Search Results (242)

In post-Soviet Georgia, the renewed visibility of religion in the public sphere has generated ambivalent effects, fostering both social cohesion and identity-based exclusion. This article focuses on the work I Am the Way by Georgian Orthodox thinker Zurab Kiknadze to explore how a non-instrumental, ethics-based conception of public religion can be sociologically conceptualized. Drawing on a qualitative, hermeneutic-narrative method, the analysis identifies two core motifs in Kiknadze’s thought—“spiritual journey” and “silent witness”—and interprets them through the lenses of public religion theory (Casanova), lived religion paradigms (McGuire, Ammerman), and post-secular debates (Habermas). The findings indicate that Kiknadze understands faith not as a marker of dogmatic or ethno-political belonging but as a practice contributing to ethical continuity and the reconstruction of social trust. Within this framework, “silent witness” is defined as a form of faith grounded in consistency, humility, and action-oriented conviction; it is proposed as a transferable sociological mechanism that supports trust, reconciliation, and inclusive citizenship in transitional societies. Centering on the Georgian case, this article offers a conceptual contribution to rethinking the public role of religion in post-authoritarian contexts within an ethical framework. Full article

Digital retail is undergoing a paradigm shift driven by the deep integration of artificial intelligence (AI) and augmented reality (AR). Although prior studies have examined the independent effects of AI-based personalized recommendation (cognitive path) and AR-enabled immersion (experiential path), how their integration systematically shapes user behavior through internal psychological mechanisms remains an important unresolved theoretical gap. To address this gap, this study develops an integrated model grounded in the stimulus–organism–response (S-O-R) framework and trust transfer theory. Specifically, the model examines how personalized recommendation, as a dynamic external stimulus, influences users’ cognitive state (perceived usefulness) and experiential state (immersion); how the overall trust of users in the integrated platform can be used as a key boundary condition to adjust the transformation efficiency from the above stimulus to the internal state; and how the above cognitive and experiential states can ultimately drive the continued usage intention through the mediation of positive emotional response. Based on survey data from 400 Chinese consumers with AR shopping experience on Taobao, analyzed using structural equation modeling (SEM), the results indicate that (1) personalized recommendation positively affects both immersion and perceived usefulness; (2) platform trust significantly and positively moderates the effects of personalized recommendation on both immersion and perceived usefulness; (3) both cognitive and experiential states stimulate positive emotions, which in turn enhance continued usage intention, with perceived usefulness exerting a stronger effect; (4) a key theoretical finding is that there is a significant positive correlation between perceived usefulness and immersion, revealing the coupling of psychological paths in an integrated environment; however, immersion does not moderate the effect of personalized recommendation on emotional responses, suggesting that the current integration mode emphasizes the formation of a stable psychological structure rather than real-time interaction. This study makes three contributions to the existing literature. First, it extends the application of S–O–R theory in a complex technological environment by analyzing the “organism” as a parallel and related cognitive-experience dual path and confirming its coupling relationship. Second, it elucidates the enabling role of trust as a moderating mechanism rather than a direct antecedent, thereby enriching micro-level evidence for trust transfer theory in the context of technology integration. Finally, by contrasting path coupling with process regulation, this study provides a more detailed distinction for understanding the theoretical connotations and boundaries of AI–AR technology integration, which may mainly be a kind of structural integration. Full article

Background: Artificial Intelligence (AI) has emerged as a transformative tool in in vitro fertilization (IVF) as it has done in other sectors. In IVF, AI offers advancements in embryo selection, treatment personalization, and outcome prediction. It does so by leveraging deep learning and computer vision, as well as AI-driven platforms such as ERICA, iDAScore, and IVY where the goal is to address the limitations of traditional embryo assessment. Key amongst them are the issues of subjectivity, labor intensity, and limited predictive power. Despite rapid technological progress, the integration of AI into routine IVF practice faces key challenges. These are issues related to clinical validation, ethical dilemmas, and workflow adaptation. Rationale/Objectives: This review synthesizes current evidence to evaluate the role of AI in IVF, focusing on six critical dimensions: (1) the evolution of AI from traditional embryology to algorithmic assessment, (2) clinical validation and regulatory considerations, (3) limitations and ethical challenges, (4) pathways for clinical integration, (5) real-world applications and outcomes, and (6) future directions and policy recommendations. The objective is to provide a comprehensive roadmap for the responsible adoption of AI in reproductive medicine. Outcomes: AI demonstrates significant potential to improve the precision and efficiency of IVF. Studies report that AI models can achieve 10 to 25% higher accuracy in predicting embryo viability and implantation potential compared to traditional morphological assessment by embryologists. This enhanced predictive power supports more consistent embryo ranking, facilitates elective single-embryo transfer (eSET) strategies, and is associated with 30 to 50% reductions in embryologist workload per embryo cohort. Early adopters report promising trends. However, large-scale randomized controlled trials have yet to conclusively demonstrate a statistically significant increase in live birth rates per transfer compared to expert embryologist selection. The most immediate and evidenced value of AI lies in hybrid decision-making models. This is where it augments embryologists by providing data-driven, objective support, thereby standardizing workflows and reducing subjectivity. Wider Implications: The sustainable integration of AI into IVF banks on three key aspects: robust evidence generation, interdisciplinary collaboration, and global standardization. To foster these, policymakers ought to establish regulatory frameworks for transparency and bias mitigation. On their part, clinicians need training to interpret AI outputs critically. Ethically, safeguarding patient trust and equity is non-negotiable. Future innovations, mainly AI-enhanced genomics and real-time monitoring, could further personalize care. However, their success depends on addressing current limitations. By balancing innovation with ethical vigilance, AI holds the potential to revolutionize IVF while upholding the highest standards of patient care. Full article

This paper discusses the evolution of convolutional (CNN) and recurrent (RNN) artificial neural networks in applications for Building Information Modeling (BIM). The paper outlines the milestones reached in the last two decades. The article organizes the current state of knowledge and technology in terms of three aspects: (1) computer visualization coupled with BIM models (detection, segmentation, and quality verification in images, videos, and point clouds), (2) sequence and time series modeling (prediction of costs, energy, work progress, risk), and (3) integration of deep learning results with the semantics and topology of Industry Foundation Class (IFC) models. The paper identifies the most used architectures, typical data pipelines (synthetic data from BIM models, transfer learning, mapping results to IFC elements) and practical limitations: lack of standardized benchmarks, high annotation costs, a domain gap between synthetic and real data, and discontinuous interoperability. We indicate directions for development: combining CNN/RNN with graph models and transformers for wider use of synthetic data and semi-/supervised learning, as well as explainability methods that increase trust in AECOO (Architecture, Engineering, Construction, Owners & Operators) processes. A practical case study presents a new application, Bimetria, which uses a hybrid CNN/OCR (Optical Character Recognition) solution to generate 3D models with estimates based on two-dimensional drawings. A deep review shows that although the importance of attention-based and graph-based architectures is growing, CNNs and RNNs remain an important part of the BIM process, especially in engineering tasks, where, in our experience and in the Bimetria case study, mature convolutional architectures offer a good balance between accuracy, stability and low latency. The paper also raises some fundamental questions to which we are still seeking answers. Thus, the article not only presents the innovative new Bimetria tool but also aims to stimulate discussion about the dynamic development of AI (Artificial Intelligence) in BIM. Full article

Ecosystem disservices (EDS)—ecosystem properties and functions that cause discomfort or harm—often shape public attitudes to urban biodiversity more strongly than ecosystem services, yet they remain weakly integrated into inclusive spatial planning. This study develops and tests an EDS classification and a decision-making tree intended to help planners recognise disservices, assess ES–EDS trade-offs, and select proportionate responses without defaulting to ecological simplification. The framework was derived from literature, survey evidence, and expert–stakeholder input from Eastern European cities, and then examined through five contrasting urban action situations in Estonia and Belarus. The cases show that a shared decision logic for EDS is transferable across settings, but that its practical uptake depends on governance conditions. Where communication was proactive and explanatory, participation was meaningful, and long-term management was institutionally secured, disservices were reframed or mitigated while ecological objectives were maintained. Where disservices were framed late, trust was low, or political intervention truncated deliberation, even modest nature-based interventions were stalled or redirected toward grey alternatives. These findings justify treating EDS as a routine planning concern and demonstrate how an EDS-aware approach can strengthen inclusive planning by making both benefits and burdens of urban nature explicit. Full article

Background/Objectives: The use of edge–cloud architectures has increased rapidly to move the analysis of AI-enabled health data to global environments. However, data security, communication overhead, cost-effectiveness, and data transmission losses are still important problems to be solved. Methods: In this paper, we propose a reliable, explainable, and energy-efficient stress detection framework supported by a cost-oriented blockchain-based content-defined chunking approach to minimise the losses during data transfer. In the proposed architecture, the Nurse Stress dataset represents IoMT data. While the chunking process reduces communication volume and storage costs by avoiding data duplication, blockchain technology eliminates the risks of unauthorised access and manipulation by ensuring the immutability and traceability of data blocks. Results: All Transformer-based models have demonstrated over 99% accuracy. The TimesNet model, in particular, has been designated as the system’s reference model, exhibiting superior performance in terms of both stability and accuracy. The main contribution of this study lies in proposing one of the first integrated frameworks that jointly employs chunking-based data management, blockchain-enabled trust mechanisms, and edge–cloud computing with XAI to ensure secure and transparent IoMT data processing. The proposed system not only performs highly accurate stress detection, but also optimises the dimensions of reliable data transmission, energy and cost efficiency, and clinical reliability. Conclusions: In this respect, the study presents a scalable, reliable, and repeatable approach in health decision support systems by combining data security, integrity, and explainability issues, which are addressed separately in the literature, in a holistic manner. Full article

To address the limitations of Retrieval-Augmented Generation (RAG) systems in handling long policy documents, mitigating information dilution, and reducing hallucinations in engineering-oriented applications, this paper proposes SPR-RAG, a retrieval-augmented framework designed for knowledge-intensive vertical domains such as electric power policy analysis. With practicality and interpretability as core design goals, SPR-RAG introduces a Semantic Parsing Retriever (SPR), which integrates community detection–based entity disambiguation and transforms natural language queries into logical forms for structured querying over a domain knowledge graph, thereby retrieving verifiable triple-based evidence. To further resolve retrieval bias arising from diverse policy writing styles and inconsistencies between user queries and policy text expressions, a question-repository–based indirect retrieval mechanism is developed. By generating and matching latent questions, this module enables more robust retrieval of non-structured contextual evidence. The system then fuses structured and unstructured evidence into a unified dual-source context, providing the generator with an interpretable and reliable grounding signal. Experiments conducted on real electric power policy corpora demonstrate that SPR-RAG achieves 90.01% faithfulness—representing a 5.26% improvement over traditional RAG—and 76.77% context relevance, with a 5.96% gain. These results show that SPR-RAG effectively mitigates hallucinations caused by ambiguous entity names, textual redundancy, and irrelevant retrieved content, thereby improving the verifiability and factual grounding of generated answers. Overall, SPR-RAG demonstrates strong deployability and cross-domain transfer potential through its “Text → Knowledge Graph → RAG” engineering paradigm. The framework provides a practical and generalizable technical blueprint for building high-trust, industry-grade question–answering systems, offering substantial engineering value and real-world applicability. Full article

Ensuring effective governance of local budgets is critical for public service delivery and sustainable development. Public audit institutions—including internal auditors and independent supreme audit bodies—are hypothesized to enhance local budget effectiveness by promoting transparency, accountability, and efficiency in the use of public funds. The main purpose of this article is to test the hypothesis that stronger and more independent public audit institutions are associated with more effective local budget governance and to answer three research questions concerning (i) how different audit models are organized, (ii) how audit strength is quantitatively related to governance outcomes, and (iii) how these relationships manifest in transfer-dependent settings such as Kazakhstan. Drawing on cross-country indicators and a case study of Kazakhstan, the empirical analysis focuses on the period 2021–2023, when the most recent and comparable data on audit oversight and budget transparency became available. This study reviews international best practices and experiences, analyzes relevant global indices, and conducts a comparative examination of advanced economies and Central Asian countries to assess how audit bodies influence local budget outcomes. Correlation analysis using cross-country data and case studies is employed to quantify and illustrate these relationships. Best-performing countries adopt performance auditing approaches that focus not only on compliance but also on evaluating value-for-money and socio-economic impact. However, gaps remain; globally, while supreme audit institutions often meet standards, legislative oversight and public participation in budgeting are frequently insufficient, and many governments fail to act on audit findings. This study underscores the need for holistic reforms—especially in transfer-dependent regions—combining empowered audit institutions with policy changes to incentivize local revenue generation and responsible financial management. Effective public audit oversight emerges as a cornerstone of good local governance, helping to safeguard public funds and improve trust in government. Full article

In many jurisdictions, property registration and transfers remain constrained by inefficient, paper-based processes that depend on multiple intermediaries and bureaucratic approvals. This paper proposes a decentralized, blockchain-based property platform designed to streamline these processes using Non-Fungible Tokens (NFTs) and artificial intelligence (AI) agents to modernize public-sector asset management. The work addresses the persistent inefficiencies of paper-based property registration and ownership transfer by embedding legal and administrative logic within smart contracts and automating compliance through an intelligent conversational interface. The system was implemented using Ethereum-based ERC-721 standards, React for the user interface, and Langfuse-powered AI integration for guided user interaction. The pilot implementation presents secure, transparent, and auditable property-transfer transactions executed entirely on-chain, while hybrid IPFS-based storage and decentralized identifiers preserve privacy and legal validity. Comparative analysis against existing national initiatives indicates that the proposed architecture delivers decentralization, citizen control, and interoperability without compromising regulatory requirements. The system reduces bureaucratic overhead, simplifies transaction workflows, and lowers user error risk, thereby strengthening accountability and public trust. Overall, the paper outlines a viable foundation for legally aligned, AI-assisted digital property registries and offers a policy-oriented roadmap for integrating blockchain-enabled systems into public-sector governance infrastructures. Full article

Living Labs (LLs) are key for collaborative and value-based innovation, though their relational and governance mechanisms are still being explored. This study focuses on examining how relational dynamics and community leadership influence the design, governance, and replicability of a Digital Living Labs (DLLs) methodology. The research examines the DLLs of Catalonia using a combination of 15 qualitative interviews and 104 survey responses, with a mixed-methods design adopted. This regional initiative is based on Quadruple Helix (4-H) collaboration and value-driven innovation. The findings show that inclusive participation is enabled through core relational infrastructures. These relationships are built on trust-building, collaboration, facilitation, and knowledge exchange. Community leaders complemented facilitators through harmonizing institutional objectives with local priorities, reinforcing distributed governance, and generating public value. Inclusion, equity, transparency, and solidarity were essential to engagement and collective ownership. The study’s results indicate that effective DLLs transferability depends more on reinforcing relational foundations and shared values than on replicating fixed structures. Full article

Background: A key barrier to realizing the full potential and long-term collection of patient-reported outcomes (PROs) is the limited understanding of user experience (UX) factors that influence sustained patient engagement with digital PRO tools. Most existing research focuses on disease-specific or country-specific solutions, leaving a gap in identifying shared UX determinants that could inform scalable, cross-disease European digital health frameworks. This fragmentation hinders interoperability and increases development costs by requiring separate tools for each context. This case study aims to address this gap by identifying key UX features that optimize PRO collection across diverse chronic conditions in Europe within the Health Outcomes Observatory project, enhancing continuous (primary use) and large-scale (secondary use) data collection. Objective: This study aimed to identify and analyze key UX factors that support adoption and sustained use of PRO collection tools among patients with chronic diseases across multiple European countries. Methods: Patient focus groups were conducted in four chronic disease areas: cancer, inflammatory bowel disease (IBD), and diabetes (type I and II) across six European countries. Participants were recruited purposively through national patient advisory boards to ensure diversity in age, gender, and disease type. Sessions were moderated by trained qualitative researchers following a standardized guide, and discussions were transcribed verbatim and coded in researcher pairs to ensure intercoder reliability through iterative consensus. A modified thematic analysis, guided deductively by the UX Honeycomb model and inductively by emergent themes, was used to identify cross-disease UX determinants. Results: In total, 17 patients and patient representatives participated (76% female; 4 diabetes, 6 IBD and 7 cancer). We identified six core UX factors driving patient engagement for all disease groups: compatibility with other technologies, direct communication with the care team, personalization, ability to share data, the need for educational material and data protection were identified as key aspects of PRO technologies. However, the customizability of the app is crucial. Not all disease groups had the same needs, and participants specifically requested that the app provide information relevant to their own condition. Disease-specific needs, like T1D patients desiring glucose monitoring integration, were identified. IBD patients highlighted flare detection abilities and cancer patients especially sought side-effect comparisons. Conclusions: Our findings indicate that a unified yet customizable PRO platform can address shared UX needs across diseases, improving patient engagement and data quality. Incorporating features such as seamless data transfer, personalization, feedback, and strong privacy measures can foster trust and long-term adoption across European contexts. In addition to some disease-specific issues, most needs for the backbone of the app were shared among the disease areas. This shows that a shared app between diseases might be preferable and, in case of comorbidities, could ease self-management for patients. Last, to ensure full potential for every user and every disease, customization is crucial. Full article

Drawing upon the Elaboration Likelihood Model (ELM) and Trust Transfer Theory, this study investigates how AI digital avatars influence consumer trust and purchase intention. Using survey data collected from 378 valid respondents, the proposed model was tested with Partial Least Squares Structural Equation Modeling (PLS-SEM). The results reveal that brand awareness and perceived quality significantly affect purchase intention through product trust, while social endorsement cues, anthropomorphism, and interaction quality indirectly influence purchase intention through AI Avatar trust. Furthermore, both AI Avatar trust and product trust have significant direct effects on purchase intention, highlighting the critical role of trust in AI-driven persuasion processes. This study validates the dual-route persuasion mechanism of the ELM in AI marketing contexts and extends the application of trust theory to human–AI interactions, offering valuable insights for future research on AI brand endorsement and consumer psychology. Full article

As AI chatbots are increasingly used in digital fundraising, it remains unclear which communication strategies are more effective in enhancing consumer trust and donation behavior. Drawing on construal level theory and adopting a human-AI interaction perspective, this research examines how message framing in AI-mediated persuasive communication shapes trust and donation willingness. Across four studies, we find that when AI chatbots employ high-level construal (abstract) message framing, consumers perceive the information as less credible compared to when the same message is delivered by a human agent. This reduced message credibility weakens trust in the charitable organization through a trust transfer mechanism, ultimately lowering donation intention. Conversely, low-level construal (concrete) framing enhances both trust and donation willingness. Moreover, the negative impact of abstract message framing by AI chatbots is significantly attenuated when the chatbot features anthropomorphic visual cues, which increase perceived credibility and restore trust and donation willingness. These findings reveal potential risks in deploying AI chatbots for interactive fundraising marketing and offer practical insights for nonprofit organizations seeking to leverage AI in donor engagement. Full article

As video games increasingly replicate real-world locations, they have become powerful tools influencing players’ perceptions and behaviors toward travel destinations. Based on the principles of Transfer Trust Theory (TTT), this research investigates how the trust established in a destination within a virtual game context, referred to as perceived destination trust, translates into real-world travel willingness. Using data from a survey of 262 Tunisian gamers who played games set in real-world environments, we employed a structural equation modeling approach incorporating SPSS and SmartPLS analyses. The results indicate that immersion and enjoyment of the game significantly strengthen emotional attachment and the image of the destination, thereby reinforcing perceived trust. This trust positively predicts the willingness to visit real-world destinations. Furthermore, moderation analysis reveals that this effect is more pronounced among individuals classified as Explorers and Achievers, highlighting the influence of motivational typologies on the translation of virtual behaviors into real-world actions. These results extend the scope of TTT to video game-induced tourism (VGIT), empirically validating the psychological mechanisms that link virtual trust to real-world travel behaviors. From a practical standpoint, tourism organizations and game developers are advised to collaborate on creating immersive and authentic environments that enhance destination credibility while aligning with brand objectives. Full article

The traditional multi-attribute group decision-making (MAGDM) method easily ignores the interference effect among decision-makers (DMs), while quantum theory can effectively portray the uncertainty in the decision-making process and quantify the preference interference among DMs. The asymmetry of evaluation information in social networks can have a significant impact on decision-making. In this paper, a quantum MAGDM algorithm based on probabilistic linguistic term sets (PLTSs) and a quantum-like Bayesian network (QLBN) is proposed (PL-QLBN), utilizing quantum theory and social network concepts and introducing a novel method for calculating interference effects based on belief entropy. Firstly, a complete trust network is constructed based on the probabilistic linguistic trust transfer operator and the minimum path method. A trust aggregation method, considering interference effects, is proposed for the QLBN to determine the DM weights. Next, the attribute weights are calculated based on the entropy weight method. Then, a probabilistic linguistic MAGDM considering interference effects is proposed based on the QLBN. Finally, the feasibility and validity of the provided method are verified through Hefei City’s selection of wastewater reuse alternatives. Full article