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Advanced Systems Engineering (ASE) is a paradigm for the efficient design and creation of tomorrow's products and services. Challenges, which the new paradigm addresses, are the increasing complexity of products and systems, increasing requirements from the regulatory side as well as the sustainability and manageability of the systems. Advanced Systems Engineering offers a variety of approaches for handling the requirements mentioned before. This paper focuses on supporting the handling of the increasing complexity and the requirements for sustainability of systems by the combined use of Model-based Systems Engineering (MBSE) and Artificial Intelligence (AI) in the early phase of product development. First, the paradigms and approaches ASE, MBSE, sustainability, and AI are presented. Subsequently, the current state of the art in the intersection of the described topics is explained in the context of a literature review. The methodology applied here follows the Systematic Literature Review approach. Based on this, potentials and white spots in research are identified and described. The paper concludes with recommendations for connecting to future research in the areas described.

Communication in a pandemic is difficult and complex. It is characterized by volatile situations associated with a high degree of uncertainty and, in some cases, social divergence in groups and societies. Orientation is expected, by politicians and individuals, from science. However, sciences are only fulfilling the expectations to a limited extent. We demonstrate that there are severe weaknesses in holistic and interdisciplinary communication in the pandemic and show that established tools from management are neglected and overlooked.We then analyze the specific needs of scientific reasoning in pandemic situations such as •a rational approach integrating both estimates and explicit evidence;•expressing and quantifying uncertainty; •considering interdisciplinary aspects in advice and decision making;•the ability to deal with ethical aspects;•simple updates with new findings and evidence.In a third step we compare from literature and own experience existing methods from management science for their suitability against those needs. We find that many of the interdisciplinary tools are deterministic, like Multi Criteria Analyses, and do not support uncertainty. The frequently adopted linear computation of utility values leads to ethical issues. Foresight methods like Delphi or Scenario methods deal with uncertainty and subjectivity. But they are not designed to integrate strong evidence. Strategic planning tools like roadmaps are comprehensible but disappoint in volatile situations. Probabilistic decision making with expected utilities is too complex and suffers from missing data. Heuristics at the other hand are simple but do not allow for comprehensive reasoning. We then argue in a fourth step to use probability in communication and to apply it to decision making in the pandemic. We propose a simple one-step method with a calculus based on Bayes’ theorem and calculate the probabilities of alternative courses of action being the best un der given conditios. With examples we show how arguments from various scientific disciplines can be integrated in decision making and adjusted as new evidence appears. Furthermore, we provide a role model and show by examples how scientists, scientific consultants and decision makers can cooperate and communicate using the method.We conclude that the method fulfils the identified needs to a high degree and is worth to be further developed. We show its epistemic and scientific limitations and give an outlook how likelihood functions may be used to replace negotiated likelihoods by parametric and model based values.

The digital transformation of entire economic sectors and occupational profiles as well as the introduction of new forms of human-machine collaboration through the increased use of cognitive systems require completely new approaches. The key to success in coping with this change, which can be seen in all industries, is to break up old structures and venture something new. The ability to adapt and innovate is becoming a central success-critical factor in entrepreneurial activity. In order to continue to achieve market success and ensure sustainable growth in an extremely dynamic and disruptive environment, companies and organizations are called upon to proactively shape change. In addition to the establishment of flexible working models and agile processes, the increased generation and integration of knowledge into and around technical systems in the course of targeted competence development of employees is indispensable. The introduction and use of technical systems thus must go hand in hand with the flexibilization of innovation and collaboration processes as well as the development of employee skills in order to generate the currently missing socio-technological link – for companies´ added value and for the benefit of people. In this paper, the authors present an overview of currently used creativity techniques and innovation methods and work out the strengths and weaknesses of the respective tools. Furthermore, the resulting need for action for the optimization of innovation processes in the interaction of established techniques and possibilities of cognitive systems is presented.

An important aspect for companies in dealing with the demands of the working world is the continuous and requirement-specific further training of employees. The possibility of workplace-related learning has a major importance in this context. In this context, digital assistance systems can be used to provide targeted support for the learning process. This paper presents current research findings from the funding priority "Work in the Digitalized world" on the use of digital assistance systems for competence development as well as on relevant design criteria for the development and implementation of workplace-related learning assistance systems. In addition, the article explores the question of what role artificial intelligence (AI) can play as a learning technology in in-house further training. In this context, the article highlights the challenges and associated design options for AI-supported learning in the process of work. Finally, the development and design of symbiotic interaction (human-machine) will be addressed and the possibility of reciprocal learning in the interaction between humans and assistance systems will be highlighted.

Monitoring learners’ mental states via a passive Brain-Computer Interface (BCI) allows to continuously estimate current abilities, available cognitive resources, and motivation. It bears the great potential to adapt educational contents, learning speed, and format to the learner’s needs via an intelligent tutoring system. We present a neurophysiological-based approach to continuously monitor learners’ current affective-emotional and cognitive states by measuring and decoding brain activity via a passive BCI. In two studies (N = 8 and N = 7), we investigate whether we can a) predict learners’ affective and cognitive states during a learning or training session, b) provide continuous feedback of recognized states to the learner and, thereby, c) increase performance and intrinsic motivation. Oscillatory power measures in the alpha (8 – 12 Hz) and theta (4 – 7 Hz) frequency band served as features for the prediction and visualization. Our results reveal that machine learning algorithms can distinguish different states of cognitive workload and affect. The approach contributes to the development of closed-loop neuro-adaptive tutoring systems which allow to monitor learners’ states, provide feedback, and adapt their parameters for an optimal learner-training fit and effective and positive learning experience.

Many companies and regions in Europe consider innovation to be their key competitive advantage. To enable these actors to successfully incorporate innovation into their strategic planning, this article seeks to scope dynamics of industrial innovation by analyzing historical developments and forecasting future trends. The insight into historical developments provides the basis for learnings and a deeper understanding of the dynamics of industrial innovation. The forecast of industrial innovation in 2030 creates a framework to discuss future orientation and positioning. Historical developments are regrouped according to five key action fields of R&D and innovation management in accordance to the action fields of industrial companies. The foresight process was carried out based on a reduced and adapted version of the scenario technique, starting bottom-up with the descriptors of innovation systems and resulting in five theses on how innovation will look like in 2030. Both perspectives are combined to reach conclusions on anticipated transformative aspects in industrial innovation systems and discuss potential consistencies, repetitions, and interruptions between historical and future dynamics.

Background: The urinary incontinence system ATOMS (A.M.I., Austria) generates suburethral compression through its sphincter cushion. To what extent the ATOMS may lead to overactive bladder (OAB) symptoms or which risk factors for these symptoms exist remain unknown to date. We report on our multicentre evaluation on the prevalence, status, and therapy of OAB after ATOMS.Methods: Between 10/09 and 01/17, a total of 361 patients received an ATOMS device in Vienna and Halle. A prerequisite for surgery was persistent male stress urinary incontinence lasting at least 6 months after the primary intervention, as well as the failure of conservative treatment. Patients with a preoperative untreated anastomotic stricture or detrusor overactivity were excluded. In addition to continence and voiding parameters, patient's age, BMI, comorbidities, and pre-treatment strategies of the underlying disease and urinary incontinence were examined. If de novo OAB was present, urodynamics were used for further clarification. Statistical analysis was performed with GraphPad Prism 7® (GraphPad Software, Inc., La Jolla, USA), p < 0.05 considered significant.Results: OAB presented 18 patients (4.9%). Regarding the degree of urinary incontinence as well as uroflowmetry, residual volume and comorbidities, patients with an OAB showed no differences compared to patients without an OAB (p < 0.05). Only previous radiotherapy or urinary incontinence surgery and urethral stricture interventions resulted in statistically significant differences based on the bivariate analysis (p = 0.030, p = 0.006, p = 0.007). The consecutive postoperative urodynamics revealed a sensory OAB in 17 patients and a low-compliance bladder in a patient with newly diagnosed insulin-dependent type II diabetes mellitus. OAB was treated with a standard dose of antimuscarinic drugs and for the low-compliance bladder with botulinum toxin type A.Conclusion: OAB symptoms can occur after ATOMS implantation, but are rare and have no clear correlation to the incontinence device but rather are due to urinary incontinence-related underlying diseases and previous treatments.

Adaptive assistance systems are able to support the user in a wide range of different situations. These systems take external information and attempt to deduce user intentions from the context of use, without requiring or allowing direct feedback from the user. For this reason, it remains unclear whether the system’s behavior was in accordance with the user’s intentions – leading to problems in the interaction between human and adaptive technology. The goal of the EMOIO project is to overcome potential barriers of use with the aid of neuroscientific methods. Merging ergonomics with the neurosciences into the new field of neuroergonomics research produces enormous potential for innovation, to make the symbiosis between humans and technology more intuitive. To this end, brain-computer interfaces (BCIs) offer a new generation of interfaces between humans and technology. BCIs make it possible to register mental states such as attention and emotions and transmit this information directly to a technological system. So-called neuroadaptive systems continuously use this information in order to adjust the behavior, functions or the content of an interactive system accordingly. A neuroadaptive system is being developed by a consortium of partners from research and industry as part of the EMOIO project. The goal of the system is to recognize, based on the users’ brain activity, whether system-initiated behaviors are approved or rejected. The system is able to use this information to provide the person with the best possible assistance and thus adapt to individual and situational demands. To do this, neuroscientific methods such as electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) are being evaluated with respect to their suitability for measuring emotions (approval/rejection).

The digitization of industrial value chain, so-called "Industry 4.0", is one of the most important economic and social development in the last years. It allows the high-wage countries, for example Germany, to maintain their business responsiveness and competitiveness. While research and development units are organizationally, personally and methodically aligned for innovation projects development, the development and introduction of innovations in a manufacturing environment face a great challenge. The process is often unstructured and has an unclear target. Especially the procedure to introduce human-centered Industry 4.0 applications focuses not only on a technical side of the question. To solve this problem, a socio-technical approach should be taken under consideration. Furthermore, employees have to be involved in the design and implementation phase. This paper presents a new approach to establish a method to introduce human-centered cyber-physical use-cases to manufacturing. After the definition of procedure models, successful and less successful projects are analyzed. This is the base to define requirements for this presented approach. Therefore a modular innovation framework is developed, which includes a systematic procedure with project phases, results of each phase, a toolkit with methods and checklists. Additionally, work functions and role models are examined. Special emphasis is given to employee work engagement. At last, the usability of this procedure is discussed.

Background: The paper presents recent results of the ongoing collaborative research project “MyCPS” (Human-centered development and application of Cyber-Physical Systems). Methods: Within the scope of the project, 14 partners, amongst seven industrial partners, develop methods and tools to set-up applications of intelligent digitalization and automation of industrial processes. Results: Within the paper they are over 385 use cases evaluated according to comparative criteria. Furthermore, use cases were classified due to their development stage of industry 4.0 goals and promises. The three levels ‘information’, ‘interaction’ and ‘intelligence’ are used to differentiate applications according to their degree of maturity in industry 4.0 terms. Conclusion: In MyCPS, special emphasis is the role of the workforce and the interactions of the technology-led use cases with employees. Thereby, the analysis helps enterprises and researchers to self-assess key-aspects of the development of industry 4.0 use cases. A part of this study was presented as oral presentation at the "24th International Conference on Production Research (ICPR 2017)" in Poznan, Poland from July 30 to August 3, 20017.

A “worker journey” (as already known from the customer journey) was created here. In this journey the daily routine of intralogistics activities are presented under the influence of digitalisation technologies. The changes that took place and their plausibility were evaluated using a two-stage Delphi study. Respondents were experts from companies in German industry who are already actively involved in the topic of Industry 4.0, not only in the context of intralogistics.

Facing a wide range of new technologies and best practices within Industry 4.0, companies are seeking a systematic approach to identify potential application scenarios in their production. Best practices are often customized use cases – generally too specific to apply in a different manufacturing setting right away. The Production Assessment 4.0 presents a pragmatic approach to support companies to develop Industry 4.0 use cases in their factories. The approach follows the Design Thinking method and focus especially on the human role as a key aspect in the use cases designing process.