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Techno-economic assessment of waste heat recovery from electrolyser plants and its application in district heating networks
No full textÖkade stängningar av Göteborgs råvattenintag - En analys av saltvatteninträngning, bakteriehalter och väder
No full textGothenburg’s drinking water supply is highly dependent on raw water from Göta
älv, which is a source that is increasingly challanged by climate change, agriculture
and urbanization. This study aims to understand the causes and timing of intake
closings at Lärjeholm by analyzing raw water quality data from Kretslopp och vatten
and metrological data such as temperature and precipitation from SMHI from 2001
to 2025. The main focus on saltwater intrusion, bacterial contamination (particular ly E.coli), and weather-related parameters. By using these data, seasonal patterns
and trends were identified and then analyzed. The results show that intake closures
are often linked to elevated E.coli levels indicating fecal contamination, or high con ductivity, indicating saltwater intrusion. This study has been able to correlate 85%
of the closings to bacteria and saltwater intrusion while 15% remain without clear
cause. The findings emphasize the importance of continuous monitoring, further re search and development of solutions to ensure a sustainable drinking water supply
for Gothenburg
Optimizing Polymeric Nanoparticle-Based mRNA Gene Delivery Materials- Aiming to Control Cellular Glycosylation
No full textThe ability to modulate cellular glycosylation through gene expression holds significant promise for advancing immunotherapies and precision medicine. In this thesis, we developed and optimized poly(lactic-co-glycolic acid) (PLGA)-based nanoparticles for mRNA delivery, aiming to establish a biocompatible platform for targeted glycosylation control. Messenger RNA was first condensed using poly(amidoamine) (PAMAM) dendrimers and then encapsulated into PLGA nanoparticles (NPs) using a modified double emulsion technique. The resulting mRNA-loaded NPs exhibited sizes ranging from 160–220 nm with favorable positive surface charge, and mRNA/PAMAM cargo loading of 20% regardless of the mRNA type. Confocal imaging confirmed successful intracellular delivery and protein expression in macrophage cells, while in vitro experiments indicated improved protection of mRNA from degradation and enhanced transfection stability compared to mRNA/PAMAM complexes alone. Collectively, this work presents a robust and scalable polymeric platform for mRNA delivery and demonstrates its potential in immune targeted applications. These findings offer a promising basis for future efforts to regulate glycosylation pathways via mRNA delivery
Fouras - Automated Android Accessibility Assessment
No full textThis thesis investigates systematic methods and tools applicable to improve the accessibility feature compliance testing of Android applications. Focusing on how software engineers can better implement design their applications to simplify testing and verify compliance with European accessibility standards, such as EN 301 549 [1] and WCAG 2.1 [2]. We aspired to assist developers and organizations in improving their Android application accessibility support and promote digital inclusivity for impaired users relying on accessibility features. We examined the capabilities and limitations of current accessibility verification tools to suggest strategies to incorporate validation tests into the development workflow. We identify accessibility verification challenges and offer automated validation methods where applicable to reduce manual labor. Our research was performed with an iterative design-based approach, resulting in a developer-approved framework
that offers systematic design approaches and automated tests to simplify the compliance process. The framework provides developers and organizations with solutions to gradually improve accessibility compliance. To ensure industry relevance, we continuously obtained feedback from senior professional Android developers throughout the study to improve the implementation of accessibility features in the development process. According to prior research, Android accessibility features are rare in Android applications. With this study, we seek to change that and provide an equal application experience for all users
Performance Evaluation of SYCL on RISC-V Vector Architectures
No full textThis paper explores the vectorization capabilities of different SYCL compilers, targeting processors with RISC-V Vector Extension, RVV. The primary aim is to evaluate the readiness of various SYCL backends of the AdaptiveCpp SYCL compiler, namely the OpenMP JIT, OpenMP AOT and OpenCL backends. We detail how each approach compiles to RISC-V assembly code, examining how vector instructions emerge or fail to do so across different these different compilation methods. A major part of the work involves discussing how SYCL backends can be configured or adapted to target RVV through toolchains such as LLVM, while also pointing out limitations in support. To support our investigations, QEMU, a general purpose emulator, was used to compile and run RISC-V binaries, and RAVE, a QEMU extension, was used to log instructions executed. These tools were essential in validating whether vector instructions are generated, but also to validate that they are executed. The results show that support for RVV across the ecosystem remains fragmented. Portable Computing Language does not support RVV, nor does it aim to. OneAPI construction kit shows signs of RVV support as vector instructions appear in assembly, however RAVE traces confirm that actual execution does not occur. LLVM offers solid RVV support, but using it with AdaptiveCpp requires modifications to enable full vectorization. In conclusion, although support for RISC-V vectorization is progressing, the broader SYCL ecosystem and its heterogeneous compilation pathways remain underdeveloped, requiring significant manual effort and tooling adaptation to achieve practical performance portability
Adversarial Inverse Reinforcement Learning for Energy-Efficient Marine Vessel Operation
No full textAbstract
The maritime industry serves as a backbone of global trade, transporting nearly 80% of goods worldwide, making it indispensable for the global economy (Statista,2024; UNCTAD, 2024). Yet, according to UNCTAD, this sector remains a notable contributor to greenhouse gas (GHG) emissions, accounting for 2-3% of total emissionsa figure projected to rise dramatically by 2050 under business-as-usual scenarios. Addressing this escalating environmental impact requires innovative strategies for optimizing vessel operations and minimizing fuel consumption, which are crucial for sustainable maritime practices. This thesis focuses exclusively on the application of adversarial inverse reinforcement learning (AIRL) to tackle these challenges.
AIRL leverages expert demonstrations from seasoned captains who have successfully navigated complex and diverse maritime conditions. By analyzing these expert behaviors, AIRL derives comprehensive reward function that encapsulate optimal operational strategies, paving the way for the development of a control policy. The strength of AIRL lies in its ability to capture the nuances of expert judgment, enabling marine vessels to replicate the energy-efficient decisions made by experienced captains. Unlike traditional control systems, which often struggle with the nonlinear dynamics and diverse challenges of maritime environments, AIRL offers a robust data-driven solution tailored to the specific operational needs of the vessels.
By integrating AIRL, this research aims to enhance energy efficiency, reduce fuel consumption, and lower carbon emissions. In doing so, it contributes not only to the operational goals of modern marine vessels but also to the broader environmental objectives of the global community. This work aligns with the maritime industry’s transition toward greener practices and supports the adoption of sustainable solutions
Investigation of k-rationality & cutting plane methods in sequentially connected 2D assignment problems
No full textInteger linear optimization problems are generally difficult to solve efficiently due to
the discrete nature of their solution spaces. A common approach to address these
difficulties is to relax the integrality constraints, solve a continuous optimization
problem, and utilize different methods to recover near-optimal integer solutions’.
This thesis explores the usage of such approaches in the context of the Time-extended
Generalised Optimal Sub-Pattern Assignment (TGOSPA) metric—a performance
measure for the evaluation of multiple object tracking algorithms. Specifically, this
can be seen as examining a binary sequentially connected 2D assignment problem
where the binary requirement has been relaxed. Two primary methods for recovering
binary optimal solutions are considered; one based on the concept of k-rationality
and the other on using different cutting plane methods derived from fractional Gomory
cuts. The results suggests that k-rationality is numerically unsuitable for this
specific problem, and that the Gomory-based cutting methods lack performance
consistency.
Finally, an outline of several different directions for future research, based on the
observations made in this thesis, is presented
