Artificial intelligence (AI) is becoming more widely used in a number of industries, including in the field of education. Applications of artificial intelligence (AI) are becoming crucial for schools and universities, whether for automated evaluation, smart educational systems, individualized learning, or staff support. ChatGPT, anAI-based chatbot, offers coherent and helpful replies based on analyzing large volumes of data. Integrating ChatGPT, a sophisticated Natural Language Processing (NLP) tool developed by OpenAI, into higher education has sparked significant interest and debate. Since the technology is already adapted by many students and teachers, this study delves into analyzing the sentiments expressed on university websites regarding ChatGPT integration into education by creating a comprehensive sentiment analysis framework using Hierarchical Residual RSigELU Attention Network (HR-RAN). The proposed framework addresses several challenges in sentiment analysis, such as capturing fine-grained sentiment nuances, including contextual information, and handling complex language expressions in university review data. The methodology involves several steps, including data collection from various educational websites, blogs, and news platforms. The data is preprocessed to handle emoticons, URLs, and tags and then, detect and remove sarcastic text using the eXtreme Learning Hyperband Network (XLHN). Sentences are then grouped based on similarity and topics are modeled using the Non-negative Term-Document Matrix Factorization (NTDMF) approach. Features, such as lexico-semantic, lexico structural, and numerical features are extracted. Dependency parsing and coreference resolution are performed to analyze grammatical structures and understand semantic relationships. Word embedding uses the Word2Vec model to capture semantic relationships between words. The preprocessed text and extracted features are inputted into the HR-RAN classifier to categorize sentiments as positive, negative, or neutral. The sentiment analysis results indicate that 74.8% of the sentiments towards ChatGPT in higher education are neutral, 21.5% are positive, and only 3.7% are negative. This suggests a predominant neutrality among users, with a significant portion expressing positive views and a very small percentage holding negative opinions. Additionally, the analysis reveals regional variations, with Canada showing the highest number of sentiments, predominantly neutral, followed by Germany, the UK, and the USA. The sentiment analysis results are evaluated based on various metrics, such as accuracy, precision, recall, F-measure, and specificity. Results indicate that the proposed framework outperforms conventional sentiment analysis models. The HR-RAN technique achieved a precision of 98.98%, recall of 99.23%, F-measure of 99.10%, accuracy of 98.88%, and specificity of 98.31%. Additionally, word clouds are generated to visually represent the most common terms within positive, neutral, and negative sentiments, providing a clear and immediate understanding of the key themes in the data. These findings can inform educators, administrators, and developers about the benefits and challenges of integrating ChatGPT into educational settings, guiding improvements in educational practices and AI tool development.

Recent studies show that biofilm substances in contact with nanoplastics play an important role in the aggregation and sedimentation of nanoplastics. Consequences of these processes are changes in biofilm formation and stability and changes in the transport and fate of pollutants in the environment. Having a deeper understanding of the nanoplastics–biofilm interaction would help to evaluate the risks posed by uncontrolled nanoplastic pollution. These interactions are impacted by environmental changes due to climate change, such as, e.g., the acidification of surface waters. We apply fluorescence correlation spectroscopy (FCS) to investigate the pH-dependent aggregation tendency of non-functionalized polystyrene (PS) nanoparticles (NPs) due to intermolecular forces with model extracellular biofilm substances. Our biofilm model consists of bovine serum albumin (BSA), which serves as a representative for globular proteins, and the polysaccharide alginate, which is a main component in many biofilms, in solutions containing Na+ with an ionic strength being realistic for fresh-water conditions. Biomolecule concentrations ranging from 0.5 g/L up to at maximum 21 g/L are considered. We use non-functionalized PS NPs as representative for mostly negatively charged nanoplastics. BSA promotes NP aggregation through adsorption onto the NPs and BSA-mediated bridging. In BSA–alginate mixtures, the alginate hampers this interaction, most likely due to alginate–BSA complex formation. In most BSA–alginate mixtures as in alginate alone, NP aggregation is predominantly driven by weaker, pH-independent depletion forces. The stabilizing effect of alginate is only weakened at high BSA contents, when the electrostatic BSA–BSA attraction is not sufficiently screened by the alginate. This study clearly shows that it is crucial to consider correlative effects between multiple biofilm components to better understand the NP aggregation in the presence of complex biofilm substances. Single-component biofilm model systems based on comparing the total organic carbon (TOC) content of the extracellular biofilm substances, as usually considered, would have led to a misjudgment of the stability towards aggregation.

In international business relationships, such as international railway operations, large amounts of data can be exchanged among the parties involved. For the exchange of such data, a limited risk of being cheated by another party, e.g., by being provided with fake data, as well as reasonable cost and a foreseeable benefit, is expected. As the exchanged data can be used to make critical business decisions, there is a high incentive for one party to manipulate the data in its favor. To prevent this type of manipulation, mechanisms exist to ensure the integrity and authenticity of the data. In combination with a fair exchange protocol, it can be ensured that the integrity and authenticity of this data is maintained even when it is exchanged with another party. At the same time, such a protocol ensures that the exchange of data only takes place in conjunction with the agreed compensation, such as a payment, and that the payment is only made if the integrity and authenticity of the data is ensured as previously agreed. However, in order to be able to guarantee fairness, a fair exchange protocol must involve a trusted third party. To avoid fraud by a single centralized party acting as a trusted third party, current research proposes decentralizing the trusted third party, e.g., by using a distributed ledger based fair exchange protocol. However, for assessing the fairness of such an exchange, state-of-the-art approaches neglect costs arising for the parties conducting the fair exchange. This can result in a violation of the outlined expectation of reasonable cost, especially when distributed ledgers are involved, which are typically associated with non-negligible costs. Furthermore, the performance of typical distributed ledger-based fair exchange protocols is limited, posing an obstacle to widespread adoption. To overcome the challenges, in this thesis, we introduce the foundation for a data exchange platform allowing for a fully decentralized fair data exchange with reasonable cost and performance. As a theoretical foundation, we introduce the concept of cost fairness, which considers cost for the fairness assessment by requesting that a party following the fair exchange protocol never suffers any unilateral disadvantages. We prove that cost fairness cannot be achieved using typical public distributed ledgers but requires customized distributed ledger instances, which usually lack complete decentralization. However, we show that the highest unilateral cost are caused by a grieving attack. To allow fair data exchanges to be conducted with reasonable cost and performance, we introduce FairSCE, a distributed ledger-based fair exchange protocol using distributed ledger state channels and incorporating a mechanism to protect against grieving attacks, reducing the possible unilateral cost that have to be covered to a minimum. Based on our evaluation of FairSCE, the worst-case cost for data exchange, even in the presence of malicious parties, is known, which allows an estimate of the possible benefit and, thus, the preliminary estimate of economic utility. Furthermore, to allow for an unambiguous assessment of the correct data being transferred while still allowing for sensitive parts of the data to be masked, we introduce an approach for the hashing of hierarchically structured data, which can be used to ensure integrity and authenticity of the data being transferred.

This work investigates the occurrence of post-event processing (PEP) in the context of test anxiety; PEP involves rumination and self-critical thinking following an event and commonly observed in social anxiety. Three short-term longitudinal studies in student samples examined whether PEP occurs after exams and how it is associated with test anxiety. University students (N =35 in Study 1, N =146 in Study 2, and N =37 in Study 3) completed measures of trait and state test anxiety before an actual exam; PEP related to the exam was assessed at various time points afterward. Results revealed that PEP occurred to a meaningful extent after exam situations. Overall, it was positively associated with trait and state test anxiety, although some variations in the relations were found across the three studies. These findings underscore the relevance of PEP in the context of test anxiety, as PEP might contribute to maintaining test anxiety in the long term. Implications for future studies are discussed.

The biodegradable polymers polylactic acid (PLA) and polyhydroxybutyrate (PHB) produced from renewable raw materials were coated with hydrogenated amorphous carbon layers (a-C:H) at different deposition angles with various thicknesses as part of this thesis. Similar to conventional polymers, biopolymers often have unsuitable surface properties for industrial purposes, e.g. low hardness. For some applications, it is therefore necessary and advantageous to modify the surface properties of biopolymers while retaining the main properties of the substrate material. A suitable surface modification is the deposition of thin a-C:H layers. Their properties depend essentially on the sp² and sp³ hybridization ratio of the carbon atoms and the content of hydrogen atoms. The sp²/sp³ ratio was to be controlled in the present work by varying the coating geometry. Since coatings at 0°, directly in front of the plasma source, contain a higher percentage of sp³ and indirectly coated (180°) a higher amount of sp², it is shown in this work that it is possible to control the sp²/sp³ ratio. For this purpose, the samples are placed in front of the plasma source at angles of 0, 30, 60, 90, 120, 150 and 180° and coated for 2.5, 5.0, 7.5 and 10.0 minutes. For the angles 0°, the layer thicknesses were 25, 50, 75 and 100 nm. The a-C:H layers were all deposited using radio-frequency plasma-enhanced chemical vapor deposition and acetylene as C and H sources after being pretreated with an oxygen plasma for 10 minutes. Following the O₂ treatment and the a-C:H deposition, the surfaces are examined using macroscopic and microscopic measurement methods and the data is then analyzed. The surface morphology is recorded using scanning electron microscopy and atomic force microscopy. In addition, data on the stability of the layer and the surface roughness can be collected. Contact angle (CA) measurements are used to determine not only the wettability, but also the contact angle hysteresis by pumping the drop volume up and down. By measuring the CA with different liquids and comparing them, the surface free energy (SFE) and its polar and disperse components are determined. The changes in barrier properties are verified by water vapor transmission rate tests (WVTR). The chemical analysis of the surface is carried out on the one hand by Fourier transform infrared spectroscopy with specular reflection and on the other hand by synchrotron-supported techniques such as near-edge X-ray absorption fine structure and X-ray photoelectron spectroscopy. When analyzing the surfaces after the O₂ treatment, which was initially assumed to serve only to clean and activate the surface for the a-C:H coating, it was found that the changes were more drastic than originally assumed. For example, if PLA is treated at 0° for 10 minutes, the roughness increases fivefold. As the angle increases, it decreases again until it returns to the initial value at 180°. This can be recognized to a lesser extent with PHB at 30°. For both polymers, it can be shown that the polar fraction of the SFE increases. In the WVTR, a decrease in permeability can be observed for PLA and an increase in the initial value for PHB. The chemical surface analysis shows that the O₂ treatment has little effect on the surface bonds. Overall, it can be shown in this work that the O₂ treatment has an effect on the properties of the surface and cannot be regarded exclusively as a cleaning and activation process. With direct a-C:H coating (at 0°), a layer failure due to internal stress can be observed for both PLA and PHB. This also occurs with PHB at 30°, but to a lesser extent. Permeability of the polymers is reduced by 47% with a five-minute coating and the layer at 10.0 minutes continues to have this effect despite cracks appearing. The application of a-C:H layers shows a dominance of sp³ bonds for both polymer types with direct coating. This decreases with increasing angle and sp² bonds become dominant for indirect coatings. This result is similar for all coating thicknesses, only the angle at which the change of the dominant bond takes place is different. It is shown that it is possible to control the surface properties by an angle-dependent coating and thus to control the ratio sp²/sp³.