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OPEN FAU

Online publication system of Friedrich-Alexander-Universität Erlangen-Nürnberg

The online publication system OPEN FAU is the central publication platform for Open Access publishing for all members of Friedrich-Alexander-Universität. Qualified works from research and teaching may be published here free of charge, either as a primary or secondary publication. The full texts are permanently available worldwide and are findable and citable via catalogues and search engines.


To search for documents in OPEN FAU, please select "Search" (via the magnifying glass at the top right); this will provide you with various search options. If you want to publish a document, go to "Login" and "My Publications". Then drag you document into the field provided and enter the metadata. In just a few steps, you can submit your document. Please note our guidelines, the publication contract and FAQs.

 

Recent Submissions

Doctoral thesis
Open Access
Stadtgespräch... Das Szene-Graffiti als Kommunikationsmittel des Urbanen - eine konversationsanalytische Untersuchung anhand einer Graffitistudie in Nürnberg
(2024) Epple, Ulrike; Habermann, Mechthild
Ulrike Epple untersucht die Kommunikationsstrukturen des Szene-Graffitis, die sich als Zeichen des menschlichen Strebens nach Sichtbarkeit, Handlungsfähigkeit, also Autonomie und Erinnerbarkeit lesen lassen. Das Szene-Graffiti wird dabei in den Kontext von Typographieforschung, Poetik, Soziolinguistik und Linguistic Landscape-Forschung gestellt. Durch Epples diachrone Studie gelingt ihr ein orginärer Blick auf das Kommunikationsmittel, der das Fluide der Kommunikation zwischen den Szene-Graffitis auf der Zeitachse fokussiert. Anhand dieser Dynamik wird mit Epples Arbeit eine konversationsanalytische und ethnomethodologische Studie zu Szene-Graffitis an ausgewählten Orten Nürnbergs vorgelegt.
Doctoral thesis
Open Access
Sterically Encumbering Tris(aryloxide)- and Cyclopentadienyl-Supported Uranium Complexes and Their Reactivity
(2024) Löffler, Sascha; Meyer, Karsten
The abundance of depleted uranium as a waste product of the enrichment process and the relatively high abundance of natural uranium makes it a considerable object of research. Any contribution of fundamental research in uranium (coordination) chemistry helps to understand the unique physicochemical properties and reactivity of the metal and its complexes. This lays the foundation for any beneficial use and application of the uranium complexes in the future. The present thesis consists of four chapters that correlate to four publications. Three chapters (3.2, 3.4, and 3.5) include uranium complexes stabilized by tris(aryloxide) ligands and their reactivity and interactions with the (small) molecules methylcyclohexane, methylene chloride, silver fluoride, water, nitrous oxide, and 9,10-diphenylanthracene-9,10-endoperoxide. Chapter 3.3 focusses on uranium complexes with cyclopentadienyl ligands and their molecular and electronic structures. In chapter 3.2, the intermolecular forces behind a uranium–alkane adduct formation are discussed. This study follows up on a publication in JACS 2003 that reports an alkane coordination to the uranium center of the complex [UIII((Ot-Bu,t-BuAr)3tacn)]. To gain insight into the forces behind the adduct formation, the adduct was resynthesized and subsequently analyzed and characterized by X-ray diffraction measurements at 6 K and a variety of spectroscopic methods. The experimental results served as a benchmark to adjust the theoretical methods and calculations that were used to describe the interactions between the uranium and the alkane. Chapter 3.3 includes bent and linear uranium pentabenzyl cyclopentadienyl complexes. It is shown that the electronic structure changes significantly when going from bent to linear. For this purpose, several bent and linear complexes were synthesized and thoroughly characterized by 1H NMR spectroscopy, CHN elemental analysis, UV/Vis/NIR spectroscopy, and SQUID magnetization measurements. The spectroscopic findings were supported and explained by theoretical studies. Chapter 3.4 deals with the activation of small molecules, such as methylene chloride, silver fluoride, water, and nitrous oxide. The primary focus is on the synthesis of a uranium(IV/V/VI) oxide and a uranium(IV/V) hydroxide series. All complexes are stabilized by the same cyclen-based tris(aryloxide) ligand. This allows for an unprecedented comparison of the oxide and hydroxide complexes, which aids drawing conclusions about the physicochemical properties of the uranium complexes. For this purpose, all compounds were extensively characterized by 1H NMR spectroscopy, CHN elemental analysis, UV/Vis/NIR spectroscopy, IR spectroscopy, cyclic voltammetry, and SQUID magnetization measurements. Chapter 3.5 describes a rare uranium-mediated peroxide activation of 9,10-diphenylanthracene-9,10-endoperoxide. The resulting uranium bis(alkoxide) complex was subsequently reduced to serve as a precursor for the formation of a uranyl complex via photochemical release of diphenyl anthracene. Theoretical calculations indicate that the synthesis of the uranyl complex proceeds through an elusive uranium cis-dioxo intermediate.
Doctoral thesis
Open Access
Optimal Design of Communication Systems Based on Wireless Power Transfer with Non-linear Energy Harvesting
(2024) Shanin, Nikita; Schober, Robert; Cottatellucci, Laura
The growth of the number of low-power Internet-of-Things (IoT) devices, such as wireless sensors and wearable medical devices, has recently fuelled interest in the design of small-scale dense IoT communication networks. Despite the notable progress in the development and optimization of these networks, efficient recharging of batteries of small low-power IoT devices still remains an unsolved problem. Since radio frequency (RF) signals are capable of transferring not only information, but also power, wireless power transfer (WPT) constitutes a promising solution to this problem. In this dissertation, we investigate the optimal transmit energy signal design and resource allocation for WPT-based gigahertz (GHz)- and terahertz (THz)-band wireless communication networks. First, we present the electrical circuits, which are typically utilized to harvest power from the received RF signals at user devices, and show that they exhibit a non-linear behaviour. Then, in contrast to other works on the design of WPT-based networks, we accurately take into account the non-linearities of practical energy harvesting (EH) circuits and we optimally design RF GHz-band multi-user WPT systems, wireless powered communication networks (WPCNs), and simultaneous wireless information and power transfer (SWIPT) systems. Furthermore, we also study the tradeoff between the achievable rate and average harvested power in single-user THz SWIPT systems.
Doctoral thesis
Open Access
Information Extraction for Data-Driven Indoor Tracking with Ultra-Wideband Signals
(2024) Kram, Sebastian; Heuberger, Albert
In indoor environments, emerging applications such robot navigation or industrial process surveillance rely on accurate radio frequency ( RF)-based tracking. It employs the commu- nication links between mobile agents and stationary anchors to infer their position. A major challenge in classical multilateration-based approaches is the influence of the environment on signal propagation, as furniture and structural components cause multipath propagation like scattering and reflections. As a solution, ultra-wideband (UWB )-signalling resolves multipath components ( MPCs) by collecting channel measurements (CMs) that contain additional spa- tial spatial information on the propagation. This has the potential to reduce deployment costs for RF-based tracking systems as it lowers the necessary anchor infrastructure. To exploit this information for tracking, however, it first has to be extracted from the CMs. The limits of the channel hereby cause overlap between MPCs and non-linear distortions. State-of-the-art data-driven machine learning ( ML ) methods, especially neural networks ( NNs), learn efficient function approximations from training data that can handle these non-linear effects. However, for cost-efficient deployment of these data-driven information extraction models to a target en- vironment, they need to generalize from environment-representative training data. This thesis proposes different NNs that learn to extract spatial information in industrial environments with harsh propagation conditions from data obtained in different environments. Specifically, these models learn how to estimate the line-of-sight ( LOS) presence based on data from artificial labeling environments using convolutional neural networks (CNNs) and variational autoencoders (VAE s)-based anomality detection. Because CM contains an arbitrary num- ber of MPC, MPC delay extraction is not a straightforward classification or regression task and state-of-the-art methods rely on computationally expensive iterative statistical signal processing. Instead, this thesis proposes a method that learns to accurately extract MPC delays from simulated data with a time-series segmentation approach relying on a compu- tationally efficient UNET CNN. A lower-dimensional representation of CM is achieved with propagation-model inspired features in the state-of-the-art. As a data-driven alternative, this thesis proposes a representation learning approach that learns to compress the spatial information in CM into a lower-dimensional latent-space representation from data obtained in similar industrial environments with an autoencoder (AE). Tracking results prove that this representation results in more accurate tracking than state-of-the-art features. Thus, compared to the state-of-the-art, the proposed spatial information extraction learn to accurately represent the spatial information in CM from easily obtainable data. The generalization abilities of the proposed methods reduce the need for cost- and labor-intensive data collection and enable the deployment of trained models to an application environments with harsh propagation conditions. Furthermore, this information extraction compresses high-dimensional CMs into a lower-dimensional representation that reduces data storage and transmission requirements in a CM-based tracking system. To exploit this extracted information for tracking, this thesis proposes a particle filter for fingerprinting (FP) that fuses the extracted LOS presence indicator and a Gaussian process regression (GPR)-based observation likelihood model of the extracted latent-space representation. As it employs this additional, compressed spatial information it enables accurate tracking in an indoor environments with harsh propagation conditions, unlike state-of-the-art LOS-focused tracking methods. FP is trained on environment-specific data that are hard to acquire and maintain. The proposed tracking methods lowers the data collection and maintenance effort over state-of-the-art CM-based FP because the learned observation likelihood model implies a reliability measure, and thus can learn from spatially sparse datasets that only contain data instances in areas within the environment that require FP for accurate tracking. Real-world evaluation shows that the proposed tracking method achieves more reliable information extraction and tracking than state-of-the-art methods, especially on a sparse FP database and with a small anchor infrastructure. The proposed information extraction reduces transmission and data storage requirements and generalizes to target environments at low data acquisition effort. So, is enables cost- and energy effi- cient deployment of data-driven CM-based positioning in indoor environments with harsh propagation conditions.
Doctoral thesis
Open Access
Diversity in Organizations: Essays in Applied Economics Based on Experimental and Administrative Data
(2024) Mayer, Lea; Rincke, Johannes
The thesis consists of three chapters that study gender and nationality diversity in different organizations using causal methods. The first chapter investigates the impact of refugee and foreign students on secondary education track choices in Germany, especially after the European refugee crisis in 2015-2016. The quick increase in the share of refugees in German schools could potentially harm educational outcomes of students. The study uses a large administrative dataset from Bavaria to explore how the share of refugee and immigrant peers in a cohort influences the secondary educational track choices of native Germans, refugees, and other foreigners. The analysis uses a within-school fixed-effects model to address biases arising from school selection. This method exploits the quasi-random variation in the share of refugees and other foreigners among cohorts. My main findings reveal positive effects of the refugee share on Germans' secondary school choices, negative effects on refugees, and limited impact on other foreigners during the years 2015-2018. These effects persist in various robustness checks. Further examination of non-refugee foreigners reveals that the share of foreigners with their own citizenship or language negatively impacts their secondary school choices, suggesting social separation within the class. While the available data limits an analysis of the mechanisms behind the positive impact of the refugee share on Germans, additional insights from further data and the literature suggest as potential factors the changes in teachers' reference points and parental preferences for their children's peers. This chapter contributes to the existing literature on peer effects of immigrants, offering insights of an exceptionally large influx of refugees to Germany. The second chapter studies the preferences of highly educated individuals regarding gender diversity in the workplace. While extensively discussed aspects of jobs, include flexibility and competitiveness, gender diversity among co-workers has received limited attention. The chapter aims to understand if women's stronger preferences for gender diversity contribute to their underrepresentation in top positions, potentially influencing career choices. We conducted a stated choice experiment involving over 9,000 participants, including undergrads and graduate students, PhD students, and non-tenured and tenured professors in Germany. Participants made choices between hypothetical job offers varying in the wage and non-wage amenities, most importantly for this chapter the percentage of women among co-workers (10%, 25%, or 40%). Results reveal a substantial valuation of a more gender-diverse work environment among high-profile co-workers. Notably, women exhibit twice the willingness-to-pay for gender diversity compared to men. Heterogeneity analyses show that more career-ambitious and competitive individuals have lower valuations of gender diversity. Women maintain anyhow high valuations when having high levels of competitiveness or ambition. Surprisingly, family preferences only impact the valuations of men. The gender gap in willingness-to-pay for gender diversity is attributed to homophily in co-workers' gender preferences or expectations about a more employee-friendly and less competitive environment with increased gender diversity. Women may also benefit from reduced gender stereotypes and less sexual harassment in a diverse setting. The chapter contributes to understanding the persistence of the gender gap in high-earning jobs by providing a detailed analysis of preferences for gender diversity among co-workers, incorporating current decade data and including top earners like professors. The third chapter analyzes the impact of gender composition on team performance and communication. With the increasing prevalence of diverse work teams, it is crucial to understand how these teams function and leverage the skills of all members. The study utilizes an online experiment, randomly assigning individuals to teams of four, either all-male, all-female, or mixed-gender composition. The teams collaborate in an online audio chat room to solve non-routine business problems, which we tested for gender neutrality in individual performance. The design ensures that communication is essential for problem-solving. The primary findings reveal that all-male teams exhibit higher communication levels, particularly on topics related to the task, and outperform both mixed and all-female teams in solving problems. The data suggests that the increased communication in all-male teams may drive their enhanced performance. Moreover, in gender-mixed teams, men speak significantly more than in all-male teams, while women adjust their communication behavior in the opposite direction, resulting in a significant gender gap. The gender gaps in mixed teams persist even between low-ability men and high-ability women. Further evidence indicates that the differences are not attributed to a more aggressive style, as sentiments and interruptions are similar in homogeneous and mixed teams. Instead, the findings suggest that gender-specific communication behaviors, influenced by self-confidence and existing gender roles, contribute to male dominance in mixed teams. The socially acquired nature of gender-specific communication behaviors emphasizes the importance of early recognition and amplification of women's voices in teams. The study contributes valuable experimental evidence to the literature, offering a systematic analysis of style and quantity of communication in teams based on their gender composition. To summarize, this thesis uses causal methods to find new evidence of how diversity in organizations affects the outcomes and decisions of individuals. The gained insights from experimental methods and administrative data may contribute to the ongoing debate on how organizations in e.g. policy, education and the private sector can reach diversity and use its full potential.