Computer Science, Mathematics and AI

The fields of computer science, mathematics, and AI are driving breakthroughs in interdisciplinary research, intelligent systems, and complex problem-solving. The explosive growth of generative AI, large language models, decentralized learning, and human–AI collaborative paradigms has created an urgent demand for rigorous theories, innovative algorithms, efficient runtime support frameworks, and responsible applications that push the boundaries of computational possibility. In response to this global trend, the Computer Sciences, Mathematics, and AI Section of this journal serves as a dedicated platform for publishing high-quality and impactful research that advances both fundamental understanding and real-world deployment of these interconnected fields.

The core intention of this section is to bridge theoretical innovations with practical technological implementations and impact. We welcome contributions spanning the entire research spectrum, including works on mathematical modeling, algorithm design, AI system development, multiscale computational methods, and cross-disciplinary integration. Special emphasis is placed on studies that demonstrate transformative potential—such as unified multi-modal scientific models, human–AI collaborative research frameworks, and solutions addressing AI safety, efficiency, equity, and trustworthiness.

This section covers all topics related to Computer Science, Mathematics, and AI as outlined above. Research fields of interest include, but are not limited to, the following:

• Computational theory.
• Computer and systems architectures.
• Computer and systems technologies and testbeds.
• Computer networks, disruptive technologies.
• Computer vision, pattern recognition.
• Computer games, computer graphics, and multimedia.
• Complex analytics and visualization.
• Concurrent, parallel, and distributed computing.
• Data structures and algorithms.
• Data, models, and systems architecture.
• Decentralized learning and unlearning.
• Databases, data mining, and large-scale and distributed data analytics.
• Responsible artificial intelligence development and prompt engineering.
• Social graphs, social computing, and human–computer interaction.
• Software, systems integration, and systems engineering.
• Applied computational science, applied mathematics, and applied AI.
• Multi-disciplinary and inter-disciplinary applications.
• AI for societal and global challenges.
• AI for engineering, business, biotechnology, health, space, and robotics.
• Autonomous computational systems—theory and practice.
• Quantum and future computational theories and practices.
• Applications for smart cities, secure societies, critical infrastructures, and digital twins.
• Open systems, interoperability, principles, and standards.
• Reproducibility, replicability, and reusability.
• Practical experience, experimental validation, and lessons learned from large-scale testbeds.