Search Results (553)

The article begins by examining Ricoeur’s critique of the Cartesian understanding of the will, which ultimately leads Ricoeur to explore Aristotle’s distinction between voluntary and involuntary acts. Later, Aquinas integrates Aristotle’s insights into a creator/creatures framework by linking voluntary acts to the pursuit of beatitude and by integrating the affective and rational elements of human action in the reflective process that precedes action. In a different metaphysical context, Confucian accounts of moral agency, particularly in the Analects and the Mencius, highlight the role of bodily dispositions, affective responses, and ritualized learning in moral cultivation. Without sharply distinguishing between voluntary and involuntary acts, Confucianism views moral development as harmonizing inner movements through learning and ritual practices. But in Confucianism, too, shaping dispositions and evaluations of possible behaviors helps consider the agent as a whole. These examinations are possible because traditional resources provide a framework. From this perspective, François Noël (1651–1729), who like Descartes trained within a Jesuit scholastic context, provides a unique synthesis. In the Third Treatise on Chinese Ethics of his Philosophia Sinica, Noël interprets Confucian texts through the lens of Aristotelian and Scholastic frameworks, using Chinese examples to illustrate distinctions among voluntary actions: free, natural, spontaneous, and strictly voluntary. Noël seeks to show the value of ancient Chinese texts, their Neo-Confucian commentaries, and their compatibility with his tradition. By doing so, he also integrates Confucian moral psychology with the scholastic framework, proposing an anthropology that preserves the embodied, affective dimensions of agency while trusting the accountability of rationality, the ability of the agent to integrate different aspects of human life in a practical unity: the one able to take action. Full article

Introduction: Knowledge of Iberian freshwater fish fauna among the general public is scarce and generally limited to a handful of species. Moreover, this knowledge gap increases as time goes by, particularly in younger generations, due to the lack of content on native fish fauna in school programmes. Nevertheless, schools across the country are proving increasingly receptive to innovative approaches that engage students in meaningful, real-world learning. Objectives: The LIFE PREDATOR programme leverages this opportunity to educate young people about freshwater fish communities. It aims to prevent the spread of the largest invasive fish in Portugal, the European catfish (Silurus glanis), by engaging students as active conservation ambassadors. Methodology: In inland Portugal, fishing is a cultural practice, and children frequently participate in angling activities alongside friends and family members. By reaching children, the programme simultaneously targets future anglers, potential decision-makers, and a channel for intergenerational knowledge transfer. Results: Thus far, the programme has reached over 5000 students and almost 60 schools, mostly throughout the Tagus basin. Preliminary assessments revealed improvements in students’ ability to name emblematic native fish species like the Iberian nase (Pseudochondrostoma spp.), European eel (Anguilla anguilla) and Iberian barbel (Luciobarbus spp.), and recognise the threats posed by invasives like the European catfish. To ensure national scalability, we have developed learning materials designed for use by teachers across Portugal, which are to be made available for free online. Beyond the dissemination directed to adult fishermen, which is often more demanding, Conclusions: LIFE PREDATOR ensures that knowledge about native river fauna, invasive species, and responsible fishing practices is conveyed through trusted, familiar voices. This intergenerational transmission model has the potential to embed long-lasting behavioural change within future fishing communities. Full article

Background: Culturally and Racially Marginalised (CaRM) communities in Australia encounter subtle and covert forms of prejudice, commonly referred to as “new racism”. Within healthcare settings, these experiences can shape trust, engagement, and patterns of help-seeking. Mental health nurses are often the first point of contact in care delivery, and their ability to recognise, respond to, and mitigate the impacts of new racism is critical for fostering therapeutic relationships and supporting equitable access. Understanding how CaRM communities perceive the conditions that influence their mental health service use is fundamental for informing more equitable and culturally responsive care. Objective: This study explored the viewpoints of CaRM community members regarding the factors they consider important for addressing new racism in healthcare systems and supporting engagement with mental health services. Design: Q methodology was used to identify statistically derived viewpoints that reflect shared viewpoints about the conditions perceived as critical for addressing the impacts of new racism on mental health service use. Setting: Participants were recruited from culturally and linguistically diverse communities across Australia through community settings, social media, and professional networks. Participants: Thirty-five individuals from CaRM backgrounds completed the Q-sort. Methods: This Q methodology consisted of five steps: (1) set up of the Q-sorting instrument, (2) selection of participants, (3) data collection, (4) factor analysis, and (5) factor interpretation. Results: Three distinct viewpoints were identified: (1) raising awareness of mental health issues within CaRM communities (community-focused), (2) providing visible anti-racism and culturally safe services (service-focused), and (3) recognising and formally addressing new racism within healthcare systems (policy-focused). Conclusions: This study offers the first empirically derived, community-informed set of viewpoints on addressing new racism in Australian mental healthcare. While exploratory, the findings highlight multi-level considerations that are potentially relevant to mental health nursing practice, and may be useful to inform future research, policy development, and service redesign aimed at strengthening cultural responsiveness and equity in mental health systems. Full article

Existing studies on trust are mainly based on rational choice theory or the relational logic of the “differential mode of association,” while neglecting the contextuality of trust and the interaction of multiple factors. This study used a within-subjects situational experiment involving 252 participants, manipulating three variables: relationship type (kin, acquaintance, general other), entrusted matter (loans of 2000 yuan, 20,000 yuan, and 200,000 yuan), and trustee attributes (high ability and integrity vs. low ability and integrity). The Friedman test and Generalized Estimating Equations (GEE) were used to examine the effects of these three factors on trust intention and the mechanisms of their interaction. The results indicate that trust intention is influenced by relationship type, the importance of the entrusted matter, and trustee attributes, with significant interactions among the three. This indicates that trust is a contextual outcome shaped by multiple interacting factors rather than a linear result. This study provides contextualized evidence that relationship type, entrusted matter, and trustee attributes jointly shape trust intention. Full article

This study examines how international tourists’ perceptions of environmental, social, and governance (ESG) practices influence their willingness to pay a premium for community-based homestays. Grounded in signaling theory, ESG perception is conceptualized as a credibility signal that reduces perceived uncertainty in community-based accommodation settings. Data were collected from 300 international tourists visiting Mae Kampong Village, Chiang Mai, Thailand, and analyzed using partial least squares structural equation modeling (PLS-SEM). To strengthen predictive assessment, the model was additionally evaluated using PLSpredict, Q²_predict, and the Cross-Validated Predictive Ability Test (CVPAT). The results indicate that ESG perception significantly enhances community sustainability image, trust, and booking intention. Trust partially mediates the relationships between ESG perception and both booking intention and willingness to pay a premium, while booking intention demonstrates the strongest effect on willingness to pay a premium. Community sustainability image does not directly influence booking intention but instead operates indirectly through trust. Environmental concern significantly influences willingness to pay a premium, although its moderating effect is not supported. The findings suggest that tourists in community-based homestay environments rely heavily on trust-based psychological assurance when making accommodation decisions. This study extends ESG tourism research into community-based accommodation contexts and highlights the importance of trust in high-uncertainty tourism environments. The findings also emphasize the importance of transparent ESG communication and trust-building strategies for strengthening sustainable tourism competitiveness. Full article

Designing an aircraft requires multidisciplinary analysis and data processing abilities, which are often spread over various partners. Effective collaboration across organisational boundaries is difficult, but essential. As the aerospace industry becomes increasingly digitalised, ever larger volumes of data and models must be exchanged. Heterogeneous tools, data formats, and infrastructures make it difficult to exchange data and to trace it. We propose using semantic graphs for data exchange to ensure interoperability, while semantic links between data models facilitate multidisciplinary and cross-organisational collaboration. Furthermore, our approach captures comprehensive metadata that describes the creation and modification of each dataset, thereby establishing a fully traceable data provenance chain. We demonstrate its functionality via a design process for an electromechanical actuator (EMA) given requirements from a different stakeholder (simulated). Having the requirements and the EMA models translated in Resource Description Framework (RDF) graphs, we are able to create links between them. This then enables the EMA model to be automatically re-evaluated when requirements change, ensuring that it complies with them. For the data exchange, we use the DLR SemanticHub, which utilises a graph database. By providing traceability of the data results provided in different data formats and the data origins, we enable transparency and accountability across organisational boundaries, which is important for trusted collaboration and compliance in intercorporational data exchange. Full article

This research investigates the factors influencing the behavioral intention and actual use of e-wallets among Generation Z by extending the UTAUT3 model to include personal innovativeness and perceived security. The study employs a quantitative approach using Partial Least Squares Structural Equation Modeling (PLS-SEM). Data were collected from 535 Generation Z e-wallet users between 15 January and 28 February 2026. The results reveal that traditional determinants such as performance expectancy, effort expectancy, facilitating conditions, and hedonic motivation do not significantly influence behavioral intention in a mature digital environment. In contrast, social influence, price value, habit, personal innovativeness, and perceived security significantly shape users’ intentions. Furthermore, the findings indicate that behavioral intention fully mediates the relationship between personal innovativeness and perceived security with actual usage behavior. This suggests that although users may possess innovative tendencies and perceive strong security, these factors influence usage only through the formation of intention. The study also shows that Generation Z demonstrates a strong ability to manage financial activities independently within digital platforms, reflecting high levels of digital and financial literacy. At the same time, users remain highly aware of potential risks, particularly regarding data privacy and transaction security, which significantly affect their intention to adopt e-wallet services. Additionally, actual usage behavior is primarily driven by habit and behavioral intention, indicating routinized usage patterns. Overall, this study highlights the critical roles of trust, social influence, and behavioral reinforcement in explaining technology adoption among Generation Z. Full article

Background/Objectives: Large language models (LLMs) and generative AI are rapidly being integrated into healthy ageing initiatives for tasks ranging from companionship and cognitive support to personalised health advice and reduction in social isolation among older adults. Current ethical discussions predominantly address bias, privacy, and accuracy, leaving unresolved three critical governance questions: How do LLM sentiments towards transformative technologies diverge from human values in ageing contexts? What epistemic status do LLM outputs hold when applied to geriatric care? When is trust in those outputs justified for older adults? And who bears responsibility when AI-informed decisions affect functional ability or well-being? Methods: The framework was developed through normative conceptual analysis, synthesizing philosophical principles of medical knowledge and trust, ethical theories of responsibility, empirical evidence on LLM sentiment divergence, digital ageism, and applications of AI in geriatric care (structured searches in PubMed, PhilPapers, and relevant databases, January 2020–March 2026). Results: The integrated framework produces (i) adaptation of SAIA for multidimensional evaluation of human–machine value convergence specific to healthy ageing values (functional ability, autonomy, dignity, equity); (ii) a four-tier classification of LLM outputs tailored to geriatric scenarios; (iii) conditions for warranted trust calibrated to age-related vulnerabilities such as cognitive decline and digital divide; and (iv) responsibility allocation via RACI models with testable hypotheses linking governance design to trust calibration and patient safety outcomes. Conclusions: Without explicit societal alignment and epistemic governance, generative AI risks reinforcing benevolent ageism, automation bias, and responsibility gaps in healthy ageing. The 2025–2027 period offers a decisive window to shape institutional norms that place functional capacity, human dignity, and value convergence at the centre of AI deployment in geriatric care. Full article

Digital participation depends not only on the formal availability of online engagement channels but also on how citizens interpret the safety, usefulness, and feasibility of participation. This article examines whether willingness to engage digitally is shaped more strongly by procedural platform cues or by underlying behavioral beliefs about security, efficacy, and personal capability. Using a survey of 500 adults in South Korea and a 2 × 2 survey-embedded vignette experiment, the study varies participation threshold (50 vs. 500 supporters) and response specificity (generic response vs. concrete action plan and timeline). The direct experimental effects are small and statistically non-significant, indicating no detectable moderate shift in stated willingness within this vignette design. In contrast, baseline participation intention, perceived outcome efficacy, and digital ability are consistently associated with scenario-based willingness to participate, while security trust is positively associated with baseline readiness to engage. The findings suggest that digital participation is better understood as a behavioral decision shaped by perceived risk, expected consequences, and self-assessed capability than as a simple response to procedural design alone. Full article

Studies explaining the variation in educational outcomes across countries, when based on “black box” models that provide high accuracy but struggle to present the decision-making mechanism transparently, carry the risk of producing limited interpretations for policy discussions. This study examines the system-level relational patterns through which the subcomponents of the Heritage Foundation Index of Economic Freedom distinguish country-average low–medium–high PISA performance profiles in mathematics, reading, and science, and interprets these patterns using machine learning and explainable artificial intelligence (XAI). The analysis draws on approximately twenty years of nominal country-year records covering 76 countries. The study design proceeds through a classification approach, treating country performance as low–medium–high profiles; thus, model outputs are presented on an interpretable reference plane for cross-country comparisons. The findings indicate that the models demonstrate consistent generalization ability in distinguishing performance profiles and that the XAI layer produces explanations that make the model’s reasoning visible in a verifiable manner. The explanation results indicate that components representing institutional trust (such as government integrity and property rights) produce strong, recurring signals alongside higher performance profiles in all three areas; while components such as public expenditure and tax burden can emerge as balancing/suppressing signals in some scenarios. Rather than offering causal policy implications, these findings transparently reveal the structural areas that stand out in distinguishing performance profiles in cross-country comparisons, thus providing an explainable, replicable evidence base for comparative analysis and further research. Full article

Traditional academic degrees often fail to capture the full range of competencies students acquire throughout their university education, particularly those developed through laboratory activities, internships, volunteering, and other extra-curricular experiences. This limitation hinders students’ ability to differentiate themselves in increasingly competitive labor markets and complicates employers’ identification of candidates with balanced technical and transversal competencies. To address this challenge, this paper presents a design-oriented research study proposing a Self-Sovereign Identity (SSI)-based framework for the decentralized issuance and verification of academic micro-credentials. The proposed approach combines a structured methodology for generating micro-credentials with a decentralized architecture supported by a prototype implementation based on SSI technologies. The framework enables universities, lecturers, and other trusted entities to issue verifiable and tamper-resistant credentials that students can securely manage, control, and share through SSI wallets. Unlike existing approaches, which typically focus either on secure credential infrastructures or on the pedagogical value of micro-credentials, the proposed framework integrates both technological and educational perspectives while explicitly supporting the certification of extra-curricular and soft skills. The system supports the creation of granular and portable competency profiles while enhancing transparency, authenticity, interoperability, and trust in credential management. Furthermore, the paper discusses key challenges associated with large-scale adoption, including trust management, governance, scalability, interoperability, and issuer credibility. The results suggest that SSI-based micro-credentialing represents a promising approach for improving the recognition of both technical and transversal competencies, contributing to better alignment between higher education outcomes and evolving labor market demands. Full article

Agricultural AI agents play a crucial role in the evolution of smart agriculture, from single-point automated applications to intelligent systems driven by tasks, collaborative decision-making, and closed-loop execution. However, their practical implementation still faces key challenges, such as heterogeneous agricultural data processing, insufficient cross-scenario generalization ability, complexity of multi-agent collaboration, difficulties in integrating software and hardware, and insufficient security and trust guarantees in real agricultural environments. This paper presents a systematic review of the architecture design, business processes, key technologies, and future challenges of agricultural AI agents. Agricultural AI agents are classified into two types: virtual agricultural AI agents and embodied agricultural AI agents. The paper summarizes a four-layer system architecture consisting of the infrastructure layer, agent management layer, agent collaboration layer, and application layer. The paper also analyzes the model capabilities required by agricultural AI agents from four typical business dimensions: perception and state understanding, knowledge memory and experience management, reasoning decision-making and task planning, and collaborative execution and resource scheduling. This research shows that technologies such as multimodal perception, knowledge graphs, retrieval-enhanced generation, digital twins, reinforcement learning, and multi-agent collaboration can provide important support for agricultural AI agents to enhance their environmental understanding, knowledge reuse, autonomous decision-making, and physical execution capabilities. Future research should focus on robust perception in open environments, long-term memory and knowledge evolution, reliable multi-agent collaboration, edge-cloud collaborative deployment, and secure and trustworthy human–machine collaboration. Integrating agricultural domain knowledge with intelligent agent technology is an important direction for promoting the large-scale, adaptive, and sustainable application of agricultural AI agents. Full article

Background: Artificial Intelligence (AI) and Machine Learning (ML) technologies, such as conversational agents, are becoming increasingly essential tools across multiple industries, particularly in healthcare. This paper presents a scoping review (PRISMA-ScR) of conversational agents (CAs) in mobile health coaching systems (MHCS). It examines existing applications of MHCS, focusing on development strategies, usage contexts, impacts on users, benefits, and research gaps, emphasizing the ability of explainable artificial intelligence (XAI) in making health guidance and decision-support recommendations transparent, trustworthy, and interpretable, if properly integrated. This scoping review identifies opportunities to maximize the use of conversational agents, explainable AI, and mobile technologies to make mobile health coaching systems more accessible and trustworthy, as well as further research gaps worth exploring. Objective: This scoping review maps the evidence on CAs and XAI-enabled technologies in MHCS, identifies trust-related design criteria, categorizes reported outcomes, and highlights opportunities for explainable conversational agents (XCA) in a mobile health context, especially in tackling general medical conditions pertinent in underserved settings. Eligibility criteria: Reported eligible resources evaluated, designed, or conceptually analyzed existing CAs, XAI techniques, and MHCS, AI-supported medical dialogue systems, e-coaching systems, and mobile health applications. We considered sources only relevant to healthcare, health coaching, trust, explainability, or patient engagement that were published between 2006 and 2025. Sources of Evidence: Searches were conducted in IEEE Xplore, Google Scholar, Springer, ScienceDirect/Elsevier, ProQuest, and ACM Digital Library, supplemented by targeted web searches and backward citation checks. Charting methods: Data were charted by system type, communication mode, health context, operational mode, technology used, XAI/trust features, degree of automation, study designs and outcome classification. We applied a revised outcome classification: generated desired outcome (GDO) and partially generated desired outcome (P-GDO), and did not generate desired outcome (DN-GDO). Results: A total of 201 resources were collected. Charted studies clustered around CAs in health, MHCS for chronic diseases and stress management, XAI methods such as LIME, SHAP, Prospector, and counterfactual explanations, and trust-related elements such as voice quality, communication style, appearance, social intelligence, privacy, and performance quality. Most health CAs and MHCS addressed chronic diseases, mental health, or behavior change; fewer addressed general medical diagnosis or autonomous mobile-based primary care support. Conclusions: Existing evidence suggests that CAs and MHCSs can support engagement, coaching, education, and selected decision-support tasks, but evidence for safe, autonomous, explainable general practice functionality remains limited. Future work should prioritize clinically supervised XCA designs, core safety assessment, interfaces with transparent explanation, data protection, culturally and linguistically responsive implementation, and future-oriented review in underserved mobile health settings. Full article

Cybersecurity has become a critical concern for individuals, organizations, and governments, especially with the rise of sophisticated cyberattacks and remote work environments. Traditional security approaches are no longer sufficient, leading to the adoption of advanced frameworks such as the zero-trust model, which operates on the principle “never trust, always verify.” This model enforces strict access controls and continuous monitoring across all network activities. Designing an intelligent zero-trust system is challenging due to the complexity of network environments and the evolving nature of malicious threats. This project proposes an advanced zero-trust architecture that integrates machine learning and multi-factor authentication (MFA) to strengthen security. Specifically, it employs Multilayer Perceptron models and k-Nearest Neighbors algorithms to analyze system logs and user behavior, enabling real-time anomaly detection and adaptive authentication mechanisms. The proposed framework is experimentally evaluated using the H-MOG behavioral–contextual authentication dataset, which captures multimodal user interaction patterns and supports continuous authentication analysis within Zero Trust environments. The integration of machine learning enhances the system’s ability to identify suspicious activities quickly and accurately, while MFA provides an additional layer of protection against unauthorized access. Moreover, the proposed framework emphasizes usability, ensuring that enhanced security does not impose excessive burden on users or IT teams. This allows the framework to respond more effectively to potential threats while maintaining usability. Overall, the proposed approach offers a practical and scalable solution that improves detection performance and strengthens continuous authentication and adaptive access control within Zero Trust environments. Full article

Visual explainable artificial intelligence (XAI) is an important mechanism for connecting analytically complex decision models with practitioners who must interpret and act upon their outputs in industrial supply chains. In facility localization problems, wafer foundries and other capital-intensive manufacturers must evaluate geographically dispersed candidate sites against multiple uncertain criteria. The ability to communicate fuzzy group decision-making (FGDM) outcomes in a transparent, interpretable form has direct operational relevance. The literature has introduced hanging gradient bar charts, gradient bidirectional scatterplots, and traceable aggregation charts as visual XAI instruments for semiconductor supply chain localization that show substantial reductions in interpretation error versus conventional plots. However, the quantitative assessment of explanation quality itself remains underdeveloped. To address such a gap, this research proposes a quantitative explainability quality index (XQI) that formalizes visual explanation quality in FGDM as a composite measurable construct. XQI integrates two complementary layers: (1) An objective explainability layer (OEI), consisting of normalized fuzzy interpretation deviation, response time, ranking fidelity, and interpretation accuracy, and (2) a subjective explainability layer (SEI), consisting of perceived understanding, perceived transparency, decision confidence, and cognitive load. Trust, acceptance, and decision quality are downstream outcome constructs rather than components of the index. A weighted linear combination of OEI and SEI produces a single index for systematic, reproducible comparison across competing visualization designs. A structural equation model is specified as a planned validation mechanism for examining how explanation quality may relate to trust, acceptance, and downstream decision quality. The proposed validation framework includes a semiconductor facility localization scenario, three visualization conditions, and a planned participant pool of 150–240 supply chain managers, engineers, and graduate students. The XQI framework transforms visual XAI from a descriptive communication aid into a testable decision-support construct, thereby addressing a key evaluation gap in the FGDM visualization literature. Full article