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Good practices in funding transdisciplinary and participatory research for sustainable development in Africa: Learning lessons from German public funding programmes on sustainable land management and agricultural development in Africa
Get PDFThis working paper explores how funding requirements enable transdisciplinary research (TDR) for sustainable development in Africa, focusing on German public funders and their TDR programmes in sustainable land management and agricultural development. It consolidates experiences from Global North-South collaborations to identify good practices in funding TDR, particularly during the initial phase of defining research problems and objectives. Key findings suggest that funders should combine research and development funding for long-term TDR processes, explicitly define TDR expectations in selection processes, create TDR-friendly budget structures, and support a collaborative problem definition phase by funding joint proposal writing and facilitating joint learning. The paper aims to foster a dialogue on good practices in funding TDR between funders, researchers, and practice organizations in Germany and Africa, facilitated by a series of workshops under the INTERFACES project. Insights are based on key expert interviews and the first workshop with funders and researchers in Germany
Making Skopéin: An autoethnographic report about the interplay between space and media art
Get PDFArt installations that engage in a dialogical relationship with their surrounding environment, transcending the confines of an isolated existence, demand a nuanced articulation of the dynamic interplay among the artwork, the spatial context, and the observer. The following report endeavors to delineate and investigate the central elements of reception and the aesthetic of production pivotal to the media art installation ‘Skopéin’, exhibited at the Evangelische Stadtkirche Karlsruhe during the late summer of 2022, through the lens of ethnographic introspection (‘autoethnography’). Given that the authors of this discourse are concurrently the creators of the aforementioned installation, the following text serves as an exploratory analysis into the fabrication process of a media art installation, employing anthropological methods
Should My Take-Away Packaging Be Reusable? An Empirical Study Of Consumer Behaviour Towards Returnable Food Packaging In Germany
Get PDFSince the new German packaging law 'VerpackG2' came into force in January 2023, German foodservice operators selling food to-go are required to provide reusable packaging alternatives to their single-use plastic food packaging. This change in legislation has led to the emergence of various reusable consumer packaging systems in the German market. Reusable packaging systems have the potential to significantly reduce the negative environmental impact of single-use plastic packaging. However, for these systems to be successful and achieve their desired positive environmental impact, also a comprehensive understanding of consumer behaviour towards these systems is needed. This study extends the Theory of Planned Behaviour (TPB) framework to identify the factors influencing consumers' intentions to use a reusable packaging system for takeaway food in the German foodservice industry. An online survey was developed and 153 valid responses were collected from consumers in Germany. Structural equation modelling revealed that consumers' personal moral norms, attitudes, subjective norms and perceived behavioural control directly influence consumers' intentions to use the reusable packaging system in this study. The results also show that context, motivation and personal moral norms are positively related to consumers' attitudes and that context has a significant positive effect on consumers' perceived behavioural control. Furthermore, the results of the study indicate that despite the high frequency of takeaway food orders in Germany, consumers' use of reusable packaging systems for takeaway food still needs to be improved
Novel Approaches in Rational Peptide Design for Recognition of FABP3 and Myoglobin in Cardiac Biosensors
No full textLattice Boltzmann method with artificial bulk viscosity using a neural collision operator
No full textThe lattice Boltzmann method (LBM) stands apart from conventional macroscopic approaches due to its low numerical dissipation and reduced computational cost, attributed to a simple streaming and local collision step. While this property makes the method particularly attractive for applications such as direct noise computation, it also renders the method highly susceptible to instabilities. A vast body of literature exists on stability-enhancing techniques, which can be categorized into selective filtering, regularized LBM, and multi-relaxation time (MRT) models. Although each technique bolsters stability by adding numerical dissipation, they act on different modes. Consequently, there is not a universal scheme optimally suited for a wide range of different flows. The reason for this lies in the static nature of these methods; they cannot adapt to local or global flow features. Still, adaptive filtering using a shear sensor constitutes an exception to this. For this reason, we developed a novel collision operator that uses space- and time-variant collision rates associated with the bulk viscosity. These rates are optimized by a physically informed neural net. In this study, the training data consists of a time series of different instances of a 2D barotropic vortex solution, obtained from a high-order Navier–Stokes solver that embodies desirable numerical features. For this specific text case our results demonstrate that the relaxation times adapt to the local flow and show a dependence on the velocity field. Furthermore, the novel collision operator demonstrates a better stability-to-precision ratio and outperforms conventional techniques that use an empirical constant for the bulk viscosity
Improved Thermal Comfort Model Leveraging Conditional Tabular GAN Focusing on Feature Selection
No full textThe indoor thermal comfort in both homes and workplaces significantly influences the health and productivity of inhabitants. The heating system, controlled by Artificial Intelligence (AI), can automatically calibrate the indoor thermal condition by analyzing various physiological and environmental variables. To ensure a comfortable indoor environment, smart home systems can adjust parameters related to thermal comfort based on accurate predictions of inhabitants’ preferences. Modeling personal thermal comfort preferences poses two significant challenges: the inadequacy of data and its high dimensionality. An adequate amount of data is a prerequisite for training efficient machine learning (ML) models. Additionally, high-dimensional data tends to contain multiple irrelevant and noisy features, which might hinder ML models’ performance. To address these challenges, we propose a framework for predicting personal thermal comfort preferences, combining the conditional tabular generative adversarial network (CTGAN) with multiple feature selection techniques. We first address the data inadequacy challenge by applying CTGAN to generate synthetic data samples, incorporating challenges associated with multimodal distributions and categorical features. Then, multiple feature selection techniques are employed to identify the best possible sets of features. Experimental results based on a wide range of settings on a standard dataset demonstrated state-of-the-art performance in predicting personal thermal comfort preferences. The results also indicated that ML models trained on synthetic data achieved significantly better performance than models trained on real data. Overall, our method, combining CTGAN and feature selection techniques, outperformed existing known related work in thermal comfort prediction in terms of multiple evaluation metrics, including area under the curve (AUC), Cohen’s Kappa, and accuracy. Additionally, we presented a global, model-agnostic explanation of the thermal preference prediction system, providing an avenue for thermal comfort experiment designers to consciously select the data to be collected
Planning Robot Placement for Object Grasping
No full textWhen performing manipulation-based activities such as picking objects, a mobile robot needs to position its base at a location that supports successful execution. To address this problem, prominent approaches typically rely on costly grasp planners to provide grasp poses for a target object, which are then are then analysed to identify the best robot placements for achieving each grasp pose. In this paper, we propose instead to first find robot placements that would not result in collision with the environment and from where picking up the object is feasible, then evaluate them to find the best placement candidate. Our approach takes into account the robot's reachability, as well as RGB-D images and occupancy grid maps of the environment for identifying suitable robot poses. The proposed algorithm is embedded in a service robotic workflow, in which a person points to select the target object for grasping. We evaluate our approach with a series of grasping experiments, against an existing baseline implementation that sends the robot to a fixed navigation goal. The experimental results show how the approach allows the robot to grasp the target object from locations that are very challenging to the baseline implementation
Studie BPM Compass 2024: Entwicklung und Zukunft des Geschäftsprozessmanagements
No full textDie Studie BPM Compass 2024 untersucht aktuelle Trends, Erfolgsfaktoren und Entwicklungen im Business Process Management (BPM). Sie behandelt Themen wie Ziele, Status Quo, Unternehmenskultur, Zufriedenheit, Erfolg und Systemintegration. Das Design lehnt sich an die Studie von 2016 an, um Vergleichsanalysen zu ermöglichen, wurde aber moderat modernisiert. Die Ergebnisse basieren auf einer Online-Befragung mit 138 Teilnehmern im Februar und März 2024 und sind indikativ. Die Studie wurde in Kooperation der Technischen Hochschule Mittelhessen (THM), der Hochschule Koblenz, der Hochschule Bonn-Rhein-Sieg, der Humboldt-Universität zu Berlin, der Deutschen Gesellschaft für Qualität (DGQ) und der Gesellschaft für Prozessmanagement (GP) durchgeführt