Munich University of Applied Sciences

Total organic carbon (TOC) presents an essential quality parameter for purified water (AP) and water for injection (WFI). For the monitoring of pharmaceutical water systems, the analysis of TOC occurs online and offline. However, monitoring data collected throughout the industry readily indicates little comparability between available online and offline measurement systems and outlier values are a common occurrence in offline samples while online devices display results with high stability. Using a recently implemented and heavily controlled WFI-system with stable online TOC values of < 4 ppb we analysed the impact of environmental air particle numbers in controlled production and technical areas on offline TOC analyses. The detected correlation strongly links environmental air particle numbers to the accumulation of organic carbon in water samples indicating outlier values do not necessarily represent a loss of quality within the generation or distribution system but rather an environmental impact or hygienic changes in the surrounding area. Our data highlights the importance of comparative and redundant offline and online analyses using various parameters to distinguish systematic and local valve contaminations from the displayed impact via the sampling environment when monitoring and evaluating complex systems.

To make different packages with various filling quantities better comparable for their packaging material use, in this study the packaging material use efficiency was defined as the ratio of fill good amount to the packaging weight. Several hundred rigid packages (tubes, bottles, cans, and carton packages) for liquid and higher-viscous fast-moving consumer goods, e.g., beverages and personal care products, were analyzed (weight) and more than >1000 data sets were taken from packaging suppliers of glass and PET packaging. As expected, glass packaging is heavier than PET packaging by a factor of around 10, and with a higher filling volume less packaging per amount of food is required. The material use efficiency of glass and PET bottles can differ by up to a factor of 3 within one filling quantity. The results are relevant for calculating life cycle assessments (LCAs) and selection of material efficient packaging.

This chapter presents an interview with renowned philosopher Sally Haslanger, exploring her intellectual journey and contributions to philosophy. The conversation delves into her experiences navigating academic institutions, addressing issues such as stereotype threat and gender bias in the field. Haslanger elaborates on her interdisciplinary approach, emphasizing the importance of integrating feminist theory, metaphysics, and social epistemology. She reflects on the evolution of her philosophical perspective, particularly her engagement with critical theory and political philosophy. The interview also touches on the challenges of public discourse in philosophy, the role of philosophers in society, and the significance of hope and activism in striving for a more just world. Through her responses, Haslanger provides a compelling narrative of her career, highlighting the interplay between personal motivation, academic rigor, and social impact.

Focusing on Sally Haslanger’s conceptual framework, where ‘gender’ and ‘race’ are perceived as social kinds, the paper reconstructs Haslanger’s view, subsequently extending her analysis to the realm of ‘disability.’ The central thesis posits that ‘disability,’ akin to ‘gender’ and ‘race,’ can be understood as a social kind, asserting that disability involves systemic subordination marked by perceived bodily impairments. The paper addresses several objections, including the natural kind standpoint and the claim of disability as a valuable cultural kind. Furthermore, it grapples with Haslanger’s non-reductive and politically engaged stance by examining whether disabilities, despite their inherent physical and mental impairments, comprise social constructs with profound normative implications.

Investigating the microstructures of materials with microscopy is a key task in quality assurance, the development of new materials, and the optimization of manufacturing processes. However, conventional image analysis often demands significant time for analysis and a large volume of images, and the predictions produced are commonly constrained. Applying deep learning, models can be trained to analyze material microstructures quickly and with greater accuracy. The objective of this study is to provide a method for the automatic segmentation of microstructural images obtained from microscopes or scanning electron microscopes using Convolutional Neural Networks. For this purpose, two software scripts were developed in Python employing OpenCV and the fastai library. The first script is designed to generate reference images, while the second is utilized for training a model and predicting the microstructure in an image. The test of the microstructural analysis using the developed software tools demonstrates that robust prediction results are attainable by using high-quality reference images. This tool has been made available as an open-source on GitHub for public use in materials analysis and can be enhanced and further developed if required.

This paper investigates the empirical relationship between predictive performance, often called predictive power, and interpretability of various Machine Learning algorithms, focusing on bicycle traffic data from four cities. As Machine Learning algorithms become increasingly embedded in decision-making processes, particularly for traffic management and other high-level commitment applications, concerns regarding the transparency and trustworthiness of complex ’black-box’ models have grown. Theoretical assertions often propose a trade-off between model complexity (predictive performance) and transparency (interpretability); however, empirical evidence supporting this claim is limited and inconsistent. To address this gap, we introduce a novel interpretability scoring system - a Machine Learning Interpretability Rank-based scale - that combines objective measures such as the number of model parameters with subjective interpretability rankings across different model types. This comprehensive methodology includes stratified sampling, model tuning, and a two-step ranking system to operationalize this trade-off. Results reveal a significant negative correlation between interpretability and predictive performance for intrinsically interpretable models, reinforcing the notion of a trade-off. However, this relationship does not hold for black-box models, suggesting that for these algorithms, predictive performance can be prioritized over interpretability. This study contributes to the ongoing discourse on explainable AI, providing practical insights and tools to help researchers and practitioners achieve a balance between model complexity and transparency. We recommend to prioritise more interpretable models when predictive performance is comparable. Our scale provides a transparent and efficient framework for implementing this heuristic and improving parameter optimization. Further research should extend this analysis to unstructured data, explore different interpretability methods, and develop new metrics for evaluating the trade-off across diverse contexts.

We use three waves of census data containing detailed characteristics of the entire population of Austrian farms to examine the causal effect of agritourism on farm survival. To control for self-selection into agritourism, we exploit regional variation in tourism intensity that is exogenous to individual farms. On average, agritourism causally increases survival probabilities by 10.9 percentage points per decade, which is economically and statistically significant. Marginal effects differ by farm characteristics, reaching up to 15.3 percentage points for certain sub-populations. Consequently, policies that support entry into agritourism can be effective in keeping farms in the market and thus in preserving the tourist appeal of many rural regions. Our analysis indicates that the magnitude of the estimated coefficients is severely biased unless endogenous self-selection into agritourism is properly addressed. This underscores that even with large microdata sets, an appropriate identification strategy is critical to derive causal and thus policy-relevant conclusions.

In this article, the phenomenon of water stress corrosion (WSC) at borofloat glass interfaces joined by hydrophilic direct bonding is explored. In particular, the impact of the surface waviness and surrounding atmosphere is studied through time-resolved measurements of the bonding energy during surface separation. We present a model for sub-critical crack growth and discuss the underlying WSC reaction. Key findings are that, firstly, the presence of humid air, and secondly, mechanical stress stored at the interface due to elastic contact point deformation increase the number of water molecules with sufficient kinetic energy to participate in the WSC reaction, that is, increase the reaction rate. This study provides crucial insights into the conditions aggravating WSC and gives implications for improving the durability and performance of mechanically stressed glass interfaces in various applications, such as micro-electro-mechanical systems (MEMS) and advanced optics.

Surface roughness plays a critical role in ultrashort pulse laser ablation, particularly for industrial applications using burst mode operations, multi-pulse laser processing, and the generation of laser-induced periodic surface structures. Hence, we address the impact of surface roughness on the resulting laser ablation topography, comparing predictions from a simulation model to experimental results. We present a comprehensive multi-scale simulation framework that first employs finite-difference-time-domain simulations for calculating the surface fluence distribution on a rough surface measured by atomic-force-microscopy followed by the two-temperature model coupled with hydrodynamic/solid mechanics simulation for the initial material heating. Lastly, a computational fluid dynamics model for material relaxation and fluid flow is developed and employed. Final state results of aluminum and AISI 304 stainless steel simulations demonstrated alignment with established ablation models and crater dimension prediction. Notably, Al exhibited significant optical scattering effects due to initial surface roughness of 15 nm—being 70 times below the laser wavelength -leading to localized, selective ablation processes and substantially altered crater topography compared to idealized conditions. Contrary, AISI 304 with ${R}_{\text{q}}$ surface roughness of 2 nm showed no difference. Hence, we highlight the necessity of incorporating realistic, material-specific surface roughness values into large-scale ablation simulations. Furthermore, the induced local fluence variations demonstrated the inadequacy of neglecting lateral heat transport effects in this context.

Biopolymers are promising sustainable alternatives to petrochemical polymers, but the recent increase in published research articles has not translated into marketable products. Here, we discuss barriers to market entry by exploring application-specific, ecological, and economic aspects, such as the utilization of biodegradable polymers to mitigate the accumulation of microplastics. We summarize previous studies revealing how fiber surface properties and the dwell time during fiber spinning affect degradability. We show how biopolymers can be processed on existing machines and how degradability can be tailored by changing process parameters. This novel approach, known as degradation by design, will allow us to rethink product development and ensure that biopolymers are not only able to replace petrochemical polymers but also reduce the environmental harm they cause.

Zusammenfassung Der Beitrag befasst sich mit der Frage, welche Bedeutung Emotionen im Ethnografischen Spiel haben, wie sie methodisch aufgegriffen werden können und welche Potenziale sich für die Analyse von Handlungssituationen und für Professionalisierungsprozesse von Studierenden ergeben. Hierfür wird zuerst das Ethnografische Spiel als Zugang zur Analyse von Handlungssituationen skizziert: Dokumentierte Handlungssituationen werden mit Studierenden nachgespielt und im Anschluss werden Erfahrungen, die die Studierenden dabei machen, reflektiert. Dabei wird der Fokus auch auf Emotionen gelegt. Anschließend werden Ziele des Ethnografischen Spiels sowie die Bedeutung von Emotionen im Analyseprozess diskutiert. Emotionen können als Anlass für Befremdungsprozesse genutzt werden – sowohl im Zuge der Selbstreflexion als auch bei der Rekonstruktion von Situationen. Schließlich wird die Arbeit mit Emotionen im Rahmen des Ethnografischen Spiels anhand eines verdichteten Beispiels aus der Lehrpraxis veranschaulicht. Dieses macht deutlich, dass der gezielte Einbezug von Emotionen Studierenden einen erleichterten Zugang zur Befremdung eigener Sichtweisen und damit zur Analyse von Handlungssituationen und deren Theoretisierung ermöglicht.

Aufgeschweißte Lamellen sind im Brücken‐ und Maschinenbau ein häufig vorkommendes Detail, das zur lokalen Querschnittsverstärkung verwendet wird und somit zur Ausführung wirtschaftlicher Konstruktionen beiträgt. Allerdings weist dieses Kerbdetail ein ungünstiges Ermüdungsverhalten auf und ist daher in vielen Fällen maßgebend beim Ermüdungsnachweis. Im Rahmen des Forschungsprojekts FOSTA P 1413 wurden numerische und experimentelle Untersuchungen an verschiedenen Lamellenformen und ‐ausführungen durchgeführt und auf Grundlage dieser Ergebnisse eine hinsichtlich Ermüdungsverhalten und Fertigungsaufwand optimierte Ausführung entwickelt. Im Rahmen des Beitrags werden die wesentlichen Ergebnisse dieser Untersuchungen und die sich daraus abgeleiteten Empfehlungen hinsichtlich Bemessung und Fertigung vorgestellt.