TY - JOUR A1 - Pedersen, Jaap A1 - Weinand, Jann Michael A1 - Syranidou, Chloi A1 - Rehfeldt, Daniel T1 - An efficient solver for large-scale onshore wind farm siting including cable routing JF - European Journal of Operational Research N2 - Existing planning approaches for onshore wind farm siting and grid integration often do not meet minimum cost solutions or social and environmental considerations. In this paper, we develop an exact approach for the integrated layout and cable routing problem of onshore wind farm planning using the Quota Steiner tree problem. Applying a novel transformation on a known directed cut formulation, reduction techniques, and heuristics, we design an exact solver that makes large problem instances solvable and outperforms generic MIP solvers. In selected regions of Germany, the trade-offs between minimizing costs and landscape impact of onshore wind farm siting are investigated. Although our case studies show large trade-offs between the objective criteria of cost and landscape impact, small burdens on one criterion can significantly improve the other criteria. In addition, we demonstrate that contrary to many approaches for exclusive turbine siting, grid integration must be simultaneously optimized to avoid excessive costs or landscape impacts in the course of a wind farm project. Our novel problem formulation and the developed solver can assist planners in decision-making and help optimize wind farms in large regions in the future. Y1 - 2024 ER - TY - GEN A1 - Kuen, Lilli A1 - Löffler, Lorenz A1 - Tsarapkin, Aleksei A1 - Zschiedrich, Lin A1 - Feichtner, Thorsten A1 - Burger, Sven A1 - Höflich, Katja T1 - Source Code and Simulation Results: Chiral and directional optical emission from a dipole source coupled to a helical plasmonic antenna T2 - Zenodo Y1 - 2024 U6 - https://doi.org/10.5281/zenodo.10598255 SP - doi: 10.5281/zenodo.10598255 ER - TY - CHAP A1 - Pedersen, Jaap A1 - Ljubić, Ivana ED - Pardalos, Panos M. ED - Prokopyev, Oleg A. T1 - Prize Collecting Steiner Tree Problem and its Variants T2 - Encyclopedia of Optimization Y1 - 2024 PB - Springer International Publishing CY - Cham ER - TY - JOUR A1 - Rodrigues Pela, Ronaldo A1 - Draxl, Claudia T1 - Speeding up all-electron real-time TDDFT demonstrated by the exciting package Y1 - 2024 U6 - https://doi.org/10.48550/arXiv.2403.04351 ER - TY - JOUR A1 - Trepte, Philipp A1 - Secker, Christopher A1 - Olivet, Julien A1 - Blavier, Jeremy A1 - Kostova, Simona A1 - Maseko, Sibusiso B A1 - Minia, Igor A1 - Silva Ramos, Eduardo A1 - Cassonnet, Patricia A1 - Golusik, Sabrina A1 - Zenkner, Martina A1 - Beetz, Stephanie A1 - Liebich, Mara J A1 - Scharek, Nadine A1 - Schütz, Anja A1 - Sperling, Marcel A1 - Lisurek, Michael A1 - Wang, Yang A1 - Spirohn, Kerstin A1 - Hao, Tong A1 - Calderwood, Michael A A1 - Hill, David E A1 - Landthaler, Markus A1 - Choi, Soon Gang A1 - Twizere, Jean-Claude A1 - Vidal, Marc A1 - Wanker, Erich E T1 - AI-guided pipeline for protein–protein interaction drug discovery identifies a SARS-CoV-2 inhibitor JF - Molecular Systems Biology N2 - Protein–protein interactions (PPIs) offer great opportunities to expand the druggable proteome and therapeutically tackle various diseases, but remain challenging targets for drug discovery. Here, we provide a comprehensive pipeline that combines experimental and computational tools to identify and validate PPI targets and perform early-stage drug discovery. We have developed a machine learning approach that prioritizes interactions by analyzing quantitative data from binary PPI assays or AlphaFold-Multimer predictions. Using the quantitative assay LuTHy together with our machine learning algorithm, we identified high-confidence interactions among SARS-CoV-2 proteins for which we predicted three-dimensional structures using AlphaFold-Multimer. We employed VirtualFlow to target the contact interface of the NSP10-NSP16 SARS-CoV-2 methyltransferase complex by ultra-large virtual drug screening. Thereby, we identified a compound that binds to NSP10 and inhibits its interaction with NSP16, while also disrupting the methyltransferase activity of the complex, and SARS-CoV-2 replication. Overall, this pipeline will help to prioritize PPI targets to accelerate the discovery of early-stage drug candidates targeting protein complexes and pathways. KW - Applied Mathematics KW - Computational Theory and Mathematics KW - General Agricultural and Biological Sciences KW - General Immunology and Microbiology KW - General Biochemistry, Genetics and Molecular Biology KW - Information Systems Y1 - 2024 U6 - https://doi.org/https://doi.org/10.1038/s44320-024-00019-8 SN - 1744-4292 VL - 20 IS - 4 SP - 428 EP - 457 PB - Springer Science and Business Media LLC ER - TY - CHAP A1 - Prause, Felix T1 - A Multi-Swap Heuristic for Rolling Stock Rotation Planning with Predictive Maintenance T2 - Proceedings of the 11th International Network Optimization Conference (INOC), Dublin, Ireland, March 11-23, 2024 N2 - We present a heuristic solution approach for the rolling stock rotation problem with predictive maintenance (RSRP-PdM). The task of this problem is to assign a sequence of trips to each of the vehicles and to schedule their maintenance such that all trips can be operated. Here, the health states of the vehicles are considered to be random variables distributed by a family of probability distribution functions, and the maintenance services should be scheduled based on the failure probability of the vehicles. The proposed algorithm first generates a solution by solving an integer linear program and then heuristically improves this solution by applying a local search procedure. For this purpose, the trips assigned to the vehicles are split up and recombined, whereby additional deadhead trips can be inserted between the partial assignments. Subsequently, the maintenance is scheduled by solving a shortest path problem in a state-expanded version of a space-time graph restricted to the trips of the individual vehicles. The solution approach is tested and evaluated on a set of test instances based on real-world timetables. Y1 - 2024 U6 - https://doi.org/10.48786/inoc.2024.11 SP - 58 EP - 63 ER - TY - JOUR A1 - Prause, Felix A1 - Borndörfer, Ralf T1 - An Iterative Refinement Approach for the Rolling Stock Rotation Problem with Predictive Maintenance N2 - The rolling stock rotation problem with predictive maintenance (RSRP-PdM) involves the assignment of trips to a fleet of vehicles with integrated maintenance scheduling based on the predicted failure probability of the vehicles. These probabilities are determined by the health states of the vehicles, which are considered to be random variables distributed by a parameterized family of probability distribution functions. During the operation of the trips, the corresponding parameters get updated. In this article, we present a dual solution approach for RSRP-PdM and generalize a linear programming based lower bound for this problem to families of probability distribution functions with more than one parameter. For this purpose, we define a rounding function that allows for a consistent underestimation of the parameters and model the problem by a state-expanded event-graph in which the possible states are restricted to a discrete set. This induces a flow problem that is solved by an integer linear program. We show that the iterative refinement of the underlying discretization leads to solutions that converge from below to an optimal solution of the original instance. Thus, the linear relaxation of the considered integer linear program results in a lower bound for RSRP-PdM. Finally, we report on the results of computational experiments conducted on a library of test instances. Y1 - 2024 ER - TY - GEN A1 - Betz, Fridtjof A1 - Hammerschmidt, Martin A1 - Zschiedrich, Lin A1 - Burger, Sven A1 - Binkowski, Felix T1 - Source code and simulation results: Efficient rational approximation of optical response functions with the AAA algorithm T2 - Zenodo Y1 - 2024 U6 - https://doi.org/10.5281/zenodo.10853692 SP - doi: 10.5281/zenodo.10853692 ER - TY - JOUR A1 - Winkler, Alexander T1 - Berliner Kulturerbe digital: Ein quantitativer Blick auf die Metadatenqualität JF - EVA BERLIN 2023. Elektronische Medien & Kunst, Kultur und Historie. 27. Berliner Veranstaltung der internationalen EVA-Serie (29. November 2023 – 01. Dezember 2023) N2 - Die Metadatenqualität bestimmt wesentlich den Nutzen und Wert von Kulturerbedaten. ‚Gute‘ Metadaten erhöhen die Auffindbarkeit, Interoperabilität und Nutzbarkeit von Daten signifikant. Mit Blick auf Retrieval bzw. Discovery, Vernetzung im Kontext von Linked Open Data und wissenschaftliches Data Mining hängt die Qualität dabei wesentlich von der Verwendung von maschinenlesbaren kontrollierten Vokabularen ab. Diese wird in der vorliegenden Arbeit quantitativ untersucht. Als Datengrundlage dienen die in der Deutschen Digitalen Bibliothek aggregierten Metadaten aus Berliner Museen (ca. 1,2 Millionen Metadatenobjekte im LIDO-Format) Y1 - 2023 SP - 101 EP - 108 ER - TY - JOUR A1 - Sherratt, Katharine A1 - Srivastava, Ajitesh A1 - Ainslie, Kylie A1 - Singh, David E. A1 - Cublier, Aymar A1 - Marinescu, Maria Cristina A1 - Carretero, Jesus A1 - Garcia, Alberto Cascajo A1 - Franco, Nicolas A1 - Willem, Lander A1 - Abrams, Steven A1 - Faes, Christel A1 - Beutels, Philippe A1 - Hens, Niel A1 - Müller, Sebastian A1 - Charlton, Billy A1 - Ewert, Ricardo A1 - Paltra, Sydney A1 - Rakow, Christian A1 - Rehmann, Jakob A1 - Conrad, Tim O.F. A1 - Schütte, Christof A1 - Nagel, Kai A1 - Abbott, Sam A1 - Grah, Rok A1 - Niehus, Rene A1 - Prasse, Bastian A1 - Sandmann, Frank A1 - Funk, Sebastian T1 - Characterising information gains and losses when collecting multiple epidemic model outputs JF - Epidemics N2 - Collaborative comparisons and combinations of epidemic models are used as policy-relevant evidence during epidemic outbreaks. In the process of collecting multiple model projections, such collaborations may gain or lose relevant information. Typically, modellers contribute a probabilistic summary at each time-step. We compared this to directly collecting simulated trajectories. We aimed to explore information on key epidemic quantities; ensemble uncertainty; and performance against data, investigating potential to continuously gain information from a single cross-sectional collection of model results. Methods We compared July 2022 projections from the European COVID-19 Scenario Modelling Hub. Five modelling teams projected incidence in Belgium, the Netherlands, and Spain. We compared projections by incidence, peaks, and cumulative totals. We created a probabilistic ensemble drawn from all trajectories, and compared to ensembles from a median across each model’s quantiles, or a linear opinion pool. We measured the predictive accuracy of individual trajectories against observations, using this in a weighted ensemble. We repeated this sequentially against increasing weeks of observed data. We evaluated these ensembles to reflect performance with varying observed data. Results. By collecting modelled trajectories, we showed policy-relevant epidemic characteristics. Trajectories contained a right-skewed distribution well represented by an ensemble of trajectories or a linear opinion pool, but not models’ quantile intervals. Ensembles weighted by performance typically retained the range of plausible incidence over time, and in some cases narrowed this by excluding some epidemic shapes. Conclusions. We observed several information gains from collecting modelled trajectories rather than quantile distributions, including potential for continuously updated information from a single model collection. The value of information gains and losses may vary with each collaborative effort’s aims, depending on the needs of projection users. Understanding the differing information potential of methods to collect model projections can support the accuracy, sustainability, and communication of collaborative infectious disease modelling efforts. Data availability All code and data available on Github: https://github.com/covid19-forecast-hub-europe/aggregation-info-loss KW - Virology KW - Infectious Diseases KW - Public Health, Environmental and Occupational Health KW - Microbiology KW - Parasitology KW - Epidemiology Y1 - 2024 U6 - https://doi.org/10.1016/j.epidem.2024.100765 SN - 1755-4365 PB - Elsevier BV ER -