This paper aims to explore the issue of human actions in Islamic thought, focusing on the various stances regarding determinism, free will, and the intermediate position between them. This topic is linked to an ontological question: What are the limits of human responsibility for their actions? Our view is that the different positions on human actions reflect the presence of pluralism within Islamic thought, specifically through the discipline of Islamic theology (kalām). The difference in positions about the human actions within the science of theology expresses the vitality of Islamic thought and its appreciation of the right to differ between theological schools such as the Mu’tazila, Shi’a, and Sunnis, especially in an era dominated by the rationalism of Mu’tazila thought influenced by the methodology of Greek philosophical thought. This difference was recognized, especially in the third and fourth centuries AH/ninth and tenth centuries AD. We consider this difference in discussing the subject of the human actions as evidence of the principle of pluralism in Islam, which allows us to speak of the existence of a significant degree of intellectual tolerance, a subject that has not been studied to date. The prevailing view in studies today on this subject is that the theological groups accuse each other of unbelief, which is a mistaken position, because the saying of unbelief did not appear until after the fourth century AH/tenth century AD when transmission, reliability, and conservatism prevailed in Islamic thought. In addressing this issue, we examine three major stances on human actions as represented by three theological schools: The Mu’tazila (who advocated free will in human actions), the Jabriya (who advocated determinism in human actions), and the Ash’ariyya (who upheld the theory of acquisition). Once this is accomplished, we will explore the philosophy of pluralism in Islam through the lens of kalām. The most important conclusion we reached is that the debate on human actions opened, by the mid-4th century AH/10th century CE, an intellectual horizon that laid the foundations for pluralism in Islamic theological discussions. However, this horizon was soon closed due to various factors, which we have discussed throughout the paper.

This study investigates the performance assessment of methanol and water as working fluid in a solar-powered vapour absorption refrigeration system. This research clarifies the system’s performance across a spectrum of operating conditions. Furthermore, the HAP software was utilized to determine and scrutinize the cooling load, facilitating a comparative analysis between software-based results and theoretical calculations. To empirically substantiate the findings, this research investigates methanol-water as a superior refrigerant compared to traditional ammonia- water and LiBr-water systems. Through experimental analysis and its comparison with previous research, the methanol-water refrigeration system demonstrated higher cooling efficiency and better environmental compatibility. The system’s performance was evaluated under varying conditions, showing that methanol-water has a 1% higher coefficient of performance (COP) compared to ammonia-water systems, proving its superior effectiveness in solar-powered applications. This empirical model acts as a pivotal tool for understanding the dynamic relationship between methanol concentration (40%, 50%, 60%) and system performance. The results show that temperature of the evaporator (5–15 ℃), condenser (30 ℃–50 ℃), and absorber (25 ℃–50 ℃) are constant, the coefficient of performance (COP) increases with increase in generator temperature. Furthermore, increasing the evaporator temperature while keeping constant temperatures for the generator (70 ℃–100 ℃), condenser, and absorber improves the COP. The resulting data provides profound insights into optimizing refrigerant concentrations for improved efficiency.

In 1859, the French invasion of Gia Dinh marked the beginning of their acquisition of Cochinchina. Shortly after their arrival, France brought printers on their ships, along with firearms and artillery. The printers were intended to quickly disseminate the policies of the invading army to the inhabitants of the occupied territory. At the end of 1861, the inaugural official newspaper in Cochinchina, ‘Le Bulletin officiel de l’expédition française de la Cochinchine’, had been published. The Royal Printing House (l’Imprimerie Impériale), the first printing facility in Cochinchina, was also established at the end of 1861 to accommodate printing tasks, particularly the production of gazettes. In 1873, various private printing houses emerged in Saigon-Cho Lon. Printing and publishing efforts gradually assimilated into the social fabric of Cochinchina after serving as a tool of the invaders. They transformed into political and cultural institutions within colonial society, notably in Saigon-Cho Lon. The progression of these activities during the process was observable, at least for those granted permission to participate. The requirements of the colonial environment and the vitality of the private sector fuelled these new activities, although the colonial authorities deemed it necessary to exert control over them. This article offers additional information on the printing and publishing activities in Saigon-Cho Lon, Vietnam, highlighting the accomplishments of some distinguished printers.

In this paper, we assess the results of experiment with different machine learning algorithms for the data classification on the basis of accuracy, precision, recall and F1-Score metrics. We collected metrics like Accuracy, F1-Score, Precision, and Recall: From the Neural Network model, it produced the highest Accuracy of 0.129526 also highest F1-Score of 0.118785, showing that it has the correct balance of precision and recall ratio that can pick up important patterns from the dataset. Random Forest was not much behind with an accuracy of 0.128119 and highest precision score of 0.118553 knit a great ability for handling relations in large dataset but with slightly lower recall in comparison with Neural Network. This ranked the Decision Tree model at number three with a 0.111792, Accuracy Score while its Recall score showed it can predict true positives better than Support Vector Machine (SVM), although it predicts more of the positives than it actually is a majority of the times. SVM ranked fourth, with accuracy of 0.095465 and F1-Score of 0.067861, the figure showing difficulty in classification of associated classes. Finally, the K-Neighbors model took the 6th place, with the predetermined accuracy of 0.065531 and the unsatisfactory results with the precision and recall indicating the problems of this algorithm in classification. We found out that Neural Networks and Random Forests are the best algorithms for this classification task, while K-Neighbors is far much inferior than the other classifiers.