In recent years multi-material Additive Manufacturing gained more interest as new applications were found in domains such as bioengineering, soft robotics and actuators, electronics and many more. Fused Filament Fabrication (FFF) is one of the most used AM technologies for multi-material 3D printing due to relatively low-cost equipment and a large variety of thermoplastic materials. Even so, issues are still to be solved regarding the adhesion mechanisms at the level of bond interface, especially for dissimilar materials. Therefore, this paper aimed to identify influencing factors over the printing process of multi-material parts at the interface level. The influencing factors were determined using a literature review and Cura slicing tool user guide and systematised using a cause-effect diagram. The main domains of influence are Materials bonding, Printing equipment, Model geometry, and Method of printing and processing. These domains were further split into more specific factors and discussed based on their influence over multi-material interface printing.

A new topology based feature extraction method for classification of interictal epileptiform discharges (IEDs) in EEG recordings from patients with epilepsy is proposed. After dimension reduction of the recorded EEG signal, using dynamical component analysis (DyCA) or principal component analysis (PCA), a persistent homology analysis of the resulting phase space trajectories is performed. Features are extracted from the persistent homology analysis and used to train and evaluate a support vector machine (SVM). Classification results based on these persistent features are compared with statistical features of the dimension-reduced signals and combinations of all of these features. Combining the persistent and statistical features improves the results (accuracy 94.7 %) compared to using only statistical feature extraction, whereas applying only persistent features does not achieve sufficient performance. For this classification example the choice of the dimension reduction technique does not significantly influence the classification performance of the algorithm.

Mit dem steigenden Forschungsinteresse im Gebiet Conversational Artificial Intelligence entsteht das Potenzial für neue Einsatzszenarien. Währenddessen legt die Tatsache, dass über die Hälfte aller Software-Projekte nicht erfolgreich fertiggestellt werden können, einen Handlungsbedarf nahe. Das Projekt-Reporting stellt dabei ein Werkzeug dar, um kontinuierlich den Status eines Projektes zu prüfen, um so frühzeitig auf ein mögliches Scheitern aufmerksam zu machen. Ziel der vorliegenden Arbeit ist die Untersuchung von Anwendungsgebieten von CAI im Projekt-Reporting sowie darauf aufbauend die Darstellung eines neuen Projekt-Reporting-Ansatzes. Der Einsatz von CAI eignet sich dabei im Wesentlichen für die Aufgaben der Datenerfassung sowie -analyse und -bereitstellung im Projekt-Reporting. Somit kann der Ablauf eines Projekt-Reporting-Prozesses mittels verschiedener Methoden der CAI automatisiert und letztendlich durch die Realisierung von Arbeitserleichterung optimiert werden.

Angesichts des demografischen Wandels, im Speziellen des Ausscheidens der Babyboomer-Generation in den Altersruhestand, nimmt die Sicherung des Erfahrungswissens langjähriger Mitarbeitender an Bedeutung zu. Unter anderem stellt Expert Debriefing mit einem Prozessbegleiter hierfür einen Baustein der Lösung dar. Die personellen Unterstützungsleistungen bedeuten jedoch nicht unerhebliche Aufwände. In diesem Beitrag wird daher untersucht, wie ein kognitiver, wissensbasierter Assistent einen Wissensträger innerhalb dieses Prozesses der Wissensdokumentation ohne die Einbeziehung eines Prozessbegleiters unterstützen kann. Damit soll angesichts des zunehmenden Risikos des drohenden Wissensverlusts eine kosteneffiziente Skalierung der Wissenssicherung ermöglicht werden. Dazu werden insbesondere ein hochstrukturiertes Prozessmodell, ein Knowledge Graph für Expert Debriefing, entsprechende Leitfragen und Checklisten, Fragestrukturen sowie weitere Automatisierungspotenziale auf der Basis von Text Mining vorgestellt. Ferner werden die Herausforderungen bei der Hebung des impliziten Erfahrungswissens und bei der Mensch-Maschine-Kommunikation fokussiert.

Research in musical interaction has been widely addressed in recent years. The term “musical expression” plays a key role in this field of research as the name of the biggest scientific conference in musical interaction “New Interfaces for Musical Expression” (nime.org) already shows. While in music and musicology the assumption is often found that mind and body act co-presently in musical performance, the research scene of music interaction essentially finds approaches that leave the mind more or less aside when models of interaction are developed, although the term "musical expression" can very well be assigned to the mind. While a huge number of musical interface development research projects have been done worldwide (Fasciani & Goode, 2021), research outcome of this scene had nearly no impact on the products musicians nowadays buy and use in their daily musical practice (Poepel & Juergens, 2022). The question arises which could be the reasons for such fruitlessness.

This paper presents an analysis of common music interaction research strategies and their assumed foundations. It shows requirements commonly found in the development of musical interfaces. It further presents understandings of music given by well-known musicians and draws conclusions which requirements for musical interaction come up facing these understandings of music and music making. The differences in the requirements of both sources are contrasted. With respect to the Whiteheadian fallacy of misplaced concreteness, these differences are analyzed. In addition, the possible meaning of the Whiteheadian findings for the general research field of musical interaction is discussed.

While the research outcome of musical interface design for the music technology market was rather small, simple music interfaces like MIDI keyboards and further digital sound generators connected to them as well as digital audio workstations are very commonly used in daily musical creative practice. The theories and models implemented in such devices could be classified as related examples of misplaced concreteness according to Whitehead. Nevertheless, these devices are used by many musicians. But the way of making music and the music itself, which is then created, is changing. This is shown by concrete little examples of making music and it is further discussed in a speculative manner.

With respect to the Whitehedian fallacy of misplaced concreteness and its possible meaning for musical interaction design, conclusions for the general area of human-computer interaction are drawn.

References:

Fasciani, S; Goode, J. (2021). 20 NIMEs: Twenty Years of New Interfaces for Musical Expression. In Proceedings of the International Conference on New Interfaces for Musical Expression (NIME 2021), Singapore.

Poepel, C.; Juergens, E. (2022). On Overcoming the Gap between Industry and Academic Research in the Field of Music Technology, Proceedings of the 4th International Conference Business Meets Technology, Ansbach.

Current research on entrepreneurial ecosystems focuses on conceptual frameworks as well as the characteristics of individual elements and actors. Yet, ecosystems require the contributions of different actors (such as successful entrepreneurs) to become self-sustaining. This study sheds light on the questions of how and why entrepreneurs contribute to the development and reproduction of their entrepreneurial ecosystem. Applying a qualitative, inductive approach, a data sample of 32 semi-structured interviews with startup entrepreneurs from Germany was examined. The analysis reveals, that, amongst the personal traits of entrepreneurial passion and altruism, a perceived affiliation, reciprocity with and gratitude to the entrepreneurial ecosystem and its actors, influenced by antecedent experiences and relationships, are the core predictors for contribution activities such as mentoring, peering, or passing on experiences. These empirical insights are synthesized in a dynamic model, which adds further theoretical grounding to research on causal mechanisms and microfoundations in entrepreneurial ecosystems.

The development of “ABCD” technology (Artificial intelligence, Blockchain, Cloud computing, and Big data) has promoted the design and development of new and innovative technologies, which fundamentally revolutionized the ways organizations manage themselves. However, digital transformations have also brought in new challenges. Against the backdrop of growing internationalization, cross-border collaborations, and increasing global trade, it is of utmost importance to explore how technology renovates the intercultural dialogues, negotiations, trainings, and the subsequent knowledge transfer processes. Researchers have suggested that technology will play a vital role in facilitating the need to work with people from all around the world. And from the perspective of intercultural exchanges, and cross-border/cross-cultural collaborations,it is necessary to know how to navigate the intercultural challenges effectively. The emergence of social robots like the Furhat allows to design rich human-robot interactions including verbal- and non-verbal communication elements, which is promising to deliver artificial intelligence (AI) -based intercultural training capabilities. This research project has two objectives: first, this research will bring in the AI factor in human landscape to explore the actions and interactions of AI and intercultural environment in transition; second, this study aims to provide orientations and recommendations for implementing interactions with social robots to enable AI-based intercultural trainings. Therefore, design criteria for implementing effective verbal and nonverbal interactions are outlined for achieving high standards of intercultural interactions to provide better understanding and interpretations of the contexts in which certain intercultural actions are practiced.

Cloud Computing allows companies to scale seamlessly, providing a broad range of state-of-the-art services. Another promise is to free users from the operational and administrative burdens [1]. However, with the advent of cloud-native applications [2], this promise becomes questionable – especially when DevOps [3] principles are used during development. Experience from practice shows that teams often struggle dealing with both the infrastructure, finding the right architecture, and implementing business logic. When working in decentralized teams, things are even worse, as standardization across teams cannot be assumed. To tackle those issues, automation by means of techniques such as Infrastructure-as-code [4] help to ease to cope with infrastructural concerns. However, when working with several teams in a decentralized manner, operational overhead is still there. Organizations struggle with standardization of infrastructure code and there is no clear centralized visibility for security-related concerns within the development lifecycle. To address these issues, we propose two things: First, building up a Platform Team [5], which serves as an organizational structure for continuous delivery. A Platform Teams can be the size of a typical small DevOps Team and support the whole organization with standardized security-hardened modules. Second, an Ops-Platform is needed that is operated by the Platform Team to centrally provide and maintain those modules. Other Dev-Teams can then consume those modules. In this paper, we report insights from the implementation of this approach in practice. We find out that developers are 75% less focused on operations by using such a platform and name specific success factors.

Research into artificial intelligence is not a very young field; its precursors can be traced back as far as the 16th century. Today’s technical development, however, is virtually leaping forward, with new intelligent chat systems and social robots playing no small part in this. This is revolutionizing a wide range of scientific and social fields. The very large publication numbers in this field illustrate this as well. In order to keep track of the discourse in the field, the representatives of the field, the publications as well as the topics and their future development, it is indispensable for academics and scientists to prepare them in a bibliometric analysis. Only in this way it is possible to uncover thematic gaps as well as further points of contact and to drive research forward in a targeted and stringent manner. It is precisely this sorting and processing of the research discourse, the topics, and the authors, which is necessary for further research, that is carried out in this paper. For this purpose, using bibliometric analysis tools, an overview of the past, present, and future of the research field is created, and the general relevant topics are uncovered. The analysis includes as performance analysis a) the total number of publications and b) the total number of citations, and for science mapping c) a co-citation analysis (past), d) a bibliographic coupling (present) and e) a co-word analysis (future). The data needed for the analysis are identified and extracted from the SCOPUS or Web of Science (ISI) databases

“China expertise” has become the keyword in European foreign politics. A look at the latest government publications shows that a clear definition is yet to be expected. It can be summarized from reports about educational programs that there are differing trends among European countries as regards their Chinese language and other China-related skills as well as their infrastructures to gain these. A linguistic assessment shows that for learning this distant language, an early onset of acquisition is recommended. Given the almost pan-European desire for China expertise a clear definition is demanded so efficient international cooperation to build up this expertise over-regionally is possible.

Ongoing assessments in a course are crucial for tracking student performance and progress. However, generating and evaluating tests for each lesson and student can be time-consuming. Existing models for generating and evaluating question-answer pairs have had limited success. In recent years, large language models (LLMs) have become available as a service, offering more intelligent answering and evaluation capabilities. This research aims to leverage LLMs for generating questions, model answers, and evaluations while providing valuable feedback to students and decentralizing the dependency on faculty. Our approach is based on the development and interaction of advanced AI-powered generative agents, built on large language models like ChatGPT, GPT-4, and Vicuna, and designed to emulate human activities such as information abstraction, context refinement, and query rating. These agents interact autonomously in a network, employing techniques like Zero-Shot and Few-Shot Prompting to generate responses and adapt to various roles and contexts. The setup includes three key agents for question generation, refinement, and quality assurance, which leverage text vectorization, document selection and filtering, and cutting-edge language models to generate, refine, and evaluate questions and answers based on specific learning objectives. In conclusion, this paper demonstrates the versatility of LLMs for various learning tasks, including question generation, model answer generation, and evaluation, all while providing personalized feedback to students. By identifying and addressing knowledge gaps, LLMs can support continuous assessment and help students improve their understanding before semester exams.