期刊信息

Computers in Industry

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影响因子:
9.1
出版商:
Elsevier
ISSN:
0166-3615
浏览:
25581
关注:
6

征稿

Computers in Industry is an academic journal published by Elsevier. (ISSN 0166-3615, impact factor 9.1).

An International, Application Oriented Research Journal The aim of Computers in Industry is to publish original, high-quality, application-oriented research papers that: • Show new trends in and options for the use of Information and Communication Technology in industry; • Link or integrate different technology fields in the broad area of computer applications for industry; • Link or integrate different application areas of ICT in industry. General topics covered include the following areas: • The unique application of ICT in business processes such as design, engineering, manufacturing, purchasing, physical distribution, production management and supply chain management. This is the main thrust of the journal. It includes research in integration of business process support, such as in enterprise modelling, ERP, EDM. • The industrial use of ICT in knowledge intensive fields such as quality control, logistics, engineering data management, and product documentation will certainly be considered. • Demonstration of enabling capabilities of new or existing technologies such as hard real time systems, knowledge engineering, applied fuzzy logic, collaborative work systems, and intelligence agents are also welcomed. • Papers solely focusing on ICT or manufacturing processes may be considered out of scope. A continuous quality policy, based on strict peer reviewing shall ensure that published articles are: - Technologically outstanding and front-end - Application-oriented with a generalised message - Representative for research at an international level
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Special Issues

Special Issue on Human-AI Symbiosis in Industrial Decision-Making, Automation, and Work Environments 截稿日期: 2026-06-30 Industrial transitions aimed at capitalising recent innovations arising from AI-enabled processes are at risk of shifting towards automation that excludes the human from the loop. Although this often brings productivity benefits, it insufficiently accounts for the new roles that humans can play in industrial environments. Increasingly decision-making and the practice of industrial automation is driven by data and automation, but the opportunities offered through the synergistic interactions between humans and AI-enabled systems are underexplored. Such opportunities are pervasive across manufacturing, logistics, and services but face challenges to deliver solutions which are trustworthy, explainable, risk-aware, and human-centric. This special issue brings together interdisciplinary perspectives on Human-AI Teaming, with emphasis on how AI systems can support, augment, and collaborate with humans in dynamic and complex industrial settings in symbiotic ways. Building on recent advances in AI, including agent-based architectures, explainable AI, multimodal foundation models, and data-driven AI=enabled systems, the aim is to bring new insights into how emerging methods, technologies, and deployments can make benefit from best of both humans and AI-enabled actors in industrial settings. Of particular interest is how human-AI teaming in decision-making, automation, production, logistics, and extended production value networks can benefit from methods, models, architectures, as well as design and evaluation approaches appropriate for industrial contexts. We welcome original research articles, case studies, and critical reviews on topics including, but not limited to Architectures and frameworks for risk-aware and trustworthy Human-AI symbiosis Design and evaluation of explainable and accountable decision-support systems Human-AI co-adaptation and mutual learning in work environments Agentic AI systems with multi-layer explainability and transparency Interactive multimodal interfaces for cognitive collaboration Interoperability for human-AI teaming (physical, technical, cognitive) Adaptive autonomy and shared control mechanisms in cyber-physical systems Human-in-the-loop of AI in industrial environments Ethics, standards and regulations – informed designs, architectures, and lifecycle management for human-centric AI in industry Data-driven and knowledge-informed human-AI teaming in industry, including methods involving Knowledge Graphs, Ontologies, and domain-specific knowledge constructs. Multi-disciplinary and co-creative approaches for designing human-AI teaming systems Pilot studies and real-world application cases and demonstrations Integrated approaches that blend mixed data modalities, AI model interpretability, system-level accountability, and human-AI interaction. Guest editors: Dr. Christos Emmanouilidis Affiliation: University of Groningen, Groningen, the Netherlands Areas of expertise: Artificial Intelligence Applications, Human-Centred Artificial Intelligence, Human-AI Teaming; Intelligent Manufacturing Systems; Maintenance and Asset Lifecycle Management Prof. Grigoris Mentzas Affiliation: National Technical University of Athens, Athens, Greece Areas of expertise: Information Systems; Trustworthy AI; Decision-Support Prof. David Romero Affiliation: Tecnológico de Monterrey, Monterrey, Mexico Areas of expertise: Operator 4.0/5.0; Human-Cyber-Physical Production Systems; Systems Integration and Interoperability Prof. Johan Stahre Affiliation: Chalmers University of Technology, Gothenburg, Sweden Areas of expertise: Human-Centred Manufacturing; Human-Automation Collaboration; Operator 4.0/5.0; Industrial Digitalisation Manuscript submission information: Open for Submission: from 02-Jan-2026 to 30-Jun-2026 Submission Site: Editorial Manager® Article Type Name: "VSI: COMIND_HAI_SYMBIOSIS" - please select this item when you submit manuscripts online All manuscripts will be peer-reviewed. Submissions will be evaluated based on originality, significance, technical quality, and clarity. Once accepted, articles will be posted online immediately and published in a journal regular issue within weeks. Articles will also be simultaneously collected in the online special issue. For any inquiries about the appropriateness of contribution topics, welcome to contact Leading Guest Editor Dr. Christos Emmanouilidis. Guide for Authors will be helpful for your future contributions, read more: Guide for authors - Computers in Industry - ISSN 0166-3615 | ScienceDirect.com by Elsevier For more information about our Journal, please visit our ScienceDirect Page: Computers in Industry | Journal | ScienceDirect.com by Elsevier Keywords: Human-AI teaming; Trustworthy industrial AI systems; AI-enabled decision-making and automation; AI maturity, lifecycle management, governance, and human agency; Industrial applications of human-centric AI; Risk-based and standards/regulatory/ethics
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