Background: Previous research has shown that mobile health (mHealth) interventions are effective in reminding older adults with chronic conditions about health care appointments and promoting adherence to medication schedules. However, the evidence is limited by the short duration and poor quality of the interventions.

Objective: We evaluated the effectiveness of the Support, Monitoring and Reminder Technology for Mild Dementia (SMART4MD) tablet app in improving the quality of life (QoL) of people with mild cognitive impairment (PwMCI) and their caregivers through medication reminders and health care appointment alerts.

Methods: An 18-month pragmatic randomized controlled trial was conducted in Spain and Sweden from December 2017 to September 2020 and included 1078 PwMCI and their informal caregivers.

Results: For PwMCI, the intervention improved the primary outcome, composite Quality of Life in Alzheimer’s Disease (QoL-AD) scale, at 18 months (mean difference 0.75, 95% CI 0.07-1.42; P=.03), and a similar effect was observed at 6 months (mean difference 0.73, 95% CI 0.09-1.36; P=.02). Confirmatory secondary outcomes for PwMCI, including medication adherence (P=.12) and Mini-Mental State Examination (MMSE) score (P=.45), did not differ significantly between groups at the 18-month follow-up. Exploratory analyses showed higher total accumulated quality-adjusted life years (QALYs) at 6 months for PwMCI (mean difference 0.035, 95% CI 0.02-0.05; nominal P<.001), informal caregivers (mean difference 0.050, 95% CI 0.04-0.07; nominal P<.001), and dyads (mean difference 0.085, 95% CI 0.06-0.11; nominal P<.001). A dropout rate of approximately 40% (429/1083, 39.61%) was observed in both the intervention and control groups.

Conclusions: The SMART4MD intervention was associated with a modest improvement in PwMCI’s QoL as measured by the composite QoL-AD score. Evidence for benefits among informal caregivers and dyads was less consistent and should be interpreted as exploratory. Nominally significant findings for exploratory outcomes should be interpreted with caution, as they were not adjusted for multiplicity. 

In dynamic social networks, the frequent changes in nodes and links pose significant challenges for community detection. Traditional community detection methods encounter with issues such as random behavior, high time complexity, or low efficiency across time steps. This paper presents a novel framework, called DyGraphSage, which integrates an enhanced GraphSage model with a Temporal GRU to identify community structures. DyGraphSage begins by defining both conventional and newly introduced structural features and employing GraphSage embeddings to learn network representations and detect communities in the initial snapshot. To address temporal evolution, two strategies are proposed for updating node labels in subsequent time steps. Primarily, a new semi-supervised method is introduced to efficiently update labels when only minor structural changes occur between consecutive snapshots. Alternatively, when substantial modifications are detected, the model retrains itself using an adaptive thresholding mechanism. A new efficient equation is proposed to compute the threshold (θ) dynamically, based on node structures and their connections across previous and current network snapshots. This process allows the model to manage community updates using adjusted weights and biases, eliminating the need for reinitialization. Experimental results demonstrate that DyGraphSage outperforms several state-of-the-art dynamic community detection algorithms, particularly in terms of NMI and ARI metrics. 

Academic tools for sustainable product development often fail to achieve widespread use in industry. Based on a case study of a consultancy firm, this study explores factors that enable consultants to adopt and adapt such tools and act as intermediaries that translate and integrate academic findings into practice. Interviews and a survey revealed that a solid conceptual foundation, clear client value, result visualization, adaptability, and integration with existing workflows are most important, and the study proposes nine lessons learned to guide future tool development and collaboration. 

The paper presents a simulation framework for evaluating fast charging and battery swapping strategies in battery-electric construction machinery. Developed using discrete-event and agent-based modeling, the framework supports scenario analysis in mining and road construction contexts. Case studies demonstrate how charging strategies impact productivity, energy costs, and battery degradation. Results highlight trade-offs between operational efficiency and long-term sustainability, offering a decision-support tool for electromobility transition in construction machinery. 

Traditional dynamic community detection methods often struggle to simultaneously preserve local structural consistency, capture global topological relationships, and efficiently adapt to continuous graph updates in large-scale environments. To solve these limitations, this paper proposes a novel dynamic community detection framework called Embedded Clustering Boundary Refinement (ECBR). The proposed method integrates unsupervised GraphSAGE and Node2Vec embeddings to jointly capture local neighborhood aggregation patterns and global structural equivalence among nodes. The generated embeddings are fused through feature concatenation and z-score normalization to construct a unified latent representation space. Subsequently, Mini-Batch KMeans clustering is employed to efficiently generate the initial community structure while maintaining scalability for large-scale graphs. To further improve partition quality, ECBR introduces a boundary-aware refinement mechanism that identifies structurally ambiguous nodes using neighborhood consistency analysis and reassigns them according to embedding-space similarity. In addition, the framework incorporates an adaptive dynamic update strategy capable of distinguishing between major topological shifts and localized structural changes. Significant graph perturbations trigger complete model retraining, whereas minor modifications are handled through computationally efficient incremental updates on local subgraphs. Experimental evaluations were conducted on synthetic LFR benchmark networks and several real-world dynamic interaction datasets, including high school, workplace, and hospital contact networks. The results demonstrate that ECBR consistently outperforms several state-of-the-art methods, including QCA, DyPerm, DCDID, IncNSA, and DCDBFE, achieving better NMI and ARI scores across diverse network conditions. The experimental findings confirm that ECBR provides a scalable, robust, and highly effective solution for dynamic community detection in evolving large-scale social networks. 

Metaheuristic algorithms remain a widely used class of solvers for solving complex, non-convex optimization problems where gradient information is unavailable, yet two failure modes continue to limit their practical reach: premature convergence caused by inadequate exploration diversity in late iterations and population stagnation that persists even when individual agents are nominally assigned to the exploration phase. This paper proposes the Stagnation-Aware Aquila Optimizer (SAAO), a hybrid algorithm that addresses both failure modes by embedding three targeted mechanisms into the Aquila Optimizer (AO) framework: (i) an adaptive exploration probability that responds to global fitness-improvement history; (ii) individual-level stagnation counters that force exploration re-entry for any agent that fails to improve for more than 30 consecutive iterations, regardless of the global phase schedule; and (iii) a diversity-maintenance module that reinitializes completely stagnant agents via random sampling or opposition-based learning. The biological repertoire of search operators is simultaneously enriched by incorporating four physics-grounded operators from the Animated Oat Optimization (AOO) algorithm centroid-guided dispersal, elite-guided dispersal, hygroscopic rolling, and spring ejection, alongside the original AO operators, yielding six complementary update rules partitioned equally between exploration and exploitation. The SAAO was evaluated against nine state-of-the-art algorithms on the CEC2015 benchmark and CEC2022 under identical experimental settings. The SAAO achieved the best Friedman mean rank on both suites and delivered competitive or superior performance against the nine baselines, with Wilcoxon rank-sum tests confirming statistically significant advantages over most competitors. On three classical engineering design problems, the SAAO achieved competitive outcomes. In a real-world equipment anomaly prediction task, an SAAO-optimized ensemble classifier attained 98.23% accuracy, surpassing the compared baseline models. These results establish SAAO as a robust and computationally tractable optimizer for both benchmark and applied settings. 

Validating threat modeling results remains difficult because completeness is hard to judge without an external oracle. Existing studies often rely on expert-produced reference models and other human baselines, but these can contain omissions or disagreements. This paper evaluates a complementary, vulnerability-grounded validation approach. We apply threat modeling to intentionally vulnerable applications with a known vulnerability set to measure the number of related vulnerabilities that can be discovered. We compare ThreMoLIA, an LLM-assisted threat modeling solution developed by our team, with the Microsoft Threat Modeling Tool (MTMT) across two vulnerable applications: AzureGoat and the Vulnerable Bank Application (VulnBank). The inputs to both tools are limited to architecture, data flow diagrams, and their descriptions. The results show that ThreMoLIA achieved higher vulnerability coverage on both systems. We show that vulnerable test applications provide a practical benchmark for assessing threat coverage and complement expert-based validation. 

This study examines how AI can support the development of Leading Sustainability Criteria in sustainable product development, comparing AI-generated outputs with human-facilitated workshop results from four Swedish companies. Results highlight AI's ability to accelerate and broaden sustainability framing, but emphasize that contextual relevance and legitimacy depend on participatory inputs. The findings suggest that AI is most effective when integrated into hybrid workflows that preserve human insight and stakeholder engagement - offering practical guidance for future implementation. 

Introduction Older adults are increasingly recognised as valuable contributors in health and social care research, yet their involvement as active research partners remains inconsistent and under-theorised across contexts. The aim of this study is to investigate how older adults are involved as active research partners in health and social care research and to build consensus on strategies and enabling conditions that can support and strengthen such involvement.

Methods and analysis This study uses a sequential exploratory mixed-methods design comprising three phases. In the preparatory phase, the study protocol was developed and the ethical approval application was prepared and submitted. In the exploratory phase, an umbrella review of international evidence, a mapping survey with older adults and researchers, qualitative interviews and a participatory concept-mapping workshop will be conducted to identify experiences, practices, barriers and strategies for involving older adults as research partners. The empirical components will primarily be conducted within a Swedish context to generate context-sensitive insights. Findings will be triangulated to develop a preliminary framework and candidate consensus statements. In the consensus-building phase, a modified Delphi study will be conducted with two expert panels of older adults and researchers, respectively. Across iterative rounds, participants will rate the importance and feasibility of each statement using 5-point Likert scales. Quantitative data will be analysed descriptively to assess consensus levels and qualitative comments will undergo content analysis. Results will be analysed overall and by panel to identify areas of agreement and divergence.

Ethics and dissemination The study has received an advisory opinion from the Swedish Ethical Review Authority (reference number 2025-08878-01). The study will be conducted in accordance with Swedish ethical regulations and applicable data protection legislation, including the General Data Protection Regulation. Older adults have been involved in shaping the study and corresponding protocol and will contribute to the interpretation and dissemination of findings. Results of this study will be shared through peer-reviewed publications and conference presentations.

Background: Physical activity (PA) has well-documented cardio-respiratory protective effects in individuals with lung diseases. Whilst most studies examining the relationship between PA, lung function and respiratory symptoms have included self-reported PA data, studies assessing PA objectively using accelerometers are scarce.

Aim: To explore the association of sedentary behavior (SED) and moderate-and-vigorous PA (MVPA), assessed using accelerometer data, with lung function and respiratory symptoms, and to compare associations in ever-smokers with never-smokers, within a large sample of middle-aged individuals.

Methods: In the population-based, cross-sectional Swedish CArdioPulmonary BioImage Study (SCAPIS), men and women aged 50 to 64 years were included. PA was assessed from triaxial hip acceleration data processed into PA intensity categories. Time spent in SED and MVPA was used. Dynamic spirometry (FEV1 and FVC) was performed post-bronchodilation and respiratory symptoms were self-reported using a questionnaire.

Results: Complete data were obtained from 25,975 individuals (52% females). Lower lung function and presence of respiratory symptoms were both associated with less MVPA, with stronger associations for lung function. An interaction between lung function and smoking status was found, so that each unit of increase in lung function was associated with a greater increase in MVPA among ever-smokers versus never-smokers.

Conclusion: Lower lung function and presence of respiratory symptoms were both associated with less MVPA in a general middle-aged Swedish population. In addition, the association between lung function and MVPA were stronger among ever-smokers. Further clinical studies are needed to clarify the role of PA in slowing lung function decline and preventing respiratory diseases. 

Världsekonomin förefaller i allt högre grad drabbas av överlappande och ihållande chocker snarare än separata och snabbt övergående kriser: finansmarknadskrisen 2008–2009, COVID-19 pandemin och Rysslands fullskaliga invasion av Ukraina utgör exempel på detta. En konsekvens är att förutsättningar och åtgärder för att bemöta kriser och upprätthålla försörjningskedjor och resiliens kan förväntas  variera beroende på det specifika krisförloppet.

Även om utvecklingen i Ukraina innebär unika möjligheter att studera hur ekonomier kan agera för att produktions- och innovationssystem ska fortsätta fungera, är det inte givet att slutsatserna är desamma för andra typer av kriser. Exempelvis är en skillnad mellan Ukrainakriget och kriser i fredstid att i det senare fallet tenderar produktion, investeringar och nyetableringar att minska. I Ukrainas fall har det varit nödvändigt att öka produktions- och innovationsförmågan samtidigt som kriget härjar. Oavsett typ av kris måste dessutom varje land utgå från sina specifika förutsättningar.

I föreliggande kunskapsöversikt visar vi att långvariga krisförhållanden utmanar den traditionella förståelsen av hur innovationssystem fungerar och hur industriell kapacitet bibehålls och byggs upp. I översikten redogörs för Ukrainas industriella omvandling under kriget baserat på företagsdata för perioden 1991–2025. Fokus ligger på den snabba utvecklingen av Ukrainas drönarindustri (Unmanned Aerial Vehicles, UAV:s) och vilken roll som entreprenörskapet har spelat för innovation och industriell utveckling. Ambitionen är att analysera hur samhällsresiliens kan utvecklas och stärkas när normala institutioner och marknadsmekanismer är satta ur spel.

Drönarindustrin är väl lämpad att studera för att förstå industriell omvandling och hur försörjningskedjor kan upprätthållas. Sedan 2022 har Ukraina snabbt utvecklat ett stort och diversifierat inhemskt ekosystem för drönare som omfattar tillverk ning i olika led, mjukvara, ingenjörstjänster, forskning och utveckling samt civilsamhället och volontärer. Detta har skett samtidigt som det varit brist på kapital och kompetens, kombinerat med att olika samhällsfunktioner varit under extrem press.

Vi visar att Ukrainas ökade produktionskapacitet  efter Rysslands fullskaliga invasion framför allt skedde genom inträde på marknaden av nya företag, en experimentell organiserad innovationsprocess och en tät, dialogbaserad kunskapsöverföring mellan användare och tillverkare som snabbt ledde till modifierade eller nya produkter. Det var således inte redan etablerade företag som i första hand drev på dessa processer. Dynamiken kännetecknades av mångfald och selektion samt en mer geografiskt spridd produktion medförhållandevis små ekosystemstrukturer som kombinerades med digitaliserade ekosystemtjänster.

This article introduces an iterative change detection (CD) method for synthetic aperture radar (SAR) imagery based on robust low-rank and sparse matrix decomposition, specifically robust principal component analysis (RPCA). Instead of using fixed pairs of reference and surveillance images, as are typically used in conventional RPCA-based CD methods, our method progressively integrates multiple reference images and adaptively updates the regularization parameter at each iteration. This iterative scheme improves the separation between static clutter and sparse changes, significantly reducing the false alarm rate (FAR). The proposed iterative CD method is also explored using an extension of RPCA to tensors, known as tensor robust principal component analysis (TRPCA), thereby enabling its application to multitemporal and multidimensional SAR datasets. The experiments were conducted using real SAR data from the Swedish CARABAS-II system. The results showed that our iterative scheme outperforms noniterative CD methods based on a pair of SAR images and achieves competitive results when compared to CD methods using SAR image stacks, indicating that the iterative approach can be an effective solution in scenarios where SAR image availability is limited and SAR acquisitions are expensive. 

Autonomous vehicles (AVs) are rapidly becoming foundational components of intelligent transportation systems (ITS), yet their complex cyber-physical architectures expose them to a broad and continuously evolving threat landscape. Existing cybersecurity solutions struggle to keep pace with the dynamic, data-intensive nature of AV ecosystems, leaving critical vulnerabilities unaddressed across perception, communication, and decision-making subsystems. Generative Artificial Intelligence (GAI), encompassing Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Diffusion Models (DMs), has emerged as a powerful paradigm for both offensive simulation and defensive reinforcement, enabling synthetic data generation, adversarial attack emulation, and enhanced anomaly and intrusion detection. Yet despite growing interest in GAI for general cybersecurity, its systematic application to AV-specific security remains fragmented and underexplored. This paper addresses that gap through a PRISMA-guided systematic review of GAI-driven defense mechanisms for AV cybersecurity, synthesizing 216 peer-reviewed studies drawn from major scientific databases and published between January 2020 and February 2026. Three principal contributions are made. First, we introduce an AV-centric, three-dimensional taxonomy that classifies defenses along generative architecture, defensive function, and AV-relevant attack surface, explicitly anchoring each study to AV subsystems and operational contexts. Second, we provide a disciplined synthesis that separates study-specific performance findings from broader design insights, exposing fundamental gaps between conventional and GAI-based approaches in scalability, adaptability, and resilience. Third, we identify critical open challenges-including training instability, the absence of standardized AV security benchmarks, real-time deployment constraints, and limited explainability-and propose targeted research directions for safety-critical environments. By grounding GAI defenses within AV system layers and cyber-physical threat models, this review serves as a practitioner- and researcher-oriented reference for building robust, scalable, and trustworthy cybersecurity solutions for next-generation autonomous vehicles.

We present a high-performance method for real-time relighting of high-fidelity volumetric clouds. Building on Relightable Neural Assets, we introduce key adaptations that enable neural shading of volumetric cloud phenomena within a rasterization-based pipeline. In particular, we incorporate a per-pixel thickness parameter to capture view-dependent opacity and replace the generalizing single light source with a sky illumination model, allowing the network to learn complex atmospheric scattering effects. To achieve real-time performance, we depart from density-field-based volumetric rendering and instead operate on mesh representations combined with a triplane feature encoding. This enables a fully rasterization-driven solution that reproduces volumetric appearance without requiring ray marching or volume integration. We further describe a complete pipeline for converting volumetric cloud assets into a neural representation trained from path-traced supervision. We evaluate our method through an ablation study analyzing both image quality and runtime performance. Our adaptations improve reconstruction quality from 18.13 dB to 22.32 dB while achieving rendering times as low as 3.7 ms per frame. These results demonstrate that our approach enables high-quality, relightable cloud rendering suitable for real-time and performance-critical applications. 

We propose a hybrid framework for detecting anomalous smart meter readings as a measurement-quality screening tool by combining predictive residual analysis with contextual population statistics. The method targets field-deployed electricity meters, where measurements must comply with standardized accuracy classes but ground-truth fault labels are scarce. In the first stage, expected active-energy values are forecast using a stacked long short-term memory (LSTM) model trained on historical meter time series. Candidate anomalies are identified when the absolute prediction residual exceeds a specified theoretical measurement-error bound, providing an interpretable, meter-specification-aligned criterion. In the second stage, the same readings are evaluated against hour-of-day contextual statistics computed across the meter population to suppress false alarms caused by legitimate but infrequent consumption or generation patterns. A reading is finally flagged only if it both violates the error-bound residual criterion and deviates significantly from its hour-conditioned population distribution. Experiments on a multi-unit smart metering dataset (over five months of measurements) demonstrate that the proposed two-level decision rule isolates a small subset of readings with strong evidence of inconsistency under both model-based expectation and contextual behavior. The results indicate that LSTM-based residual exceedance, when complemented with simple contextual filtering, offers a practical approach for screening potentially faulty measurements in large-scale metering deployments, and that the same signals can be aggregated over time to support drift-oriented monitoring of persistent meter deviations aligned with metrological fault concepts.

In Sweden, as in many other countries, the public sector has implemented merger policies to exploit economies of scale and improve efficiency. In previous work, Agrell et al. (Annals of Operations Research, 288:653–679, 2020) we showed that mergers had a positive impact on efficiency. In this study, we investigate the economies of scale argument for mergers. To measure scale elasticity, we estimate a global translog stochastic frontier input distance function covering the years 2000–2016. Our results show that the merger strategy is well supported in empirical results. In the early 2000s, the majority of district courts (94%) operated under increasing returns to scale (IRS), meaning that they were too small compared to optimal scale. About 3% operated under decreasing returns to scale (DRS) and 3% operated at optimal scale. In 2016, around 40% had targeted optimal scale, but 20% were operating at DRS, indicating that they had become too large. These findings show both the potential and the limits of public-sector mergers in demand-driven services such as courts. 

Background: Breathlessness is common, anxiety-provoking, and challenging to clarify in primary care. When it persists beyond four weeks, it markedly impairs quality of life, functional capacity, and prognosis, and can trigger urgent consultations if underlying causes are not recognized and managed. Chronic breathlessness is frequently multifactorial, combining cardiopulmonary disease with obesity, deconditioning, breathing pattern disorders, anxiety, anemia, and post-coronavirus disease 2019 conditions.

Objective: This technical sheet aimed to provide primary care physicians with a structured, pragmatic, person-centered approach to the assessment and management of chronic breathlessness.

Content: This technical sheet emphasized early identification of acute/urgent presentations of breathlessness in adults (including pulse oximetry when available), systematic evaluation (history, examination, accessible investigations such as spirometry, chest radiography, electrocardiogram, complete blood count, N-terminal pro–B-type natriuretic peptide, and screening for anxiety/depression and physical activity level), and staged diagnostic reasoning that may require several visits while safely managing uncertainty. Management includes optimizing treatment of underlying conditions according to national guidance and early introduction of evidence-based non-pharmacological strategies. The breathing–thinking–functioning model is proposed to structure symptom control: correcting maladaptive breathing patterns, addressing fear and distress, and tackling deconditioning through appropriate rehabilitation and activity support.

Conclusion: A structured primary-care pathway combined with early non-pharmacological interventions can improve consultations, support self-management, and enhance outcomes in adults with chronic breathlessness. 

Automated Fault Detection and Diagnosis (FDD) in District Heating (DH) is essential for reducing return temperatures, improving operational efficiency, and supporting the transition to low-temperature, low-carbon integrated energy systems. Practical deployment remains difficult because labelled fault data are scarce and heterogeneous across domains, while unlabelled operational data are abundant, creating a practical mismatch. To address this, we propose a Self-supervised Time Series Transformer (STST) for low-label FDD using routinely available primary-side measurements. We evaluate the method on six datasets spanning laboratory fault emulations, simulation, and real-world DH networks. The results show that primary-side temperatures contain strong fault-discriminative information, while flow measurements add sensitivity to hydraulically driven faults. The proposed method performs competitively in scarce-label and heterogeneous-field settings, achieving F1 values between 0.77 and 0.98. Cross-domain experiments indicate that fault-relevant structure is transferable across substations within the same network, although transfer remains asymmetric across networks. Overall, the results suggest that self-supervised pre-training and regularised fine-tuning can improve transformer-based DH fault detection under stricter field conditions, while CNN-based and hybrid baselines remain highly competitive in cleaner or more separable regimes. Practical deployment across new networks, therefore, still requires attention to source–target compatibility, local validation, and remaining domain shift. 

Extended reality (XR) refers to technologies that either extend the physical environment with digital content or fully replace it with virtual environment (VE), enabling users to perceive and interact with virtual objects within a spatial context. Immersion is one of the key characteristics of XR systems. It relates to sensory fidelity and influences users’ perception of presence within XR. Beyond presenting predefined content, immersive environments are increasingly expected to respond to users’ behaviors, preferences, and contexts. Previous studies on personalized smart immersive XR (PSI-XR) show that personalization can be supported through explicit input, implicit behavior information, sensing technologies, recommendation mechanisms, and user modeling. These developments have motivated the integration of XR technologies and smart techniques to create user-centered environments that adapt to users’ requirements, enhancing immersive experiences. 

This licentiate thesis investigates PSI-XR from two connected perspectives: the design perspective and the perceptual perspective. From the design perspective, the thesis first systematically reviews the application areas, enabling technologies, smart and personalized techniques, and key challenges of PSI-XR to understand how such environments can be realized. Building on this review, the thesis proposes a conceptual design of a personalized virtual reality (VR) system as a PSI-XR application, using furniture arrangement as an illustrative context and combining head-mounted display (HMD)-based VR, multimodal interaction, hand and eye tracking, and generative artificial intelligence (GenAI). From the perceptual perspective, the thesis examines whether PSI-XR can provide perceptually credible visual experiences for users. One perceptual study compares visual realism and visual acuity between real world and VR, showing that VR can achieve a high level of visual realism and convey basic visual information about virtual objects while still presenting limitations in visual acuity. Since perceptual credibility also depends on how virtual objects are digitally represented and rendered, the second perceptual study compares 3D Gaussian Splatting (3DGS) with mesh-based rendering in VR, showing that 3DGS can be perceptually comparable to mesh under distant static observation but performs less robustly under proximal viewing, free movement, and multi-object conditions. 

Overall, this thesis suggests that an important potential value of PSI-XR lies not simply in increasing immersion or intelligence, but in how effectively immersive technologies, smart personalization techniques, and perceptual credibility work together to support a user-centered environment.

Code review is a well-established practice to ensure code quality, identify potential bugs, promote knowledge sharing and maintain coding standards within a team or organization. This study aims to investigate the specific practices, challenges and information needs encountered when reviewing GUI-based test artefacts for web applications, which remain poorly understood. We conducted a qualitative interview study with 14 software testing professionals from six different companies to explore the distinct aspects of reviewing GUI-based test artefacts. We identified four practices, six challenges and four information needs related to reviewing GUI-based test artefacts. The foremost challenge is the validation of GUI-based tests under review. Furthermore, challenges concerning levels of abstraction and test robustness were not addressed in related studies. Additionally, participants proposed six potential improvements for tools and practices to better support the code review process. Notably, the absence of standardized practices and the need to run tests locally were common themes across participants. The code review process for GUI-based test artefacts differs from that of production code, highlighting the need for practices and tools tailored specifically to the unique demands of GUI-based testing.