Search Results (2,420)

Unlike B2C service failures, where firms respond to their own failures within a dyadic firm–customer relationship, C2C platform complaints often originate from third-party sellers. In such triadic platform–seller–consumer interactions, AI customer service agents become front-end governance interfaces through which consumers judge platform fairness, rule enforcement, and institutional reliability. This study examines anthropomorphic AI as a task-contingent governance cue in C2C platform complaint handling. Across two scenario-based experiments, we test how AI role framing, anthropomorphism, and complaint severity jointly shape consumer evaluations. Study 1 shows that high anthropomorphism increases service recovery satisfaction when AI is framed as a relational representative, but not when framed as a rule-based arbitrator. Study 2 reveals significant three-way interactions among complaint severity, role framing, and anthropomorphism for satisfaction, platform trust, and continuance intention. Under low severity, high anthropomorphism benefits both roles; under high severity, its benefit remains mainly for the relational representative and weakens for the rule-based arbitrator. Mechanism analyses show that social presence explains responses under low severity, whereas both social presence and procedural justice shape evaluations under high severity. Together, these findings identify governance-task fit as a key condition for the value of anthropomorphic AI in platform complaint handling, showing when human-like AI builds trust and when it may undermine governance credibility. Full article

Carbon-based materials, including graphene, carbon nanotubes, laser-induced graphene, and pyrolyzed glassy carbon, are widely used in sensing applications due to their high conductivity, large surface area, and tunable surface chemistry. Meanwhile, microfluidic systems enable precise fluid handling, reduced sample consumption, and enhanced analytical performance through improved mass transport and device miniaturization. The integration of carbon-based materials with microfluidic platforms has enabled the development of compact, portable, and highly sensitive devices for water monitoring. This review summarizes recent advances in carbon-based microfluidic sensors for water monitoring applications. Key carbon materials and their sensing mechanisms, particularly electrochemical transduction, are discussed. Various microfluidic integration strategies, including paper-based devices, polymer-based devices, MEMS-based systems, and flexible platforms, are highlighted, with emphasis on mass transport enhancement and overall system performance. Representative recent advances in carbon-based microfluidic sensors for water monitoring, including the detection of heavy metal ions, nutrients, and emerging contaminants, are reviewed. Finally, challenges related to scalable manufacturing, long-term operational stability, biofouling/surface fouling, and reproducible system integration are discussed, together with future perspectives on intelligent carbon-based microfluidic platforms featuring AI-assisted analytics, sense-response functionality, and self-healing and dynamic antifouling capabilities for water monitoring. These advances are expected to enable real-time, low-cost, and field-deployable water monitoring systems for environmental protection and public health management. Overall, this review highlights the critical role of integrating carbon-based sensing materials with microfluidic engineering in advancing next-generation water monitoring technologies. Full article

The global expansion of K-pop has increased demand for AI-driven choreography learning. However, existing motion recognition models often struggle to capture fine-grained rhythm patterns and dynamic motion transitions across consecutive frames, limiting their ability to provide accurate and objective feedback. To address these challenges, this paper proposes a Large Language Model-based Dance Generative Control (L-DGC), an integrated framework for controllable dance generation and evaluation. The framework comprises four stages: a Visual Analysis Phase (VAP) for skeletal extraction; an Audio Analysis Phase (AAP) for rhythmic synchronization; a Multimodal Data Phase (MDP), which employs Long Short-Term Memory (LSTM) and Transformer architectures to evaluate movement accuracy; and a three-dimensional (3D) Transformation Phase (3TP), which converts two-dimensional (2D) skeletal data into 3D character animations within the Unity engine. Guided by an LLM, the framework performs real-time inference and iterative refinement to optimize choreographic data without requiring subjective expert assessment. By quantifying choreographic components and transforming 2D motion data into 3D representations, L-DGC provides an objective evaluation framework for dance learning. The proposed system has significant potential for artificial intelligence (AI)-based dance education, real-time feedback applications, and automated audition platforms in the entertainment industry. Full article

Mobile reading applications often separate text reading from audiobook playback, limiting multimodal learning. This study proposes a cross-platform mobile application integrating synchronized text–audio interaction within a unified interface. Developed using the Flutter framework, the system incorporates Firebase Authentication, Cloud Firestore, and adjustable speed playback with reading progress synchronization. A mixed engineering–UX evaluation was conducted through black-box testing and a user survey involving ten participants. The application achieved an 81.2% satisfaction score, indicating strong user acceptance. The results demonstrate that integrated text–audio systems enhance accessibility and flexibility, with future scalability supported by AI-driven neural text-to-speech narration. Full article

Sustainability targets for livestock require decision support that is both scientifically credible and operationally usable on farms. This integrative narrative review synthesizes the broader peer-reviewed literature on digital farming, artificial intelligence and machine learning, simulation modeling, optimization, and digital-twin concepts as applied to sustainable dairy systems, and uses selected peer-reviewed dairy studies from one integrated research program as illustrative worked examples that show how these elements can be connected end-to-end. We organize the synthesis around a data-to-decision pipeline that links data foundations and interoperability, governance and trust, analytics, decision engines, and deployment, comparing model classes by data needs, temporal resolution, interpretability, and deployment maturity. Recurring barriers to impact—weak ground truth, data drift, fragmented identifiers, and misaligned incentives—are highlighted alongside the design principles that address them. The contribution of the review is the transferable pipeline framework, demonstrated through worked examples drawn from one integrated research program; the program’s studies appear repeatedly because they together trace decisions across all pipeline layers, not because they constitute the field. We conclude with a practical roadmap and implementation checklist for designing and scaling decision-intelligence systems with transparent tradeoffs and measurable sustainability outcomes. Full article

The rapid expansion of online health information channels, particularly emerging artificial intelligence (AI) platforms, is transforming how individuals access and evaluate health information. Drawing on an extended Comprehensive Model of Information Seeking (CMIS), this research examined how different channel types (AI-based, short-video, and text-based) influence online health information-seeking behavior (OHISB) through a pilot validation (N = 258), a cross-sectional survey (Study 1; N = 300), and a between-subjects experiment (Study 2; N = 300). Study 1 tested an extended CMIS model incorporating channel type, source credibility, information credibility, and perceived usefulness, while Study 2 examined the causal effects of channel exposure. Structural equation modeling in Studies 1 and 2 consistently showed that source and information credibility predicted OHISB indirectly through perceived usefulness. AI channels showed no advantage in Study 1, whereas Study 2 found that participants perceived AI sources as more credible and useful, which indirectly predicted stronger intentions for SAMC and information seeking through the credibility–usefulness pathway. This change may reflect methodological differences between self-report recall-based and direct exposure designs, and the public’s growing familiarity with AI technologies. By integrating channel characteristics and credibility perceptions, this study extends the CMIS framework and provides evidence for AI’s enhanced perceived credibility in health information contexts, offering insights for improving AI-driven health communication. Full article

Background/Objectives: Accurate estimation of meal composition from food images can support safer and more reliable insulin bolus decision-making for individuals with Type 1 diabetes. Existing food recognition and nutrition estimation systems are often designed for general dietary logging and do not directly integrate food analysis with personalized insulin therapy parameters. Methods: This study presents an image-based nutrition estimation and insulin decision-support module developed within the AI-assisted Diabetes Care (AIDCARE) platform. The proposed system uses a convolutional neural network (CNN) to classify food items from a single meal image, and retrieves reference nutritional values from a food composition database. A separate multimodal large language model (MLLM)-based estimation component is then used to estimate portion size, allowing carbohydrate and nutrient values to be scaled according to the observed serving. Results: A curated food image dataset containing 40 food categories was used to evaluate three CNN architectures: ResNet50, Inception V3, and EfficientNet-B0. EfficientNet-B0 achieved the best classification performance, with 94.91% validation accuracy, 95.55% precision, 94.87% recall, and 94.90% F1-score. The portion-estimation component achieved an MAE of 12.27 g and an RMSE of 15.11 g. The estimated carbohydrate value is combined with user-specific clinical parameters, including the insulin-to-carbohydrate ratio and insulin sensitivity factor, to generate advisory bolus guidance. To support safety, the system requires user confirmation or correction of the recognized food category and estimated portion before insulin guidance is displayed. Conclusions: The proposed system is intended for advisory decision support only and is not designed to replace clinical judgment or autonomous insulin delivery systems. Full article

Protein–protein interactions (PPIs) represent a vast and largely underexplored landscape of therapeutic targets, yet their structural features—including large, flat, and dynamic interfaces—have historically limited their druggability. In this context, cyclic peptides have emerged as a powerful class of PPI modulators, sitting at the interface between biologics and small molecules, and thus garnering key advantages of both classes. Their conformational constraint enhances binding affinity, proteolytic stability and, in some instances, cell permeability, thus enabling access to intracellular targets. This review provides an updated overview of cyclic peptides as modulators of PPIs, focusing on both conceptual foundations and practical strategies for their discovery and optimization. The main discovery approaches include natural sources, de novo design based on secondary structure mimetics, high-throughput screening, and computational approaches. Integration of these complementary strategies is crucial to enhance success rates in the discovery of effective and developable cyclic peptides. Accordingly, the present review aims to provide a practical guide for researchers entering this rapidly growing field, outlining current opportunities, methodological advances, and remaining challenges in the development of cyclic peptide-based PPI modulators. Full article

Generative artificial intelligence (GenAI) has transformed design education, yet growing evidence suggests that the fluency of AI-generated outputs may create a “fluency illusion”—a metacognitive bias whereby learners conflate polished AI artifacts with genuine cognitive mastery. A critical unresolved question is how to quantitatively diagnose this AI-induced fluency illusion without disrupting the natural learning process. This study introduces MBS-AIGC, a purpose-built AI-supported design education platform grounded in the Meaning–Behavior–Spirit (MBS) cultural cognition model for Chinese intangible cultural heritage. Drawing on the industrial soft-sensor paradigm, we computationally formalized six behavioral soft-sensor indicators from the digital interaction traces of 71 undergraduate design students over a four-week instructional period and applied K-means clustering to identify latent engagement patterns. Three distinct human–AI collaboration profiles emerged: Deep Explorers (n = 41), Progressive Builders (n = 16), and Surface Operators (n = 14). Crucially, expert-assessed cognitive flexibility significantly differentiated the three groups (F(2, 68) = 5.66, p = 0.005, η² = 0.143), whereas a conventional self-report questionnaire failed to distinguish among them (F(2, 36) = 0.29, p = 0.748), providing preliminary empirical evidence for the fluency illusion in design education. By addressing the lack of objective diagnostic tools for metacognitive miscalibration, this research contributes a scalable, zero-intrusion behavioral soft-sensor framework that enables educators to decode human–AI collaboration patterns and mitigate the fluency illusion in creative learning environments. Full article

With the rapid growth of electric vehicles (EVs) and renewable energy generation, Vehicle-to-Grid (V2G) technology has emerged as a promising approach for transforming EVs from passive charging loads into flexible distributed energy storage resources. By enabling bidirectional power exchange between EV batteries and the power grid, V2G can support renewable energy accommodation, peak shaving, demand response, ancillary services, and local grid balancing. This review provides a systematic synthesis of recent advances in V2G systems for renewable energy integration, with particular emphasis on coordinated scheduling, economic mechanisms, battery degradation, and user engagement. First, the technical foundations of V2G are introduced, including Vehicle-to-Everything operating modes, bidirectional charging architecture, aggregation mechanisms, grid-support services, and renewable accommodation pathways. Second, major scheduling strategies are reviewed, including price-based, load-based, renewable-forecast-driven, centralized, distributed, and hybrid approaches. Third, the economic feasibility of V2G is examined from the perspectives of revenue streams, pricing mechanisms, business models, battery aging costs, and compensation schemes. In addition, user participation barriers, such as range anxiety, battery lifetime concerns, loss of control, uncertain financial returns, and data privacy, are discussed. Key challenges related to communication standards, interoperability, cybersecurity, market access, policy design, and pilot-scale validation are also summarized. Finally, future development directions are identified, including AI-based scheduling, aggregator platforms, fleet-scale V2G, degradation-aware optimization, carbon-aware electricity markets, and user-centered participation mechanisms. This review highlights that large-scale V2G deployment requires the integrated coordination of technical scheduling, economic incentives, battery health protection, and user acceptance in renewable-rich power systems. Full article

Autonomous AI agents capable of holding digital assets, signing transactions, and executing smart contracts on public blockchain networks have moved from research prototypes to active deployment over the past two years. Despite this pace of adoption, no systematic treatment of their architecture, coordination protocols, and governance structures exists that spans the full design space. This survey addresses that gap through a systematic review of the literature from 2019 to 2026, covering 177 peer-reviewed publications and 14 system documentation sources, identified through a structured search of IEEE Xplore, the ACM Digital Library, Scopus, and arXiv. We classify deployed and proposed systems along four architectural dimensions: on-chain execution, off-chain agents with on-chain settlement, verifiable off-chain computation, and multi-agent on-chain interaction. Then, we examine the coordination mechanisms through which agents reach collective decisions, covering auction-based protocols, cooperative multi-agent reinforcement learning, token-incentive structures, and gossip-based peer-to-peer coordination. Governance is treated as a distinct dimension, analysed through a technical lens, covering on-chain parameter control, dispute resolution, and DAO structures, and an organizational one, covering accountability, incentive alignment, principal–agent dynamics, and regulatory compatibility). We survey applications across decentralized finance, supply chain, IoT, and agent marketplace domains, and identify six open research problems whose resolution is a prerequisite for broader deployment. The convergence of mechanism design and multi-agent reinforcement learning in asynchronous blockchain environments is identified as the direction of greatest near-term research value. Full article

This article examines the Integrevise^TM platform through a repeated cross-sectional, multi-cycle pilot case study of viva-based verification in AI-mediated assessment environments. Integrevise^TM pairs a submitted written artifact with a short adaptive viva in which students explain their work, reasoning, and application in their own words. Rather than functioning as an AI detector or automated grading system, the platform operates as a diagnostic assessment layer intended to surface comprehension, authorship confidence, and disengagement risk before final grades become the only available signal. The pilot was conducted across Fall 2025 and Spring 2026 at a private liberal arts college in the Midwest; these phases involved different student groups and are therefore treated as iterative implementation cycles rather than a longitudinal cohort. Results should be interpreted as preliminary pilot evidence. In Spring 2026, 52 vivas were completed, but formal student survey data were limited to seven respondents and showed mixed perceptions: only 14.3% agreed that the oral assessment helped them think more deeply about the assignment, whereas 57.1% disagreed or strongly disagreed. Platform feedback was also incomplete, with 20 of 52 vivas (38.5%) producing no student feedback record. Qualitative feedback, tutor observations, and implementation notes nevertheless suggest that viva-based verification may help identify some comprehension gaps and implementation barriers that written artifacts alone may not reveal. The findings, therefore, support continued investigation of Integrevise^TM as a process-rich assessment intervention, but not broad claims of efficacy or scalability without larger, more systematic validation. Full article

Synthetic microbial community (SynCom) biosensors are emerging from the convergence of whole-cell biosensing, synthetic ecology, and computational design. Conventional whole-cell biosensors (WCBs) use a single microbial chassis to convert analyte recognition into optical, electrochemical, gaseous, or growth-linked outputs. This compact architecture supports low-cost and field-oriented detection, but it can be limited by cellular burden, narrow dynamic range, environmental interference, and difficulty in interpreting multicomponent signals. Natural microbial consortia provide an ecological template in which sensing, transformation, stress tolerance, and response are distributed across interacting populations. SynCom biosensors seek to translate this logic into engineered platforms with defined members, assigned functional roles, designed communication, and interpretable readouts. This review traces the transition from WCBs to natural consortia and engineered multicellular biosensors, emphasizing functional partitioning, signal routing, community control, and artificial intelligence (AI)-assisted design. AI is discussed as a practical tool for narrowing design space, predicting interactions, decoding complex biosignals, and supporting adaptive operation. Key challenges remain in community stability, orthogonal communication, data quality, biosafety, standardization, and real-sample validation. Future progress will depend on parsimonious community design, reliable containment, quantitative validation, and computational workflows that connect community composition with sensing performance. Full article

Universities are increasingly expected to contribute not only to teaching and research, but also to public-sector innovation, regional development, and digitally enabled governance. This article examines how higher education institutions organize that contribution by comparing two university-based transfer architectures: Smart-EDU Hub @ SNSPA in Bucharest and the distributed transfer portfolio of RheinMain University of Applied Sciences and Arts (HSRM). Using a qualitative comparative case-study design based on the document analysis of internal strategy and regulatory documents, institutional webpages, and European policy frameworks, the study analyzes the mission framing, organizational form, program architecture, trust infra-structure, and scaling logic. The documentary analysis indicates that Smart-EDU Hub is formally presented and institutionally organized as a centralized, branded, mission-led platform that bundles conferences, courses, projects, visiting scholars, and publication channels under a recognizable public-facing identity. HSRM, by contrast, is documented as a distributed transfer portfolio linking transfer strategy, dialogue formats, digitally supported teaching, administrative digitalization, continuing education, and AI support services. The comparison should therefore be read as an analysis of formal and publicly documented transfer architectures, not as an evaluation of actual institutional performance, stakeholder experience, or societal impact. The article contributes to Administrative Sciences by conceptualizing university transfer for smart governance as a public-management and governance-design problem. It develops an analytical hybrid transfer-architecture framework in which a visible hub is combined with distributed specialist nodes, shared quality assurance, and explicit safeguards for ethics, cybersecurity, and trustworthy AI. Full article

This study synthesises evidence on whether, why, and under what conditions digital technologies improve resource efficiency across multi-tier global value chains (GVCs) and examines the theoretical adequacy of dominant explanatory lenses. Following the PRISMA 2020 protocol, we searched Web of Science, Scopus, IEEE Xplore, and ProQuest, retaining 150 articles for qualitative synthesis and 137 for bibliometric science-mapping; themes were developed via multi-cycle coding and triangulated with co-citation and keyword co-occurrence networks. Reported efficiency gains are strongest when firms deploy integrated digital stacks combining IoT sensing, AI analytics, blockchain traceability, and digital twins that jointly enable visibility, verification, and simulation-based optimisation, a pattern based predominantly on observational and cross-sectional evidence. Outcomes are contingent on cross-firm capability complementarities, data-governance arrangements, regulatory congruence, and cyber-risk maturity. A key structural finding is the digital-regionalisation paradox: stringent data-compliance demands can re-anchor sourcing within regulatory blocs, concentrating rather than extending GVC geography. Building on these findings, we propose three theoretical extensions, namely ecosystemic capability bundling, digital-sustainability spillovers, and distributed eco-innovation, that advance Transaction Cost Economics, the Resource-Based View, Dynamic Capabilities, and GVC governance theories to better account for the sustainability and platform dimensions of contemporary digitalised value chains. Full article