Anchoring and electro-optic switching of liquid crystals on nano-structured surfaces fabricated by two-photon based nano-printing - Zhang, Bingru; Plidschun, Malte; Schmidt, Markus A.; Kitzerow, Heinz-Siegfried (Optical Materials Express)
The alignment of liquid crystals on surfaces plays a central role in optimizing their performances. In this work, a cutting-edge nano-lithography-based method to control the local orientation of a thermotropic liquid crystal is applied to easily available commercial standard materials and evaluated. Parallel nanogrooves on a substrate, created through 3D nanoprinting in a negative-tone photoresin optimized for two-photon polymerization are used for this purpose. Azimuthal anchoring energies of the order from 10 −6 J/m 2 to 10 −5 J/m 2 are found, depending on the spacing, width and depth of the grooves. In part, these values are larger than those reported previously for another photopolymer. Both uniform alignment and spatial patterns of different alignment directions can be realized. Electro-optic studies confirm the suitability of the method for electrically addressable photonic applications and indicate strong polar anchoring.
Keywords: Electronic; Optical and Magnetic Materials

Zusammenarbeit von Frühförderung und Kita im Förderschwerpunkt Sehen - Jesuthasan, Jonitta

A study on the effects of normalized TSP features for automated algorithm selection - Heins, Jonathan; Bossek, Jakob; Pohl, Janina; Seiler, Moritz; Trautmann, Heike; Kerschke, Pascal (Theoretical Computer Science)
Classic automated algorithm selection (AS) for (combinatorial) optimization problems heavily relies on so-called instance features, i.e., numerical characteristics of the problem at hand ideally extracted with computationally low-demanding routines. For the traveling salesperson problem (TSP) a plethora of features have been suggested. Most of these features are, if at all, only normalized imprecisely raising the issue of feature values being strongly affected by the instance size. Such artifacts may have detrimental effects on algorithm selection models. We propose a normalization for two feature groups which stood out in multiple AS studies on the TSP: (a) features based on a minimum spanning tree (MST) and (b) nearest neighbor relationships of the input instance. To this end we theoretically derive minimum and maximum values for properties of MSTs and k-nearest neighbor graphs (NNG) of Euclidean graphs. We analyze the differences in feature space between normalized versions of these features and their unnormalized counterparts. Our empirical investigations on various TSP benchmark sets point out that the feature scaling succeeds in eliminating the effect of the instance size. A proof-of-concept AS-study shows promising results: models trained with normalized features tend to outperform those trained with the respective vanilla features.
Keywords: Feature normalization; Algorithm selection; Traveling salesperson problem

On‐Demand Indistinguishable and Entangled Photons Using Tailored Cavity Designs - Bauch, David; Siebert, Dustin; Jöns, Klaus; Förstner, Jens; Schumacher, Stefan (Advanced Quantum Technologies)
Abstract The biexciton‐exciton emission cascade commonly used in quantum‐dot systems to generate polarization entanglement yields photons with intrinsically limited indistinguishability. In the present work, it focuses on the generation of pairs of photons with high degrees of polarization entanglement and simultaneously high indistinguishability. It achieves this goal by selectively reducing the biexciton lifetime with an optical resonator. It demonstrates that a suitably tailored circular Bragg reflector fulfills the requirements of sufficient selective Purcell enhancement of biexciton emission paired with spectrally broad photon extraction and twofold degenerate optical modes. The in‐depth theoretical study combines (i) the optimization of realistic photonic structures solving Maxwell's equations from which model parameters are extracted as input for (ii) microscopic simulations of quantum‐dot cavity excitation dynamics with full access to photon properties. It reports non‐trivial dependencies on system parameters and use the predictive power of the combined theoretical approach to determine the optimal range of Purcell enhancement that maximizes indistinguishability and entanglement to near unity values, here specifically for the telecom C‐band at 1550 nm.
Keywords: tet_topic_qd

On-demand indistinguishable and entangled photons at telecom frequencies using tailored cavity designs - Bauch, David; Siebert, Dustin; Jöns, Klaus; Förstner, Jens; Schumacher, Stefan
The biexciton-exciton emission cascade commonly used in quantum-dot systems to generate polarization entanglement yields photons with intrinsically limited indistinguishability. In the present work we focus on the generation of pairs of photons with high degrees of polarization entanglement and simultaneously high indistinguishibility. We achieve this goal by selectively reducing the biexciton lifetime with an optical resonator. We demonstrate that a suitably tailored circular Bragg reflector fulfills the requirements of sufficient selective Purcell enhancement of biexciton emission paired with spectrally broad photon extraction and two-fold degenerate optical modes. Our in-depth theoretical study combines (i) the optimization of realistic photonic structures solving Maxwell's equations from which model parameters are extracted as input for (ii) microscopic simulations of quantum-dot cavity excitation dynamics with full access to photon properties. We report non-trivial dependencies on system parameters and use the predictive power of our combined theoretical approach to determine the optimal range of Purcell enhancement that maximizes indistinguishability and entanglement to near unity values in the telecom C-band at $1550\,nm$.
Keywords: tet_topic_phc; tet_topic_qd

Zwischen Überwältigung und Beliebigkeit - Wertorientierung im Unterricht als Aufgabe der Lehrpersonenbildung - Schlegel-Matthies, Kirsten (Nachdenken über Lehrerinnen- und Lehrerbildung. Anregungen zur Weiterentwicklung der Pädagogischen Hochschulen in der deutschsprachigen Schweiz. Aus Anlass des 20-Jahre-Jubiläums der Pädagogischen Hochschule Luzern)

Memory Gym: Towards Endless Tasks to Benchmark Memory Capabilities of Agents - Pleines, Marco; Pallasch, Matthias; Zimmer, Frank; Preuss, Mike (arXiv:2309.17207)
Memory Gym presents a suite of 2D partially observable environments, namely Mortar Mayhem, Mystery Path, and Searing Spotlights, designed to benchmark memory capabilities in decision-making agents. These environments, originally with finite tasks, are expanded into innovative, endless formats, mirroring the escalating challenges of cumulative memory games such as ``I packed my bag''. This progression in task design shifts the focus from merely assessing sample efficiency to also probing the levels of memory effectiveness in dynamic, prolonged scenarios. To address the gap in available memory-based Deep Reinforcement Learning baselines, we introduce an implementation that integrates Transformer-XL (TrXL) with Proximal Policy Optimization. This approach utilizes TrXL as a form of episodic memory, employing a sliding window technique. Our comparative study between the Gated Recurrent Unit (GRU) and TrXL reveals varied performances across different settings. TrXL, on the finite environments, demonstrates superior sample efficiency in Mystery Path and outperforms in Mortar Mayhem. However, GRU is more efficient on Searing Spotlights. Most notably, in all endless tasks, GRU makes a remarkable resurgence, consistently outperforming TrXL by significant margins. Website and Source Code: https://github.com/MarcoMeter/endless-memory-gym/

Assessment of long-term dietary cadmium exposure in children in Germany: Does consideration of data from total diet studies reduce uncertainties from food monitoring programmes? - Kolbaum, AE; Jung, C; Jaeger, A; Libuda, Lars; Lindtner, O (Food Chem Toxicol)

Neue Sorgfaltspflichten für Unternehmen des Agrar- und Ernährungssektors: Empfehlungen zu aktuellen Gesetzesentwicklungen - Spiller, Achim; Nieberg, Hiltrud; Renner, Britta; Balmann, Alfons; Birna, Regina; Bosy-Westphal, Anja; Buyken, Anette; Döring, Thomas; Feindt, Peter; Götz, Kai-Uwe; Linseisen, Jakob; Nöthlings, Ute; et al.
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.

A multiobjective continuation method to compute the regularization path of deep neural networks - Amakor, Augustina Chidinma; Sonntag, Konstantin; Peitz, Sebastian (arXiv)
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.

Decision Support Ecosystems: Assisted Low-Code Development of Tailored Decision Support Systems - Kirchhoff, Jonas
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 ...

Fast Convergence of Inertial Multiobjective Gradient-like Systems with Asymptotic Vanishing Damping - Sonntag, Konstantin; Peitz, Sebastian (arXiv:2307.00975)
We present a new gradient-like dynamical system related to unconstrained convex smooth multiobjective optimization which involves inertial effects and asymptotic vanishing damping. To the best of our knowledge, this system is the first inertial gradient-like system for multiobjective optimization problems including asymptotic vanishing damping, expanding the ideas laid out in [H. Attouch and G. Garrigos, Multiobjective optimization: an inertial approach to Pareto optima, preprint, arXiv:1506.02823, 201]. We prove existence of solutions to this system in finite dimensions and further prove that its bounded solutions converge weakly to weakly Pareto optimal points. In addition, we obtain a convergence rate of order O(t−2) for the function values measured with a merit function. This approach presents a good basis for the development of fast gradient methods for multiobjective optimization.

Multiobjective Optimization of Non-Smooth PDE-Constrained Problems - Bernreuther, Marco; Dellnitz, Michael; Gebken, Bennet; Müller, Georg; Peitz, Sebastian; Sonntag, Konstantin; Volkwein, Stefan (arXiv:2308.01113)
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".

Pushing the Limits of Computational Combinatorial Constructions (Dagstuhl Seminar 23161) - Moura, Lucia; Nakic, Anamari; Östergård, Patric; Wassermann, Alfred; Weiß, Charlene
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.
Keywords: automorphism groups; combinatorial algorithms; finite geometries; subspace designs

Optimal scheduling of a large-scale power-to-ammonia process: Effects of parameter optimization on the indirect demand response potential - Hochhaus, Thorben; Bruns, Bastian; Grünewald, Marcus; Riese, Julia (Computers & Chemical Engineering)
Keywords: Computer Science Applications; General Chemical Engineering

Numerical analysis of failure modeling in clinching process chain simulation - Bielak, Christian Roman; Böhnke, Max; Friedlein, Johannes; Bobbert, Mathias; Mergheim, Julia; Steinmann, Paul; Meschut, Gerson (Materials Research Proceedings)
Abstract. The application of the mechanical joining process clinching allows the assembly of different sheet metal materials with a wide range of material thickness configurations, which is of interest for lightweight multi-material structures. In order to be able to predict the clinched joint properties as a function of the individual manufacturing steps, current studies focus on numerical modeling of the entire clinching process chain. It is essential to be able to take into account the influence of the joining process-induced damage on the load-bearing capacity of the joint during the loading phase. This study presents a numerical damage accumulation in the clinching process based on an implemented Hosford-Coulomb failure model using a 3D clinching process model applied on the aluminum alloy EN AW-6014 in temper T4. A correspondence of the experimentally determined failure location with the element of the highest numerically determined damage accumulation is shown. Moreover, the experimentally determined failure behavior is predicted to be in agreement in the numerical loading simulation with transferred pre-damage from the process simulation.

Determination and Compensation of the Shrinkage Behavior of Cylindrical Elements in the FDM Process - Koers, Thorsten; Magyar, Balázs (Proceedings of the 34th Annual International Solid Freeform Fabrication Symposium 2023)
Fused Deposition Modeling (FDM) is an additive manufacturing process to produce complex thermoplastic geometries layer by layer. The filament is melted in a nozzle, iteratively deposited, and then cools down. Due to the solidification process, the deposited filament strands deviate from their intended position due to shrinkage, resulting in significant geometric deviations in the final part. In terms of dimensional accuracy, there is a need for optimization, especially for local curved geometries in relation to the global part with higher nominal dimensions. The aim of this study is to investigate the size and shape deviations for cylindrical FDM elements and to compensate the expected deformations by using an in-house software with adaptive scaling factors in the x-y plane. Previous studies mainly focus on simple, non-curved objects, this study also considers the influence of curvature and global as well as local deviations on the final part.

Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP - Seiler, Moritz Vinzent; Rook, Jeroen; Heins, Jonathan; Preuß, Oliver Ludger; Bossek, Jakob; Trautmann, Heike (2023 IEEE Symposium Series on Computational Intelligence (SSCI))
Automated Algorithm Configuration (AAC) usually takes a global perspective: it identifies a parameter configuration for an (optimization) algorithm that maximizes a performance metric over a set of instances. However, the optimal choice of parameters strongly depends on the instance at hand and should thus be calculated on a per-instance basis. We explore the potential of Per-Instance Algorithm Configuration (PIAC) by using Reinforcement Learning (RL). To this end, we propose a novel PIAC approach that is based on deep neural networks. We apply it to predict configurations for the Lin– Kernighan heuristic (LKH) for the Traveling Salesperson Problem (TSP) individually for every single instance. To train our PIAC approach, we create a large set of 100000 TSP instances with 2000 nodes each — currently the largest benchmark set to the best of our knowledge. We compare our approach to the state-of-the-art AAC method Sequential Model-based Algorithm Configuration (SMAC). The results show that our PIAC approach outperforms this baseline on both the newly created instance set and established instance sets.

proDruck 3D-Druck – Technologie der Industrie 4.0 – als Mittel der Inklusion für Menschen mit Behinderungen in die Arbeitswelt - Kruse, Anne; Müller, Laura; Ott, Manuel; Koch, Rainer; Hügel, Joachim; Finke, Florian; Winter, Stephan; Gust, Thomas (Personennahe Dienstleistungen der Zukunft)

3D-Druck – Eine Technologie als Schlüssel zur Steigerung der Teilhabe - Kruse, Anne; Müller, Laura; Ott, Manuel; Jung, Philipp; Koch, Rainer; Hügel, Joachim; Finke, Florian; Winter, Stephan; Gust, Thomas (Personennahe Dienstleistungen der Zukunft)

Investigation of the Homogenization Performance of Various Faceted Mixers and Optimization with Regard to Mixing as well as Pressure Throughput Behavior - Schöppner, Volker; Frank, Maximilian

Advances In RTM Manufacturing Of Metal-FRP Hybrids By Self-Sealing And In-Mold Cleaning Techniques - Chalicheemalapalli Jayasankar, Deviprasad
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.
Keywords: fiber-reinforced plastics; resin transfer molding; composites

Analyzing the Generalizability of Automated Algorithm Selection: A Case Study for Numerical Optimization - Ŝkvorc, Urban; Eftimov, Tome; Koro]ec, Peter (2023 IEEE Symposium Series on Computational Intelligence (SSCI))

A new dual matrix burner for one-dimensional investigation of aerosol flames - Apazeller, Sascha; Gonchikzhapov, Munko; Nanjaiah, Monika; Kasper, Tina; Wlokas, Irenäus; Wiggers, Hartmut; Schulz, Christof (Proceedings of the Combustion Institute)
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.
Keywords: Physical and Theoretical Chemistry; Mechanical Engineering; General Chemical Engineering

Optimization of Rate-Splitting Multiple Access in Beyond Diagonal RIS-assisted URLLC Systems - Soleymani, Mohammad; Santamaria, Ignacio; Jorswieck, Eduard; Clerckx, Bruno (IEEE Transactions on Wireless Communications)
Keywords: Applied Mathematics; Electrical and Electronic Engineering; Computer Science Applications

Global smooth solutions in a three-dimensional cross-diffusive SIS epidemic model with saturated taxis at large densities - Tao, Youshan; Winkler, Michael (Evolution Equations and Control Theory)
Keywords: Applied Mathematics; Control and Optimization; Modeling and Simulation

Deriving 3D Functional Brain Regions from Multi-Slice Functional Ultrasound Data Using ICA and IVA - Lehmann, Isabell; Adali, Tülay; Kruizinga, Pieter; Hunyadi, Borbála (2023 57th Asilomar Conference on Signals, Systems, and Computers)

A bi-objective hybrid vibration damping optimization model for synchronous flow shop scheduling problems - Tavana, Madjid; Hajipour, Vahid; Alaghebandha, Mohammad; Di Caprio, Debora (Machine Learning with Applications)
Keywords: General Medicine

A novel median-based optimization model for eco-efficiency assessment in data envelopment analysis - Dellnitz, Andreas; Tavana, Madjid; Banker, Rajiv (Annals of Operations Research)
Keywords: Management Science and Operations Research; General Decision Sciences

A 28-Gb/s 27.2mW NRZ Full-Rate Bang-Bang Clock and Data Recovery in 22 nm FD-SOI CMOS Technology - Iftekhar, Mohammed; Gowda, Harshan; Kneuper, Pascal; Sadiye, Babak; Müller, Wolfgang; Scheytt, Christoph (2023 IEEE BiCMOS and Compound Semiconductor Integrated Circuits and Technology Symposium (BCICTS))

A fuzzy multi-objective optimization model for sustainable location planning using volumetric fuzzy sets - Tavana, Madjid; Arman, Hosein; Hadi-Vencheh, Abdollah; Mansoori, Sadegh (Annals of Operations Research)

An optimization model with a lagrangian relaxation algorithm for artificial internet of things-enabled sustainable circular supply chain networks - Tavana, Madjid; Khalili Nasr, Arash; Santos-Arteaga, Francisco J.; Saberi, Esmaeel; Mina, Hassan (Annals of Operations Research)

Treuwidrige Darlehenskündigung durch Bank nach Herbeiführung der Zahlungsunfähigkeit des Darlehensnehmers durch eigenmächtige Aufteilung eines eingegangenen Betrags auf verschiedene Verbindlichkeiten. Anmerkung zu LG Aachen, Urt. v. 20.12.2022 - 10 O 137/22 - Bartlitz, David (Entscheidungen zum Wirtschaftsrecht (EWiR))

Optimization of optical waveguide antennas for directive emission of light - Farheen, Henna; Leuteritz, Till; Linden, Stefan; Myroshnychenko, Viktor; Förstner, Jens (Journal of the Optical Society of America B)
Optical traveling wave antennas offer unique opportunities to control and selectively guide light into a specific direction, which renders them excellent candidates for optical communication and sensing. These applications require state-of-the-art engineering to reach optimized functionalities such as high directivity and radiation efficiency, low sidelobe levels, broadband and tunable capabilities, and compact design. In this work, we report on the numerical optimization of the directivity of optical traveling wave antennas made from low-loss dielectric materials using full-wave numerical simulations in conjunction with the particle swarm optimization algorithm. The antennas are composed of a reflector and a director deposited on a glass substrate, and an emitter placed in the feed gap between them serves as an internal source of excitation. In particular, we analyze antennas with rectangular- and horn-shaped directors made of either hafnium dioxide or silicon. The optimized antennas produce highly directional emissions due to the presence of two dominant guided TE modes in the director in addition to leaky modes. These guided modes dominate the far-field emission pattern and govern the direction of the main lobe emission, which predominately originates from the end facet of the director. Our work also provides a comprehensive analysis of the modes, radiation patterns, parametric influences, and bandwidths of the antennas, which highlights their robust nature.
Keywords: tet_topic_opticalantenna

Methodenentwicklung zur Versagensanalyse aufgrund der Aushärtung vorgeschädigter Klebverbindungen in stahlintensiven Mischbaustrukturen - Beule, Felix; Teutenberg, Dominik; Meschut, Gerson; Aubel, Tobias; Matzenmiller, Anton (22. Kolloquium: Gemeinsame Forschung in der Klebtechnik)

Lebensdauerprognose für Stahlklebverbindungen bei multiaxialer Belastung mit Phasenverschiebung, veränderlicher Mittelspannung und variablen Amplituden - Carillo Beber, Vinicius; Nagel, Christof; Mayer, Bernd; Köster, Christian; Matzenmiller, Anton; Hecht, Matthias; Baumgartner, Jörg; Melz, Tobias; Tews, Karina; Çavdar, Serkan; Meschut, Gerson (22. Kolloquium: Gemeinsame Forschung in der Klebtechnik)

Weiterentwicklung der induktiven Schnellhärtung von Klebverbindungen für robuste Fertigungsprozesse unter Berücksichtigung von serienrelevanten Einflussfaktoren - Kowatz, Jannik; Teutenberg, Dominik; Meschut, Gerson (22. Kolloquium Gemeinsame Forschung in der Klebtechnik)

Analyse des Versagensverhaltens geklebter Stahl-Verbindungen beim werkstoffschonenden Entfügen in der Karosserieinstandsetzung - Chudalla, Nick; Meschut, Gerson; Bartley, Aurélie; Wibbeke, Tim Michael (22. Kolloquium: Gemeinsame Forschung in der Klebtechnik)

Experimentelle und numerische Untersuchung des Einflusses variabler Betriebstemperaturen auf das Trag- und Versagensverhalten struktureller Klebverbindungen unter Crashbelastung - Striewe, Marius; Meschut, Gerson; Schmelzle, Lars; Mergheim, Julia; Possart, Gunnar; Steinmann, Paul (22. Kolloquium: Gemeinsame Forschung in der Klebtechnik)

ROM-Based Multiobjective Optimization of Elliptic PDEs via Numerical Continuation - Banholzer, Stefan; Gebken, Bennet; Dellnitz, Michael; Peitz, Sebastian; Volkwein, Stefan (Non-Smooth and Complementarity-Based Distributed Parameter Systems)
Multiobjective optimization plays an increasingly important role in modern applications, where several objectives 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. Since 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 as is the case for models governed by partial differential equations (PDEs). To decrease the numerical effort to an affordable amount, surrogate models can be used to replace the expensive PDE evaluations. Existing multiobjective optimization methods using model reduction are limited either to low parameter dimensions or to few (ideally two) objectives. In this article, we present a combination of the reduced basis model reduction method with a continuation approach using inexact gradients. The resulting approach can handle an arbitrary number of objectives while yielding a significant reduction in computing time.

Compensation of LF Common-Mode Noise by the internal DC/DC-Stage for transformerless On-Board Chargers at Three- and Single-Phase Operation - Schafmeister, Frank (International Conference on Electric & Electronic in Hybrid and Electric Vehicles and Electric Energy Management (EEHE),)

An Open-Source Transistor Database and Toolbox as an Unified Software Engineering Tool for Managing and Evaluating Power Transistors - Förster, Nikolas; Rehlaender, Philipp; Wallscheid, Oliver; Schafmeister, Frank; Böcker, Joachim (Proc. 37th IEEE Applied Power Electronics Conference (APEC))

Robust Hysteresis Control for LLC Resonant Converters Using a Fully Isolated Measurement Scheme - Keuck, Lukas; Schafmeister, Frank; Böcker, Joachim (Proc. IEEE International Exhibition and Conference for Power Electronics, Intelligent Motion, Renewable Energy and Energy Management (PCIM))

Turnpike Property in Optimal Microbial Metabolite Production - Caillau, Jean-Baptiste; Djema, Walid; Gouzé, Jean-Luc; Maslovskaya, Sofya; Pomet, Jean-Baptiste (Journal of Optimization Theory and Applications)
Abstract We consider the problem of maximization of metabolite production in bacterial cells formulated as a dynamical optimal control problem (DOCP). According to Pontryagin’s maximum principle, optimal solutions are concatenations of singular and bang arcs and exhibit the chattering or Fuller phenomenon, which is problematic for applications. To avoid chattering, we introduce a reduced model which is still biologically relevant and retains the important structural features of the original problem. Using a combination of analytical and numerical methods, we show that the singular arc is dominant in the studied DOCPs and exhibits the turnpike property. This property is further used in order to design simple and realistic suboptimal control strategies.
Keywords: Applied Mathematics; Management Science and Operations Research; Control and Optimization

Oxide Modified Iron in Electron Beam Powder Bed Fusion—From Processability to Corrosion Properties - Torrent, Christof J. J.; Krooß, Philipp; Huang, Jingyuan; Voigt, Markus; Ebbert, Christoph; Knust, Steffen; Grundmeier, Guido; Niendorf, Thomas (Alloys)
Additive manufacturing (AM) processes are not solely used where maximum design freedom meets low lot sizes. Direct microstructure design and topology optimization can be realized concomitantly during processing by adjusting the geometry, the material composition, and the solidification behavior of the material considered. However, when complex specific requirements have to be met, a targeted part design is highly challenging. In the field of biodegradable implant surgery, a cytocompatible material of an application-adapted shape has to be characterized by a specific degradation behavior and reliably predictable mechanical properties. For instance, small amounts of oxides can have a significant effect on microstructural development, thus likewise affecting the strength and corrosion behavior of the processed material. In the present study, biocompatible pure Fe was processed using electron powder bed fusion (E-PBF). Two different modifications of the Fe were processed by incorporating Fe oxide and Ce oxide in different proportions in order to assess their impact on the microstructural evolution, the mechanical response and the corrosion behavior. The quasistatic mechanical and chemical properties were analyzed and correlated with the final microstructural appearance.

Systematisierung einer rechnergestützten Auswertemethode zur Versagensanalyse geklebter Verbindungen - Chudalla, Nick; Teutenberg, Dominik; Meschut, Gerson; Schneider, Miriam; Smart, Dominik (22. Kolloquium: Gemeinsame Forschung in der Klebtechnik)

Dataset for random uniform distributions of 2D circles and 3D spheres. - Wojciechowski, M (Data Brief)

Increased Cognitive Demands Affect Agility Performance in Female Athletes - Implications for Testing and Training of Agility in Team Ball Sports - Büchel, Daniel; Gokeler, Alli; Heuvelmans, Pieter; Baumeister, Jochen (Perceptual and Motor Skills)
Agility, a key component of team ball sports, describes an athlete´s ability to move fast in response to changing environments. While agility requires basic cognitive functions like processing speed, it also requires more complex cognitive processes like working memory and inhibition. Yet, most agility tests restrict an assessment of cognitive processes to simple reactive times that lack ecological validity. Our aim in this study was to assess agility performance by means of total time on two agility tests with matched motor demands but with both low and high cognitive demands. We tested 22 female team athletes on SpeedCourt, using a simple agility test (SAT) that measured only processing speed and a complex agility test (CAT) that required working memory and inhibition. We found excellent to good reliability for both our SAT (ICC = .79) and CAT (ICC =.70). Lower agility performance on the CAT was associated with increased agility total time and split times ( p < .05). These results demonstrated that agility performance depends on the complexity of cognitive demands. There may be interference-effects between motor and cognitive performances, reducing speed when environmental information becomes more complex. Future studies should consider agility training models that implement complex cognitive stimuli to challenge athletes according to competitive demands. This will also allow scientists and practitioners to tailor tests to talent identification, performance development and injury rehabilitation.
Keywords: Sensory Systems; Experimental and Cognitive Psychology

Distributed gradient-based optimization in the presence of dependent aperiodic communication - Redder, Adrian; Ramaswamy, Arunselvan; Karl, Holger (arXiv:2201.11343)
Iterative distributed optimization algorithms involve multiple agents that communicate with each other, over time, in order to minimize/maximize a global objective. In the presence of unreliable communication networks, the Age-of-Information (AoI), which measures the freshness of data received, may be large and hence hinder algorithmic convergence. In this paper, we study the convergence of general distributed gradient-based optimization algorithms in the presence of communication that neither happens periodically nor at stochastically independent points in time. We show that convergence is guaranteed provided the random variables associated with the AoI processes are stochastically dominated by a random variable with finite first moment. This improves on previous requirements of boundedness of more than the first moment. We then introduce stochastically strongly connected (SSC) networks, a new stochastic form of strong connectedness for time-varying networks. We show: If for any $p \ge0$ the processes that describe the success of communication between agents in a SSC network are $α$-mixing with $n^{p-1}α(n)$ summable, then the associated AoI processes are stochastically dominated by a random variable with finite $p$-th moment. In combination with our first contribution, this implies that distributed stochastic gradient descend converges in the presence of AoI, if $α(n)$ is summable.

ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-expression Learning - Dreßler, Kevin; Sherif, Mohamed; Ngonga Ngomo, Axel-Cyrille (Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia)
The creation of an RDF knowledge graph for a particular application commonly involves a pipeline of tools that transform a set ofinput data sources into an RDF knowledge graph in a process called dataset augmentation. The components of such augmentation pipelines often require extensive configuration to lead to satisfactory results. Thus, non-experts are often unable to use them. Wepresent an efficient supervised algorithm based on genetic programming for learning knowledge graph augmentation pipelines of arbitrary length. Our approach uses multi-expression learning to learn augmentation pipelines able to achieve a high F-measure on the training data. Our evaluation suggests that our approach can efficiently learn a larger class of RDF dataset augmentation tasks than the state of the art while using only a single training example. Even on the most complex augmentation problem we posed, our approach consistently achieves an average F1-measure of 99% in under 500 iterations with an average runtime of 16 seconds
Keywords: 2022 RAKI SFB901 deer dice kevin knowgraphs limes ngonga sherif simba