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Cradle to Cradle – An analysis of the market potential in the German outdoor apparel industry
(2016)
The purpose of this study is to investigate the market potential in the German outdoor apparel industry by focusing on sustainable production in terms of environmental and human health. A literature study of the Cradle to Cradle (C2C) design concept is provided, as it represents a solution for pollution, waste and environmental destruction caused by the current industrial design and waste management. The data for the subsequent market- and competitive analysis of the German outdoor apparel industry was collected through secondary research in order to identify several key market indicators for the assessment of the market potential. The outcome of this research is the identification of a positioning strategy for outdoor apparel according to the C2C design concept. The results show stagnant growth rates in recent years in the German outdoor apparel market and strong rivalry among the competitors. However, a significant market potential was calculated and beneficial trends for sustainable outdoor brands were recognised. These findings reveal the existence of a market potential for an outdoor apparel brand according to the C2C design concept. By following a positioning strategy of transparency and full commitment to a sustainable production, the company might be able to gain market shares from its competitors, as future predictions indicate slow growth rates in the market. The results of this analysis can be of great interest for entrepreneurs that plan to enter the German outdoor apparel industry.
The development of Artificial Intelligence (AI) has profound implications for improving human and computational productivity in the future. However, it also is an existential risk to human life because it could exceed human capabilities. As such, information about the technology, the direction of the development and its purpose is important. This can be achieved through openness and transparency of processes. Indeed, companies hold property rights over AI and monopolies of software, data and experts. As a countermovement to leading AI companies, the “Open AI Movement” has evolved to push open-source AI research and products, to empower users, and to bridge the digital divide through participation and access. In this thesis, the implications of the declaration of AI as a commons have been analyzed through interviews with AI experts in the United States. The legal placement of AI is controversial but it could be seen as a basic human right. Other findings are that this field is very competitive and that the best approach is to collaboratively develop software that adds additional value on the edge of the commons.
Sustainable tourism is a niche market that has been growing in recent years. At the same time, companies in the mass tourism market have increasingly marketed themselves with a “green” image, although this market is not sustainable. In order to successfully market sustainability, targeted marketing tactics are needed.
The aim of this research is to establish appropriate marketing tactics for sustainable tourism in the niche market and in the mass market. The purpose is to uncover current marketing tactics for both the mass tourism market and the sustainable tourism niche market. It also intends to explore how consumers who are more interested in sustainability differ from consumers with less interest in sustainability in terms of their perception of sustainability in tourism. Furthermore, this research paper will assess the trustworthiness of sustainable travel offers and the trustworthiness of quality seals in sustainable tourism. For this purpose, an online survey was conducted, which was addressed at German-speaking consumers. The survey showed, that consumers with more general interest in sustainability also consider sustainability to be more relevant in tourism. Offers for sustainable travel and quality seals were perceived as not very trustworthy. Moreover, no link could be found between the interest in sustainability and the perception of trustworthiness.
On the basis of the above, it is advisable to directly advertise sustainability in the niche market and to mention sustainability in the mass market only as an accompaniment or not at all. Further research could be undertaken to identify which factors influence the trustworthiness of offers, and trustworthiness of quality seals in sustainable tourism.
AlphaGo’s victory against Lee Sedol in the game of Go has been a milestone in artificial intelligence. After this success, the team behind the program further refined the architecture and applied it to many other games such as chess or shogi. In the following thesis, we try to apply the theory behind AlphaGo and its successor AlphaZero to the game of Abalone. Due to limitations in computational resources, we could not replicate the same exceptional performance.
Recent developments in the field of deep learning have shown promising advances for a wide range of historically difficult computer vision problems. Using advanced deep learning techniques, researchers manage to perform high-quality single-image super-resolution, i.e., increasing the resolution of a given image without major losses in image quality, usually encountered when using traditional approaches such as standard interpolation. This thesis examines the process of deep learning super-resolution using convolutional neural networks and investigates whether the same deep learning models can be used to increase OCR results for low-quality text images.
The purpose of this research is to explore results that are measured by social enterprises (= SEs) according to their mission and vision. Four SEs are examined for this reason. The status quo of aligned measurements was captured by conducting seven semi-structured interviews with persons from the middle and top management of the considered SEs. A conceptual framework, which categorizes output, outcome and impact measurements, is used as the basis for a structured content analysis. The findings imply that SEs’ measurements are not sufficiently aligned with their mission and vision. Outputs are measured by all considered SEs. However, they fail to measure outcomes with all its sublevels. Especially, measuring mindset change and behavior change outcomes are neglected by the examined SEs. That can lead to adjustments, where SEs only create more outputs but fail to create more outcomes and impact. Furthermore, neglecting outcome measurements makes existing but mostly unsystematic impact measurements invalid, since outputs, outcomes and impact build on each other. The research presented here provides one of the first investigations into the alignment of measurements with mission and vision in the context of SEs. Ultimately, the findings question SEs current measurements and aim to open further perspectives on improving the performance of SEs.
This research focuses on the fundamental ideas and underlying principles of E-Learning technology, as well as theoretical considerations for an optimal learning environment. This theoretical exploration was then used as a basis for the design and construction of a new, interactive Web-Based ESH-Training. The quality and effectiveness of this new course was then compared with that of the existing analog PDF-Training via a test with a diverse sample of employee learners. Learners were later surveyed to ascertain their views on both trainings in terms of the quality of the content, facilitator, resources, and length. Results clearly showed that regardless of demographic factors, most employee learners preferred the new, Web-Based ESH-Training to the analog PDF-Training.
Training and evaluating deep learning models on road graphs for traffic prediction using SUMO
(2024)
The escalation of traffic volume in urban areas poses multifaceted challenges including increased accident risks, congestion, and prolonged travel times. Traditional approaches of expanding road infrastructure face limitations such as space constraints and the potential exacerbation of traffic issues.
Intelligent Transport Systems (ITS) present an alternative strategy to alleviate traffic problems by leveraging data-driven solutions. Central to ITS is traffic prediction, a process vital for applications like Traffic Management and Navigation Systems.
Recent advancements in traffic prediction have witnessed a surge of interest, particularly in deep learning methods optimized for graph-based data processing, being considered the most promising avenue presently.
These methods typically rely on real-life datasets containing traffic sensor data such as METR-LA and PeMS. However, the finite nature of real-life data prompts exploration into augmenting training and testing datasets with simulated traffic data.
This thesis explores the potential of utilizing traffic simulations, employing the microscopic traffic simulator SUMO, to train and test deep learning models for traffic prediction. A framework integrating PyTorch and SUMO is proposed for this purpose, aiming to elucidate the feasibility and effectiveness of using simulated traffic data for enhancing predictive models in traffic management systems.