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In laser drilling, one challenge is to achieve a high drilling quality in high aspect ratio drilling. Ultra-short pulsed lasers use different concepts like thin disks, fibers and rods. The slab technology is implemented because of their flexibility and characteristics. They bring together both advantages and deliver high pulse energies at high repetition rates. Materials with a thickness > 1.5 mm demand specialized optics handling the high power and pulse energies with adapted processing strategies, integrated in a machine setup. In this contribution, we focus on all the necessary components and strategies for drilling high precision holes with aspect ratios up to 1:40.
For monitoring laser beam welding processes and detecting or actively avoiding process defects, acoustic based measurements can be used in addition to optical measurement methods such as pyrometry. To reliably detect process events, it is essential to position the respective sensors in such a way that specific signal characteristics are reproducible and significant. However, there are only few investigations regarding the positioning for airborne sound sensors, especially for the detection of process emissions in the ultrasonic range. Therefore, in this research, the influence of the process distance as well as the angle and orientation of the microphone to a laser beam deep penetration welding process is investigated with respect to the detectability of process emissions in different frequency bands. It is shown that for a wide ultrasonic range a flat sensor angle with respect to the sample surface leads to an increased signal strength of the acoustic emissions compared to steep angles.
We report on our recent progress in creating a new type of compact laser that uses thulium-based fiber CPA technology to emit a central wavelength of 2 μm. This laser can produce pulse energies of >100 μJ and an average power of >15 W. It is designed to be long-lasting and is built for industrial use, making it a great fit for integration into laser machines used for materials processing. These laser parameters are ideal for working with semiconductors like silicon, allowing for tasks such as micro-welding, cutting of filaments, dicing, bonding and more.
Laser welding of hidden T-joints, connecting the web-sheet through the face-sheet of the joint can provide advantages like increased lightweight potential in manufacturing sandwich structures with thin-walled cores. However, maintaining the correct positioning of the beam relative to the joint is challenging. A method to reduce the effort of positioning is using optical coherence tomography (OCT), that interferometrically measures the reflection distance inside of the keyhole during laser deep penetration welding. In this study new approaches for targeted data processing of the OCT-signal to automatically detect misalignments are presented. It is shown that considering multiple components from the inference pattern and the respective signal intensities improve the detection accuracy of misalignments.
Analysis of the Forensic Preparation of Biometric Facial Features for Digital User Authentication
(2023)
Biometrics has become a popular method of securing access to data as it eliminates the need for users to remember a password. Although exploiting the vulnerabilities of biometric systems increased with their usage, these could also be helpful during criminal casework.
This thesis aims to evaluate approaches to bypass electronic devices with forged faces to access data for law enforcement. Here, obtaining the necessary data in a timely manner is critical. However, unlocking the devices with a password can take several years with a brute force attack. Consequently, biometrics could be a quicker alternative for unlocking.
Various approaches were examined to bypass current face recognition technologies. The first approaches included printing the user's face on regular paper and aimed to unlock devices performing face recognition in the visible spectrum. Further approaches consisted of printing the user's infrared image and creating three-dimensional masks to bypass devices performing face recognition in the near-infrared. Additionally, the underlying software responsible for face recognition was reverse-engineered to get information about its operation mode.
The experiments demonstrate that forged faces can partly bypass face recognition and obtain secured data. Devices performing face recognition in the visible spectrum can be unlocked with a printed image of the user's face. Regarding devices with advanced near-infrared face recognition, only one could be bypassed with a three-dimensional face mask. In addition, its underlying software provided evidence about the demands of face recognition. Other devices under attack remained locked, and their software provided no clues.
The Tutte polynomial is an important tool in graph theory. This paper provides an introduction to the two-variable polynomial using the spanning subgraph and rank-generating polynomials. The equivalency of definitions is shown in detail, as well as evaluations and derivatives. The properties and examples of the polynomial, i.e. the universality, coefficient relations, closed forms and recurrence relations are mentioned. Moreover, the thesis contains the connection between the dichromate and other significant polynomials.
Analysis of Continuous Learning Strategies at the Example of Replay-Based Text Classification
(2023)
Continuous learning is a research field that has significantly boosted in recent years due to highly complex machine and deep learning models. Whereas static models need to be retrained entirely from scratch when new data get available, continuous models progressively adapt to new data saving computational resources. In this context, this work analyzes parameters impacting replay-based continuous learning approaches at the example of a data-incremental text classification task using an MLP and LSTM. Generally, it was found that replay improves the results compared to naive approaches but achieves not the performance of a static model. Mainly, the performances increased with more replayed examples, and the number of training iterations has a significant influence as it can partly control the stability-plasticity-trade-off. In contrast, the impact of balancing the buffer and the strategy to select examples to store in the replay buffer were found to have a minor impact on the results in the present case.
The GeoFlow II experiment aims to replicate Earth’s core dynamics using a rotating spherical container with controlled temperature differences and simulated gravity. During the GeoFlow II campaign, a massive dataset of images was collected, necessitating an automated system for image processing and fluid flow visualization in the northern hemisphere of the spherical container. From here, we aim to detect the special structures appearing on the post processed images. Recognizing YOLOv5’s proficiency in object detection, we apply Yolov5 model for this task.
This study explores the opportunities and risks associated with user-generated content (UGC) in the communication strategies of marketing departments from a business perspective. With the rise of social media and online platforms, UGC has become a powerful tool for brands to engage with their audience, build trust, and enhance brand awareness. However, implementing UGC also comes with inherent risks, including the loss of control over brand messaging, potential negative user-generated content, and legal implications.
To investigate these dynamics, an empirical mixed-methods approach was employed, including expert interviews and a comprehensive literature review. The findings indicate that UGC offers significant opportunities for marketing departments, such as increased customer loyalty, enhanced authenticity, brand awareness, as well as a diverse set of possible content. However, the study also reveals the potential risks associated with UGC, highlighting the importance of managing these risks effectively.
RNA tertiary contact interactions between RNA tetraloops and their receptors stabilize the folding of ribosomal RNA and support the maturation of the ribosome. Here we use FRET assisted structure prediction to develop structural models of two ribosomal tertiary contacts, one consisting of a kissing loop and a GAAA tetraloop and one consisting of the tetraloop receptor (TLR) and a GAAA tetraloop. We build bound and unbound states of the ribosomal contacts de novo, label the RNA in silico and compute FRET histograms based on MD simulations and accessible contact volume (ACV) calculations. The predicted mean FRET efficiency from molecular dynamics (MD) simulations and ACV determination show agreement for the KL-TLGAAA construct. The KL construct revealed too high FRET efficiency and artificial dye behavior, which requires further investigation of the model. In the case of the TLR, the importance of the correct dye and construct parameters in the modeling was shown, which also leads to a renewed modeling. This hybrid approach of experiment and simulation will promote the elucidation of dynamic RNA tertiary contacts and accelerate the discovery of novel RNA interactions as potential future drug targets.
The following thesis contains a detailed business plan of a formula student combustion racecar. This includes the evaluating of existing knowledge about the car combined with required information about the market and seed capital. Subsequently the already presented plan is described with the interpretation for future business plans. In this connection the acceptance of electro mobility shall be evaluated and first ideas for the presentation of an electric car shall be created.
Over recent years, Maximal Extractable Value (MEV) has gained significant importance within the decentralized finance (DeFi) ecosystem. Remarkably, within just two years of its emergence, MEV has seen an extraction of approximately 600 million USD - a phenomenon that has sparked concerns regarding potential threats to blockchain stability.
With growing interest in the Ethereum network and the growing DeFi sector, research surrounding MEV has substantially increased. This work aims to offer a comprehensive understanding of MEV. Additionally, this research quantifies the largest types of MEV (Arbitrage, Sandwich and Liquidations) from March 2022 to March 2023. The data are then compared to other sources, revealing a general upward trend, with a particularly noticeable increase in Sandwich Attacks.
In the field of Blockchain Technology applications and research, non-fungible tokens (NFTs) have gained significant attention in recent years. Whilst current research is focused on NFT use cases or the purchase of NFTs from an investor’s perspective, the NFT launch (i.e. primary market) from a creator’s perspective remains uncovered. However, the launch strategy is considered to be an important factor for the success of a product. Therefore, our research paper aims to explore launch strategies of NFTs. Thereby, we discuss the marketing mix instruments price (i.e. pricing strategy), place (i.e. mint mechanism), and promotion. Through an empirical approach of conducting eight expert interviews, we examine parameters that are used to define an NFT launch strategy and assess their preference of different stakeholders.
A Systematic Literature Review on Blockchain Oracles: State of Research, Challenges, and Trends
(2023)
To enable data exchange between the Blockchain protocol (on-chain) and the real world (off-chain), e.g., non-Blockchain-based applications and systems, a software called Oracle is used [3]. Blockchain oracle is an important component in the use of off-chain data for on-chain smart contracts. However, there is limited scientific literature available on this important blockchain topic. Therefore, in this paper, a novel systematic literature review based on intelligent methods, e.g., information linking, topic clustering and focus identification through frequency calculations, is proposed. Thus, the current state of scientific research interest, content and challenges, and future research directions for blockchain oracles are identified. This paper shows that there is little unbiased literature that does not call oracles a problem. From the results of this new literature review framework, relevant areas of data handling and verification with blockchain oracles are identified for future research.
Safety, quality, and sustainability concerns have arisen from global supply chains. Stakeholders incur risk regarding these factors, given their significance and complexity. Thus, each business's supply chain risk management must prioritize product characteristics. Accordingly, an effective traceability solution that can monitor and regulate product and supply chain aspects is crucial, especially in a given scenario. This re-search paper elucidates the potential of smart contracts in blockchain to enhancing the efficacy of business transactions and ensuring comprehensive traceability within the supply chain of paper-based coffee cups The improved levels of transaction transparency and security in traditional supply chains have been achieved through the digitization of supply chain ecosystem interactions and transactions. This approach makes verifying sources, manufacturing procedures, and quality standards easier in complex supply chains. Accordingly, the integration helps stakeholders monitor and track the whole ecosystem, promoting transparency, predictability, and dependability.
In the swiftly changing world of academic publishing, the Sea of Wisdom platform seizes the opportunity to innovate. By combining the technologies of blockchain, decentralized finance (DeFi), and Non-Fungible Tokens (NFTs) with traditional scholarly communication, we present a groundbreaking, decentralized solution. Our design, although adaptable, primarily uses Ethereum's Virtual Machine, tapping into its robust scientific community.
This desk research will initiate an exploration of present and potential blockchain applications in the higher education sector of Europe. The aim of this research is to create a theoretical base for a further postgraduate research and analysis, so to create an effective model/framework to augment the integration of blockchain technology into existing organizational processes, initially in higher educational institutions, but which may be adaptable and generalizable to other specific uses. Due to the novelty of the topic, academic resources related to the research area are limited. Most studies seem to focus on blockchain-based applications in industries such as finance, healthcare, and supply chain management, and there is little evidence of the impact of blockchain technology on education. This paper discusses present and suggests some potential blockchain-based applications in education in Europe and beyond. This research provides a groundwork for education and academia stakeholders, policymakers and researchers to exploit the potential of blockchain in different functions of an education system.
Currently, the Internet of Things (IoT) is connected to the virtual world through the Web of Things (WoT), allowing efficient utilization of real-world objects with Internet technologies. The WoT facilitates abstract interaction between applications and connected IoT devices, allowing owners to switch between devices while using multiple ones. To achieve this, virtual assets in WoT devices can be tokenized through smart contracts and transferred using hashed proof as transactions within blockchain networks that support virtual currencies. The goal of Web of Things is to establish connectivity, interoperability, and integration among IoT devices using web standards and protocols, reducing reliance on device manufacturers. This enables easy integration of Web 3.0 cryptocurrency for device management. This study proposes a solution for WoT applications involving different cryptocurrency definitions. Finally, simulation results are presented to demonstrate the tokenization-based ownership transfer in the Web of Things.
Decentralization is one of the key attributes associated with blockchain technology. Among the different developments in recent years, decentralized autonomous organizations (DAOs) have been of growing interest. DAOs are currently a key part of another emerging use case, namely decentralized science (DeSci). Given the novelty of the field, an integrative definition of DeSci has not been established, but some inherent concepts and ideas can be traced back to the Open Science movement. Although the DeSci movement has the potential to benefit the public, for example through funding underrepresented research areas or more inclusive and transparent research in general, some negative aspects of decentralization should not be neglected. Due to the novelty of blockchain and emerging use cases, research can and should precede mass adoption, to which this paper aims to contribute.
To investigate the effects of climate change on interactions within ecosystems, a microcosm experiment was conducted. The effects of temperature increase and predator diversity on Collembola communities and their decomposition rate were investigated. The predators used were mites and Chilopods, whose predation effects on several response variables were analysed. This data included Collembola abundance, biomass and body mass as well as basal respiration and microbial biomass carbon. These response variables were tested against the predictors in several models. Temperature showed high significance in interaction with mite abundance in almost all models. Furthermore, the results of the basal respiration and microbial biomass carbon support the suggestion of a trophic cascade within the animal interaction.