Blekinge Institute of Technology

Context: Code reviewers provide valuable feedback during the code review. Identifying common issues described in the reviewers’ feedback can provide input for devising context-specific software development improvements. However, the use of reviewer feedback for this purpose is currently less explored. Objective: In this study, we assess how automation can derive more interpretable and informative themes in reviewers’ feedback and whether these themes help to identify recurring quality-related issues in code changes. Method: We conducted a participatory case study using the JabRef system to analyze reviewers’ feedback on merged and abandoned code changes. We used two promising topic modeling methods (GSDMM and BERTopic) to identify themes in 5,560 code review comments. The resulting themes were analyzed and named by a domain expert from JabRef. Results: The domain expert considered the identified themes from the two topic models to represent quality-related issues. Different quality issues are pointed out in code reviews for merged and abandoned code changes. While BERTopic provides higher objective coherence, the domain expert considered themes from short-text topic modeling more informative and easy to interpret than BERTopic-based topic modeling. Conclusions: The identified prevalent code quality issues aim to address the maintainability-focused issues. The analysis of code review comments can enhance the current practices for JabRef by improving the guidelines for new developers and focusing discussions in the developer forums. The topic model choice impacts the interpretability of the generated themes, and a higher coherence (based on objective measures) of generated topics did not lead to improved interpretability by a domain expert.

Background Relative’s efforts are essential when palliative care is provided at home and support from healthcare professionals is needed. Despite this, since the support provided varies, relatives may have unmet support needs. Many people receive general palliative care at home rather than specialised care, and nurses play a significant role in supporting relatives. This study aimed to explore registered nurses’ experiences of supporting relatives before and after a patient’s death when general palliative care is provided at home. Methods This study used a qualitative explorative design. Data were collected through focus group interviews with 18 registered nurses in home care in Sweden and were analysed using content analysis. The Consolidated Criteria for Reporting Qualitative Research checklist was used for explicit reporting. Results The findings are presented in four categories with subcategories: receiving support to provide support, continuously providing understandable information, balancing different needs and building relationships facilitates safety and identifying needs. Conclusions Even if registered nurses support relatives to some extent, they rarely reflect on the support they provide and lack structure in providing support both before and after the patient’s death. The findings showed inadequacies in support after the patient’s death, which is also emphasised in previous studies. The findings also showed deficiencies in routines, local guidelines and checklists as well as in training and education on how to support relatives when palliative care is provided at home, thereby risking that relatives’ needs remain unmet. This highlights the need for creating routines and developing detailed local guidelines and checklists on providing support to relatives both before and after the patient’s death.

Similarity-based analysis is a common and intuitive tool for exploring large data sets. For instance, grouping data items by their level of similarity, regarding one or several chosen aspects, can reveal patterns and relations from the intrinsic structure of the data and thus provide important insights in the sense-making process. Existing analytical methods (such as clustering and dimensionality reduction) tend to target questions such as “Which objects are similar?”; but since they are not necessarily well-suited to answer questions such as “How does the result change if we change the similarity criteria?” or “How are the items linked together by the similarity relations?” they do not unlock the full potential of similarity-based analysis—and here we see a gap to fill. In this paper, we propose that the concept of similarity could be regarded as both: (1) a relation between items, and (2) a property in its own, with a specific distribution over the data set. Based on this approach, we developed an embedding-based computational pipeline together with a prototype visual analytics tool which allows the user to perform similarity-based exploration of a large set of scientific publications. To demonstrate the potential of our method, we present two different use cases, and we also discuss the strengths and limitations of our approach.

Depression in older adults is a significant public health issue with broad impacts on both individuals and society. The multifaceted nature of depression underscores the complexity of identifying and predicting risk factors, necessitating a sophisticated and accurate approach based on new emerging technologies. Compared to traditional statistical methods, machine learning provides a more detailed and individualized understanding of risk variables by analyzing large datasets, identifying patterns, and building predictive models. This study presented a novel feature selection method based on the relief and lasso algorithms. The proposed feature selection method selected the ten most significant features from the dataset. A neural network (NN) with hyperparameters optimized by a grid search technique was used to categorize depression. The feature selection and classification modules work together as a single unit, namely as (Relief_Lasso_NN). Data from the Swedish National Study on Aging and Care (SNAC) was used for this study. The collected dataset consists of 726 samples with 75 features per sample. Four experiments were conducted to validate the performance of the proposed (Relief_Lasso_NN) framework. The proposed model achieved an accuracy of 90.34% in predicting depression using only ten features from the dataset. The top 10 features identified by the proposed feature selection method significantly impact depression in older adults. Furthermore, the performance of seven other state-of-the-art machine learning models was also compared with the proposed framework.

In this paper, we investigate the current state and development of personalized smart immersive extended reality environments (PSI-XR). PSI-XR has gained increasing traction across various fields such as education, entertainment, and healthcare, offering customized immersive experiences that address users’ personalized needs. This study performs a systematic literature review by collecting and analyzing related journal and conference papers in the domain. Following a comprehensive search across three databases, which yielded 1276 papers, a refined selection of 94 publications was made to conduct an in-depth analysis of cutting-edge research in the field of PSI-XR. This review focused on examining application domains, relevant technologies, and smart techniques, including artificial intelligence, with particular emphasis on advancements in personalization. The study provides insights into prospective advancements while also identifying the opportunities and challenges in this evolving field. This review is beneficial for both researchers and developers interested in exploring the state-of-the-art personalized perspective in a smart immersive extended reality environment.

This final chapter provides a call to action for STEM educators, policymakers, and educational institutions to adopt a Creative Pragmatics approach to enhance existing models of learning with more participatory, hands-on, and student-directed features. Based on the wide range of case studies and methodologies presented in our book, this chapter presents multiple implications of Creative Pragmatics for educational policy, funding models, and pedagogical practice. By bolstering STEM education through Creative Pragmatics, all stakeholder groups involved in the educational ecosystem can ensure a healthy pipeline of scientific and technical talent and foster future generations of adaptive thinkers who can navigate the intricate landscape of real-world complexity.

This chapter explores the concept of Creative Pragmatics, highlighting its role in fostering situated performances of knowing and dynamic sensemaking. By integrating theoretical perspectives from pragmatism, science and technology studies (STS), and social theory, Creative Pragmatics offers a framework for understanding knowledge as an active, performative and evolving process. The chapter highlights the importance of agency and creativity as iterative elements of learning, encouraging practical engagement, interdisciplinary collaboration and the active construction of knowledge. It examines how art and design practices inform Creative Pragmatics, particularly in developing adaptability, flexible problem-solving and innovation through creative approaches. By drawing connections between interdisciplinarity and performative knowledge-making, pedagogy in Creative Pragmatics prepares learners to become “wicked scientists,” individuals capable of navigating complex challenges and fostering sustainable solutions in an ever-changing world.

Creative Pragmatics for Active Learning in STEM Education” opens with a chapter that introduces Creative Pragmatics as a flexible and evolving approach aimed at fostering active learning in STEM. This introductory chapter, provided as open access, advocates for competence-based education that prepares learners for the unpredictable and multidimensional challenges of today’s world. It challenges the traditional view of scientific knowledge as stable and complete, urging educators and students to develop competence and skills to navigate the unpredictable challenges of today’s science and society. Spanning 13 chapters, the book features contributions from a diverse array of scholars and practitioners, from Stanford, Columbia, and the University of Nebraska in the USA, with experts from Sweden, Germany and several Danish universities and university colleges. These contributors explore themes such as creative learning strategies, dynamic teacher-student interactions, innovative assessment methods, and the design of learning environments. Each chapter further develops the idea of Creative Pragmatics, emphasising a holistic approach that fosters creativity, interdisciplinary collaboration, and active student engagement. This collection is a valuable resource for educators, researchers, and policymakers who seek to rethink STEM education. The book offers practical insights and a ‘bigger picture’ perspective for fostering educational environments that enable students to develop the competencies needed to engage with complexity. It encourages an exploration of the world that nurtures curiosity, critical thinking, and creative agency, thereby promoting professional and civic responsibility towards environmental, societal, technological, and scientific challenges. Bringing together a transdisciplinary and global team with professional backgrounds spanning Asia, the Americas, and Europe, this book is part of a series on European Science Education Research and Practice, contributing significantly to the field.

The project brief is considered a pivotal component in corporate-sponsored student projects, yet many university teaching teams lack guidelines and best practices to define, frame, evaluate, and change these briefs with sponsors. We examine a rich data set of 68 project briefs used by 19 partner universities across a decade (2012–2022), drawn from a long-running project course taught at Stanford University and a related academic spinoff consortium called the SUGAR Network. All projects share a similar pedagogy for global innovation challenges and STEM-based team projects. Our study found that corporate sponsors sought seven different types of project outcomes. Although nearly two-thirds (65%) of sponsors adopted “how might we” phrasing in their briefs, other wording like “we dream…” was also used to provoke more imaginative thinking. Moreover, slightly over a third (35%) of briefs focused more on mid-term innovation horizons than near or far-term horizons. Based on these findings, we present a two-question guide for crafting a project brief with corporate sponsors to help student projects start from a stronger position.

This chapter provides the theoretical and philosophical foundations for Creative Pragmatics, exploring the nature of complexity and its implications for knowledge, skills, and competence development. It explains what complexity is, highlights the limits of reductive and predictive models of knowing, and clarifies that complexity is neither chaos nor mere complication. The chapter contrasts representational approaches to science and knowledge with performative understandings, where knowledge is actively constructed through engagement with the world. These shifts have significant implications for educational practice and policy, particularly the growing global focus on competence-based learning frameworks like the European Qualifications Framework (EQF). Emphasising transversal competencies, interdisciplinary collaboration, and autonomy in navigating complex and unpredictable environments, the chapter introduces the concept of wicked scientists—professionals equipped to address ‘wicked problems’ in a rapidly changing world. The chapter situates Creative Pragmatics as a guiding framework for competence-based education, preparing students to think critically, collaborate across boundaries, and engage with complexity and uncertainty in real-world contexts.

The financial sector plays an important yet ambivalent role in society's sustainability transition. Credit decisions have a substantial impact as they determine the allocation of large amounts of financial resources. This study applies the Framework for Strategic Sustainable Development as a lens to review literature and investigate practices in Nordic banks on sustainability considerations in corporate credit risk assessment. Three gaps and recommendations are presented: (i) banks should apply a systems perspective that goes beyond a narrow focus on climate change to avoid sub-optimisation; (ii) strategies like inclusion and exclusion should be informed by backcasting from basic sustainability principles to foresee the long-term direction of change and to assess whether solutions are scalable towards sustainability; and (iii) instead of asking whether it 'pays to be sustainable', research and practice should focus on 'how' companies can work strategically with sustainability, finding the optimal timing between being too passive and too proactive.

Personality trait identification through handwriting analysis presents a challenging area within automated document recognition based on Artificial Intelligence solutions. Recent studies relied on solutions automating graphonomic processes, while others address only a few local features, conversely few studies offer solutions based on textural features. In this work, we propose an automated approach for personality trait identification that treats a scripter’s handwriting as a texture by leveraging a diverse set of textural features, including LCP, oBIFCs, LPQ, LBP, among others. The approach is validated on FFM-annotated datasets using cost-effective classifiers such as XGBoost, Random Forest, Gradient Boost, SVM, and Naive Bayes. Our empirical study enabled the judicious selection of the most suitable textural features for each personality trait. Subsequently, we constructed a comprehensive personality trait identification solution by combining multiple textural features and integrating top-performing classifiers. The experimental results demonstrated the validity of our hypothesis, achieving performance improvements of more than 10% on both datasets.

Blockchain technology is increasingly recognized as a promising solution for managing health-related data, particularly in promoting well-being through physical activity. This is becoming more significant as the Internet of Things (IoT) and sport monitoring sensors continue to expand and become more available, leading to a growing number of users in sports and prolonged usage of these devices, which continuously capture large volumes of physical activity data. The substantial volume of data generated in sports and physical activities, combined with distinct concerns compared to medical and health-related information, makes this domain a unique case for blockchain applications. This paper presents a systematic review of blockchain applications in physical activity, exercise-based rehabilitation, fitness, sport, and exercise-based therapeutics (PARFSET). It specifically focuses on examining their quality attributes, including privacy, security, accountability, personalization, adherence, and extensibility. Our objective is to establish a foundational understanding of the benefits of a blockchain in PARFSET domains, particularly following the decline in initial hype for blockchain technology. We aim to provide a clearer perspective on potential applications, future advancements, and research directions. To this end, we assess the maturity levels of blockchain adoption in these areas and highlight specific examples where a blockchain contributes to enhanced data protection, user-centered customization, trust through accountability, and system scalability. Additionally, we present a hypothetical illustrative case to demonstrate how blockchain applications and their quality outcomes can be effectively integrated. Finally, the paper explores the challenges associated with blockchain implementation and outlines potential directions for future research.

Vowel-based voice analysis is gaining attention as a potential non-invasive tool for COPD classification, offering insights into phonatory function. The growing need for voice data has necessitated the adoption of various techniques, including segmentation, to augment existing datasets for training comprehensive Machine Learning (ML) modelsThis study aims to investigate the possible effects of segmentation of the utterance of vowel "a" on the performance of ML classifiers CatBoost (CB), Random Forest (RF), and Support Vector Machine (SVM). This research involves training individual ML models using three distinct dataset constructions: full-sequence, segment-wise, and group-wise, derived from the utterance of the vowel "a" which consists of 1058 recordings belonging to 48 participants. This approach comprehensively analyzes how each data categorization impacts the model's performance and results. A nested cross-validation (nCV) approach was implemented with grid search for hyperparameter optimization. This rigorous methodology was employed to minimize overfitting risks and maximize model performance. Compared to the full-sequence dataset, the findings indicate that the second segment yielded higher results within the four-segment category. Specifically, the CB model achieved superior accuracy, attaining 97.8% and 84.6% on the validation and test sets, respectively. The same category for the CB model also demonstrated the best balance regarding true positive rate (TPR) and true negative rate (TNR), making it the most clinically effective choice. These findings suggest that time-sensitive properties in vowel production are important for COPD classification and that segmentation can aid in capturing these properties. Despite these promising results, the dataset size and demographic homogeneity limit generalizability, highlighting areas for future research. Trial registration The study is registered on clinicaltrials.gov with ID: NCT06160674.

Objective This study explores children’s and adolescents’ experiences and opinions of routine inquiries about violence within specialised outpatient care. Utilising a mixed method with a convergent parallel design, the research combines quantitative data from 184 respondents aged 6–17 collected through survey data and qualitative interviews with four participants aged 7–14. The data presented is a byproduct of an ongoing research project that evaluates a questionnaire designed to ask children about violence. Results Findings indicate that most children and adolescents view routine questioning about violence positively or neutrally. The study highlights the importance of healthcare professionals’ responses to disclosures of violence, emphasising that supportive and empathetic reactions can impact children’s willingness to disclose such experiences in the future. The results underscore the necessity for routine inquiries about violence in healthcare settings to ensure that affected children receive appropriate support and intervention.

Similarity-based analysis is a powerful and intuitive tool for exploring large data sets, for instance, for revealing patterns by grouping items by similarity or for recommending items based on selected samples. However, similarity is an abstract and subjective property which makes it hard to evaluate by a purely computational approach. Furthermore, there are usually several possible computational models that could be applied to the data, each with its own strengths and weaknesses. With this in mind, we aim to extend the research frontier regarding what impact the choice of a computational model may have on the results. In this paper, we target the scope of embedding-based similarity calculations on text documents and seek to answer the research question: “How can a better understanding of the continuous similarity distribution captured by different models lead to better similarity calculations on document sets?”. We propose a new and generic methodology based on similarity network comparison, and based on this approach, we have developed a computational pipeline together with a prototype visual analytics tool that allows the user to easily assess the level of model agreement/disagreement. To demonstrate the potential of our method, as well as showing its application to real world scenarios, we apply it in an experimental setup using three state-of-the-art text embedding models and three different text corpora. In view of the surprisingly low level of model agreement regarding the data, we also discuss strategies for handling model disagreement.

Text-to-music generation integrates natural language processing and music generation, enabling artificial intelligence (AI) to compose music from textual descriptions. While AI-enabled music generation has advanced, challenges in aligning text with musical structures remain underexplored. This paper systematically reviews text-to-music generation across symbolic and audio domains, covering melody composition, polyphony, instrumental synthesis, and singing voice generation. It categorizes existing methods into traditional, hybrid, and end-to-end LLM-centric frameworks according to the usage of large language models (LLMs), highlighting the growing role of LLMs in improving controllability and expressiveness. Despite progress, challenges such as data scarcity, representation limitations, and long-term coherence persist. Future work should enhance multi-modal integration, improve model generalization, and develop more user-controllable frameworks to advance AI-enabled music composition.

In numerous countries, electricity Distribution System Operators (DSOs) function as local monopolies. To counter potential abuse of monopoly power, regulators, especially in Europe, often employ mechanisms like DSO-specific revenue caps to encourage cost reductions among regulated DSOs. Despite its widespread use, literature concerning ex-post evaluation of the effectiveness of revenue cap regulation, particularly divided into its individual components, is lacking. This paper offers two contributions: First, it shows the advantages of utilizing a semi-parametric panel data StoNED framework methodology as a tool for assessing the impact of revenue caps by evaluating the cost efficiency of regulated DSOs in its individual components. Second, the effectiveness of revenue cap regulation is assessed using the Danish DSOs as a case study. The empirical analysis finds evidence that part of the revenue cap incentive scheme appears to promote cost reductions among regulated Danish DSOs. JEL Classification: C14, C23, C51, L43, L51, L94, L98

This paper examines the transition to electric mobility (e-mobility) in Kisumu, Kenya’s third-largest city, focusing on the enablers, progress, barriers, and impacts of e-mobility initiatives in a secondary African city. In alignment with Kenya’s commitment to a green economy, Kisumu has emerged as a key site for experimenting and implementing e-mobility solutions aimed at lowering greenhouse gas emissions while addressing critical transportation and energy challenges. These interventions are essential in the city’s transition towards sustainable urban mobility. The study evaluates key projects which have introduced electric motorcycles and off-grid solar-powered charging hubs in urban and peri-urban regions. The overall goal of these initiatives is to mitigate the adverse environmental footprints of fossil-based vehicles while providing socioeconomic benefits to local operators such as cost reductions and job creation. Using a mixed-method approach of systematic literature review, data collection, and case study evaluations, the paper outlines the progress of e-mobility initiatives in Kisumu highlighting successes, challenges and impacts. It reveals that e-mobility has made some contribution to emissions reductions and financial gains for boda operators while significant hurdles include inadequate infrastructure, high upfront costs, and regulatory shortfalls. The paper concludes with recommendations on how to enable the scale-up of e-mobility initiatives in Kisumu, offering important lessons for secondary cities across sub-Saharan Africa that aspire to integrate e-mobility in their sustainable urban development efforts.