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Detecting and Assessing Road Damages for Autonomous Driving Utilizing Conventional Vehicle Sensors
(2021)
Environmental perception is one of the biggest challenges in autonomous driving to move inside complex traffic situations properly. Perceiving the road's condition is necessary to calculate the drivable space; in manual driving, this is realized by the human visual cortex. Enabling the vehicle to detect road conditions is a critical and complex task from many perspectives. The complexity lies on the one hand in the development of tools for detecting damage, ideally using sensors already installed in the vehicle, and on the other hand, in integrating detected damages into the autonomous driving task and thus into the subsystems of autonomous driving. High-Definition Feature Maps, for instance, should be prepared for mapping road damages, which includes online and in-vehicle implementation. Furthermore, the motion planning system should react based on the detected damages to increase driving comfort and safety actively. Road damage detection is essential, especially in areas with poor infrastructure, and should be integrated as early as possible to enable even less developed countries to reap the benefits of autonomous driving systems. Besides the application in autonomous driving, an up-to-date solution on assessing road conditions is likewise desirable for the infrastructure planning of municipalities and federal states to make optimal use of the limited resources available for maintaining infrastructure quality. Addressing the challenges mentioned above, the research approach of this work is pragmatic and problem-solving. In designing technical solutions for road damage detection, the researchers conduct applied research methods in engineering, including modeling, prototyping, and field studies. They utilize design science research to integrate road damages in an end-to-end concept for autonomous driving while drawing on previous knowledge, the application domain requirements, and expert workshops. This thesis provides various contributions to theory and practice. The investigators design two individual solutions to assess road conditions with existing vehicle sensor technology. The first solution is based on calculating the quarter-vehicle model utilizing the vehicle level sensor and an acceleration sensor. The novel model-based calculation measures the road elevation under the tires, enabling common vehicles to assess road conditions with standard hardware. The second solution utilizes images from front-facing vehicle cameras to detect road damages with deep neural networks. Despite other research in this area, the algorithms are designed to be applicable on edge devices in autonomous vehicles with limited computational resources while still delivering cutting-edge performance. In addition, the analyses of deep learning tools and the introduction of new data into training provide valuable opportunities for researchers in other application areas to develop deep learning algorithms to optimize detection performance and runtime. Besides detecting road damages, the authors provide novel algorithms for classifying the severity of road damages to deliver additional information for improved motion planning. Alongside the technical solutions, they address the lack of an end-to-end solution for road damages in autonomous driving by providing a concept that starts from data generation and ends with servicing the vehicle motion planning. This includes solutions for detecting road damages, assessing their severity, aggregating the data in the vehicle and a cloud platform, and making the data available via that platform to other vehicles. Fundamental limitations in this dissertation are due to boundaries in modeling. The pragmatic approach simplifies reality, which always distorts the degree of truth in the result.
This dissertation presents an analysis of the relations to self and technology that emerge from and in the use of self-tracking technologies. The ethnographical study, combined with the Grounded Theory approach and a media analysis, demonstrates the complex intertwining or duality of control and care towards oneself that emerge or become possible in and through the application of ST technologies. ST devices assist in strengthening one's health and well-being in a playful way, building and maintaining a positive self-feeling, self-image and agency, and discovering unknown abilities and potentials within oneself. The ST technologies used provide orientation through complexity-reducing visualizations, highlighting patterns, and trend progression. They challenge through self-overload, dissatisfaction when not achieving goals, self-deception and distraction, narcissism and even loss of control - internally through compulsion to control as well as externally through loss of data otection and exploitation of private data by third parties, as well as handing over responsibility (in the form of decisions) to technology (algorithms) instead of self-responsibility. These two seemingly opposed yet concurrently existing self-relations reflect the dynamic between today's demands for self-responsibility (in health and performance terms) and the need for self-care and guidance for the many relevant, sometimes daily, decisions. They balance possibly existing tensions and ambiguities between the modes of self-relations that at first glance seem to be opposed and yet ultimately are jointly oriented towards the same goal, namely to master one's life (life maintenance) and to be in balance. The self-relations described in this thesis are supported, reinforced, or enabled by ST technology (and practice). Three different roles that ST technology can take in self-care and self-control were elaborated: technology as a means, a counterpart, and a promise. In relation to technology, another dialectic is visible, which shows the apparent contrast between its conception as a tool and means to achieve something and the approach to technology as an intimate counterpart (partner, nanny, coach) and a promise of salvation. The relationship with technology seems to intensify in and through the ST experience and takes on or is assigned a partner-like role by the users. Finally, the results indicate that the concept of (self-)optimization, contrary to its etymological meaning of a logic of increase, can also be understood differently, namely balancing. In this context, optimization does not necessarily mean the fastest, the highest, the strongest, but something that is achievable and satisfactory for the self - within the framework of the given and the desired. At the same time, the optimization understood as harmonizing and balancing in self-tracking becomes a lifelong task that, in principle, can never be completed because with the addition of new vital areas in life and throughout a lifetime also the individually understood and conceived balance often shifts.
Assessment of forest functionality and the effectiveness of forest management and certification
(2021)
Forest ecosystems are complex systems that develop inherent structures and processes relevant for their functioning and the provisioning of ecosystem services that contribute to human wellbeing. With increasing climate change impacts, especially regulating ecosystem services such as microclimate regulation are ever more relevant to maintain forest functions and services. A key question is how forest management supports or undermines the ecosystems’ capacity to maintain those functions and services. The main objective of this thesis is the development of a concept to assess the functionality of forests and to evaluate the effectiveness of forest ecosystem management including certification. An ecosystem-based and participatory methodology, named ECOSEFFECT, was developed. The method comprises a theoretical and an empirical plausibility analysis. It was applied to the Russian National FSC Standard in the Arkhangelsk Region of the Russian Federation - where boreal forests are exploited to meet Europe's demand for timber. In addition, the influence of forestry interventions on temperature regulation in Scots pine and European beech forests in Germany was assessed during two extreme hot and dry years in 2018 and 2019. Microclimate regulation is a suitable proxy for forest functionality and can be applied easily to evaluate the effectiveness of forest management in safeguarding regulating forest functions relevant under climate change. Thus, the assessment of forest microclimate regulation serves as convenient tool to illustrate forest functionality. In the boreal and temperate forests studied in the frame of this thesis, timber harvesting reduced the capacity to self-regulate forests’ microclimate and thus impair a crucial part of ecosystem functionality. Changes in structural forest characteristics influenced by forest management and silviculture significantly affect microclimatic conditions and therefore forest ecosystems' vulnerability to climate change. Canopy coverage and the number of cut trees were most relevant for cooling maximum summer temperature in pine and beech forests in northern Germany. The Russian FSC standard has the potential to improve forest management and ecological outcomes, but there are shortcomings in the precision of targeting actual problems and ecological commitment. It is theoretically plausible that FSC prevents logging in high conservation value forests and intact forest landscapes, reduces the size and number of clearcuts, and prevents hydrological changes in the landscape. However, the standard was not sufficiently explicit and compulsory to generate a strong and positive influence on the identified problems and their drivers. Moreover, spatial data revealed, that the typical regular clearcut patterns of conventional timber harvesting continue to progress into the FSC-certified boreal forests, also if declared as "Intact Forest Landscape". This results in the need to verify the assumptions and postulates on the ground as it remains unclear and questionable if functions and services of boreal forests are maintained when FSC-certified clearcutting continues.The analysis of satellite-based data on tree cover loss showed that clearcutting causes secondary dieback in the surrounding of the cleared area. FSC-certification does not prevent the various negative impacts of clearcutting and thus fails to safeguard ecosystem functions. The postulated success in reducing identified environmental threats and stresses, e. g. through a smaller size of clearcuts, could not be verified on site. The empirical assessment does not support the hypothesis of effective improvements in the ecosystem. In practice, FSC-certification did not contribute to change clearcutting practices sufficiently to effectively improve the ecological performance. Sustainability standards that are unable to translate principles into effective outcomes fail in meeting the intended objectives of safeguarding ecosystem functioning. Clearcuts that carry sustainability labels are ecologically problematic and ineffective for the intended purpose of ecological sustainability.The overexploitation of provisioning services, i.e. timber extraction, diminishes the ecosystems' capacity to maintain other services of global significance. It also impairs ecosystem functions relevant to cope with and adapt to other stresses and disturbances that are rapidly increasing under climate change.
Maximizing the value from data has become a key challenge for companies as it helps improve operations and decision making, enhances products and services, and, ultimately, leads to new business models. While enterprise architecture (EA) management and modeling have proven their value for IT-related projects, the support of enterprise architecture for data-driven business models (DDBMs) is a rather new and unexplored field. The research group argues that the current understanding of the intersection of data-driven business model innovation and enterprise architecture is incomplete because of five challenges that have not been addressed in existing research: (1) lack of knowledge of how companies design and realize data-driven business models from a process perspective, (2) lack of knowledge on the implementation phase of data-driven business models, (3) lack of knowledge on the potential support enterprise architecture modeling and management can provide to data-driven business model endeavors, (4) lack of knowledge on how enterprise architecture modeling and management support data-driven business model design and realization in practice, (5) lack of knowledge on how to deploy data-driven business models. The researchers address these challenges by examining how enterprise architecture modeling and management can benefit data-driven business model innovation. The mixed-method approach of this thesis draws on a systematic literature review, qualitative empirical research as well as the design science research paradigm. The investigators conducted a systematic literature search on data-driven business models and enterprise architecture. Considering the novelty of data-driven business models for academia and practice, they conducted explorative qualitative research to explain "why" and "how" companies embark on realizing data-driven business models. Throughout these studies, the primary data source was semi-structured interviews. In order to provide an artifact for DDBM innovation, the researchers developed a theory for design and action. The data-driven business model innovation artifact was inductively developed in two design iterations based on the design science paradigm and the design science research framework.
This cumulative dissertation investigates food policy councils (FPCs) as potential levers for sustainability transformation. The four research papers included here on this recent phenomenon in Germany present new insights regarding the process of FPCs' emergence (Emergence paper), the legal conditions which affect their establishment (Legal paper), the different roles of FPCs in policy-making processes (Roles paper) and FPCs' potential to democratise the food system (Food democracy paper). Drawing on and contextualizing the results of the four individual studies, the framework paper uses the leverage points concept originally developed by Meadows (1999) and adopted by Abson et al. (2016) as a lens to discuss FPCs’ potential as levers for sustainability transformation. This conceptual background includes three so-called realms of leverage, which are considered to be of particular importance in transformational, solution-oriented sustainability science: first, the change, stability and learning in institutions (re-structure), second, the interactions between people and nature (re-connect) and third, the ways in which knowledge is produced and used (re-think). Framing the findings of the four research papers in terms of these three realms, the framework paper shows that FPCs could serve as cross realm levers, i.e. as interventions that simultaneously address knowledge production, institutional reform and human-nature interactions.
The dissertation consists of three scientific papers and a synopsis. The synopsis addresses the relevance of the dissertation and lists the key factors for the sustainability transition in the electricity system as a common denominator of the three papers. The relevance of the dissertation results, on the one hand, from the urgency of the sustainability transition in the electricity system and an insufficient transition willingness of the eastern European Member States. On the other hand, the Multi-Level-Perspective as one of the most important scientific frameworks to grasp transitions does not provide a sufficient explanation of its mechanisms. Moreover, Demand Response aggregators as new enterprises on the European electricity market and potential reform initiators are still under researched. The following key factors for the sustainability transition of the electricity system have been identified: supply security concerns, Europeanisation, policy making and the dominance of short-term oriented economic evaluation. Paper#1 sheds light on the roots of this problem in the context of Poland. It suggests that unfavorable regulation is symptomatic of the real, underlying barriers. In Poland, these barriers are coal dependence and political influence on energy enterprises. As main drivers, supply security concerns, EU regulatory pressure, and a positive cost-benefit profile of DR in comparison to alternatives, are revealed. A conceptual model of DR uptake in electricity systems is proposed. Applying a social mechanisms approach to the Multi-Level Perspective, paper#2 conceptualizes mechanisms of socio-technical transitions and of gaining legitimacy for transitions as co-evolutionary drivers and outcomes. Situational, action-formational, and transformational mechanisms that operate as drivers of change in a socio-technical transition require corresponding framing and framing contests to achieve legitimacy for that transition. The study illustrates the conceptual insight with the case of the coal dependent Polish electricity system. Paper #3, a qualitative study reveals Demand Response (DR) aggregators as institutional entrepreneurs that struggle to reform the still largely supply-oriented European electricity market. Unfavourable regulation, low value of flexibility, resource constraints, complexity, and customer acquisition are the key challenges DR aggregators face. To overcome them they apply a combination of strategies: lobbying, market education, technological proficiency, and upscaling the business. The study highlights DR aggregation as an architectural innovation that alters the interplay between key actors of the electricity system and provides policy recommendations including the necessity to assess the real value of DR in comparison to other flexibility sources by taking all externalities into account, a technology-neutral approach to market design and the need for simplification of DR programmes, and common standards to reduce complexity and uncertainty for DR providers.
This dissertation addresses the question of how sustainability curricula can be implemented and established in higher education institutions. Universities – as hubs for knowledge generation, innovation, and education – provide a central leverage point for sustainably developing society at large. Therefore, the institutionalization of sustainability curricula is not only socially demanded, but also stipulated in numerous political statements from the international community (e.g., those of the UN and UNESCO) and operationalized via Sustainable Development Goal No. 4: "Quality Education". Previous findings on how such implementation can be successful and what factors support or inhibit the process have come primarily through case studies of individual higher education institutions. These studies have been largely descriptive rather than analytical and leave open questions about the generalizability of their findings. The present dissertation addresses this research gap. Through a meta-study (i.e., an analytical comparison of existing case studies), generalizable findings on the implementation processes of sustainability curricula are explored. In the first step, a case universe was collected in order to provide a database for deeper analyses. In two further analysis steps that built on the case universe from Step 1, certain factors that promote or inhibit the implementation of sustainability curricula (Step 2) and specific implementation patterns (Step 3) were examined. The presented findings add a complementary empirical perspective to the discourse on the establishment of education for sustainable development (ESD) at higher education institutions. First, the case studies that specifically address the implementation processes of sustainability curricula are reviewed and analyzed here for the first time as part of a research landscape. This research landscape reveals where research on such implementation processes has been or is being conducted. On this basis, both researchers and funders can reflect on the status quo and plan further research or funding endeavors. Second, this dissertation offers the opportunity to compare a multitude of individual case studies and thus to develop new and generalizable insights into the implementation of sustainability curricula. The empirical analysis uses 133 case studies to identify key factors that promote or inhibit the implementation of sustainability curricula and to add a complementary perspective to the discourse, which has thus far been dominated by theoretical considerations and individual case studies. The analysis thereby offers a new perspective on generalizable influencing factors that appear to be important across different contexts. Thus far, specific patterns of implementation processes have been infrequently studied, and with few datasets. This dissertation analyzes the complex interplay between over 100 variables and provides one of the first research attempts at better understanding the processes that lead to the deep-rooted and comprehensive implementation of sustainability curricula. Internal and external practitioners of higher education institutions can find examples and evidence that can be useful in planning the next steps of their sustainability curriculum implementation. This dissertation offers generalizable empirical findings on how universities can succeed in recognizing their own responsibility to that end and in realizing this transformation through the implementation of ESD.
TIME for REFL-ACTION: Interpersonal Competence Development in Project-based Sustainability Courses
(2021)
This dissertation investigates interpersonal competence development in project-based sustainability courses. Visions of a sustainable, safe, and just future cannot be reached by one individual alone. Thus, future change agents need to be able to collaborate and engage with stakeholders, to approach the manifold crises, challenges, problems, and conflicts we are facing together, and to promote and push forward sustainability transitions and transformations. Therefore, this research investigates three project-based sustainability graduate courses by comparing and contrasting teaching and learning outcomes, processes, and environments. A comparative case study approach using a Grounded Theory-inspired research design which triangulates several qualitative methods and perspectives is applied to allow for generalizable insights. Thereby, this dissertation provides empirically-informed insights which are further discussed in relation to selected teaching and learning theories. This leads, first, to a discussion of practical implications within (and beyond) sustainability higher education; and second, provides a theoretical foundation for interpersonal competence development in project-based learning settings – so that educating future change agents can gain momentum. Findings of this research show that embracing conflicts when they occur (i.e. before they provoke cascading effects in the form of further conflicts down-the-road) is an effective strategy to help further develop interpersonal competence. This requires a conflict-embracing attitude. Attitude, in general, seems to be key in interpersonal competence and competence development overall. Self-reflection, if not explicitly required by outside influences (such as instructors), arises naturally from a self-reflective attitude, and is shown to provide the basis for developing interpersonal competence. This research introduces the term "Refl-Action" which stresses the importance of pairing "learning by doing" (as is often the focus in project-based learning settings) with conscious moments of "reflecting about the doing". More specifically, the research presented here identified four learning processes for interpersonal competence development: receiving input, experiencing, reflecting, and experimenting. Based on the empirical data, when the four processes are purposefully combined, following a meaningful sequence attitudes, knowledge, and skills in collaborative teamwork and impactful stakeholder engagement, are fostered (two facets of interpersonal competence). Each of the four learning processes is set in motion through various interactions students engage in during project-based sustainability courses: student-student (labeled "peer"), student-instructor (labeled "deliberate"), student-stakeholder (labeled "professional"), and student-mentor (labeled "supportive") interactions. When these interactions are made explicit subjects of inquiry - i.e. the (inter-)action is linked with (self-)reflection – different learning processes complement one another: Interpersonal competence facets (collaborative teamwork and impactful stakeholder engagement) and domains (attitudes, knowledge, skills) are fostered. While, overall, interactions, processes, and conflicts have been identified as supportive for interpersonal competence development, trust has emerged as another variable inviting further investigation.
Mental health is an important factor in an individuals' life. Online-based interventions have been developed for the treatment of various mental disorders. During these interventions, a large amount of patient-specific data is gathered that can be utilized to increase treatment outcomes by informing decision-making processes of psychotherapists, experts in the field, and patients. The articles included in this dissertation focus on the analysis of such data collected in digital psychological treatments by using machine learning approaches. This dissertation utilizes various machine learning methods such as Bayesian models, regularization techniques, or decision trees to predict different psychological factors, such as mood or self-esteem, dropout of patients, or treatment outcomes and costs. These models are evaluated using a variety of performance metrics, for example, receiver operating characteristics curve, root mean square error, or specialized performance metrics for Bayesian inference. These types of analyses can support decision- making for psychologists and patients, which can, in turn, lead to better recommendations and subsequently to increased outcomes for patients and simultaneously more insight about the interplay between psychological factors. The analysis of user journey data has not yet been fully examined in the field of psychological research. A process for this endeavor is developed and a technical implementation is provided for the research community. The application of machine learning in this context is still in its infancy. Thus, another contribution is the exploration and application of machine learning techniques for the revelation of correlations between psychological factors or characteristics and treatment outcomes as well as their prediction. Additionally, economic factors are predicted to develop a process for treatment type recommendations. This approach can be utilized for finding the optimal treatment type for patients on an individual level considering predicted treatment outcomes and costs. By evaluating the predictive accuracy of multiple machine learning techniques based on various performance metrics, the importance of considering heterogeneity among patients' behavior and affect is highlighted in some articles. Furthermore, the potential of machine learning-based decision support systems in clinical practice has been examined from a psychotherapists' point of view.
This dissertation focused on the nature and role of organizational practices for the employment of older people and the extension of their working lives. The set of four articles is driven by the objective to further deepen our understanding of how organizations can facilitate ageing at work to the benefit of both, employees and employers. Findings are empirically based on qualitative expert interview data from Germany and the U.S. and several quantitative field studies among older employees in Germany. To bridge gaps in measurement of organizational practices related to aging at work, this dissertation proposes a new comprehensive, multifaceted, and thoroughly conceptualized measure of organizational practices related to aging at work, the Later Life Workplace Index (LLWI). Through the course of the four articles the LLWI is conceptually developed based on qualitative interview data, operationalized, validated based on multiple field studies among older workers, and applied in a multi-level study among older employees of 101 organizations. Results suggest that organizational practices are not uniform, but multifaceted in their presence within organizations and their effects for the employment of older workers. The LLWI distinguishes nine domains of practices including an age-friendly organizational climate, work design, individual development, and practices tailoring the retirement transition. Thus, it may lay the foundation for more granular organizational level research in the field. Further, this dissertation's fourth article applies the LLWI and argues based on person-environment fit and socio-emotional selectivity theory that organizational practices address different individual needs and, thus, affect employment depending on employees' individual characteristics. Results suggest that older employees' retirement intentions are effected by individual development, transition-to-retirement, and continued employment practices depending on their health resources. Application of the new measure in practice to improve organizations' response to the aging workforce and opportunities for future research based on the LLWI are discussed.