Search Results (744)

Due to a complex of factors, visually impaired people are facing difficulties and increased risks during fire emergencies and evacuations from different types of buildings. Even if a lot of studies have been conducted to improve the mobility and autonomy of people with visual impairment during emergency evacuation processes, these offer only partial solutions, especially in the presence of uncertainties characteristic of fire evolution. Aiming for a more comprehensive approach to the safe evacuation of people with visual impairments, this paper proposes a support system that integrates innovative aspects related to the architecture of the application, modeling and simulation methods, and experimental realization. The system is decentralized, capable of anticipating possible fire extensions and determining, in real-time, new corresponding evacuation routes. The overall design complies with the standard norms in emergency situations. Two models, one developed in Stateflow and the other based on Delay Time Petri Nets (DTPN), were constructed to describe the dynamic behavior of the system in the presence of unexpected events that can change the initial recommended evacuation path. To test the functionality and efficiency of the proposed system, the conditions created by potential fire sources were simulated as a part of realistic scenarios. Tests were conducted with visually impaired people. Simulation and prototype testing showed that the presented system can improve evacuation times, achieving a measurable gain compared to scenarios where there is no information regarding fire evolution. Full article

In alignment with the United Nations’ Sustainable Development Goals (SDGs) and the global pursuit of net-zero emissions, higher education institutions (HEIs) are increasingly expected to demonstrate robust climate accountability and effective decarbonization strategies. This three-year longitudinal study presents a comprehensive assessment of greenhouse gas (GHG) emissions at a higher education institution, employing the ISO 14064-1:2018 framework to strengthen inventory design, boundary delineation, and data governance protocols. Findings indicate that purchased electricity constitutes the largest share; however, fugitive refrigerant leakage and Scope 3 activities—particularly commuting and business travel—represent substantial and often underestimated components of the institution’s carbon footprint. Methodological refinements, including the incorporation of updated emission factors coefficients and enhanced data verification, have revealed the sensitivity of GHG inventories to both policy reforms and behavioral changes, as well as institutional policy reforms. The study also demonstrates that targeted refrigerant management and low-carbon mobility initiatives can generate measurable mitigation effects, even under conditions of expanding campus activity. Beyond the institution-specific results, this research proposes a replicable framework that integrates ISO 14064-1 compliance with data quality assurance and digital verification tools. This framework provides HEIs globally with a structured pathway to enhance reporting credibility, develop evidence-based mitigation strategies, and accelerate progress toward carbon neutrality. These insights underline the strategic role of universities in advancing sector-wide climate leadership and contributing to sustainable development transitions. Full article

The dynamics of dense colloidal systems are not fully understood. In the study of these types of systems, computer simulations based on the so-called hard sphere model play a significant role. In the presented work, we consider a system of hard spheres of the same size but different mobilities (molecules with high mobility correspond to solvent molecules, while molecules with reduced mobility are colloid particles) at varying concentrations. For this purpose, a two-dimensional lattice and an thermal model of such systems was designed. In order to determine the properties of such systems, a Monte Carlo computer simulation was used, employing the Dynamic Lattice Liquid (DLL) algorithm. Our main aim was to determine how the dynamic behavior of the system in the short time affects the long-time behavior. For this purpose, we investigated the cross-ratios of the diffusion coefficients in the short and long time of the considered system elements. It was found that the reduction in the solvent mobility with increasing concentration of colloidal particles in a short time leads to a very similar reduction in the mobility of the colloid particles in a long time, but we do not observe such behavior in the case of the solvent, i.e., there is a decrease in the value of the solvent diffusion coefficient in the long time with the change in the concentration of colloid particles, but it is difficult to connect it in a simple way with the decrease in the diffusion coefficient in the short time. Full article

Automated collision avoidance is a central topic in multi-agent systems that consist of mobile agents. One simple approach to pursue system-wide performance is a centralized algorithm, which, however, becomes computationally expensive when involving a large number of agents. There have thus been proposed fully distributed collision avoidance algorithms that can naturally handle many-to-many encounter situations. The DSSA⁺ is one of those algorithms, which is heuristic and incomplete but has lower communication and computation overheads than other counterparts. However, the DSSA⁺ and some other distributed collision avoidance algorithms basically optimize the agents’ behavior only in the short term, not caring about the total efficiency in their paths. This may result in some agents’ paths with over-deviation or over-stagnation. In this paper, we present Distributed Stochastic Search algorithm with a deep Q-network (DSSQ), in which the agents can generate time-efficient collision-free paths while they learn independently whether to detour or change speeds by Deep Reinforcement Learning. A key idea in the learning principle of the DSSQ is to let the agents pursue their individual optimality. We have experimentally confirmed that a sequence of short-term system-optimal solutions found by the DSSA⁺ gradually becomes long-term individually optimal for every agent. Full article

Background: Plant-based diets are associated with reduced risk of chronic diseases and improved health outcomes. However, sustaining dietary changes remains challenging. Digital interventions—including virtual culinary medicine programs, web-based nutrition coaching, SMS and email reminders, mobile application–based self-management, and hybrid community programs—offer promising strategies to support behavior change, enhance cooking skills, and improve dietary adherence. These approaches are relevant for both healthy individuals and those living with chronic conditions. Methods: We conducted a narrative review of studies published between 2000 and 2025 in PubMed/MEDLINE, Scopus, and Web of Science, supplemented with manual searches. Included studies comprised randomized controlled trials, quasi-experimental designs, feasibility studies, and qualitative research. Interventions were categorized by modality (SMS, email, web platforms, mobile apps, virtual culinary programs, and hybrid formats) and population (healthy adults, patients with chronic diseases). Outcomes examined included dietary quality, self-efficacy, psychosocial well-being, and program engagement. Results: Most studies reported improvements in dietary quality, cooking skills, nutrition knowledge, and psychosocial outcomes. Virtual cooking programs enhanced dietary adherence and engagement, particularly among individuals at cardiovascular risk. Digital nutrition education supported behavior change in chronic disease populations, including patients with multiple sclerosis. SMS and email reminders improved self-monitoring and participation rates, while mobile applications facilitated real-time feedback and goal tracking. Hybrid programs combining online and in-person components increased motivation, social support, and long-term adherence. Reported barriers included limited technological access or skills, lack of personalization, and privacy concerns. Conclusions: Virtual culinary medicine programs and other digital microinterventions—including SMS, email, web, mobile, and hybrid formats—are effective tools to promote plant-based diets. Future interventions should focus on personalized, accessible, and hybrid strategies, with attention to underserved populations, to maximize engagement and sustain long-term dietary change. Full article

Urban stormwater runoff is increasingly recognized as a critical but underexplored pathway for per- and polyfluoroalkyl substances (PFAS) to enter aquatic environments. This work investigated the occurrence and behavior of 40 PFAS compounds in stormwater runoff entering a roadside rain garden in Secaucus, New Jersey, during six storm events between August 2023 and July 2024. Total PFAS concentrations (Σ40 PFAS) ranged from 1437 to 1615 ng/L, with perfluorobutane sulfonate (PFBS, 239–303 ng/L) and perfluorohexanoic acid (PFHxA, 115–137 ng/L) consistently emerging as dominant species. Perfluorocarboxylic acids (PFCAs) and perfluorosulfonic acids (PFSAs) together accounted for over 70% of the total PFAS mass. Despite its intended role in water quality improvement, the rain garden showed no measurable change in PFAS concentrations (differences of only 0.03–1.10%). These findings highlight the persistence and mobility of PFAS in urban stormwater runoff and the limited efficacy of conventional green infrastructure in mitigating PFAS contamination. Furthermore, they underscore the ineffectiveness of conventional green infrastructure for PFAS mitigation and the urgent need for advanced treatment technologies integrated into urban water management frameworks. Full article

Mobile learning (m-learning) technologies are gaining popularity in universities but not uniformly across institutions because of cognitive, affective, and behavior obstacles. This research tested and applied an expansion of the Technology Acceptance Model (TAM) with technostress (TECH) and resistance to change (RTC) as affective obstacles, as well as the core predictors of perceived usefulness (PU), perceived ease of use (PE), and perceived risk (PR). By employing a cross-sectional survey of Greek university students (N = 608) and partial least squares structural equation modeling (PLS-SEM), we tested direct and indirect impacts on behavioral intention (BI) to apply m-learning applications. The results affirm that PU and PE are direct predictors of BI, while PR has no direct impact on BI but acts indirectly through TECH and RTC. Mediation is partial in terms of PE and PU and indirect-only (complete) in terms of PR with respect to the impact of affective states on adoption. Multi-group comparisons found differences in terms of gender, age, confidence, and years of use but not frequency of use, implying that psychological and experiential characteristics have a greater impact on intention than habitual patterns. These results offer theory-driven and segment-specific guidelines for psychologically aware, user-focused m-learning adoption in higher education. Full article

Background: Online health information seeking emerged as a critical form of public health behavior during the COVID-19 pandemic, generating substantial research interest. However, empirical studies examining health information-seeking patterns among Korean populations and their behavioral outcomes during the pandemic remain limited. Grounded in the information–motivation–behavior skills model, this study investigates online health information-seeking behaviors, including information sources, search terms, and engagement patterns, while also exploring their association with actual health behaviors during the COVID-19 pandemic. Methods: A structured survey was developed based on 1014 adults aged 19 years or older using the 2021 Korean version of the Health Information National Trends Survey (K-HINTS) to obtain nationally representative data. We adopted a structural equation model and analyzed the data using SPSS 25.0 and the WordArt site. Results: Of the respondents, 74.2% sought health information online, with vaccine details being the most widely searched topic. Mobile phones were the most commonly used devices (75.8%), and 98% searched for health information online via mobile devices at least once a week. Information (β = 0.230, p < 0.001), motivation (β = 0.117, p < 0.01), and behavior skills (β = 0.117, p < 0.01) positively influenced consumers’ behavioral changes regarding health. Behavioral skills also mediated the influences that information seeking and motivation had on behavioral changes. Conclusions: This study examines four aspects of online health information seeking through nationally representative COVID-19 data in South Korea. Exploring the relationship between information-seeking and actual health behaviors provides crucial insights for predicting post-pandemic consumer behavior and developing effective public health communication strategies for future crises. Full article

Background/Objectives: Coupling mobile health (mHealth) technology with community-based nutrition programs may enhance diet quality in adolescents. This pilot study evaluated the feasibility, acceptability, and preliminary efficacy of integrating PortionSize Ed (PSEd), an image-assisted dietary assessment and education app, into the six-week Hawaii Food and Lifeskills for Youth (HI-FLY) curriculum delivered via Supplemental Nutrition Assistance Program Education (SNAP-Ed). Methods: Adolescents (grades 6–8) from two classrooms were cluster-randomized into HI-FLY or HI-FLY + PSEd. Both groups received HI-FLY and completed Youth Questionnaires (YQ) and food records (written or app-based) at Weeks 0 and 7. Feasibility and acceptability were assessed via enrollment, attrition, and User Satisfaction Surveys (USS). Diet quality was measured using Healthy Eating Index-2020 (HEI-2020) scores and analyzed via mixed-effects models. Results: Of 50 students, 42 (84%) enrolled and attrition was minimal (2.4%). The sample was 49% female and 85% at least part Native Hawaiian or Pacific Islander (NHPI). PSEd was acceptable, with average USS scores above the scale midpoint. No significant HEI-2020 changes were observed, though YQ responses indicated improvements in sugary drink intake (p = 0.03) and use of nutrition labels in HI-FLY + PSEd (p = 0.0007). Conclusions: Integrating PSEd into SNAP-Ed was feasible, acceptable, and demonstrated potential healthy behavior change among predominantly NHPI youth in Hawaii. Full article

Increased precipitation and extreme weather due to climate change can remobilize recent and legacy environmental contaminants from soil, sediment, and sewage overflows. Heavy metals are naturally distributed in Earth’s crust, but anthropogenic activity has resulted in concentrated emissions of toxic heavy metals and deposition in surrounding communities. Cities around the world are burdened with heavy metal pollution from past and present industrial activity. The city of Camden, NJ, represents a valuable case study of climate impacts on heavy metal mobilization in stormwater runoff due to similar legacy and present-day industrial pollution that has taken place in Camden and in many other cities. Various studies have shown that lead (Pb) and other toxic heavy metals have been emitted in Camden due to historic and recent industrial activity, and deposited in nearby soils and on impervious surfaces. However, it is not known if these heavy metals can be mobilized in urban stormwater, particularly after periods of high precipitation. In this study, Camden, NJ stormwater was collected from streets and parks after heavy rain events in the winter and spring for analysis with inductively coupled plasma-mass spectrometry (ICP-MS) to identify lead (Pb), mercury (Hg), cadmium (Cd), and arsenic (As). Lead was by far the most abundant of the four target elements in stormwater samples followed by Hg, Cd, and As. The locations with the highest Pb concentrations, up to 686.5 ppb, were flooded allies and streets between commercial and residential areas. The highest concentrations of Hg (up to 11.53 ppb, orders of magnitude lower than Pb) were found in partially flooded streets and ditches. Lead stormwater concentrations exceed EPA safe drinking levels at the majority of analyzed locations, and Hg stormwater concentrations exceed EPA safe drinking levels at all analyzed locations. While stormwater is not generally ingested, dermal contact and hand-to-mouth behavior by children are potential routes of exposure. Heavy metal concentrations were lower in stormwater collected from parks and restored areas of Camden, indicating that these areas have a lower heavy metal exposure risk. This study shows that heavy metal pollution can be mobilized in stormwater runoff, resulting in elevated exposure risk in industrial cities. Full article

Human behavior when navigating a dynamic environment relies on predicting the near future that describes immediate states of moving objects based on their current trajectories. Mobile robots, instead of using a similar approach in many cases, rely only on feedback from the current moment in time, which can result in close encounters with obstacles, leading to trajectory instability due to a strong control signal coming from avoidance algorithms. This paper presents an approach that uses predictive control based on dynamic environment estimation performed by an Unscented Kalman Filter. The predictor analyzes trajectories of nearby objects and assesses the possibility of collision or close encounter. This introduces an additional control signal integrated into the Artificial Potential Fields algorithm, which helps to reduce rapid changes in the control signal, in effect smoothening the robot’s trajectory, as well as reducing the likelihood of dangerous situations happening. Full article

Background: Prolonged sedentary behavior and associated obesity are recognized risk factors for poor health across the lifespan. Globally, data show that many children and adolescents aged 5 to 17 significantly increased their sedentary behaviors during the COVID-19 pandemic, failing to meet recommended physical activity levels and reporting increased smartphone use. While mobile devices and video games have been traditionally linked to physical inactivity, formats like exergaming, which combine gameplay with gross motor activity, offer potential to promote physical activity. However, many digital health tools for children are developed without incorporating feedback from key stakeholders and end-users (e.g., children, teachers, and guardians). Therefore, this paper, within the Walk around the Earth (E-Walk) project, describes a prospective study that aims (1) to identify the most influential factors or characteristics affecting engagement with and usability of a mobile application promoting physical activity among primary school students; (2) to develop a mobile application for children based on the identified factors and characteristics. Methods: This project will use a group concept mapping approach to identify the most influential features/factors/characteristics affecting engagement with and usability of an app. By involving primary stakeholders (e.g., children, teachers, guardians, and physical activity experts), the project seeks to align the app’s features with primary end-user needs and motivations. Following the app’s development, its effectiveness in increasing physical activity levels and reducing sedentary behaviors will be evaluated through a mixed-method design, incorporating anthropometric data, validated physical activity questionnaires (Physical Activity Questionnaire for Older Children (PAQ-C) and International Physical Activity Questionnaire (IPAQ)), and engagement metrics. Conclusions: The E-Walk project integrates participatory design with educational content and activity-based challenges, representing a multidimensional strategy for promoting health and learning in primary school students. Ultimately, this study contributes to the development of user-informed digital interventions that support sustainable behavioral changes, in line with broader goals of child well-being and digital health promotion. Full article

Recently, studies on driver inattention state recognition as an advanced mobility application technology are being actively conducted to prevent traffic accidents caused by driver drowsiness and distraction. The driver inattention state recognition system is a technology that recognizes drowsiness and distraction by using driver behavior, biosignals, and vehicle data characteristics. Existing driver drowsiness detection systems are wearable accessories that have partial occlusion of facial features and light scattering due to changes in internal and external lighting, which results in momentary image resolution degradation, making it difficult to recognize the driver’s condition. In this paper, we propose a transformer based spatial temporal fusion network (STFTransNet) that fuses multi-modality information for improved driver inattention state recognition in images where the driver’s face is partially occluded by wearing accessories and the instantaneous resolution is degraded due to light scattering from changes in lighting in a driving environment. The proposed STFTransNet consists of (i) a mediapipe face mesh-based facial landmark extraction process for facial feature extraction, (ii) an RCN-based two-stream cross-attention process for learning spatial features of driver face and body action images, (iii) a TCN-based temporal feature extraction process for learning temporal features of extracted features, and (iv) an ensemble of spatial and temporal features and a classification process to recognize the final driver state. As a result of the experiment, the proposed STFTransNet achieved an accuracy of 4.56% better than the existing VBFLLFA model in the NTHU-DDD public DB, 3.48% better than the existing InceptionV3 + HRNN model in the StateFarm public DB, and 3.78% better than the existing VBFLLFA model in the YawDD public DB. The proposed STFTransNet is designed as a two-stream network that can input the driver’s face and action images and solves the degradation in driver inattention state recognition performance due to partial facial feature occlusion and light blur through spatial feature and temporal feature fusion. Full article

As a key element of the sharing economy, ride-sharing plays a vital role in promoting sustainable urban mobility by optimizing vehicle utilization rates, lowering carbon emissions, and alleviating traffic congestion. Despite its cost-efficiency and sustainability benefits, ride-sharing adoption remains limited in the post-pandemic period due to behavioral changes and safety concerns. Accordingly, using survey data from 425 commuters in Hefei, concerns about COVID-19 and satisfaction with ride-sharing services were integrated into the theory of planned behavior framework. Structural equation modeling was applied to examine the relationship between ride-sharing intention and actual usage behaviors. The results indicated that ride-sharing intention was significantly positively affected by subjective norms (β = 0.428 ***), service satisfaction (β = 0.315 ***), and perceived behavioral control (β = 0.162 *), but significantly negatively affected by concerns about COVID-19 (β = −0.183 **). Concerns about COVID-19 significantly negatively affected travelers’ actual ride-sharing behaviors (β = −0.2 **). Furthermore, ride-sharing intention was identified as a significant positive predictor of travelers’ behaviors: specifically, their likelihood of accepting a ride-sharing order (β = 0.395 ***). These findings offer transport authorities evidence-based strategies for designing targeted interventions during health crises, particularly through reinforcing social norms, improving service quality, and implementing transparent health protocols to ensure both user safety and sustainability. Full article

Background: Tobacco use remains a major public health concern among young adults and is often complicated by co-occurring addictive behaviors. Objective: This study analyzed motivation for change, assessed with the decisional balance framework, in relation to multiple addictions among young smokers seeking treatment. Methods: Ninety-eight participants from the University of Granada enrolled in either an app-based cognitive–behavioral therapy (CBT) program (n = 35) or a traditional face-to-face CBT program (n = 63). Recruitment relied on self-identification and voluntary participation. Standardized instruments were applied to measure nicotine dependence (FTND), behavioral and substance-related addictions (MULTICAGE CAD-4), cannabis dependence (SDS), and motivation for change (DBQ). Logistic and stepwise regression analyses were conducted to identify predictors of treatment choice and motivational outcomes. Results: Younger participants and students were more likely to choose the app-based program. Compulsive buying was linked to perceiving more disadvantages of smoking, whereas sex addiction, cannabis dependence, and other substance addictions were associated with perceiving fewer disadvantages. Conclusions: Treatment preferences and motivational profiles differ according to age, academic status, and co-occurring addictions. These findings highlight the need to tailor smoking cessation strategies to individual profiles and support the role of mobile health tools in engaging digitally oriented populations. Full article