Numerical Simulation of Plastic Pyrolysis

Pyrolysis of plastic waste for base chemical production is a pivotal technology for closing the carbon cycle and advancing sustainable energy transition. This work presents an overview of research activities conducted at the Institute for Technical Chemistry (ITC) at KIT, focusing on the simulation of plastic pyrolysis to develop efficient and cost-effective recycling technologies. A key objective of this research is to elucidate the physicochemical behavior during pyrolysis through high-fidelity numerical simulations, leveraging high-performance computing resources. These simulations provide fundamental insights essential for process design and optimization. Several ongoing studies are discussed, systematically spanning from fundamental single-particle analyses to fully resolved laboratory-scale fluidized bed reactors. These investigations evaluate the impact of key operating conditions and reactor design parameters on pyrolysis performance, demonstrating how numerical simulations can streamline and optimize the design process

Improved Ensemble Predictive Modeling Techniques for Linked Social Media and Survey Data Sets Subject to Mismatch Error

Modern predictive modeling tools, such as random forests (and related ensemble methods), have become almost ubiquitous in research applications involving innovative combinations of survey methodology and data science. However, an important potential flaw in the widespread application of these methods has not received sufficient research attention to date. Researchers at the junction of computer and survey science frequently leverage linked data sets to study relationships between variables, where the techniques used to link two (or more) data sets may be probabilistic and non-deterministic in nature. If frequent mismatch errors occur when linking two (or more) data sets, the commonly desired outputs of predictive modeling tools describing relationships between variables in the linked data sets (e.g., variable importance, confusion matrices, RMSE, etc.) may be negatively affected, and the true predictive performance of these tools may not be realized. We demonstrate a new methodology based on mixture modeling that is designed to adjust modern predictive modeling tools for the presence of mismatch errors in a linked data set. We evaluate the performance of this new methodology in an application involving the use of observed Twitter/X activity measures and predicted socio-demographic features of Twitter/X users to accurately predict linked measures of political ideology that were collected in a designed survey, where respondents were asked for consent to link any Twitter/X activity data to their survey responses (exactly, based on Twitter/X handles). We find that the new methodology, which we have implemented in R, is able to largely recover results that would have been seen prior to the introduction of mismatch errors in the linked data set

Exklusive Authentizität? Reenactment als Form performativer Geschichtsdarstellung

In den aktuellen Auseinandersetzungen über populäre Repräsentationen des Mittelalters geraten zunehmend auch performative Darstellungsformen in den Blick der Forschung. Im Umfeld der florierenden Mittelaltermärkte, aber auch parallel dazu, entwickeln sich verschiedene Formate von Geschichtstheater, die u. a. einen Schwerpunkt auf die theatrale Vermittlung von Wissen legen. Dieser Artikel möchte Reenactment, als eine Ausprägung des Geschichtstheaters, genauer untersuchen. Dabei wird Reenactment als eine Form der populärkulturellen Aneignung von Wissen betrachtet, und als kulturelle Praxis der Gegenwart greifbar, die auf ihre gesellschaftlichen Kontexte und diskursiven Implikationen befragt werden kann

Probing in Cognitive Interviews Can Promote Acquiescence

Cognitive interviewing is widely used to pretest survey questionnaires and is considered a best practice (e.g., Willis, 2005, 2018; Beatty & Willis, 2007). However, the method has been controversial because, among other concerns, it requires interviewers to probe respondents for more detail or clarity about their experience answering draft survey questions which may lead them to report “problems’’ they have not actually experienced (e.g., Conrad & Blair, 2009). The present study investigates this possibility from the perspective of Acquiescent Response Style (ARS) – the tendency for survey respondents to select positive responses such as “yes” or “strongly agree,” irrespective of the question’s content (e.g., Baumgartner & Steenkamp, 2001). For example, respondents in a cognitive interview might affirm experiencing a problem mentioned in or implied by an interviewer’s probe even if they have not actually experienced it. We embedded a probing experiment in a pretest of a health survey in which respondents participated in cognitive interviews that used either directive probes (n=41) or non-directive probes (n=26). Directive probes explicitly queried respondents about a specific, intentionally unlikely interpretation of each question in a draft questionnaire; non-directive probes were open-ended. Directive probe (DP) respondents affirmed the interpretation queried in the probes over five times more often than respondents in the non-directive probe (NP) group volunteered these interpretations. This pattern was reversed for interpretations of the questions that were volunteered, i.e., about which DP respondents were not asked: NP respondents volunteered alternative interpretations over four times more than DP respondents. These effects were particularly pronounced for respondents with lower levels of education and who were younger. The findings suggest that directive probing in cognitive interviewing can promote responding that is reminiscent of ARS – an affirmation bias – and likely harmful for the quality of evidence produced in cognitive interviews

Improving Understanding of Survey Questions with Multimodal Clarification

If survey respondents do not interpret a question as it was intended, they may, in effect, answer the wrong question, increasing the chances of inaccurate data. Researchers can bring respondents’ interpretations into alignment with what is intended by defining the terms that respondents are at risk of misunderstanding. This article explores strategies to increase alignment between researchers’ intentions and respondents’ answers by taking advantage of the unique affordances of online surveys compared to paper or other analog formats. Web surveys are often text-based, but allow for the seamless integration of embedded audio material so that users may read, listen to, or both read and listen to survey instructions. Unimodal definitions are either spoken or textual, while multimodal definitions are both spoken and textual. Further, definitions can be designed to take advantage of the affordances of each mode. While mode-invariant definitions contain the same words irrespective of whether they are textual or spoken, mode-optimized definitions are designed to take advantage of the affordances of written and spoken communication. For example, definitions optimized for textual presentation use fewer words than corresponding mode-invariant definitions and are designed so the key information is visually salient, while definitions optimized for spoken presentation are shorter and more colloquial than corresponding mode-invariant definitions. In this study, both mode-optimized and mode-invariant formats improved alignment. Multimodal, mode-optimized definitions produced improved alignment over both types of unimodal definitions. This study suggests that multimodal definitions, when thoughtfully designed, can improve data quality in online surveys without negatively impacting respondents

Vom Archiv in die Vitrine: Zur Vermittlung mediävistischer und genderhistorischer Inhalte in universitärer Lehre und Public History

Der Beitrag bietet einen Praxisbericht mit methodisch-didaktischen Reflexionen zu einem Seminar, das sich mit den Handlungsräumen und Lebenswelten einer spätmittelalterlichen Fürstin auseinandersetzte. Erörtert werden dabei der inhaltliche wie methodische Rahmen der Seminarkonzeption, die konkrete Umsetzung unter Einsatz von Digital Humanities sowie die Chancen und Herausforderungen einer spezialisierten forschungsgeleiteten Lehrveranstaltung

Continuous Benchmarking of Numerical Algorithms Implemented in M++ via Gitlab CI/CD and Google Benchmark∗

We present an automated framework for benchmarking numerical algorithms that solve partial differential equations under consistent and reproducible conditions using the parallel finite element software M++. This framework integrates GitLab CI/CD, Google Benchmark, and the HoreKa supercomputing system to enable continuous integration and benchmarking. By incorporating ongoing software development, the framework supports improving performance and reliability, which are vital for various scientific computing applications, including wave propagation, cardiovascular simulations, dislocation dynamics, and uncertainty quantification. These applications motivate the two benchmarking examples presented in this text. We further outline the benchmarking workflow as well as the use of a research database storing comprehensive performance data, facilitating reproducibility for future studies

Fair sharing of resources between clusters with AUDITOR

For several years, we have been dynamically and opportunistically integrating the computing resources of the HPC cluster NEMO into the HTC cluster ATLAS-BFG using the COBalD/TARDIS software. To increase usage efficiency, we allow the integrated resources to be shared between the various High Energy Physics (HEP) research groups in Freiburg. However, resource sharing also requires accounting. This is done with AUDITOR (AccoUnting DatahandlIng Toolbox for Opportunistic Resources), a flexible and extensible accounting ecosystem that can cover a wide range of use cases and infrastructures. Accounting data is recorded via so-called collectors and stored in a database. So-called plugins can access the data and take measures based on the accounted data. In this work, we present how NEMO resources can be fairly shared among contributing working groups when integrated into ATLAS-BFG using AUDITOR

bwFDM – The Federal State Initiative for Research Data Management in Baden-Württemberg

This paper provides an overview of the federal state initiative for research data management in Baden- Württemberg (bwFDM) and its multi-faceted approach to addressing the challenges and opportunities presented by research data. bwFDM’s activities include the establishment of networks and collaborations, the dissemination of information and knowledge, the expansion of information services, the provision of training and consultation opportunities, and the organisation of the conference series E-Science-Tage. Overall, bwFDM’s activities emphasise the importance of collaboration, information dissemination, and training in shaping the present and future of research data management within and beyond Baden-Württemberg

Effects of the Self-View Window in Live Video Survey Interviews

The studies reported here explore how the “self-view” window (a live video feed of oneself) affects live video survey respondents’ likelihood of disclosing sensitive information and their feelings about the interview. In Study 1 (2012), 124 laboratory respondents answered sensitive and nonsensitive questions taken from US government and social scientific surveys over Skype, either with or without a self-view window. Respondents randomly assigned to having a self-view disclosed no less sensitive information than those without a self-view, and on a few questions, they disclosed more (more frequent alcohol use and more sex partners). Self-view respondents also perceived the interview as less sensitive, and they reported less copresence with the interviewer, reduced self-consciousness, and greater comfort answering many of the sensitive questions. Study 2 (2017) replicates these findings in a second sample of 133 respondents by (a) tracking where video survey respondents look on the screen—at the interviewer, at the self-view, or elsewhere—while answering the same survey questions and (b) examining how gaze location and duration differ for sensitive vs. nonsensitive questions and for more and less socially desirable answers. Findings include that self-view respondents looked less at the self-view while answering sensitive (vs. nonsensitive) questions, and that respondents who looked more at the self-view window reported feeling less self-conscious and less worried about how they presented to the interviewer. Results demonstrate that the self-view can change respondents’ experience and where they look during a video interview. They also document, for the first time in video surveys, surprising individual variability in looking at the self-view, with some respondents never once looking and others looking at their self-view as much as 50% of the time. Attending to how self-view and respondents’ choices (e.g., turning it on or off) affect respondent experience and data quality will be important as live video surveys are increasingly deployed