| 1. |
- Ayers, James, 1983-
(författare)
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Educational contexts and designs for cultivating leaders capable of addressing the wicked issues of sustainability transitions.
- 2020
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The ongoing sustainability crisis offer numerous, multifaced societal challenges as a result of the ongoing degradation of socio-ecological systems by human activity causing massive ecological damage and human suffering. Overcoming these difficulties begs for the rapid transition of society towards sustainability. This desire for urgent action has been hindered by the lack of coordinated global leadership focused on addressing these challenges and implementing a transition towards a sustainable future. The sustainability crisis and its manifestations, which include for example climate change, air and water pollution, deforestation and social segregation, are interconnected and volatile issues whose parts influence and impact each other causing the crisis to worsen. The earth system is pushed towards tipping points from beyond which it may become impossible to maintain the human civilization. The failure of leadership to address the wicked nature of these crises means humanity has been left ill-equipped to deal with the complex problems of sustainability. This thesis considers the role of Education for Sustainable Development (ESD) in overcoming these issues and operating as a leverage point towards sustainability. It focuses on investigating how the development of sustainability leadership education in Higher Education can contribute to addressing the sustainability crisis. It looks at the role that educators can play in designing learning environments that ensure leaders and leadership capable of addressing wicked problems posed by global unsustainability. The aim of this research is to investigate what educators should consider when designing learning environments that promote the qualities needed for leading in complexity towards sustainability. It does this by examining a number of ESD programs as case studies to investigate the efficacy of those programs at creating sustainability outcomes within their students. It also undertakes a literature review to describe and articulate the unique challenges faced by sustainability leaders from a personal and professional perspective. The study is situated closely to the ongoing ESD discussion regarding competencies-based learning for sustainability and the research aims to provide some contribution to that dialogue. It does this through the investigation of competencies acquisition and the discussion of emerging areas of leadership that may hold beneficial outcomes for the development and practice of sustainability leaders. The results of the thesis suggest a number of outcomes for consideration by educators and include a number of main findings. Firstly, educational programs can be capable of achieving the acquisition of ‘sustainability’ competencies within their students, but if these competencies are not taught within a larger sustainability contextualization, then students can fail to see the purpose of the competencies ‘for’ sustainability. Secondly, reflective practices, developed as the result of reflective pedagogies, can provide beneficial qualities in students as future sustainability leaders and require distinct pedagogical structures in order to guide reflective practices towards sustainability outcomes. Finally, a number of unique personal and professional challenges to sustainability leadership exist and need to be overcome if the domain of sustainability is to ensure the ongoing resilience and wellbeing of individuals and groups acting as sustainability leaders. This research suggests a novel contribution to a number of areas within ESD research, including creating knowledge within the competencies discussion regarding emerging areas of study that may influence the future of defined sustainability competencies. It also highlights the need for educators to consider the role of wellbeing and resilience in current and future sustainability leaders.
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| 2. |
- Bauer, Andreas
(författare)
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Towards Collaborative GUI-based Testing
- 2023
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Context:Contemporary software development is a socio-technical activity requiring extensive collaboration among individuals with diverse expertise.Software testing is an integral part of software development that also depends on various expertise.GUI-based testing allows assessing a system’s GUI and its behavior through its graphical user interface.Collaborative practices in software development, like code reviews, not only improve software quality but also promote knowledge exchange within teams.Similar benefits could be extended to other areas of software engineering, such as GUI-based testing.However, collaborative practices for GUI-based testing necessitate a unique approach since general software development practices, perceivably, can not be directly transferred to software testing.Goal:This thesis contributes towards a tool-supported approach enabling collaborative GUI-based testing.Our distinct goals are (1) to identify processes and guidelines to enable collaboration on GUI-based testing artifacts and (2) to operationalize tool support to aid this collaboration.Method:We conducted a systematic literature review identifying code review guidelines for GUI-based testing.Further, we conducted a controlled experiment to assess the efficiency and potential usability issues of Augmented Testing.Results:We provided guidelines for reviewing GUI-based testing artifacts, which aid contributors and reviewers during code reviews.We further provide empirical evidence that Augmented Testing is not only an efficient approach to GUI-based testing but also usable for non-technical users, making it a promising subject for further research in collaborative GUI-based testing.Conclusion:Code review guidelines aid collaboration through discussions, and a suitable testing approach can serve as a platform to operationalize collaboration.Collaborative GUI-based testing has the potential to improve the efficiency and effectiveness of such testing.
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| 3. |
- Bryant, Jayne
(författare)
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Learning as a Key Leverage Point for Sustainability Transformations
- 2021
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The global challenges of our time are unprecedented and urgent action is needed. Transformational learning and leadership development are key leverage points for supporting society’s transition towards sustainability. Many even claim that learning on an individual, organisational and societal scale is required for society’s successful transitioning towards sustainability. However, in this relatively new field, practitioners, scholars and educators grapple with what best promotes transformational learning and with how to best design and operate learning experiences that truly build capacity for leadership for sustainability. The aim of this work was to establish an improved understanding of this and to find recommendations for practitioners and educators with ambitions to create systems change for sustainability by building the capacity of people to be sustainability leaders.As an educator and facilitator of sustainability work for over a decade, working at the crossroads of local government and community change, lecturing on leadership for sustainability in Australia and currently being embedded within the faculty of the Master’s in Strategic Leadership towards Sustainability (MSLS) program in Sweden, I have rested this thesis firmly within an action-oriented transformations research paradigm in which the only way to understand a system is through a comprehensive collaborative attempt to change it. One case of action research explored an organisational change for sustainability program that spanned over five years in a local government in Perth, Western Australia and the learning and policy interventions that supported this change. Participant observation with field notes, interviews, surveys and document analysis were particular methods used in this case. Two further cases focused on the MSLS program and its practices and specific components that support such leadership development and transformational learning. Feedback surveys from students and an open question survey to alumni were key methods used in these cases.The findings suggest that community and relationships are essential for supporting and growing sustainability leadership capacity; that hope and agency are irreplaceable components for leading sustainability change; that self-reflection and dialogue are skills that will help sustainability leaders navigate complex and uncertain futures and that these can be learned. Findings also indicate that creating a shared language for sustainability work helps bridge disciplinary divides and practitioner silos, and that skills of dialogue are required to capitalise on participation. Also, the integration of the components of community, place, content, pedagogy and disorientation with hope and agency can help support transformation in sustainability leadership education and provide synergistic reinforcement of the sustainability transformation required.This thesis provides added evidence that learning can be a key leverage point for sustainability transformations in an organisation and suggests how such learning can be most effectively achieved through a conscious design of learning environments, including the use and integration of the mentioned components to improve sustainability leadership for impact in society.
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| 4. |
- Devagiri, Vishnu Manasa
(författare)
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Clustering Techniques for Mining and Analysis of Evolving Data
- 2021
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The amount of data generated is on rise due to increased demand for fields like IoT, smart monitoring applications, etc. Data generated through such systems have many distinct characteristics like continuous data generation, evolutionary, multi-source nature, and heterogeneity. In addition, the real-world data generated in these fields is largely unlabelled. Clustering is an unsupervised learning technique used to group, analyze and interpret unlabelled data. Conventional clustering algorithms are not suitable for dealing with data having previously mentioned characteristics due to memory and computational constraints, their inability to handle concept drift, distributed location of data. Therefore novel clustering approaches capable of analyzing and interpreting evolving and/or multi-source streaming data are needed. The thesis is focused on building evolutionary clustering algorithms for data that evolves over time. We have initially proposed an evolutionary clustering approach, entitled Split-Merge Clustering (Paper I), capable of continuously updating the generated clustering solution in the presence of new data. Through the progression of the work, new challenges have been studied and addressed. Namely, the Split-Merge Clustering algorithm has been enhanced in Paper II with new capabilities to deal with the challenges of multi-view data applications. A multi-view or multi-source data presents the studied phenomenon/system from different perspectives (views), and can reveal interesting knowledge that is not visible when only one view is considered and analyzed. This has motivated us to continue in this direction by designing two other novel multi-view data stream clustering algorithms. The algorithm proposed in Paper III improves the performance and interpretability of the algorithm proposed in Paper II. Paper IV introduces a minimum spanning tree based multi-view clustering algorithm capable of transferring knowledge between consecutive data chunks, and it is also enriched with a post-clustering pattern-labeling procedure. The proposed and studied evolutionary clustering algorithms are evaluated on various data sets. The obtained results have demonstrated the robustness of the algorithms for modeling, analyzing, and mining evolving data streams. They are able to adequately adapt single and multi-view clustering models by continuously integrating newly arriving data.
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| 5. |
- Flyborg, Johan
(författare)
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The use of the intelligent powered toothbrush in health technology
- 2022
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- BackgroundApplied health technology is a research field that ties together several disciplines to improve and preserve the health and quality of life of individuals and society. Helping especially elderly to meet the above goals is an important and necessary task and assistive technology and collection of health data are part of this work.AimsPaper I aims to investigate whether the use of a powered toothbrush could maintain oral health in a group of individuals with MCI and if changes in oral health affect various aspects of quality of life. Paper II and III aims to examine the capacity of a powered toothbrush as a carrier and mediator of health-related data.MethodsFor papers I and II, the participants were recruited from the Swedish site of the multicenter project Support Monitoring And Reminder Technology for Mild Dementia and for paper III from the Department of Health at Blekinge Institute of Technology. In all three papers, a powered toothbrush has been used as a tool, sensor carrier and transmitter of data. For Quality-of-life assessment two instruments are used, The QoL-AD and OHIP 14.ResultsBy introducing an intelligent powered toothbrush in the group of older individuals with mild cognitive impairment we have showed that they, regardless of cognitive level,improved their scores for plaque index, bleeding index and deepened periodontal pockets ≥ 4mm, over 12 months. The quality-of-life instrument related to oral health improved in parallel with the improvement in oral health. Furthermore, it is possible to use the intelligent powered toothbrush both as a carrier for healt related sensors and to transfer user data via Bluetooth technology to a single-core processor that stores or forwards the data via Wifi to an external computer for processing, analysis and storage. A fesibility study regarding temperature sensor for measuring body temperature during toothbrushing have been evaluated and found to be comparable to traditional oral temperature measurement. ConclusionsAn intelligent powered toothbrush is a well-functioning tool for maintaining oral health in older people with mild cognitive impairment as well as for collecting and transferring brush and health data to external units for storage and analysis.
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| 6. |
- Frattini, Julian, 1995-
(författare)
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Towards good-enough Requirements Engineering : a theoretical Foundation for Requirements Quality
- 2023
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Context: Requirements Engineering (RE) research has established a common agreement on the impact that the quality of requirements has on subsequent software development activities and artifacts. Furthermore, empirical investigations suppose that RE quality defects tend to scale in cost for remediation when left unattended. This motivates the need for requirements quality assurance.Problem: This need has been met with requirements quality research, which abounds with publications proposing writing rules and guidelines that are meant to ensure requirements of high quality. However, recent studies have questioned the rigor and relevance of these publications, which would undermine the practical applicability of requirements quality research: requirements quality is a means to an end and serves a specific purpose (i.e., minimizing the emitted risk on downstream activities), but when this purpose is not met due to lack of a rigor and practical relevance, the approach to researching requirements quality needs to be rethought.Aim: The notion of good-enough requirements engineering constitutes a context-sensitive, activity-based perspective on requirements quality. In this thesis, we aim at both (1) understanding and (2) exploring possibilities of operationalizing this notion.Methods: We employ a mixed-methods approach to achieve our aim. We use theory adoption in order to provide a theoretical foundation for requirements quality research, conduct a survey to understand the level of theory adherence in the requirements quality literature, and perform subject-based classification to generate an overview of theory-related elements proposed in literature. Results: Through theory adoption we derive a harmonized, activity-based requirements quality theory that frames requirements quality according to its impact on subsequent activities and hence ensures its relevance. The subsequent survey confirms that there is a lack of rigor and relevance in previous requirements quality publications, which likely explains the lack of adoption of the research in practice. The overview of quality factors in a subject-based classification is a first step to centralize requirements quality research for visibility and effective reuse.Conclusion: The notion of good-enough requirements engineering has the potential to re-focus requirements quality research on a more profound notion of rigor and relevance. In this thesis, we report on a first requirements quality theory. Through adherence to this requirements quality theory and contribution to the central repository of subject-based classification, the operationalization of the concept of good-enough requirements engineering can effectively support predicting the impact that requirements quality has on subsequent software development activities in the future.
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| 7. |
- Fredriksson, Henrik
(författare)
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On the use of traffic flows for improved transportation systems : Mathematical modeling and applications
- 2021
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis concerns the mathematical modeling of transportation systems for improved decision support and analysis of transportation-related problems. The main purpose of this thesis is to develop and evaluate models and methods that exploit link flows. Link flows are straightforward to obtain by measurements or estimation methods and are commonly used to describe the traffic state. The models and methods used in this thesis apply mathematical optimization techniques, computer simulations, and probabilistic methods to gain insights into the transportation network under study and provide benefits for both traffic managers and road users. First, we present an optimization model for allocating charging stations in a transportation network to serve owners of electric vehicles. The model utilizes a probabilistic route selection process to detect locations through which vehicles may pass. It also considers the limited driving range of electric vehicles. The iterative solution procedure finds the minimal number of minimal charging stations and their locations, which provides a lower bound of charging stations to cover each of the considered routes. Second, we present a case study, in which we argue that stationary and mobile measurement devices possess complementary characteristics. In that study, we investigate how speed cameras and probe vehicles can be used in conjunction with each other for the collection of detailed traffic data. The results show that the share of successfully observed and identified vehicles can be significantly improved by using both stationary and mobile measurement devices. Third, we present a simulation model with the intent of finding the most probable underlying routes based on hourly link flows. The model utilizes Dijkstra's algorithm to find the shortest paths and uses a straightforward statistical test procedure to find the most significant routes in the network based on replicated movements of trucks. Finally, we investigate the possibility to study how the traffic flow in one location reflects the flows in the surrounding area. The statistical basis of the proposed model is built upon measured link flows to study the dispersion of aggregate traffic flows in nodes. By considering the alternative ways vehicles can travel between locations, the model is able to determine the expected link flow that originates from a node in a nearby region.The results of the thesis show that the link flows, which are basic descriptors of the road segments in a transportation network, can be used to study a broad range of problems in transportation.
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| 8. |
- Ghani, Zartashia
(författare)
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Cost-effectiveness analysis of an mHealth application (SMART4MD) and analysis of the effect of dialysis treatments on labor market outcomes : Health technology assessment of two treatment methods
- 2020
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Health Technology Assessment is an important factor for decision making in the healthcare sector in Sweden. It helps to curtail the rising costs associated with the healthcare sector and aids in the efficient allocation of scarce public health resources. This thesis investigates the cost-effectiveness and the effectiveness in general of two health technologies, addressing the following research objectives: i) assessing the cost-effectiveness of mobile health (mHealth) interventions designed for older adults diagnosed with mild cognitive impairment, and ii) assessing the effectiveness of peritoneal dialysis (PD) treatment on labor market outcomes in comparison with institutional hemodialysis (IHD) treatment in Swedish settings.Study I and Study II are related to the first research objective. In Study I, we summarized and critically assessed the current evidence on the cost-effectiveness of mHealth interventions focusing on older adults; we found some evidence supporting the cost-effectiveness of these interventions. In Study II, we conducted a within-trial cost-effectiveness analysis of the software application Support, Monitoring and Reminder Technology for Mild Dementia (SMART4MD) from a healthcare perspective for a period of six months. A total of 345 Swedish dyads (MCI patient and informal caregiver) participated in this study. For a short time period of six months, we found that SMART4MD is not cost-effective for MCI patients (statistically insignificant); however, a trend was observed that indicated that it might be cost-effective for informal caregivers, although results remained statistically insignificant (p > 0.05).Study III is related to the second research objective. In Study III, we investigated the effect of PD on labor market outcomes (employment rate, work income, and disability pension) in comparison to IHD. We found that PD is associated with a treatment advantage over IHD in terms of increased employment, work income, and reduced disability pension in the Swedish population after controlling for non-random selection for the treatment.
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| 9. |
- Idrisoglu, Alper
(författare)
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Voice for Decision Support in Healthcare Applied to Chronic Obstructive Pulmonary Disease Classification : A Machine Learning Approach
- 2024
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Background: Advancements in machine learning (ML) techniques and voice technology offer the potential to harness voice as a new tool for developing decision-support tools in healthcare for the benefit of both healthcare providers and patients. Motivated by technological breakthroughs and the increasing integration of Artificial Intelligence (AI) and Machine Learning (ML) in healthcare, numerous studies aim to investigate the diagnostic potential of ML algorithms in the context of voice-affecting disorders. This thesis focuses on respiratory diseases such as Chronic Obstructive Pulmonary Disease (COPD) and explores the potential of a decision support tool that utilizes voice and ML. This exploration exemplifies the intricate relationship between voice and overall health through the lens of applied health technology (AHT. This interdisciplinary nature of research recognizes the need for accurate and efficient diagnostic tools.Objective: The objectives of this licentiate thesis are twofold. Firstly, a Systematic Literature Review (SLR) thoroughly investigates the current state of ML algorithms in detecting voice-affecting disorders, pinpointing existing gaps and suggesting directions for future research. Secondly, the study focuses on respiratory health, specifically COPD, employing ML techniques with a distinct emphasis on the vowel "A". The aim is to explore hidden information that could potentially be utilized for the binary classification of COPD vs no COPD. The creation of a new Swedish COPD voice classification dataset is anticipated to enhance the experimental and exploratory dimensions of the research.Methods: In order to have a holistic view of a research field, one of the commonly utilized methods is to scan and analyze the literature. Therefore, Paper I followed the methodology of an SLR where existing journal publications were scanned and synthesized to create a holistic view in the realm of ML techniques employed to experiment on voice-affecting disorders. Based on the results from the SLR, Paper II focused on the data collection and experimentation for the binary classification of COPD, which was one of the gaps identified in the first study. Three distinct ML algorithms were investigated on the collected datasets through voice features, which consisted of recordings collected through a mobile application from participants 18 years old and above, and the most utilized performance measures were computed for the best outcome. Results: The summary of findings from Paper I reveals the dominance of Support Vector Machine (SVM) classifiers in voice disorder research, with Parkinson's Disease and Alzheimer's Disease as the most studied disorders. Gaps in research include underrepresented disorders, limited datasets in terms of number of participants, and a lack of interest in longitudinal studies. Paper II demonstrates promising results in COPD classification using ML and a newly developed dataset, offering insights into potential decision support tools for COPD diagnosis.Conclusion: The studies covered in this dissertation provide a comprehensive literature summary of ML techniques used to support decision-making on voice-affecting disorders for clinical outcomes. The findings contribute to understanding the diagnostic potential of using ML on vocal features and highlight avenues for future research and technology development. Nonetheless, the experiment reveals the potential of employing voice as a digital biomarker for COPD diagnosis using ML.
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| 10. |
- Lindroth, Markus, 1983-
(författare)
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Low-Complexity Signal Processing for Speech Enhancement and Audio Analysis
- 2022
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In real-time signal processing there is a constraint to finish processing of an audio signal before the next audio segment is received. This makes it important to have signal processing algorithms with low computational complexity while still maintaining high quality results. This thesis presents methods for audio signal processing used in real-time systems. The publications presented cover areas of noise reduction, network echo cancellation, noise dosimeter measurements and voice analysis.A method for speech enhancement is presented with low amounts of speech distortion. The audio signal is split into several subbands, covering different frequency regions. For each subband, the noise level is estimated. A signal gain is calculated by comparing the total signal level with the noise level for each subband. The method presented here, improves performance compared to previously similar methods. Improvement is especially found in multi-speaker and noise-only scenarios.When communicating on a telephone line, network echo is introduced by hybrids in the network. In cases where multiple echo sources exist, the time range for echoes can be quite long. In devices with limited storage, it is difficult to get good echo cancellation in such cases. This thesis presents a method for network echo cancellation suited for use in a device with a larger external memory.Exposure to high noise levels will have negative health effects and methods for measuring noise level exposure is important. Included in this thesis is a study that remove the influence of own voice in noise dose measurements.For certain medical conditions it is beneficial with daily voice exercises. Methods for grading voice in four different exercises is presented, based on pitch and loudness. Evaluation is done in real-time on test medical device.
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