{"id":103569,"date":"2021-11-21T21:10:33","date_gmt":"2021-11-21T20:10:33","guid":{"rendered":"https:\/\/industry-science.com\/?page_id=103569"},"modified":"2024-08-12T20:14:58","modified_gmt":"2024-08-12T18:14:58","slug":"e-journals","status":"publish","type":"page","link":"https:\/\/industry-science.com\/en\/e-journals\/","title":{"rendered":"E-Journals"},"content":{"rendered":"\n\n
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I4S 2\/2026: Learning Factories<\/h4>\n
Drivers of research and learning environments for Industry 4.0<\/div> <\/td>\n <\/tr>\n <\/table>\n
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\n\t\t\t \n\t\t\t <\/div>\nIn recent years, learning factories have evolved into key experimental environments in the context of the Fourth Industrial Revolution. In addition to their role as training centers for skilled workers, they also serve as real-world research laboratories. This issue of Industry 4.0 Science examines learning factories as venues for exploring new approaches and technologies\u2014whether digital assistants, cobots, serious games, or digital twins. <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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I4S 1\/2026: Applied AI Ethics in the Workplace<\/h4>\n
A shared responsibility \u2014 from radiology and speech therapy to assembly<\/div> <\/td>\n <\/tr>\n <\/table>\n
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\n\t\t\t \n\t\t\t <\/div>\nAI ethics in the workplace is everyone\u2019s responsibility. It requires accountability from companies as a whole and conscious action from individuals\u2014whether developers or users, managers or employees. Key issues revolve around ethical AI skills and questions of governance and employee representation. How will the world of work change, from radiology and speech therapy to assembly and quality control? <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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I4S 6\/2025: Manufacturing in Space<\/h4>\n
Infinite possibilities for industrial production?<\/div> <\/td>\n <\/tr>\n <\/table>\n
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\n\t\t\t \n\t\t\t <\/div>\nManufacturing is leaving Earth: what was once science fiction is becoming a strategic field for the future. Falling launch costs and new space industry players are enabling production and services under conditions that are impossible on Earth\u2014from in-orbit maintenance to novel manufacturing processes in microgravity. This issue highlights opportunities, business models, and technological hurdles on the path to value chains in space. <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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I4S 5\/2025: Artificial Intelligence and Digital Assistance<\/h4>\n
How we can better support work<\/div> <\/td>\n <\/tr>\n <\/table>\n
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\n\t\t\t \n\t\t\t <\/div>\nDemographic change, skills shortages, and stagnating productivity are threatening the competitiveness of German industry. At the same time, AI and digital assistance systems are opening up new opportunities: they make work more efficient and support skilled workers. But while they have long been part of everyday life, their potential in industry remains largely untapped\u2014this is where this issue comes in with innovative concepts. <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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I4S 4\/2025: Smart Logistics<\/h4>\n
Sustainable, resilient processes along the entire value chain<\/div> <\/td>\n <\/tr>\n <\/table>\n
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\n\t\t\t \n\t\t\t <\/div>\nLogistics is entering a new era. Climate change and geopolitical uncertainties are shifting the focus to resilience and sustainability. The concept of smart logistics is gaining importance. But what exactly makes logistics smart, and how can it help us organize our societies and the economy? Approaches such as predictive analytics, demand analysis, and machine learning show why smart logistics is more than just a technological trend. <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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I4S 3\/2025: Digital Twin<\/h4>\n
Innovative concepts for manufacturing, logistics, and learning environments<\/div> <\/td>\n <\/tr>\n <\/table>\n
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\n\t\t\t \n\t\t\t <\/div>\nIn the connected world, digital twins open up completely new possibilities: they virtually replicate physical systems, processes, or products. However, key challenges remain, including the collection of current product data. This issue of Industry 4.0 Science covers a wide range of topics, from the basic concept of the digital twin to its benefits in procurement and its use in supply chain management. <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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I4S 2\/2025: The Future of Production with AI, Cobots and Virtual Worlds<\/h4>\n
Technology needs innovative, value-adding business models<\/div> <\/td>\n <\/tr>\n <\/table>\n
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\n\t\t\t \n\t\t\t <\/div>\nArtificial intelligence, collaborative robotics, and virtual worlds, such as the metaverse, are fueling visions for new forms of industrial value creation. However, innovation alone is not enough\u2014given that these technologies only develop their full potential through intelligent business models. How can companies efficiently integrate AI-supported automation, cobots and digital twins into their processes? <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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I4S 1\/2025: 40 Years of Digital Transformation in Manufacturing<\/h4>\n
Key research questions for tomorrow's production and logistics<\/div> <\/td>\n <\/tr>\n <\/table>\n
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\n\t\t\t \n\t\t\t <\/div>\nDigital transformation has been a central focus of scientific discussions for years. Questions relating to data-driven decisions, artificial intelligence and resilient supply chains are at the heart of current research. The articles in this issue explain key trends and present scientific findings and practical solutions - from automation and the circular economy to cloud computing. <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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I4S 6\/2024: Machine Learning<\/h4>\n
A technology with optimization potential in terms of efficiency, transparency and sustainability<\/div> <\/td>\n <\/tr>\n <\/table>\n
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\n\t\t\t \n\t\t\t <\/div>\nMachine learning takes automation to a new level. But what does this imply for the role of humans, who seem to remain\u00a0essential for the effective control of AI systems. The development of energy-efficient and fair algorithms and the optimization of data quality are crucial for the future viability of machine learning and artificial intelligence. The articles in this issue examine the technology's key potential and areas of application. <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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I4S 5\/2024: Double Transformation<\/h4>\n
Integrating digital and ecological change in the world of work<\/div> <\/td>\n <\/tr>\n <\/table>\n
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\n\t\t\t \n\t\t\t <\/div>\nChange is necessary for companies to maintain their competitive edge\u2014both digital and ecological change. But while external support is at hand, the drive for change must come from companies themselves. In this issue of Industry 4.0 Science, experts of the Academic Society for Work and Industrial Organization discuss how the real-world application of innovative technologies lead to resource-efficient manufacturing. <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/a>\n <\/div>\n<\/div>\n\n
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