Inklusiver Mathematikunterricht. Mathematiklernen in Vielfalt von Kompetenzen, Wegen und Lernsituationen - Häsel-Weide, Uta (Die Grundschulzeitschrift)

Addition und Subtraktion verstehen - Graf, Lara Marie; Wienhues, Inga; Häsel-Weide, Uta (Die Grundschulzeitschrift)

The Virus Is Present, Presence Is Virulent: Being Co(m)present With Others in Times of Corona - Eckel, Julia; Linseisen, Elisa (Epidemics and Othering. The Biopolitics of COVID-19 in Historical and Cultural Perspectives)

Navigationen: Tech | Demo (Navigationen - Zeitschrift für Medien- und Kulturwissenschaften)
Keywords: tech demo; technology demonstration; demo or die

Derivation of third order Runge–Kutta methods (ELDIRK) by embedding of lower order implicit time integration schemes for local and global error estimation - Mahnken, Rolf (Computational Mechanics)
Abstract Three prominent low order implicit time integration schemes are the first order implicit Euler-method, the second order trapezoidal rule and the second order Ellsiepen method. Its advantages are stability and comparatively low computational cost, however, they require the solution of a nonlinear system of equations. This paper presents a general approach for the construction of third order Runge–Kutta methods by embedding the above mentioned implicit schemes into the class of ELDIRK-methods. These will be defined to have an Explicit Last stage in the general Butcher array of Diagonal Implicit Runge–Kutta (DIRK) methods, with the consequence, that no additional system of equations must be solved. The main results—valid also for non-linear ordinary differential equations—are as follows: Two extra function calculations are required in order to embed the implicit Euler-method and one extra function calculation is required for the trapezoidal-rule and the Ellsiepen method, in order to obtain the third order properties, respectively. Two numerical examples are concerned with a parachute with viscous damping and a two-dimensional laser beam simulation. Here, we verify the higher order convergence behaviours of the proposed new ELDIRK-methods, and its successful performances for asymptotically exact global error estimation of so-called reversed embedded RK-method are shown.
Keywords: Applied Mathematics; Computational Mathematics; Computational Theory and Mathematics; Mechanical Engineering; Ocean Engineering; Computational Mechanics

PSΦ: Entwicklung von Unterstützungsmaßnahmen für Theoretische Physik - Abbas, Nilab; Bauer, Anna Brigitte; Reinhold, Peter (Lernen, Lehren und Forschen in einer digital geprägten Welt)

PSФ: Entwicklung einer abgestimmten Studieneingangsphase (Physik) - Bauer, Anna Brigitte; Reinhold, Peter (Lernen, Lehren und Forschen in einer digital geprägten Welt)

Generalised tanh-shaped hyperbolic potential: Klein-Gordon equation's bound state solution - Badalov, Vatan; Badalov, Sabuhi (Communications in Theoretical Physics)
Abstract The development of potential theory heightens the understanding of fundamental interactions in quantum systems. In this paper, the bound state solution of the modified radial Klein-Gordon equation is presented for generalised tanh-shaped hyperbolic potential from the Nikiforov-Uvarov method. The resulting energy eigenvalues and corresponding radial wave functions are expressed in terms of the Jacobi polynomials for arbitrary $l$ states. It is also demonstrated that energy eigenvalues strongly correlate with potential parameters for quantum states. Considering particular cases, the generalised tanh-shaped hyperbolic potential and its derived energy eigenvalues exhibit good agreement with the reported findings. Furthermore, the rovibrational energies are calculated for three representative diatomic molecules, namely $\rm{H_{2}}$, $\rm{HCl}$ and $\rm{O_{2}}$. The lowest excitation energies are in perfect agreement with experimental results. Overall, the potential model is displayed to be a viable candidate for concurrently prescribing numerous quantum systems.
Keywords: Physics and Astronomy (miscellaneous)

Diquat Based Dyes: A New Class of Photoredox Catalysts and Their Use in Aerobic Thiocyanation - Meier, Armin; Badalov, Sabuhi; Biktagirov, Timur; Schmidt, Wolf Gero; Wilhelm, René (Chemistry – A European Journal)
A series of new organic donor–π–acceptor dyes incorporating a diquat moiety as a novel electron-acceptor unit have been synthesized and characterized. The analytical data were supported by DFT calculations. These dyes were explored in the aerobic thiocyanation of indoles and pyrroles. Here they showed a high photocatalytic activity under visible light, giving isolated yields of up to 97 %. In addition, the photocatalytic activity of standalone diquat and methyl viologen through formation of an electron donor acceptor complex is presented.
Keywords: General Chemistry; Catalysis; Organic Chemistry

Rutile, anatase, brookite and titania thin film from Hubbard corrected and hybrid DFT - Badalov, Sabuhi; Bocchini, Adriana; Wilhelm, Rene; Kozub, A. L.; Gerstmann, Uwe; Schmidt, Wolf Gero (Materials Research Express)
As a benchmark, the structural, electronic and optical properties of the three main phases of TiO$\rm{_2}$ crystals have been calculated using Hubbard U correction and hybrid functional methods in density-functional theory. These calculations are compared concerning the available experimental observations on pristine TiO$\rm{_2}$ crystals. Modified hybrid functionals, particularly the PBE0 functional with 11.4% fraction of exact exchange, are shown to provide highly accurate atomic structures and also accurate electronic structure data, including optical excitation energies. With $\rm{DFT+U}$, accurate optical spectra are also possible, but only if the Hubbard U is applied on the O $\rm2p$ electrons exclusively. Furthermore, both methods, the 11.4%-PBE0 hybrid functional and the $\rm{DFT+U_p}$ scheme have been used to study TiO$\rm{_2}$ amorphous ultra-thin films, confirming the agreement of the two methods even with respect to small details of the optical spectra. Our results show that the proposed $\rm{DFT+U_p}$ methodology is computationally efficient, but still accurate. It can be applied to well-ordered TiO$\rm{_2}$ polymorphs as well as to amorphous TiO$\rm{_2}$ and will allow for the calculations of complex titania-based structures.

Integrating Prospective LCA in the Development of Automotive Components - Grenz, Julian; Ostermann, Moritz; Käsewieter, Karoline; Cerdas, Felipe; Marten, Thorsten; Herrmann, Christoph; Tröster, Thomas (Sustainability)
The development of automotive components with reduced greenhouse gas (GHG) emissions is needed to reduce overall vehicle emissions. Life Cycle Engineering (LCE) based on Life Cycle Assessment (LCA) supports this by providing holistic information and improvement potentials regarding eco-efficient products. Key factors influencing LCAs of automotive components, such as material production, will change in the future. First approaches for integrating future scenarios for these key factors into LCE already exist, but they only consider a limited number of parameters and scenarios. This work aims to develop a method that can be practically applied in the industry for integrating prospective LCAs (pLCA) into the LCE of automotive components, considering relevant parameters and consistent scenarios. Therefore, pLCA methods are further developed to investigate the influence of future scenarios on the GHG emissions of automotive components. The practical application is demonstrated for a vehicle component with different design options. This paper shows that different development paths of the foreground and background system can shift the ecological optimum of design alternatives. Therefore, future pathways of relevant parameters must be considered comprehensively to reduce GHG emissions of future vehicles. This work contributes to the methodological and practical integration of pLCA into automotive development processes and provides quantitative results.
Keywords: prospective LCA; life cycle engineering (LCE); lightweight design; automotive components; body parts; circular economy; steel; aluminum; hybrid materials; fiber metal laminates

Data-Driven Product Management: A Practitioner-Driven Research Agenda - Grigoryan, Khoren; Fichtler, Timm; Schreiner, Nick; Rabe, Martin; Panzner, Melina; Kühn, Arno; Dumitrescu, Roman; Koldewey, Christian (Procedia CIRP 33)
The global megatrends of digitization and sustainability lead to new challenges for the design and management of technical products in industrial companies. Product management - as the bridge between market and company - has the task to absorb and combine the manifold requirements and make the right product-related decisions. In the process, product management is confronted with heterogeneous information, rapidly changing portfolio components, as well as increasing product, and organizational complexity. Combining and utilizing data from different sources, e.g., product usage data and social media data leads to promising potentials to improve the quality of product-related decisions. In this paper, we reinforce the need for data-driven product management as an interdisciplinary field of action. The state of data-driven product management in practice was analyzed by conducting workshops with six manufacturing companies and hosting a focus group meeting with experts from different industries. We investigate the expectations and derive requirements leading us to open research questions, a vision for data-driven product management, and a research agenda to shape future research efforts.
Keywords: Product Management; Data Analytics; Data-Driven Design; Product-related data; Lifecycle Data; Tool-support

Work-related stressors and coping behaviors among leaders in small and medium-sized IT and technological services enterprises - Dannheim, Indra; Buyken, Anette; Kroke, Anja (BMC Public Health)
Abstract Background Occupational health interventions for leaders are underrepresented in small and medium-sized enterprises (SMEs). When creating and developing effective occupational health interventions, identification of the specific needs of the target group is regarded as an essential step before planning an intervention. Therefore, the aim of this study was (1) to examine the subjectively experienced work-related stressors of leaders in small and medium-sized IT and technological services enterprises, (2) to explore coping behaviors leaders use to deal with the experienced work-related stressors, (3) to investigate resources supporting the coping process and (4) to identify potentially self-perceived consequences resulting from the experienced stressors. Methods Ten semi-structured interviews with leaders in small and medium-sized IT and technological services enterprises were conducted. The interviews were transcribed and analyzed with content-structuring qualitative content analysis in accordance to Kuckartz. Results Leaders in small and medium-sized IT and technological services enterprises experience various stressors caused by work organization as well as industry-related stressors and other work-related stressors. To address the experienced stressors, leaders apply problem focused coping behaviors (e.g. performing changes on structural and personal level), emotional focused coping behaviors (e.g. balancing activities, cognitive restructuring) as well as the utilization of social support. Helpful resources for the coping process include organizational, social and personal resources. As a result of the experienced work-related stressors, interviewees stated to experience different health impairments, negative effects on work quality as well as neglect of leisure activities and lack of time for family and friends. Conclusion The identified experienced work-related stressors, applied coping behaviors, utilized resources and emerging consequences underpin the urgent need for the development and performance of health-oriented leadership interventions for leaders in small and medium- sized IT and technological services. The results of this study can be used when designing a target-oriented intervention for the examined target group.
Keywords: Public Health; Environmental and Occupational Health

Choice architecture interventions promoting sustained healthier food choice and consumption by students in a secondary school setting: A systematic review of intervention studies - Schulte, Eva A.; Winkler, Gertrud; Brombach, Christine; Buyken, Anette (Public Health Nutrition)
Abstract Objective: To systematically review the impact of choice architecture interventions (CAI) on the food choice of healthy adolescents in a secondary school setting. Factors potentially contributing to the effectiveness of CAI types and numbers implemented and its long-term success were examined. Design: PUBMED and Web of Science were systematically searched in October 2021. Publications were included following predefined inclusion criteria and grouped according to number and duration of implemented interventions. Intervention impact was determined by systematic description of the reported quantitative changes in food choice and/or consumption. Intervention types were compared with regards to food selection and sustained effects either during or following the intervention. Setting: CAI on food choice of healthy adolescents in secondary schools. Participants: Not applicable Results: Fourteen studies were included; four randomized controlled trials and five each of controlled or uncontrolled pre-post design, respectively. Four studies implemented a single CAI type, with ten implementing > 1. Three studies investigated CAI effects over the course of a school year either by continuous or repeated data collection, while ten studies’ schools were visited on selected days during intervention. Twelve studies reported desired changes in overall food selection, yet effects were not always significant, and appeared less conclusive for longer term studies. Conclusions: This review found promising evidence that CAI can be effective in encouraging favorable food choices in healthy adolescents in a secondary school setting. However, further studies designed to evaluate complex interventions are needed.
Keywords: Public Health; Environmental and Occupational Health; Nutrition and Dietetics; Medicine (miscellaneous)

Effect of a controlled lifestyle intervention on medication use and costs: The Healthy Lifestyle Community Program (cohort 2) - Kranz, Ragna-Marie; Kettler, Carmen; Anand, Corinna; Koeder, Christian; Husain, Sarah; Schoch, Nora; Buyken, Anette; Englert, Heike (Nutrition and Health)
Background: Establishing a healthy lifestyle has a great potential to reduce the prevalence of non-communicable diseases (NCDs) and their risk factors. NCDs contribute immensely to the economic costs of the health care system arising from therapy, medication use, and productivity loss. Aim: The aim of this study was to evaluate the effect of the Healthy Lifestyle Community Program (cohort 2; HLCP-2) on medication use and consequently on medication costs for selected NCDs (diabetes, hypertension, and dyslipidemia). Methods: Data stem from a 24-month non-randomised, controlled intervention trial aiming to improve risk factors for NCDs. Participants completed questionnaires at six measurement time points assessing medication use, from which costs were calculated. The following medication groups were included in the analysis as NCD medication: glucose-lowering medications (GLM), antihypertensive drugs (AHD) and lipid-lowering drugs (LLD). Statistical tests for inter- and intra-group comparison and multiple regression analysis were performed. Results: In total, 118 participants (intervention group [IG]: n = 79; control group [CG]: n = 39) were considered. Compared to baseline medication use decreased slightly in the IG and increased in the CG. Costs for NCD medication were significantly lower in the IG than in the CG after 6 ( p = 0.004), 12 ( p = 0.040), 18 ( p = 0.003) and 24 months ( p = 0.008). After multiple regression analysis and adjusting for confounders, change of costs differed significantly between the groups in all final models. Conclusion: The HLCP-2 was able to moderately prevent an increase of medication use and thus reduce costs for medication to treat NCDs with the greatest impact on AHD. Trial registration German Clinical Trials Register DRKS ( www.drks.de ; reference: DRKS00018775).
Keywords: Nutrition and Dietetics; General Medicine; Medicine (miscellaneous)

Full Breastfeeding and Allergic Diseases—Long-Term Protection or Rebound Effects? - Libuda, Lars; Filipiak-Pittroff, Birgit; Standl, Marie; Schikowski, Tamara; von Berg, Andrea; Koletzko, Sibylle; Bauer, Carl-Peter; Heinrich, Joachim; Berdel, Dietrich; Gappa, Monika (Nutrients)
A previous follow-up of the GINIplus study showed that breastfeeding could protect against early eczema. However, effects diminished in adolescence, possibly indicating a “rebound effect” in breastfed children after initial protection. We evaluated the role of early eczema until three years of age on allergies until young adulthood and assessed whether early eczema modifies the association between breastfeeding and allergies. Data from GINIplus until 20-years of age (N = 4058) were considered. Information on atopic eczema, asthma, and rhinitis was based on reported physician’s diagnoses. Adjusted Odds Ratios (aOR) were modelled by using generalized estimating equations. Early eczema was associated with eczema (aORs = 3.2–14.4), asthma (aORs = 2.2–2.7), and rhinitis (aORs = 1.2–2.7) until young adulthood. For eczema, this association decreased with age (p-for-interaction = 0.002–0.006). Longitudinal models did not show associations between breastfeeding and the respective allergies from 5 to 20 years of age. Moreover, early eczema generally did not modify the association between milk feeding and allergies except for rhinitis in participants without family history of atopy. Early eczema strongly predicts allergies until young adulthood. While preventive effects of full breastfeeding on eczema in infants with family history of atopy does not persist until young adulthood, the hypothesis of a rebound effect after initial protection cannot be confirmed.
Keywords: Food Science; Nutrition and Dietetics

Memorization-Dilation: Modeling Neural Collapse Under Noise - Nguyen, Duc Anh; Levie, Ron; Lienen, Julian; Kutyniok, Gitta; Hüllermeier, Eyke (International Conference on Learning Representations, ICLR)
The notion of neural collapse refers to several emergent phenomena that have been empirically observed across various canonical classification problems. During the terminal phase of training a deep neural network, the feature embedding of all examples of the same class tend to collapse to a single representation, and the features of different classes tend to separate as much as possible. Neural collapse is often studied through a simplified model, called the unconstrained feature representation, in which the model is assumed to have "infinite expressivity" and can map each data point to any arbitrary representation. In this work, we propose a more realistic variant of the unconstrained feature representation that takes the limited expressivity of the network into account. Empirical evidence suggests that the memorization of noisy data points leads to a degradation (dilation) of the neural collapse. Using a model of the memorization-dilation (M-D) phenomenon, we show one mechanism by which different losses lead to different performances of the trained network on noisy data. Our proofs reveal why label smoothing, a modification of cross-entropy empirically observed to produce a regularization effect, leads to improved generalization in classification tasks.

Mitigating Label Noise through Data Ambiguation - Lienen, Julian; Hüllermeier, Eyke (arXiv:2305.13764)
Label noise poses an important challenge in machine learning, especially in deep learning, in which large models with high expressive power dominate the field. Models of that kind are prone to memorizing incorrect labels, thereby harming generalization performance. Many methods have been proposed to address this problem, including robust loss functions and more complex label correction approaches. Robust loss functions are appealing due to their simplicity, but typically lack flexibility, while label correction usually adds substantial complexity to the training setup. In this paper, we suggest to address the shortcomings of both methodologies by "ambiguating" the target information, adding additional, complementary candidate labels in case the learner is not sufficiently convinced of the observed training label. More precisely, we leverage the framework of so-called superset learning to construct set-valued targets based on a confidence threshold, which deliver imprecise yet more reliable beliefs about the ground-truth, effectively helping the learner to suppress the memorization effect. In an extensive empirical evaluation, our method demonstrates favorable learning behavior on synthetic and real-world noise, confirming the effectiveness in detecting and correcting erroneous training labels.

Interview Study on Strategy Options for Platform Operation in B2B Markets - Özcan, Leon; Fichtler, Timm; Kasten, Benjamin; Koldewey, Christian; Dumitrescu, Roman
Keywords: Digital Platform; Platform Strategy; Strategic Management; Platform Life Cycle; Interview Study; Business Model; Business-to-Business; Two-sided Market; Multi-sided Market

Neural Class Expression Synthesis - KOUAGOU, N'Dah Jean; Heindorf, Stefan; Demir, Caglar; Ngonga Ngomo, Axel-Cyrille (The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023))
Many applications require explainable node classification in knowledge graphs. Towards this end, a popular ``white-box'' approach is class expression learning: Given sets of positive and negative nodes, class expressions in description logics are learned that separate positive from negative nodes. Most existing approaches are search-based approaches generating many candidate class expressions and selecting the best one. However, they often take a long time to find suitable class expressions. In this paper, we cast class expression learning as a translation problem and propose a new family of class expression learning approaches which we dub neural class expression synthesizers. Training examples are ``translated'' into class expressions in a fashion akin to machine translation. Consequently, our synthesizers are not subject to the runtime limitations of search-based approaches. We study three instances of this novel family of approaches based on LSTMs, GRUs, and set transformers, respectively. An evaluation of our approach on four benchmark datasets suggests that it can effectively synthesize high-quality class expressions with respect to the input examples in approximately one second on average. Moreover, a comparison to state-of-the-art approaches suggests that we achieve better F-measures on large datasets. For reproducibility purposes, we provide our implementation as well as pretrained models in our public GitHub repository at https://github.com/dice-group/NeuralClassExpressionSynthesis
Keywords: Neural network; Concept learning; Description logics