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Synthesis and Characterization of Dimethylarsinate-Functionalized Reduced Polyoxometalates
No full textThis dissertation presents the synthesis of novel reduced POMs functionalized with dimethylarsinate groups. Chapter 1 introduces POMs, emphasizing polyoxomolybdates. Chapter 2 reviews organofunctionalized POMs and the rationale for this study, building on prior work with dimethylarsinate-functionalized molybdenum POMs. Chapter 3 covers experimental methods, characterization techniques, and synthesis of heterophosphonic and arsonic acid ligands. Chapter 4 describes the synthesis and characterization of eight dimethylarsinate-functionalized phosphomolybdates(V), [RPMoV6O15(OH)3{AsO2(CH3)2}3]2− (R = H, HO, CH3, HO2CCH2, HO2CC2H4, C6H5, 4-FC6H4, 4-F3COC6H4), the monoanionic mixed-valent heptamolybdate [HOMoVIMoV6O15(OH)3{AsO2(CH3)2}3]−, and d-block metal-substituted analogues [MPMoV6O15(OH)3{AsO2(CH3)2}3]2− (M = Fe2+, Ni2+, Mn2+). Chapter 5 focuses on the synthesis, structural features, and antibacterial properties of eleven dimethylarsinate-functionalized arsenomolybdates(V), [RAsMoV6O15(OH)3{AsO2(CH3)2}3]2− (R = HO, CH3, C2H5, C6H5, 3,5-(HOOC)2C6H3, 4-FC6H4, 4-F3CC6H4, 4-F3COC6H4, 4-BrC6H4 and 4-N3C6H4) and [AsIIIMoV6O15(OH)3{AsO2(CH3)2}3]3−. All compounds were synthesized in aqueous media and characterized in the solid state by single-crystal X-ray diffraction, TGA, elemental analysis, FT-IR, and PXRD, while their stability in solution and the gas phase was probed using multinuclear NMR (1H, 31P, 19F, 13C), ESI-MS, ion mobility MS, and MS/MS. Chapter 6 describes the synthesis and characterization of a novel heterometallic, dimethylarsinate-capped wheel-type POM, MoV12WVI18O84{AsO2(CH3)2}18]18− (Mo12W18), prepared under mildly acidic aqueous conditions, with alternating MoV2 and WVI3 units forming a ring with a ~1.5 nm central cavity. Its solid-state and solution behavior were probed using single-crystal XRD, IR, TGA, elemental analysis, MAS and CPMAS NMR, multinuclear solution NMR (1H, 13C, 183W, DOSY), UV-Vis, Raman spectroscopy, and SAXS
Simultaneous Localization and Mapping (SLAM) as a Core Component for Open and Affordable Autonomous Underwater Vehicles (AUV)
No full textMapping challenging confined underwater environments pushes the boundaries of what is possible for state-of-the-art robotics. Current state-of-the-art high-performance equipment allows already for accurate mapping in such scenarios. However, these systems are often expensive. Affordable underwater robotic systems and sensors come with significantly reduced capabilities. Especially sonars are necessary for mapping unknown environments, due to cluttered water resulting in bad visibility for vision based sensors. Yet, affordable sonar sensors suffer from higher noise levels, reduced accuracy, and limited coverage. Consequently, developing methods to achieve reliable and accurate mapping of challenging environments with affordable hardware remains an open research question.
This thesis presents a Fourier-SOFT in 2D (FS2D) registration method for robust matching of high-noise 2D sonar scans. A Simultaneous Localization and Mapping (SLAM) framework designed to the unique challenges of affordable Mechanical Scanning Sonars (MSS) is presented, integrating this FS2D registration method.
In the context of the digitization of cultural heritage, the Bunker Valentin Memorial in Bremen is surveyed, and maps of its multiple basins are generated.
Additionally, this thesis contributes an open dataset with accurate ground truth for development and benchmarking 2D sonar navigation, mapping, and SLAM algorithms.
Overall, this thesis demonstrates that, when the unique characteristics of affordable hard ware are considered correctly, and the methods are designed accordingly, affordable underwater robots can effectively map and explore challenging, unknown environments.
The BlueAUV design, the FS2D registration method, SLAM framework for affordable hardware, and an openly available dataset provide a foundation for advancing robust mapping of challenging underwater environments within the research community
Enabling Scalable High-Performance Integrated Sensing and Communications for Next-Generation Wireless Systems
No full textWireless communications technology has gone through a significant evolution since its inception in the late 19th century, integrating itself as a critical pillar of modern society and the functions of the world. Successive generations of the mobile network systems since the first generation (1G) until the present have seen an ever-increasing demand of the key performance indicators (KPIs), such as data rate, reliability, spectral effciency, device connectivity, and more – outlined by the scope of the current fifth generation (5G) systems as enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine- type communications (mMTC), to support the emergent use cases such as autonomous and intelligent networks, extended reality (XR) applications, and Internet-of-Things (IoT), under the enabling technologies such as millimeter-wave (mmWave)/Terahertz (THz) bands, massive multiple-input multiple-output (mMIMO), cell-free MIMO (CF-MIMO), reconfigurable intelligent surface (RIS), and more. Furthermore, the imminent beyond fifth generation (B5G) and sixth-generation (6G) systems anticipate even higher requirements and ambitious paradigms in wireless technologies, aiming to improve upon the performance of 5G but also addressing other challenges which have assumed greater importance for the future, such as resource effciency (spectrum, energy, and hardware), physical layer security, system scalability, and high-mobility. In summary, this thesis entails a comprehensive investigation into the realisation of beyond fifth generation (B5G), underpinned by the two major topics of resource-effciency and low-complexity within the frameworks of the two selected enabling technologies, index modulation (IM) and integrated sensing and communications (ISAC), thereby proposing novel methods and analyses from unique yet complementary perspectives of the addressed problem of achieving scalable and high-performance next-generation wireless systems
Neurocognitive and psychological dimensions associated with gait and balance in older adults
No full textThis Ph.D. dissertation investigates the neurobehavioral, psychological, and cognitive factors influencing postural control and its age-related changes through four studies. Postural control is essential for daily activities, and its decline with age increases fall risk, leading to autonomy loss and reduced quality of life.
The first study (Imani & Godde, 2021) explores how self-efficacy mediates the relationship between falls and autonomy in older adults. While falls negatively impact autonomy, higher self-efficacy reduces this effect. Cognitive function predicts autonomy but does not moderate the relationship between falls and autonomy.
The second study examines the bidirectional link between cognitive and physical function. Results show that cognitive decline affects physical abilities over time and vice versa, highlighting the strong connection between executive function and motor control. This reinforces how cognitive decline, falls, and depressive mood contribute to reduced social participation.
The third study investigates the effects of transcranial direct current stimulation (tDCS) combined with balance training in younger adults. Targeting the sensorimotor cortex and dorsolateral prefrontal cortex (DLPFC), the study assesses how anodal tDCS enhances balance. Resting-state EEG is also explored as a predictor of training effects.
The fourth study extends this research to older adults, revealing age-related differences in neural activation during balance training. Results suggest individualized stimulation protocols may improve balance outcomes.
Overall, these studies emphasize the cognitive-motor link in postural control and propose targeted interventions to enhance balance and autonomy, particularly in older adults at risk of falls
Uncovering Pricing and Behavioural Patterns in Online Apparel: A Data Mining and Machine Learning Approach Using Clickstream Data
No full textThis project investigates customer behavior and pricing dynamics in the context of online apparel sales, using a real-world clickstream from a European e-commerce platform. Through a
structured process of exploratory data analysis and predictive modeling, the study explores how product visuals, attributes, and browsing behavior influence both purchasing patterns and pricing outcomes. Random Forest models were used for both regression and classification tasks to predict product prices and classify items into budget or premium tiers. These model outputs were then combined to detect potential pricing–perception mismatches, where a product appears over- or underpriced relative to how it is perceived based on its features. This approach helps highlight products that may benefit from a review of their pricing or presentation strategy. Key insights from the analysis show that products with frontal model photography tend to perform better in both pricing and sales, while black and blue items generate the highest revenue. These findings lead to clear business recommendations in areas such as pricing, visual merchandising, and product positioning. The project demonstrates how machine learning can be applied not only to forecast outcomes but also to guide practical, data-driven decisions in e-commerce
Engineering Plasma Membrane Transporters to Improve Organic Acid Production
No full textSuccinic acid (SA) is one of the most promising bio-based platform chemicals, as it can serve as a precursor in the synthesis of many industrially relevant chemicals and polymers. Yeasts are particularly desirable microbial hosts for SA production because they tolerate low environmental pH, allowing the direct production of the undissociated form of the acid, which results in a significantly simpler and more cost-effective overall process. The fermentation performance of yeast cell factories heavily depends on the activity of membrane transporters responsible for substrate import, metabolite exchange between intracellular compartments, and SA export from the cell. As such, membrane transporters represent key targets in metabolic engineering efforts and are the primary focus of this work. The first part of this thesis provides new insights into plasma membrane transporters from the acetate uptake transporter (AceTr) family, namely Ato1 and SatP, by identifying amino acid residues critical for their specificity and activity. This includes the engineering and characterisation of Ato1 and SatP variants capable of transporting SA in S. cerevisiae. The impact of these transporter variants on extracellular SA accumulation is assessed and compared with that of Dct-02, a known fungal SA exporter, revealing that AceTr homologues and Dct-02 have opposing effects on SA production in S. cerevisiae under industrially relevant conditions. The second part of this thesis optimizes SA production from glycerol via the CO₂-fixing reductive TCA pathway in S. cerevisiae. To increase carbon flux through this cytosolic pathway, mitochondrial membrane transporters are identified as particularly attractive engineering targets. Overall, this work expands the understanding of membrane transporters relevant for SA production and improves the fermentation performance of existing SA-producing yeast cell factories
Consistent Scalable Processing of Data Streams in a Distributed Environment
No full textThis thesis investigates consistency challenges in distributed stream processing systems. Prior work on this topic has made significant progress, with many ideas being implemented in state-of-the-art Stream Processing Engines (SPEs). In this thesis, we focus on formal modeling to better characterize existing problems and explore potential improvements.
We introduce a formal model of delivery guarantees and show that deterministic SPEs can theoretically achieve lower latency than non-deterministic ones for exactly-once guarantee. This is supported by experimental results demonstrating that a novel deterministic implementation performs better than current alternatives.
The thesis also presents a formal model for substream management, identifying a lower bound on the additional network traffic required for detecting substream termination. A corresponding framework is implemented that meets this bound and demonstrates improved performance over existing approaches.
These results contribute formal foundations and practical techniques for improving the performance and predictability of distributed stream processing systems
Pectinolytic waste valorization – Fermentation of D-galacturonic acid by Saccharomyces cerevisiae using glycerol as a co-substrate
No full textUtilizing agro-industrial waste as a raw material for the production of chemicals, fuels and materials could support a circular bioeconomy, helping to minimize carbon and energy loss. Pectin-rich biomass is a generally under-utilized feedstock, and includes residues such as citrus peel, apple pomace and sugar beet pulp. It offers itself as a feedstock for biotechnological production processes using microorganisms as a cell factory. Unlike other biomass residues, this waste is advantageous due to its high sugar and low lignin content. One key component of pectin is D-galacturonic acid (D-GalUA), an oxidized substrate that S. cerevisiae cannot metabolize naturally. To enable its consumption, S. cerevisiae was equipped with the catabolic pathway naturally present in filamentous fungi. However, this did not allow D-GalUA to serve as sole carbon and energy source. The inability to grow on D-GalUA was thought to be due to a lack of electrons, as the pathway requires NAD(P)H. In this work, this problem was addressed by providing a co-substrate - glycerol - a by-product of the biodiesel industry. The electrons provided in this way were supposed to not only enable D-GalUA utilization but also support its fermentation to ethanol. Therefore, S. cerevisiae was equipped with both the fungal pathway for D-GalUA catabolism and the ‘DHA pathway’ for glycerol utilization – the latter channeling electrons from glycerol oxidation into cytosolic NADH. The resulting strain not only consumed D-GalUA at a high specific rate, but also co-fermented the substrates into ethanol, achieving a maximum yield of 71% of the theoretical maximum. Additionally, the native Gcy1, a non-specific aldo-keto reductase, was found to convert D-GalUA into L-galactonate, an intermediate of the D-GalUA catabolic pathway. By providing valuable insights into the co-utilization of glycerol and D-GalUA, this study lays the foundation for future endeavors towards the valorization of these two industrial by-products
Straddling the border between tests and proofs
No full textTests and proofs are two main techniques in modern software verification. To test a program means running the program to check if its execution yields an expected outcome. To prove a program is to build a mathematical proof, showing the correctness of the program against its desired properties. In the traditional view, however, tests and proofs are considered as two incompatible techniques. They are often treated as warring siblings and mostly applied in isolation. The complementarity of tests and proofs — though not immediately apparent — has been relatively underexplored. Can their combination mitigate each other’s weaknesses while harnessing their respective strengths? This thesis tries to straddle the border between tests and proofs and suggest a concrete answer. It explores how the two approaches can collaborate with and mutually benefit one another. Three key contributions arise from this exploration. The first contribution is Proof2Test, a framework that transforms failed proofs into useful test cases, allowing programmers to use tests to debug failed proofs effectively. The second contribution consists of several proof-based test generation strategies, which use proofs to enhance both the efficiency and effectiveness of test generation. This thesis also extends SC with “loop unrolling”, considering not just zero or one but any number of iterations, up to a set limit. It also includes an empirical study to examine how much (if anything) testing strategies miss when they limit themselves to standard branch coverage and, conversely, how many more bugs we can find if we unroll loops. The last contribution of this thesis is an automatic program repair approach, Proof2Fix, which takes advantages of the proposed test generation methods to produce meaningful corrections to faults revealed by proofs
Applying co-creation to develop behaviour change interventions: Analysing the design, build and evaluation of digital health interventions
No full textChronic and non-communicable diseases present ongoing challenges for healthcare systems worldwide. Digital Health Interventions (DHIs) provide a promising solution by empowering patients to engage in health-related decisions and manage their behaviours. At present, many DHIs suffer from low user adoption or fail to progress beyond research. This thesis analyses the design, build, and evaluation of such tools to guide the effective co-creation of DHIs.
Across five studies, this thesis examines the design, build, and evaluation of DHIs. The first two studies explore how to effectively plan the co-creation of DHIs, identifying both facilitators and challenges. The second study applies co-creation methods to incorporate end users in developing design specifications for a DHI. The third study evaluates the impact of including end users in the build phase, whilst the final two studies assess evaluation strategies, demonstrating how synthetic data and machine learning can be used to manage missing data and predict intervention outcomes.
Findings highlight the importance of adaptive, inclusive design processes, careful planning of co-creator involvement, and the application of behavioural science across all phases. The thesis offers practical guidance for future researchers, emphasising the value of empowering patients, addressing attrition, and applying predictive analytics to maximise the real-world impact of DHIs