Employee motivation is a critical factor that influences the overall performance and productivity of an organization. This study aims to model the impact of demographic variables on employee motivation levels in the automobile industry. Demographic variables such as age, gender, education level, and years of experience are considered as potential predictors of employee motivation. The study utilizes quantitative data collected through surveys administered to employees working in various roles within the automobile industry. Statistical techniques, such as regression analysis and structural equation modeling, are employed to analyze the data and assess the relationships between demographic variables and employee motivation levels. The findings of this study provide valuable insights into the factors influencing employee motivation in the automobile industry and offer implications for human resource management practices.

Traffic sign recognition plays a crucial role in the safe and efficient operation of self-driving vehicles. This article presents a MatLab and TensorFlow-based approach for traffic sign recognition in the context of self-driving vehicles. The proposed method leverages deep learning techniques, specifically convolutional neural networks (CNNs), to detect and classify traffic signs. The article outlines the methodology, including the dataset used, pre-processing steps, model architecture, and implementation details. Results demonstrate the effectiveness of the approach, with high accuracy and reliable recognition performance. The study contributes to the advancement of self-driving vehicle technology by providing an efficient and accurate solution for traffic sign recognition.

In peer-to-peer (P2P) networks, ensuring privacy and trust is of utmost importance due to the decentralized nature of the communication. Traditional authentication methods often fall short in providing anonymity and robustness against malicious attacks. To address these challenges, we propose Secure Net, a novel framework that combines anonymous and distributed authentication mechanisms. Secure Net employs cryptographic techniques to protect user identities while enabling secure and reliable communication in P2P networks. By distributing the authentication process across multiple nodes, Secure Net mitigates the risk of single points of failure and enhances the overall resilience of the network. Through extensive evaluations, we demonstrate the effectiveness of Secure Net in safeguarding privacy, maintaining trust, and providing seamless authentication in P2P environments.

This article explores the complexity of finding small separators in temporal graphs, which are graphs that evolve over time. Separators play a crucial role in graph theory and have various applications in network analysis, community detection, and graph partitioning. The objective of this research is to investigate the computational complexity of identifying small separators in temporal graphs and analyze the implications for algorithmic design and graph-based applications. The study examines the impact of temporal dynamics on the identification and maintenance of separators and discusses the challenges and potential solutions. By addressing the complexity of finding small separators in temporal graphs, this article contributes to the understanding of graph algorithms in dynamic environments.