Search Results (587)

Background: Cerebral palsy (CP) is a lifelong neurodevelopmental disorder characterised by motor impairment and commonly associated with comorbidities such as cognitive, communicative, and behavioural difficulties. While the physical and functional aspects of CP have been extensively studied, the mental health needs of this population remain largely underexplored, particularly concerning suicidal ideation and self-injurious behaviours. The purpose of this review is to synthesise the existing literature on suicidality in individuals with CP, explore theoretical and clinical risk factors, and identify key gaps in the current evidence base. Methods: A narrative literature review was conducted focusing on studies addressing suicidal ideation, self-harm, or related psychiatric outcomes in individuals with CP. Additional literature on risks and protective factors was included to support theoretical inferences and clinical interpretations. Results: Only a limited number of studies addressed suicidality directly in CP populations. However, several reports document elevated rates of depression, anxiety, and emotional distress, particularly among adults and individuals with higher levels of functioning. Communication barriers, chronic pain, social exclusion, and lack of accessible mental health services emerged as critical risk factors. Protective elements included strong family support, inclusive environments, and access to augmentative communication. Conclusions: Suicidality in individuals with CP is a neglected yet potentially serious concern. Evidence suggests underdiagnosis due to factors such as communication barriers and diagnostic overshadowing. Future research should prioritise disability-informed methodologies and validated tools for suicidal ideation, while clinicians should incorporate routine, adapted mental health screening in CP care to ensure early detection and person-centred management. 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

With the electrification of generator sets, electric locomotives, new energy vehicles, and other industries, AC motors subject bearings to an electric field environment, leading to galvanic corrosion due to the use of variable frequency power supply drives. The phenomenon of bearing discharge breakdown in subway traction motors is a critical issue in understanding the relationship between shaft current strength and the extent of bearing damage. This paper analyzes the mechanism of impulse discharge that leads to galvanic corrosion damage in bearings at a microscopic level and conducts electric thermal coupling simulations of the traction motor bearing discharge breakdown process. It examines the temperature rise associated with lubricant film discharge breakdown during the dynamic operation of the bearing and investigates how breakdown channel parameters and operational conditions affect the temperature rise in the micro-region of bearing lubrication. Ultimately, the results of the electric thermal coupling simulation are validated through experimental tests. This study revealed that in an electric field environment, the load-bearing area of the outer ring experiences significantly more severe corrosion damage than the inner ring, whereas non-bearing areas remain unaffected by electrolytic corrosion. When the inner ring reaches a speed of 4500_rpm, the maximum widths of electrolytic corrosion pits for the outer and inner rings are measured at 89 um and 51 um, respectively. Additionally, the highest recorded temperatures for the breakdown channels in the outer and inner rings are 932 °C and 802 °C, respectively. Furthermore, as the inner ring speed increases, both the width of the electrolytic corrosion pits and the temperature of the breakdown channels rise. Specifically, at inner ring speeds of 2500_rpm, 3500_rpm, and 4500_rpm, the widths of the electrolytic pits in the outer ring raceway load zone were measured at 34 um, 56 um, and 89 um, respectively. The highest temperatures of the lubrication film breakdown channels were recorded as 612 °C, 788 °C, and 932 °C, respectively. This study provides a theoretical basis and data support for the protective and maintenance practices of traction motor bearings. Full article

The growing popularity of Personal Light Electric Vehicles (PLEVs), such as e-scooters, has revolutionized urban mobility by offering compact, cost-effective, and environmentally friendly transportation solutions. However, safety concerns, including inadequate infrastructure, poor protective measures, and high accident rates, remain critical challenges. This paper presents the design and development of an innovative self-balancing microvehicle under the H2020 LEONARDO project, which aims to address these challenges through advanced engineering and user-centric design. The vehicle combines features of monowheels and e-scooters, integrating cutting-edge technologies to enhance safety, stability, and usability. The design adheres to European regulations, including Germany’s eKFV standards, and incorporates user preferences identified through representative online surveys of 1500 PLEV users. These preferences include improved handling on uneven surfaces, enhanced signaling capabilities, and reduced instability during maneuvers. The prototype features a lightweight composite structure reinforced with carbon fibers, a high-torque motorized front wheel, and multiple speed modes tailored to different conditions, such as travel in pedestrian areas, use by novice riders, and advanced users. Braking tests demonstrate deceleration values of up to 3.5 m/s², comparable to PLEV market standards and exceeding regulatory minimums, while smooth acceleration ramps ensure rider stability and safety. Additional features, such as identification plates and weight-dependent motor control, enhance compliance with local traffic rules and prevent misuse. The vehicle’s design also addresses common safety concerns, such as curb navigation and signaling, by incorporating large-diameter wheels, increased ground clearance, and electrically operated direction indicators. Future upgrades include the addition of a second rear wheel for enhanced stability, skateboard-like rear axle modifications for improved maneuverability, and hybrid supercapacitors to minimize fire risks and extend battery life. With its focus on safety, regulatory compliance, and rider-friendly innovations, this microvehicle represents a significant advancement in promoting safe and sustainable urban mobility. Full article

Neurodegenerative diseases (NDs) pose a major challenge to global healthcare systems owing to their devastating effects and limited treatment options. These disorders are characterized by progressive loss of neuronal structure and function, resulting in cognitive and motor impairments. Current therapies primarily focus on symptom management rather than on targeting the underlying causes. However, clustered regularly interspaced short palindromic repeat (CRISPR) technology offers a promising alternative by enabling precise genetic modifications that could halt or even reverse ND progression. CRISPR-Cas9, the most widely used CRISPR system, acts as a molecular scissor targeting specific DNA sequences for editing. By designing guide RNAs (gRNAs) to match sequences in genes associated with NDs, researchers can leverage CRISPR to knockout harmful genes, correct mutations, or insert protective genes. This review explores the potential of CRISPR-based therapies in comparison with traditional treatments for NDs. As research advances, CRISPR has the potential to revolutionize ND treatment by addressing its genetic underpinnings. Ongoing clinical trials and preclinical studies continue to expand our understanding and application of this powerful tool to fight debilitating conditions. Full article

This perspective paper presents a first-person account of life with motor neurone disease (MND). Through the lens of lived experience, it explores the complex and often prolonged diagnostic journey, shaped in part by the protective grip of denial. This paper then delves into the emotional impact of MND on the individual and their close relationships, capturing the strain on identity and family dynamics. It also highlights the vital role of the multidisciplinary team in providing support throughout the journey. A central focus of the paper is the personal journey of voice banking. It reflects on the restorative experience of reclaiming a pre-disease voice through tools such as ElevenLabs^TM. This narrative underscores the critical importance of early intervention and timely access to voice banking, positioning voice not only as a tool for communication but also as a powerful anchor of identity, dignity, and agency. The paper concludes by highlighting key systemic gaps in MND care. It calls for earlier referral to speech pathology, earlier access to voice banking, access to psychological support from the time of diagnosis, and better integration between research and clinical care. Full article

This study presents the design and validation of an integrated fire safety system that leverages edge AI, hybrid sensing, and precision suppression to overcome the latency and collateral limitations of conventional smoke detection and sprinkler systems. The proposed platform features a dual-mode sensor array for early fire recognition, motorized ventilation units for rapid smoke extraction, and a 360° directional nozzle for targeted agent discharge using a residue-free clean extinguishing agent. Experimental trials demonstrated an average fire detection time of 5.8 s and complete flame suppression within 13.2 s, with 90% smoke clearance achieved in under 95 s. No false positives were recorded during non-fire simulations, and the system remained fully functional under simulated cloud communication failure, confirming its edge-resilient architecture. A probabilistic risk analysis based on ISO 31000 and NFPA 551 frameworks showed risk reductions of 75.6% in life safety, 58.0% in property damage, and 67.1% in business disruption. The system achieved a composite risk reduction of approximately 73%, shifting the operational risk level into the ALARP region. These findings demonstrate the system’s capacity to provide proactive, energy-efficient, and spatially targeted fire response suitable for high-value infrastructure. The modular design and SmartThings Edge integration further support scalable deployment and real-time system intelligence, establishing a strong foundation for future adaptive fire protection frameworks. Full article

Series elastic actuators (SEAs) represent a key technological solution to enhance safety, performance, and adaptability in robotic devices for physical training. Their ability to decouple the rigid actuator’s mechanical impedance from the load, combined with passive absorption of external disturbances, makes them particularly suitable for pediatric applications. In children aged 2 to 5 years—where motor control is still developing and movements can be unpredictable or unstructured—SEAs provide a compliant mechanical response that ensures user protection and enables safe physical interaction. This study explores the role of SEAs as a central component for imparting compliance and backdrivability in robotic systems designed for upper-limb training. A dynamic model is proposed, incorporating interaction with the user’s limb, along with a computed torque control strategy featuring integral action. The system’s performance is validated through simulations and experimental tests, demonstrating stable trajectory tracking, disturbance absorption, and effective impedance decoupling. The results support the use of SEAs as a foundational technology for developing safe adaptive robotic solutions in pediatric contexts capable of responding flexibly to user variability and promoting secure interaction in early motor development environments. Full article

Skeletal muscle atrophy is a debilitating condition characterized by the loss of muscle mass and function. It is commonly associated with aging, chronic diseases, disuse, and prolonged glucocorticoid therapy. Oxidative stress and catabolic signaling pathways play significant roles in the progression of muscle degradation. Despite its clinical relevance, few effective therapeutic options are currently available. In this study, we investigated the protective effects of an ethanolic extract of Glycine Semen Preparata (GSP), i.e., fermented black soybeans, using in vitro and in vivo models of dexamethasone (Dexa)-induced muscle atrophy. In C2C12 myoblasts and myotubes, GSP significantly attenuated both oxidative stress-induced and Dexa-induced damages by reducing reactive oxygen species levels and by suppressing the expression of the muscle-specific E3 ubiquitin ligases MuRF1 and Atrogin-1. Moreover, GSP upregulated key genes involved in muscle regeneration (Myod1 and Myog) and mitochondrial biogenesis (PGC1α), indicating its dual role in muscle protection and regeneration. Oral administration of GSP to mice with Dexa-induced muscle atrophy resulted in improved muscle fiber integrity, increased proportion of large cross-sectional area fibers, and partial recovery of motor function. Isoflavone aglycones, such as daidzein and genistein, were identified as active compounds that contribute to the beneficial effects of GSP through antioxidant activity and gene promoter enhancement. Thus, GSP is a promising nutraceutical that prevents or mitigates muscle atrophy by targeting oxidative stress and promoting myogenesis and mitochondrial function. Further studies are warranted to standardize the bioactive components and explore their clinical applications. Full article

In complex marine environments, ramp-based recovery systems for autonomous underwater vehicles (AUVs) often encounter engineering challenges such as reduced docking accuracy and success rate due to disturbances in the capture window attitude. In this study, a desktop-scale physical experimental platform for recovery compensation was designed and constructed. The system integrates attitude feedback provided by an attitude sensor and dual-motor actuation to achieve active roll and pitch compensation of the capture window. Based on the structural and geometric characteristics of the platform, a dual-channel closed-loop control strategy was proposed utilizing midpoint tracking of the capture window, accompanied by multi-level software limit protection and automatic centering mechanisms. The control algorithm was implemented using a discrete-time PID structure, with gain parameters optimized through experimental tuning under repeatable disturbance conditions. A first-order system approximation was adopted to model the actuator dynamics. Experiments were conducted under various disturbance scenarios and multiple control parameter configurations to evaluate the attitude tracking performance, dynamic response, and repeatability of the system. The results show that, compared to the uncompensated case, the proposed compensation mechanism reduces the $MSE$ by up to 76.4% and the $MaxAE$ by 73.5%, significantly improving the tracking accuracy and dynamic stability of the recovery window. The study also discusses the platform’s limitations and future optimization directions, providing theoretical and engineering references for practical AUV recovery operations. Full article

Background/Objectives: Digital technologies have become increasingly integrated into the daily lives of children and adolescents, largely because their interactive and visually engaging design is particularly suited to the younger users. The COVID-19 pandemic further accelerated this trend, significantly lowering the average age of access to the digital devices. However, scientific consensus remains divided regarding the developmental impact of digital media use—particularly its cognitive, motor, and emotional consequences—depending on whether the use is passive or active. This review aims to explore these effects across developmental stages, focusing on both behavioral and neurobiological dimensions, and to identify emerging risks and protective factors associated with digital engagement. Methods: A PRISMA review was conducted on the impact of digital media use among pre-school children and adolescents. Behavioral, psychosocial, and neurobiological aspects were examined, with specific attention to epigenetic changes, techno-stress, digital overstimulation, and immersive technologies (e.g., virtual and augmented reality). Results: The findings suggest that passive digital consumption is more often associated with negative outcomes, such as impaired attention and emotional regulation, especially in younger children. Active and guided use may offer cognitive benefits. Neurobiological research indicates that chronic exposure to digital stimuli may affect stress regulation and neural development, possibly via epigenetic mechanisms. Effects vary across developmental stages and individual vulnerabilities. Conclusions: A nuanced understanding of digital engagement is essential. While certain technologies can support development, excessive or unguided use may pose risks. This review provides age-specific recommendations to foster balanced and healthy technology use in children and adolescents. Full article

Electric vehicle applications frequently use brushless direct-current (BLDC) motors due to their high torque and efficiency. However, coil damage may result from their use at high rotating speeds and extremely high temperatures, requiring preventative maintenance. This study describes the creation of a better online monitoring platform that is coupled with an improved fault detection and protection system for small electric vehicles. Designing a fault detection system with real-time analysis to identify open-circuit problems is part of the process. The results indicate that the reliability and operating efficiency of electric vehicle applications have been greatly enhanced by the development of a potential fault-monitoring and protection solution. Full article

Background/Objectives: A large percentage of schoolchildren are children with overweight or obese, and weight gain over time increases the risk of poor health later in life. Motor coordination may be a protective factor, enabling young people to participate in healthy physical activities. However, it is unknown when the relationship between motor coordination and weight first emerges, nor whether it is evident across the body mass index (BMI) spectrum. The aim of this study was to explore what happens with coordination skills [i.e., locomotor coordination (LC), visuomotor coordination (VC), foot object control coordination (FOCC), hand object control coordination (HOCC), global motor coordination (GMC)] applying the 3JS battery, according to age (6 to 11 years) and BMI (normal weight, overweight or obesity) in a sample of 688 Primary Education participants (8.71 ± 1.66 years; 48.8% boys) from Galicia (Spain). Methods: To analyze the differences in the variables of the 3JS battery between age (6–11 years) and BMI (normal weight vs. overweight vs. obesity), they were evaluated using a multivariate analysis of covariance (MANCOVA), introducing the sex category (boy-girl) as a covariate to avoid possible confounding factors. Results: After the application of the 3JS Battery, statistically significant differences were observed depending on age [LC (p < 0.001); VC (p < 0.001); FOCC (p < 0.001); HOCC (p < 0.001); GMC (p < 0.001)] and BMI [LC (p < 0.001); VC (p = 0.008); HOCC (p < 0.001); GMC (p < 0.001)]. No statistically significant differences were found in the interactions between age and BMI (p > 0.05). Conclusions: Locomotor coordination increases progressively with age, both globally and in each of the manifestations, and this is greater the older the schoolchildren are. Schoolchildren with normal weight compared to schoolchildren with overweight or obesity have better global motor coordination, locomotor coordination, global control of objects, and control with the hand. Full article

Light-duty trucks (LDTs) are often used to tow trailers. Towing increases the load on the engine, and this additional load can affect exhaust emissions. Although heavy-duty towing impacts are widely studied, data on LDT towing impacts is sparse. In this study, portable emissions measurement systems (PEMSs) were used to measure in-use emissions from three common LDTs during towing and non-towing operations. Emission rates were characterized by operating modes defined in the Environmental Protection Agency’s (EPA’s) MOVES (MOtor Vehicle Emissions Simulator) model. The measured emission rates were compared to the default rates used by MOVES, revealing similar overall trends. However, discrepancies between measured rates and MOVES predictions, especially at high speed and high operating modes, indicate a need for refinement in emissions modeling for LDTs under towing operations. Results highlight a general trend of increased CO₂, CO, HC, and NOx when towing a trailer compared to non-towing operations across nearly all operating modes, with distinct CO and HC increases in the higher operating modes. Although emissions were observed to be notably higher in a handful of scenarios, results also indicate that three similar LDTs can have distinctly different emission profiles. Full article

Insurance is essential for financial risk protection, but claim management is complex and requires accurate classification and forecasting strategies. This study aimed to empirically evaluate the performance of classification algorithms, including Logistic Regression, Decision Tree, Random Forest, XGBoost, K-Nearest Neighbors, Support Vector Machine, and Naïve Bayes to predict high insurance claims. The research analyses the variables of claims, vehicles, and insured parties that influence the classification of high-cost claims. This investigation utilizes a dataset comprising 802 observations of bodily injury claims from the motor liability portfolio of a private insurance company in Albania, covering the period from 2018 to 2024. In order to evaluate and compare the performance of the models, we employed evaluation criteria, including classification accuracy (CA), area under the curve (AUC), confusion matrix, and error rates. We found that Random Forest performs better, achieving the highest classification accuracy (CA = 0.8867, AUC = 0.9437) with the lowest error rates, followed by the XGBoost model. At the same time, logistic regression demonstrated the weakest performance. Key predictive factors in high claim classification include claim type, deferred period, vehicle brand and age of driver. These findings highlight the potential of machine learning models in improving claim classification and risk assessment and refine underwriting policy. Full article