The aim of the research is to prove that nowadays the role of higher education, its impact on “territorial capital” and the factors of their competitiveness measurement have changed. Competitiveness should no longer be measured only in terms of rankings between higher education institutions, but also in terms of their role in territorial capital. Examining the extension of a competitiveness measurement model developed for small and medium-sized enterprises to the field of higher education can be exciting because the competitive situation between higher education institutions is strengthening, and its aspects are not limited to winning tender funds and the competition for students. The subject of this study is the Central European higher education in general and the Hungarian higher education specifically. Higher education as it appears in regional strategic documents, and the regional, third mission role of higher education institutions appearing in their strategic documents. In terms of methodology: the first part of the paper is based on document and content analysis. In the second part of the paper, institutional characteristics that may influence competitiveness are identified in the case of a Hungarian higher education institution with SME characteristics. The research concludes that the impact on territorial capital, together with the traditional characteristics of higher education and its third missionary role, may constitute the competitiveness of a given institution. If the impact of higher education institutions on location could be measured uniformly, competition between institutions would be more transparent and the role of the region would be strengthened.

Tourism stands as a cornerstone industry, experiencing continual expansion within the global economy, and is increasingly acknowledged for its crucial role as an economic catalyst. The convergence of the tourism sector with the film culture industry amplifies the economic advantages of regions and enriches cultural narratives while bolstering the international resonance of regional brands. This study examines the promotional efficacy of adapted films in fostering the development of local tourism sectors. It investigates the impact of adapted films on the development of local tourism economy and provides a new analytical perspective to describe their specific contribution to the tourism economy. Empirical findings underscore that adapted films imbued with regional nuances substantially augment local tourism revenue, catalyzing the growth of the tourism sector. This highlights the capacity of adapted films to amplify regional exposure, nurturing the expansion of the local tourism economy. The findings of the study reveal that the promotional impact of adapted films on local tourism sectors is more pronounced in less developed regions than in more developed regions. Moreover, areas with limited transportation infrastructure witness a heightened promotional effect from the film industry. Rigorous robustness and endogeneity tests corroborate the reliability of these findings.

In this study, we consider the extended Brinkman's-Darcy model for a triple diffusive convection system which consists of some parameters such as Taylor number (Ta), Solutal Rayleigh numbers (RC1 , RC2 ), and Prandtl number (Pr). To investigate the range of these parameters, a dynamical system of the Ginzburg-Landau equation is developed. The parametric analysis and comparative study of the model for the three Rayleigh numbers which leads to the clear fluid layer, sparsely packed porous layer, and densely packed porous layer is done with the help of bifurcation maps and the Lyapunov exponents. It is found that for a certain range of parameters, the system exhibits a chaotic behaviour.

Accurate drug-drug interaction (DDI) prediction is essential to prevent adverse effects, especially with the increased use of multiple medications during the COVID-19 pandemic. Traditional machine learning methods often miss the complex relationships necessary for effective DDI prediction. This study introduces a deep learning-based classification framework to assess adverse effects from interactions between Fluvoxamine and Curcumin. Our model integrates a wide range of drug-related data (e.g., molecular structures, targets, side effects) and synthesizes them into high-level features through a specialized deep neural network (DNN). This approach significantly outperforms traditional classifiers in accuracy, precision, recall, and F1-score. Additionally, our framework enables real-time DDI monitoring, which is particularly valuable in COVID-19 patient care. The model’s success in accurately predicting adverse effects demonstrates the potential of deep learning to enhance drug safety and support personalized medicine, paving the way for safer, data-driven treatment strategies.

This research uses both quantitative and qualitative research methodologies to examine the complex factors affecting community resilience in various settings. In this case, the research explores how social cohesion, governance effectiveness, adaptability, community involvement, and the specified difficulties influence resilience results by using the five pillars of resilience as variables. Descriptive and inferential statistics are used to test hypotheses on the relationships between social cohesion, governance effectiveness, adaptive capacity, and community resilience variables. Qualitative data provides further insights into the quantitative results by providing broader views and experiences of the community. The study shows how social capital is important in increasing community capacity, stressing the importance of social relations and trust in developing community solutions to disasters. Another major factor that stands out is the governance factor that ensures that decisions are made, and actions taken in line with the community’s best interest in improving its ability to prepare for and respond to disasters. Adaptive capacity is seen as a key component of resilience and this paper emphasizes the importance of communities to come up with measures that can be adjusted to the changing circumstances. In summary, this study enriches theoretical understanding and offers practical applications of the processes that can enhance community resilience based on the principles of social inclusion, sound governance, and context-specific solutions.

Objective: As the scale and importance of official development assistance (ODA) continue to grow, the need to enhance the effectiveness of ODA policies has become more critical than ever before. In this context, it is essential to systematically classify recipient countries and establish tailored ODA policies based on these classifications. The objective of this study is to identify an appropriate methodology for categorizing developing countries using specific criteria, and to apply it to actual data, providing valuable insights for donor countries in formulating future ODA policies. Design/Methodology/Approach: The data used in this study are the basic statistics on the Sustainable Development Goals (SDGs) published annually in the SDGs Report. The analytical method employed is decision tree analysis. Results: The results indicate that the 167 countries analyzed were classified into 10 distinct nodes. The study further limited the scope to the five nodes representing the most disadvantaged developing countries and suggested future directions for aid policies for each of these nodes.