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69th Annual Meeting of the German Association of Medical Informatics, Biometry and Epidemiology, GMDS 2024

作者： Moazemi, Sobhan Adams, Tim Ng, Hwei Geok Kühnel, Lisa Schneider, Julian Näher, Anatol-Fiete Fluck, Juliane Fröhlich, Holger Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing SCAI Sankt Augustin Germany Knowledge Management ZB MED - Information Centre for Life Sciences Cologne Germany Faculty of Technology Bielefeld University Bielefeld Germany Digital Global Public Health Hasso Plattner Institute for Digital Engineering University of Potsdam Potsdam Germany Institute of Medical Informatics Charité-Universitätsmedizin Germany Method Development Research Infrastructure and Information Technology Robert Koch Institute Berlin Germany The Agricultural Faculty University of Bonn Bonn Germany Institute for Geodesy and Geoinformation University of Bonn Bonn Germany University of Bonn Bonn Germany

ISBN: (纸本)9781643685366

Individual health data is crucial for scientific advancements, particularly in developing Artificial Intelligence (AI);however, sharing real patient information is often restricted due to privacy concerns. A promising solution to this challenge is synthetic data generation. This technique creates entirely new datasets that mimic the statistical properties of real data, while preserving confidential patient information. In this paper, we present the workflow and different services developed in the context of Germany's National Data Infrastructure project NFDI4Health. First, two state-of-the-art AI tools (namely, VAMBN and MultiNODEs) for generating synthetic health data are outlined. Further, we introduce SYNDAT (a public web-based tool) which allows users to visualize and assess the quality and risk of synthetic data provided by desired generative models. Additionally, the utility of the proposed methods and the web-based tool is showcased using data from Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Center for Cancer Registry Data of the Robert Koch institute (RKI). © 2024 The Authors.

关键词： Electronic health record

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Application of a Regional Data Set of the Housing Sector for Hydrogen Storage-Supported Energy System Planning

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Energy Engineering 2025年第5期122卷 1755-1770页

作者： Steffen Schedler Michael Bareev-Rudy Stefanie Meilinger Tanja Clees Department of Engineering and Communication(IWK) Institute for TechnologyResource and Energy-efficient Engineering(TREE)Hochschule Bonn-Rhein-SiegUniversity of Applied ScienceSankt Augustin53757Germany International Centre for Sustainable Development(IZNE) Hochschule Bonn-Rhein-SiegUniversity of Applied ScienceSankt Augustin53757Germany Fraunhofer Institute for Algorithms and Scientific Computing SCAI Schloss BirlinghovenSankt Augustin53754Germany

Germany aims to achieve a national climate-neutral energy system by *** residential sector still accounts for 29%of end energy consumption,with 74%attributed to the direct use of fossil fuels for heating and hot *** order to reduce fossil energy use in the household sector,great efforts are being made to design new energy concepts that expand the use of renewable energies to supply electricity *** is toconvertparts of the natural gas grid to a hydrogen-based gas grid to deliver and store energy for urban quarters of buildings,especially with older building stock where electrification of heat via heat pumps is difficult due to technical,acoustical,and economic reasons.A comprehensive dataset was generated by a bottom-up analysis with open governmental and statistical data to determine regional building types regarding energy demand,solar potential,and existing grid *** buildings’connections to the electricity,gas,and district heating networks are *** this,a representative sample dataset was chosen as input for a newly developed energy system model based on energy flow *** model simulates the interaction of hydrogen generation(HG)(from excess solar energy by electrolysis),storage in a metal-hydride storage(MHS)tank,and hydrogen use in a connected fuel cell(FC),forming a local PVPtGtHP(Photovoltaic Power-to-Gas-to-Heat-and-Power)*** to the seasonal hydrogen storage path(HSP),a battery will complete the system to forma hybrid energy storage system(HESS).Paired with seasonal time series for PV power,electricity and heat demand,and a model for connection to grid infrastructure,the simulation of different hydrogen applications and MHS placements aims to analyze operating times and energy share of the systems’equipment and existing *** method to obtain the data set together with the simulationmodel presented can be used by energy planners for cities,communities,and building developers

关键词： Hydrogen storage hybrid energy storage system simulation housing sector energy share

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PEMT: a patent enrichment tool for drug discovery (vol 39, btac716, 2023)

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BIOINFORMATICS 2023年第12期39卷 btac716页

作者： Gadiya, Yojana Zaliani, Andrea Gribbon, Philip Hofmann-Apitius, Martin Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP) Hamburg 22525 Germany. Fraunhofer Cluster of Excellence for Immune-Mediated Diseases (CIMD) Frankfurt 60590 Germany. Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) Sankt Augustin 53754 Germany. Bonn-Aachen International Center for Information Technology (B-IT) University of Bonn Bonn 53113 Germany.

MOTIVATION:Drug discovery practitioners in industry and academia use semantic tools to extract information from online scientific literature to generate new insights into targets, therapeutics and diseases. However, due to complexities in access and analysis, patent-based literature is often overlooked as a source of information. As drug discovery is a highly competitive field, naturally, tools that tap into patent literature can provide any actor in the field an advantage in terms of better informed decision-making. Hence, we aim to facilitate access to patent literature through the creation of an automatic tool for extracting information from patents described in existing public resources. RESULTS:Here, we present PEMT, a novel patent enrichment tool, that takes advantage of public databases like ChEMBL and SureChEMBL to extract relevant patent information linked to chemical structures and/or gene names described through FAIR principles and metadata annotations. PEMT aims at supporting drug discovery and research by establishing a patent landscape around genes of interest. The pharmaceutical focus of the tool is mainly due to the subselection of International Patent Classification codes, but in principle, it can be used for other patent fields, provided that a link between a concept and chemical structure is investigated. Finally, we demonstrate a use-case in rare diseases by generating a gene-patent list based on the epidemiological prevalence of these diseases and exploring their underlying patent landscapes. AVAILABILITY AND IMPLEMENTATION:PEMT is an open-source Python tool and its source code and PyPi package are available at https://***/fraunhofer-ITMP/PEMT and https://***/project/PEMT/, respectively. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

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A Dimension-adaptive Combination Technique for Uncertainty Quantification

arXiv

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arXiv 2022年

作者： Seidler, Uta Griebel, Michael Institute for Numerical Simulation University of Bonn Friedrich-Hirzebruch-Allee 7 Bonn53115 Germany Fraunhofer Institute for Algorithms and Scientific Computing SCAI Schloss Birlinghoven Sankt Augustin53754 Germany

We present an adaptive algorithm for the computation of quantities of interest involving the solution of a stochastic elliptic PDE where the diffusion coefficient is parametrized by means of a Karhunen-Loève expansion. The approximation of the equivalent parametric problem requires a restriction of the countably infinite-dimensional parameter space to a finite-dimensional parameter set, a spatial discretization and an approximation in the parametric variables. We consider a sparse grid approach between these approximation directions in order to reduce the computational effort and propose a dimension-adaptive combination technique. In addition, a sparse grid quadrature for the high-dimensional parametric approximation is employed and simultaneously balanced with the spatial and stochastic approximation. Our adaptive algorithm constructs a sparse grid approximation based on the benefit-cost ratio such that the regularity and thus the decay of the Karhunen-Loève coefficients is not required beforehand. The decay is detected and exploited as the algorithm adjusts to the anisotropy in the parametric variables. We include numerical examples for the Darcy problem with a lognormal permeability field, which illustrate a good performance of the algorithm: For sufficiently smooth random fields, we essentially recover the spatial order of convergence as asymptotic convergence rate with respect to the computational *** Codes 65N35, 65C20, 65D30, 65N30, 60H35 © 2022, CC BY.

关键词： Diffusion

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algorithms in the Orchard: An Embedding-Based Expert Answering System for Apple Rust

SSRN

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SSRN 2025年

作者： Anand, Astha Shen, Jian Cremers, Armin Bernd Jacobs, Marc Fraunhofer Institute for Algorithms and Scientific Computing SCAI Schloss Birlinghoven 1 North Rhine-Westphalia Sankt Augustin53757 Germany RPU Rhenish Plant Power Envirotech Co. Ltd. Bonn-Aachen International Center for Information Technology Friedrich-Hirzebruch-Allee 5 North Rhine-Westphalia Bonn53115 Germany

As sustainable agricultural practices gain importance, the need for intelligent pest control decision-making has grown. This paper introduces SEEDS: Similarity-based Expert Embedding Decision System, a Retrieval-Augmented Generation (RAG) based agricultural question-answering (QA) system. It is built upon a domain-specific knowledge graph (KG), representing Cedar Apple Rust disease, its host and causative agents, plant defense molecules against apple rust infection, and various pesticides. Utilizing the OpenAI embedding model, the system generates embeddings for user queries and KG data, employing similarity metrics to rank KG entries, facilitating accurate and relevant pest control recommendations. SEEDS is a promising niche AI tool in plant protection, setting the stage for scalable, extensible QA frameworks in precision agriculture. The results signify not only a step forward in agricultural expert systems but also highlight the potential for expanding this approach to other crops and pests, marking a substantial advancement in the use of AI for agricultural pest control. © 2025, The Authors. All rights reserved.

关键词： Fruits

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Proposal of an achitecture for terminology management in a research project 30

Proposal of an achitecture for terminology management in a r...

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30th Medical Informatics Europe Conference, MIE 2020

作者： Mora, Sara Madan, Sumit Gebel, Stephan Giacomini, Mauro University of Genoa Italy Fraunhofer Institute for Algorithms and Scientific Computing SCAI Department of Bioinformatics Schloss Birlinghoven Sankt Augustin Germany

ISBN: (纸本)9781643680828

Clinical and medical knowledge evolve and this causes changes in concepts and terms that describe them. The objective of this work is to formally present an ontology-based standard architecture that will be used in the scenario of neurodegeneration research to maintain terminologies and their relations updated and coherent over the time. The proposed structure is composed by three elements that will allow the user to do a list of operations on the terminology resources explicitly contemplated by the Common Terminology Service Release 2 (CTS2). © 2020 European Federation for Medical Informatics (EFMI) and IOS Press.

关键词： Ontology

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A Partition of Unity Construction of the Stabilization Function in Nitsche's Method for Variational Problems

SSRN

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SSRN 2024年

作者： Jiménez Recio, Pablo Schweitzer, Marc Alexander Institut für Numerische Simulation Universität Bonn Friedrich-Hirzebruch-Allee 7 Bonn53115 Germany Fraunhofer Institute for Algorithms and Scientific Computing Schloss Birlinghoven Sankt Augustin53757 Germany

In this paper we develop a partition-of-unity construction of the stabilization function needed in Nitsche’s method, which can be seen as a generalization of the element-wise construction that is widely used in finite element methods. This allows for the use of Nitsche’s method within the Partition of Unity Method with a stabilization function that is not simply a constant over the whole boundary. In addition to that, we introduce a patch-aggregation approach designed to avoid arbitrarily large values of the stabilization function and the associated ill-conditioned systems and deteriorated convergence rates. We present numerical results to validate the proposed methods, covering Dirichlet boundary conditions, interface constraints and higher- order problems. These results clearly show that our approach leads to optimal convergence rates. © 2024, The Authors. All rights reserved.

关键词： Boundary conditions

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Efficient solution of ill-posed integral equations through averaging

arXiv

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arXiv 2024年

作者： Griebel, Michael Jahn, Tim Institut für Numerische Simulation Universität Bonn Friedrich-Hirzebruch-Allee 7 Bonn53115 Germany Fraunhofer Institute for Algorithms and Scientific Computing SCAI Schloss Birlinghoven Sankt Augustin53754 Germany Institut für Mathematik TU Berlin Straße des 17. Juni 135 Berlin10623 Germany

This paper discusses the error and cost aspects of ill-posed integral equations when given discrete noisy point evaluations on a fine grid. Standard solution methods usually employ discretization schemes that are directly induced by the measurement points. Thus, they may scale unfavorably with the number of evaluation points, which can result in computational inefficiency. To address this issue, we propose an algorithm that achieves the same level of accuracy while significantly reducing computational costs. Our approach involves an initial averaging procedure to sparsify the underlying grid. To keep the exposition simple, we focus on regularization via the truncated singular value decomposition of one-dimensional ill-posed integral equations that have sufficient smoothness. However, the approach can be generalized to other popular regularization methods and more complicated two- and three-dimensional problems with appropriate *** Codes 65, 65R32, 65R20 © 2024, CC BY.

关键词： Integral equations

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Sizing optimization of hybrid hydrogen energy storage systems: A metamodel-based approach

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International Journal of Hydrogen Energy 2025年

作者： Bareev-Rudy, Michael Schedler, Steffen Clees, Tanja Grantham-Allee 20 Sankt Augustin53757 Germany Fraunhofer Institute for Algorithms and Scientific Computing SCAI Schloss Birlinghoven Sankt Augustin53757 Germany

Hybrid energy storage systems (HESS), consisting of a battery, hydrogen storage, electrolyzer and fuel cell, have received increasing attention from the scientific community in recent years as they can help increase the use of renewable energy. In this paper, a novel metamodel-based sizing optimization workflow is developed. The workflow is demonstrated on residential building scenarios with real solar power data. Sobol sequences are utilized to vary the component size in simulation, while radial basis functions are employed to approximate simulation results, which are then used for the optimization. The utilization of waste heat from Power-to-Gas and Gas-to-Power processes is evaluated using thermal equations by extending an existing coupled electrochemical and thermodynamic HESS model built in the multiphysical energy system simulator MEgy. The results show a functional workflow that optimizes sizing for a given scenario and outputs the component's daily energy balance and state of charge. © 2025 The Authors

关键词： Waste heat utilization

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Predicting Properties of Oxide Glasses Using Informed Neural Networks

arXiv

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arXiv 2023年

作者： Maier, Gregor Hamaekers, Jan Martilotti, Dominik-Sergio Ziebarth, Benedikt Fraunhofer Institute for Algorithms and Scientific Computing SCAI Schloss Birlinghoven Sankt Augustin53757 Germany Institut für Numerische Simulation Universität Bonn Friedrich-Hirzebruch-Allee 7 Bonn53115 Germany Fraunhofer Center for Machine Learning Schloss Birlinghoven Sankt Augustin53757 Germany Schott AG Hattenbergstrasse 10 Mainz55122 Germany

Many modern-day applications require the development of new materials with specific properties. In particular, the design of new glass compositions is of great industrial interest. Current machine learning methods for learning the composition-property relationship of glasses promise to save on expensive trial-and-error approaches. Even though quite large datasets on the composition of glasses and their properties already exist (i.e., with more than 350,000 samples), they cover only a very small fraction of the space of all possible glass compositions. This limits the applicability of purely data-driven models for property prediction purposes and necessitates the development of models with high extrapolation power. In this paper, we propose a neural network model which incorporates prior scientific and expert knowledge in its learning pipeline. This informed learning approach leads to an improved extrapolation power compared to blind (uninformed) neural network models. To demonstrate this, we train our models to predict three different material properties, that is, the glass transition temperature, the Young’s modulus (at room temperature), and the shear modulus of binary oxide glasses which do not contain sodium. As representatives for conventional blind neural network approaches we use five different feed-forward neural networks of varying widths and depths. For each property, we set up model ensembles of multiple trained models and show that, on average, our proposed informed model performs better in extrapolating the three properties of previously unseen sodium borate glass samples than all five conventional blind models. © 2023, CC BY-SA.

关键词： Extrapolation

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