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Coming theses from other universities

Please note that the date and time given on these pages is the time of electronic publication, and not the date of the public defense. To find the time and venue of the public defense, please follow the link to DiVA of the thesis in question.
  • Re:ally re:think – seeking to understand the matters of sustainable fashion

    Author: Celinda Palm
    Publication date: 2021-06-08 09:42

    Academic studies of sustainable fashion, and the discourses of actors in business and policy, under-define fashion as a system by treating the social and ecological aspects of fashion separately. This reduces the potential for academic findings to provide knowledge useful for transformation of the fashion system and obstructs desired outcomes from policy and business responses to fashion’s negative social and environmental impacts.

    Understanding how fashion works as a system presents a challenge to transdisciplinary efforts for transformation towards sustainability. In this Licentiate, I explore ways to look at fashion using a feminist critical realist social-ecological system approach. I develop a theoretical framework to understand the fashion system, and particularly to understand what is keeping it unsustainable. I view fashion as a ‘nested’ social-ecological system with inseparable social and biophysical parts.  I use a feminist lens characterized by diversity; this draws attention to gaps, what is known, missing and absent. To show that social aspects and material aspects are intertwined and cannot be studied independently of each other, I use critical realism as a metatheory. I bring its idea of a stratified reality and the model of the four-planar social being to the social-ecological system approach that forms the core of my work. I combine Ostrom’s frequently used general framework for analysing social-ecological systems with a policy-oriented framework for sustainable development. Drawing from these two frameworks I develop a five principles for a strategy framework for sustainable fashion. In summary, applying the strategy framework within the theoretical framework enables thinking more deeply about the structure and implications of knowledge contributions when taking a social-ecological perspective on actions for sustainability. 

    The two papers in this licentiate thesis examine the effects of ontological standpoints that allow environmental impacts of textile fibres to be analysed in isolation from the cultural and social aspects of fashion.   

    Paper 1, ‘Making Resilient Decisions for Sustainable Circularity of Fashion’, is recently published in the journal Circular Economy and Sustainability (Palm et al. 2021). It aimed to show how current circularity responses to global sustainability challenges have so far fallen short. The current path of the expanding fashion industry is fraught with accelerated material throughputs and increased disposal and waste, contributing to human-driven environmental changes at planetary scale. In addition the fashion industry has issues of poor working conditions, modern-day slavery, and justice. By representing a Driver – State – Response framework as an adaptive cycle of a social-ecological system, it makes it clear that reducing planetary pressure from the global fashion and textiles industry requires greater recognition of the system’s social drivers. This paper was a step towards the iterative development of my sustainable fashion framework.  

    Paper 2, ‘Reviewing and defining the concept of Sustainable Fashion: a critical social-ecological approach’, is included as an early-stage draft manuscript. It aims to provide a starting point for discussions towards a coherent science-business-policy definition of the concept of sustainable fashion itself. Using the five theoretically grounded principles of my strategy framework, I examine the manifold definitions related to sustainable fashion such as eco fashion, green fashion, ethical fashion, slow fashion, organic fashion and cradle-to-cradle-fashion. Critical realism’s idea of absence structures this paper. 

    This thesis contributes to knowledge of what a nested inseparable social-ecological system fashion is, enriching ontological descriptions for resilience research more generally.  Also, it provides concrete guidance for transdisciplinary efforts with business and policy working to decrease fashion’s negative impacts on humans and the planet, by showing that fruitful responses pay attention to social activities beyond the industry value chain, not just material flows within. Finally,  I hope my research serves as a contribution to propaedeutics of the field of sustainable fashion, i.e. giving an introductory understanding of the reality and the possibilities of fashion for people and planet.

  • Regret Minimization in Structured Reinforcement Learning

    Author: Damianos Tranos
    Publication date: 2021-06-03 17:01

    We consider a class of sequential decision making problems in the presence of uncertainty, which belongs to the field of Reinforcement Learning (RL). Specifically, we study discrete Markov decision Processes (MDPs) which model a decision maker or agent that interacts with a stochastic and dynamic environment and receives feedback from it in the form of a reward. The agent seeks to maximize a notion of cumulative reward. Because the environment (both the system dynamics and reward function) is unknown, it faces an exploration-exploitation dilemma, where it must balance exploring its available actions or exploiting what it believes to be the best one. This dilemma captured by the notion of regret, which compares the rewards that the agent has accumulated thus far with those that would have been obtained by an optimal policy. The agent is then said to behave optimally, if it minimizes its regret.

    This thesis investigates the fundamental regret limits that can be achieved by any agent. We derive general asymptotic and problem specific regret lower bounds for the cases of ergodic and deterministic MDPs. We make these explicit for ergodic MDPs that are unstructured, for MDPs with Lipschitz transitions and rewards, as well as for deterministic MDPs that satisfy a decoupling property. Furthermore, we propose DEL, an algorithm that is valid for any ergodic MDP with any structure and whose regret upper bound matches the associated regret lower bounds, thus being truly optimal. For this algorithm, we present theoretical regret guarantees as well as a numerical demonstration that verifies its ability to exploit the underlying structure.

  • To know a subject - Teachers' views about the subject of technology. : How the subject of technology is described and approached by teachers in the lower secondary school.

    Author: Birgit Fahrman
    Publication date: 2021-06-02 15:14

    For teaching to be successful, teachers must be well-educated and have knowledge in many different fields. With a combination of solid subject knowledge, good teaching skills and the ability to balance these qualities, teachers can support students’ learning. However, Swedish compulsory school technology teaching does not always meet the requirements for a desired learning environment.   This thesis aims to extend our knowledge of how teachers perceive the subject of technology, its content and purpose and our understanding of how the teachers develop this knowledge. Two sets of data have resulted in three separate studies. Study 1 (paper 1) focus on experienced technology teachers’ views of their own teaching. Study 2 and 3 (paper 2 and 3 respectively) concern the views of novice technology teachers. Different theoretical frameworks enable understanding of the analysis. The pedagogical content knowledge (PCK) framework is applied on in-depth interviews. Theories about curriculum emphases together with a conceptual framework for technology concerning the subjects’ content were applied on the short-answer interviews about purpose and content of the subject. Findings show that experienced technology teacher highlight different purposes for technology education (depending on background) but agree upon that teaching must be student-active. They emphasize the design process as important and specific for the subject and that technology teaching requires various support structures for learning to take place. The novice teachers describe the subject as being hard to grasp with a difficult to understand syllabus. They express uncertainty about planning, implementing, and assessing teaching in relation to certain content as well as practical activities.     The three studies, separately and together, point to the importance of subject knowledge. Understanding of the technology subject seems to be the first step towards grasping how the subject should be taught. Future training of technology teachers must focus on knowledge of the subject's characteristics and understanding the subject’s purpose and content. A greater effort is also needed for everyone involved to create a common vision concerning the nature, purpose, and place of the technology subject in Swedish schools that contributes to pupils' understanding of the world around them while laying a good foundation for their further studies

  • Barriers, drivers and context environment of technological innovation: An analysis of the biogas industry in Russia

    Author: Tatiana Nevzorova
    Publication date: 2021-06-02 14:48

    Global warming issues and the reduction of greenhouse gas emissions are high on many political agendas, and many scientists urge immediate changes to existing energy systems. In order to limit the drastic effects of climate change, complex solutions should be found that affect various sectors such as energy, agriculture, waste management, and transport. The value chain segments of the biogas industry belong to the above-stated sectors and biogas production can play an important role in addressing pollution control from these sectors. Despite the enormous potential of biogas, its realisation is rather slow and heterogeneous. Therefore, this PhD thesis intends to advance knowledge of the biogas industry by identifying and assessing barriers and driving forces for its deployment. Besides the identification of common development factors of the biogas industry, this thesis provides an in-depth analysis of the biogas industry in Russia. Biogas technologies can become a useful solution since they not only provide clean energy but also solve the problem of waste, which itself is a rather painful topic for residents of Russia.

    Technological innovation system (TIS) framework is taken as a theoretical point of departure in this thesis. Technological innovation has often been perceived as an essential part of any solution to tackle grand sustainability challenges, and the TIS concept constitutes a detailed model for the emergence and diffusion of innovation by covering an elaborate set of key processes and focusing on blocking and inducement mechanisms. At the same time, little is known about innovation processes in non-Western countries and emerging economies that deal with transition processes. Therefore, this PhD thesis contributes to the TIS research community by investigating the biogas industry in Russia and applying the TIS approach. 

    Building on the following theoretical and empirical scopes, this PhD thesis investigates three particular research questions related to (1) a complete picture of barriers and driving forces for the biogas industry; (2) the evolution of the biogas industry in Russia; and (3) the context environment that affects biogas technology’s formation and functioning in Russia. This thesis also discusses several specific implications of the findings for theory and practice.

    In the theoretical part, the thesis contributes to the literature on innovation systems and sustainability transition by investigating the transformations of technological innovation, seeing it as a complex process that involves diverse contextual factors, multiple dimensions, and levels. It shows the importance of extending the TIS perspective with a more elaborate understanding of the structures and processes in its context environment. Furthermore, the thesis sheds some light on the notion of system drivers and explains what system drivers conceptually mean when TIS is taken from a theoretical standpoint. Last but not least, this PhD research presents arguments for bringing together insights from two broad sets of literature on 1) socio-technical transitions (in the form of TIS) and 2) policy process theory (in the form of an advocacy coalition framework) in order to improve the TIS analytical framework so that it can more effectively be used to study policy change and scrutinise analyses of technological innovation dynamics.

    In empirical terms, this thesis has focused on the evolution of the biogas industry. This study provides a novel contribution to the literature by integrating the existing barriers and drivers to the wider uptake of biogas as a source of energy into systematic classifications. Possible solutions on how to overcome the most critical barriers and how to strengthen the drivers are also suggested. Furthermore, this PhD thesis provides a thorough analysis of the biogas industry in Russia, including an estimation of its potential, identification of driving forces and barriers for the wider uptake of biogas technologies, and specific policy recommendations to overcome the most critical barriers. It also investigates Russia’s policy development of solar, wind, and bio-energies.

  • Fully metallic antennas for millimeter wave applications

    Author: Qingbi Liao
    Publication date: 2021-05-31 15:54

    Modern societal demands for a high data throughput, short time latency and low energy consumption are difficult to satisfy using current wirelesscommunication techniques. The carrier frequency of previous wireless communication, such as broadcast, the global system for mobile communication, and wireless local area networks, are in the sub-3 GHz spectrum. Electromagnetic waves in the sub-3 GHz spectrum possess a long wavelength and small free space path loss (FSPL), but with a narrow absolute bandwidth. This absolute bandwidth limits the channel capacity according to Shannon theory. Under the assumption that relative bandwidth is fixed, the absolute bandwidth is proportional to the carrier frequency. That means, wireless communications with high carrier frequency can provide wide bandwidth and large channel capacity. Besides, the sub-3 GHz spectrum is already too crowded to have future advanced wireless communications. Nowadays, it is essential to move carrier frequencies to higher frequency spectrum. The millimeter wave (mmWave) frequency band can provide an extensive bandwidth but suffers high atmospheric attenuation and FSPL. The highattenuation and loss limit the propagation distance of mmWave to a few kilometers. Additionally there is a high attenuation due to precipitation, as the wavelength of mmWaves are of the same order in size as rain drops. Due to these losses, there are restricted applications in the mmWave band used for wireless communications. However, the electromagnetic spectrumshortage encourages new researches to look for solutions overcoming the drawbacks of mmWave.

    Specific requirements on antenna designs are imposed by using mmWave communication, including manufacturing costs, integration, efficiency, scanningrange, and directivity. Antennas designed for the mmWave have a small physical size, which requires finer manufacturing resolution and increases manufacturing costs. To compensate for the high FSPL and attenuations, high directive antennas with low side lobe level are favorable. To improve the radiation efficiency, it is preferred to use fully metallic structuresas opposed to structures containing dielectrics for antennas operating in the mmWave range. This thesis investigates the innovative techniques for designing high performance fully metallic antennas in mmWave. Antennas made in gap waveguides and geodesic lens antennas have low manufacturing costs, low loss, and high directivity. The gap waveguide technology can be used to manufacture antennas in separated pieces. These pieces are united together afterwards. The manufacturing cost is reduced in this way. In gap waveguides, the radiation leakages from gaps between separated pieces are prevented using metasurfaces. The research emphasis is placed on the properties of glide-symmetric metasurfaces. Comparing with non-glide metasurfaces, glide-symmetric metasurfaces have an extended electromagnetic bandgap. On the other side, the geodesic lens antenna is designed based on geometrical optics (GO). The graded index lenses can be transformed to geodesic shapes through GO. Since the mmWave presents optical propagation characteristics, GO can be used as a good approximation. A ray-tracing model is developed to calculate the radiation patterns of geodesic lenses and its performance is verified by full wave simulations. Geodesic lens antennas implemented in parallel plate waveguides are in full metal and allow waves to propagate in vacuum or air.

  • On the Role of Data Quality and Availability in Power System Asset Management

    Author: Wadih Naim
    Publication date: 2021-05-28 15:31

    In power system asset management, component data is crucial for decision making. This thesis mainly focuses on two aspects of asset data: data quality and data availability.

    The quality level of data has a great impact on the optimality of asset management decisions. The goal is to quantify the impact of data errors from a maintenance optimization perspective using random population studies. In quantitative terms, the impact of data quality can be evaluated financially and technically. The financial impact is the total maintenance cost per year of a specific scenario in a population of components, whereas the technical impact is the loss of a component's useful technical lifetime due to sub-optimal replacement time. Using Monte-Carlo simulation techniques, those impacts are analyzed in a case study of a simplified random population of independent and non-repairable components. The results show that missing data has a larger impact on cost and replacement year estimation than that of under- or over-estimated data. Additionally, depending on problem parameters, after a certain threshold of missing data probability, the estimation of cost and replacement year becomes unreliable. Thus, effective decision making for a certain population of components requires ensuring a minimum level of data quality.

    Data availability is another challenge that faces power system asset managers. Data can be lacking due to several factors including censoring, restricted access, or absence of data acquisition. These factors are addressed in this thesis from a decision making point of view through case studies at the operation and maintenance levels. Data censoring is handled as a data quality problem using a Monte-Carlo simulation. While the problems of restricted access and absence of data acquisition are studied using event trees and multiphysics modelling. 

    While the quantitative data quality problem can be abstract, and thus applicable to different types of physical assets, the data availability problem requires a case-by-case analysis to reach an effective decision making strategy.

  • Calibration in deep-learning eye tracking

    Author: Erik Lindén
    Publication date: 2021-05-28 15:14

    Personal variations severely limit the performance of appearance-based gaze tracking. Adapting to these variations using standard neural network model adaptation methods is difficult. The problems range from overfitting, due to small amounts of training data, to underfitting, due to restrictive model architectures. In this thesis, these problems are tackled by introducing the SPatial Adaptive GaZe Estimator (\spaze{}). By modeling personal variations as a low-dimensional latent parameter space, \spaze{} provides just enough adaptability to capture the range of personal variations without being prone to overfitting. Calibrating \spaze{} for a new person reduces to solving a small optimization problem. \spaze{} achieves an error of \ang{2.70} with \num{9} calibration samples on MPIIGaze, improving on the state-of-the-art by \SI{14}{\percent}.

    In the introductory chapters the history, methods and applications of eye tracking are reviewed, with focus on video-based eye tracking and the use of personal calibration in these methods. Emphasis is placed on methods using neural networks and the strengths and weaknesses of how these methods implement personal calibration.

  • Reassembling the Environmental Archives of the Cold War : Perspectives from the Russian North

    Author: Dmitry V. Arzyutov
    Publication date: 2021-05-27 08:58

    To what extent the environmental history of the Arctic can move beyond the

    divide between Indigenous peoples and newcomers or vernacular and academic

    ways of knowing? The present dissertation answers this question by developing the

    notion of an environmental archive. Such an archive does not have particular reference

    to a given place but rather it refers to the complex network that marks the relations

    between paper documents and human and non-human agencies as they are able to

    work together and stabilise the conceptualisation of a variety of environmental

    objects. The author thus argues that the environment does not only contain

    information about the past but just like any paper (or audio and video) archive is

    able to produce it through the relational nature of human-environment interactions.

    Through the analysis of five case studies from the Russian North, the reader is

    invited to go through various forms of environmental archives which in turn

    embrace histories of a number of disciplines such as palaeontology, biology,

    anthropology, and medicine. Every case or a “layer” is presented here as a contact

    zone where Indigenous and academic forms of knowledge are not opposed to each

    other but, on the contrary, are able to interact and consequently affect the global

    discussions about the Russian Arctic. This transnational context is pivotal for all the

    cases discussed in the dissertation. Moreover, by putting the Cold War with its

    tensions between two superpowers at the chronological center of the present work,

    the author aims to reveal the multidimensionality of in situ interactions with, for

    instance, the paleontological remains or the traces of all-terrain vehicles and their

    involvement into broader science transnational cooperations and competitions. As a

    result, the heterogeneous archives allow us to reconsider the environmental history

    of the Russian North and the wider Arctic and open a new avenue for future research

    transcending the geopolitical and epistemic borders of knowledge production.

  • Choreographing Flow : A Study in Concrete Deposition

    Author: Helena Westerlind
    Publication date: 2021-05-26 15:26

    The traditional use of concrete in architecture is fundamentally conditioned by the inverse relationship that exists between material and formwork. When poured into a mold, a homogenous mixture takes the shape of its container. More detailed adjustment of the structure of concrete, however, is not possible. As a result, concrete appears to constitute a uniform soild mass; this is one of the material’s most distinct traits. In comparison, the process of shaping concrete by deposition signifies a fundamental departure from such conventional formwork-based techniques. Rather than relying on the constraint and control imposed by a rigid mold, deposition employs a computer-controlled machine to deposit material along a programmable path. The singular operation of the pour is thus replaced with a dynamic, choreographed flow, in which the role of the line shifts from representing the perimeter of the form to constituting the path along which the material performs.  

    In addressing the main research question—How can concrete deposition lead to new ways of thinking and making architecture?—this thesis proposes that the shift from casting to programming concrete represents an opportunity for reevaluating values and conceptions that have shaped our understanding of concrete as a monolithic and uniform building material. In seeking to develop an alternative to traditional formwork-based construction methods, the research proposes that concrete deposition opens up new potentials for extending the resolution of design to encompass material distribution at a previously impossible, intermediate scale: the meso scale (10-2–10-1 m). Elaborating on this main research question, the thesis specifically asks how choreographing the flow and distribution of concrete at the scale of the deposited filament can lead to the production of intricate and porous material structures, something that was previously unfeasible due to the limitations of the mold. 

    The thesis is divided in three main parts and consists of seven chapters. The first part (Beginnings) outlines the aims of the investigation and provides a background to the research questions. The initial chapters present an overview of the research field and the theoretical and methodological framework employed in the research. In the second part (Projects), the main research question is broken down and addressed in three projects related to the preparatory processes of mixing, testing, and stitching. The third part (Synthesis) presents a discussion and a conclusion. The Appendix contains a catalogue of stitches.

  • Deep Learning for Wildfire Progression Monitoring Using SAR and Optical Satellite Image Time Series

    Author: Puzhao Zhang
    Publication date: 2021-05-26 13:14

    Wildfires have coexisted with human societies for more than 350 million years, always playing an important role in affecting the Earth's surface and climate. Across the globe, wildfires are becoming larger, more frequent, and longer-duration, and tend to be more destructive both in lives lost and economic costs, because of climate change and human activities. To reduce the damages from such destructive wildfires, it is critical to track wildfire progressions in near real-time, or even real-time.  Satellite remote sensing enables cost-effective, accurate, and timely monitoring on the wildfire progressions over vast geographic areas. The free availability of global coverage Landsat-8 and Sentinel-1/2 data opens the new era for global land surface monitoring, providing an opportunity to analyze wildfire impacts around the globe. The advances in both cloud computing and deep learning empower the automatic interpretation of spatio-temporal remote sensing big data on a large scale.

    The overall objective of this thesis is to investigate the potential of modern medium resolution earth observation data, especially Sentinel-1 C-Band synthetic aperture radar (SAR) data, in wildfire monitoring and develop operational and effective approaches for real-world applications. This thesis systematically analyzes the physical basis of earth observation data for wildfire applications, and critically reviews the available wildfire burned area mapping methods in terms of satellite data, such as SAR, optical, and SAR-Optical fusion. Taking into account its great power in learning useful representations, deep learning is adopted as the main tool to extract wildfire-induced changes from SAR and optical image time series. On a regional scale, this thesis has conducted the following four fundamental studies that may have the potential to further pave the way for achieving larger scale or even global wildfire monitoring applications. 

    To avoid manual selection of temporal indices and to highlight wildfire-induced changes in burned areas, we proposed an implicit radar convolutional burn index (RCBI), with which we assessed the roles of Sentinel-1 C-Band SAR intensity and phase in SAR-based burned area mapping. The experimental results show that RCBI is more effective than the conventional log-ratio differencing approach in detecting burned areas. Though VV intensity itself may perform poorly, the accuracy can be significantly improved when phase information is integrated using Interferometric SAR (InSAR). On the other hand, VV intensity also shows the potential to improve VH intensity-based detection results with RCBI. By exploiting VH and VV intensity together, the proposed RCBI achieved an overall mapping accuracy of 94.68% and 94.17% on the 2017 Thomas Fire and the 2018 Carr Fire.

    For the scenario of near real-time application, we investigated and demonstrated the potential Sentinel-1 SAR time series for wildfire progression monitoring with Convolutional Neural Networks (CNN). In this study, the available pre-fire SAR time series were exploited to compute temporal average and standard deviation for characterizing SAR backscatter behaviors over time and highlighting the changes with kMap. Trained with binarized kMap time series in a progression-wise manner, CNN showed good capability in detecting wildfire burned areas and capturing temporal progressions as demonstrated on three large and impactful wildfires with various topographic conditions. Compared to the pseudo masks (binarized kMap), CNN-based framework brought an 0.18 improvement in F1 score on the 2018 Camp Fire, and 0.23 on the 2019 Chuckegg Creek Fire. The experimental results demonstrated that spaceborne SAR time series with deep learning can play a significant role for near real-time wildfire monitoring when the data becomes available at daily and hourly intervals.

    For continuous wildfire progression mapping, we proposed a novel framework of learning U-Net without forgetting in a near real-time manner. By imposing a temporal consistency restriction on the network response, Learning without Forgetting (LwF) allows the U-Net to learn new capabilities for better handling with newly incoming data, and simultaneously keep its existing capabilities learned before. Unlike the continuous joint training (CJT) with all available historical data, LwF makes U-Net learning not dependent on the historical training data any more. To improve the quality of SAR-based pseudo progression masks, we accumulated the burned areas detected by optical data acquired prior to SAR observations. The experimental results demonstrated that LwF has the potential to match CJT in terms of the agreement between SAR-based results and optical-based ground truth, achieving a F1 score of 0.8423 on the Sydney Fire (2019-2020) and 0.7807 on the Chuckegg Creek Fire (2019). We also found that the SAR cross-polarization ratio (VH/VV) can be very useful in highlighting burned areas when VH and VV have diverse temporal change behaviors.

    SAR-based change detection often suffers from the variability of the surrounding background noise, we proposed a Total Variation (TV)-regularized U-Net model to relieve the influence of SAR-based noisy masks. Considering the small size of labeled wildfire data, transfer learning was adopted to fine-tune U-Net from pre-trained weights based on the past wildfire data. We quantified the effects of TV regularization on increasing the connectivity of SAR-based areas, and found that TV-regularized U-Net can significantly increase the burned area mapping accuracy, bringing an improvement of 0.0338 in F1 score and 0.0386 in IoU score on the validation set. With TV regularization, U-Net trained with noisy SAR masks achieved the highest F1 (0.6904) and IoU (0.5295), while U-Net trained with optical reference mask achieved the highest F1 (0.7529) and IoU (0.6054) score without TV regularization. When applied on wildfire progression mapping, TV-regularized U-Net also worked significantly better than vanilla U-Net with the supervision of noisy SAR-based masks, visually comparable to optical mask-based results.

    On the regional scale, we demonstrated the effectiveness of deep learning on SAR-based and SAR-optical fusion based wildfire progression mapping. To scale up deep learning models and make them globally applicable, large-scale globally distributed data is needed. Considering the scarcity of labelled data in the field of remote sensing, weakly/self-supervised learning will be our main research directions to go in the near future.