One of the most common problems faced by autistic children is sensory integration problems related to the challenges of developing social cognition. This research aims to establish the causal effect of sensory motor at home and its role in enhancing the social cognitive development of preschool children with autism spectrum disorder. Considering the difficulties of autistic children in the sensory processing and reciprocal social interaction domains, the active early intervention introduced in a familiar home environment has great potential to promote the development and improvement of the key cognitive and social skills. This qualitative research will involve interviewing parents of autistic children between the ages of three to six years. The researchers will identify the range of interesting and efficient sensory motor activities that are already taking place in the home environment and then analyse how these actions affect the social cognition development of the children in a more detailed manner. This research will provide a better perspective on the use of home-based sensory motor practices by providing empirically generated information in the process of facilitating social cognition. It can then be used to directly develop more accessible, convenient, and parent-friendly ways of the early intervention stage, enabling families to actively enhance their child's development in all areas beyond clinical environments and provide significant insights for stakeholders in the autism community, such as parents, educators, and therapists.

This study investigates the acute impact of high-intensity activity on perceptual decision-making, using computational modeling to assess changes during and after physical activity. Participants performed a two-alternative forced choice perceptual decision-making task at rest (pre- and post-exercise) and during six of eight 5-min cycling bouts (totaling 47 min) under dual-task condition, while maintaining an average intensity of 86 ± 7% of their maximum heart rate. Drift diffusion modeling was applied to accuracy and reaction time data to estimate changes in evidence accumulation (drift rate), decision threshold (boundary separation), and nondecision processes (ter). Results revealed improved post-exercise performance, characterized by shorter nondecision time, potentially reflecting a transient improvement in motor or perceptual efficiency. During ongoing physical activity, results indicate that exercise is associated with a decrease in nondecision time and an increase in the efficiency of evidence accumulation, while response caution remains stable. These findings provide novel insights into how sustained high-intensity exercise modulates perceptual decision-making dynamics under physiological stress.

Many industrial applications utilize direct current (DC) motor as an essential element. It functions as the backbone of several industries and global pillar of manufacturing applications. The predictive analytics of motor is primary for preventing unpredicted downtime, reducing protection costs, and improving system effectiveness. This paper presents a hybrid framework integrating the internet of things (IoT) and machine learning (ML) for real-time predictive analytics of DC motors. The leveraging of machine learning algorithms in predictive maintenance of DC motors has shown significant potential in reducing downtime and increasing the lifespan of the motor. Therefore, a system for predictive analytics with machine learning strategy is proposed and message queuing telemetry transport (MQTT messaging) is included for effective information transmission between sensors and gateways. The data received from the sensors is utilized to make prediction about the remaining useful life of the motor and generate alerts for maintenance before failures occur. So, the integration of machine learning algorithms in predictive maintenance of DC motors is a promising approach to increase the reliability and efficiency of DC motors. The highest performance is achieved in random forest with accuracy of 93.4%.

This paper investigates the application of machine learning (ML) models, specifically artificial neural networks (ANN) and XGBoost, for real-time motor control, focusing on switched reluctance motors (SRM) and brushless DC motors (BLDC). Traditional inverse dynamics mapping for motor control is compared with ML approaches to highlight advantages in speed, accuracy, and deployment efficiency. Datasets simulating the input-output behavior of both motor types are used to train and test the models. Key performance metrics such as mean squared error (MSE), R² score, training time, and latency are evaluated, with the goal of replacing traditional control methods in real-time applications. Results indicate that ML models outperform traditional methods in terms of prediction accuracy and deployment speed, suggesting a promising path toward more efficient and adaptive motor control systems. The novelty of this work lies in applying supervised learning directly for inverse motor control mapping, thereby eliminating the need for explicit analytical models and enabling a unified, data-driven benchmarking framework across SRM and BLDC.

This study investigates a novel dual-loop control strategy that combines sliding mode and model predictive controllers to reduce torque ripple in high-performance Brushless Direct Current (BLDC) motors, especially for automotive electric vehicle (EV) applications. The proposed control system merges the predictive features of Model Predictive Control (MPC) with the robustness of Sliding Mode Control (SMC), creating a dual-loop structure that optimizes inner-loop current regulation and outer-loop speed control. The cost function is formulated to regulate the d- and q-axis currents, enabling the calculation of the optimal output voltage signal necessary for efficient motor performance. This synergy ensures precise stator current modulation, effectively reducing torque ripple while maintaining superior motor efficiency and stability. Additionally, by incorporating adaptive heuristics and data-driven insights through a hybrid self-learning algorithm combining ANN and fuzzy logic, the SMC-MPC controller can forecast and reduce error rates in the BLDC motor, ensuring smooth torque output with minimal ripple. The performance of the SMC-MPC strategy is thoroughly evaluated through MATLAB/SIMULINK Model-in-the-Loop (MIL) simulations and validated via Hardware-in-the-Loop (HIL) testing. Comparative analysis shows that the proposed controller provides superior results, including a rapid 0.01s rise time, a minimal 0.001% steady-state error, a 0.02s settling time, and a peak overshoot of 0.066%, outperforming traditional PID and SMC controllers. Also, the experiments show a 28.57% reduction in torque ripple and efficiency maps, achieving 96.47% maximum efficiency. This endeavor validates that the SMC-MPC controller improves BLDC motor efficiency while extending the operational range of EVs.

Introduction: Reaction time is important in various situations, as it influences quick decision-making and the performance of daily activities. Objective: To correlate upper limb reaction time [TRES (expressed in hits and execution time)] with chronological age and maturity in young non-athletes. FITLIGHT® technology and system were used for this purpose. Methods: A correlational study was conducted on 73 young people aged 12 to 20 (40 males and 33 females). The sample selection was non-probabilistic. Weight and height were assessed. Body mass index (BMI) was calculated. Maturity status was determined using a regression equation that takes into account sex, chronological age, and height. The FITLIGHT® system was used for assessment (number of hits and 10 repetitions with both hands in seconds). Results: Chronological age showed low positive correlations with the number of hits in both sexes (men r=0.13 and women r=0.29). By maturity status, the correlations increased significantly in both sexes (males r=0.16 and females r=0.36). The correlations between maturity status and number of correct answers were negative and low (r = -0.11 in males and -0.25 in females), and between maturity status and time taken for 10 repetitions were low to moderate in both sexes (in males r = -0.13 and in females r = -0.28). Conclusion: TRES in adolescents and young adults showed a stronger association with maturity than with chronological age. In addition, a similar number of correct responses was observed in both sexes, but males were faster, suggesting that maturity influences motor efficiency, especially in females.

The study which is randomized control trial examines the effectiveness of increasing cup stacking speed intervention for improving handwriting speed in right handed students of age 8 to 12 years enrolled in regular schooling. The sample size of 50 students further distributed into experimental and control group which included 25 students each. Intervention included sports stacking for 30 mins everyday for 4 weeks. Handwriting speed outcomes were measured by WPM’s and stacking speed was measured in seconds through a digital stopwatch. This approach targeted fine motor coordination, neuromotor development, facilitating interhemispheric communication, enhancing neural efficiency, hand function, bilateral integration and reaction time. The findings indicate improvement in handwriting speed with increase in stacking speed and suggests that sports stacking is simple, enjoyable and powerful tool for enhancing handwriting efficiency and fine motor skills among children in their formative school years.

The green transition to reduce reliance on fossil fuels and minimize global warming is driving an increasing demand for NdFeB permanent magnets. These magnets play a crucial role in electric motors, which power vehicle propulsion, as well as in magnetic brakes, bearings, and other components. Sintered NdFeB magnets bring significant benefits to electric vehicles. Their superior magnetic properties enable electric motors to achieve a high torque-to-weight ratio, enhancing vehicle performance and acceleration. Moreover, their compact design facilitates the creation of smaller, lighter motors, contributing to more streamlined and efficient vehicle designs. These magnets also excel in heat resistance, making them ideal for the challenging conditions within electric vehicle motors. Their ability to operate effectively at relatively high temperatures ensures consistent motor efficiency and reduces the likelihood of component failures. Relatively rapid transformation from internal combustion engine vehicles to electric vehicles also requires a steep change in the demand for and supply of the raw materials used in the production process. Overall, NdFeB magnets are inevitable in modern technology and are found in numerous products and systems we encounter daily.However, NdFeB magnets are prone to degradation in harsh environments due to the low corrosion resistance of both iron and neodymium. Consequently, magnets require corrosion protection since their magnetic properties would be jeopardized due to the degradation caused by the corrosion process. Commonly used commercial coatings are Ni-Cu-Ni-based.The present study investigated the effect of temperature of automotive transmission fluid (ATF) on the corrosion of Ni-Cu-Ni coated neodymium-iron-boron permanent magnets. ATFs are complex mixtures containing various additives designed for the automatic transmissions performance. The compounds react with each other to form a stable lubricant surface. Hydrocarbons in mineral oil themselves do not cause corrosion, but the presence of water content, sulphur molecules and acid compounds can cause a chemical attack on the surface of sintered NdFeB magnets and their coatings. Therefore, additives can be chemically aggressive, potentially exacerbating the corrosion of NdFeB magnets. In particular, sulphur compounds, which are inherently contained in the purified mineral oil and originate from the ATF compositions, are highly corrosive to the metal of electric circuits and NdFeB magnets. After exposing materials to higher temperatures (ranging from 80 to 140 degrees Celsius) for extended periods, the changes in the composition of ATF were analyzed using different methods of organic analysis (nuclear magnetic resonance and liquid chromatography with mass spectrometry). On the other hand, the changes in microstructure, morphology and chemical composition of the Ni-Cu-Ni coatings were investigated using scanning electron microscopy with energy-dispersive X-ray spectroscopy. As reference materials, copper and nickel metals were studied.The results show that the temperature of automatic transmission fluid has a decisive effect on the protective ability of Ni-Cu-Ni coating deposited on NdFeB magnets.Acknowledgements: This study was part of the project financed by the Gremo i-Motion programme (No. 12816). The financial support by the Slovenian Research and Innovation Agency (ARIS) is also acknowledged (core programme funding P2-0393).

The electrification of non-road mobile machinery is advancing to enhance sustainability and reduce emissions. This study investigates how to maximize the efficiency of the retrofitting of a material handler from an internal combustion engine to a battery-powered electric motor, while keeping the hydraulic system unchanged. Using a previously validated model, this study proposes three control strategies for the electric motor and hydraulic pump to enhance efficiency and performance. The first control strategy optimizes hydraulic pump performance within its most efficient displacement range. The second strategy maximizes powertrain efficiency by considering both efficiencies of the electric motor and hydraulic pump. The third strategy uses a servo-actuated valve to adjust the load-sensing margin and exhibits energy savings up to 14.2% and an 11.5% increase in efficiency. The proposed strategies avoid complex optimization algorithms, ensuring practical applicability for small- and medium-sized enterprises, which often face cost constraints and limited scalability.

Improving the efficiency of electric vehicle traction motors requires non-oriented silicon steels with low core loss and favorable magnetic induction. This study aims to clarify the influence of annealing temperature on the microstructure, texture, and magnetic properties of a 3.2%Si–0.9%Al steel, providing guidance for process optimization. Optical metallography, X-ray diffraction, and electron backscatter diffraction were employed to characterize the evolution. Recrystallization was completed between 620 °C and 720 °C, during which fine recrystallized grains replaced the deformed structure, accompanied by the nucleation of {111}<112> and {114}<481> grains. With further annealing from 850 °C to 1050 °C, grain growth occurred, resulting in an α*-fiber texture dominated by {114}<481>. The fraction of high-angle {114}<481> grains increased, while low-angle {111}<112> grains decreased. This microstructural evolution significantly influenced the magnetic properties of non-oriented electrical steel. The P1.5/50 and P1.0/400 core losses reached minimum values of 2.02 W/kg and 16.48 W/kg at 1010 °C and 930 °C, respectively, while B50 decreased slightly from 1.670 T to 1.652 T. These findings indicate that precise control of the annealing temperature is an effective strategy to tailor microstructure and texture, thereby optimizing the magnetic properties of non-oriented electrical steel.

The electrical parameters of induction motors can be affected over time due to continuous operation and increasing motor temperature. Therefore, accurately estimating certain motor parameters is crucial to ensure that these values remain independent of the motor’s operational temperature and lifespan. Additionally, optimizing motor energy consumption is essential for reducing electrical energy usage. This study introduces a novel adaptive loss model control (ALMC) approach, designed within the framework of a model reference adaptive system (MRAS), for an indirect field-oriented control (IFOC) drive system employing sliding mode control (SMC) strategies. Unlike conventional IFOC control method that maintains a constant rotor flux during operation stator current, the proposed ALMC-based method optimizes the stator current to minimize induction motor losses under startup and no-load conditions, thereby significantly improving motor efficiency. Furthermore, SMCs are designed to ensure precise tracking of stator dq currents and accurate motor speed regulation. Due to the sensitivity of the IFOC drive to changes in stator bandwidth, precise estimation of this parameter is crucial for improving the performance of the SMC-based IFOC system. To achieve this, an MRAS-based approach utilizing active and reactive powers is aimed at identifying the dynamic bandwidths of both stator and rotor components. Consequently, the ALMC method based on MRAS is applied to minimize the vulnerability of the loss model control strategy to variations in motor parameters. Simulation results demonstrate that the combination of SMC-based current and speed controllers with the proposed adaptive ALMC method not only ensures precise tracking of the stator current and motor speed but also significantly reduces induction motor losses under low-load and startup conditions. Moreover, this approach enables precise estimation of rotor and stator bandwidth.

In India, the manufacture of brown sugar is a significant agro-based industry, especially in Tamil Nadu, where more than half of small-scale production facilities are situated. The development of hard clods during cooling and storage due to external factors and residual moisture is one of the ongoing problems producers encounter. These clods lower the shelf life of brown sugar, decrease handling efficiency, and jeopardise product homogeneity. These clods are difficult to break by hand, unsanitary, and frequently inconsistent. A small pulveriser with a 24V DC motor and rechargeable battery was created in order to solve this problem. The machine breaks down hardened sugar lumps into small granules using powerful rubber auger blades that are coupled by a chain-sprocket mechanism. According to performance evaluation, the pulveriser was able to pulverise 1 kg of hardened brown sugar in 1.5–2 minutes, with clod-breaking effectiveness above 85%. The machine maintained structural stability under moderate to high load circumstances, and the system recorded about 45% motor efficiency and 63% blade efficiency. According to the study's findings, the created pulveriser enhances product quality, hygiene, and operating efficiency, making it a good fit for small-scale brown sugar companies.

Dual-stage Conversion Framework for Efficient Renewable-powered Motor Drive

<i>Research Background: </i>Mongolia's high-altitude regions have an average elevation exceeding 1,500 meters, with some areas reaching over 3,000 meters. The unique geographical conditions (hypoxia, low temperatures, strong winds, etc.) may significantly impact the performance of medical robotic systems. As a core device in minimally invasive surgery, the stability of the da Vinci Surgical System's robotic arm driving force is critical to surgical safety and precision. However, systematic research on driving force attenuation in robotic arms under high-altitude conditions remains scarce. This study reviews existing literature and designs virtual experiments to investigate the influence mechanisms of Mongolia's high-altitude environment on the driving force of the da Vinci robotic arm, providing a theoretical basis for optimizing surgical robots in such regions. <i>Research Methods: </i>A multidimensional analytical approach was adopted: Literature Review; Integration of global empirical studies on the effects of altitude on electromechanical systems, focusing on motor efficiency, hydraulic stability, and material deformation. Simulated Environment Design; A controlled climate chamber was used to replicate Mongolia’s high-altitude conditions (2,500–3,500 m, O₂ concentration 15%–18%, temperature −10°C to 5°C), experimental Design for Comparing Robotic Arm Driving Force Output in Standard vs Simulated High-Altitude Environments. Data Modeling; A predictive model for driving force attenuation was developed based on fluid dynamics and motor thermodynamics, alongside potential mitigation strategies. <i>Research Findings: </i>Significant Driving Force Attenuation: Elevated altitudes in Mongolia are associated with diminished peak driving force, while temperatures below −5°C further increase hydraulic oil viscosity, leading to greater response latency. Key Factors: The primary contributing factors include decreased motor heat dissipation efficiency (with an 8% reduction in thermal rise rate per 1,000 m altitude gain) and pressure-induced lubrication deterioration. Nonlinear Relationship: Driving force attenuation exhibits an exponential correlation with altitude, yet the influence of subzero temperatures becomes predominant beyond 2,500 m. <i>Conclusions: </i>Mongolia’s high-altitude environment markedly impairs the da Vinci robotic arm’s driving force due to compounded factors: thermal management failure, hydraulic inefficiency, and material rigidity changes. Design optimizations are recommended, including low-temperature lubricants, enhanced motor cooling, and adaptive control algorithms. Future studies should validate these interventions through field tests to ensure surgical robotic reliability in high-altitude settings. Experimental results will address challenges in deploying the da Vinci system in such regions.

The tooth harmonics caused by stator slots are a major factor leading to low motor efficiency, high temperature rise and severe vibration. The application of a magnetic slot wedge (MSW) can effectively mitigate the adverse effects of the stator slot on the motor. However, it should also be noted that the MSW may be subject to the action of electromagnetic forces during motor operation and thus has a risk of falling off. In order to comprehensively analyze the impact of MSW on the electromagnetic and reliability performance of the motor, this paper selected three types of MSW with relative permeabilities of 5, 10 and 15 to be applied in a high-power line-start permanent magnet synchronous motor (LSPMSM). The effects of these three types of MSWs on the electromagnetic performance of the motor and the changes in the electromagnetic force acting on the MSW were studied. Finally, the research content of the paper was verified on a 630 kW, 6 kV, 4-pole LSPMSM, providing a reference for the selection and application of MSW in motors.

Due to direct shaft connection to induction motors, centrifugal pumps can only run at a limited number of speeds. Impeller trimming is normally applied to fine tune the pump operating point to match the demanded design performance with degraded pump efficiency. Due to dynamic water flow, motors are often powered by variable frequency drives (VFDs). The question is whether the VFD can replace impeller trimming to achieve the design performance without degrading pump efficiency. The objective of this paper is to investigate the overall efficiency of motor-driven pump systems powered by a VFD with and without impeller trimming through simulations. First, potential approaches to obtain pump efficiency and the equivalent circuit method to obtain motor efficiency are discussed. Second, an approach to obtain pump efficiency is identified using manufacturer data. Finally, the overall efficiency with and without impeller trimming is simulated for a 4 × 5×13.5 pump on the right and left boundaries of the preferred operation region as well as the best efficiency points. The simulation results reveal that the utilization of VFDs can provide an absolute improvement of 10% in the efficiency if the impeller is trimmed below 12.5 inch but risk of overloaded motors exists with incorrect VFD settings.

Background: Neurofeedback training has emerged as a promising tool for enhancing performance by targeting specific brain activity patterns linked to motor skills, decision-making, and concentration. This study aimed to explore the associations between neurofeedback outcomes and football-specific performance metrics, including anthropometric, physical, technical, and tactical dimensions. Methods: A quasi-experimental design was used to examine the effects of a six-week neurofeedback training program on motor skills, tactical decision-making, and physical performance in young women’s football players (n = 8, aged 14–18). Participants underwent 30-min sessions three times a week targeting sensorimotor rhythms (SMRs) in the 12–15 Hz range within virtual football scenarios. Pre- and post-intervention assessments included anthropometric measures, neurophysiological evaluations, Loughborough Soccer Shooting Test (LSST), and Yo-Yo Intermittent Recovery Test Level 1 (YYIR1). Tactical decision-making was evaluated with a FUT-SAT-based instrument, and biological maturity was estimated using the Mirwald equations. Results: Statistical analyses using Pearson’s correlations revealed significant associations between neurofeedback outcomes, motor efficiency indices (MEIs), decision-making (DM), and football performance metrics. Correlation coefficients ranged from 0.504 to 0.998, with p-values from 0.010 to <0.001, indicating significant associations across physical, technical, and tactical dimensions. Conclusions: This study highlights the beneficial impact of neurofeedback on football performance in young female athletes.

Efficient BLDC motor control using hybrid crayfish optimization and sine cosine algorithm in renewable energy systems

With the global automotive industry's development towards electrification and intelligence, as well as the promotion of environmental protection policies and market demands of various countries, automotive motors have continuously innovated to meet many requirements in terms of efficiency and power density. Motors - which are the core components determining the power, economy, and reliability of the entire vehicle - directly affect the vehicle's performance and market competitiveness. This paper focuses on the optimization of two types of motors for new energy vehicles, introducing the basic principles and characteristics of AC asynchronous motors and permanent magnet synchronous motors. For AC asynchronous motors, a system combining a variable frequency drive (VFD) and a gearbox was adopted, and the driving cycle segments were divided using a "fragmentation" method to optimize motor efficiency, energy consumption, and other data. For permanent magnet synchronous motors, a cooling system with a circulating water circuit and oil cooling structure was designed. Heat dissipation was achieved by increasing the water intake. Subsequently, the magnetic field of the built-in permanent magnet synchronous motor was analyzed, proving its accuracy and good output performance. In addition, this paper analyzed the new type of hybrid excitation motor (HEDM), which was optimized to improve parameters such as torque and met the requirements of new energy vehicles. The core significance of this research is to promote the fundamental transformation of the automotive industry from traditional fuel to electrification and intelligence.

This study proposes a compensation method for operating region variations in in-wheel PMSMs, which are widely used in small mobility applications such as e-scooters and e-bikes. As motor temperature increases during operation, electrical parameters such as inductance vary, leading to unstable control. To address this, a Single-Layer Backpropagation Neural Network (SLBPNN) is used to estimate inductance variations in real-time. The proposed algorithm adjusts the motor’s operating point to maintain stable performance under thermal stress. Simulation results using MATLAB 2024b confirm the model’s effectiveness by estimating inductance from voltage, current, speed, and position inputs. Experimental validation further demonstrates that the proposed method compensates for the shift in the operating region due to temperature changes, improving the overall motor efficiency.