Influence maximization, the identification of pivotal nodes within a network to maximize the spread of information or behaviors, plays a vital role in various domains, including social networks, marketing, and epidemiology. This paper presents "Influence Max," an innovative algorithm designed to efficiently identify influential nodes by considering the influence propagation range. Influence Max leverages the concept of influence propagation range, focusing on nodes that can reach a wide audience within a network. Through comprehensive evaluations on diverse network datasets, Influence Max demonstrates superior performance in terms of influence maximization when compared to existing methods. This algorithm offers a practical and scalable solution for influence maximization tasks, contributing to advancements in network analysis and decision-making processes.

This study investigates the impact of cryogenic treatment on the durability of cutting tools. Cryogenic treatment involves subjecting the tools to extremely low temperatures to enhance their mechanical properties and extend their lifespan. Through a comprehensive investigation, various aspects of tool durability, including wear resistance, tool life, and cutting performance, are examined before and after cryogenic treatment. Experimental data and analytical techniques are employed to assess the effectiveness of cryogenic treatment in improving cutting tool durability. The study provides valuable insights into the mechanisms underlying the enhancement of tool performance through cryogenic treatment, offering practical implications for machining processes and tooling applications.

In the rapidly evolving landscape of the Internet of Things (IoT), the need for efficient data processing and management is paramount. Fog computing, with its decentralized approach, offers a promising solution to overcome the limitations of traditional cloud computing by bringing computational resources closer to the edge of the network. This paper presents the design and implementation of a oneM2M-compliant fog computing framework tailored for IoT applications. By leveraging the standardized architecture and interoperability features of oneM2M, our proposed framework ensures seamless communication and integration across diverse IoT devices and platforms. We discuss the core components of the architecture, including the fog nodes, data management strategies, and security protocols, which collectively enhance the performance, scalability, and reliability of IoT systems. Additionally, we provide a comprehensive analysis of the framework's effectiveness through real-world case studies and performance evaluations. Our findings demonstrate significant improvements in latency reduction, bandwidth utilization, and overall system responsiveness. This research contributes to the field of IoT by providing a scalable, secure, and efficient fog computing solution, setting the stage for future advancements in smart environments and connected ecosystems.

Cloud-based video crowdsensing leverages distributed user devices to capture and analyze video data for various applications, ranging from urban monitoring to healthcare. However, optimizing the efficiency and scalability of such systems remains a significant challenge. This paper proposes scalable optimization strategies tailored for cloud-based video crowdsensing environments. We explore techniques to minimize latency, maximize resource utilization, and enhance data reliability through adaptive task allocation and scheduling algorithms. Our approach integrates cloud computing capabilities with edge processing to distribute tasks effectively, leveraging dynamic load balancing and prioritization mechanisms. Experimental evaluations demonstrate significant improvements in system performance metrics, including response time reduction and resource utilization efficiency. The findings highlight the feasibility and benefits of scalable optimization strategies in enhancing the capabilities and practicality of cloud-based video crowdsensing applications.

The growing demand for efficient urban waste management solutions has led to the development of innovative technologies designed to enhance cleanliness and sustainability. This paper presents "AutoClean," an intelligent autonomous garbage collection robot designed to address the challenges of urban waste management. Equipped with advanced sensors, machine learning algorithms, and navigation systems, AutoClean operates autonomously to identify, collect, and sort waste materials from various environments. Its adaptive learning capabilities allow it to optimize its collection routes and handle diverse types of waste, including recyclables and non-recyclables. AutoClean integrates real-time data analysis to improve operational efficiency and reduce the environmental impact of waste management. The robot's performance is evaluated in simulated urban settings, demonstrating its effectiveness in reducing manual labor and increasing the efficiency of waste collection processes. This study highlights AutoClean's potential to transform waste management practices and contribute to cleaner, more sustainable urban environments.