TY - CHAP A1 - Rauschenbach, Sina ED - Huss, Bernhard T1 - Presentism and the denial of coevalness BT - the descriptions of England and Ireland in the Seventeenth-Century ‘Elzevirian Republics’ T2 - Von Neuem: Tradition und Novation in der Vormoderne N2 - In Time and the Other Johannes Fabian analysed how modern conceptions of time were “not only secularized and naturalized but also thoroughly spatialized.” According to Fabian, this was particularly visible in modern anthropology which “promoted a scheme in terms of which not only past cultures but all living societies were irrevocably placed on a temporal slope, a stream of Time – some upstream, others downstream.”3 Anthropologists attributed otherness to a distant past which was traditionally associated with cultural retardation, i.e. a lower degree of development, progress, and civilization. Cultural difference was expressed in terms of temporal distance while temporal distance was attributed to spatial remoteness. The result was a phenomenon that Fabian coined “the denial of coevalness” which pointed towards “a persistent and systematic tendency to place the referent(s) of anthropology in a Time other than the present of the producer of anthropological discourse. Y1 - 2024 SN - 978-3-8253-8663-4 SN - 978-3-8253-9582-7 U6 - https://doi.org/10.33675/2024-82538663 VL - GRM-Beiheft 113 SP - 195 EP - 211 PB - Universitätsverlag Winter GmbH CY - Heidelberg ER - TY - CHAP A1 - Panzer, Marcel A1 - Gronau, Norbert T1 - Enhancing economic efficiency in modular production systems through deep reinforcement learning T2 - Procedia CIRP N2 - In times of increasingly complex production processes and volatile customer demands, the production adaptability is crucial for a company's profitability and competitiveness. The ability to cope with rapidly changing customer requirements and unexpected internal and external events guarantees robust and efficient production processes, requiring a dedicated control concept at the shop floor level. Yet in today's practice, conventional control approaches remain in use, which may not keep up with the dynamic behaviour due to their scenario-specific and rigid properties. To address this challenge, deep learning methods were increasingly deployed due to their optimization and scalability properties. However, these approaches were often tested in specific operational applications and focused on technical performance indicators such as order tardiness or total throughput. In this paper, we propose a deep reinforcement learning based production control to optimize combined techno-financial performance measures. Based on pre-defined manufacturing modules that are supplied and operated by multiple agents, positive effects were observed in terms of increased revenue and reduced penalties due to lower throughput times and fewer delayed products. The combined modular and multi-staged approach as well as the distributed decision-making further leverage scalability and transferability to other scenarios. KW - modular production KW - production control KW - multi-agent system KW - deep reinforcement learning KW - discrete event simulation Y1 - 2024 U6 - https://doi.org/10.1016/j.procir.2023.09.229 SN - 2212-8271 VL - 121 SP - 55 EP - 60 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Bender, Benedict A1 - Bretschneider, Sina A1 - Fattah-Weil, Jasmin T1 - Advances in demand forecasting BT - a systematic review of methods, the role of AI, and data strategies in manufacturing T2 - AMCIS Proceedings 2024 N2 - This systematic literature review highlights the gap in demand forecasting in the manufacturing sector, which is challenged by complex supply chains and rapid market change. Traditional methods fall short in this dynamic environment, highlighting the need for an approach that combines advanced forecasting techniques, high-quality data, and industry-specific insights. Our research contributes by evaluating advanced forecasting methods, the effectiveness of AI and data strategies to improve accuracy. Our analysis reveals a shift towards machine learning and deep learning to improve accuracy and highlights the untapped potential of external data sources. Key findings provide both researchers and practitioners with guidance on effective forecasting strategies and key data types and offer an integrated framework for improving forecasting accuracy and strategic decision-making in manufacturing. This work fills a critical research gap and provides stakeholders with actionable insights to manage the complexity of modern manufacturing, representing a significant advance in forecasting practice. KW - demand forecasting KW - sales forecasting KW - forecasting methods KW - manufacturing industry KW - forecasting data KW - systematic literature review Y1 - 2024 UR - https://aisel.aisnet.org/amcis2024/stratcompis/stratcompis/7/ SP - 1 EP - 11 PB - AIS CY - Atlanta ER - TY - CHAP A1 - Gonnermann-Müller, Jana A1 - Teichmann, Malte ED - Davis, Fred D. ED - Riedl, René ED - vom Brocke, Jan ED - Léger, Pierre-Majorique ED - Randolph, Adriane B. ED - Müller-Putz, Gernot R. T1 - Examining the learner’s cognitive load in response to different learning material in high and low immersive virtual learning environments BT - an eye-tracking study T2 - Information systems and neuroscience N2 - Learning in virtual, immersive environments must be well-designed to foster learning instead of overwhelming and distracting the learner. So far, learning instructions based on cognitive load theory recommend keeping the learning instructions clean and simple to reduce the extraneous cognitive load of the learner to foster learning performance. The advantages of immersive learning, such as multiple options for realistic simulation, movement and feedback, raise questions about the tension between an increase of excitement and flow with highly realistic environments on the one hand and a reduction of cognitive load by developing clean and simple surroundings on the other hand. This study aims to gain insights into learners' cognitive responses during the learning process by continuously assessing cognitive load through eye-tracking. The experiment compares two distinct immersive learning environments and varying methods of content presentation. Y1 - 2024 SN - 978-3-031-58395-7 SN - 978-3-031-58396-4 U6 - https://doi.org/10.1007/978-3-031-58396-4_29 VL - 68 SP - 333 EP - 344 PB - Springer CY - Cham ER - TY - CHAP A1 - Mucha, Anne A1 - Engels, James J. A1 - Whibley, Fred A1 - Uegaki, Wataru A1 - Wamsley, James C. A1 - Dawson, Virginia A1 - Gruzdeva, Anastasija A1 - Alhazova, Anna A1 - Golovnina, Anna A1 - Nasyrova, Regina A1 - Sadkovsky, Feudor A1 - Weingartz, Siena A1 - Hohaus, Vera A1 - Cisse, Ousmane A1 - Coppock, Elizabeth A1 - Agodio, Badiba Olivier A1 - Jenks, Peter A1 - Sande, Hannah A1 - Zimmermann, Malte A1 - Berezovskaya, Polina A1 - Chen, Sihwei A1 - Renans, Agata ED - Lecavelier des Etangs-Levallois, Jeanne ED - Geick, Niklas ED - Grubic, Mira ED - Bharadwaj, Prarthanaa ED - Zimmermann, Malte T1 - Proceedings of TripleA 10 BT - fieldwork perspectives on the semantics of African, Asian and Austronesian languages T2 - Proceedings of TripleA N2 - The TripleA workshop series was founded in 2014 by linguists from Potsdam and Tübingen with the aim of providing a platform for researchers that conduct theoretically-informed linguistic fieldwork on meaning. Its focus is particularly on languages that are under-represented in the current research landscape, including but not limited to languages of Africa, Asia, and Australia, hence TripleA. For its 10th anniversary, TripleA returned to the University of Potsdam on the 7-9th of June 2023. The programme included 21 talks dealing with no less than 22 different languages, including three invited talks given by Sihwei Chen (Academia Sinica), Jérémy Pasquereau (Laboratoire de Linguistique de Nantes, CNRS) and Agata Renans (Ruhr-Universität Bochum). Nine of these (invited or peer-reviewed) talks are featured in this volume. N2 - Die TripleA-Workshop-Reihe wurde 2014 von Linguisten aus Potsdam und Tübingen mit dem Ziel gegründet, eine Plattform für Forscherinnen und Forscher zu bieten, die theoretisch informierte Feldforschung zu sprachlicher Bedeutung betreiben. Der Fokus liegt insbesondere auf Sprachen, die in der aktuellen Forschungslandschaft unterrepräsentiert sind, einschließlich (aber nicht ausschließlich) auf Sprachen aus Afrika, Asien und Australien: daher der Name TripleA. Zu seinem 10-jährigen Bestehen kehrte TripleA vom 7. Bis 9. Juni 2023 an die Universität Potsdam zurück. Das Programm umfasste 21 Vorträge zu nicht weniger als 22 verschiedenen Sprachen, darunter drei eingeladene Vorträge von Sihwei Chen (Academia Sinica), Jérémy Pasquereau (Laboratoire de Linguistique de Nantes, CNRS) und Agata Renans (Ruhr-Universität Bochum). Neun dieser (eingeladenen oder begutachteten) Vorträge sind in diesem Band abgedruckt. KW - African languages KW - Asian languages KW - Austronesian languages KW - fieldwork KW - semantics KW - afrikanische Sprachen KW - asiatische Sprachen KW - austronesische Sprachen KW - Feldforschung KW - Semantik Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-647980 ER - TY - CHAP A1 - Grum, Marcus ED - Shishkov, Boris T1 - Researching multi-site artificial neural networks’ activation rates and activation cycles T2 - Business modeling and software design : 14th International Symposium, BMSD 2024, Luxembourg City, Luxembourg, July 1–3, 2024, proceedings N2 - With the further development of more and more production machines into cyber-physical systems, and their greater integration with artificial intelligence (AI) techniques, the coordination of intelligent systems is a highly relevant target factor for the operation and improvement of networked processes, such as they can be found in cross-organizational production contexts spanning multiple distributed locations. This work aims to extend prior research on managing their artificial knowledge transfers as coordination instrument by examining effects of different activation types (respective activation rates and cycles) on by Artificial Neural Network (ANN)-instructed production machines. For this, it provides a new integration type of ANN-based cyber-physical production system as a tool to research artificial knowledge transfers: In a design-science-oriented way, a prototype of a simulation system is constructed as Open Source information system which will be used in on-building research to (I) enable research on ANN activation types in production networks, (II) illustrate ANN-based production networks disrupted by activation types and clarify the need for harmonizing them, and (III) demonstrate conceptual management interventions. This simulator shall establish the importance of site-specific coordination mechanisms and novel forms of management interventions as drivers of efficient artificial knowledge transfer. Y1 - 2024 SN - 978-3-031-64072-8 SN - 978-3-031-64073-5 U6 - https://doi.org/10.1007/978-3-031-64073-5_12 SP - 186 EP - 206 PB - Springer CY - Cham ER - TY - CHAP A1 - Grum, Marcus ED - Shishkov, Boris T1 - Managing multi-site artificial neural networks’ activation rates and activation cycles T2 - Business modeling and software design : 14th International Symposium, BMSD 2024, Luxembourg City, Luxembourg, July 1–3, 2024, proceedings N2 - Traditionally, business models and software designs used to model the usage of artificial intelligence (AI) at a very specific point in the process or rather fix implemented application. Since applications can be based on AI, such as networked artificial neural networks (ANN) on top of which applications are installed, these on-top applications can be instructed directly from their underlying ANN compartments [1]. However, with the integration of several AI-based systems, their coordination is a highly relevant target factor for the operation and improvement of networked processes, such as they can be found in cross-organizational production contexts spanning multiple distributed locations. This work aims to extend prior research on managing artificial knowledge transfers among interlinked AIs as coordination instrument by examining effects of different activation types (respective activation rates and cycles) on by ANN-instructed production machines. In a design-science-oriented way, this paper conceptualizes rhythmic state descriptions for dynamic systems and associated 14 experiment designs. Two experiments have been realized, analyzed and evaluated thereafter in regard with their activities and processes induced. Findings show that the simulator [2] used and experiments designed and realized, here, (I) enable research on ANN activation types, (II) illustrate ANN-based production networks disrupted by activation types and clarify the need for harmonizing them. Further, (III) management interventions are derived for harmonizing interlinked ANNs. This study establishes the importance of site-specific coordination mechanisms and novel forms of management interventions as drivers of efficient artificial knowledge transfer. Y1 - 2024 SN - 978-3-031-64072-8 SN - 978-3-031-64073-5 U6 - https://doi.org/10.1007/978-3-031-64073-5_17 SP - 258 EP - 269 PB - Springer CY - Cham ER - TY - CHAP A1 - Batzel, Katharina A1 - Baum, Katharina T1 - Exploring information flow on twitter: social network analysis on gender-specific sedicine T2 - AMCIS Proceedings 2022 N2 - To date, sex and gender differences play only a minor role in medical research and practice, thereby putting individuals’ health at risk. Gender-specific medicine, or the practice of taking these differences into account when conducting research and treating patients so far is being discussed primarily by experts. With people increasingly using social media such as Twitter for sharing and searching for health-related information online, Twitter can potentially educate about gender-specific medicine. However, little is known about the information circulation and the structure of interactions on the Twitter network discussing this topic. Results of a network analysis show that the network exhibits a community-structure, with information exchange being limited and concentrated in silos. This indicates that there is untapped potential for acquiring new information by users through interacting with individuals outside their community. Public health officials may benefit from this insight and tailor online campaigns to enhance awareness on gender-specific medicine. Y1 - 2022 SN - 978-1-958200-00-1 IS - 1548 PB - AIS CY - Atlanta ER -