Objective: This study examined the prevalence and long-term survival of atrial fibrillation (AF) in the older population.

Methods: Data was recruited from the longitudinal SNAC study from baseline (2001-2004) for up to 10 years.

Results: The population comprised 6,904 persons (59% women) (mean age 73.9 years). The prevalence of AF was 4.9% and increased with age. The hazard ratio (HR) for death in those with AF at baseline was 1.29 during the 10-year observation period. Cox regression analysis in persons with AF (n = 341) showed that men had a higher HR for death (1.57). CHA2DS2-VASc scores were significantly associated with death within 10 years (HR 1.29/score). Any form of anticoagulant use was reported in 146 (42.8%) and was significantly associated with survival (p = .031).

Conclusions: The prevalence of AF in the general population was almost 5%, and it shortened life expectancy by nearly 2.4 years over a 10-year period. Despite the proven efficacy of OAC therapies, our results demonstrate that AF continues to be associated with increased mortality, especially among men, and that many older people are at high risk of developing a stroke because they do not receive appropriate anticoagulant therapy. These results emphasize the need for improved preventive and therapeutic modalities.

For decades, software engineering research and practice has focused primarily on technological and process-related factors. Today, there is a growing interest in organizational, social, and psychological factors, including well-being. Organizational studies show that well-being contributes to work outcomes, including creativity, performance, and productivity. But despite its importance, the predictors and outcomes of software engineers' well-being as a multidimensional construct to date are under-researched. This paper reports on the multidimensional well-being of professional software engineers and generates insights for the future research in this area. 44 quantitative survey studies published between 2000 and 2023 were selected and synthesized both quantitatively and qualitatively through a systematic literature review. The results of the review were further analyzed to construct a quantitatively-testable theory, detailing the predictors and outcomes of well-being in software engineering organizations. The total number of research participants included in the selected studies is 16,086 software engineering professionals from at least 42 countries. The literature review identified various measures, constructs, and indicators of well-being, as well as its predictors and outcomes. The theory, based on cumulative results of carefully selected quantitative studies, is an attempt to "correct the record" by establishing well-being in software engineering as a meta-construct of hedonic, eudaimonic, and integrated or hedaimonic qualities predicted by different individual, team and organizational factors and impacting the functioning of software engineers and their organizations. The review highlighted the under-researched aspects of well-being in software engineering and confirmed the need for more advanced quantitative studies. We hope that the theory will benefit researchers in conducting future studies and practitioners in developing nuanced and science-based interventions for improving software engineers' well-being.

The future trend of internet usage increasingly emphasizes visual modes of engagement and user interactions across complex scenarios, including multimedia video, gaming, mapping services, and immersive applications. Consequently, the success of media creators, application developers, and service providers hinges on selecting appropriate tools and technologies to optimize the user experience. Quality of Experience (QoE) offers a framework for evaluating user experience and satisfaction with applications or services by using both objective and subjective metrics. It is important to recognize that various factors can influence user feedback, including different traits of human personality, interactions with systems, and the context in which they are used.

In this thesis, we adopt a holistic approach to evaluate the QoE for video quality and outdoor applications, focusing on system, human, and contextual influence factors. From a system perspective, we investigate the performance of widely implemented video codecs and propose more effective objective QoE metrics. The objective results are benchmarked against subjective outcomes to underscore their limitations in quantifying user experience, which is influenced by numerous factors beyond system-related aspects. Regarding human influence factors, our analysis encompasses user delight with the content presented, mood, gender, and the impact of eye-tracking on user assessments. A key observation is the critical importance of selecting stimuli that effectively engage users to accurately capture the true impact of user delight in the context of mobile gaming. For contextual factors, we examine the frequency of use and highlight the distinction between assumptions and actual experiences during user evaluations of outdoor activities. Our findings reveal the notable effects of these influence factors, particularly user delight, which demonstrated significant outcomes. The impact of visual attention on user behavior and subsequent ratings was also established. Furthermore, we identify limitations in existing QoE metrics and underscore the need for more cohesive QoE models that effectively engage users with mobility-driven applications or services that are not confined to multimedia streaming. These insights are drawn from several use cases reflecting the evolving ways in which users are interacting with systems nowadays. Our findings highlight the need for future QoE methodologies to evolve, by integrating the complex interplay of technical and human-centric variables. This evolution is essential to develop flexible, dynamic, and scalable solutions that cater to the demands of diverse applications.

The hole expansion ratio (HER) observed in a standardized hole expansion test (HET) is commonly used to determine the edge fracture of steel sheets. A large variation of the measured HER restricts the practical application of the method. This study presents a systematic investigation on uncertainties in the HER of DP800 sheet material, including the hole-edge quality, pre-strain due to the hole-punching process, the friction coefficient, and the determination of fracture. An artificial neural network was trained to develop a surrogate model using a database gained from a thousand finite element simulations of the HET. Monte-Carlo simulations were performed using the trained surrogate model to characterize the distribution of the HER. Sensitivity analysis via Sobol's indices is calculated to determine the influence of the input variables on the output. It is found that the pre-strain and pre-damage generated during the hole punching process in the shear-affected zone dominate the variation of the HER. Discussions on reducing the output's variation are detailed. In general, these findings provide valuable insights for the determination of HER as well as the edge crack behavior of the investigated DP800 steel sheet.

We generalize a classical result by Passman on primeness of unital strongly group graded rings to the class of nearly epsilon-strongly group graded rings which are not necessarily unital. Using this result, we obtain (i) a characterization of prime s-unital strongly group graded rings, and, in particular, of infinite matrix rings and of group rings over s-unital rings, thereby generalizing a well-known result by Connell; (ii) characterizations of prime s-unital partial skew group rings and of prime unital partial crossed products; (iii) a generalization of the well-known characterizations of prime Leavitt path algebras, by Larki and by Abrams-Bell-Rangaswamy. © 2024 The Author(s)

Experimentation is an essential method for causal inference in any empirical discipline. Crossover-design experiments are common in Software Engineering (SE) research. In these, subjects apply more than one treatment in different orders. This design increases the amount of obtained data and deals with subject variability but introduces threats to internal validity like the learning and carryover effect. Vegas et al. reviewed the state of practice for crossover designs in SE research and provided guidelines on how to address its threats during data analysis while still harnessing its benefits. In this paper, we reflect on the impact of these guidelines and review the state of analysis of crossover-design experiments in SE publications between 2015 and March 2024. To this end, by conducting a forward snowballing of the guidelines, we survey 136 publications reporting 67 crossover-design experiments and evaluate their data analysis against the provided guidelines. The results show that the validity of data analyses has improved compared to the original state of analysis. Still, despite the explicit guidelines, only 29.5% of all threats to validity were addressed properly. While the maturation and the optimal sequence threats are properly addressed in 35.8% and 38.8% of all studies in our sample respectively, the carryover threat is only modeled in about 3% of the observed cases. The lack of adherence to the analysis guidelines threatens the validity of the conclusions drawn from crossover-design experiments. © 2024 Owner/Author.

Context: GUI-based tests for web applications are frequently broken by fragility, i.e. regression tests fail due to changing properties of the web elements. The most influential factor for fragility are the locators used in the scripts, i.e. the means of identifying the elements of the GUI.

Objective: We extend a state-of-the-art Multi-Locator solution that considers 14 locators from the DOM model of a web application, and identifies overlapping nodes in the DOM tree (VON-Similo). We augment the approach with standard Machine Learning and Learning to Rank (LTR) approaches to aid the location of web elements.

Method: We document an experiment with a ground truth of 1163 web element pairs, taken from different releases of 40 web applications, to compare the robustness of the algorithms to locator weight change, and the performance of LTR approaches in terms of MeanRank and PctAtN.

Results: Using LTR algorithms, we obtain a maximum probability of finding the correct target at the first position of 88.4% (lowest 82.57%), and among the first three positions of 94.79% (lowest 91.86%). The best mean rank of the correct candidate is 1.57.

Conclusion: The similarity-based approach proved to be highly dependable in the context of web application testing, where a low percentage of matching errors can still be accepted.

Hösten 2023 beviljades projektet Utveckling av kritisk språkfärdighet för samhällsbärande professioner medel i UHR:s satsning ”Högskolepedagogiska lyftet”. Inom projektet samarbetar sjuksköterskeutbildningen på Blekinge Tekniska Högskola, polisutbildningen på Linnéuniversitetet och officersutbildningen på Sjöstridsskolan. Projektet, som kommer att löpa mellan november 2023-juni 2025, tar avstamp i det spänningsfält som finns mellan språkanvändning i utbildningar som normalt sett inte ses som skriftbundna i förhållande till yrkeslivet (Karlsson, 2006, 2007). I professionsutbildningar såsom till sjuksköterska, polis och officer är språkförmågan därtill en kritisk professionsfärdighet, inte minst ur ett säkerhetsperspektiv (Baggens & Sandén, 2019, Ask & Byrman 2015). Detta föranleder behovet av ett tydligt och precist språk i myndighetssammanhang.  

Det finns behov i professionsutbildningar, där studenter inte alltid ser språkfärdigheten som en kärnkompetens, att justera och kommunicera språkliga målbilder som en del av yrkessocialisationen. Kompetens i professionsspråk behöver medvetet lyftas fram inom utbildningen som en väsentlig del i det professionella kunnandet och yrkesutövandet. Lärare behöver hjälp med att operationalisera sin ofta tysta kunskap kring outtalad praxis i sin profession.  

Projektet ämnar ge utrymme åt lärare att utveckla metoder och verktyg för att i läraktiviteter och examinationer lyfta fram de olika dimensioner som ryms inom ett professionsspråk. I detta rundabordssamtal ämnar vi diskutera följande frågor/aspekter:  

1. Hur främjas en kollegial kultur där vikten av att studenterna besitter ett rättssäkert professionsspråk diskuteras i syfte att i en akademisk miljö ge röst åt den tysta kunskap som lärare ser som en del av sin profession?  

2. Vilket stöd behöver utbildningar i utformandet av läraktiviteter och examinationer som främjar studenternas utveckling av ett professionsspråk och en djupare förståelse för de säkerhetsaspekter som är relevanta för deras specifika yrke? Det behövs ett ökat utbyte kring erfarenhet och kunskapsspridning gällande metoder och verktyg som främjar studenters språkutveckling i professionsutbildningar. Rundabordssamtalet syftar till att lyfta pedagogiska erfarenheter samt belysa exempel på läraktiviteter, som främjar möjligheter att med studenterna utforska och träna det gemensamma språket i en rad professionsutbildningar. Genom en variant av Open Space-metoden samlas deltagarna kring detta ämne och får därefter ta ansvar för att leda samtal i grupper kring de olika frågorna. När inga fler frågor lyfts kan deltagare byta grupp. Som avslutning samlas alla grupper kring en gemensam reflektion.  

Since the term ’human-computer interaction’ was first used in the 1970s, the way humans interact with computers has become diverse. Immersive technologies provide an interactive way to merge the physical and digital worlds. Among immersive technologies collectively referred to as extended reality (XR), virtual reality (VR) is one of the main types. In the industrial market, most of the investment in immersive technology was concentrated in the gaming industry, and healthcare-related applications were also at the forefront. Lack of consumer demand and user experience issues were among the main challenges of popularisation. With this background, this dissertation used the literature review and survey to understand the development status, opportunities, challenges, and evaluation of XR games in healthcare from academic and consumer perspectives. Based on these results, this dissertation designed and implemented an indoor VR rowing game for physical exercise aimed at the elderly and conducted a questionnaire-based test on this game. This small-scale test is a pilot study to test the experimental environment, equipment and methods, as well as collect feedback on the game and research. Based on this feedback and the problems found in the pilot study, this dissertation improved the experimental design and the game settings and conducted a large-scale formal experiment. This experiment tested the subjective evaluation method based on standardised scales and the objective evaluation based on biosensors in VR rowing games. This dissertation explored user preferences on real devices, postures, and game tasks and studied the impact of age on them. This dissertation provides a reference value for researchers and developers in related work: 1) the current status and trends of XR game applications and evaluation in the health fields from academic and industrial perspectives, 2) attempting to provide solutions to the lack of VR games for elderly users, and 3) trying to provide subjective and objective evaluation methods for user experience evaluation as well as exploration of user preferences.

The use of augmented and virtual reality (AR/VR) has recently been increased in various people’s work and life. The combination of technology with digital games have recently received much attention. The 2020 Augmented and Virtual Reality Survey Results report by Perkins Coie, XR Association, and Boost VC, pointed out that immersive techniques are leaving from laboratory research and theoretical applications and growing upas fully mature and income-generating marketplaces. Except forgames and entertainment, the most common area reported was healthcare and medical devices from immersive technologies, followed by education. The survey presented in this paper explores the usage, perceived benefits and problems of AR/VR games for mental or physical health. It also highlights future features that users would see as relevant in these applications. Through an online questionnaire, we found examples of used AR/VR games and their usage frequency, the user’s perception of their mental or physical health benefits and problems, and the prospects to physical and mental health for AR/VR games. This study is helpful for researchers and developers both in computer science and medical science. It provides people with examples of existing consumer-level products, their advantages and disadvantages, and AR/VR games’ requirements relevant to mental and physical health. In future work, the survey sample size would be extended to collect additional user experience data and further explore the challenges and opportunities of such techniques for public health.

The integration of outdoor smartphone applications with fitness trackers has introduced new opportunities and challenges for user interaction, particularly in mobility-driven activities. While these innovations offer significant benefits, they also pose challenges due to the many factors influencing the user’s quality of experience. Traditional methods of assessing user experiences, such as offline surveys and static questionnaires, often fail to capture the dynamic nature of outdoor activities. This research proposes a novel Quality of Experience (QoE) methodology for mobile applications to enhance the assessment of user experiences in cycling. Focusing on a use case in Blekinge, Sweden, where residents were encouraged to adopt cycling for daily transportation, we extracted land cover data and developed a server-side workflow for bicycle path segmentation. By incorporating dynamic surveys that adapt to users’ real-time experiences, we aim to generate a more accurate and context-aware dataset. This study makes several key contributions: First, it presents a scalable method for bicycle path segmentation; second, it demonstrates the utility and benefits of land cover data extraction; and finally, it evaluates the effectiveness of QoE influence factors through user surveys based on real-world cyclist feedback. This approach is expected to enhance the planning and development of cycling infrastructure by providing urban planners and stakeholders with valuable user insights using adaptable surveys based on route segmentation.

 Doctoral supervision is a challenging process, and supervisors use multiple supervision styles to ensure the success of their doctoral students in their research and education. To provide valuable insights into the effectiveness of these supervision practices and help identify areas for improvement, it is important to understand how doctoral students perceive different aspects of supervision. This study aims to explore the perspectives of doctoral students regarding the supervision practices of their supervisors at the Blekinge Institute of Technology (BTH). The study employs a deductive approach with a cross-sectional study design. A survey questionnaire was sent to the doctoral students at BTH. The survey included questions on the five facets of supervision based on the theoretical framework by Halse and Malfroy [1]: learning alliance, habits of mind, scholarly expertise, technè, and contextual expertise. The data collected was quantitative, and descriptive analysis was performed. The total number of respondents (N) was 51 (53.12%) out of 96 invited participants. Results indicated that while most students reported effective learning alliance, significant challenges were noted in fostering teamwork, providing constructive feedback, and offering technical guidance. Specific departments, such as the Department of Computer Science (DIDA) and the Department of Health (TIHA), reported more substantial challenges across multiple facets of supervision. In conclusion, while the overall perception of supervision at BTH is positive, there is a clear need for targeted interventions to address the identified weaknesses and enhance the quality of doctoral education. This information can help strengthen the quality of the doctoral programs at BTH.

Globally, cancer is the second-leading cause of death after cardiovascular disease. To improve survival rates, risk factors and cancer predictors must be identified early. From the literature, researchers have developed several kinds of machine learning-based diagnostic systems for early cancer prediction. This study presented a diagnostic system that can identify the risk factors linked to the onset of cancer in order to anticipate cancer early. The newly constructed diagnostic system consists of two modules: the first module relies on a statistical F-score method to rank the variables in the dataset, and the second module deploys the random forest (RF) model for classification. Using a genetic algorithm, the hyperparameters of the RF model were optimized for improved accuracy. A dataset including 10 765 samples with 74 variables per sample was gathered from the Swedish National Study on Aging and Care (SNAC). The acquired dataset has a bias issue due to the extreme imbalance between the classes. In order to address this issue and prevent bias in the newly constructed model, we balanced the classes using a random undersampling strategy. The model's components are integrated into a single unit called F-RUS-RF. With a sensitivity of 92.25% and a specificity of 85.14%, the F-RUS-RF model achieved the highest accuracy of 86.15%, utilizing only six highly ranked variables according to the statistical F-score approach. We can lower the incidence of cancer in the aging population by addressing the risk factors for cancer that the F-RUS-RF model found. © 2024 The Author(s). International Journal of Imaging Systems and Technology published by Wiley Periodicals LLC.

We introduce non-associative skew Laurent polynomial rings and characterize when they are simple. Thereby, we generalize results by Jordan, Voskoglou, and Nystedt and Öinert. © The Author(s), 2024.

The development of Quality of Experience (QoE) models using Machine Learning (ML) is challenging, since it can be difficult to share datasets between research entities to protect the intellectual property of the ML model and the confidentiality of user studies in compliance with data protection regulations such as General Data Protection Regulation (GDPR). This makes distributed machine learning techniques that do not necessitate sharing of data or attribute names appealing. One suitable use case in the scope of QoE can be the task of mapping QoE indicators for the perception of quality such as Mean Opinion Scores (MOS), in a distributed manner. In this article, we present Distributed Ensemble Learning (DEL), and Vertical Federated Learning (vFL) to address this context. Both approaches can be applied to datasets that have different feature sets, i.e., split features. The DEL approach is ML model-agnostic and achieves up to (Formula presented.) accuracy improvement of ensembling various generic and specific models. The vFL approach is based on neural networks and achieves on-par accuracy with a conventional Fully Centralized machine learning model, while exhibiting statistically significant performance that is superior to that of the Isolated local models with an average accuracy improvement of (Formula presented.). Moreover, energy-efficient vFL with reduced network footprint and training time is obtained by further tuning the model hyper-parameters. © 2021 by the authors.

Context: In large-scale software development, a large number of bug reports are submitted during software development and maintenance. Practitioners need the ability to analyze this abundant data to make data-driven decisions about bug management tasks.

Objective: This thesis aims to utilize software analytics (SA) to support practitioners in bug management. The objectives of this thesis are (1) to identify and structure the knowledge on the use of SA for software engineering (SE) tasks and (2) to investigate and evaluate the practical application of SA to support practitioners in managing invalid bug reports (IBRs).

Method: We conducted a tertiary review and systematic mapping study to achieve the first objective and comparative experiments and two industrial case studies to achieve the second objective. Throughout the thesis work, we relied on a technology transfer model to guide the research and facilitate the adoption of ML techniques for the early identification of IBRs at the case company.

Results: We provide a comprehensive map of various SA applications for SE tasks and a decision matrix that can assist in selecting the most appropriate ML technique for bug report classification for a given context. Our results indicate that an ML technique can identify IBRs with acceptable accuracy at an early stage in practice. Furthermore, the results of an SA-based approach indicate that it can support practitioners in devising preventive measures for IBRs.

Conclusion: Through industrial validations, this thesis provides evidence of the usefulness of SA in bug management, particularly in supporting practitioners in managing IBRs in large-scale software development.

Software analytics (SA) is frequently proposed as a tool to support practitioners in software engineering (SE) tasks. We have observed that several secondary studies on SA have been published. Some of these studies have overlapping aims and some have even been published in the same calendar year. This presents an opportunity to analyze the congruence or divergence of the conclusions in these studies. Such an analysis can help identify broader generalizations beyond any of the individual secondary studies. We identified five secondary studies on the use of SA for SE. These secondary studies cover primary research from 2000 to 2021. Despite the overlapping objectives and search time frames of these secondary studies, there is negligible overlap of primary studies between these secondary studies. Thus, each of them provides an isolated view, and together, they provide a fragmented view, i.e., there is no “common picture” of the area. Thus, we conclude that an overview of the literature identified by these secondary studies would be useful in providing a more comprehensive overview of the topic.

Defect inspection of the surface in ultrasonically welded wire terminations is an important inspection procedure to ensure welding quality. However, the detection task of ultrasonic welding defects based on deep learning still faces the challenges of low detection accuracy and slow inference speed. Therefore, to solve the above problems, we propose a fast and effective lightweight detection model based on You Only Look Once v8 (YOLOv8n), named LightYOLO. Specifically, first, to achieve fast feature extraction, a Two-Convolution module with FasterNet block and Efficient multi-scale attention (CTFE) structures is introduced in the backbone network. Secondly, Group-Shuffle Convolution (GSConv) is used to construct the feature fusion structure of the neck, which enhances the fusion efficiency of multi-level features. Finally, an auxiliary head training method is introduced to extract shallow details of the network. To verify the effectiveness of the proposed method, we constructed a surface defect data set of ultrasonic welding wire terminals and conducted a series of experiments. The results of experiments show that the precision of LightYOLO is 93.4%, which is 3.5% higher than YOLOv8n(89.9%). In addition, the model size was reduced to 1/2 of the baseline model. LightYOLO shows the potential for rapid detection on edge computing devices. The source code and dataset for our project is accessible at https://github.com/JianshuXu/LightYOLO. © 2024 The Authors