Mathematical Applications in Digitalization, Electrification, and Sustainable Development

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".

Deadline for manuscript submissions: 15 January 2026 | Viewed by 279

Special Issue Editor


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Guest Editor
Faculty of Computer Systems and Technologies, Department of Computer Systems, Technical University in Sofia, 8 Ohridski Blvd., 1000 Sofia, Bulgaria
Interests: artificial intelligence; mathematical modeling; control theory and applications; smart cities and smart grids
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Special Issue Information

Dear Colleagues,

This Special Issue of the journal Mathematics is dedicated to contemporary mathematical methods and applications that play a crucial role in the processes of digitalization, electrification, and sustainable development. In the context of rapidly evolving technologies and increasing demands for energy efficiency and environmental sustainability, mathematics provides powerful tools for the modeling, analysis, and optimization of complex systems. This Special Issue explores various aspects of mathematical approaches, including numerical methods, optimization techniques, stochastic modeling, machine learning, and artificial intelligence, which are applied in the automation of industrial processes, smart electrical grids, renewable energy sources, and resource management. Special emphasis is placed on the development of mathematical models for energy efficiency, the forecasting and control of complex engineering systems, and algorithms for processing large datasets in the context of digital transformation.The aim of this Special Issue is to present new research and innovative mathematical solutions that support sustainable development and technological progress. By integrating mathematical tools with modern technologies, effective and reliable methods are created to address contemporary challenges related to digitalization, electrification, and an environmentally sustainable future.

Dr. Nikolay Hinov
Guest Editor

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Keywords

  • mathematical models
  • optimization
  • numerical methods
  • digitalization
  • cybersecurity
  • electrification
  • human-centered computing
  • machine learning
  • artificial intelligence
  • energy efficiency
  • automation
  • smart grids
  • data processing

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Published Papers (1 paper)

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Research

40 pages, 4846 KiB  
Article
Comparative Analysis of Some Methods and Algorithms for Traffic Optimization in Urban Environments Based on Maximum Flow and Deep Reinforcement Learning
by Silvia Baeva, Nikolay Hinov and Plamen Nakov
Mathematics 2025, 13(14), 2296; https://doi.org/10.3390/math13142296 - 17 Jul 2025
Viewed by 149
Abstract
This paper presents a comparative analysis between classical maximum flow algorithms and modern deep Reinforcement Learning (RL) algorithms applied to traffic optimization in urban environments. Through SUMO simulations and statistical tests, algorithms such as Ford–Fulkerson, Edmonds–Karp, Dinitz, Preflow–Push, Boykov–Kolmogorov and Double [...] Read more.
This paper presents a comparative analysis between classical maximum flow algorithms and modern deep Reinforcement Learning (RL) algorithms applied to traffic optimization in urban environments. Through SUMO simulations and statistical tests, algorithms such as Ford–Fulkerson, Edmonds–Karp, Dinitz, Preflow–Push, Boykov–Kolmogorov and Double DQN are compared. Their efficiency and stability are evaluated in terms of metrics such as cumulative vehicle dispersion and the ratio of waiting time to vehicle number. The results show that classical algorithms such as Edmonds–Karp and Dinitz perform stably under deterministic conditions, while Double DQN suffers from high variation. Recommendations are made regarding the selection of an appropriate algorithm based on the characteristics of the environment, and opportunities for improvement using DRL techniques such as PPO and A2C are indicated. Full article
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