International University

This study addresses the critical challenge of fetal distress identification, with either maternal or neonatal death coming first in this case. Manual CTG interpretation, being subjective, leads to disadvantages, and a high degree of inter-observer variability may result in misdiagnosis or late clinical intervention with detrimental consequences. Thus, our research fills the gap wherein there has been very little comprehensive and rigorous comparative analysis of a large number of machine learning models. We systematically studied a wide and diverse range of classifiers in our comparison, including tree-based ensembles such as XGBoost, Random Forest, CatBoost, and LightGBM, but also Gradient Boosting, Decision Tree, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Logistic Regression, AdaBoost, Naive Bayes, and Convolutional Neural Networks (CNN). These models were trained and tested on a very large and open-source CTG dataset to validate their predictive power. Our findings reveal that the XGBoost model demonstrated superior performance with an impressive accuracy of 99.04%, while CatBoost, LightGBM, and Random Forest, which had intense predictive powers well above traditional diagnostic means. The above-mentioned accuracy-driven models have found their aptitude in capturing highly complicated nonlinear patterns occurring in CTG data and therefore hold a promising prospect to be developed and applied toward a large-scale and automated diagnostic aid. The successful implementation of these novel techniques would have huge potential for improving the quality of prenatal care and clinical decision-making in resource-poor areas, where expert supervision may be widely scarce.

Simulation/games come in all imaginable shapes and sizes. One shape and size that you may not have encountered is SIMPLEX, developed by Drew Mackie. The term means from the simple to the complex.Simulations using this format start with only essential components: people and a theme. In our example, the people are you; the theme is SIDS – Small Island Developing States.SIDS are facing immense challenges caused by global heating, particularly rising sea levels (SLR). Vulnerable stakeholders facing these challenges, as well as scientists, policymakers and other interested parties, can benefit from the use of co-created, participatory policy simulation using the SIMPLEX format.Our presentation is actually an invitation to fellow geogamers to join us during the geogames evening to help co-build a simulation/game together. It is more a simulation than a game, unless you decide otherwise! In our presentation, we will do the following:Outline the content objective (develop a policy for SLR on SIDS);Outline the procedure that we will follow at the start of the simulation;Say a few words about some of the simulation elements (e.g., time);Emphasise the importance of the debriefing;Answer any questions.On previous occasions, colleagues and DC have used the SIMPLEX format to examine or develop policy in regard, for example, to the three-day working week in the UK, the outcome of Dutch elections, an ideal education system and climate-ocean change policy. When used in anticipation of events, the simulation can have powerful and uncannily predictive power. The simulation format is adaptable to a variety of geo-situations.

Purpose: This study analyses how green investment, renewable energy capacity and climate policy influence gross domestic product (GDP), employment and CO₂ emissions in Brazil, Canada, China and India (2005–2023). Design/Methodology/Approach: A deductive explanatory strategy is utilised. Autoregressive integrated moving average and system generalised method of moments are applied to annual panel data to estimate the short- and long-run effects. Findings: Renewable energy investment increases GDP and the short-run effect is negative. Climate policy is growth friendly but has limited employment effects. Originality/Value: New empirical evidence on growth and environmental trade-offs that can guide sustainable policy choices. Practical Implications: Green investment and policy can foster growth despite transitioning difficulties. Governments need to assist in clean energy transitions without hurting employment. Keywords: Green Investment, Renewable Energy Capacity; Climate Policy; GDP; Employment; CO₂ Emissions; Panel Data. Citation: Knio, M. S. E. D. and Balikel, A. E. (2026): Green Investment, Renewable Energy Capacity and Climate Policy: An Econometric Analysis of Sustainable Development in Brazil, Canada, China and India (2005–2023). World Journal of Entrepreneurship, Management and Sustainable Development (WJEMSD), Vol. 22, No. 3, pp. 227-244.

This article examines the evolving role of District Central Cooperative Banks (DCCBs) in promoting financial inclusion across rural West Bengal. Drawing upon empirical evidence from district-level cooperative banks, official RBI data, and recent scholarly research, the study investigates how these grassroots institutions have adapted to policy shifts, technological advancements, and changing rural economic landscapes. The findings reveal that DCCBs in West Bengal have demonstrated remarkable resilience, with 15 out of 17 banks reporting profitability in 2024-25 and significant improvements in asset quality (Bhattacharyya & Nandy, 2024; Reserve Bank of India, 2025). The implementation of the Pradhan Mantri Jan-Dhan Yojana (PMJDY) in 2014 marked a critical turning point, dramatically expanding account penetration and deposit mobilization. Hooghly District Central Cooperative Bank exemplifies this transformation, reporting 2.23 lakh Kisan Credit Card holders and financing over 31,000 Self Help Groups (SHGs) as of 2022 (Cooperation Department, Government of West Bengal, n.d.). The study contributes to the growing literature on cooperative banking and sustainable development by demonstrating how DCCBs serve as vital intermediaries in reducing rural households' dependence on informal credit sources (Saravanabhavan & Rajeev, 2024; Singh et al., 2022). However, persistent challenges, including rising non-performing assets (10.4% in West Bengal), technological adoption gaps, and the need for enhanced financial literacy, require sustained policy attention.

This study examined teachers' disability-inclusive education background and its relationship with self-efficacy in instruction, collaboration, and managing behaviour within the Cambodian context. Employing a mixed methods design, quantitative data were collected from 533 teachers (403 general education, 130 special education) across five provinces using an 18-item self-efficacy questionnaire, whilst qualitative secondary data underwent SWOT analysis. Statistical analyses including Confirmatory Factor Analysis, Structural Equation Modelling, and independent samples t-tests were conducted using SPSS AMOS Version 29. Results revealed significant positive relationships between teachers' self-efficacy dimensions and both disability-inclusive education background and implementation outcomes. Special education teachers demonstrated significantly higher self-efficacy in instruction (M = 3.10 vs 2.54, p < .001) and collaboration (M = 3.02 vs 2.59, p < .001) compared to general education teachers. However, 47 per cent of teachers reported no inclusive education training, whilst 35 per cent had no experience teaching children with disabilities. SWOT analysis identified supportive policies and increased awareness as strengths, but revealed critical weaknesses including inadequate training and negative staff attitudes. These findings underscore urgent need for systematic professional development interventions to enhance teacher self-efficacy and advance Cambodia's inclusive education implementation aligned with the Inclusive Education Action Plan 2024-2028.

INTRODUCTION: Digital twin technology is at the head of smart city innovations; rapid urbanization and the rising complexity of infrastructure management have driven this technology. Real time virtual replicas of the physical urban systems - digital twins - simulate urban scenarios, monitor conditions, and improve resource allocation more accurately than ever before. Rigorous empirical studies that quantify the impact of these technologies on the efficiency of urban planning are rare, curbing evidence-based policy making and large-scale adoption strategies. OBJECTIVES: This study estimates a panel fixed effects econometric model that quantifies the impact of integrating digital twins on urban planning performance of five European countries (Estonia, Germany, Portugal, the UK, Poland) over the period of 2020–2024. METHODS: Using data from the OECD Smart Cities database, the European Commission Urban Data Platform, the World Bank’s Worldwide Governance Indicators, and national municipal APIs, annual data were assembled on a composite urban efficiency index, a digital twin integration score, real time data usage, per capita infrastructure investment, governance quality metrics, and population density. The model employs city and time fixed effects and addresses heteroskedasticity and serial correlation with clustered robust standard errors. RESULTS: The efficiency index of Germany improved from 53,52 in 2020 to 62,57 in 2024; Poland from 45,51 to 58,73; and Estonia dropped sharply from 70,22 to 39,44. Coefficients on digital twin integration (β₁ = 12,00) and governance quality (β₄ = 8,00) are positive and statistically significant. Real-time data use (β₂ = 0,08) and infrastructure investment (β₃ = 0,004) increase efficiency, while population density has a slight negative effect (β₅ = –0,005). CONCLUSION: This underscores the importance of the network and governance relationships of digital twins in realizing their potential. Future research should include non-European cities, apply spatial econometric techniques to account for intercity spillovers, and use dynamic panel models to assess feedback loops from past efficiency gains and their influence on future technology investment.

Zebrafish are commonly tested in various behavioral assays. To better understand such practices, we surveyed active global zebrafish neuroscience labs, asking to rank such assays based on their perceived utility. Overreliance on few well-established assays and their inconsistent nomenclature call for further innovation and standardizing of adult zebrafish neurobehavioral testing.

BackgroundDiagnosing oral lesions from benign conditions to oral cancer remains challenging due to overlapping visual features and reliance on histopathology. Large language models (LLMs) can integrate textual and visual cues, but their diagnostic accuracy and clinical utility in real decision-making contexts remain uncertain. To systematically evaluate the diagnostic performance, clinical usefulness, and limitations of LLMs in identifying oral lesions.MethodsPubMed, CINAHL, Embase, Web of Science, and Google Scholar were searched to 20 July 2025. Eligible studies applied LLMs (e.g., ChatGPT, Gemini, DeepSeek, Copilot, Claude) for diagnosis or differential diagnosis of oral lesions using text, images, or multimodal inputs. Outcomes included diagnostic accuracy, agreement metrics, and qualitative assessments of explanation quality and clinical applicability. Risk of bias was assessed using an adapted QUADAS-2. Narrative synthesis was performed due to heterogeneity.ResultsSeventeen studies (>1,200 cases) were included. Diagnostic accuracy ranged from 25%–96%, varying by model version, input modality, and lesion complexity. Multimodal inputs consistently improved performance, with Cohen's κ up to 0.85–0.90. Advanced models (GPT-4o, DeepSeek-R1, o1-preview) outperformed earlier versions and approached expert performance in some tasks, although specialists generally retained superior Top-1 accuracy. Clinical utility was highest when LLMs were used to structure differential reasoning, highlight red-flag features, and support communication, but limited in tasks requiring fine morphological interpretation or severity grading. Overall risk of bias was low to moderate.ConclusionsLLMs demonstrate variable diagnostic performance and context-dependent supportive utility as adjunctive tools in oral lesion assessment, particularly in multimodal settings. They should complement, rather than replace, expert clinical judgment. Future research should prioritize real-world workflow evaluation, standardized prompting strategies, and prospective clinical validation.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/view/CRD420251090315, identifier CRD420251090315.

Encouraging meaningful engagement in virtual learning contexts presents notable challenges, as the absence of physical interaction often limits spontaneity and immediacy. This study examines the tone and linguistic features employed in asynchronous exchanges within the discussion forums of a course offered by an online university. To achieve this, it is essential to consider not only the quantity but, above all, the quality of the contributions made by both teachers and students, paying particular attention to tone and linguistic features. The sample refers to a subject taught during the 23/24 academic year, Didactics of the Specialty (English) in the Master's Degree in Teaching in Secondary Education, Baccalaureate, Vocational Training and Adult Education. The total number of students is 114. The methodology responds to a triangulated qualitative analysis: on the one hand, the contributions to the forums of the students and the teacher are analysed; on the other hand, the open responses to a voluntary and anonymous questionnaire that is given to the students at the end of the subject are analysed too; and, finally, a sentiment analysis is carried out, focusing on the tone of all the contributions. The findings indicate that adopting a friendly and conversational tone, combined with consistent, timely, and active participation on the part of the teacher, contributes to transforming the forum into a dynamic environment for interaction and learning. This communicative style encourages students to mirror the teacher’s approach, promoting a sense of proximity and confidence that becomes evident in their own tone and discourse.

The increasing integration of renewable energy sources, such as solar photovoltaics, wind turbines, hydropower, and energy storage systems, introduces substantial variability and complexity into modern power grids. This variability challenges grid stability, supply-demand balancing, and operational resilience. Digital twin (DT) technology, which provides a dynamic, real-time virtual representation of physical assets and systems, has emerged as a transformative tool for monitoring, analyzing, and optimizing energy grids. The incorporation of artificial intelligence (AI) into digital twins further enhances their capabilities, enabling predictive analytics, adaptive control, fault detection, and real-time decision-making for grid-specific objectives such as voltage/frequency regulation, congestion management, DER coordination, curtailment reduction, and resilience under fast renewable ramps. Machine learning, deep learning, and reinforcement learning techniques facilitate accurate forecasting of energy generation and demand, intelligent dispatch of distributed energy resources, and predictive maintenance, while hybrid models combining physics-based simulations with AI improve prediction accuracy in data-sparse or high-uncertainty environments. Despite these advancements, challenges persist, including data quality and availability, computational scalability, cybersecurity risks, and interoperability issues. This review synthesizes current research on AI-driven digital twins in renewable energy grids, highlights methodological and technological gaps, and identifies future research directions for developing resilient, scalable, and adaptive energy systems. The findings underscore the potential of AI-integrated digital twins to accelerate the transition toward intelligent, sustainable, and climate-resilient energy infrastructures. In addition, this review incorporates sustainability-oriented intelligent-system methodologies such as energy-aware edge–cloud cyber-physical architectures and digital-twin-enabled lifecycle sustainability frameworks to align DT optimization with contemporary sustainability practice better.