This study addresses the persistent challenges that students face in understanding redox reactions, particularly the link between symbolic and particulate representations in electrochemistry. The purpose was to explore whether physical modeling with clay could enhance the conceptual understanding of electron transfer and oxidation-reduction processes. Two groups of Swedish upper secondary students participated in instructional sessions: Group A used clay models to visualize electron movement, while Group B relied solely on symbolic notation. Data were collected through a written test and follow-up interviews. Results indicate that Group A outperformed Group B in tasks involving metal displacement and identifying the number of electrons transferred in a more complex reaction combining redox and acid-base processes. However, differences were minimal in synthesis reactions and fundamental conceptual questions. Both groups exhibited widespread alternative understandings, although Group A demonstrated fewer alternative understandings and greater accuracy in applying the concept of charge. The findings suggest that clay modeling can support the visualization of electron transfer and reduce alternative understandings but does not independently foster deeper conceptual understanding. The effective integration of modeling with explicit instruction on particulate-level reasoning and scientific terminology is essential in bridging representational gaps in electrochemistry.

This study aimed to analyze the nature, scale, and consequences of cyberattacks on critical cyber-physical systems in Ukraine over the past decade, using a methodology based on classifying attacks by type, threat actor (including Russian hacking groups Sandworm, Fancy Bear, and Ember Bear responsible for half of the 22 analyzed incidents), target sector, and temporal patterns. It also included comparative analysis of cyber defense strategies. The Chinese group Volt Typhoon also demonstrated high risk through living-off-the-land techniques. While phishing remained the primary attack vector (7 cases), sophisticated supply chain attacks like NotPetya caused significant damage, with the energy sector being most targeted (7 incidents) due to its strategic importance. Six attacks involved manipulation of Industrial Control Systems/Operational Technology protocols, while four employed destructive wiper malwares. The Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privilege (STRIDE) analysis of digital platforms concluded that modern challenges require innovative solutions like Cybersecurity Mesh Architecture, digital immunity systems, and artificial intelligence, along with international coordination, while addressing barriers such as legacy systems, workforce shortages, and regulatory fragmentation, ultimately providing an evidence base for improving cybersecurity strategies at national and international levels. 

Background: Relatives play a crucial role when palliative care is provided at home. More advanced care at home places higher demands on relatives, taking great responsibility, facing challenges, and often lacking adequate knowledge and skills to provide care. Therefore, relatives need support from healthcare professionals, yet do not receive the needed support. This study aimed to elucidate relatives’ experiences of support from healthcare professionals before and after a patient’s death when general palliative care is provided at home.

Methods: A phenomenological hermeneutical method was used. The inclusion criteria were relatives of people who had died, involved in general palliative care at home. The sample consisted of 14 adult relatives involved in general palliative care at home between one week and 12 months. Data were collected through individual interviews between January and May 2025.

Results: Relatives needed to be seen as they felt left out; they felt an overwhelming responsibility; they needed to feel safe at home through guidance from and access to healthcare professionals; they felt the need to know what was happening and what to expect; and they needed help in processing the grief both before and after the patient’s death. These themes formed the main theme: Standing next to but not being part of.

Conclusions: The findings of this study showed a lack of support for relatives before and after the patient’s death but offer insights into what support relatives need from HCPs when general PC is provided at home. Relatives need to feel seen, informed and prepared, to feel safe when care is provided at home, and not feel overwhelmed by the responsibility of the situation. As research continuously reveals that relatives have unmet support needs, this highlights the need for tailored interventions and the targeting of available support actions for improved support. Since relatives play a crucial role in palliative care at home, continued work with education and training for relatives should be prioritised to support them in feeling prepared, obtaining necessary caregiving knowledge and skills, enabling them to cope with the situation at home. 

This paper introduces a new time series model based on the reflected unit Burr XII (RUBXII) distribution that is an alternative to the Kumaraswamy autoregressive moving average and Beta autoregressive moving average models for time series analysis taking values in the standard unit interval. The proposed model describes the conditional median of RUBXII-distributed discrete-time series by a dynamic structure that includes autoregressive and moving average (ARMA) terms, a set of regressors, and a link function. We perform the model's parameter estimation using the conditional maximum likelihood method. Closed-form expressions for the score vector and observed information matrix are presented. We propose and discuss techniques of diagnostic and forecasting for the new model. A Monte Carlo simulation study is carried out to evaluate the finite sample performance of the conditional maximum likelihood estimator. Finally, the proportion of stored hydroelectric energy in Northern Brazil is analyzed through the proposed model. The results evidence that the introduced RUBXII-ARMA model is suitable for describing the dynamics of the data and provides more accurate forecasts for the proportion of stored energy in Northern Brazil than those from competitors' models.

Automatic fault detection and diagnosis (FDD) are critical for maintaining reliable and efficient industrial systems. However, conventional methods rely heavily on manual inspections or threshold-based techniques, which often fail to capture the dynamic patterns in time series (TS) sensor data. As a result, faults persist for extended periods, leading to suboptimal system operations, increased energy waste, and significant economic losses. This work proposes a cross-modal framework that facilitates the efficient deployment of state-of-the-art pretrained vision models for enhanced FDD, with two novel TS-to-image transformations: first, an adapter deep encoder that learns optimal, task-specific representations from raw sensor data while generating outputs that are input-compliant with pretrained models. Second, an enhanced line plot that creates geometric shapes of two related signals. Comparative experiments against fixed methods, including spectrograms, Gramian angular fields, Markov transition fields, recurrence plots, and five deep learning baseline models, showed substantial performance gains across diverse domains. InceptionTime achieved the highest average baseline performance with an F<inf>1</inf> of 88.6%, while the adapter and shapes achieved 94.4% and 92.4%, respectively. The findings highlight the potential of the cross-modal framework for FDD to facilitate early intervention and efficient system maintenance in industrial settings.

The increasing demand for products and services coupled with growing environmental concerns has necessitated a shift towards sustainable product development. Traditional methods often prioritize functionality over environmental impact, leading to resource depletion and waste generation. To address this, as environmental concerns are increasing in importance, innovative solutions are required to integrate sustainability considerations into product lifecycles.

This study investigates the role of Artificial Intelligence (AI) in promoting sustainability within product service systems. A systematic literature review was conducted to identify key AI technologies and methodologies employed across different stages of product development. The analysis focused on the impact of these technologies on environmental sustainability and business performance.

The findings reveal that AI technologies, including machine learning, natural language processing, and virtual prototyping, can significantly enhance sustainability. These tools may optimize product design, reduce material consumption, and minimize environmental impact. Furthermore, AI applications in predictive maintenance, end-of-life management, and energy efficiency contribute to resource optimization and waste reduction.

AI has the potential to transform product service system development by integrating sustainability principles. By optimizing resource utilization, reducing waste, and enhancing decision-making, AI can drive both environmental and economic benefits. While challenges such as data quality and algorithm development exist, the overall positive impact of AI on sustainability is evident.  

Background: Evening technology use (ETU) has been associated with sleep disturbances, often attributed to blue light exposure and cognitive arousal. However, most of the existing evidence focuses on younger populations and relies primarily on subjective measures. As older adults increasingly engage with both passive and active technology use, it is important to investigate how ETU impacts objective sleep. Currently, there is also a limited understanding of how particular evening digital activities, especially active versus passive engagement, affect objective sleep in older adults.

Objective: This study aims to investigate the impact of exposure to ETU on both objective and subjective sleep outcomes in older adults.

Methods: This is a randomized crossover trial involving approximately 55 adults aged 60-75 years from the ongoing Swedish National Study on Aging and Care-Blekinge. Each participant will undergo 3 one-week intervention periods: active ETU, passive ETU, and a nondigital activity (book reading), with one-week washout periods in between. The order of interventions will be randomized. Sleep will be assessed using a home-based electroencephalography device (MUSE headband) and daily self-reports. Primary outcomes are sleep onset latency and wake after sleep onset. Secondary outcomes include objective measures such as total sleep time, sleep efficiency, and time spent in REM, deep, and light sleep, subjective sleep quality, adherence, and perception of the intervention and comfort of using the objective measurement tool, that is, the electroencephalography headband. Linear mixed-effects models (with fixed effects for condition and period and a random participant intercept) were used to analyze crossover effects on sleep outcomes.

Results: Participant recruitment and data collection began in the fall of 2025 and will continue through summer 2026 or until the target sample size is reached. Data collection is scheduled to be completed by spring 2027. Results will include participant flow, baseline characteristics, adherence data, and comparative analyses of the 3 intervention conditions. Within-subject statistical models will be used to evaluate differences in sleep outcomes and investigate the associations between ETU and sleep quality.

Conclusions: This crossover study will clarify how active and passive ETU, compared with a nondigital activity, relate to objective sleep in older adults. Findings will inform simple, practical recommendations for technology use before bed in late life. 

We present a classification framework for saturated Fell bundles over groups, utilizing data associated with their base group and unit fiber. This framework provides a unified perspective on the structure and properties of such bundles and yields key insights into their classification. In the case of discrete groups, we obtain a complete and transparent classification in terms of generalized factor systems, leading to a cohomological description of equivalence classes. For general locally compact groups, the situation is more delicate: While our construction extends to this setting, the classification depends on choices of topology on the underlying Banach bundle.

In the postpandemic era, attitudes toward remote work appeared to undergo a lasting transformation, with a high degree of location flexibility becoming increasingly common. Yet, in recent years, many organizations have introduced return-to-office (RTO) initiatives aimed at re-establishing traditional workplace dynamics and prioritizing in-person collaboration. These mandates have drawn significant attention and criticism for limiting software developers flexibility, diminishing well-being, and potentially impacting women disproportionally. This study seeks to understand software developers preferences and actual work behaviors in companies that promote in-office presence. Specifically, we investigate whether certain demographic groups, including women, are differentially affected by RTO initiatives. We also explore a range of factors that may influence individual preferences for remote or on-site work, beyond gender-based assumptions. We report findings from a survey conducted in two large Scandinavian companies engaged in the development of software-intensive systems and services. Data analysis includes descriptive statistics, contingency tables along with post hoc tests, chi-square test of association, and Cramér's (Formula presented.) for effect sizes. Our findings reveal that gender differences among software developers in both industrial cases are minimal and statistically insignificant. Instead, other variables—such as the degree of collaborative work, commute time, and responsibility to support teammates—demonstrate a stronger association with both actual and preferred office attendance. Our results challenge common narratives around gendered responses to RTO mandates, suggesting that other contextual and task-related factors may play a more decisive role. While the impact of RTO initiatives should not be dismissed, our findings indicate that a deeper understanding of work dynamics—particularly around collaboration intensity and commuting burden—is essential to designing equitable and effective work policies. Finally, our findings imply that organizational recommendations for work location must go hand in hand with task design. 

Integrating artificial intelligence (AI) with immersive analytics (IA) represents a promising means of leveraging advanced computational techniques to enhance data visualization and analysis. This study examines the state-of-the-art of AI-IA integration by addressing three key research issues: the significant application domains, the AI techniques used and their combinations, and current challenges and future directions. Results of reviewing 43 relevant studies reveal that AI-IA integration is still in its early stages, as existing research has mainly focused on a limited range of data types and application scenarios. By analyzing the application domains, this systematic literature review supports previous findings of important applications in the fields of education, manufacturing, and healthcare. At the same time, it identifies emerging applications that have progressed from XR and AI domains to AI-IA integration, such as sports events, assistive systems, urban planning, and disaster management. We contribute to extending established visual analytics (VA) pipelines into XR environments with integrated AI techniques. AI techniques are identified as contributing in five ways to this IA pipeline. Our contribution also includes identifying four key challenges and seven opportunities for future exploration. The review concludes that combining AI and IA holds the potential to create innovative applications using advanced AI and immersive visualization techniques. We present an overview of these applications and address key issues for future development. 

The problem of optimal reconstruction of a low-rank matrix subject to additive noise of arbitrary noise color is addressed. We propose a non-iterative method based on modeling the nullspace of the data. The proposed technique is shown to yield statistically efficient estimates at sufficiently high Signal-to-Noise Ratio. Yet, the computational complexity is significantly reduced compared to existing methods. The empirical efficiency is verified using simulated data. In more difficult scenarios, the proposed NullSpace Estimator (NSE) can be used to initialize an iterative approach, and in the studied cases just one iteration of Alternating Least-Squares (ALS) was found enough. 

This study proposes the inflated modified Kumaraswamy (iMK) distribution, a flexible probability model defined on the unit interval [0,1]. It captures asymmetric behaviors while accommodating inflation at zero, one, or both boundaries, as commonly observed in normalized difference vegetation index (NDVI) data. Based on the iMK distribution, we develop a new regression model (iMKreg) suitable for double-bounded responses. From this model, we derive a detection tool for invasive plant species, particularly applicable to NDVI imagery. Model performance was evaluated using synthetic NDVI data, with further assessment of predictive accuracy and detection efficacy conducted on real-world measured NDVI image. The application to detecting black-grass (Alopecurus myosuroides) in wheat crops in southern Sweden shows that the iMKreg model outperforms both standard Gaussian-based linear regression and existing inflated Kumaraswamy regression models. 

Accurate delineation of skin lesions in images is important for skin cancer detection. Existing methods often struggle with inherent complexities, such as irregular boundaries, textures, and artefacts in skin lesions. The study proposes a hybrid model comprising the edge-accurate LEDNet and Swin-UMamba for multiscale segmentation. The irregular boundaries and complex textures of skin lesions can be captured more effectively through this integration than with previous stand-alone methods. The structure of LEDNet includes components that enable it to segment lesions of various types effectively. Swin-Mamba is an encoder that uses Mamba-based architecture with the additional component of the VSS block. The proposed model is evaluated on the Ph, ISIC-2017 and ISIC-2018 skin cancer datasets and demonstrates robust performance across all datasets. The method achieved a Dice Similarity Coefficient (DSC) of 0.9734, a sensitivity of 0.9697, a specificity of 0.9858 and an accuracy of 0.9847 with ISIC 2017, DSC of 0.9753, a sensitivity of 0.9494, a specificity of 0.9902 and an accuracy of 0.9713 with ISIC 2018; and a DSC of 0.9801, a sensitivity of 0.9892, a specificity of 0.9966 and an accuracy of 0.9932 with Ph. These results show that the proposed hybrid framework has the potential to bring important benefits in the segmentation of skin lesions and is promising in clinical dermatology. 

This report details the development of a comprehensive simulation environment designed to model and analyse large-scale evacuation processes. The primary objective is to provide a structured, operationally relevant framework that represents individual and group responses to evacuation orders, movement through transportation networks, and the influence of infrastructure and resource availability on overall evacuation performance.

The environment integrates discrete-event simulation with intelligent agent modelling to operationalize established evacuation principles within a testable and verifiable framework. Development, verification, and experimentation processes are documented, emphasizing model robustness and operational relevance.

A case study based on an evacuation scenario in Karlshamn demonstrates the simulation’s ability to capture behavioural dynamics and logistical constraints, enabling scenario analysis, performance assessment, and evidence-based regional preparedness planning. Results highlight strong sensitivity to background traffic, significantly affecting rescue times and overall system performance. In the baseline scenario, private car use achieves the highest efficiency despite infrastructure limitations, whereas public transport - including buses and trains - is constrained by capacity and scheduling dependencies, potentially reducing performance without coordinated implementation. Simulation outputs align with observed traffic dynamics, supporting model validity. The framework also captures the effects of checkpoint configurations, modal distribution, and multi-destination routing on evacuation outcomes.

Pontus Braunerhjelm gör en framtidsspaning som redan idag äger rum i olika omfattning. Han konstaterar att även om AI kan bidra till effektivare finansiering av entreprenörer – och utveckla alternativa finansieringskällor med hjälp av exempelvis tokens och kryptovalutor – kommer sannolikt fysiska personliga kontakter att behövas i någon form. 

I detta kapitel beskrivs och diskuteras de kunskapsintensiva tjänsteföretagens ekonomiska betydelse i våra tre storstadsregioner. Hur har de utvecklats över perioden 1997–2021? Har Malmös och Göteborgs kunskapsintensiva tjänsteföretag närmat sig utvecklingen i Stockholm? En viktig fråga som ställts i den näringspolitiska debatten är om kunskapsintensiva tjänsteföretag förmår att bygga kluster av högteknologisk och kunskapsintensiv verksamhet i en region på liknande sätt som tillverkande ankarföretag gjort. Det har funnits en farhåga att dessa företag, trots en stor betydelse för sysselsättningen inte kan ha lika stor kunskapsspridande effekt som de traditionella ankarföretagen – stämmer detta? I kapitlet redogörs för viktiga särdrag som påverkar sannolikheten att bli ett ankarföretag och en analys hur och om de kunskapsintensiva tjänsteföretagen uppfyller dessa förutsättningar eller till och med skapar nya mekanismer för att bli ett ankarföretag. Svaret på frågan är inte ett klart ja eller nej. Den evidens som gåtts igenom antyder att potentialen f inns då såväl svensk som internationell forskning visar att spillovers är större än vad som antagits bland annat genom en betydande förmåga för spridning av kunskap och innovationer nedströms i värdekedjorna. Men då evidensen inte är tydlig kan det vara befogat med att hävda att mer forskning behövs. 

Sverige har gått från position fyra i välståndsligan år 1970 till position tolv år 2022, vilket är en kraftig försämring av det relativa välståndet. Få länder uppvisar lika stor försämring i relativt välstånd och i skrivande stund har Sverige fortsatt svag BNP-utveckling. I detta kapitel försöker vi ge ett svar på vad som förklarar denna välståndsminskning. 

I denna samlingsrapport står Sydsveriges ekonomiska utveckling och konkurrenskraft i fokus. Befolkningen i Sydsverige uppgår idag till 2,7 miljoner och den samlade regionala produkten svarar för lite drygt en femtedel av Sveriges BNP.  Med södra Sverige avser vi Skåne, Halland, Kronoberg, Småland samt Blekinge. På den regionala nivån går inte de nationella förutsättningar för konkurrenskraft att ignorera, tvärtom bestäms många av de regionala förutsättningarna av nationellt eller internationellt förd politik. Omvänt är det många gånger nödvändigt att förstå regionala skillnader för att förstå vilken effekt nationella policybeslut kan få regionalt. Befolkningstäthet, förutsättningar för infrastruktur, kvaliteten på energiförsörjningen och tillgängligheten till internationella marknader är några exempel på områden där de regionala förutsättningarna ser olika ut över landet. 

Denna rapport består utöver detta inledningskapitel av ytterligare sju kapitel som på olika sätt belyser konkurrenskraft. Detta inledningskapitel börjar med att definiera konkurrenskraft samt ger en övergripande bild av hur konkurrenskraftigt riket i sin helhet är respektive hur konkurrenskraftigt södra Sverige är. Kapitlet avslutas med att ge en kort sammanfattning av de resterande kapitlen

Sveriges försvarsindustri har en lång historia som vuxit fram ur ett nära samspel mellan politiska ambitioner, högteknologisk kompetens och industriellt kunnande. I dag ställs beslutsfattare inom politik, akademi och näringsliv inför nya och komplexa frågor till följd av förändrade säkerhets- och geopolitiska förhållanden. En central utmaning är hur olika försvarspolitiska satsningar ska förenas med största möjliga samhällsnytta.

För svenskt vidkommande aktualiseras frågan av det kommande försvarspolitiska beslutet angående fortsatt utveckling och produktion av stridsflyg. Finns det samhällsekonomiska skäl för att fortsätta med inhemsk tillverkning och vilka är i sådana fall dessa?

I en serie rapporter kommer detta att belysas närmare. Föreliggande studie är den första och fokuserar på en genomgång av internationell och svensk forskningslitteratur om de samlade effekterna av offentliga försvarspolitiska satsningar.

De viktigaste observationerna i forskningsöversikten kan sammanfattas enligt följande:

• Betydande spridningseffekter kan konstateras av offentliga satsningar på försvarsrelaterad forskning och utveckling (FoU). Samtidigt är så kallade undanträngningseffekter, det vill säga att offentlig FoU ersätter näringslivets forskningssatsningar, små eller obefintliga. Tvärtom förefaller ”inträngningseffekterna” vara betydande, det vill säga offentliga FoU-satsningar genererar ytterligare FoU-investeringar i näringslivet.

• De finanspolitiska multiplikatorerna visar att en kronas satsning på försvaret leder till BNP-ökningar på mellan 30 öre och 2,6 kronor. Den genomsnittliga storleken ligger runt ett. Den stora variationen beror bland annat på vilka satsningar som görs och vilken tidsrymd som avses.

• Effekten är störst för satsningar på FoU och teknikutveckling. Dessa förefaller slå igenom fullt ut på medellång sikt (tio till femton år) och har störst påverkan på produktivitet och tillväxt. Multiplikatorer kopplade till offentlig konsumtion och även investeringar relaterade till försvarssatsningar verkar på kortare sikt och med mer begränsade effekt på långsiktig tillväxt.

• Den försvarspolitiska spelplanen håller på att förändras. Tidigare var det avgörande att ett antal större, teknikintensiva företag kunde ta emot försvarsrelaterad kunskap. I dag är det minst lika viktigt att även mindre och entreprenörsdrivna företag deltar i processen.

• Möjligheterna till civila tillämpningar av teknologier som utvecklats för militära ändamål – ”dual use” – har ökat. Kunskapsflödena mellan militära och civila sektorer framstår emellertid som mer ömsesidiga än tidigare – kunskap rör sig inte bara från det militära till det civila, utan även i motsatt riktning. En förutsättning för den dynamiken är dock att det finns inhemska ekosystem som kan ta emot och vidareutveckla den kunskap och de metoder som högteknologisk tillverkning skapar.