TY - THES A1 - Langer, Sven T1 - The Impact of Leader Behavior on Team Processes N2 - The modern work environment is subject to constant change with a notable shift from individual to team-based work structures. This transition necessitates an improved un-derstanding of how managerial actions influence the dynamics within these teams, to optimally align them for success. Despite the substantial research about leader effec-tiveness and team effectiveness based on organizational performance indicators, it is rather infrequent that the direct influence of leader behavior on teams is explored. This thesis aims to address this gap by answering how leader behaviors impact team pro-cesses in the context of German medium to large-scale IT-enterprises. Using a quanti-tative cross-sectional methodology, individuals working in team-based structures under direct supervision were surveyed. Analyzing data from 94 respondents via hierarchical linear regression analysis, several significant relationships were identified. In general, leader behavior has a strong positive impact on team processes. Furthermore, task-oriented leaders have the greatest positive influence on action processes, while relation-oriented leaders have the greatest positive influence on interpersonal processes. Change-oriented leaders also significantly influence action processes and interpersonal processes, albeit less strongly than their counterparts. These results provide implications for team management practices, emphasizing the importance of leader behavior for aligning team processes in the direction of success. KW - Leader Behavior KW - Team Processes KW - IT-Management KW - Leader Effectiveness KW - Team Effectiveness Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:1383-opus4-18136 ER - TY - THES A1 - Acharya, Bhuwan T1 - VISUALIZATION OF FLOOD SCENARIOS IN GERMANY IN 2021 AND FORECASTING FLOOD USING MACHINE-LEARNING MODELS N2 - Floods are the most common, devastating, and frequently occurring natural disaster nowadays. Because of climate change, Europe is expected to see an even higher number of floods in the coming decade. As floods cannot be prevented, understanding the pattern and causes and being able to forecast could significantly reduce the losses during the flood. In July 2021, Germany faced a catastrophic flood, taking more than 180 people’s lives and causing around 40 billion euros of economic loss. To study the flooded region and the region at high risk, the Flood event 2021 has been visualized in this research. The Flood map has been created using a sentinel-1 image. In the visualization, floods in the Rhine River and Arh River can be seen, and the change in the pattern of flood from 12th July to 16th July has also been observed. Different models exist that try to predict floods and warn as early as possible. Since Machine learning is popular nowadays in every sector, ML models have been implemented in this study to forecast the flood. The SARIMA, Random Forest, and LSTM have been implemented using historical data to predict the flood. The Random Forest model performed better than the remaining two models. The Isolation Forest model has also been implemented to classify the data into Flood and No Flood. This model performs well with the Flood classes but struggles in capturing the No Flood class. KW - Flood KW - Machine learning KW - sentinel KW - visualization Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:1383-opus4-18780 ER - TY - THES A1 - Krishnamoorthy, Harriha T1 - Künstliche Intelligenz und nachhaltiger Fast Fashion im E-Commerce - Machbarkeitsanalyse und qualitative Analyse zur kognitiven Dissonanz der Konsumenten N2 - This thesis deals with the consumer purchasing behavior in the fast fashion segment. The central focus includes analyzing factors such as purchase intention, e-commerce trade, consequences and environmental impacts. The first section defines terms and compares current studies to each other. This is followed by a methodical discussion. In order to assess consumer purchasing behavior, semi-structured interviews were conducted, and digital diary entries were written by participants from generation Y and Z. Based on these results shopper types were identified. KW - Fast Fashion KW - Consumption KW - E-commerce KW - Generation Y and Z KW - Environment Y1 - 2023 ER - TY - THES A1 - Sharma, Neetu T1 - Analyzing Customer Behvior Patterns & Predicting Online Product Return Intentions: A Data Mining Approach N2 - During the past years, it is noticeable that the e-commerce industry has emerged drastically, offering accessibility to a variety of products to the customer where they can buy products from the comfort of their home. However, it is associated with a lot of new challenges for e-commerce businesses, particularly in understanding and managing the customer behavior patterns with the rising online product returns. Since the accessibility of online shopping has risen, assessing the critical factors related to product return and prediction has become a really challenging task for e-commerce vendors. This study also aims to segment the customers based on customer behavior prediction into two categories, i.e., high return risk and low return risk customers, and further develop strategies to reduce the online returns. This study is rather structured into four integral parts, where each part provides the comprehensive analysis. The first aspect involves analyzing and identifying the customer behavior patterns leading to product returns. The second aspect predicts the online product return based on selected features and the third is to segment the customers based on online product return prediction and categorize them into high return risk, and low return risk customers. The final step involves development of strategies to reduce the product return based on the intense analysis conducted. To achieve this meaningful research outcome, data analysis is conducted to understand the customer behavior patterns, and a Random Forest feature selector is used to identify the customer behavior patterns that lead to product return. Based on the identified features, classification models were applied to classify and predict whether the customer is going to return product or not. Furthermore, in these seven classification models such as Logistic Regression, Ada Boost, Decision Tree, Naive Bayes, XG Boost, K-Nearest Neighbors, and Random Forest were implemented and used to compare the performance of the classification models to find out the best performing model. Lastly, the segmentation of customers is carried out based on the online product prediction using Logistic regression with classification threshold method into high return risk and low return risk customer categories. The results obtained help in understanding the customer behavior and reduce the online product return by developing the strategies. This study will eventually help the online businesses in reducing returns which will also enhance the customer satisfaction. KW - Customer behavior KW - Product return KW - Data mining KW - Data analysis KW - Customer segmentation Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:1383-opus4-19240 ER - TY - THES A1 - Oha, Kingsley T1 - ADVANCEMENTS IN MICROSERVICE ARCHITECTURES: TACKLING DATA COMMUNICATION, SCALABILITY, AND CI/CD AUTOMATION CHALLENGES N2 - This paper delves into the optimization of microservice architectures, a critical aspect of contemporary software engineering, by focusing on three pivotal areas: efficient data communication, scalable design, and the automation of Continuous Integration/Continuous Deployment (CI/CD) processes. The study begins by examining the intricacies of data communication within microservices, identifying challenges such as network latency, data consistency, and fault tolerance. Strategies for efficient data handling, including advanced message queuing techniques and API gateways, are explored. The paper then shifts its focus to scalable design, assessing how microservices can be architected to gracefully handle varying loads and facilitate seamless scaling. This includes an analysis of containerization technologies, service orchestration, and load balancing strategies. The final part of the study addresses the automation of CI/CD processes, a cornerstone for achieving agility and high velocity in microservices development and deployment. The evaluation of modern CI/CD tools and methodologies, highlighting their role in enhancing deployment frequency, reducing lead time for changes, and improving overall system reliability. This comprehensive study aims to provide valuable insights and practical guidelines for software professionals and researchers, contributing to the refinement and advancement of microservice architectures in the dynamic world of software development. Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:1383-opus4-20463 ER - TY - THES A1 - Helou, Ziad T1 - Opportunities and challenges for the solar industry in Lebanon. N2 - Lebanon faces a significant energy crisis characterized by unreliable electricity supply, political instability, and outdated infrastructure. This thesis explores the potential of solar energy as a sustainable solution to address these challenges, focusing on economic, technical, and regulatory barriers while drawing comparative insights from Cyprus’s renewable energy model. The research examines three primary questions regarding the economic and technical challenges hindering solar energy adoption in Lebanon, the lessons that can be learned from Cyprus to inform solutions, and the comprehensive strategies that can support the development of Lebanon’s solar energy sector. The findings reveal that while Lebanon has experienced rapid growth in solar energy adoption, the sector is undermined by systemic issues such as low-quality imports, skill gaps, insufficient grid infrastructure, and the dominance of diesel generators. Comparative analysis with Cyprus underscores the importance of robust regulatory frameworks, financial accessibility, public-private partnerships, and accreditation systems for solar installers. Additionally, the analysis highlights the critical role of improving capacity factors in Lebanon, with findings showing that even a 1% increase could supply power to over 16,000 homes, emphasizing the potential efficiency gains for Lebanon’s solar energy sector. The thesis also evaluates Lebanon’s progress toward its 2030 renewable energy targets, noting that while the country appears to have met these targets in percentage terms, the achievement is largely a result of reduced electricity generation rather than genuine renewable capacity growth. Recommendations include enforcing quality standards, establishing national accreditation systems, addressing corruption, and leveraging international funding for infrastructure modernization and workforce development. This research emphasizes that Lebanon’s transition to renewable energy is not only a technical and economic endeavor but also a pathway to greater energy independence and environmental sustainability. KW - Solar energy KW - Renewable energy KW - Capacity factor KW - Grid modernization KW - Energy policy KW - Lebanon energy crisis KW - Solar photovoltaics KW - Energy transition KW - Distributed energy systems KW - Solar market regulation KW - Energy storage KW - Microgrid integration KW - Power grid stability KW - Reverse current flow KW - Islanding effect KW - Net metering KW - Sustainable development KW - Public-private partnerships KW - Solar waste management KW - Energy infrastructure Y1 - 2025 ER - TY - THES A1 - Fopa Mamene, Morelle T1 - Secure Access Service Edge (SASE): Architecture, Implementation, and Performance Evaluation N2 - In today’s digitally evolving world, companies are faced with modern problems like data breaches, ensuring seamless and secured access to their cloud-based assets, and the decongestion of routed traffic to their data centres which are all problems related to the ongoing digital evolution. This thesis examines the topic of Secure Access Service Edge (SASE), which is a zero-trust framework combining cutting-edge network security technologies with software-defined connectivity, and evaluates the effectiveness of its core components, such as Software Defined Wide Area Network (SD-WAN), Zero Trust Network Access (ZTNA), Cloud Access Security Broker (CASB), Secure Web Gateway (SWG), and Firewall as a Service (FWaaS), in creating a secure, fast, and reliable corporate network infrastructure. The conclusions obtained in this document are a result of analysing the network architecture of Uniper, a global company in the energy sector which produces and delivers energy, and recently shifted its resources to the cloud by adopting SASE. The outcome of the analysis revealed that, SASE indeed enhances users’ experience and provides several benefits to the company’s network connectivity like reduced latency, connections redundancy, and improved bandwidth. Going through this document will help understand how SASE components can work to provide a revolutionary solution to companies through a proposed implementation roadmap, and how SASE compares to traditional network security models like Virtual Private Network (VPN) and Multiprotocol Label Switching (MPLS). Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:1383-opus4-20700 ER - TY - THES A1 - Hanafy, Ahmed T1 - API Driven Form Rendering N2 - This master’s thesis investigates the challenges and opportunities associated with dynamic form rendering in user interfaces, specifically in the context of Galeria Karstadt Kaufhof (GKK), a key player in the German retail sector. Collaborating with CODUCT Solutions GmbH, this research aims to enhance GKK’s operational efficiency and user experience in their digital transformation journey, focusing on Project Baldeney, a cloud-native platform that automates content management workflows. The thesis begins by outlining the limitations of traditional, static methods of form rendering, highlighting the implications for development time and error frequency. It identifies the fragmentation in the existing API-driven architectures as a critical issue affecting system reliability and user experience. Against this backdrop, the work proposes a unified, API-driven framework to dynamically render forms for many user actions, such as data input and transactions. The research takes a deep dive into existing systems—particularly Informatica’s Product Information Management (PIM) software and the new software in development Article Workbench to identify user experience issues, inefficiencies, and the complexities involved. Through this examination, it offers a targeted solution to integrate various components of form management, like building, parsing, validation, and rendering, into a cohesive system. The thesis aims to contribute to software development and user experience design substantially, by targeting improved development efficiency, reduced errors, and enhanced system reliability. By achieving these objectives, the research aspires to furnish GKK with a robust solution for dynamic form rendering, thereby enhancing both user satisfaction and system performance in the fast-evolving digital landscape. KW - Dynamic Form Rendering KW - API-Driven Architecture KW - Retail Software Integration KW - User Interface Efficiency KW - Product Information Management Y1 - 2023 ER - TY - THES A1 - Gurung, Purnima T1 - Sentiment Analysis in Nepali Tweets: Leveraging TransformerBased Pre-trained Models N2 - Despite the remarkable achievements of large transformer-based pre-trained models like BERT, GPT in several Natural Language Processing (NLP) tasks including Sentiment Analysis (SA), challenges are still present for subdued source languages like Nepali. Nepali language is written in Devanagari script, has complex grammatical structure and diverse linguistic features. Due to the absence of balanced datasets, and computational resources for Nepali, achieving optimal result with the latest architecture remains challenging. For this reason, publicly available NLP modelsfor Nepali are very less, making research in this area difficult. This paper attempts to addressthis gap through the use of pre-trained transformer models specially tailored for Nepali from Hugging Face including BERT, DistilBERT, ALBERT, and DeBERTa for sentiment analysis in Nepali tweets on relatively balanced datasets. The models are trained on large Nepali datasets and optimized for NLP tasks involving Devanagari scripts. To evaluate the model’s performance, various tokenization strategies are investigated in order to capitalize on transformer-based embedding with the SoftMax function and confusion matrix. The outcomes of models are compared using the same datasets. The study’s results shows that DistilBERT achieved the highest accuracy rate of 88% in Nepali sentiment analysis tasks, followed by BERT and DeBERTa at 83% and 80%, respectively. However, ALBERT showed a low accuracy of 70%. The result of this approach shares valuable viewpoints for the field of sentiment analysis in diverse linguistic contexts. KW - Sentiment Analysis KW - NLP KW - Nepali Tweets KW - Monolingual KW - DistilBERT Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:1383-opus4-19677 ER - TY - THES A1 - Kottek, Nick T1 - Echtzeit-Erkennung von Gesten des deutschen Fingeralphabets mithilfe eines Convolutional Neural Networks N2 - Sign language is an important factor in the integration of deaf and hard of hearing people into society. Due to the limited number of people who speak sign language, there is a communication barrier that needs to be addressed. In recent years, sign language has been an important field of research. There have been numerous attempts at trying to find a solution to this problem. This thesis focuses on the German finger alphabet, which has not been researched as much. It examines whether it is possible to recognize the gestures of the German finger alphabet in real time with a convolutional neural network. For that, literature is consulted, and a prototype is developed. The prototype includes a newly created dataset with 2,500 images distributed over 25 gestures, a convolutional neural network, and software for real-time translation of a video stream. The prototype demonstrates the feasibility of realizing such a task. The convolutional neural network achieves an accuracy of 99.61%. On a powerful desktop computer, the prediction of a single frame takes between 36 and 40 ms. However, there are some limitations and restrictions to the quality of prediction caused by factors such as lighting and background. Additionally, certain similar gestures, such as M and N, are difficult to distinguish. Based on these results, ideas and suggestions for future studies are presented. KW - convolutional neural network KW - sign language KW - German finger alphabet KW - image classification KW - real-time recognition Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:1383-opus4-19768 ER - TY - THES A1 - Shrestha, Sabita T1 - Named Entity Recognition for Nepali Text Using Pre-Trained BERT-Based Model N2 - The popularity of using transformer-based models like Bidirectional Encoder Representations from Transformers (BERT) for various Natural Language Processing (NLP) tasks is increasing rapidly. Unfortunately, the research is very limited for low-resource languages like Nepali. This study examines the utilisation of pre-trained BERT-based NERNepal for Named Entity Recognition (NER) tasks in Nepali text. The main goal is to investigate the efficiency of the NERNepal model, which has been pre-trained and fine-tuned on Nepali corpus data for NER. This research provides new insights by evaluating the NERNepal model on two distinct datasets. It addresses unique linguistic challenges specific to Nepali. The study also offers a detailed analysis of the model's strengths and weaknesses. By focusing on diverse datasets, this study shows how adaptable the model is and how its performance varies. These aspects have not been explored extensively before. The EverestNER dataset is one of the largest human-annotated datasets in Nepal so far, and the Nepali_NER dataset is also BIO-annotated for the NER task, which helped to compare the model’s prediction. The performance was better on the Nepali_NER dataset in comparison with another selected dataset. The EverestNER dataset contained many complex words for ORG connected with many tokens for a single entity with different contextual meanings and different annotations for the same word in different tokens as per context. Because of this, it created more confusion for the prediction, especially for the ORG entity. It had a similar issue with another dataset as well, but the label annotation was better in comparison. Furthermore, the research tries to clarify the difficulties and constraints related to utilising pre-trained BERT models for NER in low-resource languages such as Nepali. The study focuses on research areas on the efficacy of the model and its performance on two different datasets. Post-training with the EverestNER train dataset was attempted, but due to computational resource limitations, only a maximum of 3 epochs was possible, which did not improve the evaluation. It also has implications for enhancing language processing tools for Nepali. The results show the ability of a pre-trained BERT-based model to improve NER skills for Nepali text. However, further study is required to overcome the current obstacles to identifying complex words. The availability of high computational resources and the possibility of combining other NER approaches with a transformer-based model could increase the performance and robustness of the model. Keywords: Named Entity Recognition, Natural Language Processing, BERT, low resources language KW - Named Entity Recognition KW - Natural Language Processing KW - BERT KW - low resources language Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:1383-opus4-19870 ER - TY - THES A1 - Nacke, Marius T1 - Künstliche Intelligenz zur Vorhersage von Kreditentscheidungen N2 - Seit der Verschärfung der Kreditrichtlinien im Jahr 2022 ist es zunehmend schwieriger geworden, einen Kredit in Deutschland und im europäischen Raum zu erhalten. Das Interesse an Krediten ist zuletzt ungeachtet dieser Erschwernisse angestiegen. Vergleichs- und Vermittlungsportale für Kredite können potenziellen Darlehensnehmern aktuell nur in begrenztem Maße dabei unterstützen, eine genehmigte Kreditanfrage zu erhalten. Die Vorhersage der Kreditentscheidungen von Banken kann als Basis für weitere Methoden fungieren, die Kreditnehmern helfen können, die Wahrscheinlichkeit einer Genehmigung zu erhöhen. In der vorliegenden Arbeit wird daher untersucht, welche Verfahren des Machine-Learning am ehesten für die Vorhersage von Kreditentscheidungen geeignet sind. Dazu werden die Daten der Yareto GmbH, eines Vergleichs- und Vermittlungsportals für Fahrzeugkredite, für das Training sowohl eines Logistischen-Regressions- und XGBoost-Modells als auch eines Multilayer-Perceptrons verwendet. Das XGBoost-Modell erreicht dabei die höchste Accuracy und Precision mit 73,6\% bzw. 76,1\% bei einer Trainingszeit von lediglich 55 Minuten. Das Multilayer Perceptron kann eine ähnlich hohe Accuracy erreichen, bedarf allerdings mehr als zehn Stunden für das Training. Demnach ist XGBoost am ehesten von den drei untersuchten Verfahren zur Vorhersage von Kreditentscheidungen geeignet. KW - Kreditvergleichsportale KW - Prognosen KW - Künstliche Intelligenz KW - Ensemble-Modelle KW - Kreditentscheidungen Y1 - 2024 ER - TY - THES A1 - Akter, Most Sarmin T1 - Data Analysis of the "Loyalty Program-Valeo Specialist Club" to Improve the Independent Aftermarket: Designing an Efficient Marketing Strategy N2 - Nowadays, a loyalty program is one of the most significant marketing tools for winning genuine customer loyalty. Valeo, a global automotive supplier, also offers the Valeo Specialist Club loyalty program and rewards garage or workshop members who only purchase Valeo spare parts. The core intent of this study is to understand the importance of the Valeo loyalty application, members’ insights, and their experiences for improving the future planning, aftermarket performance, and management of the Value Specialist Club. In today’s competitive market, products, services, and customer loyalty are valuable assets, so the program was created to enhance member satisfaction and loyalty with the Value Specialist Club. A mixed-method approach was used to answer the study question and findings, improve member satisfaction, and refine marketing strategies, using a survey as primary data and Qlik Sense as secondary data. Besides, using a survey methodology, the study collected 79 responses from different countries (the United Kingdom, Germany, Belgium, and the Netherlands) and analyzed the data with only 58 Specialist Club member's responses. A reliability examination was additionally accomplished to measure the accuracy of the collected survey data. Qlik Sense, Microsoft 365, and Excel 2016 were used for data analysis and visualization, which involved descriptive analysis, correlation coefficients, and text sentiment analysis. The study results show positive trends in member satisfaction across four markets, but a decreasing engagement rate is noticeable. In general, members from all countries were satisfied with Valeo products and services, contrasting with moderate dissatisfaction with the Valeo Specialist Club app and customer service. The study also found a strong correlation between the Valeo Specialist Club app, loyalty service, and customer service, suggesting improvements to meet member expectations and preferences. Nevertheless, the study also emphasizes the need to enhance the Valeo Specialist Club registration process, loyalty program features, and benefits to align with current trends, foster increased member satisfaction, and sustain loyalty within the independent aftermarket. Furthermore, the conclusions of this study will aid in developing upcoming marketing plans. KW - Loyalty Program KW - Insights KW - Member Loyalty KW - Marketing KW - Valeo Specialist Club Y1 - 2024 ER - TY - THES A1 - Grigo, Tobias T1 - Das Homeoffice in der Verwaltung – Eine Zwischenbilanz und Erfahrungsberichte zu Best Practices, Weiterentwicklung und Herausforderungen N2 - The home office has become established in the offices of numerous companies and administrations since the events of the global COVID-19 pandemic and has posed numerous challenges. Particularly in Germany, administrative authorities were confronted with the challenge of quickly responding to the events of the pandemic and taking appropriate measures to curb the spread of the virus in the offices of administrations. This paper examines the use of the home office in administration during the pandemic by combining quantitative and qualitative methods such as statistical analyses, interviews, and case studies. In doing so, it sheds light on the experiences of employees as well as the challenges and potentials of home office use. The context aims to demonstrate that the investigation of home office use in administration during the pandemic was crucial in understanding the effects of the newly designed work practices on administrative organizations to better shape future work models. The aim of this study is to gain insights that contribute to the further development of the home office concept in administration. This includes evaluating the long-term effects on work efficiency, work-life balance, and corporate culture. These insights contribute to assessing the effectiveness and efficiency of the home office in administration and lay the groundwork for how optimized work arrangements can be created. They also provide valuable insight into the adaptability of administrative structures to continuously changing work environments. The results of this study will help develop practical solutions and optimize the implementation of the home office in administration. The findings lay new foundations for future research that could address further aspects of home office use in administration, such as long-term effects on organizational performance and employee satisfaction, as well as the development of guidelines for effective implementation of hybrid work models. KW - Homeoffice KW - Administration KW - COVID-19 pandemic KW - Work practices KW - Efficiency Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:1383-opus4-20272 ER - TY - THES A1 - Regmi, Khem Raj T1 - Interactive Visualisation Tool for Teaching Environmental Data: A Guideline of how to set up the tool taking user feedback into account N2 - This thesis will study the creation of an interactive data visualization tool for environmental dataset that concentrates specifically on Nitrate concentrations in groundwater. This project is intended to create a tool for exploratory/experiential learning - one that allows learners to investigate regional data and pursue genuine learning opportunities. The plan is to use regional data in a specific user journey to create this interactive learning experience. The project uses open environmental data: Hygris dataset of LANUV(Landesamt für Natur, Umwelt und Verbraucherschutz) related to the regions Kleve and Wesel in Germany, and realizes this with help of python programming language and python streamlit framework, web development tools and data visualization libraries (pandas , plotly.express, streamlite.components.v1). This study explores tailored visualization strategies that address key challenges such as the inherent complexity of environmental datasets and the need for more engaging and comprehensible educational tools. These strategies include deploying interactive visualizations that allow hands-on data exploration, and temporal visualizations. The role of user feedback in refining educational tools for teaching environmental data is examined, particularly in designing effective visualization techniques like line char, bar chart, bubble chart, pie chart, scatter plot and intensity graph for representing groundwater nitrate concentrations. The impact of different visualization techniques on the understanding and interpretation of open environmental data among educators and students is analyzed, with an emphasis on the spatial distribution of groundwater nitrate concentrations over time. This approach aims to increase user engagement and deepen understanding of environmental data, facilitating a more profound embedding of information within learners' cognitive processes. KW - Interactive data visualization KW - Data representation KW - Environmental data KW - Nitrate concentration KW - Groundwater KW - Open data KW - User feedback KW - Education KW - Data accessibility KW - Data inclusivity KW - Educational tools KW - Digital teaching Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:1383-opus4-21594 ER - TY - THES A1 - Boulmane, Aya T1 - Development of a Web-Based Tool for Estimating Lifecycle CO₂ Emissions of Electric vs. Diesel Vehicles in Germany N2 - Due to rapid technological development, the transport sector has emerged as one of the most difficult areas to reduce decarbonization, balancing its high global greenhouse gas emissions. In Germany, the shift from internal combustion engine vehicles (ICEVs) to electric vehicles (EVs) has become a key focus of national and EU climate policies. Nevertheless, the transition to EVs is associated with a multitude of factors that need to be considered, including emissions from manufacturing, the energy used during operations, and post-operational treatment. This thesis focuses on the design, development, and refinement of a web-based CO₂ Lifecycle Emission Calculation tool that compares cradle-to-grave emissions of EVs and diesel ICEVs based on different driving scenarios in Germany. It is based on Life Cycle Assessment (LCA) Methodology as indicated in ISO 14040/14044. With the tool, the manufacturing, operational, and end-of-life emissions are estimated based on validated datasets from peer-reviewed literature and authoritative bodies (IPCC, IEA, UBA). The tool’s modular structure, which was developed in Python with the Streamlit framework, also allows for other languages to be added, selection of the region’s grid mix, scenario comparisons, and results visualization in multi-level dashboards. Findings of the literature review show that the tool is accurate for both types of vehicles as the known lifecycle emissions ranges for both types are captured. Carbon payback periods for EVs ranged from less than three years to over eight years depending on the grid context, from renewable-rich to coal-intensive. Sensitivity analysis demonstrates that both grid CO₂ intensity and annual mileage hold the greatest influence over lifecycle results. This research contributes to sustainability as it fills the void that exists between academic LCA models and consumer-facing applications with a transparent, reproducible, and user-configurable decision-support tool. Improvements can be made by incorporating real-time grid emission data, expanding the vehicle datasets, and including additional CO₂ environmental impact categories. Y1 - 2025 ER - TY - THES A1 - Okos, Martin T1 - Ethische und rechtliche Rahmenbedingungen des Maschinellen Lernens - eine systematische Analyse zur Konzeptualisierung und Regulation innerhalb der EU N2 - Die Entwicklungen des maschinellen Lernens und damit einhergehend auch der künstlichen Intelligenz umfassen immer mehr Anwendungsfälle im täglichen Leben. Je komplexer und autonomer diese Systeme werden, desto schwieriger lässt sich ihr Verhalten prognostizieren und es ist nicht auszuschließen, dass Situationen entstehen, die ethische und rechtliche Fragen aufwerfen. Das Ziel dieser Arbeit ist die Untersuchung der erforderlichen Rahmenbedingungen für den Umgang mit diesen Technologien. Neben der Erklärung wesentlicher Begriffe aus dem Bereich der KI und Ethik werden durch die Recherche geeigneter Literatur über Pflegeroboter und dem autonomen Fahren zwei Anwendungsbereiche vorgestellt, die einen Bedarf an moralischen Maschinen aufweisen. Maßnahmen zur Regulierung innerhalb der Europäischen Union erfolgen durch das Anwenden bestehender und Erlassen neuer Gesetze sowie über die Definition ethischer Leitlinien, die ebenfalls als Teil der vorliegenden Arbeit behandelt werden. N2 - Developments in machine learning along with artificial intelligence are encompassing increasingly more use cases in everyday life. The more complex and autonomous these systems become, the harder it is to predict their behaviour, and it cannot be ruled out that situations emerge where ethical and legal questions arise. This thesis aims to investigate the necessary framework conditions for dealing with these technologies. In addition to explaining essential terminology from the domain of AI and ethics, two application areas that show a need for moral machines are presented by referencing contemporary literature on care robots and autonomous driving. The European Union provides regulatory measures by applying existing laws and enacting new ones, as well as by defining ethical guidelines, which are also addressed as part of this thesis. KW - Maschinelles Lernen KW - Künstliche Intelligenz KW - Ethik KW - Pflegeroboter KW - Autonomes Fahren Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:1383-opus4-15170 ER - TY - THES A1 - Salobir, Jan T1 - Relaunch eines Kommunalen Webauftritts beim KRZN N2 - In der Arbeit wird der Prozess des Relaunches eines kommunalen Webauftritts Schritt für Schritt analysiert und besonders darauf geachtet, wie die gesetzlichen Anforderungen für so einen Webauftritt umgesetzt werden. KW - E-Government KW - Onlinezugangsgesetz KW - Webauftritt KW - Verwaltungsinformatik KW - Verwaltungsdigitalisierung Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:1383-opus4-15214 ER - TY - THES A1 - Ponten, Marvin T1 - Visualisierung von Kulturgütern am Beispiel der römischen Kaiserthermen in Trier als 3D Modell. N2 - This paper is a summary of the “Kaiserthermen Project'' for the “Landesmuseum Trier” and shows in detail how to convert a 3Ds Max 3D model for usage into an open source program like Blender. It tries to explain the basics of 3D modelling and compares it to the 3D modelling of cultural heritages. It should work as a form of manual to further convert different models into the FBX file format. It also aims to explain the high value of cultural heritage as a 3D model and compares it to the 3D model of the “Trier Kaiserthermen”. It furthermore gives examples and reasons for the usage of 3D models in different fields. It also presents the final 3D renderings and shows how they were created. At the end it explains how the perfect model for scientific research should be and how this project and paper improved this for the “Kaiserthermen”. KW - 3D Model KW - Cultural Heritage KW - Converting to FBX KW - 3D Render Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:1383-opus4-15889 ER - TY - THES A1 - Scheil, Jonathan T1 - Analyse der aktuellen und vergangenen politischen Landschaft Deutschlands im Hinblick auf die aktuelle Bundestagswahl mit Fokus auf die ansteigende Bedeutung von Social-Media im Wahlkampf N2 - Since the last federal election in germany, the CDU is no longer the strongest force and suffered large losses of votes. The SPD, Bündnis 90/Die Grünen and the FDP now make up the german parliament. Parts of the election campaign are taking place via social media. But how do the individual parties use modern media? Do the parties differ in the way they communicate their messages and, as a result, do they generate votes in diffe-rent ways? I will examine these questions in more detail in this bachelor's thesis, starting with the reunification of germany and including the last federal elections. The social me-dia platform I am investigating for political communication is twitter. I presented the past and current parliamentary election results and differences in eastern and western germany. To do this, I used sentiment analysis to examine tweets extracted from twitter at the federal and local/state political level, for their content and the accom-panying reactions. I created a detailed representation of the different social media appearances of politicians and determined whether appropriate conclusions can be drawn based on my results and data. My analysis shows that at the parliamentary level, Annalena Baerbock of Bündnis 90/Die Grünen, followed by Dr. Alice Weidel of the AfD and Christian Lindner of the FDP, have the most likes and retweets. Olaf Scholz, with 826 tweets, is the most active on twitter in 2021. At federal and local/state level, the topics of all parties are often similar. There are differences with regard to the core issues of the respective parties. I have illustrated this in each case. Based on the politicians' use of twitter, I could not draw any concrete conclusions about voter behaviour. For this it would be important to evaluate other media, e.g. facebook. KW - Sentiment-analyse KW - Politik KW - Bundestagswahl KW - Ost-/Westdeutschland KW - Social Media Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:1383-opus4-13687 ER -