Search Results (18,419)

Abstract: Background: In Ethiopia, moderate thinness (MT) is a persistent issue among children. Yet, evidence on the effects of dietary supplementation and motor skills training in these children is limited. Objective: This study aimed to assess the effect of Ready-to-Use Supplementary Food (RUSF), whether or not combined with high-intensity motor learning (HiML), on weight, height, body composition, and muscle strength in children 5–7 years old with MT living in Jimma Town, Ethiopia. Methods: A cluster-randomized controlled trial was carried out among 69 children (aged 5–7) with MT assigned to receive RUSF (n = 23), RUSF + HiML (n = 25), or no intervention (control group, n = 21). A multivariable Generalized Estimating Equations model was used and the level of significance was set at alpha < 0.05. Results:At baseline, there were no significant differences in the outcome measurements between the RUSF, RUSF + HiML, and control groups. However, after 12 weeks of intervention, there were significant mean differences in differences (DIDs) between the RUSF group and the control arm, with DIDs of 1.50 kg for weight (p < 0.001), 20.63 newton (N) for elbow flexor (p < 0.001), 11.00 N for quadriceps (p = 0.023), 18.95 N for gastrocnemius sup flexor of the leg (p < 0.001), and 1.03 kg for fat-free mass (p = 0.022). Similarly, the mean difference in differences was higher in the RUSF + HiML group by 1.62 kg for weight (p < 0.001), 2.80 kg for grip strength (p < 0.001), 15.93 for elbow flexor (p < 0.001), 16.73 for quadriceps (p < 0.001), 9.75 for gastrocnemius sup flexor of the leg (p = 0.005), and 2.20 kg for fat-free mass (p < 0.001) compared the control arm. Conclusion: RUSF alone was effective, but combining it with HiML had a synergistic effect. Compared to the control group, the RUSF and RUSF + HiML interventions improved the body composition, height, weight, and muscle strength of the studied moderately thin children. The findings of this study suggest the potential that treating moderately thin children with RUSF and combining it with HiML has for reducing the negative effects of malnutrition in Ethiopia. Future research should explore these interventions in a larger community-based study. This trial has been registered at the Pan African Clinical Trials Registry (PACTR) under trial number PACTR202305718679999. Full article

In some scenarios, electric heavy-duty trucks with battery swapping mode (ETBSm) are more cost-effective than battery charging mode. The viability of battery swapping stations is contingent upon the operational requirements and range capabilities of the ETBSm. Low temperatures have the effect of reducing the range of the ETBSm, thereby creating difficulties for battery swapping. This article proposes the use of motor waste heat recovery (MWHR) to heat batteries, which would improve range. A number of subsystem models have been established, including the ETBSm, battery, motor, and thermal management system (TMS). The calibration of battery temperature and motor efficiency is achieved with a model error of less than 5%. Comparison of performance, such as temperature, energy consumption, and range, when using only positive temperature coefficient (PTC) heating and when using both PTC heating and motor waste heat. The results indicate a 15% increase in the rate of rise in battery temperature and a 10.64 kW·h reduction in energy consumption under Chinese heavy-duty vehicle commercial vehicle test cycle (CHTC) conditions. Then, the motor waste heat percentage, energy consumption, and range are analyzed at different ambient temperatures. At an ambient temperature of −20 °C, −10 °C, and 0 °C, the percentage of the motor waste heat is 32.1%, 35%, and 40.5%; when 75% of the state of charge (SOC) is consumed, the range is improved by 6.55%, 4.37%, and 4.49%. Additionally, the effect of the PTC heater on temperature characteristics and power consumption is investigated by changing the target temperature of the coolant at the battery inlet. In accordance with the stipulated conditions of an ambient temperature of −20 °C and a target coolant temperature of 40 °C at the battery inlet, the simulation results indicated a battery temperature rise rate of 0.85 °C/min, accompanied by a PTC power consumption of 15.6 kW·h. This study demonstrates that as the ambient temperature increases, the utilization of motor waste heat becomes more effective in reducing PTC heating power consumption. At the lowest ambient temperature tested, the greatest improvement in driving range is observed. It is important to note that while an increase in the target heating temperature of the PTC helps to raise the battery temperature more rapidly, this is accompanied by a higher energy consumption. This article provides a reference for the ETBSm with MWHR. Full article

In today’s society, robots are increasingly being developed and playing an important role in many fields of industry. Combined with advances in artificial intelligence, sensors, and design principles, these robots are becoming smarter, more flexible, and especially capable of interacting more naturally with humans. In that context, a comprehensive humanoid robot with human-like actions and emotions has been designed to move flexibly like a human, performing movements to simulate the movements of the human neck and head so that the robot can interact with the surrounding environment. The mechanical design of the emotional humanoid robot head focuses on the natural and flexible movement of human electric motors, including flexible suitable connections, precise motors, and feedback signals. The feedback control parts, such as the neck, eyes, eyebrows, and mouth, are especially combined with artificial skin to create a human-like appearance. This study aims to contribute to the field of biomimetic humanoid robotics by developing a comprehensive design for a humanoid robot head with human-like actions and emotions, as well as evaluating the effectiveness of the motor and feedback control system in simulating human behavior and emotional expression, thereby enhancing natural interaction between robots and humans. Experimental results from the survey showed that the behavioral simulation rate reached 94.72%, and the emotional expression rate was 91.50%. Full article

With the increasing number of electric vehicles taking to the roads, the impact of tailpipe emissions on air quality will decrease, while resuspended road dust and brake/tire wear will become more significant. This study quantified PM₁₀ emissions from tire wear under a range of real highway conditions with measurements across different seasons and roadway surface types in Phoenix, Arizona. Tire wear was quantified in the sampled PM₁₀ using benzothiazoles (vulcanization accelerators) as tire markers. The measured emission factors had a range of 0.005–0.22 mg km⁻¹ veh⁻¹ and are consistent with an earlier experimental study conducted in Phoenix. However, these results are lower than values typically found in the literature and values calculated from emissions models, such as MOVES (MOtor Vehicle Emission Simulator). We found no significant difference in tire wear PM₁₀ emission factors for different surface types (asphalt vs. diamond grind concrete) but saw a significant decrease in the winter compared to the summer. Full article

Research on electroencephalogram-based motor imagery (MI-EEG) can identify the limbs of subjects that generate motor imagination by decoding EEG signals, which is an important issue in the field of brain–computer interface (BCI). Existing deep-learning-based classification methods have not been able to entirely employ the topological information among brain regions, and thus, the classification performance needs further improving. In this paper, we propose a multi-view graph convolutional attention network (MGCANet) with residual learning structure for multi-class MI decoding. Specifically, we design a multi-view graph convolution spatial feature extraction method based on the topological relationship of brain regions to achieve more comprehensive information aggregation. During the modeling, we build an adaptive weight fusion (Awf) module to adaptively merge feature from different brain views to improve classification accuracy. In addition, the self-attention mechanism is introduced for feature selection to expand the receptive field of EEG signals to global dependence and enhance the expression of important features. The proposed model is experimentally evaluated on two public MI datasets and achieved a mean accuracy of 78.26% (BCIC IV 2a dataset) and 73.68% (OpenBMI dataset), which significantly outperforms representative comparative methods in classification accuracy. Comprehensive experiment results verify the effectiveness of our proposed method, which can provide novel perspectives for MI decoding. Full article

High-speed dual-motor electric drive tracked vehicles (DDTVs) have emerged as a research hotspot in the field of tracked vehicles in recent years due to their advantages in fuel economy and the scalability of electrical equipment. The emergency braking of a DDTV at high speed can lead to slipping or even yawing (which is caused by a large deviation of forces at each track directly), posing significant challenges to the vehicle’s stability and safety. Therefore, the accurate real-time acquisition of critical driving parameters, such as the longitudinal force and vehicle speed, is crucial for the stability control of a DDTV. This paper developed a novel estimating algorithm of critical driving parameters for DDTVs equipped with conventional sensors such as rotary transformers at PMSMs and onboard accelerometers on the basis of their dynamics models. The algorithm includes a sensor signal preprocessing module, a longitudinal force estimation method based on a nonlinear observer, and a speed estimation method based on an adaptive Kalman filter. Through hardware-in-loop experiments based on a Speedgoat real-time target machine, the proposed algorithm is proven to estimate the longitudinal force of the track and vehicle speed accurately, whether the vehicle has stability control functions or not, providing a foundation for the further development of vehicle stability control algorithms. Full article

Background: Lower urinary tract symptoms (LUTSs) are a common complaint in adult and elderly men with bladder outlet obstruction, and have a considerable impact on their quality of life. Symptoms affect storage, voiding and post micturition stages. Among the latter, a feeling of incomplete emptying is one of the most bothersome for the patients; a condition that in turn contributes to affect urinary urgency, nocturia and frequency. Common recommendations include self-management practices (e.g., control of fluid intake, double-voiding and distraction techniques) to relieve patients’ symptoms, whose effectiveness, however, is under debate. Methods: In this report we describe two pioneering procedures to favor bladder residual content voiding in people complaining of LUTS disorders. The first is based on motor imagery and the second on the use of odors. The beneficial effects of Mental imagery techniques on various tasks (e.g., in the treatment of several pathological conditions or as valid mnemonics aids have a long tradition and have received consistently experimental support. Thus, a patient (a 68-year-old Caucasian man) complaining of LUTS was trained to use a motor imagery technique (building up a visual image comprising the bladder, the detrusor muscle and the urethra, and to imagine the detrusor muscle contracting and the flow of urine expelled) for 90 days and two odors (coffee and a lavender scented cleanser) for 10 days, as a trigger for micturition. He was asked to record—immediately after the first morning micturition—the time interval between the first (free) and the second (cued) micturition. Results: Reported data suggest the efficacy of motor imagery in favoring the bladder residual urine voiding in a few minutes (M = 4.75 min.) compared to the control condition, i.e., the baseline of the patient (M = 79.5 min.), while no differences between the odor-based procedures (M 1st odorant = 70.6 min.; M 2nd odorant = 71.1 min) and the latter were observed. Conclusions: A procedure based on an imagery technique may, therefore, be of general value—as a suggested protocol—and accordingly can be applicable clinical settings. An olfactory bladder control hypothesis cannot, however, be ruled out and is discussed as a promising future line of research. Full article

This article proposes a novel nonsingular fast terminal sliding mode control (N-NFTSMC) with a sliding mode disturbance observer (SDOB) for permanent magnet synchronous motor (PMSM) servo control. Firstly, to reduce the chattering issue, a new sliding mode reaching law (NSRL) is proposed for the N-NFTSMC. Secondly, to further improve the dynamic tracking accuracy, we introduce a sliding disturbance observer to estimate unknown disturbances for feedforward compensation. Comparative simulations via Matlab/Simulink 2018 are conducted using the traditional NFTSMC and N-NFTSMC; the step simulation results show that the chattering phenomenon was suppressed well via the N-NFTSMC scheme. The sine wave tracking simulation proves that the N-NFTSMC has better dynamic tracking performance when compared with traditional NFTSMC. Finally, we carry out experiments to validate that the N-NFTSMC adequately suppresses the chattering issue and possesses better anti-disturbance performance. Full article

Active inference describes (Bayes-optimal) behaviour as being motivated by the minimisation of surprise of one’s sensory observations, through the optimisation of a generative model (of the hidden causes of one’s sensory data) in the brain. One of active inference’s key appeals is its conceptualisation of precision as biasing neuronal communication and, thus, inference within generative models. The importance of precision in perceptual inference is evident—many studies have demonstrated the importance of ensuring precision estimates are correct for normal (healthy) sensation and perception. Here, we highlight the many roles precision plays in action, i.e., the key processes that rely on adequate estimates of precision, from decision making and action planning to the initiation and control of muscle movement itself. Thereby, we focus on the recent development of hierarchical, “mixed” models—generative models spanning multiple levels of discrete and continuous inference. These kinds of models open up new perspectives on the unified description of hierarchical computation, and its implementation, in action. Here, we highlight how these models reflect the many roles of precision in action—from planning to execution—and the associated pathologies if precision estimation goes wrong. We also discuss the potential biological implementation of the associated message passing, focusing on the role of neuromodulatory systems in mediating different kinds of precision. Full article

This paper investigated a new speed regulator using an adaptive transitioning policy iteration learning technique for the variable reluctance motor (VRM) drive. A transitioning strategy is used in this unique scheme to handle the nonlinear behavior of the VRM by using a series of learning centers, each of which is an individual local learning controller at linear operational location that grows throughout the system’s nonlinear domain. This improved control technique based on an adaptive dynamic programming algorithm is developed to derive the prime solution of the infinite horizon linear quadratic tracker (LQT) issue for an unidentified dynamical configuration with a VRM drive. By formulating a policy iteration algorithm for VRM applications, the speed of the motor shows inside the machine model, and therefore the local centers are directly affected by the speed. Hence, when the speed of the rotor changes, the parameters of the local centers grid would be updated and tuned. Additionally, a multivariate transition algorithm has been adopted to provide a seamless transition between the Q-centers. Finally, simulation and experimental results are presented to confirm the suggested control scheme’s efficacy. Full article

This study introduces envelope- and machine learning (ML)-based electrical fault type detection algorithms for electrical distribution grids, advancing beyond traditional logic-based methods. The proposed detection model involves three stages: anomaly area detection, ML-based fault presence detection, and ML-based fault type detection. Initially, an envelope-based detector identifying the anomaly region was improved to handle noisier power grid signals from meters. The second stage acts as a switch, detecting the presence of a fault among four classes: normal, motor, switching, and fault. Finally, if a fault is detected, the third stage identifies specific fault types. This study explored various feature extraction methods and evaluated different ML algorithms to maximize prediction accuracy. The performance of the proposed algorithms is tested in an emulated software–hardware electrical grid testbed using different sample rate meters/relays, such as SEL735, SEL421, SEL734, SEL700GT, and SEL351S near and far from an inverter-based photovoltaic array farm. The performance outcomes demonstrate the proposed model’s robustness and accuracy under realistic conditions. Full article

Neonatal hypoxic-ischemic encephalopathy (HIE) occurs in 1.5 per 1000 live births, leaving affected children with long-term motor and cognitive deficits. Few animal models of HIE incorporate maternal immune activation (MIA) despite the significant risk MIA poses to HIE incidence and diagnosis. Our non-invasive model of HIE pairs late gestation MIA with postnatal hypoxia. HIE pups exhibited a trend toward smaller overall brain size and delays in the ontogeny of several developmental milestones. In adulthood, HIE animals had reduced strength and gait deficits, but no difference in speed. Surprisingly, HIE animals performed better on the rotarod, an assessment of motor coordination. There was significant upregulation of inflammatory genes in microglia 24 h after hypoxia. Single-cell RNA sequencing (scRNAseq) revealed two microglia subclusters of interest following HIE. Pseudobulk analysis revealed increased microglia motility gene expression and upregulation of epigenetic machinery and neurodevelopmental genes in macrophages following HIE. No sex differences were found in any measures. These results support a two-hit noninvasive model pairing MIA and hypoxia as a model for HIE in humans. This model results in a milder phenotype compared to established HIE models; however, HIE is a clinically heterogeneous injury resulting in a variety of outcomes in humans. The pathways identified in our model of HIE may reveal novel targets for therapy for neonates with HIE. Full article

Background: Sleep disorders have been poorly described in congenital (CDM) and childhood (ChDM) myotonic dystrophy despite being highly burdensome. The aims of this study were to explore sleep disorders in a cohort of Italian CDM and ChDM and to assess their association with motor and respiratory function and disease-specific cognitive and behavioral assessments. Methods: This was an observational multicenter study. Reported sleep quality was assessed using the Pediatric Daytime Sleepiness Scale (PDSS) and Pediatric Sleep Questionnaire (PSQ). Sleep quality was correlated to motor function (6 min walk test, 6MWT and grip strength; pulmonary function (predicted Forced Vital Capacity%, FVC% pred.); executive function assessed by BRIEF-2; autism traits assessed by Autism Spectrum Screening Questionnaire (ASSQ) and Repetitive Behavior Scale-revised (RBS-R); Quality of life (PedsQL) and disease burden (Congenital Childhood Myotonic Dystrophy Health Index, CCMDHI). Results: Forty-six patients were included, 33 CDM and 13 ChDM, at a median age of 10.4 and 15.1 years. Daytime sleepiness and disrupted sleep were reported by 30% children, in both subgroups of CDM and ChDM. Daytime sleepiness correlated with autism traits in CDM (p < 0.05). Disrupted sleep correlated with poorer executive function (p = 0.04) and higher disease burden (p = 0.03). Conclusions: Sleep issues are a feature of both CDM and ChDM. They correlate with behavioral issues and impact on disease burden. Full article

The direct current (DC) motor is the core part of an electrical vehicle (EV). The unmatched perturbation of load torque is a challenging problem in the control of an EV system driven by a DC motor and hence a deep control concern is required. In this study, the proposed solution is to present two control approaches based on a backstepping control algorithm for speed trajectory tracking of EVs. The first control design is to develop the backstepping controller based on a quasi-sliding mode disturbance observer (BS-QSMDO), and the other controller is to combine the backstepping control with quasi-integral sliding mode control (BS-QISMC). In the sense of Lyapunov-based stability analysis, the ultimate boundedness of the proposed controllers has been detailedly analyzed, assessed, and evaluated in the presence of unmatched perturbation. A modified stability analysis has been presented to determine the ultimate bounds of disturbance estimation error for both controllers. The determination of ultimate bound and region-of-attraction for tracking and estimation errors is the contribution achieved by the proposed control design. The performances of the proposed controllers have been verified via computer simulations and the level of ultimate bounds for the estimation and tracking errors are the key measures for their evaluation. Compared to BS-QISMC, the results showed that a lower level of ultimate boundedness with a higher convergent rate can be reached based on BS-QSMO. However, a higher control effort can be exerted by the BS-QSMO controller as compared to BS-QISMC; and this is the price to be paid by the BS-QSMO controller to achieve lower ultimate boundedness with a faster convergence rate. Full article

Bone age assessments measure the growth and development of children and adolescents by evaluating their skeletal maturity, which is influenced by various factors like heredity, ethnicity, culture, and nutrition. The clinical standards for this assessment should be up to date and appropriate for the specific population being studied. This study validates the GP-Canary Atlas for accurately predicting bone age by analyzing posteroanterior left hand and wrist radiographs of healthy children (80 females and 134 males) from the Canary Islands across various developmental stages and genders. We found strong intra-rater reliability among all three raters, with Raters 1 and 2 indicating very high consistency (intra-class coefficients = 0.990 to 0.996) and Rater 3 displaying slightly lower but still strong reliability (intra-class coefficients = 0.921 to 0.976). The inter-rater agreement was excellent between Raters 1 and 2 but significantly lower between Rater 3 and the other two raters, with intra-class coefficients of 0.408 and 0.463 for Rater 1 and 0.327 and 0.509 for Rater 2. The accuracy analysis revealed a substantial underestimation of bone age compared to chronological age for preschool- (mean difference = 17.036 months; p < 0.001) and school-age males (mean difference = 13.298 months; p < 0.001). However, this was not observed in females, where the mean difference was minimal (3.949 months; p < 0.239). In contrast, the Atlas showed greater accuracy for teenagers, showing only a slight overestimation (mean difference = 3.159 months; p = 0.823). In conclusion, the GP-Canary Atlas demonstrates overall precision but requires caution as it underestimates the BA in preschool children and overestimates it in school-age girls and adolescents. Full article