Background: Software is at the core of most scientific discoveries today, and the reliability of research results increasingly depends on the quality of the software that underpins them. However, research software is often developed under constraints that prioritize scientific progress over engineering rigor, leaving little to no incentive for maintenance, documentation, or quality assurance.

Objective: This study examines whether embedding an existing research software into a software testing course can contribute to improving the quality of the research software and identifies the associated challenges.

Method: In an in vivo experiment, we embedded a large-scale network simulation into a university course on software testing at Blekinge Institute of Technology, Sweden, as a group project and observed the effects on the research software.

Results: We found that the research software benefited from the embedding through substantially improved documentation and fewer hardware and software dependencies. However, the embedding required significant additional effort from us, and although the student teams produced thoughtful and well-designed test suites, none of their code contributions could be merged into the research software due to uncertainties around intellectual property.

Conclusion: We strongly believe that embedding research software engineering activities into teaching can enhance the quality of research software while providing students with exposure to research. However, the uncertainty about the intellectual property of students’ code contributions substantially limits its potential. © (2026), (Ubiquity Press). All rights reserved.

Hydrometeorological time series are inherently stochastic and exhibit temporal dependence, commonly modeled using Gaussian autoregressive moving average (ARMA) models. However, the normality assumption is often too restrictive for environmental variables such as streamflow, which are nonnegative and right-skewed. We propose the Wei-ARMA model, a new class of ARMA models based on the Weibull distribution that incorporates ARMA components, external regressors, and a link function. A parametric trend test is also introduced, with parameters estimated via the conditional maximum likelihood method. Monte Carlo simulations assess finite-sample performance. An application to streamflow data from the Vacacaí River, Brazil, shows that the proposed model captures key statistical features, avoids unrealistic negative predictions, and outperforms the Gaussian ARMA. Mean absolute percentage errors in in-sample prediction are reduced by 23%, 34%, and 9% for mean, maximum, and minimum monthly streamflow, respectively, relative to the Gaussian ARMA model. The proposed trend test successfully detects monotonic trends. 

This study investigates the application of Nielsen’s 10 usability heuristics in the design of digital Sustainable Product Development (SPD) tools using a participatory design approach. Participants included representatives from three companies and SPD experts from academia. During the workshops, the ten usability criteria were presented, after which participants engaged in a brainstorming session to generate practical recommendations for SPD tool design based on their professional experience. The results suggest that SPD practitioners consider these heuristics relevant for SPD tool development. Future research will evaluate the effectiveness of integrating these guidelines into the design process and ongoing improvements to SPD tools.

Defense- and military-related R&D has shifted from component-centered hardware engineering toward software-defined, modular and continuously upgraded system architectures. This shift changes the nature of defense-to-civilian spillovers. We develop and probe a framework distinguishing between product spillovers, competence spillovers (know-how, system competence) and spin-in (civil-origin technologies integrated into defense systems). Using the SAAB Gripen fighter aircraft program (Gripen E) as a case, we combine survey data and interviews to assess the nature, magnitude and frequency of spillovers generated by the program. We find economically important competence spillovers (technology development benefits, quality-system tightening, production process improvements), while more traditional product spillovers are limited. The main conclusion is that programs based on modern digital systems-of-systems generate significant societal value primarily through spillovers of systems competence. These are economically significant but harder to observe and measure than traditional spillovers.

The overarching purpose of this pre-study report was to investigate the sustainability potential in designing so called ’energy hubs’ – a scalable concept for energy use, renewable energy production and energy storage. This could from time to time give local surplus of free renewable electricity. The american independent think-tank ReThinkX has named this situation SuperPower. More specifically this pres-study aimed to:

• Establish collaboration and partnerships around the energy hub concept.

• Identify criteria for where and how energy hubs should be built.

• Prepare for a future implementation project.

The methodology builds on the established Framework for Strategic Sustainable Development (FSSD) and its generally applicable sustainability principles (SPs). This was done to be able to plan for energy systems in full compliance with socio-ecological sustainability while ensuring sufficient economic returns on the way there. The pre-study was performed in four steps according to the ABCD procedure of the FSSD:

• Step A. Develop a vision for energy hubs within the energy system and the sustainability principles of the FSSD:

• Step B. Map the current reality of the energy hubs in relation to the vision.

• Step C. List possible solutions that can enable the vision.

• Step D. Develop a preliminary step-by-step plan for an implementation project focused on a methodology for energy hub development within a future sustainable energy system.

The results can be summarised by that:

• Energy production, energy storage, and energy use can no longer be seen as separate from each other, but must be regarded as parts of a complex integrated system. Energy users in industry, transport, and the construction and housing sector at all levels should first improve efficiency as much as possible in order to ensure efficient use of resources quickly and at low cost.

• At the same time, individual energy users such as buildings and electric vehicles can contribute their own energy production and storage and become self-sufficient units that are connected to support the whole electricity grid. This would make the whole country become an integrated energy system — a self-regulating system that, once built, can supply society with cheap renewable energy from continuous natural flows. Such a system would also, compared to today’s centrally controlled system, be much less vulnerable to military attacks, climate change, and other disturbances. It would therefore also provide advantages from a security-policy perspective in the future.

Numerous individuals grapple with chronic or recurrent pain, prompting the exploration for non-pharmacological remedies in human-computer interaction. Redirecting attention from nociceptive signals can effectively alleviate pain, but the path through fine tactile perception is rarely explored. This paper introduce Electronic Tactile Flow, a novel interaction paradigm utilizing an 8*8 electrotactile array to modulate pain through cognitive engagement and Flow theory. We investigated the analgesic effects of top-down (goal-directed) versus bottom-up (stimulus-driven) attention. Results from our user study (N=42) indicate that top-down engagement significantly reduces pain perception compared to passive stimulation. Furthermore, we implemented an adaptive difficulty mechanism that sustains users in an optimal Flow state, which was found to amplify pain relief and immersion. This work presents the first integration of electrotactile interfaces with attentional modulation, offering a promising framework for designing personalized, cognitively interactive pain interventions in HCI. 

This article is an account of a 5-year-long process of creating an AI-based tool for use in our university library to support work at the information desk, especially when it is unmanned. Our first idea of creating a machine that could answer recurring simple questions at the library's information desk developed out of frustration. Information desk work was increasingly being reduced to questions such as "do you have this book?" or "how can I get a library card”. We started out 2019 by creating a chatbot that embraced Artificial Intelligence (AI) and natural language processing (NLP), which was connected to several database sources.In 2021, we purchased a "social robot head" that mimics human faces, and dialects, and can be set to imitate different accents.It was challenging converting from chatbot-typed questions to speaking to voice recognition. The problems of misunderstandings and misinterpretations are extensive, but at the same time the advantage of using natural language is significant for the user.During a student survey, we realized that the rules processed in the Microsoft Luis natural language system often interfered with the intentions received from the Microsoft QnA maker. This problem was solved in 2023 when we were able to integrate Chat GPT into our project. This move improved the performance of our social robot. It is now faster, more reliable, can respond much better to “social” questions”. It searches successfully for book titles, authors, subjects, can filter out digital or physical books according to preference and help students with library cards and related questions.During our study, we noted that students can be hesitant to ask questions out loud to a robot in the library. We therefore would recommend using a social robot in a closed environment, like a pod, while simultaneously offering students the opportunity to ask questions via a keyboard.

Stroke detection and classification from computed tomography (CT) remains a critical and challenging task in medical imagingdue to the complexity of lesion patterns, noise variations and unbalanced datasets. In this study, we propose a novel hybriddeep learning model, StrokeFuse-AttnNet, which integrates both global (ResNet50) and local (DenseNet121) convolutionalfeature extractors with a self-attention mechanism to improve spatial focus and semantic interpretability. A hierarchical featurefusion strategy concatenates multi-scale features, which are then processed by a self-attention module to highlight key strokeregions and reduce irrelevant activations. We use data augmentation and SMOTE on training samples to address imbalanceand improve generalization. The proposed model was evaluated on both publicly and privately available brain CT datasets.StrokeFuse-AttnNet achieved an accuracy of 98.27% and an AUC of 0.983 on the public dataset and an accuracy of 96.04%and an AUC of 0.9501 on the private dataset. The results show that the model has higher accuracy, reliability and generalizationthan existing and baseline methods. The proposed model is lightweight, with only 32 million parameters and can be used inreal-time clinical diagnostic processing systems that require 40 GFLOPs. The model has the potential to support radiologistsin the efficient and rapid diagnosis of strokes on non-contrast CT images.