Global University

Conventional solar stills produce only 2-4 L/m2 per day of freshwater; consequently, they cannot satisfy the daily freshwater demand, even for a single family. Literature indicates that tubular solar stills (TSSs) are more effective, although they encounter challenges such as heat loss and reduced productivity in winter due to lower heat flux. This necessitates advancements in the TSSs to address these challenges. The present research investigates the effectiveness of a double-tubular solar still when loaded with phase change materials (PCMs), such as paraffin wax and lauric acid, in producing more freshwater. The developed solar stills were tested with PCM containers in strategic locations to store and release heat. This compensated for the fact that solar irradiation was lower in the winter months. The results revealed a significant increase in distillate production (15.2 % with paraffin wax and 11.4 % with lauric acid) compared with the baseline (i.e., no PCM). Thus, paraffin wax was found to be better for storing heat and boosting production. The results showed that integrating the PCM improved the distillation efficiency. This study provides vital information for selecting and arranging PCM to optimise the performance of solar stills in semi-arid areas prone to winter weather.

This article discusses the movement of education to online learning. It features the contributions and challenges of digital education in today's educational system.

As the popularity of android smartphone operating system growing now a days. In the Android platform, the Google play store contains millions of android mobile applications those are downloaded by user for multiple purpose. Mobile apps built on Android have launched and it contains some malicious and malware attacks. So, users become a target of unethical intrusions due to open-source platform. In this work, malware with changing properties cannot be detected or predicted using typical malware detection methods. Machine learning classification techniques have been employed for many years to address these problems, and it has been found that ensemble learning produces the greatest results when it comes to identifying malware for Android devices. The ensemble learning method employs multiple learning algorithms to improve predictions, resulting in improved prediction performance. It assists with further developing AI results by combining a few models. In order to build the classification model, one must focus on both static and dynamic (hybrid analysis) aspects of the code while maintaining a balance between the learnt model’s accuracy and processing time. In this work, machine learning algorithms like K-Nearest Neighbor and Support Vector Machine (SVM) are used, along with ensemble learning techniques including Random Forest, Extra Tree Classifier, and Voting Classifier. The model’s performance is assessed using F1 score, accuracy, recall, and precision.

Objective - To present issues related to rivers and urban landscapes with a view to the branches of the creek and the preservation of the Rio do Meio in the city of Magalhães Barata. Methodology - Bibliographic research covering books, periodicals, theses, dissertations, specialized websites as a way to contemplate knowledge already produced and contextualize with situations experienced in everyday life, establishing a relationship with the proposed themes. Results – A partnership between the government and the local community is necessary, and the urgency of establishing dialogue and partnership is still visible in order to strengthen sustainable environmental practices in order to benefit the collective in favor of sustainable environmental practices.