As distributed cloud systems continue to evolve and become integral to modern computing, ensuring data reliability remains a significant challenge. Distributed environments, while offering flexibility and scalability, face issues such as data inconsistency, network failures, and system downtime. Traditional methods of addressing these concerns, such as manual interventions or simple fault tolerance mechanisms, often fall short in maintaining consistent data reliability. This research explores the role of artificial intelligence (AI) in enhancing decision-making processes to ensure data reliability within distributed cloud systems. The study highlights how AI-driven models, particularly machine learning and predictive analytic, can pro-actively identify potential failures, optimize data storage, and implement self-healing strategies. By integrating AI into the decision-making framework of cloud systems, this paper demonstrates improved reliability, fault tolerance, and performance in real-time scenarios. Through a detailed analysis of an AI-powered framework, this research showcases the potential for AI to transform distributed cloud management, enabling adaptive and automated solutions for maintaining data consistency and reliability. The findings indicate that AI-driven approaches offer significant advantages over traditional methods, offering scalability, precision, and efficiency in ensuring the integrity of data across distributed cloud environments. Finally, the study concludes with recommendations for future research, including the integration of emerging AI technologies for even greater improvements in system reliability.

Over the past few years, considerable interest has been focused on semiconducting nanoparticles due to
their potential applications in diverse fields including catalysis, magnetic recording media,
microelectronics, gas sensors, etc. In our present study, we describe the design, fabrication and gas sensing
performance of p-NiO/n-MoO3 (MN) nanocomposites functionalized by noble metal Au (MNA) in order to
develop a reliable sensor for CO2 gas detection since it is odorless, highly hazardous and toxic green house
gas which affects the environment and human health. The formation of the nanocomposite was
systematically reviewed and confirmed by X-Ray Fluorescence Spectroscopy (XRF) and X-Ray Diffraction
(XRD) patterns. The MN nanocomposite impregnated with noble metal – Au (MNA) showed better
efficiency of S=87.86% towards Carbon dioxide (CO2) gas compared to MN (S=80%) and selectivity
towards CO2 in comparison to other interfering gases with superior stability.

This study was conducted involving a group of 500 dental students to directly compare perceived stress
levels encountered during their education and Clinical years. A modified questionnaire based on
Demographic informations, Physical Symptoms (PHQ-15), Perceived Stress Scale (PSS) and General
Health “GHQ-12”provided to the students by papers. The purpose of the investigation was to determine if
the sources of stress reported by first or Fourth dental students, A several statistical analysis was also
conducted to measure stress differences between didactic and clinical year dental student. The overall
findings show that the fourth year dental students had greater levels of stress than first year students.