This article explores architectural space in residential care in Sweden during the Covid-19 pandemic, when residential care was subject to spatial strategies. Community, which is at the heart of the predominant collective care model and underpins the design of architectural space, was identified as a potential source of contagion and thus restricted by spatial measures. Lefebvre's spatial triad is the theoretical backbone of this study. The research design is an intuitive enquiry and qualitative data collection methods include interviews, observations and analysis of drawings. The lived space of staff and residents is primarily investigated in semi-structured individual interviews, and there is particular focus on their experiences of spatial strategies. Findings show that the collective care model and residents' everyday lives changed significantly when the virus entered residential care, creating a different lived space. The most common spatial strategies were isolation, social distance and managing movements in line with existing research. These were enacted in existing spatial conditions. The study findings challenge the value of community in the collective care model. For instance, the staff found it very awkward to isolate the residents in their flats, whereas the residents themselves did not view life during the pandemic as very different from normal, everyday life. The findings also challenge the relevance of the architectural models that are in use today. The article concludes that it is necessary to develop new architectural models, a caring architecture in which handling epidemics is less strenuous, and where residents' diverse wishes can better be met.

Objectives: Crime prevention strategies often rely on the small set of micro-places where crime is most concentrated, the so-called hotspots, yet it has remained unclear how close existing hotspot detection methods come to the maximum coverage theoretically possible. This study introduces GraphVenn, the first algorithm that identifies the globally optimal placement of N fixed-radius hotspots directly from the empirical crime distribution, without relying on heuristic or approximate approaches.

Methods: GraphVenn was evaluated on three years of crime data from Malm & ouml;, Boston, and New York City (in total 1.75 million crimes) and compared against kernel density estimation (KDE), greedy PAI maximization (PAI-Max), and GraphTrace. Both the globally optimal and the greedy (fast approximation) modes of GraphVenn were evaluated across different spatial resolutions, demonstrating scalability to large urban datasets.

Results: In optimal mode, GraphVenn identified the absolute maximum coverage of incidents achievable under fixed-radius constraints. The greedy variant reached within 0.1-1.9% of this optimum while reducing runtimes by up to two orders of magnitude. By contrast, existing methods consistently fell short, e.g., in New York City the optimal GraphVenn captured 51,522 crimes within its top-100 hotspots compared to 35,098 with KDE and 28,241 with GraphTrace, while PAI-Max was excluded due to its runtimes. In practical terms, the baselines therefore missed between 16,000 and 23,000 crime incidents that could have been covered.

Conclusions: Globally optimal detection of fixed-radius hotspots that maximize the distinct crime count is now computationally feasible at city scale. GraphVenn offers (i) a practical tool for researchers, law enforcement, and crime analysts to identify the most effective fixed-radius hotspot locations with confidence that no better configuration exists, and (ii) a benchmark for evaluating approximate methods against the true maximum crime count. Open-source code is provided to support replication and further research.

Healthy aging implies physical and psychological well-being, maintaining social relationships and engaging in meaningful activities. Thus, socially inclusive initiatives that address loneliness are needed. The aim of this ethnographic study was to describe interpersonal interactions in a community-based activity program targeting loneliness among older adults. Over 10 weeks, 10 participants were observed during interpersonal interactions on 20 occasions, focusing on when, where, and how the interactions occurred. Field and reflective notes were analyzed using an ethnographic approach. The findings show that support was promoted through communication, which created togetherness in the activities by encouraging each other and exchanging knowledge and information. Furthermore, connecting by embracing openness highlighted a willingness and courage to share life experiences and bring memories into conversations. These findings provide valuable insights for designing future activity programs that reduce loneliness and promote social connectedness among older adults. 

Context: Software systems in regulated domains must continually adapt to legal changes, yet practitioners often handle updates manually with limited support, making compliance work costly and error prone. Recent advances in LLMs prompt the question of how automation can reliably assist this process.

Objectives: We aim to (1) characterize the nature of regulatory changes and derive a systematic taxonomy, (2) understand through the lens of practitioners where automation is most useful, and (3) assess the feasibility of using LLMs for detecting and classifying regulatory changes.

Method: We conducted a mixed-methods study grounded in the German social security (DEÜV) in collaboration with practitioners from a FinTech company. First, we developed a taxonomy of regulatory changes through manual document analysis of four Regulatory Implementation Specifications (RIS), followed by a workshop and expert interviews. Second, we validated the taxonomy and elicited challenges through semi-structured practitioner interviews. Third, we built a gold-standard dataset of 93 annotated change instances and evaluated seven state-of-the-art LLMs within an automated detection and classification pipeline.

Results: The taxonomy defines five change scopes and four optional context dimensions. Practitioners found it intuitive and useful for filtering relevant changes, particularly Data and Field updates, but reported challenges such as tight deadlines, legal ambiguity, limited traceability, and overlapping categories. In automation, proprietary LLMs performed best, while performance dropped on narrative or weakly structured documents, highlighting sensitivity to document format.

Conclusion: The proposed taxonomy provides a practical lens for organizing regulatory change information, and LLMs can support the identification and classification of recurring, structurally explicit changes. Their limitations on context-dependent and infrequent categories suggest that automation should complement, rather than replace, expert assessment, motivating future work on human-in-the-loop compliance tooling across broader regulatory ecosystems. 

Internal hackathons are widely used to drive innovation and collaboration in software companies, yet not all succeed. Many fall short due to low employee engagement. Why do some employees dive in, while others hold back? This article explores what drives and hinders engagement based on insights from five companies practicing regular hackathons. Using Social-Cognitive Theory, we show that engagement depends on two key beliefs: one’s ability to contribute (self-efficacy) and the value of participation (outcome expectations). These drivers are not arbitrary; they are predictable, and thus manageable. By understanding the psychology behind engagement, organizers can create hackathons that help more employees to feel confident and inspired to participate. We offer practical guidance to make your next hackathon truly engaging. 

Background: As paediatric day surgery becomes increasingly common, postoperative care is transferred to the home setting where parents play a central role in assessing and managing their child's postoperative pain. No validated Swedish instrument currently exists to support parents in evaluating their child's pain at home. Aim: To assess the reliability and validity of the Swedish translation of the PPPM-S in children aged 2–12 years during the first two postoperative days.

Method: The instrument was earlier translated from English into Swedish in accordance with the WHO Guidelines for translation and adaptation of an instrument. A backward-forward translation was done with a bilingual expert panel, and cognitive interviews were done in the target population. This study was conducted at three Swedish hospitals between 2022 and 2025 involving 80 parents of children aged 2–12 years who underwent day surgery. Parents completed the PPPM-S on postoperative days 1 and 2. To evaluate the accuracy of the instrument, the results were compared with scores from an established pain rating scale, the Coloured Analogue Scale (CAS).

Findings: PPPM-S demonstrated good psychometric properties: good internal consistency (Cronbach's alpha 0.842–0.851) and satisfactory criterion validity demonstrated by strong correlations with CAS (Spearman's rho = 0.683–0.630, p < 0.01). ROC analysis identified 5/15 as an optimal cut-off, with acceptable sensitivity and specificity. Parents reported high levels of satisfaction and found it easy to use at home.

Conclusion: The PPPM-S is a valid and practical tool for assessing children's postoperative pain at home. It can help parents better understand and evaluate their child's pain, potentially improving postoperative care in the home setting. 

Organizations often migrate monolithic architectures to microservices based on ad hoc data, expert opinions, or industry trends without assessing their specific context and needs. Such transitions tend to coincide with increased architectural complexity and technical debt (TD), making it crucial to understand how TD evolves over time in industrial settings to manage it effectively. This observational study explores the evolution of technical debt density (TDD) in a single software product consisting of both monolithic and microservice architectures at a Swedish fintech company, without aiming to establish causality between architectural styles and TDD trends. We further investigate TDD trends across various microservice size categories, team types, and the relationship between size and TDD. We analyzed SonarQube TD data collected from one monolith and 78 microservices from August 2022 to December 2024, and conducted semi-structured interviews with practitioners (a development manager, a product owner, and a lead developer) to validate and contextualize the quantitative findings. Our results show that, in this case, the monolithic system exhibits a decreasing TDD trend over time despite continued growth in size, while a gradual increase in TDD is observed across microservices. Furthermore, TDD trends appear inconsistent among small microservices, more consistently growing in medium-sized microservices, and comparatively stable in larger services. Differences in TDD trends are observed across services owned by platform teams and product teams. Overall, the findings from this specific case suggest that TDD evolves differently in monolith and microservices, highlighting the importance of continuous monitoring and context-aware interpretation of TDD trends in practice. 

This article highlights the urgent need to explore how transformative planning can be put into practice and what competencies urban and regional planners need to facilitate this transition. There is a consensus that traditional planning, with its rigid and structured approaches, is inadequate to address the challenges of climate change. Instead, more proactive, transformative planning practices are needed. This study presents the results of a questionnaire conducted among planning managers in Swedish municipalities, aiming to deepen the understanding of how municipalities can achieve climate neutrality by identifying the necessary competencies among planners. The results identify five competence categories: technical knowledge (including climate adaptation), broad knowledge in various areas, analytical skills, coordination and cooperation, and communication. Our findings thus suggest that current planning practices in Swedish municipalities are largely traditional rather than transformative, but also that the focus is on adaptation rather than mitigation. We argue that this can partly be explained by the concept of obduracy, and how frames and persistent traditions affect the way planning managers think of the future, making it difficult to envision a future beyond current challenges and demands, but also the importance of thinking more visionary about planning to achieve climate neutrality and transformations. 

This paper proposes SCENTS, a Streaming Consensus Embedding for Time Series that fuses streaming windows into a single low-dimensional representation suitable for diverse downstream tasks. First, it learns a denoised low-dimensional latent basis for state initialisation (). Second, for each newly arriving stream, it performs a near-linear time, multi-pass consensus update that fuses new affinities directly into Z. We prove convergence and validate our assumptions using various real-world multi-source industrial datasets on a streaming consensus clustering task. In contrast to conventional pipelines that accumulate noise over time, require costly  co-association matrices, or  eigendecompositions, SCENTS yields a linear memory consensus embedding that improves monotonically along the streaming horizon and produces high-quality partitions. Moreover, SCENTS is designed to be integrated with any fixed encoder, enabling the implementation of a lightweight streaming consensus adaptation layer. The resulting embeddings offer a compact, reusable representation that supports various downstream tasks beyond clustering, thereby providing a scalable and generalisable fusion layer for data analysis.

This article builds on the argument that design for complex interactive systems should shift from creating transactional interactions to ‘organizing relational complexity’. Grounded in agential realism, we reframe computational agents from black-box predictors to material-discursive apparatuses. We utilize a standard reservoir computing architecture, conceptualized here as the Affective Reservoir, as a diffractive instrument to visualize the co-constitution of gameplay. In doing so, we replace the teleological concept of a fixed ‘goal’ with the agential realist concept of a ‘yearning’: the continuous negotiation of situated tension. By analyzing the reservoir’s dynamics, we show how coherent regimes of interaction emerge within the agent’s internal state space, not from error minimization but from Dynamical Friction; the intense interference pattern generated when the agent’s Re-membered Inertia (habitual momentum) resists the Affective Gradients (situational forcing) of its environment. Ultimately, we argue that by orchestrating an agent’s capacity to be affected via its resistance to and resonance with the environment, designers can move beyond transactional logic to sustain emergent relational phenomena.

This article builds on the argument that design for complex interactive systems should shift from creating linear transactional interactions toward organizing relational complexity. Grounded in Karen Barad’s agential realism, we argue that a designer’s role can benefit from not predefining interactions but from curating the material-discursive conditions under which meaningful relations can emerge. To explore the empirical and temporal dimensions of this practice, we conducted an exploratory workshop setting the conditions for emergent gameplay dynamics and discussions on agential realist anticipation. Participants utilized a custom-designed game and built their own physical controllers to anticipate and adapt to shifting gameplay conditions. Our results demonstrate how alterations in relational constraints, rather than explicit pre-programmed goals, drove the emergence of non-predefined gameplay rhythms. The findings provide empirical grounding for an agential realist understanding of anticipation, showing that an interactive system’s identity lies in its unfolding processual patterns rather than a static final state. Based on these findings, we propose three design principles for further exploration: Design for Relational Emergence, Design for Re-membering, and Design for Emergent Patterns. Consequently, we conclude by outlining a conceptual approach for non-linear computational architectures, drawing on principles from Enactive AI and reservoir computing.

En examination där studenterna blir medskapare av tentamen med hjälp av artificiell intelligens (AI), är det möjligt? Bedömning av studenters lärande är en central del av högre utbildning. Det ska vara väl valda examinationer för att kunna bedöma att lärandemålen är uppnådda. Examinationsupplägg diskuteras därför regelbundet av alla som är verksamma inom högre utbildning. Ett annat ämne som diskuterats flitigt är användningen av AI som under de senaste åren både har skapat nya möjligheter och nya utmaningar. Under hösten 2025 utvecklades och utvärderades ett nytt upplägg för en examination i en kurs om sjukdomslära, där lärande och examination skapas gemensamt av studenter, lärare och AI. I denna artikel delas erfarenheterna från detta nya studentaktiva upplägget, både från lärares och studenters perspektiv

Efficient task scheduling remains a key challenge in High-Performance Computing and Internet of Things (IoT) systems, where the sequential execution of nested loops often limits parallelism. This paper proposes a hybrid approach that dynamically parallelizes nested loops in heterogeneous IoT environments. The suggested method (PSOALS) combines Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and wave-angle scheduling to model nested loops as two-dimensional iteration spaces and minimize communication overhead. By encoding loop iterations as particles and using a dependency-aware fitness function, PSOALS enhances makespan, resource utilization, and scalability. The key contributions of this work include: a dynamic scheduling framework for efficient loop parallelization and dependency management, a wave-angle scheduling mechanism to improve task execution order by balancing load and communication delays, and the integration of mutation and diversity techniques to enhance the quality of the solution. Experimental results across various IoT configurations show that PSOALS outperforms block-based, cyclic, and GA-based scheduling methods in convergence speed, stability, and execution time. The proposed approach offers a scalable and adaptive solution to future IoT challenges, including real-time processing, energy efficiency, and large-scale deployment.

This paper presents a psychophysiological analysis using galvanic skin response (GSR) signals recorded during a subjective quality assessment of 360° videos viewed on head-mounted displays (HMDs). During the test, participants provided opinion scores based on their perceived video quality. Participants were grouped by their prior virtual reality (VR) experience level into experts, sometimes used, and never used. Additionally, participants whose ratings deviated significantly from the majority were classified as outliers, and their GSR data were also analyzed. The novelty of this work lies in the comparative analysis of GSR responses across different VR experience levels, including the outliers during subjective quality assessment tests for 360° ' videos. Results show that experts exhibited lower GSR amplitudes and fewer peaks, indicating reduced emotional or cognitive arousal. Conversely, participants with sometimes used and never used showed elevated physiological responses and peaks compared to experts. While the outlier group reported the highest number of peaks, it also showed statistically significant differences between the experts and sometimes used experience levels. These findings suggest that prior VR experience influences subjective quality assessments and that GSR metrics can serve as a valuable indicator for participant screening and refining experimental design in immersive multimedia evaluations. 

Context Boundary artefacts are shared artefacts that support collaboration by allowing different groups to interpret the same information in different ways. Software development activities benefit from them, as a single artefact can support stakeholders across different organisational boundaries. When these artefacts contain inconsistencies, such as incorrect information, practitioners' trust in them may decrease, leading to inefficiencies in task execution.

Objective This study developed and evaluated a guideline to support the creation of boundary artefacts in software engineering contexts.

Method We conducted a longitudinal, multi-phase study embedded in an industrial setting. The guideline was developed based on a literature review and prior findings from a previous case study and was then submitted for practitioner evaluation. A post-implementation analysis of the guideline was carried out after a period without researcher intervention.

Results Our guideline consists of 10 principles grouped into three categories: (1) Scope: stakeholders, boundaries, and terminology; (2) Structure: artefact format, transference, granularity, and additions; and (3) Management: evaluation, ownership, governance, and integration. Practitioner evaluations suggested that these principles support the creation of reliable, predictable, and functional boundary artefacts. However, practitioners also noted challenges during use, including the time-consuming nature of the activity and difficulties in understanding the concept of boundary artefact.

Conclusions Overall, the guideline was well received. After the non-intervention period, it was adopted as a standard by the partner company for artefacts such as security testing, standards documentation, and requirements specifications. Adoption challenges persisted, including cultural barriers and comprehension issues. Further applications across different artefacts could clarify how the principles influence their reliability, functionality, and predictability.

This paper proposes SHIODEG, a hybrid metaheuristic that integrates the success-history intelligent optimizer (SHIO) with differential evolution (DE) and a Gaussian transformation (GT) to tackle two persistent challenges in optimization for engineering design: (i) the absence of a universally best optimizer across problem classes (as implied by the No-Free-Lunch perspective) and (ii) the limited ability of purely gradient-based methods to produce substantial improvements in complex, constrained, and often non-smooth real-world problems, motivating hybrid strategies that balance exploration and exploitation. SHIODEG follows a staged search process in which DE generates diverse trial solutions, GT injects normally distributed perturbations to reduce premature convergence and diversity collapse, and SHIO refines promising regions using success-history guidance from the best three leaders. SHIODEG is evaluated on the IEEE CEC2022 benchmark suite (12 functions) using 30 independent runs, a population size of 100, and a budget of 1000D function evaluations. The results show that SHIODEG consistently delivers top-tier performance across the benchmark suite, showing strong competitiveness, low variability, and statistically significant improvements over a wide range of alternative optimizers. It also demonstrates robust effectiveness on multiple constrained engineering design problems, achieving high-quality solutions across diverse real-world constraints.

Lyme borreliosis (LB), the most common European tick-borne disease, can manifest as an erythema migrans (EM) rash or as disseminated LB. The prospective Burden of Lyme Disease (BOLD) study evaluated the frequency of LB clinical manifestations, including signs, symptoms, and treatment patterns in 14 healthcare practices in endemic regions of six European countries: the Czech Republic, Germany, Poland, Slovakia, Slovenia, and Sweden. Between April 2021 and December 2022, patients with suspected LB were evaluated using predefined case definitions that were applied by investigators to identify medically attended LB cases. Enrolled cases were interviewed about their symptoms. Among the 797 LB cases, 615 (77.2%) had EM and 182 (22.8%) had disseminated disease; 154 of the disseminated cases had Lyme arthritis (LA), five had Lyme neuroborreliosis, and three had Lyme carditis. Geographically, the proportion of disseminated disease varied by country, from 1.1% in Slovenia to 78.0% in Slovakia. Overall, 76.3% of all LB cases in Slovakia were LA. Antibiotic use varied by country, although every country prescribed doxycycline. The frequency of LB manifestations varied substantially between countries. EM was the most common manifestation in all countries except Slovakia, where LA was most common. This study underscores the need for improved prevention strategies.

Structural software testing is an essential stage in the software development lifecycle, where achieving high coverage and fault detection remains a significant challenge. Manual testing is costly and inefficient for a program with a large number of modules and functions. Automated test data generation addresses this issue, but its effectiveness depends on the optimization strategies used. This study introduces a novel hybrid optimization algorithm that combines the Gray Wolf Optimizer (GWO) and Particle Swarm Optimization (PSO) to cover deep paths of the program and generate efficient test data. By balancing exploration and exploitation through the proposed hybrid PSO-GWO approach, this method adapts well to programs of varying size and complexity. The proposed method was evaluated on 26 standard benchmark programs. Experimental results demonstrate its superior performance, achieving 88.37% coverage, which is higher than the state-of-the-art methods, and a mutation score of 67.45%, reflecting improved fault detection capability. Moreover, it produces fewer test cases and executes an average of 1257.7 s, approximately half the time required by GA, GWO, and PSO individually. In this study, the symmetric and asymmetric structural aspects of program control flow and execution paths are analyzed to generate automated tests. The suggested deep path coverage technique uses optimization principles based on symmetry to achieve effective and reliable structural testing of software. Overall, the proposed hybrid algorithm delivers test data that is smaller, faster, and more effective. The proposed method is a reliable and efficient test generator compared to the state-of-the-art methods.

A hybrid metaheuristic methodology that combines the Red-billed Blue Magpie Optimization (RBMO) algorithm with the Aquila Optimizer (AO) is introduced in this work as the RBMOAO method. The novel algorithm addresses a critical shortcoming of the standard AO: its exploration-to-exploitation ratio across different optimization stages is inefficient, yielding premature convergence and low diversity within the population. This is achieved by using RBMO's Group-Based Directional Perturbation (GDP) and its dynamic convergence factor (CF) as part of the methodology. The early stages of the optimization process are characterized by a grouping methodology to maintain population diversity through coordinated exploration across subgroups of varying sizes using GDP. Later iterations are characterized by a CF-guided updating process that increases the resolution of the search for the best areas, thereby improving convergence precision without sacrificing solution quality. Empirical testing of the proposed methodology using the CEC 2015 and CEC 2020 test sets demonstrated RBMOAO's superior performance compared to other metaheuristics, outperforming other optimizers in 73.33% of CEC 2015 functions and 80% of CEC 2020 functions, with statistical significance in the increased precision and robustness of solutions across all problem types. Additionally, the RBMOAO methodology demonstrated outstanding performance in constrained engineering design problems. In addition to optimization, an RBMOAO-optimized ensemble architecture was implemented to predict cybersecurity intrusion threats, achieving an accuracy of 89.6%. Through the dynamic calibration of the base learner weights via metaheuristic search, the RBMOAO ensemble achieved the top ranking. These results illustrate the wide range of applications of the RBMOAO methodology and provide support for its deployment in the context of high-stakes predictive analytics.