Search Results (910)

The transportation sector is a major contributor to global greenhouse gas emissions, making electric vehicle (EV) adoption critical for decarbonization. This study investigates EV adoption determinants in Turkey using explainable machine learning, focusing on economic, infrastructure, and attitudinal factors while exploring driver behavior and fuel-efficiency awareness. Data from 304 participants were collected; after excluding undecided responses, the final analytical sample comprised 232 participants. Multiple algorithms (Random Forest, XGBoost, Logistic Regression, and SVM) were evaluated, addressing class imbalance via SMOTETomek. SHAP analysis identified policy-relevant predictors. Results reveal that EV adoption intentions are primarily driven by perceived cost impact, EV knowledge, and charging infrastructure accessibility, showing substantially stronger effects than driver behavior. Exploratory analysis indicates that aggressive driving correlates with lower fuel-efficiency awareness, whereas maintenance and eco-driving support higher awareness. The best-performing Random Forest model achieved 89.36% accuracy and a 0.9348 F1-score. Rather than claiming novelty in ML application, this study contributes an interpretable framework and emerging-market evidence contrasting economic/infrastructure factors against behavioral variables. Findings provide actionable insights for policy, highlighting cost-focused incentives, infrastructure deployment, and targeted awareness campaigns. Full article

Extended Reality (XR) technologies are rapidly reshaping human–computer interaction; however, persistent ambiguity in the use of core terms (VR, AR, MR) hampers cumulative knowledge building, cross-study comparability, and technical standardisation. This review evaluates the XR conceptual landscape across four primary dimensions: the historical evolution of core definitions, the synthesis of contemporary theoretical frameworks, the critical extensions of the Reality-Virtuality (RV) Continuum, and the alignment between academic taxonomies and industry practices. This review evaluates the XR conceptual landscape across four primary dimensions: the historical evolution of core definitions, the synthesis of contemporary theoretical frameworks, the critical extensions of the Reality-Virtuality (RV) Continuum, and the alignment between academic taxonomies and industry practices. To address this issue, we conducted a PRISMA-guided systematic literature review across four major databases (IEEE Xplore, ACM Digital Library, Scopus, and Web of Science), complemented by seminal and industry sources. Of the 173,677 retrieved records, 59 studies were included in the synthesis. Using thematic synthesis, we mapped the historical evolution of definitions and conceptual models and identified recurring analytical dimensions. The results indicate a clear paradigm shift from Milgram’s one-dimensional Reality–Virtuality continuum—originally grounded in visual display technology—towards a multidimensional conceptual space that integrates subjective user-experience constructs (e.g., coherence and plausibility) with objective system characteristics. The included studies cover 1968–2025, with marked acceleration in the 2020s: 2022 alone accounts for the highest annual count (9 studies), and nearly half of the corpus (47.5%) was published in 2021–2025. We further show that industry actors pragmatically re-bound these academic concepts for product and market positioning, leading to systematic divergences between academic and industrial definitions. By distilling key turning points and synthesising core analytical dimensions into a structured lens, this review provides a historically grounded, actionable understanding of the XR conceptual landscape to support terminological alignment across research and practice. Full article

Heart failure (HF) remains a leading cause of morbidity and mortality worldwide and is driven by complex, interconnected pathophysiological processes, including maladaptive cardiac remodeling, fibrosis, hypertrophy, metabolic dysregulation, and cardiomyocyte loss. MicroRNAs (miRNAs), small non-coding RNAs that act as key post-transcriptional regulators of gene expression, have emerged as important coordinators of these processes across cardiomyocytes and non-myocyte cardiac cell populations. In addition to altered expression patterns, accumulating evidence indicates that miRNA activity is dynamically influenced by regulated biogenesis, maturation, and context-dependent mechanisms of action. Through reversible translational repression and longer-term mRNA destabilization, miRNAs support adaptive responses to acute cardiac stress, whereas their persistent dysregulation contributes to remodeling pathways that promote HF progression. This comprehensive narrative review provides an integrative overview of current knowledge on the role of miRNA networks in shaping the molecular heterogeneity of heart failure across disease stages, phenotypes, and cardiac cell types. Drawing on a broad body of experimental and clinical literature, we discuss advances in understanding miRNA biogenesis, post-transcriptional control, and cell-specific effects, while highlighting conceptual developments rather than applying systematic selection criteria. We further examine the translational and clinical implications of miRNA biology, critically considering the progress of miRNA-based therapeutics alongside the biological and practical challenges that continue to limit their widespread clinical implementation. In parallel, we explore the emerging potential of circulating miRNAs as minimally invasive biomarkers that reflect upstream regulatory stress at the level of RNA processing and post-transcriptional regulation. Finally, we address the growing application of artificial intelligence and machine learning approaches to high-dimensional miRNA datasets, which enable integrative analyses across clinical, imaging, and multi-omics domains and support biomarker discovery, patient stratification, and prediction of therapeutic response. Collectively, miRNA biology, supported by systems-level and AI-driven analytical frameworks, offers a unifying perspective for understanding, classifying, and monitoring cardiac remodeling in heart failure. Full article

Reinforcement learning for robotic manipulation is often limited by poor sample efficiency and unstable training dynamics, challenges that are further amplified in federated settings due to data privacy constraints and task heterogeneity. To address these issues, we propose F-DRL,a federated dynamics-aware representation learning framework that enables multiple robotic tasks to collaboratively learn structured latent representations without sharing raw trajectories or policy parameters. The framework combines robotics priors with an action-conditioned latent dynamics model to learn low-dimensional state and state–action embeddings that explicitly capture task-relevant geometric and transition structure. Representation learning is performed locally at each client, while a central server aggregates encoder parameters using a similarity-weighted scheme based on second-order latent geometry. The learned representations are then used as frozen auxiliary inputs for downstream model-free reinforcement learning. We evaluate F-DRL on seven heterogeneous robotic manipulation tasks from the MetaWorld benchmark. While achieving performance comparable to centralized training and standard federated baseline, F-DRL substantially improves training stability relative to FedAvg on heterogeneous manipulation tasks with partially shared dynamics (e.g., Drawer-Open and Window-Open), reducing the mean across-seed standard deviation and the AUC of this deviation by over 60%. The method remains neutral on simple tasks and performs less consistently on contact-rich manipulation tasks with task-specific dynamics, indicating both the benefits and the practical limits of representation-level knowledge sharing in federated robotic learning. Full article

The exponential growth of data over the past decade has created new challenges in transforming raw information into actionable knowledge, particularly through the development of data products. The latter is essentially the result of querying and retrieving specific portions of data from a data storage architecture at various levels of granularity. Traditionally, this transformation depends on domain experts manually analyzing datasets and providing feedback to effectively describe or annotate data that facilitates data retrieval. Nevertheless, this is a very time-consuming process that highlights the need for its potential automation. To address this challenge, the present paper proposes a framework which utilizes Large Language Models to support data product discovery through semantic metadata reasoning and executable query prototyping. The framework is evaluated across two domains and three levels of concept complexity to assess the LLM’s ability to identify relevant datasets and generate executable data product queries under varying analytical demands. The findings indicate that LLMs perform effectively in simpler scenarios, but their performance declines as conceptual complexity and dataset volume increase. Full article

Traditional macroscopic fundamental diagram (MFD)-based traffic perimeter metering control strategies rely on full knowledge of vehicle accumulation and inter-regional flow dynamics, assumptions that seldom hold in heterogeneous and highly variable real-world networks. Classical data-driven reinforcement learning methods face similar constraints, often converging slowly and exhibiting low sample efficiency when confronted with such complexities. Motivated by these limitations, this paper proposes a Parameterized Deep Q-Network perimeter control (P-DQNPC) scheme designed for multi-region urban road networks. The framework jointly optimizes discrete actions (regional routing choices) and continuous actions (signal-timing or flow-duration regulation) within a model-free learning structure. The approach is first trained and validated on synthetic MFD data to establish stable and interpretable policy behavior under controlled conditions. It is then transferred and further evaluated using real-world measurements from the Performance Measurement System—San Francisco Bay Area (PeMS-SF), a dataset collected from 18,954 loop detectors across the California State Highway System. PeMS-SF is selected due to its high spatial and temporal resolution, broad network coverage, and strong ability to capture realistic and diverse congestion patterns qualities that support both rigorous validation and generalization to other metropolitan regions. Experimental results show that P-DQNPC consistently outperforms state-of-the-art baselines, including deep deterministic policy gradient, deep Q-network, and No-Control schemes. The proposed method achieves superior regulation of regional accumulations and demonstrates enhanced robustness in large, heterogeneous, and uncertain urban traffic environments. Full article

Cognitive impairment is a serious comorbidity in chronic obstructive pulmonary disease (COPD), consistently associated with adverse clinical outcomes, including impaired self-management, poor treatment adherence, reduced participation in pulmonary rehabilitation, and increased risk of mortality. Despite this, it remains inconsistently recognised and insufficiently addressed during routine COPD assessment. This narrative review synthesises current evidence on the recognition and management of cognitive impairment in COPD, with a particular focus on understanding why it continues to be under-recognised and inadequately managed in clinical practice. Across care settings, cognitive concerns are commonly identified informally, assessed selectively, or deferred altogether, even when clinicians acknowledge their relevance to respiratory assessment, treatment implementation, and patient engagement. This persistent evidence–practice gap suggests the influence of factors extending beyond disease- or patient-related explanations alone. Emerging evidence indicates that clinician-level determinants, particularly clinical confidence, play a central role in shaping cognitive care practices. Limited clinical confidence appears to mediate the translation of existing knowledge and competence into clinical action, influencing decisions to initiate assessment, communicate cognitive concerns, assume clinical ownership, and pursue follow-up or referral. These confidence-related barriers are further reinforced by educational limitations, time constraints, diagnostic ambiguity, particularly in the early cognitive impairment stage, and the absence of clear operational guidance within COPD-specific frameworks. Conceptualising cognitive care through the lens of clinical confidence provides a coherent explanation for the underrecognition of cognitive impairment in COPD. It also helps account for observed variability in clinical decision-making, highlighting clinical confidence as a modifiable intermediary between knowledge, competence, and practice and a potential target for strengthening integrated, patient-centred COPD care. Full article

Farmers’ adoption of green grain production practices is essential for advancing China’s ecological civilization and achieving carbon neutrality. However, adoption remains uneven because farmers’ decisions are embedded in local social structures and shaped by short-term economic incentives and constraints. Drawing on an embeddedness framework, this study investigates how social mobilization influences farmers’ green grain production practices, while also examining the moderating role of household resource endowments and the mediating role of non-market value perceptions. Using multi-stage survey data collected in Heilongjiang Province between June and September 2023, the results show that grassroots cadres foster farmers’ green production adoption through four dimensions of social mobilization—technical, knowledge, cultural, and relational embeddedness. Moreover, household endowments positively moderate these effects, and non-market value perceptions partially mediate the relationship between social mobilization and green production practices. These findings are robust to alternative model specifications. This study provides micro-level evidence on how a cadre-led, governance-based social mobilization process is associated with farmers’ adoption of green production practices. Overall, this study advances understanding of the behavioral foundations of farmers’ green transitions and highlights actionable policy levers for grassroots governance, helping translate external policy directives into internalized and sustainable production practices. Full article

Background and Objectives: Social Determinants of Health (SDoH) are factors that can contribute to health inequities. Improving the conditions in which people are born, grow, and live requires collaboration between professionals from different health sectors. Given their health and well-being-focused care, nurses are crucial to promoting health equity in the care they provide. Thus, their knowledge, attitudes, and actions—i.e., practice—(KAP) regarding SDoH could serve as a helpful starting point for promoting care that also focuses on non-medical factors. This study aims to map the SDoH assessed in the literature in relation to nurses’ and nursing students’ KAPs, using the Dahlgren–Whitehead Rainbow Model as a logical framework. Methods: Following PRISMA guidelines, a systematic literature review was conducted using PubMed, Scopus, Web of Science, CINAHL, and PsycINFO. Records published until June 2024 were selected from primary studies involving nurses and nursing students, with no time limits. The assessed determinants were adapted and categorised according to the Rainbow Model Levels. Results: 22 results were eligible. The SDoH (in general), poverty, social justice, social gradient, social inclusion and exclusion, discrimination, diversity, equity and inequality, food insecurity and access to nutritious food, employment status, geographical isolation, healthcare services, housing difficulties, transportation, social support, individual lifestyle factors, and health literacy were assessed on KAPs. Conversely, health equity has been assessed just for knowledge and attitudes. Considering the latter level of the Rainbow Model and the relative categorisation of the results, age, sex, and constitutional factors were not examined in the studies included in this review. Conclusions: This review maps the most and least frequently assessed SDoH in relation to KAP. As nurses are essential to providing care that considers SDoH, improving health outcomes, and addressing health inequities, and advocating for community health, it would be valuable to enhance nursing education from baccalaureate through postgraduate courses. Moreover, a strong relationship with different healthcare professionals is needed. Full article

The rapidly expanding body of biomedical literature encompasses a wealth of information concerning the pharmacological effects, mechanisms of action, adverse reactions, and repurposing potential of small-molecule therapeutics. Nevertheless, the systematic extraction and integration of this knowledge continue to pose substantial challenges. In this study, we propose an integrated text-mining framework for the automated extraction and structured representation of information on the biological activities of low-molecular-weight compounds, exemplified by angiotensin-converting enzyme (ACE) inhibitors as a representative pharmacological class. A corpus comprising over 20,000 PubMed titles and abstracts reporting in vitro, in vivo, and clinical investigations of ACE inhibitors was assembled. Chemical compounds, proteins/genes, and diseases were recognized using a previously developed named entity recognition model based on conditional random fields. Entity-level associations were extracted at the sentence level through a rule-based approach employing manually curated pattern phrases, followed by normalization via automated queries to PubChem, UniProt, and the Human Disease Ontology. The proposed methodology facilitated the extraction of approximately 22,000 unique and normalized associations encompassing drug-target, drug-disease, and drug-drug relationships. In addition to confirming well-established therapeutic effects and clinically recognized drug combinations, the analysis identified underexplored pharmacological activities of ACE inhibitors, including antineoplastic, antifibrotic, and neuropsychiatric properties, along with mechanistic associations involving matrix metalloproteinases and neurotrophic signaling pathways. Collectively, these findings underscore the potential of automated literature mining to advance systematic knowledge integration and data-driven hypothesis generation in the contexts of drug repurposing and safety evaluation. Full article

Although Palestine ratified the Convention on the Elimination of All Forms of Discrimination against Women (CEDAW) in 2014 without reservations, women’s labour-force participation has remained largely stagnant over the past fifteen years, fluctuating between 16% and 20%, raising critical questions about the operational effectiveness of international gender-equality commitments. Focusing on Article 11 of CEDAW, this study adopts a mixed-methods design that integrates administrative labour-force statistics, a survey of 529 economically active women, and qualitative evidence from key-informant interviews, legal texts, and policy documents. Quantitative findings reveal a systematic divergence between symbolic awareness of CEDAW and actionable knowledge of Article 11, with substantially higher levels of informed awareness among respondents engaged through authoritative institutional or civil-society channels. Qualitative evidence further demonstrates that labour-market reforms associated with Article 11 have been uneven and selective, constrained by weak enforcement capacity, fragmented institutional coordination, and employer cost-avoidance practices, particularly in the private sector. Taken together, the findings indicate that CEDAW ratification in Palestine has functioned primarily as a normative anchor rather than as an operational driver of labour-market transformation. By situating these findings within the Sustainable Development Goals framework, the study contributes to SDG 5 (Gender Equality) and SDG 8 (Decent Work) by demonstrating how rights awareness and enforcement credibility condition women’s employment outcomes, while highlighting the central role of institutional coordination and civil-society mediation in line with SDG 17. The study advances debates on treaty implementation by showing that, in fragile governance contexts, progress toward gender-equality targets depends less on formal legal adoption and more on the institutional pathways through which rights are translated into practice. Full article

Cultivated meat is a novel food and therefore must undergo safety assessments and regulatory review to identify risks and establish appropriate mitigations prior to commercialisation. The culture media used within the cell cultivation process may contain components that lack a long history of use in food, necessitating safety evaluation. However, there is no clearly defined framework outlining the evaluations needed to generate robust and reliable data. The aim of this work was two-fold: first, to convene a multi-stakeholder workshop to identify knowledge gaps related to culture medium safety assessment, and second, to provide a case study addressing one knowledge gap through the evaluation of ELISAs for quantifying growth factors in culture media and cultivated meat products. The workshop findings highlighted critical needs for standardised residue measurement methods, Certificates of Analysis, characterisation of metabolites and breakdown products, as well as open databases. Our case study evaluates the use of ELISAs to quantify six commonly used growth factors for cultivated meat production, comparing their presence in cultivated meat and conventional meat. Growth factor levels varied depending on the medium formulation but were generally reduced to conventional levels or were non-detectable after simulated cooking. Several methodological challenges were identified around the use of ELISAs, such as cross-reactivity between species, limited antibody availability for non-traditional species, and a lack of reference data and standards to support validation of ELISAs and establishment of suitable limits of detection. This work therefore provides actionable guidance for future research in this field for standardisation and emphasises the need for a clearly defined framework and standardised analytical methods to ensure consistent and transparent evaluation of cultivated meat. Full article

Engineering education is increasingly shaped by two converging developments: accelerating sustainability transitions and rapid advances in artificial intelligence (AI). However, in many application-oriented undergraduate programs, sustainability learning remains fragmented, methodologically limited, and weakly connected to authentic engineering decision-making. To address this gap, this study proposes AI-SEE (Artificial Intelligence-Integrated Sustainable Engineering Education), a pedagogical framework that integrates AI across the curriculum as both a cognitive scaffold and a resource for system-level analysis. Emphasizing human–AI collaboration, AI-SEE is designed to be feasible and scalable within application-oriented higher education contexts. The framework comprises four interrelated pillars: intelligence-driven, green-empowered, responsibility-leading, and practice-integrated. Drawing on an empirical case from transportation-related programs at Nantong University, the study employs a qualitative comparative design and conducts semi-structured interviews with 144 undergraduates at the end of their eighth semester (control group n = 70; pilot group n = 74). Interview data were analyzed using thematic analysis informed by constructivist grounded theory and the Gioia coding approach. The findings suggest that participation in AI-SEE is associated with differentiated patterns of sustainability consciousness. At the knowledge level, students reported more systematic and interdisciplinary understandings that extended beyond environmentally reductionist perspectives to include life-cycle thinking, social equity, and long-term considerations. At the attitudinal level, students described enhanced ethical reflexivity and evolving professional self-concepts, shifting from a focus on technical execution toward broader value-oriented roles. At the behavioral level, students reported more extensive knowledge-to-action translation across personal, academic, and career-related domains. Overall, AI-SEE provides a transferable pedagogical pathway for integrating AI into engineering education to support the development of sustainability consciousness in higher education. Full article

The Atlantic blue crab (Callinectes sapidus) has rapidly expanded across the Mediterranean Sea, forming self-sustaining populations in coastal and transitional ecosystems. Its ecological plasticity, high reproductive potential, and tolerance to wide salinity and temperature ranges have enabled a rapid basin-wide colonization, particularly evident in Italian lagoons and estuaries. This invasion has generated substantial ecological alterations, such as predation on bivalves, competition with native decapods, and disruptions of trophic dynamics, as well as significant economic losses for fisheries and aquaculture sectors, especially in northern Adriatic clam-farming areas. Social perceptions vary widely, and management actions remain fragmented, limiting the effectiveness of control and mitigation efforts. This review analyzes the scientific and gray literature published from its first Mediterranean records to 2025, synthesizing evidence on the species’ distribution, ecological impacts, socio-economic consequences, and existing regulatory responses, with a focus on the Mediterranean basin and Italy. Studies on consumers’ and fishers’ perceptions are examined to identify emerging opportunities for sustainable utilization. By integrating ecological and socio-economic dimensions, the review outlines priority knowledge gaps and management needs, providing a science-based framework to support coordinated monitoring, adaptive control strategies, and potential valorization pathways consistent with the EU Green Deal, the Blue Economy, and Circular Bioeconomy principles. Full article

Manufacturing companies face significant challenges in leveraging artificial intelligence for equipment management due to high infrastructure costs and limited availability of labeled data for failures. While most manufacturing AI applications focus on structured sensor data, vast amounts of unstructured textual information containing valuable maintenance knowledge remain underutilized. This study presents a practical generative AI-based framework for structured information extraction that automatically converts unstructured equipment maintenance texts into predefined semantic fields to support predictive maintenance in manufacturing environments. We adopted and evaluated three representative generative models—Bidirectional and Auto-Regressive Transformers (BART) with KoBART, Text-to-Text Transfer Transformer (T5) with pko-t5-base, and the large language model Qwen—to generate structured outputs by extracting three predefined fields: failed components, failure types, and corrective actions. The framework enables the structuring of equipment management text data from Manufacturing Execution Systems (MES) to build predictive maintenance support systems. We validated the approach using a large-scale MES dataset consisting of 29,736 equipment maintenance records from a major automotive parts manufacturer, from which curated subsets were used for model training and evaluation. Our methodology employs Generative Pre-trained Transformer 4 (GPT-4) for initial dataset construction, followed by domain expert validation to ensure data quality. The trained models achieved promising performance when evaluated using extraction-aligned metrics, including exact match (EM) and token-level precision, recall, and F1-score, which directly assess field-level extraction correctness. ROUGE scores are additionally reported as a supplementary indicator of lexical overlap. Among the evaluated models, Qwen consistently outperformed BART and T5 across all extracted fields. The structured outputs are further processed through domain-specific dictionaries and regular expressions to create a comprehensive analytical database supporting predictive maintenance strategies. We implemented a web-based analytics platform enabling time-series analysis, correlation analysis, frequency analysis, and anomaly detection for equipment maintenance optimization. The proposed system converts tacit knowledge embedded in maintenance texts into explicit, actionable insights without requiring additional sensor installations or infrastructure investments. This research contributes to the manufacturing AI field by demonstrating a comprehensive application of generative language models to equipment maintenance text analysis, providing a cost-effective approach for digital transformation in manufacturing environments. The framework’s scalability and cloud-based deployment model present significant opportunities for widespread adoption in the manufacturing sector, supporting the transition from reactive to predictive maintenance strategies. Full article