Search Results (5,466)

Background: The aim was to explore independent wheeled mobility in children with CP, and identify predictors of early independent wheeled mobility and changes over time across birth cohorts. Methods: We included data from the Norwegian Quality and Surveillance Registry for Cerebral Palsy (NorCP) comprising 11,565 assessments of 1780 children born in 2002–2019. Variables included demographic data, Gross Motor Function Classification System (GMFCS) and Manual Ability Classification System (MACS) levels, wheelchair use, and independent wheeled mobility. Cox proportional hazard regression was used to identify predictors for early independent wheeled mobility. Kaplan–Meier survival curves were used to compare birth cohorts. Results: Of 769 (43%) children who used a wheelchair, 511 (67%) had independent wheeled mobility. Two thirds of the children (n = 337) achieved independent wheeled mobility before age 7. Most children with independent wheeled mobility used powered wheelchairs. Children at GMFCS levels III and IV were more likely to reach independent wheeled mobility at an early age. Children at MACS levels III–V had a lower probability of early independent wheeled mobility. The average age of achieving independent mobility decreased from 9.5 to 4.0 years between birth years 2002 and 2019. Conclusions: Two in three children were independent wheelchair users before 7 years of age, and the age of obtaining independent wheeled mobility has decreased over the last 20 years. Children with better hand function were more likely to obtain early independent wheeled mobility. Early intervention programs to promote mobility, development and participation should include powered mobility, adapted steering options, and interventions for hand function. Full article

In today’s global scenario, any aspiration for sustainable development centers around cities. Decades of a hegemonic motor vehicle culture have led to unprecedented levels of noise and environmental pollution, urban congestion, sedentary lifestyles, and increased vulnerability to the effects of climate change. Mobility, thus, becomes a cornerstone in transitioning toward more sustainable urban models, where active mobility is unquestionable. More and more cities are focusing on promoting urban cycling, not only as a leisure practice, but also as an alternative mode of transport. This article delves into the progress and current situation of cycling in Bizkaia, a city in the Basque Country in northern Spain. Based on the insights from and knowledge of seventeen experts, it explores the main challenges and strategic actions necessary to promote its expansion as a sustainable urban transport mode. The study employs a qualitative methodology, with results affirming the importance of some of the “key factors” identified in the literature as essential for any transition toward sustainable urban mobility through cycling. Among these key factors, sports leisure is highlighted due to its central role as a facilitator in the acquisition and consolidation of these new mobility habits. The study concludes by advancing five fundamental premises, the main contribution of this work, that encapsulate essential knowledge, without which any strategy to promote cycling as an alternative mode of sustainable urban mobility may falter. Full article

Species of the genus Eryngium L. of the Apiaceae family are successfully used in folk medicine in various countries worldwide, but they are hardly implemented in official medicinal and pharmaceutical practices. Therefore, it is advisable to conduct phytochemical and pharmacological research in E. planum L. herb extracts to develop and implement new phytomedicines based on this raw material. Purified water, and 40% and 70% ethanol were used for obtaining soft extracts. A total of seven hydroxycinnamic acids, six flavonoids, and three tannin metabolites were identified and quantified in the E. planum extracts by HPLC. These extracts were characterized as practically non-toxic medicines (V toxicity class, LD₅₀ > 5000 mg/kg). The hepatoprotective activity of the E. planum extracts has been established. They were affected to reduce serum thiobarbituric acid (TBA) levels by 29.3%, 31.5%, and 32.4%, respectively, compared to untreated animals and in liver homogenate by 59.5%, 65.4%, and 66.8%, respectively. The alanine transaminase (ALT) activity decreased by 26.9%, 30.8%, and 33.8%, respectively. The aspartate transaminase (AST) activity decreased by 23.9 %, 25.7 %, and 30.5 %, respectively. The sedative activity of E. planum extracts was studied for the first time. The 70% ethanol extract has the most pronounced sedative effect due to a significant decrease in motor activity (by 2.4 times compared to the control group) in the «Open Field test» and reduction in concern in the «Cube tests», «Raised Cross Maze», and "Black and White Camera". This extract also demonstrates a moderate antibacterial effect against gram-positive microorganisms (8–10 mm), including staphylococci, streptococci, and bacilli. These results provide a foundation for further preclinical and clinical studies of the dry extract of E. planum herb (extractant: 70% ethanol) to integrate it into modern medical practice and develop new supplements. Full article

Metal amorphous nanocomposite (MANC) soft magnetic materials exhibit remarkably low iron loss and high saturation magnetization. However, they have not been widely used in electric motors largely due to a lack of demonstrated manufacturing processing methods and an absence of proven motor designs well suited for their use. Recent developments in these two areas have prompted the optimization study of flux-switching with permanent magnet motor topology using MANCs presented here. This study uses population-based optimization in conjunction with a simplified electromagnetics model to seek rare earth-free designs that attain or exceed the state of the art in power density and efficiency. To predict the maximum mechanically safe rotational speed for each design with minimal computational effort, a new method of quantifying the rotor assembly mechanical limit is presented. The resulting population of designs includes motor designs with a specific power of up to 6.1 kW/kg and efficiency of up to 99% without the use of rare earth permanent magnets. These designs, while exhibiting drawbacks of high electrical frequency and significant manufacturing complexity, exceed the typical power density of representative state-of-the-art EV motors while increasing efficiency and eliminating rare earth elements. Full article

In this work, a tachometer based on a Metglas/PZT/Metglas magnetoelectric (ME) composite was developed to achieve high-precision rotational speed measurement over a wide temperature range (−70 °C to 160 °C). The tachometer converts external magnetic signals into electrical signals through the ME effect and operates stably in extreme temperature environments. COMSOL Multiphysics software was used for simulation analysis to investigate the ME response characteristics of the composite in such environments. To evaluate the properties of the ME composite under different conditions, its response characteristics at various frequencies, DC bias, and temperatures were systematically investigated. A permanent magnet and a DC motor were used to simulate gear rotation, and the voltage output was analyzed by adjusting the position between the sensor and the DC motor. The results show that the measured values of the ME tachometer closely match the set values, and the tachometer demonstrates high measurement accuracy within the range of 480 to 1260 revolutions per minute (rpm). Additionally, the properties of the ME composite at different temperatures were examined. In the temperature range from −70 °C to 160 °C, the ME coefficients exhibit good regularity and stability, with the measured trend closely matching the simulation results, ensuring the reliability and accuracy of the ME tachometer. To verify its practicality, the measurement capability of the ME tachometer was comprehensively tested under extreme temperature conditions. The results show that in high-temperature environments, the tachometer can accurately measure speed while maintaining a high signal-to-noise ratio (SNR), demonstrating excellent anti-interference ability. The proposed ME tachometer shows promising application potential in extreme temperature conditions, particularly in complex industrial environments that require high reliability and precision. Full article

This research focuses on the design and implementation of a movement strategy for a photovoltaic (PV) system, presented through four phases: First came the design of the mechanical part and the selection of geared motors with high torque and low power consumption, while having a solid mechanical structure that supports the panel. An open-loop control was selected using solar positioning equations, with the inputs defined as solar equations. The Intel Edison development board was chosen for programming the solar equations in Python. Two linear potentiometers served as sensing elements, where analog–digital characterization was conducted for each movement. The plant started with the static panel at a latitude of 7° south, oriented toward the equator, achieving a performance of 177.62 kWh. With the solar tracker, a performance of 232.38 kWh was obtained, resulting in an efficiency increase of 27%. Given the aim of enhancing PV efficiency through increased utilization, satisfactory results were achieved. Another advantage of the unit is that it is designed to support more than one panel. Full article

Introduction: This study presents a Convolutional Neural Network (CNN) approach for detecting depression and schizophrenia using motor activity patterns represented as images. Participants’ motor activity data were captured and transformed into visual representations, enabling advanced computer vision techniques for the classification of these mental disorders. The model’s performance was evaluated using a three-fold cross-validation, achieving an average accuracy of 95%, demonstrating the effectiveness of the proposed approach in accurately identifying mental health conditions. Objectives: The objective of the study is to develop a model capable of identifying different mental disorders by processing motor data using CNN in order to provide a support tool to areas specialized in the diagnosis and efficient treatment of these psychological conditions. Methods: The methodology involved segmenting and transforming motor activity data into images, followed by a CNN training and testing phase on these visual representations. This innovative approach enables the identification of subtle motor behavior patterns, potentially indicative of specific mental states, without invasive interventions or self-reporting. Results: The results suggest that CNNs can capture discriminative features in motor activity to differentiate between individuals with depression, schizophrenia, and those without mental health diagnoses. Conclusions: These findings underscore the potential of computer vision and deep neural network techniques to contribute to early, non-invasive mental health disorder diagnosis, with significant implications for developing mental health support tools. Full article

Background/Objectives: Traumatic spinal cord injury (TSCI) profoundly impacts patients by precipitating a loss of motor and sensory capabilities, largely due to oxidative stress and inflammation during the secondary injury phase. Methods:This investigation explores the diagnostic potential of zinc (Zn) and free zinc (fZn) as biomarkers by analyzing their serum concentration dynamics in 48 TSCI individuals with TSCI, with the aim of correlating these levels with neurological impairment. Serum samples collected at admission, 4 h, 9 h, 12 h, 24 h, and 3 days post-injury were analyzed for total serum Zn and fZn concentrations. The patients were compared to a control group comprised of individuals with vertebral fractures but no neurological deficits. Results:The study revealed injury-specific fluctuations in Zn and fZn levels following TSCI, with significantly lower Zn levels observed post-TSCI compared to controls (p = 0.016). The American Spinal Injury Association (ASIA) Impairment scale (AIS) assessments at admission and three months post-injury showed Zn level differences are linked to neurological recovery (AIS+:1 > AIS+:2, 0 h: p = 0.008; AIS+:0 < AIS+:1, 4 h: p = 0.016), highlighting the critical role of Zn and trace elements in the early remission process after TSCI. Notably, significant differences in fZn levels were detected between the control and TSCI groups (TSCI < Control; 12 h: p = 0.045; 24 h: p = 0.001; 3 d: p = 0.016), with the peak diagnostic performance of fZn at 24 h post-injury, as indicated by an Area Under the ROC Curve (AUC) of 83.84% (CI: 0.698–0.978). Conclusions:These findings underscore the potential of fZn as a biomarker to guide early diagnostic and therapeutic interventions aimed at mitigating secondary injury and enhancing recovery outcomes. This study contributes insights into the dynamics of serum Zn and its importance, holding specific diagnostic properties that could be critically relevant in the early phase of biomarker signature development for TSCI diagnostics and prognosis. Full article

Kappa opioid receptor (KOR) agonists have well-established antinociceptive effects. However, many KOR agonists have negative side effects, which limit their therapeutic potential. Some researchers have suggested that the development of biased agonists that preferentially stimulate KOR G-protein pathways over β-arrestin pathways may yield drugs with fewer adverse side effects. This was investigated in the current study. We describe the synthesis and characterization of three U50,488 analogues, 1, 2, and 3. We evaluated the acute and chronic antinociceptive effects of these compounds in mice using the warm-water tail flick assay and in a paclitaxel-induced neuropathic pain model. Side effects were investigated using open-field, passive wire hang, rotarod, elevated zero maze, conditioned place aversion, and whole-body plethysmography, with some tests being conducted in KOR or β-arrestin2 knock out mice. All compounds were highly potent, full agonists of the KOR, with varying signaling biases in vitro. In the warm-water tail withdrawal assay, these agonists were ~10 times more potent than U50,488, but not more efficacious. All KOR agonists reversed paclitaxel-induced neuropathic pain, without tolerance. Compound 3 showed no significant side effects on any test. Signaling bias did not correlate with the antinociceptive or side effects of any compounds and knockout of β-arrestin2 had no effect on U50,488-induced sedation or motor incoordination. These findings highlight the therapeutic potential of 3, with its lack of side effects typically associated with KOR agonists, and also suggest that G-protein signaling bias is a poor predictor of KOR agonist-induced side effects. Full article

Goals of automated detection of epileptic seizures using wearable devices include objective documentation of seizures, prevention of sudden unexpected death in epilepsy (SUDEP) and seizure-related injuries, obviating both the unpredictability of seizures and potential social embarrassment, and finally to develop seizure-triggered on-demand therapies. Automated seizure detection devices are based on the analysis of EEG signals (scalp-EEG, subcutaneous EEG and intracranial EEG), of motor manifestations of seizures (surface EMG, accelerometry), and of physiologic autonomic changes caused by seizures (heart and respiration rate, oxygen saturation, sweat secretion, body temperature). While the detection of generalized tonic-clonic and of focal to bilateral tonic-clonic seizures can be achieved with high sensitivity and low false alarm rates, the detection of focal seizures is still suboptimal, especially in the everyday ambulatory setting. Multimodal seizure detection devices in general provide better performance than devices based on single measurement parameters. Long-term use of seizure detection devices in home environments helps to improve the accuracy of seizure diaries and to reduce seizure-related injuries, while evidence for prevention of SUDEP is still lacking. Automated seizure detection devices are generally well accepted by patients and caregivers. Full article

This paper presents a comprehensive study of parameter estimation for three-phase induction motors (IMs) using hybrid optimization methods and a comparative evaluation of static and dynamic modeling approaches. A hybrid metaheuristic combining the Sine Cosine Algorithm (SCA) and Particle Swarm Optimization (PSO) is developed to identify optimal motor parameters efficiently. The approach utilizes a static model for rapid estimation, with final parameter values validated against a dynamic model to ensure accuracy in operational predictions. Results confirm that the static model provides robust parameter estimates for key performance metrics, including torque, power factor, and current, aligning well with experimental results from real-motor no-load tests. Parameters estimated by the proposed method demonstrate a high adherence with the motor real measurements. Comparisons also reveal the limitations of static models in scenarios requiring state-space accuracy, such as observer-based control applications. This study concludes by recommending further exploration of alternative motor modeling structures and the hybrid optimization algorithm for parameter estimation. Full article

Since the introduction of robot-assisted laparoscopic surgery, efforts have been made to incorporate force sensing technologies to monitor critical components and to provide force feedback. The advanced laparoscopic robotic system (AdLap RS) is a robotic platform that aims to make robot technology more sustainable through the use of the fully reusable shaft-actuated tip-articulating (SATA) instruments. The SATA instrument driver features electronics and sensors exposed to the sterile environment, which complicate the sterilisation process. The aim of this study was to develop and validate smart sensing in stepper motors using the back electromotive force in a newly developed Smart SATA Driver (SSD), eliminating the need for sensors in the sterile environment. Methods: The stepper drivers were equipped with TMC2209 ICs featuring StallGuard technology to measure back EMF. The tip was actuated up until a set StallGuard threshold value was reached, at which the resulting tip force was measured. This cycle was repeated ten times for a range of threshold levels. A regression analysis with a power series model was used to determine the quality of the fit. Results: The SSD is capable of exerting tip forces between 2.4 and 8.2 N. The back EMF force test demonstrated a strong correlation between obtained StallGuard values and measured tip forces. The regression analysis showed an R-squared of 0.95 and a root Mean squared error of 0.4 N. Discussion: The back EMF force test shows promise for force feedback, but its accuracy limits real-time use due to back EMF fluctuations. Future improvements in motor stability and refining the back EMF model are needed to enable real-time feedback. Conclusion: The strong correlation during the back EMF force test shows its potential as a low-budget method for detecting motor stalls and estimating tool–tissue forces without the need for sensors in laparoscopic instruments. Full article

A robust experimental-based vehicle drive system model and parameters tuned to match the real-world performance of the electro-mechanical systems is presented. The model is based on battery electric vehicle systems, focusing on the individual components of the drive system. The model includes motors, a battery pack, a driveline, and a DC-DC converter. Based on the collected empirical data using an on-board data acquisition system, and fine-tuning the critical vehicle parameters to align the simulation with the empirical data, stationary and drive cycle tests were designed and performed to replicate standard day-to-day driving conditions. Performance metrics such as the power consumption, the state of charge and the range were employed for the model validation. The validated model was then used for the performance study at different operating temperatures. The results of the study demonstrate that the model successfully mirrored the performance of the powertrain under various conditions and lined up with existing research work, providing a valuable tool for further research and development in battery electric vehicle technology. Full article

Motor-bearing fault diagnosis is critical for industrial equipment reliability, yet traditional data-driven methods require extensive labeled data, which are often scarce in real-world applications. To address this challenge, we propose a Transformer transfer learning network (TTLN) for accurate fault diagnosis under cross-condition scenarios, particularly when target domain data are limited. First, we develop a Transformer-based fault diagnosis model that captures long-range dependencies in sequential data through self-attention, achieving high accuracy under single operating conditions. Second, we introduce the TTLN framework, which integrates domain adaptation to align marginal and conditional distributions, enabling robust cross-condition fault diagnosis with minimal target domain samples. Finally, we validated the proposed method on the CWRU and PU datasets, demonstrating the TTLN’s superior performance in data-scarce scenarios. For example, the TTLN achieved over 95% accuracy with only 100 target samples, outperforming traditional methods and fine-tuning-based approaches. The results underscore the TTLN’s effectiveness in cross-condition fault diagnosis, offering a practical solution for industrial applications with limited labeled data. Full article

It is generally recommended that bed-resting patients be mobilised early to promote recovery. The aim of this work was to develop and evaluate the usability of a prototype in-bed lower-limb therapy device that offers various training patterns for the feet and legs, featuring an intuitive user interface and interactive exergames. Based on clinical interviews, the user requirements for the device were determined. The therapy device consisted of two compact foot platforms with integrated electric motors and force sensors. Movement control strategies and a user interface with computer games were developed. Through a touch screen, the target force and position trajectories were defined. Using automatic position and force control algorithms, the device produced leg flexion/extension with synchronised ankle plantarflexion/dorsiflexion as well as leg pressing with adjustable resistive loading. An evaluation test on 12 able-bodied participants showed that the device produced passive (mean position control errors: 8.91 mm linearly and 1.62° in the ankle joints) and active leg training (force control error: 2.52 N). The computer games were proven to be interesting, engaging, and responsive to the training movement. It was demonstrated that the device was technically usable in terms of mechatronics, movement control, user interface, and computer games. The advancements in well-controlled movement, multi-modal training patterns, convenient operation, and intuitive feedback enable the compact therapy device to be a potential system for bed-resting users to improve physical activity and cognitive functionality. Full article