Search Results (442)

This article analyses trading-performance patterns in a stock market simulation conducted with 134 second-year students at the University of Mons (Belgium) on 11 December 2025. Participants had a virtual capital of 100,000 euros and were free to trade CAC 40 securities without any restrictions on the number or volume of transactions. An academic incentive scheme, combining a participation bonus and bonuses for the three best portfolios, created a tournament-style environment with continuous ranking feedback. This feature is considered as part of the experimental context rather than as a separately identified causal mechanism. We estimate a quadratic model linking performance to activity, measured by the number of mean-centered transactions to reduce the collinearity between the first-degree term and its square, and control exposure via the average percentage of cash in the portfolio, portfolio variability (measured as the standard deviation of portfolio value) and the average trade size. Breusch–Pagan and White tests indicate heteroscedasticity, justifying a robust inference. The results highlight a convex relationship between activity and performance: the marginal association is initially negative but becomes positive above a model-implied upper-tail level corresponding to approximately 46 transactions. This value should not be interpreted as a behavioral level or as a trading rule. The percentage of cash in the portfolio and the average trade size are negatively associated with performance, while the portfolio variability does not show a statistically significant association with performance. Overall, the results indicate heterogeneous trading patterns rather than a single activity–performance profile. Full article

This paper explores how higher education institutions (HEIs) in the Gulf Cooperation Council (GCC) are advancing the Sustainable Development Goals (SDGs) amid rapidly evolving national development agendas. This study reviews publicly available institutional documents and global SDG ranking data to identify patterns of SDG integration: through academic programs, research, and community engagement. The data shows active engagement of the universities in the region linked with varying SDGs. The analysis also reveals that sustainability initiatives in Gulf universities are not purely educational or environmental undertakings; rather, they function as strategic instruments aligned with national visions, international positioning and soft power objectives. Accordingly, this study assesses institutional commitment to the SDGs as expressed through, and made visible by, publicly available reporting, rather than the effectiveness or real-world impact of that engagement, which the available data cannot establish. Guided by theoretical perspectives, the paper argues that SDG engagement remains largely shaped by global ranking frameworks and policy imperatives. While the GCC higher education sector is increasingly embedded in the global sustainability discourse, meaningful localization of SDG practices and data transparency remain limited. By drawing attention to these dynamics, the study contributes to the literature on higher education and sustainable development in the Arab Gulf, emphasizing the need for context-sensitive frameworks and stronger regional collaboration to advance the 2030 Agenda. It calls for strengthened collaboration, capacity development, and tailored policy approaches to fully harness the transformative potential of the SDGs. Future research should explore the sociopolitical drivers of SDG adoption to deepen understanding of HEIs’ contributions to sustainable development in the region. Full article

The pervasive integration of artificial intelligence across e-commerce ecosystems has fundamentally transformed the competitive landscape, rendering systematic and reproducible platform evaluation frameworks an operational necessity rather than an academic exercise. Conventional multi-criteria decision analysis approaches for e-commerce evaluation remain structurally constrained by their dependency on human expert panels, which introduce recruitment costs, cognitive biases, limited reproducibility, and the practical infeasibility of assembling genuinely multidisciplinary panels spanning e-commerce strategy, machine learning engineering, and financial technology simultaneously. This study proposes a novel decision support framework that integrates Large Language Model (LLM) multi-agent simulation with the Best–Worst Method (BWM) to derive reproducible priority weights for AI-driven e-commerce platform evaluation within a rigorous business intelligence architecture. Twelve domain-differentiated LLM agents—organized into three expertise groups representing e-commerce management, AI and machine learning technology, and digital payment systems—were instantiated with structured system prompts encoding professional domain knowledge and deployed across three independent simulation rounds to perform BWM pairwise comparisons across a comprehensive six-dimensional, 30-sub-criterion evaluation hierarchy. Inter-agent consensus was synthesized through geometric mean aggregation, with consistency verification conducted via BWM’s xi* indicator and inter-round stability assessed through coefficient of variation analysis. Results reveal that Transaction Security and Trust achieves the highest dimension-level weight (w = 0.248), followed by AI Recommendation Effectiveness (w = 0.213), with Personal Data Protection (G = 0.0750), Recommendation Accuracy (G = 0.0607), and Transaction Transparency (G = 0.0549) emerging as the three highest globally ranked sub-criteria. The aggregated consistency indicator xi* = 0.062 confirms logical coherence of the multi-agent judgment consensus, and all dimension weights exhibit CV values below 2.8%, demonstrating exceptional inter-round stability. Spearman rank correlations among the three domain-expertise groups exceed 0.92, confirming strong inter-group convergence. Sensitivity analysis under perturbations of ±10% and ±20% demonstrates that the top-five priority indicators are structurally stable. This study establishes LLM multi-agent BWM simulation as a methodologically rigorous, institutionally accessible, and computationally reproducible alternative to traditional expert elicitation for complex platform evaluation tasks. Full article

Teacher education programs often fail to bridge the gap between sustainability knowledge and its practice, leaving pre-service teachers ill-equipped to implement Education for Sustainable Development (ESD). Addressing this knowledge–action gap, the present study draws on Mezirow’s ten-phase perspective transformation model and the UNESCO ESD competency framework. It examines how a competency-based pedagogical design is associated with (a) pre-service social studies teachers’ sustainability awareness and (b) the transformation of their experiences across the stages of transformative learning. A sequential explanatory mixed-methods design was employed with 33 pre-service teachers enrolled in a “Sustainable Development and Education” course during the fall semester of the 2025–2026 academic year. The ten-week instructional program was organized around four core processes: disorienting dilemma, critical reflection, rational discourse, and action. Quantitatively, the Wilcoxon Signed-Rank Test revealed statistically significant increases in overall sustainability awareness and across all three sub-dimensions—economy, society, and environment—with large effect sizes according to Cohen’s criteria. Qualitatively, participants shifted from individual responsibility to systemic awareness, revised their consumption practices, and reframed sustainability as a pedagogical responsibility. Disconfirming patterns also emerged: some retained their initial perspectives, while others reported heightened feelings of helplessness despite greater awareness. Findings suggest that transformative learning offers a robust framework for action-oriented sustainability education, while demonstrating that behavioral and professional transfer remains a complex process. Full article

This study proposes a hybrid artificial intelligence (AI) framework for graduate subject allocation that enhances fairness, transparency, and operational efficiency in higher education institutions. Traditional subject allocation processes are predominantly manual and time-consuming in increasingly complex academic environments. The proposed framework integrates a custom Python-based rule engine for institutional constraint reasoning with advanced deep learning models, including XGBoost, Wide-and-Deep Neural Networks (WDNNs), and Graph Neural Networks (GNNs), to ensure policy-compliant and data-driven subject allocation decisions. Subsequently, a systematic hyperparameter optimization strategy is applied to enhance predictive accuracy and model stability across all architectures. Experimental evaluation demonstrates that the proposed framework significantly improves predictive and ranking performance. The GNNs model achieved the highest results with Accuracy = 0.964, Precision = 0.953, Recall = 0.941, F1-score = 0.947, and AUC = 0.976, outperforming WDNN (Accuracy = 0.956, AUC = 0.972) and XGBoost (Accuracy = 0.934, AUC = 0.942). Ranking effectiveness was also validated with HR@10 = 0.784 and NDCG@10 = 0.622. Feature-importance analysis using SHAP revealed that Digital Pedagogical Competence (12.6%), Research Productivity (10.8%), and Postgraduate Supervision (9.7%) are the most influential factors in allocation decisions. To ensure institutional alignment, a multi-objective reranking mechanism was introduced to balance suitability, workload fairness, research alignment, and diversity. This approach reduced workload variance from 0.26 to 0.18 and improved research–subject alignment by 21%. Overall, the proposed framework provides a scalable, explainable, and data-driven solution for optimizing graduate subject allocation in modern higher education systems. Full article

Background/Objectives: Home mechanical ventilation (HMV) has become an essential component of long-term respiratory support for patients with chronic respiratory failure. Despite the growing number of publications, the characteristics and citation patterns of the most influential studies have not been systematically evaluated. This study aimed to analyze the 50 most-cited publications on home mechanical ventilation indexed in the Web of Science Core Collection and explore citation patterns and potential associations with citation impact. Methods: This study was designed as a descriptive citation-based bibliometric analysis. A bibliometric analysis was performed using the Web of Science Core Collection. Publications related to home mechanical ventilation were ranked by total citation count, and the 50 most-cited articles were included. Extracted variables included publication year, total citations, citations per year, journal quartile, impact factor, index status, article type, topic category, geographic origin, and ventilation population category. Descriptive statistics were calculated. Group comparisons were performed using the Kruskal–Wallis and Mann–Whitney U tests, and correlations were evaluated with Spearman’s analysis. Funding status was summarized descriptively because funding was reported in only 8 studies. Results: The median total citation count was 15.5 (range: 1–118), and the median citations per year was 0.89 (range: 0.02–9.83). Most articles were published in Q1 journals and indexed in SCI-Expanded. Exploratory associations were observed between citation metrics and journal quartile/index status (p < 0.05). Articles published between 2005 and 2009 had the highest total citations, whereas those published after 2020 showed the highest citations per year. No association was observed with geographic origin. Conclusions: Distinct exploratory citation patterns were observed according to publication period, journal quartile, and index status. Bibliometric evaluation may help characterize the academic development and visibility of home mechanical ventilation research, but these findings should not be interpreted as confirmatory determinants of citation impact. Full article

Accurate prediction of student performance and identification of key influencing factors are essential for improving teaching quality and enabling data-driven educational decision-making. However, conventional metaheuristic optimization algorithms often suffer from premature convergence, insufficient population diversity, and an inadequate balance between exploration and exploitation when solving complex optimization problems. To address these limitations, this study proposes an Improved Educational Competition Optimizer (IECO), which integrates three complementary strategies: an elite exemplar-guided cooperative learning mechanism to preserve population diversity, a rank-adaptive stage-wise search control strategy to dynamically regulate search intensity, and an elite-mean opposition-based refinement strategy to strengthen global exploration capability and local exploitation performance. To evaluate the effectiveness of the proposed method, IECO is applied to optimize the hyperparameters of the K-nearest neighbors (KNN) classifier, leading to the construction of an IECO-KNN grade prediction model. Extensive experiments conducted on the CEC2017 and CEC2022 benchmark suites demonstrate that IECO achieves superior optimization accuracy, faster convergence speed, and stronger robustness compared with several classical and advanced metaheuristic algorithms. Statistical analyses based on the Wilcoxon rank-sum test and Friedman ranking test further confirm the significance and stability of the proposed algorithm. Furthermore, experiments on a real-world educational dataset show that the proposed IECO-KNN model consistently outperforms the other optimization-based KNN models in terms of accuracy, Cohen’s Kappa coefficient, macro-precision, and macro-recall. In particular, the proposed model achieves the highest classification performance and demonstrates more stable prediction capability across independent runs. Correlation analysis further reveals that learning interest, classroom interaction frequency, and extracurricular information acquisition are the most influential factors affecting students’ academic performance. Overall, the proposed IECO and IECO-KNN framework provide an effective and reliable solution for complex optimization and intelligent educational prediction tasks, offering both theoretical contributions to swarm intelligence optimization and practical value for intelligent teaching evaluation systems. Full article

Selecting the appropriate academic track is a critical decision for students, as misalignment between program requirements and individual cognitive, personality, and competency profiles can significantly impact academic performance, persistence, and overall educational outcomes. Traditional educational recommender systems often rely solely on skill matching or on the correlation of interests, failing to account for the dimension of competency that is required for success in specific academic tracks. This paper introduces a novel Multi-Dimensional Psychometric Alignment (MDPA) algorithm that moves beyond simple rank-order correlation between skills and programs by jointly integrating multiple psychometric perspectives and evaluating both preference similarity and competency sufficiency. Based on a structured synthesis of Cognitive Preferences (MBTI), Cognitive Modalities (Gardner’s Multiple Intelligences), and Personality Stability (Big Five), the proposed profile captures complementary dimensions of student readiness that are usually examined separately in prior educational recommender systems. Then applies an alignment algorithm-which is based on a hybrid similarity metric that fuses Spearman’s Rank Correlation (Interest Shape) with Weighted Euclidean Distance (Competency Magnitude), enforced by non-linear threshold penalties for critical traits- in order to find the best options for students. This approach constitutes a deterministic, explainable recommender system whose novelty lies in combining heterogeneous psychometric evidence with an explicit magnitude–shape matching mechanism and threshold-based academic viability constraints. Our approach is validated through a case study of university students in Kazakhstan, and the results demonstrate how “academic fit” is better modeled as a function of both interest pattern and trait sufficiency, offering a robust alternative to “black-box” skill-based recommenders. Full article

Engineering consultants and design firms are central to the success of construction projects. However, the systematic evaluation of their performance in the Saudi Arabian context remains methodologically fragmented and empirically underdeveloped. Existing prequalification frameworks rely predominantly on administrative criteria and single-method ranking approaches that cannot adequately differentiate between high- and low-performing firms. To address this gap, the study proceeds in two distinct parts. Part I—Literature Review: A PRISMA-compliant systematic literature review across five major academic databases was conducted to map the existing evidence base, identify three substantive gaps in the Saudi and GCC engineering firm evaluation literature, and derive a consensus-based set of 29 performance criteria grouped into seven dimensions. This review constitutes an independent contribution: it establishes the gap that motivates the empirical work and provides the criterion framework on which that work is built. Part II—Practical Application: A structured questionnaire was administered to 288 construction professionals in Saudi Arabia (Cronbach’s α = 0.936), and the collected data were analyzed through a hybrid RII–Shannon Entropy Weighting (EWM)–TOPSIS pipeline that produced a Composite Priority Index (CPI) for each criterion, enabling a stable and discriminating ranking that integrates subjective expert consensus with objective distributional information. The main finding revealed that five criteria attained Very High Priority status (CPI > 0.70): Supervisory Experience (CPI = 0.740), Engineers’ Capability Index (CPI = 0.717), License Class (CPI = 0.709), Client Satisfaction Index (CPI = 0.708), and Average Delay Time (CPI = 0.705). These top-ranked criteria collectively center on technical leadership, regulatory standing, client-reported outcomes, and schedule reliability, indicating that procurement decisions should prioritize demonstrable competence over structural size or geographic footprint. The consistently lower importance of physical branch networks and headquarters location further suggests that remote management capabilities and digital coordination tools are reshaping performance expectations under Saudi Vision 2030. The Quality Indicators dimension achieved the highest mean CPI across all seven dimensions. The findings provide actionable evidence for procurement authorities, regulatory bodies, and engineering firms seeking to strengthen performance-evaluation practices in the Saudi construction sector. Full article

Background: Diversity in academia promotes research that can reduces health disparities and addresses equity issues for marginalized populations. This study aims to examine the effect of visible minority status on academic achievements in cancer physicians and scientists. Methods: Faculty at the tertiary cancer center in Calgary, Alberta, Canada, completed a survey in 2023 to evaluate demographics, academic rank, leadership positions, number of trainees mentored, number of publications, and amount of grant funding. Chi-square tests and regression analyses examined the impact of race and ethnicity on these academic achievements. Results: The survey was completed by 74 faculty members (47% male, 43% female, 9% gender fluid or providing no answer) with a response rate of 26%. Seven percent were Black or Latin American, 18% East Asian or Southeast Asian, 19% West or South Asian, 39% Caucasian, 6% mixed race, and 11% not providing an answer. Visible minorities were underrepresented in the full professor rank (19%) compared to non-visible minorities (38%) and were overrepresented in assistant/associate professors (28% and 53%, respectively), with 41% of non-visible minorities having the title of assistant professor and 21% as associate professor (p = 0.02). Visible minorities were less likely to have both parents college-educated (OR 0.30, 95% CI 0.09–0.92, p = 0.042) and also less likely to have been raised in a home with household income above $100,000 (OR 0.26, 95% CI 0.07–0.90, p = 0.040). Discussion: Visible minorities are underrepresented in the full professor academic rank. Larger studies are needed to evaluate whether race and ethnicity significantly impact achievements in oncology academics. Full article

The increasing adoption of data-driven educational systems requires reliable methods to predict student readiness for future coursework and support personalised learning pathways. This study proposes a graph-enhanced ensemble framework that integrates curriculum structure and skill-gap awareness to estimate student course readiness. A global prerequisite directed acyclic graph (DAG) of university subjects was constructed to model curriculum dependencies, from which structural features including the PageRank, in-degree, out-degree, and prerequisite chain depth were derived. In parallel, a domain-informed skill cluster mapping grouped subjects into nine interpretable competency domains to enable skill-gap analysis. These curriculum-aware features were combined with academic history, behavioural engagement, and demographic indicators to produce 38 engineered features for each student–subject pair. Six models (CatBoost, XGBoost, LightGBM, FT-Transformer, MLP and TabPFN) were trained and combined using a weighted ensemble. Experiments on a real-world institutional dataset containing 20,581 students and 727,168 records achieved an AUC of 0.8908 for predicting course success. Ablation experiments demonstrate that graph-derived and skill-cluster features provide modest but statistically significant incremental value. The resulting model was integrated into a prototype personalised recommender that prioritizes curriculum-consistent learning pathways. The proposed framework provides an interpretable and curriculum-aware approach for personalised learning. While the model demonstrates strong overall performance, a moderate gender disparity in the false positive rate was observed. Results were obtained on a large longitudinal dataset from a single university, and external validation at other institutions is needed to confirm generalizability. Full article

Environmental Impact Assessment (EIA) is intended to function as a predictive, spatially grounded decision-support mechanism. Yet in many developing contexts, its operationalization remains fragmented, descriptive, and weakly standardized. Thus, this study addresses limitations in conventional EIA systems related to transparency, reproducibility, and uncertainty integration by proposing a spatially explicit, digital rule-based decision-support framework that operationalizes hierarchical receptor-based structuring, lifecycle-sensitive modelling, risk classification, and uncertainty propagation within an integrated Geographic Information Systems (GISs) architecture. The academic objective is to advance computational environmental assessment methodologies by formalizing EIA logic into a structured computational workflow that translates spatial interactions (including land use, population density, ecological sensitivity, hydrological zones) and project attributes (including project type, activities and operational conditions) into quantified risk profiles and mitigation mappings. This necessitates combining receptor proximity, overlap intensity, contextual sensitivity, operational conditions, and receptor vulnerability. The framework was applied to three airport case studies in Egypt—representing urban, peri-urban/desert expansion, and coastal–ecological environmental contexts—using standardized spatial preprocessing and normalized analytical scales. Validation was conducted using Monte Carlo uncertainty simulation, sensitivity analysis, Spearman rank correlation, and Cohen’s Kappa agreement analysis. The results demonstrated stable comparative risk classification across receptor categories, lifecycle phases, and impact mechanisms under moderate parameter perturbation (±15%). Cohen’s Kappa agreement values ranging from 0.71 to 0.79 indicated substantial consistency between model-generated exceedance zones and regulatory environmental classifications. In sum, the results demonstrate that receptor proximity, operational intensity, and lifecycle stage function as primary determinants of differentiated environmental risk configurations, and that the proposed framework can support transparent, reproducible, and spatially explicit environmental assessment. Full article

Background: Bladder cancer disproportionately affects non-high-income countries, yet clinical trials underrepresent global diversity. We assessed global availability of bladder cancer trials, their alignment with disease burden, and barriers to equitable care. Methods: We queried ClinicalTrials.gov for adult bladder cancer trials from June 2019 to June 2024, excluding observational and non-oncologic trials. Trial characteristics were summarized descriptively, and country data came from the Global Cancer Observatory. Countries were classified per World Bank Ranking (WBR) into high-income (HICs), upper middle-income (UMICs), lower middle-income (LMICs), and low-income countries (LICs). Trials were categorized as HIC-only, non-HIC, or mixed-income trials. Fisher’s exact and Kruskal–Wallis tests compared groups. Multivariable logistic regression assessed associations between trial availability and WBR, national health expenditure, and gross national income (GNI). Univariable linear regression and ANOVA assessed the association between the mortality-to-incident ratio and WBR. Results: Of 611 trials, 75.1% were HIC-only, 16.9% non-HIC, and 8.0% mixed-income trials. Non-HIC trials were mainly academic-sponsored (80.6%), while all mixed-income trials had pharmaceutical sponsorship (p < 0.001). Non-HIC trials had lower enrollment, less pharmaceutical funding, fewer multinational collaborations, and fewer basket, multi-arm, early-phase designs (all p < 0.001). Mixed-income trials were larger, led by HICs, had broader eligibility criteria, more novel therapies, and more frequent use of overall survival endpoints. Trial availability was lower in UMICs (p = 0.011), LMICs (p = 0.024), and absent in LICs, and positively associated with higher national health expenditure (p = 0.007) and GNI (p = 0.001). Conclusions: Bladder cancer trials remain concentrated in HICs. Mixed-income trials expand access in non-high-income countries, but are exclusively led by HICs and require balanced sponsorship, early-phase research, and lasting local benefits. Full article

Autonomous mobile robots (AMRs) are increasingly used in warehouse intralogistics to improve material flow, flexibility, productivity, and operational continuity. However, selecting an appropriate AMR is no longer limited to mechanical performance or acquisition cost, since modern warehouse robots operate as networked cyber-physical systems that must interact with enterprise software, fleet management platforms, communication infrastructures, and cybersecurity mechanisms. This study proposes an integrated Pythagorean fuzzy multi-criteria decision-making (MCDM) framework for evaluating AMR alternatives in warehouse operations by jointly considering economic, technical, physical, software-related, integration-oriented, and security-related criteria. Expert judgments obtained from eight specialists, including four academics and four private-sector professionals, were modeled using Pythagorean fuzzy numbers to capture uncertainty and hesitation in linguistic assessments. The Pythagorean Fuzzy Indifference Threshold-Based Attribute Ratio Analysis (PF-ITARA) method was employed to determine criterion weights based on threshold-sensitive discrimination among alternatives, while Pythagorean Fuzzy VIšekriterijumsko KOmpromisno Rangiranje (PF-VIKOR) was used to rank four AMR alternatives according to a compromise solution logic. The results show that investment cost, maneuverability, total cost of ownership, integration and validation requirements, and ease of programming and commissioning are the most influential criteria. Cybersecurity-related criteria, particularly data confidentiality, system integrity, monitoring and incident response readiness, authentication control, and role-based access control, also received notable importance levels. Among the evaluated alternatives, MiR250 achieved the best overall performance and emerged as the most suitable compromise solution, followed by OMRON LD-250, HIKROBOT Forklift AGV, and KUKA KMP 600-S diffDrive. The proposed framework provides a transparent and practically applicable decision-support tool for AMR procurement by integrating operational performance, digital interoperability, and cybersecurity readiness into a unified evaluation structure. Full article

Timor-Leste, Asia’s youngest nation since its independence in 2002, has been making progress in its education sector. However, these gains have not translated into leadership representation as expected, with women remaining significantly underrepresented in senior academic and managerial roles in higher education. While existing studies highlight the potential of intersectional disaggregated data to enhance the visibility of layered inequalities and inform more targeted leadership interventions, its application in Timor-Leste remains at an early stage. This study examines respondents’ perception of barriers and enablers influencing the collection and use of intersectional disaggregated data, and their association with perceived leadership interventions aimed at advancing women in higher education leadership in Timor-Leste. A survey design was employed, with questionnaires administered to purposively selected academic and non-academic staff across selected universities in Timor-Leste. Data were analysed using descriptive and inferential techniques, including the Kruskal–Wallis test, and Spearman’s rank correlation (ρ). The findings suggest that respondents perceive key leadership interventions to include women’s leadership development programmes, mentorship, mental health support, and establishment of dedicated equality and diversity units Respondents also identified key enablers and barriers influencing the collection and use of intersectional disaggregated data, including staff training in ethical data practices, the use of mixed-method approaches, and the provision of privacy protections, alongside constraints related to data systems, capacity, and leadership support. Spearman’s analysis showed significant associations between perceived enablers and barriers influencing the collection and use of intersectional disaggregated data and perceived leadership interventions. This study contributes to the gender equity literature by providing empirical insights on perceived institutional conditions, reported barriers, enablers and perceived mechanisms through which intersectional data may inform leadership-related interventions in the context of Timor-Leste’s higher education system. Full article