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Senast publicerade

Minskar klädföretags miljöbelastning vid strategiskt arbete mot returer?
(2026) Björk, Alma; Forsaeus, Tilda
Returns constitute a significant problem within the fashion industry, where the increasing popularity of fast fashion bought online contributes to a higher number of incorrect purchases. These returns result in high levels of carbon emissions, partly because clothing production is largely fossil-fuel based, and partly because returned clothes often do not reach a new customer. The purpose of this study is to examine how strategic and preventive efforts to reduce returns can lower companies’ carbon emissions and to evaluate whether this is a prerequisite for more sustainable consumption. The study is based on a mixed-method approach, combining qualitative interviews with quantitative analysis of sustainability data from companies in the industry. The results indicate that strategies such as improved product descriptions, size guides, return fees and analysis of return reasons are central to reducing returns. Furthermore, unsustainable consumption patterns, such as incorrect purchases because of fast fashion, contribute to increased return volumes. Reducing returns can therefore lead to decreased overproduction and reduced carbon emissions. The study concludes that at more personalized shopping experience and greater size consistency can reduce incorrect purchases, while customer behaviour also needs to change through increased awareness. To enable this companies must also adapt their strategies in line with EU directives under the European Green Deal.
Rare Earth Elements in Moss in Sweden Spatial Patterns and Temporal Comparisons
(2026) Gunnarsson, Johanna
Rare earth elements (REEs) are increasingly used in modern technologies such as wind turbines, electric vehicles and electronics. Despite their growing importance, there is still limited knowledge about how these elements spread and behave in the environment. Mosses are often used as biomonitors of air pollution due to their ability to accumulate elements directly from atmospheric deposition. This thesis investigates temporal and spatial patterns of REEs in Swedish moss between 2015 and 2025, compares Swedish concentrations with those reported in international studies and evaluates correlations between REEs and selected metals in order to assess potential sources. The work was based on concentration data from moss samples collected within the Swedish national environmental monitoring programme. Samples from 2015 and 2025 were analysed using descriptive statistics, temporal comparisons and GIS-based spatial analysis. In addition, enrichment factor calculations and correlation analyses with aluminium (Al), lead (Pb) and antimony (Sb) were carried out to support interpretation of REE sources and behaviour. The results showed that REE concentrations in Swedish moss were generally low and followed the natural abundance pattern of the Earth's crust, with light REEs (LREEs) occurring at higher concentrations than heavy REEs (HREEs). Similar spatial distributions were observed in both 2015 and 2025, with somewhat elevated concentrations mainly in southern and central Sweden. Most REEs showed modest increases in concentration between 2015 and 2025, while the overall spatial patterns remained similar. Strong correlations between REEs and aluminium indicated that crustal material is an important contributor to the observed concentrations. However, local hotspots and enrichment patterns suggested that anthropogenic influences may occur in specific areas. Overall, the results suggest that REEs in Swedish moss are predominantly influenced by natural background sources, although the observed temporal increases and local enrichment indicate that continued environmental monitoring remains important.
Modelling and inference of interacting cell dynamics using single time-point images
(2026) Franzén Dennis, Patrik
Understanding cell dynamics, such as migration, proliferation, and interactions are central in cancer biology, particularly for modeling invasive tumors like glioblastoma. High-throughput in vitro assays frequently rely on single time-point (endpoint) im- ages, which lack direct temporal data, presenting a significant methodological chal- lenge for parameter estimation. This thesis develops a comprehensive computational pipeline to infer interacting cell dynamics exclusively from endpoint phase-contrast microscopy images using Approximate Bayesian Computation (ABC). The workflow integrates a robust U-Net segmentation model with a ResNet34 back- bone and boundary-aware probability shaping, alongside a velocity-predictive track- ing algorithm to mitigate survival bias and extract unbiased biological features. We evaluate an agent-based model utilizing overdamped Langevin dynamics proposed and used in against an advanced Fractional Brownian Motion (fBm) framework de- signed to capture directional persistence. Our results demonstrate that while the fBm model accurately captures the super-diffusive memory inherent to glioblastoma motility, this increased model capacity introduces parameter degeneracy at a sin- gle temporal endpoint. To navigate the resulting non-linear parameter manifolds, Sequential Monte Carlo (SMC-ABC) proved strictly superior to standard Rejection and Regression-Adjusted ABC variants. Furthermore, we expose an important "Sim-to-Real Gap" regarding summary statis- tics: while continuous Topological Data Analysis (TDA) via Persistence Images excels on idealized in vitro data, it suffers from ”topological fragility” when ex- posed to real-world biological noise and segmentation artifacts. Consequently, a hybrid summary statistic combining the Pair Correlation Function (PCF) with dis- crete Betti curves emerged as the most robust metric for experimental inference. Ultimately, this work establishes that extracting complex dynamic memory from static snapshots is mathematically viable, provided that physical model capacity is carefully balanced with robust spatial statistics and adaptive, non-linear Bayesian algorithms.
Video as Observational Data for Machine Resetting
(2026) Jonasson, Samuel; Rask, Benjamin
Machine resetting is a growing share of lost production time in high-mix, low-volume manufacturing. At SKF, resets on the SGP 320 shoe centerless grinding machine have become more frequent, longer, and more variable, motivating the search for new observational data sources to inform a future multimodal AI-based operator assistance system. Within this context, this exploratory single-case study evaluates video as such a source. Reset events were recorded with a wide-angle RGB-D camera and an action camera. These recordings were analyzed at four levels: manual phase annotation in BORIS, operator tracking using YOLOv8 with depth-based zone analysis, R3D-18 video embeddings with cosine similarity and PCA, and visibility detection using Qwen3-VL 8B. Video analysis was further complemented by two expert interviews, the official machine manual, an OPC parameter-snapshot pipeline, and a VNC-based HMI screen recording. While video reliably captured the observable execution of a reset, it missed internal machine states and experience-based decisions. Its usefulness depended on camera placement, visibility, and analytical method. The findings position video as a valuable observational layer for capturing the execution of a reset, but one that must be combined with machine-state and interface data to support a future multimodal system.
Power-to-heat-to-power (P2H2P) A scenario analysis
(2026) Persson, Linus; Sonnerup, Alve