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Auswirkungen einer anhaltenden, inflationären Geldpolitik in der Eurozone auf den privaten Sparer
(2022)
Die vorliegende Bachelorarbeit setzt sich kritisch mit den Auswirkungen einer anhaltenden, inflationären Geldpolitik in der Eurozone auf den privaten Sparer auseinander. Im Rahmen dieser Arbeit wird aufgezeigt, wie die starke Erhöhung der Geldmenge Einfluss auf die Möglichkeiten und Entscheidungen des Sparers hat und wie weit eine solche Geldpolitik mit den Interessen des Sparers vereinbar ist.
Self-supervised learning has proved to be a powerful approach to learn image representations without the need of large labeled datasets. For underwater robotics, it is of great interest to design computer vision algorithms to improve perception capabilities such as sonar image classification. Due to the confidential nature of sonar imaging and the difficulty to interpret sonar images, it is challenging to create public large labeled sonar datasets to train supervised learning algorithms. In this work, we investigate the potential of three self-supervised learning methods (RotNet, Denoising Autoencoders, and Jigsaw) to learn high-quality sonar image representation without the need of human labels. We present pre-training and transfer learning results on real-life sonar image datasets. Our results indicate that self-supervised pre-training yields classification performance comparable to supervised pre-training in a few-shot transfer learning setup across all three methods. Code and self-supervised pre-trained models are be available at https://github.com/agrija9/ssl-sonar-images
We introduce canonical weight normalization for convolutional neural networks. Inspired by the canonical tensor decomposition, we express the weight tensors in so-called canonical networks as scaled sums of outer vector products. In particular, we train network weights in the decomposed form, where scale weights are optimized separately for each mode. Additionally, similarly to weight normalization, we include a global scaling parameter. We study the initialization of the canonical form by running the power method and by drawing randomly from Gaussian or uniform distributions. Our results indicate that we can replace the power method with cheaper initializations drawn from standard distributions. The canonical re-parametrization leads to competitive normalization performance on the MNIST, CIFAR10, and SVHN data sets. Moreover, the formulation simplifies network compression. Once training has converged, the canonical form allows convenient model-compression by truncating the parameter sums.
Comparative study of 3D object detection frameworks based on LiDAR data and sensor fusion techniques
(2022)
Estimating and understanding the surroundings of the vehicle precisely forms the basic and crucial step for the autonomous vehicle. The perception system plays a significant role in providing an accurate interpretation of a vehicle's environment in real-time. Generally, the perception system involves various subsystems such as localization, obstacle (static and dynamic) detection, and avoidance, mapping systems, and others. For perceiving the environment, these vehicles will be equipped with various exteroceptive (both passive and active) sensors in particular cameras, Radars, LiDARs, and others. These systems are equipped with deep learning techniques that transform the huge amount of data from the sensors into semantic information on which the object detection and localization tasks are performed. For numerous driving tasks, to provide accurate results, the location and depth information of a particular object is necessary. 3D object detection methods, by utilizing the additional pose data from the sensors such as LiDARs, stereo cameras, provides information on the size and location of the object. Based on recent research, 3D object detection frameworks performing object detection and localization on LiDAR data and sensor fusion techniques show significant improvement in their performance. In this work, a comparative study of the effect of using LiDAR data for object detection frameworks and the performance improvement seen by using sensor fusion techniques are performed. Along with discussing various state-of-the-art methods in both the cases, performing experimental analysis, and providing future research directions.
Wo individuelle Nutzenkalküle und geteilte Erwartungen enden, beginnt das Terrain der Kunstfertigkeit. Unter der Annahme, dass politisches Entscheiden als pragmatischer Problemlösungs- und Abwägungsprozess zu betrachten ist, rollen die Beiträge dieses interdisziplinär angelegten Bandes die Frage neu auf, wie in der Politik unter Bedingungen begrenzter Rationalität Handlungsalternativen entworfen, verhandelt und ausgewählt werden. Die Pandemie hat diesem Anliegen eine ungeahnte Dramatik verliehen, eingeübte Grundsätze, Entscheidungsarenen und Praktiken der Politik stehen mehr denn je zur Disposition. Es ist an der Zeit, diese neu zu vermessen.
(Verlagsangaben)
Vietnam requires a sustainable urbanization, for which city sensing is used in planning and de-cision-making. Large cities need portable, scalable, and inexpensive digital technology for this purpose. End-to-end air quality monitoring companies such as AirVisual and Plume Air have shown their reliability with portable devices outfitted with superior air sensors. They are pricey, yet homeowners use them to get local air data without evaluating the causal effect. Our air quality inspection system is scalable, reasonably priced, and flexible. Minicomputer of the sys-tem remotely monitors PMS7003 and BME280 sensor data through a microcontroller processor. The 5-megapixel camera module enables researchers to infer the causal relationship between traffic intensity and dust concentration. The design enables inexpensive, commercial-grade hardware, with Azure Blob storing air pollution data and surrounding-area imagery and pre-venting the system from physically expanding. In addition, by including an air channel that re-plenishes and distributes temperature, the design improves ventilation and safeguards electrical components. The gadget allows for the analysis of the correlation between traffic and air quali-ty data, which might aid in the establishment of sustainable urban development plans and poli-cies.
Zum Geleit
(2022)
Bionik
(2022)
Wie machen die das… kann angesichts der erstaunlichen Fähigkeiten mancher Lebewesen gefragt werden. Die Bionik fragt noch weiter …und wie kann man das nachmachen? Hier liegt ein Schwerpunkt dieses Lehrbuches, das die Bionik nicht nur an zahlreichen Beispielen erklärt, sondern auch eine Vorgehensweise für die Identifizierung biologischer Lösungen und deren Übertragung auf technische Anwendungen vermittelt. Basisinformationen der Biologie und Grundlagen der Konstruktionstechnik gewährleisten einen leichten Zugang zum Stoff. Mit dem 3D-Druck als Schlüsseltechnologie und der Thematisierung der Nachhaltigkeit geht das Buch zudem auf aktuelle Entwicklungen ein. Dieser ganzheitliche Blick auf die Bionik soll den Leser zur Durchführung bionischer Projekte befähigen und motivieren. Die vorliegende Auflage wurde überarbeitet und um aktuelle Forschungserkenntnisse und Entwicklungen ergänzt. (Verlagsangaben)
Jahresbericht 2021
(2022)
In Forschung, Lehre und Transfer neue Wege beschreiten und Akzente setzen – das hat die Hochschule Bonn-Rhein-Sieg (H-BRS) im vergangenen Jahr trotz der Corona-Pandemie geschafft. Talente, Ideen und Kooperationen sind in unterschiedlicher Weise zur Geltung gekommen, stets im engen Austausch zwischen angewandter Wissenschaft, Gesellschaft und Wirtschaft. „Entfalten“ lautet deshalb das Motto des Jahresberichts der H-BRS für das Jahr 2021, der jetzt erschienen ist.
Im Zuge der Migrationsbewegung in den Jahren 2015 und 2016 hat die menschenwürdige Unterbringung von geflüchteten Menschen in Kommunen in Deutschland an Aufmerksamkeit gewonnen. Der Anstieg der Asylbewerber:innen in den Kommunen sowie die Bundesinitiative „Schutz von geflüchteten Menschen in Flüchtlingsunterkünften“ haben Veränderungen im Hinblick auf Schutzstandards in der kommunalen Unterbringung geflüchteter Menschen hervorgerufen. Der Artikel erklärt diese Veränderungen mittels einer akteurszentrierten organisationssoziologischen Herangehensweise. Grundlage sind empirische Forschungsergebnisse des Projektes „Organisational Perspectives on Human Security Standards for Refugees in Germany“ aus zwei deutschen Kommunen.
The white ground crater by the Phiale Painter (450–440 BC) exhibited in the “Pietro Griffo” Archaeological Museum in Agrigento (Italy) depicts two scenes from Perseus myth. The vase is of utmost importance to archaeologists because the figures are drawn on a white background with remarkable daintiness and attention to detail. Notwithstanding the white ground ceramics being well documented from an archaeological and historical point of view, doubts concerning the compositions of pigments and binders and the production technique are still unsolved. This kind of vase is a valuable rarity, the use of which is documented in elitist funeral rituals. The study aims to investigate the constituent materials and the execution technique of this magnificent crater. The investigation was carried out using non-destructive and non-invasive techniques in situ. Portable X-ray fluorescence and Fourier-transform total reflection infrared spectroscopy complemented the use of visible and ultraviolet light photography to get an overview and specific information on the vase. The XRF data were used to produce false colour maps showing the location of the various elements detected, using the program SmART_scan. The use of gypsum as the material for the white ground is an important result that deserves to be further investigated in similar vases.
Comparative study of 3D object detection frameworks based on LiDAR data and sensor fusion techniques
(2022)
Estimating and understanding the surroundings of the vehicle precisely forms the basic and crucial step for the autonomous vehicle. The perception system plays a significant role in providing an accurate interpretation of a vehicle's environment in real-time. Generally, the perception system involves various subsystems such as localization, obstacle (static and dynamic) detection, and avoidance, mapping systems, and others. For perceiving the environment, these vehicles will be equipped with various exteroceptive (both passive and active) sensors in particular cameras, Radars, LiDARs, and others. These systems are equipped with deep learning techniques that transform the huge amount of data from the sensors into semantic information on which the object detection and localization tasks are performed. For numerous driving tasks, to provide accurate results, the location and depth information of a particular object is necessary. 3D object detection methods, by utilizing the additional pose data from the sensors such as LiDARs, stereo cameras, provides information on the size and location of the object. Based on recent research, 3D object detection frameworks performing object detection and localization on LiDAR data and sensor fusion techniques show significant improvement in their performance. In this work, a comparative study of the effect of using LiDAR data for object detection frameworks and the performance improvement seen by using sensor fusion techniques are performed. Along with discussing various state-of-the-art methods in both the cases, performing experimental analysis, and providing future research directions.
Breaking new ground and setting new trends in research, teaching and transfer - this is what the Hochschule Bonn-Rhein-Sieg (H-BRS) managed to do last year despite the Corona pandemic. Talents, ideas and cooperations have come to fruition in various ways, always in close exchange between applied science, society and business. "expand" is therefore the motto of the annual report of the H-BRS for the year 2021, which has now been published.
Recent advances in Natural Language Processing have substantially improved contextualized representations of language. However, the inclusion of factual knowledge, particularly in the biomedical domain, remains challenging. Hence, many Language Models (LMs) are extended by Knowledge Graphs (KGs), but most approaches require entity linking (i.e., explicit alignment between text and KG entities). Inspired by single-stream multimodal Transformers operating on text, image and video data, this thesis proposes the Sophisticated Transformer trained on biomedical text and Knowledge Graphs (STonKGs). STonKGs incorporates a novel multimodal architecture based on a cross encoder that uses the attention mechanism on a concatenation of input sequences derived from text and KG triples, respectively. Over 13 million so-called text-triple pairs, coming from PubMed and assembled using the Integrated Network and Dynamical Reasoning Assembler (INDRA), were used in an unsupervised pre-training procedure to learn representations of biomedical knowledge in STonKGs. By comparing STonKGs to an NLP- and a KG-baseline (operating on either text or KG data) on a benchmark consisting of eight fine-tuning tasks, the proposed knowledge integration method applied in STonKGs was empirically validated. Specifically, on tasks with a comparatively small dataset size and a larger number of classes, STonKGs resulted in considerable performance gains, beating the F1-score of the best baseline by up to 0.083. Both the source code as well as the code used to implement STonKGs are made publicly available so that the proposed method of this thesis can be extended to many other biomedical applications.
Contextual information is widely considered for NLP and knowledge discovery in life sciences since it highly influences the exact meaning of natural language. The scientific challenge is not only to extract such context data, but also to store this data for further query and discovery approaches. Classical approaches use RDF triple stores, which have serious limitations. Here, we propose a multiple step knowledge graph approach using labeled property graphs based on polyglot persistence systems to utilize context data for context mining, graph queries, knowledge discovery and extraction. We introduce the graph-theoretic foundation for a general context concept within semantic networks and show a proof of concept based on biomedical literature and text mining. Our test system contains a knowledge graph derived from the entirety of PubMed and SCAIView data and is enriched with text mining data and domain-specific language data using Biological Expression Language. Here, context is a more general concept than annotations. This dense graph has more than 71M nodes and 850M relationships. We discuss the impact of this novel approach with 27 real-world use cases represented by graph queries. Storing and querying a giant knowledge graph as a labeled property graph is still a technological challenge. Here, we demonstrate how our data model is able to support the understanding and interpretation of biomedical data. We present several real-world use cases that utilize our massive, generated knowledge graph derived from PubMed data and enriched with additional contextual data. Finally, we show a working example in context of biologically relevant information using SCAIView.
For research in audiovisual interview archives often it is not only of interest what is said but also how. Sentiment analysis and emotion recognition can help capture, categorize and make these different facets searchable. In particular, for oral history archives, such indexing technologies can be of great interest. These technologies can help understand the role of emotions in historical remembering. However, humans often perceive sentiments and emotions ambiguously and subjectively. Moreover, oral history interviews have multi-layered levels of complex, sometimes contradictory, sometimes very subtle facets of emotions. Therefore, the question arises of the chance machines and humans have capturing and assigning these into predefined categories. This paper investigates the ambiguity in human perception of emotions and sentiment in German oral history interviews and the impact on machine learning systems. Our experiments reveal substantial differences in human perception for different emotions. Furthermore, we report from ongoing machine learning experiments with different modalities. We show that the human perceptual ambiguity and other challenges, such as class imbalance and lack of training data, currently limit the opportunities of these technologies for oral history archives. Nonetheless, our work uncovers promising observations and possibilities for further research.
The non-scientific questioning of scientific research during the COVID-19 pandemic, the unwillingness of a president of the United States of America to accept the result of a democratically held election: just in recent times, there have been quite a few striking examples of long-held certainties appearing as nothing more than just illusions. This essay reflects on the severe consequences of the loss of such certainties in the spheres of democratic politics on the one hand and of science, especially for highly differentiated societies, on the other hand as well as on their interdependencies. Furthermore, the author tries to make the case that this disillusionment could prove to be a salutary shock – reminding us that we need to take a stand for the things we hold as certainties, oftentimes even as calming ones, if we want them to stay how we always thought they were.
State-of-the-art object detectors are treated as black boxes due to their highly non-linear internal computations. Even with unprecedented advancements in detector performance, the inability to explain how their outputs are generated limits their use in safety-critical applications. Previous work fails to produce explanations for both bounding box and classification decisions, and generally make individual explanations for various detectors. In this paper, we propose an open-source Detector Explanation Toolkit (DExT) which implements the proposed approach to generate a holistic explanation for all detector decisions using certain gradient-based explanation methods. We suggests various multi-object visualization methods to merge the explanations of multiple objects detected in an image as well as the corresponding detections in a single image. The quantitative evaluation show that the Single Shot MultiBox Detector (SSD) is more faithfully explained compared to other detectors regardless of the explanation methods. Both quantitative and human-centric evaluations identify that SmoothGrad with Guided Backpropagation (GBP) provides more trustworthy explanations among selected methods across all detectors. We expect that DExT will motivate practitioners to evaluate object detectors from the interpretability perspective by explaining both bounding box and classification decisions.
21 pages, with supplementary
ProtSTonKGs: A Sophisticated Transformer Trained on Protein Sequences, Text, and Knowledge Graphs
(2022)
While most approaches individually exploit unstructured data from the biomedical literature or structured data from biomedical knowledge graphs, their union can better exploit the advantages of such approaches, ultimately improving representations of biology. Using multimodal transformers for such purposes can improve performance on context dependent classication tasks, as demonstrated by our previous model, the Sophisticated Transformer Trained on Biomedical Text and Knowledge Graphs (STonKGs). In this work, we introduce ProtSTonKGs, a transformer aimed at learning all-encompassing representations of protein-protein interactions. ProtSTonKGs presents an extension to our previous work by adding textual protein descriptions and amino acid sequences (i.e., structural information) to the text- and knowledge graph-based input sequence used in STonKGs. We benchmark ProtSTonKGs against STonKGs, resulting in improved F1 scores by up to 0.066 (i.e., from 0.204 to 0.270) in several tasks such as predicting protein interactions in several contexts. Our work demonstrates how multimodal transformers can be used to integrate heterogeneous sources of information, paving the foundation for future approaches that use multiple modalities for biomedical applications.
Due to the COVID-19 pandemic, health education programs and workplace health promotion (WHP) could only be offered under difficult conditions, if at all. In Germany for example, mandatory lockdowns, working from home, and physical distancing have led to a sharp decline in expenditure on prevention and health promotion from 2019 to 2020. At the same time, the pandemic has negatively affected many people’s mental health. Therefore, our goal was to examine audiovisual stimulation as a possible measure in the context of WHP, because its usage is contact-free, time flexible, and offers, additionally, voice-guided health education programs. In an online survey following a cross-sectional single case study design with 393 study participants, we examined the associations between audiovisual stimulation and mental health, work engagement, and burnout. Using multiple regression analyses, we could identify positive associations between audiovisual stimulation and mental health, burnout, and work engagement. However, longitudinal data are needed to further investigate causal mechanisms between mental health and the use of audiovisual stimulation. Nevertheless, especially with regard to the pandemic, audiovisual stimulation may represent a promising measure for improving mental health at the workplace.
Differential-Algebraic Equations and Beyond: From Smooth to Nonsmooth Constrained Dynamical Systems
(2022)
Effective Neighborhood Feature Exploitation in Graph CNNs for Point Cloud Object-Part Segmentation
(2022)
Part segmentation is the task of semantic segmentation applied on objects and carries a wide range of applications from robotic manipulation to medical imaging. This work deals with the problem of part segmentation on raw, unordered point clouds of 3D objects. While pioneering works on deep learning for point clouds typically ignore taking advantage of local geometric structure around individual points, the subsequent methods proposed to extract features by exploiting local geometry have not yielded significant improvements either. In order to investigate further, a graph convolutional network (GCN) is used in this work in an attempt to increase the effectiveness of such neighborhood feature exploitation approaches. Most of the previous works also focus only on segmenting complete point cloud data. Considering the impracticality of such approaches, taking into consideration the real world scenarios where complete point clouds are scarcely available, this work proposes approaches to deal with partial point cloud segmentation.
In the attempt to better capture neighborhood features, this work proposes a novel method to learn regional part descriptors which guide and refine the segmentation predictions. The proposed approach helps the network achieve state-of-the-art performance of 86.4% mIoU on the ShapeNetPart dataset for methods which do not use any preprocessing techniques or voting strategies. In order to better deal with partial point clouds, this work also proposes new strategies to train and test on partial data. While achieving significant improvements compared to the baseline performance, the problem of partial point cloud segmentation is also viewed through an alternate lens of semantic shape completion.
Semantic shape completion networks not only help deal with partial point cloud segmentation but also enrich the information captured by the system by predicting complete point clouds with corresponding semantic labels for each point. To this end, a new network architecture for semantic shape completion is also proposed based on point completion network (PCN) which takes advantage of a graph convolution based hierarchical decoder for completion as well as segmentation. In addition to predicting complete point clouds, results indicate that the network is capable of reaching within a margin of 5% to the mIoU performance of dedicated segmentation networks for partial point cloud segmentation.
As cameras are ubiquitous in autonomous systems, object detection is a crucial task. Object detectors are widely used in applications such as autonomous driving, healthcare, and robotics. Given an image, an object detector outputs both the bounding box coordinates as well as classification probabilities for each object detected. The state-of-the-art detectors are treated as black boxes due to their highly non-linear internal computations. Even with unprecedented advancements in detector performance, the inability to explain how their outputs are generated limits their use in safety-critical applications in particular. It is therefore crucial to explain the reason behind each detector decision in order to gain user trust, enhance detector performance, and analyze their failure.
Previous work fails to explain as well as evaluate both bounding box and classification decisions individually for various detectors. Moreover, no tools explain each detector decision, evaluate the explanations, and also identify the reasons for detector failures. This restricts the flexibility to analyze detectors. The main contribution presented here is an open-source Detector Explanation Toolkit (DExT). It is used to explain the detector decisions, evaluate the explanations, and analyze detector errors. The detector decisions are explained visually by highlighting the image pixels that most influence a particular decision. The toolkit implements the proposed approach to generate a holistic explanation for all detector decisions using certain gradient-based explanation methods. To the author’s knowledge, this is the first work to conduct extensive qualitative and novel quantitative evaluations of different explanation methods across various detectors. The qualitative evaluation incorporates a visual analysis of the explanations carried out by the author as well as a human-centric evaluation. The human-centric evaluation includes a user study to understand user trust in the explanations generated across various explanation methods for different detectors. Four multi-object visualization methods are provided to merge the explanations of multiple objects detected in an image as well as the corresponding detector outputs in a single image. Finally, DExT implements the procedure to analyze detector failures using the formulated approach.
The visual analysis illustrates that the ability to explain a model is more dependent on the model itself than the actual ability of the explanation method. In addition, the explanations are affected by the object explained, the decision explained, detector architecture, training data labels, and model parameters. The results of the quantitative evaluation show that the Single Shot MultiBox Detector (SSD) is more faithfully explained compared to other detectors regardless of the explanation methods. In addition, a single explanation method cannot generate more faithful explanations than other methods for both the bounding box and the classification decision across different detectors. Both the quantitative and human-centric evaluations identify that SmoothGrad with Guided Backpropagation (GBP) provides more trustworthy explanations among selected methods across all detectors. Finally, a convex polygon-based multi-object visualization method provides more human-understandable visualization than other methods.
The author expects that DExT will motivate practitioners to evaluate object detectors from the interpretability perspective by explaining both bounding box and classification decisions.
We describe a systematic approach for rendering time-varying simulation data produced by exa-scale simulations, using GPU workstations. The data sets we focus on use adaptive mesh refinement (AMR) to overcome memory bandwidth limitations by representing interesting regions in space with high detail. Particularly, our focus is on data sets where the AMR hierarchy is fixed and does not change over time. Our study is motivated by the NASA Exajet, a large computational fluid dynamics simulation of a civilian cargo aircraft that consists of 423 simulation time steps, each storing 2.5 GB of data per scalar field, amounting to a total of 4 TB. We present strategies for rendering this time series data set with smooth animation and at interactive rates using current generation GPUs. We start with an unoptimized baseline and step by step extend that to support fast streaming updates. Our approach demonstrates how to push current visualization workstations and modern visualization APIs to their limits to achieve interactive visualization of exa-scale time series data sets.
Trojanized software packages used in software supply chain attacks constitute an emerging threat. Unfortunately, there is still a lack of scalable approaches that allow automated and timely detection of malicious software packages and thus most detections are based on manual labor and expertise. However, it has been observed that most attack campaigns comprise multiple packages that share the same or similar malicious code. We leverage that fact to automatically reproduce manually identified clusters of known malicious packages that have been used in real world attacks, thus, reducing the need for expert knowledge and manual inspection. Our approach, AST Clustering using MCL to mimic Expertise (ACME), yields promising results with a 𝐹1 score of 0.99. Signatures are automatically generated based on characteristic code fragments from clusters and are subsequently used to scan the whole npm registry for unreported malicious packages. We are able to identify and report six malicious packages that have been removed from npm consequentially. Therefore, our approach can support the detection by reducing manual labor and hence may be employed by maintainers of package repositories to detect possible software supply chain attacks through trojanized software packages.
Hydrogen is a versatile energy carrier. When produced with renewable energy by water splitting, it is a carbon neutral alternative to fossil fuels. The industrialization process of this technology is currently dominated by electrolyzers powered by solar or wind energy. For small scale applications, however, more integrated device designs for water splitting using solar energy might optimize hydrogen production due to lower balance of system costs and a smarter thermal management. Such devices offer the opportunity to thermally couple the solar cell and the electrochemical compartment. In this way, heat losses in the absorber can be turned into an efficiency boost for the device via simultaneously enhancing the catalytic performance of the water splitting reactions, cooling the absorber, and decreasing the ohmic losses.[1,2] However,integrated devices (sometimes also referred to as “artificial leaves”), currently suffer from a lower technology readiness level (TRL) than the completely decoupled approach.
Integrated solar water splitting devices that produce hydrogen without the use of power inverters operate outdoors and are hence exposed to varying weather conditions. As a result, they might sometimes work at non-optimal operation points below or above the maximum power point of the photovoltaic component, which would directly translate into efficiency losses. Up until now, however, no common parameter describing and quantifying this and other real-life operating related losses (e.g. spectral mismatch) exists in the community. Therefore, the annual-hydrogen-yield-climatic-response (AHYCR) ratio is introduced as a figure of merit to evaluate the outdoor performance of integrated solar water splitting devices. This value is defined as the ratio between the real annual hydrogen yield and the theoretical yield assuming the solar-to-hydrogen device efficiency at standard conditions. This parameter is derived for an exemplary system based on state-of-the-art AlGaAs//Si dual-junction solar cells and an anion exchange membrane electrolyzer using hourly resolved climate data from a location in southern California and from reanalysis data of Antarctica. This work will help to evaluate, compare and optimize the climatic response of solar water splitting devices in different climate zones.
Research-Practice-Collaborations Addressing One Health and Urban Transformation. A Case Study
(2022)
One Health is an integrative approach at the interface of humans, animals and the environment, which can be implemented as Research-Practice-Collaboration (RPC) for its interdisciplinarity and intersectoral focus on the co-production of knowledge. To exemplify this, the present commentary shows the example of the Forschungskolleg “One Health and Urban Transformation” funded by the Ministry of Culture and Science of the State Government of Nord Rhine Westphalia in Germany. After analysis, the factors identified for a better implementation of RPC for One Health were the ones that allowed for constant communication and the reduction of power asymmetries between practitioners and academics in the co-production of knowledge. In this light, the training of a new generation of scientists at the boundaries of different disciplines that have mediation skills between academia and practice is an important contribution with great implications for societal change that can aid the further development of RPC.
In Unterkünften für geflüchtete Menschen lebt ein hoher Anteil Kinder in einem Umfeld, das häufig für Erwachsene geschaffen wurde und/oder von diesen dominiert wird. Die Beschaffenheit, die Struktur und das Zusammenleben vor Ort bestimmen daher wesentlich die Lebenswelten von Kindern. Dabei haben Kinder besondere Rechte und Bedarfe. Der Schutz von Kindern und ein förderliches Umfeld für eine gute Entwicklung sind wesentliche Aspekte, die durch internationale Abkommen, wie die UN-Kinderrechtskonvention verbrieft sind und umgesetzt werden müssen. Zwar sind die Bundesländer im nationalen gesetzlichen Rahmen dazu verpflichtet, den Schutz von Kindern in Unterkünften für geflüchtete Menschen zu gewährleisten, die Umsetzung ist jedoch oft nicht verbindlich geregelt. Die vorliegende Analyse diskutiert kinderrechtliche Aspekte für den Schutz von Kindern in Unterkünften für geflüchtete Menschen, zudem wirft sie einen Blick auf Aktivitäten und Maßnahmen der Bundesinitiative Schutz von geflüchteten Menschen in Flüchtlingsunterkünften initiiert durch das Bundesministerium für Familien, Senioren, Frauen und Jugend (BMFSFJ) und dem Kinderhilfswerk der Vereinten Nationen (UNICEF) und gibt Einblick in die neueste Sekundärliteratur zum Kinderschutz in Sammelunterkünften. Ziel des Beitrags ist es, aufzuzeigen, welche Aspekte den Schutz von Kindern begünstigen und wo die Herausforderungen liegen.
Schulungen in neun Prozessschritten gestalten! Digitalisierung des Masterfaches „Integrierte Managementsysteme“ im Studiengang „Material Science and Sustainability Methods“ im Fachbereich Naturwissenschaften an der Hochschule Bonn-Rhein-Sieg. Am Beispiel einer jahrelang in Präsenz gelehrten Lehrveranstaltung mit Vorlesungen und seminaristischen Übungen wird gezeigt, wie das Gestalten und Durchführen zur Vermittlung prüfungsrelevanter Kompetenzen auch „online“ gelingt. Das passende „Setting“ des Lehr- und Lernprozesses unter Beachtung von Qualitätskriterien und Handlungsempfehlungen ist für jede Art von Schulung in Universitäten, Behörden, Unternehmen und anderen Organsitationen relevant.
Forschungsdatenmanagement (FDM) nimmt im Forschungsalltag der Hochschulen für angewandte Wissenschaften (HAW) eine zunehmende größere Rolle ein und stellt an viele Forschende bisher unbekannte Anforderungen. So gilt es ein FAIRes und nachhaltiges Datenmanagement im Sinne des Kodex „Leitlinien zur Sicherung guter wissenschaftlicher Praxis“ der DFG zu betreiben – für sich selbst, für die eigene Forschungsgruppe und für die Forschungsgemeinschaft. In dieser Veranstaltung anlässlich des Tags der Forschungsdaten in NRW am 15.11.2022 wurden die wesentlichen Grundzüge des Forschungsdatenmanagements anhand des Forschungsdatenlebenszyklus nähergebracht und Praxisbeispiele und nützliche Links vorgestellt.
50 Jahre: Von der FH zur HAW
(2022)
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.