Search Results (839)

This study examines the relationships between technology readiness, Augmented Reality Consumer Experience Scale (ARCES), and purchase intention in digital retail environments. Unlike prior augmented reality studies that primarily focus on technology adoption or isolated experiential effects, this study integrates technology readiness, multidimensional AR-based consumer experience, and purchase intention within a single correlational framework. Data were collected from 385 university students using a correlational research design. The factor structure of the adapted measurement scale was assessed through exploratory and confirmatory factor analyses, and the relationships among the variables were examined using correlation analysis. The findings indicate significant positive relationships: technology readiness is positively associated with AR-based consumer experience, and AR-based consumer experience is positively associated with purchase intention. From a sustainability-oriented perspective, these findings suggest that AR-enabled retail experiences may support more informed and reflective pre-purchase evaluation processes in digital environments. Full article

Gaze estimation, a critical facet of understanding user intent and enhancing human–computer interaction, has seen substantial advancements with the integration of deep learning technologies. Despite the progress, the application of deep learning in gaze estimation presents unique challenges, notably in the adaptation and optimization of these models for precise gaze tracking. This paper conducts a thorough review of recent developments in deep learning-based gaze estimation, with a particular focus on the evolution from traditional methods to sophisticated appearance-based techniques. We examine the key components of successful gaze estimation systems, including input feature processing, neural network architectures, and the importance of data preprocessing in achieving high accuracy. Our analysis extends to a comprehensive comparison of existing methods, shedding light on their effectiveness and limitations within various implementation contexts. Through this systematic review, we aim to consolidate existing knowledge in the field, identify gaps in current research, and suggest directions for future investigation. By providing a clear overview of the state-of-the-art in gaze estimation and discussing ongoing challenges and potential solutions, our work seeks to inspire further innovation and progress in developing more accurate and efficient gaze estimation systems. Full article

This study challenges familiar readings of John Calvin’s theology of worship by reframing it through the lens of contemporary liturgical theology. Rather than offering a purely historical account, it probes Calvin’s intentions, achievements, and limitations, with particular attention to the formative interplay between lex orandi and lex credendi. Drawing on Calvin’s writings, liturgical texts, and patristic sources, the analysis highlights his Christological and pneumatological grounding, his integration of Word and Sacrament, his pastoral flexibility in applying the regulative principle, and his creative retrieval of ancient liturgical practices to encourage active congregational participation. At the same time, the article identifies tensions within Calvin’s approach, including the risk that doctrinal oversight may constrain liturgical vitality and contribute to an overly intellectualized understanding of worship. By juxtaposing Calvin’s historical context with contemporary ecclesial realities, the study offers both a critical reassessment and a constructive proposal: to reclaim God-centered, Scripture-shaped worship while cultivating the adaptive balance that Calvin himself sought to model. In this way, the article rearticulates the significance of Calvin’s legacy for the theological integrity and missional vitality of worship in the twenty-first century. Full article

Minimal residual disease (MRD) refers to the persistence of low-level malignant cells or tumor-derived nucleic acids that remain after curative-intent therapy and are undetectable by conventional diagnostic methods. In oncology, MRD has emerged as a powerful biomarker with well-established prognostic value in hematologic malignancies and rapidly expanding relevance in solid tumors. Advances in sensitive detection technologies, including multiparameter flow cytometry, quantitative real-time polymerase chain reaction, next-generation sequencing, and digital polymerase chain reaction, have enabled the identification of residual disease at the molecular level, often preceding clinical or radiological relapse. Beyond its conventional role as a binary indicator of treatment response or cure, MRD is increasingly recognized as a dynamic longitudinal biomarker that supports personalized disease management. Within this evolving paradigm, patient-informed MRD strategies that incorporate tumor-specific molecular profiling and serial monitoring, particularly through circulating tumor DNA, offer the potential to guide treatment adaptation, including escalation, de-escalation, maintenance optimization, and surveillance strategies across both hematologic and solid malignancies. In this review, we summarize the biological basis of MRD, current and emerging detection methodologies, and clinical applications across cancer types, with a focus on patient-informed approaches. We also discuss key limitations, including assay standardization, biological variability in solid tumors, and the lack of clearly defined actionability thresholds. Finally, we highlight future directions for integrating MRD with multi-omics and AI-driven analytical frameworks to enable adaptive, risk-informed cancer management and advanced precision oncology. Full article

The Hyakuzahōdan Kikigakishō (百座法談聞書抄, hereafter Hyakuza 百座), compiled in the late Heian period, is an important Buddhist document that records a hundred-day lecture series on the Lotus Sutra (法華経). While previous scholarship has recognized the constructed nature of the text as a kikigaki (聞書), it has predominantly focused on content analysis, implicitly treating the text as a transparent window into the actual preaching event. To move beyond this limitation, this study proposes the analytical framework of dual performativity and, drawing on Diana Taylor’s theory of the archive and the repertoire, reexamines the text’s generative logic and political implications. This study argues that the Hyakuza embodies two interrelated forms of performance: first, the performativity of the hōdan (法談) as a live ritual, understood as a repertoire performance that constructs immediate authority through body, voice, and situational dynamics; second, the performativity of the kikigaki as textual construction, understood as an archival performance that transforms the ephemeral oral event into an authoritative, transmissible text through formulaic rhetoric, localized adaptation, and systematic arrangement. Integrating methodologies from textual history, rhetorical analysis, ritual theory, and intellectual history, this study demonstrates that the Hyakuza is not a neutral transcript of sermons but a meticulous, intentional act of writing with two fundamental aims: on a cultural level, to hierarchically integrate shinbutsu shūgō (神仏習合) through narrative appropriation; on a social level, to symbolically bind Buddhist merit with the institutional identities of aristocrats such as naishinnō (内親王), ultimately serving the self-affirmation internal cohesion, and cultural demarcation of the elite community from the masses, while simultaneously contributing to the state’s project of constructing a unified ideology in the late Heian period. By examining both cross-civilizational universal logic and specific historical context, this study reveals how the Hyakuza’s dual performativity produces and categorizes knowledge narratives while embedding political power dynamics, offering a critical path for the study of kikigaki-genre literature from discourse analysis to politics of textuality. Full article

The acceptance and effective use of digital technologies constitute a critical prerequisite for the adaptability and sustainability of organizations in tourism and hospitality, particularly in environments characterized by technological acceleration and continuous transformation. Drawing on the Technology Acceptance Model (TAM) and established extensions, this study examines determinants of behavioral intention to use digital technologies, focusing on perceived usefulness (performance expectancy), perceived ease of use (effort expectancy), trust/security, and facilitating conditions. The empirical analysis is based on survey data collected from tourism professionals in the metropolitan area of Thessaloniki (N = 634) and employs covariance-based Structural Equation Modeling (CB-SEM) using IBM SPSS AMOS v.21. Results indicate that all examined predictors are positively associated with behavioral intention, with facilitating conditions emerging as the strongest predictor. The findings are interpreted through an organizational agility lens—treated as a contextual perspective rather than a measured construct—to explain why organizational enablement is pivotal in digital transformation settings. Full article

The implementation of the new national electric bike (e-bike) standard has imposed stringent compliance requirements on equipment and e-bikes in the instant delivery sector, which directly affects the delivery efficiency and the work adaptability of food delivery riders. This study aims to investigate food delivery riders’ continued usage intention of e-bikes under China’s new e-bike regulation. Based on valid data collected from food delivery riders in Nanjing, this study employs ordered logit regression to examine the primary factors influencing their continued usage intention of e-bikes. The findings reveal that: (1) Male riders’ willingness to continue using e-bikes is comparatively lower, whereas older riders show a stronger intention. (2) Food delivery riders with higher incomes and those who need to replace their e-bikes show a stronger inclination to continue using them. (3) Limited e-bike options have a significant negative effect on riders’ continued usage intention, while speed limits exert no significant influence. Based on these empirical findings, corresponding policy recommendations are proposed to promote riders’ continued use of e-bikes, such as developing age-friendly delivery models, establishing an income guarantee mechanism for riders, and optimizing platform delivery time allocation. The findings could provide a theoretical basis and practical insights for policymakers and food delivery platforms to improve e-bike management policies. Full article

Multistep-ahead prediction of critical states in the iron ore sintering process is essential for maintaining production stability, enhancing energy efficiency, and reducing industrial emissions. However, large time delays, strong coupling, and condition drifts challenge existing spatiotemporal graph neural networks (STGNNs). This paper proposes Process-aware Mixed Dynamic Graph Network (ProMix-DGNet), which integrates a Decoupled Two-Stream Topology Learning mechanism—fusing Adaptive Static Graph with a Radial Basis Function (RBF)-driven Dynamic Graph Constructor—to ensure robust spatial modeling under high-noise conditions. Furthermore, Process-View Global Mixer explicitly captures long-range process coupling across the entire sintering strand, overcoming the receptive field limitations of traditional graph convolutions. In the decoding phase, a future control-informed module utilizes a bidirectional Long Short-Term Memory (BiLSTM) and a global mixer to align known future control setpoints with the system’s spatial topology. These features are integrated via a gated residual mechanism that dynamically modulates the interaction between control intents and historical representations. Extensive experiments conducted on two real-world industrial datasets, Sinter-A and Sinter-B, demonstrate that ProMix-DGNet consistently outperforms mainstream baselines across multiple metrics, including Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results verify the model’s higher accuracy and robustness in complex large-time-delay systems, offering a reliable framework for the intelligent monitoring and closed-loop optimization of sintering process. Full article

Ensuring the rights of involuntary resettlers is fundamental to a law-based state and essential for achieving social equity and sustainable development. However, institutional improvement depends not only on the intent of top-level design but also on the capacity for dynamic adaptation amid evolving social contexts. Moving beyond the predominant research focus on policy design principles, this study investigates the dynamic evolution of China’s reservoir resettlement rights protection policies from 1949 to 2025. We first constructed a corpus of 32 core policy documents. Employing a bibliometric analysis within a multi-dimensional framework, we statically examined the developmental patterns of these policies. Subsequently, we applied the Punctuated Equilibrium Theory (PET) to dynamically analyze their policy changes, identifying a trajectory marked by both long-term stability and significant punctuations. Our findings reveal that over 76 years, the policy process has undergone two major equilibrium periods and two critical punctuation nodes, demonstrating a clear pattern of “protracted stability interspersed with short bursts of rapid transformation.” The policy image has correspondingly evolved through four distinct stages: “Administratively Mobilized Resettlement,” “Development-Oriented Resettlement,” “Harmonious Society for Resettlers,” and “Common Prosperity.” The study argues that this evolution is driven by the interplay of shifting central government attention, the occurrence of focusing events, and the reinforcement of evolving Policy Images, which collectively broadened the policy venue and led to non-linear institutional change. Based on these findings, the paper recommends: first, adopting a dynamic approach to policy formulation; second, maintaining sustained political commitment and robust institutional safeguards; and third, fostering multi-stakeholder consultation and collaborative governance mechanisms. These strategies are essential to more effectively secure the multifaceted rights of reservoir resettlers. Full article

This paper presents an interactive inference behavior tree (IIBT) framework, integrating behavior trees (BTs) with interactive inference based on the free energy principle for distributed decision-making in multi-UAV (unmanned aerial vehicle) systems. The proposed IIBT framework enhances conventional BTs by incorporating probabilistic inference, enabling online joint planning and execution among multiple UAVs. The framework maintains full compatibility with standard BT architectures, allowing seamless integration into existing UAV control systems. In this framework, cooperative behavior is modeled as a free-energy minimization process, where each UAV dynamically updates its preference matrix based on perceptual inputs and peer intentions, achieving adaptive coordination in dynamic and partially observable environments. The validation tasks, including cooperative navigation in uncertain environments and task coordination, directly mirror the decision-making and coordination challenges faced in UAV missions. Experimental results demonstrate that the IIBT framework achieves a reduction of over 70% in BT node complexity while maintaining robust, interpretable, and adaptive cooperative behavior in uncertain environments. Full article

This selective integrative narrative review article examines creativity awareness as a critical cognitive state and highlights its intentional development in teaching and learning as part of essential 21st-century skill building. The article is organized around three interconnected themes: (1) Creativity as a 21st-century skill; (2) Exploring the journey to promoting creativity awareness in education: A multi-dimensional focus; and (3) Enhancing creativity awareness through research and science communication. Grounded in established and emerging research in the field, this review provides research-informed insights and practical recommendations for educators, researchers, and school counselors to recognize, appreciate, and promote creativity awareness in ways that contribute to more adaptive, innovative, and equitable educational environments. Full article

Accurate pedestrian trajectory prediction and intention estimation are crucial for autonomous systems and intelligent transportation applications. However, existing methods often address these two highly correlated tasks in isolation and rely on static or heuristic interaction modeling, leading to insufficient adaptability and limited generalization capability in dynamic traffic scenarios. To this end, this paper proposes MTG-TPNet, a Multi-task dynamic Graph Transformer network for joint Trajectory Prediction and intention estimation. The research framework integrates three key innovations: First, a dynamic graph neural network enhanced with motion features, whose graph topology can be adaptively learned end-to-end based on semantic and motion contexts to accurately capture evolving interactions. Second, a multi-granularity attention mechanism that collaboratively fuses geometric proximity, semantic similarity, and physical hard constraints to achieve fine-grained modeling of spatiotemporal dependencies. Third, a dynamic correlation loss based on Bayesian uncertainty, which balances multi-task learning in an adaptive manner and encourages beneficial interactions across tasks. Extensive experiments on the publicly available PIE and ETH/UCY datasets demonstrate that MTG-TPNet achieves state-of-the-art performance. On the PIE dataset, the proposed model significantly outperforms the best baseline model in trajectory prediction metrics, achieving an Average Displacement Error (ADE) of 0.21 and a Final Displacement Error (FDE) of 0.29. This represents a 27.6% reduction in ADE while maintaining stability in intention estimation. Systematic ablation studies validate the effectiveness of each proposed module, with the model retaining an average performance of 69.3%. Furthermore, cross-dataset evaluations confirm its superior generalization capability. This study provides a powerful unified framework for robust pedestrian behavior understanding in complex urban traffic scenarios. Full article

Open-ended, team-based problem solving demands (i) a bridge between stochastic language models and symbolic control, (ii) mechanisms for idea elaboration, (iii) feature-level concept combination, and (iv) internal representations that support understanding beyond mere association. We present a cognitive architecture (CA) that couples an LLM with an editable knowledge-graph (KG) scaffold and a controller that adaptively schedules five reasoning strategies. Elaborations are cast as graph updates validated against coverage and consistency checks; combinations produce property- and relation-level recompositions. On 30 collaborative programming dialogs (nine representative scenarios), adaptive prompting improves solution completeness by 19.1% and reduces required turns by 18.5% over a CoT baseline; explicit concept combinations increase Distinct-3 by 12.4 points with a +0.7 gain in human-rated creativity. Ablations show that Soft→Pruning scaffolds best support early elaboration, while Hard partitioning helps under ambiguity. The CA demonstrates a practical route to aligning LLMs with team intent in open-ended tasks. Full article

Deep learning approaches for egocentric video understanding often lack a principled theoretical treatment of stability, particularly when dealing with the sparse, noisy, and temporally ambiguous observations characteristic of first-person imaging. In this work, we frame egocentric video question answering not merely as a classification task, but as an ill-posed inverse problem aimed at reconstructing latent semantic intent from stochastically perturbed visual signals. To address the instability inherent in standard dual-encoder architectures, we present a framework with a mathematical interpretation that incorporates gated cross-modal interaction within the transformer backbone. Formally, the video-side update analyzed in this work is defined as a learnable convex combination of unimodal feature representations and cross-modal attention residuals; the full implementation applies analogous gated cross-modal updates bidirectionally. From a regularization perspective, the gating mechanism can be interpreted as an adaptive parameter that balances data fidelity against language-conditioned structural constraints during feature reconstruction. We provide the Bounded Update Property (Lemma 1) and an analytical layer-wise sensitivity bound and empirically demonstrate that the proposed framework achieves measurable improvements in both accuracy and stability on the EgoTaskQA and MSR-VTT benchmarks. On EgoTaskQA, our model improves accuracy from 27.0% to 31.7% (+4.7 pp) and reduces the accuracy drop under 50% frame drop from 3.93 pp to 0.94 pp. On MSR-VTT, our model improves accuracy by 13.0 pp over the dual-encoder baseline. Under severe perturbation (50% frame drop) on MSR-VTT, our model retains 97.7% of its clean performance, whereas the baseline exhibits near-zero drop accompanied by majority-class behavior. These results provide empirical evidence that the proposed interaction induces stable behavior under perturbations in an ill-posed multimodal inference setting, mitigating sensitivity to sampling variability while preserving query-relevant temporal structure. Furthermore, an entropy-based analysis indicates that the gating mechanism prevents excessive diffusion of attention, promoting coherent temporal reasoning. Overall, this work offers a mathematically informed perspective on designing interaction mechanisms for stable multimodal systems, with a focus on robust reasoning under temporal ambiguity. Full article

This research addresses a structural cybernetic anomaly within strategic management precipitated by the integration of artificial intelligence into the organizational core. Traditional paradigms, specifically the resource-based view and the dynamic capabilities framework, operate under closed-system, first-order cybernetic assumptions that fail to capture the dissipative nature of algorithmic agents. By conceptualizing the enterprise as a complex adaptive system operating far from thermodynamic equilibrium, this study introduces the theory of dynamic cognitive advantage. Grounded in second-order cybernetics, the framework posits that competitive differentiation emerges from the historical, recursive, structural coupling of human semantic intent and machine syntactic processing. This research formalizes this co-evolutionary dynamic utilizing coupled non-linear differential equations and time decay integrals. Furthermore, it operationalizes the central mechanism of this capability—the cognitive flywheel—and proposes a fractal governance architecture to mitigate systemic vulnerabilities such as automation bias. To transition these propositions into management science, a proposed mixed-methods empirical research agenda is presented. It outlines a future partial least squares–structural equation modeling (PLS-SEM) approach to test the mediating role of the cognitive flywheel and the moderating effect of fractal governance on organizational resilience. This research provides a mathematically formalized, empirically testable architecture for navigating the artificial intelligence economy. Full article