Photovoltaic (PV) systems could be a promising option for accelerating sustainable transition in the power sector. However, it is not straightforward to implement solar PV in Sweden. While the huge gap between current and desired solar capacity generates great opportunities for solar PV technologies, the challenges arise regarding accurate performance prediction, optimization of sizing and installation, optimization for high latitude regions and integration with other technologies. This study focuses on a multi-dimensional probe into the potential and feasibility of PV systems in Sweden with a case study of Stockholm. The techno-economic potential of PV systems is evaluated regarding weather, space, infrastructure, operation configuration and economics. The results reveal the technical and economic feasibility of PV systems in Swedish contexts, despite limitations on existing infrastructure. The research highlights the significant PV generation loss due to snow conditions. The annual electricity generation loss is found to be 14.7%, which is greater than most prior research findings. Regarding this significant snow loss, bifacial PV can reduce snow-induced PV generation losses by up to 6 percentage points under heavy snow conditions. It also outperforms monofacial PV with lower levelized cost of electricity (LCoE) and shorter payback year in Sweden. Wall-mounted PV could also be an alternative. Compared to fixed-tilt PV, wall-mounted PV can achieve comparable annual benefits due to higher generation during the snow season when the electricity price is rather high. Future projections indicate an anticipated increase in PV generation by approximately 5% compared to historical periods. The change in PV generation is expected to be relatively minor during future periods, with an estimated variation of less than 30 kWh/kWp by 2100. Additionally, an optimal tilt angle has been determined for Sweden, applicable across all cities, which could enhance PV generation by 3-6% compared to the common installation angle.

Modern control systems provide services that are indispensable for society, e.g., transportation, energy production, healthcare etc. Hence, it is important to guarantee safe and reliable functioning of such systems. However, they are increasingly relying on networking technologies, which makes them susceptible to cyberattacks that could potentially jeopardise their safety. Moreover, such systems typically have a complex distributed architecture and dynamic behaviour. Hence, it is hard to ensure correctness and safety of their design. Formal methods are used to tackle system complexity and guarantee correctness of the design via abstract mathematical modelling and rigorous verification. Various formal modelling techniques have been successfully used in the design of safety-critical systems in different domains. However, they primarily focused on modelling and verification of system safety. Since modern safety-critical systems are increasingly becoming the subject of cyberattacks, formal modelling techniques should be extended to address the emerging problem of safety-security interactions. In this thesis, we propose a rigorous approach to modelling the impact of cyberattacks on safety of networked control systems. Our approach integrates graphical modelling in Systems Modelling Language – SysML and formal specification and verification in the Event-B framework. Graphical models provide support in visualising system architecture and interactions between the components as well as facilitate the analysis of safety and security interactions by the interdisciplinary teams. Modelling and proof- based verification in Event-B allows us to formally identify the cyberattacks that jeopardise system safety. To bridge the gap between the graphical and formal modelling, we developed software automatically translating graphical system models into formal specifications in Event-B. We believe that this thesis makes both theoretical and practical contributions towards an integration of safety and security engineering, which is necessary for the development of modern trustworthy networked control systems.

Artificial intelligence (AI) is rapidly evolving into one of the most impactful technologies of our time. Advancements in machine learning algorithms, rise in computational power, and increasing amounts of available data have enabled these systems to handle complex tasks like natural language processing and advanced analytics with remarkable speed and accuracy. Today, AI is considered a driving force behind the Fourth Industrial Revolution. Despite its potential, companies struggle to integrate artificial intelligence into their operations beyond the pilot stages (Enholm et al. 2021). AI imposes high requirements on data availability, IT infrastructure, and sufficient technical expertise to navigate this evolving landscape. Emerging regulatory frameworks for AI compliance add another layer of complexity, particularly in highly regulated sectors, such as the pharmaceutical industry. With its rigorous standards for product quality and patient safety, organizations within this industry face greater challenges in adopting artificial intelligence within their business processes. These barriers are even more pronounced in small and medium-sized enterprises (SMEs). Besides being burdened by general AI adoption challenges, SMEs face unique constraints stemming from inherent limitations in financial resources, underdeveloped IT infrastructures, and fragmented data structures. Despite these barriers, scholars believe adopting AI offers SMEs significant opportunities to transform their operations and gain a competitive edge (Schwaeke et al. 2024). SMEs constitute a significant share of the global economy and play a crucial role in job creation by contributing to more than half of global employment (Iyelolu et al. 2024). Given their substantial impact on the global economy, it is imperative to assist SMEs in overcoming barriers to AI adoption. This thesis investigates how artificial intelligence can enhance business processes within small and medium-sized enterprises (SMEs) in the pharmaceutical industry. Furthermore, the study explores barriers hindering AI adoption in pharmaceutical SMEs. To achieve this, a single case study methodology was employed in collaboration with Unimedic Pharma AB, a Swedish pharmaceutical SME. The thesis finds several critical barriers to AI adoption in pharmaceutical SMEs, including data structures, limited AI knowledge, lack of formal AI strategy, and regulatory requirements. A particular inhibitor is regulatory ambiguity, where evolving frameworks for using artificial intelligence make compliance requirements unclear and challenging to navigate. Despite these constraints, AI holds significant potential to enhance business processes within pharmaceutical SMEs. For instance, partial AI adoption strategies can be employed in highly regulated processes such as Product of the month (PM) process, leveraging AI as a recommendation-based system augmented by human oversights rather than fully automating the process. Furthermore, Intelligent Process Automation (IPA) and Machine Learning (ML) can be leveraged to expedite time-consuming activities and significantly improve insight generation in less-regulated processes such as Business Development (BD) and purchasing. As a result, the thesis provides a structured and detailed overview of AI inhibitors faced by pharmaceutical SMEs. It also presents technical examples of AI applications that can enhance Business Development, Product of the month (PM), and purchasing by leveraging cloud-based and on-premises solutions.

This thesis investigates Xylem's strategic shift towards Agile methodologies within its Research and Development (R&D) division, driven by the increasing complexity of integrating smart technologies into its water technology solutions. The study examines the transition from traditional Stage-Gate and Waterfall approaches to more iterative frameworks. Through a qualitative research approach, consisting of semi-structured interviews and on-site observations at Xylem's Sundbyberg facility, this study identifies critical challenges in the Agile transformation process, including cultural resistance, the complexities of scaling Agile practices, and limitations in tool integration. The findings emphasize the need for adaptive leadership, context-specific Agile frameworks tailored to hardware development, and the fostering of a continuous learning culture. By exploring Agile implementation in a nonsoftware industrial context, this case study offers actionable insights and proposes a practical model to support organizations aiming to enhance responsiveness, flexibility, and innovation in their product development processes.

As solar PV projects are being developed, bifacial panels, which capture energy from both sides of the modules, have gained attention due to their potential to enhance energy yield. This master thesis research is motivated by the need for more effective modeling tools and better understanding of realworld bifacial performance, with a focus on three case studies: operational PV plants in different regions and with different design parameters. This study aims at addressing the challenges of accurately modeling bifacial performance and identifying key factors that influence bifacial gain. Weather and yield data from bifacial, fixed-tilt PV plants located in Greece, the Caribbean, and South America were collected, processed, and cleaned using various data processing techniques to ensure reliability. PVSyst models for each plant were then developed and refined, using the processed weather data as input, and results were compared with measured yields. Key parameters—such as tilt angle, module height, inter-row distance, and albedo— were analyzed for their impact on bifacial gain, both by comparing the performance of the different plants and through sensitivity analysis within PVSyst when possible. Additionally, a financial analysis was conducted for one of the plants, using business model data to assess the economic feasibility of bifacial technology. The study finds that bifacial gain ranges from 2% to 5% in locations with albedo values between 0,15- 0,20. Height above ground, tilt angle, and inter-row distance are essential factors affecting bifacial gain. The study finds that ideal systems should have a height above ground of at least 1,5 meters, tilt angles greater than 15°, and inter-row distances of 4 to 5 meters for optimal bifacial gain, and of 6,5 meters for optimal LCOE. Additionally, the research demonstrates that bifacial gain varies linearly with albedo. Albedo enhancement solutions can increase bifacial gain by up to 10%, with a payback period of 1,5 years for one of the case studies, though these solutions bring complexities in terms of cost, land impact, and maintenance. The study also finds that PVSyst tends to slightly underestimate bifacial gain, but the overall agreement between the model and measured results remains strong. Finally, bifacial modules provide a competitive advantage by reducing the LCOE compared to monofacial modules, by 6,4% for the project under study, despite their higher initial investment costs. This thesis highlights the importance of accurate data collection, precise modeling, and site-specific optimization in deploying bifacial solar technology. The findings provide valuable insights into the financial and technical feasibility of integrating bifacial modules in future PV projects, contributing to more efficient solar energy generation.

Menstrual discomfort is a prevalent, diverse, and cyclical lived experience, impacting everyday lives. However, in HCI, it has been mostly approached as a data point, leaving much unknown on how technologies can care for these experiences. In response, we designed Touchware, a collection of on-body touch probes with pneumatic shape-change and weight components, which invite wearers to engage with and care for their menstrual discomfort. We report on the participatory soma design process of making Touchware and its two-week-long deployment study with 6 participants in a workplace setting. Our data analysis highlights diffuse and lingering qualities of menstrual discomfort, shedding light on how technologies may touch bodies in vulnerable states. We discuss the importance and challenges of designing touch technologies for and with bodies in the moments of menstrual discomfort. We conclude with a reflection on the agency of touch and its potential to support the self-care labour and nurturing the radical normalization of rest.

Over 90% of climbers report pain while wearing climbing footwear and advanced climbers are likely to suffer from foot deformities and overstrain injuries. Analysing the effect of climbing footwear during climbing activity can lead to increased understanding of the underlying cause of foot pathologies. Therefore, this thesis aims to develop a method to measure the impact of climbing footwear on the forefoot joints during motion.To achieve this, a see-through climbing shoe was specifically developed to allow the observation of forefoot joints; video motion analysis was performed to collect joint motion data on the medial plane; a load sensor was installed on a climbing wall to measure the reaction forces. The developed method was evaluated both quantitatively, through interviews, and qualitatively, through rigorous validity and reliability testing.The results of this thesis show that it is possible to measure the impact of climbing footwear on biomechanical variables during motion. High quality data was collected for the metatarsophalangeal joint motion; however, further work is necessary to improve the quality of interphalangeal joint motion data. The collected force data was evaluated as reliable and adequately valid for the intended application. Moreover, a significant joint motion was measured, which highlights the importance of studying the effect of footwear during motion, which has not been done in prior research. Qualitative analysis indicates that the method is reproducible and applicable both for research and industrial settings.The developed method shows potential to contribute to further research on the underlying causes of foot pathologies and possible preventive solutions. The developed method can also be incorporated into climbing footwear development processes to effectively pinpoint what to improve in each prototype.

Combating climate change and achieving a sustainable future are the main objectives of the Sustainable Development Goals, requiring low-carbon energy systems. The SDGs, adopted by the United Nations in 2015, aim to create a more equitable and sustainable world by addressing pressing global challenges such as climate change. This thesis explores and provides insights into reducing greenhouse gas (GHG) emissions by focusing on energy systems modeling for infrastructure planning, strategies, and targets in developing economies. By identifying key research gaps, this thesis offers practical methods, insights, and open data into GHG reductions through the integration of renewables in specific locations. 

This dissertation uses an open-source energy modeling system generator to develop long-term models addressing the challenges of transitioning to low-carbon energy systems in Ecuador, Kenya, and the State of Goa (India). These real-world case applications show how financial, technological, and policy factors influence the adoption of renewable energy in specific geographical locations. In addition, by utilizing an open-source framework, it is possible to integrate cross-sectoral systems, such as the Climate, Land, Energy, and Water systems (CLEWs), to analyze the interaction between energy, land, and water resources.

Research article I focuses on the impact of discount rates on long-term energy planning and how variations in these rates can influence decision-making processes. In addition, the discount rate is separated into global/social and individual/hurdle rates for electricity supply technologies. Research article II presents a whole energy system model tailored for a peculiar location in India, the state of Goa. The state lacks local electricity supply capacity and relies on allocated coal power plants. The study emphasizes achieving emission reduction goals in one scenario, accounting for a high share of renewables by integrating local stakeholders’ knowledge.

In the third research article, the geographic location is Kenya. A Climate, Land, Energy, and Water (CLEWs) model is developed focusing on land and energy systems, specifically the cooking and agricultural sectors. Special attention is given to clean cooking technologies and reducing crop imports, with the variability of input parameters being cataloged and discussed. This detailed cataloging is novel in this kind of models, as it extends beyond the energy sector to include parameters from land, agriculture, and water systems, and their interlinkages with the cooking and agricultural sectors. Such variability in inputs also highlights the associated uncertainties in these parameters. 

In contrast, in research article IV, the methodological approach goes beyond cataloging and discussing to identify the most influential and non-influential parameters driving modeling results through a global sensitivity analysis (GSA). This sensitivity analysis is performed on a CLEWs model for Kenya, which is the result of merging two existing models, one related to cooking and agriculture (research article III) and another related to the whole energy system. Combining these models addresses the limitation of each in isolation and provides a more comprehensive view of cross-system interlinkages. The GSA approach not only reveals which parameters are influential on model results but also helps to better understand model behavior and the interactions across energy, land, and water systems.

Through these four scientific articles, this thesis highlights how financial barriers and cross-sectoral complexities can either hinder or enable low-carbon energy transitions. It catalogs and discusses the uncertainty surrounding the techno-economic representations of land, energy, and water systems and demonstrates the influence of specific parameters on energy systems optimization models via sensitivity analysis. Ultimately, this integrated approach offers a blueprint for policymakers and stakeholders in other developing economies searching to balance financial viability with resource interdependencies on the path to reduce emissions.

Rail vehicle passengers demand comfortable journeys. A passenger who wishes to work or readduring her or his journey needs to be able to focus without being distracted by disturbing sounds orvibrations that makes writing difficult. In addition to the direct disturbance effect, such sounds andvibrations significantly affect passengers’ perception of product quality and are therefore importantfactors to attract and keep passengers from other less energy effective modes of transportation. Inthis perspective the acoustic and vibrational interior comfort of rail vehicles is an important factorwhen seeking to promote travel with relatively low energy and environmental impact.  A study of annoying sounds and vibrations generated by train interiors is reported. Anumber of different types of annoying sounds are discussed with respect to the effects they have onthe passengers and a notation for distinguishing annoying sounds of different character is defined.Annoying sounds in vehicles are categorized with respect to the underlying generation mechanismsand measures for mitigation are discussed in general terms as well as the state‐of‐art regardingmetrics for analysis of disturbing sounds. Furthermore, a literature survey of annoying sounds andvibrations in cars is presented together with procedures and methodologies to reduce theoccurrence of such sounds. It is suggested that pro‐active methodologies to minimize annoyingsound and vibration in cars could be transferred and adapted to be used in rail vehicle design andmanufacturing, for example component testing in shaker rigs.  An investigation of disturbing sounds and interior vibrations on Swedish intercity trains isalso reported. It is found that a large majority of the annoying sounds onboard a Swedish intercitytrain is of tapping and rattling type, originating from components like ceiling panels, light covers,cabinet doors, interior sliding doors and foldable tables. A number of case studies are presentedbased on observations on operating vehicles. From the survey it is found that for some vehicles thenumber of annoying sounds and vibration issues related to interiors is substantial. Also for vehicleswith less than 10 year operation. This observation underlines the need for systematic abatementprocedures and proactive activities from the manufacturers to ensure comfortable train journeys.  Finally, best practice design solutions to reduce interior vibrations and annoying soundsfrom train interiors are presented. The solutions discussed include:  • Monitoring and reporting programs in operating vehicles.  • Systematic application of vibration testing in the component and system quality assurance programs.  • Effective source isolation systems for important vibrating systems like compressors andpropulsion systems.  • Squeake and rattle free mounting techniques for interior panels, doors and lightingsystem.  • Low vibration design and mounting strategies for passenger chairs and interior tables. 

Our ability to lubricate machine components effectively sets the boundaries for the technologies we can reliably deploy. This is patently clear in fields like wind power, wave and tidal power, and space. These applications face challenges due to low-speed oscillating motions where conventional lubrication methods struggle. By means of ionic material design, we probe these limitations with the objective of enabling progress in key machine technologies towards sustainable development. Our research shows that ionic liquids (ILs), when used as grease additives, can delay lubricant ejection from fretting contacts and provide remarkable lubricity. Nuclear magnetic resonance reveals that the ability of the IL to enact this effect depends on whether it is sequestered with the other grease components (thickener – base oil blend) or is readily available and mobile. Wide angle x-ray scattering shows that when these ionic liquids are subjected to pressures in the GPa range they have similar structural compliance and liquid-to-solid transitions when compared to a conventional PAO synthetic oil, and that these can be modified by changing the structure of the constituent ions. These findings illustrate that the effect of lubricant retention within the contact is linked to the ability of the IL to reach the surfaces and strongly adsorb, and that distinct behaviors under pressure may be achieved by tuning the architecture of the ionic species. We then explore the impact of these effects at the component level in oscillating bearings, using a custom-built frameless bearing test rig. Furthermore, these ILs are shown to have remarkable performance in vacuum environments, reducing wear by multiple orders of magnitude when compared to heritage space lubricants, and offering a pathway towards PFAS-free vacuum lubricants. The unique versatility and potential of these non-halogenated ILs is further highlighted when we then show that it is possible to use carbon capture and biomass products to synthesize ionic lubricants. Overall, it is shown that ionic materials can be leveraged to expand the limits set by our current technology in lubrication practice.

The amount of medical data has exploded in the past decades. While this may pose a challenge to doctors and medical personnel, it also yields great opportunities due to the rise of new technology. In particular, the advent of artificial intelligence (AI) raises hopes for the future of healthcare. Within AI, the development of language models makes it possible to handle and perform operations on text-based data, such as patient journals. One important element of many language models is word embeddings, which represent a model's vocabulary numerically, using several numerical values to describe each contained term. To use language models on Swedish medical data, it could be helpful to have access to a word embedding that is adapted to Swedish medical terms. Up to this point, there has not existed any such publicly available word embedding and thus this project has aimed to develop one. The developed language model, from which a word embedding is extracted, is pre-trained on the available Swedish general-purpose language model KB-BERT using sentences from the medical-oriented Swedish journal Läkartidningen. The resulting model was evaluated on a separate test set and compared with the base model, which showed that the pre-trained model had higher scores on two versions of an accuracy test. 

Autonomous vehicles (AVs) must navigate uncertain environments while ensuring safety, particularly in scenarios involving risk and occlusions. This thesis develops structured approaches to risk- and occlusion-aware motion planning, integrating theoretical advancements with real-world validation.

To address risk in motion planning, we introduce a framework that quantifies both the probability and severity of safety violations, enabling AVs to reason about risk while maintaining operational efficiency. Complementing this, we investigate pedestrian-aware motion planning in urban environments, incorporating a harm-based risk model to balance safety and progress in interactions with vulnerable road users.

Occlusions pose a major challenge by limiting direct visibility of critical road users. We develop a method for tracking and reasoning about hidden obstacles using reachability analysis and formal logics. By incorporating prior observations, our approach systematically refines possible states of occluded agents, reducing unnecessary conservatism. For high-speed driving, we refine velocity bounds on occluded traffic participants, preventing worst-case assumptions that could lead to excessive braking. Additionally, we explore vehicle-to-everything (V2X) communication to enhance situational awareness, enabling AVs to infer and share information about occluded regions in real time.

Finally, we propose an occlusion-aware planning framework that integrates tree-based motion planning with reachability-based occlusion tracking. This enables AVs to proactively reason about future observations—or their absence—ensuring robust decision-making under limited sensing. By reducing overly conservative constraints while maintaining safety guarantees, our approach addresses key issues in occlusion-aware motion planning.

Together, these contributions advance the ability of AVs to operate safely and efficiently in demanding environments, supporting scalable real-world deployment.

The Philippines has experienced significant growth in electricity consumption in recent decades due to population growth and economic development, but its power grid is still heavily reliant on fossil fuels. To combat climate change, it is necessary to reduce their use and associated greenhouse gas emissions by replacing them with renewable energy sources (e.g. solar and wind) and consequently developing more storage and flexible capacity. Therefore, this report investigates the feasibility of a 1000 MW Pumped Hydro Energy Storage (PHES) facility with a 10-hour storage capacity in a mountainous area of the island of Luzon, where a hydropower plant is already under design to serve as a lower reservoir. To achieve this, a Geographical Information System is used to screen alternatives, followed by design and a techno-economic comparison of several options. After the analysis, two alternatives emerge as more promising, one more attractive economically and the other more attractive environmentally. A multi-criteria analysis is then carried out to objectively compare the economic and environmental criteria. This report shows that the area under consideration is very promising for the development of PHES and recommends further investigations as well as more detailed design studies.

This thesis examines the overall environmental impact of large-scale solar power construction in Finland. It takes a critical eye into the EU Solar Energy Strategy and its targets for solar power development in the coming years with a purpose to determine if and how the targets can be achieved without causing disproportionate damage to the environment in the process. Through three case studies of recently built large-scale solar photovoltaic installations, the research highlights significant variances in environmental impacts associated with different solar installation types. Case 1, a roof-mounted system, exhibited minimal adverse effects, primarily linked to the climate impact from component manufacturing. In contrast, Case 2, which was built beside an industrial plant, involving extensive forest clearing and heavy civil works, demonstrated the most detrimental impacts. Case 3 revealed that constructing solar PV on agricultural land with minimal civil works led to improved biodiversity, as the land was allowed to revert to natural grassland. The cases were chosen to represent the main different types of commercial, industrial and utility scale solar construction projects and to have as much variance between them as possible, to gain meaningful insight into the differences. The study is based on the standard Environmental Impact Assessment process by means of how the impact categories are chosen and the assessment is executed. The analysis is quantitative when it comes to determining carbon footprint by separately calculating all major emission sources in Annex 1, and qualitative regarding more abstract effects such as landscape impacts which are evaluated by observation. Data for the studies was gathered from project managers and engineers responsible for the construction of the three case study projects. The findings suggest that strategic site selection is crucial for minimizing environmental harm. The thesis advocates for the re-purposing of lower-value forest, peat production- and farmland for solar development and hybrid land use alongside wind power and agriculture. The study found that less than 5 % of the recognized re-purposable and hybrid land use areas would suffice to reach EU’s targets. It also identifies challenges related to site suitability, stakeholder interests, and the need for national standards in permitting processes. Ultimately, the thesis emphasizes the importance of integrating environmental considerations into solar power project planning to ensure development is truly sustainable.

Bakgrund: Byggbranschen är en av de farligaste branscherna i Sverige. Arbetsgivare harenligt lagen skyldighet att se till att arbetstagare inte skadar sig eller blir sjuka på sitt arbete.Trots tydlig lagstiftning så finns det brister i arbetsmiljöarbetet. Satsningar för att förändraoch förbättra säkerheten inom branschen har successivt gjorts. Det har skapatsledningssystem, rutiner och lagar kring hur arbetet skall utföras men alltmer talas det omvikten av att arbeta med säkerhetsklimatet och säkerhetskulturen.

Syfte: Den här uppsatsen kommer att undersöka hur byggindustrin kan arbeta för att utvecklaoch förbättra säkerhetsklimatet. Huvudsakligt fokus kommer att vara på säkerhetsklimatet,säkerhetskultur baseras ofta på åtgärder för säkerhetsklimat.

Teoretiskt ramverk: Vi definierade säkerhetskultur som gemensamma tankeprocesser,värderingar och beteende hos chefer och yrkesarbetare. Säkerhetsklimat definierade vi somgemensamma uppfattningar av hur säkerhet prioriteras och hanteras inom en organisation.

Metod: En kvalitativ studie genomfördes med individuella intervjuer av sex personer på treolika byggföretag i Halland. Intervjuerna var semistrukturerade och baserades på kategorierframtagna av forskare inom området säkerhetsklimat. Analys av intervjuerna gjordes medinnehållsanalys.

Resultat: Fyra teman identifierades, tid är pengar, det ska vara enkelt att göra rätt, leverledningen som man lär och kommunikation och delaktighet.

Diskussion: Urvalet var begränsat till ett mindre antal byggföretag i Halland vilket kanpåverka möjligheten till generaliserbarhet. Dock har vi uppfattat svaren på frågorna somlikartade och materialet kan därmed ha närmat sig mättnad. En styrka var att vi har varit tvåsom intervjuat och analyserat materialet.

Slutsats: Baserat på resultatet i denna studie föreslår vi följande områden för att utveckla ochförbättra säkerhetsklimat, kommunikation, närvarande ledarskap, reflektion över egetbeteende, prioritera säkerhet, enkel rapportering, utbildning samt feedback.Främjande för ett gott säkerhetsklimat är att det skall vara enkelt att göra rätt, att ledningenlever som de lär samt att det finns en god kommunikation och delaktighet inom företaget.Hinder för ett gott säkerhetsklimat i vår studie är tidspress, konkurrens och prestationsbaseradlön.

The use of natural refrigerants in modern heat pumping systems as well as building them to be more efficient are key factors towards attaining a more sustainable future. Through this work two systems using CO2 as a refrigerant for an ASHP and a GSHP have been analyzed through use of field measurements. The ASHP system is reversible meaning it can provide AC as well as heating, while the GSHP has a shared borehole with an adjacent supermarket. Through models created on these field measurements the performance has been evaluated as well as compared to alternative refrigerants to see how they compare and what improvements could be made. The study concludes that the systems have high potential for improvements by properly tuning the discharge pressure levels to more efficiently extract heat. The SP F of the systems could be improved from 1.8 to 3.3 for the ASHP and from 3.2 to 3.3 for the GSHP. It was also shown that other natural refrigerants are feasible alternatives though CO2 does perform well and without compromising the safety of the buildings it operates in. A future connection for the GSHP to support cooling will probably be positive for performance. While the current borehole in the GSHP is beneficial for the apartment building investigated it could be out-performed by more direct supply of recovered heat from the adjacent supermarket although it creates a dependency towards the supermarket.

The global energy sector faces the dual challenge of achieving universal electricity access while transitioning to renewable energy sources. In Sub-Saharan Africa, particularly in rural South Africa, electrification efforts face barriers ranging from financial and technical challenges to governance and social obstacles. This thesis investigates these barriers to the adoption of solar systems and explores how innovative business models can alleviate these challenges to accelerate solar adoption in rural South Africa. The research includes a comprehensive literature review of the barriers to adoption, semi-structured interviews conducted with a rural solar installer to assess practical barriers, synthesis of new business model innovation and an economic assessment of proposed business models. Key barriers identified include high upfront costs, lack of financing, limited customer awareness, and governance hurdles. Drawing from these insights, the study synthesizes new business model designed to address these barriers, with a focus on affordability and sustainability. The economic analysis evaluates these models using discounted cash-flows to assess the customer affordability through it’s expected average monthly costs. The literature review and interviews provided detailed insights into the key financial, social, and institutional barriers to solar adoption and outlined a number of business model archetypes upon which five new business models were developed. These include Revived Solar, which focuses on using second-life equipment to reduce capital costs; Community Energy Empowerment, which aims to enhance microgrid economic viability through productive use of electricity; SWARM, which leverages installed solar systems to create a micro-grid of prosumers and consumers; Guarantee-Based Solar Systems, which relies on international cooperation to provide guarantees for securing subsidies; and Result-Based Financing, which incentivize market-driven solutions by externalizing profits. The economic assessment suggests that innovative models like Revived Solar, ResultBased Financing, and Guarantee-Based Solar Systems are most effective at improving affordability, achieving monthly cost reductions of 30%, 36%, and 41%, respectively.The Guarantee-Based Solar model can achieve even greater reductions of up to 65% for lower-tier electrification. While the SWARM and Community Energy Empowerment models are less effective from an economic perspective, they deliver significant social benefits. By integrating practical insights with economic analysis, this thesis offers actionable recommendations for stakeholders to support a just energy transition in South Africa’s rural electrification landscape.

For a Rk−action generated by vector fields X1,...,Xk we define an operator −(X12 + ... + Xk2), the orbitwise laplacian. In this paper, we study and classify Rk−actions whose orbitwise laplacian is globally hypoelliptic (GH). In three different settings we prove that any such action is given by a translation action on some compact nilmanifold, (i) when the space is a compact nilmanifold, (ii) when the first Betti number of the manifold is sufficiently large, (iii) when the codimension of the orbitfoliation of the action is 1. As a consequence, we prove the Greenfield-Wallach conjecture on all nilmanifolds. Along the way, we also calculate the cohomology of GH Rk−actions, proving, in particular, that it is always finite dimensional. 

We prove global rigidity results for actions of higher rank lattices on nil- manifolds containing a partially hyperbolic element. We consider actions of higher rank lattices on tori or on 2 ́step nilpotent nilmanifolds, such that the actions contain a partially hyperbolic element with 1 ́dimensional center. In this setting we prove, under a techni- cal assumption on the partially hyperbolic element, that any such action must be by affine maps. This extends results from [4] to lattice actions that are not Anosov. 

The device, still at the mock-up stage, on which this Master’s thesis project is based, aims to improve medical diagnosis by offering a portable, rapid and accessible solution capable of providing haematological analyses directly at the point-of-care. This mock-up of an innovative haematology machine includes microtechnologies and a reusable microfluidic cartridge in which blood tests are carried out. Despite the progress made in medical diagnostics, current haematology analysers remain bulky, expensive and often inaccessible in regions with limited resources. Responding to major societal challenges such as universal access to affordable, high-quality healthcare solutions, in line with the United Nations’ Sustainable Development Goals, this project aims for future industrialisation, which would represent a significant advance in the medical field and innovation in healthcare.Although promising, this portable machine still faces a number of technical challenges that need to be addressed before it can be considered for industrialisation. The methodological approach adopted is based on mostly iterative experimental investigations: the malfunctions observed led to the implementation of successive modifications. These adjustments were then evaluated through repeatability tests to validate their effectiveness and eliminate the initial problems.The main results concern investigations and improvements relating to three major aspects: fluid transfer, cleaning of the reusable cartridge and improvement of analysis cycles. Numerical simulation work focused on identifying and resolving fluid transfer problems inside the microfluidic cartridge during shutdown of the mock-up. This led to the implementation of modifications that reduced fluid transfer, thereby limiting the risk of pollution of the various reagents, a crucial factor for accurate blood analysis. In addition, the cleaning protocol for the reusable microfluidic cartridge was improved, helping to maintain the cartridge’s long-term usability while reducing the risk of contamination between analyses. Finally, modifications were made to certain blood cells counting cycles. By working on the amount of reagents used, analysis cycles have been stabilised, guaranteeing accurate blood cell counts.All the improvement actions implemented during my Master’s thesis, although limited in time, have enabled the continuous development of the mock-up onthe points mentioned above, while identifying some areas for optimisation at later stages before its industrialisation. Thus, it is proposed to investigate points such as blood sample collection and the reproducibility of haematological performance under different environmental conditions. This project illustrâtes the potential of emerging technologies to have a sustainable impact on medical diagnosis, as close as possible to the patients’ needs.

I denna rapport redogörs för vilka grunder som är bra att börja med för implementering och förståelse av Lean som koncept. Arbetet är utfört och anpassat efter Platt ABs förutsättningar, en processindustri som inte tidigare har arbetat med Lean och vill komma igång med att jobba med Lean.

Experter menar på att ett bra sätt att börja jobba med Lean är att jobba med att identifiera och ta bort slöserier, också kallat Muda på japanska. Inom Lean har slöserierna summerats med ”7+1”, slöserier som består av: onödiga rörelser, väntan, överarbete, kassationer, lager, överproduktion, transport och outnyttjad kreativitet. Denna implementeringen med att ta bort slöserierna har gjorts genom 4P-modellen som Toyota har en egen version av, som kallas ”Toyota Way”. 4P-modellen består av 4 lager av implementeringsnivåer;

1. Filosofi (Philosophy) som är företagets vision,

2. Process (Process), har att göra med företagets flöden som optimalt ska vara anpassade efter att gagna visionen,

3. Medarbetare (People) handlar om att värdesätta, vägleda och utveckla personalen och intressenter för att få bäst förutsättningar att lyckas med implementeringen av processerna,

4. Problemlösning (Problem solving), har att göra med ständiga förbättringar och testa sig fram för att anpassa verksamheten till kundens krav till exempel genom olika Lean verktyg.

Resultatet för rapporten är en omarbetad version av ”Toyota Way” som är anpassad för Platt ABs verksamhet med utgångs punkten från dess vision. Resultatet har tagits fram på en del av tillverkningsprocessen som ett pilotarbete för att sätta ett exempel och kunna skalas upp till hela verksamheten. Resultatet har blivit Platt AB Way.

This thesis presents the development and validation of a model designed to simulate dynamic transient gas behavior in an Intra Aortic Balloon Pump with the aim of reducing the reliance on physical experimentation. By utilizing non-linear springs and dampers, the model captures the necessary properties of the system while avoiding the complexity of viscoelastic models.

Experimental validation demonstrates strong agreement with observed behaviors, including overshoot magnitude, rise time, and plateau dynamics, though minor discrepancies in undershoot behavior and decay time highlight areas for further refinement captured enables system-wide analysis of key parameters such as the influence of varying internal resistance, tube volume, alternating drive pressure and varying external pressure  and their impact on balloon inflation, component displacement, and flow dynamics. While certain physical phenomena, such as thermal effects and viscoelastic stress relaxation, were approximated or excluded, their impact was deemed negligible within the scope of this study. The inclusion of dynamic interactions between system components highlights the model's potential as a design and diagnostic tool.

The findings of this thesis provide a robust foundation for understanding and optimizing  pneumatic models, offering valuable insights into their dynamic interactions and system-level behavior.

Future work will focus on incorporating thermal dynamics, modeling tube compliance together with a dynamic external pressures to enhance the model’s predictive accuracy and applicability to real-world scenarios.

The need for high-quality tool steel is ever rising in many applications. One factor which impacts the quality of tool steel is the cleanliness of the steel, which can be defined as the number, sizes, and types of nonmetallic inclusions present in the steel. Electroslag remelting is a process that is good at improving the cleanliness of steel. The final quality of the remelted ingot is influenced by a range of different factors, one being the quality of the electrode used for the remelting. In this study, the characteristics of the nonmetallic inclusions found in the ladle treatment and casting process were analyzed and compared to help map out the evolution of the non-metallic inclusions during these two steps in the process chain. The characteristics investigated were the size, morphology, and composition of the non-metallic inclusions, which were done using electrolytic extraction on several steel samples to gather the non-metallic inclusions and using a scanning electron microscope to investigate them. The results showed that the nonmetallic inclusions found in the electrode ingot were MgO – Al2O3 spinels and Al2O3. The inclusions found in the ladle were Al2O3 and inclusions composed of Al2O3 – MgO – CaO with S and SiO2. Additionally, the size of the inclusions was not affected by their location within the electrode ingot, but there were signs that clusters formed in higher numbers towards the center of the ingot, as well in the bottom part of the ingot.

Syftet med detta examensarbete är att undersöka kompatibiliteten mellan Revit och RFEM och hur man på ett lämpligt sätt kan modellera i Revit för att kunna arbeta vidare med beräkningar i RFEM med samma modell. Många företag arbetar idag med separata modeller i dessa två program, vilket innebär en viss mängd dubbelarbete. Genom att undersöka detta är målet att komma fram till arbetsmetoder där denna process kan effektiviseras.Arbetet påbörjades med inlärning av grunderna i programmen för att sedan möjliggöra olika tester och på så vis kunna analysera olika metoder för modellering samt överföring av dessa modeller till RFEM från Revit. Slutligen genomfördes en fallstudie där överföring av en av Swecos färdiga modeller undersöktes. I denna rapport presenteras resultatet av fallstudien, slutsatsen den ledde till samt viss teori för att med enkelhet kunna följa denna rapport.Vid undersökning av en ”verklig” modell i form av fallstudien kunde konstateras att en hel del problem med överföringen uppstod. Problemen som stöttes på redovisas under resultatdelen av rapporten och under diskussionsdelen presenteras lösningsförslag på dessa problem.

I dagsläget använder AOD-processen hårdbränd kalk och dolomit för att bilda slagg. Slaggens uppgift är att skydda infodringen och ta upp föroreningar. Tillverkningen av hårdbränd kalk släpper ut CO2 . Det innebär att användning av en alternativ kalkkälla, där behovet av det förädlingssteget är mindre kan sänka utsläppen, tillsammans med andra besparingar såsom kylskrot och kostnader. Målet med projektet är att studera olika kalkkällors beteende i AOD-processen, samt när i processen de bör tillsättas för ett bra resultat. Detta uppnåddes med en inledande litteraturstudie, följt av termodynamiska simuleringar samt försök på industriell skala i stålverket i Avesta. Med information från litteraturstudien planerades simuleringarna och försöken. Simuleringar genomfördes i Thermo-Calc, och betraktade variationer i sammansättning och massa för stål och slag vid användning av olika blandningar av kalkkällor och stålsorter. Industriförsöken använde kalksten som tillsattes tidigt i processen, och genomfördes med flera olika stålsorter. Data från produktionsstyrningssystemet, samt tagna prover, jämfördes sedan med charger av samma stålsorter tillverkade enligt normala produktionsprinciper. Med hjälp av simuleringarna och jämförelserna av produktionsdatan analyserades de möjliga effekterna på produktion, materialåtgång och slutgiltig stålkvalité vid ett delvist utbyte av hårdbränd kalk med kalksten eller mjukbränd kalk.

We obtain rigidity results for abelian partially hyperbolic higher rank actions with 1−dimensional center on certain 2−step nilmanifolds XΓ. Our main result is global rigidity, namely, under certain natural assumptions, all such actions are C∞−conjugated to an affine model. Using this, we derive a centralizer rigidity result, classifying all possible centralizers for any C1−small perturbation of an irreducible, affine partially hyperbolic map on XΓ. Additionally we prove a local rigidity result for C1−small perturbations of algebraic partially hyperbolic actions on XΓ. Along the way, we also prove two results of independent interest. We describe fibered partially hyperbolic diffeomorphisms on XΓ and we show that topological conjugacies between partially hyperbolic actions and higher rank affine actions are necessarily C∞. 

Peri-urban landscapes are the meeting point of a wide range of human activities, power dynamics, and social-ecological processes in the Anthropocene. Multiple interests, as well as differences in governance regimes and decision-making processes increase the complexity of peri‑urban landscapes. With this complexity as a background, we place our focus on the interactions between two highly desirable human activities in peri‑urban landscapes: outdoor recreation and biodiversity conservation. The multifunctional role peri‑urban landscapes can fulfill to respond to biodiversity conservation and outdoor recreation goals has the capacity to minimize the potential clash between these two functions. Despite this, little attention has been paid to the realization of multifunctionality in peri‑urban landscapes when addressing the recreation-conservation nexus. In this Perspective, we provide a foundation for further inter-disciplinary explorations in this area by identifying knowledge gaps and priorities for future research on the recreation-conservation nexus in peri‑urban landscapes with particular emphasis on the “spatial” dimension. 

Magnetic confinement fusion represents one of the most promising pathways to achieving sustainable and clean energy production. In this approach, strong magnetic fields are used to confine hot plasma within a device preventing it from coming into direct contact with the vessel walls. However, plasma-wall interactions remain an unavoidable challenge, as some heat and particles inevitably escape confinement, particularly during energetic transient events. These interactions pose a significant threat to the integrity of plasma-facing components (PFCs), which are subjected to extreme thermal and particle loads. Among the various forms of damage caused by such loads, melt damage is particularly concerning due to its potential to severely degrade the performance and longevity of PFCs. 

To address these challenges, the MEMOS-U physics model was developed to simulate macroscopic melt motion in fusion environments. MEMOS-U simplifies the computational heavy thermoelectric magnetohydrodynamic equations by employing the shallow water approximation, which reduces the dimensionality of the problem. MEMOS-U has been validated against a series of dedicated tokamak experiments, demonstrating its ability to capture the essential features of melt motion in fusion environments.

Building on the MEMOS-U model, the MEMENTO code was developed as a modern numerical implementation designed to further enhance the predictive capabilities of melt motion simulations. MEMENTO leverages the AMReX framework to create and maintain a non-uniform, adaptive grid, enabling efficient simulations of large PFCs over long time scales. The code includes solvers for heat transfer, fluid dynamics, and current propagation, all of which are fully coupled to accurately model the interplay between thermal loading, melt motion, and electromagnetic effects. 

The MEMENTO code has been validated against experimental data from dedicated controlled melting experiments carried out in the ASDEX-Upgrade and WEST tokamaks. Predictive studies with MEMENTO have provided valuable insights into the potential melt damage in future tokamaks. In summary, MEMENTO represents a significant advancement in the modeling of macroscopic melt motion in fusion environments. By implementing the MEMOS-U physics model in a new code, MEMENTO provides a reliable and computationally efficient tool able to accurately predict melt damage in future reactors for regimes that could not be probed before. 

Organic aerosols (OA) are a major component of fine particulate matter and are strongly linked to elevated mortality rates. Since different OA sources have distinct impacts on human health, accurately identifying their spatial and temporal distributions is essential for effective health risk assessments and policy-making. In this thesis, we leverage a large European dataset that provides 85,000 daily OA mass concentration measurements from 175 stations, only 15,000 of which include detailed source information. While physical models (e.g., chemical transport models) can estimate OA sources, their accuracy often suffers from incomplete or uncertain emission data. To address these limitations, we incorporate outputs from these models alongside land-use and weather information as input features for a suite of Machine Learning and Deep Learning methods. Specifically, we compare Random Forest, several Gradient Boosting models, Multi-Layer Perceptrons, Gated Recurrent Units, and a beta Variational AutoEncoder, with all models fine-tuned to optimize performance and tested under different target normalization schemes. Our findings show that every Machine Learning approach outperforms CAMx—the baseline physical model used in our study. By improving source estimations at sites lacking detailed data, this work contributes to more accurate OA modeling, supporting refined health risk analyses and more effective policy interventions.

Embedded systems in aeronautics and space applications require reliable and predictable performance to handle complex computations and real-time processing. Multi-Processing System-on-Chip (MPSoC) architectures like the Xilinx Zynq UltraScale+ (XZU+) MPSoC, which combine Processing Systems (PS) and Programmable Logic (PL), face significant challenges in achieving determinism due to interference in shared resources, particularly the DDR memory. This thesis explores methods to mitigate interference and improve Quality of Service (QoS) in the XZU+ MPSoC through optimized DDR Memory Controller (DDRMC) configurations. Techniques such as port-level isolation, command prioritization, and resource allocation are employed to minimize PS performance degradation, reduce variability across PL loads, and maintain acceptable PL service levels. Evaluations using metrics like relative performance reveal that tailored QoS setups enhance system predictability and stability, particularly under varying PL loads. The identified DDRMC QoS configuration successfully constrained interference to below 10% while maintaining PL performance. Future work will focus on incorporating cost and security considerations and expanding these QoS strategies to encompass a wider range of MPSoC platforms and deployment scenarios.

The identification of suitable reference projects is a critical yet time-consuming aspect of the architectural tendering process. This thesis investigates how arti- ficial intelligence (AI) can be leveraged to automate and optimize this task, fo- cusing on Cedervall Arkitekter as a case study. A data-driven retrieval system was developed to mine internal datasets—specifically the Milltime database— encompassing both structured project metadata and unstructured user notes. After evaluating multiple AI methods, an embedding-based retrieval approach integrated with keyword filtering was selected, striking a balance between computational efficiency and retrieval accuracy. Deployed on-premise as a web application, the final solution enables ar- chitects and procurement staff to query project records using natural language inputs. The system applies semantic similarity modeling and a customized ranking algorithm to provide rapid, relevant search results, cutting manual search time by more than 50% according to user testing. Structured interviews further demonstrated its capacity to enhance the reference project selection process and reduce reliance on personal memory. Taken together, these find- ings underscore the value of AI-driven retrieval systems in architectural prac- tices, while highlighting promising directions for expanded machine learning integration within tendering and other knowledge-intensive workflows in the architecture, engineering, and construction (AEC) sector.

With the increase of renewable electricity generation, and subsequent loss of inertia, in power systems all over the world, ensuring power system stability will be a more challenging task. To combat this, ancillary services are employed all over the world, letting external actors contribute to a stable power system operation. In many power systems, the external actors receive financial reimbursement for this. Recently, batteries have come to play a large part in providing these ancillary services. The similar type of batteries that commonly provide ancillary services can also be found in the millions of telecom cell sites all over the world, to ensure functionality and access to cellular coverage during blackouts. The aim of this project is to explore the feasibility for using these telecom batteries located in cell sites for participation in ancillary service markets. This could help the host company Telefonaktiebolaget LM Ericsson expand their revenue model, as well as help the system operator ensure power system stability by enabling more regulating capacity. This project included two main parts. First, investigating the technical requirements stipulated by the transmission system operators in the Nordic Synchronous Area (NSA). That specific region was chosen due to the amount of publicly available data, as well as being a state-of-the-art power system when it comes to ancillary services, and the fact that the host company Telefonaktiebolaget LM Ericsson originated from Sweden. Second, the potential economic benefits were assessed quantitively by simulating battery participation in two Swedish ancillary service markets, based on historical market data, as well as making forecasts for market development, to then calculate key economic indicators to assess historic and future potential economic benefits. The results show that it can be technically feasible to use telecom batteries to provide ancillary services. However, there are some concerns with respect to communication between different cell sites, as well as between cell sites and the system operator. The prices for the investigated ancillary service products have historically been volatile, which made forecasting difficult. However, the simulation results show that the investments needed for ancillary service provision have a payback time of 4-44 months. The length of the payback period much depends on the market forecasting method used.

Lithium-ion battery (LiB) dominates the traction battery market for battery electric vehicles (BEVs), in no small part due to its high energy density and long life-cycle. But the safety risks involved with the use of LiBs include overheating of battery cells that can potentially lead to fire incidents. This highlights the need for early fault prediction systems. Moreover, with the adoption of cloud technology, an increasing number of data-driven approaches have been enabled for the fault prediction of BEV LiBs. In particular, machine learning (ML) approaches have found success within this field, but there are some key challenges it faces such as limited fault data and the need for timely detection. This thesis proposes leveraging diagnostic trouble codes (DTCs) as proxy fault labels to predict critical BEV LiB faults using ML. DTCs are alerts generated by a vehicle’s self-diagnosis system and indicate a wide range of battery issues, but they are inherently reactive. By predicting critical DTCs using real-world BEV monitoring data, this work can transform these reactive alerts into proactive tools for fault prediction. Traditional supervised ML models—Decision Tree, Random Forest, and XGBoost—were trained on battery signals such as voltage, current, temperature, and state of charge (SoC) to predict critical DTCs. Short-term (120 minutes) and long-term (30 days) prediction frameworks were explored to explore the influence of temporal differences. The results demonstrates that ML models have robust performance when predicting critical DTCs. Random Forest achieved a F1-score of 0.9722 when predicting critical DTCs 45- minutes ahead while Decision Tree had a F1-score of 0.833 when predicting 3-days ahead on the hold-out test set. Long-term predictions benefited from XGBoost’s ability to handle non-linear feature interactions, with extended prediction windows yielding consistent improvements. Feature importance analysis revealed current-based signals as the most predictive, while SoC emerged as a key factor in identifying slower-evolving faults. This study highlights the potential of DTCs as proxy fault labels for ML-based BEV LiB fault prediction, advancing research for BEV predictive maintenance.

Biology and medicine have seen groundbreaking discoveries, from ion channels to induced pluripotent stem cells, resulting in a paradigm shift. The advancements in physical sciences and engineering have always been pivotal in unlocking mysteries of biology and highlighting that the new frontiers lie in deepening our understanding at the single-cell and single-molecule levels. Applying different physical and engineering principles sheds new light on our understanding of complex biological systems at the single-cell and single-molecule level, enabling the development of various technologies such as single-molecule detection, organ-on-chip platforms, and organoids. The development of these technologies offers valuable insights into disease progression and personalized therapeutic strategies. The advancements in micro and nanofabrication propel the development of sensing platforms and biological devices that pave the way for novel solutions, ensuring the best of both worlds. This thesis aims to contribute to advancing the fields of single-molecule sensing and cell therapy by integrating biological discoveries and engineering advancements to develop novel engineering toolboxes. 

The first part of this thesis introduces and describes two approaches for single-molecule sensing and detection, specifically tunneling nanogaps and solid-state nanopore-based sensing platforms. The first work reports the custom measurement setup built during the project, which facilitates automated probing and testing arrays with hundreds of tunnel junctions in liquid with integrated microfluidics, current in the pA range, and at sampling rates up to 200 kHz. This setup highlights key electrical and microfluidic components and design choices to achieve a scalable measurement method, providing a platform for further studies and development in this field and enabling the potential for dynamic sensing. The second work in this thesis investigates the fabrication and electrical behavior of tunnel junctions in various gaseous and liquid media by feedback-controlled electromigration of microfabricated gold nanoconstrictions. This work maps the conductance stability and characteristics of the resulting tunnel junctions, highlighting various considerations and challenges in working with on-chip integrated tunnel junctions to guide future efforts. 

In the third work, we shift our focus to solid-state nanopores and demonstrate that the nanopores fabricated by controlled dielectric breakdown could be localized at the site of femtosecond laser exposure on a pristine silicon nitride membrane. We analyze the sensing potential of these nanopores by the translocation of double-stranded DNA through the pores. The fourth work uses the solid-state nanopore platform to detect and study the binding of Estrogen Receptor Alpha to the Estrogen Receptor Elements on the DNA. The work on tunnel junction and solid-state nanopore-based sensing modalities holds potential for further development in the field of single-(bio)molecule sensing.

The second part of this thesis presents a microfluidic chip platform that enables simple and fast reprogramming of somatic cells, such as fibroblasts, into induced pluripotent stem cells (iPSCs). These iPSCs can then be differentiated further into functional ectodermal cell types towards neural lineage, resulting in neural stem cells on the chip. Furthermore, using bulk-RNA sequencing, we observed that the microfluidic platform boosted commitment toward generating neural stem cells while reducing biological variability compared to a conventional well plate. Our method provides a simple platform with considerably reduced reagent requirements, cellular input, and manual labor, leading to substantial cost savings and holding potential for the highly controlled generation of clinical-grade iPSCs and differentiated cells for cellular therapeutics.

Chemical Vapor Deposition (CVD) is a critical process for depositing thin films, where coating quality, specifically texture and thickness, directly impacts product performance. However, maintaining consistent coating quality across different production lines is challenging due to the complex and interdependent process parameters involved in CVD. This thesis explores the use of advanced Multivariate Data Analysis (MVDA) techniques, including Principal Component Analysis (PCA) and Orthogonal Partial Least Squares (OPLS), through SIMCA software, to develop predictive models for improving coating quality control. The main objective of this research was to generate accurate predictive models for key coating quality metrics such as the thickness of TiCN and Al₂O₃ coatings and their texture, while identifying critical process variables, trends, and potential areas for optimization across the production lines. MVDA tools like PCA and OPLS regression were employed to reduce data dimensionality and uncover correlations between process parameters. By using historical process data from three CVD lines, the SIMCA-based models allow for the detection of deviations from optimal process conditions, offering insights for process optimization and better-quality control. The results revealed that temperature is a key driver in early phases of texture formation across lines, while pressure emerged as a dominant factor affecting TiCN thickness. Additionally, temperature variables strongly correlated with oxide layer formation, though pressure played a significant role in later phases. Environmental factors, such as humidity, were also shown to have a minor influence on oxide thickness. In the end, comparing the performance of the models across three production systems, the study uncovered distinct differences in process conditions, with Line J exhibiting a greater shift in variability of process parameters compared to Lines A and H. This shift suggests differing machine conditions and process control strategies, which were further explored in combined models. The integration of additional variables, such as area and volume, significantly improved the predictive power of the models, offering a more comprehensive understanding of the process dynamics

This thesis explores an advanced additive manufacturing process that integrates Selective Laser Melting (SLM) with bound metal deposition to overcome the limitations of conventional metal FDM. Traditional metal FDM is constrained by complex post-processing stages, including debinding and furnace sintering, which introduce issues like excessive shrinkage, porosity, and limited design complexity. These challenges highlight the need for an innovative approach that can produce dense, mechanically robust metal components more efficiently. The development of such a process is crucial for industries demanding high-strength, precision-engineered metal parts, yet this goal remains elusive due to the complexities of achieving uniform layer fusion and optimal material properties. To address this, the project proposed and developed a novel sintering process that combines SLM with an eco-friendly binder application method, aiming to streamline production by eliminating debinding and bulk sintering steps. Using tool steel powder bound with corn starch, an organic binder, the research employed a manual layering technique, followed by controlled laser melting to achieve layer-by-layer bonding. Parameter optimization focused on adjusting laser power, velocity, and layer thickness to achieve an optimal energy density that promotes complete fusion without defects like melt pools or excessive spattering. Experimental trials were structured through a Design of Experiments (DOE) approach, enabling systematic evaluation of laser settings and material behavior. The results demonstrate that a balanced energy density—achieved through medium laser power and low velocity—yielded high-density sintered layers with minimal porosity and high hardness, confirming the process’s potential for creating robust, durable metal parts. Microstructural analysis revealed a martensitic matrix with well-distributed carbides, while surface porosity was minimized at a 0.2 mm layer thickness, indicating optimal structural cohesion. The successful integration of SLM with metal FDM principles offers a streamlined manufacturing solution, reducing time and cost while enhancing part quality. This work lays a foundation for further advancements in additive manufacturing, with implications for creating large, complex metal parts without traditional postprocessing constraints, opening new possibilities for industrial applications.

This project addresses the challenges of file management within Kanthal AB's HS HHR (Heating Systems Hallstahammar) department, where disorganized file storage practices have led to inefficiencies and reduced productivity. The study aimed to streamline file management across three platforms: PDM (Product Data Management) Vault, Microsoft SharePoint, and On-Premises storage. Through qualitative research, including interviews with key stakeholders, the project identified critical issues such as inconsistent file naming conventions, lack of standardized storage guidelines, and inefficient search functionalities. To resolve these challenges, the project resulted in a series of proposed solutions, including the implementation of structured folder systems, enhanced metadata functionalities within SharePoint. In the project the PDM system was explored to understand ways to support better integration with the ERP system, M3, and the use of Microsoft Power Automate (MPA) to automate workflows. A comprehensive guideline document is also developed to assist users across all platforms, ensuring consistent practices and ease of use. The outcome of this project is a more organized and efficient file management system, expected to improve overall productivity and user satisfaction within the department.

In the recent years, there has been a global shift towards sustainability and circular economy and this necessitates a transformative approach in managing product lifecycles. This thesis addresses the need for integrating digital product passport into the existing PLM systems. This in turn helps in enhancing transparency, traceability and sustainability within the context of circular economy. As mandated by European Commisions’s Ecodesign for Sustainable Products Regulation (ESPR), DPP will be compulsory for various product groups. DPP includes detailed product data from initial design stage till the End-of-life. The thesis presents a structure framework for companies in adapting PLM system in facilitating DPP integration. A mixed-method research approach was utilized for this thesis. The study includes a survey to assess the current PLM maturity across industries along with interviews of PLM and sustainability experts. By proposing a system architecture and roadmap, the study offers a detailed blueprint for companies to meet the upcoming regulatory requirements. The research contribute to the field by providing actionable insights to streamline DPP integration process facilitating a sustainable shift that enhances both regulatory compliances and competitive advantage.

The thesis focuses on optimizing inventory management and procurement of raw materials in a manufacturing industry that produces essential raw materials for battery production. The thesis aims to study various inventory control and procurement strategies that can help the Organization lower the holding costs of the inventory and increase the adaptability of the supply chain to sudden changes in market demand. Due to the constraints, studying the trends of all the raw materials was challenging. Therefore, according to the ABC classification of materials, all the raw materials used for direct production were classified under different classes based on several factors. The EOQ was calculated by considering the costs of transportation and ordering costs, while the JIT model focused on lowering the holding costs through frequent deliveries. The result of this thesis demonstrates a potential cost reduction of 11.63% and 20.84% if the JIT model was implemented for Material-A and Material-B when compared to that of current state. A detailed study of the incorporation of Safety stock is considered to be a potential future work of this research. Finally, the thesis provided the best recommendations that can be implemented in the future to improve inventory management and enhance maximum procurement efficiency at the organization.

Fr8Rail II/Work-Package 3 Real-time network management and improved methods for timetable planning addresses the problem to improve capacity and punctuality in the railway system by developing concepts and methods for tactical planning and operational traffic. In this report the state-of-the-art has been summarised.

The aim of the project is to:

• Propose concepts and methods that improve the annual and short-term timetable planning.
• Demonstrate how the proposed timetable planning concepts improve the prerequisites for real-time network management.
• Develop methods and tools that can reduce inefficiencies in real time network management.

An important aspect is to improve the coordination between yards/terminals and the line network, and between Infrastructure Manager, Yard Managers, and freight Rail Undertakings.

We motivate our research by the current situation in Sweden, which is characterised by low on-time performance for freight trains, dense and heterogenous traffic on the major railway lines, and a rigid annual timetabling process, which is non-suitable for short-term changes. We believe that better tools for network planning and management on tactical and operational level can help to connect planning and operational processes.

Aiming for improvements of the operational traffic, there is a need for systematic development of methods applied at several planning horizons, based on both simulation and optimization techniques. Close to operation fast methods are needed, for example, based on meta-heuristics.

The maintenance planning process and improvement potential have been described. This is a new piece of the puzzle and it is important to close the gap between timetable planning and operational traffic. The different planning processes at the Infrastructure Manager, the Rail Undertakings and the Maintenance Contractors should be aligned.

When developing new approaches for computational decision-support tools for real-time network management, it is important — but very challenging — to evaluate and benchmark with existing software tools. We also observe that the research stream on computational decision-support and algorithm development for railway traffic management has not yet been sufficiently merged with the corresponding research stream focusing on aspects of human computer interaction.

The aim of this document is to provide an assessment of possibilities for simulation approaches, both microscopic and macroscopic, that do not require detailed data on all aspects of the simulated scenario, for example for incompletely specified railway freight traffic.

In a macroscopic simulation, only a choice of aspects is included, e.g. stations and stops, but no detailed track plan between these network nodes. In that case, a stochastic approach is required because failures of different infrastructure elements of the same type on a specific macroscopic network-edge can have different consequences for train travelling times. This approach ignores the causal connection between disturbance, re-routing, possible waiting times and the resulting delay, and simply assigns a probability to different delay times given a type of disturbance.

The simulation tool PRISM uses a macroscopic model. It makes it less important to have a high level of detail in the data. Missing such data is thus less problematic than for microscopic simulation tools such as RailSys. In either case, it is prudent to start by establishing a well-calibrated scenario, before scenarios with missing observations are run.

However, missing data makes the comparison to real data more difficult. Especially if many assumptions are used or the input is generalised to a large amount, the results will not be comparable for enabling an accurate analysis. In that case, other ways of validation have to be chosen.

The conclusions reached in this report are that there are certain situations where the available information is not sufficient for performing a simulation, but that there are ways to overcome these shortcomings.

This project started when I got in touch with the non-profit cultural centre Royal in Eskilstuna, a smallcity 1,5 hours west of Stockholm. They had just moved in to their new premises, an old cinema fromthe 30’s, and were gaining influence over the cultural scene of Eskilstuna. The first time I visited themwas late autumn last year and I quickly realised that I could develop a cooperation with them duringmy thesis project. I was interested in the phenomenon of cultural centres since I believed it to be a pro-grammatic padding or alibi used by politicians to gain popularity instead of more specifically payingculture a greater overall attention. Royal were keen to let me know that I had freedom in doing whate-ver I wanted as my project, but they also told me that they were interested in a renovation and concep-tualisation of their premises as well as a wardrobe, office space and conference room which were likelyto be built if it was what I decided to do.As I began the project with a scepticism towards cultural centres in general I wanted to look if it wasany need of Royal in Eskilstuna, and if it was – what kind of culture Royal should focus on. I realisedthat Eskilstuna had its cinema, theatre, concert stage etc., but there was no cultural centre that combi-ned the different activities, and there wasn’t anyone who did it non-profit. Further analysis of the cultu-ral atmosphere in Eskilstuna also showed that Royal were focusing on the right kind of culture attrac-ting a wide range of people from different ages, genders and backgrounds.I understood that part of the success of Royal was probably because it was driven by ambitious pe-ople residing in Eskilstuna and not by the municipality, and I realised that I had little to add in terms ofcultural content. I felt as Royal was doing something good and my motivation to contribute as an archi-tect grew. Solely to restore their premises and do what they told me to felt like taking the easy way out,hence I focused on what they needed but didn’t tell me.By then I had visited Eskilstuna a few times, and had become familiar with the location of Royal in rela-tion to the city. The area is called Nyfors and is located just south of the central station. Geographicallyin the centre of the city, however, the railway that separates Nyfors from the city centre works as a bar-rier reducing the importance of Nyfors to the point where it is regarded to be a suburb. Since there isn’tmuch activities in Nyfors there is no point for people not residing in that particular part of the city to gothere, which is one of the big reasons for why many people doesn’t even know that Royal exists. I figu-red that was one thing they didn’t tell me.While looking at the social aspects of Nyfors, I found that it had bad reputation due to high criminality.Much of the criminality was focused on a shady restaurant very close to Royal. I, however, discoveredthat the same restaurant had closed and reopened with new owners which had started a cooperationwith Royal. At that point I started to see Royal as a beginning of a development of the area alreadycausing a ripple effect.From that point my focus turned from being a restoration project of an old cinema to be more of a cityplanning project. Through my investigation I came to see that the square where Royal was locatedneeded a lift that in turn would draw attention to the cultural centre.I started to look at the existing square, with the intention to point out the things that worked and thethings that didn’t. Furthermore, I made an investigation of squares around the world that I had visitedaiming to figure out what characteristics defines a good square.My conclusion was that the square which I had decided to work with had almost everything against it.It was inclined to the north, it had no clear limits and it was too big for the surrounding city scale. Na-turally making it very hard to work with. At the same time I was motivated by the work of Royal, and Iwanted to give them something in form of a realistic proposal.By trying to understand the positives of the site and boost them while identifying the deficits and fixthem, I have meant to create a space for culture and recreation in the heart of Nyfors.I have intended to be perceptive towards my process and always see my customers’ needs in the big-ger picture, letting the project lead me towards the final intervention – a process successively runningto a result which came out to be a square far from my initial thoughts.

This thesis investigates the economic, and technical feasibility of using Additive Manufacturing (AM), specifically Laser Metal Deposition (LMD-p), for the repair of forging tool. Forging tools require frequent repairs and eventually replacement due to their severe wear and tear, especially from abrasive wear and plastic deformation. Although traditional machining is effective but the , traditional machining techniques wastes materials, wears tools more frequently, and use more energy. This study investigates AM as a more economical and environmentally friendly substitute. Furthermore, AM contributes to environmental goals by reducing energy use. Although AM infrastructure requires a capital investment up front, the long-term advantages it offers over conventional techniques make it a very compelling alternative for tool maintenance in the future. The study's concentration on a single particular tool used by a company, however, puts limits on it.

The Battery Regulation 2023/1542 necessitates recyclers to conduct extensive reporting on waste battery details, recycling efficiency, and material recovery. To ensure compliance, a robust traceability system must be implemented throughout the battery recycling supply chain. This research delves into the specific traceability requirements for recyclers, the suitability of information systems, and the effective translation of these requirements into a traceability system. To address these questions, a mixed-methods approach, combining a thorough literature review, regulation analysis, and a case study, was employed. The findings include defined achievable traceability levels for each recycling process step, an Entity-Relationship Diagram (ERD) for database design, a traceability system architecture, and Data Flow Diagrams (DFDs) for each recycling process step. These findings significantly contribute to research by focusing specifically on battery recycling and providing a detailed data collection process. The results empower recyclers to fully adhere to regulatory requirements while implementing a modern, scalable, accessible, and secure traceability system. Future research should explore the integration of advanced technologies into recycling traceability systems and adapt the proposed system to accommodate evolving regulations.

The aim of this study is to investigate the performances of semi-active suspension systems onto Scania AB terrain vehicles. This will be conducted by assessing the potential benefits for the road handling by implementing controllable dampers on 4x4 Scania AB chassis. The goal of the study is to conduct Multi-Body Simulation (MBS) on a virtual rugged terrain to analyze the impacts on parameters influencing the road-holding. The simulations will be performed through co-simulation between Adams and Matlab/Simulink. The truck will be modeled in Adams, and the shock absorbers will be controlled using a control algorithm developed in Matlab/Simulink. A comparison will be made between the results of the vehicle equipped with a passive damping system and those of the vehicle equipped with semi-active shock absorbers. Different scenarios will be explored based on the road profile, vehicle payload and driving speed. The ultimate goal is to identify benefits and/or challenges associated with integrating this system into the vehicle and to provide recommendations for future development. This study has demonstrated that, across all tested scenarios in the co-simulations, no significant improvements in road-holding or comfort were observed with the semiactive dampers. The main reasons identified include the inherent stiffness of the suspension system and the use of rigid axles, which limit suspension travel and the independent movement of the wheels. While the simplicity of the control algorithm may also contribute to the results, it is more likely that the mechanical and dynamic characteristics have a more substantial impact on performance. These findings suggest that, passive dampers may be as effective as semi-active dampers for off-road Scania AB vehicles.

This master’s thesis report set out to explore the user experience of mobile applications for public EV charging. The project was conducted in collaboration with VCG, a company that operates public EV charging infrastructure and provides a mobile application to manage charging sessions. The purpose of the project was to investigate the user needs related to mobile applications for EV charging and to identify the challenges and opportunities arising from this interaction. The project followed a double-diamond methodology, with a service design approach. The project began with a literature review exploring previous research on EV charging technology, as well as on consumer behavior and habits among EV drivers. This was followed by the main study, in which 25 users, EV retailers, and EV charger retailers were interviewed, and observations were conducted at 15 different locations. Various analysis methods were applied after the main study, resulting in seven major insights. These insights established the foundation for the subsequent concept development phase, which included ideation and prototyping. The concept development phase resulted in a main concept featuring a selection of key functions. These functions were categorized into five distinct solution areas. The project concluded with the development of a mobile application mockup that showcases the five different solution areas and functions identified in the study. In addition to the mockup, three recommendations are presented at a societal level to enhance the overall user experience.

Understanding which factors affect people's choices when traveling helps planners and policymakers build better infrastructure that fosters more sustainable practices. Gaining insights into how cyclists choose their routes is one major key to improving infrastructure and promoting cycling. Particularly commuters and e-bike users have been noted to have preferences unconstrained from shortest path logic. There are other more human centric factors can be of importance, such as closer to nature and away from busy intersections. To that end, this paper aims to uncover cyclists' preferences and the affecting nature-related attributes of routes for commuters to high school in Nijmegen, Netherlands. Based on a Dutch dataset, the study analyzed 1284 e-bike cycling trips, each with four route alternatives, including the chosen one. The primary objective is to identify the most influential parameters affecting e-bike commuters' route choices and understand their contributions. The approach employed both a simple path size Logit (PSL) and a Pairwise Combinatorial Logit (PCL) model, incorporating nature-related and interaction variables. Additionally, the research compared the predictive performance of Logit-based models with deep learning models. The findings shed light on the factors influencing e-bike commuters' route choices and demonstrate the superior predictive capabilities of deep learning techniques, with a validation accuracy of 80.16\%. By adopting a sensitivity analysis approach, we uncovered the key factors that influence our deep learning model in predicting cycling routes, giving a precise interpretation of our results. Our findings show that commuter e-bike cyclists prefer shorter routes with fewer traffic lights and favor routes that have more natural settings. However, their primary concern is efficiency in their commutes.

Dynamic Discrete Choice Models (DDCMs) allow us to analyze trip attribute choices within an activity-based framework where the sequence of activities matters. These models have been primarily developed for travel survey data. However, the advent of big data presents new opportunities for mobility analysis. This paper bridges traditional approaches with novel data sources by adapting an established activity-based DDCM to handle anonymized mobile network data. The proposed framework advances methods within transportation planning in two ways. First, dynamic models allow for detailed assessments of sequential travel decisions, for instance related to activity, departure time, and modes. Second, automatically collected data are advantageous as compared to travel surveys that are limited by respondents’ ability and willingness to disclose travel behavior. However, this approach poses methodological challenges, particularly because individuals’ exact locations over time are not directly observed. We address these in a latent-based framework in which the positions of the individuals are treated as latent variables and the location of cell towers as observations. We estimate the model using a stochastic expectation-maximization (StEM) algorithm. The performance is highlighted in a case study of daily travel demand in Stockholm, Sweden. Our paper demonstrates the agility of activity-based DDCMs in adapting to new data sources, by incorporating appropriate modifications within a well-established modeling paradigm.

Denna avhandling föreslår en ny strategi för utveckling av benvävnad in vitro i makroskala. För att möta de utmaningar som nuvarande käkkirurgitekniker innefattar, föreslår denna avhandling en ny metod, som testas in vitro med användningen av stamceller deriverade från fettvävnad (ADSCs) och deras förmåga att differentiera till osteoblaster. Användandet av Fibronectin (FN)-silke är dessutom avsedd att ge ett 3D-strukturellt stöd för ADSC. FN-silke är ett biokompatibelt biomaterial, konstruerat från rekombinant spindelsilkeprotein 4RepCT, och visat sig kunna bilda ett starkt, elastiskt nätverk som stödjer 3D-cellodling på ett sätt som liknar in vivo förhållanden. Målet är därmed att etablera en 3D-modell av benvävnad och att utforska potentialen hos ADSC i samverkan med FN-silke nätverk.

Denna studie visar att ADSC framgångsrikt integreras med FN-silkenätverket och - efter tillsats av osteogena faktorer - genomgår osteogen differentiering inom FN-silkenätverket, vilket bekräftas genom ökad Alkaline Phosphatase-aktivitet och tecken på mineralisering visat genom Alizarin Red S-färgning. Studien påvisar också att Simulerad Kroppsvätska (SBF) ensam i odlingsmediet inte främjar osteogenes in vitro. Dessutom har en metod för att inkorporera ADSC inom FN-silkenätverket i zirkoniumformar för kliniska tillämpningar framgångsrikt genomförts. 

Developing crosslinked elastomers that are easily produced and easily recyclable is complex, usually requiring a compromise between performance and recyclability. However, combining reversible exchange reactions together with phase separation phenomena appears as a promising approach. Herein, a simple and up‐scalable extrusion process is proposed, involving commercial maleated ethylene propylene rubber (EPRgMA), maleated polypropylene (PPgMA), and a suitable crosslinker. It is shown that a crosslinker enabling disulfide exchange reactions can provide local and long‐range rearrangements required for extrusion, yielding a robust crosslinked blend (BlendSS) with strength of 15 MPa and an impressive elongation of 1000%. Moreover, the presence of the disulfide crosslinker provided the required fast exchanges for three repetitive recycling cycles by extrusion with close to 80% retention of initial properties. In comparison, the use of a crosslinker without the capability to establish reversible reactions (BlendCC), yielded crosslinked blends of marginal compatibility, strength of 4 MPa and only 40% elongation. The absence of reversible reactions restricted chain rearrangements and consecutive recycling is only possible by compression molding. The recycled blends presented even lower compatibility, elasticity and thermomechanical performance, demonstrating that the proper design of interfacial interactions between PPgMA and EPRgMA can build a bridge between processability, performance, and high‐throughput recyclability.