Journal of Computing and Information Science in Engineering

SPECIAL ISSUE: Challenges and Opportunities in Computing Research to Enable Next Generation Engineering Applications

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J. Comput. Inf. Sci. Eng. Dec 2023, 23(6): 060301

Recent advances in computing and information science such as artificial intelligence (AI), machine learning (ML), edge computing, cloud computing, metacomputing, and quantum computing are creating new computing paradigms. These advances are providing new opportunities for new research and application development. For instance, the adoption of Industry 4.0 enabled by AI/ML is fundamentally changing how products are designed, manufactured, maintained, and recycled. It enables consideration of all aspects of the product life cycle and realizing sustainable designs and helps us in achieving carbon neutrality. Intelligent machines such as robots and autonomous vehicles are revolutionizing human–machine interactions and increasing digitalization in the manufacturing and transportation industries. It is important for the Journal of Computing and Information Science in Engineering (JCISE) community to identify challenges and opportunities in these emerging areas and inspire new researchers to join the field and become a part of the community. This Special Issue consists of 19 position papers that span a wide variety of topics of interest to the JCISE community. These position papers identify challenges and opportunities, outline new areas of research, and point out new applications that will be enabled by advances in this field.


Janet K. Allen – University of Oklahoma, USA
Ehsan Esfahani – University at Buffalo, USA
SK Gupta – University of Southern California, USA
Balan Gurumoorthy – Indian Institute of Science, Bangalore, India
Bin He – Shanghai University, China
Ying Liu – Cardiff University, UK
John G. Michopoulos – Naval Research Laboratory, USA
Jitesh H. Panchal – Purdue University, USA
Anurag Purwar – State University of New York, USA
Kristina Wärmefjord – Chalmers University of Technology, Sweden

Position Papers

Challenges in Geometry Assurance of Megacasting in the Automotive Industry 

Kristina Wärmefjord, Josefin Hansen, Rikard Söderberg


Framing Supradisciplinary Research for Intellectualized Cyber-Physical Systems: An Unfinished Story 

Imre Horváth


Making Robotic Swarms Trustful: A Blockchain-Based Perspective 

Atul Thakur, Swagatika Sahoo, Arnab Mukherjee, Raju Halder


Towards Data and Model Interoperability for Industrial Extended Reality in Manufacturing 

William Z. Bernstein, Andrew Bowman, Ryan Durscher, Andrew Gillman, Sean Donegan


Digital Twin-Driven Product Sustainable Design for Low Carbon Footprint 

Bin He, Hangyu Mao


Exploring the Intersection of Metaverse, Digital Twins, and Artificial Intelligence in Training and Maintenance 

Monica Bordegoni, Francesco Ferrise


Harnessing Multi-Domain Knowledge for User-Centric Product Conceptual Design 

Xin Guo, Zechuan Huang, Ying Liu, Wu Zhao, Zeyuan Yu


Challenges and Opportunities for Machine Learning in Multiscale Computational Modeling 

Phong C. H. Nguyen, Joseph B. Choi, H. S. Udaykumar, Stephen Baek


Carbon Neutrality: A Review 

Bin He, Xin Yuan, Shusheng Qian, Bing Li


Research Issues in the Generative Design of Cyber-Physical-Human Systems 

David W. Rosen, Christina Youngmi Choi


Deep Learning-Driven Design of Robot Mechanisms 

Anurag Purwar, Nilanjan Chakraborty


Zero-Trust for the System Design Lifecycle 

Douglas L. Van Bossuyt, Britta Hale, Ryan Arlitt, Nikolaos Papakonstantinou


Information Embedding for Secure Manufacturing: Challenges and Research Opportunities 

Karim A. ElSayed, Adam Dachowicz, Mikhail J. Atallah, Jitesh H. Panchal


Bayesian, Multifidelity Operator Learning for Complex Engineering Systems–A Position Paper 

Christian Moya, Guang Lin


Designing Evolving Cyber-Physical-Social Systems: Computational Research Opportunities 

Janet K. Allen, Anand Balu Nellippallil, Zhenjun Ming, Jelena Milisavljevic-Syed, Farrokh Mistree


The Role of Deep Learning in Manufacturing Applications: Challenges and Opportunities 

Rishi Malhan, Satyandra K. Gupta


Opportunities and Challenges of Quantum Computing for Engineering Optimization 

Yan Wang, Jungin E. Kim, Krishnan Suresh


Design of Next-Generation Automotive Systems: Challenges and Research Opportunities 

Jitesh H. Panchal, Ziran Wang


Human Digital Twin, the Development and Impact on Design 

Yu (Wolf) Song


Metacomputing for Directly Computable Multiphysics Models 

John G. Michopoulos, Athanasios P. Iliopoulos, John C. Steuben, Nicoleta A. Apetre


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Special Issue on Geometric Data Processing and Analysis for Advanced Manufacturing

Geometric information, such as three-dimensional (3D) shapes and network topologies, has been increasingly explored in manufacturing research. For example, characterizing geometric information in 3D-printed parts, in-situ or ex-situ, opens opportunities for defect detection, quality improvement, and product customization. However, geometric data mining remains critically challenging. Geometric information is embedded in complex data structures, such as 3D point clouds, graphs, meshes, voxels, high-dimensional images, and tensors, which possess challenges for analysis due to their high-dimensionality, high-volume, unstructured, multimodality characteristics. Additional challenges stem from compromised data quality (e.g., noisy and incomplete data), the need for registration, etc.


2023 Reviewer’s Recognition

The Editor and Editorial Board of the Journal of Computing and Information Science in Engineering would like to thank all of the reviewers for volunteering their expertise and time reviewing manuscripts in 2023. Serving as reviewers for the journal is a critical service necessary to maintain the quality of our publication and to provide the authors with a valuable peer review of their work.