Search Results (6,460)

(1) Background: Technological advancements provide new opportunities to objectively assess motor deficits in people with Multiple Sclerosis (PwMS). This pilot study aimed to evaluate the performance and usability of iFeel, a novel wearable system which integrates inertial sensors, instrumented shoes, and an AI-based algorithm. (2) Methods: Sixteen adult PwMS (Expanded Disability Status Scale—EDSS ≤ 6) performed motor tests (Timed 25-Foot Walk—T25FW; Timed Up and Go—TUG) both with and without the iFeel suit. Patient-reported outcomes (PROs) were also collected to assess perceived fatigue, dual-task impact, and walking difficulties. System Usability Scale (SUS) and ad hoc questionnaires have been further administered to test usability. (3) Results: No significant differences were found between the clinician and system-based scores for both T25FW (p = 0.383) and TUG (p = 0.447). Reliability analyses showed good agreement for T25FW (Intraclass Correlation Coefficient—ICC = 0.83) and excellent agreement for TUG (ICC = 0.92). Sensor-derived measures correlated strongly with PROs on fatigue, dual-task interference, and mobility. Usability was rated high (SUS: 78.6 ± 16.1), with participants reporting minimal discomfort and positive perceptions of iFeel usefulness for rehabilitation, health monitoring, and daily activities. (4) Conclusions: This pilot study provides preliminary yet promising evidence on the feasibility, usability, and perceived usefulness of the iFeel technology for motor assessment in PwMS. The findings support its further development and potential integration into clinical practice, particularly for remote or continuous motor monitoring. Full article

In the context of the growing promotion of sustainable urban mobility policies, traffic calming is one of the main actions adopted by local, regional, and national administrations to support the liveability and vitality of residential and commercial areas through improvements in non-motorists’ safety, mobility, and comfort. Traffic calming is achieved through the implementation of several actions and physical features such as speed bumps. These elements are generally accompanied by surface drainage elements (grated inlets) located upstream. The presence of speed bumps modifies the hydraulic performance of the inlets. This work aimed to evaluate, by experimental tests, the effects produced by the presence of two different speed bumps on two grated inlets commonly used in Barcelona. The results indicate that the hydraulic efficiency of grated inlets located upstream of speed bumps increases with respect to conventional situations (without speed bumps). These increments are relevant (up to 60%) for flat areas and streets with longitudinal slopes of up to 4–6%, but can be neglected for steep roads (more than 6%). The increase in grate inlet hydraulic performance means modifications in terms of inlet spacing, with significant economic savings for local administrations in charge of the design, implementation, and maintenance of surface drainage systems. Full article

Background: Mobility assessment is a crucial aspect of rehabilitation for individuals with lower limb amputation, as it directly influences their independence and quality of life. The objective of this study was to translate and cross-culturally adapt the Special Interest Group in Amputee Medicine (SIGAM) mobility grades questionnaire in the Persian language and to investigate its psychometric properties. Methods: The SIGAM mobility scale was translated into Persian according to international guidelines for cross-cultural adaptation of self-reported measures and was administered to forty Persian-speaking people with lower limb amputations. Measurement properties were evaluated following COSMIN (COnsensus-based Standards for the Selection of Health Measurement INstruments) recommendations and included internal consistency, test–retest reliability, and hypotheses testing for construct validity by comparing SIGAM mobility grades to the Locomotor Capabilities Index-5 (LCI-5), Houghton scale, Activities-specific Balance Confidence (ABC) scale, the 2-Minute Walk Test (2-MWT), and the Timed Up and Go (TUG). Results: SIGAM mobility scale demonstrated acceptable internal consistency (Kuder-Richardson 20 coefficient = 0.72) and excellent test–retest reliability (Cohen Kappa coefficient = 0.85). Hypothesis testing for construct validity confirmed the good to very good correlations of the Persian SIGAM mobility scale with the LCI-5 (r = 0.63, 0.55, and 0.63 for the general, basic, and advanced activities components, respectively), Houghton scale (r = 0.63), ABC scale (r = 0.73), 2-MWT (r = 0.50), and TUG test (r = −0.51). Conclusion: The Persian version of the SIGAM mobility scale demonstrated preliminary evidence of acceptable psychometric properties, supporting its clinical applicability. Full article

(1) Background: The global increase in life expectancy has generated growing interest in strategies that support functional independence and quality of life among older adults. Functional fitness—including strength, mobility, flexibility, and aerobic endurance—is essential for preserving autonomy during aging. In this context, physical exercise, particularly High-Intensity Interval Training (HIIT), has gained attention for its time efficiency and physiological benefits. This randomized controlled trial aimed to evaluate the effects of a group-based HIIT program on functional fitness in older adults; (2) Methods: Functional outcomes were assessed before, during, and after a 65-week intervention using standardized field tests, including measures of upper and lower body strength, flexibility, aerobic endurance, and agility. This study was prospectively registered at ClinicalTrials.gov (NCT07170579); (3) Results: Significant improvements were observed in the HIIT group across multiple domains of functional fitness compared to the control group, notably in upper body strength, lower limb flexibility, cardiorespiratory endurance, and mobility; (4) Conclusions: These results suggest that HIIT is an effective and adaptable strategy for improving functional fitness in older adults, with the potential to enhance performance in daily activities and support healthy aging in community settings. Full article

Visual inspection remains a cornerstone of quality control in welded structures, yet manual evaluations are inherently constrained by subjectivity, inconsistency, and limited scalability. This study presents WeldVGG, a deep learning-based visual inspection model designed to automate weld defect classification using radiographic imagery. The proposed model is trained on the RIAWELC dataset, a publicly available collection of X-ray weld images acquired in real manufacturing environments and annotated across four defect conditions: cracking, porosity, lack of penetration, and no defect. RIAWELC offers high-resolution imagery and standardized class labels, making it a valuable benchmark for defect classification under realistic conditions. To improve trust and explainability, Grad-CAM++ is employed to generate class-discriminative saliency maps, enabling visual validation of predictions. The model is rigorously evaluated through stratified cross-validation and benchmarked against traditional machine learning baselines, including SVC, Random Forest, and a state-of-the-art architecture, MobileNetV3. The proposed model achieves high classification accuracy and interpretability, offering a practical and scalable solution for intelligent weld inspection. Furthermore, to prove the model’s ability to generalize, a test on the GDXray was performed, yielding positive results. Additionally, a Wilcoxon signed-rank test was conducted separately to assess statistical significance between model performances. Full article

To counter the high unmet need for family planning in urban areas of Sindh province, Pakistan, Greenstar Social Marketing began implementation of The Challenge Initiative (TCI) in collaboration with the government departments of Population Welfare and Health in eight urban districts of Sindh province. This study aimed to assess the effectiveness of TCI’s Community Health Volunteers (CHVs) on public sector service provision of family planning services in eight urban districts of Sindh province, Pakistan. The Contraceptive Logistics Management Information System (cLMIS) and District Health Information System 2 (DHIS2) were used to obtain monthly contraceptive data from June 2022 to December 2024. CHVs began implementation at different time points in each district, starting from January 2023 to October 2023, when CHVs became operational in all eight districts. Descriptive statistics and two-sample t-tests were used for data analysis. CHVs significantly improved family planning service provision, particularly for short- and long-acting methods at the facility level, with greater change observed in Department of Health facilities. This study provides preliminary evidence of the effectiveness of CHVs in increasing public sector service provision of contraceptives, particularly for Department of Health facilities. CHVs bridge the gap between the community and the facility, particularly in areas uncovered by the government’s existing mobilization staff. Full article

The transition to electric mobility has intensified efforts to develop battery technologies that are not only high-performing but also environmentally sustainable. A critical element in battery system design is the structural housing, which must provide effective impact protection to ensure passenger safety and prevent catastrophic failures. This study examines the impact response of an innovative sheet molding compound (SMC) composite battery housing, manufactured from an Elium resin modified with Martinal ATH matrix, reinforced with glass fibers, that combines fire resistance and recyclability, unlike conventional thermoset and metallic housings. The material was characterized through standardized mechanical tests, and its impact performance was evaluated via drop-weight experiments on plates and a full-scale housing. The impact tests were conducted at varying energy levels to induce barely visible impact damage (BVID) and visible impact damage (VID). A finite element model was developed in LS-DYNA using the experimentally derived material properties and was validated against the impact tests. Parametric simulations of ground and pole collisions revealed the critical velocity thresholds at which housing deformation begins to affect the first battery cells, while lower-energy impacts were absorbed without compromising the pack. The study provides one of the first combined experimental and numerical assessments of Elium SMC in battery enclosures, emphasizing its potential as a sustainable alternative for next-generation battery systems for transport applications. Full article

(1) Background: Caffeine is one of the most widely consumed psychoactive substances. Its safety profile and short half-life make it an ideal drug model for studying the pharmacokinetics of caffeine. This study aimed to develop a method for determination of caffeine in a small volume of saliva (200 µL). (2) Methods: Solid-phase extraction was employed to isolate caffeine from saliva, followed by quantitative analysis using liquid chromatography coupled with diode-array detection. Chromatographic separation was achieved on a C18 column, using a gradient mobile phase of acetonitrile and 0.1% formic acid. (3) Results: The method was validated for selectivity, linearity, precision, and accuracy. Linearity was established over the range of 10–10,000 ng/mL (R² = 0.995). The coefficients of variation for intra- and inter-day precision for the three tested caffeine concentrations did not exceed 12.11%. Recovery from spiked saliva samples exceeded 90.53%. The developed method was applied to preliminary studies to follow the pharmacokinetics of caffeine in saliva. The concentration of the substance was studied in the saliva obtained from a volunteer after espresso consumption. (4) Conclusions: The developed method will offer a reliable approach for non-invasive caffeine monitoring in clinical and research applications. Full article

The rapid growth of urban populations intensifies congestion, air pollution, and energy demand. Green mobility is central to sustainable smart cities, and the Internet of Things (IoT) offers a means to monitor, coordinate, and optimize transport systems in real time. This paper presents an Internet of Things (IoT)-based architecture integrating heterogeneous sensing with edge–cloud orchestration and AI-driven control for green routing and coordinated Electric Vehicle (EV) charging. The framework supports adaptive traffic management, energy-aware charging, and multimodal integration through standards-aware interfaces and auditable Key Performance Indicators (KPIs). We hypothesize that, relative to a static shortest-path baseline, the integrated green routing and EV-charging coordination reduce (H1) mean travel time per trip by ≥7%, (H2) CO₂ intensity (g/km) by ≥6%, and (H3) station peak load by ≥20% under moderate-to-high demand conditions. These hypotheses are tested in Simulation of Urban MObility (SUMO) with Handbook Emission Factors for Road Transport (HBEFA) emission classes, using 10 independent random seeds and reporting means with 95% confidence intervals and formal significance testing. The results confirm the hypotheses: average travel time decreases by approximately 9.8%, CO₂ intensity by approximately 8%, and peak load by approximately 25% under demand multipliers ≥1.2 and EV shares ≥20%. Gains are attenuated under light demand, where congestion effects are weaker. We further discuss scalability, interoperability, privacy/security, and the simulation-to-deployment gap, and outline priorities for reproducible field pilots. In summary, a pragmatic edge–cloud IoT stack has the potential to lower congestion, reduce per-kilometer emissions, and smooth charging demand, provided it is supported by reliable data integration, resilient edge services, and standards-compliant interoperability, thereby contributing to sustainable urban mobility in line with the objectives of SDG 11 (Sustainable Cities and Communities). Full article

This study investigates the use of deep reinforcement learning techniques to improve traffic signal control systems through the integration of deep learning and reinforcement learning approaches. The purpose of a deep reinforcement learning architecture is to provide adaptive control via a reinforcement learning interface and deep learning for the representation of traffic queues with regards to signal timings. This has driven recent research, which has reported success in the use of such dynamic approaches. To further explore this success, we apply a deep reinforcement learning algorithm over a grid of 21 interconnected traffic signalized intersections and monitor its effectiveness. Unlike previous research, which often examined isolated or idealized scenarios, our model is applied to the real-world traffic network of Via “Prenestina” in eastern Rome. We utilize the Simulation of Urban MObility (SUMO) platform to simulate and test the model. This study has two main objectives: ensure the algorithm’s correct implementation in a real traffic network and assess its impact on public transportation, incorporating an additional priority reward for public transport. The simulation results confirm the model’s effectiveness in optimizing traffic signals and reducing delays for public transport. Full article

Accurate mapping of small water structures and streambeds is essential for hydrological modeling, erosion control, and landscape management. While traditional geodetic methods such as GNSS and total stations provide high precision, they are time-consuming and require specialized equipment. Recent advances in mobile technology, particularly smartphones equipped with LiDAR sensors, offer a potential alternative for rapid and cost-effective field data collection. This study assesses the accuracy of the iPhone 14 Pro’s built-in LiDAR sensor for mapping streambeds and retention structures in challenging terrain. The test site was the Dílský stream in the Oslavany cadastral area, characterized by steep slopes, rocky surfaces, and dense vegetation. The stream channel and water structures were first surveyed using GNSS and a total station and subsequently re-measured with the iPhone. Several scanning workflows were tested to evaluate field applicability. Results show that the iPhone LiDAR sensor can capture landscape features with useful accuracy when supported by reference points spaced every 20 m, achieving a vertical RMSE of 0.16 m. Retention structures were mapped with an average positional error of 7%, with deviations of up to 0.20 m in complex or vegetated areas. The findings highlight the potential of smartphone LiDAR for rapid, small-scale mapping, while acknowledging its limitations in rugged environments. Full article

This study aimed to test the reliability of seven functional performance tests in amateur trail runners, including ankle mobility, balance, hopping, and countermovement jump (CMJ) tests. The sample consisted of 35 runners who were evaluated in two sessions separated by 7 to 14 days, which varied due to participants’ scheduling constraints. Relative reliability was assessed using the Intraclass Correlation Coefficient (ICC, which indicates consistency between repeated measures), the Standard Error of Measurement (SEM, which reflects measurement precision), and the Minimal Detectable Change (MDC, which represents the smallest real change beyond measurement error). The results show high reliability in almost all tests. The Lunge Test obtained an ICC of 0.990 and 0.983 for distance, and 0.941 and 0.958 for angular measurements in both legs. The Hop Tests showed moderate reliability with ICC above 0.7 In contrast, the Y Balance Test demonstrated lower reliability, with ICC values ranging from 0.554 to 0.732. The CMJ test showed good reliability, with an ICC ranging from 0.753 to 0.894, an SEM between 5.79% and 11.3%, and an MDC ranging from 15.54% to 31.44%, making it useful for assessing lower limb explosive strength. Both tests presented comparatively higher error values, which should be considered when interpreting individual changes. These findings support the use of these tests as valid and reliable tools for evaluating ankle dorsiflexion, balance, functional symmetry, and lower limb explosive strength in amateur trail runners, prior to training programs or injury prevention strategies, provided that standardized protocols and validated measuring instruments are used. Full article

Background: Muscle latency is an often-overlooked factor contributing to increased implant wear and higher rates of hip and knee osteoarthritis. Latency reduces the protective role of muscles against external joint loads during movement initiation, leading to cumulative microtrauma. This study investigates whether preliminary quadriceps contraction can mitigate these adverse effects during early rehabilitation after arthroplasty. Materials and methods: The study was conducted in two university hospitals in Sofia, Bulgaria, including 46 patients (mean age 63.76 ± 9.49 years): 25 with hip arthroplasty and 21 with knee arthroplasty. Participants were randomly assigned to a control group (n = 25; 13 hip, 12 knee: standard postoperative advice) or an experimental group (n = 21; 12 hip, 9 knee: standard advice plus preliminary quadriceps contraction). Primary outcome: pain intensity (VAS). Secondary outcomes: range of motion (ROM, %), manual muscle testing (MMT, %), thigh circumference difference (cm), and success rate of preliminary quadriceps contraction (%). Results: Both groups improved after one month (p < 0.05), but the experimental group showed significantly greater improvement (p < 0.05). Higher success rates of preliminary quadriceps contraction correlated with greater improvements in all outcomes (p < 0.05). Conclusions: Preliminary quadriceps contraction enhances standard postoperative advice by reducing pain, improving mobility and muscle strength, and reducing hypotrophy during early rehabilitation after hip and knee arthroplasty. Patients should be encouraged to perform it consistently, even when pain subsides. Full article

The ubiquitous presence of smartphones has led to new phenomenon such as “phubbing” (the act of ignoring one’s immediate surroundings in favor of using a mobile phone). This behavior has become increasingly common among university students, making it an important subject of study due to its potential negative impact on learning environments. The aim of the present study is to analyze the prevalence of phubbing among university students, the existence of significant differences as a function of specific sociodemographic variables (such as gender, age, academic performance, and connection frequency), and, lastly, the predictive capacity of these elements with the different levels of phubbing experienced. The sample was composed of 1121 Spanish university students, and the instrument selected for the collection of data was the Phubbing Scale, which was divided into three factors, “attachment to the mobile phone”, “communication disturbance”, and “smartphone obsession”, through different validity and reliability tests. The results indicated a moderately high prevalence of phubbing among the population studied. Likewise, statistically significant differences were identified at a multivariate level in the three dimensions. Lastly, it is notable that the frequency of smartphone usage significantly and positively predicted the three dimensions of phubbing. Full article

Crack monitoring in concrete structures is essential to maintaining structural integrity. Therefore, this paper proposes a mobile ground robot equipped with a 2-DoF manipulator and stereo vision sensors for autonomous crack monitoring and mapping. To facilitate crack detection over large areas, a 2-DoF motorized manipulator providing linear and rotational motions, with a stereo vision sensor mounted on the end effector, was deployed. In combination with a manual rotation plate, this configuration enhances accessibility and expands the field of view for crack monitoring. Another stereo vision sensor, mounted at the front of the robot, was used to acquire point cloud data of the surrounding environment, enabling tasks such as SLAM (simultaneous localization and mapping), path planning and following, and obstacle avoidance. Cracks are detected and segmented using the deep learning algorithms YOLO (You Only Look Once) v6-s and SFNet (Semantic Flow Network), respectively. To enhance the performance of crack segmentation, synthetic image generation and preprocessing techniques, including cropping and scaling, were applied. The dimensions of cracks are calculated using point clouds filtered with the median absolute deviation method. To validate the performance of the proposed crack-monitoring and mapping method with the robot system, indoor experimental tests were performed. The experimental results confirmed that, in cases of divided imaging, the crack propagation direction was predicted, enabling robotic manipulation and division-point calculation. Subsequently, total crack length and width were calculated by combining reconstructed 3D point clouds from multiple frames, with a maximum relative error of 1%. Full article