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 rapid rise of live streaming commerce in China has transformed the retail environment, with electronic word-of-mouth (eWOM) emerging as a pivotal factor in shaping consumer behavior. As a digital evolution of traditional word-of-mouth, eWOM gains particular significance in live streaming contexts, where real-time interactions foster immediacy and engagement. This study investigates how eWOM influences consumer purchase intentions within Chinese live streaming platforms, employing the Information Adoption Model (IAM) as theoretical framework. Using a grounded theory approach, this research applies NVivo for data coding and analysis to explore the cognitive and emotional processes triggered by eWOM during live streaming. Findings indicate that argument quality, source credibility, and information quantity significantly enhance consumer trust and perceived usefulness of information, which, in turn, drives information adoption and purchase intention. Furthermore, the study reveals that social interaction between live streaming anchors and audiences amplifies the influence of consumers’ internal states on information adoption. This study enhances the Information Adoption Model (IAM) by introducing social interaction as a moderator between consumers’ internal states toward live streaming eWOM and their adoption of information, highlighting the value of social interaction in live streaming. It also incorporates information quantity, showing how eWOM quantity affects trust and perceived usefulness. Furthermore, the study contributes to exploring how factors like argument quality, source credibility, and information quantity shape consumer trust and perceived usefulness, offering insights into the cognitive and emotional processes of information adoption in live streaming.

This study aims to evaluate theories and ideas about social values and determine the high quality of virtues that potentially change social practices, thinking, self-awareness, and behavior of the individual and society. The relevance of the study of value components is determined by the fact that such values as “spirituality and morality”, “responsibility”, “justice”, “rationality”, and “security” are capable of capturing the greatest value of many interests, which allows for the integration of society. An experimental study was conducted using sociological research methods based on developed questionnaires with questions touching on the parameters of sustainable development of society, determining the high quality of virtues and behavior of the individual and society. The study was conducted from May to June 2023 (N = 1387). Based on Demoethical values, special attention is paid to global problems related to climate change and inefficient use of energy and water resources, thereby achieving the Sustainable Development Goals. As a result of the study, Demoethical values are revealed in interaction with the economic components of demography, democracy, and demoeconomics as a tool for social transformation, as they shape the harmonious vision of the world, human behavior, decisions, and relationships with other people.

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.