About the Journal

Computational Integration Science (CIS) focuses on the development and application of computational tools, algorithms, and frameworks that integrate multi-scale data, models, and knowledge systems, enabling holistic insights into multifaceted phenomena. Topics of interest include, but are not limited to the following:

1. Interdisciplinary Computational Modeling

Multiscale and multiphysics modeling: Integrating computational models across spatial/temporal scales (e.g., quantum to continuum)

Hybrid AI-physics models: Combining machine learning with traditional simulations for predictive modeling

Data-driven scientific discovery: Leveraging materials informatics, bioinformatics, or climate informatics for hypothesis generation

Computational reproducibility: FAIR (Findable, Accessible, Interoperable, Reusable) data practices and open-source toolkits

2. AI-Driven Data Fusion and Analysis

Integrating heterogeneous data sources (e.g., IoT, genomics, social media) using AI, deep learning, and big data analytics

Healthcare (e.g., personalized medicine), urban planning, and environmental monitoring

3. Computational Social Science and Policy Simulation

Understanding socio-economic systems, public policy impacts, and crisis management agent-based modeling, network analysis, and machine learning

Ethical AI frameworks for policy decision-making

4. Cross-Domain Numerical Methods

Advanced algorithms for solving partial differential equations (PDEs) in multi-physics systems (e.g., fluid-structure interactions, quantum computing simulations)

Optimization techniques for engineering design, logistics, and energy systems

5. Computational Education and Curriculum Innovation

Pedagogical strategies for teaching integrated computational science, including inquiry-based learning and virtual labs

Tools for visualizing interdisciplinary concepts (e.g., merging biology with computational modeling)

6. Bioinformatics and Computational Biology

Genomics, proteomics, and systems biology using AI and high-performance computing

Multi-scale modeling of biological processes (e.g., cellular dynamics to ecosystem interactions)

7. Software and Tools for Integrated Systems

Development of open-source platforms for reproducible research, emphasizing interoperability across disciplines

Quantum-classical hybrid algorithms and their implementation in software frameworks

8. Ethics, Security, and Reproducibility

Addressing data privacy, algorithmic bias, and ethical AI in integrated systems

Standards for reproducibility in multi-disciplinary computational workflows

9. Smart Environments and Ubiquitous Computing

IoT-enabled systems for smart cities, agriculture, and healthcare, integrating real-time data with predictive analytics

Energy-efficient algorithms for edge computing and distributed systems

10. Economic and Financial Computational Integration

Computational econometrics for market prediction, risk assessment, and policy impact analysis

Blockchain and AI applications in financial systems and supply chain management

11. Computer Vision and Signal/Image Analysis

Object detection, segmentation, and multimodal data fusion (e.g., medical imaging, satellite imagery, autonomous systems)