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The initial idea of Robotic Process Automation (RPA) is the automation of business processes through the presentation layer of existing application systems. For this simple emulation of user input and output by software robots, no changes of the systems and architecture is required. However, considering strategic aspects of aligning business and technology on an enterprise level as well as the growing capabilities of RPA driven by artificial intelligence, interrelations between RPA and Enterprise Architecture (EA) become visible and pose new questions. In this paper we discuss the relationship between RPA and EA in terms of perspectives and implications. As workin- progress we focus on identifying new questions and research opportunities related to RPA and EA.
The initial idea of Robotic Process Automation (RPA) is the automation of business processes through a simple emulation of user input and output by software robots. Hence, it can be assumed that no changes of the used software systems and existing Enterprise Architecture (EA) is
required. In this short, practical paper we discuss this assumption based on a real-life implementation project. We show that a successful RPA implementation might require architectural work during analysis, implementation, and migration. As practical paper we focus on exemplary lessons-learned and new questions related to RPA and EA.
Digital twins enable the modeling and simulation of real-world entities (objects, processes or systems), resulting in improvements in the associated value chains. The emerging field of quantum computing holds tremendous promise forevolving this virtualization towards Quantum (Digital) Twins (QDT) and ultimately Quantum Twins (QT). The quantum (digital) twin concept is not a contradiction in terms - but instead describes a hybrid approach that can be implemented using the technologies available today by combining classicalcomputing and digital twin concepts with quantum processing. This paperpresents the status quo of research and practice on quantum (digital) twins. It alsodiscuses their potential to create competitive advantage through real-timesimulation of highly complex, interconnected entities that helps companies better
address changes in their environment and differentiate their products andservices.
Reliable methods for automatic readability assessment have the potential to impact a variety of fields, ranging from machine translation to self-informed learning. Recently, large language models for the German language (such as GBERT and GPT-2-Wechsel) have become available, allowing to develop Deep Learning based approaches that promise to further improve automatic readability assessment. In this contribution, we studied the ability of ensembles of fine-tuned GBERT and GPT-2-Wechsel models to reliably predict the readability of German sentences. We combined these models with linguistic features and investigated the dependence of prediction performance on ensemble size and composition. Mixed ensembles of GBERT and GPT-2-Wechsel performed better than ensembles of the same size consisting of only GBERT or GPT-2-Wechsel models. Our models were evaluated in the GermEval 2022 Shared Task on Text Complexity Assessment on data of German sentences. On out-of-sample data, our best ensemble achieved a root mean squared error of 0:435.
Acknowledging that a diverse workforce could be a potential source of innovation, the current research deals with the fine details of why diversity management is central to achieving innovation in heterogeneous research groups and how this could be effectively realized in an organization. The types of heterogeneities addressed mainly include gender, qualification, academic discipline and intercultural perspectives. The type of organization being dealt with in this work is a complex association of research institutes at a technical university in Germany (RWTH Aachen University), namely a 'Cluster of Excellence', whereby several institutes of the university work collaboratively in different sub-projects. The 'Cluster of Excellence' is a part of the 'Excellence Initiative' of the German federal and state governments German Research Foundation (DFG) and German Council of Science and Humanities, with the ultimate aim of promoting cutting-edge research. To support interdisciplinary collaboration and thus the performance of the cluster, the development of a diversity and innovation management concept is presently in the conceptual phase and will be described in the frame of this paper. The 3-S-Diversity Model, composed of the three elements: skills, structure and strategy, serves as a basis for the development of the concept. The proposed concept consists of six phases; the first two phases lay the ground work by developing an understanding of the status quo on the forms of diversity in the Cluster of Excellence, the type of organizational structure of the member institutes and the varieties of specialist work cultures of the same. The third and the fourth phases build up on this foundation by means of qualitative and quantitative studies. While the third phase deals with the sensitization of the management level to the close connection between diversity and innovation; the need to manage them thereafter and find tailor-made methods of doing so, the fourth phase shall mainly focus on the mindset of the employees in this regard. The fifth phase shall consolidate the learnings and the ideas developed in the course of the first four phases into an implementable strategy. The ultimate phase shall be the implementation of this concept in the Cluster. The first three phases have been accomplished successfully and the preliminary results are already available.
Retinal vessels are similar to cerebral vessels in their structure and function. Moderately low oscillation frequencies of around 0.1 Hz have been reported as the driving force for paravascular drainage in gray matter in mice and are known as the frequencies of lymphatic vessels in humans. We aimed to elucidate whether retinal vessel oscillations are altered in Alzheimer's disease (AD) at the stage of dementia or mild cognitive impairment (MCI). Seventeen patients with mild-to-moderate dementia due to AD (ADD); 23 patients with MCI due to AD, and 18 cognitively healthy controls (HC) were examined using Dynamic Retinal Vessel Analyzer. Oscillatory temporal changes of retinal vessel diameters were evaluated using mathematical signal analysis. Especially at moderately low frequencies around 0.1 Hz, arterial oscillations in ADD and MCI significantly prevailed over HC oscillations and correlated with disease severity. The pronounced retinal arterial vasomotion at moderately low frequencies in the ADD and MCI groups would be compatible with the view of a compensatory upregulation of paravascular drainage in AD and strengthen the amyloid clearance hypothesis.
Edge-based and face-based smoothed finite element methods (ES-FEM and FS-FEM, respectively) are modified versions of the finite element method allowing to achieve more accurate results and to reduce sensitivity to mesh distortion, at least for linear elements. These properties make the two methods very attractive. However, their implementation in a standard finite element code is nontrivial because it requires heavy and extensive modifications to the code architecture. In this article, we present an element-based formulation of ES-FEM and FS-FEM methods allowing to implement the two methods in a standard finite element code with no modifications to its architecture. Moreover, the element-based formulation permits to easily manage any type of element, especially in 3D models where, to the best of the authors' knowledge, only tetrahedral elements are used in FS-FEM applications found in the literature. Shape functions for non-simplex 3D elements are proposed in order to apply FS-FEM to any standard finite element.
The mechanical behavior of the large intestine beyond the ultimate stress has never been investigated. Stretching beyond the ultimate stress may drastically impair the tissue microstructure, which consequently weakens its healthy state functions of absorption, temporary storage, and transportation for defecation. Due to closely similar microstructure and function with humans, biaxial tensile experiments on the porcine large intestine have been performed in this study. In this paper, we report hyperelastic characterization of the large intestine based on experiments in 102 specimens. We also report the theoretical analysis of the experimental results, including an exponential damage evolution function. The fracture energies and the threshold stresses are set as damage material parameters for the longitudinal muscular, the circumferential muscular and the submucosal collagenous layers. A biaxial tensile simulation of a linear brick element has been performed to validate the applicability of the estimated material parameters. The model successfully simulates the biomechanical response of the large intestine under physiological and non-physiological loads.
Die Chance der fiktionalen Literatur : Literatur der Fiktion als Anstoß für Information und Diskurs
(2022)
Der informative Charakter fiktionaler Texte sollte nicht unterschätzt werden. Mit dem Projekt wird durch „Buddenbrooks“ von Thomas Mann gezeigt, wie relevante und redebedürftige Themen in der Literatur angesprochen werden. Dafür gibt es zusätzliche Text- und Info-Impulse als ergänzendes Buch zum Roman. Neben diesen Vertiefungsbüchern gibt es eine Inszenierung des Buches mit Beispielen aus den Büchern. Die Ausstellung kann Teil eines Messestandes sein oder für sich existieren und bietet einen ersten Kontakt mit der Idee des Projektes und mit den Büchern selbst. Zusätzliches Interesse an der Arbeit wird durch Social Media Posts generiert, die den wichtigen Part des Austauschs und der Diskussion fördern, wozu das Projekt auch anregen möchte. „Die Chance der fiktionalen Literatur“ regt Leser:innen an, fiktionale Literatur anders zu betrachten und einen größeren Nutzen aus der Fiktion zu ziehen.
Sportvereine verlieren zunehmend Mitglieder. Grund dafür ist der wachsende Wunsch nach unabhängigen Trainingsmöglichkeiten. GYMLET bietet den Sportler:innen die Möglichkeit, ihre Fitness funktional, selbstorganisiert und unabhängig zu trainieren. Dabei kann durch den selbstbestimmten Trainingsort neben der körperlichen Fitness vor allem die Motivation zur sportlichen Aktivität und das Wohlbefinden gesteigert werden. Unterstützt wird das Trainingssystem durch eine App, welche den Sportler:innen neben Nutzungsmöglichkeiten als Coach und Trainingsplaner dient. Um diese Trainingsmöglichkeit an die Zielgruppe heranzutragen, ist eine Kampagnenstrategie aus zwei Phasen entstanden. In dieser sollen Postings und Clips über Instagram und Youtube veröffentlicht werden. Zusätzlich werden Zeitschriften und mögliche Kooperationen mit Therapeuten oder Freizeitunterkünften zur Veröffentlichung der Kampagne genutzt.
Gastronomiebetriebe, insbesondere diese ohne zeitgemäße und durchdachte Konzepte wurden durch die Pandemie stark getroffen. Während große Fast-Food-Ketten bereits vor der Pandemie digitale Lösungen einsetzten, stehen nun auch andere Restaurantbetreiber:innen vor der Herausforderung der Digitalisierung. Gerade die Pandemie hat an dieser Stelle verdeutlicht, welche Restaurants ihre Prozesse im Sinne der User Experience durchdacht haben und welche nicht. Die Tisch-Stuhl-Kombination der „SUE“-Serie bietet Restaurants, smarte und elegante Gastronomiemöbel, die je nach Konzept in Materialität und Farbe frei anpassbar sind. Wandelbarkeit, Komfort und die Integration digitaler Lösungen sind die Kernkompetenzen und Anforderungen, die das Gastronomie-Interior in Zukunft erfüllen muss. Der smarte Bistrotisch und der wandelbare Stuhl der „SUE“-Serie zeigt einen digitalisierten und anpassbaren Lösungsansatz.
Promoting diversity and combatting discrimination in research organizations: a practitioner’s guide
(2022)
The essay is addressed to practitioners in research management and from
academic leadership. It describes which measures can contribute to creating an inclusive climate for research teams and preventing and effectively dealing with discrimination. The practical recommendations consider the policy and organizational levels, as well as the individual perspective of research managers. Following a series of basic recommendations, six lessons learned are formulated, derived from the contributions to the edited collection on “Diversity and Discrimination in Research Organizations.”
Diversity management is seen as a decisive factor for ensuring the development of socially responsible innovations (Beacham and Shambaugh, 2011; Sonntag, 2014; López, 2015; Uebernickel et al., 2015). However, many diversity management approaches fail due to a one-sided consideration of diversity (Thomas and Ely, 2019) and a lacking linkage between the prevailing organizational culture and the perception of diversity in the respective organization. Reflecting the importance of diverse perspectives, research institutions have a special responsibility to actively deal with diversity, as they are publicly funded institutions that drive socially relevant development and educate future generations of developers, leaders and decision-makers. Nevertheless, only a few studies have so far dealt with the influence of the special framework conditions of the science system on diversity management. Focusing on the interdependency of the organizational culture and diversity management especially in a university research environment, this chapter aims in a first step to provide a theoretical perspective on the framework conditions of a complex research organization in Germany in order to understand the system-specific factors influencing diversity management. In a second step, an exploratory cluster analysis is presented, investigating the perception of diversity and possible influencing factors moderating this perception in a scientific organization. Combining both steps, the results show specific mechanisms and structures of the university research environment that have an impact on diversity management and rigidify structural barriers preventing an increase of diversity. The quantitative study also points out that the management level takes on a special role model function in the scientific system and thus has an influence on the perception of diversity. Consequently, when developing diversity management approaches in research organizations, it is necessary to consider the top-down direction of action, the special nature of organizational structures in the university research environment as well as the special role of the professorial level as role model for the scientific staff.
Industrial production systems are facing radical change in multiple dimensions. This change is caused by technological developments and the digital transformation of production, as well as the call for political and social change to facilitate a transformation toward sustainability. These changes affect both the capabilities of production systems and companies and the design of higher education and educational programs. Given the high uncertainty in the likelihood of occurrence and the technical, economic, and societal impacts of these concepts, we conducted a technology foresight study, in the form of a real-time Delphi analysis, to derive reliable future scenarios featuring the next generation of manufacturing systems. This chapter presents the capabilities dimension and describes each projection in detail, offering current case study examples and discussing related research, as well as implications for policy makers and firms. Specifically, we discuss the benefits of capturing expert knowledge and making it accessible to newcomers, especially in highly specialized industries. The experts argue that in order to cope with the challenges and circumstances of today’s world, students must already during their education at university learn how to work with AI and other technologies. This means that study programs must change and that universities must adapt their structural aspects to meet the needs of the students.
Image reconstruction analysis for positron emission tomography with heterostructured scintillators
(2022)
The concept of structure engineering has been proposed for exploring the next generation of radiation detectors with improved performance. A TOF-PET geometry with heterostructured scintillators with a pixel size of 3.0×3.1×15 mm3 was simulated using Monte Carlo. The heterostructures consisted of alternating layers of BGO as a dense material with high stopping power and plastic (EJ232) as a fast light emitter. The detector time resolution was calculated as a function of the deposited and shared energy in both materials on an event-by-event basis. While sensitivity was reduced to 32% for 100 μm thick plastic layers and 52% for 50 μm, the CTR distribution improved to 204±49 ps and 220±41 ps respectively, compared to 276 ps that we considered for bulk BGO. The complex distribution of timing resolutions was accounted for in the reconstruction. We divided the events into three groups based on their CTR and modeled them with different Gaussian TOF kernels. On a NEMA IQ phantom, the heterostructures had better contrast recovery in early iterations. On the other hand, BGO achieved a better contrast to noise ratio (CNR) after the 15th iteration due to the higher sensitivity. The developed simulation and reconstruction methods constitute new tools for evaluating different detector designs with complex time responses.
In this paper research activities developed within the FutureCom project are presented. The project, funded by the European Metrology Programme for Innovation and Research (EMPIR), aims at evaluating and characterizing: (i) active devices, (ii) signal- and power integrity of field programmable gate array (FPGA) circuits, (iii) operational performance of electronic circuits in real-world and harsh environments (e.g. below and above ambient temperatures and at different levels of humidity), (iv) passive inter-modulation (PIM) in communication systems considering different values of temperature and humidity corresponding to the typical operating conditions that we can experience in real-world scenarios. An overview of the FutureCom project is provided here, then the research activities are described.
Biomedical applications of magnetic nanoparticles (MNP) fundamentally rely on the particles’ magnetic relaxation as a response to an alternating magnetic field. The magnetic relaxation complexly depends on the interplay of MNP magnetic and physical properties with the applied field parameters. It is commonly accepted that particle core size is a major contributor to signal generation in all the above applications, however, most MNP samples comprise broad distribution spanning nm and more. Therefore, precise knowledge of the exact contribution of individual core sizes to signal generation is desired for optimal MNP design generally for each application. Specifically, we present a magnetic relaxation simulation-driven analysis of experimental frequency mixing magnetic detection (FMMD) for biosensing to quantify the contributions of individual core size fractions towards signal generation. Applying our method to two different experimental MNP systems, we found the most dominant contributions from approx. 20 nm sized particles in the two independent MNP systems. Additional comparison between freely suspended and immobilized MNP also reveals insight in the MNP microstructure, allowing to use FMMD for MNP characterization, as well as to further fine-tune its applicability in biosensing.
Frequency mixing magnetic detection (FMMD) has been widely utilized as a measurement technique in magnetic immunoassays. It can also be used for the characterization and distinction (also known as “colourization”) of different types of magnetic nanoparticles (MNPs) based on their core sizes. In a previous work, it was shown that the large particles contribute most of the FMMD signal. This leads to ambiguities in core size determination from fitting since the contribution of the small-sized particles is almost undetectable among the strong responses from the large ones. In this work, we report on how this ambiguity can be overcome by modelling the signal intensity using the Langevin model in thermodynamic equilibrium including a lognormal core size distribution fL(dc,d0,σ) fitted to experimentally measured FMMD data of immobilized MNPs. For each given median diameter d0, an ambiguous amount of best-fitting pairs of parameters distribution width σ and number of particles Np with R2 > 0.99 are extracted. By determining the samples’ total iron mass, mFe, with inductively coupled plasma optical emission spectrometry (ICP-OES), we are then able to identify the one specific best-fitting pair (σ, Np) one uniquely. With this additional externally measured parameter, we resolved the ambiguity in core size distribution and determined the parameters (d0, σ, Np) directly from FMMD measurements, allowing precise MNPs sample characterization.
Next Generation Manufacturing promises significant improvements in performance, productivity, and value creation. In addition to the desired and projected improvements regarding the planning, production, and usage cycles of products, this digital transformation will have a huge impact on work, workers, and workplace design. Given the high uncertainty in the likelihood of occurrence and the technical, economic, and societal impacts of these changes, we conducted a technology foresight study, in the form of a real-time Delphi analysis, to derive reliable future scenarios featuring the next generation of manufacturing systems. This chapter presents the organization dimension and describes each projection in detail, offering current case study examples and discussing related research, as well as implications for policy makers and firms. Specifically, we highlight seven areas in which the digital transformation of production will change how we work, how we organize the work within a company, how we evaluate these changes, and how employment and labor rights will be affected across company boundaries. The experts are unsure whether the use of collaborative robots in factories will replace traditional robots by 2030. They believe that the use of hybrid intelligence will supplement human decision-making processes in production environments. Furthermore, they predict that artificial intelligence will lead to changes in management processes, leadership, and the elimination of hierarchies. However, to ensure that social and normative aspects are incorporated into the AI algorithms, restricting measurement of individual performance will be necessary. Additionally, AI-based decision support can significantly contribute toward new, socially accepted modes of leadership. Finally, the experts believe that there will be a reduction in the workforce by the year 2030.