This thesis is aims to answer the following questions to get detailed information about buildings element such as rebars, from 2D-drawing to information in a BIM-model

·       What obstacles are there to start using Dynamo for project planning?

·       Is there a need for prior knowledge of Dynamo?

This has been investigated through empirical research and interviews with educators who run courses on Dynamo and students who have learnt to use Dynamo. In the study, the author has learned to use Dynamo from scratch, through self-study of courses and online articles. 

In the study, the author identifies some barriers to start to use Dynamo for design, and there is no need for prior knowledge of Dynamo to start using the programme. 

The barriers to start to use Dynamo can be divided into two areas:

·       the learning process of Dynamo and knowledge of Dynamo and Revit 

·       dealing with technical issues with Dynamo. 

Where the learning process was the biggest barrier to start to use Dynamo, because Dynamo can have a steep learning curve that can take a long time to overcome. This is because a lot of knowledge of Dynamo and Revit is required to create advanced scripts such as adding detailed information to the BIM model. Th author of the study had to spend a lot of time finding the right information about Dynamo. 

It is also clear from the interviews that the learning process is the biggest barrier to start to use Dynamo. The students interviewed learned to use Dynamo by taking a course in Parametric Design at university. 

The author concluded from the study and the interviews that it would be easier to learn through a structured course at university. And all civil engineering students should take a course in Parametric Design as part of their education, where you learn visual or text-based prediction.

MHL is the ability to accumulate and apply knowledge about how to gain and maintain positive mental health. Studenthälsan is an organization that in conjunction with Swedish universities offers health counseling for students. The purpose of this study is to illuminate how employees at Studenthälsan in Swedish universities are working to promote MHL in students and describe what the employees consider to be areas fit for development within this context. Ten semi-structured interviews were conducted with employees at Studenthälsan across seven universities. The study result revealed that the employees enhanced students’ MHL by using pedagogical tools in individual counseling and strategies outside of counseling to distribute information regarding tactics for preventing mental disorders. The areas the employees considered to be important for further development were to reach out to students to a greater degree and to raise the competence of other employees at the universities to manage students' mental disorders at an early stage. Therefore the studys’ conclusion is summarized by Studenthälans’ approach to enhancing students’ MHL consists of utilizing different tools and methods and that further development is needed regarding the structural undertaking within the universities to support the work of Studenthälsan. This study provides knowledge about Studenthälsans’ work and MHL in Swedish context by documenting the experiences of the employees. 

This thesis investigates how environmental contaminants affect the performance of millimeter-wave radar, focusing on the AWR1843BOOST sensor. To evaluate how dirt water, salt water, and water attenuate radar signals, controlled laboratory studies were carried out. By comparing the reflections from a reference target on a test surface with and without impurities, the strength of the radar signal was determined. The findings demonstrate the substantial effects of each form of contamination on radar transmissivity. The water decreased signal strength with a 0.11mm layer from -47.5533 dB to -51.7395 dB with a 0.66 mm layer. Saltwater decreased signal strength with a 0.11 mm layer from -48.6587 dB to -50.7752 dB with a 0.66 mm layer. Dirt water decreased signal strength with a 0.11 mm layer from -48.8468 dB to -51.2691 dB with a 0.66 mm layer. These results highlight how important it is to study the reduction in the signal power of radar systems in various environmental conditions. 

The purpose of this study was to describe preschool staff's experiences of working with meal pedagogy in preschools and how they work to promote healthy eating habits. Using an interview guide, the study was conducted through qualitative interviews with preschool staff involved in meal pedagogy. The results indicated that staff face both opportunities and challenges, with lack of time and financial constraints being significant obstacles, while engaged staff, good collaboration and a supporting principalare key opportunities. Meal pedagogy enhanced children's engagement with food, increasing their curiosity and interest. The Sapere method, which allows children to explore food with all five senses, contributed to their language development and willingness to try new foods. The staff emphasized that successful implementation requires resources, clear guidelines, and sufficient time and knowledge to integrate it into daily activities. The study raises suggestions for collaborative models and ongoing training to effectively incorporate meal pedagogy into preschool routines.

This paper aims to identify key elements of investment in digital security and the changes in the current and future financial landscape regarding the rate of return on created network security capital. Cybersecurity cost and benefits Using a cost-benefit analysis framework and a Kaggle database set the twin challenge of protecting and preserving an organization’s information capital while simultaneously enabling access to IT resources by different users can be immense from a financial point of view. Highlighted areas for further study include regulatory compliance and risk management as well as investment prospects and their outcomes in various organizations, which can be considered as the major outline of the analysis. The report reveals the deficiencies of the literature indicating the need for more thorough research topics. It includes agile software security integration and industrial IoT risk evaluation. These quantitative approaches resulted in vital policy recommendations for the decision-makers, and thus, the role of constant monitoring and risk management was accentuated. Thus, the adaption splits cover some areas such as investment types, job profiles, year/industry-wise trend analysis, and investment analysis from the cost-benefit model 2021 to 2023. It reveals positive net cash flow in the future that reflects that organizations should allocate efforts and resources toward cybersecurity as these augments the financial capacity and reduce vulnerability. It helps to stress that more theoretical models should be applied not as mere concepts but as a base for practical recommendations that can be useful for various companies and organizations of different sizes and from different sectors. The issues cover the appraisal of the return on network security capital, the issues at Markun CFOs, the technology debts of finance, and applying digital twin compliance for the built environment. 

Survival Analysis is a major sub-field of statistics that studies the time to an event, like a patient's death or a machine's failure. This makes survival analysis crucial in critical applications like medical studies and predictive maintenance. In such applications, safety is critical creating a demand for trustworthy models. Machine learning and deep learning techniques started to be used, spurred by the growing volume of collected data. While this direction holds promise for improving certain qualities, such as model performance, it also introduces new challenges in other areas, particularly model explainability. This challenge is general in machine learning due to the black-box nature of most machine learning models, especially deep neural networks (DNN). However, survival models usually output functions rather than point estimates like regression and classification models which makes their explainability even more challenging task. 

Other challenges also exist due to the nature of time-to-event data, such as censoring. This phenomenon happens due to several reasons, most commonly due to the limited study time, resulting in a considerable number of studied subjects not experiencing the event during the study. Moreover, in industrial settings, recorded events do not always correspond to actual failures. This is because companies tend to replace machine parts before their failure due to safety or cost considerations resulting in noisy event labels. Censoring and noisy labels create a challenge in building and evaluating survival models.    

This thesis addresses these challenges by following two tracks, one focusing on explainability and the other on improving performance. The two tracks eventually merge providing an explainable survival model while maintaining the performance of its black-box counterpart.

In the explainability track, we propose two post-hoc explanation methods based on what we define as Survival Patterns. These are patterns in the predictions of the survival model that represent distinct survival behaviors in the studied population. We propose an algorithm for discovering the survival patterns upon which the two post-hoc explanation methods rely. The first method, SurvSHAP, utilizes a proxy classification model that learns the relationship between the input space and the discovered survival patterns. The proxy model is then explained using the SHAP method resulting in per-pattern explanations. The second post-hoc method relies on finding counterfactual explanations that would change the decision of the survival model from one source survival pattern to another. The algorithm uses Particle Swarm Optimization (PSO) with a tailored objective function to guarantee certain explanation qualities in plausibility and actionability.

On the performance track, we propose a Variational Encoder-Decoder model for estimating the survival function using a sampling-based approach. The model is trained using a regression-based objective function that accounts for censored instances assisted with a differentiable lower bound of the concordance index (C-index). In the same work, we propose a decomposition of the C-index where we found out that it can be expressed as a weighted harmonic average of two quantities; one quantifies the concordance among the observed event cases and the other quantifies the concordance between observed events and censored cases. The two quantities are weighted by a factor that balances the contribution of event and censored cases to the total C-index. Such decomposition uncovers hidden differences among survival models that seem equivalent based on the C-index. We also used genetic programming to search for a regression-based loss function for survival analysis with an improved concordance ability. The search results uncovered an interesting phenomenon, upon which we propose the use of the continuously differentiable Softplus function instead of the sharp-cut Relu function for handling censored cases. Lastly in the performance track, we propose an algorithm for correcting erroneous observed event labels that can be caused by preventive maintenance activities. The algorithm adopts an iterative expectation-maximization-like approach utilizing a genetic algorithm to search for better event labels that can maximize a surrogate survival model's performance.

Finally, the two tracks merge and we propose CoxSE a Cox-based deep neural network model that provides inherent explanations while maintaining the performance of its black-box counterpart. The model relies on the Self-Explaining Neural Networks (SENN) and the Cox Proportional Hazard formulation. We also propose CoxSENAM, an enhancement to the Neural Additive Model (NAM) by adopting the NAM structure along with the SENN loss function and type of output. The CoxSENAM model demonstrated better explanations than the NAM-based model with enhanced robustness to noise.

Athletes run a risk of receiving injuries when engaging in their sport, and one commonand severe injury among Swedish female football players is when the anterior cruciateligament (ACL) in the knee gets torn off by an acute trauma, and it is typically treated withsurgery. Physical test of the knees function has traditionally been the only determiner forwhether the athlete is ready to return or not. However, the psychological aspects of return tosport (RTS) have become increasingly interesting for researchers. The current study had twoobjectives: (1) to explore Swedish female football players’ perceptions and experiences ofpsychological readiness to RTS, after undergoing an ACL-reconstruction surgery, and (2) toinvestigate which potential factors might have influenced a successful outcome for theseathletes. Two participants were recruited, Alexandra, 23, and Johanna, 25, and both hadinjured their ACL while playing football, had the reconstruction surgery, and successfullyreturned to the same level as before their injury. To answer the objectives, the holistic-formstructural analysis and the narrative thematic analysis was used. The first analysis resulted intwo narratives: Alexandra had a family and personal growth narrative, and Johanna had aloneliness and performance narrative. The second analysis resulted in four themes: socialsupport, relation to football, psychological readiness, and desire to RTS. The four themes arecommon factors that influenced the girls in different ways. Future research should focus onexamining larger samples to establish common factors of success in a larger population, indifferent divisions of football, as well as other countries and sports. 

Meta-learning, or learning to learn, is a rapidly evolving area in machine learning that aims to enhance the adaptability and efficiency of learning algorithms. Inspired by the human ability to learn new concepts from limited examples and quickly adapt to unforeseen situations, meta-learning leverages prior experience to prepare models for fast adaptation to new tasks. Unlike traditional machine learning systems, where models are trained for specific tasks, meta-learning frameworks enable models to acquire generalized knowledge during training and efficiently learn new tasks during inference. This ability to generalize from past experiences to new tasks makes meta-learning a key focus in advancing artificial intelligence, offering the potential to create more flexible and efficient AI systems capable of performing well with minimal data.

In this thesis, we begin by formally defining the meta-learning framework, establishing clear terminology, and synthesizing existing work in a comprehensive survey paper. Building on this foundation, we demonstrate how meta-learning can be integrated into various fields to enhance model performance and extend capabilities to few-shot learning scenarios. We show how meta-learning can significantly improve the accuracy and efficiency of transferring knowledge across domains in domain adaptation. In scenarios involving a multimodal distribution of tasks, we develop methods that efficiently learn from and adapt to a wide variety of tasks drawn from different modes within the distribution, ensuring effective adaptation across diverse domains. Our work on personalized federated learning highlights meta-learning's potential to tailor federated learning processes to individual user needs while maintaining privacy and data security. Additionally, we address the challenges of continual learning by developing models that continuously integrate new information without forgetting previously acquired knowledge. For time series data analysis, we present meta-learning strategies that automatically learn optimal augmentation techniques, enhancing model predictions and offering robust solutions for real-world applications. Lastly, our pioneering research on unsupervised meta-learning via in-context learning explores innovative approaches for constructing tasks and learning effectively from unlabeled data.

Overall, the contributions of this thesis emphasize the potential of meta-learning techniques to improve performance across diverse research areas and demonstrate how advancements in one area can benefit the field as a whole.

Background: Despite ongoing advancements in the diagnosis and treatment of tuberculosis, millions of people contract tuberculosis each year. Correct treatment for tuberculosis is important for patients and society at large in order to reduce tuberculosis mortality. Healthcare professionals have the responsibility to help patients understand the risks of tuberculosis and provide information about the disease and its treatment. Patients' adherence regarding treatment against tuberculosis has a direct link to their experiences of the treatment and disease. To increase patients’ adherence and ensure good care it is therefore of importance to acknowledge patients experiences of tuberculosis. Aim: The aim of this literature study was to describe patients' experiences of living with tuberculosis in high prevalence countries. Method: The study was a literature study, where nine scientific articles were compiled and analyzed based on article searches in the databases PubMed, CINAHL, and APA PsychINFO. Results: The results were divided into two categories which were Patients' experiences of healthcare and Patients' experiences of physical and psychological impact. Conclusion: Patients experience an increase in discrimination due to stigma and a lack of knowledge about tuberculosis. Patients are at both physical and psychological risk when they prematurely discontinue their treatment on their own initiative. When healthcare professionals develop a deeper understanding of patients' experiences of living with tuberculosis, patient adherence to treatment may increase, thereby easing the disease process for the patient.

Background: Eating disorders cause physical and psychological suffering and globally more than 55 million people suffer from eating disorders and the number of unrecognized cases is expected to be high. Causes of eating disorders can be environmental, psychological, genetic and social media influences. Eating disorders are complex and psychological conditions that require individualized care. Teamwork is important as patients often need interventions from different professions. The most common eating disorders are anorexia nervosa, bulimia nervosa and binge eating disorder. Aim: The aim was to elucidate patients' experiences of nursing care for eating disorders. Method: The study was a general literature review that included eleven qualitative articles. Results: The results are presented in three categories, which were Experiences of not being taken seriously, Experiences of lack of knowledge about eating disorders among health professionals and Experiences of nursing care in connection with compulsory care for eating disorders. Conclusion: The central finding of the study was patients' experiences of ignorance among health professionals. The most prominent experiences were how patients felt ignored by staff and how they felt their medical condition was minimized. Further research from the patient's perspective is needed to develop health professionals' knowledge of eating disorders. 

A B S T R A C TIn recent years, machine learning (ML) algorithms have been used tominimize maintenance costs and identify problems early in the auto-motive sector. The breakdown of a component or equipment impactsthe performance and cost, and hence it is considered a crucial stepin various domains. To fulfill this purpose, different approaches canbe considered to improve the application in real-world problems. Thedetermination of an asset’s residual useful life of a component at aspecific time is known as "remaining useful life" (RUL). The exten-sive evolution of data makes it challenging to analyze and interprethigh-level and valuable features from the data. The issue arises inall disciplines, and the automotive industry is no exception, giventhe large number of sensors to consider. Existing RUL research hasnot given much thought to the influence of high dimensionality dataon component maintenance and deterioration. The fundamental pur-pose of feature selection (FS) is to select a subset of features from thedata without compromising model performance. Lately, the trend ofresearch in feature selection has been based on bio-inspired methods.This work proposes a hybrid approach to the FS problem that com-bines Ant Colony Optimization (ACO) and Particle Swarm Optimiza-tion (PSO). When tested on public datasets, our results demonstrate arise in regression accuracy and a reduction in the number of selectedfeatures. ACO-PSO applies advantage from ACO to handle directlywith nominal attributes and uses advantage from PSO to handle con-tinuous features, utilizing the "the best of both worlds" in a singlealgorithm. This approach is combined with the random forest classi-fier for choosing the relevant and appropriate features. To gain betterresults from the algorithm, the performance of these approaches iscompared and the feature set with the best accuracy has been usedfor regression tasks. The results are evaluated in 5 public domaindatasets as well, and results show an increase in accuracy for the re-gression and the reduction of selected features number.

Educational outcomes are heavily reliant on student engagement, yet this concept is complex and subject to diverse interpretations. The intricacy of the issue arises from the broad spectrum of interpretations, each contributing to the understanding of student engagement as both complex and multifaceted. Given the emergence and increasing use of Learning Analytics (LA) within higher education to provide enhanced insight into engagement, research is needed to understand how engagement is conceptualised by LA researchers and what dimensions and indicators of engagement are captured by studies that use log data. This systematic review synthesises primary research indexed in the Web of Science, Scopus, ProQuest, A + Education, and SAGE journals or captured through snowballing in OpenAlex. Studies were included if they were published between 2011 and 2023, were journal articles or conference papers and explicitly focused on LA and engagement or disengagement within formal higher education settings. 159 studies were included for data extraction within EPPI Reviewer. The findings reveal that LA research overwhelmingly approaches engagement using observable behavioural engagement measures, such as clicks and task duration, with very few studies exploring multiple dimensions of engagement. Ongoing issues with methodological reporting quality were identified, including a lack of detailed contextual information, and recommendations for future research and practice are provided. © The Author(s) 2024.

Contemporary higher education institutions are marked by diverse, internationalized classrooms that bring together various linguistic and cultural backgrounds. However, realizing the full potential of this diversity poses challenges, as academics, key players in maximizing the benefits of international classrooms, often lack the necessary competence, resources, and tools. Despite universities offering continuing professional development (CPD) initiatives, these suffer from low enrollment and high drop-out rates. Past research highlights the oversight of academics' input in the design of CPD initiatives. In our study, conducted at a medium-sized university in Sweden, we surveyed the perceptions and CPD needs of academics. The findings emphasize the importance of immersive international experiences of staff over disciplinary affiliation, reveal a disconnect between perceived challenges for teaching in the international classroom and academics' interest in CPD, and underscore the importance of adopting an andragogical adult learning centered approach in the design and delivery of CPD.

Over the past decade there has been an increase in scientific publications addressing intercultural competence (IC) of students. The sheer volume of publications available makes it difficult to determine the extent, breadth, and nature of research within the area. The aim of this scoping review was to describe the state of peer reviewed research related to IC, including academic disciplines addressing the issue, regions of the world conducting research, types of interventions used to foster IC and how outcomes are being evaluated. Six databases were searched, resulting in 15,128 articles. A total of 464 met the inclusion criteria. A trend was observed towards studying IC in interdisciplinary student populations as well as a post-COVID-19 trend towards more online interventions. Most research was conducted in North America (n = 198; 42.7%) within the discipline of education (n = 87; 18.8%). The most common intervention was pedagogical approaches delivered at the students’ home institution (n = 161; 34.7%). Results highlight a gap in research from the Global South and a lack of consensus regarding appropriate tools for evaluating IC. Continued work is required to determine the effects of specific interventions and to support educators in identifying appropriate tool(s) for measuring outcomes. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Continual learning (CL) refers to the ability to continually learn over time by accommodating new knowledge while retaining previously learned experience. While this concept is inherent in human learning, current machine learning methods are highly prone to overwrite previously learned patterns and thus forget past experience. Instead, model parameters should be updated selectively and carefully, avoiding unnecessary forgetting while optimally leveraging previously learned patterns to accelerate future learning. Since hand-crafting effective update mechanisms is difficult, we propose meta-learning a transformer-based optimizer to enhance CL. This meta-learned optimizer uses attention to learn the complex relationships between model parameters across a stream of tasks, and is designed to generate effective weight updates for the current task while preventing catastrophic forgetting on previously encountered tasks. Evaluations on benchmark datasets like SplitMNIST, RotatedMNIST, and SplitCIFAR-100 affirm the efficacy of the proposed approach in terms of both forward and backward transfer, even on small sets of labeled data, highlighting the advantages of integrating a meta-learned optimizer within the continual learning framework. 

Within Human-Computer Interaction (HCI), the interaction between humans and computers is explored to create user-friendly and accessible digital products, which shape the development and use of social media by promoting new ways to communicate, express ourselves, and build communities online. This thesis explores the interaction and construction of social media and its effect on user behavior, with a special focus on young adults aged 18-24. Through qualitative methodology that includes semi-structured interviews and observations, the theory is examined on how social media like Instagram and TikTok develop an individual's online identity. The study identifies both positive and negative aspects of the effects of social media, from how they promote solidarity and self-expression to how they can contribute to addiction and a negative impact on norms. The results highlight the importance of understanding the role of social media in societies and provide insights that can be used to improve the user experience on these platforms with the help of a developed persona. Future research should continue to explore this area, especially focusing on HCI strategies within social media to create better user awareness for the future development of social media.

Background: Anxiety is a common experience among patients undergoing mastectomy and negatively affects their health by contributing to stress anddistress. Purpose: The aim was to explore women’s experiences of anxietyin connection with mastectomy. Method: A general literature reviewcomprising a total of ten studies: five qualitative, three quantitative, and twousing mixed methods. Results: The results indicate that women’sexperiences of anxiety related to mastectomy can be organized into fourmain categories. The first category, Women’s experiences of anxiety as aresult of physical changes, highlights how the loss of a breast and changesin body image affect women’s self-esteem, sense of femininity, andattractiveness. The second category, Women’s anxiety about the future andcancer recurrence, focuses on the fear of recurrence and uncertainty about prognosis and longevity, which create significant anxiety impacting both psychological well-being and social roles. The third category, Women’sexperiences of anxiety due to inadequate information, emphasizes how insufficient and unclear communication from healthcare professionals canlead to feelings of helplessness and uncertainty. The fourth and finalcategory, Women’s strategies for managing anxiety, underscores the importance of social support from family, friends, and healthcareprofessionals, as well as the use of coping strategies such as mindfulnessand relaxation techniques. Conclusion: The results of this study underscore the importance of providing relevant information, social support, and psychological strategies to reduce anxiety associated with mastectomy.Creating a supportive care environment is essential to improve women’spreoperative experiences and mitigate their feelings of anxiety. 

Background: Stroke is an acute medical emergency that occurs when blood flow to part of the brain is turned off, leading to a lack of oxygen and damage to brain cells. Aphasia is a language disorder caused by damage to the language areas of the brain, often after a stroke. About 30 percent of those who suffer from an ischemic stroke develop this type of disorder. Communication is a fundamental element in nursing that has a major impact on the relationship between nurse and patient. Aim: The aim was to describe nurses' experiences of communication with patients who have aphasia after a stroke. Method: The study was conducted as a literature study based on eight articles that were processed and analyzed with Popenoe et al., (2021). Results: Aspects affecting the communication between the nurse and the patient in nursing meetings, the three prominent main categories were: experience of insufficient education and knowledge, experience of lack of time and experience using strategies Conclusion: Several factors show that lack of knowledge, insufficient training in communication strategies and lack of time among healthcare professionals, especially nurses, lead to poorer quality of care and support for patients with aphasia, which underlines the need for increased education and resources to promote effective communication and participation.