1.583 cc, p = 0.026). Conclusions: This study demonstrates widespread structural brain differences in individuals with CA, including reduced GM and increased WM volumes across multiple cortical and sub-lobar regions. These findings suggest that congenital olfactory deprivation may impact brain maturation beyond primary olfactory pathways, potentially reflecting altered synaptic pruning and increased myelination during early neurodevelopment. The involvement of the cerebellum further implies potential adaptations beyond motor functions. These structural differences may serve as potential neuroimaging markers for monitoring CA-associated cognitive or emotional comorbidities. Full article

Racket sports are very popular not only in professional sports but also among recreational players. As a result, their impact on the field of education has grown significantly in recent years. Table tennis (TT) offers significant advantages in terms of skill acquisition and health improvement. Nevertheless, its application within physical education (PE) curricula remains undetermined. The aim of this systematic review was to analyze the use of TT as educational content in the subject of PE in primary school. The PRISMA 2020 methodology was used to conduct the systematic review. Six databases (ERIC, Pubmed, ScienceDirect, Scopus, Sport Discus, and Web of Science) were used during the search process. The search cutoff date was December 31, 2024. After applying the eligibility criteria, 3595 articles were found. Only seven studies were selected for the final analysis and the data included 1526 students from primary school. The results indicate that TT is rarely used in primary education during PE classes. Research indicates interest among teachers and students in playing it during PE. Furthermore, due to the benefits, motivation, and interest this sport generates, educational experiences have been developed, such as roundtable discussions, table top tennis, balloon TT, and TT triathlon. A lack of materials, facilities, and teacher training for teaching this sport is notable. The teaching experiences analyzed in this review confirm that TT is a highly versatile and interesting sport as a subject matter in school PE. The use of TT allows for innovative, comprehensive, and inclusive PE, thanks to the sport’s multiple possibilities. Given its adaptability and low entry barrier, TT could serve as an effective tool for increasing children’s physical activity levels, improving motor skills, and fostering social–emotional development. However further research is needed to quantify its impact on health outcomes such as cardiovascular fitness or obesity prevention. Full article

In high-dynamic bipedal locomotion control, robotic systems are often constrained by motor torque limitations, particularly during explosive tasks such as jumping. One of the key challenges in reinforcement learning lies in bridging the sim-to-real gap, which mainly stems from both inaccuracies in simulation models and the limitations of motor torque output, ultimately leading to the failure of deploying learned policies in real-world systems. Traditional RL methods usually focus on peak torque limits but ignore that motor torque changes with speed. By only limiting peak torque, they prevent the torque from adjusting dynamically based on velocity, which can reduce the system’s efficiency and performance in high-speed tasks. To address these issues, this paper proposes a reinforcement learning jump-control framework tailored for tendon-driven bipedal robots, which integrates dynamic torque boundary constraints and torque error-compensation modeling. First, we developed a torque transmission coefficient model based on the tendon-driven mechanism, taking into account tendon elasticity and motor-control errors, which significantly improves the modeling accuracy. Building on this, we derived a dynamic joint torque limit that adapts to joint velocity, and designed a torque-aware reward function within the reinforcement learning environment, aimed at encouraging the policy to implicitly learn and comply with physical constraints during training, effectively bridging the gap between simulation and real-world performance. Hardware experimental results demonstrate that the proposed method effectively satisfies actuator safety limits while achieving more efficient and stable jumping behavior. This work provides a general and scalable modeling and control framework for learning high-dynamic bipedal motion under complex physical constraints. Full article

Spinal cord injury (SCI) is a major disability that, to this day, does not have a permanent cure. The spinal cord extends caudally through the body structure of the vertebral column and is part of the central nervous system (CNS). The spinal cord enables neural communication and motor coordination, so injuries can disrupt sensation, movement, and autonomic functions. Mechanical and traumatic damage to the spinal cord causes lesions to the nerves, resulting in the disruption of relayed messages to the extremities. Various forms of treatment for the spinal cord include functional electrical stimulation (FES), epidural electrical stimulation (EES), ‘SMART’ devices, exoskeleton and robotic systems, transcranial magnetic stimulation, and neuroprostheses using AI for the brain–computer interface. This research is going to analyse and review these current treatment methods for spinal cord injury and identify the current gaps and limitations in these, such as long-term biocompatibility, wireless adaptability, cost, regulatory barriers, and risk of surgery. Future advancements should work on implementing wireless data logging with AI algorithms to increase SCI device adaptability, as well as maintaining regulatory and health system integration. Full article

Background/Objectives: Early experiences can significantly influence brain development, particularly when they occur during specific time windows known as sensitive or critical periods. Therefore, the early promotion of neurodevelopmental functions is crucial in children at risk for neurodevelopmental disabilities, such as those with cerebral palsy. This article introduces AMIRA (A Multidimensional and Integrated Rehabilitation Approach), a rehabilitative framework designed for infants at risk of neurodevelopmental disabilities. Methods: AMIRA is intended to guide clinical–rehabilitation reasoning rather than prescribe a rigid sequence of predetermined activities for the child. The theoretical foundation and structure of AMIRA are presented by formalizing its criteria, objectives, tools, and intervention procedures. The framework comprises four distinct sections, each supported by adaptive strategies to facilitate access to materials and to promote play-based interactions among the child, their environment, and communication partners. Particular attention is given to optimizing both micro- and macro-environments for children with, or at risk of, co-occurring visual impairment. Each rehabilitative section includes three progressive phases: an initial observation phase, a facilitation phase to support the child’s engagement, and an active experimentation phase that gradually introduces more challenging tasks. Results: The intervention pathways in AMIRA are organized according to six core developmental domains: behavioral–emotional self-regulation, visual function, postural–motor skills, praxis, interaction and communication, and cognitive function. These are outlined in structured charts that serve as flexible guidelines rather than prescriptive protocols. Each chart presents activities of increasing complexity aligned with typical developmental milestones up to 24 months of age. For each specific ability, the corresponding habilitation goals, contextual recommendations (including environmental setup, objects, and tools), and suggested activities are provided. Conclusions: This study presents a detailed intervention approach, offering both a practical framework and a structured set of activities for use in rehabilitative settings. Further studies will explore the efficacy of the proposed standardized approach. Full article

This study explores the potential of craft-based activities in the context of special education, focusing on a papier mâché sculpting workshop implemented at the Special Kindergarten of Komotini, Greece, as part of the Horizon Europe Craeft project. The initiative aimed to assess how such creative activities could enhance the learning experience of children with intellectual and motor impairments, foster socialization, and develop fine motor skills. With reference to literature in art therapy, craft education, and inclusive pedagogy, the study applied a mixed-methods approach combining observation, visual analysis, and a survey. The findings indicate that, despite varied levels of participation based on individual needs, all students engaged meaningfully with the materials and activities. School professionals observed increased student engagement, emotional comfort, and communication, while also identifying the activity as well adapted and replicable in similar contexts. The results highlight the value of crafts in special education, not only as a sensory and cognitive stimulus but also as a means of fostering inclusion and self-expression. The study concludes with a call for further research into the role of tactile materials and hand gestures in relation to specific impairments. Full article

In unmanned systems, especially in large-scale and complex ones, sensor and communication failures occur from time to time and are hard to avoid. Therefore, this paper studies the fault detection problem of a class of unknown nonlinear singular uncertain time-varying delay Markov jump systems (UNSUTVDMJSs). Firstly, the corresponding sliding mode controller (SMC) is designed by using the equivalent control principle, and the unknown nonlinearity is equivalently replaced by changing the system input. Then, a fault detection filter adapted to this system is designed, thereby obtaining the unknown nonlinear stochastic singular uncertain Augmented filter residual system (UNSSUAFRS) model. To obtain the sufficient conditions for the random admissibility of this augmented system, a weak infinitesimal generator was used to design the required Lyapunov-Krasovskii functional. With the help of the Lyapunov principle and $H_{\infty }$ performance analysis method, the sufficient conditions for the random admissibility of UNSSUAFRS under the $H_{\infty }$ performance index $\gamma$ were derived. Finally, with the aid of the designed residual evaluation function and threshold, simulation analysis was conducted on the examples of DC servo motors and numerical calculation examples to verify the effectiveness and practicability of this fault detection filter. Full article

Background: This study investigates differences in muscle co-contraction and peak electromyography (EMG) activity between novice and advanced Taekwondo athletes during consecutive roundhouse (bandal chagui) kicks, examining the influence of body composition and experience level. Methods: Sixteen Taekwondo athletes (12 males, 4 females; mean age: 20.5 ± 4.3 years) were divided into novice (n = 8) and advanced (n = 8) groups. Muscle co-contraction indices and peak EMG activity across 15 consecutive kicks were assessed in key lower limb muscles, including the biceps femoris (BF), lateral gastrocnemius (LG), rectus femoris (RF), soleus (SO), semitendinosus (ST), tibialis anterior (TA), vastus lateralis (VL), and vastus medialis (VM). Results: Advanced athletes exhibited significantly higher co-contraction indices in BF–RF, VM–BF, and SO–TA pairs (p < 0.05) and increased peak EMG trends in the BF and LG (p < 0.05). Novice athletes showed significantly reduced peak EMG increases in the RF, VM, and VL. EMG trends were influenced by body composition, with principal component analysis indicating that higher fat mass and lower muscle mass were associated with greater variations in muscle activation. Conclusions: These findings suggest that advanced athletes refine motor control through increased co-contraction, improving stability and efficiency, while novices exhibit less optimized coordination patterns. This study provides insights into the neuromechanical adaptations associated with expertise development in Taekwondo. Full article

This paper presents a structured and experimentally validated approach to the parameter identification, modeling, and real-time speed control of a brushless DC (BLDC) motor. Electrical parameters, including resistance and inductance, were measured through DC and AC testing under controlled conditions, respectively, while mechanical and electromagnetic parameters such as the back electromotive force (EMF) constant and rotor inertia were determined experimentally using an AVL dynamometer. The back EMF was obtained by operating the motor as a generator under varying speeds, and inertia was identified using a deceleration method based on the relationship between angular acceleration and torque. The identified parameters were used to construct a transfer function model of the motor, which was implemented in MATLAB/Simulink R2024b and validated against real-time experimental data using sinusoidal and exponential input signals. The comparison between simulated and measured speed responses showed strong agreement, confirming the accuracy of the model. A proportional–integral (PI) controller was developed and implemented for speed regulation, using a low-cost National Instruments (NI) USB-6009 data acquisition (DAQ) and a Kelly controller. A first-order low-pass filter was integrated into the control loop to suppress high-frequency disturbances and improve transient performance. Experimental tests using a stepwise reference speed profile demonstrated accurate tracking, minimal overshoot, and robust operation. Although the modeling and control techniques applied are well known, the novelty of this work lies in its integration of experimental parameter identification, real-time validation, and practical hardware implementation within a unified and replicable framework. This approach provides a solid foundation for further studies involving more advanced or adaptive control strategies for BLDC motors. Full article

This study introduces a paradigm shift in permanent magnet synchronous motor (PMSM) control through the development of hybrid dual fractional-order sliding mode control (HDFOSMC) architecture integrated with evolutionary parameter learning (EPL). Conventional PMSM control frameworks face critical limitations in ultra-precision applications due to their inability to reconcile dynamic agility with steady-state precision under time-varying parameters and compound disturbances. The proposed HDFOSMC framework addresses these challenges via two synergistic innovations: (1) a dual fractional-order sliding manifold that fuses the rapid transient response of non-integer-order differentiation with the small steady-state error capability of dual-integral compensation, and (2) an EPL mechanism enabling real-time adaptation to thermal drift, load mutations, and unmodeled nonlinearities. Validation can be obtained through the comparison of the results on PMSM testbenches, which demonstrate superior performance over traditional fractional-order sliding mode control (FOSMC). By integrating fractional-order theory, sliding mode control theory, and parameter self-tuning theory, this study proposes a novel control framework for PMSM. The developed system achieves high-precision performance under extreme operational uncertainties through this innovative theoretical synthesis and comparative results. Full article