@techreport{50741, abstract = {{Zitieren als: Wissenschaftlicher Beirat für Agrarpolitik, Ernährung und gesundheitlichen Verbraucher- schutz beim BMEL (2023): Neue Sorgfaltspflichten für Unternehmen des Agrar- und Ernährungssektors: Empfehlungen zu aktuellen Gesetzesentwicklungen. Gutachten. Berlin.}}, author = {{Spiller, Achim and Nieberg, Hiltrud and Renner, Britta and Balmann, Alfons and Birna, Regina and Bosy-Westphal, Anja and Buyken, Anette and Döring, Thomas and Feindt, Peter and Götz, Kai-Uwe and Linseisen, Jakob and Nöthlings, Ute and Martínes, José and Pischetsrieder, Monika and Voget-Kleschin, Lieske and Weingarten, Peter and Wesseler, Justus and Wieck, Christine}}, publisher = {{Wissenschaftlicher Beirat für Agrarpolitik, Ernährung und gesundheitlichen Verbraucherschutz beim BMEL}}, title = {{{Neue Sorgfaltspflichten für Unternehmen des Agrar- und Ernährungssektors: Empfehlungen zu aktuellen Gesetzesentwicklungen}}}, year = {{2023}}, } @unpublished{51159, abstract = {{Sparsity is a highly desired feature in deep neural networks (DNNs) since it ensures numerical efficiency, improves the interpretability of models (due to the smaller number of relevant features), and robustness. In machine learning approaches based on linear models, it is well known that there exists a connecting path between the sparsest solution in terms of the $\ell^1$ norm,i.e., zero weights and the non-regularized solution, which is called the regularization path. Very recently, there was a first attempt to extend the concept of regularization paths to DNNs by means of treating the empirical loss and sparsity ($\ell^1$ norm) as two conflicting criteria and solving the resulting multiobjective optimization problem. However, due to the non-smoothness of the $\ell^1$ norm and the high number of parameters, this approach is not very efficient from a computational perspective. To overcome this limitation, we present an algorithm that allows for the approximation of the entire Pareto front for the above-mentioned objectives in a very efficient manner. We present numerical examples using both deterministic and stochastic gradients. We furthermore demonstrate that knowledge of the regularization path allows for a well-generalizing network parametrization.}}, author = {{Amakor, Augustina Chidinma and Sonntag, Konstantin and Peitz, Sebastian}}, booktitle = {{arXiv}}, title = {{{A multiobjective continuation method to compute the regularization path of deep neural networks}}}, year = {{2023}}, } @phdthesis{51352, abstract = {{Erfolg und Misserfolg eines Unternehmens werden maßgeblich durch getroffene Entscheidungen beeinflusst. Daher verlassen sich Entscheider oft auf Entscheidungsunterstützungssysteme, die durch Datensimulation, -optimierung und -visualisierung bei der Identifizierung von geeigneten Entscheidungen unterstützen. Für eine optimale Unterstützung muss ein Entscheidungsunterstützungssystem (EUS) jedoch auf den Entscheidungsprozess eines Entscheiders abgestimmt sein und verfügbare Daten, Optimierungsziele, persönliche Präferenzen sowie weitere Einflussfaktoren berücksichtigen. EUS-Entwickler können aufgrund der Komplexität und Volatilität von Geschäftsumgebungen allerdings nicht alle potenziellen Entscheidungsprozesse während des Entwurfs eines EUS vorhersehen, wodurch ein EUS einem Entscheider häufig nur unzureichende Anpassungsmöglichkeiten an den individuellen Entscheidungsprozess bietet. Die Einzelanfertigung eines EUS, das auf einen Entscheidungsprozess zugeschnitten ist, ist ein kosten- und zeitintensives Unterfangen aufgrund der begrenzten Verfügbarkeit von Softwareentwicklern oder Missverständnissen zwischen Entwicklern und Entscheidern während der Entwicklung. Daher geben sich Entscheider möglicherweise mit einem handelsüblichen EUS zufrieden, das nicht vollständig mit ihrem Entscheidungsprozess übereinstimmt, suboptimale Entscheidungen begünstigt und so den Unternehmenserfolg negativ beeinflusst. In dieser Arbeit wird ein Ansatz vorgeschlagen, der es Entscheidern ermöglicht, selbst maßgeschneiderte Entscheidungsunterstützungssysteme zu entwickeln und so die Diskrepanz zwischen benötigter und tatsächlicher Entscheidungsunterstützung zu vermeiden. Dazu stellen EUS-Entwickler einen Teil der EUS-Funktionalität als wiederverwendbare Software-Dienste bereit ...}}, author = {{Kirchhoff, Jonas}}, publisher = {{Universität Paderborn}}, title = {{{Decision Support Ecosystems: Assisted Low-Code Development of Tailored Decision Support Systems}}}, doi = {{10.17619/UNIPB/1-1845}}, year = {{2023}}, } @unpublished{46578, abstract = {{Multiobjective optimization plays an increasingly important role in modern applications, where several criteria are often of equal importance. The task in multiobjective optimization and multiobjective optimal control is therefore to compute the set of optimal compromises (the Pareto set) between the conflicting objectives. The advances in algorithms and the increasing interest in Pareto-optimal solutions have led to a wide range of new applications related to optimal and feedback control - potentially with non-smoothness both on the level of the objectives or in the system dynamics. This results in new challenges such as dealing with expensive models (e.g., governed by partial differential equations (PDEs)) and developing dedicated algorithms handling the non-smoothness. Since in contrast to single-objective optimization, the Pareto set generally consists of an infinite number of solutions, the computational effort can quickly become challenging, which is particularly problematic when the objectives are costly to evaluate or when a solution has to be presented very quickly. This article gives an overview of recent developments in the field of multiobjective optimization of non-smooth PDE-constrained problems. In particular we report on the advances achieved within Project 2 "Multiobjective Optimization of Non-Smooth PDE-Constrained Problems - Switches, State Constraints and Model Order Reduction" of the DFG Priority Programm 1962 "Non-smooth and Complementarity-based Distributed Parameter Systems: Simulation and Hierarchical Optimization".}}, author = {{Bernreuther, Marco and Dellnitz, Michael and Gebken, Bennet and Müller, Georg and Peitz, Sebastian and Sonntag, Konstantin and Volkwein, Stefan}}, booktitle = {{arXiv:2308.01113}}, title = {{{Multiobjective Optimization of Non-Smooth PDE-Constrained Problems}}}, year = {{2023}}, } @techreport{52127, abstract = {{This report documents the program and the outcomes of Dagstuhl Seminar 23161 "Pushing the Limits of Computational Combinatorial Constructions". In this Dagstuhl Seminar, we focused on computational methods for challenging problems in combinatorial construction. This includes algorithms for construction of combinatorial objects with prescribed symmetry, for isomorph-free exhaustive generation, and for combinatorial search. Examples of specific algorithmic techniques are tactical decomposition, the Kramer-Mesner method, algebraic methods, graph isomorphism software, isomorph-free generation, clique-finding methods, heuristic search, SAT solvers, and combinatorial optimization. There was an emphasis on problems involving graphs, designs and codes, also including topics in related fields such as finite geometry, graph decomposition, Hadamard matrices, Latin squares, and q-analogs of designs and codes.}}, author = {{Moura, Lucia and Nakic, Anamari and Östergård, Patric and Wassermann, Alfred and Weiß, Charlene}}, keywords = {{automorphism groups, combinatorial algorithms, finite geometries, subspace designs}}, pages = {{40--57}}, publisher = {{Schloss Dagstuhl - Leibniz-Zentrum für Informatik}}, title = {{{Pushing the Limits of Computational Combinatorial Constructions (Dagstuhl Seminar 23161)}}}, doi = {{10.4230/DagRep.13.4.40}}, volume = {{13, Issue 4}}, year = {{2023}}, } @article{47551, author = {{Hochhaus, Thorben and Bruns, Bastian and Grünewald, Marcus and Riese, Julia}}, issn = {{0098-1354}}, journal = {{Computers & Chemical Engineering}}, keywords = {{Computer Science Applications, General Chemical Engineering}}, publisher = {{Elsevier BV}}, title = {{{Optimal scheduling of a large-scale power-to-ammonia process: Effects of parameter optimization on the indirect demand response potential}}}, doi = {{10.1016/j.compchemeng.2023.108132}}, volume = {{170}}, year = {{2023}}, } @inbook{52696, author = {{Kruse, Anne and Müller, Laura and Ott, Manuel and Koch, Rainer and Hügel, Joachim and Finke, Florian and Winter, Stephan and Gust, Thomas}}, booktitle = {{Personennahe Dienstleistungen der Zukunft}}, isbn = {{9783658388126}}, issn = {{2366-1127}}, publisher = {{Springer Fachmedien Wiesbaden}}, title = {{{proDruck 3D-Druck – Technologie der Industrie 4.0 – als Mittel der Inklusion für Menschen mit Behinderungen in die Arbeitswelt}}}, doi = {{10.1007/978-3-658-38813-3_16}}, year = {{2023}}, } @inbook{52698, author = {{Kruse, Anne and Müller, Laura and Ott, Manuel and Jung, Philipp and Koch, Rainer and Hügel, Joachim and Finke, Florian and Winter, Stephan and Gust, Thomas}}, booktitle = {{Personennahe Dienstleistungen der Zukunft}}, isbn = {{9783658388126}}, issn = {{2366-1127}}, publisher = {{Springer Fachmedien Wiesbaden}}, title = {{{3D-Druck – Eine Technologie als Schlüssel zur Steigerung der Teilhabe}}}, doi = {{10.1007/978-3-658-38813-3_7}}, year = {{2023}}, } @inproceedings{34736, author = {{Schöppner, Volker and Frank, Maximilian}}, location = {{Fukuoka}}, title = {{{Investigation of the Homogenization Performance of Various Faceted Mixers and Optimization with Regard to Mixing as well as Pressure Throughput Behavior}}}, doi = {{10.1063/5.0135825}}, year = {{2023}}, } @phdthesis{50449, abstract = {{The importance of fiber-reinforced plastics for lightweight construction applications is steadily increasing due to their outstanding weight-specific property values. However, a decisive disadvantage of these composite materials has so far been the high material and process costs, which is why fiber-reinforced plastics are almost exclusively used in small to medium-sized series. Optimization of manufacturing methods is of great importance to reduce the production cost. In this study, two concepts are proposed that can optimize vacuum assisted light resin transfer molding (VA-LRTM) further, leading to a possibility of fully automatic process. Conventional VA-LRTM methods are used to produce complex fiber-reinforced plastics (FRP) and hybrid components. Traditional molds used to produce components via VA-LRTM are sealed using polymer materials to prevent the leakage of matrix system. The seals undergo tremendous amounts of thermal, chemical, and mechanical loadings. Thus, sealings must be replaced in short intervals. In the current study, a concept where sealing is achieved by accelerating the curing of matrix system itself with the help of heating elements and catalysts resulting in a self-sealing approach is proposed. Another concern is mold surface contamination during component production. To address this, a modified automatic cleaning technique based on ultrasonic cleaning was proposed which can be integrated into the production line with minimum modification. Both the proposed concepts were validated and optimized using experiments, simulations, and analytical approaches by producing metal-FRP hybrid shafts.}}, author = {{Chalicheemalapalli Jayasankar, Deviprasad}}, keywords = {{fiber-reinforced plastics, resin transfer molding, composites}}, title = {{{Advances In RTM Manufacturing Of Metal-FRP Hybrids By Self-Sealing And In-Mold Cleaning Techniques}}}, year = {{2023}}, } @inproceedings{52863, author = {{Ŝkvorc, Urban and Eftimov, Tome and Koro]ec, Peter}}, booktitle = {{2023 IEEE Symposium Series on Computational Intelligence (SSCI)}}, publisher = {{IEEE}}, title = {{{Analyzing the Generalizability of Automated Algorithm Selection: A Case Study for Numerical Optimization}}}, doi = {{10.1109/ssci52147.2023.10371868}}, year = {{2023}}, } @article{53078, abstract = {{In spray-flame synthesis of nanoparticles, a precise understanding of the reaction processes is necessary to find optimal process parameters for the formation of the desired products. Coupling the chemistries of flame, solvent, and gas-phase species initially formed from the particle precursor in combination with the complex flow geometry of the spray flame means a special challenge for the modeling of the reaction processes. A new burner has been developed that is capable to observe the reaction of precursor solutions frequently used in spray-flame synthesis. The burner provides an almost flat, laminar, and steady flame with homogeneous addition of a fine aerosol and thus enables detailed investigation and modeling of the coupled reactions inde-pendent of spray formation and turbulent mixing. With its two separate supply channel matrices, the burner also enables the use of reactants that would otherwise react with each other already before reaching the flame. These features enable the investigation of a wide range of flame-based synthesis methods for nanoparticles and, due to the flat-flame geometry, kinetics models for these processes can be developed and validated. This work describes the matrix burner development and its gas flow optimization by simulation. Droplet-size dis-tributions generated by ultrasonic nebulization and their interaction with the burner structure are investigated by phase-Doppler anemometry. As an example for nanoparticle-for ming flames from solutions, iron-oxide nanoparticle-generating flames using iron(III) nitrate nonahydrate dissolved in 1-butanol were investigated. This effort includes measurements of two-dimensional maps of the flame temperature by a thermocouple and height-dependent concentration profiles of the main species by time-of-flight mass spectrometry. Exper-imental data are compared with 1D simulations using a reduced reaction mechanism. The results show that the new burner is well suited for the development of reaction models for precursors supplied in the liquid phase usually applied in spray-flame synthesis configurations.& COPY; 2022 The Combustion Institute. Published by Elsevier Inc. All rights reserved.}}, author = {{Apazeller, Sascha and Gonchikzhapov, Munko and Nanjaiah, Monika and Kasper, Tina and Wlokas, Irenäus and Wiggers, Hartmut and Schulz, Christof}}, issn = {{1540-7489}}, journal = {{Proceedings of the Combustion Institute}}, keywords = {{Physical and Theoretical Chemistry, Mechanical Engineering, General Chemical Engineering}}, number = {{1}}, pages = {{909--918}}, publisher = {{Elsevier BV}}, title = {{{A new dual matrix burner for one-dimensional investigation of aerosol flames}}}, doi = {{10.1016/j.proci.2022.07.166}}, volume = {{39}}, year = {{2023}}, } @article{53261, author = {{Soleymani, Mohammad and Santamaria, Ignacio and Jorswieck, Eduard and Clerckx, Bruno}}, issn = {{1536-1276}}, journal = {{IEEE Transactions on Wireless Communications}}, keywords = {{Applied Mathematics, Electrical and Electronic Engineering, Computer Science Applications}}, pages = {{1--1}}, publisher = {{Institute of Electrical and Electronics Engineers (IEEE)}}, title = {{{Optimization of Rate-Splitting Multiple Access in Beyond Diagonal RIS-assisted URLLC Systems}}}, doi = {{10.1109/twc.2023.3324190}}, year = {{2023}}, } @article{53317, author = {{Tao, Youshan and Winkler, Michael}}, issn = {{2163-2480}}, journal = {{Evolution Equations and Control Theory}}, keywords = {{Applied Mathematics, Control and Optimization, Modeling and Simulation}}, number = {{6}}, pages = {{1676--1687}}, publisher = {{American Institute of Mathematical Sciences (AIMS)}}, title = {{{Global smooth solutions in a three-dimensional cross-diffusive SIS epidemic model with saturated taxis at large densities}}}, doi = {{10.3934/eect.2023031}}, volume = {{12}}, year = {{2023}}, } @inproceedings{53235, author = {{Lehmann, Isabell and Adali, Tülay and Kruizinga, Pieter and Hunyadi, Borbála}}, booktitle = {{2023 57th Asilomar Conference on Signals, Systems, and Computers}}, publisher = {{IEEE}}, title = {{{Deriving 3D Functional Brain Regions from Multi-Slice Functional Ultrasound Data Using ICA and IVA}}}, doi = {{10.1109/ieeeconf59524.2023.10477081}}, year = {{2023}}, } @article{53219, author = {{Tavana, Madjid and Hajipour, Vahid and Alaghebandha, Mohammad and Di Caprio, Debora}}, issn = {{2666-8270}}, journal = {{Machine Learning with Applications}}, keywords = {{General Medicine}}, publisher = {{Elsevier BV}}, title = {{{A bi-objective hybrid vibration damping optimization model for synchronous flow shop scheduling problems}}}, doi = {{10.1016/j.mlwa.2022.100445}}, volume = {{11}}, year = {{2023}}, } @article{53223, author = {{Dellnitz, Andreas and Tavana, Madjid and Banker, Rajiv}}, issn = {{0254-5330}}, journal = {{Annals of Operations Research}}, keywords = {{Management Science and Operations Research, General Decision Sciences}}, number = {{2}}, pages = {{661--690}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{A novel median-based optimization model for eco-efficiency assessment in data envelopment analysis}}}, doi = {{10.1007/s10479-022-04937-4}}, volume = {{322}}, year = {{2023}}, } @inproceedings{48961, author = {{Iftekhar, Mohammed and Gowda, Harshan and Kneuper, Pascal and Sadiye, Babak and Müller, Wolfgang and Scheytt, Christoph}}, booktitle = {{2023 IEEE BiCMOS and Compound Semiconductor Integrated Circuits and Technology Symposium (BCICTS)}}, location = {{Monterey, CA, USA}}, title = {{{A 28-Gb/s 27.2mW NRZ Full-Rate Bang-Bang Clock and Data Recovery in 22 nm FD-SOI CMOS Technology}}}, doi = {{10.1109/BCICTS54660.2023.10310954}}, year = {{2023}}, } @article{54182, author = {{Tavana, Madjid and Arman, Hosein and Hadi-Vencheh, Abdollah and Mansoori, Sadegh}}, issn = {{0254-5330}}, journal = {{Annals of Operations Research}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{A fuzzy multi-objective optimization model for sustainable location planning using volumetric fuzzy sets}}}, doi = {{10.1007/s10479-023-05505-0}}, year = {{2023}}, } @article{54180, author = {{Tavana, Madjid and Khalili Nasr, Arash and Santos-Arteaga, Francisco J. and Saberi, Esmaeel and Mina, Hassan}}, issn = {{0254-5330}}, journal = {{Annals of Operations Research}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{An optimization model with a lagrangian relaxation algorithm for artificial internet of things-enabled sustainable circular supply chain networks}}}, doi = {{10.1007/s10479-023-05219-3}}, year = {{2023}}, }