This study validates the Intercultural Competence and Inclusion in Education Scale (ICIES), a novel instrument designed to assess students’ perceptions of inclusivity and intercultural competence in multiethnic secondary schools. Using a sample of 276 high school students from Western Romania, the ICIES identified three dimensions: ethnic appreciation and support, intercultural engagement and integration, and school unity and cohesion. Exploratory factor analysis confirmed the scale’s structural validity, while network analysis revealed key interconnections among its components. Findings highlight the critical role of inclusive teaching strategies and school cohesion in fostering intercultural competence. The ICIES provides educators and policymakers with actionable insights for designing interventions that promote empathy, mutual respect, and a sense of belonging in diverse school settings. These results contribute to the development of educational policies aimed at fostering inclusion and addressing the needs of increasingly multicultural classrooms.

The debate on relocating Indonesia’s national capital from Jakarta stems from critical issues such as overpopulation, social inequality, environmental degradation, and natural disaster risks. These challenges highlight the need to reassess Jakarta’s viability as the nation’s administrative center. This study evaluates Indonesia’s readiness to address the complexities of relocation by analyzing Jakarta’s socio-economic, political, cultural, and geographical conditions. Using a systematic literature review (SLR) with a qualitative approach, the research explores key questions: Do Jakarta’s conditions necessitate relocation? What challenges might arise from the move? How prepared is Indonesia to tackle these challenges? The SLR process includes defining questions, sourcing literature from reputable databases, applying inclusion/exclusion criteria, and synthesizing data for analysis. Findings reveal Jakarta’s multifaceted challenges, including social disparities, environmental degradation, disaster risks, and governance issues, which emphasize the urgency of considering relocation. However, the study also identifies significant hurdles, such as high costs, logistical complexities, potential social conflicts, and environmental risks at the new capital site. Relocating the capital is a strategic and complex undertaking that requires meticulous planning. Indonesia must weigh Jakarta’s current issues, address potential relocation challenges, and ensure readiness for risk mitigation and sustainable development. Comprehensive and thoughtful planning is essential to achieve a successful and balanced transition.

The integration of Big Earth Data and Artificial Intelligence (AI) has revolutionized geological and mineral mapping by delivering enhanced accuracy, efficiency, and scalability in analyzing large-scale remote sensing datasets. This study appraisals the application of advanced AI techniques, including machine learning and deep learning models such as Convolutional Neural Networks (CNNs), to multispectral and hyperspectral data for the identification and classification of geological formations and mineral deposits. The manuscript provides a critical analysis of AI’s capabilities, emphasizing its current significance and potential as demonstrated by organizations like NASA in managing complex geospatial datasets. A detailed examination of selected AI methodologies, criteria for case selection, and ethical and social impacts enriches the discussion, addressing gaps in the responsible application of AI in geosciences. The findings highlight notable improvements in detecting complex spatial patterns and subtle spectral signatures, advancing the generation of precise geological maps. Quantitative analyses compare AI-driven approaches with traditional techniques, underscoring their superiority in performance metrics such as accuracy and computational efficiency. The study also proposes solutions to challenges such as data quality, model transparency, and computational demands. By integrating enhanced visual aids and practical case studies, the research underscores its innovations in algorithmic breakthroughs and geospatial data integration. These contributions advance the growing body of knowledge in Big Earth Data and geosciences, setting a foundation for responsible, equitable, and impactful future applications of AI in geological and mineral mapping.

The target area of the survey is the rehabilitated flat area behind the capital cities of Vienna and Bratislava, which lies in the tourist area of Győr. Wetlands provide a backdrop for tourism products such as kite flying, cycling and walking. The city centre offers tourists an easy sightseeing tour behind the natural scenery of the Danube tributary (Szigetköz). Objective: The demographic characteristics of demand and preferences for active tourism product types and the extent of the scope of supply were analyzed. The present research also analyses the cycling routes in the region with regard to the EUROVELO 6 road network. The primary research was a quantitative (questionnaire) survey conducted between 10 September 2023 and 30 October 2023. The survey sample of 666 respondents is not representative and was selected by random sampling. The results of the research include an analysis of the demand for participation in cycling tourism and tour programs as activities requiring activity. The findings of the research provide a basis for demand-supply segmentation of sustainable active tourism product development based on physical experience according to demographic characteristics (e.g. age, education). The landscape of the wetland can be positioned for the bicycle tourists. Especially for the target group of people over 40 and for people with higher education. The scope of the guided tours, linked to the central offer, extends over an area of more than 50 km. Activating the target group helps the rehabilitated natural scenery to connect to sustainable tourism.

The COVID-19 pandemic has fundamentally transformed the global education landscape, compelling institutions to adopt e-learning as an essential tool to sustain academic activities. This research examines the critical impact of e-learning on arts and science college students in Coimbatore, with an emphasis on its influence on their readiness for campus recruitment. Using a survey of 300 students, this study investigates their perceptions of online education, highlighting both its advantages, such as flexibility and accessibility, and its challenges, including engagement barriers and technical limitations. Data was collected through structured questionnaires and analyzed using statistical methods to draw meaningful insights. The research also explores the efficacy of online assessments in recruitment processes and assesses students’ awareness of available e-learning platforms and courses. The urgency of this study lies in addressing the pressing need to optimize digital education models as institutions globally transition toward blended learning post-pandemic. The findings underline the dual potential and limitations of e-learning, concluding with actionable recommendations to enhance its effectiveness, particularly in preparing students for competitive employment opportunities.

Credit risk assessment is one of the most important aspects of financial decision-making processes. This study presents a systematic review of the literature on the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques in credit risk assessment, offering insights into methodologies, outcomes, and prevalent analysis techniques. Covering studies from diverse regions and countries, the review focuses on AI/ML-based credit risk assessment from consumer and corporate perspectives. Employing the PRISMA framework, Antecedents, Decisions, and Outcomes (ADO) framework and stringent inclusion criteria, the review analyses geographic focus, methodologies, results, and analytical techniques. It examines a wide array of datasets and approaches, from traditional statistical methods to advanced AI/ML and deep learning techniques, emphasizing their impact on improving lending practices and ensuring fairness for borrowers. The discussion section critically evaluates the contributions and limitations of existing research papers, providing novel insights and comprehensive coverage. This review highlights the international scope of research in this field, with contributions from various countries providing diverse perspectives. This systematic review enhances understanding of the evolving landscape of credit risk assessment and offers valuable insights into the application, challenges, and opportunities of AI and ML in this critical financial domain. By comparing findings with existing survey papers, this review identifies novel insights and contributions, making it a valuable resource for researchers, practitioners, and policymakers in the financial industry.