@article{59585, abstract = {{Similar to bulk metal forming, clinch joining is characterised by large plastic deformations and a variety of different 3D stress states, including severe compression. However, inherent to plastic forming is the nucleation and growth of defects, whose detrimental effects on the material behaviour can be described by continuum damage models and eventually lead to material failure. As the damage evolution strongly depends on the stress state, a stress-state-dependent model is utilised to correctly track the accumulation. To formulate and parameterise this model, besides classical experiments, so-called modified punch tests are also integrated herein to enhance the calibration of the failure model by capturing a larger range of stress states and metal-forming-specific loading conditions. Moreover, when highly ductile materials are considered, such as the dual-phase steel HCT590X and the aluminium alloy EN AW-6014 T4 investigated here, strong necking and localisation might occur prior to fracture. This can alter the stress state and affect the actual strain at failure. This influence is captured by coupling plasticity and damage to incorporate the damage-induced softening effect. Its relative importance is shown by conducting inverse parameter identifications to determine damage and failure parameters for both mentioned ductile metals based on up to 12 different experiments.}}, author = {{Friedlein, Johannes and Böhnke, Max and Schlichter, Malte Christian and Bobbert, Mathias and Meschut, Gerson and Mergheim, Julia and Steinmann, Paul}}, issn = {{2504-4494}}, journal = {{Journal of Manufacturing and Materials Processing}}, number = {{4}}, publisher = {{MDPI AG}}, title = {{{Material Parameter Identification for a Stress-State-Dependent Ductile Damage and Failure Model Applied to Clinch Joining}}}, doi = {{10.3390/jmmp8040157}}, volume = {{8}}, year = {{2024}}, } @inproceedings{66172, author = {{Malcher, Jannik and Biebert, Daniel and Chen, Kuan-Hsun and Buschjäger, Sebastian and Hakert, Christian and Chen, Jian-Jia}}, booktitle = {{Proceedings of the 25th ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems}}, publisher = {{ACM}}, title = {{{Language-Based Deployment Optimization for Random Forests (Invited Paper)}}}, doi = {{10.1145/3652032.3659366}}, year = {{2024}}, } @article{66174, title = {{{Register Your Forests: Decision Tree Ensemble Optimization by Explicit CPU Register Allocation}}}, doi = {{10.48550/ARXIV.2404.06846}}, year = {{2024}}, } @article{21199, abstract = {{As in almost every other branch of science, the major advances in data science and machine learning have also resulted in significant improvements regarding the modeling and simulation of nonlinear dynamical systems. It is nowadays possible to make accurate medium to long-term predictions of highly complex systems such as the weather, the dynamics within a nuclear fusion reactor, of disease models or the stock market in a very efficient manner. In many cases, predictive methods are advertised to ultimately be useful for control, as the control of high-dimensional nonlinear systems is an engineering grand challenge with huge potential in areas such as clean and efficient energy production, or the development of advanced medical devices. However, the question of how to use a predictive model for control is often left unanswered due to the associated challenges, namely a significantly higher system complexity, the requirement of much larger data sets and an increased and often problem-specific modeling effort. To solve these issues, we present a universal framework (which we call QuaSiModO: Quantization-Simulation-Modeling-Optimization) to transform arbitrary predictive models into control systems and use them for feedback control. The advantages of our approach are a linear increase in data requirements with respect to the control dimension, performance guarantees that rely exclusively on the accuracy of the predictive model, and only little prior knowledge requirements in control theory to solve complex control problems. In particular the latter point is of key importance to enable a large number of researchers and practitioners to exploit the ever increasing capabilities of predictive models for control in a straight-forward and systematic fashion.}}, author = {{Peitz, Sebastian and Bieker, Katharina}}, journal = {{Automatica}}, publisher = {{Elsevier}}, title = {{{On the Universal Transformation of Data-Driven Models to Control Systems}}}, doi = {{10.1016/j.automatica.2022.110840}}, volume = {{149}}, year = {{2023}}, } @article{35428, abstract = {{This paper presents a model of an energy system for a private household extended by a lifetime prognosis. The energy system was designed for fully covering the year-round energy demand of a private household on the basis of electricity generated by a photovoltaic (PV) system, using a hybrid energy storage system consisting of a hydrogen unit and a lithium-ion battery. Hydrogen is produced with a Proton Exchange Membrane (PEM) electrolyser by PV surplus during the summer months and then stored in a hydrogen tank. Mainly during winter, in terms of lack of PV energy, the hydrogen is converted back into electricity and heat by a fuel cell. The model was created in Matlab/Simulink and is based on real input data. Heat demand was also taken into account and is covered by a heat pump. The simulation period is a full year to account for the seasonality of energy production and demand. Due to high initial costs, the longevity of such an energy system is of vital interest. Therefore, this model was extended by a lifetime prediction in order to optimize the dimensioning with the aim of lifetime extension of a hydrogen-based energy system. Lifetime influencing factors were identified on the basis of a literature review and were integrated in the model. An extensive parameter study was performed to evaluate different dimensionings regarding the energy balance and the lifetime of the three components, electrolyser, fuel cell and lithium-ion battery. The results demonstrate the benefits of a holistic modelling approach and enable a design optimization regarding the use of resources, lifetime and self-sufficiency of the system}}, author = {{Möller, Marius Claus and Krauter, Stefan}}, issn = {{2673-9941}}, journal = {{Solar}}, number = {{1}}, pages = {{25--48}}, publisher = {{MDPI AG}}, title = {{{Dimensioning and Lifetime Prediction Model for a Hybrid, Hydrogen-Based Household PV Energy System Using Matlab/Simulink}}}, doi = {{10.3390/solar3010003}}, volume = {{3}}, year = {{2023}}, } @article{47992, abstract = {{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.}}, author = {{Acevedo-Salas, Ulises and Croes, Boris and Zhang, Yide and Cregut, Olivier and Dorkenoo, Kokou Dodzi and Kirbus, Benjamin and Singh, Ekta and Beccard, Henrik and Rüsing, Michael and Eng, Lukas M. and Hertel, Riccardo and Eliseev, Eugene A. and Morozovska, Anna N. and Cherifi-Hertel, Salia}}, issn = {{1530-6984}}, journal = {{Nano Letters}}, keywords = {{Mechanical Engineering, Condensed Matter Physics, General Materials Science, General Chemistry, Bioengineering}}, number = {{3}}, pages = {{795--803}}, publisher = {{American Chemical Society (ACS)}}, title = {{{Impact of 3D Curvature on the Polarization Orientation in Non-Ising Domain Walls}}}, doi = {{10.1021/acs.nanolett.2c03579}}, volume = {{23}}, year = {{2023}}, } @inproceedings{47522, abstract = {{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.}}, author = {{Prager, Raphael Patrick and Dietrich, Konstantin and Schneider, Lennart and Schäpermeier, Lennart and Bischl, Bernd and Kerschke, Pascal and Trautmann, Heike and Mersmann, Olaf}}, booktitle = {{Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms}}, isbn = {{9798400702020}}, keywords = {{Benchmarking, Instance Generator, Black-Box Continuous Optimization, Exploratory Landscape Analysis, Neural Networks}}, pages = {{129–139}}, publisher = {{Association for Computing Machinery}}, title = {{{Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features}}}, doi = {{10.1145/3594805.3607136}}, year = {{2023}}, } @inproceedings{46297, abstract = {{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.}}, author = {{Prager, Raphael Patrick and Trautmann, Heike}}, booktitle = {{Applications of Evolutionary Computation}}, editor = {{Correia, João and Smith, Stephen and Qaddoura, Raneem}}, isbn = {{978-3-031-30229-9}}, pages = {{411–425}}, publisher = {{Springer Nature Switzerland}}, title = {{{Nullifying the Inherent Bias of Non-invariant Exploratory Landscape Analysis Features}}}, year = {{2023}}, } @inproceedings{46298, abstract = {{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.}}, author = {{Schäpermeier, Lennart and Kerschke, Pascal and Grimme, Christian and Trautmann, Heike}}, booktitle = {{Evolutionary Multi-Criterion Optimization}}, editor = {{Emmerich, Michael and Deutz, André and Wang, Hao and Kononova, Anna V. and Naujoks, Boris and Li, Ke and Miettinen, Kaisa and Yevseyeva, Iryna}}, isbn = {{978-3-031-27250-9}}, pages = {{291–304}}, publisher = {{Springer Nature Switzerland}}, title = {{{Peak-A-Boo! Generating Multi-objective Multiple Peaks Benchmark Problems with Precise Pareto Sets}}}, year = {{2023}}, } @article{46299, abstract = {{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.}}, author = {{Prager, Raphael Patrick and Trautmann, Heike}}, issn = {{1063-6560}}, journal = {{Evolutionary Computation}}, pages = {{1–25}}, title = {{{Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python}}}, doi = {{10.1162/evco_a_00341}}, year = {{2023}}, } @inproceedings{48377, author = {{Weber, Katharina S. and Schlesinger, Sabrina and Lang, Alexander and Straßburger, Klaus and Maalmi, Haifa and Zaharia, Oana P. and Goletzke, Janina and Buyken, Anette and Trenkamp, Sandra and Wagner, Robert and Lieb, Wolfgang and Roden, Michael and Herder, Christian}}, booktitle = {{Diabetologie und Stoffwechsel}}, issn = {{1861-9010}}, publisher = {{Georg Thieme Verlag}}, title = {{{Plant-based dietary patterns and cardiometabolic risk factors in subgroups of individuals with diabetes}}}, doi = {{10.1055/s-0043-1767936}}, year = {{2023}}, } @inproceedings{48095, abstract = {{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.}}, author = {{Koers, Thorsten and Magyar, Balázs}}, booktitle = {{Macromolecular Symposia}}, editor = {{Lamanna, Giuseppe and Opran, Constantin}}, location = {{Bucharest, Romania}}, number = {{1}}, publisher = {{Wiley}}, title = {{{Compensation of the Shrinkage Behavior Occurring in Cylindrical Components in the FDM Process}}}, doi = {{https://doi.org/10.1002/masy.202200185}}, volume = {{411}}, year = {{2023}}, } @article{48086, abstract = {{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.}}, author = {{Burs, Carina and Gries, Thomas and Müller, Veronika}}, issn = {{2158-3609}}, journal = {{Journal of Organizational Psychology}}, keywords = {{Economics, Ideology, Decision-making}}, number = {{1}}, publisher = {{North American Business Press}}, title = {{{The Choice of Ideology and Everyday Decisions}}}, doi = {{10.33423/jop.v23i1.6033}}, volume = {{23}}, year = {{2023}}, } @article{48374, abstract = {{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 (HbA1c, 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. }}, author = {{Schulze, Matthias B. and Haardt, Julia and Amini, Anna M. and Kalotai, Nicole and Lehmann, Andreas and Schmidt, Annemarie and Buyken, Anette and Egert, Sarah and Ellinger, Sabine and Kroke, Anja and Kühn, Tilman and Louis, Sandrine and Nimptsch, Katharina and Schwingshackl, Lukas and Siener, Roswitha and Zittermann, Armin and Watzl, Bernhard and Lorkowski, Stefan}}, issn = {{1436-6207}}, journal = {{European Journal of Nutrition}}, keywords = {{Nutrition and Dietetics, Medicine (miscellaneous)}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{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}}}, doi = {{10.1007/s00394-023-03234-5}}, year = {{2023}}, } @article{48373, abstract = {{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”. }}, author = {{Ellinger, Sabine and Amini, Anna M. and Haardt, Julia and Lehmann, Andreas and Schmidt, Annemarie and Bischoff-Ferrari, Heike A. and Buyken, Anette and Kroke, Anja and Kühn, Tilman and Louis, Sandrine and Lorkowski, Stefan and Nimptsch, Katharina and Schulze, Matthias B. and Schwingshackl, Lukas and Siener, Roswitha and Stangl, Gabriele I. and Volkert, Dorothee and Zittermann, Armin and Watzl, Bernhard and Egert, Sarah}}, issn = {{1436-6207}}, journal = {{European Journal of Nutrition}}, keywords = {{Nutrition and Dietetics, Medicine (miscellaneous)}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{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}}}, doi = {{10.1007/s00394-023-03220-x}}, year = {{2023}}, } @article{48456, abstract = {{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/m2 (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. }}, author = {{Jankovic, Nicole and Schmitting, Sarah and Stutz, Bianca and Krüger, Bettina and Buyken, Anette and Alexy, Ute}}, issn = {{1436-6207}}, journal = {{European Journal of Nutrition}}, keywords = {{Nutrition and Dietetics, Medicine (miscellaneous)}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Alignment between timing of ‘highest caloric intake’ and chronotype in relation to body composition during adolescence: the DONALD Study}}}, doi = {{10.1007/s00394-023-03259-w}}, year = {{2023}}, } @inproceedings{48586, author = {{Lückenkötter, Julian and Aydin, Simon and Marten, Thorsten and Tröster, Thomas}}, location = {{Belfast}}, title = {{{Analysis and Optimization of Joint Formation in Hybrid Compression Molding}}}, year = {{2023}}, } @inproceedings{48747, author = {{Moritzer, Elmar and Held, Christian}}, location = {{Singapur (Singapur)}}, publisher = {{Springer Verlag}}, title = {{{Optimization of additively manufactured parts for direct screwing}}}, year = {{2023}}, } @article{48900, author = {{Diederich, Sarah and Iseke, Anja and Pull, Kerstin and Schneider, Martin}}, issn = {{0958-5192}}, journal = {{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}}, pages = {{1--29}}, publisher = {{Informa UK Limited}}, title = {{{Role (in-)congruity and the Catch 22 for female executives: how stereotyping contributes to the gender pay gap at top executive level}}}, doi = {{10.1080/09585192.2023.2273331}}, year = {{2023}}, } @article{49446, author = {{Diederich, Sarah and Iseke, Anja and Pull, Kerstin and Schneider, Martin}}, issn = {{0958-5192}}, journal = {{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}}, pages = {{1--29}}, publisher = {{Informa UK Limited}}, title = {{{Role (in-)congruity and the Catch 22 for female executives: how stereotyping contributes to the gender pay gap at top executive level}}}, doi = {{10.1080/09585192.2023.2273331}}, year = {{2023}}, }