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Simulating Light Dark Matter Signals at SHiP
No full textDark matter remains one of the most compelling mysteries in modern physics, with
a wide range of astrophysical and cosmological observations providing strong evidence
for its existence. Among the many proposed candidates, light dark matter,
with masses below the GeV scale, has gained increasing interest, partly due to its
accessibility in current and next-generation accelerator-based experiments. An interesting
framework for light dark matter involves a massive dark photon mediator
A′, which kinetically mixes with the Standard Model photon, enabling interactions
between the visible and dark sectors.
This thesis focuses on the sensitivity of the SHiP (Search for Hidden Particles)
experiment – a planned proton beam-dump experiment at the CERN Super-Proton-
Synchrotron (SPS) – to light dark matter scenarios. This experiment is designed
to probe feebly interacting particles and is expected to significantly extend the
parameter space coverage for light dark sector particles. The analysis adopts a
benchmark model characterized by a minimal dark sector with a complex scalar
dark matter candidate χ, a dark coupling αD = 0.1, and a mediator mass set to
mA′ = 3mχ.
Simulations are performed using two independent tools: BdNMC, a standalone
dark matter Monte Carlo generator, and MadDump, a plugin for the Mad-
Graph5_aMC@NLO framework tailored for beam dump and fixed-target experiments.
A comparative analysis of these tools is conducted in the context of
the complex scalar model, highlighting methodological differences and their impact
on predicted signal rates. Furthermore, the sensitivity studies for the SHiP experiment
are extended to fermionic dark matter scenarios, including both Dirac and
Majorana candidates, resulting in updated exclusion contours and emphasizing the
experiment’s discovery potential across these different dark matter hypotheses
Exploring Customer-Centric Energy Service Innovation & Development
No full textFacing challenges in the context of digitalization in Sweden’s energy sector, tradi-
tional firms are moving towards providing services and solutions to their customers,
distancing themselves from the traditional utility-driven perspective. This study
explores how digitalization enables customer-centric service innovation and devel-
opment by viewing the customer as an active participant in the process and by
using the benefits derived from digital customer touchpoints. Using a qualitative
approach, semi-structured interviews were held with energy service providers and
their customers to gather insights into customer engagement, digital interactions,
and market actors.
The findings show that customers are being increasingly included in the service in-
novation and development process, positioning them as active co-creators of value.
Respondents discussed that customers engage both explicitly through active touch-
points such as online customer panels and webinars, and implicitly through passive
touchpoints from which usage data and behavioral patterns can be derived. Ac-
cordingly, digital touchpoints were categorized into passive and active based on the
degree of provider involvement in interactions. The study also found that digital-
ization is changing the actor constellations in the energy sector. These changes pose
challenges for traditional energy service providers since they shift the use of digital
touchpoints to various degrees, reducing the possibilities for co-creative innovation
and development. Despite these challenges, the study highlights that effective man-
agement of digital touchpoints and customer co-creation enhance service innovation
and development. By matching digital touchpoints to customers based on their
behavior, needs, and preferences they can be used more efficiently when includ-
ing customers in the process of innovation and development. Therefore the thesis
concludes that energy service providers should take a more structured approach re-
garding digital customer touchpoints to match these to the needs of the customer.
In this the categorization into active and passive digital touchpoints and implicit or
explicit customer engagement may prove helpful
Gastroenterologisk endoskopimottagning En kartläggning över nuvarande behov och produktionsnivå vid Gastroenterologiska endoskopimottagningen vid Sahlgrenska Universitetssjukhus
No full textGastroenterologiska endoskopimottagningen (GEA) vid Sahlgrenska Universitetssjukhuset står
inför en utmanande situation där kapaciteten inte längre möter det växande vårdbehovet. Ökad
efterfrågan på avancerade undersökningar, förändrade sjukdomsbilder samt effekter från
coronapandemin har lett till en omfattande kö med över 3000 patienter, vilket i sin tur försvårar
möjligheten att uppfylla den nationella vårdgarantin. Mot denna bakgrund genomfördes denna
studie i syfte att kartlägga mottagningens efterfrågan och produktionsnivå samt identifiera
möjliga förbättringsåtgärder för att minska vårdkön och säkerställa att patienter får vård inom
medicinskt motiverad tid.
Studien har använt en mixad metodansats där kvalitativa data samlades in genom observationer
och intervjuer med personal på GEA, medan kvantitativ data inhämtades från verksamhetens
interna system. Detta möjliggjorde en bred analys av både flöden, resursutnyttjande och
schemaläggningspraxis.
Resultaten visar att det finns ett tydligt gap mellan inflödet av patienter och den faktiska
produktionen. Framför allt har kontrollpatienter trängts undan till följd av prioriteringar mot
akuta och elektiva flöden, vilket resulterat i att en majoritet av vårdkön utgörs av denna grupp.
Dessutom identifierades ojämnt utnyttjande av operationssalar, bristande standardisering i
administrativa processer och dynamiska flaskhalsar i den dagliga verksamheten.
Som lösning föreslås bland annat att en fast sal dedikeras för kontrollpatienter, att produktionen
utjämnas över arbetsdagen samt att en veckovis produktionsplanering införs. Dessa åtgärder
syftar till att förbättra resursutnyttjandet, öka förutsägbarheten och skapa ett mer balanserat
vårdflöde. Studien understryker även behovet av utökade resurser för att säkerställa en hållbar
produktionsökning. Genom ett mer strukturerat och strategiskt arbetssätt kan GEA på sikt åter
uppnå en vårdproduktion i linje med efterfrågan
User Study for In-Vehicle Displays
No full textThis master’s thesis investigates how Head-Up Displays (HUDs) influence driver behaviour in real-world traffic, with a focus on attention, distraction, and cognitive load. The study examines a conventional HUD installed in a Volvo XC60 using eyetracking technology. A mixed-methods approach was employed, combining quantitative analysis of gaze dwell time and NASA-TLX scores with qualitative insights from thematic analysis of participant feedback. The research compares driver behaviours in HUD and non-HUD conditions. Findings indicate that while the presence of a HUD reduces visual attention to Head-Down Displays (HDDs) and is generally perceived as enhancing safety, usability concerns were raised—especially regarding the navigation interface. Although the HUD did not significantly affect the overall cognitive load, some participants reported visual discomfort. The study highlights both the potential benefits and challenges of HUD implementation and calls for further research with more diverse participants and multimodal HUD designs to ensure safe and user-friendly automotive interfaces
AI in Construction Management: Preparedness and potential. A case study on implementing a predictive machine learning framework for construction project scheduling
No full textPersistent challenges related to project delays continue to plague the construction
industry, an industry often characterized as outdated, low-productivity, and unpre dictable. These challenges are amplified by the complexity of infrastructure projects,
external pressures, and the historically slow adoption of digital technologies. De spite generating large volumes of data, the industry struggles with inconsistent data
collection and effective utilization. To address these limitations and underline the
importance of robust data management, this thesis explores the integration of ML based predictive models to improve decision-making in project management. In col laboration with NCC, the complex Ingelkärr–Stenkullen transmission line project
served as a case study. A hybrid forecasting model was developed, combining
Monte Carlo simulations with a neural network-based ML approach. The Monte
Carlo simulations generate a wide range of potential project completion timelines,
incorporating variations in task durations and task-specific characteristics. These
simulated outcomes serve as the foundational training data for the neural network.
A key technical contribution of this work lies in the model’s dynamic weekly updates
with real-world progress data, enabling adaptive learning. Task dependencies were
processed using GPU acceleration, and an attention mechanism allowed the neural
network to capture task interactions, enhancing predictive accuracy. Interviews with
NCC and Svenska Kraftnät project managers and engineers informed the model’s
user interface, ensuring transparency and improved decision-making. Results showed
high predictive accuracy (R2 = 0.92), which improved over time, highlighting the
value of combining data-driven methods with traditional management strategies.
Ultimately, this thesis demonstrates the critical need for next generation planning
systems in construction, focusing on intelligence, adaptability, and transparency.
The proposed framework shows strong potential to transform industry practices by
significantly improving risk forecasting, optimizing resource management, and in creasing responsiveness to uncertainty, thereby offering a pathway to more efficient
and resilient project management in constructio
Energy Efficient Open-Source RISC-V Processor for Automotive Workloads
No full textModern vehicles integrate a growing number of electronic control units to perform
key tasks, such as driver assistance, smart braking, and steer by wire. This presents a
challenge for the transition towards battery electric vehicles as any power consumed
by embedded systems cannot be used for driving, directly reducing vehicle range.
As a result, energy efficiency is of central concern as automotive software grows
more complex. Another factor is ease of development, with closed systems requiring
expensive and specialized software for development, making them expensive and
difficult to maintain. Moreover, processors traditionally used in the automotive
space are proprietary which enables vendor lock in. Exploring alternative processor
architectures has great potential for meeting the evolving requirements. The open
and flexible nature of RISC-V makes it particularly well-suited to address the
performance, safety, and energy-efficiency challenges of modern automotive systems.
The instruction set being open allows anyone to design a compatible processor which
can enable competition and result in cheaper and more efficient designs. Additionally,
the design can be tailored to the specific performance and power requirements of the
application. Finally, software development can leverage open-source tools, reducing
the need for expensive licenses. This thesis investigates the suitability of the NOEL-V,
an open-source RISC-V processor, for use in automotive systems. It is compared
to a commercial automotive ECU based on Infineon TriCore TC399XP in terms of
performance and power consumption by porting a climate system to RISC-V and
evaluating it on an FPGA implementation of NOEL-V. The performance is assessed
through cycle count and schedulability of real-time tasks, while power consumption
on a 45 nm process is estimated using EDA tools. For the ECU, the performance
metrics of the TriCore are collected on the real hardware through Lauterbach debug
tools, and power consumption is estimated indirectly via the system-level power
increase during workload execution. The results show that although the NOEL-V
platform does not achieve the same raw performance as the TC399XP, it is sufficient
for the climate application and requires fewer clock cycles per instruction. The
project also identifies major power hotspots in the RISC-V processor under the
target workload, and proposes directions on optimizing power dissipation for future
RISC-V-based automotive processors
On automated testing of ship control and monitoring system in submarine
No full textAs technology advances, increasingly complex and autonomous engineering systems emerge. Given our daily dependence on them, these systems must work as intended. Testing is used to verify that the systems behave correctly according to given specifications. In industry, testing is often performed manually by test engineers. However, this approach is expensive, time-consuming, repetitive, and error-prone. A potential solution to these issues is test automation. This thesis employs a design-science research methodology to investigate test automation at a case company. This resulted in the development of an automated testing system for a ship control and monitoring system in a submarine. The automated testing system consists of three different variants of test automation. The first variant involves creating scripts corresponding to manual test procedures in the current documentation. The second and third variants of the automated testing system comprise the testing approach falsification. Optimization-based falsification involves using a simulation model to automatically identify input signals that cause a system to violate given specifications. Quantitative semantics are used to assess how close a scenario is to violating the specifications. The optimization problem includes decision variables that determine the type and shape of the generated input signals. The proposed automated testing system has been assessed according to different measures. Combined, the automated testing system has proved that it confronts the business needs of the case company, where it enhances the productivity and quality of the testing activities. While the initial time spent on creating test scripts may be considerable, the long-term benefits become evident with saved efforts and costs, especially when most systems are tested frequently
Linear and nonlinear mapping of sea surface waves imaged by synthetic aperture radar
No full textOcean observation is a powerful tool for ocean monitoring of sea surfaces. With
the help of SAR it becomes easier to monitor large wave fields. The purpose of
this project is to provide a simulator which when given the wind speed and wind
direction, to generate the ocean wave field equivalent to those parameters, to simu late the SAR image, and finally to provide the transform for the linear, quasilinear,
and nonlinear mapping. In the simulation, an analysis is performed on the validity
of the three transforms, where different wind speeds, wind directions, and Slant
Range-Velocity ratios β are used. The results, show that the linear and quasilinear
transforms can be used when the value is β is low. On the other hand, the non linear transform can always be used, in many different scenarios. Remaining work
to be done is an inversion algorithm to extract the ocean parameters from the SAR
spectrum, an analysis of the effect of the ocean currents and finally the analysis of
nonlinear waves
Lock-Free Queues in Rust
No full textThe Rust programming language offers strong compile-time safety guarantees and a modern approach to concurrency, positioning it as a promising candidate for developing concurrent data structures. Despite this, the ecosystem of lock-free data structures in Rust remains fragmented with significant variation in quality, performance and correctness. This thesis addresses these deficiencies by surveying the current state of lock-free concurrent queue implementations in Rust, identifying critical flaws in existing crates and benchmarking them alongside well-established C/C++ implementations. To enable consistent evaluation, we designed and developed a benchmarking framework to measure throughput, fairness, memory usage and ordering guarantees under various configurable workloads. Furthermore, we implemented several state-of-the-art lock-free queue designs in Rust and analysed their performance, highlighting challenges such as memory reclamation and limitations in Rust’s type system in low-level concurrency contexts. Our results expose serious issues in popular crates and demonstrate through our implementations that high-performance, safe, and verifiable lock-free queues are achievable in Rust. In doing so, this work takes a step toward addressing the fragmentation in Rust’s lock-free concurrency landscape