Search Results (207)

Objective: To analyze the association between physical activity, health-related quality of life (HRQoL), and perceived barriers to physical activity with the risk of symptoms of depression, anxiety, and stress in Chilean adolescents. Method: A quantitative, cross-sectional, descriptive-correlational study was conducted with a sample of 351 secondary school students (mean age = 15.75 ± 1.47 years) from several educational institutions in the south-central region of Chile. Validated instruments were used to assess physical activity (PAQ-A), symptoms of mental health (DASS-21), HRQoL (Kidscreen-52), and the short scale of barriers to physical activity. For exploratory purposes, mental health outcomes were dichotomized based on standard cut-off scores, and binary logistic regression models were estimated to identify associated factors. Results: Based on the binary categorization, a substantial proportion of students exceeded the risk thresholds for depressive (54.4%), anxiety (63%), and stress symptoms (42.2%). Across models, lower physical activity levels, reduced autonomy and weaker relationships with parents, and barriers related to self-concept and motivation were consistently associated with higher mental health risk. Additionally, passive commuting and the perceived school environment emerged as specific predictors of stress and depression risk, respectively. Conclusions: These findings suggest that individual and contextual factors linked to lifestyle behaviors and perceived social support may play a critical role in adolescent mental health, and could represent key targets for school-based interventions. Full article

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disorder that progressively impairs motor and communication abilities. Globally, the prevalence of ALS was estimated at approximately 222,800 cases in 2015 and is projected to increase by nearly 70% to 376,700 cases by 2040, primarily driven by demographic shifts in aging populations, and the lifetime risk of developing ALS is 1 in 350–420. Despite international advancements in assistive technologies, a recent national survey in Saudi Arabia revealed that 100% of ALS care providers lack access to eye-tracking communication tools, and 92% reported communication aids as inconsistently available. While assistive technologies such as speech-generating devices and gaze-based control systems have made strides in recent decades, they primarily support English speakers, leaving Arabic-speaking ALS patients underserved. This paper presents SOUTY, a cost-effective, mobile-based application that empowers ALS patients to communicate using gaze-controlled interfaces combined with a text-to-speech (TTS) feature in Arabic language, which is one of the five most widely spoken languages in the world. SOUTY (i.e., “my voice”) utilizes a personalized, pre-recorded voice bank of the ALS patient and integrated eye-tracking technology to support the formation and vocalization of custom phrases in Arabic. This study describes the full development life cycle of SOUTY from conceptualization and requirements gathering to system architecture, implementation, evaluation, and refinement. Validation included expert interviews with Human–Computer Interaction (HCI) expertise and speech pathology specialty, as well as a public survey assessing awareness and technological readiness. The results support SOUTY as a culturally and linguistically relevant innovation that enhances autonomy and quality of life for Arabic-speaking ALS patients. This approach may serve as a replicable model for developing inclusive Augmentative and Alternative Communication (AAC) tools in other underrepresented languages. The system achieved 100% task completion during internal walkthroughs, with mean phrase selection times under 5 s and audio playback latency below 0.3 s. Full article

Inertial navigation systems (INSs) exhibit distinctive characteristics, such as long-duration operation, full autonomy, and exceptional covertness compared to other navigation systems. However, errors are accumulated over time due to operational principles and the limitations of sensors. To address this problem, this study theoretically explores a numerically simulated integrated inertial navigation system consisting of a single-axis cold atom interferometer gyroscope (CAIG) and a conventional inertial measurement unit (IMU). The system leverages the low bias and drift of the CAIG and the high sampling rate of the conventional IMU to obtain more accurate navigation information. Furthermore, an adaptive gradient ascent (AGA) method is proposed to estimate the variance of the measurement noise online for the Kalman filter. It was found that errors of latitude, longitude, and positioning are reduced by 43.9%, 32.6%, and 32.3% compared with the conventional IMU over 24 h. On this basis, errors from inertial sensor drift could be further reduced by the online Kalman filter. Full article

Accurate localization plays a critical role in enabling underwater vehicle autonomy. In this work, we develop a robust infrastructure-based localization framework that estimates the position and orientation of underwater vehicles using only range measurements from long baseline (LBL) acoustic beacons to multiple on-board receivers. The proposed framework integrates three key components, each formulated as a convex optimization problem. First, we introduce a robust calibration function that unifies multiple sources of measurement error—such as range-dependent degradation, variable sound speed, and latency—by modeling them through a monotonic function. This function bounds the true distance and defines a convex feasible set for each receiver location. Next, we estimate the receiver positions as the center of this feasible region, using two notions of centrality: the Chebyshev center and the maximum volume inscribed ellipsoid (MVE), both formulated as convex programs. Finally, we recover the vehicle’s full 6-DOF pose by enforcing rigid-body constraints on the estimated receiver positions. To do this, we leverage the known geometric configuration of the receivers in the vehicle and solve the Orthogonal Procrustes Problem to compute the rotation matrix that best aligns the estimated and known configurations, thereby correcting the position estimates and determining the vehicle orientation. We evaluate the proposed method through both numerical simulations and field experiments. To further enhance robustness under real-world conditions, we model beacon-location uncertainty—due to mooring slack and water currents—as bounded spherical regions around nominal beacon positions. We then mitigate the uncertainty by integrating the modified range constraints into the MVE position estimation formulation, ensuring reliable localization even under infrastructure drift. Full article

In the context of global demographic aging, falls among the elderly remain a major public health concern, often leading to injury, hospitalization, and loss of autonomy. This study proposes a real-time fall detection system that combines a modern computer vision model, YOLOv11 with integrated pose estimation, and an Artificial Intelligence (AI)-based voice assistant designed to reduce false alarms and improve intervention efficiency and reliability. The system continuously monitors human posture via video input, detects fall events based on body dynamics and keypoint analysis, and initiates a voice-based interaction to assess the user’s condition. Depending on the user’s verbal response or the absence thereof, the system determines whether to trigger an emergency alert to caregivers or family members. All processing, including speech recognition and response generation, is performed locally to preserve user privacy and ensure low-latency performance. The approach is designed to support independent living for older adults. Evaluation of 200 simulated video sequences acquired by the development team demonstrated high precision and recall, along with a decrease in false positives when incorporating voice-based confirmation. In addition, the system was also evaluated on an external dataset to assess its robustness. Our results highlight the system’s reliability and scalability for real-world in-home elderly monitoring applications. Full article

While electric unmanned aerial vehicles (UAVs) offer advantages in noise reduction, safety, and operational efficiency, their endurance is limited by current battery technology. Extending flight autonomy without compromising performance is a critical challenge in UAV system development. Previous studies introduced hybrid micro-turbogenerator architectures, but limitations in control stability and output power constrained their practical implementation. This study aimed to finalize the design and experimental validation of an optimized hybrid power system featuring a micro-turboprop engine mechanically coupled to an upgraded electric generator. A fuzzy logic-based control algorithm was implemented on a single-board computer to enable autonomous voltage regulation. The test bench architecture was reinforced and instrumented to allow stable multi-stage testing across increasing power levels. Results demonstrated stable voltage control at 48 VDC and electrical power outputs up to 3 kW, with an estimated maximum of 3.5 kW at full throttle. Efficiency was calculated at approximately 67%, and analysis of the generator’s KV constant revealed that using a lower KV variant (KV80) could reduce required rotational speed (RPM) and improve performance. These findings underscore the value of adaptive hybridization in UAVs and suggest that tuning generator electromechanical parameters can significantly enhance overall energy efficiency and platform autonomy. Full article

The integration of advanced computer vision and artificial intelligence (AI) techniques into collaborative robotic systems holds the potential to revolutionize human–robot interaction, productivity, and safety. Despite substantial research activity, a systematic synthesis of how vision and AI are jointly enabling context-aware, adaptive cobot capabilities across perception, planning, and decision-making remains lacking (especially in recent years). Addressing this gap, our review unifies the latest advances in visual recognition, deep learning, and semantic mapping within a structured taxonomy tailored to collaborative robotics. We examine foundational technologies such as object detection, human pose estimation, and environmental modeling, as well as emerging trends including multimodal sensor fusion, explainable AI, and ethically guided autonomy. Unlike prior surveys that focus narrowly on either vision or AI, this review uniquely analyzes their integrated use for real-world human–robot collaboration. Highlighting industrial and service applications, we distill the best practices, identify critical challenges, and present key performance metrics to guide future research. We conclude by proposing strategic directions—from scalable training methods to interoperability standards—to foster safe, robust, and proactive human–robot partnerships in the years ahead. Full article

Hip fractures are among the most serious health events in older adults, frequently leading to disability, loss of independence, and elevated mortality. In 2019, an estimated 9.6 million new cases occurred globally among adults aged ≥ 55 years, with an incidence rate of 681 per 100,000. Despite improved surgical care, one-year mortality remains high (15–30%), and fewer than half of survivors regain their pre-fracture functional status. Traditionally regarded as mechanical injuries, hip fractures are now increasingly recognized as systemic events reflecting and accelerating biological vulnerability and frailty progression. We synthesize evidence across biological, clinical, and social domains to explore the systemic implications of hip fracture, from the acute catabolic response and immune dysfunction to long-term functional decline. The concept of intrinsic capacity, introduced by the World Health Organization, offers a resilience-based framework to assess the multidimensional impact of hip fracture on physical, cognitive, and psychological function. We highlight the importance of orthogeriatric co-management, early surgical intervention, and integrated rehabilitation strategies tailored to the individual’s functional reserves and personal goals. Innovations such as digital health tools, biological aging biomarkers, and personalized surgical approaches represent promising avenues to enhance recovery and autonomy. Ultimately, we advocate for a shift toward interdisciplinary, capacity-oriented models of care that align with the goals of healthy aging and enable recovery that transcends survival, focusing instead on restoring function and quality of life. Full article

This paper presents a multi-criteria decision-making (MCDM) approach for optimizing a microgrid system to achieve Plus-Energy Building (PEB) performance at the University of Coimbra’s Electrical Engineering Department. Using Python 3.12.8, Rhino 7, and PVsyst 8.0.1, simulations considered architectural and visual constraints, with economic feasibility assessed through a TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) analysis. The system is projected to generate approximately 1 GWh annually, with a 98% probability of exceeding 1076 MWh based on Gaussian estimation. Consumption is estimated at 460 MWh, while a 3.8 MWh battery ensures up to 72 h of autonomy. Rooftop panels and green parking arrays, fixed at 13.5° and 59°, minimize visual impact while contributing a surplus of +160% energy injection (or a net surplus of +60% energy after self-consumption). Assuming a battery cost of EUR 200/kWh, each hour of energy storage for the building requires 61 kWh of extra capacity with a cost of 12,200 (EUR/hr.storage). Recognizing environmental variability, these figures represent cross-validated probabilistic estimates derived from both PVsyst and Monte Carlo simulation using Python, reinforcing confidence in system feasibility. A holistic photovoltaic optimization strategy balances technical, economic, and architectural factors, demonstrating the potential of PEBs as a sustainable energy solution for academic institutions. Full article

Background/Objectives: In the context of active ageing, functional assessment is key to preserving autonomy in older women. The six-minute walk test (6MWT) is a practical tool for estimating general health, but its results can be influenced by various factors. This study analysed cardiorespiratory variations during the 6MWT in older women according to their physical activity level and age-related variables such as pain, sarcopenia, frailty, and motivation to exercise. Methods: A total of 163 older women with musculoskeletal pain, but without cardiac or respiratory conditions, were classified into groups with high (HPA), moderate (MPA), and low (LPA) physical activity. During the 6MWT, heart rate (HR), dyspnoea, and oxygen saturation (SpO₂) were recorded. Pain, sarcopenia, frailty, and motivation to exercise were also assessed. A repeated-measures multivariate analysis of (co)variance (RM-MANCOVA) was performed. Results: The results showed differences in HR depending on the level of physical activity, conditioned by sarcopenia (p < 0.05) and walked distance (p < 0.001), and in dyspnoea conditioned by pain perception (p < 0.01) and social (p < 0.001) and psychological (p < 0.05) motivation to exercise. There were also differences in SpO₂ depending on the level of physical activity (p < 0.0001). There were differences between the HPA group and both the MPA and LPA group, which had higher HR, higher dyspnoea, and lower SpO₂ when undergoing the 6MWT test. Conclusions: To accurately interpret 6MWT results in older adult women, it is essential to consider physical activity level, perceived pain, sarcopenia, and motivation to exercise, as these factors influence HR, dyspnoea, and SpO₂. These variables should guide physical activity recommendations for healthy ageing. Full article

This paper introduces a methodology for estimating transition intensities in a multi-state model for disability and long-term care insurance. We propose a novel framework that integrates observable risk factors, such as demographic (age and sex), lifestyle (smoking and exercise habits) and health-related variables (body mass index), into the estimation and graduation of transition intensities, using a parametric approach based on the Gompertz–Makeham law and generalised linear models. The model features four states—autonomous, dead, and two intermediate states representing varying disability levels—providing a detailed view of disability/lack of autonomy progression. To illustrate the proposed framework, we simulate a dataset with individual risk profiles and model trajectories, mirroring Portugal’s demographic composition. This allows us to derive a functional form (as a function of age) for the transition intensities, stratified by relevant risk factors, thus enabling precise risk differentiation. The results offer a robust basis for developing tailored pricing structures in the Portuguese market, with broader applications in actuarial science and insurance. By combining granular disability modelling with risk factor integration, our approach enhances accuracy in pricing structure and risk assessment. Full article

Environmental regulation has become a central policy tool for reconciling the tensions between ecological sustainability and industrial development. Although most existing studies focus on its impact on green innovation or firm behavioral change, attention to how environmental regulation affects the structural resilience of manufacturing systems under external shocks remains limited. This paper constructs a balanced panel dataset covering 287 prefecture-level cities in mainland China from 2006 to 2021 to quantify the impact of environmental regulation intensity on the resilience of manufacturing development. Manufacturing resilience is assessed through a comprehensive indicator system, including the dimensions of adaptive capacity, recovery potential, and industrial continuity. The empirical results show that environmental regulation has a significant inhibitory effect on manufacturing resilience, and this effect is supported in a number of robustness analyses using instrumental variable estimation and lagged structural tests. Mechanism analysis suggests that, despite the overall negative effect, environmental regulations can indirectly enhance resilience performance by promoting industrial autonomy and digital transformation under certain conditions. Heterogeneity analysis further reveals that the negative effect is more pronounced in regions with higher regulatory intensity, in non-self-employed firms, in industries not subject to U.S. sanctions, and in eastern China. These findings suggest that the dynamic needs of the industrial system should be taken into account in the formulation of environmental policies, and that digital capacity building and autonomy upgrading should be the key paths to mitigate regulatory shocks. Full article

A stable and high-precision autonomous orbit determination scheme for a Low Lunar Orbit (LLO) spacecraft is proposed, leveraging satellite-to-satellite tracking (SST) measurement data and lunar laser ranging data. One satellite orbits around the LLO, while the other satellite orbits around the Distant Retrograde Orbit (DRO). An inter-satellite ranging link is established between the two satellites, while the LLO satellite conducts laser ranging with a Corner Cube Reflector (CCR) on the lunar surface. Both inter-satellite ranging data and lunar laser ranging data are acquired through measurements. By integrating these data with orbital dynamics and employing the Extended Kalman Filter (EKF) method, the position and velocity states of the two formation satellites are estimated. This orbit determination scheme operates independently of ground measurement and control stations, achieving a high degree of autonomy. Simulation results demonstrate that the position accuracy of the LLO satellite can reach 0.1 m, and that of the DRO satellite can reach 10 m. Compared to the autonomous orbit determination scheme relying solely on SST measurement data, this proposed scheme exhibits several advantages, including shorter convergence time, higher convergence accuracy, and enhanced robustness of the navigation system against initial orbit errors and orbital dynamic model errors. It can provide a valuable engineering reference for the autonomous navigation of lunar-orbiting satellites. Full article

Accurate human pose estimation is essential for anti-cheating detection in unattended truck scale systems, where human intervention must be reliably identified under challenging conditions such as poor lighting and small target pixel areas. This paper proposes a human joint detection system tailored for truck scale scenarios. To enable efficient deployment, several lightweight structures are introduced, among which an innovative channel hourglass convolution module is designed. By employing a channel compression-recover strategy, the module effectively reduces computational overhead while preserving network depth, significantly outperforming traditional grouped convolution and residual compression structures. In addition, a hybrid attention mechanism based on depthwise separable convolution is constructed, integrating spatial and channel attention to guide the network in focusing on key features, thereby enhancing robustness against noise interference and complex backgrounds. Ablation studies validate the optimal insertion position of the attention mechanism. Experiments conducted on the MPII dataset show that the proposed system achieves improvements of 8.00% in percentage of correct keypoints (PCK) and 2.12% in mean absolute error (MAE), alongside a notable enhancement in inference frame rate. The proposed approach promotes computational efficiency, system autonomy, and operational sustainability, offering a viable solution for energy-efficient, intelligent transportation systems, and long-term automated supervision in logistics and freight environments. Full article

Operations by space drones mandate significant autonomy. This study experimentally evaluates key proposed applications of autonomy. Center of gravity auto-location is proposed using autonomous identification of mass properties, necessitating nonlinear state estimation. Nonlinear, coupled governing kinetics are strictly adopted as the control, and inversion provides closed-form estimates of mass properties. Seminally neglecting the diagonal inertia moments, the inertia cross-products are utilized to exactly find the mass center coordinates using the parallel axis theorem to parameterize the location coordinates. In December 2024, experiments were performed in space for hours, validating the approaches proposed. The findings indicate the longitudinal distribution was quite symmetric. Meanwhile, the lateral distribution was quite off-balance. Estimation convergence of the mass center coordinates was improved compared to the state-of-the-art comparative benchmark. In hundreds of days, the latter achieved millimeter convergence, while in minutes, the former achieved hundreds of millimeters convergence. Full article