Delays

Teaching cars to drive

April 2, 2026 by Filippos N. Tzortzoglou, Andreas A. Malikopoulos

Abstract: In recent decades, society has witnessed significant advancements in emerging mobility systems. These systems refer to transportation solutions that incorporate digital technologies, such as, automation and connectivity, to support sustainability goals, creating safer, more efficient, and user-centered mobility. Examples include connected and automated vehicles … [Read more...] about Teaching cars to drive

Teaching cars to drive

November 14, 2025 by Filippos N. Tzortzoglou, Andreas A. Malikopoulos

Abstract: In recent decades, society has witnessed significant advancements in emerging mobility systems. These systems refer to transportation solutions that incorporate digital technologies, such as, automation and connectivity, to support sustainability goals, creating safer, more efficient, and user-centered mobility. Examples include connected and automated vehicles … [Read more...] about Teaching cars to drive

Meal delivery services: Current practices, challenges, and future directions

February 6, 2024 by Maria Michalopoulou

Meal delivery services have undergone a tremendous evolution during the last decade and, according to market predictions, this trend will continue to grow in the following years. The service growth forecast in combination with the high complexity of the problem indicate the importance of achieving efficient solutions. This article presents a detailed description of the meal … [Read more...] about Meal delivery services: Current practices, challenges, and future directions

Privacy-preserving distributed learning: Techniques, applications, and future challenges

The rapid increase in data generated by connected devices has created a pressing need for privacy-preserving techniques in distributed learning. This article examines methods that enable collaborative machine learning (ML) while maintaining data security and user privacy. Key approaches such as federated learning (FL), differential privacy (DP), secure multiparty computation (SMPC), and homomorphic encryption (HE) are analyzed for their unique capabilities and various applications. FL facilitates model training across decentralized data sources, ensuring data remain local, while DP mitigates privacy risks by adding controlled noise. SMPC and HE support secure computations on encrypted data, maintaining confidentiality during processing. Despite their effectiveness, these techniques face challenges related to computational complexity, scalability, and regulatory compliance. The article reviews current advancements, practical implementations, and future directions, emphasizing the need for optimized, accessible solutions to enhance data security in distributed systems.