Search Results (5,727)

The implementation of an artificial intelligence (AI)-based system for monitoring, forecasting, and learner performance support has been intensified by the rapid expansion of online education systems. Existing online educational platforms completely rely on learning analytics and machine learning to customize content delivery. On the other hand, these platforms fundamentally focus on behavioral and cognitive indicators, whereas the integration of affective computing into learning analytics and adaptive decision-making processes is lacking. During the learning process, emotions like engagement, boredom, and confusion play a vital role. Nonetheless, the integration of adaptive online learning systems is still fragmented and underdeveloped. The latest progress in affective computing and multimodal sensing technologies allow for the inference of the affective state of learners in real-time, which creates a range of potential opportunities to create emotionally sensitive learning spaces. Despite technological innovations, the existing studies do not have a conceptual framework that is unified, design-oriented, and clearly incorporates affective computing with AI-based learning analytics to inform real-time pedagogical adaptation. To address this gap, this study introduces a design-oriented conceptual framework for AI-based online education systems that incorporate real-time affective computing. This conceptual framework combines the theoretical foundation of learning analytics, affective computing, and adaptive learning systems. The suggested framework offers a clear and scalable basis of online learning environments that are affective-aware by offering a clear framework of development, assessment, and consequent empirical validation. Full article

Introduction: Traditional instruments for measuring lifelong learning of health professionals fail to capture digital-age learning, creating a critical measurement disconnect. To address this gap, we developed a 16-item Self-Pursuits, Aspirations, and Knowledge Endeavors in the Digital Era (SPArKED) instrument. Methods: To gather validity evidence for SPArKED, a cross-sectional survey was deployed to health professional students (n = 558). The survey questionnaire included: SPArKED, Jefferson scale of lifelong learning for students in health professions, basic psychological needs satisfaction scale, and human–computer trust scale assessing students’ trust in generative technology to support lifelong learning. Exploratory factor analysis (EFA) and correlation analysis were performed. Results: The EFA of the SPArKED revealed a three-component structure: networked learning, i-learning (individual mastery), and AI-powered learning, together explaining 55% of the total variance. The SPArKED demonstrated good internal consistency (α = 0.86) and convergent validity with the Jefferson scale of lifelong learning (r = 0.75). The correlations between SPArKED and psychological needs satisfaction scores were moderately high: autonomy (r = 0.50), competence (r = 0.48), and relatedness (r = 0.51). SPArKED had a higher correlation with students’ trust in generative technology to support lifelong learning than the Jefferson scale (r = 0.52 and r = 0.32, respectively). Conclusions: Compared to the Jefferson scale, the SPArKED instrument appears to better capture digital-age learning behaviors among students in health professions. By assessing these evolving behaviors in learners, education programs can better guide future health practitioners in developing desired lifelong learning competencies and digital literacies. Future research should gather validity evidence for SPArKED across diverse learner samples and educational stages, informing a critical re-assessment of established instruments in the rapidly evolving learning landscape. Full article

Government data governance has become an important institutional mechanism for reducing information frictions, improving data-resource allocation, and supporting firm innovation in the digital economy. However, whether government data governance can promote firms’ technological catch-up remains insufficiently understood. Based on the quasi-natural experiment of the establishment of Big Data Administrations, this study constructs a multi-period difference-in-differences model to examine the impact of government data governance on firms’ technological catch-up. Using panel data from Chinese A-share listed firms from 2011 to 2021, the DID estimates indicate that the establishment of Big Data Administrations significantly improves firms’ technological catch-up. This estimated effect remains robust across placebo tests, specifications controlling for differential trends associated with pre-treatment city characteristics, and double/debiased machine learning estimation. Mechanism analyses provide evidence consistent with three channels: technology stimulation, digital-ecosystem optimization, and competition strengthening. Heterogeneity analyses further show that the effect is evident among non-state-owned enterprises, firms with higher information asymmetry, and larger firms. Additional spatial analyses suggest that neighboring cities’ data governance capacity does not generate stable positive spillovers; instead, it may be associated with negative spatial externalities, potentially reflecting siphoning or competitive crowding-out pressures. These findings highlight government data governance as an institutional driver of firm technological progress and provide policy implications for improving digital governance capacity, optimizing digital ecosystems, and promoting high-quality development. Full article

Cybersecurity risk management is often complicated by fragmented solutions for threat identification and detection, vulnerability assessment, and control selection across multiple frameworks. This paper presents a unified, dynamically updated, threat-based cybersecurity control platform that addresses this challenge by integrating Information Technology (IT), Operational Technology (OT), and Internet of Things (IoT) standards, including ISO/IEC 27001:2022, National Institute of Standards and Technology Cybersecurity Framework (NIST CSF) 2.0, and IEC 62443-3-3. The platform enables (1) querying a selected threat to identify associated vulnerabilities, (2) recommending applicable security controls across multiple frameworks, and (3) identifying overlapping or unique controls to avoid redundant implementation. Automated integration of Common Vulnerabilities and Exposures (CVEs) from the NIST National Vulnerability Database (NVD) links vulnerabilities to mapped threats and controls, supporting proactive risk management. A structured evaluation was conducted across 100 threat scenarios spanning IT, OT, and IoT domains, producing approximately 1000 threat–control relationships across 3 integrated frameworks. Performance evaluation demonstrates that the platform is scalable. While integrating additional frameworks, it maintains an average query latency of 0.40 s to 0.43 s, which implies an insignificant incremental latency increase of 0.03 s, while its web-based interface provides dynamic querying and visualization in a user-friendly manner for technical and non-technical users. By unifying threat, vulnerability, and control data, the platform streamlines compliance, reduces control retrieval time, and ensures traceable, consistent, and cross-framework mitigation strategies, enhancing informed cybersecurity decision making. Full article

This study examines the determinants of electric vehicles adoption focusing on environmental awareness, user preferences, and user experience. Specifically, it investigates whether EV ownership reflects environmental concerns or pragmatic factors such as economic and technological advantages. The analysis is based on survey data obtained from 209 EV users in Türkiye. An Environmental Awareness Index consisting of eleven Likert-scale statements was constructed, and its reliability and validity were verified using Cronbach’s alpha coefficient and factor analysis, demonstrating a unidimensional structure. The empirical strategy relies on a combination of descriptive statistics, non-parametric tests, and multivariate OLS regression models. Robustness analyses were performed using ordered logit and z-standardized OLS estimations. The findings indicate that EV users generally possess a relatively high level of environmental awareness. Descriptive results suggest that economic considerations constitute the most frequently reported motivation for EV adoption, whereas the multivariate analyses provide only limited evidence regarding the independent role of adoption motives in explaining environmental awareness. Econometric results reveal that the frequency of access to environmental information and driving experience are positively correlated with environmental awareness, while household vehicle ownership has a negative effect. These relationships are statistically significant in the baseline models but weaken in the full-specification models, indicating model specification sensitivity. Robustness checks provide partial support for the directional stability of the main associations, although statistical significance varies across alternative specifications. Overall, EV ownership does not necessarily reflect a strong environmentally conscious identity. Instead, adoption is shaped by a combination of cost advantages, technological appeal, and environmental sensitivity. The study highlights the importance of exposure to environmental information on awareness and offers policy implications for both increasing EV adoption and broader environmental awareness. Full article

Forging is a key manufacturing route for high-performance structural components, but its process design, quality prediction, and adaptive control still rely heavily on empirical rules, offline simulations, and fragmented production data. This review examines intelligent forging from the perspective of mechanism–data–knowledge fusion, with emphasis on forging-specific process chains, real alloy systems, model validation, and industrial maturity. To improve methodological traceability, a structured literature search was conducted using Web of Science Core Collection, Scopus, ScienceDirect, SpringerLink, and Google Scholar, covering studies published from 1996 to 2026. The screened literature was organized around process perception, mechanism-based modeling, data-driven learning, hybrid modeling, knowledge representation, digital twins, online prediction, and adaptive regulation. Representative cases are discussed for closed-die forging, open-die/large forging, multistage forging, radial forging, and forging of aluminum alloys, titanium alloys, steels, and Ni-based superalloys. Particular attention is given to how specific models are validated, including independent experiments, finite-element benchmarks, industrial datasets, new geometries, sensor noise, and cross-material or cross-equipment transfer. The review further distinguishes consolidated technologies, such as FEM-based process simulation and die/preform optimization, from methods still under validation, including hybrid digital twins, sensor-updated models, and adaptive control. Large-model-assisted forging is considered a prospective direction mainly for information retrieval, case recovery, diagnostic support, and engineer-supervised recommendation rather than unsupervised real-time control. This review provides a more process-specific and critically assessed reference for developing explainable, validated, and deployable intelligent forging systems. Full article

Inner Triangle Outer Diamond (ITOD) Framework, presented and published in 2025, informed and guided the introduction of Artificial Intelligence (AI) ethics education into a Medical Humanities course for medical students. Central to the ITOD Framework is the premise that education takes place in an ecosystem, with outcomes shaped by the dynamic interplay and interactions among multiple components. In planning and designing the instruction, the ITOD’s inner triangle helped connect pedagogy (team-based learning), curriculum (AI in Medical Humanities), and assessment (reflective practice). The outer diamond’s elements were also considered to ensure proper alignment of the educational intervention with programme requirements, with special emphasis on resources, policies, instructional design, curricular structure, standard practices, including the educational ecosystem dynamics, and alignment of outcomes with overall programme objectives. Very importantly, the ITOD also aided in calibrating an effective response to external conceptual factors such as technological and cultural advances, and professional advancements. A pre- and post-intervention survey design was adopted to evaluate the impact of the educational intervention (n = 113 pre-session; n = 103 post-session). Results showed significant positive shifts in self-reported AI understanding, increased support for formal AI integration into the curriculum, and enhanced confidence in critically engaging with AI outputs (94.1% reporting ‘yes’ or ‘somewhat’ post-session). Following the educational intervention and the feedback received from the learners, it is accurate to state that the ITOD was a reliable framework for introducing technology-related topics, specifically Artificial Intelligence ethics education, into Medical Humanities within an undergraduate medical programme. The approach is hereby presented in this work and recommended to educators, academic leaders, and other stakeholders. Full article

The increasing reliance on digital infrastructures has positioned electronic records management systems (ERMS) as critical mechanisms for supporting organisational governance, accountability, transparency, and effective service delivery. This study presents a structured qualitative literature review examining ERMS implementation across developed and developing institutional contexts to identify key determinants, recurring implementation challenges, and contextual variations in adoption patterns. Drawing on studies published between 2012 and 2026, the review adopts a socio-technical analytical framework that categorises implementation determinants into environmental, technological, and organisational dimensions, specifically: governance and policy alignment; technological infrastructure readiness; interoperability and system integration; and human re-source capacity and organisational culture. The findings indicate that successful ERMS implementation depends on the alignment and interaction of governance frameworks, technological capabilities, and organisational readiness. The analysis further demonstrates that these determinants are highly interdependent and vary according to levels of institutional and digital maturity. In developing contexts, implementation is primarily constrained by inadequate infrastructure, financial limitations, weak policy enforcement, and shortages of skilled personnel. In contrast, digitally mature environments increasingly focus on interoperability, metadata standardisation, usability optimisation, and long-term digital preservation. This study contributes to the literature by synthesising fragmented empirical findings into an integrated socio-technical framework, thereby advancing a more structured understanding of ERMS implementation across diverse governance environments. The review also identifies major methodological limitations within the existing literature, including limited empirical validation, weak longitudinal analysis, language bias, and the predominance of single-institution case study designs. The findings provide practical implications for policymakers, information managers, and institutions seeking to strengthen electronic records management and information governance practices. Future research should prioritise longitudinal, comparative, and cross-national studies to further advance theoretical and empirical understanding of ERMS implementation. Full article

Digital technologies are increasingly transforming teaching and learning, particularly through technology-enhanced assessment and feedback systems. This study examines the feasibility and pedagogical utility of the Kit-Build Concept Map (KBCM) system as a technology-supported approach for systematizing serial concept mapping in a human–computer interaction course. A three-week study was conducted with 258 undergraduate students, integrating a re-composition framework with real-time feedback to support continuous refinement of students’ externalized conceptual representations. Pre-tests, post-tests, and concept map analytics were used to evaluate learning gains and concept map structures across instructional sessions. The results show that the KBCM system enabled lecturers to identify individual and class-level map gaps and provide timely, data-informed feedback to support instructional monitoring and pedagogical decision-making. Students showed statistically significant improvements in learning outcomes, consistent progress across instructional weeks, along with a measurable reduction in discrepancies between student-generated maps and the expert map. These findings suggest that serial concept mapping with re-composition and feedback support may help students refine their externalized conceptual representations to become more closely aligned with target knowledge over time. Overall, this study highlights the potential of technology-enhanced concept mapping systems to support continuous instructional feedback, assessment, and data-informed pedagogical practices in higher education. The findings should be interpreted within the context of a short-term, three-week implementation focusing on changes in externalized conceptual representations rather than direct measurement of internal cognitive processes. Full article

Epithelial ovarian cancers (EOCs) histotypes are characterized by marked molecular heterogeneity and limited effectiveness of current screening and monitoring strategies. Earlier identification of tumor-associated alterations may support timely intervention, especially in genetically predisposed or early-onset patient populations. While liquid biopsy approaches have primarily focused on circulating DNA, RNA, and proteins, increasing evidence indicates that cancer-associated metabolic reprogramming generates measurable informative signals in peripheral biofluids. This review summarizes recent progress in liquid biopsy-derived metabolomics in EOCs, covering analytical platforms applied to serum, plasma, urine, and ascites. Recurrent metabolic signatures linked to tumor burden, disease stage, treatment response, and clinical outcome are described, and their significance in discriminating malignant and non-malignant conditions is critically discussed. Collectively, these findings suggest that metabolomics may provide complementary functional information alongside genomic and histopathological profiling. Although its clinical implementation still requires further validation and methodological standardization, ongoing advances in analytical technologies and the integration of high-dimensional metabolic data into machine learning-based frameworks may progressively support the identification of early tumor-associated alterations and contribute to more accurate disease stratification and biologically informed clinical management. Full article

The rapid advancement of internet technology has transformed the tourism industry from traditional offline services to digital networked, and intelligent platforms. WebGIS has become critical infrastructure for tourism information retrieval and spatial decision-making. However, the growing volume and heterogeneity of multi-source tourism data expose fundamental limitations in conventional relational database architectures, particularly in handling complex spatial semantic queries. To address this, the present study proposes a WebGIS design methodology that integrates knowledge graphs with relational databases through a dual-database collaborative architecture. Using tourist attraction data from China’s Xinjiang Uyghur Autonomous Region as a case study, a prototype Xinjiang Smart Tourism WebGIS system was constructed, which consists of an asynchronous synchronization mechanism based on Change Data Capture (CDC) to ensure data consistency across heterogeneous databases. Subsequently, tourism semantic queries of varying depths were constructed and comprehensively tested across different data scales. The experimental results indicate that the proposed methodology effectively decouples business transactions and supports complex relationship computations, achieving shorter cross-domain semantic query times and higher latency stability. These findings offer practical guidance for designing high-performance regional tourism information services. Full article

The growing global demand for energy and rising greenhouse gas emissions require effective mitigation strategies, including carbon capture and storage (CCS) technologies. This study reviews 16 widely used simulation tools, including Aspen Plus, MATLAB, Fluent, and gPROMS, for steady-state and dynamic modeling of post-combustion, pre-combustion, and oxy-fuel combustion carbon capture processes. The tools are evaluated using five criteria: chemical process simulation capability, dynamic modeling functionality, thermodynamic property management, heat transfer accuracy, and tool integration features. The results reveal distinct strengths across platforms. Aspen Plus and Aspen Plus Dynamics perform strongly in chemical process simulation and thermodynamic property modeling, reflecting their robustness in reaction modeling and property estimation. gPROMS excels in dynamic modeling, demonstrating strong capability for time-dependent and transient process analysis. MATLAB achieves the highest score in tool integration, highlighting its flexibility in coupling with optimization solvers, control systems, and external programming environments. Fluent shows strong performance in heat transfer modeling, particularly for detailed thermal analysis in oxy-fuel combustion systems. Most existing studies focus on individual carbon capture technologies rather than simulation tool capabilities. Following the PRISMA 2020 guidelines, a systematic search of Scopus yielded 53 peer-reviewed papers on CCS simulation, which were analyzed to identify dominant tools and inform the AHP-based evaluation. This work addresses that gap by clarifying tool-specific advantages, supporting informed model selection to improve the efficiency and sustainability of CCS process design. Full article

Multiple Myeloma (MM) is a hematological malignancy characterized by the clonal proliferation and survival of neoplastic plasma cells (PCs) within the bone marrow (BM), where disease progression is critically supported by interactions with the BM tumor microenvironment (TME). Despite significant advances in therapeutic strategies, MM remains incurable, underscoring the need for improved preclinical models to better understand the disease biology and therapeutic response. This review summarizes current and emerging MM treatment approaches and critically examines the development of models designed to more accurately recapitulate interactions between MM-PCs and the surrounding BM niche. We describe established and emerging modeling platforms, with emphasis on advanced three-dimensional (3D) culture systems and highlight their unique contributions to the preclinical assessment of both existing and novel therapies. The advantages of 3D models, including in vitro and in silico systems, over traditional two-dimensional (2D) models are discussed, alongside a comparative evaluation of scaffold-free and scaffold-based approaches. In addition, the benefits and recent advances in the customization of BM niche simulation using microfluidic technologies and organ-on-a-chip platforms are reviewed. The application of 3D models in MM research is increasingly enabling the study of disease pathogenesis, progression, drug resistance and precision-medicine approaches (informed by biomarker discovery). Although standardized preclinical approaches for evaluating MM therapeutics are currently lacking, the growing imperative to reduce reliance on preclinical animal models highlights the importance of alternate systems. Consequently, the development and adoption of physiologically relevant models that accurately recapitulate MM-PC interactions with the BM TME will be critical for advancing future therapeutic strategies in MM. Full article

High-throughput technologies such as RNA-seq and single-cell transcriptomics generate increasingly large and high-dimensional gene expression datasets in which nonlinear dependence structures are common. Because classical methods primarily capture linear associations, they may fail to characterize many biologically relevant patterns of dependence. To address this limitation, diverse nonlinear dependence measures—including information-theoretic, rank-based, kernel-based, distance-based, copula-based, and clustering-based approaches—have been developed. However, the field remains fragmented, and comparative evaluations are often inconsistent. This review organizes nonlinear methods into major methodological families and critically compares their statistical behavior, strengths, limitations, and characteristic modes of failure. We emphasize that method selection depends on matching inferential objectives to estimator assumptions, analytical constraints, and characteristic failure modes. By identifying recurring trade-offs among flexibility, robustness, interpretability, and computational scalability, we provide scenario-based guidance for method selection in transcriptomics, network inference, and functional genomics. In doing so, we aim to align inferential objectives with analytical requirements, supporting principled and application-specific use of nonlinear dependence methods in modern omics research. Full article

Centered on lean production, this study integrates operational technologies (OT), communication technologies (CT), and information technologies (IT) within an open-system software architecture. Under stochastic customer demand, reliance on static data and experience-based decision-making constrains firms’ responsiveness to market. The integration of lean management with a data-driven database enhances operational flexibility and decision quality, enabling small and medium-sized enterprises (SMEs) in the bicycle industry to develop responsive digital factory environments with real-time monitoring and improved operational transparency. The proposed platform is applicable to both manufacturing processes and operational management, improving overall equipment effectiveness (OEE), production efficiency, process optimization, and reducing quality losses, inventory levels, and workforce misallocation. This study investigates the application of the Analytic Hierarchy Process (AHP) and multi-criteria decision-making (MCDM) within a performance framework integrating ESG indicators and a balanced scorecard to identify key success factors for digital lean improvement in the bicycle industry. A case study of a bicycle manufacturer was conducted using questionnaire surveys and expert interviews with exporters. The results indicate that the five most critical success factors are: enhancing return on invested capital, strengthening digital capabilities, improving product quality, minimizing inventory waste, and reducing lead time. These findings provide practical guidance for decision-makers in designing more effective lean management strategies in highly competitive digital markets. Furthermore, by facilitating the adoption of appropriate digital technologies under a reasonable return on investment, this approach supports the systematic implementation of Industry 4.0 initiatives and transforms traditional lean practices into more efficient and sustainable digital lean operations. Full article