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In diesem Band werden die Beiträge der ersten Konferenz „Interdisziplinäre Lehre für nachhaltige Entwicklung“ an der Technischen Hochschule Nürnberg Georg Simon Ohm aus dem Jahr 2023 präsentiert. Die inhaltliche Vielfalt der 13 Beiträge verdeutlicht, dass die Diskussion der Interdisziplinarität in der hochschulgebundenen Ausbildung junger Menschen für die sich transformierende Gesellschaft gerade erst begonnen hat. Vorgestellt werden unterschiedlich wissenschaftlich verankerte und praxisorientierte Modelle zur Vermittlung notwendiger Kompetenzen, um gesellschaftliche Zukunft zu gestalten. Es gilt am Ball zu bleiben, den Austausch fortzusetzen und Chancen für interdisziplinäre Kooperationen für die Bildung für nachhaltige Entwicklung (BNE) zu identifizieren und zu ergreifen. Die Open Source Publikation soll in diesem Sinne Anschlusskommunikation ermöglichen.
Data engineering is an integral part of the data science process. It comprises tasks such as data ingestion, data transformation, and data quality assurance. In order to fulfill these tasks, schema inference is an important capability. Its goal is to detect the structure of a dataset and to derive metadata on hierarchies, data types, etc. Artificial intelligence (AI) has the potential to automate schema inference and thus increase the efficiency of the data science process. However, as government institutions are subject to special regulations, explainability of AI models can be a mandatory requirement. Goal of this research protocol is to plan a systematic review of literature on schema inference for tabular data with explainable AI (XAI). This third version was derived from two earlier review protocols.
Carbon-doped titania was fabricated via carbothermal treatment in nitrogen–acetylene gas flow and further used as a precursor for multiwalled titanate nanotube (TNT) synthesis via alkaline hydrothermal route. Investigation of the reaction products after hydrothermal treatment of carbon-doped titania using Transmission electron microscopy, X-ray diffraction, and Brunauer–Emmett–Teller method shows the successful formation of TNTs. The presence of carbon was proved although the type of incorporation could not be certified. All samples show approximately the same carbon content before and after hydrothermal treatment. An increasing pretreatment temperature of titania precursor powders yields more secondary products in the nanotube samples, indicating lower reactivity of the titanium oxycarbide phase during hydrothermal treatment. In this study, TNTs with 6 wt% carbon and with the highest specific surface area of 340 m2/g were formed via hydrothermal treatment of carbon-doped titania precursor powder exposed to 700 °C during carbothermal pretreatment.
Data engineering is an integral part of the data science process. It comprises tasks such as data ingestion, data transformation, and data quality assurance. In order to fulfill these tasks, schema inference is an important capability. Its goal is to detect the structure of a dataset and to derive metadata on hierarchies, data types, etc. Artificial intelligence (AI) has the potential to automate schema inference and thus increase the efficiency of the data science process. However, as government institutions are subject to special regulations, explainability of AI models can be a mandatory requirement. Goal of this research protocol is to plan a systematic review of literature on schema inference with explainable AI (XAI) for data engineering in government institutions. This second version includes adjustments resulting from the first iteration of the review.
The design of robust architectures for software-intensive systems in the automotive domain is a complex task and a considerable engineering challenge. Even conventional systems are usually subject to a multitude of conflicting design goals, such as unit cost and weight minimization, dependability augmentation and timing control. One feasible approach to resolve these conflicting levers on a system's architecture is to perform an optimization analysis on a concretely defined design space, which in general is vast. For the purpose of the approach presented here, this design space is represented by an automated identification of variation points relevant for design space exploration. The identified variability information is then transformed into a convenient mathematical representation for product-line-aware architecture optimization.
Highly connected with the environment via various interfaces, cars have been the focus of malicious cyber attacks for years. These attacks are becoming an increasing burden for a society with growing vehicle autonomization: they are the sword of Damocles of future mobility. Therefore, research is particularly active in the area of vehicle IT security, and in part also in the area of dependability, in order to develop effective countermeasures and to maintain a minimum of one step ahead of hackers. This paper examines the known state-of-the-art security and dependability measures based on a detailed and systematic analysis of published cyber attacks on automotive software systems. The sobering result of the analysis of the cyber attacks with the model-based technique SAM (Security Abstraction Model) and a categorization of the examined attacks in relation to the known security and dependability measures is that most countermeasures against cyber attacks are hardly effective. They either are not applicable to the underlying problem or take effect too late; the intruder has already gained access to a substantial part of the vehicle when the countermeasures apply. The paper is thus contributing to an understanding of the gaps that exist today in the area of vehicle security and dependability and concludes concrete research challenges.
Diese Studienarbeit untersucht den Stromverbrauch auf regionaler Ebene in Deutschland und vergleicht drei verschiedene Systemdesigns für erneuerbare Energien. Die Systeme basieren auf dem Einsatz von geotechnischen, chemischen und Batteriespeicher-technologien. Es werden reale Wetter- und Lastdaten für einen Zeitraum von drei Jahren vom 01.09.2020 bis zum 31.08.2023 verwendet.
Semiconducting transition metal oxides such as TiO 2 are promising photo(electro)catalysts for solar water splitting and photoreduction of CO 2 as well as for antibacterial, self-, water and air-cleaning coatings and admixtures in paints, building materials, on window glass or medical devices. In photoelectrocatalytic applications TiO 2 is usually used as photoanode only catalyzing the oxidation
reaction. In coatings and admixtures TiO 2 works as heterogeneous catalyst and has to catalyze a complete redox cycle. While photoelectrochemical charge transport parameters are usually quite well accessible by electrochemical measurements, the quantitative description of photocatalytic properties is more challenging. Here, we present a systematic structural, photoelectrocatalytic, photocatalytic and antimicrobial study to understand if and how photoelectrochemical parameters can be used to predict the photocatalytic activity of TiO 2. For this purpose TiO 2 thin films on
flourine-doped tin oxide substrates were prepared and annealed at temperatures between 200 and 600 ◦C. The film morphologies and thicknesses were studied by GIXRD, FESEM, and EDX. Photoelectrochemical properties were measured by linear sweep voltammetry, photoelectrochemical impedance spectroscopy, chopped light chronoamperometry, and intensity modulated photocurrent/photovoltage spectroscopy. For comparison, photocatalytic rate constants were determined by
methylene blue degradation and Escherichea coli inactivation and correlated with the deducedphotoelectrocatalytic parameters. We found that the respective photoactivities of amorphous and crystalline TiO 2 nanolayers can be best correlated, if the extracted photoelectrochemical parameters such as charge transfer and recombination rates, charge transfer efficiencies and resistances are measured close to the open circuit potential (OCP). Hence, the interfacial charge transport parameters at the OCP can be indeed used as descriptors for predicting and understanding the photocatalytic
activity of TiO 2 coatings.
The increasing use of software and connectivity in modern vehicles has made cybersecurity an important issue in the automotive industry. ISO 21434 is a standard for automotive cybersecurity engineering that provides guidelines for the development and validation of secure automotive systems. For effective implementation and practical use of ISO 21434, it must be incorporated into existing automotive industry development workflows.
In this paper, we investigate the practical applicability of ISO 21434 in the context of the Security Abstraction Model (SAM), a security modeling approach for the domain-specific modeling language EAST-ADL, and provide insights into the benefits and consequences of this approach. In doing so, we describe the methodological opportunities of integrating ISO 21434 into SAM on the one hand and present a case study illustrating the application of this integrated approach in the development of a secure automotive system on the other hand. Our results suggest that the integration of ISO 21434 into SAM better supports automotive system security in the early development phases and makes it transparent to a wide range of stakeholders. At the same time, it becomes clear that a representation of the interrelationships in the form of a metamodel, in contrast to ISO 21434 in which these are only described textually, significantly improves conceptual understanding and ultimately enables pragmatic usability in industrial development.