Dynamic contrast-enhanced MRI (DCE-MRI) is widely used for breast cancer diagnosis and treatment monitoring, but repeated injections of gadoliniumbased contrast agents (GBCAs) raise safety, and environmental concerns. This thesis investigates whether contrast-free, multi-sequence breast MRI can be used to synthesize multi-phase DCE-like images under realistic data constraints. We develop a conditional generative adversarial network (cGAN) based on Pix2PixHD that maps pre-contrast T1-weighted images, diffusionweighted imaging (DWI) at multiple b-values, and apparent diffusion coefficient (ADC) maps to several post-contrast phases in a single forward pass. To emphasise enhancement dynamics rather than absolute intensities, we introduce a multi-scale subtraction consistency (MSSC) loss that compares pre-/post-contrast subtraction maps across multiple image scales.

Experiments on public breast MRI datasets show that the proposed MSSC model with full multi-sequence input improves standard reconstruction metrics (PSNR, SSIM, LPIPS, lesion-wise RMSE) compared with strong Pix2PixHD baselines. Lesion-level enhancement curves and qualitative assessments further indicate better preservation of tumour morphology and parenchymal texture with fewer artifacts. These results suggest that, given limited public data, a contrast-free, multi-sequence cGAN with scale-aware subtraction supervision can synthesize multi-phase DCE-like breast MRI that is competitive with real DCE-MRI while more faithfully capturing lesion-level enhancement behaviour.

Over the past few decades, code reuse attacks have shown how malicious actors can alter a program’s intended execution flow by taking advantage of benign code already present in the application. Class Pollution in the Python programming language is a novel variant of a code reuse attack, which can enable a malicious party to surgically mutate a variable in any part of the application in order to trigger a change in its execution flow. However, until now, little to no research has explored class pollution in detail, and no tool is readily-available to detect it. For this reason, as part of this degree project, a literature review on the causes and consequences of class pollution has been conducted, in addition to the methodical development of a tool capable of detecting class pollution, Classa. Additionally, an empirical study on the prevalence of class pollution in realworld Python code has been performed by running Classa against a dataset of 3000 Python projects, revealing, most notably, a critical vulnerability in a popular PyPI package with more than 30 million downloads. This vulnerability allowed for Denial of Service and Remote Code Execution, having since been responsibly disclosed and patched. Altogether, the results revealed that while not many real-world Python projects are susceptible to class pollution, it is a vulnerability that must be accounted for when building a secure application due to the serious consequences it can lead to.

Future wireless networks face growing energy demands due to dense infrastructure and computationally intensive processing. Cell-Free Massive Multiple-Input Multiple-Output (CF-mMIMO) has been recognized as a promising architecture to enhance user fairness and spectral efficiency through joint transmission among distributed Access Pointss (APs). However, largescale deployment of CF-MIMO with optical fronthaul introduces high endto- end power consumption across radio, fronthaul, and cloud domains. More recently, Open Radio Access Network (O-RAN) allows controlling radio and cloud resources jointly, allowing potential energy savings. To reduce the energy consumption of CF-mMIMO, this thesis considers CF-mMIMO on top of O-RAN architecture, and proposes a Reinforcement Learning (RL)-based framework for energy-efficient joint radio, fronthaul, and processing resource allocation in centralized CF-MIMO networks. This study first models the end-to-end power consumption of centralized CF-mMIMO, demonstrating that the power consumption in both cloud and radio dominantly depends on active antenna ports and the number of active radio units. Then, Proximal Policy Optimization (PPO) algorithm is designed to learn near-real-time control policies at the near-real-time RAN Intelligent Controller (near-RT RIC), dynamically adjusting active antennas based on long-term channel characteristics to minimize total network power while maintaining users’ Spectral Efficiency (SE) requirements. To enhance the performance, a greedy post-processing stage is developed to further prune redundant activations without compromising service quality. Simulation results show that the proposed RL-based controller achieves over 50% total power reduction compared to the full activation scheme, and around 15% reduction over the heuristic baseline. Specifically, by integrating a greedy strategy with the RL-based control, the total power consumption can be further reduced by an additional 50%. Moreover, the trained model performs well to different user densities and SE targets, demonstrating high robustness and scalability. Its inference time is at the millisecond (ms) level, offering a significant speed advantage over traditional optimization methods. The joint orchestration of radio and processing resources reduces the O-Cloud power consumption by 60%, demonstrating the significant energy-saving potential of the proposed end-to-end control framework.

Platform engineering teams in large enterprises face growing operational challenges due to the increasing scale and complexity of cloud-native systems. A significant portion of their workload consists of repetitive workflows, such as repeatedly searching logs, consulting documentation, and updating incident tickets. These tasks consume valuable engineering time, fragment knowledge across multiple systems, and delay efficient problem resolution. This thesis addresses this problem by designing and implementing an AI-driven chatbot that automates repetitive operational workflows through a conversational interface. The system is built on a LangGraph-based orchestration framework that coordinates a master-agent workflow, routing queries to specialized tools: Elasticsearch for log retrieval, Qdrant as a vector database for semantic documentation search, and ServiceNow for incident management. A continuous embedding pipeline, based on HuggingFace models, ensures that the knowledge base is regularly updated with documentation, logs, and historical incident data. The contribution of this work lies in providing a unified, context-aware interface for platform engineers that not only retrieves information but also executes repetitive workflows automatically. By integrating knowledge management with workflow automation, the system reduces the need for manual effort in operational tasks and supports more efficient platform operations. The solution is evaluated through deployment in an enterprise case study at Volvo Cars, demonstrating its potential to improve workflow efficiency, reduce repetitive work, and enhance the overall effectiveness of platform engineering teams.

The decarbonisation of European industry coincides with rising shares of variable renewable generation and increasing electricity prices, making industrial load flexibility a critical resource for cost-efficient, low-carbon operation. Yet many planning tools still treat demandside management in coarse, system-level terms and cannot represent the plant-specific operating rules, inter dependencies, and validity windows that determine what flexibility is actually realizable in factories. This thesis proposes a simulation-based planning framework that embeds a fine-grained model of Energy Flexibility Measures (EFMs) into an opensource environment. The formulation captures bidirectional load changes, hold and clean-up phases, recovery and minimum activation windows, cooldown dynamics, one-to-one pairings, and group-level exclusivity at 15-minute resolution, and couples them with dynamic tariffs, peak-power charges, and on-site assets such as photovoltaic (PV) system and battery storage. The framework is implemented as a modular Mixed Integer Linear Programming (MILP) component and evaluated for two real industrial systems: an oven-dominated diecasting plant and a site-infrastructure-oriented system with ventilation, electric vehicle (EV) charging station, PV system, and battery storage. For each system, multiyear simulations (2019–2024) compare baseline operation with and without EFMs under four tariff designs: static prices, static prices with peak shaving, day-ahead prices, and day-ahead prices with peak shaving. The results show that explicit EFM modeling systematically reshapes load toward low-price periods, reduces grid peak demand, and yields consistent grid-cost savings and, in most years, CO₂ reductions while respecting process and staffing constraints. Batteries are found to complement rather than replace demand response, primarily shifting midday PV surplus into higher-value evening hours, and peak-shaving charges can be reduced without increasing annual consumption. Overall, the thesis contributes a transparent, Energy Flexibility Data Model (EFDM) inspired EFM module and data layer, alongside a reproducible 15-minute planning workflow. It calls on industrial planners and tool developers to integrate structured, plant-level demand-side flexibility into energy system planning and tariff design so that future factories can co-optimize production and energy use rather than treating flexibility as an afterthought.

The goal of the project presented in this report was to implement the photons as aprojectile in the Liège Intranuclear Cascade model (INCL), elaborated to simulate nuclearreactions. The energy of interest ranges from a few MeV up to 300 MeV. For this purpose,it is essential to understand the structure of the model, how it works and all its finedetails. The complexity is to describe the interactions that take place when the incidentparticle interacts with the nucleus. Two main mechanisms need to be taken into accountfor the realisation of the project. At low energies (below 30 MeV), the Giant DipoleResonance is supposed to be dominant. Going up in energy, a smooth transition towardsthe Quasi-Deuteron model must be implemented. This report is also dedicated to thevalidation of the photo-nuclear reaction model through comparisons with experimentaldata available on databases. Globally, the model predictions are consistent with theexperimental data. Looking at specific data, some discrepancies appear. One of themencouraged the implementation of the Delta production mechanism for photons withenergies higher than 150 MeV while others are probably due to missing mechanisms inthe photo-nuclear reaction model. The INCL predictions are also analysed in light ofexperimental data on 209Bi obtained via laser-based accelerators by collaborators fromthe University of Valencia, Spain.

Denna rapport presenterar resultaten från forskningsprojektet "Sårbarheter och hotanalys från sidokanaler med maskininlärning i fokus", finansierat av Myndigheten för civilt försvar (MCF) Projektet har genomförts i samarbete mellan KTH Royal Institute of Technology och Lunds universitet.

Projektets övergripande ambition har varit att öka kunskapen om fysiska attacker mot digitala enheter, med särskilt fokus på sidokanalsattacker förstärkta med maskininlärningstekniker. Genom att systematiskt studera nya attackvektorer, utveckla metoder för sårbarhetsanalys och utvärdera motåtgärder har projektet syftat till att bidra till ett mer proaktivt arbete med hårdvarusäkerhet.MCF:s stöd har varit avgörande för att möjliggöra långsiktig och explorativ forskning inom detta strategiskt viktiga område. Vi vill särskilt tacka Erik Sundström, som varit projektets kontaktperson och handledare vid MCF.

Vi hoppas att resultaten som presenteras i denna rapport ska vara till nytta inte bara för forskarsamhället utan även för beslutsfattare, systemutvecklare, utvärderare och andra aktörer som arbetar med att skydda samhällsviktiga funktioner.

This degree project comprises 15 credits and was carried out within the programme in Industrial Technology and Production Maintenance at KTH Södertälje in collaboration with Coca-Cola Europacific Partners in Jordbro during the autumn semester of 2025. The study focuses on the end of line section of the production process, where finished beverages are grouped and wrapped before being sent to the warehouse and before being sent to the customers. The parts of the facility examined include the wrapping robots and the pallet conveyor between the infeed and the pallet labellers.

The purpose of the study was to analyse the causes of production disturbances in the end of line area and to develop improvement proposals that could contribute to increased operational reliability and a reduced number of unplanned stoppages. To map the current situation, semi-structured interviews and Gemba Walks were conducted. Operational and failure data were collected from SAP, while trend data and information on machine faults were gathered from LineView. The collected material was analysed using methods such as Pareto analysis, Ishikawa, 5 Whys, FMEA and a simplified RCM approach.

The results showed that the end of line area is a part of the production process where maintenance work is less developed than in other sections, despite recurring disturbances and machine faults affecting availability. The study resulted in proposed improvement measures and a recommended maintenance strategy focused on preventing recurring faults, improving disturbance follow-up, and creating better conditions for a more systematic maintenance approach.

Today, there are several uses for satellites, one of them is communication. Having the ability send audio, video, or other types of data through electromagnetic waves, people all around the globe can communicate with each other. This kind of technology enables a lot of possibilities, and there exist both stationary and mobile communication. Ovzon AB is a Satellite Communication company which works with both these solutions. This Master thesis will be focusing on their SATCOM on the Move, SOTM, solution. It uses sensor fusion to combine multiple sensor data in order to track the antenna mounted on the vehicle. This technology has been around for a while, but the sensors in the system are still susceptible to disturbances. The scope of the project is to compare different sensor combinations through Unscented Kalman filter against the common combination INS and GNSS. The results indicate that there are differences between the sensors. However, the result also shows that there were some outliers depending on which sensor combination that was used. Overall, the conclusion of the thesis is that there were two groups which improved the antenna performance, the GNSS + Gyroscope and GNSS + Gyroscope + Accelerometer.

Moreover, this project has given more insight into design choices as well as optimization alternatives.

The increase in the number of simultaneous proton-proton collisions, pile-up interactions, will be a major challenge for the future High-Luminosity phase of the Large Hadron Collider (LHC) at CERN, making pile-up mitigation an important issue for the ATLAS experiment. The High-Granularity Timing Detector (HGTD), to be installed in the upcoming years, is one of the detector upgrades contributing to this pile-up mitigation. To report the current status of the HGTD development, an HGTD test-beam analysis was performed using data taken during two weeks in October 2025 at the CERN SPS test-beam facility. Synchronization between different detector systems in the test-beam environment was achieved. The full data reconstruction chain was demonstrated to be feasible although the measured hit efficiency and timing resolution were limited by non-optimal chip tuning including a very high threshold setting, as well as by low statistics. To study the effect of pile-up, the impact of jet pile-up contamination on the jet flavor-tagger was investigated under current LHC running conditions. A substantial degradation in flavor-tagging performance was seen, highlighting the importance of understanding pile-up effects in jet reconstruction. A supplementary timing-based pile-up mitigation study was also performed.

Railway infrastructure requires a more circular use of materials, but much research still focuses on single metrics and assumes that higher recycling rates are always better. This paper constructs a simple and consistent framework for evaluating recycling strategies for three railway assets (sleepers, rails, and overhead contact lines). The framework covers four dimensions: material utilization, greenhouse gas emissions, energy consumption, and financial costs. It follows the EN 15804 life cycle structure (Modules A-D) and links all metrics to the basic material flow. Recycling rate (R) and reuse or maintenance rate (U) are used as the primary decision variables.To avoid unrealistic results at extremely high recycling or reuse rates, the model introduces penalty functions. These penalties represent additional effort and losses, such as more complex dismantling, lower waste quality, or higher processing energy consumption. All metrics are standardized and can be combined through a simple weighted scheme, allowing for comparison of different strategies on the same scale.Case studies show that the optimal range for R and U largely depends on the asset type. For railway sleepers, moderate steel recycling and low reuse rates already offer significant benefits in terms of material utilization and emissions, while pushing recycling rates close to their maximum leads to higher costs and fewer additional benefits. Rails can more easily achieve higher recycling rates due to well-established scrap and rerolled steel routes. Overhead contact wires are primarily made of copper, a material that is highly recyclable in principle. However, in practice their effective recycling rate remains narrow because dismantling, contamination, and alloy degradation significantly constrain recoverability. As a result, low recycling rates fail to capture substitution benefits, while excessively high rates increase energy use and costs.Overall, the results indicate that higher recycling rates are not always better in practice. Recycling targets should be set based on asset type, rather than a single value for the entire railway system. This framework provides a practical approach to testing different recycling and reuse levels and supports procurement and maintenance decisions that balance material utilization, climate impact, energy demand, and costs.

This study presents the development and validation of a fast, iterative design workflow for a new class of fully electric long-range delivery drones that combine simultaneous vertical take-off and landing (VTOL) capability with high cruise efficiency in a fixed-wing configuration. With a growing presence of unmanned aerial vehicles (UAVs) in today's world, efficient and reliable design methodologies become increasingly important to decrease costs and accelerate development, especially in startup environments. The proposed workflow integrates multi-objective optimization of the drone's geometry with an aerodynamic analysis of increased fidelity level through computational tools. Rapid potential flow solvers are employed for preliminary design, followed by computational fluid dynamics (CFD) simulations of greater accuracy for further refinement, advanced analysis and validation. To test the speed and modularity of the workflow, two different UAV geometries are designed and optimized with the potential solver VSPAERO, then further analyzed with advanced CFD models in Ansys Fluent. The results obtained demonstrate that this approach significantly reduces design time and usage of computational resources compared to CFD-only methods, while maintaining accuracy and scalability for further iterations.

A major challenge in developing connected and automated vehicles~(CAVs) for urban environments is achieving a comprehensive understanding of the surrounding traffic scene. This relies on situational awareness, defined as the ability to perceive, interpret, and anticipate the behavior of surrounding road-users, which is essential to ensure safety. In particular, unprotected road-users, such as pedestrians and cyclists, are often occluded or located in sensor blind-spots of the CAV, which remains a critical challenge. This thesis aims to improve the situational awareness of the ego-vehicle, the CAV of primary interest, in urban environments by leveraging vehicle-to-everything (V2X) communication to incorporate information from connected road-users. A framework using set-based methods is developed to systematically handle uncertainties in measurements and initial conditions of detected pedestrians.

The objective is to address several key challenges that arise in real-world scenarios, including data inconsistency, data association, pedestrian motion prediction, and efficient reduction of redundant information. The thesis first proposes a shared situational awareness framework for occluded pedestrian-crossing scenario to compute an estimated set for the pedestrian. The framework is extended to handle measurements from V2X units that may be inconsistent with the ground truth of the detected pedestrian. To address scenarios involving multiple occluded pedestrians, a data association method based on intersection-over-union heuristics is introduced. Pedestrian motion prediction is further studied using both a data-driven approach and a bounded velocity–acceleration model applied to the estimated set obtained from the framework. An occlusion-aware extension is also developed to handle situations where occlusions affect both the ego-vehicle and V2X units by exploiting previously observed measurements. Finally, a method for selecting and filtering relevant information from multiple V2X units is proposed to reduce the computational load while maintaining effectiveness. The proposed methods are validated through numerical simulations and real-world experiments using Scania prototype automated vehicles.

The structural certification of transport category aircraft mandates a rigorous demonstration of static strength and stability for all primary load-carrying components. This thesis presents the development and validation of ASTRA (Airframe STRuctural Analysis), a computational framework designed to automate the transition from linear Global Finite Element Models (GFEM) to margins of safety for metallic semi-monocoque structures.ASTRA implements a hybrid analysis methodology applicable to any skin-stringer-frame assembly, integrating linear elastic internal loads with non-linear stability correlations. By capturing complex stiffened panel interactions ASTRA enables rapid identification of critical failure modes across large structural assemblies.The tool contributes to a more efficient, effective, and comprehensive airframe structural analysis, offering a faster and less error-prone approach than traditional methods. It helps identify the most critical regions, drives design decisions, and facilitates compliance demonstration. Additionally, ASTRA can assist in devising the structural load applicable at a given fuselage reference station by checking which are the maximum loads that yield positive margins of safety.Developed in a modular way, ASTRA allows each failure mode method to be used independently and can be further expanded to include new failure modes, alternative methods, and extensions to address more detailed FEMs, potentially broadening its scope significantly.

Freshwater availability is a critical planetary boundary currently under threat from intensified climate change and agricultural over-exploitation. Agriculture accounts for over 70% of global water use, yet the ability to monitor crop water consumption accurately remains a significant challenge. Satellite remote sensing has become an essential tool for monitoring agricultural water use at regional to global scales, primarily through the estimation of evapotranspiration (ET). However, most operational ET studies rely on moderate-resolution satellite missions such as the Landsat program or Sentinel-2, which may limit the detection of short-term dynamics and field-scale variability in agricultural systems.

This thesis, conducted at KTH Royal Institute of Technology in collaboration with Planet Labs, evaluates the operational potential of three distinct evapotranspiration (ET) modeling frameworks: STIC (Surface Temperature Initiated Closure), PT-JPL (Priestley-Taylor, Jet Propulsion Laboratory), and GLEAM (Global Land Evaporation Amsterdam Model). The objective of this research is to assess how these widely used ET models perform when driven by high-frequency commercial satellite observations and to investigate whether combining their outputs can improve the reliability of agricultural water use estimation across diverse climatic conditions. By leveraging Planet's unique satellite constellation, this study explores the potential of high-temporal-resolution Earth observation data to enhance evapotranspiration monitoring at the field scale. This research benchmarks these models against in-situ AmeriFlux data across diverse climate zones in North America between 2020 and 2024.

Results indicate that the PT-JPL model demonstrated the highest overall consistency (R²: 0.64 and RMSE: 1.04 mm/day), driven by its robust eco-physiological scaling. While STIC proved highly accurate during peak biomass, but vulnerable to thermal inputs from bare soil during early growth. Conversely, GLEAM provided the most robust performances over the different climates, though sometimes underestimating the transpiration rates of mature crops.

A central contribution of this research is the development of a multi-model ensemble, which significantly improved validation metrics (R²: 0.68, RMSE: 0.96 mm/day) by mitigating individual model biases. The study further identifies climatic dependencies, showing higher accuracy in subtropical and continental climates compared to arid regions, where GLEAM emerged as the most resilient framework. By translating these biophysical fluxes into actionable Water Stress and Deficit Indices, this thesis demonstrates a clear bridge between satellite-based modeling and practical irrigation scheduling. Beyond model benchmarking, the results highlight the potential of high-frequency commercial satellite constellations to support operational agricultural water monitoring within the fields of geoinformatics and remote sensing. This research provides a foundation for scalable to similar climates, model-driven tools that can optimize agricultural water use, ensuring both economic viability for farmers and ecological sustainability in a water-constrained future.

Modern cellular networks have gotten more complex over the years, transitioning from sparse macro-cell deployments to ultra-dense, heterogeneous systems. In this thesis we consider a radio resource management (RRM) problem called remote electrical tilt (RET), in particular. The objective in RET opimization is to tune antenna tilt parameters in the network to allocate radio resources where they are the most needed. As cellular networks evolve toward 6G, we expect an unprecedented increased need for autonomous decision making in the networks, introducing new coordination challenges exacerbated by the denser networks. Traditional network management has been reliant on manual engineering and rule-based heuristics and is insufficient for the needs of the next generation as it scales poorly. While Multi-Agent Reinforcement Learning appears as a promising tool for autonomously adapting the network, currently deployed solutions often struggle with the large scale of the problem. Additionally, they fail to provide formal guarantees, and remain limited by myopic and step-wise reward structures that cannot capture complex constraints communication service providers (CSPs) may impose on the network. Lacking these attributes holds back deployment in live networks beyond small scale pilot studies.

This thesis proposes a series of approaches that aim to provide high-assurance autonomous network parameter control. The contributions progressively build on each other from spatial interference coordination to long-horizon, risk-aware planning to satisfy CSP network intents. First, we address the myopic constraints by leveraging graph-based decomposition and coordination graphs to factorize the joint action space, enabling scalable \textit{constrained} learning in dense urban environments. Recognizing that critical infrastructure demands reliability beyond mean performance, we also introduce a risk-aware constrained learning framework utilizing Conditional Value-at-Risk to provide probabilistic reasoning over constraints in the network.

To bridge the gap between low-level control and high-level CSP intents, we transition from scalar rewards to formal specifications. We utilize Signal Temporal Logic (STL) and transformer-based architectures to satisfy complex intents, enabling agents to reason over  long-horizon requirements. Finally, we move beyond traditional control policies toward generative planning of trajectory rollouts. We aim to enable the generation of safe, high-quality plans that respect hard constraints with probabilistic guarantees by using diffusion probabilistic.

The proposed methods are evaluated on high-fidelity simulators modeled after real-world urban topologies. The results demonstrate that by integrating structural coordination, formal logic, and generative modeling, it is possible to address many of the issues that plague contemporary autonomous network management. The policies that are obtained by these approaches are not only high-performing but also interpretable, safe, and aligned with the rigorous demands of next-generation telecommunications infrastructure.

This thesis investigates the structural response of reinforced concrete elements subjected to blast-type loading using advanced finite element modelling. In engineering practice, blast-resistant design is often performed using simplified equivalent static methods, which may not fully capture the transient behaviour of structures subjected to short-duration loads.The objective of this work is to evaluate the differences between equivalent static approaches and nonlinear dynamic time-history analyses for reinforced concrete structures. A numerical modelling framework was developed in the SOFiSTiK finite element environment. Blast pressure-time histories were generated using empirical relations from the Kingery–Bulmash and UFC 3-340-02 formulations and implemented as time-dependent surface loads.The modelling approach was first verified through comparison with published numerical and experimental studies involving reinforced concrete beams and slabs subjected to blast loading. The methodology was then applied to two engineering case studies: the blast response of an anaerobic digester structure and the accidental drop of a heavy container on a reinforced concrete slab.The results show that nonlinear dynamic analyses capture the transient interaction between load evolution and structural deformation more realistically than simplified static approaches. While dynamic simulations often predict larger displacements, they generally lead to lower internal forces due to energy dissipation mechanisms. This behaviour highlights the limitations of equivalent static methods and demonstrates the potential of nonlinear dynamic analysis to provide more realistic and less conservative structural assessments.The study provides a practical framework for implementing blast loading in SOFiSTiK and illustrates the benefits of dynamic modelling for engineering design applications.

The primary objective of this master’s thesis was to automate the seismic design and analysis cycle of buildings. The automation of the building models was performed using the computer-aided design (CAD) software Rhinoceros and its implemented visual programming environment Grasshopper. Additionally, one of the third-generation programming languages, C#, embedded within Grasshopper, was used to develop custom components to export the building models to external structural and seismic analysis software, ETABS. The seismic analyzes were performed in ETABS using the nonlinear time-history analysis (THA) method, with strong ground motion recordings from the 2023 Kahramanmaraş (Türkiye) earthquake obtained at the station in Narlı, Pazarcık, in Kahramanmaraş. The guidelines for conducting seismic analyzes specified in the European seismic building codes and regulations, Eurocode 8 (2004), were primarily used. However, for certain aspects, such as the selection of material and structural properties and coefficients related to dynamic analysis, some of the guidelines specified in the Turkish Building Earthquake Code (2018) were adopted.A further objective of this study was to compare different lateral force resisting systems (LFRS), with a focus on configurations consisting of shear walls. In total, five configurations were compared, where these differed in terms of shear wall placement, including locations at the building periphery, such as corners, edges, and inner parts, and also at the center of the building, and combinations of these. Moreover, different shapes of shear walls were constructed, such as L-shaped, rectangular-shaped, and box-shaped, the latter representing the shear core located at the center of the building. The performance of these configurations was evaluated from different perspectives, including building height, where a variety of total floors were examined, and the column length on the ground floor, for which the same column length as and a longer column length than other floors were investigated. Nevertheless, in this study, a total of 70 building models were analyzed, all of which had the same material and structural properties, except for the length of the shear walls.The results indicated that the shear wall configuration with only the shear core performed best in contrast to other configurations, when comparing the result outputs such as story displacement, story drift, story shear, and story stiffness. At the same time, this configuration also exhibited the highest natural frequencies, where one of the building models experienced resonance due to the earthquake’s close match of frequency content. It was also implied that the shear walls located along the building periphery were more efficient in decreasing the base shear force demand. However, the configuration with only the shear core has shown a higher shear resistance. Another crucial finding was that the lengths of the shear walls that define the shear core in the L-shaped and rectangular-shaped shear wall configurations were insufficient, as there was no significant impact of the shear core on increasing the earthquake resistance. Consequently, the results for these shear wall configurations were remarkably close to those without a shear core. In general, the L-shaped shear wall configurations performed better than the rectangular-shaped shear wall configurations in the x-direction, whereas the opposite was observed in the y-direction. Notably, none of the analyzed building models has shown soft and weak story failure modes.For further research, it was recommended to expand the scope of the study by including other shear wall configurations and a wider range of building heights and column lengths on the ground floor in the comparison frame. Furthermore, incorporating enlarged structural properties of columns, beams, shear walls, and slabs into the comparison frame could provide deeper insights into their influence on earthquake resistance. In addition, using strong ground motion recordings from other stations and comparing them to the findings of this study may provide a broader perspective for the conclusions. However, based on the suggested recommendations for further research, another significant factor affecting earthquake resistance is the structural layout. Therefore, considering other placements of columns was also addressed as an enhancement of this study. Finally, to perform a more comprehensive analysis, the inclusion of soil-structure interaction effects was mentioned as a crucial factor influencing building responses and overall results.

Emergency Operating Procedures (EOPs) are guidelines developed to ensure a safeand effective response to emergency situations in nuclear power plants. This reportpresents the work carried out during a five-and-a-half-month internship at Framatome in the DTIPRO unit, which is responsible for emergency procedures andthermal-hydraulic studies.The first objective was to further develop a simulation tool (OSCIA) used to test, derisk, and pre-validate early versions or modifications of EOPs. This tool was designedto be simple and easily accessible, serving as an alternative to a full-scope simulator.The second objective was to create a best-estimate transient CATHARE dataset fora Steam Generator Tube Rupture accident and to test the tool using this scenariowhile following the emergency operating procedures. The aim was to demonstratethat, even with a delayed initial operator action, the accident can be mitigated whilekeeping radioactive releases to the atmosphere within acceptable limits. The resultsshow that the OSCIA EOPs applications enables the reactor to be brought to a safestate, with radioactive releases remaining within prescribed limits. Furthermore, theOSCIA meets its intended objectives and proves to be an effective and user-friendlytool for simulating EOPs and pre-validating new strategies.

Power harrows are key implements for seedbed preparation, yet soil–tool interactions remain difficult to characterize experimentally due to the variability of field conditions and the cost of instrumented trials. This thesis aims to further integrate Discrete Element Method (DEM) simulations into KUHN’s design workflow by improving model robustness and quantifying the influence of operating and modelling parameters on key performance indicators.A DEM workflow was developed primarily in ThreeParticle, with complementary sensitivity investigations in EDEM. Moist soil cohesion was represented using the Johnson–Kendall–Roberts (JKR) model for soil–soil contacts, while a computationally efficient fragmentation framework was implemented via a Voronoi-based particle-replacement API to model friable soil and breakable stones. The tool geometry was progressively refined to mitigate non-physical particle interpenetration, and a robust post-processing pipeline (winsorization, Hampel filtering, rolling median, EMA) was introduced to extract reliable power signals and key performance indicators. Work-quality was assessed using displacement, mixing and density indices, extended to fragmentation through lineage tracking based on a persistent “Master_shape” identifier.Results show that fragmentation acts as a stress-relief mechanism that disrupts highly loaded contact networks, consistently reducing mean power demand and limiting extreme load regimes, while cohesion increases power demand and promotes stress transmission below the active layer. Fragmentation and cohesion also shape the bulldozing heap dynamics and associated displacement patterns, whereas the density response is dominated by cohesion and working depth, with deeper sublayers remaining weakly affected. Across a 24-case screening design of experiments spanning forward speed, rotational speed, working depth, cohesion and fragmentation for two harrow configurations, operating parameters (notably forward speed and working depth) govern most KPI variability, while configuration-to-configuration differences remain second order. Overall, the predicted power levels fall within the expected order of magnitude for real machines, supporting the credibility of the DEM trends and providing actionable guidance for targeted testing and design trade-offs.

Parameter estimation is a pivotal task across various domains such as system identification, statistics, and machine learning. The literature presents numerous estimation procedures, many of which are backed by well-studied asymptotic properties. In the contemporary landscape, highly advanced digital twins (DTs) offer the capability to faithfully replicate real systems through proper tuning. Leveraging these DTs, simulation-driven estimators can alleviate challenges inherent in traditional methods, notably their computational cost and sensitivity to initializations. Furthermore, traditional estimators often rely on sensitive data, necessitating protective measures. In this thesis, we consider simulation-driven and privacy-preserving approaches to parameter estimation that overcome many of these challenges.

The first part of the thesis delves into an exploration of modern simulation-driven estimation techniques, focusing on the two-stage (TS) approach. Operating under the paradigm of inverse supervised learning, the TS approach simulates numerous samples across parameter variations and employs supervised learning methods to predict parameter values. Divided into two stages, the approach involves compressing data into a smaller set of auxiliary statistics, and the second stage utilizes these statistics to predict parameter values. The simplicity of the TS estimator underscores its interpretability, necessitating theoretical justification, which forms a core motivation for this thesis. We establish statistical frameworks for the TS estimator, yielding its Bayes and minimax versions, alongside developing an improved minimax TS variant based on gradient boosting that excels in computational efficiency. We conduct both asymptotic and non-asymptotic analyses of the TS estimator, establishing strong consistency, asymptotic normality, and finite-sample deviation bounds that characterize the estimation error in terms of the number of training samples and observation length. Finally, we address the question of generating diverse and informative training samples by proposing a Metropolis-Adjusted Langevin Algorithm (MALA)-based scheme for sampling training parameters from Jeffreys prior, which reflects the intrinsic geometry of the parameter space via the Fisher Information Matrix.

The second part of the thesis considers the problem of adapting pretrained TS estimators to out-of-distribution scenarios. We introduce two fine-tuning methods: a supervised approach that combines feature-space anomaly detection with Fisher-information-guided retraining, and an unsupervised approach that minimizes the discrepancy between simulated and observed trajectories without requiring labeled data.

The third part of the thesis introduces applications of simulation-driven estimation methods in the design of tuning rules for PI controllers. Leveraging synthetic datasets generated from DTs, we train machine learning algorithms to meta-learn tuning rules, streamlining the calibration process without manual intervention. We explore both TS-based approaches with explicit parameter estimation and direct learning approaches using neural network architectures such as convolutional neural networks and WaveNet.

In the final part of the thesis, we tackle scenarios where estimation procedures must handle sensitive data. Here, we introduce differential privacy constraints into the Bayes point estimation problem to protect sensitive information. By proposing a unified approach, we integrate the estimation problem and differential privacy constraints into a single convex optimization objective, thereby optimizing the accuracy-privacy trade-off. In cases where both observations and parameter spaces are finite, this approach reduces to a tractable linear program which is solvable using off-the-shelf solvers.

In essence, this thesis endeavors to address theoretical foundations, robustness, and privacy concerns within the realm of simulation-driven parameter estimation.

We present a framework and proof of concept for flexible access control in WebAssembly Components. This mechanism enables custom mediation and supports varying trust levels amongst components. It enforces policies via shims, allowing mediation logic to be expressed as any logic presentable as WebAssembly code. This is a vast improvement over the limited capabilities previously granted. This paper provides flexible access control in WebAssembly through a new composition-time enforcement mechanism at build. The system inserts shims that can enforce any access control policy that can be described with code, without the need for specific runtime support. Two tools, Wacky and Shimmer, automate shim insertion based on change manifests derived from declarative policies and generate shim scaffolds, respectively. This enables fine-grained permission without modifying component code or runtimes. Evaluation shows negligible performance impact for function calls, with an average overhead of 4.5 ns per mediated call. Instantiation has a higher relative overhead of 53𝜇s, in absolute terms the cost is low and since it occurs only once the impact is deemed acceptable. Despite limitations in supporting certain WebAssembly Interface Type (WIT) type support, such as resources due to their stateful nature and the static nature of the framework that operates at binding before execution, our results show that build-time enforcement is a practical and efficient way to improve WebAssembly access control. We see hybrid approaches combining build and runtime linking as promising future work, which could further help adapt to cloud-native settings.

The increasing integration of renewable energy sources into power systems increases fluctuations which can lead to oscillations that can threaten the reliable and stable operation of the power system. Power system oscillations can be divided into two types of oscillations: natural and forced oscillations. In order to maintain reliable and stable power system operation, it is essential to understand the cause of these oscillations and locate where such oscillations have their origin so that the system operator can perform corrective operations. This thesis project analyses different case studies on both forced and natural oscillations. The aim of the project is to develop a test model for simulating both oscillation types and using PMU data from these simulations to verify the potential and limitations of the Dissipating Energy Flow (DEF) method of localising the oscillation source. DIgSILENT PowerFactory version of Nordic 32 test system was used as a benchmark for this project. Test models were constructed to simulate forced oscillation in the generator governor and excitation system. Moreover, simulations of forced oscillation in Battery Energy Storage System (BESS) were simulated in PQ control of the battery. A Matlab version of the DEF method was used to process the simulation data and to see the potentials and limitations of the DEF method of localising the oscillation source. A total of fourteen case studies were conducted in this project: twelve involving forced oscillations with constant and variable amplitudes in the generator governor, excitation system, and BESS, and two involving natural oscillations. In order to simulate the natural oscillation, gain in generator Power System Stabiliser (PSS) parameter was set to negative value along with short-circuit event that lasted for 110 ms. The different load models used in the project show no effect on the results of the DEF method. The method correctly localised the oscillation source in all cases with the different load models. The key observation of the analysis of the DEF method shows the capability and limitations of the method. DEF method gave accurate and reliable results for both forced and natural oscillations. The cases where the oscillation source is located inside the generator showed results that were accurate and consistent. For natural oscillation case studies, the DEF method could locate the oscillation source accurately. DEF method showed limitations in localising the oscillation source correctly when the oscillation was located inside BESS. The results of these cases showed that the DEF method identified some generators and transmission lines as oscillation sources which were wrong. The main conclusion of this analysis is that the DEF method has a good potential to localise different types of oscillation that originate from various parts of the generator. For oscillations that originate from the BESS, the DEF method shows limitations in identifying the oscillation source.

Cobots have become increasingly common in industry for their versatility and manufacturing in conjunction with operators. Deviating from spherical wrist 6-Degrees of Freedom (DOF) manipulator designs, offset-wrist manipulators give rise to less structured singularities and added difficulties in the Inverse Kinematics (IK) problem. This thesis proposes a more intuitive and visual way of determining singularities for an offset-wrist 6-DOF robot by visualizing the determinant for all combinations of the wrist joint angles for a given arm configuration, with singularities appearing as the zero crossings of this 3D function. From this representation, we can reduce our domain of possible values for wrist singularities, represent them in Cartesian space, and relate phenomena such as an increased number of IK solutions and the conditions under which cuspidal changes can occur.

The ability to perceive the environment with minimal energy is a remarkable characteristic of biological vision. Engineers are still far from achieving comparable efficiency in conventional computer vision, where cameras and processors consume significant power to handle dense frame-based data. Event cameras, inspired by the principles of biological vision, represent an effort to move closer to this efficiency by outputting only pixel-level brightness changes instead of full frames. This results in both high temporal resolution and sparse data that can be exploited for fast, energy-efficient computation. However, the baseline power required to acquire and transmit event data in embedded platforms is still not well characterized, and recent work has emphasized the need for realistic end-to-end benchmarks of complete computer vision pipelines in biologically inspired architectures. To address this gap, this thesis presents the design and benchmarking of a scalable embedded system combining the Prophesee GENX320 event camera with the quad-core Alif Ensemble E7 microcontroller featuring integrated Ethos-U55 hardware accelerators. Results show that the camera consumed less than 3 mW even in high-activity scenes, while the MCU dominated overall system consumption. The findings establish a practical lower bound for eventdriven perception on the studied architecture and provide a reference point for future designs, while also identifying areas where further optimization is possible. Building on this work, future research can utilize the embedded system for comparative benchmarking of complete computer vision algorithms to evaluate power – performance trade-offs.

Given stricter regulations and the transfer of sensitive data to the cloud, there is a clear need to further strengthen cloud security. The latest advances fall under the term confidential computing, which complements existing methods of protecting data during storage and transfer with memory encryption and remote attestation. The introduction of these countermeasures significantly raises the security bar for both remote attackers who operate malicious virtual machines and exploit vulnerabilities in cloud infrastructure, and malicious actors with physical access. AMD SEV-SNP and Intel TDX are the latest developments implementing confidential computing for server-grade processors. For wider adoption of this technology, effective management of confidential virtual machines, i.e., virtual machines utilizing the protection provided by confidential computing chips, is essential. To facilitate the lifecycle management of confidential virtual machines, the Secure VM Service Module (SVSM) has been introduced as a common layer that can be used across different vendors.

This thesis investigates live migration of confidential virtual machines running under AMD SEV-SNP using the SVSM module. First, the current state of the art is investigated. Since there is no solution for migrating confidential machines with the SVSM module, a migration design is developed and proof of concept is provided for the most time-consuming part of the migration process, random access memory (RAM) migration. The proposed solution is analyzed and the steps needed to increase its scope and functionality are outlined. A new methodology for evaluating incomplete migration is developed and used to assess the upper limit of the overhead that AMD SEV-SNP confidential machines would represent for the live migration process. Our single-threaded proof-of-concept resulted in a tenfold slowdown in memory page transfers.

Spatial Active noise control (ANC) aims to manipulate sound fields within target regions through active control methods, with applications including noise cancellation and high-fidelity audio reproduction. The placement of secondary sources and sensors has a significant impact on system performance; however, traditional approaches optimise these placements independently, resulting in suboptimal results. This thesis addresses the joint source and sensor placement optimisation problem in spatial ANC systems. The challenge lies in formulating an effective cost function that simultaneously optimises secondary source placement for sound field synthesis and sensor placement for accurate spatial interpolation, while maintaining reasonable computational complexity. A novel joint cost function formulation was developed that explicitly incorporates interpolation error alongside synthesis error by comparing interpolated field estimates against the true desired field across the entire control zone. This approach addresses fundamental limitations in existing methods, including clustering behaviour and convergence issues, by properly accounting for estimation uncertainty inherent in limited sensor measurements. A comprehensive simulation environment was implemented to evaluate four placement algorithms: Random (baseline), Regular (geometric), Greedy (optimisationbased), and Matching Pursuit (signal processing-based). The algorithms were systematically compared using normalised mean square error and computational efficiency metrics across multiple resolution configurations. Results demonstrate that optimisation-based approaches provide meaningful performance improvements over simple placement strategies, with the Greedy algorithm achieving the best acoustic performance. However, significant computational trade-offs were revealed, with optimisation methods requiring substantially longer execution times. The novel cost function formulation successfully resolved convergence issues and demonstrated superior robustness across varying acoustic conditions.

This thesis explores the potential of gamification to enhance the onboarding experience in a factory simulation tool designed for the corrugated fiber industry. The tool, developed for Van Den Bos Robotics, allows users to model transport systems and simulate production flows. While rich in functionality, the tool’s complexity may pose a challenge for new users during onboarding. To address this issue, a gamified onboarding system was developed, incorporating game design elements such as missions, achievements, levels, and in-app rewards to guide users through key features of the tool. Most gamification work focuses on employee performance and motivation; very little work is done on learning complex simulation software. This is a crucial gap provides a strong justification for the present study. By examining gamification in the context of digital tool onboarding, this thesis contributes new insights into improving learnability and user experience in industrial simulation environments. To evaluate the impact of this approach, a between-subjects experiment was conducted with 16–20 participants of diverse academic backgrounds. One group used a traditional onboarding method (verbal intro and manual), while the other group experienced the gamified onboarding. Task performance, usability (SUS), engagement, and cognitive load (NASA-TLX) were measured and compared.

Lightweight composite structures have become essential in modern aerospace engineering, where increasing demands for fuel efficiency, reduced emissions, and improved operational reliability place new requirements on both materials and manufacturing. As composite components grow more advanced, featuring co-cured components, complex geometries, and thinner design margins, the need for improved insight into their internal behaviour becomes critical. Existing sensing technologies struggle to provide local, in-situ information from the composite’s interior during manufacturing or throughout its service life, without compromising structural integrity. This creates a gap between the capability of current sensing approaches and the monitoring demands required by the complexity of next-generation composites.

This thesis addresses this gap by investigating the feasibility of embedding nanomaterial-based sensing structures, primarily vertically aligned carbon nanotube (VACNT) forests, into fibre-reinforced polymer composites. The overarching aim is to explore how such sensors can be integrated with minimal structural intrusion, from where their sensing behaviour originates, and how they can provide reliable, multifunctional monitoring both during manufacturing and in the cured state. The work spans the development of embedding and contacting strategies, bottom-up characterisation to investigate sensing mechanisms, and the exploration of both direct current (DC) and alternating current (AC) measurement approaches. Collectively, the research seeks to expand the understanding of how nanomaterial sensors interact with composite materials and how they can support the design of future multifunctional aerospace structures.

The findings demonstrate that VACNT forests can be embedded into composite laminates without compromising the composite’s mechanical structure, while providing robust and reproducible sensing capabilities. A bottom-up analysis helps determine that the embedded VACNT forests’ thermoresistive behaviour is governed by fluctuation-assisted tunnelling, and their linear piezoresistive response originates in the intrinsic piezoresistivity of individual CNTs. The VACNT forests enable local in-situ cure monitoring of prepreg laminate, detecting key process transitions. Strategies for sensing in conductive carbon fibre environments are established, as well as comparisons with alternative nanomaterial-based sensors such as graphene coatings. Finally, by transitioning from DC resistance to AC impedance measurements, the work shows that embedded CNT structures can detect high transverse pressures and exhibit frequency-dependent sensing sensitivity.

Together, these results establish VACNT forests as a promising, multifunctional, and structurally compatible sensing concept for advanced composite structures, offering new pathways for embedded process monitoring, structural health monitoring, and the development of next-generation multifunctional aerospace components.

As algorithmic systems increasingly shape human experiences, ensuring fairness and diversity has become a central challenge. This thesis studies fairness and diversity through the lens of algorithm design and optimization theory, providing formal frameworks and efficient algorithms across three domains: ranking-based recommendation, streaming recommendation, and graph analysis.

The first part of the thesis investigates diversity maximization in recommender systems with stochastic user engagement. We first study how to rank items in recommendation systems, where users engage with content sequentially and probabilistically. We introduce two novel diversity measures, sequential sum diversity and sequential coverage diversity, which account for uncertainty in user engagement. Our goal is to find a ranking of items that maximizes these sequential diversity measures. We show that sequential coverage diversity is ordered submodular, enabling a greedy 1/2-approximation. For sequential sum diversity, we provide polynomial-time constant-factor approximation algorithms. Separately, we study a streaming setting where items arrive continuously and users may visit the system multiple times at arbitrary moments. For this setting, we aim to design a streaming algorithm that maximizes a stochastic coverage diversity measure. We show that a classic greedy algorithm achieves a tight 1/2-competitive ratio but requires memory linear in the stream length. With sublinear memory and an upper bound T' on the number of user visits T, we propose STORM, which achieves a 1/4(T'-T+1)-competitive ratio. We further propose STORM++, improving the competitive ratio to 1/8delta, where the integer parameter delta controls the tradeoff between solution quality and computational cost.

The second part of the thesis studies diversity as a constraint in densest subgraph discovery and addresses the problem of finding dense communities in networks with heterogeneous relationship types. We model relationship types as edge colors and formulate the At Least h Colored Edges Densest Subgraph problem (ALHCEDGESDSP), which seeks subgraphs that are both dense and contain at least h_i edges of each color i. We prove that even the simplest variant of this problem is NP-hard and develop constant-factor approximation algorithms. Our key technical contribution links the edge-constrained and node-constrained versions of the densest subgraph problem. We first show that algorithms for the At Least k Nodes Densest Subgraph problem (DalkS) can approximate the At Least h Edges Densest Subgraph problem (ATLEASTHEDGESDSP), and then extend the algorithm for DalkS to handle colored edge constraints for solving ALHCEDGESDSP.

The third part of the thesis studies graph interventions for fairness in networks. We examine two fairness measures, PageRank fairness and hitting-time fairness, developing methods to balance influence and improve accessibility across groups. For each demographic group, the sum of PageRank scores within it quantifies the influence of that group. PageRank fairness measures how far the current group-wise influence deviates from a given target. We formulate the PageRank fairness problem as an optimization problem that adjusts edge weights such that the resulting graph achieves a group-wise influence distribution as close to the target as possible. The optimization problem involves a nonconvex objective over a convex feasible set under practical constraints, such as not adding new edges and limiting the magnitude of weight changes. We solve this PageRank fairness maximization problem using efficient projected gradient descent, proving convergence to a stationary point. For hitting-time fairness in bipartite graphs, we formulate two problems, minimizing the average (BMAH) and the maximum hitting time (BMMH) from one group to another via strategic edge additions. We provide a (2+epsilon)-approximation for BMAH by combining fast random walk simulation with greedy supermodular minimization. For the more challenging BMMH problem, we develop two approaches: the first leverages its connection to the BMAH problem, and the second employs a method based on the asymmetric k-center problem. Both approaches yield provable approximation guarantees for BMMH.

The algorithms and analysis techniques presented in this thesis contribute to the growing body of work on fairness and diversity in algorithmic systems. By formalizing new problem variants that capture realistic constraints in interactive and networked settings, and by providing approximation algorithms with provable guarantees, this work expands the toolkit available for addressing fairness and diversity challenges in computational systems.

Waste management is particularly challenging for Small Island Developing States (SIDS) due to their high per-capita infrastructure costs, remoteness, narrow resource bases and high dependence on tourism. The lack of integrated planning frameworks considering these SIDS-characteristics has stalled progress on sustainable waste management. To address this challenge, this paper proposes an integrated methodology for long-term waste management planning to deliver on the United Nations’ Sustainable Development Goals (SDGs) in SIDS. This explicitly combines multi-level participatory SDG visioning and back-casting with waste infrastructure modelling. This methodological development is piloted using a national-scale demonstration on Curacao. Three island-specific waste management portfolios (Inaction, Circular Economy, Technology-led), developed through stakeholder back-casting, are modelled for SDG delivery using a national accounting model under different socio-economic futures. The results highlight the importance of waste prevention and material re-use strategies within islands that engage local populations. Evidence-based identification and evaluation of waste management strategies, grounded in participatory processes, can itself contribute to SDG delivery.

We explore a novel problem in streaming submodular maximization, inspired by the dynamics of news-recommendation platforms. We consider a setting where users can visit a news website at any time, and upon each visit, the website must display up to k news items. User interactions are inherently stochastic: each news item presented to the user is consumed with a certain acceptance probability by the user, and each news item covers certain topics. Our goal is to design a streaming algorithm that maximizes the expected total topic coverage.

To address this problem, we establish a connection to submodular maximization subject to a matroid constraint. We show that we can effectively adapt previous methods to address our problem when the number of user visits is known in advance or linear-size memory in the stream length is available. However, in more realistic scenarios where only an upper bound on the visits and sublinear memory is available, the algorithms fail to guarantee any bounded performance. To overcome these limitations, we introduce a new online streaming algorithm that achieves a competitive ratio of 1/(8δ), where δ controls the approximation quality. Moreover, it requires only a single pass over the stream, and uses memory independent of the stream length. Empirically, our algorithms consistently outperform the baselines.

In this paper, two strategies to isolate resistive vertically aligned carbon nanotube (VACNT) forestsfrom the conductive carbon fibre environment are presented, enabling embedded sensing with resistivecarbon nanotube sensors in carbon fibre laminates. VACNT forests are used due to their already proventemperature and strain sensing capabilities and ease of placement in prepregs, enabling localisedsensing. To achieve this, a non-permeable separator and a permeable separator are used and compared.Performing cure-monitoring on the VACNT forests during the sample manufacture, it can be concludedthat short-circuits of the resistive sensor are avoided. After manufacture, the temperature and strainsensing capabilities of the VACNT forests when using the two isolation strategies are evaluated. Fromthese measurements, differences in temperature sensing range and sensitivity to strain are observed.

Link-analysis algorithms, such as PageRank, are instrumental in understanding the structural dynamics of networks by evaluating the importance of individual vertices based on their connectivity. Recently, with the rising importance of responsible AI, the question of fairness in link-analysis algorithms has gained traction.

In this paper, we present a new approach for incorporating group fairness into the PageRank algorithm by reweighting the transition probabilities in the underlying transition matrix. We formulate the problem of achieving fair PageRank by seeking to minimize the fairness loss, which is the difference between the original group-wise PageRank distribution and a target PageRank distribution. We further define a group-adapted fairness notion, which accounts for group homophily by considering random walks with group-biased restart for each group. Since the fairness loss is non-convex, we propose an efficient projected gradient-descent method for computing locally-optimal edge weights. Unlike earlier approaches, we do not recommend adding new edges to the network, nor do we adjust the restart vector. Instead, we keep the topology of the underlying network unchanged and only modify the relative importance of existing edges. We empirically compare our approach with state-of-the-art baselines and demonstrate the efficacy of our method, where very small changes in the transition matrix lead to significant improvement in the fairness of the PageRank algorithm.

Diversification is a useful tool for exploring large collections of information items. It has been used to reduce redundancy and cover multiple perspectives in information-search settings. Diversification finds applications in many different domains, including presenting search results of information-retrieval systems and selecting suggestions for recommender systems.

Interestingly, existing measures of diversity are defined over sets of items, rather than evaluating sequences of items. This design choice comes in contrast with commonly-used relevance measures, which are distinctly defined over sequences of items, taking into account the ranking of items. The importance of employing sequential measures is that information items are almost always presented in a sequential manner, and during their information-exploration activity users tend to prioritize items with higher ranking.

In this paper, we study the problem of maximizing sequential diversity. This is a new measure of diversity, which accounts for the ranking of the items, and incorporates item relevance and user behavior. The overarching framework can be instantiated with different diversity measures, and here we consider the measures of sum diversity and coverage diversity. The problem was recently proposed by Coppolillo et al. [11], where they introduce empirical methods that work well in practice. Our paper is a theoretical treatment of the problem: we establish the problem hardness and present algorithms with constant approximation guarantees for both diversity measures we consider. Experimentally, we demonstrate that our methods are competitive against strong baselines.

In this paper, a graphene-coating on glass fiber weave has been evaluated as a multifunctional sensingmaterial based on previous work performed by the authors on embedded vertically aligned carbonnanotube forests. First, the graphene-coating has been assessed as a resistive cure-monitoring sensorwhen embedded in thermosetting glass fiber/epoxy laminate to monitor its production. The graphene-coating showed similar results to the resistive cure monitoring of carbon nanotubes. However, thegraphene-coating diverges in its resistive signature upon reaching cure temperature, showing acontinuous resistance decrease in this phase, suggesting a sensitivity to cure-induced shrinkage of theepoxy, not seen in the carbon nanotube sensor. Later, in the cured state, the embedded graphene-coatingfunctions as an excellent temperature sensor, possessing a negative thermoresistive effect. However, asa strain sensor, the graphene-coating does not perform as well as the embedded carbon nanotube sensor,possessing an initial drift in resistance upon its first load cycle and additional drift during constant strainconditions, and when unloaded.

Diffusion models have shown great potential in generating trajectory plans for agents in environments with unknown dynamics. However, such models provide no safety guarantees. In this work, we focus on risk-aware planning with respect to safety constraints and introduce a probabilistically risk-aware variant of Diffuser (PRA-Diffuser). The diffusion model initially learns a distribution over trajectories that may or may not be unsafe. We then fine-tune this model to reduce the probability of sampling such unsafe trajectories. We analyze the proposed solution and introduce a provable lower bound on risk of safety violation leveraging concentration inequalities for conditional Value-at-Risk. Our approach can be applied to models that have been pre-trained, potentially from datasets containing unsafe trajectories. Our empirical results demonstrate that our approach significantly reduces unsafe trajectories generated by the diffusion model across multiple environments.

We study multi-agent reinforcement learning (MARL) under temporally extended Signal Temporal Logic(STL) objectives, which require reasoning over both long-horizon dynamics and inter-agent relations. Wepropose TD-MAT, a transformer-based architecture with multivariate positional encodings, causal temporalmasking, and a decomposed reward based on arithmetic–geometric mean robustness with variance regularization. Experiments on coordination tasks ranging from unstructured multi-objective problems to strict temporalsequencing show that TD-MAT learns effective long-term behaviors and generalizes to heterogeneous agentsettings. Ablation studies highlight the necessity of temporal masking, positional encodings, and reward decomposition, while comparisons to MAPPO, RMAPPO, and MAT reveal that transformers provide the greatestbenefit on unstructured, long-horizon tasks.

Global floor area is increasing every year which is subsequently leading to an increase in electricity and heating demand in buildings. Residential buildings have huge potential for energy savings and there is an immediate need to decarbonize them by the end of 2050. Machine learning is finding application across all fields and will thus have an important role to play in the building sector also. One of the important challenges that building owners need to tackle is occupancy detection in residential apartments which can help save considerable amounts of energy and costs. However, occupancy is highly variable, and it is difficult to quantify and predict occupancy because of the random and individualistic nature of humans. In addition, scalable approaches for occupancy detection should prioritize data from common and cost-effective sensors like temperature sensors. In contrast to existing literature which has stated that occupancy detection based on the data from a single environmental sensor is not appropriate for obtaining good results, this paper aims to detect occupancy in a real residential building using only indoor temperature as the feature to train the model. Different machine learning models and techniques are studied and tested to understand how the accuracy of occupancy detection can be increased. With the right techniques, it has been possible to obtain promising results in the form of an accuracy of 95% using machine learning models and only indoor temperature to train it.

The construction sector, deeply entrenched in a linear model of resource extraction, consumption, and disposal, significantly contributes to environmental degradation. In response, the circular economy paradigm has emerged as a key alternative. Given their high resource intensity, large-scale urban development projects call for a reconfiguration of their material flows. These interventions offer a strategic opportunity to develop closed loops at a fine spatial scale. This study explores the development of circular economy strategies for construction and demolition materials in French urban renewal projects led by the National Agency for Urban Renewal (ANRU). A qualitative research design is adopted, combining a literature review, document analysis, ten semi-structured interviews, a site visit and participant observation. Following a contextual analysis of the ANRU Framework, two case studies of NPNRU projects in the Est Ensemble area are examined. The literature review engages with conceptual approaches to the circular economy in the built environment and identifies urban metabolism as a key theoretical concept at the macro scale. The subsequent analysis is informed by this framework, complemented by the Multi-Level Perspective (MLP). The empirical results first identify opportunities associated with intense material flows and conditions conducive to innovation, as well as barriers related to the spatial, temporal and material characteristics of this specific context. The case studies further reveal key implementation mechanisms, particularly financial instruments, physical platforms, and monitoring tools. Finally, the findings point to conditions for scaling up circular practices, including institutional changes, enhanced synergies and a broader paradigm shift in urban production. In brief, this thesis contributes to both the literature on circular economy in the built environment and to research on ANRU projects. The integration of circular economy theory with empirical evidence highlights the potential of these urban renewal programmes in the transition to a circular regime, while also acknowledging the diverse associated limitations.

The study of pattern avoidance stems from a question in computer science: which sequences of distinct numbers can be ordered by a single pass through a stack? Knuth (1968) found that these sequences are characterized by having no subsequence a, b, c, such that c < a < b. Such a subsequence has the same relative order as the permutation 231, so we say that our original sequence avoids 231.

In more general terms, pattern avoidance is a natural way to restrict the structure of permutations by forbidding subsequences with a certain relative order. This has become a popular topic in enumerative combinatorics, and it has connections to various other fields.

Determining the number of 1324-avoiding permutations of length n is the most important open problem in pattern avoidance. This thesis is comprised of two papers contributing to the inversion monotonicity conjecture by Claesson, Jelínek and Steingrímsson (2012), according to which av_n^k(1324), the number of 1324-avoiders of length n with a fixed number k of inversions, is weakly increasing in n. If the conjecture is true, it improves our understanding of the asymptotic behavior of the number of 1324-avoiders.

In Paper A, we provide an explicit formula for av_n^k(1324) for all n ≥ (k + 7)/2. The proof relies on a novel structural characterization of 1324-avoiders with few inversions. As a byproduct, we show that the inversion monotonicity conjecture holds when n ≥ (k + 7)/2.

In Paper B, we study the inversion monotonicity of classes of permutations avoiding multiple patterns. We show the sets {1324, 231} and {1324, 2314, 3214, 4213} are inversion monotone via explicit injections, and introduce a general procedure for constructing large inversion-monotone sets. We also analyze the limiting structure of large permutations with a fixed number of inversions avoiding 1324 and another pattern of length four, and prove several half-monotonicity results similar to Paper A.

Understanding the degradation mechanisms in lithium-ion batteries is essential for the development of next generation batteries. Herein, a post-mortem protocol described in “Quantifying lithium lost to plating and formation of the solid-electrolyte interface in graphite and commercial battery components” by Fang et.al is used to track the redistribution of metallic lithium in two types of carbonaceous anode materials: graphite, and hard carbon. In this report, two different commercially available graphite anodes were used, one power optimized while the other energy optimized. The hard carbon anode was prepared in house.

Metallic lithium in plated samples displayed an immediate redistribution to ionic compounds at low chemical shifts on a complex manner. Graphite displayed a behaviour that could be modelled by a double-exponential function whilst hard carbon displayed a single-exponential function decaying to a baseline. There was no observed difference between the power optimized and energy optimized graphite. “Dead” lithium in delithiated samples showed a period of stability followed by a fast redistribution into ionic compounds. No attempt was made to identify these ionic compounds due to their large NMR linewidths. 

The plating kinetics of hard carbon differed significantly from graphite, requiring a much greater overcharge to reach the deposition underpotential which was the same for both anode materials.

Ultra-endurance cycling exposes athletes to prolonged static postures, leading to a high prevalence of overuse injuries, particularly in the lower back and knees. While aerobars are widely used to reduce aerodynamic drag, their internal musculoskeletal loads remain poorly understood. This study aims to quantify the biomechanical impact of aerobar use compared to standard road positions and evaluate the specific influence of stack and inclination settings on lumbar and lower limb joint reaction forces. Ten experienced cyclists performed trials on an instrumented ergometer at 70% of their Functional Threshold Power (FTP) across six conditions: hoods, drops, and four aerobar configurations varying in stack (high/low) and inclination (0°/15°). A custom automated musculoskeletal modeling workflow using OpenSim was developed to compute compressive and shear forces for lumbar and lower limb joints. Results revealed that the flat aerobar position (0°) significantly reduced L4-L5 compressive forces (17.5 ± 10.9 N/kg) compared to the standard hoods position (20.9 ± 4.8 N/kg), likely due to effective skeletal support. However, tilting the aerobars to 15° significantly increased spinal loading. A mechanical trade-off was identified: aerobar use shifted the load distally, more than doubling hip compression forces (~46 N/kg) and significantly increasing knee forces compared to road positions. These findings suggest that while flat aerobars reduce spinal joint loading, they impose severe stress on the lower limbs, highlighting the need for injury-specific bike fitting strategies in ultra-endurance.

Photocuring is central to many biomedical polymer applications because light-activated photoactivators (PAs) generate radicals that rapidly convert acrylate functions into a crosslinked network, enabling fast, localised curing. For clinical devices the PA must therefore combine efficient, rapid crosslinking under the clinical light source with an acceptable safety profile.

Following a regulatory change, three candidate photoactivators (A, B and C) were evaluated as possible replacements for the current PA in a medical polymer formulation. Absorption at 405 nm (the emission of the clinical light source) was confirmed for all candidates, and formulations were prepared by mixing the base polymer with PA solutions in ethanol at the highest feasible loading. Target concentrations of 2500 ppm and 5000 ppm were tested. Crosslinking kinetics and efficiency were characterized using real-time FTIR, ATR-FTIR and photo-DSC; efficient crosslinking was defined as ≥90% acrylate conversion within 15 s at the Lowest Acceptable Polymerization Irradiance (70 mW・cm⁻2).

Here we show that Candidate A, despite slower polymerization kinetics, offers the best balance of performance and safety: minimal concentrations to meet the 90%/15 s criterion were ~2500 ppm for the current PA and ≥2500 ppm for Candidates B and C, whereas Candidate A required ≥5000 ppm. All formulations gave similar depth of cure and were stable to ambient light for 7h except Candidate C, which hardened more rapidly. Regulatory database review indicated Candidate A had the lowest toxicity, Candidate C was more cytotoxic and classified as skin sensitizer 1A, and less data were available for Candidate B.

These findings revise the expectation that all candidate PAs would perform like the incumbent PA at the same dose: structural and photophysical differences alter required operating concentrations and residual risk. Practically, the study identifies Candidate A as the preferred replacement and also shows that reducing the current PA below labelling thresholds is infeasible (900 ppm failed to achieve effective crosslinking even at higher irradiance and longer cure). Ongoing work evaluates POL004 formulations with Candidate A after supercritical CO₂ purification to confirm crosslinking efficiency and overall performance in an industrial, clinically relevant context.

Autonomous systems have become integral to industry and society, spanning applications from robotic platforms to autonomous vehicles and beyond. Most contemporary engineering systems exhibit complex nonlinear dynamics and are often subject to time-varying uncertainties, making high-fidelity modeling difficult. At the same time, these systems are increasingly required to execute complex tasks that extend beyond classical objectives such as set-point tracking and stabilization. In many real-world scenarios, they must satisfy spatiotemporal specifications, requirements that depend jointly on space and time. Such specifications can be formulated as time-varying constraints in control systems, and enforcing them is crucial for enhanced performance, guaranteed safety, and reliable, timely task execution. 

Funnel-based control methods provide closed-form feedback laws that enforce certain classes of time-varying constraints for uncertain nonlinear systems. The main focus of this thesis is to develop new robust closed-form control schemes, rooted in the core ideas of funnel-based control, to address broader classes of time-varying constraints that cannot be treated directly by conventional funnel-based methods. In addition, the thesis investigates distributed coordination of multi-agent systems under spatial constraints, as well as the application of funnel-based methods to multi-agent formation control under transient performance requirements.

The first part of the thesis is devoted to extending funnel-based control methods, particularly prescribed performance control (PPC), to address time-varying hard (safety) and soft (performance) funnel-type specifications. We then revisit the standard PPC design to highlight its limitations and to motivate the need for a new control framework. Building on the PPC design philosophy, we propose a novel robust closed-form control scheme that enforces generic time-varying set invariance for high-relative-degree, multi-input multi-output uncertain nonlinear systems, thereby accommodating classes of time-varying constraints beyond those handled by standard PPC. Finally, we extend the proposed design to treat potentially conflicting generalized time-varying hard and soft specifications, further broadening the applicability of the method.

In the second part of the thesis, we shift the focus to multi-agent coordination problems. First, we present a novel coordinate-free formation control scheme for directed leader–follower multi-agent systems that achieves almost-global convergence to a desired shape. Fully decentralized robust controllers are synthesized by leveraging the PPC framework to impose prescribed transient and steady-state performance on the agents’ formation errors, while ensuring robustness to system uncertainties. A key ingredient of the approach is the use of bipolar coordinates to obtain orthogonal (decoupled) formation-error coordinates for each follower. This not only promotes almost-global convergence to the desired shape but also enables a systematic and effective application of PPC. Finally, we introduce a distributed, task-based implicit formation determination and control problem in which each agent is subject to spatial constraints with respect to other agents and the environment. We reformulate the problem as a distributed optimization scheme and, based on this formulation, develop a control protocol for kinematic agents.

PurposeThe study investigates how openBIM workflows can support standardised digital validation processes, aiding the transition towards circular construction. Specifically, it examines the use of the Information Delivery Specification (IDS) standard to validate Industry Foundation Classes (IFC) models for the reuse of precast hollow core slabs, in accordance with the Norwegian standard NS 3682:2022 Hollow Core Slabs for Reuse.Design/methodology/approachThis study proposes an Automated Checking Compliance (ACC) method to verify the compliance of IFC models with reuse-driven information contents for precast hollow core slabs. To achieve this, the IDS standard was selected to develop and test an openBIM validation workflow. The methodology includes three main steps: (1) identifying the minimum set of information requirements derived primarily from NS 3682:2022; (2) implementing these as IDS specifications linked to IFC entities, and (3) applying the workflow to a case study of a precast hollow core slab modelled in Autodesk Revit and exported to IFC4x3. The validation is performed using the open-source Bonsai add-on for Blender, while a complementary buildingSMART Data Dictionary (bsDD) is developed to ensure semantic consistency and standardised property definitions.FindingsThe results confirm that IDS enables effective automation of compliance checking for data presence and structure within IFC models. The IDS-based workflow reliably identifies missing information, highlighting the specific objects that fail the validation. By aligning rule-based validation with recognised standards, the proposed approach supports quality assurance for the digital representation of reusable hollow core slabs. Furthermore, the study establishes a standardised, machine-readable database of reuse requirements. The approach can be adapted and applied to other building components, promoting interoperability and reliability in digital marketplaces for reclaimed materials.Research limitations/implicationsWhile the proposed ACC workflow guarantees consistency and completeness of digital information, it does not assess the correctness or validity of underlying physical test results and of the initial data entry, such as mechanical properties or service life parameters. The applicability of the approach also depends on the digital maturity of stakeholders and the completeness of IFC models. Broader applicability will require further harmonisation of reuse-related standards and increased awareness of information requirements.Practical implicationsThe study illustrates how openBIM standards can be used within reuse-driven design and procurement processes through automated data validation. The combined use of IDS and bsDD enhances regulatory compliance, data transparency, and reliability of digital inventories of reclaimed components, lowering barriers to reuse, especially for small and medium enterprises.Originality/valueThis paper is among the first to apply the IDS framework to the context of building component reuse, translating reuse-oriented standards into machine-readable validation rules. It extends the use of openBIM standards from model verification to circular construction practices, supporting both digitalisation and sustainability efforts in the built environment. Specifically, the study contributes to improved interoperability and trust in digital marketplaces for reclaimed construction products.

This thesis investigates the thermal-hydraulic phenomena during a LBLOCA ina Nordic BWR with a focus on the temperature and pressure response in thewetwell gas space. The phenomena in the pressure suppression system weresimulated using GOTHIC thermal-hydraulic code. Reliable response of a pressuresuppression system is crucial for containing main steam line break accidents inBWRs to avoid leakage of radioactive materials to the environment. Therefore,it is a thoroughly researched subject but a gap seems to exist in evaluating theaccident with a maximum possible break area for the steam line. The objectiveof this thesis is to model the accident with maximum break size and evaluate theresults and suitability of chosen methodology.Two models with different approaches regarding steam venting were developedto compare which one leads to a better alignment with expected results oftemperature and pressure. Sensitivity studies were performed on the mainsimplifications of the model to study the impact on the response.Comparison with other studies shows that a model with lumped blowdownpipes yields more conservative results than the model with one large subdividedblowdown pipe in GOTHIC. However, the model with lumped pipes lacks incapability of monitoring processes in the pipe and modeling the heat gradientaccurately throughout the length of the pipe. Sensitivity studies emphasized theimportance of wetwell gas space grid resolution, number of modeled blowdownpipes and incorporating the heat radiation from blowdown pipe to the wetwellgas space into the model. The pressure safety criteria for the containment was notexceeded in any of the simulations in this study.Additional studies could be done on the condensation phenomena in the wetwellpool with total number of pipes and horizontal asymmetry of temperaturedistribution in the wetwell containment.

Generative video editing has enabled several intuitive editing operations for short video clips that would previously have been difficult to achieve, especially for non-expert editors. Existing methods focus on prescribing an object's 3D or 2D motion trajectory in a video, or on altering the appearance of an object or a scene, while preserving both the video's plausibility and identity. Yet a method to move an object's 3D motion trajectory in a video, i.e. moving an object while preserving its relative 3D motion, is currently still missing. The main challenge lies in obtaining paired video data for this scenario. Previous methods typically rely on clever data generation approaches to construct plausible paired data from unpaired videos, but this approach fails if one of the videos in a pair cannot easily be constructed from the other. Instead, we introduce TrajectoryAtlas, a new data generation pipeline for large-scale synthetic paired video data and a video generator TrajectoryMover fine-tuned with this data. We show that this successfully enables generative movement of object trajectories.

One of the most important problems in society today is the abundance of plastics and microplastics causing harm in nature. Because of this, the current need for a switch to wood fibre-based solutions to replace plastics is urgent, and one promising emerging process is the dry-forming of cellulosic fibres. Dry-forming is advantageous since it can produce biobased, recyclable products in complex shapes with mechanical properties in par with plastics. Since dry-forming is an emerging field, additional research is required to better understand the process. In this work the effect of different conditions for forming and the effect of hydrophobisation using Alkyl Ketene Dimer (AKD) has been investigated. It was found that an increase in tensile properties could be observed when increasing the pressing temperature from 100 to 140 °C with a difference in the type of tensile failure between these temperatures. Below 100 °C the main mechanism is fibre pull-out, and above 140 °C the failure is more chaotic indicating stronger interfibre bonding. The results from analysis of AKD-treatment were unexpected with small differences in tensile properties, where previous experiences show decreased mechanical properties when material is treated with AKD. The reason for this is not yet understood, but an interesting area for further research.

The transition toward sustainable energy systems requires the development of environmentally benign and resource-efficient materials, including membranes used in electrochemical and humidification processes. This thesis focuses on the sustainability assessment and optimization of Cellfion’s pilot-scale production of PFAS-free((per- and polyfluoroalkyl substances-free)), cellulose-based humidifier membranes, positioning the work at the intersection of sustainable energy engineering, materials innovation, and circular economy principles.

The study begins by mapping the pilot plant’s resource consumption through a Material and Energy Flow Analysis (MEFA), quantifying inputs such as water, energy, and chemical solvents. This baseline assessment identifies critical environmental and operational hotspots, with drying emerging as the largest energy consumer, washing stages driving significant water demand, and solvent losses contributing to hazardous waste generation. A Techno-Economic Assessment (TEA) complements this analysis by linking these environmental hotspots to cost structures, revealing that while fixed costs dominate at pilot scale, solvent losses and waste disposal remain non-trivial variable costs.

To address these specific challenges (excess energy use in drying, high water consumption, and solvent wastage), a scenario-based approach is employed, developing three optimization pathways: Energy Optimization through waste-heat recovery in drying, Water Optimization via process-water recycling, and Material Reduction through improved solvent management. These scenarios are informed by analogous studies in sustainable materials manufacturing and modeled using MEFA and TEA frameworks to assess their environmental and economic impacts.

The results show that targeted circular economy interventions can achieve notable sustainability gains without increasing production costs. Specifically, the Water Optimization scenario reduced freshwater use by over 35% and lowered material intensity by 34%, the Energy Optimization scenario decreased energy intensity by approximately 7%, and the Material Reduction scenario cut hazardous solvent waste by around 30%. All scenarios maintained unit production costs within ±1% of the baseline, confirming cost neutrality. Trade-off analysis reveals minimal cross-impacts, with the most notable being a slight energy increase in the water-recycling case.

In conclusion, the integrated assessment demonstrates that PFAS-free membrane production can be made significantly more resource-efficient while retaining economic viability. The findings provide actionable guidance for Cellfion’s scale-up strategy, offering a replicable methodological framework for early-stage sustainable process design in the energy materials sector.