Hochschule Bonn-Rhein-Sieg
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Development of Gold Nanoparticle-Based SERS Substrates on TiO2-Coating to Reduce the Coffee Ring Effect
No full textHydrophilic surface-enhanced Raman spectroscopy (SERS) substrates were prepared by a combination of TiO2-coatings of aluminium plates through a direct titanium tetraisopropoxide (TTIP) coating and drop coated by synthesised gold nanoparticles (AuNPs). Differences between the wettability of the untreated substrates, the slowly dried Ti(OH)4 substrates and calcinated as well as plasma treated TiO2 substrates were analysed by water contact angle (WCA) measurements. The hydrophilic behaviour of the developed substrates helped to improve the distribution of the AuNPs, which reflects in overall higher lateral SERS enhancement. Surface enhancement of the substrates was tested with target molecule rhodamine 6G (R6G) and a fibre-coupled 638 nm Raman spectrometer. Additionally, the morphology of the substrates was characterised using scanning electron microscopy (SEM) and Raman microscopy. The studies showed a reduced influence of the coffee ring effect on the particle distribution, resulting in a more broadly distributed edge region, which increased the spatial reproducibility of the measured SERS signal in the surface-enhanced Raman mapping measurements on mm scale
Institutional Settings Surrounding Agriculture and Biodiversity: Challenges, Potentials and Obstacles of a Contract-based Nature Protection Scheme in the Rhine-Sieg District of Germany
No full textContract-based nature protection schemes are a voluntary mechanism, with a limited contract duration, that aim to raise the acceptance of biodiversity conservation practices in agriculture among farmers and other land users. The purpose of this paper is to analyse the institutional settings of contract-based nature protection based on the– “Institutions of Sustainability” (IoS) framework in the German Rhine-Sieg district, and to outline the way in which policy measures should be designed to encourage farmers to participate in contract-based nature protection programmes. This was achieved by answering research questions to identify the challenges, potentials and obstacles of a contract-based nature protection scheme in different “sub-arenas” as defined in the IoS framework. Qualitative research methods were used as the methodology. The analysis shows that main constraints for sufficient implementation of contract-based nature protection schemes are the limited consideration of the impact of climate change during the contract period, the limited consideration of regional conditions as regards the measures taken on the ground and an inflexible contract duration
Canonical convolutional neural networks
No full textWe 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
Formelsammlung Finanzmathematik: Wissen kompakt für Studierende und Praktiker
No full textDiese Formelsammlung enthält und erklärt finanzmathematische Formeln innerhalb finanzwirtschaftlicher Zusammenhänge, wie sie in den Wirtschaftswissenschaften und in der wirtschaftswissenschaftlichen Praxis fundamental notwendig sind. Das Verständnis der Formeln und deren praktische Anwendung werden durch nützliche Hilfen und verständliche Beispiele sinnvoll unterstützt, so dass der Kontext finanzmathematischer Formeln klar und erklärlich dargestellt wird. Diese Formelsammlung ist ein unverzichtbares Tool für Studierende der Wirtschaftswissenschaften, aber auch ein nützliches Nachschlagewerk für Verantwortliche aus Wirtschaft, Politik und Lehre. (Verlagsangaben
From Zero to Hero: Generating Training Data for Question-To-Cypher Models
No full textGraph databases employ graph structures such as nodes, attributes and edges to model and store relationships among data. To access this data, graph query languages (GQL) such as Cypher are typically used, which might be difficult to master for end-users. In the context of relational databases, sequence to SQL models, which translate natural language questions to SQL queries, have been proposed. While these Neural Machine Translation (NMT) models increase the accessibility of relational databases, NMT models for graph databases are not yet available mainly due to the lack of suitable parallel training data. In this short paper we sketch an architecture which enables the generation of synthetic training data for the graph query language Cypher
Bachelor’s Program “Sustainable Engineering” at Bonn-Rhein-Sieg University of Applied Sciences: Development, Concept and Practical Experience
No full textResearch-Practice-Collaborations Addressing One Health and Urban Transformation. A Case Study: Commentary on "Research-Practice-Collaborations in International Sustainable Development and Knowledge Production-Reflections from a Political-Economic Perspective"
Get PDFOne 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.« Une Santé» (en anglais: One Health) est un approche intégratif situé à l’interface entre les humains, les animaux, et l’environnement, qui peut être implémenté tel qu’une collaboration entre recherche et pratique (CRP) grâce à son interdisciplinarité et son accent sur la cocréation du savoir. Pour illustrer ce point, cet article prend le Forschungskolleg « Une Santé et Transformation Urbaine» (en anglais: One Health and Urban Transformation) financé par le Ministère de la Culture et Sciences du gouvernement du Nord-Rhin Westphalie en Allemagne. D’après nos analyses, les facteurs identifiés comme soutenant une meilleure implémentation du CRP pour le programme One Health sont ceux qui permettent une communication constante et une réduction des asymétries causés par le pouvoir entre les praticiens et les entités académiques dans la cocréation du savoir. Sur ce point, l’éducation d’une nouvelle génération de scientifiques, à l’intersection des différentes disciplines et avec des fortes aptitudes à la médiation entre la pratique et le monde académique, est une contribution important avec des grandes implications pour le changement sociétal, et qui peut en outre soutenir le développement du CRP
Selbstverständlich – ohne Zweifel?: Ein Versuch über das Wesen des Selbstverständlichen in der Spätmoderne
No full textThe 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
Explainable product backorder prediction exploiting CNN: Introducing explainable models in businesses
Get PDFDue 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