Search Results (3,513)

The design of low-emission alternative-propulsion aircraft requires multidisciplinary collaboration across distributed academic and industrial environments, challenging the applicability of conventional multidisciplinary design analysis and optimization (MDAO) frameworks. This paper presents the Holistic Collaborative MDAO Selection (HCMS) methodology, which provides a structured approach for selecting MDAO architectures based on socio-technical feasibility (intellectual property protection, disciplinary autonomy, and IT governance) and computational feasibility (coupling strength and model fidelity). The methodology supports a transition from centralized to distributed workflows while ensuring secure and efficient cross-organizational integration. The approach is demonstrated through a multi-institutional case study of a dual-fuel (hydrogen and kerosene) business jet using Remote Component Environment (RCE) and Common Parametric Aircraft Configuration Schema (CPACS). Results demonstrate that the proposed methodology enables stable and scalable distributed MDAO execution while explicitly accounting for socio-technical constraints, with consistent convergence behavior and communication overhead (approximately 25 s per iteration) remaining small relative to disciplinary computation time. The case study further illustrates the impact of hydrogen integration, showing an increase in operating empty weight of approximately 14.06% for a 600 NM mission and a reduction in kerosene capacity of approximately 12.9%, while enabling hydrogen-powered operation for the primary mission segment. These findings confirm that the proposed framework effectively supports secure, collaborative MDAO under realistic socio-technical constraints while providing meaningful system-level design insights. Full article

Academic stress is prevalent among university students and affects their evaluation of educational environment quality, fairness, and supportiveness. Based on the challenge–hindrance stressor framework and transactional stress-coping model, this study explores how challenge and hindrance stressors (HSs) shape perceived decent education (DE), focusing on the mediating role of artificial intelligence use (AIUSE) and moderating effect of interpersonal interaction (II). Using partial least squares structural equation modeling (PLS-SEM) to analyze survey data from 520 university students, the results show that both stressors positively predict AIUSE, which in turn improves perceived DE and mediates the stressor-DE relationship. II negatively moderates the AIUSE–DE link: the positive effect weakens as II increases. Moderated mediation analysis indicates that the indirect effects via AIUSE are only significant at low II levels. These findings highlight AI-enabled learning as an adaptive coping strategy and the necessity of integrating technological and interpersonal resources to enhance student well-being in higher education. Full article

Background: Delivering comprehensive cancer prevention, diagnosis, and treatment across Hawaiʻi and the U.S.-Affiliated Pacific Islands (USAPI) is constrained by geographic isolation, oncology workforce shortages, and persistent cancer inequities. Objectives: The University of Hawaiʻi Cancer Center, the state’s only National Cancer Institute-designated cancer center, partners with community healthcare systems to address cancer health disparities. Here, we describe an implementation-focused strategy initiated in December 2024 that is designed to improve equitable access to evidence-based oncology services across the catchment area. Approach: This program description integrates publicly available demographic and health system data and presents a structured implementation framework centered on (1) workforce development and oncology training pathways; (2) a statewide clinical oncology network supported by telehealth; (3) community-engaged screening and early detection outreach; and (4) strengthening clinical research and trial infrastructure with deliberate inclusion of underserved populations. Evaluation: We outline an evaluation framework incorporating process and outcome metrics spanning workforce capacity, screening participation, timeliness of care, clinical trial enrollment, and equity indicators stratified by county, island, and population group. Conclusions: This approach offers a scalable, implementation-oriented model for developing an academic oncology ecosystem that emphasizes measurement, accountability, and equity, with potential applicability to other geographically dispersed and ethnically diverse regions. Full article

This study examines the hypothetical issue of the impact of ancient Egyptian beliefs on Africa as a whole. Several focal points are explored. These include (1). The situation of the discipline of folklore within allied academic specializations. (2). Culture diffusion within Africa, and (3). Spoken folk stories as the only field that integrates, in the space and time continuum, culture on the one hand, with its bearers/(society), on the other. (4). [Beside the] colonial past, the problem, is a result of a number of academic factors that include: (a). The establishment at universities of African studies departments that confine the continent to the sub-Saharan tier excluding Africa of the North; thus, folklore is isolated without a proper stage for studying it academically (see Dorson 1972); (b). The stereotyping concerning the capacity of scholars with unfamiliar names or recognized departmental membership as capable of dealing with theory or innovation, though some of their ideas are adopted by the famous without accrediting the source; (c). Ignoring the unfamiliarity for the family (especially under conditions of secrecy; cf. bias, ethnocentrism); and (d). Inadequacy of academic classroom pedagogy on the basics of verbal lore. Folklore in its original, mainly verbal branches, as represented by Stith Thompson’s monumental works on motif (1955–1958), and its predecessor by Antti Aarne on Type, (1910, 1928, 1961/1964), whose coverage, especially on Africa of the North, is seriously lacking in both the Type and Motif Indexes. The tracking of this line begins with recent calls for need for morphological studies of a South African tale (Dseagu [²⁰⁰¹] 2021). An association among various regions of Africa with ancient Egypt concerning mythological contacts merits this investigation. Full article

Value co-creation among diverse actors in digital innovation ecosystems (DIEs) exhibits characteristics of high complexity and dynamic evolution. Grounded in the Quadruple Helix Theory, this study develops a conceptual model that interlinks “supervisory guides, knowledge providers, technology transformers, and user demand parties.” This model is defined by organizational oversight as its nexus, knowledge and technology as its foundation, outcome transformation as its core, and user needs as its orientation. Building upon this conceptual foundation, we establish a four-party evolutionary game model involving “innovation regulators (government), innovation producers (academic/research institutions), innovation decomposers (enterprises), and innovation consumers (users).” This analytical framework is then applied to systematically investigate the dynamic evolutionary mechanisms and collaborative pathways for value co-creation in DIEs. We construct the payoff matrix and replicator dynamics to derive the system’s Evolutionarily Stable Strategies (ESSs). Numerical simulations via MATLAB R2023b identify the stability conditions for each party’s strategic choices and unravel the influence mechanisms of key parameters. The results demonstrate nine distinct ESSs, categorized into three types: low-level stability, regulation-dominated transitional stability, and high-level cooperative stability. While the agents’ initial strategies do not alter the system’s final equilibrium state, they significantly impact the speed of evolutionary convergence. Critical factors—including regulators’ intervention costs, subsidy and penalty mechanisms, producers’ and decomposers’ cooperation and default costs, and consumer feedback behaviors—collectively drive the system toward the ideal (1, 1, 1, 1) equilibrium. Theoretically, this study enriches the perspective on multi-agent collaboration in value co-creation by introducing a dynamic quantitative analytical framework, thereby addressing a notable gap in the literature. Practically, it provides actionable insights for mechanism design and a solid foundation for policy optimization, aiming to foster a synergistic governance system that integrates “regulatory guidance, market incentives, and social feedback.” Full article

Cardiometabolic diseases, encompassing obesity, insulin resistance, type 2 diabetes (T2D), metabolic dysfunction-associated steatotic liver disease (MASLD), hypertension, and atherosclerotic cardiovascular disease (ASCVD), represent a vast continuum driven by multi-organ network dysregulation. Clinical risk assessment remains dominated by late-stage measures (e.g., fasting glucose, HbA1c, standard lipids). While these assessments predominate the literature and clinical trial endpoints, each incompletely capture early mechanistic risk, inter-individual heterogeneity, and differential response to interventions. Multiomics (genomics, epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, microbiomics, and extracellular vesicle/exosome cargo profiling) expands the biomarker landscape but introduces translational barriers: high dimensionality, cohort heterogeneity, limited causal inference, and insufficient validation pipelines. AI-driven systems biology platforms can support cardiometabolic biomarker discovery and therapeutic translation by enabling systems-level biological inference across heterogeneous datasets, prioritizing mechanism and traceability over purely correlation-based models. GATC Health’s Operon™ platform is described as a proprietary, AI-driven internal scientific computing platform designed to support therapeutic discovery and development decision-making across the pharmaceutical lifecycle, including evaluation of drug efficacy, safety, off-target effects, pharmacokinetics (PK), pharmacodynamics (PD), and overall development risk. Operon evolved from earlier generations of GATC Health’s internal multiomic modeling systems (formerly referred to as the Multiomics Advanced Technology, MAT) and incorporates expanded data types, orchestration layers, validation workflows, and productization frameworks. Operon is operated by GATC scientists and generates structured, productized outputs (e.g., formal assessments, analyses, and decision frameworks) that are reviewed by experts. Operon methodologies have undergone internal validation and independent academic evaluation under blinded conditions, with reported classification performance (true positive rate 86% and true negative rate 91%) in controlled evaluation settings; these performance metrics should not be interpreted as guarantees of clinical success. This review provides a T2D-centered cardiometabolic biomarker landscape with cardiovascular extension and outlines how Operon-enabled multiomic integration and scenario-based simulation can support early screening, endotype stratification, mechanistic interpretation, and precision intervention design, including AI-guided polypharmacology strategies. Full article

The integration of artificial intelligence (AI) into the diagnosis and prognosis of heart diseases is transforming cardiovascular and cardiac healthcare, improving predictive accuracy, and personalizing treatment plans. This review presents a novel contribution by providing a comprehensive overview of both diagnosis and prognosis in heart diseases through AI, covering ML and DL models. Following the PRISMA guidelines, a total of 84 recent research articles sourced from significant journals are reported. A bibliometric analysis using the VOSviewer tool was performed to map the impact of AI, enabling a detailed examination of academic connections and contributions. The findings reveal that DL models were employed 63% for diagnosis tasks, while ML models were utilized in 37% of the studies. Key recommendations include the incorporation of essential model evaluation metrics, as clinical validation indicators, integrating explainable artificial intelligence (XAI) to improve the transparency and interpretability of models, and adopting standardized frameworks to enable smooth clinical integration. This review highlights the potential of AI to improve cardiac and cardiovascular diagnosis and prognosis, providing an overview of its strengths, limitations, challenges and the possible application as AI-driven tools in patient monitoring and to support specialists in the decision-making process. Full article

The reorganization of academic work following the COVID-19 pandemic has intensified debate regarding the effects of work modality on faculty well-being, particularly in Latin American contexts characterized by structural inequalities and digital divides. This study examines the influence of work modality on the integrated well-being of university faculty in Peru. A quantitative, non-experimental explanatory design was employed with a sample of 448 faculty members from public and private universities. Occupational well-being and quality of life were assessed using validated instruments and subsequently integrated into a higher-order construct due to the absence of discriminant validity. Structural relationships were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with bootstrapping (5000 resamples). The results indicate that work modality significantly predicts integrated well-being (β = 0.823; p < 0.001), although the model explains a limited proportion of variance (R² = 0.168). Comparative analysis revealed that faculty in in-person modality report significantly higher levels of well-being and quality of life than those in virtual modality. These findings suggest that work modality operates as a structural condition shaping faculty well-being and highlight the need for institutional policies that promote balanced and human-centered work designs in higher education. Full article

While valorisation pathways are increasingly promoted as sustainable solutions, their ability to genuinely minimise environmental harm and contribute to long-term material circularity remains uneven. This study systematically identifies and maps existing valorisation routes across the EU and UK, with particular attention to their environmental performance and economic viability through a material value retention lens. A literature review highlights a spectrum of practices—from soil amendment and composting to bioenergy recovery and bio-based construction materials—each offering different sustainability benefits but varying significantly in their capacity to preserve material quality and function. To address the absence of robust comparative approaches, this paper introduces a novel evaluative framework centred on intrinsic material value retention, a key principle in sustainable and circular material systems. Building on established scholarship, the framework provides a structured means of comparing valorisation options based on how effectively they conserve material properties, particularly in terms of the material’s structural and functional values, and enable high-value reuse. Supported by a dedicated classification tool and a set of guiding questions refined through expert interviews, the framework complements existing environmental assessment methods by foregrounding material circularity. In doing so, it supports more integrated, holistic decision-making for the development of a resilient and sustainable circular bioeconomy. This research is intended for academic audiences and may also be of relevance to industry practitioners. Full article

The rapid expansion of Generative Artificial Intelligence (GAI) is transforming higher education systems, particularly public institutions seeking to advance toward smart governance models and digital transformation. In this context, digital teaching competence emerges as a strategic factor for the effective, ethical, and pedagogically sound adoption of these technologies. This study assesses the level of digital competence among public higher education faculty in Paraguay and examines its predictive capacity regarding the adoption of GAI tools using machine learning models. A nationwide quantitative study was conducted with a sample of 800 faculty members from public universities across Paraguay. Data were collected through a structured questionnaire based on international digital competence frameworks, incorporating additional variables such as attitudes toward GAI, technological experience, institutional infrastructure, and perceived organizational support. Data analysis involved the application of machine learning techniques, including Logistic Regression, Random Forest, and Gradient Boosting, to identify the variables with the strongest predictive power regarding faculty readiness and willingness to integrate GAI into teaching practices. Model performance was evaluated using metrics such as accuracy, F1-scores, and the AUC-ROC. The findings identify key predictors of technological readiness and structural gaps within Paraguay’s public higher education system. This research provides empirical evidence from Latin America on the factors influencing GAI adoption in public sector educational contexts and contributes to the design of educational policies aimed at fostering smart universities and digitally sustainable academic ecosystems. Full article

The sustainability of traditional vernacular dwelling heritage has become an important academic concern. This study takes the traditional vernacular dwellings of the Ancient City of Pingyao as its research object and develops a macro–meso–micro multi-scale analytical framework. Drawing on four dimensions—environment, layout, architecture, and culture—it systematically investigates the geographical environment, spatial pattern, and architectural forms of Pingyao’s traditional vernacular dwellings using GIS spatial analysis, UAV oblique photogrammetry, and 3D laser scanning technologies. On this basis, an AHP–FCE comprehensive evaluation model is introduced to assess their sustainability. The results indicate that the formation and persistence of these dwellings are closely associated with favourable natural environmental conditions, a clear and orderly spatial pattern, and well-structured courtyard and architectural forms. The comprehensive evaluation yields a score of F = 3.23, indicating a moderately high level of sustainability. The four criterion layers are ranked as follows: architecture, layout, environment, and culture. The key determinants are structural safety, material authenticity, spatial integrity, and the continuity of traditional character. By combining multi-scale analysis with comprehensive evaluation, this study aims to clarify the priority directions for the conservation of traditional vernacular dwelling heritage in the Ancient City of Pingyao, thereby providing a scientific basis for its sustainable development. Full article

Security and stability constitute fundamental preconditions for long-term sustainable development. Russia’s aggression against Ukraine and the return of high-intensity interstate warfare to Europe have profoundly transformed the European security environment and challenged long-standing assumptions underpinning European integration and economic development. This article analyses the ongoing transformation of European security with particular attention to industrial resilience, the evolution of the defence technological and industrial base, and the expanding institutional role of the European Union in strengthening strategic autonomy. Using a qualitative analytical approach based on the examination of strategic documents, policy initiatives, and academic literature, the study identifies structural weaknesses in Europe’s defence-industrial system and evaluates recent institutional and financial responses aimed at enhancing resilience and sustainability. The findings demonstrate that security, industrial capacity, and institutional adaptation are increasingly interconnected, and that strengthening resilience and reducing strategic dependencies are essential conditions for Europe’s long-term sustainable development in an unstable geopolitical environment. Full article

The integration of administrative staff into research processes within higher education institutions (HEIs) remains underexplored, particularly in Latin American contexts. This study aimed to examine the perceptions, motivations, and structural barriers experienced by administrative personnel regarding their involvement in institutional research at the Universidad Politécnica Estatal del Carchi (UPEC), Ecuador. A quantitative, cross-sectional design was employed using a validated Likert-type instrument. Data were collected from 70 administrative employees and analyzed through Exploratory Factor Analysis (EFA), revealing seven latent factors: personal motivation, structural barriers, regulatory knowledge, institutional recognition, contribution to efficiency, training and participation, and institutional vision. The EFA yielded a cumulative explained variance of 54.5%, and Cronbach’s alpha coefficients ranged from 0.64 to 0.94 across factors, indicating strong internal consistency. Correlation analysis demonstrated moderate to strong associations between motivation and participation (r = 0.65), and between regulatory knowledge and institutional recognition (r = 0.50). Multiple regression analysis revealed that only the institutional recognition factor significantly predicted research participation among administrative staff (β = 0.41, p = 0.004), while other predictors—including motivation and structural barriers—did not reach statistical significance. These findings underscore the need to design inclusive research policies that strategically engage administrative personnel. The study contributes to expanding the discourse on research ecosystems by highlighting the overlooked potential of non-academic actors in institutional scientific output. Full article

Mobile thermal energy storage (M-TES) demonstrates significant commercialization potential in industrial waste heat recovery, distributed heating, and clean heating applications, which is primarily based on three technical pathways: sensible heat storage, latent heat storage using phase change materials (PCMs), and thermochemical heat storage. The updated status of M-TES, mainly on PCMs and thermochemical ones, and the challenges facing application were reviewed, and potential development trends were discussed in the present study. Sensible heat storage is relatively mature and cost-effective; however, it suffers from low energy density and comparatively high heat loss during storage and transport. Latent heat storage utilizes the phase transition enthalpy of PCMs to store thermal energy, offering higher energy density and near-isothermal heat release, making it a focal point of current academic and industrial research. Nevertheless, latent heat storage still faces technical bottlenecks, including low thermal conductivity, phase separation, and supercooling of PCMs. Thermochemical heat storage relies on reversible chemical reactions to convert and store thermal energy as chemical energy, theoretically achieving the highest energy density and minimal heat loss. However, due to its technical complexity and high system cost, thermochemical storage remains largely in the early stages of research and demonstration. Overall, as a bridge between heat supply and demand, the development trend emphasizes the design of high-performance composite PCMs, enhanced system integration, and intelligent operational management. However, its large-scale deployment is still constrained by challenges related to energy density, heat transfer enhancement, long-term material stability, and techno-economic feasibility. Full article

The construction industry accounts for approximately 13% of global GDP but suffers from chronic productivity stagnation. Although artificial intelligence (AI) offers transformative potential, its adoption is constrained by three key barriers: data integrity issues (H1), socio-technical challenges (H2), and system integration problems (H3). This study investigates whether academic research attention aligns with these practitioner-identified barriers through a bibliometric analysis of 4668 publications from OpenAlex (1990–2025), applying a five-pillar analytical framework synthesized into composite scores (0–100 scale) via min-max normalization, weighted summation, and bootstrap validation. H3 achieved a nominal 15.9% prevalence rate (adjusted to ~13.0% after correcting for an 18.2% false positive rate in keyword classification), robust growth (R² = 0.654), significant overrepresentation in top-cited works (risk ratio = 1.31, p = 0.003), and received a composite score of 62/100 (confirmed). H1 (2.7%, score: 17/100) and H2 (4.6%, score: 13/100) were both rejected. The rank ordering by prevalence (H3 > H2 > H1) remains robust under all adjustment scenarios. These findings contrast notably with the RICS Global Construction Monitor (2025, n = 2200+), where practitioners most frequently reported socio-technical barriers (46%), followed by system integration (37%) and data quality (30%), yielding practitioner-to-publication ratios of 4.7:1, 5.2:1, and 1.1:1, respectively. This apparent research–practice paradox appears primarily volume-driven rather than clearly quality-driven: H1/H2 publications receive citation attention broadly comparable to the baseline, though this comparison is limited by control group heterogeneity. We call for rebalanced research agendas addressing data governance frameworks, competency development, and organizational change management. Full article