Search Results (1,191)

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

The integration of Generative Artificial Intelligence (GenAI) tools, such as ChatGPT, into higher education has introduced new opportunities and challenges for students and lecturers alike. This study investigates the psychological, ethical, and institutional factors that shape the adoption of GenAI tools in Saudi Arabian universities, drawing on an extended Technology Acceptance Model (TAM) that incorporates constructs from Self-Determination Theory (SDT) and ethical decision-making. A cross-sectional survey was administered to 578 undergraduate students and 309 university lecturers across three major institutions in Southern Saudi Arabia. Quantitative analysis using Structural Equation Modelling (SmartPLS 4) revealed that perceived usefulness, intrinsic motivation, and ethical trust significantly predicted students’ intention to use GenAI. Perceived ease of use influenced intention both directly and indirectly through usefulness, while institutional support positively shaped perceptions of GenAI’s value. Academic integrity and trust-related concerns emerged as key mediators of motivation, highlighting the ethical tensions in AI-assisted learning. Lecturer data revealed a parallel set of concerns, including fear of overreliance, diminished student effort, and erosion of assessment credibility. Although many faculty members had adapted their assessments in response to GenAI, institutional guidance was often perceived as lacking. Overall, the study offers a validated, context-sensitive model for understanding GenAI adoption in education and emphasises the importance of ethical frameworks, motivation-building, and institutional readiness. These findings offer actionable insights for policy-makers, curriculum designers, and academic leaders seeking to responsibly integrate GenAI into teaching and learning environments. Full article

Open-weight generative large language models (LLMs) can be freely downloaded and modified. Yet, little empirical evidence exists on how these models are systematically altered and redistributed. This study provides a large-scale empirical analysis of safety-modified open-weight LLMs, drawing on 8608 model repositories and evaluating 20 representative modified models on unsafe prompts designed to elicit, for example, election disinformation, criminal instruction, and regulatory evasion. This study demonstrates that modified models exhibit substantially higher compliance: while an average of unmodified models complied with only 19.2% of unsafe requests, modified variants complied at an average rate of 80.0%. Modification effectiveness was independent of model size, with smaller, 14-billion-parameter variants sometimes matching or exceeding the compliance levels of 70B parameter versions. The ecosystem is highly concentrated yet structurally decentralized; for example, the top 5% of providers account for over 60% of downloads and the top 20 for nearly 86%. Moreover, more than half of the identified models use GGUF packaging, optimized for consumer hardware, and 4-bit quantization methods proliferate widely, though full-precision and lossless 16-bit models remain the most downloaded. These findings demonstrate how locally deployable, modified LLMs represent a paradigm shift for Internet safety governance, calling for new regulatory approaches suited to decentralized AI. Full article

Background/Objectives: The COVID-19 pandemic highlighted how infodemics—an excessive amount of both accurate and misleading information—undermine health responses. Artificial intelligence (AI) and digital tools have been increasingly applied to monitor, detect, and counter health misinformation online. This scoping review aims to systematically map digital and AI-based interventions, describing their applications, outcomes, ethical and equity implications, and policy frameworks. Methods: This review followed the Joanna Briggs Institute methodology and was reported according to PRISMA-ScR. The protocol was preregistered on the Open Science Framework . Searches were conducted in PubMed/MEDLINE, Scopus, Web of Science, and CINAHL (January 2017–March 2025). Two reviewers independently screened titles/abstracts and full texts; disagreements were resolved by a third reviewer. Data extraction included study characteristics, populations, technologies, outcomes, thematic areas, and domains. Quantitative synthesis used descriptive statistics with 95% confidence intervals. Results: A total of 63 studies were included, most published between 2020 and 2024. The majority originated from the Americas (41.3%), followed by Europe (15.9%), the Western Pacific (9.5%), and other regions; 22.2% had a global scope. The most frequent thematic areas were monitoring/surveillance (54.0%) and health communication (42.9%), followed by education/training, AI/ML model development, and digital engagement tools. The domains most often addressed were applications (63.5%), responsiveness, policies/strategies, ethical concerns, and equity/accessibility. Conclusions: AI and digital tools provide significant contributions in detecting misinformation, strengthening surveillance, and promoting health literacy. However, evidence remains heterogeneous, with geographic imbalances, reliance on proxy outcomes, and limited focus on vulnerable groups. Scaling these interventions requires transparent governance, multilingual datasets, ethical safeguards, and integration into public health infrastructures. Full article

Background: Exposure therapy is an established intervention for treating specific phobias. This study evaluates a Virtual Exposure Therapist (VET), a virtual reality (VR)-based system enhanced with artificial intelligence (AI), designed to reduce spider fear symptoms. Methods: The VET system delivers three progressive exposure scenarios involving interactive 3D spider models and features an adaptive relaxation mode triggered when physiological stress exceeds preset thresholds. AI integration is rule-based, enabling real-time adjustments based on session duration, head movement (degrees/sec), and average heart rate (bpm). Fifty-five participants (aged 18–35) with self-reported moderate to high fear of spiders completed seven sessions using the VET system. Participants were not clinically diagnosed, which limits the generalizability of findings to clinical populations. Ethical approval was obtained, and informed consent was secured. Behavioral responses were analyzed using AR(p)–GARCH (1,1) models to account for intra-session volatility in anxiety-related indicators. The presence of ARCH effects was confirmed through the Lagrange Multiplier test, validating the model choice. Results: Results demonstrated a 21.4% reduction in completion time and a 16.7% decrease in average heart rate across sessions. Head movement variability declined, indicating increased user composure. These changes suggest a trend toward reduced phobic response over repeated exposures. Conclusions: While findings support the potential of AI-assisted VR exposure therapy, they remain preliminary due to the non-clinical sample and absence of a control group. Findings indicate expected symptom improvement across sessions; additionally, within-session volatility metrics (persistence/half-life) provided incremental predictive information about later change beyond session means, with results replicated using simple volatility proxies. These process measures are offered as complements to standard analyses, not replacements. Full article

Artificial intelligence (AI) is increasingly embedded within organizational infrastructures, yet the foundational role of data in shaping AI outcomes remains underexplored. This study positions data at the center of complexity, uncertainty, and strategic decision-making in AI development, aligning with the emerging paradigm of data-centric AI (DCAI). Based on in-depth interviews with 74 senior AI and data professionals, the research examines how experts conceptualize and operationalize data throughout the AI lifecycle. A thematic analysis reveals five interconnected domains reflecting sociotechnical and organizational challenges—such as data quality, governance, contextualization, and alignment with business objectives. The study proposes a conceptual model depicting data as a dynamic infrastructure underpinning all AI phases, from collection to deployment and monitoring. Findings indicate that data-related issues, more than model sophistication, are the primary bottlenecks undermining system reliability, fairness, and accountability. Practically, this research advocates for increased investment in the development of intelligent systems designed to ensure high-quality data management. Theoretically, it reframes data as a site of labor and negotiation, challenging dominant model-centric narratives. By integrating empirical insights with normative concerns, this study contributes to the design of more trustworthy and ethically grounded AI systems within the DCAI framework. Full article

The use of generative artificial intelligence (GenAI) chatbots in brands is growing exponentially, and higher education institutions are not unaware of how such tools effectively shape the attitudes and behavioral intentions of students. These chatbots are able to synthesize an enormous amount of data input and can create contextually aware, human-like conversational content that is not limited to simple scripted responses. This study examines the factors that determine chatbot brand trust in the adoption of GenAI in higher education. By extending the Technology Acceptance Model (TAM) with the construct of brand trust, the study introduces a novel contribution to the literature, offering fresh insights into how trust in GenAI chatbots is developed within the academic context. Using the convenience sampling technique, a sample of 609 students from public universities in North Central and Southwestern Nigeria was selected. The collected data were analyzed via partial least squares structural equation modelling. The results indicated that attitudes toward chatbots determine behavioral intentions and GenAI chatbot brand trust. Surprisingly, behavioral intentions do not affect GenAI chatbot brand trust. Similarly, the perceived ease of use of chatbots does not determine behavioral intention or attitudes toward GenAI chatbot adoption but rather determines perceived usefulness. Additionally, the perceived usefulness of chatbots affects behavioral intention and attitudes toward GenAI chatbot adoption. Moreover, social influence affects behavioral intention, perceived ease of use, perceived usefulness and attitudes toward GenAI chatbot adoption. The implications of the findings for higher education institutions are that homegrown GenAI chatbots that align with the principles of the institution should be developed, creating an environment that promotes a positive attitude toward these technologies. Specifically, the study recommends that policymakers and university administrators establish clear institutional guidelines for the design, deployment, and ethical use of homegrown GenAI chatbots, ensuring alignment with educational goals and safeguarding student trust. 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

The review “Symbolic Approaches for Explainable Artificial Intelligence” discusses the potential of symbolic AI to improve transparency, contrasting it with opaque deep learning systems. Though connectionist models perform well, their poor interpretability means that they are of concern for bias and trust in high-stakes fields such as healthcare and finance. The authors integrate symbolic AI methods—rule-based reasoning, ontologies, and expert systems—with neuro-symbolic integrations (e.g., DeepProbLog). This paper covers topics such as scalability and integrating knowledge, proposing solutions like dynamic ontologies. The survey concludes by advocating for hybrid AI approaches and interdisciplinary collaboration to reconcile technical innovation with ethical and regulatory demands. Full article

Generative Adversarial Networks (GANs) have emerged as powerful tools in artificial intelligence (AI) with growing relevance in medical imaging. In dermatology, GANs are revolutionizing image analysis, enabling synthetic image generation, data augmentation, color standardization, and improved diagnostic model training. This narrative review explores the landscape of GAN applications in dermatology, systematically analyzing 27 key studies and identifying 11 main clinical use cases. These range from the synthesis of under-represented skin phenotypes to segmentation, denoising, and super-resolution imaging. The review also examines the commercial implementations of GAN-based solutions relevant to practicing dermatologists. We present a comparative summary of GAN architectures, including DCGAN, cGAN, StyleGAN, CycleGAN, and advanced hybrids. We analyze technical metrics used to evaluate performance—such as Fréchet Inception Distance (FID), SSIM, Inception Score, and Dice Coefficient—and discuss challenges like data imbalance, overfitting, and the lack of clinical validation. Additionally, we review ethical concerns and regulatory limitations. Our findings highlight the transformative potential of GANs in dermatology while emphasizing the need for standardized protocols and rigorous validation. While early results are promising, few models have yet reached real-world clinical integration. The democratization of AI tools and open-access datasets are pivotal to ensure equitable dermatologic care across diverse populations. This review serves as a comprehensive resource for dermatologists, researchers, and developers interested in applying GANs in dermatological practice and research. Future directions include multimodal integration, clinical trials, and explainable GANs to facilitate adoption in daily clinical workflows. Full article

The sustainable preservation of cultural heritage, as articulated in Sustainable Development Goal (SDG) 11.4, requires strategies that not only safeguard tangible and intangible assets but also enhance their long-term cultural, social, and economic value. Artificial intelligence (AI) and digital technologies are increasingly applied in heritage conservation. However, most research emphasizes technical applications, such as improving data accuracy and increasing efficiency, while neglecting their integration into a broader framework of cultural sustainability and heritage tourism. This study addresses this gap by developing a set of practical guidelines for the sustainable use of AI in cultural heritage preservation. The guidelines highlight six dimensions: inclusive data governance, data authenticity protection, leveraging AI as a complementary tool, balancing innovation with cultural values, ensuring copyright and ethical compliance, long-term technical maintenance, and collaborative governance. To illustrate the feasibility of these guidelines, the paper analyses three representative case studies: AI-driven 3D reconstruction of the Old Summer Palace, educational dissemination via Google Arts & Culture, and intelligent restoration at E-Dunhuang. By situating AI-driven practices within the framework of cultural sustainability, this study makes both theoretical and practical contributions to heritage governance, to enhance cultural sustainability commitments and align digital innovation with the enduring preservation of humanity’s shared heritage, providing actionable insights for policymakers, institutions, and the tourism industry in designing resilient and culturally respectful heritage strategies. Full article

The emergence of large language models (LLMs) has redefined the potential of artificial intelligence in clinical domains. In this context, retrieval-augmented generation (RAG) systems provide a promising approach to enhance traceability, timeliness, and accuracy in tasks such as biomedical question answering (QA). This article presents a narrative and thematic review of the evolution of these technologies in maternal health, structured across five axes: technical foundations of RAG, advancements in biomedical LLMs, conversational agents in healthcare, clinical validation frameworks, and specific applications in obstetric telehealth. Through a systematic search in scientific databases covering the period from 2022 to 2025, 148 relevant studies were identified. Notable developments include architectures such as BiomedRAG and MedGraphRAG, which integrate semantic retrieval with controlled generation, achieving up to 18% improvement in accuracy compared to pure generative models. The review also highlights domain-specific models like PMC-LLaMA and Med-PaLM 2, while addressing persistent challenges in bias mitigation, hallucination reduction, and clinical validation. In the maternal care context, the review outlines applications in prenatal monitoring, the automatic generation of clinically validated QA pairs, and low-resource deployment using techniques such as QLoRA. The article concludes with a proposed research agenda emphasizing federated evaluation, participatory co-design with patients and healthcare professionals, and the ethical design of adaptable systems for diverse clinical settings. Full article

Sustainable innovation represents both a strategic priority and survival imperative for small- and medium-sized enterprises in resource-constrained economies. While digital transformation offers potential solutions, the synergistic effects of digital leadership, employee behaviors, and emerging technologies remain poorly understood. This study bridges this gap by developing and testing a behavioral-tech leadership framework grounded in the Job Demands-Resources (JD-R) model and Technology Acceptance Model. Analyzing survey data from 400 Iraqi SME employees using partial least squares structural equation modeling, we demonstrate that digital leadership directly enhances sustainable innovation while reducing counterproductive cyberloafing. Crucially, social cyberloafing, when properly managed, emerges as a positive mediator, improving employee well-being and creativity, particularly among mid-career and educated workers. Artificial Intelligence’s integration further amplifies these effects by optimizing operational efficiency and reducing human-resource strain. These findings challenge conventional perspectives by repositioning cyberloafing as a conditional resource within the JD-R framework and provide actionable insights for achieving sustainable innovation even in challenging environments. Practical implications include gender-inclusive digital leadership programs, ethical AI implementation guidelines and restorative cyberloafing policies. The study contributes to United Nations Sustainable Development Goals 8 (decent work), 9 (industry innovation) and 12 (responsible consumption) while highlighting the transformative potential of human-centric digital strategies in resource-constrained contexts. 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

As artificial intelligence (AI) reshapes production, its integration into manufacturing offers gains in precision, efficiency, and sustainability. Globally, AI supports additive, subtractive, and forming processes through optimization, monitoring, defect detection, and design innovation. In Latin America, however, adoption is limited and uneven, with most evidence from surveys, policy reports, and pilot projects rather than large-scale implementations. This review addresses that gap by examining the global landscape of AI in manufacturing and the specific conditions influencing its adoption in the region. The study is guided by the question: What structural conditions are required to enable successful and sustainable AI integration in Latin American manufacturing? To answer, it applies the Triadic Integration Framework, which identifies three pillars: digital infrastructure, policy and governance, and socio-industrial capacity. The analysis highlights barriers, including fragmented regulation, skills shortages, cybersecurity risks, and cost–benefit uncertainties, while also pointing to opportunities in various industrial sectors. To translate insights into practice, a phased roadmap is proposed, outlining short-term, medium-term, and long-term actions, along with the responsible stakeholders and the necessary resources. As an integrative review, the study synthesizes existing knowledge to build a framework, defining directions for future research, emphasizing that successful adoption requires technical progress, inclusive governance, and regional coordination. Full article