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In honour of Seymour Papert
(2018)
Forth is nice and flexible but to a philosopher and teacher educator Logo is the more impressing language. Both are relatives of Lisp, but Forth has a reverse Polish notation where as Logo has an infix notation. Logo allows top down programming, Forth only bottom up. Logo enables recursive programming, Forth does not. Logo includes turtle graphics, Forth has nothing comparable. So what to do if you can't get Logo and have no information about its inner architecture? This should be a case of "empirical modelling": How can you model observable results of the behaviour of Logo in terms of Forth? The main steps to solve this problem are shown in the first part of the paper.
The second part of the paper discusses the problem of modelling and shows that the modelling of making and the modelling of recognition have the same mathematical structure. So "empirical modelling" can also serve for modelling desired behaviour of technical systems.
The last part of the paper will show that the heuristic potential of a problem which should be modeled is more important than the programming language. The Picasso construal shows, in a very simple way, how children of different ages can model emotional relations in human behaviour with a simple Logo system.
Enhancing economic efficiency in modular production systems through deep reinforcement learning
(2024)
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.
Increasingly fast development cycles and individualized products pose major challenges for today's smart production systems in times of industry 4.0. The systems must be flexible and continuously adapt to changing conditions while still guaranteeing high throughputs and robustness against external disruptions. Deep rein- forcement learning (RL) algorithms, which already reached impressive success with Google DeepMind's AlphaGo, are increasingly transferred to production systems to meet related requirements. Unlike supervised and unsupervised machine learning techniques, deep RL algorithms learn based on recently collected sensor- and process-data in direct interaction with the environment and are able to perform decisions in real-time. As such, deep RL algorithms seem promising given their potential to provide decision support in complex environments, as production systems, and simultaneously adapt to changing circumstances. While different use-cases for deep RL emerged, a structured overview and integration of findings on their application are missing. To address this gap, this contribution provides a systematic literature review of existing deep RL applications in the field of production planning and control as well as production logistics. From a performance perspective, it became evident that deep RL can beat heuristics significantly in their overall performance and provides superior solutions to various industrial use-cases. Nevertheless, safety and reliability concerns must be overcome before the widespread use of deep RL is possible which presumes more intensive testing of deep RL in real world applications besides the already ongoing intensive simulations.
CpG-oligonucleotides modulate sphingosine-1-phosphate metabolism in normal human keratinocytes
(2012)
This first volume of the DIGAREC Series holds the proceedings of the conference “The Philosophy of Computer Games”, held at the University of Potsdam from May 8-10, 2008. The contributions of the conference address three fields of computer game research that are philosophically relevant and, likewise, to which philosophical reflection is crucial. These are: ethics and politics, the action-space of games, and the magic circle. All three topics are interlinked and constitute the paradigmatic object of computer games: Whereas the first describes computer games on the outside, looking at the cultural effects of games as well as on moral practices acted out with them, the second describes computer games on the inside, i.e. how they are constituted as a medium. The latter finally discusses the way in which a border between these two realms, games and non-games, persists or is already transgressed in respect to a general performativity.
The game itself?
(2020)
In this paper, we reassess the notion and current state of ludohermeneutics in game studies, and propose a more solid foundation for how to conduct hermeneutic game analysis. We argue that there can be no ludo-hermeneutics as such, and that every game interpretation rests in a particular game ontology, whether implicit or explicit. The quality of this ontology, then, determines a vital aspect of the quality of the analysis.
The game itself?
(2020)
In this paper, we reassess the notion and current state of ludohermeneutics in game studies, and propose a more solid foundation for how to conduct hermeneutic game analysis. We argue that there can be no ludo-hermeneutics as such, and that every game interpretation rests in a particular game ontology, whether implicit or explicit. The quality of this ontology, then, determines a vital aspect of the quality of the analysis.
Breaking down barriers
(2024)
Many researchers hesitate to provide full access to their datasets due to a lack of knowledge about research data management (RDM) tools and perceived fears, such as losing the value of one's own data. Existing tools and approaches often do not take into account these fears and missing knowledge. In this study, we examined how conversational agents (CAs) can provide a natural way of guidance through RDM processes and nudge researchers towards more data sharing. This work offers an online experiment in which researchers interacted with a CA on a self-developed RDM platform and a survey on participants’ data sharing behavior. Our findings indicate that the presence of a guiding and enlightening CA on an RDM platform has a constructive influence on both the intention to share data and the actual behavior of data sharing. Notably, individual factors do not appear to impede or hinder this effect.
Social media constitute an important arena for public debates and steady interchange of issues relevant to society. To boost their reputation, commercial organizations also engage in political, social, or environmental debates on social media. To engage in this type of digital activism, organizations increasingly utilize the social media profiles of executive employees and other brand ambassadors. However, the relationship between brand ambassadors’ digital activism and corporate reputation is only vaguely understood. The results of a qualitative inquiry suggest that digital activism via brand ambassadors can be risky (e.g., creating additional surface for firestorms, financial loss) and rewarding (e.g., emitting authenticity, employing ‘megaphones’ for industry change) at the same time. The paper informs both scholarship and practitioners about strategic trade-offs that need to be considered when employing brand ambassadors for digital activism.
Insertion of artificial cell surface receptors for antigen-specific labelling of hybridoma cells
(2012)
Scaling up CSP
(2023)
Concentrating solar power (CSP) is one of the few scalable technologies capable of delivering dispatchable renewable power. Therefore, many expect it to shoulder a significant share of system balancing in a renewable electricity future powered by cheap, intermittent PV and wind power: the IEA, for example, projects 73 GW CSP by 2030 and several hundred GW by 2050 in its Net-Zero by 2050 pathway. In this paper, we assess how fast CSP can be expected to scale up and how long time it would take to get new, high-efficiency CSP technologies to market, based on observed trends and historical patterns. We find that to meaningfully contribute to net-zero pathways the CSP sector needs to reach and exceed the maximum historical annual growth rate of 30%/year last seen between 2010-2014 and maintain it for at least two decades. Any CSP deployment in the 2020s will rely mostly on mature existing technologies, namely parabolic trough and molten-salt towers, but likely with adapted business models such as hybrid CSP-PV stations, combining the advantages of higher-cost dispatchable and low-cost intermittent power. New third-generation CSP designs are unlikely to play a role in markets during the 2020s, as they are still at or before the pilot stage and, judging from past pilot-to-market cycles for CSP, they will likely not be ready for market deployment before 2030. CSP can contribute to low-cost zero-emission energy systems by 2050, but to make that happen, at the scale foreseen in current energy models, ambitious technology-specific policy support is necessary, as soon as possible and in several countries.
The digitalization of value networks holds out the prospect of many advantages for the participating compa- nies. Utilizing information platforms, cross-company data exchange enables increased efficiency of collab- oration and offers space for new business models and services. In addition to the technological challenges, the fear of know-how leakage appears to be a significant roadblock that hinders the beneficial realization of new business models in digital ecosystems. This paper provides the necessary building blocks of digital participation and, in particular, classifies the issue of trust creation within it as a significant success factor. Based on these findings, it presents a solution concept that, by linking the identified building blocks, offers the individual actors of the digital value network the opportunity to retain sovereignty over their data and know-how and to use the potential of extensive networking. In particular, the presented concept takes into account the relevant dilemma, that every actor (e. g. the machine users) has to be able to control his commu- nicated data at any time and have sufficient possibilities for intervention that, on the one hand, satisfy the need for protection of his knowledge and, on the other hand, do not excessively diminish the benefits of the system or the business. Taking up this perspective, this paper introduces dedicated data sovereignty and shows a possible implementation concept.
Visual Social Networking Sites (SNSs) enable users to present themselves favorably to gain likes and the attention of others. Especially, Instagram is known for its focus on beauty, fitness, fashion, and dietary topics. Although a large body of research reports negative weight-related outcomes of SNS usage (e.g., body dissatisfaction, body image concerns), studies examining how SNS usage relates to these outcomes are scarce. Based on the visual normalization theory, we argue that SNS content facilitates normalization of so-called thin- and fit-ideals, thereby leading to biased perceptions of the average body weight in society. Therefore, this study tests whether Instagram use is associated with perceiving that the average person weighs less. Responses of 181 survey participants confirm that Instagram use is negatively related to average weight perception of both women and men. These findings contribute to the growing body of research on how SNS use relates to negative weight-related outcomes.
Data sharing requires researchers to publish their (primary) data and any supporting research materials. With increased attention on reproducibility and more transparent research requiring sharing of data, the issues surrounding data sharing are moving beyond whether data sharing is beneficial, to what kind of research data should be shared and how. However, despite its benefits, data sharing still is not common practice in Information Systems (IS) research. The panel seeks to discuss the controversies related to data sharing in research, specifically focusing on the IS discipline. It remains unclear how the positive effects of data sharing that are often framed as extending beyond the individual researcher (e.g., openness for innovation) can be utilized while reducing the downsides often associated with negative consequences for the individual researcher (e.g., losing a competitive advantage). To foster data sharing practices in IS, the panel will address this dilemma by drawing on the panelists’ expertise.