Social media is a powerful tool that influences public perception, serving both positive and negative purposes. It is used by individuals, the government, and developers to promote specific narratives. On the positive side, social media facilitates protests and mobilization efforts aimed at addressing bad governance and amplifying citizens' concerns. However, it also has a negative aspect, as political candidates in Nigeria exploit it for manipulation through disinformation and deception, influencing public opinion and creating divisions based on ideology, beliefs, and values such as small gaps can have a huge impact.

This paper examines the role of influencers and activists in addressing corruption and human rights violations in Nigeria. It provides an overview of Nigeria’s democratic system, explaining how party systems function and their role in governance. However, social media platforms such as Twitter, Instagram, and Facebook have been instrumental in enabling youth to call out bad governance. The study explores how these platforms can be used by activists and influencers to express their dissatisfaction with bad governance, as political parties are said to be deeply rooted in corrupt practices. It also analyses the impact of policies and social factors on media usage, including censorship during movements such as #EndSARS, #EndBadGovernance, and #RevolutionNow. Additionally, it analyses the role of influencers as advocates and the challenges they encounter. Lastly, it discusses the influence of activists and influencers in promoting good governance and explores strategies to combat disinformation while demanding accountability and transparency from the government.

Osteoporosis is a geriatric disease characterized by decreased bone density, commonly treated with bisphosphonates. This class of drugs inhibits bone resorption and increases bone mineral density, effectively reducing overall fracture risk. However, long-term bisphosphonate therapy has been associated with atypical femoral fractures, which are insufficiency fractures that can occur with minimal or no trauma. This effect necessitates careful assessment when considering extended bisphosphonate treatment, and therefore accurately classifying such fractures from normal femoral fractures that usually stem from high-trauma impact is of clinical concern. 

This thesis explores deep learning models, namely Transformer-based models, to aid in classifying atypical femoral fractures from normal femoral fractures. Fusion of two modalities, radiographic images of fractures and electronic health records of the patients, was performed to allow the networks to make predictions using more available information. For this, a vision transformer and a tabular transformer models were employed. The fusion was done using a conventional fusion strategy, but also an attention-based one. 

The dataset used comprises data from 1, 073 patients in Sweden who suffered a femur fracture, with radiographs obtained from 72 hospitals in 2011, totaling 4, 014 images. All images have a fracture present. This was coupled with detailed register information from the Swedish National Patient Register. The data was preprocessed using common techniques for each respective modality and split on patient-level using 5-fold cross-validation. 

Five models were employed to perform the binary classification of the fracture, (1) unimodal vision transformer, (2) unimodal tabular transformer, (3) multimodal conventional late fusion, (4) multimodal intra self-attention fusion, (5) multimodal inter cross-attention fusion. The models were assessed using performance metrics (Accuracy, AUC, F1-score, Matthews Correlation Coefficient) and prediction uncertainties from MC dropout. Model predictions were also aggregated from the image-level to the patient-level using the inverse uncertainties to increase clinical relevance of the results. Model evaluation was performed using the DeLong test to compare AUCs and Wilcoxon rank-sum test to compare uncertainties. 

Among the models, the unimodal vision model had majority of best test metrics at the patient-level when averaged over the folds, with an accuracy of 99.07%, F1-score of 0.9714, and an MCC of 0.9665. The multimodal conventional late fusion model and the multimodal inter cross-attention fusion model had the highest average AUC of 0.9994. There were no significant differences between the AUC of the respective models. The comparison of uncertainty showed significant outcomes for all tests except for the conventional late fusion compared to the multimodal inter cross-attention fusion model. In conclusion, the unimodal vision model performed best according to the performance metrics, however when accounting for uncertainties, the fusion models showed an increased performance. 

This paper examines the health implications of introducing a new pricing model for medicines used to treat rare diseases, known as a rarity staircase. The rarity staircase implies that a higher cost per health gain should be accepted for medicines targeting rare diseases in Sweden. The health consequences of implementing the rarity staircase are examined by quantifying the health implications as net health effects. The calculations indicate that the net health loss from introducing the severity staircase over the next ten years amounts to 654-1,521 years in full health, depending on how pharmaceutical companies respond to the changes in incentives brought by implementing the severity staircase. Sensitivity analyses highlight uncertainties in the estimates but indicate that the net health effect will be negative. The results imply that implementing the rarity staircase displaces more health than it generates. Our estimates can be used to assess whether this displacement is acceptable based on arguments regarding equal access to healthcare. 

6DoF object pose estimation is important in many applications, such as robotics, autonomous driving, and virtual reality. Many methods today require expensive equipment, such as depth sensors, or high-quality hand-made 3D models, such as CAD models, to achieve high accuracy. NeRF and Gaussian Splatting are two recent methods for Novel View Synthesis, which solve these problems by creating 3D reconstructions of objects using only RGB images. This thesis explores whether the two state-of-the-art camera pose estimation methods, IFFNeRF and 6DGS, which make use of NeRF and Gaussian Splatting, can be used for 6DoF object pose estimation, and how they are affected by images containing occlusion or background. This report shows that IFFNeRF and 6DGS can be used for object pose estimation, with 6DGS generally performing better than IFFNeRF. The results also indicate that IFFNeRF and 6DGS are resistant to occlusion but highly sensitive to changes in the background.

Bakgrund: Långvarig smärta är ett vanligt besvär som kan påverka individen fysiskt, socialt och psykiskt. Det nationella vårdförloppet för långvarig smärta används för att främja en mer jämlik vård med hög kvalitet. Implementering av ett vårdförlopp är en krävande process ur olika perspektiv. Till vårdförloppet har det skapats en konsekvensanalys för barriärer och underlättande faktorer för implementering. 

Syfte: Syftet med studien var att utforska perspektiven från fysioterapeuter i Region Östergötland gällande potentiella barriärer och underlättande faktorer i att implementera det nationella vårdförloppet för långvarig smärta för vuxna. 

Metod: En kvalitativ intervjustudie genomfördes med semistrukturerade intervjuer. Det inkluderades sex fysioterapeuter inom primärvården i rehab öst, rehab väst och centrala Östergötland samt smärtenheten i Region Östergötland. Det insamlade materialet analyserades genom kvalitativ innehållsanalys med induktiv ansats. 

Resultat: Det analyserade materialet delades in i fem huvudkategorier, vilket var ”sambandet mellan implementeringsprocessen och lokal nivå”, ”behov kopplat till samarbete på olika nivåer”, ”behov kopplat till resurser”, ”strategier för implementering av vårdförlopp” och ”underlättande faktorer för implementering”. 

Konklusion: Resultatet visar en variation av tankar och erfarenheter angående faktorer som påverkar en implementeringsprocess. Faktorer som uppkom berörde bland annat kunskap, samarbete, tid, personalresurser, ansvar, tydlig struktur av vårdförloppet, kostnader samt olika strategier för implementering. Det finns behov av framtida studier för att stärka slutsatserna i denna studie. 

Efficient and reliable handling of fluctuating medical imaging workloads is critical in modern healthcare systems. This thesis explores the performance of reactive autoscaling for DICOM image import in Kubernetes environments, with a focus on evaluating and extending the Horizontal Pod Autoscaler. We simulate realistic hospital workload patterns which consists of ramp-up, ramp-down, and burst scenarios. We compare scaling behaviors across both default CPU-based metrics and custom metrics such as active DICOM associations. The simulations are run in a cloud-based Azure Kubernetes Service cluster, using a benchmarking setup that sends DICOM images via the DCMTK protocol suite.Our results show that metric selection and scaling policy configuration significantly affect responsiveness, stability, and resource efficiency. We also identify practical challenges, such as load balancer limitations and the time it takes to provision new nodes during scaleup events, both of which impact system responsiveness and reliability. The findings highlight both the potential and limitations of reactive scaling and suggest directions for future improvements, including more intelligent metric tuning, load-aware routing, and proactive or hybrid scaling approaches tailored to healthcare environments.

Most modern navigation systems rely heavily on global navigation satellite systems (GNSS), which are vulnerable to jamming, spoofing and interference. To overcome these issues, alternative methods for navigation are needed. This thesis presents a GNSS-free framework for fusing inertial measurements with geophysical map data for airborne navigation. Specifically, measurements from two map modalities, terrain elevation and magnetic anomaly maps, are fused with inertial sensor measurements within a marginalized particle filter (MPF).

Real-world terrain and magnetic anomaly data from surveys over Sweden are used, while sensor measurements are generated synthetically. Four geographic scenarios, ranging from mountainous to lake environments, are used to analyze how the performance varies with different map characteristics.

The results show that the use of both topographic and magnetic anomaly data significantly improves estimation accuracy and reduces inertial sensor requirements compared to using the maps individually. The results also highlight the complementary nature of the two map modalities. In a simulated flight over lake Vättern in Sweden, using only the terrain map data gave poor results, as expected. But by also leveraging the magnetic anomaly map in the filter, performance was drastically improved.

By fusing the two map modalities, the root mean square error (RMSE) of the estimates is reduced, but at the cost of decreased filter robustness in some scenarios. Moreover, robustness is further reduced in some scenarios with large map gradients. To address this, two robust sensor fusion techniques are evaluated: Huber’s M-estimation and P-norm opinion pooling. The results show that robustness can be greatly improved using these methods, though at the expense of estimation accuracy. Overall, the thesis concludes that fusion of inertial sensor measurements with two map modalities using an MPF framework is a promising alternative to GNSS-based navigation.

Konsumtion på kredit har vuxit snabbt på den svenska e-handelsmarknaden, särskilt genom tjänster som Buy Now, Pay Later (BNPL). Generation Z som präglas av teknologisk utveckling, snabb informationsåtkomst och otålighet, utgör den största användargruppen. Kombinationen av generationens egenskaper och BNPL:s lättillgänglighet kan bidra till förändrade ekonomiska beteenden, särskilt i en tid där konsumtion kan ske utan omedelbara ekonomiska konsekvenser. Mot denna bakgrund syftar uppsatsen till att undersöka hur tillgången samt attityden till BNPL-tjänster påverkar Generation Z:s förutsättningar att hantera konflikten mellan kortsiktiga konsumtionsköp och långsiktiga ekonomiska mål.

För att besvara detta genomfördes tolv intervjuer med personer födda mellan år 1995 och år 2005 vilket utgör en grupp som både befinner sig i aktiv konsumtionsålder och har haft möjlighet att använda BNPL-tjänster. Studien utgår från beteendeekonomiska teorier för att förstå hur BNPL:s utformning samspelar med psykologiska mekanismer som kan underminera långsiktig ekonomisk planering.

Studien visar att BNPL-tjänster på kort tid har etablerats som ett standardalternativ inom e-handeln, särskilt bland Generation Z. Tjänsternas utformning och marknadsföring sänker tröskeln för konsumtion och skapar en betalningslogik som uppmuntrar snabba beslut. Trots att många respondenter uppger att de konsumerar mer till följd av BNPL, upplevs detta inte som problematiskt och sparandet anses opåverkat. Detta tyder på att BNPL-köp ofta hålls mentalt åtskilda från övrig ekonomi, vilket skapar en kognitiv distans mellan impulsiva beslut och långsiktiga mål. Tjänsten framställs som rationell och flexibel, vilket förstärker känslan av ekonomisk kontroll, en känsla som inte alltid utgår från faktisk reflektion. Att respondenter lättare identifierar riskbeteenden hos andra än hos sig själva tyder även på en övertro på den egna förmågan, och pekar på hur BNPL inte bara påverkar konsumtion, utan även hur unga strukturerar och förstår sin ekonomiska verklighet.

Slutsatsen är att BNPL skapar nya villkor för ungas ekonomiska disciplinering. Även om tjänsterna inte direkt förhindrar sparande, kräver de ett ökat mått av självdisciplin och ansvarstagande för att motverka att omedelbar konsumtion prioriteras framför långsiktig ekonomisk nytta.

Background: Crossfit is an increasingly popular form of exercise. Several studies report that injuries are common in the sport, particularly affecting the knees, shoulders, and lower back. Currently, research on athletes’ thoughts and attitudes toward injury prevention in Crossfit is limited.

Aim:The aim of this interview study was to explore and describe Crossfit athletes’ thoughts and attitudes toward injury prevention training within the sport.

Method: This qualitative interview study included eight participants with experience in Crossfit. Recruitment initially involved contacting training facilities in Östergötland, later expanding to facilities in Värmland and Småland. After limited response, individual athletes were contacted via email, Instagram, or Messenger. Prior to the interviews, participants completed a questionnaire containing background information. The interviews were semi-structured, and a qualitative content analysis was conducted according to Graneheim and Lundman (2004) with an inductive approach.

Results:The interview material was categorized into three main themes: “Athletes’ thoughts and attitudes toward injury prevention training,” “Injury prevention approaches used by athletes,” and “Knowledge acquisition regarding injury prevention.” Each main category included subcategories. Participants emphasized that injury prevention training is important but often deprioritized. They also described a change in attitude toward the subject since beginning Crossfit. Injury prevention was viewed as a holistic approach involving a combination of multiple training forms and exercises.

Conclusion:The participants stated that injury prevention training is important for long-term sustainability among Crossfit athletes but is often deprioritized due to it being perceived as boring, time-consuming, and lacking motivation. They also reported a shift in attitude and a broader, more holistic understanding of injury prevention over time.

Keys-words:Crossfit, Injury Prevention Training, Sustainability, Preventive Strategies for Injuries, Injuries

Recent advancements in artificial intelligence (AI), especially large language models (LLMs), have sparked significant interest in their potential applications. Code review is a widely adopted software development practice that helps participants understand the codebase and maintain software quality. However, this process is highly time-consuming, prompting interest in improving efficiency through advances in code review tools.

This study investigates the applicability and effectiveness of LLMs as assistive tools in the code review process. We generate and systematically evaluate AI-generated code review comments, which are assessed by both the study authors (using scientifically derived criteria) and professional software engineers (reflecting industrial best practices). A review of scientific literature and a series of interviews with engineers reveal substantial theoretical alignment on what constitutes a good code review comment, particularly regarding the identification of functional errors, edge cases, maintainability concerns, and suggestions for more efficient solutions. However, analysis of inter-rater reliability and thematic categorization indicates a substantial divergence of what constitutes a useful code review comment between study authors and engineers in practice.

Our results show that current state-of-the-art LLMs can generate code review comments that are often perceived as useful by both academics and practitioners. Specifically, 69.4% (95% CI [65.5%, 73.1%]) of comments were found useful by the study authors, and 92.8% (95% CI [88.9%, 95.4%]) by professional software engineers. While LLMs are not positioned to replace humans in the code review process, they show promise as effective assistive tools. We highlight challenges related to prompt engineering, subjectivity in usefulness assessments, and the potential of LLMs as a component in future code reviewing tools.

Cloud-native technologies are being widely embraced by operators and vendors in the telecommunications (telco) sector. These technologies are transforming how telco workloads are managed and executed, enabling more scalable and flexible systems. However, to function as intended, telco applications demand low latencies. This thesis investigates the effectiveness of the Latency-criticality Aware Virtual Deadline (LAVD) scheduler, implemented via Linux’s new sched_ext framework, in handling performance-critical telco workloads. A simulator was developed to model realistic traffic based on statistical analysis of Ericsson trace data in combination with interfering workloads. This simulator was used to evaluate the latency performance of LAVD and scx_simple, another sched_ext scheduler, against the default Linux kernel scheduler EEVDF (Earliest Eligible Virtual Deadline First) under varying load conditions, both on native Linux and in a local Kubernetes cluster. The results show that LAVD has the potential of significantly reducing the mean turnaround time, especially in high-load scenarios with interfering workloads, outperforming the baseline EEVDF scheduler by up to 81% in certain configurations. scx_simple performed surprisingly well in comparison to LAVD, outperforming it when the number of interfering workloads was low. This indicates that the key takeaway is not the superiority of one specific scheduler, but rather the potential of the sched_ext framework as a whole. These findings suggest that sched_ext schedulers like LAVD can provide substantial performance benefits for latency-sensitive workloads.

2+1 roads represent a cost-effective and safety-enhancing design for rural highways, offering alternating overtaking lanes that reduce the need for dangerous passing maneuvers into oncoming traffic. However, a persistent operational concern on these roads is the merging area, where the transition from two lanes to one may result in sudden deceleration and reduced capacity. Understanding and replicating the complex driver behavior associated with overtaking and merging is essential for improving the planning and safety evaluation of such infrastructure. This thesis investigates the extent to which the microscopic traffic simulation software PTV VISSIM 2024 can be calibrated to accurately simulate traffic operations on a 2+1 road. Using cross-sectional field measurements from a two-lane segment of the E18 highway in Sweden—where individual vehicles were tracked across multiple sensors—the thesis builds, calibrates, and validates a series of progressively refined simulation models. The calibration process incorporates parameter sensitivity analysis, parameter optimization, the usage of the network object reducing the speed in the merging area, and the use of the COM interface for dynamic adjustment of vehicle behavior. Four models were evaluated. The baseline model M0 failed to replicate real-world traffic conditions, exhibiting excessive overtaking and congestion. M1 introduced calibrated behavioral parameters, improving lane usage and merging. M2 incorporated speed adjustments via the Reduced Speed Area, improving alignment with observed speed distributions in the merging area. M3 implemented dynamic speed modifications for overtaking vehicles through the COM interface, yielding the closest replication of the Field Data. The study concludes that with calibrated parameters and behavioral extensions, VISSIM can realistically model traffic operations on 2+1 roads. Nevertheless, it also highlights challenges such as overfitting to specific datasets and the necessity for detailed Field Data. The developed methodology demonstrates the feasibility of modeling overtaking and merging dynamics and provides a robust foundation for further research and application to broader road networks.

With the rise in AI usage and the gap of knowledge of how it may affect different groups of people,this thesis aimed to grow the body of research by performing a study that compared how differentusers were comfortable interacting with AI chatbots, based on how lonely they were. The studyused a survey to gather data, mainly collecting quantitative data. It used an adapted Almere modelto assess AI acceptance, the De Jong Gierveld Loneliness Scale to access participants lonelinessscores and a scenario-based approach to measure participants comfort levels in different scenarios.The findings showed that severely lonely individuals were more comfortable asking AI chatbotsintimate and personal questions compared to non-lonely and moderately lonely individuals. Thisshow that when someone who is severely lonely feels the need for companionship or socialconnection, they may be more comfortable turning to an AI for emotional support, which, althoughpotentially offering short-term relief, may in the long-term lead to risks of forming emotionaldependency with AI, further exacerbating their condition.

The Swedish real property law system is based on the premise that a real property must have an owner. Despite this, there are properties that are ownerless. Thus, a paradox has occurred, which leads to several legal questions. The main purpose of this study is to highlight and investigate real property law challenges that arise as a result of real property becoming ownerless. Furthermore, the purpose is to analyse existing legislation linked to these challenges.

In order to fulfil the purpose of the study the doctrinal method is applied, which involves determining current law, by using sources of law. To give the reader a better view of the issue with ownerless real properties, news articles as well as interviews with professionals have been used to some extent. As a result of the study, the authors reach three final conclusions. The first conclusion is that the main legal challenge is the lack of an owner, which causes problems as no one can be accountable for the real property in question. The law places responsibility on a real property owner in situations such as in maintenance of the property and payment of fees associated with real property ownership. The second conclusion is that a lack of legislation regarding these properties constitutes a legal challenge. There is currently no statutory law that places responsibility on neither an individual nor the government when a real property has become ownerless. The last conclusion is that expropriation of an ownerless real property works in theory but not as well in practice. 

I dagens samhälle är digitala medier en integrerad del i individers vardag, detta innefattar såväl vuxna som barn. I vår studie undersöker vi hur digitala medier påverkar elevers hälsa och liv, med särskilt fokus på den sociala hälsan och fritidshemselevernas egna perspektiv. Studien utgår från ett barndomssociologiskt perspektiv och ett salutogent perspektiv på hälsa. 

Syftet med studien är att synliggöra hur digitala medier kan bidra till elevers välbefinnande, och forskningsfrågorna fokuserar på elevernas relation till, motiv för och känslor kring digitala medier samt hur deras användning kan upplevas som hälsofrämjande. 

Den bakgrundslitteratur vi utgått ifrån visar på en spänning mellan läroplanens krav på digital kompetens och de praktiska begränsningarna i skolmiljön, såsom brist på resurser och kunskap. Texterna visar även att den digitala världen kan ha positiva effekter på elevernas relationskapande, vilket innefattar den sociala hälsan. Tidigare forskning och myndighetsrapporter pekar på ett vuxenperspektiv i diskussionen om barns medieanvändning, vilket motiverar behovet av att lyfta fram barnens egna röster. 

Datainsamlingen genomfördes genom semistrukturerade fokusgruppsintervjuer med totalt 15 elever i årskurs 1–3 på fyra fritidshem i två kommuner. Analysen genomfördes med tematisk analys, där fem centrala teman identifierades: elevers användning av digitala medier; vuxnas begränsningar; digitala relationer; risk och medvetenhet samt fritidshemmets digitala förutsättningar.  

Resultatet visar att digitala medier är en stor del i elevernas vardagliga liv och att användning främst är ett sätt för dem att skapa och upprätthålla relationer baserat på deras intressen. Vuxna begränsar elevernas användning baserat på deras uppfattningar och attityder kring ämnet utan att vara delaktiga eller närvarande i deras digitala värld eller att se till elevernas egna perspektiv. Studien visar på stor variation i elevernas erfarenheter och belyser vikten av att inkludera deras perspektiv i utvecklingen av digital pedagogik i fritidshemmet

The scope of the project was to investigate the use of a machine learning-enhanced electronic nose (e-nose) to distinguish between different species. The project is a collaboration between the Department of Physics, Chemistry and Biology (IFM) and the Swedish National Board of Forensic Medicine (RMV).

The samples include post-mortem human, pig, and chicken tissue, with an additional analysis performed on chicken samples contaminated with soil, to assess the impact of environmental contamination. The samples were analysed using an e-nose equipped with 32 sensors, consisting of eight sensor types distributed across four sensor banks. Mean signal of the model classes was visualised as an initial analysis. The models use a machine learning (ML) approach to optimise an ensemble method to the dataset. Prior to the training of the model, the dataset is preprocessed and exclusion of both sensors and extracted features was performed. The results were presented in confusion matrices and the accuracy, sensitivity and specificity were calculated. All models were evaluated using an independent dataset, and assessed with a confusion matrix and a final decision algorithm. The final decision algorithm classifies one sample by using intermediate decisions from each sensor and performing a majority vote.

Five classification models are developed. Model 1 distinguishes between human and animal tissue, while Model 2 further separates the animal class into pig and chicken. Model 3 compares human samples to a contaminated class, which includes both soil contaminated chicken tissue and chicken contaminated soil. Model 4 focuses on distinguishing clean chicken from soil-contaminated chicken tissue. Finally, Model 5 separates the contaminated class into its two components: contaminated soil and contaminated chicken tissue. All models demonstrate high performance with test accuracies between 94.0%-99.5%, sensitivity performances between 95.5%-100% and specificity performances between 91.3%-100% for the training dataset. The values were reduced for the independent dataset and ranged between 72.7%-100%. The addition of soil as a contaminant did not significantly change the model performance.

The method of using an ML-enhanced e-nose was shown to be a good alternative based on the presented model performances, both in regard to clean and contaminated samples. To improve the models further, larger datasets would be required.

Finding secure and reliable student housing remains a challenge for many students atLinköping University. Existing online platforms such as Facebook groups and Blocket areoften unregulated, lack verification systems, and offer limited support for the specific needsof student renters and landlords. This thesis explores how trust is established in an onlinehousing rental platform, with a focus on three factors: social presence, ease of use, and se-curity. To investigate this, a prototype platform "liuHome" was developed and evaluatedthrough two user tests with a total of 51 student participants. The two versions differedin key features with respect to the login method, payment integration, user profiles, andfiltering functionality. Participants completed predefined tasks and responded to Likert-based surveys and questions about trust. The data were later analyzed using mean scoresand Mann-Whitney U tests to evaluate differences. The results indicate that the inclusionof LiU Single Sign-On (SSO) and Swish payment integration significantly improved users’perception of security. Similarly, the introduction of user profiles and review systems im-proved the social presence and trust of other users on the platform. Filtering tools andimproved designs positively influence ease of use. The findings suggest that targeted de-sign innovation that focuses on the chosen factors in trust can meaningfully enhance userconfidence in online housing marketplaces.

Many financial systems opened up to interconnection as a direct result of new legislationon an EU level. This caused many financial institutions to adopt API:s for intercommu-nication of important data and to execute transactions. This paper seeks to examine onesuch implementation.This paper examines the implementation of an API gateway to validate and facilitateOpen Banking authentication flow access, focusing on the challenges related to security.The study evaluates the target of evaluation accessible through an API gateway. Byemploying quantitative assurance metric the paper aims to derive a qualitative secu-rity assessment. The evaluation utilizes the Goal-Question-Metric (GQM) methodology,drawing from the OWASP REST Security Cheat Sheet to establish security requirementsand metrics. The findings reveal a moderate to high level of security compliance, and theresults provide insights into the effectiveness of quantitative security metrics and highlightareas for improvement in securing Open Banking authentication flows.

This thesis explores how multinational companies manage ongoing tensions during digital servitization. A case study of Globauto Japan shows how global strategies often conflict with local realities, creating persistent paradoxes. Instead of formal restructuring, employees relied on informal practices, such as mentoring, role-shifting, and goal translation, to manage contradictions in daily work. The study highlights how transformation is not just strategic or technical, but relational and interpretive. It argues that paradoxes are not problems to solve but conditions to live with, requiring flexible routines and local agency.

I många delar av världen är utbildning en självklarhet, men inte för unga på landsbygden iKenya. Denna studie undersöker hur digitala yrkesutbildningar genom Kenswed m-schoolpåverkar unga kenyaners motivation, framtidstro och livsvillkor. Genom en enkätstudie med139 elever från sex platser i olika delar av Kenya, åskådliggörs deras erfarenheter av att studeramed hjälp av solcellsdrivna läsplattor, i en vardag präglad av begränsad tillgång till el, internetoch resurser. Resultaten visar att eleverna i sitt val av yrkesutbildning drivs av både nyfikenhetoch framtidstro där ett starkt personligt intresse för studierna kombineras med tron på attutbildningen kan förbättra deras framtid. Samtidigt finns tydliga hinder: delad utrustning, långaavstånd och en vardag där tid för studier ofta måste konkurrera med arbete och ansvar. Ändåtyder mycket på att m-school kan vara ett verktyg för att möjliggöra en ljusare framtid, förutsattatt utbildningen får stöd av fungerande infrastruktur, teknik, lokalt engagemang och långsiktigsamhällsutveckling

As TikTok has become a central platform for information-seeking among young voters, the Swedish political landscape has shifted to a space where fast-paced, entertaining, and visual content is prioritized. This study aims to examine how the Swedish political parties, the Social Democratic Party and the Moderate Party, use TikTok to build brand awareness among young voters aged 18–25. The study is grounded in theories of political branding, human branding, and social media marketing (SMM), with a focus on four key dimensions: entertainment, trendiness, interaction, and electronic word-of-mouth.

Adopting a deductive approach and a qualitative methodology, data was collected through semiotic observations of the parties’ TikTok content and semi-structured interviews with young voters. The data was analyzed using open coding, content analysis, and thematic analysis.

The study concludes that Swedish parties use TikTok as a tool to build brand awareness among young voters by combining ideological messages, entertaining elements, and the personality of the party leader. By adapting content to the platform’s logic, parties generate recognition, trust, and engagement among young audiences. The study recommends that Swedish parties combine value-driven messages with clear visual elements, and present their party leaders as both professional and relatable. They should use humor and trends authentically and engage actively with users on TikTok to build a coherent, credible, and engaging brand identity that strengthens party brand awareness.

Imaginaries of safe informal sports facilities can offer valuable insights into urban planning, particularly when shaped by those who are often excluded from decision-making processes. This study explores how women in Linköping, Sweden, experience and imagine informal sport facilities, with a focus on elements of safety. Drawing on feminist planning theory and the concept of imaginaries, three workshops were conducted using visual and narrative methods to elicit both lived experiences and alternative imaginaries of informal sport facilities.The results show that safety is not solely a matter of physical infrastructure but is created in the interaction between movement, openness and visibility, color and greenery, and social presence. A central finding is the presence of ambiguous imaginaries: women imagined spaces that are open but not exposing, visible but not surveilled, and lively but not crowded. The study does not offer ready-made solutions but contributes to broader reflection and dialogue on gender and urban planning, on how planning practices can become more inclusive. By highlighting how women negotiate safety, the findings invite planners and municipal actors to engage with complexity, challenge established norms, and reconsider how informal sport facilities can be planned and designed.

This thesis explores how the establishment of Northvolt’s battery factory in Skellefteå has influenced the municipality’s planning by focusing on the opportunities and challenges related to its recent changes in infrastructure, housing development, and social sustainability. The study also looks at how access to key resources and regional competition can be examined in the context of Sweden’s green transition. 

By combining document analysis with semi-structured interviews, I explore the opportunities and challenges that have emerged in infrastructure development, housing development, and social sustainability. Further, I also analyze how access to critical raw materials, and power dynamics in global supply chains have influenced Skellefteå’s development trajectory. 

The findings highlight how Skellefteå’s rapid transformation was guided by optimistic sociotechnical imaginaries, emphasizing industrial growth, job creation, and green leadership. However, these future-oriented visions often outpaced practical realities, leading to tensions as the accelerated industrial development clashed with the slower, more deliberate pace of strategic planning. At the time of writing, Northvolt filed for bankruptcy, adding a critical layer of context to the study. This development revealed the fragility of relying on a single company and underscored the municipality’s vulnerability to external market forces.

Furthermore, the thesis shows how Sweden’s national strategy to reduce reliance on foreign critical raw materials – while aligned with broader EU goals – remains constrained by regulatory delays. By integrating resource dependency theory and sociotechnical imaginaries, the study reveals how imagined futures and resource dependencies shape both national policy and strategic planning. It concludes that effective governance of the green transition requires balancing visionary ambitions with grounded, diversified, and inclusive regional development strategies.

Abstract

Syftet med föreliggande examensarbete är att undersöka elevers uppfattning om specialundervisning inom fältet språk-, skriv- och läsutveckling. Frågeställningen är: Hur erfar elever i år 3 - år 6 specialundervisning inom fältet språk-, skriv- och läsutveckling?

För att studera elevers uppfattning av fenomenet specialundervisning antogs en fenomenografisk vetenskapsteoretisk ansats. Fenomenografi används inom bland annat pedagogisk forskning, där syftet är att utforska variationer mellan individers sätt att uppfatta eller erfara ett fenomen, i stället för att söka efter likheter. Datainsamlingen utgick från semistrukturerade intervjuer utifrån en på förhand utarbetad intervjuguide. De 14 intervjupersonerna valdes ut baserat på ålder (9-12 år) och utifrån premissen att de tagit del av specialundervisning inom läs- och skrivundervisning, gärna under en längre tidsperiod. Samtliga elever hade upptäckta läs- och skrivsvårigheter av olika karaktär och svårighetsgrad, där vissa var diagnostiserade med dyslexi.

Resultatet av studien består av fyra olika beskrivningskategorier som utgör beskrivningar på kollektiv nivå. Kategorierna är följande: Eleverna erfar specialundervisning inom läs- och skrivundervisning som 1) lärtillfälle, 2) störningsmoment, 3) tillflykt och trygghet samt 4) utpekande. Beskrivningskategorierna sorteras i vår analys horisontellt, det vill säga att de är kvalitativt skilda men utan en intern relation till varandra. Samtliga beskrivningskategorier kan sägas utgöra svar på vår frågeställning om hur elever erfar specialundervisning inom språk-,skriv- och läsutveckling.

Skolans uppdrag är att skapa plats för alla elever och förbereda dem för att kunna vara delaktiga samhällsmedborgare. I vår roll som speciallärare har vi som uppdrag att ge stöd till elever som lätt hamnar i periferin, och som riskerar att exkluderas från samhället. Med det här examensarbetet vill vi bidra genom att låta dessa elevers röster höras. Detta för att vi som speciallärare ska kunna anpassa undervisningen för att på bästa sätt utveckla elevernas språk-,skriv- och läsförmåga, men även för att stödja och stärka elevernas akademiska självbild.

Efficient operation of bus terminals is crucial to the performance of large public transportation systems, particularly in urban environments where space constraints limit physical expansion. This thesis investigates methods for detecting and analyzing congestion in bus terminals using Automatic Vehicle Location (AVL) positioning data. The study utilizes historical real-time and static transit data in the General Transit Feed Specification (GTFS) format, combined with video footage from a drone for validation. A methodology is developed to detect stop occurrences, classify them according to their cause (e.g. at a berth, at traffic lights), validate these classifications using the video observations, and isolate the possible cases of congestion between buses. Data cleaning techniques and algorithmic adjustments are presented to improve detection accuracy. The main result of this approach is a table containing the occurrences of vehicles stopping within a determined time frame, with the detected possible stopping causes as an attribute for each occurrence. A numerical analysis is also conducted to assess berth occupancy and travel times within the terminal. The methodology is applied to the case of a typical Swedish bus terminal, Linköping Central. The proposed method involves recording the vehicle stop occurrences and the possible reasons for each stop. The algorithm detects the berth the vehicle is stopping at, if any, as well as the berths it is supposed to stop at according to its schedule. It also detects other possible reasons, such as at the traffic light at the exit of the terminal, at a pedestrian crossing or because of another stopped vehicle ahead of them. Validation of the results using the video observations is fully integrated within this process. Applying the methodology to the case of Linköping Central allowed for its evaluation, as well as several findings on the cases of congestion from the dataset to be drawn. The results from the berth detection algorithm were deemed as satisfying, as well as the detection of “regular” stops (in this case, traffic lights at the exit), while the algorithms for the detection of other stopped vehicles ahead and pedestrian crossings led to poorer results. All 6 cases of congestion found in the dataset happened near a different berth, but all of them occurred either because of a bus badly parked at its berth or another vehicle attempting to stop at an already-occupied berth. However, the dataset was restricted by the availability of drone footage, which might have limited too much the occurrences of congestion. Lastly, the numerical analysis led to the observation that vehicle distribution among berths was not optimal, as well as that some particular berths were potentially overloaded. The study proposes a generalizable framework, offering potential for broader application across different terminals and transit networks. As it is also a pre-study for a larger project, several suggestions are made for further work on the topic, including the expansion of the hereby presented approach, some other approaches such as microsimulation, and the possibility of taking into account other factors such as pedestrian movement and the needs of the stakeholders.

This essay aims to examine how the United Nations portrays its own responsibility regarding women in the global south who are subjected to sexual violence by UN personnel, using two annual reports. Through a critical discourse study using Fairclough’s three-dimensional model, the study intends to highlight how the organization uses discourses and linguistic strategies through a feminist theoretical framework. As well as highlighting how notions of gender, power and marginalized women are reflected in the language.

The findings in the study reflect recurring patterns which demonstrate how the UN uses formal and neutral discourses to create a distance from the responsibility. The discourses used by the UN, which implies responsibility, will create an image that efforts are made for the women in the global south. When in reality, it lacks clarity on what is being done in practice for the women. The repercussions of using this narrative can affect individuals and other actors, based on their context, to perceive the UN as taking responsibility. The gendered narrative and postcolonial feminist framework highlights how the UN reinforce and maintain discourses which reproduce colonial power structures and gender imbalances. Strengthening damaging gender roles and reinforcing perceptions about women in the global south as vulnerable and passive. The United Nations, as an architect of norms, may prevent a possible change for the women in the global south when they reproduce and maintain colonial and gendered roles and present themselves as responsible.

Separating a payload from a launch vehicle in space poses a unique engineering challenge due to the intense random vibration environment during ascent. One of the most critical components in the separation mechanism is the separation spring assembly, where a spring is used to gently separate the payload from its launch vehicle. To simulate and evaluate the mechanical behaviour of such devices, this study applies finite element modeling (FEM) to investigate dynamic responses, stress development, bolt load behaviour, and damping characteristics. The modeling approach considers combined static and random vibration loading, focusing on how structural modeling choices influence simulation outcomes.

The study shows that simplified models, while computationally efficient, tend to overestimate internal stresses and underestimate bolt loads, potentially leading to non-conservative designs. Including a detailed spring model was essential for accurately predicting eigenfrequencies and stress distributions. It was also found that the best method for combining static and dynamic stress tensors is through direct summation of their individual components, resulting in a conservative and realistic representation of the stress state. It correctly accounts for worst-case sign combinations and the non-negative nature of stress results typical of random vibration analysis.

Damping analysis showed that the commonly assumed 2\% structural damping ratio is only valid above 800 Hz, while significantly lower damping (around 0.6\%) was observed at lower frequencies, indicating that frequency-dependent damping models offer improved accuracy. The study further recommends using lumped mass representations only when the added components do not contribute to global stiffness, and emphasizes the use of RBE3 elements (a Nastran element that distributes loads without adding stiffness) for stiffness-neutral connections. Physical testing is advised to validate simplified configurations and extend findings to similar spacecraft secondary structures.

Designing large-scale data interfaces that cater to a diverse audience is an increasingly critical challenge in modern information systems. This thesis examines how Nielsen’s 10 Usability Heuristics can be effectively utilized to enhance user experience in a genealogy platform, particularly for audiences with varying degrees of computer literacy and spanning multiple age groups. Adopting a Research through Design (RTD) methodology, the study explores iterative, user-centered design techniques that align with accessibility standards and inclusive design practices. A functioning genealogy website is developed, allowing practical assessment of key usability and accessibility goals. Findings highlight the importance of consistent visual structures, intuitive navigational metaphors, and robust help features in facilitating user engagement with complex family history data. By integrating iterative usability testing in the workflow, the final interface demonstrates an inclusive model that improves overall interaction efficiency and user satisfaction. The outcomes offer actionable insights for designers seeking to balance powerful data tools with accessible, user-friendly experiences in genealogical and other data-intensive platforms.Keywords: Genealogy Website, Nielsen’s Heuristics, Accessibility, Research through Design, Digital Literacy

At airports with high traffic volumes, efficient ground handling is critical to minimising delays and optimising capacity utilisation. Stockholm Arlanda, one of Scandinavia’s largest airports, manages a complex daily flow of arriving and departing aircraft. A particularly critical part of these ground movements is the pushback sequcne, in which an aircraft is pushed back from its stand and starts its engines before taxiing towards the runway. During this phase, the aircraft may block other ground movements, potentially disrupting the entire airside flow. According to Swedavia, 3.5% of all delays between 2023 and 2025 were attributed to “Trafik bakom (Traffic behind)”, meaning that one aircraft blocked the ability of another aircraft to commence pushback from its stand. This study aims to conduct a root cause analysis of blocked pushback at Stockholm Arlanda Airport, with the objective of identifying the underlying causes, the most affected areas of the airport, and potential mitigation strategies to reduce the number of delay incidents. The study is based on a combination of interviews with relevant airport operational actors, on-site observations, and analysis of Swedavia’s delay data. The results show that during the period 2018-2024, a total of 4,451 delay incidents were recorded, corresponding to 28,292 minutes of delay and estimated additional cost of €2.8 million. The root cause analysis concludes that blocked pushback at Arlanda is a complex issue arising at the intersection of physical infrastructure, scheduling strategies, and operational limitations. The analysis identifies that certain areas, specifically ramp AB, show a significantly higher frequency of traffic behind, while other parts of the airport experience lower traffic volumes. The study concludes that improving stand availability at Pier C could help redistribute traffic flow across Terminal 5, that the installation of additional jet blast deflector between the baggage sorting facility and ramp AB could improve pushback sequences, and that modern aircraft types should be allocated to gates outside of ramp AB during peak hours.

A min-entropy is defined as a conservative measure of uncertainty, which correspondsto the probability of guessing the most common output in a dataset. This thesis evaluatesthe performance of two min-entropy estimators defined in NIST Special Publication 800-90B, i.e. MultiMCW estimator and the Compression estimator. The comparison focuseson two key metrics: accuracy and execution time for estimating min-entropy values acrossdatasets with known true min-entropy. To ensure a fair comparison, both estimators weretested under identical conditions using synthetic datasets. All tools, configurations, andenvironments were consistent throughout the experiments. The motivation for this workis the increasing demand for reliable true random number generation (TRNG), especiallythe Quantum Random Number Generators (QRNGs), where accurate entropy estimationcan be helpful for ensuring the randomness quality and security.

This thesis considers list star edge colorings of the Petersen graph. A star edge coloring of a graph G is a proper edge coloring in which there are no 2-colored paths or cycles of length four. The minimum number of colors in a star edge coloring of a graph is called its star chromatic index χ_st'(G). For a list star edge coloring we are only allowed to choose an edge's color from its assigned list L(e). If every edge e ∈ E(G) has list size |L(e)| ≥ k then we say that L is a k -edge-list-assignment. The smallest k such that any k -edge-list-assignment guarantees the existence of a star edge coloring of G is called the list star chromatic index ch_st'(G). The aim of this thesis is to asses whether the Petersen graph satisfies the conjectured statement that ch_st'(G) = χ_st'(G) for all graphs G. The star chromatic index of the Petersen graph is known to be five.

We have shown that with some special constraints on the lists L(e) of each edge, all of size five, a star edge coloring with support in the lists can be found. Lastly, a final open problem is presented to which some possible approaches are given.

This thesis investigates how Swedish municipalities integrate the UN Convention on the Rights of the Child (CRC) and the Convention on the Rights of Persons with Disabilities (CRPD) into their crisis preparedness. Using a mixed-methods design combining surveys and interviews, the study explores the extent to which these rights-based frameworks are implemented at the local level and identifies the factors and governance structures that influence this process. The analysis is grounded in implementation theory and governance perspectives to understand both the institutional and practical challenges faced by municipalities. The findings reveal a fragmented and uneven implementation, where legal commitments are often not translated into operational practice. The results point to deficiencies in knowledge, prioritization, resources, and vertical as well as horizontal coordination. Furthermore, the study identifies an implementation gap caused by limited national guidance, weak policy enforcement, and a lack of systematic collaboration with civil society and affected groups. By shedding light on these challenges, the thesis contributes to the understanding of how rights-based crisis preparedness can be strengthened in decentralized welfare systems.

In neural prostheses, intensity modulation of a single channel (i.e., through a single stimulating electrode) has been achieved by increasing the magnitude or width of each stimulation pulse, which risks eliciting pain or paraesthesia; and by changing the stimulation rate, which leads to concurrent changes in perceived frequency. In this study, we sought to render a perception of tactile intensity and frequency independently, by means of temporal pulse train patterns of fixed magnitude, delivered non-invasively. Our psychophysical study exploits a previously discovered frequency coding mechanism, where the perceived frequency of stimulus pulses grouped into periodic bursts depends on the duration of the inter-burst interval, rather than the mean pulse rate or periodicity. When electrical stimulus pulses were organised into bursts, perceived intensity was influenced by the number of pulses within a burst, while perceived frequency was determined by the time between the end of one burst envelope and the start of the next. The perceived amplitude was modulated by 1.6× while perceived frequency was varied independently by 2× within the tested range (20-40 Hz). Thus, the sensation of intensity might be controlled independently from frequency through a single stimulation channel without having to vary the injected electrical current. This can form the basis for improving strategies in delivering more complex and natural sensations for prosthetic hand users.

The growing scale of data and the complexities of machine learning models have led to significant energy-related carbon emissions, which threaten ecosystems and communities worldwide, prompting the search for greener alternatives. Research regarding the energy consumption in machine learning has been biased towards large NLP models such as BERT and GPT, leaving a gap in understanding the sustainability implications of other fundamental algorithms. This thesis investigates how representative subsets can reduce carbon emissions on the example of the k-means algorithm without significantly compromising performance.

Three well-known subset constructions are evaluated: Uniform sampling, the Lightweight Coreset and the Strong Coreset for Bregman clustering, along with two novel approaches designed to improve the construction time of the Strong Coreset: one replaces the k-means++ initialization in the Strong Coreset with the AFK-MC² approximation, while the other reduces the dimension of the dataset with the Clarkson-Woodruff transform before the coreset construction. The experiments were run on 12 datasets of different sizes and complexities, testing two k values per dataset and six subset sizes. Performance was measured using Sum of Squared Distance (SSD) and Davis-Bouldin Score (DBS) along with measurements of execution time and energy consumption. Each combination of dataset, k value and subset size was also tested with the Kruskal-Wallis test to determine whether a significant difference existed between the methods for any of the measured metrics. When a Kruskal-Wallis test indicated a significant result, the Dunn’s-test was applied to identify which specific methods differed from each other.

Results demonstrated that while in most cases there is a significantly different distribution between running k-means on the full dataset and using a coreset regarding the performance metrics SSD and DBS, the practical magnitude of these differences is minimal. A subset size of 5 percent typically remains less than 5 percent away of the same performance of running on the full dataset across the tested datasets and k values used in this thesis for the AFK-MC², Lightweight, Strong Coresets. All combinations of datasets, subset sizes and k values have a significantly different distribution in execution time and energy consumption compared to the full data, with all of the results from representative subsets having a much lower median. A strong correlation between time and energy reduction was observed for every dataset with the lowest value of 0.966. However, the slope of this relationship varied depending on the dataset, k and sometimes also between representative subset methods. The Lightweight Coreset often outperformed the Strong Coresets in efficiency except in very specific configurations unlikely to occur in practice. Dimension reduction via the Clarkson-Woodruff Transform showed promising time and energy improvements for the Strong Coreset with benefits increasing for larger k values and subset sizes.

Additional experiments on parallelization for the k-means algorithm with a k of 50 on small subsets of 397 and 1983 observations and 58 features, showed a significant reduction in energy consumption when running k-means in parallel. These findings suggest that representative subsets combined with algorithmic optimization offer an effective approach to reduce the carbon emissions of k-means clustering while maintaining high performance levels.

Aims: Assessment of cardiac function is essential for diagnosis and treatment planning in cardiovascular disease. Volume of cardiac regions and the derived measures of stroke volume (SV) and ejection fraction (EF) are most accurately calculated from imaging. This study aims to develop a fully automatic deep learning approach for calculation of cardiac function from computed tomography (CT).

Methods and results: Time-resolved CT data sets from 39 patients were used to train segmentation models for the left side of the heart including the left ventricle (LV), left atrium (LA), and left atrial appendage (LAA). We compared nnU-Net, 3D TransUNet, and UNETR. Dice Similarity Scores (DSS) were similar between nnU-Net (average DSS = 0.91) and 3D TransUNet (DSS = 0.89) while UNETR performed less well (DSS = 0.69). Intra-class correlation analysis showed nnU-Net and 3D TransUNet both accurately estimated LVSV (ICCnnU-Net = 0.95; ICC3DTransUNet = 0.94), LVEF (ICCnnU-Net = 1.00; ICC3DTransUNet = 1.00), LASV (ICCnnU-Net = 0.91; ICC3DTransUNet = 0.80), LAEF (ICCnnU-Net = 0.95; ICC3DTransUNet = 0.81), and LAASV (ICCnnU-Net = 0.79; ICC3DTransUNet = 0.81). Only nnU-Net significantly predicted LAAEF (ICCnnU-Net = 0.68). UNETR was not able to accurately estimate cardiac function. Time to convergence during training and time needed for inference were both faster for 3D TransUNet than for nnU-Net.

Conclusion: nnU-Net outperformed two different vision transformer architectures for the segmentation and calculation of function parameters for the LV, LA, and LAA. Fully automatic calculation of cardiac function parameters from CT using deep learning is fast and reliable.

Industrial automation systems require high-performance execution of control applications while maintaining portability across diverse hardware platforms. This thesis investigates alternatives to traditional interpreter-based execution of IEC 61131-3 control applications by developing an LLVM IR translator that converts ABB's proprietary intermediate representation, used for IEC 61131-3 programs, into LLVM IR, enabling compilation to both native machine code and WebAssembly.

The research evaluates four distinct execution modes: the current ABB interpreter (baseline), native code execution, WebAssembly interpreted execution, and WebAssembly ahead-of-time (AOT) compilation. A comprehensive experimental framework measures performance metrics including average execution time, first scan execution time, and execution stability across two representative control applications.

Results demonstrate that native code execution with LLVM optimizations achieves a significant performance improvement, reducing execution time by up to 96.8% compared to the interpreter-based approach. WebAssembly AOT compilation delivers nearly identical performance to native code in function-intensive applications, while WebAssembly interpreted execution shows mixed results depending on the application complexity and optimization settings. The study reveals that LLVM optimization impacts vary dramatically across execution modes, yielding improvements between 6.4% and 84.9% for most configurations.

This work contributes to the industrial automation field by demonstrating how modern compilation frameworks can enhance the performance and portability of control systems without sacrificing functionality. The findings suggest that leveraging LLVM's optimization capabilities and WebAssembly's portability provides a promising approach for next-generation industrial control systems that require both high performance and platform independence.

SammanfattningDenna studie syftar till att öka kunskapen om hur elevhälsoarbete struktureras och genomförs inom sfi. Genom semistrukturerade intervjuer med rektorer och pedagogisk personal från tre sfi-verksamheter analyseras informanternas erfarenheter av förebyggande och åtgärdande elevhälsoarbete, samt vilka utmaningar och möjligheter de upplever i det dagliga elevhälsoarbetet. Studien utgår från ett kvalitativt angreppssätt och en tematisk analys, förankrad i tre teoretiska perspektiv: det kompensatoriska perspektivet, dilemmaperspektivet samt Bronfenbrenners ekologiska systemteori. Resultatet antyder att elevhälsoarbetet i vissa fall kan ha en mer individinriktad prägel och ibland vilar på enskilda medarbetares engagemang, snarare än vara fullt ut integrerat i verksamhetens övergripande strukturer. Samverkan mellan olika yrkesroller förekommer, men verkar i flera fall sakna tydliga ramar. Det relationella perspektivet framstår som betydelsefullt, där goda relationer mellan lärare och elever ofta lyfts som viktiga för både lärande och välbefinnande.

        Slutsatserna antyder att det kan finnas ett behov av ökad systematik och långsiktighet i elevhälsoarbetet, samt att det systematiska kvalitetsarbetet möjligen skulle kunna användas mer aktivt för att följa upp och utveckla stödinsatser. Studien bidrar med fördjupad kunskap om hur elevhälsa inom vuxenutbildning kan förstås, organiseras och utvecklas.

This systematic literature review examines the conceptual and cognitive challenges that students in grades 7–9 face when working with tree diagrams in probability. The aim is to identify common misconceptions and highlight didactic strategies and tools that support students’ understanding. Research articles were selected from databases such as ERIC, DiVA, and Google Scholar and analyzed thematically. The findings show that students often struggle to differentiate between dependent and independent events, apply probability rules correctly in compound situations, and recognize when order is important. These difficulties are generally rooted in a lack of conceptual understanding rather than procedural ability. Tree diagrams are frequently used in a mechanical way, without insight into their function or the relationships they represent. However, the study also emphasizes the pedagogical potential of using tree diagrams as visual and exploratory tools—especially when integrated with digital simulations, real-life contexts, and reflective activities. Such methods can support the shift from intuitive to more systematic probabilistic reasoning. The review concludes that effective probability instruction requires a long-term, conceptually grounded approach. Teachers play key role in fostering a classroom environment that supports reasoning, exploration, and discussion. Future research should explore classroom-based interventions and examine how factors such as language skills, prior knowledge, and teaching strategies influence students’ probabilistic thinking.

During the autumn of 2025, the national tests in mathematics are planned to transition to a digital format. The purpose of this study is partly to examine students' attitudes toward digital mathematics tests. The aim is also to investigate which reading strategies students use when reading mathematics digitally versus on paper, and whether their reading comprehension is affected. To explore this, a mathematics test and a survey were conducted in two different classes taking Mathematics 4 at an upper secondary school in Linköping. One class received the test questions digitally, and the other class received them on paper. The test was used to examine students' reading comprehension, while a questionnaire was used to investigate students’ opinions about digital mathematics tests and which reading strategies they used during the test. The results show that most students prefer paper-based tests over digital ones. It also turns out that the number of reading strategies used does not differ significantly between the students who took the test digitally and those who took it on paper. However, a difference emerges in the extent to which different types of reading strategies are used by each group. The results show that students who took the test on paper demonstrated a higher level of reading comprehension than those who took it digitally. However, this result should be interpreted with caution, as several factors may have influenced the outcome. Finally, the study highlights the importance of teachers working together with their students on reading strategies for both digital and paper-based reading in mathematics education.

Den här småskaliga studien handlar om hur nio förskollärare går tillväga för att skapa förutsättningar för en inkluderande utbildning för barn i förskolan utifrån barn i behov av särskilt stöd. Alla som arbetar inom förskolan ska bidra till alla människors lika värde vilket innebär att inget barn i förskolan ska särbehandlas (Skolverket, 2018, s. 1).

Genom semistrukturerade intervjuer och observationer inhämtades data kring förskollärares strategier för att främja inkludering med fokus på hur en god utbildning för alla skapas. Data analyserades sedan med stöd av tematisk analys och resultatet som framkom presenteras genom fyra teman: medvetna strategier, anpassningar av lärmiljön, individuellt särskilt stöd och inget man gör på egen hand.

Studien bidrar med kunskap om metoder som används och även utmaningar som förskollärarna upplever i arbetet att skapa en god utbildning för alla. Det kan i sin tur visa specialpedagoger och andra i ledningsgrupper hur de skulle kunna verka stödjande för förskollärare i arbetet med barns lärande och utveckling i en inkluderande förskolegrupp. 

Syftet med denna studie var att studera övergången mellan förskola och skola för barn med intellektuell funktionsnedsättning. Studien har fokuserat på hur övergången går till i två olika kommuner i Sverige, vad som är betydelsefullt och vad som utmanar i processen enligt de yrkeskategorier som deltar, till exempel förskollärare, lärare, rektorer och specialpedagoger. Studien har en kvalitativ ansats och har genomförts med hjälp av semistrukturerade intervjuer. Dokument som har med övergången att göra har också samlats in. Analysstrategin som användes var tematisk analys. Bronfenbrenners systemteori utgör studiens teoretiska ramverk. Resultatet visar att de båda kommunerna har upprättat en organisation för hur övergången går till. Organisationen ser olika ut. En skillnad är vilka aktörer som är delaktiga. Vad som tydligt framkommer för att övergången ska bli lyckad är samverkan med hemmet och externa aktörer, att göra vårdnadshavare trygga i processen samt att genomföra individanpassade övergångsaktiviteter både för barn och vårdnadshavare. Resultatet visar även att det finns dilemman och utmaningar i övergången såsom organisatoriska faktorer för att genomföra övergångsaktiviteterna, samt att ”hitta lämplig väg” in i skolan för barn med intellektuell funktionsnedsättning, då förskoleklass ej finns i anpassad grundskola. Studiens slutsats är att övergångar för barn med intellektuell funktionsnedsättning upplevs få goda förutsättningar att bli framgångsrika när det finns en tydligt strukturerad organisation kring övergången, både gällande ansvar och utifrån tidsperspektivet. Framgångsfaktorer och en implikation till specialpedagoger som arbetar med övergångar är: betydelsen av en tydlig organisation i övergångsarbetet, att samverka, att starta tidigt, att skapa trygghet samt att använda övergångsaktiviteter formade utifrån barnets och vårdnadshavares behov.

Inom skolformen anpassad grundskola arbetar förutom lärare även elevassistenter. Yrkesgruppen elevassistenter har ett komplext uppdrag som omfattar både omsorg och stöd i lärandet. De samarbetar med lärare och andra elevassistenter i arbetslag. Trots det ansvar och komplexa uppdrag elevassistenter har, saknar de ofta pedagogisk utbildning och nämns inte i skolans styrdokument. Det råder ett bristande forskningsläge vad gäller yrkesgruppen, särskilt inom anpassad skola. Studiens syfte var att undersöka hur elevassistenterna uppfattar sin roll och identifiera framgångsfaktorer och hinder i samarbete med läraren, samt att undersöka eventuella behov av utbildning och kompetensutveckling.

En kvalitativ metod användes och materialet samlades in genom semistrukturerade intervjuer. Nio intervjuer har genomförts med erfarna elevassistenter inom anpassad skola. Det insamlade materialet analyserades tematiskt med ett professionsteoretiskt perspektiv. Resultaten visar att goda relationer har betydelse för trivsel och samarbete i arbetslaget samt vikten av en tydlig organisation som möjliggör detta samarbete. Vår studie bekräftar elevassistenternas komplexa uppdrag och vi ser att detta uppdrag kräver många kompetenser. Dessa kompetenser kräver kunskaper som antingen erhålls genom utbildning eller kompetensutveckling, något som ofta saknas. Vidare visar resultatet att elevassistenterna önskar tydliggöra sitt uppdrag samtidigt som de önskar ökad delaktighet och ett utökat ansvar. Studiens resultat visar att elevassistenterna vill synliggöras och erkännas av rektor och andra beslutsfattare. De efterfrågar en ökad status.

Vi tolkar dessa resultat som att elevassistentyrket behöver genomgå en professionaliseringsprocess. Vidare forskning på området föreslås eftersom elevassistenter i anpassad skola är ett outforskat område, men också för att ökade kunskaper kan bidra till en utveckling av elevassistentyrket. Detta kan leda till en kvalitetshöjning för eleverna i den anpassade skolan.

Det finns en stor risk att flickor med ADHD går igenom en större del av sin skolgång med oupptäckta svårigheter i matematik. Detta kan bero på att symptom och tecken på ADHD hos flickor visar sig på andra sätt jämfört med pojkar. Det är till exempel vanligt att flickor i stor utsträckning maskerar symptom och svårigheter. De viktiga exekutiva funktionerna som behövs för matematikinlärning, så som uppmärksamhet, arbetsminne, förmåga att organisera och planera en arbetsinsats påverkas av ADHD. Skolan har ansvar att erbjuda alla elever, vare sig de har en diagnos eller inte, god undervisning i en väl fungerande lärmiljö.

Syftet med denna studie är att bidra med kunskaper och synliggöra svårigheter flickor med diagnosen ADHD kan uppvisa, möta och uppleva i grundskolans matematikundervisning.

Denna småskaliga studie inspireras av den fenomenologiska filosofin och genomfördes med semistrukturerade intervjuer. Kvinnor med ADHD-diagnos i åldern 17-27 år intervjuades om deras erfarenheter och upplevelser av matematikundervisningen i grundskolan. Insamlade data analyserades enligt tematisk analys. 

Resultatet visar att kvinnorna har haft svårigheter i matematik utan att de fått rätt stöd. De har även känt sig underlägsna och dumma jämfört med sina kompisar på grund av att de ofta tänkte på ett annat sätt. De menar att nyckelfaktorer för bättre matematikinlärning är förmågan att se bakom fasaden och hitta det som trycks undan, samt att se helhetsbilden för flickorna i skolan. Ytterligare viktiga faktorer i undervisningen är struktur, varierad undervisning, verktyg som stöttar de exekutiva funktionerna, anpassat tempo, små grupper och kompetenta och omtänksamma lärare med specialpedagogisk kompetens. Respektfullt bemötande samt att förstå alla elevers olikheter, lära känna och lyssna på individen ansågs viktigt för ett mer riktat och individanpassat stöd.

Denna studie syftar till att undersöka förskollärares och specialpedagogers erfarenheter av attarbeta med barn som de misstänker far illa i sin hemmiljö.

Genom semistruktureradeintervjuer med verksamma förskollärare och specialpedagoger har följande frågeställningarundersökts: Hur beskriver förskollärare och specialpedagoger sitt arbete med barn som farilla?, Vilken betydelse beskriver förskollärare och specialpedagoger att förskolan har för attbemöta barn som far illa? och Vilka utmaningar upplever förskollärare och specialpedagogeri arbetet med barn som far illa?

Resultaten från studien visar att förskollärare och specialpedagoger ser sig ha en betydelsefullroll i att identifiera och stötta barn som far illa. I intervjuerna framträder roller som Detektivensom söker efter tecken, Brobyggaren som strävar efter en god relation till föräldrarna och somskapar broar mellan hem och förskola. Men även Barnens talesperson, en person som står uppför barnens rättigheter och sätter barnet i främsta rummet samt Räddaren i nöden som bådeemotionellt och genom handling stöttar de barn som de misstänker far illa. Slutligenpresenteras rollen som Struts, en person som i väljer att ducka för sina misstankar och i ställetlåter någon annan agera. Utöver dessa roller framhävs även rollen som Behövande för de somprofessionellt ska stötta både barn och föräldrar. Detta då det framkommer att det råder enosäkerhet kring när och hur man bör agera.

Genom att analysera med hjälp av Bronfenbrenners ekologiska systemteori går det också attdra slutsatsen att frågan om barn som far illa och hur stödet kan utformas involverar allanivåer kring barnet. Vilket talar för att ett ökat samarbete mellan de olika aktörerna skulle ökamöjligheten för att skapa ett skyddande nätverk för dessa barn.

När människor åldras uppstår ofta psykiska och fysiska begränsningar. Ensamheten blir många gånger påtaglig för äldre personer och kan leda till en försämrad livskvalitet. Detta kan i sin tur resultera i psykisk ohälsa och uppkomst av suicidala ideationer. Trots att många äldre lever med psykisk ohälsa söker de sig sällan till psykiatrin. Ett stort antal äldre har däremot hjälp från hemsjukvården.Aktuell studie syftar till att undersöka sjuksköterskors erfarenheter av att uppmärksamma och bemöta äldre patienter med suicidala ideationer i hemsjukvården. Behovet att studera detta ämne finns eftersom antalet äldre (65 år och uppåt) ökar och så även den psykiska ohälsan. Detta innebär fler patienter med suicidala ideationer i hemsjukvården. Hemsjukvårdens sjuksköterskor spelar många gånger en stor roll i patienternas liv. En empirisk studie gjordes. Den baserades på kvalitativa, semistrukturerade intervjuer med tolv sjuksköterskor från hemsjukvården. Data analyserades med konventionell innehållsanalys. I resultatet framkom att sjuksköterskorna upplever bristande kunskap i psykiatri, att tidsbristen gör det svårt att prioritera patienter med psykiska besvär, att sjuksköterskorna arbetar för att ge ett gott bemötande i den utsträckning det är möjligt och att ensamheten hos patienterna är en riskfaktor för försämrat mående och suicid. Sjuksköterskorna önskar mer utbildning i bemötandet av dessa patienter. Mer forskning krävs för att ta reda på hur hemsjukvårdens sjuksköterskor kan bidra till det suicidpreventiva arbetet med den äldre generationen.

Bakgrund: Postoperativt delirium (POD) är en allvarlig och oftaunderdiagnostiserad komplikation hos äldre patienter efter kirurgi, och är kopplat tillförsämrad återhämtning, ökad sjuklighet och dödlighet. Tidig identifiering ochriktade förebyggande åtgärder är avgörande för att förbättra utfall och minskabelastningen på vården.

Syfte: Att undersöka evidensbaserade förebyggande åtgärder för postoperativtdelirium hos äldre.

Metod: En systematisk litteraturstudie med meta-analys genomfördes. Totaltinkluderades 39 randomiserade kontrollerade studier (RCT).

Resultat: Farmakologiska insatser visade signifikant minskad risk för POD, särskiltmed dexmedetomidin och ketamin, vilka återkom i flera studier och visade starkasteffekt med upp till 55% (OR 0,45). Icke-farmakologiska åtgärder som tidigmobilisering, kognitiv träning, användning av ögonmask och familjeinterventionerminskade risken med upp till 65 % (OR 0,35). Tidig mobilisering visade särskiltkraftfull effekt i vissa studier.

Slutsats: Förebyggande åtgärder mot postoperativt delirium bör integreras i hela denperioperativa vårdprocessen. Studien visar att både farmakologiska och ickefarmakologiska insatser har potential att minska risken för postoperativt delirium hosäldre patienter, och därmed förbättra återhämtning, patientsäkerhet och vårdkvalitet.Anestesisjuksköterskan har en central roll i att identifiera riskpatienter och tillämpaevidensbaserade strategier. Med stöd av erfarenhetsbaserat lärande kan vårdpersonalomvandla kunskap till praktisk handling och utveckla hållbara arbetssätt för enpersoncentrerad och trygg vård.

Background: Colorectal surgery is a central treatment for both malignant and benign medical conditions. Postoperative complications affect quality of life, recovery, hospital stay, and healthcare resources. Immunonutrition, aimed at modulating the immune system, may improve postoperative outcomes by reducing complications and accelerating recovery.

Objective: To investigate and summarize the impact of immunonutrition on postoperative outcomes after colorectal surgery.

Method: A narrative synthesis was conducted following PRISMA guidelines. Studies were identified through PubMed and EMBASE in December 2024. Included studies involved adult patients who underwent colorectal surgery and received immunonutrition. Studies published before 2025, non-English, and animal studies were excluded. Risk of bias was assessed using RoB 2 and ROBINS-I.

Results: Eight studies with 1,650 patients were included. The results demonstrated that immunonutrition was associated with fewer wound infections, reduced antibiotic use, shorter time to gastrointestinal function, and shorter hospital stays. A pooled analysis of total postoperative complications revealed a significant reduction in favor of immunonutrition (OR 0.61, 95% CI 0.38-0.96). No difference was observed in mortality.

Conclusion: Immunonutrition shows modest effects on certain postoperative outcomes, the heterogeneous evidence limits the ability to draw definitive conclusions. There is insufficient support for immunonutrition being superior to standard nutrition in colorectal surgery

Climate change and urbanization place high demands on well-planned and sustainable stormwater management, making reliable stormwater investigations increasingly important. These investigations often follow a similar structure and include several recurring elements, which makes them suitable for streamlining. The purpose of this study is to investigate how Ramboll’s AI tool, RamGPT, can be used to streamline repetitive parts of report writing in stormwater investigations, with the goal to achieve an accuracy of at least 70% - meaning that at least 70% of the AI-generated texts should be considered “good enough” according to a developed evaluation scale. To achieve the goal and answer the research questions, a literature study, interviews with stormwater investigators, and testing of prompts in RamGPT were conducted. The prompts were assessed using a criteria-based evaluation scale for objective assessment of content, structure, and language accuracy. The study focused on two parts of the report - the summary and geological conditions - as these were identified in interviews with Ramboll’s stormwater investigators as both time-consuming and suitable for standardization. With the help of theory and practical testing in RamGPT, prompt templates could be developed for both parts. The tests showed that RamGPT was able to generate text of sufficiently high quality to be used as a first draft for further development, achieving an accuracy of 73%, which exceeds the goal. Generative AI can be a valuable support in report writing as it increases efficiency by quickly providing a first draft and can process large amounts of data. Well-thought-out and clear prompts reduce the risk of misinterpretations and misunderstandings, but it is also important to be aware of the tool’s limitations, such as the risk of incorrect information. Therefore, manual review is always required to ensure the quality and accuracy of the content. In conclusion, the study shows that generative AI has the potential to streamline parts of the stormwater investigation process, especially when clear prompt templates are combined with manual review. There are also opportunities to further develop the method for additional parts of the reporting process.

This thesis project is a collaboration between the American Museum of Natural History (AMNH) and Linköping University. The aim is to integrate visualizations from astrophysical simulations generated with the python package yt into the OpenSpace software. This integration was developed due to yt’s limited support for contextual visualization and support for immersive environments. However, yt is effective at processing and rendering unstructured adaptive mesh refinement hierarchical grids. OpenSpace, on the other hand, provides the tools for public exploration through its immersive experience and user-friendly interface, but lacks the support for AMR hierarchical grids. By combining the strengths of yt with the visualization capabilities of OpenSpace, it will enhance the accuracy of scientific visualizations and make complex data more accessible. This collaboration bridges the gap between detailed scientific research and broader public understanding, promoting enhanced scientific understanding and encouraging the development of future researchers. To integrate yt into OpenSpace, volume rendering is accomplished and displayed through a pyglet application window, utilizing the yt-idv package within yt. The volume rendering is then processed as an OpenGL texture and transmitted to OpenSpace via a Spout sender. This results in a prototype with two separate windows: OpenSpace and the Pyglet application window, each with its own individual functionality. The aim of the project was achieved by integrating yt into OpenSpace. The pipeline adapts yt’s AMR hierarchical grids for OpenSpace’s structured grid, ensuring accurate volume rendering with minimal data loss. The use of Spout for media transfer demonstrates the feasibility of displaying these grids in OpenSpace. This integration also enhances the presentation of astrophysical research by facilitating interactive 3D visualizations of simulations, which can assist researchers and educators in explaining complex astrophysical phenomena. However, there remains potential for improvements, such as design aspects, compatibility with macOS and Linux operating systems, and sending information from OpenSpace back to yt.