This study explores the complex environment of smart applications technology in the UAE's public and private sectors, looking into the complex interactions between technological adaptability and accessibility as well as the effects of Total Quality Management (TQM) studies presented by policies and regulations. Data was collected by a multistage sampling strategy, mixing probability and non-probability methods, using a cross-sectional research design. To ensure impartial item creation and adherence to best practices, a pretested questionnaire underwent extensive examination. Through the revelation of unexpected results, the research tests and expands upon the Technology Acceptance Model (TAM). There is a request for an enlarged TAM framework because there is no substantial correlation found between accessibility and the performance of smart applications. A negative link is resulted, unexpectedly, between technology adaptation and TQM adoption, underscoring the necessity of a cautious integration strategy. The report also emphasizes how important TQM policies and regulations are to the functioning of smart applications, supporting the need for a thorough TAM framework that takes in regulatory dynamics into consideration. Practical implications for stakeholders, quality policymakers, and institutions are presented by the findings, which highlight several adoption aspects beyond accessibility. This research adds a contribution on smart application performance and develops theoretical models for integrating technology in organizational settings. The rigorous design of the methodology highlights the integrity of the study and guarantees the validity of the insights obtained from the investigation.

The effectiveness of organizations and the satisfaction of customers are both significantly improved by providing outstanding quality. To better meet the needs of their consumers, an increasing number of firms are implementing quality improvement programs such as total quality management (TQM). The major objective of this research paper is to examine the effects of TQM components on the efficiency and effectiveness of UAE higher education institutions (HEIs). The association between TQM components and organization performance was investigated using a quantitative research technique based on a survey study design. The partial least square structural equation model (PLS-SEM) was used to quantify the reflectiveness of 99 data. The study found that TQM has a positive impact on employee satisfaction and service quality in HEIs. It was revealed that TQM has a significant impact on organizational performance, with service quality serving as a mediator. Furthermore, the results revealed that service quality and students’ satisfaction are fully mediating the relationship between TQM and organizational performance. The current study concludes that HEIs are implementing TQM to enhance market performance and comprehend the evolving business trends. The study has added to the body of information about the relationship between TQM elements and organization performance.

Climate change is a threat that is being faced by us and it is a reality that this is a man-made phenomenon. Studies have shown that human activities after the 1850s have increased emission of carbon gases into the atmosphere leading to increased global atmospheric temperatures and this impacting sustainable development on a large scale. A solution to this disaster should also be provided by us and it lies in creating policies and practices that will lead to reduced carbon emissions and atmospheric temperature. Artificial intelligence techniques can analyse big data that will help build models to predict changes in the climatic patterns. This study uses traditional, machine learning and deep learning models to predict temperature based on a data set from the year 1850 to 2013. The findings suggest that XG boost and LightGBM are better than other models in terms of prediction accuracy. This indicates that AI models are critical in terms of predicting climate change patterns and in proactive monitoring and adaptation to environmental issues that affect sustainable development.