Search Results (573)

Background: Human immunodeficiency virus (HIV) affects more than 39 million people worldwide, with Uganda ranked 10th among countries with the highest number of cases. As new preventative HIV injectables emerge, it is vital to think about how best to tailor strategies to promote these injectable drugs, like PrEP and vaccines, when available, to the different populations most in need. Discrete choice experiments (DCEs) are economics-derived methods used to determine factors that influence engagement in a certain behavior. Objective: This study used a DCE to determine the preferences for a preventative HIV injectable drugs/vaccines among people at risk of HIV acquisition in urban and peri-urban areas of Uganda. Methods: In June 2024, we implemented a cross-sectional DCE survey in three urban sites in Uganda in English and Luganda. The survey collected information on demographics, HIV risk, vaccine confidence and responses to the 13 injection product choice tasks presented to determine preferences. We used community-based, respondent-driven sampling methods to recruit participants from three key populations: (1) female sex workers; (2) people who identify as lesbian, gay, bisexual or transgender; and (3) young women (18–24 years). We collected the data on tablets using the Sawtooth Lighthouse Studio software (v. 19.15.6), taking into consideration privacy and confidentiality, given the sensitivity of the information and recent governmental policies in Uganda. Data were analyzed using a split-sample mixed logit regression analysis. The study was approved by local ethical regulatory bodies. Results: From the total of 406 participants screened for this study, 376 participants met the eligibility criteria and were included in the final analysis (85 young women, 159 female sex workers, and 132 who identified as lesbian, gay, bisexual or transgender). The average age was 23.7 (SD: 5.7). The majority of participants had received some secondary school or vocational school (202, 53.7%) The attributes that explained the preferences were primarily severe compared to mild side effects (β: −0.69, 95% CI: −0.78, −0.60), a 30% increase in vaccine/drug effectiveness (β: 0.39, 95% CI: 0.34, 0.44), and a 50,000 UGX (or USD ~13.64) increase in cost (β: −0.22, 95% CI: −0.27, −0.17). There were no significant differences between the preferences for different injectable types. The sensitivity analyses suggested potential differences in preferences by the amount of help participants received from research assistants when completing the survey, although not by income level. Conclusions: Side effects had the greatest impact on participants’ preferences for injectable HIV prevention methods, followed closely by effectiveness and cost. It is therefore essential to develop affordable or free prevention options with minimal side effects. Policymakers should focus on reducing the financial barriers to access and emphasize transparent communication about the effectiveness and safety of these injectables in health promotion campaigns to maximize adoption and improve public health outcomes. Full article

Background: Burnout is a widespread concern across frontline professions, with healthcare, education, and emergency services workers experiencing particularly high rates of stress and emotional exhaustion. Passive artificial intelligence (AI) technologies may provide novel means to monitor and predict burnout risk using data collected continuously and non-invasively. Objective: This review aims to synthesize recent evidence on passive AI approaches for detecting stress and burnout among frontline workers, identify key physiological and behavioral biomarkers, and highlight current limitations in implementation, validation, and generalizability. Methods: A narrative review of peer-reviewed literature was conducted across multiple databases and digital libraries, including PubMed, IEEE Xplore, Scopus, ACM Digital Library, and Web of Science. Eligible studies applied passive AI methods to infer stress or burnout in individuals in frontline roles. Only studies using passive data (e.g., wearables, Electronic Health Record (EHR) logs) and involving healthcare, education, emergency response, or retail workers were included. Studies focusing exclusively on self-reported or active measures were excluded. Results: Recent evidence indicates that biometric data (e.g., heart rate variability, skin conductance, sleep) from wearables are most frequently used and moderately predictive of stress, with reported accuracies often ranging from 75 to 95%. Workflow interaction logs (e.g., EHR usage patterns) and communication metrics (e.g., email timing and sentiment) show promise but remain underexplored. Organizational network analysis and ambient computing remain largely conceptual in nature. Few studies have examined cross-sector or long-term data, and limited work addresses the generalizability of demographic or cultural findings. Challenges persist in data standardization, privacy, ethical oversight, and integration with clinical or operational workflows. Conclusions: Passive AI systems offer significant promise for proactive burnout detection among frontline workers. However, current studies are limited by small sample sizes, short durations, and sector-specific focus. Future work should prioritize longitudinal, multi-sector validation, address inclusivity and bias, and establish ethical frameworks to support deployment in real-world settings. 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

Location-based services (LBSs), which are used for navigation, tracking, and mapping across digital devices and social platforms, establish a user’s position and deliver tailored experiences. Collecting and sharing such trajectory datasets with analysts for business purposes raises critical privacy concerns, as both symmetry in recurring behavior mobility patterns and asymmetry in irregular movement mobility patterns in sensitive locations collectively expose highly identifiable information, resulting in re-identification risks, trajectory disclosure, and location inference. In response, several privacy preservation models have been proposed, including k-anonymity, l-diversity, t-closeness, LKC-privacy, differential privacy, and location-based approaches. However, these models still exhibit privacy issues, including sensitive location inference (e.g., hospitals, pawnshops, prisons, safe houses), disclosure from duplicate trajectories revealing sensitive places, and the re-identification of unique locations such as homes, condominiums, and offices. Efforts to address these issues often lead to utility loss and computational complexity. To overcome these limitations, we propose a new ($\xi$, $ϵ$)-privacy model that combines data generalization and suppression with sliding windows and R-Tree structures, where sliding windows partition large trajectory graphs into simplified subgraphs, R-Trees provide hierarchical indexing for spatial generalization, and suppression removes highly identifiable locations. The model addresses both symmetry and asymmetry in mobility patterns by balancing generalization and suppression to protect privacy while maintaining data utility. Symmetry-driven mechanisms that enhance resistance to inference attacks and support data confidentiality, integrity, and availability are core requirements of cryptography and information security. An experimental evaluation on the City80k and Metro100k datasets confirms that the ($\xi$, $ϵ$)-privacy model addresses privacy issues with reduced utility loss and efficient scalability, while validating robustness through relative error across query types in diverse analytical scenarios. The findings provide evidence of the model’s practicality for large-scale location data, confirming its relevance to secure computation, data protection, and information security applications. Full article

The rise of the smart city reflects a transformative shift in urban development, defined by the integration of advanced technologies and data-driven solutions seeking to address rapid urbanization, environmental externalities, and the ever-increasing pressing need for optimal resource use. Nevertheless, a better understanding of the factors that shape citizens’ behavioral intentions towards smart-city living is becoming a sheer necessity. This study is among the first to empirically examine determinants describing the propensity to use smart-city services in an urban setting of south-eastern Europe. In this regard, we employ the smart-city stakeholders’ adoption (SSA) model in order to shed light on smart-city technology acceptance, further focusing on the underlying impact of demographics in shaping citizen attitudes and perceptions. Findings suggest that key predictors of acceptance (latent variables describing self-efficacy, price value, and trust in technology), all positively affect behavioral intention while the non-significance of effort expectancy contradicts the relevant results of previous studies and warrants further investigation. Furthermore, the analysis supports the theorized indirect effects of the model, whereas perceived privacy and perceived security both influence behavioral intention via trust in technology, while price value mediates the effect of citizen’s trust in government. The role of demographics was examined for potential moderating effects and was found to be significant, particularly in the case of age and education. Even though the demographic moderators we opted for do not substantially affect the explanatory power of the model, they seem to improve its specificity, particularly regarding perceptions on effort expectancy across the different demographic groups. Such results offer actionable insights on the relevance of smart-city acceptance models to the different demographic groups and in tailoring policies according to demographic segmentation groups with common characteristics. Full article

Unmanned aerial vehicles (UAVs), commonly known as drones, have emerged as promising solutions to overcome the shortcomings of traditional highway-monitoring approaches. UAVs have been used extensively for highway traffic monitoring, infrastructure inspection, safety analysis, and environmental management. This review summarizes the latest applications, contributions, and challenges of UAV technology in highway systems, highlighting their transformative impacts on traffic monitoring, infrastructure inspection, and safety assessment. Several UAV-based highway traffic datasets significantly improve research in traffic behavior analysis and automated driving system validation. The integration of UAVs with advanced technologies, such as artificial intelligence (AI), the Internet of Things (IoT), and 5G, further enhances their capabilities, enabling enhanced real-time analytics and better decision-making support. Addressing ethical, regulatory, and social implications through transparent governance and privacy-preserving technologies is essential for sustainable deployment. Full article

In increasingly networked environments, artificial agents are required to operate not with fixed roles but with identities that adapt, evolve, and emerge through interaction. Traditional identity modeling approaches, whether symbolic or statistical, fail to capture this dynamic, relational nature. This paper proposes a network-based framework for constructing and analyzing AI identity by modeling interaction, representation, and emergence within complex networks. The goal is to uncover how agent identity can be inferred and explained through structural roles, temporal behaviors, and community dynamics. The approach begins by transforming raw data from three benchmark domain, Reddit, the Interaction Network dataset, and AMine, into temporal interaction graphs. These graphs are structurally enriched via motif extraction, centrality scoring, and community detection. Graph Neural Networks (GNNs), including GCNs, GATs, and GraphSAGE, are applied to learn identity embeddings across time slices. Extensive evaluations include identity coherence, role classification accuracy, and temporal embedding consistency. Ablation studies assess the contribution of motif and temporal layers. The proposed model achieves strong performance across all metrics. On the AMiner dataset, identity coherence reaches 0.854, with a role classification accuracy of 80.2%. GAT demonstrates the highest temporal consistency and resilience to noise. Role trajectories and motif patterns confirm the emergence of stable and transient identities over time. The results validate the fact that the framework is not only associated with healthy quantitative performance but also offers information on behavioral development. The model will be expanded with semantic representations and be more concerned with ethical considerations, such as privacy, fairness, and transparency, to make identity modeling in artificial intelligence systems responsible and trustworthy. Full article

This study examines Chinese university students’ adoption of generative artificial intelligence (GenAI) tools by integrating the Theory of Planned Behavior (TPB), the Technology Acceptance Model (TAM), and AI literacy dimensions into a hybrid framework. Survey data from 1006 students across various majors and regions are analyzed using partial least squares structural equation modeling. Notably, AI literacy (i.e., students’ AI ethics, evaluation, and awareness) positively affect their attitudes, subjective norms, and perceived behavioral control, although the influence patterns vary according to the literacy dimension. Perceived privacy risks reduce AI trust, which mediates adoption behavior. Overall, core TPB pathways are validated, with behavioral intentions significantly predicting students’ actual use. Gender and regional differences moderate the key relationships. The results of this study suggest that enhancing students’ ethical and evaluative competencies, building user trust, and addressing privacy concerns could promote generative AI integration in education. Full article

The increasing digitization and decentralization of modern energy systems have heightened their vulnerability to sophisticated cyber threats, necessitating advanced, scalable, and privacy-preserving detection frameworks. This paper introduces a novel Federated Quantum Machine Learning (FQML) framework tailored for anomaly detection in multi-agent energy environments. By integrating parameterized quantum circuits (PQCs) at the local agent level with secure federated learning protocols, the framework enhances detection accuracy while preserving data privacy. A trimmed-mean aggregation scheme and differential privacy mechanisms are embedded to defend against Byzantine behaviors and data-poisoning attacks. The problem is formally modeled as a constrained optimization task, accounting for quantum circuit depth, communication latency, and adversarial resilience. Experimental validation on synthetic smart grid datasets demonstrates that FQML achieves high detection accuracy (≥96.3%), maintains robustness under adversarial perturbations, and reduces communication overhead by 28.6% compared to classical federated baselines. These results substantiate the viability of quantum-enhanced federated learning as a practical, hardware-conscious approach to distributed cybersecurity in next-generation energy infrastructures. Full article

Background/Objectives: The aging population faces a multitude of health challenges, particularly when it comes to maintaining proper nutrition. Age-related physiological changes, such as decreased metabolism, diminished taste perception, and difficulty in chewing, can lead to insufficient nutrient intake, ultimately resulting in malnutrition. It is crucial to address these issues to promote not only physical health but also overall well-being. In this modern era, artificial intelligence (AI) technologies, including robots and machine learning algorithms, are being increasingly harnessed to encourage healthy eating habits among older adults. This is critical to support healthy aging and mitigate diet-related chronic diseases. However, little or no synthesis has established their effectiveness in delivering personalized, scalable, and adaptive interventions for older adults. This systematic review considers the state-of-the-art application of AI-based interventions aimed at improving dietary behaviors and nutritional outcomes in older adults. Methods: Following the PRISMA 2020 guidelines and a registered PROSPERO protocol (ID: CRD420241045268), we systematically analyzed 30 studies we collected from five databases, published between 2015 and 2025 based on different AI techniques, including machine learning, natural language processing, and recommender systems. We synthesized data collected from these studies to examine the intervention types, outcomes, and methodological approaches. Results: Findings from our review highlight the potential of AI-based interventions to promote engagement among older adults and improve adherence to healthy eating guidelines. Additionally, we found some challenges related to ethical concerns such as privacy and transparency, and limited evidence of their long-term effectiveness. Conclusions: AI-based interventions offer significant promise in promoting healthy eating among older adults through personalized, adaptive, and scalable interventions. Yet, current evidence is constrained by some methodological limitations and ethical concerns, which calls for future research to design inclusive, evidence-based AI interventions that address the unique physiological, psychological, and social needs of older adults. Full article

Generative Artificial Intelligence (GAI) has become a topic of increasing societal and academic relevance, with its rapid diffusion reshaping public debate, policymaking, and scholarly inquiry across diverse disciplines. Building on this context, the present study explores the factors influencing GAI adoption among Spanish digital natives (Millennials and Zoomers), using data from a large national survey of 1533 participants (average age = 33.51 years). The theoretical foundation of this research is the Theory of Planned Behavior (TPB). Accordingly, the study examines how perceived usefulness (USEFUL), innovativeness (INNOV), privacy concerns (PRI), knowledge (KNOWL), perceived social performance (SPER), and perceived need for regulation (NREG), along with gender (FEM) and generational identity (GENZ), influence the frequency of GAI use. A mixed-methods design combines ordered logistic regression to assess average effects and fuzzy set qualitative comparative analysis (fsQCA) to uncover multiple causal paths. The results show that USEFUL, INNOV, KNOWL, and GENZ positively influence GAI use, whereas NREG discourages it. PRI and SPER show no statistically significant differences. The fsQCA reveals 17 configurations leading to GAI use and eight to non-use, confirming an asymmetric pattern in which all variables, including PRI, SPER, and FEM, are relevant in specific combinations. These insights highlight the multifaceted nature of GAI adoption and suggest tailored educational, communication, and policy strategies to promote responsible and inclusive use. Full article

As cities strive for human-centered and fine-tuned development, Agent-Based Modeling (ABM) has emerged as a powerful tool for simulating pedestrian behavior and optimizing walkable neighborhood design. This study presents a comparative bibliometric analysis of ABM applications in block-scale walkability research from 2015 to 2024, drawing on both Chinese- and English-language literature. Using visualization tools such as VOSviewer, the analysis reveals divergences in national trajectories, methodological approaches, and institutional logics. Chinese research demonstrates a policy-driven growth pattern, particularly following the introduction of the “15-Minute Community Life Circle” initiative, with an emphasis on neighborhood renewal, age-friendly design, and transit-oriented planning. In contrast, international studies show a steady output driven by technological innovation, integrating methods such as deep learning, semantic segmentation, and behavioral simulation to address climate resilience, equity, and mobility complexity. The study also classifies ABM applications into five key application domains, highlighting how Chinese and international studies differ in focus, data inputs, and implementation strategies. Despite these differences, both research streams recognize the value of ABM in transport planning, public health, and low-carbon urbanism. Key challenges identified include data scarcity, algorithmic limitations, and ethical concerns. The study concludes with future research directions, including multimodal data fusion, integration with extended reality, and the development of privacy-aware, cross-cultural modeling standards. These findings reinforce ABM’s potential as a smart urban simulation tool for advancing adaptive, human-centered, and sustainable neighborhood planning. Full article

As the global economy undergoes rapid digital transformation, cryptocurrencies have emerged as a prominent alternative class of financial assets. Their decentralized nature, pseudonymity, and lack of centralized oversight have attracted considerable interest among investors while simultaneously raising significant concerns among regulators and compliance professionals. While cryptocurrencies offer benefits such as enhanced accessibility and transactional privacy, they also pose notable risks, particularly their potential misuse in financial crimes, including money laundering. This study explores the perceived risks associated with cryptocurrencies in the context of money laundering, drawing on insights from a survey conducted among 50 financial sector professionals. A quantitative research design was employed, using a structured online questionnaire to assess participants’ awareness, investment behavior, and perceptions of the role of cryptocurrencies in illicit finance and financial system security. The results reveal a complex perspective: while 70% of respondents acknowledged the potential for cryptocurrencies to facilitate money laundering, 60% expressed support for their wider adoption. Notably, statistically significant correlations emerged between active investment in cryptocurrencies and the belief that they could enhance financial market security and reduce laundering risks. However, self-reported knowledge levels and general awareness did not show a significant relationship with perceived risk. The findings underscore the importance of a balanced approach to regulation, one that fosters innovation while mitigating illicit finance risks. The study recommends increased investment in user education, the development of blockchain analytics, the adoption of global regulatory standards and enhanced international cooperation to ensure the responsible evolution of the cryptocurrency ecosystem. Full article

A personalized framework for smart home automation is introduced, utilizing machine learning to predict user activities and allow for the context-aware control of living spaces. Predicting user activities, such as ‘Watch_TV’, ‘Sleep’, ‘Work_On_Computer’, and ‘Cook_Dinner’, is essential for improving occupant comfort, optimizing energy consumption, and offering proactive support in smart home settings. The Edge Light Human Activity Recognition Predictor, or EL-HARP, is the main prediction model used in this framework to predict user behavior. The system combines open-source software for real-time sensing, facial recognition, and appliance control with affordable hardware, including the Raspberry Pi 5, ESP32-CAM, Tuya smart switches, NFC (Near Field Communication), and ultrasonic sensors. In order to predict daily user activities, three gradient-boosting models—XGBoost, CatBoost, and LightGBM (Gradient Boosting Models)—are trained for each household using engineered features and past behaviour patterns. Using extended temporal features, LightGBM in particular achieves strong predictive performance within EL-HARP. The framework is optimized for edge deployment with efficient training, regularization, and class imbalance handling. A fully functional prototype demonstrates real-time performance and adaptability to individual behavior patterns. This work contributes a scalable, privacy-preserving, and user-centric approach to intelligent home automation. 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