Perspective: A Conceptual Framework for Adaptive Personalized Nutrition Advice Systems - Renner, Britta; Buyken, Anette; Gedrich, Kurt; Lorkowski, Stefan; Watzl, Bernhard; Linseisen, Jakob; Daniel, Hannelore; Conrad, Johanna; Ferrario, Paola G.; Holzapfel, Christina; Leitzmann, Michael; Richter, Margrit; et al. (Advances in Nutrition)
Keywords: Nutrition and Dietetics; Medicine (miscellaneous); Food Science

Calibration of Large Coherent MIMO Radar Arrays: Channel Imbalances and 3D Antenna Positions - Greiff, Christian; Mateos-Núñez, David; Simoni, Renato; González-Huici, Maria; Kruse, Stephan; Scheytt, J. Christoph; Kolk, Karl; Höller, Christian; Kurz, Heiko Gustav; Meinecke, Marc-Michael; Gisder, Thomas (2023 24th International Radar Symposium (IRS))
One of the main challenges for next generation automotive radars is the improvement of angular resolution to a sub-degree level. In this context, wide aperture automotive radars of 1m length or more and resolution close to 0.1° in azimuth and 0.5° in elevation could be beneficial. To enable coherent processing of arrays with such large aperture, prior (i.e offline) and online calibration are necessary: channel imbalances (gains and phases) and three dimensional coordinates of transmit and receive elements need to be determined. We propose a calibration strategy based on alternating steps between the two subtasks of i) channel imbalance estimation with ‘known’ array positions, by applying a singular value decomposition to the resulting tensor calculus problem; and ii) antenna position estimation with ’known’ channel imbalances, by numerically maximizing the Bayesian posterior probability; in both cases operating on range/Doppler snapshots of disjoint targets (with potentially unknown locations). Simulation studies based on the parameters of a MIMO 8x6 linear sparse array show promising results as long as the initial position errors do not exceed half a wavelength (2mm), beyond which we observe strong effects of ambiguity. Experimental results with real measurements show that after calibration in laboratory conditions, our MIMO 8x6 demonstrator with 50cm aperture is able to resolve two targets at the same range with angular separation at least as close as 0.4°.

Multi-Objective Optimization for Sparse Deep Neural Network Training - Hotegni, Sedjro Salomon; Peitz, Sebastian; Berkemeier, Manuel Bastian (arXiv:2308.12243)
Different conflicting optimization criteria arise naturally in various Deep Learning scenarios. These can address different main tasks (i.e., in the setting of Multi-Task Learning), but also main and secondary tasks such as loss minimization versus sparsity. The usual approach is a simple weighting of the criteria, which formally only works in the convex setting. In this paper, we present a Multi-Objective Optimization algorithm using a modified Weighted Chebyshev scalarization for training Deep Neural Networks (DNNs) with respect to several tasks. By employing this scalarization technique, the algorithm can identify all optimal solutions of the original problem while reducing its complexity to a sequence of single-objective problems. The simplified problems are then solved using an Augmented Lagrangian method, enabling the use of popular optimization techniques such as Adam and Stochastic Gradient Descent, while efficaciously handling constraints. Our work aims to address the (economical and also ecological) sustainability issue of DNN models, with a particular focus on Deep Multi-Task models, which are typically designed with a very large number of weights to perform equally well on multiple tasks. Through experiments conducted on two Machine Learning datasets, we demonstrate the possibility of adaptively sparsifying the model during training without significantly impacting its performance, if we are willing to apply task-specific adaptations to the network weights. Code is available at https://github.com/salomonhotegni/MDMTN.

Modeling and Parameterization for a 3D Simulation of Clinching with an Extensible Die - Rossel, M.; Meschut, G. (Lecture Notes in Mechanical Engineering)

Training matters: Heading incidence and characteristics in children's and youth football (soccer) players - Reeschke, Rebecca; Haase, Franziska Katharina; Dautzenberg, Lena; Krutsch, Werner; Reinsberger, Claus (Scandinavian Journal of Medicine & Science in Sports)
Abstract Objective Concerns about short‐ and long‐term consequences of repetitive heading contributed to heading restrictions in youth football in some countries. This prospective longitudinal cohort study aims to describe heading exposure in children's and youth football over two seasons using standardized video analysis. Methods All matches and training sessions of a male Under‐11 ( n = 29), Under‐15 ( n = 28), Under‐19 ( n = 38), and female Under‐17 ( n = 39) team were videotaped during the seasons 2019–2020 and 2020–2021. Heading frequencies and characteristics were analyzed. Individual heading exposure is presented as average incidence rates (IR) per 1000 match/training hours. Results In 275 matches and 673 training sessions, 22 921 headers were observed. Heading IR per player in matches was 1256 (Under‐11 m), 1608 (Under‐15 m), 1050 (Under‐17 f), and 1966 (Under‐19 m). In training sessions, IR per player was 739 (Under‐11 m), 2206 (Under‐15 m), 1661 (Under‐17 f), and 1419 (Under‐19 m). Five Under‐15 males headed the ball five to eight times per training on average. Most headers were performed without heading duels. Flight distance was predominantly 5–20 m (54%) in matches and <5 m (65%) in training. While head impact location most frequently was at frontal areas, one‐third of all headers in Under‐11 in matches hit temporal, parietal, and occipital parts of the head. Conclusion Heading incidence was low in the youngest age group, whereas (predominantly five) Under‐15 males showed very high heading exposures in training. In assessment and regulation of heading burden, training sessions and individual heading behavior should specifically be addressed. Recommendations for heading the ball in practice should account for individual and age‐related differences.
Keywords: Physical Therapy; Sports Therapy and Rehabilitation; Orthopedics and Sports Medicine

Methodical Approach to Reducing Design Time by using Neural Networks in Early Stages of Concept Development - Ott, Manuel; Meihöfener, Niclas; Mozgova, Iryna (Proceedings of the 34rd Annual International Solid Freeform Fabrication Symposium 2023)
Modern companies often face various challenges in concept development of products or systems. Design engineers prepare initial concepts as 3D models. These are then simulated by computational engineers. If requirements are not met, this necessitates an iterative process that runs between the design and computation departments until a valid concept is created. Design methods such as topology optimization are often used here. The upcoming result is then attempted to be adapted to certain manufacturing processes. These iteration loops can sometimes take a very long time, since the model construction and structural optimization generate large computational efforts. The present work shows on an example a methodical approach, which represents a first proof of concept, to solving this problem, including a description of methods and techniques, as well as possible problems in a detailed analysis concerning training data for neural networks and their abstraction capabilities. It is evident that additional research work needs to be conducted for further utilization in order to address all arising questions.

A Sub-Threshold Microwave RFID Tag Chip, Compatible With RFID MIMO Reader Technology - Haddadian, Sanaz; Scheytt, J. Christoph; von Bögel, Gerd; Grenter, Thorben (IEEE Journal of Radio Frequency Identification)
We present a fully integrated radio frequency identifications transponder chip operating at 5.8 GHz, which is compatible with the class-1 generation-2 of the Electronic Product Code protocol (EPC-C1 G2). The tag chip including the analog front-end and the digital baseband processor, are designed in the sub-threshold regime (0.5 V) with a total supply current of less than 50 μA. As a power scavenging unit, a single-stage differential-drive rectifier structure is designed and fabricated with standard threshold voltage (SVT) MOS elements in a commercial 65-nm CMOS process, to provide 0.8 V of rectified voltage. Measurements performed on the fabricated single-stage structure show a maximum power conversion efficiency of 69.6% for a 22 kΩ load and a sensitivity of -12.5 dBm, which corresponds to more than 1 m of reading range. The power conversion efficiency at this range is about 64%.

Impact of 3D Curvature on the Polarization Orientation in Non-Ising Domain Walls - Acevedo-Salas, Ulises; Croes, Boris; Zhang, Yide; Cregut, Olivier; Dorkenoo, Kokou Dodzi; Kirbus, Benjamin; Singh, Ekta; Beccard, Henrik; Rüsing, Michael; Eng, Lukas M.; Hertel, Riccardo; Eliseev, Eugene A.; et al. (Nano Letters)
Ferroelectric domain boundaries are quasi-two-dimensional functional interfaces with high prospects for nanoelectronic applications. Despite their reduced dimensionality, they can exhibit complex non-Ising polarization configurations and unexpected physical properties. Here, the impact of the three-dimensional (3D) curvature on the polarization profile of nominally uncharged 180° domain walls in LiNbO3 is studied using second-harmonic generation microscopy and 3D polarimetry analysis. Correlations between the domain-wall curvature and the variation of its internal polarization unfold in the form of modulations of the Néel-like character, which we attribute to the flexoelectric effect. While the Néel-like character originates mainly from the tilting of the domain wall, the internal polarization adjusts its orientation due to the synergetic upshot of dipolar and monopolar bound charges and their variation with the 3D curvature. Our results show that curved interfaces in solid crystals may offer a rich playground for tailoring nanoscale polar states.
Keywords: Mechanical Engineering; Condensed Matter Physics; General Materials Science; General Chemistry; Bioengineering

Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features - Prager, Raphael Patrick; Dietrich, Konstantin; Schneider, Lennart; Schäpermeier, Lennart; Bischl, Bernd; Kerschke, Pascal; Trautmann, Heike; Mersmann, Olaf (Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms)
Artificial benchmark functions are commonly used in optimization research because of their ability to rapidly evaluate potential solutions, making them a preferred substitute for real-world problems. However, these benchmark functions have faced criticism for their limited resemblance to real-world problems. In response, recent research has focused on automatically generating new benchmark functions for areas where established test suites are inadequate. These approaches have limitations, such as the difficulty of generating new benchmark functions that exhibit exploratory landscape analysis (ELA) features beyond those of existing benchmarks.The objective of this work is to develop a method for generating benchmark functions for single-objective continuous optimization with user-specified structural properties. Specifically, we aim to demonstrate a proof of concept for a method that uses an ELA feature vector to specify these properties in advance. To achieve this, we begin by generating a random sample of decision space variables and objective values. We then adjust the objective values using CMA-ES until the corresponding features of our new problem match the predefined ELA features within a specified threshold. By iteratively transforming the landscape in this way, we ensure that the resulting function exhibits the desired properties. To create the final function, we use the resulting point cloud as training data for a simple neural network that produces a function exhibiting the target ELA features. We demonstrate the effectiveness of this approach by replicating the existing functions of the well-known BBOB suite and creating new functions with ELA feature values that are not present in BBOB.
Keywords: Benchmarking; Instance Generator; Black-Box Continuous Optimization; Exploratory Landscape Analysis; Neural Networks

Nullifying the Inherent Bias of Non-invariant Exploratory Landscape Analysis Features - Prager, Raphael Patrick; Trautmann, Heike (Applications of Evolutionary Computation)
Exploratory landscape analysis (ELA) in single-objective black-box optimization relies on a comprehensive and large set of numerical features characterizing problem instances. Those foster problem understanding and serve as basis for constructing automated algorithm selection models choosing the best suited algorithm for a problem at hand based on the aforementioned features computed prior to optimization. This work specifically points to the sensitivity of a substantial proportion of these features to absolute objective values, i.e., we observe a lack of shift and scale invariance. We show that this unfortunately induces bias within automated algorithm selection models, an overfitting to specific benchmark problem sets used for training and thereby hinders generalization capabilities to unseen problems. We tackle these issues by presenting an appropriate objective normalization to be used prior to ELA feature computation and empirically illustrate the respective effectiveness focusing on the BBOB benchmark set.

Peak-A-Boo! Generating Multi-objective Multiple Peaks Benchmark Problems with Precise Pareto Sets - Schäpermeier, Lennart; Kerschke, Pascal; Grimme, Christian; Trautmann, Heike (Evolutionary Multi-Criterion Optimization)
The design and choice of benchmark suites are ongoing topics of discussion in the multi-objective optimization community. Some suites provide a good understanding of their Pareto sets and fronts, such as the well-known DTLZ and ZDT problems. However, they lack diversity in their landscape properties and do not provide a mechanism for creating multiple distinct problem instances. Other suites, like bi-objective BBOB, possess diverse and challenging landscape properties, but their optima are not well understood and can only be approximated empirically without any guarantees.

Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python - Prager, Raphael Patrick; Trautmann, Heike (Evolutionary Computation)
The herein proposed Python package pflacco provides a set of numerical features to characterize single-objective continuous and constrained optimization problems. Thereby, pflacco addresses two major challenges in the area optimization. Firstly, it provides the means to develop an understanding of a given problem instance, which is crucial for designing, selecting, or configuring optimization algorithms in general. Secondly, these numerical features can be utilized in the research streams of automated algorithm selection and configuration. While the majority of these landscape features is already available in the R package flacco, our Python implementation offers these tools to an even wider audience and thereby promotes research interests and novel avenues in the area of optimization.

Plant-based dietary patterns and cardiometabolic risk factors in subgroups of individuals with diabetes - Weber, Katharina S.; Schlesinger, Sabrina; Lang, Alexander; Straßburger, Klaus; Maalmi, Haifa; Zaharia, Oana P.; Goletzke, Janina; Buyken, Anette; Trenkamp, Sandra; Wagner, Robert; Lieb, Wolfgang; Roden, Michael; et al. (Diabetologie und Stoffwechsel)

Compensation of the Shrinkage Behavior Occurring in Cylindrical Components in the FDM Process - Koers, Thorsten; Magyar, Balázs (Macromolecular Symposia)
Fused deposition modeling (FDM) is an additive manufacturing process that can be used to manufacture three-dimensional thermoplastic components layer by layer. One disadvantage of FDM is the shrinkage of the components during the manufacturing process. The filament is molten in the nozzle, deposited layer by layer, and cools down again. During solidification, the filament shrinks inhomogeneously in the x/y/z direction, which leads to distortion of the component geometries. Particularly with higher nominal lengths and complex local geometries, there is a need for optimization with regard to dimensional accuracy. The aim of this paper is to counteract this on the software side with global as well as local shrinkage factors. The expected shrinkage within a layer is predicted with an in-house developed software. The geometric accuracy of the model is verified by experimental investigations on cylindrical test specimens. In these, the so-called clover effect occurs as a result of the shrinkage. The circular shape of the deposited layer is deformed by the distortion in the x–y plane comparable to a clover. Finally, the results are validated by analyzing a demonstrator in the form of a bracket.

The Choice of Ideology and Everyday Decisions - Burs, Carina; Gries, Thomas; Müller, Veronika (Journal of Organizational Psychology)
Individuals strive to make decisions that are consistent with not only their consumer preferences but also their psychological needs. However, they are confronted with complex, ambiguous or even false information. Ideologies and belief systems provide guidance when processing and evaluating information and give a coherent and comprehensible interpretation of reality. The first question is: why is an individual attracted to a particular ideology? Individuals choose ideologies that resonate with their subjective psychological needs and preferences. Second, how do individuals search for ideologies and find out which suit them best? We model an individual’s sequential information search for the best matching ideologies by applying Bayesian learning and utility optimization. Additional information enhances utility by reducing uncertainty. As a search is costly, the process may stop once an individual adopts an ideology even if the information set remains incomplete. Third, once they have chosen a particular ideology, individuals adhere to its rules and norms when making everyday decisions. Consumers not only physically consume, but they also act in accordance with their psychological needs.
Keywords: Economics; Ideology; Decision-making

Protein intake and type 2 diabetes mellitus: an umbrella review of systematic reviews for the evidence-based guideline for protein intake of the German Nutrition Society - Schulze, Matthias B.; Haardt, Julia; Amini, Anna M.; Kalotai, Nicole; Lehmann, Andreas; Schmidt, Annemarie; Buyken, Anette; Egert, Sarah; Ellinger, Sabine; Kroke, Anja; Kühn, Tilman; Louis, Sandrine; et al. (European Journal of Nutrition)
Abstract Purpose Protein-rich foods show heterogeneous associations with the risk of type 2 diabetes (T2D) and it remains unclear whether habitual protein intake is related to T2D risk. We carried out an umbrella review of systematic reviews (SR) of randomised trials and/or cohort studies on protein intake in relation to risks of T2D. Methods Following a pre-specified protocol (PROSPERO: CRD42018082395), we retrieved SRs on protein intake and T2D risk published between July 1st 2009 and May 22nd 2022, and assessed the methodological quality and outcome-specific certainty of the evidence using a modified version of AMSTAR 2 and NutriGrade, respectively. The overall certainty of evidence was rated according to predefined criteria. Results Eight SRs were identified of which six contained meta-analyses. The majority of SRs on total protein intake had moderate or high methodological quality and moderate outcome-specific certainty of evidence according to NutriGrade, however, the latter was low for the majority of SRs on animal and plant protein. Six of the eight SRs reported risk increases with both total and animal protein. According to one SR, total protein intake in studies was ~ 21 energy percentage (%E) in the highest intake category and 15%E in the lowest intake category. Relative Risks comparing high versus low intake in most recent SRs ranged from 1.09 (two SRs, 95% CIs 1.02–1.15 and 1.06–1.13) to 1.11 (1.05–1.16) for total protein (between 8 and 12 cohort studies included) and from 1.13 (1.08–1.19) to 1.19 (two SRs, 1.11–1.28 and 1.11–1.28) (8–9 cohort studies) for animal protein. However, SRs on RCTs examining major glycaemic traits (HbA 1c , fasting glucose, fasting insulin) do not support a clear biological link with T2D risk. For plant protein, some recent SRs pointed towards risk decreases and non-linear associations, however, the majority did not support an association with T2D risk. Conclusion Higher total protein intake was possibly associated with higher T2D risk, while there is insufficient evidence for a risk increase with higher intakes of animal protein and a risk decrease with plant protein intake. Given that most SRs on plant protein did not indicate an association, there is possibly a lack of an effect.
Keywords: Nutrition and Dietetics; Medicine (miscellaneous)

Protein intake and body weight, fat mass and waist circumference: an umbrella review of systematic reviews for the evidence-based guideline on protein intake of the German Nutrition Society - Ellinger, Sabine; Amini, Anna M.; Haardt, Julia; Lehmann, Andreas; Schmidt, Annemarie; Bischoff-Ferrari, Heike A.; Buyken, Anette; Kroke, Anja; Kühn, Tilman; Louis, Sandrine; Lorkowski, Stefan; Nimptsch, Katharina; et al. (European Journal of Nutrition)
Abstract Purpose This umbrella review aimed to assess whether dietary protein intake with regard to quantitative (higher vs. lower dietary protein intake) and qualitative considerations (total, plant-based or animal-based protein intake) affects body weight (BW), fat mass (FM) and waist circumference (WC). Methods A systematic literature search was conducted in PubMed, Embase and Cochrane Database of Systematic Reviews for systematic reviews (SRs) with and without meta-analyses of prospective studies published between 04 October 2007 and 04 January 2022. Methodological quality and outcome-specific certainty of evidence of the retrieved SRs were assessed by using AMSTAR 2 and NutriGrade, respectively, in order to rate the overall certainty of evidence using predefined criteria. Results Thirty-three SRs were included in this umbrella review; 29 were based on randomised controlled trials, a few included cohort studies. In studies without energy restriction, a high-protein diet did not modulate BW, FM and WC in adults in general (all “possible” evidence); for older adults, overall certainty of evidence was “insufficient” for all parameters. Under hypoenergetic diets, a high-protein diet mostly decreased BW and FM, but evidence was “insufficient” due to low methodological quality. Evidence regarding an influence of the protein type on BW, FM and WC was “insufficient”. Conclusion “Possible” evidence exists that the amount of protein does not affect BW, FM and WC in adults under isoenergetic conditions. Its impact on the reduction in BW and FM under hypoenergetic conditions remains unclear; evidence for an influence of protein type on BW, FM and WC is “insufficient”.
Keywords: Nutrition and Dietetics; Medicine (miscellaneous)

Alignment between timing of ‘highest caloric intake’ and chronotype in relation to body composition during adolescence: the DONALD Study - Jankovic, Nicole; Schmitting, Sarah; Stutz, Bianca; Krüger, Bettina; Buyken, Anette; Alexy, Ute (European Journal of Nutrition)
Abstract Purpose Our aim was to assess alignment in timing of ‘highest caloric intake’ with individual chronotype and its association with body composition in adolescents. Methods We used repeatedly collected data from n = 196 adolescents (age 9–16 years, providing N = 401 yearly questionnaires) of the DONALD open cohort study. Chronotype was assessed by the Munich Chronotype Questionnaire from which midpoint of sleep (MSFsc) was derived. A sex- and age-specific diet-chrono-alignment score (DCAS) was calculated as the difference in hours between the chronotype-specific median timing of highest caloric intake of the studied population and the individual timing of ‘highest caloric intake’ or vice versa. Repeated-measures regression models were applied to study cross-sectional and longitudinal associations between the DCAS and body composition, e.g., Fat Mass Index (FMI) or Fat Free Mass Index (FFMI). Results DCAS ranged from −6:42 h to + 8:01 h and was not associated with body composition. Among adolescents with a later chronotype ( N = 201) a 1 h increase in DCAS (later consumption of ‘highest caloric intake’ in comparison to the median intake of that group), increased FFMI by 1.92 kg/m 2 (95% CI: 0.15, 3.69, p value = 0.04) over a median follow-up of 0.94 year. Conclusion Alignment of energy intake with individual chronotype appears beneficial for FFMI among those with a late chronotype.
Keywords: Nutrition and Dietetics; Medicine (miscellaneous)

Analysis and Optimization of Joint Formation in Hybrid Compression Molding - Lückenkötter, Julian; Aydin, Simon; Marten, Thorsten; Tröster, Thomas

Optimization of additively manufactured parts for direct screwing - Moritzer, Elmar; Held, Christian

Role (in-)congruity and the Catch 22 for female executives: how stereotyping contributes to the gender pay gap at top executive level - Diederich, Sarah; Iseke, Anja; Pull, Kerstin; Schneider, Martin (The International Journal of Human Resource Management)
Keywords: Management of Technology and Innovation; Organizational Behavior and Human Resource Management; Strategy and Management; Business and International Management; Industrial relations

Role (in-)congruity and the Catch 22 for female executives: how stereotyping contributes to the gender pay gap at top executive level - Diederich, Sarah; Iseke, Anja; Pull, Kerstin; Schneider, Martin (The International Journal of Human Resource Management)
Keywords: Management of Technology and Innovation; Organizational Behavior and Human Resource Management; Strategy and Management; Business and International Management; Industrial relations

Reconstruction of 3D Surfels from Neural Radiance Fields - Telaprolu, Vipasyan

On the Impact of Basic Mutation Operators and Populations within Evolutionary Algorithms for the Dynamic Weighted Traveling Salesperson Problem - Bossek, Jakob; Neumann, Aneta; Neumann, Frank (Proceedings of the Genetic and Evolutionary Computation Conference)
Evolutionary algorithms have been shown to obtain good solutions for complex optimization problems in static and dynamic environments. It is important to understand the behaviour of evolutionary algorithms for complex optimization problems that also involve dynamic and/or stochastic components in a systematic way in order to further increase their applicability to real-world problems. We investigate the node weighted traveling salesperson problem (W-TSP), which provides an abstraction of a wide range of weighted TSP problems, in dynamic settings. In the dynamic setting of the problem, items that have to be collected as part of a TSP tour change over time. We first present a dynamic setup for the dynamic W-TSP parameterized by different types of changes that are applied to the set of items to be collected when traversing the tour. Our first experimental investigations study the impact of such changes on resulting optimized tours in order to provide structural insights of optimization solutions. Afterwards, we investigate simple mutation-based evolutionary algorithms and study the impact of the mutation operators and the use of populations with dealing with the dynamic changes to the node weights of the problem.
Keywords: dynamic optimization; evolutionary algorithms; re-optimization; weighted traveling salesperson problem

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

Practical Network Conditions for the Convergence of Distributed Optimization - Redder, Adrian; Ramaswamy, Arunselvan; Karl, Holger (IFAC-PapersOnLine)

XCS on Embedded Systems: An Analysis of Execution Profiles and Accelerated Classifier Deletion - Hansmeier, Tim; Brede, Mathis; Platzner, Marco (GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion)

Methodenentwicklung zur Verbesserung der Schädigungsmodellierung in der numerischen 3D-Belastungssimulation mechanischer Fügeverfahren unter Berücksichtigung der fügeinduzierten Vorbeanspruchung - Otroshi, Mortaza; Meschut, Gerson

Estimation of Clinch Joint Characteristics Based on Limited Input Data Using Pre-Trained Metamodels - Zirngibl, Christoph; Schleich, Benjamin; Wartzack, Sandro (AI)
Given strict emission targets and legal requirements, especially in the automotive industry, environmentally friendly and simultaneously versatile applicable production technologies are gaining importance. In this regard, the use of mechanical joining processes, such as clinching, enable assembly sheet metals to achieve strength properties similar to those of established thermal joining technologies. However, to guarantee a high reliability of the generated joint connection, the selection of a best-fitting joining technology as well as the meaningful description of individual joint properties is essential. In the context of clinching, few contributions have to date investigated the metamodel-based estimation and optimization of joint characteristics, such as neck or interlock thickness, by applying machine learning and genetic algorithms. Therefore, several regression models have been trained on varying databases and amounts of input parameters. However, if product engineers can only provide limited data for a new joining task, such as incomplete information on applied joining tool dimensions, previously trained metamodels often reach their limits. This often results in a significant loss of prediction quality and leads to increasing uncertainties and inaccuracies within the metamodel-based design of a clinch joint connection. Motivated by this, the presented contribution investigates different machine learning algorithms regarding their ability to achieve a satisfying estimation accuracy on limited input data applying a statistically based feature selection method. Through this, it is possible to identify which regression models are suitable to predict clinch joint characteristics considering only a minimum set of required input features. Thus, in addition to the opportunity to decrease the training effort as well as the model complexity, the subsequent formulation of design equations can pave the way to a more versatile application and reuse of pretrained metamodels on varying tool configurations for a given clinch joining task.
Keywords: Industrial and Manufacturing Engineering

Using second-order information in gradient sampling methods for nonsmooth optimization - Gebken, Bennet (arXiv:2210.04579)
In this article, we show how second-order derivative information can be incorporated into gradient sampling methods for nonsmooth optimization. The second-order information we consider is essentially the set of coefficients of all second-order Taylor expansions of the objective in a closed ball around a given point. Based on this concept, we define a model of the objective as the maximum of these Taylor expansions. Iteratively minimizing this model (constrained to the closed ball) results in a simple descent method, for which we prove convergence to minimal points in case the objective is convex. To obtain an implementable method, we construct an approximation scheme for the second-order information based on sampling objective values, gradients and Hessian matrices at finitely many points. Using a set of test problems, we compare the resulting method to five other available solvers. Considering the number of function evaluations, the results suggest that the method we propose is superior to the standard gradient sampling method, and competitive compared to other methods.

Methodenentwicklung zur rechnerischen Auslegung geklebter Stahlverbindungen unter Alterungsbeanspruchung im Stahl- und Anlagenbau - Sander, Sascha; Teutenberg, Dominik; Meschut, Gerson; Kötz, Fabian; Matzenmiller, Anton; Kasper, Yann; Ummenhofer, Thomas (22. Kolloquium Gemeinsame Forschung in der Klebtechnik)

The Scale4Edge RISC-V Ecosystem - Ecker, Wolfgang; Adelt, Peer; Müller, Wolfgang; Heckmann, Reinhold; Krstic, Milos; Herdt, Vladimir; Drechsler, Rolf; Angst, Gerhard; Wimmer, Ralf; Mauderer, Andreas; Stahl, Rafael; Emrich, Karsten; et al. (In Proceedings of the Design Automation and Test Conference and Exhibition (DATE 2022))
This paper introduces the project Scale4Edge. The project is focused on enabling an effective RISC-V ecosystem for optimization of edge applications. We describe the basic components of this ecosystem and introduce the envisioned demonstrators, which will be used in their evaluation.

Broadband optical Ta2O5 antennas for directional emission of light - Farheen, Henna; Yan, Lok-Yee; Quiring, Viktor; Eigner, Christof; Zentgraf, Thomas; Linden, Stefan; Förstner, Jens; Myroshnychenko, Viktor (Optics Express)
Highly directive antennas with the ability of shaping radiation patterns in desired directions are essential for efficient on-chip optical communication with reduced cross talk. In this paper, we design and optimize three distinct broadband traveling-wave tantalum pentoxide antennas exhibiting highly directional characteristics. Our antennas contain a director and reflector deposited on a glass substrate, which are excited by a dipole emitter placed in the feed gap between the two elements. Full-wave simulations in conjunction with global optimization provide structures with an enhanced linear directivity as high as 119 radiating in the substrate. The high directivity is a result of the interplay between two dominant TE modes and the leaky modes present in the antenna director. Furthermore, these low-loss dielectric antennas exhibit a near-unity radiation efficiency at the operational wavelength of 780 nm and maintain a broad bandwidth. Our numerical results are in good agreement with experimental measurements from the optimized antennas fabricated using a two-step electron-beam lithography, revealing the highly directive nature of our structures. We envision that our antenna designs can be conveniently adapted to other dielectric materials and prove instrumental for inter-chip optical communications and other on-chip applications.
Keywords: tet_topic_opticalantenna

Kronecker Decomposition for Knowledge Graph Embeddings - Demir, Caglar; Lienen, Julian; Ngonga Ngomo, Axel-Cyrille (arXiv:2205.06560)
Knowledge graph embedding research has mainly focused on learning continuous representations of entities and relations tailored towards the link prediction problem. Recent results indicate an ever increasing predictive ability of current approaches on benchmark datasets. However, this effectiveness often comes with the cost of over-parameterization and increased computationally complexity. The former induces extensive hyperparameter optimization to mitigate malicious overfitting. The latter magnifies the importance of winning the hardware lottery. Here, we investigate a remedy for the first problem. We propose a technique based on Kronecker decomposition to reduce the number of parameters in a knowledge graph embedding model, while retaining its expressiveness. Through Kronecker decomposition, large embedding matrices are split into smaller embedding matrices during the training process. Hence, embeddings of knowledge graphs are not plainly retrieved but reconstructed on the fly. The decomposition ensures that elementwise interactions between three embedding vectors are extended with interactions within each embedding vector. This implicitly reduces redundancy in embedding vectors and encourages feature reuse. To quantify the impact of applying Kronecker decomposition on embedding matrices, we conduct a series of experiments on benchmark datasets. Our experiments suggest that applying Kronecker decomposition on embedding matrices leads to an improved parameter efficiency on all benchmark datasets. Moreover, empirical evidence suggests that reconstructed embeddings entail robustness against noise in the input knowledge graph. To foster reproducible research, we provide an open-source implementation of our approach, including training and evaluation scripts as well as pre-trained models in our knowledge graph embedding framework.

Computation and analysis of Pareto critical sets in smooth and nonsmooth multiobjective optimization - Gebken, Bennet
Mehrzieloptimierung behandelt Probleme, bei denen mehrere skalare Zielfunktionen simultan optimiert werden sollen. Ein Punkt ist in diesem Fall optimal, wenn es keinen anderen Punkt gibt, der mindestens genauso gut ist in allen Zielfunktionen und besser in mindestens einer Zielfunktion. Ein notwendiges Optimalitätskriterium lässt sich über Ableitungsinformationen erster Ordnung der Zielfunktionen herleiten. Die Menge der Punkte, die dieses notwendige Kriterium erfüllen, wird als Pareto-kritische Menge bezeichnet. Diese Arbeit enthält neue Resultate über Pareto-kritische Mengen für glatte und nicht-glatte Mehrzieloptimierungsprobleme, sowohl was deren Berechnung betrifft als auch deren Struktur. Im glatten Fall erfolgt die Berechnung über ein Fortsetzungsverfahren, im nichtglatten Fall über ein Abstiegsverfahren. Anschließend wird die Struktur des Randes der Pareto-kritischen Menge analysiert, welcher aus Pareto-kritischen Mengen kleinerer Subprobleme besteht. Schlussendlich werden inverse Probleme betrachtet, bei denen zu einer gegebenen Datenmenge ein Zielfunktionsvektor gefunden werden soll, für den die Datenpunkte kritisch sind.

Dietary protein intake and health-related outcomes: a methodological protocol for the evidence evaluation and the outline of an evidence to decision framework underlying the evidence-based guideline of the German Nutrition Society - Kroke, Anja; Schmidt, Annemarie; Amini, Anna M.; Kalotai, Nicole; Lehmann, Andreas; Haardt, Julia; Bauer, Jürgen M.; Bischoff-Ferrari, Heike A.; Boeing, Heiner; Egert, Sarah; Ellinger, Sabine; Kühn, Tilman; et al. (European Journal of Nutrition)
Abstract Purpose The present work aimed to delineate (i) a revised protocol according to recent methodological developments in evidence generation, to (ii) describe its interpretation, the assessment of the overall certainty of evidence and to (iii) outline an Evidence to Decision framework for deriving an evidence-based guideline on quantitative and qualitative aspects of dietary protein intake. Methods A methodological protocol to systematically investigate the association between dietary protein intake and several health outcomes and for deriving dietary protein intake recommendations for the primary prevention of various non-communicable diseases in the general adult population was developed. Results The developed methodological protocol relies on umbrella reviews including systematic reviews with or without meta-analyses. Systematic literature searches in three databases will be performed for each health-related outcome. The methodological quality of all selected systematic reviews will be evaluated using a modified version of AMSTAR 2, and the outcome-specific certainty of evidence for systematic reviews with or without meta-analysis will be assessed with NutriGrade. The general outline of the Evidence to Decision framework foresees that recommendations in the derived guideline will be given based on the overall certainty of evidence as well as on additional criteria such as sustainability. Conclusion The methodological protocol permits a systematic evaluation of published systematic reviews on dietary protein intake and its association with selected health-related outcomes. An Evidence to Decision framework will be the basis for the overall conclusions and the resulting recommendations for dietary protein intake.
Keywords: Nutrition and Dietetics; Medicine (miscellaneous)

Generation of a Reduced, Representative, Virtual Test Drive for Fast Evaluation of Tire Wear by Clustering of Driving Data - Muth, Lars; Noll, Christian; Sextro, Walter (Advances in Dynamics of Vehicles on Roads and Tracks II - Proceedings of the 27th Symposium of the International Association of Vehicle System Dynamics, IAVSD 2021)
Tire and road wear are a major source of emissions of nonexhaust particulate matter (PM) and make up the largest share of microplastics in the environment. To reduce tire wear through numerical optimization of a vehicle's suspension system, fast simulations of the representative usage of a vehicle are needed. Therefore, this contribution evaluates if instead of a full simulation of a representative test drive, only specific driving maneuvers resulting from a clustering of the driving data can be used to predict tire wear. As a measure for tire wear, the friction work between tire and road is calculated. It is shown that enough clusters result in negligible deviations between the total friction work of the full simulation and the cluster simulations as well as between the distributions of the friction work over the tire width. The calculation time can be reduced to about 1% of the full simulation.
Keywords: Tire Wear; Vehicle Dynamics; Clustering; Virtual Test

A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials - Gevers, Karina; Tornede, Alexander; Wever, Marcel Dominik; Schöppner, Volker; Hüllermeier, Eyke (Welding in the World)
Abstract Heated tool butt welding is a method often used for joining thermoplastics, especially when the components are made out of different materials. The quality of the connection between the components crucially depends on a suitable choice of the parameters of the welding process, such as heating time, temperature, and the precise way how the parts are then welded. Moreover, when different materials are to be joined, the parameter values need to be tailored to the specifics of the respective material. To this end, in this paper, three approaches to tailor the parameter values to optimize the quality of the connection are compared: a heuristic by Potente, statistical experimental design, and Bayesian optimization. With the suitability for practice in mind, a series of experiments are carried out with these approaches, and their capabilities of proposing well-performing parameter values are investigated. As a result, Bayesian optimization is found to yield peak performance, but the costs for optimization are substantial. In contrast, the Potente heuristic does not require any experimentation and recommends parameter values with competitive quality.
Keywords: Metals and Alloys; Mechanical Engineering; Mechanics of Materials

Multi-Objective Trust-Region Filter Method for Nonlinear Constraints using Inexact Gradients - Berkemeier, Manuel Bastian; Peitz, Sebastian (arXiv:2208.12094)
In this article, we build on previous work to present an optimization algorithm for nonlinearly constrained multi-objective optimization problems. The algorithm combines a surrogate-assisted derivative-free trust-region approach with the filter method known from single-objective optimization. Instead of the true objective and constraint functions, so-called fully linear models are employed and we show how to deal with the gradient inexactness in the composite step setting, adapted from single-objective optimization as well. Under standard assumptions, we prove convergence of a subset of iterates to a quasi-stationary point and if constraint qualifications hold, then the limit point is also a KKT-point of the multi-objective problem.

Brief Announcement: The (Limited) Power of Multiple Identities: Asynchronous Byzantine Reliable Broadcast with Improved Resilience through Collusion - Götte, Thorsten; Scheideler, Christian (SPAA ’22: 34th ACM Symposium on Parallelism in Algorithms and Architectures, Philadelphia, PA, USA, July 11 - 14, 2022)

Decision Support Ecosystems: Deﬁnition and Platform Architecture - Kirchhoff, Jonas; Weskamp, Christoph; Engels, Gregor (Decision Support Systems XII: Decision Support Addressing Modern Industry, Business, and Societal Needs)
Decision support systems are crucial in helping decision makers to quickly identify optimal business decisions in increasingly volatile and complex business environments. However, the ideal DSS for one decision maker may not optimally address the requirements for decision support of another decision maker. This is due to differences between decision makers in business goals, regulatory restrictions or availability of resources such as data. By using a suboptimal DSS, decision makers risk implementing suboptimal decision recommendations which endanger the success of their business. This presents DSS developers with the challenge to implement a customizable DSS which can be tailored to the individual requirements for decision support of a single decision maker. In order to address this challenge, we suggest a decision support ecosystem in which DSS developers, decision makers and other domain experts collaborate using a shared platform to provide and combine reusable decision support services into a tailored DSS. The contribution of our paper is twofold: First, we define the concept of a decision support ecosystem with respect to existing digital business ecosystems and discuss expected benefits and challenges. Second, we present a reference architecture for a shared platform supporting the realization of a decision support ecosystem. We demonstrate our contributions in the example application domain of regional energy distribution network planning.

Modeling and Characterization of a 3D Environment for the Design of an Inductively Based Locating Method by Coil Couplings - Lange, Sven; Hedayat, Christian; Kuhn, Harald; Hilleringmann, Ulrich (2022 Smart Systems Integration (SSI))
In this work, methods will be evaluated to numerically calculate the passive electrical parameters of planar coils. These parameters can then be used to optimize inductive applications such as wireless power transmission. The focus here will be on inductive localization, which uses high-frequency magnetic fields and the resulting induced voltage to provide localization through the coupling parameter mutual inductance. To achieve localization with high accuracy and best possible operation (resonance, signal strength, etc.), the coil parameters need to be well known. For this reason, some numerical methods for the calculation of these quantities are presented and validated. In addition, the physical effects are thereby considered in more detail, allowing the localization procedure to be better optimized compared to simulative black-box methods. The goal should be a dedicated simulation platform for planar coils to be able to develop training data for neural networks and to test and optimize localization algorithms.
Keywords: Simulation Environment; Inductive Localization; Coil Parameters; Inductive Applications; Near-Field

Evaluation of Industry 4.0 Applications in Production - Joppen, Robert; Kühn, Arno; Förster, Magdalena; Dumitrescu, Roman (Journal of the Knowledge Economy)
Abstract Industry 4.0 promises many potentials in production. Examples are a data-driven optimization of production processes of individual machines, driverless transport systems, and assistance systems. Nevertheless, companies are still hesitant to invest in Industry 4.0 applications. Studies show that one of the main reasons for that is the unclear economic benefit. In this work, we present a systematic approach for the evaluation of Industry 4.0 applications in production. The main goal of the systematic is to create transparency over the evaluation process of an investment in an Industry 4.0 application in production. The evaluation of a concrete technical solution in an existing production system is supported. As a theoretical foundation, a characterization of investments in Industry 4.0 applications is given. From that, a procedure model is derived. It puts the activities to be carried out, the tools to be used and results in a temporal context. The application of the systematic is shown on the basis of an application example.
Keywords: Economics and Econometrics

Collaborative Modeling of Use Case & Damage Scenarios in Online Workshops Using a 3D Environment - Japs, Sergej; Schmidt, Sebastian; Kargl, Frank; Kaiser, Lydia; Kharatyan, Aschot; Dumitrescu, Roman (Proceedings of the Design Society)
Abstract The development of technical systems requires close cooperation of stakeholders from different disciplines. This collaboration takes place in workshops. Driven by digitalization and by the current pandemic such workshops take place primarily online. Suitable collaboration tools and methods are crucial to success. At the beginning of such workshops, use and damage scenarios are identified. In this paper, we presented a method and tool for identifying and modeling use and damage scenarios, which we evaluated in 14 online workshops with a total of 118 participants over a period of almost 3 years.

Development of a Numerical 3D Model for Analyzing Clinched Joints in Versatile Process Chains - Bielak, C. R.; Böhnke, M.; Bobbert, M.; Meschut, G. (The Minerals, Metals & Materials Series)

On the Treatment of Optimization Problems with L1 Penalty Terms via Multiobjective Continuation - Bieker, Katharina; Gebken, Bennet; Peitz, Sebastian (IEEE Transactions on Pattern Analysis and Machine Intelligence)
We present a novel algorithm that allows us to gain detailed insight into the effects of sparsity in linear and nonlinear optimization, which is of great importance in many scientific areas such as image and signal processing, medical imaging, compressed sensing, and machine learning (e.g., for the training of neural networks). Sparsity is an important feature to ensure robustness against noisy data, but also to find models that are interpretable and easy to analyze due to the small number of relevant terms. It is common practice to enforce sparsity by adding the ℓ1-norm as a weighted penalty term. In order to gain a better understanding and to allow for an informed model selection, we directly solve the corresponding multiobjective optimization problem (MOP) that arises when we minimize the main objective and the ℓ1-norm simultaneously. As this MOP is in general non-convex for nonlinear objectives, the weighting method will fail to provide all optimal compromises. To avoid this issue, we present a continuation method which is specifically tailored to MOPs with two objective functions one of which is the ℓ1-norm. Our method can be seen as a generalization of well-known homotopy methods for linear regression problems to the nonlinear case. Several numerical examples - including neural network training - demonstrate our theoretical findings and the additional insight that can be gained by this multiobjective approach.

2022 Edsger W. Dijkstra Prize in Distributed Computing - Aguiliera, Marcos; Richa, Andréa W.; Schwarzmann, Alexander A.; Panconesi, Alessandro; Scheideler, Christian; Woelfel, Philipp (PODC ’22: ACM Symposium on Principles of Distributed Computing, Salerno, Italy, July 25 - 29, 2022)

A Data Driven Modelling Approach for the Strain Rate Dependent 3D Shear Deformation and Failure of Thermoplastic Fibre Reinforced Composites: Experimental Characterisation and Deriving Modelling Parameters - Gerritzen, Johannes; Hornig, Andreas; Gröger, Benjamin; Gude, Maik (Journal of Composites Science)
The 3D shear deformation and failure behaviour of a glass fibre reinforced polypropylene in a shear strain rate range of γ˙=2.2×10−4 to 3.4 1s is investigated. An Iosipescu testing setup on a servo-hydraulic high speed testing unit is used to experimentally characterise the in-plane and out-of-plane behaviour utilising three specimen configurations (12-, 13- and 31-direction). The experimental procedure as well as the testing results are presented and discussed. The measured shear stress–shear strain relations indicate a highly nonlinear behaviour and a distinct rate dependency. Two methods are investigated to derive according material characteristics: a classical engineering approach based on moduli and strengths and a data driven approach based on the curve progression. In all cases a Johnson–Cook based formulation is used to describe rate dependency. The analysis methodologies as well as the derived model parameters are described and discussed in detail. It is shown that a phenomenologically enhanced regression can be used to obtain material characteristics for a generalising constitutive model based on the data driven approach.
Keywords: Engineering (miscellaneous); Ceramics and Composites

Long-Term Behavior of Clinched Electrical Contacts - Kalich, Jan; Matzke, Marcus; Pfeiffer, Wolfgang; Schlegel, Stephan; Kornhuber, Ludwig; Füssel, Uwe (Metals)
Joining by forming operations presents powerful and complex joining techniques. Clinching is a well-known joining process for use in sheet metalworking. Currently, clinched joints are focusing on mechanically enhanced connections. Additionally, the demand for integrating electrical requirements to transmit electrical currents will be increased in the future. This integration is particularly important, for instance, in the e-mobility sector. It enables connecting battery cells with electrical joints of aluminum and copper. Systematic use of the process-specific advantages of this joining method opens up the possibility to find and create electrically optimized connections. The optimization for the transmission of electrical currents will be demonstrated for clinched joints by adapting the tool geometry and the clinched joint design. Based on a comparison of the electrical joint resistance, the limit use temperature is defined for the joining materials used based on the microstructural condition and the aging condition due to artificial aging. As a result of the investigations carried out, reliable current transmission at a constant conductor temperature of up to 120 °C can be achieved for clinched copper–copper joints. In the case of pure aluminum joints and mixed joints of aluminum and copper, long-term stable current transmission can be ensured up to a conductor temperature of 100 °C.
Keywords: General Materials Science; Metals and Alloys

Modelling and Simulation Strategies for Fluid–Structure-Interactions of Highly Viscous Thermoplastic Melt and Single Fibres—A Numerical Study - Gröger, Benjamin; Wang, Jingjing; Bätzel, Tim; Hornig, Andreas; Gude, Maik (Materials)
A virtual test setup for investigating single fibres in a transverse shear flow based on a parallel-plate rheometer is presented. The investigations are carried out to verify a numerical representation of the fluid–structure interaction (FSI), where Arbitrary Lagrangian–Eulerian (ALE) and computational fluid dynamics (CFD) methods are used and evaluated. Both are suitable to simulate flexible solid structures in a transverse shear flow. Comparative investigations with different model setups and increasing complexity are presented. It is shown, that the CFD method with an interface-based coupling approach is not capable of handling small fibre diameters in comparison to large fluid domains due to mesh dependencies at the interface definitions. The ALE method is more suited for this task since fibres are embedded without any mesh restrictions. Element types beam, solid, and discrete are considered for fibre modelling. It is shown that the beam formulation for ALE and 3D solid elements for the CFD method are the preferred options.
Keywords: General Materials Science

Robust estimation of clinch joint characteristics based on data-driven methods - Zirngibl, Christoph; Schleich, Benjamin; Wartzack, Sandro (The International Journal of Advanced Manufacturing Technology)
Given a steadily increasing demand on multi-material lightweight designs, fast and cost-efficient production technologies, such as the mechanical joining process clinching, are becoming more and more relevant for series production. Since the application of such joining techniques often base on the ability to reach similar or even better joint loading capacities compared to established joining processes (e.g., spot welding), few contributions investigated the systematic improvement of clinch joint characteristics. In this regard, the use of data-driven methods in combination with optimization algorithms showed already high potentials for the analysis of individual joints and the definition of optimal tool configurations. However, the often missing consideration of uncertainties, such as varying material properties, and the related calculation of their impact on clinch joint properties can lead to poor estimation results and thus to a decreased reliability of the entire joint connection. This can cause major challenges, especially for the design and dimensioning of safety-relevant components, such as in car bodies. Motivated by this, the presented contribution introduces a novel method for the robust estimation of clinch joint characteristics including uncertainties of varying and versatile process chains in mechanical joining. Therefore, the utilization of Gaussian process regression models is demonstrated and evaluated regarding the ability to achieve sufficient prediction qualities.
Keywords: Industrial and Manufacturing Engineering; Computer Science Applications; Mechanical Engineering; Software; Control and Systems Engineering