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Accurate global horizontal irradiance (GHI) forecasting is critical for integrating solar energy into the power grid and operating solar power plants. The Weather Research and Forecasting model with its solar radiation extension (WRF-Solar) has been used to forecast solar irradiance in different regions around the world. However, the application of the WRF-Solar model to the prediction of GHI in West Africa, particularly Ghana, has not yet been investigated. The aim of this study is to evaluate the performance of the WRF-Solar model for predicting GHI in Ghana, focusing on three automatic weather stations (Akwatia, Kumasi and Kologo) for the year 2021. We used two one-way nested domains (D1 = 15 km and D2 = 3 km) to investigate the ability of the fully coupled WRF-Solar model to forecast GHI up to 72-hour ahead under different atmospheric conditions. The initial and lateral boundary conditions were taken from the ECMWF high-resolution operational forecasts. Our findings reveal that the WRF-Solar model performs better under clear skies than cloudy skies. Under clear skies, Kologo performed best in predicting 72-hour GHI, with a first day nRMSE of 9.62 %. However, forecasting GHI under cloudy skies at all three sites had significant uncertainties. Additionally, WRF-Solar model is able to reproduce the observed GHI diurnal cycle under high AOD conditions in most of the selected days. This study enhances the understanding of the WRF-Solar model’s capabilities and limitations for GHI forecasting in West Africa, particularly in Ghana. The findings provide valuable information for stakeholders involved in solar energy generation and grid integration towards optimized management in the region.
In vision tasks, a larger effective receptive field (ERF) is associated with better performance. While attention natively supports global context, convolution requires multiple stacked layers and a hierarchical structure for large context. In this work, we extend Hyena, a convolution-based attention replacement, from causal sequences to the non-causal two-dimensional image space. We scale the Hyena convolution kernels beyond the feature map size up to 191$\times$191 to maximize the ERF while maintaining sub-quadratic complexity in the number of pixels. We integrate our two-dimensional Hyena, HyenaPixel, and bidirectional Hyena into the MetaFormer framework. For image categorization, HyenaPixel and bidirectional Hyena achieve a competitive ImageNet-1k top-1 accuracy of 83.0% and 83.5%, respectively, while outperforming other large-kernel networks. Combining HyenaPixel with attention further increases accuracy to 83.6%. We attribute the success of attention to the lack of spatial bias in later stages and support this finding with bidirectional Hyena.
Green infrastructure has been widely recognized for the benefits to human health and biodiversity conservation. However, knowledge of the qualities and requirements of such spaces and structures for the effective delivery of the range of ecosystem services expected is still limited, as well as the identification of trade-offs between services. In this study, we apply the One Health approach in the context of green spaces to investigate how urban park characteristics affect human mental health and wildlife support outcomes and identify synergies and trade-offs between these dimensions. Here we show that perceived restorativeness of park users varies significantly across sites and is mainly affected by safety and naturalness perceptions. In turn, these perceptions are driven by objective indicators of quality, such as maintenance of facilities and vegetation structure, and subjective estimations of biodiversity levels. The presence of water bodies benefited both mental health and wildlife. However, high tree canopy coverage provided greater restoration potential whereas a certain level of habitat heterogeneity was important to support a wider range of bird species requirements. To reconcile human and wildlife needs in green spaces, cities should strategically implement a heterogeneous green infrastructure network that considers trade-offs and maximizes synergies between these dimensions.
Pipeline transport is an efficient method for transporting fluids in energy supply and other technical applications. While natural gas is the classical example, the transport of hydrogen is becoming more and more important; both are transmitted under high pressure in a gaseous state. Also relevant is the transport of carbon dioxide, captured in the places of formation, transferred under high pressure in a liquid or supercritical state and pumped into underground reservoirs for storage. The transport of other fluids is also required in technical applications. Meanwhile, the transport equations for different fluids are essentially the same, and the simulation can be performed using the same methods. In this paper, the effect of control elements such as compressors, regulators and flaptraps on the stability of fluid transport simulations is studied. It is shown that modeling of these elements can lead to instabilities, both in stationary and dynamic simulations. Special regularization methods were developed to overcome these problems. Their functionality also for dynamic simulations is demonstrated for a number of numerical experiments.
In recent years, eXtended Reality (XR) technology like Augmented Reality and Virtual Reality became both technically feasible as well as affordable which lead to a drastic demand of professionally designed and developed applications. However, this demand combined with a rapid pace of innovation revealed a lack of design tool support for professional interaction designers as well as a knowledge gap regarding their approaches and needs. To address this gap, this thesis engages with the work of professional XR interaction designers in a qualitative research into XR interaction design approach. Therefore, this thesis applies two complementary lenses stemming from scientific design and social practice theory discourses to observe, describe, analyze, and understand professional XR interaction designers' challenges and approaches with a focus on application prototyping.
This paper seeks to explore the framework within which the International Labour Office should promote a principled, practical approach to social security policy in the new millennium. Integration has to be built around a joint coherent policy vision and building such a policy vision requires debate. This paper is a product of a joint effort of members of the ILO Social Security Department and social security specialists working in the ILO field offices.
Farming communities confronted with climate change adopt formal and informal adaptation strategies to mitigate the effects of climate change. While the environmental and social effects of climate change are well documented, there is still a dearth of literature on girl-child marriage (formal marriage or informal union between a child under the age of 18 and an adult or another child) as a response to the effects of climate change. In this research, we ask if girl-child marriage is promoted as a social protection mechanism first, rather than as simply a response to climate-induced poverty. We use qualitative semi-structured interviews and focus group discussions to explore this question in a rural farming community in Northern Ghana. Our findings reveal that climate change shocks result in poverty and compel farmers to marry off their young daughters. The unmarried girl-child is perceived as an ‘extra mouth to feed’, a liability whose marriage becomes a strategy for protecting the family, the family’s reputation, and the girl child. The emphasis in girl-child marriage is not on the girl-child as an individual but on the family as a group. Hence, what is good for the family is assumed to be in the best interest of the girl-child. We place our analysis at the intersection of climate change, social protection, and the incidence of girl-child marriages. We argue that understanding this link is crucial and can contribute significantly to our knowledge of girl-child marriage as well as our ability to address this in Sub-Saharan Africa.
The transport sector is a major source of air pollution and thus a major contributor to the changing climate. As a result, in the recent past, driving bans have been imposed on cars with critical pollutant groups. As an international UN campus and self-proclaimed climate capital, the Federal City of Bonn declared a climate emergency in 2019 and participated in a federally funded “Lead City” project to optimise air quality. A key goal of the project is to reduce private motorised transport and strengthen public transport. Among the implemented measures, a “climate ticket” was introduced in 2019 whereby consumers could purchase an annual 365 € ticket for all local public transport. This paper reports on an analysis of that ticket’s changes in travel behavior.
A quantitative survey (n = 1,315) of the climate ticket users as well as the multiple regressions confirm that the climate ticket attracted more customers to the buses and trams and that a modal shift for the period of the measure was recognisable. The multiple regressions showed that the ticket was perceived significantly more positively by full-time employed users than by unemployed people. The results also show that, in addition to the price, it is essential that travel time and reliability are ensured. Furthermore, the eligible groups of people, the area of coverage, and good connecting services should be extended. To sustainably improve air quality, this type of mobility service must be optimised and introduced on a permanent basis.
Improved Thermal Comfort Model Leveraging Conditional Tabular GAN Focusing on Feature Selection
(2024)
The 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.
Design and characterization of geopolymer foams reinforced with Miscanthus x giganteus fibers
(2024)
This paper presents the effects of different amounts of fibers and foaming agent, as well as different fiber sizes, on the mechanical and thermal properties of fly ash-based geopolymer foams reinforced with Miscanthus x giganteus fibers. The mechanical properties of the geopolymer foams were measured through compressive strength, and their thermal properties were characterized by thermal conductivity and X-ray micro-computed tomography. Furthermore, design of experiment (DoE) were used to optimize the thermal conductivity and compressive strength of Miscanthus x giganteus reinforced geopolymer foams. In addition, the microstructure was studied using X-ray diffraction (XRD), Field emission scanning electron microscopy (SEM) and Fourier-Transform Infrared Spectroscopy (FTIR). Mixtures with a low thermal conductivity of 0.056 W (m K)−1 and a porosity of 79 vol% achieved a compressive strength of only 0.02 MPa. In comparison, mixtures with a thermal conductivity of 0.087 W (m K)−1 and a porosity of 58 vol% achieved a compressive strength of 0.45 MPa.
Trade of wild-caught animals is illegal for many taxa and in many countries. Common regulatory procedures involve documentation and marking techniques. However, these procedures are subject to fraud and thus should be complemented by routine genetic testing in order to authenticate the captive-bred origin of animals intended for trade. A suitable class of genetic markers are SNPSTRs that combine a short tandem repeat (STR) and single nucleotide polymorphisms (SNPs) within one amplicon. This combined marker type can be used for genetic identification and for parentage analyses and in addition, provides insight into haplotype history. As a proof of principle, this study establishes a set of 20 SNPSTR markers for Athene noctua, one of the most trafficked owls in CITES Appendix II. These markers can be coamplified in a single multiplex reaction. Based on population data, the percentage of observed and expected heterozygosities of the markers ranged from 0.400 to 1.000 and 0.545 to 0.850, respectively. A combined probability of identity of 5.3*10-23 was achieved with the whole set, and combined parentage exclusion probabilities reached over 99.99%, even if the genotype of one parent was missing. A direct comparison of an owl family and an unrelated owl demonstrated the applicability of the SNPSTR set in parentage testing. The established SNPSTR set thus proved to be highly useful for identifying individuals and analysing parentage to determine wild or captive origin. We propose to implement SNPSTR-based routine certification in wildlife trade as a way to reveal animal laundering and misdeclaration of wild-caught animals.
The 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.
This article deals with the under-researched phenomenon of rural health entrepreneurship and its major characteristics. The purpose of this study is to explicate the process of providing health services in rural areas of a developing country and their relation to SDGs. The paper is based on six semi-structured interviews conducted with Serbian health entrepreneurs in rural areas (two private practices, two policlinics, and two dental practices), a review of laws and strategies relevant to the field, and three sessions of discussions with eight experts (four authors and four additional experts). The research methodology follows an empirical, mixed-method case study research procedure. The results are presented in relation to the aspects of frugality, family orientation, and sustainability-oriented innovation. The timeline of the six case studies demonstrates the increasing importance of health entrepreneurs in rural areas due to the aging population and, therefore, increased needs for quality healthcare in these areas. The financing instruments have also become more formal and substantial in recent years, enabling the growth of healthcare businesses in rural areas. However, a major obstacle to further sustainable development remains the non-refundability of services before the state-owned, obligatory health fund, creating major social inequalities, especially in rural areas.
The Peren Theorem
(2024)
The Peren-Clement Index
(2024)
Network Analysis Method
(2024)
Dynamic Programming
(2024)
Heuristic Methods
(2024)
Sequencing Problems
(2024)
Queueing Theory
(2024)
Linear Optimization
(2024)
The Decision Tree Procedure
(2024)
In addition to the long-term goal of mitigating climate change, the current geopolitical upheavals heighten the urgency to transform Europe's energy system. This involves expanding renewable energies while managing intermittent electricity generation. Hydrogen is a promising solution to balance generation and demand, simultaneously decarbonizing complex applications. To model the energy system's transformation, the project TransHyDE-Sys, funded by the German Federal Ministry of Education and Research, takes an integrated approach beyond traditional energy system analysis, incorporating a diverse range of more detailed methods and tools. Herein, TransHyDE-Sys is situated within the recent policy discussion. It addresses the requirements for energy system modeling to gain insights into transforming the European hydrogen and energy infrastructure. It identifies knowledge gaps in the existing literature on hydrogen infrastructure-oriented energy system modeling and presents the research approach of TransHyDE-Sys. TransHyDE-Sys analyzes the development of hydrogen and energy infrastructures from “the system” and “the stakeholder” perspectives. The integrated modeling landscape captures temporal and spatial interactions among hydrogen, electricity, and natural gas infrastructure, providing comprehensive insights for systemic infrastructure planning. This allows a more accurate representation of the energy system's dynamics and aids in decision-making for achieving sustainable and efficient hydrogen network development integration.
Chain models as a tool to quantify the relation between soil, crop quality and human exposure
(2008)
„from stable to table“
(2008)
Chloritesare found in igneous rocks, and more commonly
in sediments and low grade metamorphic rocks (BrinJiey &
Brown 1980). Because chlorites are less stable than most of
the other clay minerals when placed into an acidic
environment (Barnhisel & Bertsch 1989), they play
significant roles in the solution chemistry and may be an
important source of magnesium and micronutrients in soils
(Ross 1975). The objective of the present study was to
compare the weathering behavior of different chlorites in
different chemical environments.
Introduction: The paper analyses – basing itself on reports and other documents created by different parts of the International Labour Organisation (ILO) – the process which led to the adoption of Social Protection Floor Recommendation No. 202 and the shift in focus of social policy advice towards basic protection and to the Global South countries. We look at the actions of different actors which shape the standard setting and policy stand of the organisation. Objective: To provide a comprehensive analysis of the historical trajectory of ILO social security standards, examining the evolution of principles, conventions, and the global dynamics that have shaped the organization's approach to social protection over time. Materials and methods: The methods include examining ILO documents, relevant subject literature, and the author's participant observations from over twenty-years of service in the ILO's Social Security Department, aiming to provide insights into the decision-making processes within the organization. Conclusion: We conclude that change was brought by: 1) shift in the membership of the ILO and of its decision-making bodies towards the increased presence and powers of representatives from countries of the Global South, 2) the shift in the global development community policy priorities towards poverty reduction, 3) emergence of experimental social assistance schemes in Global South countries, with designs often ignoring principles embedded in the ILO standards. The Social Protection Floor Recommendation complements previous standards in response to the challenges of widespread poverty and informality and spreading atypical forms of employment. It provides two directions of policy responses: 1) formalizing informal employment relationships and 2) expanding universal or targeted rights-based social assistance schemes. Assistance provided by ILO to member states focuses now more on building the non-contributory schemes and on identifying the fiscal space necessary to close the coverage gaps. Nowadays, the ILO must collaborate more than before with other development partners and the main challenge is to build among them awareness and acceptance of the principles of the ILO social security standards.
There are several recent works which had proposed an automatic computer-aided diagnosis (CAD) deep learning (DL) model to diagnose coronavirus disease 2019 (COVID-19) using chest x-ray images (CXR) to propose a high-accuracy CAD method to detect COVID-19 automatically. In this study, seven different models including Convolutional Neural Network (CNN) models such as VGG-16 and vision transformer (ViT) models, are proposed. The different proposed models are trained with a three-class balanced dataset consisting of 3,000 CXR images consisting of 1,000 CXR images for each class of COVID-19, Normal, and Lung-Opacity. A publicly available dataset to train and test the models is used from Kaggle-COVID-19-Radiography-Dataset. From the experiments, the accuracy of the VGG16 model is 93.44% and ViT's is 92.33%. Besides, the binary classification between two classes of COVID-19 and Normal CXR with a limited number of just 100 images for each class, using a transfer learning technique, with a validation accuracy of 97.5% is proposed.
The transport of carbon dioxide through pipelines is one of the important components of Carbon dioxide Capture and Storage (CCS) systems that are currently being developed. If high flow rates are desired a transportation in the liquid or supercritical phase is to be preferred. For technical reasons, the transport must stay in that phase, without transitioning to the gaseous state. In this paper, a numerical simulation of the stationary process of carbon dioxide transport with impurities and phase transitions is considered. We use the Homogeneous Equilibrium Model (HEM) and the GERG-2008 thermodynamic equation of state to describe the transport parameters. The algorithms used allow to solve scenarios of carbon dioxide transport in the liquid or supercritical phase, with the detection of approaching the phase transition region. Convergence of the solution algorithms is analyzed in connection with fast and abrupt changes of the equation of state and the enthalpy function in the region of phase transitions.
This work proposes a novel approach for probabilistic end-to-end all-sky imager-based nowcasting with horizons of up to 30 min using an ImageNet pre-trained deep neural network. The method involves a two-stage approach. First, a backbone model is trained to estimate the irradiance from all-sky imager (ASI) images. The model is then extended and retrained on image and parameter sequences for forecasting. An open access data set is used for training and evaluation. We investigated the impact of simultaneously considering global horizontal (GHI), direct normal (DNI), and diffuse horizontal irradiance (DHI) on training time and forecast performance as well as the effect of adding parameters describing the irradiance variability proposed in the literature. The backbone model estimates current GHI with an RMSE and MAE of 58.06 and 29.33 W m−2, respectively. When extended for forecasting, the model achieves an overall positive skill score reaching 18.6 % compared to a smart persistence forecast. Minor modifications to the deterministic backbone and forecasting models enables the architecture to output an asymmetrical probability distribution and reduces training time while leading to similar errors for the backbone models. Investigating the impact of variability parameters shows that they reduce training time but have no significant impact on the GHI forecasting performance for both deterministic and probabilistic forecasting while simultaneously forecasting GHI, DNI, and DHI reduces the forecast performance.
Integrating physical simulation data into data ecosystems challenges the compatibility and interoperability of data management tools. Semantic web technologies and relational databases mostly use other data types, such as measurement or manufacturing design data. Standardizing simulation data storage and harmonizing the data structures with other domains is still a challenge, as current standards such as the ISO standard STEP (ISO 10303 ”Standard for the Exchange of Product model data”) fail to bridge the gap between design and simulation data. This challenge requires new methods, such as ontologies, to rethink simulation results integration. This research describes a new software architecture and application methodology based on the industrial standard ”Virtual Material Modelling in Manufacturing” (VMAP). The architecture integrates large quantities of structured simulation data and their analyses into a semantic data structure. It is capable of providing data permeability from the global digital twin level to the detailed numerical values of data entries and even new key indicators in a three-step approach: It represents a file as an instance in a knowledge graph, queries the file’s metadata, and finds a semantically represented process that enables new metadata to be created and instantiated.
This paper presents the b-it-bots RoboCup@Work team and its current hardware and functional architecture for the KUKA youBot robot. We describe the underlying software framework and the developed capabilities required for operating in industrial environments including features such as reliable and precise navigation, flexible manipulation, robust object recognition and task planning. New developments include an approach to grasp vertical objects, placement of objects by considering the empty space on a workstation, and the process of porting our code to ROS2.
The Global Compact for Safe, Orderly and Regular Migration defines Global Skill Partnerships (GSP) as an innovative means of strengthen skills development among origin countries and countries of destination in mutually beneficial manner. However, GSPs are very limited in number and scope, and empirical analyses of them are, to date, relatively rare. This study helps fill this gap in data by presenting and examining existing GSPs or GSP-like approaches (e.g., transnational training partnerships). The aim of the study is to take stock of the various conceptual discourses on and practical experience with transnational skill partnerships. Using Kosovo as a case study, the study details the structure of such partnerships and the processes they entail. It documents the experience of those involved and catalogues the factors contributing to success. On this basis, the authors propose a means of categorizing the various practices that will help structure the empirical diversity of such approaches and render them conceptually feasible: Transnational Skills and Mobility Partnerships (TSMP).
This paper presents the preliminary results of the Socialist Republic of Vietnam country case study conducted as part of the research project Sustainable Labour Migration implemented by the University of Applied Science Bonn-Rhein-Sieg. The project focuses on stakeholder perspectives on countries of origin benefits and the sustainability of different transnational skill partnership schemes. Existing and ongoing small-scale initiatives indicate that opportunities exist for all three types of labour mobility pathways, from recruiting youth for apprenticeships and subsequent skilled work to recruitment and recognition of skilled 'professionals' certificates for direct work contracts to initial vocational education and training programs in a dual-track approach. While the latter has the highest potential to be more beneficial than other approaches, pursuing and supporting the scaling up of all three pathways in parallel will have additional, mutually reinforcing and supporting effects. The potential for benefits over and above those already realised by existing skill partnerships appears high, especially considering the favourable framework conditions specific to the long-standing German-Vietnamese relationship. If the potential of well-managed skill partnerships was realised, such sustainable models of skilled labour migration could serve as a unique selling point in the international competition for skilled labour.
What does ‘desirable’ or ‘sustainable’ mean in the context of labour migration? And what should programmes geared towards making migration more compatible with development look like? These questions provided the starting point for the ‘Sustainable Labour Migration’ research project implemented by Hochschule Bonn-Rhein-Sieg University of Applied Sciences between December 2020 and August 2022. The project looked at how sustainability in different transnational skills partnership schemes was perceived by different stakeholders in three countries chosen as case studies: Georgia, Kosovo and Vietnam. Embracing the notion of a ‘triple win’, many transnational skills partnership schemes aim to deliver benefits for their main stakeholder groups. As well as reflecting critically on this triple-win narrative, this paper also argues for a more nuanced approach in order to grasp the complexity of skilled labour migration. The paper introduces one such approach, namely the sustainable labour migration framework, and highlights the key elements of the research project. It details the methodology used in the study (systematic literature review – employer survey – semi-structured, in-depth interviews – focus group interviews) and explores the perception of sustainability in skilled labour migration with a focus on the cost/benefit ratio, the relationship between vocational education and training and labour migration, and the various arrangements for the partnerships employed in the schemes. In introducing the key findings from the three countries selected for the case studies, the paper highlights that the perception of sustainability is not underpinned by a comprehensive understanding of the term amongst most stakeholders. Within all the schemes, however, some stakeholders identified elements which make a positive contribution to development in the respective countries of origin and thus identify elements of sustainable labour migration with room for improvement in multiple areas. The paper concludes with a presentation of overall policy recommendations: The ongoing reform of Germany’s labour migration policy should be accompanied by more development-oriented activities. As part of this, the German Federal Ministry for Economic Cooperation and Development (BMZ) should stand up more forcefully for the needs of migrants and potential partner countries in order to reduce existing inequalities
In intensively used agricultural landscapes, path margins are one of the few refuges and nurseries for wildlife. They provide e. g. food sources and overwintering opportunities for many insects, serve as migration corridors for animals, protect soil from erosion, increase its water-holding capacity, and increase soil organic carbon, contributing thus directly to biodiversity conservation and climate change mitigation. Path margins are often municipally owned but used and managed by agriculture. For a path margin to be functional, certain conditions must be fulfilled, such as the width, the botanical composition, and how it is managed through the seasons. Therefore, it must be managed under specific requirements. A multifunctional path margin can be achieved only through the commitment of all stakeholders (e.g., farmers, municipalities, conservationists, and civil society).
The art of nudging
(2023)
Do simple and subtle changes in the living and study environment improve the eating behaviour of students in an educational setting? This dissertation provides a not-so-simple answer to this simple question based on the outcomes of four studies that explore the effects and design of artwork nudges (specifically the artwork of Alberto Giacometti) on the eating behaviour of students by applying different research designs. Study 1 explores the effects of a Giacometti-like nudge (a more contemporary version of the original nudge) regarding the dietary behaviour of high school students in a controlled setting. Study 2 applies different artwork nudges within a virtual vignette setting to measure their effects on virtual meal choices made. Also, the degree to which individuals were aware of the nudge’s presence is included as an influential factor in nudge effectiveness. Study 3 assesses the susceptibility to nudges as measured with a questionnaire. Susceptibility to nudges is defined as nudgeability. Study 4 assesses the effects of the original Giacometti nudge in a real-world university cafeteria setting. Specifically, the immediate and sustained effects of the original Giacometti nudge on students’ meal purchases in the university cafeteria are considered. In addition, the role of awareness of the nudge’s presence as well as the acceptance of this specific nudge are discussed. The conclusion is drawn that the original Giacometti nudge should only be applied in an educational setting to improve healthy eating behaviour if the intended target groups and environment meet certain conditions. Artwork nudges in general should be applied only after rigorous testing of various types of different nudges and more research reflecting healthy eating in its entirety.
Accurate forecasting of solar irradiance is crucial for the integration of solar energy into the power grid, power system planning, and the operation of solar power plants. The Weather Research and Forecasting (WRF) model, with its solar radiation (WRF-Solar) extension, has been used to forecast solar irradiance in various regions worldwide. However, the application of the WRF-Solar model for global horizontal irradiance (GHI) forecasting in West Africa, specifically in Ghana, has not been studied. This study aims to evaluate the performance of the WRF-Solar model for GHI forecasting in Ghana, focusing on 3 health centers (Kologo, Kumasi and Akwatia) for the year 2021. We applied a two one-way nested domain (D1=15 km and D2=3 km) to investigate the ability of the WRF solar model to forecast GHI up to 72 hours in advance under different atmospheric conditions. The initial and lateral boundary conditions were taken from the ECMWF operational forecasts. In addition, the optical aerosol depth (AOD) data at 550 nm from the Copernicus Atmosphere Monitoring Service (CAMS) were considered. The study uses statistical metrics such as mean bias error (MBE), root mean square error (RMSE), to evaluate the performance of the WRF-Solar model with the observational data obtained from automatic weather stations in the three health centers in Ghana. The results of this study will contribute to the understanding of the capabilities and limitations of the WRF-Solar model for forecasting GHI in West Africa, particularly in Ghana, and provide valuable information for stakeholders involved in solar energy generation and grid integration towards optimized management of in the region.
Neuromorphic computing aims to mimic the computational principles of the brain in silico and has motivated research into event-based vision and spiking neural networks (SNNs). Event cameras (ECs) capture local, independent changes in brightness, and offer superior power consumption, response latencies, and dynamic ranges compared to frame-based cameras. SNNs replicate neuronal dynamics observed in biological neurons and propagate information in sparse sequences of ”spikes”. Apart from biological fidelity, SNNs have demonstrated potential as an alternative to conventional artificial neural networks (ANNs), such as in reducing energy expenditure and inference time in visual classification. Although potentially beneficial for robotics, the novel event-driven and spike-based paradigms remain scarcely explored outside the domain of aerial robots.
To investigate the utility of brain-inspired sensing and data processing in a robotics application, we developed a neuromorphic approach to real-time, online obstacle avoidance on a manipulator with an onboard camera. Our approach adapts high-level trajectory plans with reactive maneuvers by processing emulated event data in a convolutional SNN, decoding neural activations into avoidance motions, and adjusting plans in a dynamic motion primitive formulation. We conducted simulated and real experiments with a Kinova Gen3 arm performing simple reaching tasks involving static and dynamic obstacles. Our implementation was systematically tuned, validated, and tested in sets of distinct task scenarios, and compared to a non-adaptive baseline through formalized quantitative metrics and qualitative criteria.
The neuromorphic implementation facilitated reliable avoidance of imminent collisions in most scenarios, with 84% and 92% median success rates in simulated and real experiments, where the baseline consistently failed. Adapted trajectories were qualitatively similar to baseline trajectories, indicating low impacts on safety, predictability and smoothness criteria. Among notable properties of the SNN were the correlation of processing time with the magnitude of perceived motions (captured in events) and robustness to different event emulation methods. Preliminary tests with a DAVIS346 EC showed similar performance, validating our experimental event emulation method. These results motivate future efforts to incorporate SNN learning, utilize neuromorphic processors, and target other robot tasks to further explore this approach.
This study addresses the underrepresentation of women and the so-far neglected process perspective in empirical entrepreneurial research. It aims to identify the personality traits that differentiate successful female entrepreneurs from their less successful peers and to determine which traits are crucial for pre-launch, launch, and post-launch success. Independent t-tests on 305 female entrepreneurs (and 476 male entrepreneurs) from the DACH region highlight the role of self-efficacy, proactivity, locus of control, and need for achievement for female entrepreneurs. Multiple regression analyses further reveal the importance of self-efficacy for every phase of women’s entrepreneurial journey. While the need for autonomy was critical during pre-launch and launch, locus of control significantly predicted female entrepreneurial success in the pre-launch and post-launch phases. Contrary to previous research, risk-taking was not a crucial trait for female entrepreneurs when compared to their male counterparts, while both showed similar levels of need for autonomy, proactivity, need for achievement, perseverance, self-control, and locus of control. The study offers valuable insights into successful entrepreneurship and highlights the need for female- and phase-specific support programs to enhance self-efficacy among female entrepreneurs.
The decline of insect abundance and richness has been documented for decades and has received increased attention in recent years. In 2017, a study by Hallmann and colleagues on insect biomasses in German nature protected areas received a great deal of attention and provided the impetus for the creation of the project Diversity of Insects in Nature protected Areas (DINA). The aim of DINA was to investigate possible causes for the decline of insects in nature protected areas throughout Germany and to develop strategies for managing the problem.
A major issue for the protection of insects is the lack of insect-specific regulations for nature protected areas and the lack of a risk assessment and verification of the measures applied. Most nature protected areas border on or enclose agricultural land and are structured in a mosaic, resulting in an abundance of small and narrow areas. This leads to fragmentation or even loss of endangered habitats and thus threaten biodiversity. In addition, the impact of agricultural practices, especially pesticides and fertilisers, leads to the degradation of biodiversity at the boundaries of nature protected areas, reducing their effective size. All affected stakeholders need to be involved in solving these threats by working on joint solutions. Furthermore, agriculture in and around nature protected areas must act to promote biodiversity and utilise and develop methods that reverse the current trend. This also requires subsidies from the state to ensure economic sustainability and promote biodiversity-promoting practices.
Background: Bloodstream infections (BSIs) remain a significant cause of mortality worldwide. Causative pathogens are routinely identified and susceptibility tested but only very rarely investigated for their resistance genes, virulence factors, and clonality. Our aim was to gain insight into the clonality patterns of different species causing BSI and the clinical relevance of distinct virulence genes.
Methods: For this study, we whole-genome-sequenced over 400 randomly selected important pathogens isolated from blood cultures in our diagnostic department between 2016 and 2021. Genomic data on virulence factors, resistance genes, and clonality were cross-linked with in-vitro data and demographic and clinical information.
Results: The investigation yielded extensive and informative data on the distribution of genes implicated in BSI as well as on the clonality of isolates across various species.
Conclusion: Associations between survival outcomes and the presence of specific genes must be interpreted with caution, and conducting replication studies with larger sample sizes for each species appears mandatory. Likewise, a deeper knowledge of virulence and host factors will aid in the interpretation of results and might lead to more targeted therapeutic and preventive measures. Monitoring transmission dynamics more efficiently holds promise to serve as a valuable tool in preventing in particular BSI caused by nosocomial pathogens.
Introduction: As historically verified, countries with comprehensive, well designed social protection systems in place are better prepared to cope with large scale catastrophes of all kinds, always in such situation there is still a need for government interventions other than social protection and larger scale discretionary social protection or related interventions. Objective: The article presents the actions of countries to minimize the negative social effects of the COVID-19 coronavirus pandemic. The text is an attempt to answer how social security systems should be adapted to aforementioned crisis? Materials and methods: The text uses research methods such as: literature criticism and statistical analysis of data and revision of implemented state intervention policies based on reports of Organisation for Economic Cooperation and Development, International Labour Organizaton, European Foundation for the Improvement of Living and Working Conditions and International Monetary Fund. Results: 1) For social security institutions of key importance to ensure continuity of operations of all services – of contributory social insurance as well of social assistance - was to ensure continuous payment of all benefits due and quick response to the new entitlement emerging. It is also necessary to ensure that all citizens are fully insured, regardless of the form of contract under which they perform work. 2) In many countries, special emergency measures that extended coverage and increased benefits were taken by governments without clearly identifying the sources of funding and very often burdening social security funds with non-statutory expenses and affecting their long-term financial sustainability. 3) In the longer run, there is a need to ensure universal health care coverage of the adequate quality, there is a need to develop policies which will secure at least minimum income security to all – independently of their labour market status, forms of employment, sex, ethnicity or nationality.
Striated muscle contraction is regulated by the translocation of troponin-tropomyosin strands over the thin filament surface. Relaxation relies partly on highly-favorable, conformation-dependent electrostatic contacts between actin and tropomyosin, which position tropomyosin such that it impedes actomyosin associations. Impaired relaxation and hypercontractile properties are hallmarks of various muscle disorders. The α-cardiac actin M305L hypertrophic cardiomyopathy-causing mutation lies near residues that help confine tropomyosin to an inhibitory position along thin filaments. Here, we investigate M305L actin in vivo, in vitro, and in silico to resolve emergent pathological properties and disease mechanisms. Our data suggest the mutation reduces actin flexibility and distorts the actin-tropomyosin electrostatic energy landscape that, in muscle, result in aberrant contractile inhibition and excessive force. Thus, actin flexibility may be required to establish and maintain interfacial contacts with tropomyosin as well as facilitate its movement over distinct actin surface features and is, therefore, likely necessary for proper regulation of contraction.
Atomic oxygen is a key species in the mesosphere and thermosphere of Venus. It peaks in the transition region between the two dominant atmospheric circulation patterns, the retrograde super-rotating zonal flow below 70 km and the subsolar to antisolar flow above 120 km altitude. However, past and current detection methods are indirect and based on measurements of other molecules in combination with photochemical models. Here, we show direct detection of atomic oxygen on the dayside as well as on the nightside of Venus by measuring its ground-state transition at 4.74 THz (63.2 µm). The atomic oxygen is concentrated at altitudes around 100 km with a maximum column density on the dayside where it is generated by photolysis of carbon dioxide and carbon monoxide. This method enables detailed investigations of the Venusian atmosphere in the region between the two atmospheric circulation patterns in support of future space missions to Venus.
A Fourier scatterometry setup is evaluated to recover the key parameters of optical phase gratings. Based on these parameters, systematic errors in the printing process of two-photon polymerization (TPP) gray-scale lithography three-dimensional printers can be compensated, namely tilt and curvature deviations. The proposed setup is significantly cheaper than a confocal microscope, which is usually used to determine calibration parameters for compensation of the TPP printing process. The grating parameters recovered this way are compared to those obtained with a confocal microscope. A clear correlation between confocal and scatterometric measurements is first shown for structures containing either tilt or curvature. The correlation is also shown for structures containing a mixture of tilt and curvature errors (squared Pearson coefficient r2 = 0.92). This compensation method is demonstrated on a TPP printer: a diffractive optical element printed with correction parameters obtained from Fourier scatterometry shows a significant reduction in noise as compared to the uncompensated system. This verifies the successful reduction of tilt and curvature errors. Further improvements of the method are proposed, which may enable the measurements to become more precise than confocal measurements in the future, since scatterometry is not affected by the diffraction limit.
In memoriam Willy Lehnert
(2023)
A Fourier scatterometry setup is evaluated to recover the key parameters of optical phase gratings. Based on these parameters, systematic errors in the printing process of two photon polymerization (TPP) gray-scale lithography 3d printers can be compensated, namely tilt and curvature deviations. The proposed setup is significantly cheaper than a confocal microscope, which is usually used to determine calibrations parameters for compensation of the TPP printing process. The grating parameters recovered this way are compared to those obtained with a confocal microscope. A clear correlation between confocal and scatterometric measurements is first shown for structures containing either tilt or curvature. The correlation is also shown for structures containing a mixture of tilt and curvature errors (squared Pearson coefficient $r^2$ = 0.92). This new compensation method is demonstrated on a TPP printer: A diffractive optical element (DOE) printed with correction parameters obtained from Fourier scatterometry shows a significant reduction in noise as compared to the uncompensated system. This verifies the successful reduction of tilt and curvature errors. Further improvements of the method are proposed, which may enable the measurements to become more precise than confocal measurements in the future, since scatterometry is not affected by the diffraction limit.
Solar photovoltaic power output is modulated by atmospheric aerosols and clouds and thus contains valuable information on the optical properties of the atmosphere. As a ground-based data source with high spatiotemporal resolution it has great potential to complement other ground-based solar irradiance measurements as well as those of weather models and satellites, thus leading to an improved characterisation of global horizontal irradiance. In this work several algorithms are presented that can retrieve global tilted and horizontal irradiance and atmospheric optical properties from solar photovoltaic data and/or pyranometer measurements. The method is tested on data from two measurement campaigns that took place in the Allgäu region in Germany in autumn 2018 and summer 2019, and the results are compared with local pyranometer measurements as well as satellite and weather model data. Using power data measured at 1 Hz and averaged to 1 min resolution along with a non-linear photovoltaic module temperature model, global horizontal irradiance is extracted with a mean bias error compared to concurrent pyranometer measurements of 5.79 W m−2 (7.35 W m−2) under clear (cloudy) skies, averaged over the two campaigns, whereas for the retrieval using coarser 15 min power data with a linear temperature model the mean bias error is 5.88 and 41.87 W m−2 under clear and cloudy skies, respectively.
During completely overcast periods the cloud optical depth is extracted from photovoltaic power using a lookup table method based on a 1D radiative transfer simulation, and the results are compared to both satellite retrievals and data from the Consortium for Small-scale Modelling (COSMO) weather model. Potential applications of this approach for extracting cloud optical properties are discussed, as well as certain limitations, such as the representation of 3D radiative effects that occur under broken-cloud conditions. In principle this method could provide an unprecedented amount of ground-based data on both irradiance and optical properties of the atmosphere, as long as the required photovoltaic power data are available and properly pre-screened to remove unwanted artefacts in the signal. Possible solutions to this problem are discussed in the context of future work.
Electrical signal transmission in power electronic devices takes place through high-purity aluminum bonding wires. Cyclic mechanical and thermal stresses during operation lead to fatigue loads, resulting in premature failure of the wires, which cannot be reliably predicted. The following work presents two fatigue lifetime models calibrated and validated based on experimental fatigue results of an aluminum bonding wire and subsequently transferred and applied to other wire types. The lifetime modeling of Wöhler curves for different load ratios shows good but limited applicability for the linear model. The model can only be applied above 10,000 cycles and within the investigated load range of R = 0.1 to R = 0.7. The nonlinear model shows very good agreement between model prediction and experimental results over the entire investigated cycle range. Furthermore, the predicted Smith diagram is not only consistent in the investigated load range but also in the extrapolated load range from R = −1.0 to R = 0.8. A transfer of both model approaches to other wire types by using their tensile strengths can be implemented as well, although the nonlinear model is more suitable since it covers the entire load and cycle range.
Battery lifespan estimation is essential for effective battery management systems, aiding users and manufacturers in strategic planning. However, accurately estimating battery capacity is complex, owing to diverse capacity fading phenomena tied to factors such as temperature, charge-discharge rate, and rest period duration. In this work, we present an innovative approach that integrates real-world driving behaviors into cyclic testing. Unlike conventional methods that lack rest periods and involve fixed charge-discharge rates, our approach involves 1000 unique test cycles tailored to specific objectives and applications, capturing the nuanced effects of temperature, charge-discharge rate, and rest duration on capacity fading. This yields comprehensive insights into cell-level battery degradation, unveiling growth patterns of the solid electrolyte interface (SEI) layer and lithium plating, influenced by cyclic test parameters. The results yield critical empirical relations for evaluating capacity fading under specific testing conditions.
The UN Declaration on the Right to Development (UNDRTD) adopted in 1986 and the 2030 Agenda for Sustainable Development adopted in 2015 share a universal concept of development that refers both to individual and collective dimensions of prosperity and thus includes the rights of future generations.2 They thus offer a definition of the relationship between development and human rights that is very relevant for the 21st century. The core norm of the UNDRTD has been defined later as “the right of peoples and individuals to the constant improvement of their wellbeing and to a national and global enabling environment conducive to just, equitable, participatory and human-centred development respectful of all human rights”3.
Microorganisms not only contribute to the spoilage of food but can also cause illnesses through consumption. Consumer concerns and doubts about the shelf life of the products and the resulting enormous amounts of food waste have led to a demand for a rapid, robust, and non-destructive method for the detection of microorganisms, especially in the food sector. Therefore, a rapid and simple sampling method for the Raman- and infrared (IR)-microspectroscopic study of microorganisms associated with spoilage processes was developed. For subsequent evaluation pre-processing routines, as well as chemometric models for classification of spoilage microorganisms were developed. The microbiological samples are taken using a disinfectable sampling stamp and measured by microspectroscopy without the usual pre-treatments such as purification separation, washing, and centrifugation. The resulting complex multivariate data sets were pre-processed, reduced by principal component analysis, and classified by discriminant analysis. Classification of independent unlabeled test data showed that microorganisms could be classified at genus, species, and strain levels with an accuracy of 96.5 % (Raman) and 94.5 % (IR), respectively, despite large biological differences and novel sampling strategies. As bacteria are exposed to constantly changing conditions and their adaptation mechanisms may make them inaccessible to conventional measurement methods, the methods and models developed were investigated for their suitability for microorganisms exposed to stress. Compared to normal growth conditions, spectral changes in lipids, polysaccharides, nucleic acids, and proteins were observed in microorganisms exposed to stress. Models were developed to discriminate microorganisms, independent of the involvement of various stress factors and storage times. Classification of the investigated bacteria yielded accuracies of 97.6 % (Raman) and 96.6 % (IR), respectively, and a robust and meaningful model was developed to discriminate different microorganisms at the genus, species, and strain levels. The obtained results are very promising and show that the methods and models developed for the discrimination of microorganisms as well as the investigation of stress factors on microorganisms by means of Raman- and IR-microspectroscopy have the potential to be used, for example, in the food sector for the rapid determination of surface contamination.
Vehicle emissions have been identified as a cause of air pollution and one of the major reasons why air quality in many large German cities such as Berlin, Bonn, Hamburg, Cologne or Munich does not meet EU-wide limits. As a result, in the recent past, judicial driving bans on diesel vehicles have been imposed in many places since those vehicles emit critical pollutant groups. For the increasing urban population, the challenge is whether and how a change of the modal split in favor of the more environmentally and climate-friendly public transport can be achieved.
This paper presents the case of the Federal City of Bonn, one of five model cities sponsored by the German federal government that are testing measures to reduce traffic-related pollutant emissions by expanding the range of public transport services on offer. We present the results of a quantitative survey (N = 14,296) performed in the Bonn/Rhein-Sieg area and the neighboring municipalities as well as the ensuing logistic regressions confirming that a change in individual mobility behavior in favor of public transport is possible through expanding services. Our results show that individual traffic could be reduced, especially on the city's main traffic axes. To sustainably improve air quality, such services must be made permanently available.