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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
Deciphering the role of the presenilins Psen1 and Psen2 in intestinal epithelial homeostasis and inflammation
(2024) Erkert, Lena; Becker, Christoph
Intestinal epithelial cells (IECs) form a protective barrier against harmful pathogens present in the intestinal lumen, while acting as a communication platform between the microbiome in the lumen and adjacent immune cells in the lamina propria. Tight control of IEC differentiation, proliferation and death is therefore essential to ensure intestinal homeostasis. If the balance between these vitally important processes is disturbed, intestinal pathologies such as inflam-matory bowel diseases (IBD) can result. To date, the pathophysiology of IBD is incompletely understood and requires further investigation. However, several factors have been identified that contribute to disease pathology, including genetic and environmental factors, such as al-terations in the microbiome that induce the recruitment of mucosal immune cells to the in-flamed areas, ultimately leading to barrier defects. In this context, different human studies have already identified various risk genes associated with IBD. Preliminary analysis by our group of RNA sequencing data from inflamed tissue of mice with experimentally induced colitis identified, for the first time, deregulation of several molecules involved in the Alzheimer’s Dis-ease pathway, including the presenilin Psen1 and Psen2. Although presenilins are well studied molecules in the brain due to their association with Alzheimer's disease and their important function in the γ-secretase complex, their role in the gut remains largely unknown. Therefore, the aim of this work was to verify the hypothesis that presenilins play an important role during intestinal homeostasis and under inflammatory conditions. To this end, an IEC-specific Psen1 deletion in mice (Psen1ΔIEC) was established. The absence of Psen1 in IECs had no effect on cell differentiation, proliferation or death, which could be explained by a compensatory overex-pression of Psen2. Based on these findings, an inducible Psen1/Psen2 double knockout (Psen2-/- Psen1iΔIEC) mouse line was established. Mice lacking both presenilins in the intestinal epitheli-um rapidly and severely lost weight and developed spontaneous intestinal inflammation. In addition, Psen2-/- Psen1iΔIEC mice showed a breakdown of the intestinal barrier with bacterial translocation into the lamina propria. However, attempts to rescue the observed phenotype by antibiotic treatment to eliminate the microbiota improved the inflammatory signature but not the wasting disease. Most importantly, Psen2-/- Psen1iΔIEC mice showed a disturbance of IEC differentiation, with a significant upregulation of the number of secretory cells and an almost complete loss of absorptive enterocytes. This alteration in cell composition might be responsi-ble for the observed wasting disease, attributed to compromised nutrient uptake. Molecularly, the present thesis was able to demonstrate that the observed phenotype is mainly the result of a complete blockade of Notch signaling. In conclusion, this work has identified the prese-nilins Psen1 and Psen2 as important molecules in the maintenance of intestinal homeostasis. Their absence results in barrier breakdown with subsequent bacterial translocation, goblet cell hyperplasia and, most importantly, absorptive enterocyte hypoplasia and hence malnutrition.
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.