The Internet of Things (IoT) connects numerous sensor nodes and devices, resulting in an increase in the bandwidth and data rates. However, this has led to a surge in data-hungry applications, which consume significant energy at battery-limited IoT nodes, causing rapid battery drainage. As a result, it is imperative to find a reliable solution that reduces the power consumption. A power optimization model utilizing a modified genetic algorithm is proposed to manage power resources efficiently and reduce high power consumption. In this model, each access point computes the optimal power using the modified genetic algorithm until it meets the fitness criteria and assigns it to each cellular user. Additionally, a weight-based user-scheduling algorithm is proposed to enhance network efficiency. This algorithm considers both the distance and received signal strength indicator (RSSI) to select a user for a specific base station. Furthermore, it assigns appropriate weights for the distance, and the RSSI helps increase the spectral efficiency performance. In this paper, the user-scheduling algorithm was assigned equal weights and combined with the power optimization model to analyze the power consumption and spectral efficiency performance metrics. The results demonstrated that the weight-based user-scheduling algorithm performed better and was supported by the optimal allocation of weights using a modified genetic algorithm. The outcome proved that the optimal allocation of transmission power for users reduced the cellular users’ power consumption and improved the spectral efficiency.

Accurate measurement of the microspores, mesopores, and macropores on the surface of the activated carbon is essential due to its direct influence on the material’s adsorption capacity, surface area, and overall performance in various applications like water purification, air filtration, and gas separation. Traditionally, Scanning Electron Microscopy (SEM) images of activated carbons are collected and manually annotated by a human expert to differentiate and measure different pores in the surface. However, manual analysis of such surfaces is costly, time-consuming, and resource-intensive as requires supervision from experts. In this paper, we propose an automatic Deep-learning-based solution to address this challenge of activated carbon surface segmentation. We introduce a novel SEM Image segmentation dataset for activated carbon. We then evaluate the state-of-the-art deep learning models on the novel semantic segmentation task that shows promising results. Finally, we outline the key research challenges and discuss potential research directions to address these challenges.

National elections are a big deal which has a major role in the development and progress of a country. The security of an election is in fact the matter of national security. Current voting systems in the context of Nepal and other parts of the world are still traditional and insecure. From the dawn of democracy, elections are still based on pen and papers. While there are new electronic voting machines, these systems have a high risk of rigging and are prone to failure, crashes, and attacks from intruders. The stated problems can be addressed through the implementation of new technology namely Blockchain for a transparent and reliable voting process. A blockchain based voting platform has been built to make the voting process digital, secure, reliable and intuitive. The votes casted by voters are stored in the blockchain, thus making it immutable and secure. The system can be scaled based on the requirements of an election. Voters can login to the system with the help of encrypted identification. This encrypted identification will lead to secure voting process. A separate portal for both voters and the election commission reduces the election hassle and time. So, in this system, it is demonstrated how the use of blockchain platform could contribute to modern elections using Ethereum, smart contracts and distributed systems to provide voters with secure, transparent and reliable voting platform.