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The global push for increased renewable energy in power production is reshaping how industries approach energy systems. As the urgency to combat climate change grows, industries are integrating alternative power pathways alongside existing systems. This shift is driven by factors such as renewable energy adoption, energy storage advances, decentralization, electrification, circular economy principles, regulatory support, sustainability goals, and technological progress. These changes not only yield economic benefits but also enhance environmental and social impact. Integrating alternative pathways necessitates strategic planning, optimization, and a phased approach for seamless integration. Through these transformations, industries position themselves as sustainability leaders, align with climate goals, and ensure long-term energy security.

The proposed implementation of a photovoltaic (PV) system at SSAB's steel production plant in Borlänge, specifically for forming line 4's electricity needs, will have a positive impact. This integration introduces renewable energy generation, offsetting the load and reducing reliance on the grid during peak hours, potentially leading to lower costs. It aligns with SSAB's environmental goals by curbing emissions, bolsters energy resilience, and aiding peak demand management. However, challenges in grid integration and infrastructure adjustments must be addressed for successful implementation. Overall, this move embodies SSAB's commitment to sustainability and efficient operations. 

Through the utilization of simulation tools such as PVsyst and Homer Pro, an extensive study was conducted to investigate diverse scenarios involving combinations of a PV system, hydrogen modules, batteries, and a grid-connected load. The primary aim was to assess the feasibility of these scenarios within the energy system context. By leveraging PVsyst's capabilities for photovoltaic system analysis and Homer Pro's system optimization features, the study comprehensively examines interactions between electricity generation, storage, and consumption. This simulation-driven approach provided valuable insights into the performance dynamics, energy balance, and economic viability of each configuration, aiding in the informed selection of optimal combinations that align with the project's feasibility objectives.

The results obtained suggest that the ideal size for the PV system in this context is 2.7 MW, allowing for an annual energy generation of 2.5 GWh. The electricity output aligns well with the yearly demand of 2.4 GWh for Forming Line 4

The results from different scenarios offer valuable insights into how integrating renewable energy and incorporating energy storage affect the overall efficiency and cost-effectiveness of the system. Each scenario was assessed in comparison to the base case of grid connection, uncovering a spectrum of LCOE values. It is noteworthy that the highest LCOE, reaching 0.12 €/kWh, was observed when all renewable resources were combined, whereas the lowest LCOE, at 0.059 €/kWh, was achieved with the PV system-only configuration.

This study explores how attention mechanisms impact representation distributions within neural networks, focusing on catastrophic forgetting and robustness to input noise. We compare Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), their attention-enhanced counterparts (RNNA, LSTMA, GRUA), and the Transformer model using musical sequences from "Daisy Bell". A key finding is the difference in how these models distribute the information in their representation. Base models like RNN, LSTM, and GRU concentrate information within specific nodes, while attention-enhanced models spread information across more nodes, demonstrating greater robustness to input noise. This is shown by significant differences in performance deterioration between base models and their attention-augmented versions. However, base models such as RNN and GRU exhibit better resistance to catastrophic forgetting compared to their attention-enhanced counterparts. Despite this, attention models show a positive correlation between higher overlap percentages in their representations and improved accuracy for certain tasks, alongside a negative correlation with higher numbers of empty nodes. The Transformer model stands out by maintaining high accuracy across tasks, likely due to its self-attention mechanisms. These results suggest that while attention mechanisms enhance robustness to noise, further research is needed to address catastrophic forgetting in neural networks.

This study is a thesis within the construction engineering program at Dalarna University in Borlänge. This thesis highlights the new hybrid element and its moisture-related properties, as well as comparing hybrid elements with the old proven solution of sandwich elements out of moisture perspective. This study is of interest as the construction industry needs new, more sustainable solutions that this hybrid element offers.

The purpose of this study is to compare moisture properties of both sandwich and hybrid elements out of interviews and simulation of hybrid- and sandwich element with the help of a WUFI program. A further purpose is to analyse the behaviour of both elements when exposed to different amounts of moisture, investigate possible moisture-related problems in these elements and the possibility of building with hybrid elements instead of sandwich elements.

The results of the study confirm that hybrid elements are theoretically an acceptable solution when it comes to moisture safety. At the same time, the conclusion concludes that a good tightness of a wall is an important parameter, but is not always enough to guarantee moisture safety, but tightness needs to be completed with careful design and execution.

The focus of this study was to evaluate different search engines on Swedish text. Information retrieval is widely used by both people and organizations, and it is important to be able to efficiently retrieve needed information at the right time. The study determined that relevance and speed are the most important factors in search engines. The evaluation measures the precision and recall which are relevance measurements, and speed of two search engines, Elastic search and MarkLogic. The evaluation has determined that there is no significant difference in the relevance of the retrieved results between the engines. The evaluation has also determined that there is a statistically significant difference in speed between the engines, with Elastic search outperforming MarkLogic. Both search engines performed very well in terms of successful searches, meaning to return a relevant document in the first 20 results. Both engines succeeded in fulfilling the information need 96% of the time.

This paper highlights the importance of economic incentives in stimulating the reuse of building materials in Sweden. Although the construction industry recognizes the benefits of reuse, the study shows that there are insufficient economic incentives to make it a standard practice. Through semi-structured interviews with representatives from the construction industry and a literature review, the study identified several barriers such as quality assurance, building regulations, lack of knowledge, and economic challenges.The purpose of the study was to investigate what economic incentives could promote the use of reused building materials and identify potential barriers to their implementation.The findings suggest that there are currently no direct economic incentives that encourage construction companies to reuse building materials. Several obstacles have been identified, including challenges related to quality guarantees, building regulations, lack of knowledge, and economic difficulties. The study also emphasizes that more extensive political and economic initiatives are needed to stimulate the use of reused building materials. The paper contributes to the existing research by providing recommendations to policy makers and other actors in the construction sector who are interested in promoting sustainability and environmentally friendly methods. It suggests that future research should focus on designing and testing specific economic incentives to promote the reuse of building materials. In its conclusions, it emerges that construction companies identify a multitude of obstacles and challenges when reusing building materials. It also appears that there is a noticeable lack of economic incentives from the state to promote the use of reused building materials in construction projects. Construction companies suggested different forms of economic incentives that could promote the use of reused building materials, including grants, tax relief, and digital platforms. There was also a variation in political opinions regarding the idea of introducing economic incentives to promote the reuse of building materials, with some supporting the idea and others opposing it.

Climate change temperatures expected to rise and extreme heat events (HW) canbe intensified. The influence of climate change on the built environment willbecame more apparent over the coming years. For example, there would be ashift in the risk of overheating in buildings, as well as the cooling and heatingneeds.Studies found that design strategies used to optimize buildings for winter like:good thermal insulation, high airtightness, and extra heat gains increase the riskof overheating. Thus, because of climate change, there is a need for checking thebuildings for summer conditions even in heating dominated countries.This study aims to investigate the potential of two main passive design strategiesto mitigate indoor overheating: ventilation and shading. The main focus of thisstudy is on single-family homes within the Swedish context. Bysjöstrand EkobyAssociation’s Bysjöstrand eco-village project is used as case study. 30 singlefamilyhomes are simulated using Honeybee to run EnergyPlus for calculatingindoor mean air temperature values, extracting the number of hour andpercentages of overheating for each building.Six alternative scenarios were used to evaluate the eco-village. The firststructures were assessed to determine the hours and percentage of time spentoverheating in the present and future situations. The second scenarios, whichinvolved utilizing natural ventilation, was tested to determine if and to what extentit can help to reduce the overheating risk in present and future.A combination of natural ventilation and shading was used for the last scenariosboth for current and future climate.According to the findings, natural ventilation has the greatest influence in reducingoverheating. Combining these two strategies in 2020 and 2070 can lower theaverage percentages of overheating from 17.5 % to 0.6 % and 52.8 % to 12.4%,respectively.The majority of the overheating risk may be addressed using passive strategies,based on the results. More detailed building design is likely be able to eliminateoverheating in single family homes, however, as this study showed it is importantto consider passive strategies from the early stage on the design process.

The application YouTube released in 2005 is today known as the most prominent platform on the world wide web for sharing, creating, and discovering video content(s). By utilizing technology trends such as Machine learning, YouTube can take advantage of algorithms such as deep neural networks to entice the users with a massive amount of personalized recommended content(s)t to watch based on the user's data.

The algorithm in the YouTube recommendation system collects the users' personalized data such as location, watch history and search history to recommend video content. The YouTube recommendation system gives personalized recommended content(s) to a YouTube user with the collected data. The user may deem the recommended content as ethically wrong. Therefore, bring up meaningful discussions such as a YouTube consumer's sentiment towards those contents they deem unethical.

This research study examines what ethical challenges exist in the YouTube recommendation system and how those challenges affect the YouTube consumers trust in the recommendation system.

• The research questions that were examined in this study are: What kind of ethical challenges does the YouTube recommendation system have?

• What kind of impact do those ethical challenges have on the user’s trust in the YouTube recommendation system?

The respondents consisted of students at Dalarna University. This research was conducted with semistructured interviews and a questionnaire that consisted of 11 questionnaires.

By examining the results from the literature reviews, interviews, and questionnaire, the gathered results show that challenges such as inappropriate, misinformed content and privacy exist with the YouTube recommendation system. Numerous of our respondents felt like they did not trust the YouTube recommendation system with their personalized data, primarily because of receiving video content that is not relevant, which has made some of the respondent question what YouTube does with the influx of the individual data.

Based on the result it can be concluded that there are many concerns related to ethical issues in the YouTube recommendation system. In essence, the respondents highlighted the significance of privacy when using a platform such as YouTube. The respondents desired more transparency with their data.

Purpose: This study conducts a comparative analysis of Displacement Ventilation (DV) and Mixed Ventilation (MV) strategies in landscape offices, considering thermal comfort, indoor air quality, energy performance, and life cycle assessment (LCA) in HVAC design. Methods: IDA-ICE 5 software was used for air stratification analysis of the indoor environment, Magi-CAD for ventilation system design, One Click LCA for calculating the environmental impact, hand calculations and an extensive literature review. Results: The energy performance simulations indicate that DV is more energy efficient than MV. This efficiency is largely attributed to the higher heat exchange capacity of the DV system´s air handling unit, benefiting from the stratification of indoor air temperatures, because for the DV case, the exhaust air temperature is higher than in the MV system, enabling more effective heat recovery. Conversely, the MV system exhibit a more homogeneous air temperature distribution, resulting in a lower exhaust air temperature and consequently less effective heat recovery. Additionally, the supply air temperature for MVS is lower (16°C) than DVS (19°C), leading to increased energy consumption for indoor heating. Regarding environmental impact, the LCA results from stages A1 to A5 (“cradle to practical completion”) show that DVS has a higher Global Warming Potential (GWP) compared to MVS. However, when considering the complete LCA (“cradle to grave”), including energy consumption over the entire lifespan, DVS has a lower GWP than MVS. This outcome is due to the operational energy efficiency of DV, leading to a lower overall environmental impact over the system´s lifetime. Sensitivity analysis confirms also that DV has a lower overall environmental impact, even when accounting for future climate scenarios. Regarding indoor air quality, DV delivers better indoor air quality for the same airflow rate compared to the MV system. MVS requires a higher airflow rate to meet the WELL Building Standard's CO2 level requirements, leading to higher energy consumption and global warming potential. Thus, the DV system maintain better indoor air quality at lower airflow rates, contributing to greater energy efficiency and reduced environmental impact. However, MV systems demonstrate better performance in thermal comfort. The homogeneous air distribution of MV systems results in lower Predicted Percentage of Dissatisfied (PPD) values, enhancing overall occupant comfort. In contrast, DV systems' air temperature stratification characteristics result in cooler temperatures at the floor level, increasing PPD values, and decreasing thermal comfort, particularly in the summertime when temperature differences are more pronounced. Conclusion: DV is more energy-efficient due to higher heat recovery from stratified air temperatures. DV is more environmentally friendly over its lifespan due to lower operational energy use. DV provides better air quality at lower airflow rates, reducing energy consumption and GWP. MV offers a slightly better thermal comfort with more uniform air distribution.

Background According to teachers for a higher education for a general degree, there is a perceived recurring problem that students who study a three-year bachelor's program do not understand how courses are connected to each other and that courses can be qualifying. What does the problem really look like from a student's perspective? Can what teachers describe be confirmed by students? How do they view their program time, how do they absorb information about their studies and how would they like to receive that information?

Aim The purpose of the research study is to contribute knowledge about the type of information students in a higher education program need during their program time. The study's two questions are: What information do students need in order to understand their own program time? How can higher education institutions design information to support students' understanding of their own time in the program?

Method The methods for collecting empirical data were questionnaires, semi-structured interviews and document analysis. The three years of the Information Designprogram at Dalarna University, together with documents such as study guides, syllabi, study plan and the information pages that are available to the students, have formed the empirical basis for the study. A total of seven interviews were conducted with students from these year groups.

Results The study identified two main themes in relation to how students perceived the program, "Context / Identity" and"Understanding / Relevance". Recurring expressions of misunderstanding of how the content of the program is connected and an uncertainty about its end goal.

Conclusion Students need the content of the education to be linked to the final goals of the program and to future working life.The program is perceived as very broad and difficult to grasp and that some courses need a clearer relevance to increase understanding, commitment and motivation. The study guide is a central document that should be partially standardized and contain specific information. An interactive solution over the program with filtering function is needed. 

Utilizing edge and cloud computing to empower the profitability of manufacturing is drastically increasing in modern industries. As a result of that, several challenges have raised over the years that essentially require urgent attention. Among these, coping with different faults in edge and cloud computing and recovering from permanent and temporary faults became prominent issues to be solved. In this paper, we focus on the challenges of applying fault tolerance techniques on edge and cloud computing in the context of manufacturing and we investigate the current state of the proposed approaches by categorizing them into several groups. Moreover, we identify critical gaps in the research domain as open research directions. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Cloud computing is a game-changer model that opens new directions for modern manufacturing. It enables services and solutions that help improve the productivity and efficiency of smart production plants. The main objective of the paper is to provide a summary of the various cloud-based manufacturing services currently being offered to manufacturers or that could be offered in the future. Additionally, the paper aims to discuss the various enabling technologies used to support the integration of cloud manufacturing in the manufacturing industry. Furthermore, the paper categorizes the different services based on their functionalities and maps them to four levels of production such as plant level, production line level, machine level, and process level. The categorization of services and mapping them to appropriate levels in production can enhance efficiency and productivity in the manufacturing industry. The study advances the discussion on cloud-based manufacturing from the types of services and enabling technologies perspective. © IFIP International Federation for Information Processing 2024.

In the era of the IoT revolution, applications are becoming ever more sophisticated and accompanied by diverse functional and non-functional requirements, including those related to computing resources and performance levels. Such requirements make the development and implementation of these applications complex and challenging. Computing models, such as cloud computing, can provide applications with on-demand computation and storage resources to meet their needs. Although cloud computing is a great enabler for IoT and endpoint devices, its limitations make it unsuitable to fulfill all design goals of novel applications and use cases. Instead of only relying on cloud computing, leveraging and integrating resources at different layers (like IoT, edge, and cloud) is necessary to form and utilize a computing continuum. The layers’ integration in the computing continuum offers a wide range of innovative services, but it introduces new challenges (e.g., monitoring performance and ensuring security) that need to be investigated. A better grasp and more profound understanding of the computing continuum can guide researchers and developers in tackling and overcoming such challenges. Thus, this paper provides a comprehensive and unified view of the computing continuum. The paper discusses computing models in general with a focus on cloud computing, the computing models that emerged beyond the cloud, and the communication technologies that enable computing in the continuum. In addition, two novel reference architectures are presented in this work: one for edge–cloud computing models and the other for edge–cloud communication technologies. We demonstrate real use cases from different application domains (like industry and science) to validate the proposed reference architectures, and we show how these use cases map onto the reference architectures. Finally, the paper highlights key points that express the authors’ vision about efficiently enabling and utilizing the computing continuum in the future. © 2024 The Author(s)

Parkinson's disease (PD) is a neurodegenerative disorder that affects millions of people worldwide, causing various motor and non-motor symptoms. Early diagnosis of PD is crucial for timely intervention and management. Gait analysis provides insights into the motor impairments with PD, aiding in early detection. In this study, different deep learning models such as CNN, LSTM, and CNN-LSTM with varying neural network depths were explored to classify PD using gait data acquired through sensor technology. The study then compared the results of deep learning models with machine learning algorithms (Random Forest (RF) and Decision Trees (DT)). The dataset used in this study consists of 93 persons with PD and 73 healthy controls (HC) collected through sensor technology. The findings reveal that the RF algorithm achieved the highest accuracy of 96%, followed by the CNN-LSTM model of 95.49 %. © 2024 IEEE.

BACKGROUND: Dementia is a general term for several progressive neurodegenerative disorders including Alzheimer's disease. Timely and accurate detection is crucial for early intervention. Advancements in artificial intelligence present significant potential for using machine learning to aid in early detection.

OBJECTIVE: Summarize the state-of-the-art machine learning-based approaches for dementia prediction, focusing on non-invasive methods, as the burden on the patients is lower. Specifically, the analysis of gait and speech performance can offer insights into cognitive health through clinically cost-effective screening methods.

METHODS: A systematic literature review was conducted following the PRISMA protocol (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). The search was performed on three electronic databases (Scopus, Web of Science, and PubMed) to identify the relevant studies published between 2017 to 2022. A total of 40 papers were selected for review.

RESULTS: The most common machine learning methods employed were support vector machine followed by deep learning. Studies suggested the use of multimodal approaches as they can provide comprehensive and better prediction performance. Deep learning application in gait studies is still in the early stages as few studies have applied it. Moreover, including features of whole body movement contribute to better classification accuracy. Regarding speech studies, the combination of different parameters (acoustic, linguistic, cognitive testing) produced better results.

CONCLUSIONS: The review highlights the potential of machine learning, particularly non-invasive approaches, in the early prediction of dementia. The comparable prediction accuracies of manual and automatic speech analysis indicate an imminent fully automated approach for dementia detection.

The concept of future sustainability is driving efforts toward the efficient improvement of energy systems. District heating systems play a key role in balancing the energy system by improving performance and flexibility. As system efficiency increases, fuel consumption decreases, resulting in reduced greenhouse gas emissions and mitigating potential climate impacts, especially when using fossil fuels. In Sweden, district heating has shown significant growth, with a 75 % increase in total heat production over the last 30 years. Furthermore, carbon dioxide emissions have been reduced by approximately 50 % for each delivered kWh in the past 20 years, while renewable energy sources have doubled in the district heating sector. This thesis focuses on optimizing the cooling process to achieve lower return temperatures and higher system efficiency. It also emphasizes the importance of implementing an efficient heat consumption strategy to reduce peak loads and improve overall system efficiency. This approach involves managing demand to minimize peak heat requirements and distribute the load evenly throughout the day, leading to a more resource-effective and efficient system. The case study examines district heating supplied by VB Energy to Ludvikahem AB buildings, using data from Dec 2022 to Jan 2023. Quantitative data from district heating substations and the district heating plant are collected and analyzed to generate qualitative insights. The study proposes theoretical optimization measures based on the findings. The evaluation of substation performance reveals 20 poorly performing substations with various issues. Eight of these substations have technical problems related to substation components, while another eight experience management issues not aligned with specific activity profiles. The load shifting simulation demonstrates a 3% reduction in heat rate peak levels, resulting in approximately 7 kW of subscribed heat rate savings. Energy usage savings reach approximately 0.9%, leading to an increase in energy usage effectiveness. The cost savings amounted to about 3000 SEK over two months for a single building. This research emphasizes the importance of routine control, inspection, and documentation of substation performance to ensure optimal efficiency. Furthermore, indicates that the poorly performing substations that contribute to inefficiencies in the district heating network. Additionally, real-time regulation and load-shifting strategies are vital for optimizing customer consumption and maintaining an efficient district heating system, benefiting both suppliers and consumers.

Robotic Process Automation (RPA) is a wide variety of approaches and technical solutions that work as a support to automate repetitive tasks. In 2017, a new legislation was enacted in Sweden that made it possible for the public sector to automate decision support. This in turn led to more and more Swedish municipalities starting to implement RPA. Of Sweden's total 290 municipalities, 259 of them are classified as smaller municipalities. For these smaller municipalities, it is important to balance between having enough staff to manage both the finances and the tasks. In addition, several municipalities in Sweden cannot afford to hire new staff due to the current pandemic. Most of these smaller municipalities have not yet begun theirwork with RPA.

The study examines six smaller Swedish municipalities that have implemented RPA with theaim of finding out the effects and changes that have arisen after their implementation. This has been done through semi-structured interviews to find out how their implementation went, what processes and in which areas they have chosen to automate and what they have seen for effects of this. Thematic analysis was then used to find patterns and themes in the interviews to present a common overall result with rich descriptions.

This result showed successful implementations where the municipalities have come far different in their work and reached different levels of success, but where all through RPA have freed up time for their employees. These six municipalities also have a few processes to automate in the planning stage for the coming year.T

he conclusion of this study shows that RPA gives good results for the smaller municipalities that have chosen to invest resources in it. In the absence of other ways to solve the current problems, RPA is a good method for balancing finance and the growing challenges for a smallermunicipality. 

This study investigates the performance of Logistic Regression, k-Nearest Neighbors (KNN), and Random Forest algorithms in predicting customer churn within an e-commerce platform. The choice of the mentioned algorithms was due to the unique characteristics of the dataset and the unique perception and value provided by each algorithm. Iterative models ‘examinations, encompassing preprocessing techniques, feature engineering, and rigorous evaluations, were conducted. Logistic Regression showcased moderate predictive capabilities but lagged in accurately identifying potential churners due to its assumptions of linearity between log odds and predictors. KNN emerged as the most accurate classifier, achieving superior sensitivity and specificity (98.22% and 96.35%, respectively), outperforming other models. Random Forest, with sensitivity and specificity (91.75% and 95.83% respectively) excelled in specificity but slightly lagged in sensitivity. Feature importance analysis highlighted "Tenure" as the most impactful variable for churn prediction. Preprocessing techniques differed in performance across models, emphasizing the importance of tailored preprocessing. The study's findings underscore the significance of continuous model refinement and optimization in addressing complex business challenges like customer churn. The insights serve as a foundation for businesses to implement targeted retention strategies, mitigating customer attrition, and promote growth in e-commerce platforms.

In this thesis a future-world case was undertaken of the life cycle assessment (from cradleto grave) of a single-family house. The house is expected to be constructed by the end of2021 in Borlänge, a city located in Dalarna County. The aim of this study is to investigatewhether the building materials in external walls and roof surpass in terms ofenvironmental impacts compared with other building materials suggested by the authorsof this thesis.Six scenarios were evaluated in terms of the environmental impacts for two buildingelements, external wall, and roof. A base case scenario is taken into consideration foreach building element, considering the fact, that the thermal performance characteristicsof the building materials are comprehensively provided. Consequently, four scenarios aredetermined by the authors.One Click LCA program was used to calculate the environmental impacts of thebuilding materials through the lifecycle of the house during a time horizon of 50 years.Hemp fiber insulation material is planned to be used in the external walls; thus, it isconsidered the baseline case for this thesis. The first study is corresponded to the externalwalls, and it was found that glass wool insulation is more environmentally friendly thanhemp fiber and rock wool insulation.Wood material is planned to be used in the roof; thus, it is considered the baseline casefor this thesis. The second study is corresponded to the roof, and it was found that woodmaterial is more environmentally friendly than concrete and steel.Therefore, the suggested material by the authors surpasses the baseline case materialenvironmentally in respect of external walls, but this was not the case regarding the roof.

Amidst escalating cyber threats, the necessity for robust cybersecurity education is more pressing than ever, particularly within university environments that are increasingly targeted. This study, "CyberSafe in Action," evaluates the effectiveness of an interactive digital prototype designed to enhance cybersecurity awareness among Swedish university students. Through a mixed-methods approach, combining quantitative pre- and post-intervention quizzes with qualitative interviews, the study assesses the prototype's impact on students' cybersecurity knowledge and behavioral intentions.The research involved 20 participants divided into an experimental group, which interacted with the prototype, and a control group, which did not. Results indicated a significant improvement in cybersecurity awareness in the experimental group, with average quiz scores increasing from 16.2 to 24.8, representing a 40.9% improvement in cybersecurity knowledge. In contrast, the control group experienced a decline in scores from 17.1 to 13.1, indicating a -25.6% change. Statistical analysis using paired and independent t-tests confirmed these enhancements were significant (p<0.01), demonstrating the prototype’s efficacy. Qualitative feedback highlighted increased engagement and a proactive approach to cybersecurity, affirming the benefits of interactive educational tools. This study contributes to pedagogical practices by demonstrating that interactive, problem-based learning can substantially enhance cybersecurity education. Recommendations for future research include larger-scale studies to explore long-term retention of knowledge and behavioural changes. This study underscores the critical role of innovative educational strategies in preparing students to navigate and secure increasingly digital landscapes effectively.

Numerous studies have studied the impacts of automated driving (AD) technology on e.g. accident rates or CO2 emissions using various frameworks. In this paper we present an overview of previous frameworks used for societal impacts and review their advantages and limitations. Additionally, we introduce the Total Impact Assessment (TIA) framework developed by the Swedish Transport Administration and use this framework to evaluate three scenarios for AD bus services in Stockholm. We conclude that the reviewed frameworks cover different aspects of AD technology, and that e.g. cybersecurity and biodiversity are areas largely neglected. Furthermore, most frameworks assume effects to be homogenous, when there may be large variation in e.g. perceived security. The TIA framework does not manage to include all societal aspects of AD technology, but has great benefits and manages to provide important insights of the societal impacts of AD technology, especially how effects may wary for different actors.

This study takes a unique approach by investigating the integration of Brain–Computer Interfaces (BCIs) and Building Information Modeling (BIM) within residential architecture. It explores their combined potential to foster neuro-responsive, sustainable environments within the framework of Construction 5.0. The methodological approach involves real-time BCI data and subjective evaluations of occupants’ experiences to elucidate cognitive and emotional states. These data inform BIM-driven alterations that facilitate adaptable, customized, and sustainability-oriented architectural solutions. The results highlight the ability of BCI–BIM integration to create dynamic, occupant-responsive environments that enhance well-being, promote energy efficiency, and minimize environmental impact. The primary contribution of this work is the demonstration of the viability of neuro-responsive architecture, wherein cognitive input from Brain–Computer Interfaces enables real-time modifications to architectural designs. This technique enhances built environments’ flexibility and user-centered quality by integrating occupant preferences and mental states into the design process. Furthermore, integrating BCI and BIM technologies has significant implications for advancing sustainability and facilitating the design of energy-efficient and ecologically responsible residential areas. The study offers practical insights for architects, engineers, and construction professionals, providing a method for implementing BCI–BIM systems to enhance user experience and promote sustainable design practices. The research examines ethical issues concerning privacy, data security, and informed permission, ensuring these technologies adhere to moral and legal requirements. The study underscores the transformational potential of BCI–BIM integration while acknowledging challenges related to data interoperability, integrity, and scalability. As a result, ongoing innovation and rigorous ethical supervision are crucial for effectively implementing these technologies. The findings provide practical insights for architects, engineers, and industry professionals, offering a roadmap for developing intelligent and ethically sound design practices. © 2024 by the authors.

In this study, we critically examine the potential of recycled construction materials, focusing on how these materials can significantly reduce greenhouse gas (GHG) emissions and energy usage in the construction sector. By adopting an integrated approach that combines Life Cycle Assessment (LCA) and Material Flow Analysis (MFA) within the circular economy framework, we thoroughly examine the lifecycle environmental performance of these materials. Our findings reveal a promising future where incorporating recycled materials in construction can significantly lower GHG emissions and conserve energy. This underscores their crucial role in advancing sustainable construction practices. Moreover, our study emphasizes the need for robust regulatory frameworks and technological innovations to enhance the adoption of environmentally responsible practices. We encourage policymakers, industry stakeholders, and the academic community to collaborate and promote the adoption of a circular economy strategy in the building sector. Our research contributes to the ongoing discussion on sustainable construction, offering evidence-based insights that can inform future policies and initiatives to improve environmental stewardship in the construction industry. This study aligns with the European Union's objectives of achieving climate-neutral cities by 2030 and the United Nations' Sustainable Development Goals outlined for completion by 2030. Overall, this paper contributes to the ongoing dialogue on sustainable construction, providing a fact-driven basis for future policy and initiatives to enhance environmental stewardship in the industry.

The study aimed to prepare for future software to estimate the solar energy production on rooftops by establishing specific criteria and developing a method to estimate the solar energy potential for PV systems on rooftops. The research initiated with a comprehensive literature review and the gathering of geographical data which proceeded in parallel. To verify the study's findings, an existing roof were studied. The developed method involves converting the total roof area into the annual solar energy production output, which is visually represented in a flowchart. The method starts by adjusting the total roof area using LiDAR data and adhering to building regulations and guidelines concerning solar- and architectural suitability. The suitable area is then further refined into usable area based on the dimensions and installation requirements of the PV panels. The final step in the method calculates the solar energy production of the PV system considering the location and specific system conditions. This method was tested on an existing building to verify the results. The calculations indicated that 39% of the total roof area was suitable for horizontal (landscape)-oriented panels, producing an annual solar energy output of 6,900 kWh/year. For vertical (portrait)-oriented panels, 32% of the total roof area was applicable, yielding an annual solar energy production of 5,800 kWh/year. The study's conclusion highlights that criteria include distances from obstacles and solar viability. Integrating various geographical data would enhance the accuracy of solar energy production estimation, when planning PV panel installations. Future software should incorporate visualisation features representing physical barriers and shading elements that may prevent PV installations, as well as safety distance regulatory.

Background ChatGPT is a chatbot released in November 2022. Its usage has grown to include being used in academia and for scientific writing, with varying results. We investigate if ChatGPT can be used for the technical part in a Bachelor’s thesis in System Sciences.

Aim We evaluate if it is possible to generate the code for detecting potential gender bias in previous responses from ChatGPT, in the form of a dialogue.

Method We use an exploratory case study where an iterative dialogue with ChatGPT is used to generate Python code to be able to analyse previous responses made byChatGPT. The methods for development were chosen by the authors from suggestions by ChatGPT.

Results Two separate dialogues resulted in a program that combined a fine-tuned Natural Language Processing model together with sentiment analysis and word frequency analysis. The program successfully identified responses in the dataset as having a female or male gender bias or being gender neutral.

Conclusions ChatGPT serves as a powerful tool for coding, although it currently falls short of being a one-stop solution that can generate code sufficient for more complex tasks witha single prompt. Our experience suggests that ChatGPT accelerates one’s work when the user possesses some programming knowledge. With further development, ChatGPT could transform coding workflows and increase productivity in related fields.

Implications ChatGPT as a tool is very capable in supporting students in the technical aspect of a Bachelor’s thesis and it is not unreasonable to assume that it works in other contexts, as well. As such, one can achieve more with the tool than without, and consequently it would be for the better to integrate ChatGPT into thesis work. This stresses the point that we need to find better regulations for cheating and plagiarism.

This study was made for Dellner Couplers and is regarding a development of their electrical coupler cover (ECC).

The electrical coupler includes several electrical contacts where the current flows, which lets electrical signals to be transported between all carriages in a train when the electrical couplers are connected to each other. An electrical coupler that is not connected to another one will have an ECC that automatically folds down in in front of the coupler to protect the electrical contacts from dust, water, and damage. The current ECC Dellner Couplers have today is made from an extruded aluminium profile, which has desirable properties in all aspects, except that it’s conductive.

If the ECC encounter the electrical contacts, which can happen during a collision between the train and an animal, the contact between them could cause short circuit since the electricity is always running and can’t be turned off. The development of the ECC is aimed to remove the occurrence of short circuit in case of collision with an animal, but still fulfil the mechanical requirements.

The method used to find a solution was based around well know concept development methods involving problem identification, identifying customer needs to create a target specification on what the ECC must fulfil, search for external and internal ideas for solving similar problems, performing concept screening on several concept ideas, and implement a finite element analysis (FEA) on the final concepts to see which one has the best potential for success. A part of the focus in the method has also been around material exchange by searching for potential materials in a material database.

The results imply that adding thickness to the extruded aluminium profile could be a promising concept, which was discovered in the concept screening and FEA. The FEA of the concept shows lower displacement of the ECC and a decrease of stress level of about 57% compared to the original when being exposed to 10kN, and the result in displacement is ¼ of the original concept when being exposed to 100kN.

The conclusion of this work is that a significant improvement can be made by adding thickness to the cross-section, but it is limited by the strength of the screws and the attachment. The ECC can handle up to 100kN before it will reach the electrical contacts, but the screws can only endure up to 53kN before they rupture. The screws and the attachment are the ones limiting what the electrical coupler can handle, but if the real force of an impact is below 53kN, this solution could be seen as acceptable in terms of the strength.

The concept also involves a significant addition of weight and space required. The weight (+2,4kg) needs to be examined so it doesn’t affect other parts of the coupler negatively, and evaluation needs to be done to find out if the space required (+12mm at the center of the ECC) is available and that the meeting electrical couplers won’t collide in each other when connecting.

A suggestion is to focus on the avoidance of short circuit, rather than the avoidance of reaching the electrical contacts. A more potential solution could potentially be found in a protective coating, which will allow the ECC to deform and not exhaust the screws and the attachment as much. The thicker and stronger geometry of the ECC will set higher requirements for the screws and the attachment. 

One challenge in today’s district heating systems is the relatively high distribution heat loss. Lowering distribution temperatures is one way to reduce operational costs resulting from high heat losses, while changing the distribution system from steel pipes to plastic pipes and changing the heat distribution concept can reduce investment costs. The result is that the overall life cycle cost of the district heating system is reduced, leading to the improved cost competitiveness of district heating versus individual heating options. The main aim of this study was to determine the most cost-efficient distribution system for a theoretical solar district heating system, by comparing the marginal life cycle cost of two different distribution systems. A secondary aim was to determine the influence of the employed pipe type and insulation level on the marginal life cycle cost by comparing detailed economic calculations, including differences in pipe installation costs and construction costs, among others. A small solar-assisted district heating system has been modeled in TRNSYS based on a real system, and this “hybrid” model is used as a basis for a second model where a novel distribution system is employed and the heating network operating temperature is changed. Results indicate that a novel distribution concept with lower network temperatures and central domestic hot water preparation is most efficient both from an energy and cost perspective. The total life cycle costs vary less than 2% for a given distribution concept when using different pipe types and insulation classes, indicating that the investment costs are more significant than operational costs in reducing life cycle costs. The largest difference in life cycle cost is observed by changing the distribution concept, the novel concept having approximately 24% lower marginal life cycle cost than the “hybrid” system. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

The high operating temperatures in today’s district heating networks combined with the low energy demand of new buildings lead to high relative network heat losses. New networks featuring lower operating temperatures have reduced relative heat losses while enabling an increase in the use of solar heat. The primary aim of this study was to determine if a particular district heating system can be made more effective with respect to heat losses and useful solar energy, by considering different distribution concepts and load densities. A small solar assisted district heating system with a novel hybrid distribution system has been modelled based on a real case study. This model serves as a basis for two other models where the distribution system and heating network operating temperature is changed. A secondary aim of the study was to determine the economic implications of making these changes, by using costs estimates to calculate the contribution of essential system components to total system cost. Results indicate that a novel distribution concept with lower network temperatures and central domestic hot water preparation is most energy efficient in a sparse network with a heat density of 0.2 MWh/m∙a and a performance ratio of 66%, while a conventional district heating system performs worst and has a performance ratio of less than 58% at the same heat density. In an extremely sparse network with heat density of 0.05 MWh/m∙a, the performance ratio is 41% and 30% for these systems, respectively. A simple economic analysis indicates that the novel distribution concept is also best from an economic point of view, reducing the initial investment cost by 1/3 compared to the conventional concept, which is the most costly. However, more detailed calculations are needed to conclude on this.

This study takes its point of departure in the 1950s when the car became more accessible to the public and its impact on the use of public transport. There are many positive aspects to using public transport which in turn lead to better sustainability, reduced carbon dioxide emissions and other societal factors. Despite these advantages, this study addresses several obstacles such as distance, travel times and safety aspects as reasons for travelers to opt out of means of transport such as public transport. The purpose of the study is to investigate and understand the obstacles that affect commuters' choice of means of transport between Falun and Borlänge, with a focus on the reasons that lead to some commuters opting out of public transport as a means of transport. The study has used qualitative interviews to bring out themes such as limitations and time aspects, which are some of the aspects that influence the choice of means of transport. The study focuses on finding out the obstacles that influences commuters to opt out of public transport to travel betweenFalun and Borlänge. The discussion part of the study also addresses how individual behaviors and preferences affect the choice of means of transport.

The different counties in Sweden have been reporting about the increasing housingshortage in the country for a long time. To counteract this shortage, 60 000- 70 000residences need to be produced yearly in Sweden till 2029. In the 60s-70s Swedenalso faced big challenges with housing shortage due the urbanization from thecountryside of Sweden to the cities. The result was to build on a large scale in aIndustry-like ways and the consequences of this was that green areas around thecities were exploited to make room for new residential areas. To not repeat thehistory again an alternative option could be to densify the already built urbanenvironment with for example building extensions.In an already existing urban environment, there are many different types ofbuilding, this also means that there are many different types of extensions. Thepurpose of the work is to: for a reference house located in the central area of Falun,find an alternative way for a building extension in wood where the already existingbuilding is not added by any load from the extension. Another purpose with thework is therefore to contribute with knowledge around the subject, buildingextensions in wood.The building that has been the starting point of this work is located in the centralarena of Falun and was built in the late 60s. The building extension that the projectcame up with is of “light construction” and the reinforcement frame in massivewood technique with columns and beams. The reason why to use a real referenceobject is to give the work a real construction to relate to when analyzing thepossible placing of the columns. The dimensioning of the building extension andthe reinforcement frame is done with manual calculations that are based on theapplicable rules in Sweden, Eurocode and EKS. The building has also beenmodeled in Revit to give an illustration on the building extension with thereinforcement frame but also the building in its entirety after the buildingextension.The result of the reinforcement frame is in many ways similar to a “trä8”-system.Columns and beams take the vertical load and the existing buildings system ofjoist, walls and stairwells in concrete contribute to the frame stability. Throughanalyzing the drawing of the existing building, the placement of the columns hasbeen done according to different parameters. The beams that are placed on top ofthe columns have then been controlled for the current spans with the current loadsfrom the manual calculations of the building extension. The result and theconclusions that can be done from this work is that the construction's critical partis the beams placed on the columns and not the columns itself. The dimensions ofthe beams are crucial for how many floors that are possible to build on thecolumns that are placed. Also, the weight of the building extension has an impactin the quantity of the columns and therefor longest spans for the beams

The building and construction sector is transitioning from a linear to circular economy to meet the global climate goals set by the Paris Agreement. Historically, most GHG emissions from buildings occurred during the user phase. In Sweden, heating emissions have decreased over the past 20 years, but construction and renovation emissions remained consistent. As operational energy efficiency improves, embodied GHG emissions now play a larger role in a building’s total life cycle emissions. A majority of the emissions occurs in the production of structural elements in addition to waste generation. One way to lowers both waste and production emissions is using reused materials. The purpose of the study is to assess the climate impact of a rowhouse in Drottninghög, by comparing the global warming potential (GWP) of three structural options: 1. Reused concrete 2. Traditional concrete 3. Light timber construction. The goal is to determine the most climate friendly option and provide valuable insights for future projects within the industry. This is a case study which focuses on comparing the GWP that is given by the Life cycle analysis (LCA) of each case. The results from the LCA shows that reused concrete has the lowest GWP, followed by the option with timber construction. Exchanging the new concrete structure to a reused concrete structure can reduce the GWP by 77%, and by 52% for the timber construction. This suggests that reused concrete can even be considered a competitive alternative to the traditional timber construction for this type of buildings. The construction sector must become more sustainable to reach climate goals. Reusing concrete structural elements is shown in this study to be a good solution to decrease the GHG in the production phase in comparison to timber construction and new concrete structure.

Geographical information systems are becoming increasingly important in today´s society where spatial data can be stored, collected, analysed, and visualized. By compiling spatial data reality can be abstracted. Detailed information on road networks and objects (traffic islands, noise barriers, signs, etcetera) for analysis leads to more efficient operation and maintenance work. Which in turn provides increased accessibility for road users. The technology company Triona has a map application where algorithmic connection of traffic islands (Norway-dataset) to the Norwegian road network has been challenging. A traffic island is an elevation in the street that delimits lanes and is reminiscent of a sidewalk in appearance. This case study addressed a sub-problem where classification of traffic islands could facilitate the connection and prerequisites for analysis. The aim was to present methods that could classify the traffic islands with supervised machine learning. With the algorithms K-nearest neighbors (KNN) and Decision tree, the possibility of automatically classifying the traffic islands was studied. A traffic island consisted of a vector polygon which is a list storing its corners (latitude and longitude). The Norway-dataset was not previously labelled into its eleven types. A data collection of 2157 refuges with seven types from Portland, USA was therefore applied instead. The traffic islands were transformed with Elliptical Fourier Descriptors which extracted an approximation of its contours to train the machine learning models on. Conclusions could be drawn by analysing the contours and observing performance. Performance was evaluated based on accuracy with precision and recall on the Port-land-dataset. Accuracy is the proportion of correct classifications. KNN achieved 64% and Decision Tree 69% accuracy. As both datasets contained real traffic islands in road networks, an assumption could be made that the accuracy would not be much higher if applied on the Norway-dataset. The result was not considered sufficient for a recommendation.

This study examines the use of generative AI (GAI) coding assistants for system development and testing within government agencies and big companies, focusing on security risks and functional requirements. This is done through a survey where interviews and both internal and public documents are used as datacollection methods.

With the rapid development of large language models (LLMs) and their growing use in system development, it is crucial for organizations to choose AI tools that     meet both security and functionality requirements. This survey compares various tools based on these criteria, aiming to assist organizations in making wellbalanced choices in an ever-growing range of GAI tools for coding.

The security risks identified include the risk of information leaks and the generation of insecure code. The study suggests mitigations such as the use of tools that do not save or train on user data to reduce these risks. Functional requirements identified include the ability to generate and explain code, as well as integration with popular development environments like Visual Studio and Visual Studio Code. The tools were then filtered based on the identified mitigations, resulting in a recommendation of six tools that meet the security requirements. Four of these six tools, in turn, meet the set criteria from the partner (SwedishTransport Administration) regarding functional requirements.

This study contributes to academic research by offering an increased understanding of the security and functionality aspects of GAI tools (ChatGPT, Claude, Codeium, Codiumate, Cody, GitHub Copilot, Gemini, Q, Tabnine) within system development and testing. This through a list of potential security risks and an interactive comparison table where identified GAI tools can be filtered according to different requirements. 

Within the construction industry the advantages of looking at the logistics todifferent projects has been acknowledged during the past couple of years.This study has been written in collaboration with Skanska Sverige AB. Thepurpose of the study is to, in different ways, examine what advantages anddisadvantages there are in using the logistics solutions terminalization (also knownto be a part of Third Party Logistics) and inside delivery to a specific project inDalarna, Sweden. This project is a remodeling project of a school with studentsfrom six to sixteen years old. Within the school area new parts of the school isbuilt while students are located in the yet existent buildings. This infer thatstudents move around the construction sites daily. The study also involves thirdparty people and how these people feel about the large transports connected to theproject. The inside delivery utility is then discussed based on the securityexperience within and around the construction site along with the economic andecological perspective of the service.The methods used in this study are interviews with three people of the projectmanagement, one person that works with logistics at the company, the headmasterof the younger students, skilled workers and third party people. Furthermore afield study is done that includes measurements and observations. As a close,calculations are done to determine if a economical profitability could be possibleby using inside delivery.Results from the study show how the logistics solutions terminalization and insidedelivery is looked at within Skanska today. Here, the advantages anddisadvantages with the different services are displayed from the perspectives of thepeople who were interviewed. The results from the measurements from the fieldstudy presents how many third party people that move adjacent to the two activeconstruction sites when the school day begins. The calculations show that it iseconomically beneficial to use inside delivery after the end of the workday whileused with terminalization.Conclusions of the study indicate that there are no overall solutions regarding howthe logistics must be used in different projects. If there is a possibility to use terminalization in the area where the project is situated, this, along with insidedelivery, is a good alternative for the project.

Background: AI, specifically conversational AI like OpenAI's ChatGPT, is rapidly expanding in personal and professional settings, offering cost-cutting and modernization opportunities for businesses. This technology, capable of simulating human-like conversations, holds promise across various industries, potentially enhancing productivity through human-AI collaboration. The main research problem is to identify factors influencing system developers' adoption of ChatGPT, considering its design and implementation to mitigate potential negative impacts. Aim: This study aims to investigate the factors that influence user adoption of ChatGPT as a tool to support system developers. Additionally, it aims to identify how ChatGPT can aid system developers in their daily work, and challenges associated with incorporating ChatGPT in this context. Method: Using a case study approach with qualitative and quantitative data collection methods, the study employs positivist and interpretivist philosophical paradigms. Results: Results showed that the perceived ability of ChatGPT to enhance efficiency and generate accurate responses significantly impacts adoption intentions. When examining aspects related to timesaving, productivity enhancement, and user-friendliness, no statistically significant results were found. Among developers, ChatGPT is considered valuable for simplifying tasks and assisting junior developers. There are concerns regarding its capability to handle complex tasks and potential security issues. Suggestions for improvement include better integration with integrated development environments (IDEs) and enhanced accuracy. Conclusions: The findings highlight perceived accuracy and efficiency as driving factors for user adoption regarding ChatGPT. ChatGPT can support tasks like debugging, code generation, code refactoring, code optimization, and technical documentation. However, there may be some potential limitations when dealing with overly complex code. Barriers to adoption include concerns about integrity and security, lack of awareness, and functional limitations. Implications: The insights gained can indirectly benefit companies, including our business partner CGI, by guiding decision-making processes related to the effective adoption and utilization of ChatGPT.

With the growing use of cloud computing and need for resource effectiveness, the use of container technology has increased compared to virtual machines. This is since containers require fewer resources and are significantly faster to start up. A popular containerplatform is Docker which lets users manage and run containers. The containers are run from images that can be downloaded from different sources, Docker Hub being a popular choice. Because of container technology sharing the OS-kernel with the host, there is a great need to increase and monitor the security of containers and the images they are run from. To find vulnerabilities in images, there are image scanning tools available. In this dissertation, we study 5 different image scanning tools and their performance. Twentyfive random images were selected from popular images on Docker Hub and were then scanned for vulnerabilities with the tools in the study. We aimed to answer the following questions: (1) Are there any clear differences between the number of vulnerabilities found by different image vulnerability scanning tools? (2) Are there any differences between the types of vulnerabilities found by different image vulnerability scanning tools? (3) What is the relative effectiveness of different image vulnerability scanning tools? The results show that there are considerable differences between different container image scanning tools regarding the number of found vulnerabilities. We also found that there were differences regarding the severity-grading of found vulnerabilities between the tested tools. When using our proposed metric for calculation of relative effectiveness, we discovered that the tool with the highest relative effectiveness could still miss approximately 39 percent of the vulnerabilities in images. The tool with the lowest relative effectiveness could miss approximately 77 percent of the vulnerabilities in images. 

Job Recommendation Systems have traditionally been using machine learning and information retrieval techniques such as Word2Vec, Term Frequency – Inverse Term Frequency and Support Vector Machines. The rise of Large Language Models (LLM) could potentially bridge the shortcomings of the traditional techniques. LLMs have other shortcomings which could be mitigated using the Retrieval Augmented Generation (RAG) architecture, which grounds output of an LLM to a dataset. This thesis explores the use of LLMs and RAG in job recommendation systems. Addressing gaps in existing methods that overlook user’s skills, geographic preferences, and multilingual datasets. LLMs and RAG enhance recommendation systems by processing unstructured data and providing grounded, personalized suggestions. This thesis aims to evaluate the trustworthiness and utility of an LLM in recruitment and competence development. It examines how an LLM can enhance traditional recruitment processes and support job seekers by recommending skills needed for specific job roles and regions, based on a user’s existing skills – resulting in a curated list of skills that could be improved upon. The methodology involves using Swedish IT job market data to provide personalized skill recommendations for job seekers based on their existing skills and desired job titles for specific regions. Data is collected, cleaned, and formatted to meet system requirements, and the models’ usefulness is evaluated through questionnaires, experiments and interviews with IT-students and recruitment professionals and analyzed using thematic and quantitative analysis. The inclusion of LLMs in recruitment and competence development shows promising potential. Findings suggest LLMs can refine the recruitment process, identify skill gaps, and offer insights for both job seekers and recruiters. Both IT-students and professionals in recruitment express optimism about LLMs ability to enhance traditional methods. Respondents given examples of the model’s output showed positive reactions and valuable insights in potential.

Data breaches from email attacks have been an issue since email was first implemented. Common attack methods like phishing are still a threat to organizations to this very day. That is because it never seems to stop evolving and keeps becoming more and more convincing. Email compromises have caused billions of dollars in damage worldwide, and it shows no sign of stopping. The purpose and research questions of this thesis are formulated to find guidelines or solutions that organizations can follow to improve their overall email security and awareness. In this thesis, both a systematic literature review and interviews are methods used to conduct the research. That way, both the technical portion of the subject, as well as the human perspective are covered. We found that the most common and significant email threats to organizations are phishing, BEC, and APT attacks. This thesis provides methods to mitigate these threats. What has also become clear is that human mistakes are a large portion of the problem concerning email attacks.

To ensure milk quality and detect cows with signs of mastitis, visual inspection of milk by prestripping quarters before milking is recommended in many countries. An objective method to find milk changed in homogeneity (i.e., with clots) is to use commercially available inline filters to inspect the milk. Due to the required manual labor, this method is not applicable in automatic milking systems (AMS). We investigated the possibility of detecting and predicting changes in milk homogeneity using data generated by AMS. In total, 21,335 quarter-level milk inspections were performed on 5,424 milkings of 624 unique cows on 4 farms by applying visual inspection of inline filters that assembled clots from the separate quarters during milking. Images of the filters with clots were scored for density, resulting in 892 observations with signs of clots for analysis (77% traces or mild cases, 15% moderate cases, and 8% heavy cases). The quarter density scores were combined into 1 score indicating the presence of clots during a single cow milking and into 2 scores summarizing the density scores in cow milkings during a 30-h sampling period. Data generated from the AMS, such as milk yield, milk flow, conductivity, and online somatic cell counts, were used as input to 4 multilayer perceptron models to detect or predict single milkings with clots and to detect milking periods with clots. All models resulted in high specificity (98-100%), showing that the models correctly classified cow milkings or cow milking periods with no clots observed. The ability to successfully classify cow milkings or cow periods with observed clots had a low sensitivity. The highest sensitivity (26%) was obtained by the model that detected clots in a single milking. The prevalence of clots in the data was low (2.4%), which was reflected in the results. The positive predictive value depends on the prevalence and was relatively high, with the highest positive predictive value (72%) reached in the model that detected clots during the 30-h sampling periods. The misclassification rate for cow milkings that included higher-density scores was lower, indicating that the models that detected or predicted clots in a single milking could better distinguish the heavier cases of clots. Using data from AMS to detect and predict changes in milk homogeneity seems to be possible, although the prediction performance for the definitions of clots used in this study was poor.

Background At the end of 2019 the spread of the SARS-CoV-2 virus began in China. Inorder to stall the spread of the virus, government agencies in Sweden havefaced a challenge with communica<ng informa<on in an effec<ve way. Oneway to reach a large number of people is through social media.Aim The study analyzed how three local government agencies had used Instagramto convey informa<on about the pandemic to their ci<zens. As well aswhat strategy has worked the best.Method Interviews were conducted in order to find out how the regions saw theirown work and goal with their use of Instagram. To accompany the interviews,an online form was used to pinpoint the target audience's aVtudetowards different types of Instagram posts. Together with a mul<modalanalysis of the posts, a triangula<on of the results was made.Results The strategies, target groups and goals with Instagram varied between theregional government agencies. Certain mul<modal strategies like culturalsymbols, analy<cal processes and textual metafunc<ons can have a posi<veimpact on the ci<zens aVtude towards the posts. It also showed the difficultywith designing informa<on for Instagram in a way that encourages citizensto read the post in its en<rety. This was especially prominent whenusing cultural symbols in a careless way or the message conveyed by theimage was perceived to be irrelevant. Finally, it showed that the local governmentsaim to follow the governmental wri<ng guidelines but don’t alwayssucceed.Conclusions The target audience and purpose of using Instagram varies between the regionalgovernment agencies and this is reflected in their mul<modal execu-<on of Instagram posts. Regional governments need to be aware that themessage of the image and the use of cultural symbols can have a nega<veimpact on the ci<zens' will to understand the content of the post.

Byggfel vid upprättande av byggnationer genomsyrar byggbranschen och står för onödigt höga kostnader av den totala byggkostnaden. Denna problematik har under de senaste åren uppmärksammats markant av både aktörer inom byggbranschen, samt av samhället. Enligt en rapport upprättad av Boverket (2022) uppnår dessa kostnader närmare 100 miljarder kronor och därmed sänker värdigheten i de investerade pengarna. Syftet med denna rapport är att upplysa problemområdet, och utifrån samverkan med aktörer som är delaktiga i byggprocessens olika skeden, försöka undersöka vilka byggfel som är vanligast och vart dess brister uppkommer. Utifrån det som påvisas så ska konkreta åtgärder och förbättringstips lyftas fram. Syftet och frågeställningarna i rapporten förväntas besvaras och uppnås via kvalitativa studier. Dessa studier kommer att upprättas i from av intervju och litteraturstudie. Intervjuerna kommer att hållas med aktörer inom byggbranschen som medverkar i byggprocessens olika skeden, detta urval ska ge möjligheten att få tankar och idéer utifrån olika synvinklar. Byggfelen som anses vara vanligast förekommande är fukt och vattenrelaterade skador, samt montering och infästning. Denna typen av skador uppkommer på olika platser i byggnationer, men främst i badrum och våtrum, takfönstermontering, fönsterdörrar och tyngre dörrar. De bakomliggande orsakerna till uppkomsten av dessa skador är bristande i kunskap, erfarenhet och tidspress. Bristande kunskapen och erfarenheten anses vara en central faktor i både projekteringen och produktionen, medan tidspressen framhävs tydligast i produktionsfasen. För att få bukt med byggfel-problematiken krävs det specifika enskilda åtgärder för de olika problemområdena genomförs. Ansvarstagandet hos de olika aktörerna i byggbranschen behöver öka, både i planering, utförande och kravsättning.

E-learning has transformed the healthcare education system by providing healthcare professionals with training and development opportunities, regardless of their location. However, healthcare professionals in remote or rural areas face challenges such as limited access to educational resources, lack of reliable internet connectivity, geographical isolation, and limited availability of specialized training programs and instructors. These challenges hinder their access to e-learning opportunities and impede their professional development. To address this issue, a study was conducted to identify the factors that influence the effectiveness of e-learning in healthcare. A literature review was conducted, and two questionnaires were distributed to e-learning experts to assess primary variables and identify the most significant factor. The Fuzzy Analytic Network Process (Fuzzy ANP) was used to identify the importance of selected factors. The study found that success, satisfaction, availability, effectiveness, readability, and engagement are the main components ranked in order of importance. Success was identified as the most significant factor. The study results highlight the benefits of e-learning in healthcare, including increased accessibility, interactivity, flexibility, knowledge management, and cost efficiency. E-learning offers a solution to the challenges of professional development faced by healthcare professionals in remote or rural areas. The study provides insights into the factors that influence the effectiveness of e-learning in healthcare and can guide the development of future e-learning programs.

The global EV stock is expected to increase from 7.2 million in 2019 to nearly 140 million vehicles by 2030. So, the demand for the battery also increases due to the increase in the number of EVs. In any EV, battery degradation is an unavoidable phenomenon and EV batteries are assumed to arrive at their end-of-life in EV application when the state of health reaches 80 %, repurposing the eligible EV batteries after end of first life is expected to extend their lifetime by another 5-15 years in the second life applications. 

This thesis aims to conduct a techno-economic study on the usage of second life EV batteries as an alternative storage option in off-grid PV systems compared to lead-acid batteries and new Li-ion batteries. A single-family house with an annual demand of 2245 kWh/year located in Athens was chosen as the primary location, the off-grid PV system is pre-sized for Athens and based on the pre-sizing results and what is state of art in the market. The system components were chosen for system design (4 kW bi-directional inverter, 2.9 kW PV array, 7.2 kW genset and three battery bank options i.e., 16.5 kWh of lead-acid, 8 kWh new Li-ion and 12.6 kWh of second life EV battery). PV off-grid system with different storage options is simulated using HOMER for both locations and the results are compared. 

 The simulation results show that the designed off-grid PV system can reach a solar fraction of 90 % in Athens and 73 % in Gotland when 16.5 kWh of lead-acid batteries are used with an allowed depth of discharge of 50 %. When a new Li-ion battery of 8 kWh with an allowed depth of discharge of 80 % is used then the achievable solar fraction is 84 % in Athens and 71 % in Gotland, When the second life EV battery of 12.6 kWh with an allowed depth of discharge of 60 % is used then the achievable solar fraction is 90 % in Athens and 74 % in Gotland. Sensitivity analysis is performed on the depth of discharge and results showed that the solar fraction can be increased by allowing the battery to discharge more, but it also decreases the battery lifetime. 

 The simulation results also show that the net present cost was lower in Athens for all the reference cases compared to Gotland. Net present cost and levelized cost of electricity for the off-grid system are 25.3 k€, 0.9 €/kWh in Athens and 29.2 k€, 1.0 €/kWh in Gotland when a lead-acid battery is used. When a new Li-ion battery is used then 26.2 k€, 0.9 €/kWh in Athens and 29.3 k€, 1.0 €/kWh in Gotland, when the second life EV battery is used then 26.7 k€, 0.9 €/kWh in Athens and 30.7 k€, 1.1 €/kWh in Gotland. 

 Overall, the net present cost and levelized cost of electricity are lower in Athens in all cases compared to Gotland. For the reference house in Athens, lead acid battery system has shown slightly lower net present cost than new Li-ion battery and second life EV battery. For the reference house in Gotland, both lead acid battery and new Li-ion battery system have shown similar net present cost and they are slightly lower than second life EV battery.  

Also, the second life EV battery levelized cost of electricity is fairly comparable to the new Li-ion and lead acid battery system. In future, the massive inflow of used batteries from EV are expected to be available on the market for the second life application at a lower price than today. Thus, in future, second life EV batteries can become economically viable.

Bipolar supercapacitors that can store many fold higher capacitance in negative voltage compared to positive voltage are of great importance if they can be engineered for practical applications. The electrode material encompassing high surface area, better electrochemical stability, high conductivity, moderate distribution of pore size, and their interaction with suitable electrolytes is imperative to enable bipolar supercapacitor performance. Apropos of the aforementioned aspects, the intent of this work is to ascertain the effect of ionic properties of different electrolytes on the electrochemical properties and performance of a porous CNT-MoS2 hybrid microstructure toward bipolar supercapacitor applications. The electrochemical assessment reveals that the CNT-MoS2 hybrid electrode exhibited a two- to threefold higher areal capacitance value of 122.3 mF cm-2 at 100 μA cm-2 in 1 M aqueous Na2SO4 and 42.13 mF cm-2 at 0.30 mA cm-2 in PVA-Na2SO4 gel electrolyte in the negative potential window in comparison to the positive potential window. The CNT-MoS2 hybrid demonstrates a splendid Coulombic efficiency of ∼102.5% and outstanding stability with capacitance retention showing a change from 100% to ∼180% over 7000 repeated charging-discharging cycles. © 2023 American Chemical Society.

A statistical model is developed in order to simulate the melt composition in electric arc furnaces (EAFs) with respect to uncertainties in 1) scrap composition, 2) scrap weighing and 3) element distribution factors. The tramp element Cu and alloying element Cr are taken into account. The model enables simulations of a charge program as well as backwards estimations of the element concentrations and their variance in scrap. In the backwards calculation, the maximum likelihood method is solved by considering three cases corresponding to the involved uncertainties. It is shown that the model can estimate standard deviations for elements so that the real values lie within the estimated 95% confidence interval. Moreover, the results of the model application in each target product show that the estimated scrap composition results in a melt composition, which is in good agreement with the measured one. The model can be applied to increase our understanding of scrap chemical composition and lower the charged material cost and carbon footprint of the products. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Water, which is predicted to be one of the most critical resources for the near future, also plays a vital role in society's sustainable development. Wastewater treatment is a critical part of the circular water management system and offers various technological alternatives. Taking appropriate decision for the technology selection is, therefore, essential for a long-term perspective. A complex yet imperative process is the sustainable selection of the wastewater treatment process. This paper presents the use of multi-criteria decision-making (MCDM) in the sustainability assessment of wastewater treatment technologies that may be very relevant to the growing sector with many emerging options. A comparison of six wastewater treatment technologies based on four sustainability parameters using three MCDM techniques, namely FSWARA, FMOORA and FTOPSIS is presented in detail. FSWARA is used for weighting criteria and the other two for technology ranking. The detailed step-by-step comparison study is presented and the results were somewhat predictable for the study, and this confirms the reliability of the methodology. This paper's primary objective is to propose a well-defined increscent practice for making sustainable wastewater treatment decisions among state-of-the-art technologies.

Social media has recently become an essential component of our daily modern life, with platforms like Facebook, YouTube, and Twitter serving as popular venues for people to share their opinions on various topics, including sustainable food. The interest in consumer sentiments towards sustainable practices has increased particularly after Covid-2019. This study investigates the attitudes and needs of Swedish consumers regarding sustainable food consumption as reflected in their social media interactions using 4588 comments from Facebook and YouTube. The methodology used are sentiment analysis and topic modelling with VADER and Latent Dirichlet Allocation (LDA) respectively. The results reveal a generally strong positive attitude toward sustainable food. However, the study observes further a decline in positive sentiments over time, indicating changing consumer opinions. The primary topic identified is market challenges, such as high pricing. Furthermore, health concerns and environmental considerations are identified both as important factors influencing the choice of sustainable food. The findings highlight the necessity for policy interventions to enhance the affordability and accessibility of sustainable food, as well as the effective use of social media for raising consumer awareness.

Road ruts are depressions or grooves worn into a road. Increases in rut depth are highly undesirable due to the heightened risk of hydroplaning. Accurately predicting increases in road rut depth is important for maintenance planning within the Swedish Transport Administration. At the time of writing this paper, the agency utilizes a linear regression model and is developing a feed-forward neural network for road rut predictions. The aim of the study was to evaluate the possibility of using a Recurrent Neural Network to predict road rut. Through design science research, an artefact in the form of a LSTM model was designed, developed, and evaluated.The dataset consisted of multiple-multivariate short time series where research was limited. Case studies were conducted which inspired the conceptual design of the model. The baseline LSTM model proposed in this paper utilizes the full dataset in combination with time-series individualization through an added index feature. Additional features thought to correlate with rut depth was also studied through multiple training set variations.

The model was evaluated by calculating the Root Mean Squared Error (RMSE) and the Mean Absolute Error (MAE) for each training set variation. The baseline model predicted rut depth with a MAE of 0.8110 (mm) and a RMSE of 1.124 (mm) outperforming a control set without the added index. The feature with the highest correlation to rut depth was curvature with a MAEof 0.8031 and a RMSE of 1.1093. Initial finding shows that there is a possibility of utilizing an LSTM model trained on multiple-multivariate time series to predict rut depth. Time series individualization through an added index feature yielded better results than control, indicating that it had the desired effect on model performance.

Both business networks and institutional forces are relevant to firm internationalization but they have seldom been studied together. We investigate under what circumstances firms are more likely to adopt non-predictive strategy in light of the influence of the business networks, the institutional forces, and the home market background affecting their internationalization. Based on survey data from 758 small and medium-sized enterprises (SMEs) from Brazil, China, Poland, Italy, and Sweden, our results support the effects of formal institutional distance and cultural differences on the use of non-predictive strategies by SMEs in internationalization decisions, as well as the contingency effects of business network stability and of having an emerging market background. We integrate research on the liability of foreignness and the liability of outsidership and find that business network stability is critical. It does not moderate the relation between cultural difference and NPS adoption but attenuates the negative relation between institutional distance and NPS adoption, indicating that the liabilities of foreignness and outsidership play different roles in internationalization. © 2021 The Author(s)

By shedding light on the often-overlooked early stages of opportunity development initiated by entrepreneurial alertness and its performance outcomes in an international setting, this study examines the interplay between alertness and two decision-making logics, effectual flexibility and causal prediction, in the context of small- and medium-sized enterprises (SMEs) internationalization. Leveraging structural equation modeling on a data set of 680 manufacturing SMEs across five countries, this study uncovers distinct effects: effectual flexibility moderates, while causal prediction mediates, the positive relationship between alertness and the international performance of SMEs. The research makes noteworthy contributions to international entrepreneurship literature by linking alertness with performance in the international context and disentangling the nuanced pathways through which SMEs can achieve success in the global arena.