Das iwe-Institut für Wasser und Energiemanagement an der Hochschule Hof hat eine Metastudie zum Stand der Digitalisierung der Wasserwirtschaft in den deutschsprachigen Ländern durchgeführt. Das Fazit: Die Digitalisierung ist zu einem festen Bestandteil technologischer Lösungen und strategischer Entscheidungen geworden. In einer dreiteiligen Serie berichtet die WasserWirtschaft über die Ergebnisse der Studie. Im zweiten Teil der Serie geht es um die Erkenntnis, dass Technik nicht alles ist - und wie Menschen auf dem Weg der Digitalisierung mitgenommen werden können.

This book comprises the select proceedings of the International Conference on Recent Trends in Developments of Thermofluids and Renewable Energy (TFRE 2020). The major topics covered include aerodynamics, alternate energy, bio fuel, bio heat transfer, computational fluid dynamics, control mechanism for constant power generation, and energy storage. The book also discusses latest developments in the fields of electric vehicles, hybrid power systems, and solar and renewable energy. Given the scope of its contents, this book will be useful for students, researchers, and professionals interested in the field of thermofluids and renewable energy resources.

To provide robots for a wide range of users, there needs to be an easy and intuitive way to program them. This issue is addressed by the robot programming by demonstration or imitation learning paradigm, where the user demonstrates the task to the robot by teleoperation. Although single-shot approaches could save a lot of time and effort, they are still a niche due to some drawbacks, like ambiguities in selecting the relevant features.In this work we try to enhance a single shot programming by demonstration approach on sub-symbolic level by extending it to a multi modal input. While most approaches mainly focus on the trajectories and visual detection of objects, we combine speech and kinestethic teaching in order to resolve ambiguities and to rise the level of transferred information.

Die Informationen unserer DNA sind die persönlichsten Daten, die wir kennen. Kaum eine andere Datenkategorie erlaubt ähnlich tiefe Einblicke in die Kundenbedürfnisse. Die individuelle Zuschneidung von Angeboten kann demnach heute auch auf Basis unseres Erbgutes erfolgen, und zwar im Rahmen eines „DNA-Targetings“. Auf der Grundlage einer detaillierten Analyse des Erbgutes – vermarktet als eigenständiges Produkt, mit dem sich etwa ethnische Herkunft oder Krankheitsrisiken bestimmen ­lassen – eröffnen sich zahlreiche Einsatzmöglichkeiten, von der, „streuverlustfreien“ Kommunikation über ein DNA-basiertes Kunden-Clustering bis hin zu personalisierten Ernährungsprodukten und Medikamenten. Im internationalen Kontext spielen diese Anwendungsfälle eine noch ungleich größere Rolle als in Europa, ungeachtet der auch andernorts durchaus erkannten besonderen Datenschutzproblematik. Eine Auseinandersetzung mit der Thematik ist daher auch dringend im akademischen Marketing hierzulande dringend geboten.

While many speech synthesis systems based on deep neural networks are thoroughly evaluated and released for free use in English, models for languages with far less active speakers like German are scarcely trained and most often not published for common use. This work covers specific challenges in training text to speech models for the German language, including dataset selection and data preprocessing, and presents the training process for multiple models of an end-to-end text to speech system based on a combination of Tacotron 2 and Multi- Band MelGAN. All model compositions were evaluated against the mean opinion score, which revealed comparable results to models in literature that are trained and evaluated on English datasets. In addition, empirical analyses identified distinct aspects influencing the quality of such systems, based on subjective user experience. All trained models are released for public use.

Das iwe-Institut für Wasser und Energiemanagement an der Hochschule Hof hat eine Metastudie zum Stand der Digitalisierung der Wasserwirtschaft in den deutschsprachigen Ländern durchgeführt. Das Fazit: Die Digitalisierung ist zu einem festen Bestandteil technologischer Lösungen und strategischer Entscheidungen geworden. In einer dreiteiligen Serie wird die WasserWirtschaft über die Ergebnisse der Studie berichten. In dieser Ausgabe beginnen die Autoren mit einer Übersicht: Wo finden sich Digitalisierungselemente verstärkt wieder und welche digitalen Lösungen dominieren aktuell?

Mobility is a central element of the new networked world, and customers expect highly integrated features in their vehicles and want to be able to use services or features at any time in a highly integrated manner. As a result, the entire automotive industry is facing a major change process, both technological as well as in its own core business processes and functions. This chapter examines the impact of this transition on the conduct and sustainability of IT projects in the German automotive industry. Information distilled from in-depth interviews with industry practitioners reveals how project management methods, tools, and culture have to evolve, as value chains in the industry are re-evaluated and re-defined. The chapter puts forward a framework for the interaction of project management methods and digital technologies to achieve sustainable project processes and outcomes. It is hoped this may act as a building block for future research in this field to advance the transitioning of the industry and its inherent IT projects to a more sustainable future.

When it comes to addressing climate change, water is at the centre of many aspects and measures. Especially in urban areas where the negative consequences of heavy rainfall events and prolonged dry periods are increasing worldwide. In the future, urban water management will have to examine water use in terms of its various objectives and provide alternative water resources for different purposes (groundwater, river water, rainwater, treated wastewater, etc.). The technological networking of water management systems requires intelligent and digital systems to manage the challenges of the future. Similarly, the contribution of water management to global CO2 reduction through more efficient procedural treatment processes will only succeed with adequate digital systems ((Balogun, 2020), (Kröhling, 2017), (Randhahn, 2020)) Researchers at Hof University of Applied Sciences have investigated the status quo of digitization within the first meta-study in the German-speaking water industry (WaterExe 4.0 project) funded by the German Federal Ministry of Education and Research. The research was conducted with four methodologically different sub-surveys (literature and market research, survey, expert interview and workshops). 120 industry participants took part in an online survey and 30 water sector experts were interviewed.

Kaum ein Aspekt der digitalen Transformation wird bis heute so intensiv diskutiert, wie das Phänomen der „Plattformökonomie“, der Wirkungsweise der Geschäftsmodelle der großen Tech-Unternehmen wie Google, Facebook oder Amazon. Die daraus erwachsenen Konsequenzen greifen tief in die wirtschaftlichen Ökosphären ein, Plattformmechanismen haben sich inzwischen aber auch als gesellschaftspolitischer Faktor etabliert, insbesondere bei der Nachrichtenverbreitung und der politischen Meinungsbildung. Plattformen sorgen für mehr Effizienz in der Distribution, auf den Produktmärkten wie auch bei der Informationsbereitstellung, aber gleichzeitig eben auch für mehr Konzentration und weniger Ausgewogenheit. Sie haben die Eigenschaft, den Wettbewerb in einem Angebots-Monopol zu absorbieren. Ihr Handlungsprinzip lässt sich dabei durchaus als parasitär beschreiben, mit weitreichenden Folgen für Wirtschaft und Gesellschaft.

Citizen science allows involving interested citizen in the entire research process in science. In the past, various citizen science projects have been performed in different research fields, especially in human medicine. We conducted a rapid scoping review to determine which citizen projects in human medicine already used software-based systems to engage citizens in the research process. Furthermore, we analysed which of the software-systems are publicly available, especially in the field of rare diseases, how citizens can participate using those tools and whether the usability was rated by the participants. To get insights for our project “SelEe (Seltene Erkrankungen bürgerwissenschaftlich erforschen)”, which is a citizen science project in rare diseases funded by the Federal Ministry of Education and Research (BMBF), we aimed to identify projects in this research area. We searched PubMed for articles between 2011 and 2021 and performed a title- and abstract screening, as well as a full-text screening. Finally, 12 studies were identified in different research areas like public health, genetic research and infectious diseases. We could not identify any study directly associated with rare diseases. None of the studies investigated usability of those systems. Furthermore, five publicly available citizen science software-systems were identified. Three of them are general systems that allow creating, operating, managing citizen science projects and including citizens in the research process. In further investigations, we will check and compare these systems, if they are appropriate for use in our SelEe-project.

This paper presents the design and the results of a novel approach to predict air pollutants in urban environments. The objective is to create an artificial intelligence (AI)-based system to support planning actors in taking effective and adequate short-term measures against unfavourable air quality situations. In general, air quality in European cities has improved over the past decades. Nevertheless, reductions of the air pollutants particulate matter (PM), nitrogen dioxide (NO2) and ground-level ozone (O3), in particular, are essential to ensure the quality of life and a healthy life in cities. To forecast these air pollutants for the next 48 hours, a sequence-to-sequence encoder-decoder model with a recurrent neural network (RNN) was implemented. The model was trained with historic in situ air pollutant measurements, traffic and meteorological data. An evaluation of the prediction results against historical data shows high accordance with in situ measurements and implicate the system’s applicability and its great potential for high quality forecasts of air pollutants in urban environments by including real time weather forecast data.