Search Results (5,520)

The development of motor competence (MC) is a key objective in preschool education. It is essential to assess MC from a pedagogical perspective using valid and applicable instruments in educational settings. This study aimed to validate the MOBAK-KG test in Colombian preschool children and to describe their motor performance. The sample consisted of 495 children from public schools in Bogotá, Colombia (48.1% girls; M = 5.8 years, SD = 0.60). Factorial validity of the MOBAK-KG test and its correlations with sex, age, and body mass index (BMI) were examined. Confirmatory factor analysis of eight MOBAK-KG items supported a bifactorial structure with object control and self-movement as dimensions (χ² = 33.55; df = 19; p = 0.021; CFI = 0.959; RMSEA = 0.039). Including the covariates yielded significant associations in basic motor competencies (χ² = 67.61; df = 33; p = 0.0004; CFI = 0.941; RMSEA = 0.046). Results showed sex differences (boys performed better in object control), BMI (negatively related to self-movement), and age (older children performed better). This study demonstrates that the MOBAK-KG test provides a feasible, educationally oriented assessment tool for preschool settings in Colombia. Moreover, it underscores the importance of considering factors such as sex, BMI, and age in the development of motor skills among children. Full article

Background: Reaction time and coordination are key performance components in team sports such as handball, particularly during the developmental years. Integrating visual and cognitive stimuli through smart technologies has been shown to facilitate motor skill development in young athletes. Methods: This study evaluated the effects of a BlazePod-based training protocol on reaction time, visuomotor coordination, movement quickness, and change-of-direction performance in junior male handball players aged 12–14 years. Thirty-two athletes (mean age = 13.37 ± 0.29 years) were randomly assigned to an experimental group (n = 16), in which the traditional neuromotor/coordination block of regular practice was replaced with BlazePod-based drills three times per week for eight weeks, or to a control group (n = 16), which trained the same capacities with traditional handball-specific exercises without technology. Training frequency (3 sessions/week), session duration (90 min), and the workload of the 30 min neuromotor block were matched between groups. Motor performance was assessed using four tests: Focus Reactions, Fast Feet, Clap Challenge, and the Agility T-Test. Paired- and independent-samples t-tests were applied to compare pre- and post-intervention scores. Results: The experimental group showed significant within-group improvements in Focus Reactions (p = 0.002) and AgilTT_ShuffleLeft (p = 0.014), whereas the control group showed no improvements and a small but significant worsening in Focus Reactions. Between-group comparisons at post-test revealed significant differences in favor of the experimental group for Fast Feet (p = 0.036), Clap Challenge (p = 0.008), AgilTT_Overall (p < 0.001), and AgilTT_SprintBack (p = 0.003). Conclusions: The integration of BlazePod technology into handball training produced measurable improvements in reaction speed and lateral agility among junior players. These findings suggest that technology-assisted neuromotor training represents a viable training modality that can replace a traditional neuromotor block within youth handball practice while maintaining overall training dose. Full article

This paper presents experimental testing results which describe the response of modern residential loads and electric vehicle (EV) chargers to various voltage magnitude, frequency, and phase angle disturbances. The purpose of these tests is to replicate real life network conditions and assist Network Service Providers and the Australian Energy Market Operator in identifying and predicting potential power variation and system stability issues caused by load behaviour during power system transient phenomena. By examining the behaviour of typical loads connected to distribution networks, a deeper understanding of their response can be achieved, enabling the refinement of composite load models that are compatible with the Western Electricity Coordinating Council dynamic composite load model (CMPLDW) structure presently used for dynamic studies. The performance of a wide range of common appliances found in residential settings, such as refrigerators, microwave ovens, air conditioners, direct-on-line motor-based appliances, and EV chargers, has been evaluated. The results obtained from these tests offer valuable insights into the behaviour of different load types and illustrate differing performances from established model parameters, identifying the need to refine existing CMPLDW models. The results also support the reclassification of several appliances within the composite load model, motivate the introduction of a dedicated EV charger component, and empower network operators to improve the modelling of modern power network responses. Full article

Peripheral nerve injuries affect 13–23 out of 100,000 people annually, with Wallerian degeneration and subsequent inflammatory/oxidative responses critically impacting recovery. Aloperine, a natural alkaloid from Sophora alopecuroides L., exhibits potent anti-inflammatory and antioxidant properties but has never been studied for nerve repair. In this study, we aimed to investigate whether aloperine could enhance peripheral nerve regeneration by modulating inflammation and oxidative stress in a rat sciatic nerve injury model. Thirty male Wistar rats underwent sciatic nerve neurotmesis with epineural repair. Animals were divided into surgical controls (Group A), aloperine-treated rats (Group B; single 100 mg/kg intraperitoneal dose), and intact controls (Group C). After 8 weeks, outcomes were assessed via functional tests (pinprick, hot plate, extensor postural thrust), biochemical analyses (TNF-α, IL-6, IL-10, TOS/TAS), and histomorphometric evaluations (axon counts, diameter indices, immunohistochemistry). Aloperine treatment significantly improved functional recovery, with near-normal hot plate latency and motor performance. Biochemically, it reduced pro-inflammatory markers (TNF-α) while elevating IL-10. Oxidative stress was attenuated. Histologically, treated nerves showed better-preserved axonal architecture (reduced inflammation). This first investigation of aloperine for nerve repair demonstrates its therapeutic potential through dual anti-inflammatory and antioxidant mechanisms, significantly improving functional and structural outcomes. These findings support its development as a novel treatment for peripheral nerve injuries. Full article

Background/Objective: Parkinson’s disease (PD) has long been viewed from a neurocentric perspective; however, increasing evidence indicates that glial dysfunction also contributes to dopaminergic neurodegeneration. Although neurotoxic glial phenotypes have been described in amyotrophic lateral sclerosis and Alzheimer’s disease in vivo models, it remains unclear whether similar states arise in the pathological milieu of PD. This study aimed to determine whether glial cells with intrinsic neurotoxic properties emerge in the substantia nigra pars compacta (SNpc) in a PD context. Methods: The classical 6-hydroxydopamine rat model was used to obtain glial cultures from the ipsilateral, toxin-damaged SNpc. These cultures were characterized by quantifying cell number and morphology, as well as by assessing the expression of glial markers. Their neurotoxic potential was evaluated in vitro through co-cultures with PC12 cells, and in vivo by transplanting the isolated cells into the SNpc of naïve rats. Assessments included PC12 cell survival, and integrity of the nigrostriatal pathway and motor performance in the cylinder test. Results: Ipsilateral SNpc cultures yielded 25-fold more cells than contralateral controls. Cultured cells co-expressed astrocytic and microglial markers, thus defining a population of damage-derived reactive glia (DDRG). When co-cultured, DDRG reduced PC12 cell survival, whereas control glial cells showed no neurotoxic effects. In vivo, DDRG transplantation induced a dose-dependent loss of dopaminergic neurons and motor impairments, while vehicle and control glia produced no detectable effects. Conclusions: Our findings suggest that glial cells emerging from a neuroinflammatory/neurodegenerative environment in the SNpc may contribute to dopaminergic neuron loss. Within the context of the experimental PD model used, DDRG appears to represent a glial population with potential pathogenic relevance and may constitute a candidate target for further investigation as a therapeutic strategy in Parkinson’s disease. Full article

Electric motors are the workhorse of industries owing to their precise speed and torque control technologies. Despite their ruggedness, faults are inevitable due to wear and tear, their prolonged usage and multiple factors. Bearing faults are among the most frequently occurring faults in electric motors. Detecting faults at an early stage is crucial for avoiding complete shutdown. Deep learning has gained significant attention in the fault detection domain owing to its inherent advantages. This paper proposes a hybrid multi-scale convolutional neural network and Transformer model for bearing fault detection. The model combines the strengths of multi-scale convolutional front-ends for fine-grained feature extraction with Transformer encoder blocks for capturing long-range temporal dependencies. This approach combines the advantages of both models for effective bearing fault detection. The proposed method was tested on a bearing dataset to show its performance and efficacy. This method achieved high-performance accuracy in bearing fault detection. Full article

Background/Objectives: Falls are a major cause of injury in older adults, closely related to declines in muscle strength, balance control, and sensory integration. Although exercise-based fall prevention programs are well supported, evidence on combining such programs with cerebellar transcranial direct-current stimulation (c-tDCS) remains limited. This study investigated the effects of c-tDCS applied before a modified Otago Exercise Program (OEP) on lower-extremity strength, balance, and fall efficacy in older adults. Methods: In this randomized controlled study, twenty-six community-dwelling older adults (median age [IQR]: experimental, 74.00 [10] years; control, 71.00 [10] years) were randomly assigned to either a c-tDCS + exercise group (n = 13) or a sham + exercise group (n = 13). The intervention was administered twice weekly for four weeks. The experimental group received 15 min of c-tDCS followed by 30 min of OEP-based exercise; the control group received sham stimulation under identical conditions. The outcome measures included the Five Times Sit to Stand Test (FTSST), Timed Up and Go (TUG), Balancia-based static balance (velocity average), and Falls Efficacy Scale—Korea (FES-K). Assessments were performed pre- and post-intervention. Results: The experimental group demonstrated significantly greater improvements than the control group (p < 0.05) in the Five Times Sit to Stand Test (r = 0.44) and Timed Up and Go test (r = 0.56). No significant changes were observed in static balance or fall efficacy in either group (p > 0.05). Conclusions: The combined use of c-tDCS and an OEP-based fall prevention exercise program effectively improved lower-extremity strength and dynamic balance in older adults. However, short-term intervention did not influence static balance or fall efficacy. Further studies using longer intervention periods and larger samples are warranted to verify these findings and clarify the mechanisms underlying c-tDCS-enhanced motor performance. Full article

Neurodevelopmental disorders (NDDs) are a heterogeneous group of conditions characterised by impairments in cognition, motor function, behaviour, and social interaction. Their genetic basis is highly diverse, and next-generation sequencing has become central to improving diagnostic yield. We retrospectively analysed 94 paediatric patients (0–18 years) with NDDs referred to the Paediatric and Rare Diseases Clinic, Microcitemico Hospital “A. Cao,” between January 2019 and July 2024. Each patient underwent detailed clinical evaluation and whole-exome sequencing (WES). Variants were prioritised according to ACMG guidelines. Gene burden analysis of rare predicted loss-of-function variants was performed using the Cohort Allelic Sums Test to detect enrichment in NDD cases relative to controls. WES identified 12 pathogenic variants, 16 likely pathogenic variants, and 10 variants of uncertain significance. Autosomal dominant disorders were the most frequent (n = 35 patients), while autosomal recessive and X-linked dominant conditions were identified in a single case each. The findings of this study further highlight the importance of WES in identifying novel genetic variants and in providing explanations for previously unexplained NDD cases. Moreover, the Cohort Allelic Sums Test (CAST) demonstrated that rare variants are enriched in genes implicated in neuronal development in affected individuals. Full article

Wheel load is a critical information source reflecting the status of vehicle load distribution and motion. Yet, existing in-wheel motor products are primarily designed as propulsion units and inherently lack the load-sensing capabilities required by intelligent vehicles. To address this research gap, this paper presents a novel intelligent electric drive wheel (i-EDW) with an integrated transmission system and a load-sensing unit (LSU). The i-EDW adopts an Axial Flux Permanent Magnet Synchronous Motor (AFPMSM), while the integrated LSU ensures high-precision measurement of six-dimensional wheel forces and moments. According to this multi-axis force information, a real-time estimation and stability control method based on the tire–road friction circle concept is proposed. Instead of the complex decoupling and multi-objective optimization with the multi-actuator systems, this paper focuses on minimizing the tire load rate of i-EDWs, which significantly advances the state of the art in terms of calculation efficiency and respond speed. To validate this theoretical framework, a full-vehicle model equipped with four i-EDWs is developed. In the MATLAB R2022A/Simulink co-simulation environment, a virtual prototype is tested under typical driving scenarios, including the straight-line acceleration and double-moving-lane (DML) steering. The simulation results prove a reliable safety margin from the friction circle boundaries, laying a solid foundation for precise motion control and improved system robustness in future intelligent vehicles. Full article

Parkinson’s disease (PD) is a common neurodegenerative disorder characterized by motor dysfunction and non-motor symptoms, including cognitive decline. Animal models that replicate PD’s clinical features are essential for therapeutic research. The widely used subacute 1-methyl-4-phenyl-1,2,3,6tetrahydropyridine (MPTP)-induced mouse model effectively mimics motor deficits but fails to fully represent aging-related non-motor symptoms. In this study, we established an aging-associated PD mouse model by combining MPTP with D-galactose treatment. Compared to mice treated with MPTP alone, MPTP + D-galactose-treated mice exhibited typical motor impairments alongside cognitive deficits in the Morris water maze and Y-maze tests. D-galactose alone induced cognitive impairment without motor dysfunction. Pathological analysis showed that the MPTP + D-galactose treatment caused tyrosine hydroxylase-positive neuron loss similar to MPTP, while D-galactose did not damage these neurons. Additionally, Micro-CT revealed bone loss in both the MPTP + D-galactose and D-galactose groups. This model recapitulates both the motor and aging-related non-motor symptoms of PD, including cognitive impairment and bone loss, providing a more comprehensive tool for studying PD pathogenesis and evaluating potential therapies. Full article

Condition-based maintenance strategies using AI-driven health monitoring have emerged as valuable tools for industrial reliability, yet their implementation remains challenging in industries with limited operational data. Class I induction motors (≤15 kW), which power critical equipment in industries such as grain handling facilities, represent a significant portion of industrial assets but lack established healthy vibration baselines for effective monitoring. A fundamental challenge exists in deploying AI-based health monitoring systems when no historical performance data is available, creating a ’cold-start’ problem that prevents industries from adopting predictive maintenance strategies without costly pilot programs or prolonged data collection periods. This study developed a data-driven health monitoring framework for Class I induction motors that eliminates the dependency on long-term historical trends. Through extensive experimental testing of 98 configurations on new motors, a correlation between vibration amplitude at rotational frequency and motor power rating was established, enabling the creation of a synthetic signal generation algorithm. A robust Health Index (HI) model with integrated diagnostic capabilities was developed using the JPCCED-HI framework, trained on both experimental and synthetically generated healthy vibration data to detect degradation and diagnose common failure modes. The regression analysis revealed a statistically significant relationship between motor power rating and healthy vibration signatures, enabling synthetic generation of baseline data for any Class I motor within the rated range. When implemented at an operational grain silo facility, the HI model successfully detected faulty behavior and accurately diagnosed probable failure modes in equipment with no prior monitoring history, demonstrating that maintenance decisions could be made based on condition data rather than reactive responses to failures. This framework enables immediate deployment of AI-based condition monitoring in industries lacking historical data, eliminating a major barrier to adopting predictive maintenance strategies. The synthetic data generation approach provides a cost-effective solution to the data scarcity problem identified as a critical challenge in industrial AI applications, while the successful industrial implementation validates the feasibility of this approach for small-to-medium industrial facilities. Full article

A new type of transportation vehicle, the maglev car, is gaining attention in the automotive and maglev industries due to its potential to meet personalized urban mobility and future travel needs. To optimize the chassis layout of maglev cars, this paper proposes a compact powertrain integrating electrodynamic suspension with in-wheel motor technology, in which a permanent magnet electrodynamic in-wheel motor (PMEIM) enables integrated propulsion and levitation. First, the PMEIM external magnetic field distribution is characterized by analytical and finite element (FEM) approaches, revealing the magnetic field distortion of the contactless powertrain. Subsequently, the steady-state electromagnetic force is modeled and the operating states of the PMEIM powertrain are calculated and determined. Next, the PMEIM electromagnetic design is conducted, and its electromagnetic structure rationality is verified through magnetic circuit and parametric analysis. Finally, an equivalent prototype is constructed, and the non-contact electromagnetic forces of the PMESM are measured in bench testing. Results indicate that the PMEIM powertrain performs propulsion and levitation functions, demonstrating 14.2 N propulsion force and 45.8 N levitation force under the rated condition, with a levitation–weight ratio of 2.52, which hold promise as a compact and flexible drivetrain solution for maglev cars. Full article

The article presents issues related to fossil fuel energy consumption and CO₂ emissions from motor vehicles. It identifies the main areas of research in this field in the context of motor vehicles, namely driver behavior, fuel consumption, and OBD systems. The research sample consisted of experimental data containing records of a series of test drives conducted with a passenger vehicle equipped with a gasoline-powered internal combustion engine, collected via an OBD diagnostic interface. Three subsets related to engine operation and energy demand patterns were distinguished for the study: during vehicle start-up and low-speed driving (vehicle start-up mode), during urban driving, and during extra-urban driving. Multiple regression models were constructed for the analyzed subsets to predict CO₂ emissions based on engine energy output parameters (power, load) and vehicle kinematic parameters. The developed models were subjected to detailed evaluation and mutual comparison, taking into account their predictive performance and the interpretability of the results. The analysis made it possible to identify the variables with the most substantial impact on CO₂ emissions and fuel energy consumption. The models allow individual drivers to monitor and optimize vehicle energy efficiency in real-time. The extra-urban driving model achieved the highest predictive accuracy, with a mean absolute error (MAE) of 19.62 g/km, which makes it suitable for real-time emission monitoring during highway driving. Full article

Background/Objectives: Chronic psychomotor and cognitive slowing after stroke can persist despite standard rehabilitation, especially in young adults with subcortical injuries. Innovative, integrated interventions are crucial for patients who have reached a plateau in their rehabilitation. We present a case of a 41-year-old male with chronic psychomotor and cognitive slowing following a left lenticulostriate infarction (NIHSS score = 5 at onset), who had plateaued after conventional rehabilitation. Methods: Over 4 weeks the patient underwent 20 sessions of a multimodal approach including high-frequency repetitive transcranial magnetic resonance stimulation over the supplementary motor area and bilateral temporo-parietal junctions and simultaneous computerized cognitive training targeting attention and executive function. Both motor and cognitive assessments, along with quantitative EEG (qEEG) evaluations, were conducted before and after the treatment. Results: At the end of treatment, the patient showed significant clinical improvement: speed and coordination in upper extremities (Finger Tapping Test) increased by 66% (dominant hand) and 74% (non-dominant hand), while finger dexterity (Nine-Hole Peg Test) increased by 25% (dominant hand) and 19% (non-dominant hand). Cognitive scores improved in alertness (58%), visual exploration (25%), and flexibility (24%), while divided attention remained stable. qEEG investigation showed increases in alpha (79%), gamma (33%), and beta (10%) power, with topographic shifts in the stimulated regions. Conclusions: These findings highlight the feasibility of combining targeted rTMS and cognitive training to enhance neuroplasticity in the chronic phase of stroke. Clinical recovery was accompanied by normalized cortical rhythms, suggesting qEEG biomarkers may be useful for tracking treatment response. Multimodal precision neurorehabilitation may offer a path forward for patients with persistent cognitive–motor deficits post-stroke. Full article

Background and Objectives: Although several meta-analyses have evaluated the effects of motor imagery (MI) on upper-limb recovery using the Fugl-Meyer Assessment for the Upper Extremity (FM-UE), evidence based on more specific (Action Research Arm Test, ARAT) and functional (Barthel Index, BI) outcomes remains scarce. This study examined the effect of MI combined with conventional rehabilitation therapy (CRT), which translates into meaningful improvements in upper-limb performance and functional independence after stroke, accounting for methodological quality and publication bias. Materials and Methods: A systematic review and meta-analysis were carried out in accordance with PRISMA recommendations, with prior registration in PROSPERO (CRD420251120044). Comprehensive searches were conducted across six electronic databases up to July 2025. The methodological rigor of the included studies was evaluated using the PEDro scale, and risk of bias was appraised with the Cochrane RoB 2 instrument. Random-effects models estimated pooled effect sizes (ESs) for the ARAT and BI, alongside analyses of heterogeneity, publication bias, and moderators. Results: Eleven RCTs (n = 425) were included. A small pooled improvement in ARAT was observed (ES = 0.25; 95% CI: 0.13–0.37; p < 0.001); however, this effect was rendered non-significant after correction for publication bias (ES = 0.08; 95% CI: −0.14–0.31). No significant differences were found for the BI (ES = 0.41; 95% CI: −0.35–1.18; p = 0.268), with substantial heterogeneity (I² = 96.6%). The mean PEDro score was 6.6, indicating moderate methodological quality. Conclusions: MI combined with CRT yields small and inconsistent effects on upper-limb recovery and no improvement in functional independence. Current evidence does not support its routine use in stroke rehabilitation. Well-designed, adequately powered randomized controlled trials employing standardized MI protocols are required to determine its true clinical relevance. Full article