Advanced computing methods and related technologies are changing the way engineers interact with the information infrastructure. Explicit knowledge representation formalisms and new reasoning techniques are no longer the sole territory of computer science. For knowledge-intensive tasks in engineering, a new philosophy and body of knowledge called Engineering Informatics is emerging.

Advanced Engineering Informatics solicits research papers with particular emphases both on 'knowledge' and 'engineering applications'. As an international Journal, original papers typically:

• Report progress in the engineering discipline of applying methods of engineering informatics.
• Have engineering relevance and help provide the scientific base to make engineering decision-making more reliable, spontaneous and creative.
• Contain novel research that demonstrates the science of supporting knowledge-intensive engineering tasks.
• Validate the generality, power and scalability of new methods through vigorous evaluation, preferably both qualitatively and quantitatively.

In addition, the Journal welcomes high quality review articles that summarise, compare, and evaluate methodologies and representations that are proposed for the field of engineering informatics. Similarly, summaries and comparisons of full-scale applications are welcomed, particularly those where scientific shortcomings have hindered success. Typically, such papers have expanded literature reviews and discussion of findings that reflect mastery of the current body of knowledge and propose novel additions to contemporary research.

Papers missing explicit representation and use of knowledge, such as those describing soft computing techniques, mathematical optimization methods, pattern recognition techniques, and numerical computation methods, do not normally qualify for publication in the Journal. Papers must illustrate contributions using examples of automating and supporting knowledge intensive tasks in artifacts-centered engineering fields such as mechanical, manufacturing, architecture, civil, electrical, transportation, environmental, and chemical engineering. Papers that report application of an established method to a new engineering subdomain will qualify only if they convincingly demonstrate noteworthy new power, generality or scalability in comparison with previously reported validation results. Finally, papers that discuss software engineering issues only are not in the scope of this journal.

Special Issue on Airspace Optimization and Intelligent Air Traffic Models for Sustainable Air Transportation
截稿日期: 2024-08-01

The forecasted traffic growth in the aviation industry is expected to increase significantly over the coming years. According to the International Air Transport Association (IATA), pre-COVID-19 estimates, the number of air passengers is projected to double by 2037, with the Asia-Pacific region being the most significant contributor to this growth. Additionally, the growth of unmanned aerial vehicles (UAVs) and commercial space transportation is also expected to add to the traffic in the airspace. This creates a significant challenge for the ATM industry, which must adapt and handle growing traffic safely, efficiently, and at an economically acceptable cost.

The current airspace design and procedures have evolved over the years. They are constrained by limited capacity, poor scalability, fixed routes, fixed national airspace structures, limited automation, low level of information sharing, and fragmented Air Traffic Management (ATM) infrastructure. Although it has served its purpose well, it has now reached its operational limits, where it will be challenging for air navigation service providers (ANSPs) and airlines to accommodate future air traffic growth.

Future air traffic demand and challenges cannot be met with incremental changes in the ATM system and an automation plug-in approach. A holistic strategy is needed to achieve a resilient airspace that seamlessly integrates various ATM sub-systems, including en-route, terminal, and airside. Such an approach will accommodate forecasted growth in air traffic, reduce air traffic delays, and minimize fuel consumption through efficient demand-capacity balancing. Integrating sub-systems will enable advanced air traffic services, including time-based separation, trajectory-based operations, smart sequencing of traffic, and conformal automation support tools. The emergent challenges of coordinating air traffic across airspace sub-systems lead to the requirement for developing novel concepts of operations and artificial intelligence (AI) tools to support air traffic controllers (ATCO) in handling the growing traffic demand safely and efficiently.

Furthermore, the emergence of AI and advanced Communication, Navigation, and Surveillance (CNS) technologies is driving a profound transformation in ATM research and is part of ATM strategic planning worldwide. To achieve a green, safe, efficient, and seamless gate-to-gate experience for passengers, it is essential to leverage these emerging technologies to optimize and control air traffic planning, operations, and control.

This special issue aims to bring together the latest advancements in concepts of operations and AI models for integrated airspace management in the context of future air transportation systems. We seek to showcase innovative methodologies for strategic planning, tactical management, and operational control, leveraging emerging technologies such as data-driven decision support, AI-based machine learning, and large-scale simulation and optimization. Additionally, this proposal aims to explore the integration of various ATM sub-systems, including en-route, terminal, and airside, to enhance coordination and improve the efficiency and sustainability of the air transportation system.

The research topics of interest include but are not limited to:

Intelligent airspace design for seamless air traffic operations,
Novel representation and intelligent algorithms for dynamic airspace management and trajectory-based operations,
Human-machine collaboration in air traffic management,
Personalized automation models and Human-Machine Interface for supporting ATCOs,
Trust of humans in automation/recommendation systems,
Simulation platform for training and evaluation of the AI algorithms and novel concepts of operations.

Guest editors:
Assoc. Prof. Sameer Alam
Nanyang Technological University, Singapore City, Singapore
Prof. Eri Itoh
The University of Tokyo, Tokyo, Japan
Assist. Prof. Max Li
University of Michigan, Ann Arbor, Ann Arbor, United States
Dr. Duc-Thinh Pham
Nanyang Technological University, Singapore City, Singapore
Prof. Michael Schultz
Universität der Bundeswehr München, Neubiberg, Germany
Assoc. Prof. Yanjun Wang
Nanjing University of Aeronautics and Astronautics, Nanjing, China

Manuscript submission information:

Open for Submission: from 25-Mar-2024 to 01-Aug-2024 

Special Issue on Human-AI Collaboration for Engineering Designs and Services in the Evolution of Industry 5.0 and Beyond
截稿日期: 2025-03-31

In the field of engineering, the arrival of the digital transformation era has brought about a fundamental paradigm change that presents both previously unthinkable possibilities and difficult challenges (Lee et al., 2021). One notable challenge is the increasing complexity of cybersecurity threats, as interconnected systems become more prevalent, posing risks to integrity and security of critical engineering infrastructure. Additionally, the rapid pace of technological evolution has led to challenges in workforce adaptation, requiring continuous skill development to keep pace with emerging technologies and methodologies. Moreover, ethical implications surrounding the use of artificial intelligence (AI) in engineering, such as bias in algorithms and responsible technology integration, represent another significant challenge that necessitates careful consideration and resolution within the evolving digital landscape (Lee et al., d2021; Lepri et al., 2021; Rožanec, et al., 2023). The fast adoption of modern technologies by various industries has made the integration of AI and human intelligence (HI) a crucial focus in the field of engineering designs and services (Lee et al., 2022; Lepri et al., 2021; Rožanec, et al., 2023, Agrawal et al., 2023). The transition from Industry 4.0 to Industry 5.0 represents a critical turning point in the dynamic environment of the digital transformation age, stressing a deep reorientation towards human-centric, linked systems (Zhang et al., 2023; Zizic et al., 2022). The essential requirement for research on human-AI collaboration, which is emphasized by a number of aspects, is at the center of this shift. Industry 5.0 sees a future where human creativity and intuition are crucial, encouraging collaborative innovation in engineering designs and services, whereas Industry 4.0 was primarily focused on technology efficiency (Trappey et al., 2017; Marcon et al, 2022; Zizic et al., 2022). The shift to Industry 5.0 demands efficient decision-making that combines human contextual awareness with AI-driven insights. Furthermore, social and ethical issues take center stage, necessitating a responsible integration of AI that is consistent with human values (Rožanec et al., 2023; Grabowska et al., 2022, Colabianchi et al., 2023). Research on human-AI collaboration is crucial for developing educational initiatives that highlight the symbiotic relationship between people and AI as we equip the labor force for Industry 5.0 (Grabowska et al., 2022; Lepri et al., 2021; Zhang et al., 2023). Digital Twin as a highly automated, AI-enabled artifact heavily impacts the Human-Machine collaboration: “Where humans fit in?” (Agrawal et al., 2023, Colabianchi et al., 2023). Human-AI collaboration refers to the synergistic and interactive partnership between HI and AI systems to achieve shared goals or tasks. It involves the seamless integration of human expertise, creativity, and contextual understanding with the computational capabilities of AI, fostering a mutually beneficial relationship (Grabowska et al., 2022; Lepri et al., 2021; Zhang et al., 2023, Agrawal et al., 2023). This collaboration often encompasses joint decision-making, problem-solving, and information processing, where the strengths of both human and AI entities are leveraged to enhance overall performance and outcomes. In the context of Industry 5.0 and digital transformation, human-AI collaboration emphasizes a cooperative and symbiotic approach, recognizing the unique strengths of each component and optimizing their collective potential for innovation, efficiency, and responsible technological integration.
We extend an invitation to researchers, academics, and industry professionals to participate in a thorough examination of the field of human-AI collaboration as it develops in relation to engineering designs and services as Guest Editors of this special issue. The combination of AI and HI is changing the engineering design and service landscape in the age of digital transformation. In the context of engineering, this special issue seeks to investigate and present cutting-edge research, approaches, and case studies that demonstrate the dynamic interplay between human expertise and AI technologies. A wide range of subjects pertaining to human-AI collaboration in engineering designs and services will be covered. Among the possible topics of interest include, but not
1. Theoretical foundations and concepts of human-AI collaboration on engineering designs and services

Exploring cognitive models for Human-AI interaction in engineering designs
Developing conceptual frameworks for ethical Human-AI Collaboration in engineering services
Exploring innovation theories in Human-AI Co-Creation for engineering solutions

2. Human-AI collaborative design processes

Examining how humans and AI systems collaborate in the design process, exploring the challenges and opportunities for enhancing creativity, efficiency, and innovation.
Exploration of collaborative frameworks that seamlessly integrate human and AI contributions in the design and innovation processes within engineering disciplines.
Analyze the evolving skillsets required in the era of human-AI collaboration and propose strategies for upskilling the workforce.

3. Human-AI interaction and human-AI systems design

Development of interfaces and interaction models that facilitate effective communication and collaboration between human (designer, engineer, etc.) and AI algorithms, ensuring seamless integration and mutual understanding.
Investigating User Experience (UX) principles that optimize the integration of AI tools into daily workflows, ensuring a positive and efficient user experience.
Exploring how AI technologies can augment human cognitive abilities, leading to enhanced problem-solving, decision-making, and creativity in the workplace.

4. Human-centric and AI-augmented designs and services in the evolution of Industry 5.0

Integrating user-centric approaches in the design and implementation of digital transformations under Industry 5.0.
Exploring generative AI-driven digital transformations from a human factor perspective under Industry 5.0.
Examining human-AI collaboration in enhancing engineering services, such as manufacturing monitoring, and optimization, quality assurance and maintenance, and also exploring new service models enabled by human-AI collaboration.

5. Neuro-informed AI systems for enhancing human design decision-making in engineering

Leveraging neuroscientific principles for effective decision making and management strategies.
Understanding the impact of neuro management on organizational performance and effectiveness of product and service design.
Exploring scenarios of neuro management, emotional intelligence in engineering management and decision making.

6. Adaptive learning environments for user acceptance and adoption of human-AI collaboration in engineering

Exploring approaches to integrating human-AI collaboration into engineering education and training programs, preparing the next generation of engineers for a collaborative digital future.
Studies on factors influencing the acceptance and adoption of human-AI collaboration by engineering professionals and the broader community.
Examining human-AI collaboration in educational settings, tailoring learning experiences to the individual needs and learning styles of students.

7. Ethical considerations in human-AI cooperative Industry 5.0
Understanding ethical and responsible AI for human-centered technological innovation.
Investigations into ethical implications, challenges, and responsible practices concerning the collaboration between humans and AI in engineering contexts.
Addressing societal concerns and ensuring responsible technological innovation in Human-AI Cooperative Industry 5.0.
Handling issues of trust and risks in human-AI collaborative Industry 5.0 and beyond.
Handling of legislative and regulatory rules and practices in human-AI collaborative Industry 5.0.

8. Case studies and best practices in human-AI collaborations

Evolution of human roles and responsibilities in human-AI collaborative Industry 5.0 and beyond.
Presenting real-world engineering applications and success stories in human-centered digital transformations.
Exploring best practices and changing dynamics with advanced digital transformation enablers, emphasizing human-AI collaborative Industry 5.0.

Guest editors:
Prof. Amy TrappeyNational Tsing Hua University, Hsinchu, Taiwan
Dr. Josip StjepandicPROSTEP AG, Darmstadt, Germany
Prof. John MoRMIT University, Melbourne, Australia
Dr. Ching-Hung LeeXi'an Jiaotong University, Xi'an, China
Dr. Yi ZhangUniversity of Technology Sydney, Sydney, Australia