This study examines why collaboration in the practical work against honour-related violence and oppression in Sweden often fails, despite an extensive policy framework, and how a governance perspective can help to understand these obstacles. Honour-related violence and oppression is a complex societal problem involving numerous actors from different parts of the public sector and civil society. Previous research has shown that collaboration is crucial for effective protection efforts, but also that accountability, information sharing, and organizational coordination often present significant challenges.

The study is based on six qualitative interviews with representatives from a government agency, the judiciary, municipal administration, civil society organization, and two non-profit organizations. The analysis is thematically structured and built upon a theoretical framework focusing on governance, network governance, collaboration, policy instruments, and policy implementation.

The results show that the governance structure in honour-related violence and oppression efforts is characterized by unclear mandates, person-dependent collaboration, and a lack of long-term steering mechanisms. The role of civil society appears central in practice but is weakly anchored in formal governance structures, creating a gap in accountability. At the same time, examples of effective collaboration are identified, where trust, informal channels of communication, and shared forums have contributed to increased efficiency.

The study contributes to a deeper understanding of how governance functions in practice in a policy context where multiple actors share responsibility, but where the conditions for joint governance are still insufficient. The thesis concludes with a discussion on policy implications and the need to institutionalize effective forms of collaboration.

Traditional camouflage development relies heavily on manual design and iterative field testing, which are resource-intensive and limited in adaptability. This thesis explores the use of generative artificial intelligence for the design of camouflage patterns, with a focus on Latent Diffusion Models (LDMs), such as Stable Diffusion 3 (SD3). A method is proposed for generating context-specific patterns that visually blend into natural envi- ronments, using a pipeline that transforms target backgrounds into shuffled data grids as input to the generative model, with the possibility of applying structural segmentation and a constrained color palette. To evaluate the effectiveness of the generated camouflage, both computational metrics such as Image Color Similarity Index(ICSI) and Gradient Magnitude Standard Devia- tion(GMSD) is used in conjunction with a human visual assessment. Results show that key preprocessing parameters, particularly image block size and latent strength, have a significant impact on the quality. While digital patterns showed promising alignment be- tween human perception and edge- and color-based computational metrics, SSIM proved unreliable for camouflage assessment. Printed patterns presented additional challenges due to material properties influencing visibility. The findings suggest that LDMs can produce effective camouflage patterns suitable for specific environments, although improvements in environment representation and evaluation are possible.

Determining the postmortem interval (PMI)–the time since death–is critical in forensic science. Yet, traditional methods often become unreliable due to the variable nature of biological processes after death. Recent advances in machine learning (ML) and metabolomics offer new opportunities for more accurate and interpretable PMI estimation. However, most ML approaches provide only point estimates without conveying the model’s confidence.

This study uses Monte Carlo Dropout (MCD) to predict PMI from metabolomics data, providing a corresponding prediction interval, and investigates Variational Autoencoder(VAE) for capturing uncertainty in the data structure, reflecting variability in the metabolomics data.

The best-performing MCD model, a multilayer perceptron with dropout active during inference, achieved a mean absolute error (MAE) of 1.57 days and showed a moderate correlation between Mean Prediction Interval Width (MPIW) and model error, indicating that uncertainty estimates can reflect predictive confidence. Increasing the dropout rate improved Prediction Interval Coverage Probability (PICP) but also widened MPIW, highlighting a trade-off between reliability and precision. The VAE, while effective at reconstructing data, showed limited utility for detecting out-of-distribution samples.

These findings highlight the importance of incorporating uncertainty estimation into PMI prediction, as it enhances the interpretability and applicability of the results, making them more defensible in forensic contexts.

This thesis explores the potential for automating the risk classification of medical devices under the European Medical Device Regulation (MDR) Annex VIII. The classification process, typically requiring expert regulatory knowledge, poses a significant challenge for start-ups and small manufacturers. 

To address this, the project first examined the MDR and the classification process in detail to identify key decision points and rule structures. Based on this analysis, a rule-based decision tree was implemented in Python, and an ontology was developed in Protégé using OWL and GCAs to structure and interpret classification logic. The decision tree offers a transparent and user-friendly pathway through MDR rules, while the ontology demonstrates the potential for representing semantic relationships within regulatory content.

A case study evaluation reveals that both approaches are effective for well-defined scenarios but fall short in edge cases where interpretation beyond the text of Annex VIII is required, in this case, external guidance documents such as MEDDEV. These findings suggest that while partial automation is achievable, full automation is limited by the need for contextual interpretation and expert input.

The project concludes that automated tools can support, but not replace, regulatory judgment. They offer value as educational and decision-support resources, particularly for non-expert users. 

One of the biggest industries within sports is football. With increased access to valuable sports data, the need for sports analytics has grown and become an important resource for increased competition in professional football. This thesis explores the improvement of football data dashboards through a human-centered design approach, with the goal of optimising usability and reducing the risk of misinterpreting the data. Specifically, to simplify the creation of customised dashboards tailored to users’ needs. The study focuses on the PlaymakerAI platform. Where existing usability limitations were addressed through interviews with the target group; scouts and match analysts. The literature review and interviews helped shape the mockups, which were based on UX design principles and dashboard layout patterns. Key improvements focused on clearer visuals, a more flexible workflow and easier data filtering and navigation. The final mockups introduced features such as linked views, interactive filtering and clearer visual explanations. These were evaluated through an early concept evaluation and a comparative analysis with the current platform. Results indicate that the proposed solutions improve user experience by sup- porting different analytical tasks and decision-making processes for both scouts and match analysts. The thesis highlights the importance of tailoring visualisation tools to specific user goals within sports analytics platforms.

Automatic facial recognition technologies are widely used in modern society and users should be able to trust that these systems are fair and non-discriminatory. The fairness of a system can be measured by the performance across demographic groups. To ensure trust that these systems are fair, the evaluation should be based on statistical tests instead of observed values.

This thesis explores how statistical validation can be performed for an algorithm performing automatic facial recognition. The algorithm can make two types of errors: a false non-match and a false match. A false non-match occurs when the algorithm fails to recognize two images of the same individual as being the same individual. A false match occurs when the algorithm incorrectly identifies two images of different individuals as the same individual.

Four different evaluation metrics were considered for statistical validation. Differential error measures the difference between the demographic group the algorithm performs the worst with the demographic group the algorithm performs the best. The ratio of worst and best case error rates measures how many times worse the algorithm performs between the two demographic groups where the algorithm performs the worst and the best. The ratios normalized by the arithmetic mean measures how many times worse the algorithm performs for the worst relative to the arithmetic mean. The ratios normalized by the geometric mean measures how many times worse the algorithm performs for the worst relative to the geometric mean.

Simulations were performed to investigate whether there could be biases in the evaluation metrics. The largest observed error rate can be biased towards larger values if the true sampling distribution of demographic group where the algorithm performs the worst overlaps with the true sampling distributions of any other demographic group. Conversely, the smallest observed error rate can be biased towards lower values if the true sampling distribution of the demographic group where the algorithm performs the best overlaps with the true sampling distribution of any other demographic group. These potential biases could affect the point estimation of the evaluation metrics to be biased towards larger values.

Statistical validation was performed on an algorithm using the Color Facial Recognition Technology data set. Individuals were divided into demographic groups based on age, sex, and skin color. For each demographic group, the sampling distribution was estimated with bootstrapping and normal approximation. Bootstrapping could be applied for all demographic groups, whereas normal approximation could only be applied to seven out of twenty demographic groups.

Improved self-esteem and body image, as well as reduced experiences of weight stigma are important patient-reported obesity treatment outcomes. However, more knowledge is needed about how individuals who have undergone metabolic and bariatric surgery (MBS) perceive themselves and their bodies and use different coping strategies in relation to body image and self-esteem long-term after MBS. In this qualitative study body image, self-esteem, weight stigma and coping strategies were explored among 18 individuals who underwent MBS more than 10 years ago when interviewed. Using reflexive thematic analysis, two primary themes were identified: 'Experiences of living with a stigmatised body' and 'Coping with weight stigma, body image and self-esteem', and eight sub-themes. Findings capture frequent experiences of weight stigma before bariatric surgery, the need for coping with stigma and body dissatisfaction before and after MBS, and how different coping strategies are related to participants' perceptions of their bodies and self-concepts. More adaptive coping strategies, such as confrontation and cognitive restructuring may facilitate more positive body image outcomes, than more ruminative and avoidant strategies. Understanding adaptive coping strategies can be useful to develop interventions to reduce negative consequences of weight stigma on body image and self-esteem.

The rise of satellite-based Wi-Fi through large-scale satellite constellations offers unprecedented global connectivity, requiring only a small receiver for internet access fromvirtually anywhere. However, reliance on this technology introduces new risks that are not present in normal land based networks. As the satellite communications are reliant on vulnerable GNSS signals to properly function, attackers are provided with an easy target to potentially disrupt service. Further, space phenomena regularly disrupt communications between ground stations and satellites, risking serious disruptions for users. While previous research has explored the viability of different GPS spoofing techniques, the impact of such attacks on satellite communication systems remains largely unexplored. In a similar vein, little research has been done to attempt to quantify the impact of space weather phenomena on low earth orbit satellite communication systems. This thesis seeks to fill these research gaps through a controlled experiment using a HackRF One to perform an impersonation GPS spoofing attack against a Starlink receiver and data collection and correlation of user error reports with different space weather statistics. For the experiment, a total of 63 tests were successfully performed, with 54 being shorter network speed tests and 9 being TCP-traces of large file downloads. For the data correlation, data was collected for 36 days over a three month period. The experimental results showed that the network performance remained largely unaffected, with the main change being a increased variance in network performance metrics when the spoofing was occurring. One example of this can be seen in the download speed, which changed at most +/-10 Mbit/s for all spoofing distances. These findings indicate that Starlink receivers either employ additional built-inprotections against spoofing or mitigate such attacks by leveraging the Starlink satellite constellation for location verification. The space weather data correlation indicate that the Starlink network behaves in line with other satellite systems, with both magnetospheric charge and the number of high energy solar protons being correlated with the number of user error reports with a high degree of certainty. While this research provides some initial insights, further research is recommended, especially in relation to the effects of GPS spoofing. Modifying the Starlink hardware to obtain accurate GPS information directly from the receiver or replacing the impersonation attack with a more sophisticated attack method could provide further information on the topic.

Objective: To examine income-related inequality changes in the outcomes of an osteoarthritis (OA) first-line intervention. Design: Retrospective cohort study. Setting: Swedish health care system. Participants: We included 115,403 people (age: 66.2§9.7 years; females 67.8%; N=115,403) with knee (67.8%) or hip OA (32.4%) recorded in the “Swedish Osteoarthritis Registry” (SOAR). Interventions: Exercise and education. Main Outcome Measures: Erreygers’ concentration index (E) measured income-related inequalities in “Pain intensity,” “Self-efficacy,” “Use of NSAIDs,” and “Desire for surgery” at baseline, 3-month, and 12-month follow-ups and their differences over time. E-values range from-1 to +1 if the health variables are more concentrated among people with lower or higher income. Zero represents perfect equality. We used entropy balancing to address demographic and outcome imbalances and bootstrap replications to estimate confidence intervals for E differences over time. Results: Comparing baseline to 3 months, “pain” concentrated more among individuals with lower income initially (E=-0.027), intensifying at 3 months (difference with baseline: E=-0.011 [95% CI:-0.014;-0.008]). Similarly, the “Desire for surgery” concentrated more among individuals with lower income initially (E=-0.009), intensifying at 3 months (difference with baseline: E=-0.012 [-0.018;-0.005]). Conversely, “Self-efficacy” concentrated more among individuals with higher income initially (E=0.058), intensifying at 3 months (difference with baseline: E=0.008 [0.004; 0.012]). Lastly, the “Use of NSAIDs” concentrated more among individuals with higher income initially (E=0.068) but narrowed at 3 months (difference with baseline: E=-0.029 [-0.038;-0.021]). Comparing baseline with 12 months, “pain” concentrated more among individuals with lower income initially (E=-0.024), intensifying at 12 months (difference with baseline: E=-0.017 [-0.022;-0.012]). Similarly, the “Desire for surgery” concentrated more among individuals with lower income initially (E=-0.016), intensifying at 12 months (difference with baseline: E=-0.012 [-0.022;-0.002]). Conversely, “Self-efficacy” concentrated more among individuals with higher income initially (E=0.059), intensifying at 12 months (difference with baseline: E=0.016 [0.011; 0.021]). The variable ’Use of NSAIDs’ was not recorded in the SOAR at 12-month follow-up. Conclusion: Our results highlight the increase of income-related inequalities in the SOAR over time.

Purpose: To investigate the influence of surgical volume, and various patient-, injury-, and surgery-related factors, on meniscal treatment strategies in primary anterior cruciate ligament reconstruction (ACLR).

Methods: This retrospective cohort study analyzed patients with concomitant meniscal injuries undergoing primary ACLR, from 2008 to 2022, using data from the Swedish National Knee Ligament Registry. Surgeons and clinics were stratified by total caseload (cutoff: 50 ACLRs/surgeon, 500 ACLRs/clinic) and annual volume (cutoff: 29 ACLRs/year/surgeon, 56 ACLRs/year/clinic). To assess factors influencing medial meniscus or lateral meniscus repair, adjusted multivariable logistic regression was conducted, with results presented as odds ratios (OR) and 95% confidence intervals (CI).

Results: In total, 20,699 patients undergoing primary ACLR with concomitant meniscal injuries were included. Lower percentages of meniscus repair were seen among surgeons with low caseload and annual volume (LCLV) (13.3%-20.8%) compared with high caseload and annual volume (HCHV) surgeons (19.0%-29.8%), and at LCLV clinics (11.1%-18.3%) compared to HCHV clinics (21.5%-33.8%), all P < .001. Significantly decreased odds of medial meniscus repair were seen for patients operated on by LCLV surgeons (OR 0.82, 95% CI 0.70-0.96; P = .015) or at LCLV clinics (OR 0.56, 95% CI 0.50-0.64; P < .001. Similar results were seen for lateral meniscus repair with LCLV surgeons (OR 0.83, 95% CI 0.69-1.01; P = .067) and LCLV clinics (OR 0.62, 95% CI 0.53-0.72; P < .001). Additionally, younger age, female sex, shorter time from injury to surgery, and ACLRs performed more recently were associated with increased odds of repair.

Conclusions: Lower surgical volume significantly decreased the rates and odds of performing meniscal repair during primary ACLR. In contrast, ACLRs performed during more recent years, younger age, female sex, shorter time from injury to surgery, absence of chondral injuries, and injuries sustained during nonpivoting activities, positively influenced meniscal preservation.

Level of evidence: Level III, retrospective cohort study.

This thesis investigates which teaching strategies are used by teachers from preschool to grade 6 to enhance students’ motivation and engagement in mathematics education. The study is based on qualitative interviews with six teachers and classroom observations, analyzed through the frameworks of Self-Determination Theory (SDT) and engagement theory. The results show that variation, clear structure, positive reinforcement, physical activity, and individual adaptation are key strategies for increasing motivation. Playfulness and movement are emphasized in the early years, while structure and cognitive challenges become more important in later grades. The study also explores how the experience of success and reward, linked to the dopamine-based reward system, influences student learning. The conclusion is that a balanced and differentiated teaching approach that activates both emotional and cognitive aspects of engagement is essential for promoting student motivation in mathematics. 

This study examines the importance of language for young adults attitudes towards credit purchases, focusing on the growing use of buy now, pay later (BNPL) services, especially Klarna. The background is a worrying increase in debt among young adults, where BNPL services are attributed as a contributing factor. The aim is to analyze whether linguistic framing, specifically the use of terminology such as Klarna versus credit purchases, affects attitudes, propensity to use and understanding of credit costs. Through an experimental survey design where respondents were randomly exposed to different wordings, it is investigated whether the term Klarna evokes more positive attitudes and a lower perceived financial risk compared to the more neutral term credit purchases. 

Regression analyses are used to analyze how choice of words affect respondents' responses. The results show that wording has a clear impact on both attitudes and intentions, where Klarna is perceived more positively despite the payment terms being the same as for a credit purchase. The study is interpreted based on behavioral economic theories such as mental accounting, bounded rationality, and cognitive dissonance. Our conclusion is that linguistic framing combined with limited financial knowledge may contribute to normalizing credit behaviors among young adults and increasing the risk of debt. 

A substantial period of an individual’s early years occurs in school, whereas experiencing social exclusion has profound psychological consequences. Despite this, it is not always perceived to be immoral or harmful, and there is a scarcity of interventions. This master’s thesis addresses this gap by exploring the potential of Outdoor Education to foster the social inclusion of socially excluded children, based on the outdoor educators’ perception and their understanding of the problem. Semi-structured interviews with five outdoor educators from Switzerland were conducted and analyzed using the problematization approach inspired by Foucault (1985) and Bacchi (2009). The findings identified three different categories in the problematization of social exclusion: social exclusion because of norm deviation, social synchrony, and social structure. Within these categories, the Outdoor Educators highlighted different aspects of their problem, focusing either on the excluded child, the excluder, or group dynamics, wherein their suggested approaches and implementation in the outdoors were influenced by their problematization. Importantly, the categories often overlapped, revealing deeper issues within school structures, such as limited opportunities for social role shifts, strengths-based activities, or group cohesion. Furthermore, insecurities - whether in the excluded child or the excluder - emerged as central to the exclusion problem. Thus, this study shows that Outdoor Education has the potential to foster social inclusion of socially excluded children; however, it does not solve ‘magically’ when going outdoors but depends on the form of Outdoor Education and whether or not the outdoor educator is attuned to the problem.

This study aimed to examine the range of Outdoor Education (OE) practitioners’ perspectives on how OE should be integrated in Teacher Formation (TF). This research follows a phenomenographic approach in which OE practitioners from various countries throughout Europe were interviewed in order to determine how they perceived the possible integration of OE in TF. An outcome space was used to represent the findings of the research, allowing correlations and differences amongst the various perspectives to be brought to light. The findings revealed a spectrum of viewpoints, influenced by the practitioners’ own foundational views on OE. Key themes include the importance of embedding OE either across subjects or as a stand-alone course, the relevance of both theory and practice in training, and the significance of place in outdoor learning environments. Additionally, the study identified a range of practical skills considered essential for preservice teachers, such as organizational and collaborative skills, adaptability, creativity, safety awareness, and confidence in nature-based settings. Challenges related to time, resources and curriculum constraints as well as cultural attitudes were also discussed, along with recommendations such as institutional support and educator networks.

With the advent of modern GPU’s, it has become increasingly practical to simulatelarge numbers of photons for Monte Carlo-based radiance transfer. In this thesis, we ex-plore this topic for the purpose of simulating interactions between infrared light and cloudsusing the volumetric radiance transfer equation and Mie theory to accurately compute scat-tering probabilities and angles for homogeneous media. We create a path tracer which wevalidate against real world measurements of clouds and look at different important param-eters for modeling said clouds. We compare the more expensive but accurate Mie phasefunction to the widely used approximative Henyey Greenstein (HG) phase function. Wefind that HG only performs up to 3.5% faster while failing to accurately approximate theMie function. Finally, we derive a formula for precomputing radiance values of a cloud,including contributions of multiple scattering, for use in a simpler path tracer that doesn’tsupport multiple scattering. We test this formula and find that the resulting renders appearvisually reasonable.

This thesis aims to assess the spillover effects among Scandinavian housing markets and to compare them with those from the large US economy, notable for its significant global influence. We employ a Bayesian Global Vector Autoregressive framework (BGVAR) with trade-weighted linkages to estimate how shocks related to monetary policy and housing demand, transmit to and within Scandinavia. Sweden serves as the primary case study for a Scandinavian receiving country, due to its geographical ties to Norway and Denmark. However, we also conducted additional estimations with Norway and Denmark as receiving countries to ensure the robustness of the results. We utilized quarterly data from 1993 to 2019, and our findings show significant spillover effects from monetary policy and housing demand shocks among Scandinavian housing markets. When comparing the effects to those from an identical shock in the US, we see that monetary policy shocks in the US have a significantly greater impact on the Scandinavian housing markets than those originating from neighboring Scandinavian countries. Regarding housing demand shocks, the results are less clear-cut. For Denmark and Sweden, US housing demand has a greater impact than that from neighboring countries, while Norway shows greater sensitivity to housing demand shocks from its Scandinavian neighbor. Our findings suggest that spillover effects from monetary policy shocks are more influenced by global forces, whereas the effects of housing demand shocks are more closely tied to proximity. 

This thesis investigates the design and implementation of a low-frequency beamforming system using FPGA, with a focus on optimizing computational complexity. The methodology followed a structured approach, starting with simulations, followed by the development of a fixed-point model and FPGA implementation.

Several beamforming methods, including frequency-domain subband, tapped delay lines, and time-domain subband, were compared for their computational efficiency, alongside the consideration of adaptive beamforming techniques such as MVDR. Based on this analysis, the frequency-domain subband method was selected for its low computational cost, particularly in scenarios where adaptive beamforming is not required.

Two FPGA architectures were evaluated for implementing the selected FFT-based beamforming system. Architecture 1, which utilizes a single delay path FFT, was initially considered but found to be inefficient in terms of hardware utilization, particularly since parallelism was not necessary at the low sample rates involved. In contrast, Architecture 2, which employs serial processing, was implemented due to its ability to fully utilize the hardware while maintaining a relatively low clock frequency.

The FPGA implementation, designed with 25×18 DSP multipliers and distributed memory, successfully demonstrated spatial perception, enabling the accurate detection of signal direction and power, even in the presence of noise. While the system effectively utilized available resources, accuracy of detecting angles could be enhanced by increasing the number of FFT points and expanding the sine/cosine tables.

The putative F3p pheromone is commonly used and sold as a tool to reduce unwanted and abnormal behaviors in cats. Therefore, this study assessed the effect of the putative F3p pheromone on the behavior of big cats and if it could reduce stereotypic behaviors when transitioning from an old to a new enclosure. Behavior sampling with instantaneous recording was used to collect data during test- and control periods, alternating according to a pseudo-random schedule. During the test periods the animals were sprayed with the putative F3p pheromone and during the control periods with water. The study was conducted on four big cats (nLion = 3 and nTiger = 1) living at FELIDA Big Cat Sanctuary. The results showed no significant difference for the stereotypic behavior category when transitioning between the old and new enclosure during the pheromone treatment. However, for one lion a significant increase was found for the stereotypic behavior category when transitioning between the old and new enclosure during the water control. Additionally, across the whole observation period there was a significant increase for the behavior categories inactive, active, vocalizations, maintenance, other and explorative, where behaviors significant result depended on individuals and spray types. Previous studies show varying results, where some studies are consistent with the present study, and some are not. These findings highlight the importance of further research to fully understand the effect of the putative F3p pheromone on cat behavior and if it can be used as a tool to alleviate stressful transitions.   

This thesis examines the intersectional dynamics of class, gender, and religion in the artwork of Swedish artist and activist Monica Sjöö, focusing on two paintings: Housewives (1973) and God Giving Birth (1968). Through a methodology that combines feminist visual analysis and narratology, I examine how Sjöö's visual narratives and symbolic elements create spaces for resistance and meaning-making in her artwork.

I investigate thematics like domestic labor/space and male/oppositional gaze, helping problematizing Sjöö’s visual narratives. In supporting my analysis, I explore Sjöö's artwork within the context of her political activism and feminist spiritual movements, showing how her works embody a critique of capitalist, patriarchal, and religious systems. Finally, by situating my own positionality as a situated reader, I reflect on the ethical and epistemological implications of analysing feminist art from structures of privilege and marginality. 

Background

Practical training in a birthing unit is an important part of midwifery education. Previous research on the preceptor role predominantly consists of qualitative interview studies and have not explored pedagogical models like peer learning.

Objectives

The purpose of this study was to quantitatively describe midwives' experiences, conditions, and needs in their role as preceptors in a birthing unit, as well as their attitudes towards and experiences of peer-learning.

Design

A cross-sectional study, using a web-survey.SettingsThe survey was distributed to all midwives working in a birthing unit in Sweden.ParticipantsA total of 573 valid responses were obtained.MethodsA questionnaire consisting of 22 questions, which included background questions, questions to be answered on a four-point Likert scale, and open-ended questions, was sent out from November 2022 to March 2023. The collected data were analysed using descriptive statistics, Chi-square, and Mann Whitney U test analyses. The open-ended responses were analysed using quantitative content analysis.

Results

Midwives with ≤10 years of experience were more hesitant about taking on the preceptor role. They also described a lack of support and found it more challenging to integrate supervision into their working hours than midwives with >10 years of experience. Time for supervision and time for reflection with midwifery students were identified as areas that could improve the quality of supervision. Of the respondents, 42.8 % had experience of peer learning. “Students learn from each other” was identified as the most common advantage of peer learning.

Conclusions

Long-term experience as a midwife plays a crucial role in how midwives perceive their role as a preceptor and the conditions they experience. The midwives in this study had started supervising students earlier than they deemed advantageous. As the preceptor plays a critical role in the midwifery student's learning process, it is important to have experienced preceptors.

Globally, females are at twice the risk of AD than males; in Canada, over 700,000 are living with Alzheimer’s disease and related dementia (ADRDs), with 72% being female. However, females maintain verbal memory in the face of more AD pathology than men. It is unclear how multilingualism, considered a resilience factor, might interact with the risk and resilience of sex. Thus, we wondered if female sex and multilingualism might interact to confer more resilience in individuals with Mild Cognitive Impairment (MCI) in a Canadian cohort: The Comprehensive Assessment of Neurodegeneration and Dementia (COMPASS-ND).MethodNeuropsychology data from 270 female and male participants diagnosed with MCI were analyzed using female or male sex as a categorical variable with 2 levels: 116 women and 156 men. They self-reported language use and history allowing a multilingualism score which identified 148 multilinguals, and 124 monolinguals. First univariate analysis was used to explore individual differences among the 4 groups: women, men, monolinguals, multilinguals. Then, different models using Path analysis and Structural Equation Modelling (SEM) were used to create a Cognitive Reserve index (CRI) which accounted for multilingualism and biological sex. Simple regressions were used to estimate cognitive performance in relation to the CRI.ResultFemales with MCI had higher scores than men in The Rey Auditory Verbal Learning Test (RAVLT); this effect was even stronger in those women who had ever used Hormone replacement therapy. Multilinguals outperformed monolinguals in the verbal fluency component of the Delis-Kaplan Executive Function System (D-KEFS) which was performed in English. Moreover, CRI significantly predicted increased performance in tasks measuring visuospatial memory and attention.ConclusionPrevious mixed-sex studies have suggested that multilingualism may be a proxy for cognitive reserve delaying the onset of AD symptoms for approximately 4-5 years. Other studies have indicated that women are more affected by AD than men but that verbal memory may be a form of reserve for them. Here, we show that multilingualism and biological sex together may associate with increased neuropsychological performance even in the presence of MCI.

Startups in the manufacturing industry face challenges in transforming their innovations into

commercially successful products. A key obstacle is the evident gap between strategic planning

and actual implementation, particularly in the context of entering a market. Although theories on

go-to-market-strategies are well-established, there is a lack of practically applicable and

generalizable frameworks tailored to startups in this context. Based on this, the aim of the study

was to develop a go-to-market-strategy framework that guides startups in the manufacturing

industry in commercializing their technology solutions in a target market.

Grounded in academic literature, an analytical model was developed. This model comprises three

main analytical steps, which together aim to build an understanding of how manufacturing startups

can structure their go-to-market-strategy. The three steps include an examination of market

attributes, factors for the development of a value proposition and revenue model. Within each step,

factors influencing the go-to-market-strategy are analyzed.

The qualitative empirical data collection consists of eleven semi-structured interviews, one with

the case company of the study and ten with contract manufacturers in the manufacturing industry.

Through the study, several key market attributes and factors were identified that startups in the

manufacturing industry should consider in order to succeed in their go-to-market-strategy.

The study resulted in three main conclusions. The first conclusion identifies four key market

attributes for segmenting the manufacturing market, which vary across different actors and

influence the conditions for entering a market. The second conclusion highlights 19 critical factors

for developing a customer-tailored value proposition that addresses diverse needs and challenges.

The third conclusion outlines 17 factors to consider when developing a revenue model,

emphasizing how customers assess value when investing in machinery solutions.

Intrusion Detection Systems (IDS) are critical for cybersecurity, yet their performance is frequently compromised by class imbalance in datasets, where underrepresented attack classes lead to degraded detection rates. This study rigorously evaluates machine learning-based Network Intrusion Detection Systems (NIDS) using the CICIDS2017 dataset, addressing class imbalance through Synthetic Minority Oversampling Technique (SMOTE). This study systematically evaluates five supervised models Logistic Regression, k-Nearest Neighbors, Naive Bayes, Random Forest, and Deep Neural Networks for binary classification, along with two unsupervised approaches (K-Means and Isolation Forest), across seven randomly selected attack sample instances from the training set. Controlled imbalance experiments, where the attack sample size as a proportion of the normal data in the training set is progressively increased (1%, 5%, 10%, 30%, 60%, 80%, 100%) to analyze their impact on model performance. This study analysis leverages balanced accuracy, precision, recall, F1-score, and Matthews Correlation Coefficient (MCC) to quantify performance. Results reveal that Random Forest dominates supervised methods, achieving near-flawless detection on balanced data. In stark contrast, unsupervised techniques exhibit severe limitations. K-Means and Isolation Forest fail to generalize beyond rare anomalies. This study provides valuable insights into optimizing NIDS with machine learning, emphasizing the importance of data balancing and algorithm selection for robust cybersecurity solutions. The findings suggest that SMOTE-enhanced supervised models, particularly Random Forest, offer the most reliable intrusion detection, while unsupervised techniques require further refinement for practical deployment.

Denna studie syftar till att analysera de samhällsekonomiska konsekvenserna av övergången från traditionell tv till streamingtjänster i Sverige. Med bakgrund i ett snabbt förändrat medielandskap har konsumtionsmönster för rörlig bild förändrats markant, med ökad digitalisering, individualisering och teknisk utveckling som centrala drivkrafter. För att bedöma om denna övergång har varit samhällsekonomiskt fördelaktig har en kostnads-nyttoanalys (CBA) genomförts. Analysen omfattar tre huvudsakliga värderade effekter: investeringar i digital infrastruktur, kostnader för innehållsproduktion samt konsumenternas betalningsvilja. Därutöver diskuteras icke-värderade effekter såsom digitalt utanförskap och selektiv mediekonsumtion. Studien jämför två referensår: 2012, då traditionell tv dominerade, och 2024, ett år där streamingtjänster är den dominerande plattformen för rörlig bild. Resultaten visar att den värderade nettonyttan är cirka 17,9 miljarder kronor högre år 2024 än 2012, vilket indikerar att övergången till streaming genererat ett betydande samhällsekonomiskt överskott. Den huvudsakliga förklaringen är att konsumenterna i dag får mer värde för samma resursförbrukning, vilket tyder på ett mer effektivt utnyttjande av samhällets resurser. Även efter beaktande av potentiella negativa, icke-värderade effekter kvarstår denna slutsats som robust. Studien visar därmed att övergången från traditionell tv till streaming, givet de undersökta parametrarna, sannolikt har medfört en ökad samhällsnytta i Sverige.

This master's thesis has explored the opportunities and challenges associated with using Microsoft 365 Copilot to automate and streamline administrative processes within the Swedish construction industry. In a sector long characterized by low productivity and limited digitalization, the focus has been on how an AI-powered virtual assistant can contribute to more structured and efficient workflows. The study aimed to answer three central questions: which administrative areas Copilot can support, what effects the tool may have on workflows and what challenges may arise during implementation and use. To address these questions, a combination of quantitative and qualitative methods was applied. A digital survey was distributed to professionals across various types of construction companies to provide a broad overview of how Copilot is perceived and used. In addition, five semi-structured interviews were conducted with individuals who had varying levels of experience with AI and Copilot, including daily users. Through methodological triangulation, both general trends and individual perspectives were analyzed. The findings show that Microsoft 365 Copilot is perceived as particularly useful in document management, reporting and communication. Respondents highlighted increased productivity, improved quality in written work, easier access to information and clear time savings as key benefits. At the same time, challenges such as legal uncertainty, data security issues, poor data quality, organizational resistance and limited training were identified. The study concludes that Microsoft 365 Copilot holds significant potential to improve administrative workflows in the construction sector, especially in information-intensive environments. However, realizing this potential requires active efforts in organizational support, technical readiness, and continuous skills development. As Copilot is still in the early stages of adoption within the construction sector, further research with larger and more diverse samples is needed. Follow up studies could provide insight into how the tool’s use and impact evolve over time. The generalizability of the results should therefore be interpreted with caution.

Public transport is an essential part of people’s daily life, enabling them to move between homes, schools, workplaces and more. Swedish public transport is divided into two parts. General public transport, which follows fixed routes, and special public transport, which includes services such as transportation for people with disabilities, medical trips, and schoolchildren. In special public transport, journeys often start from the passengers’ homes, resulting in numerous different stops depending on the passengers in transit. In contrast, general public transport operates fixed stops following a predetermined route and schedule. This also means that higher requirements are placed on the planning of special public transport, due to trips beginning and ending at varying locations. Meanwhile, coordinated planning is important to minimize the driving distance and the number of vehicles in use. This study investigates how Dalatrafik's route planning for fixed trips within special public transport in Dalarna can be improved. Based on data collected by interviews, observations, and information from the company’s data systems, the potential for more efficient route planning was estimated, along with the possible effects of such improvements. An optimization model aided the study in evaluating whether improvements in the route planning of ten different vehicle loops are feasible, with regards to driving distance and vehicle resources. While simultaneously not compromising the level of service for passengers. The results of the study show that the total driving distance for ten routes spanning one day can be reduced from 720 km to 655 km. This corresponds to a reduction of 66 km, or about 10%. However, there may be potential for even greater improvements, as the study assumed that single-passenger trips must be planned with individual vehicles, whereas Dalatrafik sometimes plans single-passenger trips together regardless. If the study had included coordinated planning of certain single-passenger trips, the distance might have been further reduced. The study highlights that the times 7:40–8:30 and 13:45–14:30 were the most critical, because many school trips took place during these hours. The number of vehicles required during these times of the day could potentially be reduced from ten to nine, while a maximum of eight vehicles was sufficient to satisfy the demand during the rest of the day. The presented improvements are largely due to better coordination of passengers, resulting in shorter driving distances with passengers in vehicles. The coordination with which passengers to combine in each trip is seen as the part of the planning process with the greatest potential for improvement since it covers the largest share of the total distance. Another important planning aspect is to arrange each respective trip into complete schedules in a way that minimizes driving distances without passengers. This is something that could be further evaluated in future studies.

The view of mathematics in today’s society is often limited and one-sided. This study aims to challenge this narrow perspective by exploring the deeper cultural and philosophical significance of mathematics. The purpose is to examine how arithmetic operations were performed in Mayan mathematics and what algorithms were behind these calculations. The work begins with an introduction to the Mayan number system, a vertical system based on the primary base of 20, a sub-base of 5, and the concept of zero — a symbol that the Maya civilization was among the first to use. Despite the rich scientific heritage of Mayan culture, much of their mathematical knowledge remains unknown. To investigate this, a mathematical analysis of both Spanish and English sources was conducted to identify the methods the Maya might have used for their calculations. The work presents methods for addition, subtraction, multiplication, and division, both with and without the use of grids. These methods were then compared to the Hindu-Arabic numeral system and the algorithms used in it. The discovered procedures for calculation proved to be simple, elegant, and intelligent. The work also highlights the ongoing debate among researchers about the extent to which the Maya performed arithmetic operations and calculations with decimal numbers. The final section presents how Mayan mathematics can be used as a valuable resource in contemporary mathematics education.

This thesis has examined impacts on mobility from a mobility justice perspective in the area around the closed tram stop ‘Roddföreningen’ in Gothenburg, Sweden. The tram stop had been in use for 116 years before being removed for network efficiency reasons in late 2024. Interviews, a GIS analysis and a questionnaire have been used to gather results that have given insights on how residents and visitors have been affected. This includes how they have adapted and acted following the relatively unordinary event of a public transit stop being removed without replacement. Particular focus has also been put on the role of the area’s high-income status. The findings suggest a high local engagement, with strong perceived feelings of injustice among many locals, directed both at the planning process and at the practical outcome. The results also show a lacking local knowledge over the exact cause for the tram stop’s closure as well as the functionality of alternative public transport modes. Meanwhile, the GIS analysis shows a major impact on physical accessibility and a lacking access to alternative mobility modes, with the clear exception of personal vehicles. The area’s high-income status is reflected in the high degree of automobility amongst residents, as well as the general adaptability of them. Questionnaire respondents manage to find solutions, but instead describe stress, challenging logistics, inconvenience, worry and changes of routines. Where long-term plans and habits have been changed, it has been for particularly vulnerable groups. This especially includes those who have a low automobility or where a high income plays a limited mobility role - particularly children and elderly with disabilities.

This thesis investigates how building materials can be adapted to climate conditions by promoting reuse and circular practices within the construction industry. The research is motivated by the environmental challenges associated with high raw material consumption and significant carbon dioxide emissions from conventional building production methods, which have spurred an increasing interest in sustainable and circular solutions. The purpose of this study is to identify smart strategies for effectively reusing building materials and to analyse the obstacles as well as potential technical and economic solutions that could enhance their practical implementation. The central research questions focus on the identification of the barriers and challenges that hinder the reuse of building materials, and on determining which alternative materials and techniques can be employed to address these issues. The study employs a mixed-methods approach that combines an extensive literature review with quantitative surveys and qualitative interviews with industry stakeholders, complemented by the analysis of historical data. By integrating both quantitative and qualitative findings, the research provides a comprehensive understanding of the current conditions and practical challenges involved in the reuse of construction materials. The results indicate that the reuse of building materials offers significant environmental benefits, such as reduced carbon dioxide emissions and lower consumption of virgin raw materials. However, practical difficulties were also uncovered. Key challenges include limited availability of high-quality reused materials, uncertainties regarding quality assurance and certification, and substantial economic and logistical barriers. Moreover, despite the recognized environmental benefits, the study reveals that current market and regulatory frameworks do not sufficiently support the widespread adoption of recycled materials. Digital solutions, notably AI-based systems for material identification and classification, emerged as promising tools to improve transparency and efficiency in the reuse process. Furthermore, the research underscores the need for revised legislation and enhanced incentive structures to facilitate a successful transition toward a circular construction process. In conclusion, while the reuse of building materials holds great promise as a pathway toward a more sustainable future in the construction sector, achieving this transition requires coordinated efforts in technological innovation, economic strategy, and regulatory development. The findings provide concrete insights into both the opportunities and the challenges associated with material reuse and serve as a foundation for further research aimed at optimizing digital solutions and policy frameworks to support a full-scale implementation of circular practices in construction.

 In 2023 the Swedish parliament voted for the biggest reform to the country’s press subsidy system in its over 50 years long history. With more and more legacy newspapers qualifying, and an ever-growing share of the subsidy ending up in the pockets of Sweden’s largest newspaper corporations, it was clear that the system was outdated and obsolete. The new system sought to address issues such as the waning plurality of high-quality news outlets and the dominance of the large corporations. But does the new system solve these issues? To try and find that out, this paper employs a theoretical framework based on the economic theory behind externalities and the role of journalism according to the liberal and republican philosophers who shaped our view of it. By comparing the design, its effect on the distribution subsidies and the economic impact on the recipient news outlets, preliminary conclusions can be drawn. The new system does to some degree address the identified issues. At the same time the system is not faultless. The new subsidy system is only for local and regional news outlets, shutting out not only the large national newspapers, but also niche ones. The new system can thus be described as advocating for local news representation but at the same time dismissing a crucial part of the Swedish media ecology.               

Black holes are among the most bewildering and complex phenomena in our universe. Consequently, most people have a limited understanding of the physics or even the visual characteristics of black holes. This thesis explores developing and implementing a real-time, interactive black hole visualisation within OpenSpace, an open-source platform for astronomical data representation. The project aims to bridge the gap between scientific accuracy and user engagement by integrating GPU-accelerated techniques and custom interpolation methods to simulate the visual and gravitational effects of both Schwarzschild (non-rotating) and Kerr (rotating) black holes. The research investigates how these black hole models affect rendering fidelity and system responsiveness. Results show that the Schwarzschild model offers smoother interactivity due to its lower computational complexity. In contrast, the Kerr model captures more intricate relativistic effects, such as frame-dragging and asymmetrical lensing, albeit at the cost of performance. This trade-off between physical realism and real-time usability highlights a core challenge in scientific visualisation: faithfully representing extreme physical phenomena without overwhelming computational resources or compromising user experience. Additionally, a 2D layered star map was implemented to enhance immersion by introducing parallax effects that improve depth perception and scale illusion. This technique effectively situates the viewer within a vast and dynamic cosmos, reinforcing the sense of spatial presence and enhancing cognitive immersion. While current limitations include the lack of dynamic interactions between black holes and 3D scene elements, the system’s modular design allows for future expansion. Proposed improvements include adaptive resolution techniques for handling high-complexity metrics and better integrating astrophysical datasets.

This thesis investigates the collaboration between two Swedish municipalities and the Swedish Public Employment Service, focusing on labour market policy measures and support for job seekers. The aim is to analyse how this cooperation is organised and functions in practice, the opportunities and challenges involved, and how the collaboration addresses job seekers who are repeatedly subject to sanctions. The study is exploratory and based on qualitative interviews with key personnel within the municipalities and the Employment Service. Two municipalities with similar socio-economic conditions but different organisational models were selected for comparison. The results show that although the conditions for cooperation between the Public Employment Service and the municipalities are good and relationships are important, the collaboration is often hindered by regulatory constraints, shortcomings in information exchange, and organisational differences. This leads to unclear division of responsibilities and delays, which can complicate efforts for individuals with complex needs, particularly those who are repeatedly sanctioned. The study highlights the need for increased flexibility, clearer dialogue, and better coordination between the Public Employment Service and the municipalities.

Security and audit trail events play a critical role in intrusion detection, incident response, and damage assessment following security breaches. Attackers target these logs to evade detection or remove evidence. While log protection is well-studied in traditional environments, few studies have specifically addressed the challenge within Cloud Radio Access Network (Cloud RAN) infrastructures, where logs must remain secure despite an adversary potentially having privileged access to the entire platform.

This thesis investigates the feasibility of integrating Trusted Execution Environments (TEEs) into Cloud RAN products to protect symmetric keys used for generating Hash-based Message Authentication Codes (HMACs). Based on a STRIDE threat assessment, identified threats are addressed by deriving a hardware-agnostic design, implemented using the Open Enclave SDK and OpenSSL, facilitating compatibility across diverse hardware environments. The resulting proof of concept performs secure key establishment through a protocol involving remote attestation and cryptographic key provisioning. To preserve integrity, security and audit trail events are securely signed within the TEE using HMACs. Subsequently, they are sent to and verified externally by a centralized Security Information and Event Management (SIEM) system.

The proof of concept was evaluated in a Kubernetes-based environment with SGX-enabled hardware, assessing its impact on container image footprint, deployment latency, and scalability. Results demonstrate that while TEE integration introduces moderate overhead, it offers significant improvements in protecting critical assets even against infrastructure-level adversaries. These findings offer actionable insights for operators aiming to enhance log integrity in cloud-native deployments exposed to high-privilege threats.

The fundamental theorem of arithmetic states that every integer n > 1 can be factored uniquely into prime numbers: n=p₁···p_r. In this thesis, we explore how algebraic number theory emerged from an attempt to generalize the arithmetic properties of Z to more general domains. In particular, the arithmetic of number fields K and their ring of integers O_K. The main topic of this thesis is the concept of unique factorization, which, in general, fails in O_K. We investigate Dedekind's approach to restore unique factorization, by proving that ideals of O_K factor uniquely into products of prime ideals. Since unique factorization fails in general in O_K, we want to understand the extent of this failure. We do this by constructing the class group C_K of a number field K, which measures the failure of unique factorization in O_K. We also take a closer look at some special cases of rings of integers O_K without unique factorization, and derive weaker factorization results. Lastly, we demonstrate how algebraic number theory can be used to determine the primes of the form x²+my² for a fixed positive integer m.

Communication Service Providers (CSPs), meaning network operators, want to adopt the O-RAN architecture and run their Kubernetes within Hyperscaler Cloud Providers (HCPs) in multi-tenant clusters. To reduce the load on the root Certificate Authority (CA) in the Service Management and Orchestration (SMO), an intermediate CA can be deployed inside the Kubernetes cluster. However, this introduces new security risks that must be carefully assessed. This thesis conducts a threat analysis using the STRIDE framework and proposes a secure design that leverages remote attestation with Trusted Platform Modules (TPMs) inspired by the Keylime framework. It also outlines a certificate distribution workflow using encrypted One Time Passwords (OTPs) and Kubernetes custom resources. The proposed solution is summarized into a secure protocol that mitigates 82.76% of the identified threats in the threat model, significantly reducing identified threats. This thesis sets the foundation for trusted Kubernetes deployments in HCPs specific for securing telecommunication environments and offers a basis for further research into securing dynamic and distributed infrastructure components.

Most of the knowledge about religion is learned in school. That comes with a responsibility to know how the religious education represents the religions. The purpose of this study is to investigate how different textbooks used in religious education in Swedish high schools represent the concept of God in the Abrahamic religions. The study is based on representation using the interpretative approach of theory. The focus is to examine the representations of the religion's concepts of God. The method used in this study to analyze is a qualitative text analysis. It's based on the various aspects of the definition of God concepts. They are God’s revelation, persona, and the relationship between God and humanity. The study concludes that there are large similarities between how the books represent the religions. All three religions are described to perceive God as one, bigger, and with no gender. They are also described to perceive God as an almighty maker, good and a just judge who rewards and punishes. They also write that the religions think that God has given humankind free will, with which they can choose good or bad, and that God also has given humans the task of caring for the world and other humans. How each religion differs from each other is described almost the same in all the textbooks. However, the literature gives more of a divided view when it comes to the description within each religion. There are different perspectives, and the books sometimes are even at risk to over-simplify. This shows the teacher's importance and ability to know when the books are not enough or more perspectives should be explained. By doing that the school, and teachers, can not only counteract religious students feeling misunderstood and excluded, but also prevent misconceptions among students that, eventually, can help reduce prejudice in society.

In this thesis, we study several related variants of graph coloring: list coloring, separated list coloring, adapted list coloring, single conflict coloring, and DP-coloring. Each variant is associated with a corresponding invariant: choosability ch, separation choosability ch$_{sep}$, adaptable choosability ch$_{ad}$, single conflict chromatic number $\chi_{\nleftrightarrow}$, and DP-chromatic number $\chi_{DP}$. 

We begin by presenting definitions, examples and previous results for each of these graph coloring variants. We then establish new results, which, to the best of our knowledge, have not previously appeared in the literature.

More specifically, we characterize graphs with $\chi_{\nleftrightarrow}=2$, and partially characterize those with ch$_{sep}=2$. We compute the values of ch, ch$_{ad}$, ch$_{sep}$, and $\chi_{\nleftrightarrow}$ for a selection of small standard graphs. Furthermore, we investigate the possible values of the invariants ch$_{sep}$, ch$_{ad}$, and $\chi_{\nleftrightarrow}$ for planar graphs, and provide a partial characterization. In particular, we show the existence of a planar multigraph satisfying $(\text{ch}_{sep}, \text{ch}_{ad}, \chi_{\nleftrightarrow})=(3,3,4)$. Finally, we introduce single conflict coloring in the setting of $r$-uniform hypergraphs and establish several properties of this hypergraph coloring model. 

The trucking industry is undergoing a significant transformation of business models, which is driven by technological advancements and increasing demands for operational efficiency. A central piece of this transition is the growth of availability services, which are service offerings that aim to maximize uptime and minimize unplanned stops for the customer. These services represent a shift to a more service-based business model through servitization and demand that truck manufacturers reconfigure how they create and capture value during the customer journey. As product and relationship quality become increasingly commodified, the customer experience of these services becomes the main differentiating component in value creation. Therefore, the purpose of this study is to develop a framework for how OEMs in the trucking industry can create and capture the value of availability services throughout the customer journey. 

The study was designed as a qualitative single case study, where the case company is a large European truck manufacturer. The data collection took place through semistructured interviews with case company employees and customers. Interviews were conducted with 17 customers from varying industries and with five respondents from the case company. Thematic data analysis was utilised with inspiration from the Gioia methodology. 

The findings of the study include a total of 16 value driving factors distributed into four dimensions of value in business markets. Namely, table stakes, functional value, ease of doing business value and individual value. Additionally, 16 activities that influence the customer experience of availability services were found and categorised into five groups. These groups are activities related to the purchase phase, daily interactions during the usage phase, moments of truth, background activities performed by the supplier and external activities performed by the customer. Lastly, a framework was created for how OEMs in the trucking industry can create and capture value of availability services throughout the customer journey by combining the identified value driving factors and activities. 

The conclusions showed that the different activities maintain different roles in creating and capturing value throughout the customer journey. Activities related to the purchase phase showcase the expected value for the customer and act as a door opener by selling a win-win solution that the customer accepts as economically beneficial. Phase spanning activities from the customer and the supplier ensure that the economical and individual value-in-use for the service offering is increased over time by creating a more coherent and contextually adapted experience. Daily interactions during the usage phase facilitate that the customer experience differentiates the supplier from its competitors. This is achieved by enabling and simplifying daily tasks within administration. Moments of truth are when the customer experience becomes accentuated. By utilising a quick and adaptive service organisation that uses transparent and factual communication, suppliers ensure that these instances take the customer experience to the next level. 

This study explores the concept of strategic pricing in an immature and unstandardized market, using a business ecology perspective. The focus is on a service aimed at protecting manufacturing organizations against cyber threats. Despite growing cybersecurity threats and a worsening geopolitical climate in Europe, the service has had limited growth on the market. As operational technology (OT) environments, often integral to critical infrastructure, become more connected to IT systems through digitalization, they also become more vulnerable to cyber threats according to authorities. Yet, protecting these systems remains a challenge to justify for manufacturing companies, as the value of such services often becomes visible only after an incident. This creates difficulties for vendors offering OT security services when trying to communicate their offering and price it accordingly.

By using a qualitative analysis of perspectives within a broader business ecology, this research moves away from traditional views on competitive markets and pricing logics. Instead, it explores how industry-spanning trends and conflicting interpretations influence what are considered suitable price models. This approach enables a deeper exploration of how perceptions of price are influenced by broader trends, rather than only determined solely by internal thoughts. In doing so, it uncovers dynamics that would likely remain hidden in more conventional pricing approaches.

Insights captured from the first research question reveal that a business ecology approach introduces several challenges. These include trust, stakeholder alignment, and interpreting vastly different perspectives, all of which complicate the practical application of strategic pricing. Further, price models, seen as communication tools, require contextual clarity to support meaningful dialogue. The second research question explores industry-spanning trends such as perceptions of risk, competition and thoughts on IT/OT convergence and its connection to how people argue for fair and effective price models. Our findings show that perceptions of these trends affect people’s reasonings to varying extents, which in extension illustrate differences in expectations of what the service entails to provide. Moreover, the different perspectives illustrate an underlying hesitation to other’s perspectives, which affects current power dynamics in the business ecology.

The study contributes to academic research by providing an empirical application of a business ecology, demonstrating insights into the complexities of the framework. Lastly, the research offers a starting point for future research on the evolution of actor dynamics over time, the strategic use of business ecologies for market positioning, and the importance of internal alignment in developing effective pricing strategies.

This thesis explores the use of column generation methods to solve an extended vehicle routing problem tailored to surveillance missions using UAVs. The goal is to minimise the number of UAVs required by finding optimal routes while still meeting surveillance requirements. A mission involves a fixed set of locations that must be continuously monitored and a number of depots from which UAVs can be deployed. The problem takes into account factors such as location-specific time constraints, a heterogeneous UAV-fleet and multiple depots. Two classes of problem instances are studied. In the first, multiple UAVs are allowed to collaborate on a single tour to satisfy strict time constraint on revisits. In the second, a single UAV may perform multiple tours, where the revisit time requirements are less strict. The goal is to solve these problems using column generation and demonstrate their practical value in real-world applications.

To evaluate the models, fictional instances with a varying number of depots and monitoring locations were constructed. An upper time limit was used for all computations. The results show that the models are capable of producing useful solutions. In particular, the second class of problems proved significantly easier to solve within the given time frame. However, for larger problem instances, timeouts were frequent due to the computation time required to solve large subproblems. Lastly, a post-processing step was used on all solutions to reduce total route times and thus improve usefulness in real-world applications.

Exergames, being games that promote physical activity, havebeen proposed as a solution to combat sedentary lifestyles.This study explores the design and evaluation of a 3D survival-themed exergame intended to be an effective and enjoyableexperience that promotes moderate- to vigorous physical activ-ity over short sessions. The game was developed with Godot,and utilized MediaPipe for real-time pose tracking with a web-cam. To evaluate its effectiveness and appeal, nine participantsplayed the game for two minutes, and completed standardizedquestionnaires regarding perceived physical exertion, enjoy-ment and mental appeal. The results indicated that the game,while it was highly enjoyable, it did not achieve its goal ofphysical exertion. These findings suggest that survival-themedexergames can maintain player interest. However, further re-finement of the movement mechanics is required to enhanceits effectiveness and desired output.

This study compares the implementation processes of peace efforts conducted by the United Nations (UN) and the European Union (EU). It presents an analysis of the institutional structures, mandates and cooperation mechanisms, which along with the study’s empirical material explains differences in the processes. To contextualize the UN:s and EU:s implementations efforts, a case study on each organization's interventions in Macedonia between 1992 and 2006 is conducted. The case study provides concrete examples of how the organizations’ implementation processes are structured in practice, thus forming the empirical basis for the comparative analysis. The study’s analysis draws on the theoretical perspectives of implementation of top-down versus bottom-up governance, as well as the triad on “understanding, wanting and being able to”. 

The study uses a comparative qualitative method to highlight the differences between the UN and EU implementation processes. Process tracing of the case study on the organisations efforts in Macedonia is conducted, which identifies factors in the process that shapes the final outcomes and influence goals fulfillment. The “Most Similar Systems Design” model is applied to highlight the differences that were observed in the analysis. The empirical material is primarily based on official UN and EU documents, supplemented by academic research and scholarly articles.

The conclusions suggest that a universally accepted model for the design of implementation processes for peace work cannot be established. However, it emphasizes that the coordination of an organization’s governance, understanding, willingness and capacity for implementation is important for achieving intended outcomes, thereby acting as important pillars for a sustainable implementation of peace. 

Something that many people lack today is regular movement which is partly caused by sitting still for long periods of time without breaks. A possible solution to breaking up the long periods of sitting is by playing an exergame. The goal of this work is to design a Pacman-like exergame that uses arm movements and skeletal tracking while being enjoyable and effective exercise for moderate physical activity. To create the game the Godot game engine was used and for the skeletal tracking Googles MediaPipe was used. To make sure that the game was enjoyable previous research on exergame design principles were analyzed and applied where possible. To evaluate the game it was tested by people who played the game and then answered a form about their enjoyment and physical exertion. The results were generally very positive but with some elements of the game needing slight improvement.

This study presents a comparative analysis of three AI models: BERT, FastText, and Perspective API, for detecting harmful content in social media communications, focusing on the trade-offs between detection effectiveness and computational efficiency. The research addresses the growing need for scalable and accurate automated content moderation systems, given the limitations of manual review. Using two datasets (Davidson et al. and HateXplain), the study evaluates the models' performance through standard metrics such as F1-score, accuracy, precision, and recall, while also assessing computational metrics like latency and throughput.Surprisingly, FastText, a lightweight embedding-based model, outperformed the more complex BERT and Perspective API in most classification metrics, achieving the highest F1-scores and demonstrating superior computational efficiency. However, FastText showed a conservative classification pattern with higher true negatives and lower false positives, resulting in missing more harmful content compared to the other models. Perspective API, while offering ease of deployment and multilingual support, faced scalability challenges due to rate limits in its free tier. These findings challenge the assumption that advanced deep learning models consistently outperform simpler alternatives in harmful content detection. The study shows the importance of balancing model complexity, detection accuracy, and computational efficiency in practical moderation scenarios. Limitations include dataset biases, English-language focus, and simplified binary classification. Future work could explore hybrid approaches, multilingual datasets, and evolving linguistic trends to enhance moderation systems.

The focus of this study is Eva-Marie Liffner’s (b. 1957) historical novel Lacrimosa (2011) and especially its queer protagonist Ros. Liffner imagines that Ros was in fact a real historical person who inspired one of the most famous Swedish authors, Carl Jonas Love Almqvist (1793–1866), to create the mysterious Tintomara, featured in The Queen’s Diadem (1834). With a particular focus on Ros, including their upset reactions to Almqvist’s portrayal, this study seeks to illuminate queer people’s motivations and opportunities to create the image of themselves and write as well as rewrite their own histories. I do this primarily by reading Lacrimosa as queer archive, but I also study it in relation to postmodern aesthetics, notably historiographic metafiction and intertextuality. The analysis centres around three different themes: identities, objects, and texts. It shows the importance of nuanced representations of queer people, including the desire to be a recognised part of history. Compared to their predecessor Tintomara, Ros has greater opportunities here. Ros is given a clearer voice of their own and also departs from stereotypical and degrading representations of queer people, at least to some extent. They also leave something behind in the form of objects and an autobiographical manuscript, preserving their history. Nevertheless, it is still unclear whether Ros actually manages to rewrite and reach out with their history, especially in a long-term perspective. Thus, through Lacrimosa, Liffner emphasises the possibilities as well as the limitations that the queer archive entails. 

This article aims to explore LGBTQ+ youth’s narrative about Swedish school space. The analysis is based on interviews with 16 participants and is based on a queer theoretical perspective on school as a space where norms are both reproduced and changed. School emerges in the analysis as an ambiguous and contradictory space. The results partly reiterate the dominant narrative from previous research, of the school as a space where students experience exclusion and invisibility as LGBTQ+ people. But there are also examples of young people highlighting a different narrative of school as a space that fosters belonging and important relationships with peers. The example of students running an LGBTQ+ group at their upper secondary school raise questions concerning young people’s participation and agency verses the school’s responsibilities. School emerges as a space where values are negotiated and where young people seek to create change.

This paper examines the different methods used in Android OS for fetching the current position of the phone, and compares them in terms of accuracy and battery usage. The use cases for position data are plenty. It spans from different types of navigational apps, where Google Maps is a well known example, to applications where positioning is not the main feature of the app, but may use position data to provide a better experience. An example of this are applications where the application might just need to know the approximate location of the user, such as weather, social media and news applications.

This thesis investigates whether aggregate gamma exposure (GEX) in the S&P 500 index options market contains predictive information about future equity returns and whether its inclusion can enhance short-term forecasting models. Motivated by the rapid growth of the options market and the increasing influence of delta-hedging flows on equity price dynamics, the study examines the relationship between changes in GEX and subsequent movements in the S&P 500 index. Using daily data from 2011 to 2025, we estimate an Autoregressive Distributed Lag (ARDL) model to capture dynamic short- and long-run effects and construct two predictive models: one including GEX and one excluding it. These models are evaluated against each other and a random walk benchmark using out-of-sample, rolling-window forecasts and Diebold-Mariano tests. Our results show that changes in GEX are significantly and positively associated with S&P 500 returns across multiple time horizons. Moreover, incorporating GEX significantly improves forecast accuracy compared to both the GEX-excluding model and the benchmark. These findings suggest that gamma-related hedging activity by options market makers introduces mechanically induced flows with directional implications for short-term equity market returns. The study contributes to the literature by quantifying this relationship at the index level for the following day(s) and offers practical insights for asset managers seeking to integrate market microstructure variables into predictive frameworks.

The conference pays close attention to questions concerning interspecies relations, aiming to promote multidisciplinary research collaborations. 

The purpose is to gather researchers in a new Nordic network focusing on the study of human-animal relations in narrative forms, such as children’s literature and young adult fiction, film, art, games, fanfiction, fanart, and exhibitions.

Keynote speakers are Zoe Jaques (University of Cambridge), children’s literature scholar within posthumanism and ecocriticism, as well as Helena Pedersen (University of Gothenburg), researcher within critical animal studies and critical animal pedagogy.