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Traditionelle Organisationen wandeln sich in komplexe Wertschöpfungssysteme mit zunehmend dezentralisierten und digitalisierten Formen der Arbeitsorganisation. Indem sich die Vorstellungen von Mitgliedschaft verändern und sich Arbeit in digitalisierte Räume verschiebt, lösen sich die Grenzen von Organisationen auf. Im Beitrag wird argumentiert, dass sich in solch grenzaufgelösten Organisationen die Machtdynamiken verändern. Im Beitrag werden zwei Dynamiken exemplarisch betrachtet: erstens diejenigen, die sich aus der abnehmenden Wirkung bürokratischer Strukturen als Machtressourcen ergeben, wenn sich die Formen der organisationalen Mitgliedschaft und Zugehörigkeit verändern (z. B. Freelancer, hybride Arbeit). Zweitens werden die Schnittstellen zwischen Menschen und intelligenten Technologien in digitalen Arbeitsräumen und die sich dadurch verschiebenden Machtverhältnisse betrachtet. Der Beitrag zielt darauf ab, die veränderten Machtdynamiken sichtbarer zu machen und damit einen reflektierten Umgang mit Macht in digital transformierten Organisationen zu ermöglichen.
Offshore-Windenergie wird global zunehmend intensiver ausgebaut. Auch die deutsche Bundesregierung hat die Ausbauziele auf 30 GW installierte Leistung bis 2030 erhöht, von derzeit ca. 8 GW. Wie kann die deutsche Offshore-Windenergiebranche dies erreichen und was bedeutet das für ihre Zulieferer und Dienstleister? Vier Szenarien beschreiben mögliche Zukünfte. Technischer Fortschritt entlang der gesamten Wertschöpfungskette, Lieferkettensicherheit, Regulatorik sowie Fachkräfteverfügbarkeit sind die kritischen Erfolgsfaktoren.
Silicon neurons represent different levels of biological details and accuracies as a trade-off between complexity and power consumption. With respect to this trade-off and high similarity to neuron behaviour models, relaxation-type oscillator circuits often yield a good compromise to emulate neurons. In this chapter, two exemplified relaxation-type silicon neurons are presented that emulate neural behaviour with energy consumption under the scale of nJ/spike. The first proposed fully CMOS relaxation SiN is based on mathematical Izhikevich model and can mimic a broad range of physiologically observable spike patterns. The results of kinds of biologically plausible output patterns and coupling process of two SiNs are presented in 0.35 μm CMOS technology. The second type is a novel ultra-low-frequency hybrid CMOS-memristive SiN based on relaxation oscillators and analog memristive devices. The hybrid SiN directly emulates neuron behaviour in the range of physiological spiking frequencies (less than 100 Hz). The relaxation oscillator is implemented and fabricated in 0.13 μm CMOS technology. An autonomous neuronal synchronization process is demonstrated with two relaxation oscillators coupled by an analog memristive device in the measurement to emulate the synchronous behaviour between spiking neurons.
OpenAPI, WADL, RAML, and API Blueprint are popular formats for documenting Web APIs. Although these formats are in general both human and machine-readable, only the part of the format describing the syntax of a Web API is machine-understandable. Descriptions, which explain the meaning and purpose of Web API elements, are embedded as natural language text snippets into documents and target human readers but not machines. To enable machines to read and process these state-of-practice Web API documentation, we propose a Transformer model that solves the generic task of identifying a Web API element within a syntax structure that matches a natural language query. For our first prototype, we focus on the Web API integration task of matching output with input parameters and fined-tuned a pre-trained CodeBERT model to the downstream task of question answering with samples from 2,321 OpenAPI documentation. We formulate the original question answering problem as a multiple choice task: given a semantic natural language description of an output parameter (question) and the syntax of the input schema (paragraph), the model chooses the input parameter (answer) in the schema that best matches the description. The paper describes the data preparation, tokenization, and fine-tuning process as well as discusses possible applications of our model as part of a recommender system. Furthermore, we evaluate the generalizability and the robustness of our fine-tuned model, with the result that it achieves an accuracy of 81.46% correctly chosen parameters.
Distributed Ledger Technologies for the energy sector: facilitating interoperability analysis
(2023)
The use of distributed data storage and management structures, such as Distributed Ledger Technologies (DLT), in the energy sector has gained great interest in recent times. This opens up new possibilities in e.g. microgrid management, aggregation of distributed resources, peer-to- peer trading, integration of electromobility or proof-of-origin strategies. However, in order to benefit from those new possibilities, new challenges have to be overcome. This work focuses on one of these challenges, which is the need to ensure interoperability when integrating DLT-enabled devices in energy use cases. Firstly, the use of DLTs in the energy sector will be analyzed and the main use cases will be presented. Then, a classification of DLT-Energy use cases will be proposed. Secondly, the need for a common reference architecture framework to analyze those use cases with a focus on interoperability will be discussed and the current activities in research and standardization in this field will be presented. Finally, a new common reference architecture framework based on current activities in standardization will be presented.
It is widely recognized that Education for Sustainable Development (ESD) plays a critical role in creating a more sustainable world by fostering the development of the knowledge, skills, understanding, values, and actions necessary for such change (UNESCO, 2020). In this context, ESD represents a holistic approach that focuses on lifelong learning to create informed people who can make decisions today and in the future. Related to the textile and fashion industry, ESD is an appropriate approach to continuously implement sustainability aspects in education and training. To achieve this goal, the European project "Sustainable Fashion Curriculum at Textile Universities in Europe - Development, Implementation and Evaluation of a Teaching Module for Educators" (Fashion DIET) has developed a digital teaching module in a partnership between a University of Education and universities with textile departments. The main objective of the project is to elaborate an ESD module for university lecturers in order to introduce a sustainable fashion curriculum in textile universities in Europe and implement it in educational systems. The project therefore aims to train educators along the textile supply chain, to inform the young generation about the latest aspects of sustainability and raise awareness by implementing ESD in textile education. This paper presents the learning outcomes of the modules on sustainable fashion design and related production technologies developed by the technical university partners, as part of the total of 42 courses covering didactic-methodological approaches and the sustainable orientation of the fashion market, offered at the consortium level. The project content is made available as Open Educational Resources through Glocal Campus, an open-access e-learning platform that enables virtual collaboration between universities.
Es fällt in Unternehmen immer schwerer, in komplexen Zeiten zukünftige Entwicklungen zu antizipieren. Ein Umstand, der auch das Change Management prägen sollte. Dennoch ist ein methodisch fundierter Umgang mit Nicht-Wissen in der Change-Praxis kaum verbreitet. Erst seit Kurzem wird darüber nachgedacht, wie man beispielsweise das Experiment als zukunftsfähige Methode nutzbar machen kann. Ein Ansatz sind Experimentierräume als ein besonders kennzeichnendes Format einer experimentellen Organisationsentwicklung.
Context
Web APIs are one of the most used ways to expose application functionality on the Web, and their understandability is important for efficiently using the provided resources. While many API design rules exist, empirical evidence for the effectiveness of most rules is lacking.
Objective
We therefore wanted to study 1) the impact of RESTful API design rules on understandability, 2) if rule violations are also perceived as more difficult to understand, and 3) if demographic attributes like REST-related experience have an influence on this.
Method
We conducted a controlled Web-based experiment with 105 participants, from both industry and academia and with different levels of experience. Based on a hybrid between a crossover and a between-subjects design, we studied 12 design rules using API snippets in two complementary versions: one that adhered to a rule and one that was a violation of this rule. Participants answered comprehension questions and rated the perceived difficulty.
Results
For 11 of the 12 rules, we found that violation performed significantly worse than rule for the comprehension tasks. Regarding the subjective ratings, we found significant differences for 9 of the 12 rules, meaning that most violations were subjectively rated as more difficult to understand. Demographics played no role in the comprehension performance for violation.
Conclusions
Our results provide first empirical evidence for the importance of following design rules to improve the understandability of Web APIs, which is important for researchers, practitioners, and educators.
Rapid and robust quality monitoring of the composition of meat pastes is of fundamental importance in processing meat and sausage products. Here, an in-line near-infrared spectroscopy/micro-electro-mechanical-system-(MEMS)-based approach, combined with multivariate data analysis, was used for measuring the constituents fat, protein, water, and salt in meat pastes within a typical range of meat paste recipes. The meat pastes were spectroscopically characterized in-line with a novel process analyzer prototype. By integrating salt content in the calibration set, robust predictive PLSR models of high accuracy (R2 > 0.81) were obtained that take interfering matrix effects of the minor and NIR-inactive meat paste recipe component “salt” into account as well. The nonlinear blending behavior of salt concentration on the spectral features of meat pastes is discussed based on a designed mixture experiment with four systematically varied components.