Open Access

This study investigates the initial stage of the thermo-mechanical crystallization behavior for uni- and biaxially stretched polyethylene. The models are based on a mesoscale molecular dynamics approach. We take constraints that occur in real-life polymer processing into account, especially with respect to the blowing stage of the extrusion blow-molding process. For this purpose, we deform our systems using a wide range of stretching levels before they are quenched. We discuss the effects of the stretching procedures on the micro-mechanical state of the systems, characterized by entanglement behavior and nematic ordering of chain segments. For the cooling stage, we use two different approaches which allow for free or hindered shrinkage, respectively. During cooling, crystallization kinetics are monitored: We precisely evaluate how the interplay of chain length, temperature, local entanglements and orientation of chain segments influence crystallization behavior. Our models reveal that the main stretching direction dominates microscopic states of the different systems. We are able to show that crystallization mainly depends on the (dis-)entanglement behavior. Nematic ordering plays a secondary role.

Silicon carbide and graphene possess extraordinary chemical and physical properties. Here, these different systems are linked and the changes in structural and dynamic properties are investigated. For the simulations performed a classical molecular dynamic (MD) approach was used. In this approach, a graphene layer (N = 240 atoms) was grafted at different distances on top of a 6H-SiC structure (N = 2400 atoms) and onto a 3C-SiC structure (N = 1728 atoms). The distances between the graphene and the 6H are 1.0, 1.3 and 1.5 Å and the distances between the graphene layer and the 3C-SiC are 2.0, 2.3, and 2.5 Å. Each system has been equilibrated at room temperature until no further relaxation was observed. The 6H-SiC structure in combination with graphene proves to be more stable compared to the combination with 3C-SiC. This can be seen well in the determined energies. Pair distribution functions were influenced slightly by the graphene layer due to steric and energetic changes. This becomes clear from the small shifts of the C-C distances. Interactions as well as bonds between graphene and SiC lead to the fact that small shoulders of the high-frequency SiC-peaks are visible in the spectra and at the same time the high-frequency peaks of graphene are completely absent.

Finis Siegler, Beate: Entwicklung einer Ökonomik Sozialer Arbeit aus der Retrospektive, Wiesbaden: Springer VS, 2021; 266 Seiten

In the field of autonomous robotics, sensors have played a major role in defining the scope of technology and to a great extent, limitations of it as well. This cycle of constant updates and hence technological advancement has made given birth to some serious industries which were once inconceivable. Industries like autonomous driving which has a serious impact on safety and security of people, also has an equally harsh implication on the dynamics and economics of the market. With sensors like LiDAR and RADAR delivering 3D measurements as point clouds, there is a necessity to process the raw measurements directly and many research groups are working on the same. A sizable research has gone in solving the task of object detection on 2D images. In this thesis we aim to develop a LiDAR based 3D object detection scheme. We combine the ideas of PointPillars and feature pyramid networks from 2D vision to propose Pillar-FPN. The proposed method directly takes 3D point clouds as input and outputs a 3D bounding box. Our pipeline consists of multiple variations of proposed Pillar-FPN at the feature fusion level that are described in the results section. We have trained our model on the KITTI train dataset and evaluated it on KITTI validation dataset.

Due to expected positive impacts on business, the application of artificial intelligence has been widely increased. The decision-making procedures of those models are often complex and not easily understandable to the company’s stakeholders, i.e. the people having to follow up on recommendations or try to understand automated decisions of a system. This opaqueness and black-box nature might hinder adoption, as users struggle to make sense and trust the predictions of AI models. Recent research on eXplainable Artificial Intelligence (XAI) focused mainly on explaining the models to AI experts with the purpose of debugging and improving the performance of the models. In this article, we explore how such systems could be made explainable to the stakeholders. For doing so, we propose a new convolutional neural network (CNN)-based explainable predictive model for product backorder prediction in inventory management. Backorders are orders that customers place for products that are currently not in stock. The company now takes the risk to produce or acquire the backordered products while in the meantime, customers can cancel their orders if that takes too long, leaving the company with unsold items in their inventory. Hence, for their strategic inventory management, companies need to make decisions based on assumptions. Our argument is that these tasks can be improved by offering explanations for AI recommendations. Hence, our research investigates how such explanations could be provided, employing Shapley additive explanations to explain the overall models’ priority in decision-making. Besides that, we introduce locally interpretable surrogate models that can explain any individual prediction of a model. The experimental results demonstrate effectiveness in predicting backorders in terms of standard evaluation metrics and outperform known related works with AUC 0.9489. Our approach demonstrates how current limitations of predictive technologies can be addressed in the business domain.

The implementation of the Sustainable Development Goals (SDGs) and the conservation and protection of nature are among the greatest challenges facing urban regions. There are few approaches so far that link the SDGs to natural diversity and related ecosystem services at the local level and track them in terms of increasing sustainable development at the local level. We want to close this gap by developing a set of indicators that capture ecosystem services in the sense of the SDGs and which are based on data that are freely available throughout Germany and Europe. Based on 10 SDGs and 35 SDG indicators, we are developing an ecosystem service and biodiversity-related indicator set for the evaluation of sustainable development in urban areas. We further show that it is possible to close many of the data gaps between SDGs and locally collected data mentioned in the literature and to translate the universal SDGs to the local level. Our example develops this set of indicators for the Bonn/Rhein-Sieg metropolitan area in North Rhine-Westphalia, Germany, which comprises both rural and densely populated settlements. This set of indicators can also help improve communication and plan sustainable development by increasing transparency in local sustainability, implementing a visible sustainability monitoring system, and strengthening the collaboration between local stakeholders.

Professor Dr. Dietmar Fink, Inhaber des Lehrstuhls für Unternehmensberatung an der Hochschule Bonn-Rhein-Sieg und Geschäftsführender Direktor der Wissenschaftlichen Gesellschaft für Management und Beratung (WGMB) in Bonn, über den Mehrwert von Consulting-Rankings und den Sinn von Beraterprojekten bei Versicherern

Social protection has been increasingly recognized by experts from different fields as a key instrument for social, economic, political, and environmental development. It is also known for tackling multiple goals related to the reduction of risk, poverty and inequality at once. Yet, its instruments are often seen in isolation, programmes are still managed in silos and the systemic aspect is often overlooked. Engaging in critical discussions about the systemic aspect of social protection and outlining what it really takes to pursue a systemic approach has motivated the two editors, Prof. Dr. Esther Schüring from H-BRS and Dr. Markus Loewe from the German Institute of Development and Sustainability (IDOS) to launch the very first Handbook on Social Protection Systems in late 2021.

Selbstfahrende Arbeitsmaschinen für den Einsatz auf dem Boden der Tiefsee von beispielsweise 6000 Meter Tiefe existieren derzeit noch nicht. Im Rahmen der vorliegenden Untersuchung (BMFT-Förderkennzeichen MTK 0328) wurden die Grundlagen elektrohydraulischer Antriebe für Umgebungsdrücke von 60 MPa geschaffen und in einer Druckkammer erfolgreich getestet. Die dabei erhaltenen Erkenntnisse sind bei der konstruktiven Weiterentwicklung solcher Antriebe übertragbar.

Die Untersuchungen zum vorliegenden Beitrag wurden im Rahmen des deutsch-französischen Gemeinschaftsprojektes "Entwicklung von Manganknollenabbau- und Gewinnungsverfahren" durchgeführt. Auf deutscher Seite waren die Unternehmen PREUSSAG AG, Abteilung Meerestechnik in Hannover, die Versuchsanstalt für Wasser- und Schiffbau in Berlin, das Institut für Förderwesen der TH Karlsruhe, das Institut für Strömungsmechanik der Universität GH Paderborn und das Institut für Konstruktion der Universität GH Siegen beteiligt. Im zuletzt genannten Institut wurden elektrohydraulische Antriebe für Versuchsprototypen von geschleppten Manganknollenkollektoren und deren Steuerungen ausgelegt und auf ihre Einsatzfähigkeit im Tiefsee-Simulator getestet.