Search Results (2,535)

With the increasing number of patients with Parkinson’s disease, the detection of Parkinson’s disease is crucial for the early intervention and treatment of this condition. The motor characteristics of Parkinson’s disease primarily include typical motor features. Flexible pressure sensor arrays, due to their unique mechanical properties and biocompatibility, have shown great potential for capturing movement characteristics. This research aims to develop a deep learning model based on foot pressure data for the detection of Parkinson’s disease. By collecting the pressure data of patients during walking and analyzing the distribution of foot pressure, the model can capture the unique biomechanical characteristics of Parkinson’s disease patients. To address the core challenges of spatial irregularity and data disorder in footprint data, we propose an innovative approach that leverages the Transformer-based attention mechanism and tensor fusion technique to enable accurate identification of Parkinson’s disease. This attention mechanism has inherent permutation invariance, which is highly suitable for the feature learning of footprint data. The tensor fusion technique can effectively integrate the foot features at different levels. A large-scale dataset of foot pressure data was used for training and validation. The experimental results show that the model achieves a high accuracy of 87.03% and good stability in Parkinson’s disease detection, enabling effective differentiation between patients and healthy individuals. On the one hand, our work is critical for analyzing pressure data and fusion features from large-area flexible force-sensitive sensors, which enables the accurate identification of foot data. On the other hand, it greatly facilitates gait analysis, gait evaluation, and the diagnosis of Parkinson’s disease. Full article

Current research and development in understanding road users’ driving behaviors play a key role in improving traffic safety. Recently, several driving simulators have been employed as a suitable approach to investigate several drivers’ responses in challenging traffic scenarios. Although professional drivers represent a particular category among driving populations, the body of literature about their comparative behavioral and psychological characteristics remains limited. This study examined the differences in driving performance and visual and physiological responses between civilian and professional drivers in a simulated environment. A total of 30 drivers, with an equal split between professional and civilian categories, took part in a series of driving simulations. The simulations incorporated various infrastructure types, including four cone avoidance tasks and a high-speed motorway task. This study collected comprehensive data on performance metrics, hand usage, heart rate, and eye movements. Eye-tracking technology was used to measure visual attention. The findings revealed that during cone avoidance scenarios, civilian drivers exhibited a similar performance, visual behavior, and physiological response, except for the speed, experiment duration, and throttle, to professional drivers. In the motorway scenario, all metrics showed no significant difference between the two driver groups. These results highlight the need for cautious interpretation, particularly given the limitations of the sample. Revalidation is needed in larger studies, especially for understanding the differences between drivers’ metrics, which is crucial to elevate drivers’ safety, and assessing training programs in Hungary. Full article

Ticks use multiple sensory organs to facilitate host detection, including Haller’s organs (HOs) that allow ticks to sense infrared (IR) radiation from potential hosts. Additionally, ticks have primitive eyes to sense light sources. The possibility exists that these senses may detect stimuli that attract ticks to road edge habitat, where IR radiation tends to be elevated. We investigated the role of the HOs and eyes in the attraction of adult American dog ticks, Dermacentor variabilis, towards road edge habitat(s). Adult D. variabilis were collected from multiple study sites and separated into three groups: (1) Haller’s organs removed; (2) eyes painted with black nail polish; and (3) unmodified ticks (control). All tick groups were marked with a unique fluorescent paint color and released 7.5 m from the road edge at two study sites. Tick movements were tracked at night using ultraviolet lights, tick position(s) were recorded using flags, and measurements were recorded to track tick movement in relation to the release point and road edge. Surface temperatures were recorded at the road edge and in the field to detect a potential thermal stimulus. Mixed-effects models were applied to investigate the significance of tick proximity to the road edge between the groups and sites. Our results demonstrated that the control unmodified group was significantly closer to the road edge than the modified groups lacking Haller’s organ or eyes (p ≤ 0.0001, p = 0.0049), leading to the conclusion that unmodified ticks move towards road edges. Modifying ticks, either by removing the HO or eyes of adult D. variabilis decreased tick movement toward road edges. Full article

Branching structures such as vascular networks are representative morphological patterns in living systems, and they often arise from collective cell migration. Angiogenesis, the sprouting of new blood vessels from pre-existing ones, is a fundamental process in development. Experimental and theoretical studies have demonstrated that sprout formation depends on the collective movements and shapes of endothelial cells, as well as the remodelling of the extracellular matrix. Many discrete models have been proposed to describe cell dynamics, successfully reproducing vascular patterns and collective behaviours. In this study, we present a two-dimensional mathematical model that represents each endothelial cell as an ellipse and incorporates the effects of the extracellular matrix. We performed computer simulations under two scenarios: invasion from a pre-formed sprout and collective advancement into an extracellular matrix region. The results show that the extracellular matrix helps maintain linear sprout extension and suppresses the formation of dispersed or curved branches, while elongated cell shapes promote sprouting more effectively than round cells. The model also reproduces experimentally observed behaviours such as tip-cell replacement and the mixing of cells within sprouts. These findings highlight the importance of integrating cell shape and extracellular matrix remodelling to understand early blood vessel formation. Full article

The green economy has become an economic necessity and a cultural discourse due to the rapid global movement towards sustainability. This paper discusses the representation of green economy in Qatar and Malaysia, two countries with different political and cultural background but similar ambitions to attain sustainable development on social media. Through the application of qualitative techniques, namely thematic analysis and critical discourse analysis, the re-search analyzed Twitter, Facebook, Instagram, and LinkedIn posts discussing sustainability, renewable energy, and green innovation by using hashtags and stories on the topic. The results indicate that four major themes exist in both settings, and they are sustainability as national pride and identity, corporate–government branding of green efforts, grassroot and citizen involvement, and conflicts around contradictions and skepticism. Green economy in Qatar is constructed as a symbol of prestige and international presence, which is directly connected to the Qatar National Vision 2030, and popularized at the state and corporate levels. Big projects, financial solutions like green bonds, and sustainable infrastructure are mentioned in narratives and criticism is afforded little space. The environmental sustainability is part of cultural representation and collective accountability, grassroots mobilization, youth activism, and defiance of official and corporate language in Malaysia. A dynamic and critical digital discourse is often criticized by the citizens when they face perceived greenwashing. The research adds to the theoretical knowledge of understanding of framing theory that civic space plays a role in the development of sustainability discourses and the importance of critical discourse analysis in studying power relations in environmental discourse. In practice, the study recommends that Qatar should engage its citizens in more than just symbolic branding; Malaysia should enhance transparency and consistency of its policies to curb the skepticism of its people. In general, the paper highlights the fact that social media is not simply a medium of communication but rather a controversial field on which the definitions of sustainability are actively discussed. Full article

This study presents a comparative analysis of two bio-inspired optimization techniques: the Dragonfly Algorithm (DA) and Cuckoo Search (CS). The DA models the collective behavior of dragonflies, replicating dynamic processes such as foraging, evasion, and synchronized movement to effectively explore and exploit the solution space. In contrast, the CS algorithm draws inspiration from the brood parasitism strategy observed in certain Cuckoo species, where eggs are laid in the nests of other birds, thereby leveraging randomization and selection mechanisms for optimization. To enhance the performance of both algorithms, Type-2 fuzzy logic systems were integrated into their structures. Specifically, the DA was fine-tuned through the adjustment of its inertia weight (W) and attraction coefficient (Beta), while the CS algorithm was optimized by calibrating the Lévy flight distribution parameter. A comprehensive set of benchmark functions, F1 through F10, was employed to evaluate and compare the effectiveness and convergence behavior of each method under fuzzy-enhanced configurations. Results indicate that the fuzzy-based adaptations consistently improved convergence stability and accuracy, demonstrating the advantage of integrating Type-2 fuzzy parameter control into swarm-based optimization frameworks. Full article

Traditional control of robotic systems relies on the availability of an exact model, which assumes complete knowledge of the robot’s parameters and all dynamic effects. However, this idealized scenario rarely holds in practice, as real-world interactions introduce unpredictable environmental influences, friction, and edge effects. This paper presents a novel data-driven approach to modeling and estimating robot dynamics by leveraging data collected during the robot’s movements. The proposed method operates without prior knowledge of the system parameters, thereby addressing the limitations of conventional model-based control strategies in complex and uncertain environments. Our unified data-driven framework integrates classical control theory with modern machine learning techniques, including system identification, physics-informed neural networks (PINNs), and deep learning. We demonstrate its efficacy in the case of a two-link robotic manipulator that achieves superior trajectory tracking and robustness to unmodeled dynamics. The technique is modular and can be extended to manipulators with more joints. Full article

This research aims to investigate the practicality and feasibility of pneumatic muscle actuators (PMAs) applied in the pneumatic servo system. The mechanism consists of closed symmetrical planar robotic arms driven by two pairs of opposing PMAs, whose structure is similar to human arms. Importantly, the two distal links (or wrist parts) are combined into a collective end-effector, whose desired position is controlled only by the two shoulder angle joints. When two pairs of PMAs are attached to the upper arms, they actuate each shoulder and assist in the movement of the arms. However, the nonlinear behavior, high hysteresis, low damping, and time-varying characteristics of PMAs significantly limit their controllability. Therefore, to effectively address these challenges, a Fourier series-based adaptive sliding mode controller with $H_{\mathrm{\infty }}$ (FSB-ASMC + $H_{\mathrm{\infty }}$) is employed to achieve accurate path tracking of the PMAs. This control approach not only compensates for approximation errors, disturbances, and unmodeled dynamics but also ensures the desired $H_{\mathrm{\infty }}$ positioning performance of the overall system. The controller method can not only effectively prevent approximation errors, disturbances, and un-modeled dynamics but can also ensure the required $H_{\mathrm{\infty }}$ positioning performance of the whole system. Thus, the results of the experiment showed that the control strategy for the system collocating the FSB-ASMC + $H_{\mathrm{\infty }}$ can attain excellent control performance. Full article

Beaver dam–pond systems reshape the hydrology of lowland landscapes by slowing water flow and trapping sediments, thereby reducing the movement of pollutants. This study examined how such beaver-engineered wetlands can naturally filter and signal contamination risks associated with lead (Pb). We combined data from three matrices—bottom sediments, riparian vegetation, and non-invasively collected beaver fur—across three Lithuanian sites (2022–2024). Previously published datasets on plants and sediments were complemented with new information from beaver fur to explore seasonal and age-related effects as well as differences inside and outside dam zones. Lead levels were consistently higher in sediments than in plants, while beaver fur reflected variable, site-specific exposures. These results show that beaver activity contributes to the capture and redistribution of sediment-bound Pb in wetland buffers. The approach demonstrates how beaver habitats can serve as low-cost, nature-based sentinels for pollutant monitoring. Using beaver fur as a non-invasive bioindicator and managing dam stability can improve the ecological and policy relevance of buffer zones. Overall, the findings support the integration of beaver-engineered wetlands into environmental management and EU water policy, contributing to SDG 6 goals for clean water and sustainable wetland use. Full article

Beet armyworm (BAW), Spodoptera exigua (Hübner), is a polyphagous and migratory lepidopteran pest that causes damage to a broad range of host plants, including row crops and vegetables, with increasing reports of insecticide-resistant populations. Understanding its movement ecology is essential for integrated pest management (IPM) and insect resistance management (IRM) programs. We investigated the population dynamics and migratory origins of BAW during the 2024 cropping season in two counties of the Florida Panhandle, USA. Larval sampling and adult pheromone trapping were conducted in commercial peanut fields, and hydrogen stable isotopes were used to infer the probable geographic origins of moths. Across the season, the presence of BAW accounted for 7.1% of all individuals collected, with infestation occurring only in July and August. Moth captures were significantly higher in the eastern Florida panhandle than in the western Florida Panhandle. Stable isotope analysis revealed four distinct migratory patterns, with most BAW likely originating from South Florida, the Caribbean, or the central and southern United States, indicating northward movement. Our findings provide critical insights into the movement ecology of BAW and highlight its implications for outbreak forecasting, resistance management, and regional IPM/IRM strategies in the southeastern United States. Full article

Anterior cruciate ligament (ACL) injuries in football mostly occur during defensive (pressing) cut maneuvers. Football-specific cutting movements are key to identifying dangerous biomechanics but hard to evaluate clinically. This study aimed to develop a practical field-based tool—Anterior Cruciate Ligament Injury Risk Profile Detection (ACL-IRD)—to assess ACL injury risk during return to sport (RTS). It was hypothesized that the ACL-IRD could detect ACL injury risk profiles after ACLR players had RTS clearance. Sixty-one footballers (21 ACLR, 40 healthy; 16.2 ± 2.2 years old, >14 months post-surgery) were tested on a regular football pitch. Players performed pre-planned (AGTT) and unplanned football-specific cut maneuvers simulating defensive pressing (FS deceiving action). Kinematic data were collected via eight wearable inertial sensors (MTw Awinda, Movella) on trunk and lower limbs. The ACL-IRD analyzed biomechanics in three risk categories, knee valgus collapse, sagittal knee loading, and trunk–pelvis imbalance, using thresholds from healthy players. A clinician-friendly, automatic report was generated. At-risk biomechanics were identified in 36–37/104 AGTT trials and 25–41/97 FS deceiving actions (at initial contact and peak knee flexion). Over 60% of risky trials involved the ACLR limb. Major risk factors were altered knee/hip flexion ratio, knee valgus, and hip abduction. The ACL-IRD is a novel, clinical-friendly tool designed to identify potential ACL injury risk profiles and is intended to support safer RTS decisions. Full article

Heart rate (HR) monitoring is a practical method for assessing internal load (IL). However, it remains unclear for which age group HR would be an appropriate predictor of IL considering the relationship with external load (EL). Thus, this study aims to evaluate the relevance and applicability of HR monitoring by exploring the relationship between EL and IL among U16 soccer players. EL was measured using global positioning system (GPS) data, while IL was assessed through training impulse (TRIMP), Edward’s TRIMP, HR exertion, rate of perceived exertion (RPE), and session-RPE (s-RPE). Nineteen (N = 19) male footballers from an elite football academy participated, with data collected from 50 training sessions and 11 matches. In the analysis of the training sessions, TRIMP demonstrated a near-perfect correlation with total distance (TD) (p < 0.001), and eTRIMP correlated strongly with TD (r = 0.82) and player load (r = 0.79). HR exertion also correlated significantly with TD, medium-speed running, decelerations, inertial movement analysis (IMA) events, and player load (p < 0.001). In matches, a large correlation was observed between TRIMP and TD (r = 0.73), while the strongest correlation was between RPE and s-RPE with TD and PL (p < 0.001). Furthermore, TD emerged as the best GPS-derived predictor of both TRIMP and HR exertion in training contexts. These findings provide evidence for the validity and usability of heart rate-based and RPE-based measures to indicate IL in U16 soccer players. Future research should focus on contextual factors in exploring the relationship between EL and IL. Full article

In less than 30 years, Deep Brain Stimulation (DBS) has evolved from an antiparkinsonian rescue intervention into a flexible neuromodulatory therapy with the potential for personalized, adaptive, and enhancement-focused interventions. In this review we collected evidence from seven areas: (i) modern eligibility criteria, and ways to practically improve on these, outside of ‘Core Assessment Program of Surgical Interventional Therapies in Parkinson’s Disease’ (CAPSIT-PD); (ii) cost-effectiveness, where long-horizon models now show positive incremental net monetary benefit for Parkinson’s disease, and rechargeable-devices lead the way in treatment-resistant depression and obsessive–compulsive disorder; (iii) anatomical targets, from canonical subthalamic nucleus (STN) / globus pallidus internus (GPi) sites, to new dual-node and cortical targets; (iv) mechanistic theories from informational lesions, antidromic cortical drive, and state-dependent network modulation made possible by optogenetics and computational modeling; (v) psychiatric and metabolic indications, and early successes in subcallosal and nucleus-accumbens stimulation for depression, obsessive–compulsive disorder (OCD), anorexia nervosa, and schizophrenia; (vi) procedure- and hardware-related safety, summarized through five reviews, showing that the risks were around 4% for infection, 4–5% for revision surgery, 3% for lead malposition or fracture, and 2% for intracranial hemorrhage; and (vii) future directions in connectomics, closed-loop sensing, and explainable machine learning pipelines, which may change patient selection, programming, and long-term stewardship. Overall, the DBS is entering a “third wave” focused on a better understanding of neural circuits, the integration of AI-based adaptive technologies, and an emphasis on cost-effectiveness, in order to extend the benefits of DBS beyond the treatment of movement disorders, while remaining sustainable for healthcare systems. Full article

This study evaluates four developed external Human–Machine Interface (eHMI) concepts for automated shuttles, focusing on improving communication with other road users, mainly pedestrians and cyclists. Without a human driver to signal intentions, eHMI systems can play a crucial role in conveying the shuttle’s movements and future path, fostering safety and trust. The four eHMI systems’ purple light projections, emotional eyes, auditory alerts, and informative text were tested in a virtual reality (VR) environment. Participant evaluations were collected using an approach inspired by Kansei engineering and Likert scales. Results show that auditory alerts and informative text-eHMI are most appreciated, with participants finding them relatively clear and easy to understand. In contrast, purple light projections were hard to see in daylight, and emotional eyes were often misinterpreted. Principal Component Analysis (PCA) identified three key factors for eHMI success: predictability, endangerment, and practicality. The findings underscore the need for intuitive, simple, and predictable designs, particularly in the absence of a driver. This study highlights how eHMI systems can support the integration of automated shuttles into public transport. It offers insights into design features that improve road safety and user experience, recommending further research on long-term effectiveness in real-world traffic conditions. Full article

Despite growing evidence on the adaptive movement patterns that horses adopt during water treadmill (WT) exercise, underlying adaptations in muscle activity remain uninvestigated. This feasibility study aimed to develop a method for the synchronous measurement of muscle activity and movement of horses during WT exercise. Combined surface electromyography (sEMG) (2000 Hz) from selected hindlimb (biceps femoris, gluteus medius, tensor fasciae latae) and epaxial (longissimus dorsi) muscles, and three-dimensional kinematic (200 Hz) data from the back and pelvis of one (n = 1) horse were collected during overground (OG), dry treadmill (TM), and WT walking conditions. Statistical parametric mapping evaluated differences in time- and amplitude-normalised sEMG and thoracolumbar and pelvis kinematic waveforms between conditions. Distinct, significant (p < 0.05) adaptations in hindlimb and epaxial muscle activation patterns and axial and pelvic kinematics, were observed in this horse across exercise conditions. Adaptive muscle activity was most pronounced in this horse during WT, compared to OG walking. These findings demonstrate the feasibility of this method, which combines sEMG and motion capture technologies to synchronously quantify equine movement and muscle activation patterns during WT exercise. This justifies the replication of this work in a larger sample of horses to inform evidence-based training and rehabilitation programmes. Full article