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.

The focus of the article is the evaluation of the interaction between regional state bodies and business structures in Kazakhstan, specifically in terms of the development of public-private partnerships. The purpose of the research is to enhance the understanding of the theoretical and practical aspects of the mechanism of interaction between the state and business structures. Through an examination of the various structural components of the partnership development strategy, the study aims to identify the elements of the mechanism for the implementation of the state and business development strategy. Additionally, the research seeks to establish the correlation between the outcomes of the joint entrepreneurship mechanism and the criteria used to evaluate the performance of regional state bodies. To assess the effectiveness of the interaction between business and government at the regional level in Kazakhstan, a survey-based evaluation was conducted to measure the satisfaction levels of public utilities, entrepreneurs, and businesses with the activities of local authorities. The survey also evaluated the degree of corruption among local authorities. A matrix of interaction between business and government was created, and various models and algorithms for the interaction between government representatives and business structures were studied. The research findings highlight the importance of enhancing the collaboration between the state and the business sector, promoting the implementation of public-private partnerships, and establishing social partnerships to cultivate mutually beneficial relationships.

The border is a strategic area within the Republic of Indonesia because it has potential natural resources and market opportunities and is related to aspects of sovereignty, defense and security. The division of the Papua region based on astronomical lines causes the traditional region, inhabited by tribes with the same spiritual-culture, to be divided into two countries. The Kanum tribe, who live in the border region of PNG and Indonesia, have close kinship relations. This research aims to analyze the social interactions of cross-border communities, especially the interdependence of the Kanum Tribe in Sota, Merauke Regency, with Papua New Guinea. The research used social interaction theory and interdependence theory, as well as qualitative descriptive methods by interviewing 15 informants. The research results support Polanyi’s statement but refute Omolomo’s, confirm positive competition, and eliminate indicators of conflict in the social interactions of the Kanum Tribe. The main problem found was unclear population data for the Kanum community who live in PNG but receive facilities from Indonesia. The dominant inhibiting factor comes from the PNG border condition, and the dominant supporting factor comes from the Sota border conditions (geography, infrastructure, economics and government policy). However, the condition that is equally a during factor in the conditions of the PNG and Sota borders is culture.

The objective of this research is to examine the effects of income inequality, governance quality, and their interaction on environmental quality in Asian countries. Time series data are obtained from 45 Asian countries for the period 1996–2020 for this empirical analysis. The research has performed various econometric tests to ensure the robustness and reliability of the results. We have addressed different econometric issues, such as autocorrelation, heteroskedasticity, and cross-sectional dependence, using the Driscoll-Kraay (DK) standard error estimation and endogeneity issues by the system generalized method of moments (S-GMM). The results of the study revealed that income inequality and governance quality have a positive impact on environmental degradation, while the interaction of governance quality with income inequality has a negative effect on it. In addition, economic growth, population growth, urbanization, and natural resource dependency are found to deteriorate the quality of the environment. The findings of the study offer insightful policies to reduce environmental degradation in Asian countries.