Huntington's disease (HD) is a dominant neurodegenerative disease that is inherited from an affected parent who carries a mutation of the huntingtin gene (mHtt). The mHtt gene, with repeated CAG-sequences at the N-terminal, causes aggregations and oligomers. Failure of protein degradation processes causes toxic levels of aggregations and oligomers. Heat shock proteins perform chaperone functions, such as refolding misfolded proteins and are activated by increased temperatures. The temperature rise contributes to aggregated and misfolded proteins which stimulate the heat shock response. In this project we used two different mouse model cells. Cells with seven repeats of glutamines in the Htt gene, called Q7, are wild type cells, while cells with 111 repeats, called Q111, serve as a model for Huntington. The purpose of this study was to do a computational analysis and compare genome-wide mRNA expression in the Q7 and Q111 mouse models. Using mRNA-seq, differential gene expression between Q7 and Q111 cells was analysed, and up- and down-regulated genes have been detected and analysed. Additionally some selected chaperones and ubiquitins were investigated. With ChIP-seq, binding of a master activator of chaperone genes was compared in Q7 and Q111 cells, and with PRO-seq, the profile of nascent transcription investigated at selected genes and enhancers. The distribution of the down-regulated and up-regulated genes is spread equally, which means there is no general pattern of regulation in the sick mouse model. A connection between heat shock and HD, as well as the immune system and HD can be drawn from our result. These connections are interesting to study further in the future.

Cell cultivation is a frequently used method for production of biomolecules, like the enzyme β-Galactosidase used in the food industry to hydrolyze lactose [1]. In this bachelor thesis the feasibility of a high cell density chemostat cultivation of the Escherichia coli strain ML308, for continuous production of β-Galactosidase was examined. A chemostat cultivation was set up, based on the fed-batch process design which is currently used for β-Galactosidase production at the department of Industrial Biotechnology at AlbaNova, Royal Institute of Tehchnology. A fedbatch, however, is less productive than a chemostat connected to downstream processes for protein purification, because of the absence of downtime.

The goal of the project was to reach a cell dry weight of over 100 gL⁻¹ inside the bioreactor, and to remain at steady state for one week. A cell dry weight (CDW) corresponding to a biomass concentration of 96.3 gL⁻¹ was achieved, however, the cell pellets turned out to contain precipitated salt which, at the highest cell concentration, overestimated the CDW with approximately 8 gL⁻¹. The culture reached steady state twice during the cultivation process at different cell densities. At the first steady state, the enzyme activity was 809 U. A peak enzyme activity of 1610 U was achieved at the second steady state. At both instances, the yield of β-Galactosidase over biomass was 0.028 g_pg_x⁻¹.

In conclusion, a chemostat cultivation is a feasible method for high cell density cultivation of E. coli ML308 and continuous production of β-Galactosidase. In order to reach a cell density of 100 gL⁻¹, improvements of the process are required, mainly regarding pressure regulation, foam control, oxygen transfer rate and medium composition.

This thesis investigates the role of Battery Energy Storage System (BESS) in Ancillary Service Market (ASM) across three key regions: Great Britain, Sweden, and Texas. As the deployment of BESS increases, these markets are experiencing saturation, where the capacity for frequency regulation services exceeds demand, leading to declining service prices and economic challenges for BESS operators. The study addresses these issues by examining how market saturation impacts the economic benefits of BESS and explores strategies for optimizing market participation. A mixed-methods approach was used, incorporating a detailed market analysis, stakeholder surveys, and an Analytic Hierarchy Process (AHP) model to assess key criteria from the perspectives of Transmission System Operator (TSO) and BESS asset owners. The analysis revealed that Sweden has the most advanced market design, excelling in areas such as procurement expansion and bid size flexibility, followed closely by Great Britain, which benefits from innovative market mechanisms. Texas demonstrates strengths in competitive pricing and flexibility but ranks lower overall due to challenges in other regulatory areas. The results highlight the increasing saturation in ASM across all three regions and its impact on BESS revenue. The findings emphasize the need for regulatory adjustments and market design improvements to enhance BESS integration and ensure long-term profitability. This thesis offers practical recommendations for policymakers and stakeholders seeking to address the challenges of BESS deployment in ancillary services while maintaining grid stability.

This thesis explores the possibility of integrating a V-shaped hydrofoil on a rowing boat. Hydrofoils are said to reduce drag and improve performance by lifting the boat out of the water. Through a literature review, previous research and technical solutions for hydrofoils are analyzed, focusing on V-shaped hydrofoils as a more cost-effective and simpler alternative to T-shaped hydrofoils.

The results show that it is technically feasible to integrate a V-shaped hydrofoil with a rowing boat. By reducing the hull's contact with the water, resistance can be lowered, leading to a reduction in fuel consumption by approximately 50–80% and an increase in speed. It is also possible to reduce travel time by half, as demonstrated by test runs of the Candela P-12 ferry.

The report examines which of the UN's 17 Sustainable Development Goals are addressed by hydrofoils. It turns out that reduced energy consumption, lower emissions in maritime transport, and sustainable urban transport, especially in coastal cities and areas with many islands, are relevant. However, the consequences of this have not been explored, which is a recommendation for future work.

The conclusion is that V-shaped hydrofoils offer a solution to improve boat performance, but further investigation, practical tests, and adjustments are needed to ensure optimal performance and explore potential drawbacks of the technology. The work contributes theoretical insights and recommendations for how small boat owners and others can experiment with hydrofoils, and it inspires further research in the field.

In recent years, climate change and human activities have caused increasing numbers of wildfires. Earth observation data with various spatial and temporal resolutions have shown great potential in detecting and monitoring wildfires. Sensors with different spatial and temporal resolutions detect wildfires in different stages. For low spatial resolution and high temporal resolution satellites, they are mostly used in active fire detection and early-stage burned area mapping because of their frequent revisit. While these products are very useful, the existing solutions have flaws, including many false alarms due to cloud cover or buildings with roofs in high temperatures. Also, the multi-criteria threshold-based method does not leverage rich temporal information of each pixel at different timestamps and rich spatial information between neighbouring pixels. Therefore, advanced processing algorithms are needed to detect active fires. For medium spatial resolution and low temporal resolution satellites, they are often used to detect post-fire burned areas. Optical sensors like Sentinel-2 and Landsat-8/9 are commonly used but their low temporal resolution makes them difficult to monitor ongoing wildfire as they are likely to be affected by clouds and smoke. Synthetic Aperture Radar (SAR) satellites like Sentinel-1, ALOS-2 and RADARSAR Constellation Mission (RCM) can penetrate through the cloud and their spatial resolutions are around 30 meters. However, limited studies have compared the effectiveness of C-band and L-band data and investigating the usage of compact polarization on burned area mapping.

The main objective of this thesis is to develop deep learning methods for improved active fire detection, daily burned area mapping and post-fire burned area mapping utilizing multi-sensor multi-resolution earth observation images. 

 Temporal models such as Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Transformer networks are promising for effectively capturing temporal information embedded in the image time-series produced by high temporal resolution sensors. Spatial models, including ConvNet-based and Transformer-based architectures, are well-suited for leveraging the rich spatial details in images from mid-resolution sensors. Furthermore, when dealing with image time-series that contain both abundant temporal and spatial information, spatial-temporal models like 3D ConvNet-based and Transformer-based models are ideal for addressing the task. 

In this thesis, the GRU-based GOES-R early detection method consists of a 5-layer GRU network that utilizes GOES-R ABI pixel time-series and classifies the active fire pixels at each time step. For 36 study areas, the proposed method detects 26 wildfires earlier than VIIRS active fire product. Moreover, the method mitigates the problem of coarse resolution of GOES-R ABI images by upsampling and the results show more reliable early-stage active fire location and suppresses the noise compared to GOES-R active fire product.

Furthermore, the VIIRS time-series images are investigated for both active fire detection and daily burned area mapping. For active fire detection, the image time-series are tokenized into vectors of pixel time-series as the input to the proposed Transformer model. For daily burned area mapping, the 3-dimensional Swin-Transformer model is directly applied to the image time-series. The attention mechanism of the Transformer helps to find the spatial-temporal relations of the pixel. By detecting the variation of the pixel values, the proposed model classifies the pixel at different time steps as an active fire pixel or a non-fire pixel. The proposed method is tested over 18 study areas across different regions and provides a 0.804 F1-Score. It outperforms the VIIRS active fire products from NASA which has a 0.663 F1-Score. For daily burned area mapping, it also outperforms the accumulation of VIIRS active fire hotspots in the F1 Score (0.811 vs 0.730). Also, the Transformer model is proven to be superior for active fire detection to other sequential models GRU and spatial models like U-Net. Additionally, for burned area detection, the proposed AR-SwinUNETR also shows superior performance over spatial models and other baseline spatial-temporal models.

To address the limitation of optical images due to cloud cover,  C-bBand data from Sentinel-1 and RCM, as well as L-band data from ALOS-2 PALSAR-2, are evaluated for post-fire burned area detection. To assess the effectiveness of SAR at different wavelengths, the performance of the same deep learning model is cross-compared on burned areas of varying severities in broadleaf and needleleaf forests using both Sentinel-1 SAR and PALSAR-2 SAR data. The results indicate that L-band SAR is more sensitive to detecting low and medium burn severities. Overall, models using L-band data achieve superior performance, with an F1 Score of 0.840 and an IoU Score of 0.729, compared to models using C-band data, which scored 0.757 and 0.630, respectively, across 12 test wildfires. For the RCM data, which provides compact polarization (compact-pol) at C-band, the inclusion of features generated from m-$\chi$ compact polarization decomposition and the radar vegetation index, combined with the original images, further enhances performance. The results demonstrate that leveraging polarization decomposition and the radar vegetation index improves detection accuracy for baseline deep learning models compared to using compact-pol images alone.

In conclusion, this thesis demonstrates the potential of advanced deep learning methods and multi-sensor Earth observation data for improving wildfire detection and burned area mapping, achieving superior performance across various sensors and methodologies.

Monitoring wildfires has become increasingly critical due to the sharp rise in wildfire incidents in recent years. Optical satellites like Sentinel-2 and Landsat are extensively utilized for mapping burned areas. However, the effectiveness of optical sensors is compromised by clouds and smoke, which obstruct the detection of burned areas. Thus, satellites equipped with Synthetic Aperture Radar (SAR), such as dual-polarization Sentinel-1 and quad-polarization RADARSAT-1/-2 C-band SAR, which can penetrate clouds and smoke, are investigated for mapping burned areas. However, there is limited research on using compact polarisation (compact-pol) C-band RADARSAT Constellation Mission (RCM) SAR data for this purpose. This study aims to investigate the capacity of compact polarisation RCM data for burned area mapping through deep learning. Compact-pol m-$\chi$ decomposition and Compact-pol Radar Vegetation Index (CpRVI) are derived from the RCM Multi-look Complex product. A deep-learning-based processing pipeline incorporating ConvNet-based and Transformer-based models is applied for burned area mapping, with three different input settings: using only log-ratio dual-polarization intensity images images, using only compact-pol decomposition plus CpRVI, and using all three data sources. The training dataset comprises 46,295 patches, totalling 90 GB, generated from 12 major wildfire events in Canada. The test dataset includes seven wildfire events from the 2023 and 2024 Canadian wildfire seasons in Alberta, British Columbia, Quebec and the Northwest Territories. The results demonstrate that compact-pol m-$\chi$ decomposition and CpRVI images significantly complement log-ratio images for burned area mapping. The best-performing Transformer-based model, UNETR, trained with log-ratio, m-$\chi$ decomposition, and CpRVI data, achieved an F1 Score of 0.718 and an IoU Score of 0.565, showing a notable improvement compared to the same model trained using only log-ratio images (F1 Score: 0.684, IoU Score: 0.557).

Earth observation-based burned area mapping is critical for evaluating the impact of wildfires on ecosystems. Optical satellites, such as Landsat and Sentinel-2, are often to map burned areas. However, they suffer from interference caused by clouds. Capable of penetrating through clouds, Synthetic Aperture Radar (SAR) at C- and L-band is also widely used for burned area mapping. With a longer wavelength than C-band SAR, L-band SAR is more sensitive to trunks and branches. Conversely, C-band SAR is prone to reflection off tree canopy leaves. Thus, the wavelength differences between the two types of sensors result in varying abilities to detect burned areas with different burn severities, as different burn severities cause structural changes in the forests. This research compares ALOS Phased-Array L-band Synthetic Aperture Radar-2 (PALSAR-2) to Sentinel-1 C-band SAR for mapping burned areas across low, medium, and high burn severities. Moreover, a deep-learning-based workflow is utlized to segment burned area maps from both C-band and L-band images. ConvNet-based and Transformer-based segmentation models are trained and tested on global wildfires in broadleaf and needle-leaf forests. The results indicate that L-band data show higher backscatter changes compared to C-band data for low and medium severity. Additionally, the segmentation models with L-band data as input achieve higher F1 (0.840) and IoU Scores (0.729) than models with C-band data (0.757, 0.630). Finally, the ablation study tested different combinations of input bands and the effectiveness of total-variation loss. The study highlights the importance of SAR Log-ratio images as input and demonstrates that total- variation loss can reduce the noise in SAR images and improve segmentation accuracy.

Wildfire management and response requires frequent and accurate burned area mapping. How to map daily burned areas with satisfactory accuracy remains challenging due to missed detections caused by accumulating active fire points as well as the low temporal resolution of sensors onboard satellites like Sentinel-2/Landsat-8/9 and monthly burned area product generated from the Visible Infrared Imaging Radiometer Suite (VIIRS) data. ConvNet-based and Transformer-based deep-learning models are widely applied to mid-spatial-resolution satellite images. But these models perform poorly on low-spatial-resolution images. Also, cloud interference is one major issue when continuously monitoring the burned area. To improve detection accuracy and reduce cloud inference by combining temporal and spatial information, we propose an autoregressive spatial-temporal model AR-SwinUNETR to segment daily burned areas from VIIRS time-series. AR-SwinUNETR processes the image time-series as a 3D tensor but considers the temporal connections between images in the time-series by applying an autoregressive mask in Swin-Transformer Block. The model is trained with 2017-2020 wildfire events in the US and validated on 2021 US wildfire events. The quantitative results indicate AR-SwinUNETR can achieve a higher F1-Score than baseline deep learning models. The quantitative results of testset which consists of eight 2023 long-duration wildfires in Canada show a better F1 Score (0.757) and IoU Score (0.607) than baseline accumulated VIIRS Active Fire Hotspots (0.715) and IoU Score (0.557) compared with labels generated from Sentinel-2 images. In conclusion, the proposed AR-SwinUNETR with VIIRS image time-series can efficiently detect daily burned area providing better accuracy than direct burned area mapping with VIIRS active fire hotspots. Also, burned area mapping using VIIRS time-series and AR-SwinUNETR keeps a high temporal resolution (daily) compared to other burned area mapping products. The qualitative results also show improvements in detecting burned areas with cloudy images.

The ability to modify the structure of the wood-pulp fibre cell wall structure is an attractive means to obtain increased accessibility to the fibre interior and enable functionalization such as controlled drug delivery, interpenetrated networks, and selective removal of metal ions from aqueous mixtures just to mention a few examples. By changing the physical state of water, it should be possible to significantly alter the structure of the wet fibre wall, providing the possibility to perform cell wall modifications under extreme conditions. To address this challenge, we have focussed on investigating the structural development of the wet softwood kraft pulp fibre wall under high pressure (HP) conditions (up to 2 GPa). The experiments aim to clarify the effect of the HP conditions on the porosity and the accessibility of the fibre wall for treated and untreated fibres. The second goal is to observe the changes in the crystalline structure of cellulose due to HP conditions. Different characterization techniques, including Electron microscopy, X-ray diffraction, Small and wide-angle X-ray scattering, and Cross-polarized/magic angle spinning 13C-NMR, are used to characterize material that has been exposed to HP. Key findings from the experiments relate to changes in crystallinity, specific surface area, bound water content and surface morphology.

Studies of molecular changes in cellulose structure as a response to different physical conditions are essential for understanding the fundamental mechanisms that can be used to optimise processing conditions and contribute to improved sustainability. Cellulose strongly interacts with water, raising the question of whether it is possible to change its molecular structure by changing the physical structure and properties of the surrounding water. Previous studies have established that the hyperbaric treatment of bio-materials permanently affects molecular structure in terms of crystallinity and accessibility. The present study shows the changes in cellulose-rich pulps on the molecular level in response to static extreme-pressure conditions. This is achieved by statically compressing the pulp-water mixture up to 3 GPa pressure using a resistive-heated di-amond anvil cell (DAC). During compression, the water transforms through a phase transformation from liquid to ice VI and ice VII, inducing a permanent increase in the crystallinity of the pulp. High-pressure, cellulose, X-ray diffraction, diamond anvil cell, crystallinity, morphological changes

The use of occupancy sensing technology is imperative in augmenting safety, comfort, and efficacy in diverse settings, especially in the automotive sector. With the use of this technology, people inside vehicles and other enclosed spaces may be detected and monitored, allowing for the installation of intelligent systems and customized features based on the requirements of the occupants. The European Union's requirements for driver monitoring systems and the automotive industry's explosive rise in in-cabin sensing highlight how important it is to enhance both safety and user experience in heavy-duty vehicles like trucks. This thesis work explores the state-of-the-art in current occupant sensing technologies and focuses on its application within truck cabin environments. It discusses about the different sensor technologies present for occupant sensing especially radar. The work aims to investigate the possibility of radar-based sensors in detecting the presence of occupants in different seating positions and various types of truck cabin environments. Furthermore, the work also emphasizes on the dependability of the sensor positioning on detecting human presence inside the truck cabins. The findings of this work is to enhance occupant sensing technology and integrate it into heavyduty vehicle environments, thereby improving truck occupant's comfort, convenience, and security by tackling the possibilities and limitations.

With the growing concerns about our negative impact on the environment, interest in research focusing on greener solutions has increased. Companies like the Swedish truck manufacturer Scania have responded by investing in the development of more environmentally friendly solutions and materials. This thesis explores the potential of sustainable rubber materials as alternatives to traditional rubber in trucks. The research aims to evaluate the performance of sustainable EPDM and AEM rubber, which is produced with lower emissions than conventional rubber, and identifying suitable applications for rubber within the vehicle. 

Sulphur-vulcanized and peroxide-vulcanized EPDM and AEM rubber was supplied from two different companies. In total, twelve different rubber materials were subjected to testing based on international ISO standards, including tensile testing, compression set and change in hardness and volume after ageing in air, coolant or oil. The materials were evaluated in accordance with Scania's standards for automotive components. The results demonstrate the potential of sustainable rubber for automotive applications. The peroxide-vulcanised rubber resulted in the best performance, followed by the sulphur vulcanized EPDM and AEM rubber which were more sensitive to temperature and the different media. Suitable applications for sustainable rubber within trucks were identified, including components in the cab and front areas environment is less demanding.

This thesis showed the potential of sustainable rubber in the automotive industry. The results provide insights for Scania to be able to integrate sustainable materials in its truck production and thereby reduce environmental impact and promote a more sustainable future. Future research is recommended to further explore the potential of sustainable rubber in various automotive components.

Drinking water safety is an essential pillar of a healthy society. Recently, concerns regarding the impact ofmicro- (5 mm to 1 μm) to nanoplastics (<1 μm) on human health have intensified from both regulatory andindustrial perspectives. Acoustofluidics has become a microfluidic technique of high potential for themanagement of all particles below 50 μm, owing to its versatility and recent development in theenvironmental field. In this work, we integrate the EchoGrid, a device optimized for high-throughput microand nanoplastic enrichment, with 3-D fluorescence microscopy and Raman spectroscopy for the structuraland functional characterization of levitating acoustic clusters created by the silica-enhanced seed particlemethod. This multidisciplinary approach has allowed the study of the geometry of these clusters, howparticles of different sizes behave within and around them, and how different concentrations can impact thefinal cluster conformation after enrichment. Moreover, we studied how our silica-enhanced seed particlemethod is essential for stability and inertness when analyzing enriched micro and nanoplastics. With thisapproach, we detected PS, PE, and PMMA simultaneously in a silica seed cluster, at various size ranges,including nanoplastics. In conclusion, we demonstrate the effectiveness of the EchoGrid as anacoustofluidic platform compatible with the endpoint analysis of even complex samples using 3-DFluorescence and Raman spectroscopy, allowing the detection of various types of micro and nanoplasticsas a highly promising proof-of-concept monitoring solution.

As global energy demand has increased over the years, so too has the consumption of fossil fuels to meet these needs, resulting in substantial emissions of greenhouse gases (GHGs) and other pollutants. In response to the impact of these emissions on the climate crisis, there is a growing global consideration of alternative energy sources. Given that the transport sector is a major contributor to GHG emissions, it is imperative to implement changes to reduce its environmental impact.

One promising solution to this crisis is the use of hydrogen produced through electrolysis using renewable energy sources. This approach involves retrofitting combustion engines to use hydrogen gas as the primary fuel. While this method allows for the utilization of the efficiency of conventional engines with minimal modifications, it faces a challenge during cold starts: hydrogen's high autoignition temperature leads to ignition delays. A common solution to this issue is the introduction of a small amount of diesel to initiate ignition. However, this approach contradicts the primary goal of using hydrogen to reduce carbon emissions.

An alternative solution, which avoids such compromises, involves integrating a catalyst before the combustion chamber, through which a mixture of air and hydrogen flows. This process heats the surrounding air and the system itself during cold starts.

The primary objective of this study was to evaluate the feasibility of hydrogen combustion at subzero temperatures using a catalyst. For this experiment, a platinum-alumina (Pt/Al2O3) catalyst was selected, and a test rig capable of operating at subzero temperatures was designed. Experiments were conducted using two platinum-based catalysts with different platinum loadings (1% and 3% by weight), at three inlet temperatures (25 °C, -12 °C, and -20 °C), and with varying hydrogen concentrations (1%, 3%, and 4% by volume). The results indicate that hydrogen combustion at subzero temperatures is feasible, and the temperature differences achieved were sufficient to enable the effective operation of a hydrogen internal combustion engine. These findings suggest that the use of catalysts in hydrogen internal combustion engines could be a viable pathway for reducing carbon emissions in the transport sector.

The health hazards of micro- and nanoplastic contaminants in drinking water has recently emerged as an area of concern to policy makers and industry. Plastic contaminants range in size from micro- (5 mm to 1 μm) to nanoplastics (<1 μm). Microfluidics provides many tools for particle manipulation at the microscale, particularly in diagnostics and biomedicine, but has in general a limited capacity to process large volumes. Drinking water and environmental samples with low-level contamination of microplastics require processing of deciliter to liter sample volumes to achieve statistically relevant particle counts. Here, we introduce the EchoGrid, an acoustofluidics device for high throughput continuous flow particle enrichment into a robust array of particle clusters. The EchoGrid takes advantage of highly efficient particle capture through the integration of a micropatterned transducer for surface displacement-based acoustic trapping in a glass and polymer microchannel. Silica seed particles were used as anchor particles to improve capture performance at low particle concentrations and high flow rates. The device was able to maintain the silica grids at a flow rate of 50 mL/min. In terms of enrichment, the device is able to double the final pellet’s microplastic concentration every 78 s for 23 μm particles and every 51 s for 10 μm particles at a flow rate of 5 mL/min. In conclusion, we demonstrate the usefulness of the EchoGrid by capturing microplastics in challenging conditions, such as large sample volumes with low microparticle concentrations, without sacrificing the potential of integration with downstream analysis for environmental monitoring.

The Sustainable Risk Awareness (SRA) Index, a newly designed Key Performance Indicator (KPI), reflects an organization's risk awareness through Work Environment (WE) deviations data. However, there is still room for improvement of the SRA Index considering the quality of definitions, usage, visualization, etc. The study aims to analyze the SRA Index from different aspects and propose improvements based on feedback from participating companies.

The thesis consists of three main components: first, discussing different definitions of safety culture, safety approach, and risk awareness, and exploring their relationship with the SRA Index; second, collecting WE deviations data from five companies, calculating and analyzing the SRA Index; and third, conducting interviews with safety managers from these companies to gather feedback on the SRA Index.

The results have shown that individuals with different work experiences, knowledge of safety and work environment have varying interpretations of risk awareness, which influences their understanding of the SRA Index definition. Adjusting the definitions of both the SRA Index and risk awareness could help reduce this interpretive bias. Additionally, factors such as the utilization rate of the IA system, employee education levels, and management support were found to impact the SRA Index. With the aid of suggested visual enhancements, the SRA Index has the potential to help companies foster a safer working environment in the future.

While academic papers give us space to express our knowledge andexpertise, we also need spaces to express our views, feelings, andcreative expressions towards a more sustainable life on this planet,where ICT is not always directly implicated.We are therefore very excited to share the contributions from theICT4S Zine 2024!As an alternative to the official program and traditional, peer-reviewed publications, we have taken inspiration from zineculture to gather a set of alternative and DIY contributions thatencourage authors to embrace creativity that might not always beencouraged in more traditional academic outputs focused on ICTand sustainability.This zine is a result of a call for contributions to that exploresalternative visions, ideas and critiques on the topic ofsustainability and ICT.We look forward to hear what you think about the zine.Creativity is where new ideas can grow and be nurtured. Our hopeis that the zine encourages the ICT4S community to build space forcreativity and new ideas in the future.

The City of Stockholm is conducting a scenario planning exercise to explore where potential future office development should be planned: closer to the city centre as in the status quo, in peripheral hubs on the outskirts of the city, or dispersed throughout multiple neighbourhoods. To support this exercise, this paper models these three scenarios using a nested work location and dynamic activity-based scheduling model. Our model predicts that high-income individuals have the highest consumer welfare benefits and are over-represented as workers in all scenarios. Developing more central office space will likely reinforce existing geographical patterns of income inequality in Stockholm; developing peripheral or dispersed office space, especially in the south of the city, will challenge these patterns. However, the model also illustrates a tension between the goals of equity and the environment. By taking advantage of existing transit infrastructure and congestion patterns, more central office development will result in lower vehicle kilometers travelled and lower car mode share for commuting than more peripheral or dispersed development.

Fatigue analysis is critical when evaluating a products lifespan and determining the structural integrity of a component under cyclic loads. While software suits like Ansys and Comsol offer built-in fatigue modules, Abaqus CAE does not. This thesis addresses this gap by developing and evaluating a post-processor plug-in tailored for fatigue life calculations in the Abaqus CAE. By scripting in python, the plug-in was designed to integrate with Abaqus´s existing user interface. The literary review was based upon an array of literature, including user manuals from industry leaders andrelevant research articles. The plug-in was validated through benchmarking against established solutions, particularly Ansys. The findings indicate a promising convergence between the plug-in and Ansys, for high-cycle fatigue situations, but caution its use for high-stresses, resulting in low-cycle fatigue conditions, and more complex stress states. Future work on this research include the incorporation of thermal stresses, multi-axial stress criterion’s and notch sensitivity. While the plug-in offers promising initial results, the study highlights areas for refinements and serves as afirst step towards a more comprehensive fatigue analysis solution in the future.

This study aims to explore the potential of a hovering umbrella that can autonomously take off using an ultrasonic sensor, self-regulate with advanced PID control and scan a personal QR code to identify the owner. The methodology involves extensive testing of various drone designs to identify the most aerodynamic and functional construction. Furthermore, detailed tests of the ultrasonic sensor’s precision and the PID control’s stabaility were conducted to assess their performance. The results demonstrate that the drone can successfully lift of the ground and maintain a stable flight for one minute. Additionally, the drone’s capability to identify individuals by scannig QR codes was confirmed. The conclusion indicate that it is feasible to develop an autonomously hovering umbrella with the investigated technologies which holds potential practical applications in areas such as security, convenience and personal identification.

NoviOcean is a company dedicated to the development of wave power plants. In order to efectively demonstrate and explain the functionality of their construction to investors and clients, they sought to create a small prototype of the wave power plant capable of movement. The purpose of this report was to examine the feasibility of replicating ocean waves using a mechanical approach by constructing a Stewart Platform for NoviOcean. A Stewart Platform consists of 6 linked legs connected by a top and a base plate. By adjusting the length of the legs, the top plate can move in 6 degrees of freedom. Depending on the angle of the servo motors, the length of each leg changes, thus afecting the position of the top plate. The length of each leg is determined using inverse kinematics. The fnal product can move like a particle in the ocean, in a circular path, with satisfactory tolerances. However, there are visible tremors in the movement, likely due to material and manufacturing defciencies. Further work is needed to address this issue.

Identifying embedded system design solutions that utilize platform resources effectively is often difficult. Due to the demands of modern applications and the complexity of heterogeneous platforms, tailoring the system design to maximize throughput, minimize power consumption, or meet other design goals may pose a highly demanding task for system designers. Design Space Exploration (DSE) is a technique commonly applied within the embedded system design process that utilizes a systematic search to find optimal design solutions. These solutions include where applications should be executed, how applications should be scheduled, and where application data should be stored. The design space is the core of DSE, defining the possible design alternatives based on platforms and application specifications. This thesis explores how DSE can evaluate design spaces where applications are mapped to hardware- or software-programmable processing units. In this work, IDeSyDe is the targeted DSE tool, that originally only supported DSE of software-programmable platforms, and ForSyDe IO is used for creating the system specifications. These tools were adapted to support hardware-programmable platforms and hardware implementations, wherein analysis was necessary to decide relevant parameters. A quality assessment of these novel DSE capabilities was performed on a model of the heterogeneous Zynq UltraScale+ XCZU9EG Multi-processor System on Chip through test cases and a realistic video streaming application. An FPGA is a hardware-programmable processing unit providing hardware resources to implement functionality. Extensions to the tools mainly focus on providing support for specifying FPGA components and applications implemented on an FPGA. The achieved DSE support covers FPGA implementations through the specification of execution latency, required block RAM, and required logic blocks, relevant for FPGA modeling due to its characterizing resource limitations. These parameters are suitable as they are commonly found in libraries for reusable FPGA implementations. Likewise, the FPGAcomponent is modeled with the availability of blockRAM and logic blocks. The project’s goals were met and can thus be considered successful. Assessment of the test cases shows that the novel extensions are well incorporated into IDeSyDe. However, IDeSyDe did not find a solution for the realistic application within a reasonable time due to high computational demands, and the modeling did not adequately account for certain real-world aspects of the platform. Also, although the platform model assumed rather pessimistic performance, the lack of performance guarantees between modeling and reality results poses a significant limitation and is left for future work. 

Object detection (OD) is a computer vision problem that involves the identification and localization of objects within an image or video stream. 5G and edge computing technologies have enabled distributed OD systems to operate more efficiently. This thesis addresses the challenge of improving an existing edge-assisted OD pipeline, developed at the Sensing and Perception team, Ericsson Research. 

The existing pipeline uses support vector machines (SVM)-based classifier in order to identify images where lightweight OD has failed, and calculates an introspective score based on the classifier. It combines two failure scores, i.e., the introspective score with comparison based score so-called, a golden score. Combining is done by taking the weighted average between the scores that results in detection failure metric, (DFM), which is then used to offload OD from a resource-constraint device to a more powerful device (edge). There is room for improvement, mainly in two areas. Firstly, SVM is a simple classifier and requires features from OD to infer OD failures. Secondly, the pipeline does not consider end-to-end latency, therefore it worsens significantly in degraded network conditions. Due to its significance and degree of difficulty, this problem is important to tackle and appropriate topic for a Master's thesis.

To solve the first problem, this thesis proposes a deep learning-based detection failure classifier that replaces the previous SVM-based classifier in order to identify images where lightweight OD has failed. For the second problem, several approaches to make offloading decision are proposed in order to consider end-to-end latency as well as network conditions by combining two failure scores with a new latency score. The effectiveness of the proposed methods is then tested against the ImageNet Large Scale Visual Recognition Challenge 2017 (ILSVRC2017) VID dataset using several accuracy metrics and end-to-end latency.

Experimental evaluation shows that the final proposed method significantly enhances the edge-assisted OD pipeline compared to SVM-based implementation and fixed weight scheduler, and increases overall pipeline performance. As a result, this work could help enhance accuracy and efficiency for edge-assisted OD pipelines in various applications, including surveillance systems, virtual reality environments and autonomous vehicles. 

In this paper, we introduce a ROS based framework designed for the planning and control of robotic systems within the context of precision agriculture, with an emphasis on human-in-the-loop capabilities. Utilizing Linear Temporal Logic to articulate complex task specifications, our algorithm creates high-level robotic plans that are not only correct by design but also adaptable in real time by human operators. This dual-focus approach ensures that while humans have the flexibility to modify the high-level plan on-the-fly or even take over low-level control of the robots, the system inherently safeguards against any human actions that could potentially breach the predefined task specifications. We demonstrate our algorithm within the dynamic and challenging environment of a real vineyard, where the collaboration between human workers and robots is critical for tasks such as harvesting and pruning, and show the practical applicability and robustness of our software. This work marks a pioneering application of formal methods to complex, real-world agricultural environments.

This study presents novel active-flow-control (AFC) strategies aimed at achieving drag reduction for a three-dimensional cylinder immersed in a flow at a Reynolds number based on freestream velocity and cylinder diameter of (Re_D=3900). The cylinder in this subcritical flow regime has been extensively studied in the literature and is considered a classic case of turbulent flow arising from a bluff body. The strategies presented are explored through the use of deep reinforcement learning. The cylinder is equipped with 10 independent zero-net-mass-flux jet pairs, distributed on the top and bottom surfaces, which define the AFC setup. The method is based on the coupling between a computational-fluid-dynamics solver and a multi-agent reinforcement-learning (MARL) framework using the proximal-policy-optimization algorithm. Thanks to the acceleration in training facilitated by exploiting the local invariants with MARL, a drag reduction of (8\%) was achieved, with a mass cost efficiency two orders of magnitude lower than those of the existing classical controls in the literature. This development represents a significant advancement in active flow control, particularly in turbulent regimes critical to industrial applications.

Designing active-flow-control (AFC) strategies for three-dimensional (3D) bluff bodies is a challenging task with critical industrial implications. In this study we explore the potential of discovering novel control strategies for drag reduction using deep reinforcement learning. We introduce a high-dimensional AFC setup on a 3D cylinder, considering Reynolds numbers (ReD) from 100 to 400, which is a range including the transition to 3D wake instabilities. The setup involves multiple zero-net-mass-flux jets positioned on the top and bottom surfaces, aligned into two slots. The method relies on coupling the computational-fluid-dynamics solver with a multi-agent reinforcement-learning (MARL) framework based on the proximal-policy-optimization algorithm. MARL offers several advantages: it exploits local invariance, adaptable control across geometries, facilitates transfer learning and cross-application of agents, and results in a significant training speedup. For instance, our results demonstrate 21% drag reduction for ReD=300, outperforming classical periodic control, which yields up to 6% reduction. To the authors' knowledge, the present MARL-based framework represents the first time where training is conducted in 3D cylinders. This breakthrough paves the way for conducting AFC on progressively more complex turbulent-flow configurations.

This project covers the designing, testing and evaluation of an infantry target system called “The Turtle”. The problems identified with the currently fielded target systems in the Swedish Armed Forces resulted in an idea to create a target system to better fill the gap between training and reality for soldiers on exercise. The goal was to make a target that gave the soldier realistic feedback in real-time by responding to the soldier shot placement, that was usable with indirect fire and that was field expedient. The importance of a system that offers realistic training possibilities is not lost on military organisations. The closer a unit can train to a realistic situation the better they will perform when the times comes. The system was constructed using raw materials cut with a water-jet, and electronic components such as motor drivers, accelerometers and hall-effect sensors. After testing the prototype, the conclusion was drawn that the concept was functional for the purposes of the project. While many tweaks and adjustments will have to be made in the future, good groundwork has been laid.

Within research on societal transitions, ‘post-normal’ scientific approaches such as transdisciplinary research are increasingly prominent. The difficulties of interdisciplinary and transdisciplinary research are well-established, but less attention has been paid to the underlying causes of these difficulties. In this essay, we argue that the political natures of both ‘transdisciplinarity’ and ‘transitions’ themselves underlie the more visible research challenges. While recent work has outlined how transitions research, embedded as it is in the sociopolitical milieu, can reproduce or challenge existing regimes, here we discuss more specifically the politics of projects themselves, which necessarily affect how they inform societal transitions. Using literature and examples from our own work, we outline three politically contested areas in projects – stakeholder inclusion, understanding of transitions, and research questions that are considered – and identify two broad orientations that research can follow to address these: incremental or fundamental. The interconnectedness of the political aspects of transdisciplinary transitions research requires explicit attention, we argue, if such work is to effectively address complex and ‘wicked’ societal challenges.

Alloying ZnO with isovalent compounds allows tailoring the material’s optoelectronic properties. In this work, we theoretically analyze the ZnO-based alloys ZnO–X ≡ (ZnO)1−x(X)x where X = GaN and InN, employing a first-principles Green’s function method GW0 based on the density functional approach. Since the alloy compounds are isovalent to ZnO, we find relatively small distortion of the crystalline structure, however, nanocluster structures are expected to be present in the alloy. ZnO–X reveal intriguing optoelectronic properties. Incorporating GaN or InN in ZnO strongly narrows the energy gap. The band gap energy is reduced from Eg = 3.34 eV in intrinsic ZnO to ∼2.17 and ∼1.89 eV in ZnO–X by alloying ZnO with 25% GaN and InN, respectively. Moreover, clustering enhances the impact on the electronic structure, and the gap energy in ZnO–InN is further reduced to 0.7–1.5 eV if the 25% compound contains nanoclusters. The dielectric function ε2(ω) varies weakly in ZnO–GaN with respect to alloy composition, while it varies rather strongly in ZnO–InN. Hence, by properly growing and designing ZnO–X, the alloy can be optimized for a variety of novel integrated optoelectronic nano-systems.

Very recently, Cu3BiS3 has been suggested as an alternative material for photovoltaic (PV) thin-film technologies. In this work, we analyze the electronic and optical properties of Cu3BiY 3 with the anion elements Y = S, Se, and Te, employing a first-principles approach within the density function theory. We find that the three Cu3BiY 3 compounds have indirect band gaps and the gap energies are in the region of 1.2–1.7 eV. The energy dispersions of the lowest conduction bands are small, and therefore the direct gap energies are only ∼0.1 eV larger than the fundamental gap energies. The flat conduction bands are explained by the presence of localized Bi p-states in the band gap region. Flat energy dispersion implies a large optical absorption, and the calculations reveal that the absorption coefficient of Cu3BiY 3 is larger than 105 cm−1 for photon energies of ∼2.5 eV. The absorption is stronger than other Cu-S based materials like CuInS2 and Cu2ZnSnS4. Thereby, Cu3BiY 3 has the potential to be a suitable material in thin-film PV technologies.

Information communication and technology (ICT) services and solutions can improve resource efficiency in a variety of sector, but also result in direct environmental impacts. This study assesses the direct environmental impacts of an ICT service that improves vehicle fuel efficiency using a cradle-to-grave life cycle assessment (LCA). This is one of the first studies to examine the entire life cycle of an ICT service from development to use and maintenance, with a focus on software—an aspect that is typically neglected in previous studies. The results suggest that software development and maintenance and the use of in-vehicle communicators for data transmission have the largest environmental impacts across multiple categories. Deployed across a fleet of 150,000 vehicles over 5 years, we estimate that the ICT service is responsible for 174 tCO2e. However, this is negligible compared with the total emissions of the fleet and the potential savings from the service, given a single diesel vehicle in this fleet emits around 130 tCO2e over the same period. We explore several scenarios to reduce the footprint of the ICT service. The largest potential reduction of around one-third is achieved by replacing in-house servers with cloud computing in a data center located in a region with low-carbon electricity. The study demonstrates how LCA can be used to assess the environmental impacts of ICT services and the importance of considering software in these assessments.

Software testing is the process of verifying that a software system behaves as it is intended to behave. Significant resources are invested in creating and maintaining strong test suites to ensure software quality. However, in-house tests seldom reflect all the scenarios that may occur as a software system executes in production environments. The literature on the automated generation of tests proposes valuable techniques that assist developers with their testing activities. Yet the gap between tested behaviors and field behaviors remains largely overlooked. Consequently, the behaviors relevant for end users are not reflected in the test suite, and the faults that may surface for end-users in the field may remain undetected by developer-written or automatically generated tests.

This thesis proposes a novel framework for using production observations, made as a system executes in the field, in order to generate tests. The generated tests include test inputs that are sourced from the field, and oracles that verify behaviors exhibited by the system in response to these inputs. We instantiate our framework in three distinct ways.

First, for a target project, we focus on methods that are inadequately tested by the developer-written test suite. At runtime, we capture objects that are associated with the invocations of these methods. The captured objects are used to generate tests that recreate the observed production state and contain oracles that specify the expected behavior. Our evaluation demonstrates that this strategy results in improved test quality for the target project.

With the second instantiation of our framework, we observe the invocations of target methods at runtime, as well as the invocations of methods called within the target methods. Using the objects associated with these invocations, we generate tests that use mocks, stubs, and mock-based oracles. We find that the generated oracles verify distinct aspects of the behaviors observed in the field, and also detect regressions within the system.

Third, we adapt our framework to capture the arguments with which target methods are invoked, during the execution of the test suite and in the field. We generate a data provider using the union of captured arguments, which supplies values to a parameterized unit test that is derived from a developer-written unit test. Using this strategy, we discover developer-written oracles that are actually generalizable to a larger input space.

We evaluate the three instances of our proposed framework against real-world software projects exercised with production workloads. Our findings demonstrate that runtime observations can be harnessed to generate complete tests, with inputs and oracles. The generated tests are representative of real-world usage, and can augment developer-written test suites.

The transition to circular construction practices within Sweden’s public housing sector, particularly the reuse of building materials, faces significant barriers despite a growing interest and the practice’s potential environmental and societal benefits. This study investigates the key obstacles and driving forces towards large-scale material reuse among municipally owned housing companies, using Strategic Niche Management (SNM) theory as a framework. Through a combination of literature review and semi-structured interviews with professionals from public housing companies as well as other experts, the research identifies critical barriers such as regulatory limitations, conservative cultural mindset, and technical challenges. Potential driving forces include procurement practices in favor of circular practices, collaborative pilot projects, and increasing industry-level competence. Recommendations to overcome these barriers emphasize the importance of strategic experimentation, clear documentation of built-in materials, proactive collaboration between actors in the value chain, and leveraging existing networks like the Center for Circular Building (CCBuild). The findings emphasize the role of public housing companies as niche managers in promoting a transition towards a more sustainable construction sector, in particular in initiating the innovative experimentation and collaboration to achieve a circular economy through their procurement capacity. Future research should focus on resolving legal uncertainties and warranty issues to further facilitate the reuse of building materials in public construction projects. 

As part of their commitment to the Paris Agreement and the European Union’s climate objectives, banks across the world are setting ambitious goals to reduce the carbon emissions intensity of their mortgage portfolios by 2030, Swedbank being one of them. The real estate sector, which accounts for the largest share of energy consumption in Europe, plays a pivotal role in achieving these targets. However, a significant challenge facing the banking industry is the widespread absence of Energy Performance Certificates (EPCs) in their mortgage portfolios, which are critical for accurately calculating financed emissions. This data gap creates substantial uncertainty in tracking progress toward climate goals. 

This master’s thesis aims to develop a methodology for estimating energy performance ratings for real estate lacking EPCs. The methodology is to utilize Ordinary Least Squares (OLS) regression to estimate EPCs. The methodology developed in this thesis provides a practical tool for banks to predict energy performance based on available data. The study also explores what adjustments are needed within mortgage portfolios to achieve a targeted reduction in financed emissions by 2030. 

The findings demonstrate that OLS regression is an effective method for banks to estimate energy performance in-house, although the accuracy of predictions is highly contingent on the appropriate selection of dummy variables within the model. The research offers valuable insights for the banking sector on how to strategically align mortgage portfolios with long-term climate targets, even in the absence of complete EPC data. 

This study explores how Alfa Laval, a global leader in the marine industry, can reduce the environmental impact of its supply chains, particularly by optimizing transportation and adopting more sustainable logistics strategies. The research focuses on the company’s marine division, where global transportation plays a significant role in its carbon footprint. By analyzing three specific cases of inefficient supply chains and mapping two of the largest product groups within the marine division, the study identifies key areas for improvement. The findings reveal that reliance on air freight leads to unnecessary emissions and costs. The study suggests the implementation of Smart Supply Chain Management and smart logistics solutions, such as IoT and blockchain technology, to enhance transparency, efficiency, and sustainability in the supply chain. Additionally, it recommends restructuring the supply chain to reduce reliance on air transport and relocate production closer to end customers.

Nitrogen is an essential element for life on Earth, it plays a key role in several environmental processes. However, nitrogen oxides (NOx) are strong greenhouse gases that make a major contribution to global warming and climate change as they build up in the atmosphere. It is crucial to tackle NOx emissions in order to mitigate the negative effects on the environment.  The aim of this thesis was to assess the efficiency of the Selective Non-Catalytic Reduction (SNCR) system at the Heidelberg Materials plant in Slite, with a focus on NOx reduction and ammonia use. In order to achieve the aim, the study investigated the current efficiency of NOx reduction and ammonia usage preformed by the SNCR system, followed by a comparison of these findings with the efficiencies reported in the literature. The study also looked into what the result from the measurements could translate to over time. 

The system's performance was evaluated using manual and continuous data measurements. Based on the findings, it was observed that the manual measurements displayed a NOx reduction efficiency of 27%. However, the continuous data indicated a higher efficiency, which is more in line with the efficiencies reported in the literature. However, the continuous data also showed fluctuations in the efficiency over time. A key finding of this study was the difference between the theoretical ammonia requirements and the actual amounts used in the SNCR system. This difference in the ammonia dosing indicate that there could be possibilities for improvement. Enhancing the control mechanisms and operational parameters has the potential to decrease ammonia slip, improve system efficiency, and reduce environmental impact. 

At last, this study offers insights into the performance of the SNCR system at the Slite plant. It also emphasizes the need for additional investigation and optimization to enhance efficiency and minimize ammonia usage. 

In the last few decades, the study of identifying the network topology of multi-agent systems has attracted increasing attention, where the communication topology is not always accessible to controller design or analysis of multi-agent systems. One challenging objective is to generally identify the unknown network structure from the measurements of multi-agent systems and decide where and how to stimulate it to achieve the desired response. In this thesis, we investigate the problem of topology identification for multi-agent systems with unknown topology and the problem of simultaneous topology identification and synchronization of the multi-agent systems. 

In the first part of the thesis, we address the topology identification problem for complex dynamical networks with both unknown constant and switching topology. We propose a finite-time identification scheme that ensures accurate topology estimation by leveraging a finite-time adaptive controller tracking reference signals and providing sufficient excitation. Accurate topology estimation is achieved once a relaxed excitation condition holds. This scheme removes the assumption of linear independence conditions or persistent excitation conditions during the identification process and guarantees the success of accurate topology identification. In addition, we provide a new scheme that achieves topology identification and synchronization in finite time, which provides a solution to combine topology identification and other control tasks.  We adjust this scheme to solve the finite-time topology identification problem for the directed general topological matrix and its extensions for the cases of a symmetric matrix and a Laplacian matrix, thereby broadening its applicability to a wider range of complex networks. With a partial priori knowledge of network structure, adjusted algorithms improve efficiency and reduce computational complexity.  Moreover, we extend the scheme to handle the networks with unknown switching topology, which identifies both the switching instant and graph sequences.

In the second part of the thesis, we propose a novel approach for simultaneous topology identification and synchronization for dynamical directed networks to overcome the conflicting goals of topology identification and synchronization. A new perspective which relies on the edge-agreement framework is presented for the study of the topology identification problem and a new adaptive-control-based topology-identification algorithm based on the concept of $\delta$-persistency of excitation is employed to achieve simultaneous topology identification and synchronization. By the edge-agreement representation, strong stability results for the identification errors in terms of uniform semi-global practical asymptotic stability are provided. In addition, We also extend this adaptive controller-based approach to simultaneous estimation of topology and synchronization in complex dynamical networks with time-varying topology. Our approach transforms the problem of time-varying topology estimation into a problem of estimating the time-varying weights of a complete graph, based on the edge-agreement framework. Two auxiliary networks are introduced to bound the weight estimation errors: one that satisfies the persistent excitation condition to facilitate topology estimation, while the other, a uniform-$\delta$ persistently exciting network, ensures the boundedness of both weight estimation and synchronization errors, assuming bounded time-varying weights and their derivatives.

Adhesive joining technologies have received great praise within industries such as aerospace, transportation, electronics, and mechanical systems, owing to the benefits of lightweight design and durability. Recently, well-established adhesive manufacturing, application, and testing techniques are experiencing increased demand for reliable and effective modelling methodologies. Polymer-based adhesives, which fall into categories such as elastic-plastic, viscoelastic, hyperelastic, viscoplastic, and thermosensitive materials, present significant challenges to structural modelling due to computational complexity and costs.

Currently, novel characterisation methods and modelling techniques for fracture analysis of adhesive joints are readily available. However, in many engineering applications, the precise stress and strain states around propagating cracks within adhesive layers are of lesser importance. Consequently, cohesive zone models have been successfully implemented in most finite element (FE) software, offering cost-efficient methods for simulating the debonding behaviour of adhesive joints. While fracture mechanics and other static/quasi-static analyses of adhesive joints have been extensively studied in recent years, there is an incentive for developing reliable methods for modelling the dynamic response of adhesively bonded structures. In this study, three alternative adhesive layer FE representations for modal-based analyses are discussed and evaluated. Continuum models, phenomenological models, and contact models are described and applied to lap-joint specimens with different adhesive materials and configurations. A vibration testing method for parametric investigation of lap-joints is presented, and all FE models were validated against the test results. Constant material damping and Rayleigh damping are investigated as alternatives to capture the global damping behaviour of the joints.

Results suggest that stiff structural adhesives are adequately modelled using constant material damping or bonded contact representations, whereas softer adhesives require alternative representation and damping techniques, likely due to the associated increased damping and viscous effects. Rayleigh damping proved suitable for capturing the response of soft adhesives; however, it faces challenges in generalising to multi-component structures. Additionally, thinlayer phenomenological representations of adhesive layers in lap joints offered significant time savings compared to in-use bonded contact models. Furthermore, thin-layer models offer the flexibility of material-specific damping.

Forests are essential ecosystems that provide habitats for a wide range of species and deliver ecosystem services of great importance to society. Forests are recognized as hotspots for biodiversity and play a crucial role in efforts to combat climate change. However, recently, forest landscapes have been increasingly affected by intensive forest management, resulting in higher carbon emissions and a shift towards more homogeneous and even-aged forest stands. This transformation not only alters the age structure of forests but also negatively impacts species that rely on diverse forest environments. To assess and monitor the effects of forestry on forest cover, Remote Sensing (RS) and Geographic Information Systems (GIS) have proven to be valuable tools in ecological research.

This study specifically examines changes in forest age structure and biodiversity in the Sörmland region over the past 20 years based on loggings as disturbance regime. The results show that the forest landscape in Sweden is increasingly dominated by younger, homogeneous tree populations, which negatively affects biodiversity and species that rely on ecological characteristics that older forests provide. Furthermore, the study identifies inherent flaws and uncertainties in the methodology applied and the geodata used. In particular, the quality of data sources was found to play a critical role in how the results are interpreted and utilized in decision-making processes. Citizen science, which collects data on species, proved to be especially sensitive to factors such as participant expertise, geographic coverage, and temporal biases. Addressing these uncertainties is therefore crucial for improving future data collection and analysis. 

Finally, the study highlights the importance of using robust methodologies, while also emphasizing the need to expand protected forest areas, implement more sustainable forestry practices, and foster increased collaboration between stakeholders. These measures are necessary to prevent potential biodiversity loss and to ensure the long-term resilience of forests and the continued provision of their critical ecosystem services.

The objective of the thesis was to create a finite element (FE) model for the boat CANDELA P-12 which can be used to analyze various impact scenarios. The boat's structural components are primarily constructed from quasi-isotropic carbon fibre reinforced polymer (CFRP) laminates. First, an analytical model was developed to assess the in-plane elastic and failure properties of a laminate made from CFRP. This model was used to evaluate the isotropy of a quasi-isotropic laminate. The findings suggested that for a quasi-isotropic CFRP laminate, the in-plane properties exhibit minimal variation with different angles in the laminate plane. However, the error in the bending stiffness of an isotropic model for the CFRP with equivalent stiffness was found to be large when the laminate includes a core of a significant thickness. Subsequently, the FE model was developed. For simplicity, the CFRP laminates in the boat were modelled using an isotropic material model. The FE model's sensitivity to the tensile strength of the isotropic CFRP material model and the viscoplastic material properties of the ductile materials in the boat were studied in terms of the acceleration of the passenger deck and global damage mode. The tensile strength of the CFRP model and the consideration of viscoplastic material behaviour had little impact on acceleration and the global damage mode.

Cities and municipalities play a pivotal role in the transition towards a low-carbon economy. Local climate actions by municipalities are often more readily implemented than those at national or global levels. To develop effective municipal climate change mitigation strategies, it is imperative to obtain high-quality greenhouse gas (GHG) emissions data, which serves as the foundation for robust GHG inventories. 

Despite increasing interest and advancements in recent years, disparities in GHG inventory approaches among municipalities persist, hindering accurate comparisons and consistent accounting. Currently, there are no well-established global standards for municipalities to accurately account for GHG emissions at the local level. This study aims to enhance the understanding of climate inventory and accounting practices at the municipal level by examining existing policy frameworks across various levels and identifying key GHG emission inventory procedures and approaches through comprehensive literature reviews. Additionally, it investigates whether current municipal GHG inventory practices in Sweden effectively support municipalities in meeting their emission reduction goals and mitigation targets, utilizing expert interviews at both national and municipal levels. 

The study highlights the crucial role of municipalities in Sweden’s climate initiatives and emphasizes the importance of reliable GHG inventories in informing policy decisions and enabling municipalities to develop and implement robust reduction strategies. While current GHG inventory frameworks serve as useful preliminary indicators, they fall short of fully supporting municipalities in consistently addressing their reduction efforts. A globally or nationally agreed-upon GHG inventory standard for municipalities is necessary to ensure data availability and enhance data collection capabilities. Achieving this requires collaborative efforts among stakeholders at all different and the continuous improvement of GHG inventory methodologies.  

The transition to electrified road transportation is crucial for achieving sustainability goals and reducing greenhouse gas emissions. However, therapid adoption of battery electric vehicles (BEVs) depends heavily on the availability of a robust charging infrastructure. Effective charging infrastruc-ture planning faces numerous challenges stemming from deep uncertainties inherent in transport electrification. These uncertainties encompass aspectssuch as rapid technological advancements, the variability of technology adoption and behavioral changes, the shifting landscapes of regulations, policiesand subsidies, the variability in availability, cost, development lead-time for grid transmission capacity, real-estate, and related services, and the evolving market dynamics arising from competition. This thesis examines the complexities of charging infrastructure planning, addressing two critical knowledge gaps identified in the literature: the inadequate utilization of transport route information in charging network placement optimization and the lack of planning methods and tools that can help manage the uncertainties.

To this extent the research presented in this thesis, after a quick background on transport electrification needs, challenges, current status and future ambitions, analyzes charging infrastructure planning support needs with respect to the knowledge gaps. Specifically, through a clear example, the thesis argues for the need to adopt a demand-centric charging network design where the adequate use of transport route information to achieve logical network design objectives is undeniable. Moreover, quite logically, the thesis argues the use of a dynamic adaptive planning approach that requires interactive decision support tools to manage the deep uncertainties of transport electrification during the system transition. Then, through a broad and systematic literature review, the thesis establishes that the prior work has not fully addressed these planning needs.

To extend scientific knowledge on the adequate use of transport route information in demand-centric charging network design, the thesis formalizes a data-driven simulation based transport electrification scenario model and in it an incremental charging network placement optimization problem where both the model and the problem require the use of detailed information in the transport routes. Then, the thesis proposes a series of greedy network expansion based charging network placement optimization methods to tackle the combinatorial network design problem and the incremental planning support need. First a set of baseline methods are introduced that guide the exploration of the search space using inaccurate but easily pre-computable static demand proxies and attempts to correct inaccurate guidance of the proxies by enforcing spatial constraints on the network placements to avoid the demand losses within the network and increase the electric coverage provision of the network. Next, the thesis proposes the Route Based Network Demand (RBND) method that does not use approximations or heuristics but rather selectively recomputes via computationally demanding simulations the exact values of the objective functions for promising network expansion candidates during the search space explorations. Empirical evaluations assess the methods’ optimization quality, empirical optimality, sensitivity to model parameters, and runtime scalability. The results show that the RBND method outperforms the baseline methods in optimization objectives and, among a practically infinite number of possible solutions, identifies statistically provable near-optimal solutions within minutes.

To address the lack of planning methods and tools that can help manage the uncertainties, the thesis contributes to scientific knowledge in two ways.  First, the thesis proposes the parameter sensitivity analysis of optimized charging network placements and as a case study explores and aggregates the optimized network placement information for 324 combinations of 5 key transport electrification scenario parameters as an attempt to derive the likelihood that a given location is part of an optimized network and what is the average charging demand at that location across all tested scenarios. Notably, the sensitivity analysis methodology entails the selective evaluation and simulation of the entire Swedish road freight system with 10.5 million annual transport routes for the most promising few million charging network placements for each of the 324 transport electrification scenario parameter combinations. The results of this vast charging network placement- and scenario search exploration are presented in a single table and two maps that represent frequency- and the average demand distributions of locations in optimized networks. Second, motivated by the argument that charging networks are developed by individual economic actors with their unique opportunities, challenges, and strategies in a competitive environment, based on qualitative feedback from 33 stakeholder organizations, the thesis describes the design, the components, and the visual analytics features of a flexible, spatial decision support system that can support the dynamic adaptive planning in stakeholders’ competing and collaborative settings.

Together, these contributions provide a scalable and flexible framework to support the planning and deployment of sustainable and resilient charginginfrastructures, while addressing uncertainties and enabling dynamic adaptive planning to meet the evolving requirements of electric transport.

Finally, the thesis points to three distinct future research directions. First, the extension of the methodologies and systems is called for to provide plan-ning support for charging network designs that include a mix of electrification techniques, e.g., dynamic charging on electric roads and static chargingon stations. Second, with a similar methodology as the developed sensitivity analysis, the thesis highlights the importance of network resilience and callsfor the methods that evaluate and integrate network resilience in the designs. Finally, realizing that regardless of the sophistication of long-term strategic planning not all uncertainties can be mitigated, the thesis calls for methods and tools that can increase the operational and cost efficiency of charging networks and electrified transports on them.

The rapid expansion of the global e-commerce market, with Sweden's e-commerce accounting for 15% of the total retail sector, has led to increased GHG emissions, especially during the last-mile delivery phase, the most environmentally detrimental segment of the distribution chain. Last-mile delivery options in Sweden, such as home delivery, delivery to service points, and stationary parcel lockers, vary in their environmental impact, primarily measured by GHG emissions. While research has predominantly focused on urban areas, there is a growing need to understand the environmental implications in rural regions where e-commerce is also expanding. 

This study investigates the carbon footprint of last-mile delivery options in rural Sweden, comparing home delivery with delivery to collection points and factoring in customer collection trips. Using data from Early Bird, a Swedish distribution company, the carbon footprint of both delivery methods was calculated. Survey data was used to estimate the average carbon footprint of consumer trips to the nearest collection point. Findings indicate that home delivery is more environmentally efficient in rural Sweden, a conclusion that gains importance as competition and stricter requirements from e-commerce companies are likely to drive down parcel delivery emissions in the coming years. 

Sweden has significant potential to develop sustainable aviation fuels for military aviation and enhance supply security. Electrofuels and forest-based biofuels hold promise, leveraging abundant raw materials. Lifecycle analyses demonstrate their potential to reduce emissions compared to fossil fuels, yet obstacles such as political instability and infrastructure collaboration must be addressed. Establishing new value chains and infrastructure for access to carbon dioxide and renewable electricity is essential for sustainable production. Overall, the study underscores the potential of electrofuels to mitigate emissions in the aviation sector, contingent upon collaborative efforts, resources, and political stability for large-scale implementation. 

Exoskeletons hold much promise for humanity in their capacity to off-load movement and aid in physical rehabilitation. Off-board actuators have been used to accelerate the development of active exoskeletons, bringing them closer to a reality outside of the labs. An off-board actuation module for exoskeletons is designed and modeled in this project, implementing some of the safety guidelines provided by the medical device standard most applicable at the time, IEC 80601-2-78. Following stakeholder provided design requirements led to a modular design that facilitates expansions and modifications. The module provides two degrees of freedom, enabling bidirectional control of a single joint or unidirectional control of two joints through different motion control modes. The modeled actuator takes joint torque profiles as inputs into the developed torque controller. Simulations were performed that compared torque tracking accuracy for different cases with the goal of verifying the selected motors in the physical prototype. An iterative learning algorithm was developed for the purpose of reducing torque tracking errors and was verified in simulations. An investigation on the regulatory framework available to exoskeleton developers was also done in parallel to the design of the module in this project. The investigation indicated that developer satisfaction towards the regulatory standards can be improved and that having distinct standards documents, based on device application field, is more often preferred over generalized documents.

This paper surveys 560 publications about music genre recognition (MGR) published between 2013–2022, com- plementing the comprehensive survey of [474], which cov- ered the time frame 1995–2012 (467 publications). For each publication we determine its main functions: a review of research, a contribution to evaluation methodology, or an experimental work. For each experimental work we note the data, experimental approach, and figure of merit it ap- plies. We also note the extents to which any publication engages with work critical of MGR as a research problem, as well as genre theory. Our bibliographic analysis shows for MGR research: 1) it typically does not meaningfully engage with any critique of itself; and 2) it typically does not meaningfully engage with work in genre theory. 

This paper (a product of artistic research) engages with the following challenge: combine publicly available generative AI tools to simulate a commercial radio station, complete with dialogue, news and advertisements, and music programming. Our five success criteria for the “station” are: 1) it runs autonomously; 2) it features diverse content; 3) its content is generated and assembled in faster than real-time; 4) it sounds like commercial radio; and 5) it is engaging for longer than its novelty factor. We consider a variety of generative AI systems for text and dialogue, synthesizing expressive speech, and generating music audio. We describe our engineered pipeline and illustrate its components with several audio examples. We compare our results to other “endless” streams of content. Our resulting stream — “Stochastic Pirate Radio (KSPR)” — can be heard here: https://www.youtube.com/@KSPRStochasticPirateRadio.

A text-to-music (TTM) model should synthesize audio that reflects the concepts in a given prompt as long as it has been trained on those concepts. If a prompt references concepts that the TTM model has not been trained on then the audio it synthesizes will likely not match. This paper investigates the application of a simple gradient-based approach called textual inversion (TI) to expand the concept vocabulary of a trained TTM model without compromising the fidelity of concepts on which it has already been trained. We apply this technique to MusicGen and measure its reconstruction and editability quality, as well as its subjective quality. We see TI can expand the concept vocabulary of a pretrained TTM model, thus making it personalized and more controllable without having to finetune the entire model.