Search Results (6,771)

This paper presents the design and implementation of a SLAM-based online mapping and autonomous trajectory execution system for the Nimbulus-e, a concept vehicle designed for agile maneuvering in confined spaces. The Nimbulus-e uses individual steer-by-wire corner modules with in-wheel motors at all four corners. The associated eight joint variables serve as control inputs, allowing precise trajectory following. These control inputs can be derived from the vehicle’s trajectory using nonholonomic constraints. A LiDAR sensor is used to map the environment and detect obstacles. The system processes LiDAR data in real time, continuously updating the environment map and enabling localization within the environment. The inclusion of vehicle odometry data significantly reduces computation time and improves accuracy compared to a purely visual approach. The A* and Hybrid A* algorithms are used for trajectory planning and optimization, ensuring smooth vehicle movement. The implementation is validated through both full vehicle simulations using an ADAMS Car—MATLABco-simulation and a scaled physical prototype, demonstrating the effectiveness of the system in navigating complex environments. This work contributes to the field of autonomous systems by demonstrating the potential of combining advanced sensor technologies with innovative control algorithms to achieve reliable and efficient navigation. Future developments will focus on improving the robustness of the system by implementing a robust closed-loop controller and exploring additional applications in dense urban traffic and agricultural operations. Full article

In this article, we propose an equivalent 2-D finite element analysis (FEA) process considering the 3-D flux of an outer rotor permanent magnet synchronous motor (PMSM). In the motor, 3-D flux such as axial leakage flux (ALF) and overhang fringing flux (OFF) are influenced based on design variables. Three-dimensional FEA is required to consider the components of 3-D flux. However, 3-D FEA is inefficient to use during the design process because of time-consuming. Therefore, we propose an equivalent FEA that considers the 3-D flux. First, the effects of ALF and OFF according to design variables such as rotor inner and outer diameter, stack length, and overhang length. Second, the 3-D flux is converted into a coefficient. Finally, it is applied to 2-D FEA. Using the proposed process, motor performance considering 3-D flux can be quickly predicted. The proposed performance prediction process is verified through simulation and experiment. Full article

The Gross Motor Function Measure-66 (GMFM-66, range of values: 0 to 100 points) is one of the most widely used clinical tests to quantify motor function in children with cerebral palsy (CP). A disadvantage of the GMFM-66 is that it can take up to one hour to complete. The aim of the study was to evaluate whether the GMFM-66 can be predicted with sufficient accuracy by the results of an instrumental gait analysis (IGA) in ambulant children with CP. A retrospective analysis was conducted on n = 256 ambulant children with CP enrolled in a rehabilitation program between 2018 and 2023. The sample consisted of 97 females and 159 males, with a mean age of 9.0 years (SD 3.6 years). The IGA was performed with a Zebris FDM pressure plate. For the prediction of the GMFM-66, different statistical models were used (multiple linear regression and machine learning algorithms). Among the models considered, the XGBoost model had the best predictive performance (mean absolute error 6.32 (95%CI 5.35–7.28)). Agreement between results from gait analyses by the Zebris FDM pressure plate and GMFM-66 is not yet sufficient to predict the GMFM-66 score with acceptable accuracy for clinical purposes. Full article

Background and Objectives: Dual-task training (DTT) is an innovative therapeutic approach that involves the simultaneous application of two tasks, which can be motor, cognitive, or a combination of both. Children with cerebral palsy (CP) often exhibit impairments in balance, motor skills, and gait, conditions that may be amenable to improvement through DTT. The aim of this study was to determine the effectiveness of DTT in enhancing balance, walking speed, and gross motor function-related balance in children with CP. Materials and Methods: In accordance with PRISMA guidelines, a comprehensive systematic review with meta-analysis (SRMA) was conducted. Electronic databases like PubMed Medline, Scopus, Web of Science, CINAHL, and PEDro were searched up to March 2025, with no language or publication date restrictions. Only randomized controlled trials (RCTs) examining the effectiveness of DTT on balance, gross motor function, and walking speed in children with CP were included. The methodological quality and risk of bias of the included RCTs were assessed using the PEDro scale. Pooled effects were calculated using Cohen’s standardized mean difference (SMD) and its 95% confidence interval (95% CI) within random-effects models. Results: Eight RCTs, providing data from 216 children, were included. Meta-analyses suggested that DTT was more effective than conventional therapies for increasing functional (SMD = 0.65; 95% CI 0.18 to 1.13), dynamic (SMD = 0.61; 95% CI 0.15 to 1.1), and static balance (SMD = 0.46; 95% CI 0.02 to 0.9), as well as standing (SMD = 0.75; 95% CI 0.31 to 1.18; p = 0.001) and locomotion dimensions (SMD = 0.65; 95% CI 0.22 to 1.08) of the Gross Motor Function Measure (GMFM) and walking speed (SMD = 0.46; 95% CI 0.06 to 0.87). Subgroup analyses revealed that a motor–cognitive dual task is better than a motor single task for functional, dynamic, and static balance and standing and locomotion dimensions for the GMFM. Conclusions: This SRMA, including the major number of RCTs to date, suggests that DTT is effective in increasing balance, walking and gross motor function-related balance in children with CP. Full article

Electric motors play a decisive role in electric vehicles by converting electrical energy into mechanical motion across various drivetrain components. However, failures in these motors can interrupt the motor function, with approximately 40% of these failures stemming from bearing issues. Key contributors to bearing degradation include shaft voltage, bearing current, and electric discharge material spalling current, especially in motors powered by inverters or variable frequency drives. This review explores the tribological and vibrational aspects of bearing currents, analyzing their mechanisms and influence on electric motor performance. It addresses the challenges faced by electric vehicles, such as high-speed operation, elevated temperatures, electrical conductivity, and energy efficiency. This study investigates the origins of bearing currents, damage linked to shaft voltage and electric discharge material spalling current, and the effects of lubricant properties on bearing functionality. Moreover, it covers various methods for measuring shaft voltage and bearing current, as well as strategies to alleviate the adverse impacts of bearing currents. This comprehensive analysis aims to shed light on the detrimental effects of bearing currents on the performance and lifespan of electric motors in electric vehicles, emphasizing the importance of tribological considerations for reliable operation and durability. The aim of this study is to address the engineering problem of bearing failure in inverter-fed EV motors by integrating electrical, tribological, and lubrication perspectives. The novelty lies in proposing a conceptual link between lubricant breakdown and damage morphology to guide mitigation strategies. The study tasks include literature review, analysis of bearing current mechanisms and diagnostics, and identification of technological trends. The findings provide insights into lubricant properties and diagnostic approaches that can support industrial solutions. Full article

Background: Severe damage to one side of the brain often leads to adverse consequences and can also cause widespread changes throughout the brain, especially in the contralateral area. Studying molecular changes in the contralateral cerebral hemisphere, especially with regard to genetic regulation, can help discover potential treatment strategies to promote recovery after severe brain trauma on one side. Methods: In our study, the right motor cortex was surgically removed to simulate severe unilateral brain injury, and changes in glial cells and synaptic structure in the contralateral cortex were subsequently assessed through immunohistological, morphological, and Western blot analyses. We conducted transcriptomic studies to explore changes in gene expression levels associated with the inflammatory response. Results: Seven days after corticotomy, levels of reactive astrocytes and hypertrophic microglia increased significantly in the experimental group, while synapsin-1 and PSD-95 levels in the contralateral motor cortex increased. These molecular changes are associated with structural changes, including destruction of dendritic structures and the encapsulation of astrocytes by synapses. Genome-wide transcriptome analysis showed a significant increase in gene pathways involved in inflammatory responses, synaptic activity, and nerve fiber regeneration in the contralateral cortex after corticorectomy. Key transcription factors such as NF-κB1, Rela, STAT3 and Jun were identified as potential regulators of these contralateral changes. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) confirmed that the mRNA expression levels of Cacna1c, Tgfb1 and Slc2a1 genes related to STAT3, JUN, and NF-κB regulation significantly increased in the contralateral cortex of the experimental group. Conclusions: After unilateral brain damage occurs, changes in the contralateral cerebral hemisphere are closely related to processes involving inflammation and synaptic function. Full article

Allicin (ALC), a naturally occurring organosulfur compound derived from garlic (Allium sativum), exhibits potential neuroprotective properties. Parkinson’s disease (PD) is a progressive neurodegenerative disease characterized by degeneration of dopaminergic neurons and motor dysfunction. This study utilized bioinformatics and network pharmacology methods to predict the anti-PD mechanism of ALC and established in vivo and in vitro PD models using 6-hydroxydopamine (6-OHDA) for experimental verification. Network pharmacological analysis indicates that apoptosis regulation and the PKA/p-CREB/BDNF signaling pathway are closely related to the anti-PD effect of ALC, and protein kinase A (PKA) and dopamine transporter (DAT) are key molecular targets. The experimental results show that ALC administration can alleviate the cytotoxicity of SH-SY5Y induced by 6-OHDA and simultaneously improve the motor dysfunction and dopaminergic neuron loss in PD mice. In addition, ALC can also activate the PKA/p-CREB/BDNF signaling pathway and increase the DAT level in brain tissue, regulate the expression of BAX and Bcl-2, and reduce neuronal apoptosis. These results indicate that ALC can exert anti-PD effects by up-regulating the PKA/p-CREB/BDNF/DAT signaling pathway and inhibiting neuronal apoptosis, providing theoretical support for the application of ALC in PD. Full article

To enhance the reliability of electric propulsion in electric aircraft and address power interruptions caused by current sensor failures, this study proposes a current sensorless fault-tolerant control strategy for permanent magnet synchronous motors (PMSMs) based on a long short-term memory (LSTM) network. First, a hierarchical architecture is constructed to fuse multi-phase electrical signals in the fault diagnosis layer (sliding mode observer). A symbolic function for the reaching law observer is designed based on Lyapunov theory, in order to generate current predictions for fault diagnosis. Second, when a fault occurs, the system switches to the LSTM reconstruction layer. Finally, gating units are used to model nonlinear dynamics to achieve direct mapping of speed/position to phase current. Verification using a physical prototype shows that the proposed method can complete mode switching within 10 ms after a sensor failure, which is 80% faster than EKF, and its speed error is less than 2.5%, fully meeting the high speed error requirements of electric aircraft propulsion systems (i.e., ≤3%). The current reconstruction RMSE is reduced by more than 50% compared with that of the EKF, which ensures continuous and reliable control while maintaining the stable operation of the motor and realizing rapid switching. The intelligent algorithm and sliding mode control fusion strategy meet the requirements of high real-time performance and provide a highly reliable fault-tolerant scheme for electric aircraft propulsion. Full article

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

Background/Objectives: Children with severe physical and communication disabilities face many challenges. They have very limited opportunities for upright, hands-free, self-initiated mobility. Current findings in neuroscience and theories on child development suggest that self-initiated mobility can have positive cascading effects on various developmental areas, including language and communication. This study was conducted to examine the current use of hands-free support walkers with children who have severe physical and communication disabilities and use augmentative and alternative communication and to identify the benefits and problems perceived by their parents and professionals from different disciplines. Methods: Online surveys were utilized to collect information from 127 participants, including 31 parents and 96 professionals or paraprofessionals. Results: The participants reported that these children could perform various motor activities in the hands-free support walkers to achieve different goals. Benefits identified by both parents and professionals included providing a way to exercise and stay active, improving motor control, enhancing independence, and bringing enjoyment. Professionals also observed positive impacts on communication, vocalization, use of eye contact, and problem solving. Conclusions: Results suggest that children with severe physical and communication disabilities can benefit from the upright, hands-free, self-initiated mobility provided by hands-free support walkers. Clinical implications and needs for future research are discussed. Full article

Automated movement intention is crucial for brain–computer interface (BCI) applications. The automatic identification of movement intention can assist patients with movement problems in regaining their mobility. This study introduces a novel approach for the automatic identification of movement intention through finger tapping. This work has compiled a database of EEG signals derived from left finger taps, right finger taps, and a resting condition. Following the requisite pre-processing, the captured signals are input into the proposed model, which is constructed based on graph theory and deep convolutional networks. In this study, we introduce a novel architecture based on six deep convolutional graph layers, specifically designed to effectively capture and extract essential features from EEG signals. The proposed model demonstrates a remarkable performance, achieving an accuracy of 98% in a binary classification task when distinguishing between left and right finger tapping. Furthermore, in a more complex three-class classification scenario, which includes left finger tapping, right finger tapping, and an additional class, the model attains an accuracy of 92%. These results highlight the effectiveness of the architecture in decoding motor-related brain activity from EEG data. Furthermore, relative to recent studies, the suggested model exhibits significant resilience in noisy situations, making it suitable for online BCI applications. Full article

This paper catalogues a series of experiments we conducted to explore how to 3D print a DC electric motor. The individual parts of the electric motor were 3D printed but assembled by hand. First, we focused on a rotor with soft magnetic properties, for which we adopted ProtoPasta^TM, which is a commercial off-the-shelf PLA filament incorporating iron particles. Second, we focused on the stator permanent magnets, which were 3D printed through binder jetting. Third, we focused on the wire coils, for which we adopted a form of laminated object manufacture of copper wire. The chief challenge was in 3D printing the coils, because the winding density is crucial to the performance of the motor. We have demonstrated that DC electric motors can be 3D printed and assembled into a functional system. Although the performance was poor due to the wiring problem, we showed that the other 3D printing processes were consistent with high performance. Nevertheless, we demonstrated the principle of 3D printing electric motors. Full article

The implementation of the trans-European tourist cycle route network “EuroVelo” is fostering new strategic importance for non-motorized mobility and the associated practice of cycling tourism. Indeed, slow tourism offers a pathway for the development of inland areas. The infrastructure supporting it, such as long-distance cycling and walking paths, can act as a vital connection, stimulating regeneration in peripheral territories by enhancing environmental and landscape assets, as well as preserving heritage, local identity, and culture. The regeneration of peri-urban landscapes through soft mobility is recognized as the cornerstone for accessibility to material and immaterial resources (including ecosystem services) for multiple categories of users, including the most vulnerable, especially following the restoration of green-area systems and non-urbanized areas with degraded ecosystems. Considering the forthcoming implementation of the Magna Grecia cycling route, the southernmost segment of the “EuroVelo” network traversing three regions in southern Italy, this contribution briefly examines the necessity of defining new development policies to effectively integrate sustainable slow tourism with the enhancement of environmental and landscape values in the coastal areas along the route. Specifically, this case study focuses on a coastal stretch characterized by significant morphological and environmental features and notable landscapes interwoven with densely built environments. In this area, environmental and landscape values face considerable threats from scattered, irregular, low-density settlements, abandoned sites, and other inappropriate constructions along the coastline. Full article

Functional recovery after traumatic brain injury (TBI) is hindered by progressive neurodegeneration resulting from neuroinflammation and other secondary injury processes. Dexamethasone (DX), a synthetic glucocorticoid, has been shown to reduce inflammation, but its systemic administration can cause a myriad of other medical issues. We aim to provide a local, sustained treatment of DX for TBI. Previously, we demonstrated that PEG-bis-AA/HA-DXM hydrogels composed of polyethyleneglycol-bis-(acryloyloxy acetate) (PEG-bis-AA) and dexamethasone-conjugated hyaluronic acid (HA-DXM) reduced secondary injury and improved motor functional recovery at 7 days post-injury (DPI) in a rat moderate controlled cortical impact (CCI) TBI model. In this study, we evaluated the effect of PEG-bis-AA/HA-DXM hydrogel on cognitive function and secondary injury at 14 DPI. Immediately after injury, hydrogel disks were placed on the surface of the injured cortex. Cognitive function was evaluated using the Morris Water Maze test, and secondary injury was evaluated by histological analysis. The hydrogel treatment group demonstrated significantly shorter latency to target, decreased distance to find the hidden target, increased number of target crossings, increased number of entries to the platform zone, and decreased latency to first entry of target zone compared to untreated TBI rats for probe test. We also observed reduced lesion volume, inflammatory response, and apoptosis in the hydrogel treatment group compared to the untreated TBI group. Full article

Today, mechatronic systems are required to operate reliably and safely. However, actuators can fail, causing the system to malfunction or, in the worst case, resulting in an accident. A clear example of this is the motors of unmanned aerial vehicles. If any of them fail, the vehicle loses control, resulting in a catastrophe and potentially leading to the partial or total loss of the system. Therefore, there is a need to design robust control strategies that allow the system to continue operating even with the loss of one of its actuators. Based on the above, this work presents a controller capable of performing output regulation while tolerating actuator faults in actuated robotic platforms. In contrast to traditional output regulation theory, where a known exosystem provides the reference signal, the proposed approach employs a High-Gain Observer (HGO) to estimate and generate the reference signal from an unknown exosystem. Additionally, an Unknown Input (UI) observer is used to estimate actuator faults, enabling the computation of a fault-tolerant control. The methodology is tested in simulation and real-time experiments on the well-known Furuta pendulum system to illustrate the effectiveness of the proposed approach. Full article