Search Results (4,729)

Background: The widespread use of social media among children and adolescents has raised increasing concern about its potential impact on mental health. Given the unique neurodevelopmental vulnerabilities during adolescence, understanding how digital platforms influence psychiatric outcomes is critical. Objectives: This narrative review aims to synthesize current evidence on the relationship between social media exposure and key psychiatric symptoms in youth, including depression, anxiety, body image disturbances, suicidality, and emotional dysregulation. Methods: We conducted a comprehensive narrative review of the literature, drawing from longitudinal, cross-sectional, and neuroimaging studies published in peer-reviewed journals. Specific attention was given to moderators (e.g., age, gender, and personality traits) and mediators (e.g., sleep, emotion regulation, and family context) influencing the relationship between social media use and mental health outcomes. Results: Evidence indicates that certain patterns of social media use, especially passive or compulsive engagement, are associated with increased risk of depression, anxiety, body dissatisfaction, and suicidal ideation. Adolescent girls, younger users, and those with low self-esteem or poor emotional regulation are particularly vulnerable. Neuroimaging studies show that social media activates reward-related brain regions, which may reinforce problematic use. Family support and digital literacy appear to mitigate negative effects. Conclusions: Social media use is not uniformly harmful; its psychological impact depends on how, why, and by whom it is used. Multilevel prevention strategies, including media education, parental involvement, and responsible platform design, are essential to support healthy adolescent development in the digital age. Full article

The proliferation of social network data has unlocked unprecedented opportunities for extensive, data-driven exploration of human behavior. The structural intricacies of social networks offer insights into various computational social science issues, particularly concerning social influence and information diffusion. However, modeling large-scale social network data comes with computational challenges. Though large language models make it easier than ever to model textual content, any advanced network representation method struggles with scalability and efficient deployment to out-of-sample users. In response, we introduce a novel approach tailored for modeling social network data in user-detection tasks. This innovative method integrates localized social network interactions with the capabilities of large language models. Operating under the premise of social network homophily, which posits that socially connected users share similarities, our approach is designed with scalability and inductive capabilities in mind, avoiding the need for full-graph training. We conduct a thorough evaluation of our method across seven real-world social network datasets, spanning a diverse range of topics and detection tasks, showcasing its applicability to advance research in computational social science. Full article

Personal comfort systems (PCSs), which provide targeted heating or cooling to specific body parts, have emerged as a promising solution to enhance occupant comfort while reducing energy use in buildings. Among the many factors influencing PCS performance, heat transfer mechanisms (HTMs) play a pivotal role. However, a critical gap remains in the literature regarding the identification of optimal HTMs for achieving both thermal comfort and energy efficiency in PCSs. To address this gap, our study investigates the impact of conduction, convection, and radiation in PCSs on thermal comfort enhancement and energy performance under both heating and cooling modes. A meta-analysis was conducted, extracting data from 64 previous studies to evaluate the effects of HTMs of PCSs on thermal sensation vote (TSV), overall comfort (OC) and corrective energy power (CEP). Results indicate that PCSs typically improve users’ thermal sensation and comfort by about one scale unit in both heating and cooling modes. Radiative HTM is the most effective individual method, while combined conductive and convective HTMs perform best overall. Most PCSs operate efficiently, consuming less than 200 W/°C, with conduction in heating and convection in cooling being recommended for optimal comfort and energy efficiency. These findings suggest that selecting optimal HTMs for PCSs is crucial for achieving maximum comfort performance and energy savings. Data on combined HTMs of PCSs remain limited, underscoring the need for further research in this area. Future research should prioritize optimizing HTMs, especially radiative and combined methods, to maximize comfort and energy savings in PCS design. Full article

Background: Medication adherence is one of the critical factors in optimizing treatment outcomes for chronic diseases such as atopic dermatitis (AD). Existing studies use aggregate data, but there is a need for assessment of medication adherence phases, such as the initiation and discontinuation of therapy. The aim of this study was to assess medication adherence across patients with moderate to severe AD, investigating the impact of drug treatment characteristics, particularly the route of administration, on adherence levels during treatment. Methods: A retrospective observational study on an Italian sample included 821 newly diagnosed AD patients from January 2021 to June 2022. Medication adherence was evaluated by EMERGE guidelines, focusing on initiation and discontinuation. Discontinuation was assessed at 6 and 12 months, comprising sensitivity analysis. Statistical analysis included chi-square tests and descriptive statistics on treatment duration. Results: Treatment initiation is significantly lower for tacrolimus ointment (38% non-initiation) than for dupilumab injection (12% non-initiation), due to initial healthcare support for dupilumab patients. After six months, 75.6% of dupilumab injection patients remained on therapy, while 24.4% of patients continued tacrolimus ointment treatment. After one year, therapy persistence was 68.7% among users of dupilumab, while only 22.5% of patients remained on tacrolimus therapy. Dupilumab demonstrated a significantly longer median treatment duration compared to tacrolimus (4.4 vs. 2.6 months; p < 0.01). Conclusions: The observed differences in adherence patterns between topical tacrolimus and subcutaneous dupilumab suggest that distinct contextual and behavioral factors influence patient adherence during therapy. Full article

Instagram is a hugely popular social media site; however, it has also been cited in many times as being a source of low self-esteem, unhappiness, and body dissatisfaction. Despite this, there is potential to use Instagram as a self-care delivery system and create positive changes in users’ mental health by showing them a specific type of image. In this paper, we use Stress Reduction Theory to demonstrate that viewing images of nature on Instagram can improve well-being (H1), by increasing feelings of connectedness with nature (H2). Furthermore, we posit this same influence will elicit more altruistic behaviour from users (H3). In a laboratory experiment, participants accessed images using either the #naturephotography hashtag, or a control hashtag (#bookshelves). Analyses showed that, in line with the proposed positive effects of SRT, viewing natural images improved well-being and positive emotions, and this was at least partially mediated by increased connectedness to nature. Future studies that use a more longitudinal approach, and examine how images can be presented within a more robust psychiatric intervention are then discussed. Full article

With the widespread integration of Artificial Intelligence-Generated Content (AIGC) into e-commerce platforms, understanding how users perceive, evaluate, and respond to such content has become a critical issue for both academia and industry. This study examines the influence mechanism of AIGC Content Quality (AIGCQ) on users’ Purchase Intention (PI) by constructing a cognitive model centered on Trust (TR) and Perceived Risk (PR). Additionally, it introduces two moderating variables—Ethical Concern (EC) and Perceived Platform Responsibility (PLR)—to explore higher-order psychological influences. The research variables were identified through a systematic literature review and expert interviews, followed by structural equation modeling based on data collected from 507 e-commerce users. The results indicate that AIGCQ significantly reduces users’ PR and enhances TR, while PR negatively and TR positively influence PI, validating the fundamental dual-pathway structure. However, the moderating effects reveal unexpected complexities: PLR simultaneously amplifies both the negative effect of PR and the positive effect of TR on PI, presenting a “dual amplification” pattern; meanwhile, EC weakens the strength of both pathways, manifesting a “dual attenuation” effect. These findings highlight the nonlinear cognitive mechanisms underlying users’ acceptance of AIGC, suggesting that PLR and EC influence decision-making in more intricate ways than previously anticipated. By uncovering the unanticipated patterns in moderation, this study extends the boundary conditions of the trust–risk theoretical framework within AIGC contexts. In practical terms, it reveals that PLR acts as a “double-edged sword,” providing more nuanced guidance for platform governance of AI-generated content, including responsibility frameworks and ethical labeling strategies. Full article

Smart home technologies are increasingly powered by artificial intelligence (AI), offering convenience, energy efficiency, and security, but also raising serious concerns around privacy and cybersecurity. This study seeks to explore the factors that affect the adoption of AI-powered smart home devices by extending the Trust in Technology Model (TTM) to incorporate cybersecurity awareness. The objective is to better understand how users’ trust in technology, institutions, and specific devices, combined with their cybersecurity awareness, influences adoption behavior. A quantitative research design was used, and Structural Equation Modeling (SEM) was employed to examine the assumed relationships among the variables. The results confirm that propensity to trust, in general, technology significantly enhances institution-based trust, which in turn positively influences trust in specific technologies. Trust in specific technologies and cybersecurity awareness were both found to strongly increase users’ intention to adopt AI-powered smart home devices. Moreover, users’ intentions showed the strongest effect on deep structure use, highlighting that positive behavioral intention is a key driver of actual, advanced utilization of these technologies. These results highlight the importance of trust-building and awareness initiatives for fostering wider adoption. This research extends the current literature on technology adoption and provides a framework that can help explain the user’s adoption of AI-powered smart home devices. Its originality lies in integrating cybersecurity awareness into the TTM, offering both theoretical contributions and practical implications for policymakers, developers, and marketers. Full article

This study investigated how icon array layouts influence comprehension of medical risk information, particularly in relation to users’ cognitive abilities. In a within-subjects experiment (N = 121), participants reviewed clinical scenarios with treatment-related risks and side effect risks displayed in either separated or integrated icon arrays. Comprehension was significantly higher for separated treatment-related risk layouts (p < 0.001), while side effect layout showed no effect. Numeracy and graph literacy significantly predicted comprehension. Crucially, individuals with lower numeracy showed marked gains when viewing separated formats, whereas those with higher numeracy performed well regardless of layout. Despite this, participants preferred hybrid formats—separated treatment-related risk with integrated side effect risks—revealing a critical preference–performance gap. By demonstrating how visual layout interacts with user abilities, this study provides actionable guidance for patient decision aid design. The findings show that comprehension accuracy must take precedence over layout compactness and user preference, with separated layouts recommended for treatment-related risks—especially for individuals with lower numeracy—and greater flexibility allowed for side effect risks when space is limited. Full article

This work explores the integration of Proton Exchange Membrane (PEM) electrolysis waste heat with district heating networks (DHN), aiming to enhance the overall energy efficiency and economic viability of hydrogen production systems. PEM electrolysers generate substantial amounts of low-temperature waste heat during operation, which is often dissipated and left unutilised. By recovering such thermal energy and selling it to district heating systems, a synergistic energy pathway that supports both green hydrogen production and sustainable urban heating can be achieved. The study investigates how the electrolyser’s operating temperature, ranging between 50 and 80 °C, influences both hydrogen production and thermal energy availability, exploring trade-offs between electrical efficiency and heat recovery potential. Furthermore, the study evaluates the compatibility of the recovered heat with common heat emission systems such as radiators, fan coils, and radiant floors. Results indicate that valorising waste heat can enhance the overall system performance by reducing the electrolyser’s specific energy consumption and its levelized cost of hydrogen (LCOH) while supplying carbon-free thermal energy for the end users. This integrated approach contributes to the broader goal of sector coupling, offering a pathway toward more resilient, flexible, and resource-efficient energy systems. Full article

Meeting the demands for advanced technology and superior quality in the automotive industry has become essential. Continuous evolution requires a rigorous analysis of every step taken. Customers demand high performance in the technology, design, and digitalization, as well as, of course, quality at a competitive price. To meet these expectations, engineers ensure transparency and trust at every stage of the project, guaranteeing flawless execution. This paper aims to highlight a clear and transparent approach to the 8D analysis method, demonstrating its effectiveness in identifying and solving engineering problems. Furthermore, quality planning and 8D analysis are fundamental pillars of quality management in the automotive industry. To ensure a comprehensive and well-founded approach, this paper combines several research methods: a review of the specialized literature, a hypothetical case study approach, and comparative analysis. The proposed methodology allows for a deep understanding of the concepts addressed, facilitating their applicability in real situations. The main conclusions drawn from this research are that quality planning in an automotive buckle development project has proven to be an essential and complex process, directly influencing the success of the project, the safety of end users, and their satisfaction. The analysis of the implementation of the quality planning process, as previously described, has highlighted several fundamental aspects that must be considered to ensure the success and performance of such a project. Full article

The growing complexity of distribution-level virtual power plants (VPPs) demands a rethinking of how flexible demand is modeled, aggregated, and dispatched under uncertainty. Traditional optimization frameworks often rely on deterministic or homogeneous assumptions about end-user behavior, thereby overestimating controllability and underestimating risk. In this paper, we propose a behavior-aware, two-stage stochastic dispatch framework for VPPs that explicitly models heterogeneous user participation via integrated behavioral economics and social interaction structures. At the behavioral layer, user responses to demand response (DR) incentives are captured using a Prospect Theory-based utility function, parameterized by loss aversion, nonlinear gain perception, and subjective probability weighting. In parallel, social influence dynamics are modeled using a peer interaction network that modulates individual participation probabilities through local contagion effects. These two mechanisms are combined to produce a high-dimensional, time-varying participation map across user classes, including residential, commercial, and industrial actors. This probabilistic behavioral landscape is embedded within a scenario-based two-stage stochastic optimization model. The first stage determines pre-committed dispatch quantities across flexible loads, electric vehicles, and distributed storage systems, while the second stage executes real-time recourse based on realized participation trajectories. The dispatch model includes physical constraints (e.g., energy balance, network limits), behavioral fatigue, and the intertemporal coupling of flexible resources. A scenario reduction technique and the Conditional Value-at-Risk (CVaR) metric are used to ensure computational tractability and robustness against extreme behavior deviations. Full article

Background: In an era of unprecedented technology adoption in healthcare, it is imperative to understand and predict factors influencing users’ perspective. This study employs a risk-integrated technology acceptance model aiming to identify the determinants of the intention to use mobile health applications among patients with chronic diseases in Romania. Methods: A face-to-face survey method was used to collect research data from 207 subjects, and the partial least squares structural equation modeling approach was employed for data analysis. Results: The behavioral intention to use mobile health applications (INT) was influenced positively by the perceived ease of use (PEOU, f² = 0.358, β = 0.500, p < 0.001) and perceived usefulness (PU, f² = 0.271, β = 0.678, p < 0.001). Another core predictor, with a negative effect on the intention to use, was the user’s perceived risk of using the technology (RISK, f² = 0.239, β = −0.321, p < 0.001), in turn influenced by the perceived degree of cyber-insecurity (CYBER, f² = 0.492, β = 0.639, p < 0.001). Digital self-efficacy (DSE) was identified as an external determinant with strong positive influence on PEOU (f² = 0.486, β = 0.610, p < 0.001). The model shows strong performance, reflected in a high Tenenhaus goodness-of-fit index (0.770) and solid explanatory power for the outcome variable (adjusted R² = 0.718). Conclusions: This study validates an extended risk-integrated technology acceptance model, offering robust insights into the determinants of mobile health application adoption among chronic patients in Romania. The findings provide actionable guidance for designing targeted interventions and healthcare policies to enhance technology adoption in this population. Full article

Three-wheelers (3Ws—autos or auto-rickshaws) occupy a unique yet salient and substantive position within the context of India’s urban mobility. They provide critical first-and-last-mile connectivity, fill public transit coverage gaps, boost local and urban agglomeration economies, and are a major income source for millions. Their value and utility are especially pronounced in rapidly emerging Tier-II cities such as Amritsar. The city’s 7500-strong diesel 3W (d3W) fleet is the backbone of its transportation network but also contributes to air pollution. Though Amritsar’s favorable policies to transition the d3W fleet to electric (e3W) have reduced purchase costs by 40–60%, barriers remain. This study investigates the influence of the e3W user experience through a first-of-a-kind three-day pilot trial for ~300 d3W drivers. By leveraging a pre- and post-intervention framework combining surveys and trip diaries, this study evaluated how direct exposure influences adoption intentions, perceptions, and the social dynamics underpinning decision-making. In total, ~6% of participants switched to e3Ws following the trial, and there was a 20% drop in “don’t know” answers regarding charging duration and range. The results show non-random and meaningful shifts in attitudes, a greater awareness of range and charging times, improved views on charging convenience and vehicle safety, and air quality benefits. Full article

With the rapid advancement of artificial intelligence, the deployment of social robots has significantly broadened, extending into diverse fields such as education, medical services, and business. Despite this expansive growth, there remains a notable scarcity of empirical research addressing the underlying psychological mechanisms that influence human–robot interactions. To address this critical research gap, the present study proposes and empirically tests a theoretical model designed to elucidate how users’ multi-dimensional perceived values of social robots influence their attitudinal responses and outcomes. Based on questionnaire data from 569 social robot users, the study reveals that users’ perceived utilitarian value, emotional value, and hedonic value all exert significant positive effects on active involvement, thereby fostering their identification and reinforcing attitudinal loyalty. Among these dimensions, emotional value emerged as the strongest predictor, underscoring the pivotal role of emotional orientation in cultivating lasting human–robot relationships. Furthermore, the findings highlight the critical mediating function of active involvement in linking perceived value to users’ psychological sense of belonging, thereby elucidating the mechanism through which perceived value enhances engagement and promotes sustained long-term interaction. These findings extend the conceptual boundaries of human–machine interaction, offer a theoretical foundation for future explorations of user psychological mechanisms, and inform strategic design approaches centered on emotional interaction and user-oriented experiences, providing practical guidance for optimizing social robot design in applications. Full article

Background/Objectives: Correct inhalation technique (IT) is crucial in the management of airway obstructive diseases. However, inhaler misuse among patients is frequent. The aim of the study was to assess IT and analyze factors influencing inhalation errors in adults with asthma and COPD. Methods: This single-center case–control study involved 180 adults with asthma or COPD. IT was evaluated using a checklist of common errors, a four-grade dedicated scale, and peak inspiratory flow. Patients with correct and incorrect IT were compared across multiple factors, including demographics, disease duration and severity, motivation for treatment, spirometry results, cognitive function, visual or hearing disorders and prior training in inhaler use. Results: A total of 115 patients with asthma and 65 patients with COPD were analyzed. Among them, only 59 patients (32.8%) were treated with 1 inhaler. Sixty-eight patients (37.8%) used all their inhalers properly. Correct IT was observed more frequently among DPI compared to MDI users (p < 0.001). Only 76 patients (42.2%) reported previous training in IT. No differences were found between correct and incorrect inhaler users (MDI or DPI) regarding age, gender, education, treatment motivation, visual or hearing impairments or cognitive disorders. Among MDI users, those with correct IT more often read the drug leaflet (p = 0.015). Among DPI users, proper technique was associated with better self-assessment (p = 0.046) and a higher rate of prior inhalation training (p = 0.001). Conclusions: Most adults with asthma or COPD do not use their inhalers properly, particularly patients using MDI. Insufficient education in the field of proper IT is still a burning issue. Full article