Climate change requires us to fundamentally change the way we plan and develop our cities and regions. This raises questions about the role and capacity of spatial planning in general and the agency and expertise of planners in particular. Based on 27 qualitative interviews with Swedish planning professionals, we identify four key constraints: the increasing politicisation and complexity of planning practice, a cultivated practice of bypassing planners' expertise and a loss of control over their own professional sphere. Our findings suggest that the Swedish planning community is 'congealed' rather than agile when it comes to promoting zero-carbon cities and regions.

Accurate knowledge of seawater optical properties is essential for underwater imaging, sensing, and optical communication, particularly in coastal and shallow-water environments where geometric light propagation effects can influence measurement accuracy. While empirical formulations describing the refractive index of seawater are well established and widely used in the visible spectral range, their applicability in the near-ultraviolet region has received limited experimental validation. In this work, the applicability of an established empirical seawater refractive-index formulation in the near-ultraviolet band is investigated through a combined numerical and experimental approach. First, the empirical model is evaluated numerically to examine its spectral behavior across the visible-near-ultraviolet transition. The results indicate smooth and physically consistent refractive-index variation near the ultraviolet boundary. Second, a controlled laboratory experiment is conducted in which near-ultraviolet beam refraction through stratified seawater is measured using a multi-compartment tank designed to emulate discrete ocean depth intervals. Beam displacement measurements at two near-ultraviolet wavelength bands are compared directly with predictions obtained from a multi-layer ray-tracing simulation based on the empirical formulation. The close agreement between simulated and experimentally measured beam displacement across multiple depth configurations provides physical validation of the empirical refractive-index model in the near-ultraviolet region under the investigated conditions. These findings support the use of established refractive-index formulations for near-ultraviolet underwater optical modeling and contribute to a more reliable foundation for near-UV marine optical sensing and measurement applications.

In this paper, a two-dimensional (2D) autoregressive (AR) model is employed as a mitigation algorithm for interference in the Frequency Modulated Continuous Wave (FMCW) radars. The AR model, due to its simple structure, can perform super efficiently in multi-dimensional estimation problems and can be a suitable replacement for complex Neural Network (NN) based algorithms. This study addresses the advanced requirements of the 2DAR algorithm for mitigating interference in actual frames collected from real-world experiments. Three approaches to interference generation were incorporated to reproduce and study the most common and likely circumstances of mutual interference. A dynamic sampling direction selection framework is developed to address the unpredictable shapes of interfered segments within a frame. An iterative signal reconstruction algorithm is proposed to reconstruct the damaged areas using clean samples. Finally, the parallelizable processes were vectorized to make them implementable in the real world. The 2DAR mitigator’s performance was assessed using a diverse dataset of frames collected from real-world experiments, each containing unique target, noise, and interference attributes. The derived mitigator improved the signal in all experimental cases, down to the noise floor, and increased the Signal to Interference plus Noise Ratio (SINR) to almost 15 dB. Finally, the performance of different order models was compared in an identical hardware and software environment to provide a scaled indicator of the computation escalation in different model orders.

Team-based capstone projects are vital in preparing computer science students for real-world work by developing teamwork, communication, and industry-relevant technical skills. Their assessment, however, is challenging, requiring alignment between academic criteria and external stakeholder expectations, fair evaluation of individual contributions, recognition of diverse skills, and clarity on external partners involvement in the evaluation process. The high stakes of these projects further demand transparent and equitable assessment methods that are perceived as fair by all involved. Our working group (WG) addresses the challenges of capstone project assessment by examining the perspectives of instructors, students, and external stakeholders to support fair and effective evaluation. Building on insights from our previous WG and a comprehensive review of the literature, we used a mixed-methods approach combining online surveys (quantitative) and in-depth interviews (qualitative) with instructors, students, and external stakeholders. In total, we collected 66 survey responses and conducted 30 interviews across multiple countries and institutions, capturing a diverse range of global perspectives on capstone course assessments. Insights from instructors and students revealed several commonalities, for example, in the types of assessed components and the challenges of identifying and addressing non-contributing group members. Our findings also revealed clear variation between instructor and student perspectives on how contributions are measured and weighted. Instructors were reluctant to rely heavily on peer or self-evaluation due to concerns about reliability, preferring scaffolded assessments and early-warning systems to gather contribution data and moderate team dynamics. They viewed contribution-based grading as positive but resource-intensive. Students, in contrast, emphasized the need for more transparency, formative feedback, and accurate recognition of individual contributions. They also expressed concerns about the lack of recognition for hidden labor (e.g., project management, team coordination), assessor inconsistency, and a reluctance to critique peers. Instructors treated peer input as supplementary evidence, whereas students perceived it as high-stakes and socially risky. Stakeholder involvement in assessment was generally limited to providing formative feedback and participating in final showcase events. We also identified generative AI as a rapidly evolving challenge, with both students and instructors seeking guidance on acceptable use and exploring opportunities to automate aspects of assessment. Our results offer actionable evidence-based guidance for designing transparent and equitable assessment practices in team-based computing capstones. 

One of the most important choices a researcher makes is selecting a research paradigm and methodology, without which they will be hampered in their search for knowledge and answers. Ideally, researchers consider all possible approaches and select the most appropriate one, but several factors constrain this: Time, familiarity with certain approaches, and the uncertainty of the benefit of change. \Cer draws from many research disciplines, exposing new possibilities that may not be seized due to these limitations. The Cards for Alternative Research Design (CARD) deck is designed to expand researchers awareness of different research approaches through a card-based prototyping exercise. This serious card-based game approach could be used by graduate students, early-career researchers, research course instructors, research mentors, and even experienced researchers. CARD games are intended to reduce the formality and potentially confrontational aspects of being asked to consider new approaches, allowing participants to examine their current research and plans through different paradigms, methodologies, and constraints, without it being a direct criticism of their current choices. This can increase the level of understanding of research framing and practice, strengthening the arguments for using a given approach and introducing valid arguments to adopt different approaches, with low time investment. This report summarizes the current evolution of the CARD deck, including an accompanying glossary and multiple games that can be played with the cards. 

INTRODUCTION: Sexual life is important for many people and may be limited by breathlessness. We evaluated associations between breathlessness and individuals' perceived satisfaction with sexual lives and explored mediating factors in this relationship.

METHODS: A cross-sectional, online, population-based survey of Australian adults with key demographics (age, sex, rurality, state/territory) reflected the 2016 national census. Assessments included: demographics, breathlessness (modified Medical Research Council (mMRC) scale); satisfaction with overall sexual life; whether breathlessness had affected overall sexual life and physical, social and emotional functioning. Binomial and ordinal logistic regression evaluated associations; a structural equation model evaluated direct, indirect and total associations.

RESULTS: Of 10 033 respondents (52% women; mean age 45.4 (SD 18.6)), 4245/10 033 (42%) reported mMRC ≥1, 1214/10 033 (12%) reported being very dissatisfied with their overall sexual life and 943/10 033 (9%) reported that breathlessness had impacted their overall sexual life. Compared with mMRC 0, there was an unadjusted association between mMRC 1 and satisfaction with overall sexual life which remained after adjusting for age, sex and body mass index (adjusted OR (aOR): 1.50; 95% CI 1.29 to 1.74).Breathlessness severity was associated with worse unadjusted and adjusted impact on overall sexual life: mMRC 0 versus mMRC 1 (aOR: 1.84; 95% CI 1.36 to 2.47). The associations increased stepwise for each higher mMRC level. There may be moderation of the effect of breathlessness on satisfaction with sexual life through emotional functioning.

CONCLUSIONS: Increasing breathlessness severity increases the likelihood of a person's overall sexual life being impacted negatively, potentially mediated partly through emotional functioning. 

Carefully selecting the source data is crucial to achieve high performance of transfer learning methods for brain–computer interfaces (BCIs). Especially so in settings where a large amount of source data is available, and finding the optimal source is not computationally feasible. This paper presents a novel method for source selection, the so-called Transfer Performance Predictor (TPP) method. The TPP method is based on computationally simple features, a choice made to enable real-time implementation and reduce calibration time. The presented method outperforms other comparable source selection methods in BCI settings where a large amount of source data is available. By using the TPP method, source selection can be performed quickly with good results for transfer learning performance, which means that the BCI calibration time can be reduced and a new target user can more quickly start using the BCI. 

Generative AI (GenAI) is playing an increasingly influential role in computing education across all levels, offering new opportunities to support both teaching and learning. However, its effective integration raises critical concerns related to trust, academic integrity, and broader social and ethical implications. While substantial attention has been given to GenAI use in introductory programming courses (e.g., CS0/CS1), there remains a notable gap in research addressing its application in upper-level computing courses, such as software engineering, human-computer interaction, algorithms, operating systems, and theoretical computer science. This working group report presents two complementary studies: A systematic literature review of GenAI interventions in upper-level computing education, and a survey of computing instructors on their practices and perspectives regarding GenAI integration in these contexts. Based on the combined findings, this report presents an overview of current practice and practical guidance for computing instructors. The report is intended to inform the design of engaging, pedagogically sound, and forward-looking curricula that align with modern educational and workforce standards and expectations. 

This dissertation, part of the urbanHIST project, employs digital geospatial tools to examine the morphological transformation of post-socialist cities, focusing on Košice, Slovakia. Integrating urban morphology, history, geoinformatics, and sociology, the research uses Historical Geographic Information Systems (HGIS) to analyse tempo-spatial changes and underlying socioeconomic processes after the fall of communism.

Three key phenomena are identified in post-socialist urban development: a shift from centralized planning to neoliberal governance, urban sprawl paired with increased core density, and the emergence of new development axes and commercial subcentres. The study applies theoretical frameworks such as the Conzenian approach, path dependency, and the post-metropolis model to situate Košice’s evolution within broader Central and Eastern European patterns.

The findings are presented across three analytical levels: post-socialist transformation, city-wide spatial dynamics, and focused analyses of active development zones. A typology of transformation hotspots in Košice is established, including revitalized industrial zones, housing estates, and commercial subcentres. Case studies such as the revitalization of Kasárne Kulturpark and suburban expansion highlight the interplay between historical legacies and contemporary urban needs. Key challenges include reconciling socialist-era infrastructure, addressing socioeconomic needs, and ensuring sustainable growth.

Košice’s post-socialist development is categorized into three phases: crisis management (1990s), uncoordinated development (2000s), and semi-organized development (2010s), reflecting shifts from neoliberal to more strategic planning approaches. Quantitative analyses of building density, land use, and cluster development are combined with qualitative insights into decision-making processes.

This study underscores the significance of historical context in post-socialist urban transformations, demonstrating HGIS’s utility in capturing spatial changes and morphological patterns. Its multidisciplinary approach bridges historical analysis and modern planning, offering practical insights for managing urban development and serving as a reference for other Central and Eastern European cities undergoing similar transitions.

The aim of this thesis was to understand and explore how a strategic sustainable development perspective can be integrated in product sustainability performance assessment practices. Based on an interactive qualitative research approach, three studies were conducted. The first study reviewed early-phase assessment approaches available in literature and found that although many approaches exist, a systemic and strategic perspective was often not completely addressed. Six criteria were therefore developed to support integration of such a perspective in assessment approaches. The second study focused on two EU policy instruments and while both instruments presented opportunities to incentivize and support product sustainability performance assessments, they provide limited support for considering the full socio-ecological systems perspective and long-term strategic decision-making. The third study used a multi-case study to deepen the understanding of how product developing companies work with and use product sustainability performance assessments. The findings showed that product sustainability performance was assessed, but companies face challenges and their practices were not fully aligned with a strategic sustainable development perspective. Ten propositions were formulated for what must be considered to integrate such a perspective in company practices, so that assessments can function as strategic decision-support for developing products that can contribute to society’s transition towards sustainability in ways that benefit the own organization. Together, the three studies highlight the need to develop both individual assessment approaches and the surrounding practices in which they are embedded to support effective integration of a strategic sustainable development perspective into assessment practices in the product development process.

This paper presents a novel low-complexity discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-s-OFDM) scheme designed to reduce computational overhead while maintaining robust performance in wireless communication systems. The core idea is to modify the signal processing chain by merging the computations of multiple functional blocks, resulting in a significant reduction in the complexity of conventional DFT-s-OFDM implementations. A comparative analysis is conducted to evaluate the proposed scheme against traditional DFT-s-OFDM, using key metrics such as error vector magnitude (EVM) and computational complexity. Simulation results demonstrate that the proposed scheme achieves performance comparable to conventional DFT-s-OFDM in terms of EVM under various system configurations, while significantly reducing computational complexity. This makes the proposed scheme a promising candidate for next-generation wireless communication systems, particularly in uplink scenarios involving battery-powered devices. 

Artificial intelligence (AI) has entered a transformative era, as traditional pretraining paradigms face the constraints of “peak data.” This article explores the implications of a “post-pretraining” era, where AI systems transcend static data dependency and evolve into agentic AI entities. 

In the context of increasing climate variability and the gradual decline of ground-based observation networks, satellite-based rainfall estimates (SREs) have become indispensable tools for hydrological monitoring disaster preparedness, and climate modeling. Satellite technology has evolved rapidly in recent years, with new missions, sensors, and techniques implemented by agencies and researchers to improve SRE products. This study presents a global systematic review explicitly applying the PRISMA methodology, offering a structured and reproducible framework for evidence synthesis. It evaluates the performance of the most widely used SREs across all continents from January 2018 to November 2025, with particular emphasis on their application in data-scarce and hydrologically complex regions. Drawing from 636 peer-reviewed studies, the review identifies key factors affecting the accuracy of SREs, including topography, rainfall type, and seasonality. Notably, products that integrate satellite data with ground-based observations consistently demonstrate superior performance compared to satellite-only estimates. Among them, IMERG-Final and CHIRPS stand out as the most widely used datasets worldwide, with IMERG-Final showing particularly promising performance across most continents. The findings highlight the need for future research to prioritize the development of advanced bias correction algorithms, region-specific calibration methods, and hybrid models that incorporate additional meteorological variables. Although previous reviews have addressed this approach, the present synthesis offers an updated and concise reference for selecting suitable SREs across diverse environmental and operational contexts. 

Context: Software Engineering (SE) education equips students for the complexities of the software industry, emphasizing not only technical skills but also teamwork and communication with stakeholders. SE team projects courses with industrial customers provide learning environments where students develop both technical and social skills. However, such courses often face challenges related to teamwork, accountability, and assessment.

Objectives: This thesis aims to support teamwork and learning in SE team projectcourses by i) identifying the challenges students face, ii) exploring peer evaluation as a teamwork support strategy, and iii) evaluating the perceived effectiveness of various teamwork support strategies, including peer evaluation, team contracts, and collaborative peer review.

Methods: This research adopts a qualitative, evidence-based approach. We analyzed student reflection reports using qualitative document analysis to capture students’ experiences. Teacher focus groups were conducted to gather educators’ in-sights. Additionally, a systematic literature review was performed to create a peer evaluation taxonomy, which was then validated via semi-structured interviews with SE educators.

Results: The findings show that challenges in SE team project courses are mainly socio-technical rather than purely technical. Several strategies, such as peer evaluation, team contracts, and collaborative peer review workshops, are used to support teamwork. However, peer evaluation practices in literature vary widely in their design and reporting. To address this, the thesis proposes a taxonomy of peer evaluation with guidelines for designing peer evaluation processes. The thesis also finds that both students and teachers perceive peer evaluation and team contracts as useful strategies; however, they need structured follow-up.

Conclusion: This thesis provides empirical insights into teamwork challenges and lessons learned. It also contributes taxonomy and design guidelines for peer evaluation. Further, it provides qualitative evidence on the perceived usefulness of different teamwork strategies. The findings highlight the importance of context-sensitive design and structured follow-up when implementing teamwork support strategies in SE team project courses.

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.