Search Results (3,688)

This study integrates the Theory of Planned Behavior and the Theory of Consumption Values through a mixed-methods approach (structured interview and structural equation model) to investigate cross-platform disposal behaviors for idle clothing on social media and second-hand platform ecosystems. The study reconstructs traditional theoretical variables: psychological motivation dimension (platform-enabled green attitude, social circle environmental demonstration, and cross-platform behavioral control) and perceived value dimension (functional integration value perception, socialized emotional empowerment, and community identity value). Key findings: Cross-platform behavioral control is the strongest predictor of behavioral intention. In the value dimension, emotional value has the strongest direct impact on disposal intentions, functional integration is key to enhancing behavioral control, and community identity value most significantly impacts the platform-enabled green attitude and the social circle environmental demonstration. Finally, proposing a governance framework of “technological empowerment–emotional resonance–identity motivation”, offering theoretical foundations for optimizing platform interoperability and formulating digital environmental policies. Full article

Social trust has been linked to the development and severity of depression, but trust is a complex, multidimensional construct. This study examines the extent to which two distinct forms of trust, general trust and specific trust, predict depressive symptoms. Drawing on longitudinal data from the Directorate of Health’s national surveys conducted in 2007 and 2009, the analysis includes responses from 3211 Icelanders selected through a stratified random sample. Depressive symptoms were assessed using the Depression, Anxiety, and Stress Scale (DASS), while specific trust captured trust in close relationships, and general trust measured broader perceptions of trustworthiness in others. The two forms of trust together explained 7.6% of the variance in depressive symptoms, with specific trust contributing a substantially greater share. Both remained significant predictors after controlling for prior depression and physical health. These findings highlight the protective role of specific trust and suggest that general trust, an indicator of broader social capital, may also help buffer against depression. The results underscore the relevance of trust as a public health resource and support continued research into social determinants of mental health in Iceland. Full article

To enable safe and effective human–robot collaboration (HRC) in smart manufacturing, it is critical to seamlessly integrate sensing, cognition, and prediction into the robot controller for real-time awareness, response, and communication inside a heterogeneous environment (robots, humans, and equipment). The proposed approach takes advantage of the prediction capabilities of nonlinear model predictive control (NMPC) to execute safe path planning based on feedback from a vision system. To satisfy the requirements of real-time path planning, an embedded solver based on a penalty method is applied. However, due to tight sampling times, NMPC solutions are approximate; therefore, the safety of the system cannot be guaranteed. To address this, we formulate a novel safety-critical paradigm that uses an exponential control barrier function (ECBF) as a safety filter. Several common human–robot assembly subtasks have been integrated into a real-life HRC assembly task to validate the performance of the proposed controller and to investigate whether integrating human pose prediction can help with safe and efficient collaboration. The robot uses OptiTrack cameras for perception and dynamically generates collision-free trajectories to the predicted target interactive position. Results for a number of different configurations confirm the efficiency of the proposed motion planning and execution framework, with a 23.2% reduction in execution time achieved for the HRC task compared to an implementation without human motion prediction. Full article

Surface electromyographic (sEMG) signal-driven joint-angle estimation plays a critical role in intelligent rehabilitation systems, as its accuracy directly affects both control performance and rehabilitation efficacy. This study proposes a continuous elbow joint angle estimation method based on time–frequency domain analysis. Raw sEMG signals were processed using the Short-Time Fourier Transform (STFT) to extract time–frequency features. A Scale Temporal–Channel Cross-Encoder (STCCE) network was developed, integrating temporal and channel attention mechanisms to enhance feature representation and establish the mapping from sEMG signals to elbow joint angles. The model was trained and evaluated on a dataset comprising approximately 103,000 samples collected from seven subjects. In the single-subject test set, the proposed STCCE model achieved an average Mean Absolute Error (MAE) of $2.96\pm {0.24}^{\circ }$, Root Mean Square Error (RMSE) of $4.41\pm {0.45}^{\circ }$, Coefficient of Determination ($R^2$) of $0.9924\pm 0.0020$, and Correlation Coefficient (CC) of $0.9963\pm 0.0010$. It achieved a MAE of ${3.30}^{\circ }$, RMSE of ${4.75}^{\circ }$, $R^2$ of 0.9915, and CC of 0.9962 on the multi-subject test set, and an average MAE of $15.53\pm {1.80}^{\circ }$, RMSE of $21.72\pm {2.85}^{\circ }$, $R^2$ of $0.8141\pm 0.0540$, and CC of $0.9100\pm 0.0306$ on the inter-subject test set. These results demonstrated that the STCCE model enabled accurate joint-angle estimation in the time–frequency domain, contributing to a better motion intent perception for upper-limb rehabilitation. Full article

Sensor fusion in intelligent suspension systems constitutes a fundamental technology for optimizing vehicle dynamic stability, ride comfort, and occupant safety. By integrating data from multiple sensor modalities, this study proposes a hierarchical multi-sensor fusion framework for active suspension control, aiming to enhance control precision. Initially, a binocular vision system is employed for target detection, enabling the identification of lane curvature initiation points and speed bumps, with real-time distance measurements. Subsequently, the integration of Global Positioning System (GPS) and inertial measurement unit (IMU) data facilitates the extraction of road elevation profiles ahead of the vehicle. A BP-PID control strategy is implemented to formulate mode-switching rules for the active suspension under three distinct road conditions: flat road, curved road, and obstacle road. Additionally, an ant colony optimization algorithm is utilized to fine-tune four suspension parameters. Utilizing the hardware-in-the-loop (HIL) simulation platform, the observed reductions in vertical, pitch, and roll accelerations were 5.37%, 9.63%, and 11.58%, respectively, thereby substantiating the efficacy and robustness of this approach. Full article

Introduction: Low medication adherence and low hypertension control are a public health challenge, particularly in low- and middle-income countries (LMICs). Healthcare system- and patient-related barriers hinder the successful management of hypertension. This study aimed to identify the perceptions of barriers and facilitators to hypertension management among health system stakeholders in Santander, Colombia. Materials and Methods: We conducted a qualitative, phenomenological, and interpretative study, comprising five focus groups, to explore the barriers and facilitators to managing people with hypertension. Each focus group was formed by stakeholders from territorial entities, healthcare insurers, or healthcare providers. Meetings were held between December 2022 and February 2023. The sessions were recorded and transcribed using NVivo Transcription and analyzed using NVivo version 1.6.1. Results: Seven categories of barriers and facilitators were identified: strategies, resources, access, risk assessment, cross-sector collaboration, articulation, and stewardship. Of these categories, articulation and stewardship emerged as the main barriers, as revealed through axial coding and cluster analysis, which highlighted deficiencies in stewardship practices, a lack of clear objectives, and misalignment with public policy frameworks. Conclusions: Multisectoral actions extending beyond healthcare providers and aimed at improving coordination and intersectoral collaboration are essential for enhancing hypertension control in LMICs, such as Colombia. Addressing social determinants and strengthening primary healthcare through community-based strategies are critical, making stewardship and improved access key priorities. Full article

Fish coloration plays an important role in reproduction and camouflage, yet capturing color variation under field conditions remains challenging. We present a standardized, semi-automated protocol for measuring body coloration in the popular model fish threespine stickleback (Gasterosteus aculeatus). Individuals are photographed in a controlled light box within minutes of capture, and color is sampled from eight anatomically defined standard sites in human-perception-based CIELAB space. Analyses combine univariate color metrics, multivariate statistics, and the ΔE* perceptual difference index to detect subtle shifts in hue and brightness. Validation on pre-spawning fish shows the method reliably distinguishes males and females well before full breeding colors develop. Although it currently omits ultraviolet signals and fine-scale patterning, the approach scales efficiently to large sample sizes and varying lighting conditions, making it well suited for population-level surveys of camouflage dynamics, sexual dimorphism, and environmental influences on coloration in sticklebacks. Full article

The integration of digital twin technology with robotic automation holds significant promise for advancing sustainable horticulture in controlled environment agriculture. This article presents DigiHortiRobot, a novel AI-driven digital twin architecture tailored for hydroponic greenhouse systems. The proposed framework integrates real-time sensing, predictive modeling, task planning, and dual-arm robotic execution within a modular, IoT-enabled infrastructure. DigiHortiRobot is structured into three progressive implementation phases: (i) monitoring and data acquisition through a multimodal perception system; (ii) decision support and virtual simulation for scenario analysis and intervention planning; and (iii) autonomous execution with feedback-based model refinement. The Physical Layer encompasses crops, infrastructure, and a mobile dual-arm robot; the virtual layer incorporates semantic modeling and simulation environments; and the synchronization layer enables continuous bi-directional communication via a nine-tier IoT architecture inspired by FIWARE standards. A robot task assignment algorithm is introduced to support operational autonomy while maintaining human oversight. The system is designed to optimize horticultural workflows such as seeding and harvesting while allowing farmers to interact remotely through cloud-based interfaces. Compared to previous digital agriculture approaches, DigiHortiRobot enables closed-loop coordination among perception, simulation, and action, supporting real-time task adaptation in dynamic environments. Experimental validation in a hydroponic greenhouse confirmed robust performance in both seeding and harvesting operations, achieving over 90% accuracy in localizing target elements and successfully executing planned tasks. The platform thus provides a strong foundation for future research in predictive control, semantic environment modeling, and scalable deployment of autonomous systems for high-value crop production. Full article

Background: Chronic stress in childhood and adolescence leads to excessive cortisol secretion, adipokines production and obesity with all the negative mental and physical effects on the health of individuals and adulthood. Objectives: The aim of the present non-randomized controlled trial was to investigate the effect of a stress management protocol with diaphragmatic breathing (DB) and physiotherapy exercise on stress, body composition, cardiorespiratory and metabolic markers of children and adolescents with morbid obesity. Methods: The study included 31 children and adolescents (5–18 years old) with morbid obesity (22 in the intervention arm and 9 controls). All participants completed anxiety questionnaires and a self-perception scale. Forced expiratory volume in the first second (FEV1), forced vital capacity (FVC), blood pressure (BP) and SpO₂ were measured. Fasting glucose, uric acid, triglycerides, HbA1c, (AST/SGOT), (ALT/SGPT), HDL, LDL, insulin, ACTH, cortisol, HOMA-IR, 17-OH, S-DHEA, SHBG were assessed, and anthropometric measurements were also performed. Results: In the intervention group, 4 months after the treatment, an improvement was noted in the BMI, BMI z-score, waist-to-height ratio, FEV1, SpO₂, pulse and systolic BP. HDL increased, ALT/SGPT and insulin resistance improved. Positive changes were observed in temporary and permanent stress and self-esteem of children in the intervention group, including anxiety, self-perception, physical appearance, etc. Conclusions: A combined exercise and DB protocol has a positive effect on stress, by improving body composition, reducing insulin resistance, and ameliorating physical and mental health and quality of life of pediatric patients with morbid obesity. Full article

Background: Taste changes are common during pregnancy and can have a significant impact on dietary habits. Objective: This study aimed to investigate the influence of the perception of sweet and fat taste on diet in pregnant diabetic women. Methods: This cross-sectional observational study included 66 pregnant women, 33 with gestational diabetes and 33 with pre-gestational type 2 diabetes. Taste perception tests were conducted to evaluate thresholds for detecting sweet and fatty tastes. Dietary surveys were used to assess daily nutrient intake, and various biochemical parameters, such as glycemia, HbA1c, and cholesterol, were analyzed. Results: The low-fat taster group (threshold > 0.75 mmol/L) included more patients with diabetes compared to those with gestational diabetes. All diabetic patients had low sucrose perception. Although pregnant women with gestational diabetes detected sweetness at high concentrations, pregnant women with diabetes detected it at lower concentrations (0.012 ± 0.023 mmol/L vs. 0.006 ± 0.005 mmol/L; p = 0.3). High-fat tasters exhibited elevated glycemia compared to low-fat tasters (6.04 ± 1.88 mmol/L vs. 7.47 ± 3.4 mmol/L; p = 0.03). They also had higher cholesterol (p = 0.04) and lower HDL-C levels (4.96 ± 1.04 mmol/L vs. 1.36 ± 0.29 mmol/L; p = 0.03). High-fat tasters showed more frequent daily consumption of oil, butter, cheese, and chocolate. The highly sweet tasters had higher cholesterol levels and lower LDL levels. Individuals who reported being highly sensitive to sweet taste consumed more daily oil, sweetened yogurt, or cream desserts, as well as white sugar. Conclusions: These findings indicate that altered sensitivity to fat and sweet tastes is associated with different dietary habits and metabolic profiles in pregnant women with diabetes. Specifically, reduced sensitivity to the taste of fat is associated with higher consumption of high-fat foods and poorer lipid profiles. In contrast, sensitivity to sweet taste correlates with an increased intake of sugary and fatty foods. Understanding these taste-related behaviors can help develop personalized nutritional strategies to improve metabolic control and maternal–fetal outcomes in this high-risk group. Full article

Automated vehicles controlled by artificial intelligence are becoming capable of making moral decisions independently. This study investigates the differences in participants’ perceptions of the moral decision-maker’s permissibility when viewing scenarios (pre-test) and after witnessing the outcomes of moral decisions (post-test). It also investigates how permissibility, ten typical moral emotions, and perceived moral agency fluctuate when AI and the human driver make deontological or utilitarian decisions in a pedestrian-sacrificing dilemma (Experiment 1, N = 254) and a driver-sacrificing dilemma (Experiment 2, N = 269) from a third-person perspective. Moreover, by conducting binary logistic regression, this study examined whether these factors could predict the non-decrease in permissibility ratings. In both experiments, participants preferred to delegate decisions to human drivers rather than to AI, and they generally preferred utilitarianism over deontology. The results of perceived moral emotions and moral agency provide evidence. Moreover, Experiment 2 elicited greater variations in permissibility, moral emotions, and perceived moral agency compared to Experiment 1. Moreover, deontology and gratitude could positively predict the non-decrease in permissibility ratings in Experiment 1, while contempt had a negative influence. In Experiment 2, the human driver and disgust were significant negative predictor factors, while perceived moral agency had a positive influence. These findings deepen the comprehension of the dynamic processes of autonomous driving’s moral decision-making and facilitate understanding of people’s attitudes toward moral machines and their underlying reasons, providing a reference for developing more sophisticated moral machines. Full article

Background: Postoperative pain management after a cesarean section remains a significant challenge, as inadequate control can delay maternal recovery and hinder early bonding and breastfeeding. While multimodal analgesia is the standard approach, non–pharmacological strategies like immediate skin–to–skin contact (SSC) are often underused despite their potential benefits in reducing pain, improving uterine contractions, and increasing maternal satisfaction. Objective: To evaluate the effects of immediate SSC on postoperative pain perception, uterine contraction quality, and maternal satisfaction, and to explore ways to incorporate SSC into routine post–cesarean care to promote recovery and humanized care. Method: A randomized clinical trial was conducted with 80 women undergoing elective cesarean sections, divided into two groups: SSC (40 women) and control (40 women). Postoperative pain was measured using the Visual Analog Scale (VAS) at various intervals, while uterine contraction quality and maternal satisfaction were assessed through clinical observation and a Likert scale, respectively. Results: We found that women in the SSC group experienced significantly lower pain scores (VAS2 and VAS3, p < 0.001), stronger infraumbilical uterine contractions (92.5%, p < 0.001), and higher satisfaction levels (average 9.98 vs. 6.50, p < 0.001). An inverse correlation was observed between pain intensity and satisfaction, indicating that SSC enhances both physiological and psychological recovery. Conclusions: Immediate SSC after cesarean is an effective, humanizing intervention that reduces pain, supports uterine contractions, and boosts maternal satisfaction. These findings advocate for integrating SSC into standard postoperative care, aligning with ethical principles of beneficence and autonomy. Further research with larger samples is necessary to confirm these benefits and facilitate widespread adoption in maternity protocols. Full article

Autonomous Unmanned Surface Vehicle (USV) operations in complex ocean engineering scenarios necessitate robust navigation, guidance, and control technologies. These systems require reliable sensor-based object detection and efficient, safe, and energy-aware path planning. To address these multifaceted challenges, this paper proposes a novel synergistic AI framework. The framework integrates (1) a novel adaptation of the Swin-Transformer to generate a dense, semantic risk map from raw visual data, enabling the system to interpret ambiguous marine conditions like sun glare and choppy water, enabling real-time environmental understanding crucial for guidance; (2) a Transformer-enhanced A-star (T-ASTAR) algorithm with spatio-temporal attentional guidance to generate globally near-optimal and energy-aware static paths; (3) a domain-adapted TD3 agent featuring a novel energy-aware reward function that optimizes for USV hydrodynamic constraints, making it suitable for long-endurance missions tailored for USVs to perform dynamic local path optimization and real-time obstacle avoidance, forming a key control element; and (4) CUDA acceleration to meet the computational demands of real-time ocean engineering applications. Simulations and real-world data verify the framework’s superiority over benchmarks like A* and RRT, achieving 30% shorter routes, 70% fewer turns, 64.7% fewer dynamic collisions, and a 215-fold speed improvement in map generation via CUDA acceleration. This research underscores the importance of integrating powerful AI components within a hierarchical synergy, encompassing AI-based perception, hierarchical decision planning for guidance, and multi-stage optimal search algorithms for control. The proposed solution significantly advances USV autonomy, addressing critical ocean engineering challenges such as navigation in dynamic environments, object avoidance, and energy-constrained operations for unmanned maritime systems. Full article

Objectives: The objective of this manuscript is to translate, adapt, and validate an Arabic version of the Diabetes Eating Problem Survey—Revised (DEPS-R) questionnaire to assess disordered eating behaviors (DEBs) in adolescents with T1D in Saudi Arabia. Additionally, the study sought to estimate the prevalence of DEBs and analyze its associations with glycemic control and diabetes-related complications. Methods: A cross-cultural validation study was conducted following the COSMIN guidelines. The DEPS-R questionnaire was translated into Arabic through forward and backward translation involving expert panels, including psychiatrists, diabetologists, and linguists. A sample of 409 people with type 1 diabetes (PwT1D) (58.4% females) aged 12–20 years was recruited from outpatient diabetes clinics in the five main regions of Saudi Arabia. Participants completed the Arabic DEPS-R and the validated Arabic version of the SCOFF questionnaire. Sociodemographic, anthropometric, and biochemical data were collected, and statistical analyses, including confirmatory factor analysis (CFA) and internal consistency tests, were conducted. Results: The Arabic DEPS-R exhibits strong internal consistency (Cronbach’s alpha = 0.829) and high test–retest reliability (ICC = 0.861), with a CFA supporting a three-factor structure, namely body weight perception, disordered eating behaviors (DEBs), and bulimic tendencies. Notably, higher DEPS-R scores are significantly linked to elevated HbA1c levels, increased BMI, and more frequent insulin use. Alarmingly, 52.8% of participants show high-risk DEB, which is directly associated with poor glycemic control (HbA1c ≥ 8.1%) and a heightened risk of diabetic ketoacidosis (DKA). Conclusions: The Arabic DEPS-R is a valid and reliable tool for screening DEBs among Saudi adolescents with T1D. Findings underscore the necessity for early identification and intervention to mitigate the impact of EDs on diabetes management and overall health outcomes. Full article

Swarm robots often need to cover the designated area to complete specific tasks. While robots possess local perception and limited communication capabilities, they struggle to handle coverage issues in dynamic environments. This paper proposes a self-organizing algorithm for swarm robots based on Dynamic Virtual Force (DVF) to cover dynamic areas. Robots in the swarm can locally perceive their surrounding robots and dynamically select adjacent ones to generate virtual repulsion, thereby controlling their movement. The algorithm enables swarm robots to be rapidly and evenly deployed in unknown areas, adapt to dynamic area changes, and solve the problem of symmetrical robot distribution during coverage. It also allows for adaptive coverage of different density areas, divided as needed. Experimental validation across 20 benchmark scenarios (including obstacles, dynamic boundaries, and multi-density zones) demonstrates that the DVF method outperforms existing approaches in coverage rate, total robot movement distance, and coverage uniformity. The results validate its effectiveness and superiority in addressing area coverage problems. By addressing these challenges, the DVF algorithm can be widely applied to forest firefighting, oil spill cleanup in the ocean, and other swarm robot tasks. Full article