Serverless computing has revolutionized application development but faces a significant challenge: cold starts, which introduce delays when a function is called after a period of inactivity. Addressing these delays is crucial because they affect efficiency, performance, cost, and scalability. Existing mitigation strategies come with trade-offs, such as increased resource overhead and the need for precise resource management predictions. Also, optimizing the function startup process requires detailed knowledge of the runtime characteristics and the isolation technique used, such as using a container-based or a micro virtual machine setup. This work presents PARSEC, a comprehensive solution for cold start issues in serverless computing. By focusing on reducing initialization latency in idle containers, this research seeks to preserve scalability and ease of deployment features of serverless computing while overcoming cold start limitations. The proposed architecture improved cold start by streamlining the initialization of containers to reduce overhead. This involves minimizing unnecessary operations and customizing launches for serverless needs, aiming for a faster and more efficient setup. It also enhances the provisioning of Zygotes to speed up sandbox launches. The results show better performance for PARSEC when compared with other architectures, particularly at shorter wait times, suggesting effective cold start management. The strategic management of Zygotes and their provisioning scaling plays a critical role in managing large numbers of packages and instances, thereby enhancing the performance of package management. The cache system also evolves to become more selective, reducing overhead by focusing on essential packages.

This paper aims to compare and evaluate several approaches from the state-of- the-art literature for developing a Multi-objective Particle Swarm Optimization (MOPSO) Algorithm that aims to classify victims in emergency response for evacuation and generate decision support information for agents during emergency response. The paper compares the results of the proposed approach with those obtained from other state-of-the-art approaches with similar objectives to evaluate their performance and improvements. One of the critical metrics for evaluating the MOPSO algorithm is the response time of agents, which is critical in disaster situations. Other metrics may include the accuracy of victim classification, efficiency of the algorithm, and ease of implementation. Moreover, ways in which the proposed MOPSO algorithm can be adapted to other disaster events of similar nature are explored. Overall, this paper aims to provide a comprehensive evaluation of the proposed MOPSO algorithm and highlight its potential usefulness in emergency response situations.

We live in a world where most foods and beverages are unhealthy due to their high content of sugars, sodium, fat, among others. Although many of these foods, especially packaged foods (snacks) for children have nutritional information, this is not very clear, and its interpretation is not easy for a normal person. For this reason, HealthCAM is developed. HealthCAM is a mobile application that uses augmented reality and machine learning techniques to detect the type of snack and indicate to the user through a visual warning on the phone's camera in real time the degree of sugar, sodium and fat that the snack has.