Instability is inherent in global capitalism, impacting all countries, particularly those directly reliant on this economic framework. The USA shapes tourism metrics in dependent nations and influences inbound tourism spending. Using logarithmic models and power tests, the study delineated four dynamic fields (Cn) supporting the thesis of the fusion of tourism and temporary residency. This study demonstrates that tourism and migration correlate with political, economic, and social instability, as evidenced by high statistical correlations. Variance increases during instability, leading to more residency petitions per tourist entry. This pattern is repeated during three major crises: the 2008–2009 financial crisis, the 2011–2013 conflicts in the Middle East and Africa, and the 2016–2017 regional political turmoil and Venezuelan migration. Economic classification tests confirm the association between instability, armed conflict, and heightened tourism and residency tendencies. Tourism income rises steadily, and residency averages increase, especially during periods of regional instability. The study highlights the tight link between tourism and migration with political, economic, and social instability. The statistical analysis reveals significant correlations, showing higher residency pressure during unstable periods. The applied tests confirm that countries in turmoil exhibit heightened tourism and migration tendencies.

Using the Intercultural Competence and Inclusion in Education Scale (ICIES), this study examines variations in intercultural competence and inclusion between mainstream and multiethnic high schools. The sample consisted of 384 high school students, aged 17 to 18, from both rural and urban areas in Western Romania, enrolled in grades 11 and 12. The ICIES demonstrated strong reliability, with a Cronbach’s alpha of 0.721. Exploratory factor analysis revealed three distinct dimensions: Intercultural opportunities and activities, Comfort in diverse settings, and Cultural reflection and values. Independent samples t-tests identified significant differences between mainstream and multiethnic schools across several items, with students in multiethnic schools reporting higher levels of intercultural competence and inclusion. These findings highlight the critical role of multicultural educational settings in fostering students' cultural awareness and inclusive attitudes. This study provides actionable insights for enhancing multicultural education practices and policies, including teacher training programs, inclusive curricula, and extracurricular initiatives that promote intercultural engagement and reduce intergroup biases.

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.

Corporate social responsibility (CSR) is an important concept of modern economic theory. In the last few decades, it has become an increasingly popular marketing tool used by companies. Consumers too want to see more CSR activities, especially those focused on environmental protection. The petroleum industry produces both toxic and non-toxic waste at almost all stages of production. While petroleum companies satisfy market demand, they also want to meet consumers’ moral and ethical demands. In this light, CSR has become vital for the development of industry. This paper looks at CSR in the petroleum industry, and its effect on customer satisfaction and subsequently toward the customer repurchase intention in Malaysia. The starting point of this paper is the Stakeholder Theory. It then examines CSR endeavors within the oil and gas sector and its link to customer repurchase intentions. It also looks at the established hypotheses between the activities of CSR (Economic Responsibility, Legal Responsibility, Ethical Responsibility, Philanthropic Responsibility), customer satisfaction and repurchase intention. This paper aims to learn about the customer’s sense of fulfilment with the CSR activities, and what could be the reaction base on the customer’s expectation.

The Consumer Price Index (CPI) is a vital gauge of economic performance, reflecting fluctuations in the costs of goods, services, and other commodities essential to consumers. It is a cornerstone measure used to evaluate inflationary trends within an economy. In Saudi Arabia, forecasting the Consumer Price Index (CPI) relies on analyzing CPI data from 2013 to 2020, structured as an annual time series. Through rigorous analysis, the SARMA (0,1,0) (12,0,12) model emerges as the most suitable approach for estimating this dataset. Notably, this model stands out for its ability to accurately capture seasonal variations and autocorrelation patterns inherent in the CPI data. An advantageous feature of the chosen SARMA model is its self-sufficiency, eliminating the need for supplementary models to address outliers or disruptions in the data. Moreover, the residuals produced by the model adhere closely to the fundamental assumptions of least squares principles, underscoring the precision of the estimation process. The fitted SARMA model demonstrates stability, exhibiting minimal deviations from expected trends. This stability enhances its utility in estimating the average prices of goods and services, thus providing valuable insights for policymakers and stakeholders. Utilizing the SARMA (0,1,0) (12,0,12) model enables the projection of future values of the Consumer Price Index (CPI) in Saudi Arabia for the period from June 2020 to June 2021. The model forecasts a consistent upward trajectory in monthly CPI values, reflecting ongoing economic inflationary pressures. In summary, the findings underscore the efficacy of the SARMA model in predicting CPI trends in Saudi Arabia. This model is a valuable tool for policymakers, enabling informed decision-making in response to evolving economic dynamics and facilitating effective policies to address inflationary challenges.