In today’s fast-evolving digital landscape, cloud-native environments have emerged as the cornerstone of scalable and flexible computing. However, ensuring data reliability within these environments remains a critical challenge due to the dynamic nature of cloud infrastructure, resource variability, and the increased frequency of system failures. Traditional data reliability mechanisms, such as redundancy and replication, often fall short in addressing the complex demands of modern cloud-native applications. This paper proposes an innovative approach to enhancing data reliability through the integration of Artificial Intelligence (AI)-orchestrated processes. AI techniques, including machine learning algorithms, predictive analytic, and real-time data monitoring, offer promising solutions to detect, predict, and mitigate issues related to data consistency, availability, and fault tolerance in cloud-native environments.

The research examines the application of AI-driven orchestration in managing cloud infrastructure, focusing on automation of error detection, real-time anomaly identification, and dynamic adjustment of resources to ensure continuous data reliability. By leveraging  AI's capabilities, cloud-native systems can autonomously identify potential data inconsistencies, optimize resource allocation, and rapidly recover from failures, all while maintaining high system performance. Through a comprehensive review of existing literature, coupled with practical case studies and quantitative evaluation, the study demonstrates the substantial advantages of AI-enhanced processes over traditional data management strategies. These benefits include increased operational efficiency, reduced human intervention, improved system resilience, and enhanced fault tolerance.

While AI orchestration offers significant potential, challenges such as the computational complexity of AI models, data security concerns, and the need for robust AI model training must be addressed for broader adoption. The findings of this research contribute to a deeper understanding of AI’s role in modernizing cloud-native data management and provide actionable insights for organizations looking to adopt AI-driven solutions to enhance data reliability in their cloud environments.

This study focused on investigating the effects of foreign exchange rates on the market operations of bottling companies in Sierra Leone, using the Sierra Leone Bottling Company (SLBC) as the case study. The problem was found around bottling companies not maintaining the regular market operations and performance due to the drawbacks they face in foreign exchange risks. This effect often rests on the staff working for bottling companies like Sierra Leone Bottling Companies (SLBC) situated in Freetown, the capital city of Sierra Leone. Therefore, the research included a sample of 100 staff of SLBC as respondents to the study. Qualitative or descriptive analysis was employed, and the operational results of the exchange rate was targeted to test the outcomes of a consistent exchange rate ratio in promoting the profits of the Company. The problem revolves around bottling companies not being able to maintain the expected pro due to the drawbacks they face in the foreign exchange rate. SLBC staff should ensure that raw materials are properly managed and accounted for to avoid stock of materials to ensure total production for customers' satisfaction. Government should play an active role to see that the exchange rate is brought to its most favourable rate to ensure a normal/favourable market operating environment for firms.

Health is a fundamental human right. It is enshrined as such in the preamble of the WHO Constitution. Unfortunately, in southern countries in general, and particularly in Côte d'Ivoire, the effectiveness of this fundamental right leaves much to be desired. The factors generally highlighted to account for this state are the material and infrastructural aspects. Few works deal with psychosocial and anthropological factors. In reproductive health, for example, the existence in certain rural localities of health centers, provided with drugs, materials and personnel, and the free provision of certain reproductive care, are not enough to decide pregnant women to undergo antenatal consultations. . Through this research, we therefore wish to question the role of the aforementioned factors in the decision of pregnant women in rural areas to undergo prenatal consultations. This qualitative research was carried out in the department of Kokomiani in April 2021.