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Utveckling av Rscueds hemsida - Redesign av en hållbarhetsinriktad hemsida med fokus på optimering av användbarhet och användarupplevelse.
No full textRscued is a company dedicated to reducing food waste by rescuing fruits and vegetables that would otherwise be discarded. These rescued fruits are then used to produce products such as juices, shots and smoothies. To increase product sales and spread their message, the company aims to improve the usability and user experience of their website. The purpose of this project was therefore to redesign Rscued’s website with a focus on simplified navigation, enhanced usability, an optimized shop and a stronger emphasis on their core message.
To achieve the project goals, an initial theoretical evaluation was conducted using methods such as HTA, CW and PHEA. This was followed by an initial user study with participants to identify problems with the current website. Based on these evaluations, analyses were carried out, including problem descriptions and a KJ-analysis. These formed the foundation for a requirements list that guided the following redesign process. The redesign began with idea generation in the form of wireframes, which were later conceptualized using the design tool Figma. With guidance from Rscued and in alignment with the requirements list, a final website concept was developed.
In the final concept, the implementation and optimization of functions and layout resulted in an improved version of Rscued’s website. In particular, five areas-homepage, product range, webshop, sustainability section and recipes were developed to create a more cohesive and user-friendly experience. The overall layout and structure including categorization and content were also enhanced to simplify navigation and improve usability.
To evaluate whether the website had actually improved, a second theoretical evaluation and a second user study was made. The updated usability test provided both objective and subjective data indicating improvements, with many of the original interface problems either reduced or entirely eliminated. Together, these changes have resulted in a new interface that is more user-friendly, functional and engaging, thereby supporting and strengthening Rscued as a company
Creation of a digital twin for simulation of melt pool geometry in metal additive manufacturing
No full textAdditive manufacturing (AM) is a production technique that allows for the creation of
complex geometries and components in near net shape by fusing the material layer by
layer. It is a versatile but expensive method and due to this cost, simulating the process is
of utmost interest. Since it requires complex multi-physics models at a very small scale,
producing simulations of desired accuracy takes a lot of time and computational power.
Simpler analytical models have shown promise as they run in a fraction of the time, but at
the cost of accuracy. The present work explores the possibility to train an analytical model
by adjusting material and process parameters to compensate for the lack of physics. The
goal is a digital twin of the more complex model, which generates melt pool geometries
comparable to the multi-physics model in a fraction of the time. This was accomplished
by minimising a cost function based on the width and depth of a number of isotherms in a
simulated melt pool in an electron beam melting AM process. The work demonstrates the
feasibility of using a low resolution analytical model with modified material and process
parameters as a digital twin to a multi-physics based simulation. Thus demonstrating
the feasibility of bridging the gap between computational efficiency and model accuracy.
It also establishes a foundation for more cost-effective and scalable simulations in AM,
advancing the potential for innovation in the field
Route planning for electric vehicles using Adaptive Large Neighborhood Search
No full textThe Electric Vehicle Routing Problem (EVRP) is an extension of the well-known
Traveling Salesperson Problem (TSP) including battery limitations and multiple
vehicles. Variants of this problem are formulated as mathematical optimization
models consisting of objective functions and constraints, that may vary depending
on the specific variant and the assumptions made.
A method that can be used to solve this problem is the Adaptive Large Neighborhood
Search (ALNS) which is a so-called meta-heuristic method where the solution
is destroyed and repaired iteratively to improve the solution over time. ALNS is
flexible and can be designed to fit various formulations of the EVRP. In this project
the aim is to investigate how our design and its implementation performs on different
variants of the problem.
To evaluate the performance, ALNS was compared against a MILP solver by
giving them the same initial solution and time limit. The evaluations were performed
on data of different sizes as well as with different customer distributions. The bestfound
objective value from an article using the same data instances and a problem
formulation similar to ours was used as an additional reference.
Our evaluation displays the efficiency of the ALNS, even if it was not possible
to make any strong conclusions due to the quality of the reference solutions for the
larger data instances. However, the algorithm and its implementation show great
promise for further development, both for adapting it to other problem formulations
and for improving the overall computational performance
Evaluating Guest Isolation on a Hypervised System
No full textAs mixed-critical systems become more prevalent in automotive systems, virtualization has emerged as a promising solution to reduce system complexity and improve costefficiency. This thesis investigates the ability of hypervisors to maintain temporal isolation between virtual machines (VMs) under conditions that simulate disturbances. Two general-purpose hypervisors, Xen and QEMU/KVM, are evaluated on an ARM-based Pi 4B using ZephyrOS as a Real-Time Operating System (RTOS) in both measurer and stressor roles. A test framework was developed to benchmark low-level latency operations and applicationlevel performance using adapted MiBench workloads (Qsort and Basicmath), and longterm scheduling behavior through thread metrics. Performance metrics were collected under various configurations, including stressed and unstressed scenarios across different CPU core assignments. The results show that while both hypervisors provide a baseline level of temporal isolation, their behaviors diverge under stress. QEMU/KVM generally demonstrates better raw performance and responsiveness, whereas Xen offers more predictable behavior in specific scheduling configurations. These findings underscore the trade-offs involved in selecting a hypervisor for real-time automotive applications and contribute to a broader understanding of how virtualization affects temporal determinism in embedded systems
Supporting snus cessation among young women in Sweden through digital nudging and a conversational AI
No full textThe rising prevalence of snus use among young women in Sweden has become a growing public health concern, especially with the increased popularity of tobaccofree nicotine pouches. Among women aged 16-29, daily snus use has increased by 500% between 2018 and 2024, highlighting an urgent need for tailored cessation strategies. Digital health interventions, such as mobile applications, offer a scalable and accessible alternative to traditional cessation methods. This thesis explores the development of a mobile snus cessation application tailored to young Swedish women aged 16-29, combining principles from nudge theory with support from a conversational AI chatbot. Through a literature review, state-of-the-art cessation
apps, and semi-structured interviews with young female snus users, the study identifies key behavioural, emotional, and social factors influencing snus use and quitting attempts. The resulting application incorporates digital nudges such as milestone tracking and health progress visualizations, alongside conversational support from the chatbot. Usability testing revealed that participants responded very positively to the conversational AI chatbot, describing it as helpful and empathetic. The majority also expressed that the integrated features designed in line with nudge theory,
such as the health timeline and achievement feature, would motivate them to use
the app regularly as part of their daily routine. This study contributes to the growing
field of digital health interventions and demonstrates the potential of combining
nudge theory and conversational AI in promoting snus cessation among targeted
populations
Camera simulation tool for automotive applications
No full textAs technology in the automotive industry consistency evolves, and driver aids get more advanced the need for advanced driver assistance systems (ADAS) grows larger. The need for quicker and more reliable simulation tools becomes essential to further evolve ADAS. This thesis presents the design and development of a simulation tool for 3D camera position testing. The tool created using blender and blender’s own Application Programming Interface (API) and blenders own node functions to create a realistic environment to improve performance in the sense that it produces a reduced gap in between simulation and actual performance. The simulation tool was created in collaboration with Polestar to create an improved simulation tool to an already existing version with the limitation of the previous tool being setup-time and a steep learning curve. By creating a personalized interface with real-time visualization before rendering set in a realistic environment and with adjustable camera settings the simulation tool. The tool enables engineers to seamlessly integrate CAD models into the simulation tool without adjusting coordinates. The finished tool was tested by Polestar’s ADAS team and found the tool to be both smoother while using the tool and a more efficient setup time which minimized time consumptio