Search Results (548)

Electrochemical sensors are user-friendly devices designed for the rapid and straightforward detection of target analytes. Serotonin (5-hydroxytryptamine, 5-HT) is a key neurotransmitter and neuromodulator that regulates diverse neuronal processes. Using a custom-designed screen-printed carbon electrode (SPCE) incorporating ordered mesoporous carbon–bimetal oxides of Cu and Ni (CuO–NiO–OMC), rapid and real-time detection of 5-HT was achieved. The CuO–NiO–OMC structure featured highly active CuO and NiO catalytic sites that effectively promoted the irreversible oxidation of 5-HT (vs. Ag/AgCl reference electrode). The CuO–NiO–OMC/SPCE sensor, connected to a portable potentiostat, exhibited exceptional electrocatalytic performance for the oxidation of 5-HT, with a detection limit of 42.5 nM. The sensitivity was 1.56 A M⁻¹ cm⁻², and the linear dynamic range was 0.0–80.0 µM. The CuO–NiO–OMC/SPCE sensor also demonstrated outstanding selectivity in the presence of competing neurochemicals, including norepinephrine, epinephrine, dopamine, and glutamate, as well as high concentrations of tested biomolecules and inorganic ions. Furthermore, the practicality of the sensor was demonstrated using human serum and urine samples, with recovery percentages ranging from 91.1% to 98.3%. Thus, the CuO–NiO–OMC/SPCE sensor offers an effective approach for 5-HT sensing, thereby permitting molecular-level understanding of brain function. Full article

This paper presents how an ontology-based Knowledge Graph (KG) for basketball box scores can be exploited to support several real use cases, and also presents competency questions, including sports analytics, complex question answering and data browsing with semantic filters. To illustrate this, we present the BBall ontology, the modeling decisions and the key advantages of creating a KG based on this ontology. Then, we introduce a KG covering 25 seasons of the EuroLeague and more than 5 million triples, and we showcase the functionality of three research prototypes based on that KG, particularly a faceted search application with semantic filters and two text-to-SPARQL applications leveraging LLMs, including support for multilingual queries. The first LLM-based application enables SPARQL query editing, and the second is a chat-based application offering interactive dialogue between the user and the system. For these applications, we describe their functionality and approach, and we compare them (along with a classical SPARQL query editor) in several dimensions. Finally, we provide the statistics for the constructed KG, indicative SPARQL queries addressing the competency questions, results and error analysis for the text-to-SPARQL method, efficiency results, and a historical analysis showing the evolution of several factors of EuroLeague basketball from 2000 to 2025. Full article

The rapid proliferation of large language models has created significant opportunities for personalized education, yet existing systems rarely account for user competency as a determinant of interaction quality. This study introduces Persona in The Loop (PITL), a dual-mode adaptive framework that recommends AI personas for blockchain and smart contract education applications. PITL employs 100 AI personas organized across two domains, ten sub-specialties, and five Dreyfus competency levels, recommending personas via either similarity-based mode grounded in Cognitive Load Theory or complementary mode grounded in the Zone of Proximal Development, with an adaptive switching mechanism driven by NASA-TLX cognitive load feedback. A mixed-method study with 150 participants using a 2 × 5 factorial design showed that the complementary mode produced higher learning gains, while the similarity-based mode yielded lower cognitive load and higher code quality. The adaptive mechanism outperformed both fixed-mode conditions on learning gain and code quality. The Mode × Dreyfus interaction was significant for cognitive load and task duration but not for learning gains, suggesting mode effects on learning outcomes are consistent across competency levels. Qualitative interviews with 20 participants corroborated quantitative findings. PITL offers a theoretically grounded and empirically validated approach to competency-based AI persona recommendation in educational contexts. Full article

Architects and interior design (AID) practitioners have a professional responsibility to advocate and design for minority occupants, yet it is not always possible to consult with all future users due to commercial project constraints. In lieu of occupant engagement, this paper asks what self-directed inquiry might guide more inclusive strategic decision-making in AID practice? Taking a systems perspective, a novel framework for interpreting the occupant–building system is proposed. By deductively extending Shove, Panzar and Watson’s existing Social Practices Theory (SPT) operationalisation, their omission of space is remedied through integrating Reckwitz’s affective spaces of social practices. The framework changes the unit of analysis from the physical by describing occupancy as a social practice with three elements: material, the physical assemblage including human bodies and space; competences, the rules and habits of using the space; and meanings of space for occupant cohorts. The revised theory elevates the social to equal status of material, thus reinforcing their reciprocal relationship and making this explicit for AID practice. The framework is proposed as an interpretive sensemaking tool for AID practitioners to identify different spatial occupations beyond stereotypical expectations. It also offers a framework for AID practitioners to critically reflect on their agency in stabilising or evolving the spatialisation of culture. Three interpretations are demonstrated for contemporary Australian multicultural and inclusion scenarios. It is argued that this theory offers a framework for practice to enable strategic inclusive outcomes in projects with or without user consultation. Furthermore, in addressing the social practices of the built environment, this organising framework offers broader and holistic future built environment research and education. Full article

Present-day artificial intelligence systems (AI), virtual assistants, and devices connected to the Internet of Things (IoT) are playing an increasingly important role in decision-making processes in the everyday lives of individuals and daily operations of organizations. In this respect, the users’ trust is a key factor determining their acceptance and effective use. In contemporary digital ecosystems, this trust increasingly becomes a component of sustainable digital marketing, in which transparent data practices and responsible communication shape long-term consumer–technology relationships. This paper analyzes the halo effect as a psychological mechanism affecting the perception of competences, reliability, and ethics in the case of technologies based on AI. Based on the literature on behavioral economics, it was shown how positive associations with the interface, brand, or previous experience of the user may lead to excessive trust in technology. Such mechanisms also play a significant role in shaping sustainable consumption patterns, as users—guided by cognitive shortcuts—can adopt technologies in ways that either strengthen or weaken responsible digital behaviors. Moreover, the potential risks associated with this phenomenon were also indicated. The aim of this paper was to present how the utilization of the halo effect influences the generation of trust in smart systems and the formulation of implication for management practices and technology design. These implications are increasingly important in the context of sustainable digital marketing policy, where organizations must align persuasive communication with ethical standards and with rising expectations regarding sustainable digital transformation. Relationships between variables were analyzed using structural equation modeling (SEM), making it possible to verify complex dependencies between the perceived image of technology, the halo effect, and the users’ trust. This study tested three core hypotheses regarding the halo effect’s role, the foundational importance of security, and the mediating function of trust in technology adoption. The results of these analyses indicate that the halo effect significantly affects the level of trust in each of the investigated areas, with the strongest effect observed in the case of virtual assistants, where perception of the human-like characteristics of the interface considerably strengthened trust in the competences and reliability of the system. This finding has particular relevance for AI-driven personalization mechanisms, which increasingly guide consumer decision-making and shape their long-term behavioral patterns in online environments, with direct implications for sustainable consumption. This paper provides contribution to innovation management and technical marketing, stressing the importance of cognitive and emotional factors in the acceptance of new technologies. At the same time, it highlights the theoretical need to integrate responsible AI design with sustainable digital marketing strategies The findings suggest that ensuring trust, once established, has the potential to support not only technological innovation but broader societal goals related to responsible consumption, environmental stewardship, and long-term digital well-being aligned with sustainable development principles. However, this study stops short of empirically measuring sustainable consumption behaviors, offering instead a conceptual link that requires further empirical validation. Full article

The design of satellite constellations is a complex optimization problem interdependent with other decision problems and multiple competing, user-specific criteria. Consequently, it is very difficult to make a final decision on the constellation design. This study proposes a full simulation and evaluation framework for designing a satellite constellation. Firstly, constructing a solution space by constraining orbital parameters and varying satellite count and plane configuration. Secondly, employing six evaluation metrics—covering both cost and coverage—that are weighted via the case company, Sternula’s setting, with the TOPSIS approach for ranking the candidate constellations. A subsequent sensitivity analysis evaluates robustness to shifts in criterion weights and per-satellite cost. The study indicates that a Walker Star constellation with 97.5° inclination, 105 satellites in 15 planes (phasing 7) achieves the best cost–coverage balance for the case company and remains stable under weight and cost variations. Full article

Human–robot collaboration (HRC) can be defined as the close interaction between a human user and a robot working together to accomplish a specific task. True collaboration, however, can only be realized when humans and robots can share the same workspace simultaneously and move freely within it. To address these problems, Learning from Demonstrations (LfD) helps robots become competent in solving plenty of complicated tasks, greatly reducing programming times and allowing task generalization. However, complex robot tasks require complex path planning modeling for a robot to move from one place to another in a heavily constrained workspace following a collision-free path. To this end, a robot programming framework based on Dynamic Movement Primitives (DMPs) is proposed. The framework derives and implements a solution for robot path planning and includes a new DMP formulation with volumetric obstacle avoidance for robot LfD. The formulation equips robotic systems with the capability of online adaptation in the presence of dynamic obstacles. Quantitative evaluations demonstrate high success rates (>96% in tested scenarios) in collision avoidance and typical trajectory adaptation times in the order of milliseconds (<5 ms), supporting its applicability. These methods have been applied in both simulation and real robotic scenarios using a UR10e collaborative robot from Universal Robots for testing and validation purposes. The results indicate that the proposed approach can effectively make the robot follow a user-defined trajectory and learn how to adapt it to avoid collisions with volumetric obstacles of different shapes and poses in an unconstrained human–robot collaborative environment. Full article

Digital technologies such as the Internet, social media, and online games have become integral to an adult’s everyday life, yet their implications for health-related behaviors remain the subject of ongoing debate. While existing research has extensively examined risks and benefits of digital media use, evidence focused specifically on adult populations and on the psychological processes supporting health-oriented engagement remains fragmented. This systematic review with narrative synthesis, conducted in accordance with PRISMA 2020 guidelines, examined peer-reviewed studies published between 2015 and 2025 involving adults (≥18 years). Searches across PubMed, Scopus, Web of Science, and PsycINFO identified 27 eligible studies addressing spontaneous use of the Internet, social media, or online games in relation to actual health behaviors. Across studies, a consistent pattern emerged in which self-efficacy, health literacy, motivation, risk perception, and perceived social support were associated with the adoption of health-related behaviors, particularly physical activity, preventive practices, healthy eating, and health information seeking. However, the literature was characterized by a predominance of cross-sectional designs, a strong geographical concentration in East Asian contexts, and a marked imbalance across digital environments, with social media and informational Internet use being far more frequently studied than online games. Overall, the findings suggest that digital technologies are neither inherently beneficial nor harmful for adult health; rather, their effects depend on users’ psychological competencies and modes of engagement. By synthesizing evidence across digital contexts, this review proposes a competence-oriented framework that helps explain how everyday digital media use may translate into health-promoting behaviors in adulthood, while also highlighting critical gaps that future longitudinal, cross-cultural, and gaming-focused research should address. Full article

Background/Objectives: Pressure injury stage classification was performed using object detection models to address inconsistencies in clinical assessment due to variability in nurses’ experience and education levels. Methods: A dataset of 1282 pressure injury images from a medical institution was used to train and compare five representative architectures, YOLOv5x, YOLOv7, YOLOv8x, YOLOv8n, and YOLOv11x, and Faster R-CNN across Stages 1–4, excluding Deep Tissue Injury and unclassified cases. A mobile application incorporating YOLOv7 was deployed at Eulji University Daejeon Medical Center and tested by 10 nurses over 2 weeks, processing 46 cases. Results: YOLOv7 demonstrated superior performance with mAP@0.5 of 0.97 and mAP@0.5:0.95 of 0.68, achieving 93% accuracy for Stage 2 classification, the most challenging diagnostic category. Clinical validation demonstrated 87% diagnostic accuracy, 4.0/5 user satisfaction, and workflow improvement with assessment time reduced from 4–6 min to 1 min. The application proved valuable as both a diagnostic support tool and educational resource for novice nurses, with zero critical misclassifications recorded. Conclusions: This study establishes the practical utility of AI-based pressure injury classification systems in clinical practice and their potential for enhancing nursing competency and workflow efficiency. Full article

The rapid integration of artificial intelligence (AI) into higher education is reshaping how learners develop academic, linguistic, and research competencies. This mixed-methods study examines how second-year EFL computer science students employ prompt engineering techniques across four task domains—research summarization, academic video note-taking, style transformation, and concept mapping—within a smart learning environment. Sixty-nine students completed a structured survey requiring AI-assisted draft generation followed by student-led revision. Quantitative analyses included descriptive statistics, chi-square tests, Cramer’s V, t-tests, ANOVA, Kruskal–Wallis tests, and three text-similarity measures (cosine, Jaccard, and Levenshtein). Qualitative evidence was drawn from students’ revised outputs and reflective responses. Results indicate that students consistently preserved semantic meaning while significantly rephrasing AI-generated text, demonstrating moderate conceptual alignment but substantial lexical and structural transformation. Frequent AI users said they were better at searching and revising, but the type of prompt didn’t have much of an effect on how deep the revision was or how well they learned. Iterative prompting and revision emerged as central drivers of metacognitive growth, academic language development, and sustainable learning behaviors. Across tasks, students viewed AI prompts as effective scaffolds for organizing information and synthesizing multimodal input, though reliance varied by learner. The findings underscore that sustainable AI use in EFL technical education depends not on AI output alone, but on structured prompting, iterative human revision, and critical engagement—practices that cultivate autonomy, digital literacy, and long-term academic resilience. Full article

This study aims to examine the mechanisms fostering users’ intention to follow generative artificial intelligence (GenAI)-assisted travel destination recommendations (ATDRs) in the context of digital transformation. A research framework was developed by integrating perceived value, trust, and perceived intrusiveness with the Self-Determination Theory (SDT). Data were collected from 469 respondents who expressed an intention to visit GenAI-ATDRs. This study utilizes structural equation modeling (SEM) to examine the research model. The findings indicate that perceived value and trust are affected by SDT-related dimensions, namely perceived competence, perceived relatedness, perceived autonomy, and perceived intrusiveness. However, trust does not influence perceived value. Furthermore, all three GenAI-related constructs, including perceived value, trust, and perceived intrusiveness, significantly affect users’ intention to follow GenAI-ATDRs. Finally, this work contributes to the body of knowledge on GenAI and user engagement by enlightening its necessity as a helpful virtual assistant and providing practical guidance for industry practitioners on how to enhance users’ willingness to adopt GenAI-ATDRs. Full article

Digital skills play a central role in the ongoing transformation of tourism and hospitality. This study analyzes several issues, such as the structure of digital competencies and workforce segmentation, within the tourism sector in Albania areas where empirical evidence remains scarce. The motivation for inclusion in this study focuses on the void between advanced technologies and the skills gap, while identifying opportunities for change in this direction. The quantitative design considers a sample composed of owners, managers, and operational staff. Data analysis includes descriptive statistics, Pearson correlation analysis, heatmap visualization, cluster analysis, and workforce segmentation. The data processing reveals that while foundational digital literacy is well-evidenced, a significant gap exists in advanced analytical competencies and emerging technologies. The findings regarding workforce segmentation into three distinct groups: Digital Pioneers, Functional Users, and Digital Laggards are of high importance. Equally significant are the findings concerning disparities in digital maturity across sectors. Sectoral analysis reveals higher advanced digital skill levels in accommodation and destination management, while food and beverage businesses show lower digital proficiency. This study contributes by providing an empirical framework that links digital skill structures, workforce segmentation, and sectoral differences, offering evidence-based insights for targeted digital upskilling in tourism and hospitality. The findings highlight the need for differentiated digital training strategies tailored to workforce segments, organizational roles, and tourism subsectors. Full article

The rapid integration of generative AI in education often frames teachers as technology users who primarily need technical training. Existing prompt engineering frameworks offer technical guidance but have limited grounding in theories of teacher professional development or reflective practice. This misses a key feature of prompt engineering: prompting can externalize pedagogical thinking, making AI interaction a process of knowledge externalization. Through systematic conceptual analysis, this paper proposes a reconceptualization of prompt engineering from a technical competency to a reflective professional practice. The methodology integrates three theoretical traditions: Schön’s reflective practice theory (for externalizing tacit knowledge), Wiggins and McTighe’s backward design (for structuring instructional decisions), and Celik’s AI-TPACK framework (as integrated knowledge base). This synthesis suggests that effective prompting can be understood as an act of pedagogical externalization requiring integrated professional knowledge. The paper develops a seven-strategy framework (RPE framework) as an analytic lens for examining prompt engineering sophistication. This theoretical framework offers theory-derived hypotheses that require future empirical validation rather than presenting verified outcomes. Ultimately, the RPE framework offers a conceptual basis for potentially shifting the focus from technical training to teacher professional development by repositioning educators as AI-assisted instructional designers rather than mere AI users. Full article

Background: The delivery of nursing care based on available evidence and centered on the individuals who require it and has a positive impact on the quality of professional interventions, leading to health benefits for the population across all core domains of nursing practice, as well as contributing to the advancement of the profession. Objective: To analyze nurses’ perspectives on the effects of their participation in evidence implementation programs in clinical nursing. Methods: In the Qualitative Descriptive Study, the study participants were nurses who took part in the “Safe Transition” project, a collaborative initiative involving three institutions: a nursing school, a hospital, and a network of primary healthcare institutions. The data were collected through semi-structured interviews that were analyzed by two researchers using content analysis techniques, with the support of the qualitative data analysis software. Results: From the content analysis of semi-structured interviews conducted with 17 nurses involved in knowledge implementation projects in clinical practice, the following categories emerged: Cultivating a spirit of inquiry and an evidence-based practice culture; Critically appraising established practices and evidence-based recommendations; and Integrating evidence into clinical expertise to drive change, improve outcomes, and enhance the quality of care. Conclusions: Effective communication and structured opportunities for knowledge sharing emerged as central to the critical examination of clinical practice and to the development of professionals’ competencies in evidence use. Evidence implementation was further motivated by professionals’ recognition that it can generate tangible benefits for healthcare service users. Collectively, these findings inform recommendations for clinical practice, nursing education, and future nursing research. Full article

Task-oriented dialogue systems face a tension between comprehensive constraint elicitation (task adequacy) and conversational efficiency (minimizing turns). Current preference learning frameworks treat preferences as static, unable to capture the dynamic evolution of interaction states that evolve across dialogue progression. We present Dual-DPO, a framework that embeds multi-objective preferences into data construction via turn-aware scoring. Our approach decouples objective balancing from policy updates through offline preference scalarization, addressing the optimization instability challenges in online multi-objective reinforcement learning. Experiments on MultiWOZ 2.4 demonstrate 28–35% dialogue turn reduction while maintaining Joint Goal Accuracy > 89% ($p<0.001$). Pareto frontier analysis shows 94% coverage with hypervolume $HV=0.847$. Independent expert evaluation by 10 PhD-level researchers ($n=300$ assessments, inter-rater agreement $\alpha =0.78$) confirms 32% user satisfaction improvement ($p<0.001$). Theoretical analysis demonstrates that offline scalarization, which correlates with improved optimization stability, achieves $3.2\times$ lower gradient variance than online multi-reward optimization by eliminating sampling stochasticity. Our approach enables balanced treatment of competing objectives through Pareto-optimal trade-offs. These results highlight a symmetric and balanced treatment of competing objectives within a Pareto-optimal optimization framework. Full article