Refine
H-BRS Bibliography
- yes (1604) (remove)
Departments, institutes and facilities
- Fachbereich Angewandte Naturwissenschaften (531)
- Fachbereich Wirtschaftswissenschaften (411)
- Fachbereich Informatik (265)
- Fachbereich Ingenieurwissenschaften und Kommunikation (200)
- Institut für funktionale Gen-Analytik (IFGA) (183)
- Fachbereich Sozialpolitik und Soziale Sicherung (177)
- Institut für Technik, Ressourcenschonung und Energieeffizienz (TREE) (176)
- Institute of Visual Computing (IVC) (65)
- Internationales Zentrum für Nachhaltige Entwicklung (IZNE) (63)
- Institut für Sicherheitsforschung (ISF) (43)
- Institut für Verbraucherinformatik (IVI) (39)
- Institut für Cyber Security & Privacy (ICSP) (26)
- Centrum für Entrepreneurship, Innovation und Mittelstand (CENTIM) (11)
- Institut für Detektionstechnologien (IDT) (11)
- Zentrum für Ethik und Verantwortung (ZEV) (7)
- Verwaltung (6)
- Institut für Medienentwicklung und -analyse (IMEA) (3)
- Sprachenzentrum (3)
- Bibliothek (2)
- Institut für KI und Autonome Systeme (A2S) (2)
- Präsidium (2)
- Zentrum für Innovation und Entwicklung in der Lehre (ZIEL) (2)
- Gleichstellungsstelle (1)
- Institut für Soziale Innovationen (ISI) (1)
- Zentrum für Campus-IT (1)
- Zentrum für Wissenschafts- und Technologietransfer (ZWT) (1)
Document Type
- Article (1604) (remove)
Year of publication
Keywords
- cytokine-induced killer cells (9)
- GC/MS (8)
- immunotherapy (8)
- Deutschland (6)
- ISM: molecules (6)
- Qualitätsmanagement (6)
- virtual reality (6)
- Africa (5)
- Corporate Social Responsibility (5)
- Gene expression (5)
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.
The human gut microbiota harbors untapped potential for biotechnological applications. Within the phylum of Bacteroidota, Phocaeicola vulgatus stands out as a promising candidate for sustainable production of key platform chemicals like succinate. However, genetic engineering of Phocaeicola sp. remains challenging due to its intricate molecular landscape. This study lays the groundwork for manipulating the central carbon pathways in Phocaeicola vulgatus, offering insights into overcoming genetic hurdles for increased succinate yields.
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.
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.
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.
Interne Audits können mehr
(2024)
Dieser Beitrag zeigt, wie das Deutsche Zentrum für Luft- und Raumfahrt e. V. (DLR) Zufriedenheitsanalysen aus zwei Sichtweisen durchführt: Aus Sicht der Auditoren und aus Sicht der Managementbeauftragten der auditierten Institute und Einrichtungen. Die Ergebnisse fließen in die jährliche Auditprogrammplanung ein. Damit wird der Nutzen von internen Audits gesteigert.
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.
Der Impuls von Remi Maier-Rigaud behandelt die Einführung der europäischen Gesundheitsunion als Reaktion auf die begrenzten Handlungsmöglichkeiten der Europäischen Union (EU) im Bereich der Gesundheitspolitik während der Coronapandemie. Die Gesundheitsunion wurde geschaffen, um die koordinierte Reaktion auf grenzüberschreitende Gesundheitsgefahren zu verbessern. Maier-Riguad beleuchtet im Beitrag die Vorteile einer europäischen Gesundheitspolitik, betont jedoch, dass die Frage der ausreichenden Vorbereitung auf zukünftige Gesundheitskrisen offen bleibt.
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.
Polyamorie und Ethik
(2023)
„Polyamorie“ ist die Bezeichnung für das explizite und konsensuelle Eingehen mehrerer Liebesbeziehungen zur selben Zeit. Mit polyamoren Konstellationen korreliert, dafür wird in diesem Beitrag argumentiert, eine Ethik der Beziehungsgestaltung und sie schließen darüber hinaus für gewöhnlich das Teilen äquivalenter gesellschaftspolitischer Prämissen ein, auf die im Folgenden an einigen Stellen hingewiesen wird. Eine Ethik der Polyamorie umfasst zwei Eigenschaften: Polyethiken brechen zum einen mit dem traditionellen Verständnis vom Individuum oder Handlungssubjekt. Ich werde zeigen, dass polyamoren Beziehungsnetzwerken und der Ethik, die in ihnen idealiter gelebt wird, ein relationales Denken zugrunde liegt. Zum anderen zeichnen sich Polyethiken durch ein besonderes Augenmerk auf die Beziehungen, die als poly bezeichnet werden können, aus, in denen typischerweise eine Reihe von Werten realisiert werden. Zuvor gilt es allerdings, das Verständnis von Liebe zu definieren, das nach meinem Verständnis einem Polycule bzw. polyamoren Beziehungsnetzwerken im Allgemeinen zugrunde liegt.