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XAutoML : A Visual Analytics Tool for Understanding and Validating Automated Machine Learning
(2023)
XAI for Semantic Dependency : How to understand the impact of higher-level concepts on AI results
(2023)
The bachelor thesis investigates the development and impact of electronic payment methods, specifically concerning the COVID-19 pandemic, consumer preferences, and the future of such methods. The primary objective of this study was to examine the effect of the pandemic and security concerns on consumer behaviour towards electronic payments. That was achieved through a literature review and a survey of 605 participants. The survey results indicate an increasing trend among consumers towards cashless payments due to their numerous advantages over cash transactions, including enhanced hygiene, quicker processing times, and improved security. As such, users exhibit a high level of comfort and convenience with electronic payment methods and are generally rarely worried about the security of their data. The study further reveals that electronic payment methods are gaining popularity worldwide, particularly mobile payments. The pandemic and improved security measures have led to a notable increase in their use, which is expected to continue to rise. Based on these findings, the study suggests that digital payments are the way forward and that these methods will be further developed to improve user-friendliness and security.
Increasing Resilience of Production Systems by Dynamic Context Modelling and Process Adaption
(2023)
This chapter introduces the technology Non-Intrusive Load Monitoring, a method for detecting individual devices from an overall signal. Non-Intrusive Load Monitoring is the research area and technology behind the third word in Smart Meter Inclusive. Using a smart meter as a basis and recognizing devices from the power profile is not a new idea but is now a common practice in Non-Intrusive Load Monitoring. However, the approach to creating such a measurement system that classifies appliances in real-time and visualizes the results directly on the same hardware has not been existing yet. Smart Meter Inclusive wants to leave the data where it originates, namely with the customer. This book chapter provides a general overview of non-intrusive load monitoring to be able to understand the basics and approaches for such a Smart Meter Inclusive.
The purpose of this study is to explain the concept of heart work and to demonstrate that long-term excellence can be achieved through it. The study examines the implementation of corporate values at METRO Markets from 2020 to 2022 and investigates the extent to which the implementation of company values, the perception of autonomy and championing, and the quality of the relationship with managers are linked to the level of heart work. The hypotheses suggest that better implementation of values and the greater feelings of autonomy and championing, and better relationships with managers are associated with higher levels of heart work. Employee data from METRO Markets measuring employee engagement over two years is analysed using exploratory data analysis and quantitative regression analysis to test the hypotheses. The study shows that a value-oriented work environment and self-leadership skills are linked to higher levels of heart work. No conclusive statement could be made about heart work and the relationship with managers due to unmet assumptions in the regression analysis, indicating the need for further research. The conclusion highlights the importance of personal responsibility, mindfulness and self-leadership in achieving and maintaining healthy levels of performance.
In recent years, several studies have reported inadequate financial capability. Studies in Germany have shown similar results. While there was no statewide initiative, learning offers were available. However, the financial capability level did not increase over time. As a possible reason, the Dunning-Kruger Effect was hypothesised. According to the Dunning-Kruger Effect, participants with low ability would simply lack the ability to realise their lack of ability. This study investigated the investment capability of participants in Germany and tested for a possible Dunning-Kruger Effect. The results have shown no evidence for a Dunning-Kruger Effect. The study’s results were supportive of literature critical on the Dunning-Kruger Effect.
Selected case studies regarding research-based education in the area of machine and civil assemblies
(2023)
Regional Flow Index may predict weaning outcomes in patients under prolonged mechanical ventilation
(2023)
This bachelor thesis examines the emerging concept of re-commerce in the fashion industry with a particular focus on the factors influencing its adoption in the German market. In the context of changing economic dynamics and the increasing importance of sustainability, the thesis analyzes the basic principles of the circular economy in relation to the fashion industry and discusses the role of re-commerce initiatives in promoting resource conservation.
The study uses a comprehensive PEST analysis of the German market to uncover political, economic, sociocultural, and technological factors influencing the adoption of re- commerce. In addition, a detailed market analysis reveals the current state and growth prospects of re-commerce in Germany. This includes an examination of trends, target groups, demand for sustainable fashion and the acceptance of second-hand goods.
Using the case study of the fashion brand Levi's, which has successfully integrated a re- commerce platform, the study shows how established companies can achieve their sustainability goals and discover new business opportunities.
Finally, the paper concludes by summarizing the identified factors and the opportunities and challenges for companies looking to enter the German re-commerce market. The assessment of the potential benefits and risks associated with launching re-commerce initiatives in Germany provides a comprehensive basis for future decision-making. All in all, the German fashion resale market offers promising prospects. The current expansion and future potential of this sector are strongly influenced by consumer behavior and driven by extrinsic factors.
Thus, this work contributes to a deeper understanding of the relevance of re-commerce practices in the fashion industry and provides valuable insights for companies in the German fashion market aiming at sustainable business strategies.
The common occurrence of impulsive buying is present in our everyday lives, but what is behind this occurrence? Impulse buying behaviour is described as a sudden, spontaneous, and compelling phenomenon that accounts for a great part of regular shopping. However, shopping should not only be considered in physical stores but also on online websites and social commerce platforms. With the advancement of technology, the number of social media users is also increasing and with it the importance of online purchases. In order for businesses, marketers or e-retailers to capitalize on this surge, it is necessary to understand what factors drive impulse buying behaviour, and particularly what the key driving factors of online impulse purchases are. Therefore, this paper address impulse buying behaviour and its general influences as well as social media marketing and online stimuli. Moreover, it aimed to examine the relationship between online impulse buying behaviour and social media marketing. To identify the relevant insights on the topic, a systematic literature review was conducted by using databases and literature search engines such as Emerald, Google Scholar, EBSCO, and ResearchGate. After the selection process, 39 articles were further reviewed for this study. The main findings that emerge from the paper are hedonic motivation, price promotions, payment facility, website quality and content, and trust beliefs on online websites are the key driving factors of online impulse buying behaviour. Furthermore, certain literature indicates that social media marketing influences impulsive purchases through so-called influencers and adapts influencing factors of online impulse buying to their social network sites.
As a leading medical technology company, KARL STORZ SE & Co. KG continuously wants to bring innovations and applications into the market and expand its existing product portfolio. As strategic initiatives, the company has set priorities and goals to ensure its sustained success. One of those strategic initiatives is to start developing new products in the field of robotic surgery and compete in this segment. To achieve these ambitions, the company decided to spin off its robotic solutions department and establish a new business called KARL STORZ VentureONE Pte. Ltd. With a smaller and more agile structure, the new venture seeks to implement new ways of working and promote innovation.
This new-born company is playing in a fast-growing market that one single competitor has traditionally dominated. However, robotic surgery still has much potential, and existing suppliers have not yet reached different market segments. One segment that has called the attention of the spin-off is the ambulatory surgery centers in the US.
These surgical institutions are growing and gaining relevance in the American surgery landscape. Hence, the company is particularly interested in developing a robotic solution for this segment. Before introducing a product, the company must understand the target customer intended to address. Management at the new venture is also convinced that new business models can be successfully implemented in this segment. Thus, this academic work aims to evaluate the ambulatory surgery center market in the US and propose a suitable business model for this target customer.
For this purpose, this thesis was divided into two main parts. The first aimed to conduct market research on the ambulatory surgery centers in the US by collecting secondary data from articles, reports, and governmental publications. The second part focused on proposing a business model for the ambulatory surgery center segment. To do so, primary data was obtained by interviewing experts in this market.
The research found that the ambulatory surgery centers segment is a growing market where robotic surgery has not yet been fully implemented due to its high cost and reduced reimbursement rates for outpatient procedures. Another barrier to robotic surgery in surgery centers is the high upfront investment needed to buy the technology. Based on these insights, different business models, such as leasing, renting, pay-per-use, and subscription, were assessed. A subscription model was selected and further developed by presenting the most advantages. Using the “Business Model Navigator” methodology, this work defined and documented the target customer, the value proposition, the value chain, and the profit mechanism.
Advantest Company ("the company") is the world’s leading manufacturer of automatic test and measurement equipment used in the design and manufacturing of semiconductors. Because of the complexity of technologies and the supply chain of the semiconductor business segment, the company depends on both internal and external suppliers to manage some aspects of the supply chain of its main product, the System on Chip (“SoC”) test system. As a result, it is of ongoing interest to the company to have tools to continually strive for increased quality, reliability, capacity, and speed. Above all, these tools must enhance the relationships with the suppliers, and ensure the profitability and the company's position in the future.
The goal of this thesis is to understand the supply chain procedure at Advantest Company’s current state from the data-driven perspective by using the process mining technique. This technique is an emerging discipline, providing a comprehensive set of tools to provide fact-based insights and support process improvement. This new discipline is built on process model-driven approaches and data mining.
In this thesis, we discuss opportunities for process mining with an approach to analyse the process and how it can help the company enhance the procedure. This approach, which consists of data extraction from both third-party and internal software solutions, provides better visibility and faster disruptive event notification of the supply chain at the company.
In today's fast-paced business environment, customers expect more than just high-quality products or services. They also demand excellent customer support that is both efficient and personalized. With the growth of businesses and the increasing complexity of products and services, providing efficient customer support has become a critical component of any successful business strategy to fulfill customers and user’s expectations. The present study investigates the need for a technical support helpdesk solution within the business unit Industrial Hydraulics of Bosch Rexroth, a global leader in drive and control technology. The primary objective of this thesis is to assess the current state of the support processes, identify areas of weakness, and leverage these insights to optimize and enhance them. The focus will be on finding a way to enhance transparency of customer data.
Based on the theory to process optimization, a current state analysis was conducted, and expert interviews were carried out to identify weaknesses and potential solutions for improving customer support. The research question "How can the Industrial Hydraulics Services of Bosch Rexroth deliver more effective and efficient customer support?" is addressed through the findings that a central platform is necessary to handle customer inquiries more efficiently, given the diverse communication channels and varying modes of operation within the business unit. The use of the existing SAP CRM ticketing system Robin is suggested as an efficient solution path, which should be optimized for future use based on the identified optimization approaches. The results underscore the importance of a unified approach to working, which is vital for the success of the business unit, resulting in increased efficiency, quality, collaboration, scalability, and customer orientation.
Building and further strengthening customer relationships is of vital significance for organizations per diem, as maintaining an existing customer base is proven to be less costly than acquiring new customers. Therefore, customer loyalty programs serve as the ideal foundation to enhance customer-centric businesses in the long-run and reward continuous engaging customer behavior. The B2B automotive aftermarket industry with respect to loyalty programs is a general under-researched area. Thus, this study aims to close the gap.
The objective of this research study was to create a suitable loyalty program for the automotive aftermarket on the example of Parts Trading at Mercedes-Benz AG. In order to achieve that, literature concerning individual factors that compose a loyalty program were reviewed as well as twelve qualitative expert interviews in the German market conducted. The experts provide specific, deep-rooted practical knowledge in the automotive aftersales field and assist in gathering valuable information in terms of desired aspects, challenges and experiences regarding loyalty programs. The results were assigned to six categories which were further used to provide recommended action for a loyalty program in Parts Trading and the general automotive aftermarket.
The results of this study display that an automotive aftermarket loyalty program is associated with a great deal of effort, as various challenges have to be overcome. These include the issue of data management, specialized rewards that provide high-perceived value that need to be offered in an easy-to-understand scheme, the signif-icant topic of support and accessibility from the company that needs to be ensured before implementing a loyalty program and lastly, the subject of profitability for the organization. Consequently, it is suggested to derive further international studies where quantitative research with a greater sample size is conducted to review whether the results are in alignment along with a long-term study in which the impact on customer buying behavior and profitability of the program with the same experts is evaluated. However, these findings serve as the ideal starting point for Parts Trading with a lot of possibilities to further develop the loyalty program.
Explainable Artificial Intelligence (XAI) seeks to enhance transparency and trust in AI systems. Evaluating the quality of XAI explanation methods remains challenging due to limitations in existing metrics. To address these issues, we propose a novel metric called Explanation Significance Assessment (ESA) and its extension, the Weighted Explanation Significance Assessment (WESA). These metrics offer a comprehensive evaluation of XAI explanations, considering spatial precision, focus overlap, and relevance accuracy. In this paper, we demonstrate the applicability of ESA and WESA on medical data. These metrics quantify the understandability and reliability of XAI explanations, assisting practitioners in interpreting AI-based decisions and promoting informed choices in critical domains like healthcare. Moreover, ESA and WESA can play a crucial role in AI certification, ensuring both accuracy and explainability. By evaluating the performance of XAI methods and underlying AI models, these metrics contribute to trustworthy AI systems. Incorporating ESA and WESA in AI certification efforts advances the field of XAI and bridges the gap between accuracy and interpretability. In summary, ESA and WESA provide comprehensive metrics to evaluate XAI explanations, benefiting research, critical domains, and AI certification, thereby enabling trustworthy and interpretable AI systems.
Data scientists, researchers and engineers want to understand, whether machine learning models for object detection work accurate and precise. Networks like Yolo use bounding boxes as a result to localize the object in the image.
The principal aim of this paper is to address the problem of a lack of an effective metric for evaluating the results of bounding box regression in object detection networks when boxes do not overlap or lie completely within each other.
The standard known metrics, like IoU, lack of differentiating results, which do not overlap but differ in the distance between predicted bounding box and label.
To solve this challenge, we propose a new metric called UIoU (Unified Intersection over Union) that combines the best properties of existing metrics (IoU, GIoU and DIoU) and extends them with a similarity factor. By assigning weight to each component of the metric, it allows for a clear differentiation between the three possible cases of box positions (not overlapping, overlapping, boxes inside each other).
The result of this paper is a new metric that outperforms the existing metrics such as IoU, GIoU and DIoU by providing a more understandable measure of the performance of object detection models. This provides researchers and users in the field of explainable AI with a metric that allows the evaluation and comparison of prediction and label bounding boxes in an understandable way.
As industrial networks continue to expand and connect more devices and users, they face growing security challenges such as unauthorized access and data breaches. This paper delves into the crucial role of security and trust in industrial networks and how trust management systems (TMS) can mitigate malicious access to these networks.
The TMS presented in this paper leverages distributed ledger technology (blockchain) to evaluate the trustworthiness of blockchain nodes, including devices and users, and make access decisions accordingly. While this approach is applicable to blockchain, it can also be extended to other areas. This approach can help prevent malicious actors from penetrating industrial networks and causing harm. The paper also presents the results of a simulation to demonstrate the behavior of the TMS and provide insights into its effectiveness.
A Review on Digital Wallets and Federated Service for Future of Cloud Services Identity Management
(2023)
In today’s technology-driven era, managing digital identities has become a critical concern due to the widespread use of online services and digital devices. This has led to a fragmented landscape of digital identities, burdening individuals with multiple usernames, passwords, and authentication methods. To address this challenge, digital wallets have emerged as a promising solution. These wallets empower users to store, manage, and utilize their digital assets, including personal data, payment information, and credentials. Additionally, federated services have gained prominence, enabling users to access multiple services using a single digital identity. Gaia-X is an example of such a service, aiming to establish a secure and trustworthy data infrastructure. This paper examines digital identity management, focusing on the application of digital wallets and federated services. It explores the categorization of identities needed for different cloud services, considering their unique requirements and characteristics. Furthermore, it discusses the future requirements for digital wallets and federated identity management in the cloud, along with the associated challenges and benefits. The paper also introduces a categorization scheme for cloud services based on security and privacy requirements, demonstrating how different identity types can be mapped to each category.
Defining tasks and activities for academic nursing in community and long-term care arrangements
(2023)
The cheerleader effect describes the phenomenon, where pictures of faces are rated to be more attractive when presented as part of a group, than when presented in isolation. This study employs electroencephalography (EEG) and event-related potentials (ERP) to explore the origins of the cheerleader effect. We found significant ERP differences which suggest that the cheerleader effect is not caused by perceptual differences, but instead through social cues which distort the decision-making process that gives rise to subjects’ trait judgments.
This thesis explores the dynamics of digital marketing, brand positioning, and
branding strategies within the agri-food sector, and delves into the current efforts of Finca San Antonio, a Peruvian cacao farm that manufactures their own products. The rapid growth of digitalization in Peru, emphasize the changing landscape of consumer behavior and media channels. This creates a need for companies to enter the digital era and update their traditional marketing efforts, in order to cater to the new arisen needs.
The study further delves into the dimensions and strategies for brand positioning
in the agri-food industry, shedding light on the relevance of product differentiation
and market segmentation. The case of Finca San Antonio exemplifies the
incorporation of sustainability and eco-branding in building brand identity and value proposition, which is instrumental to establishing brand loyalty. Overall, this research serves as a comprehensive overview of the interplay between digital marketing and brand positioning, especially in the agri-food sector.
In-situ SEM analysis tool for stretchable metal-elastomer-laminate-membranes for flexible sensors
(2023)
This thesis will examine the impact of cashless payments on E-commerce industry in India. In November 2016 Indian government banned the use of 500 INR (Indian rupee note) and 1000 INR note. Since November 2016 Indian government is encouraging cashless transaction or digital payments. There are several modes of online payments. This study will put some light on the basics and facts to understand what demonetization is and on how it increases popularity of cashless payments, the benefits, and challenges it imposes. In the end this study answers the question that if digital transactions will keep growing in India and it effects on e-commerce industry
This thesis examines the relationship between environmental, social and governance (ESG) scores and portfolio returns using ordinary least squares (OLS) regressions and statistical tests. ESG scores were collected from Refinitiv Datastream between January 2007 and February 2023 and consist primarily of companies listed in the Standard & Poor's 500 (S&P 500) Index. The risk factors were calculated using data from Kenneth R. French’s data library. OLS regressions are used to estimate abnormal returns. The portfolios are created by dividing stocks into five equal quantiles and allocating them to five portfolios according to their ESG scores. The portfolios were then reconstructed each year based on the company's ESG rating. This analysis concludes that there is no significant evidence of abnormal returns for high-rated ESG portfolios. Instead, it shows that the portfolio with the lowest 20% ESG ratings outperformed all other portfolios, including the sample market, over the indicated period.
In the Job Satisfaction- Performance literature largely, job satisfaction is portrayed and
discussed positively as a performance driver for organizations that embark on the journey
of progress. This research asks whether job satisfaction inevitably leads to negative
implementation outcomes and how. This research focuses on factors other than job
satisfaction that can be used to drive better performance of employees and how
satisfaction could also lead to negative outcomes. In an attempt to answer this question,
the research undertakes three key tasks. First, it contextualizes how job satisfaction has
minor affects on performance. Second, it outlines the factors that can increase
performance. Third, it discusses crisply the adverse affects on job satisfaction through
empirical evidence to provide focus and 'evidential grounding' for the research.
Essentially, the research contends that for institutions that are unable to manage and lead
productivity from employees, this research serves a powerful purpose ensuring the multi-dimensional approach to view employee performance and modelled to take account of
the people on whom it may impact as well as the overall value it may deliver.
As machine learning becomes increasingly pervasive, its resource demands and financial implications escalate, necessitating energy and cost optimisations to meet stakeholder demands. Quality metrics for predictive machine learning models are abundant, but efficiency metrics remain rare. We propose a framework for efficiency metrics, that enables the comparison of distinct efficiency types. A quality-focused efficiency metric is introduced that considers resource consumption, computational effort, and runtime in addition to prediction quality. The metric has been successfully tested for usability, plausibility, and compensation for dataset size and host performance. This framework enables informed decisions to be made about the use and design of machine learning in an environmentally responsible and cost-effective manner.
Transcultural Student Research on SDGs - A Higher Education Project for Sustainable Development
(2023)
Prophylaxis in pink: Susceptibility of human oral bacteria to roseoflavin, a vitamin B2 analogue
(2023)
With the increasing popularity of the home office model and the limited exploration of associated stressors necessitate a comprehensive investigation of potential drawbacks. This thesis embarks on an investigation of how the home office paradigm can be leveraged to alleviate distress, nurture mental well-being. Specifically, it delves into an analysis of five prominent stressors inherent in the home office context, identifying effective strategies for mitigating each one adeptly. The data and research in this thesis are drawn from existing literature on the work concept. The findings underscore the substantial influence of these stressors on employees’ mental health and productivity. However, specific measures to mitigate these challenges are identified and thoroughly expounded upon. This research provides valuable insights into optimizing the home office environment and promoting the holistic well-being of employees.