Sustainability and Resilience in Alliance-Driven Manufacturing Ecosystems: A Strategic Conceptual Modeling Perspective

The challenge of sustainability rests on the ability of organizations to change their practices to meet the needs of current and future generations. To date, most research on organizational change has focused on how to change within a single organization. However, an increasing number of sustainability challenges require changes across multiple organizations. In this paper, we summarize strategic challenges faced in such a setting and outline a conceptual modeling approach for strategic analysis of alliance-driven solutions. We illustrate our ideas with a case study in digital agriculture, a field particularly relevant to sustainability, and end with the identification of issues for further research

Gamification Support for Learning in Spatial Computing Environments

With the rise of mixed reality hardware and software, new opportunities in formal higher education arise, e.g. in anatomy, as the United States of Americage of 3D structures instead of 2D images or anatomical models supports a better understanding and enhances the learning process. But even with access to virtual 3D models, motivation is a key element for successful learning and for progressing over a longer period of time. Mixed reality spaces offer new opportunities for combining a 3D stereoscopic depth perception of anatomic models together with gamification and interactive learning. Virtual 3D models can be enhanced with additional information which can name and explain separate elements. Therefore, we developed GaMR, a gamified framework for learning in mixed reality, where 3D models can be experienced on the Microsoft HoloLens and the HTC Vive. Quiz creation is supported by placing annotations on the model. Progress is rewarded by badges. The gamification strategy guides the student and gives feedback about the learning progress. This open source gamification framework for mixed reality was evaluated with students and doctors from a medical university. It showed that it can be employed in many academic and industrial use cases

Transgenic rabbit models for cardiac disease research.

To study the pathophysiology of human cardiac diseases and to develop novel treatment strategies, complex interactions of cardiac cells on cellular, tissue and whole heart levels need to be considered. As in vitro cell-based models do not depict the complexity of the human heart, animal models are used to obtain insights that can be translated to human diseases. Mice are the most commonly used animals in cardiac research, however, differences in electrophysiological and mechanical cardiac function and a different composition of electrical and contractile proteins limit the transferability of the knowledge gained. Moreover, the small heart size and fast heart rate are major disadvantages. In contrast to rodents, electrophysiological, mechanical, and structural cardiac characteristics of rabbits resemble the human heart more closely, making them particularly suitable as an animal model for cardiac disease research. In this review, various methodological approaches for the generation of transgenic rabbits for cardiac disease research - such as pronuclear microinjection, the sleeping beauty transposon system and novel genome editing methods (ZFN and CRISPR/Cas9) - will be discussed. In the second section, we will introduce the different currently available transgenic rabbit models for monogenic cardiac diseases (such as long-QT syndrome, short-QT syndrome, and hypertrophic cardiomyopathy) in detail, especially in regards to their utility to increase the understanding of pathophysiological disease-mechanisms and novel treatment options

Transgenic LQT2, LQT5, and LQT2-5 rabbit models with decreased repolarisation reserve for prediction of drug-induced ventricular arrhythmias

Background and Purpose Reliable prediction of pro‐arrhythmic side effects of novel drug candidates is still a major challenge. Although drug‐induced pro‐arrhythmia occurs primarily in patients with pre‐existing repolarisation disturbances, healthy animals are employed for pro‐arrhythmia testing. To improve current safety screening, transgenic long QT (LQTS) rabbit models with impaired repolarisation reserve were generated by overexpressing loss‐of‐function mutations of human HERG (HERG‐G628S , loss of IKr; LQT2), KCNE1 (KCNE1‐G52R , decreased IKs; LQT5), or both transgenes (LQT2‐5) in the heart. Experimental Approach Effects of K+ channel blockers on cardiac repolarisation and arrhythmia susceptibility were assessed in healthy wild‐type (WT) and LQTS rabbits using in vivo ECG and ex vivo monophasic action potential and ECG recordings in Langendorff‐perfused hearts. Key Results LQTS models reflect patients with clinically “silent” (LQT5) or “manifest” (LQT2 and LQT2‐5) impairment in cardiac repolarisation reserve: they were more sensitive in detecting IKr‐blocking (LQT5) or IK1/IKs‐blocking (LQT2 and LQT2‐5) properties of drugs compared to healthy WT animals. Impaired QT‐shortening capacity at fast heart rates was observed due to disturbed IKs function in LQT5 and LQT2‐5. Importantly, LQTS models exhibited higher incidence, longer duration, and more malignant types of ex vivo arrhythmias than WT. Conclusion and Implications LQTS models represent patients with reduced repolarisation reserve due to different pathomechanisms. As they demonstrate increased sensitivity to different specific ion channel blockers (IKr blockade in LQT5 and IK1 and IKs blockade in LQT2 and LQT2‐5), their combined use could provide more reliable and more thorough prediction of (multichannel‐based) pro‐arrhythmic potential of novel drug candidates

The coming decade of digital brain research: a vision for neuroscience at the intersection of technology and computing

In recent years, brain research has indisputably entered a new epoch, driven by substantial methodological advances and digitally enabled data integration and modelling at multiple scales— from molecules to the whole brain. Major advances are emerging at the intersection of neuroscience with technology and computing. This new science of the brain combines high-quality research, data integration across multiple scales, a new culture of multidisciplinary large-scale collaboration and translation into applications. As pioneered in Europe’s Human Brain Project (HBP), a systematic approach will be essential for meeting the coming decade’s pressing medical and technological challenges. The aims of this paper are to: develop a concept for the coming decade of digital brain research, discuss this new concept with the research community at large, to identify points of convergence, and derive therefrom scientific common goals; provide a scientific framework for the current and future development of EBRAINS, a research infrastructure resulting from the HBP’s work; inform and engage stakeholders, funding organisations and research institutions regarding future digital brain research; identify and address the transformational potential of comprehensive brain models for artificial intelligence, including machine learning and deep learning; outline a collaborative approach that integrates reflection, dialogues and societal engagement on ethical and societal opportunities and challenges as part of future neuroscience research

DevOpsUse: community-driven continuous innovation of web information infrastructures

Since its invention in 1989, the only reliable factor on the Web has been its continuous change and diffusion into more and more application areas. The evolution was shaped by an interplay of new technologies on the one hand, and innovative application ideas from communities on the other. At a technological scale, alternation between vastly distributed and centralized architectures can be observed. The current challenges caused by the ongoing digital transformation are changing workplace settings and the adoption of the Internet of Things in industrial use cases, as for example in the context of Industry 4.0. On the Web, new technologies and device types sprawl together with new communication protocols and revised application programming interfaces (APIs). This inhibits the demanded rapid innovation cycles and creates a disruptive and unstable environment in which the requirements of endless communities must be met. Information systems infrastructure, while only partially visible and thus hard to grasp, has a strong influence on user practices. Therefore, the aim of this thesis is to stabilize the dichotomies apparent in the Web by means of an agile information systems development methodology. It supports the evolution of infrastructure through community-driven and model-based technologies to guide it on a sustainable path of continuous innovation. Our DevOpsUse methodology includes users in the process of infrastructuring, i.e. the appropriation of infrastructure during its usage. Agile development practices in software engineering, in particular DevOps, promote stronger cooperation between development and operating teams. DevOpsUse additionally fosters a stronger involvement of end users in software development processes. It intends to empower communities of practice to create and run their own software on their specific infrastructure, with the help of various newly developed software artifacts. The instantiation of our DevOpsUse life cycle model starts with Requirements Bazaar, a Web-based tool involving end users in the idea generation and evolution phases. Direwolf is a model-based framework bridging the gap between technocratic API descriptions created by developers, and user interfaces understood by end users. Faster development times require a streamlined deployment, which we achieve with the software container-based Layers Box. Ultimately, distributed development and operation go hand in hand with our evolutionary analytics platform SWEVA. The newly developed DevOpsUse methodology with its four areas, all involving end users, has been successfully validated by the transitions between three generations of technologies: near real-time peer-to-peer Web architectures, edge computing, and the Internet of Things. All technological leaps could be adequately mastered and supported by significantly end-user-oriented measures. In particular, we were able to demonstrate our methodology's capabilities through longitudinal studies in several large-scale international digitalization projects. DevOpsUse scalability and involvement aspects were confirmed in entrepreneurial and medical teaching courses. Beyond Web information systems, the framework and its open source tools are applicable in further innovative areas like mixed reality and Industry 4.0. Its broad adaptability testifies that DevOpsUse has the potential to unlock sustainable innovation capabilities