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Get PDFA fully automated explainable predictive model for diagnosing pre-capillary and post-capillary pulmonary hypertension on routine unenhanced CT: results from the ASPIRE registry
Get PDFAims
Unenhanced chest CT is frequently used to assess lung malignancy and parenchymal disease. Harnessing CT data to quantify cardiac and vascular structures has the potential to improve the diagnosis of heart failure and pulmonary hypertension (PH). This study aims to develop a deep learning model to segment and analyse cardiothoracic structures from unenhanced CT images to diagnose PH, pre-capillary PH and PH associated with left heart disease (LHD).
Methods and results
A twelve-structure cardiothoracic segmentation model was developed using an institutional cohort (n = 55, 35/9/11 training/validation/testing). Model performance was evaluated using Dice similarity coefficients (DSC). Volumetric measurements were compared to manual values using intra-class correlation (ICC) and visually assessed by four observers using an external cohort (n = 50, from 26 hospitals). Univariable and multivariable regression analyses were performed using a cohort of 368 patients (254/114 training/testing). Receiver-operating characteristic curves were plotted and the area under the curves (AUC) with confidence intervals (CI) were calculated. The model yielded a DSC segmentation performance of ≥0.87 for 9/12 segmented structures and ICC > 0.95 for 10/12 structures. Most of the segmented structures scored as excellent in the external cohort visual assessment. Diagnostic accuracy for predicting PH was high [AUC = 0.88 (CI: 0.80–0.96), sensitivity = 70%, specificity = 100%], including pre-capillary PH [AUC = 0.84 (CI: 0.74–0.94), sensitivity = 72%, specificity = 94%] and PH-LHD [AUC = 0.86 (CI: 0.79–0.93), sensitivity = 94%, specificity = 63%].
Conclusion
A fully automated model for multi-structure cardiothoracic segmentation on unenhanced CT is achievable. The model can predict PH and identify patients with pre-capillary PH and PH-LHD with promising performance
Thermodynamics of stacking faults in GaAs-based system revealed by in-situ heating in TEM
Get PDFStacking faults (SFs) are a type of two-dimensional defect that can significantly degrade the performance of III-V semiconductor devices. In this study, we investigate the thermal evolution of intrinsic SFs in (In)GaAs-on-Si systems using in-situ heating in an aberration-corrected scanning transmission electron microscopy. Our results indicate that chiral intrinsic SFs near the InGaAs/GaAs interface undergo thermally induced migration and interaction, leading to the formation of Lomer-Cottrell locks at 700 °C. Between 200 and 700 °C, SFs exhibit sliding behaviour, which triggers their reaction into a characteristic three-layer defect (TLD) structure, which could be quickly annihilated during the baking environment. Using Lorentz transmission electron microscopy (LTEM) to image magnetization configurations, we observed the formation of intrinsic stacking fault (SF)-induced magnetic vortices. These vortices arise from the competition between the Heisenberg exchange interaction and the Dzyaloshinskii-Moriya interaction (DMI). Notably, as field-driven dipole oscillations intensify, the magneto-Stark effect enables manipulation of transitions between out-of-plane and in-plane magnetic vector fields. This work advances the understanding of defect dynamics in III-V compound semiconductors and provides new strategies for tailoring crystal quality during epitaxial growth
Beyond the projection postulate and back:Quantum theories with generalized state-update rules
No full textAre there consistent and physically reasonable alternatives to the projection postulate? Does it have unique properties compared to acceptable alternatives? We answer these questions by systematically investigating hypothetical state-update rules for quantum systems that Nature could have chosen over the Lüders rule. Among other basic properties, any prospective rule must define unique post-measurement states and not allow for superluminal signalling. Particular attention will be paid to consistently defining post-measurement states when performing local measurements in composite systems. Explicit examples of valid unconventional update rules are presented, each resulting in a distinct, well-defined foil of quantum theory. This framework of state-update rules allows us to identify operational properties that distinguish the projective update rule from all others and to put earlier derivations of the projection postulate into perspective
Lithium-ion battery thermal runaway propagation prevention — predicting critical parameters considering uncertainty
Get PDFLi-ion batteries (LIBs) are integral to modern society, driving the electrification of transport and supporting renewable energy generation to meet Net Zero. However, LIBs suffer from the potential to undergo thermal runaway (TR) which can lead to fire and explosions. Computational modelling of TR is essential to understanding its hazards, and to accurately quantify risks there is a need to account for the uncertainty in TR behaviour. To adequately predict the safe limits of battery operation we incorporate the stochasticity of thermo-physical and kinetic reaction parameters in module thermal runaway propagation (TRP) analysis. A 0-dimension heat transfer model for TRP predictions is validated against experimental findings. From this, Monte Carlo simulations are undertaken to determine the uncertainty in the predicted cell temperatures, times to cell TR and times to TRP. The critical heat dissipation coefficient to prevent TRP considering cell uncertainty was found to be 2.5 and 4.6 times larger for LFP and NMC stacks, respectively, compared to the scenario where cell uncertainty was not considered. For the LFP stack, the less severe TR events mean, in theory, that TRP can be prevented by heat pipe or submersion cooling thermal management systems. Without considering cell stochasticity there is a significant overestimate of TRP time and an underestimate of critical heat dissipation coefficient to prevent TRP. Hence, the predicted safe time for evacuation and appropriate thermal management methods are inaccurate. This work highlights the need to incorporate uncertainty in predictions of risk
Low-cost hybrid copper–carbon nanotube coating with antimicrobial properties in ambient conditions
No full textBackground
The development of bactericidal surfaces using nanotechnology has gained traction in high-tech sectors due to their effectiveness against pathogens. However, widespread adoption in low-income regions remains limited by the high cost of materials such as copper nanoparticles and the need for specialized application personnel. This study aims to develop a cost-effective bactericidal coating that minimizes nano-copper usage while maintaining strong antimicrobial performance and practical applicability in resource-limited environments.
Results
A polymer-based coating incorporating ≤3 wt% nano-copper and carbon nanotubes was formulated to enhance conductivity and mechanical stability. The fabrication process was optimized for on-site application under ambient conditions. Scanning Electron Microscopy (SEM) revealed a uniform surface distribution of nano-copper particles. Bactericidal activity tests confirmed efficacy against Escherichia coli, Listeria monocytogenes, and Salmonella spp. Techno-economic analysis indicated that the coating could be integrated into existing surface finishing systems at an incremental cost of 2.6–3.5 USD per gallon.
Conclusions
This work demonstrates the feasibility of producing and applying affordable nano-based bactericidal coatings under real-world conditions. The approach provides a practical pathway for implementing antimicrobial surface technologies in low-resource settings. Although the present study focused on wood substrates, future research should assess performance on diverse materials to broaden applicability. The combination of cost-effectiveness, efficacy, and scalability underscores the potential for both commercial adoption and significant public health benefits
Outdoor lighting and active travel: A high-resolution analysis using satellite imagery and Strava data in Glasgow
Get PDFIntroduction:
The benefits of active travel are well-established. While previous research has explored how built environment factors (such as population density, accessibility, land use, and infrastructure) influence active travel, micro-scale features like outdoor lighting have received less attention. This study examines associations between outdoor lighting levels and active travel in Glasgow, accounting for broader contextual factors and distinguishing between daylight and dark conditions.
Methods:
We used Strava data, satellite-derived outdoor lighting imagery, and other spatial datasets aggregated to small-area zones in Glasgow. Bayesian spatial models (Besag–York–Mollié) were fitted to estimate associations between contextual variables and distances travelled on foot, by bike, and by both modes combined, separately for daylight and dark hours.
Results:
Outdoor lighting levels derived from night-time satellite imagery were positively associated with walking, cycling, and overall active-travel distances during both light conditions (daylight and dark). These associations were stronger during dark hours, particularly for cycling. Several contextual relationships also varied by light condition: industrial density was positively associated with cycling only during daylight, while quietness and gradient showed stronger associations during daylight. Population and income deprivation were negatively associated across all modes under both light conditions.
Conclusions:
Our findings underscore the potential relevance of lighting in shaping active travel patterns after dark, particularly for cycling. They also highlight the need for future research that considers light conditions and time of day in environmental studies of mobility, as well as across broader contexts, specific locations, and diverse population groups – to better inform equitable and effective active travel policy
Thick Forests
Get PDFWe consider classes of graphs, which we call thick graphs, that have the vertices of a corresponding thin graph replaced by cliques and the edges replaced by cobipartite graphs. In particular, we consider the case of thick forests, which we show to be the largest class of perfect thick graphs.
Recognising membership of a class of thick C-graphs is NP-complete unless the class C is triangle-free, so we focus on this case. Even then membership can be NP-complete. However, we show that the class of thick forests can be recognised in polynomial time.
We consider two well-studied combinatorial problems on thick graphs, independent sets and proper colourings. Since determining the independence or chromatic number of a perfect graph is known to be tractable, we examine the complexity of counting all independent sets and colourings in thick forests.
Finally, we consider two parametric extensions to larger classes of thick graphs: where the parameter is the size of the thin graph, and where the parameter is its treewidth
Gamification and sustainability: A review of approaches for urban mobility and climate change resilience
Get PDFThe global urbanisation trend is exacerbating environmental issues and demanding innovative solutions. In this context, the role of human behaviour is increasingly significant, hence it is crucial to increase awareness and promote behavioural change. This systematic literature review explores the critical intersection of sustainable mobility and climate change resilience within urban contexts, focusing on the transformative potential of gamification to drive behavioural change. Sustainable mobility mitigates climate impacts by reducing emissions, while climate change resilience ensures transport systems can withstand environmental disruptions. Integrating these two concepts is crucial for a climate-secure urban future.
The research findings highlight the underexplored potential of Artificial Intelligence techniques for tailoring the gaming experience to the user or context, the need for a more diverse geographical distribution of studies, and the lack of attention given to the characteristics and needs of target users. Further, this review identifies the lack of studies combining sustainable urban mobility and climate change resilience
Mitigating future glacial lake outburst floods in the Himalaya
No full textGlacial lake outburst floods (GLOFs) are among the most severe cryospheric hazards in the Himalaya. While previous studies have primarily focused on the characteristics and causes of GLOFs, strategies for mitigating their disaster impacts remain underexplored. This study introduces China’s Glacial Lake Management System (GLMS) and evaluates its potential for regional replication in reducing damage caused by GLOFs. We find that while GLOF frequency shows a statistically insignificant decrease from 1990 to 2023, downstream damage has intensified, yet appears relatively mitigated within China across the Himalaya following the implementation of the GLMS. Further hydrodynamic modelling suggests that glacial lakes will continue to expand in the future, with total growth expected to triple relative to the 2000–2020 period. These expansions could increase GLOF exposure by over 27% for high-risk lakes and by more than 40% in regions outside China without targeted interventions. However, implementing GLMS engineering measures could reduce the intensity of future floods by 24%, with even greater reductions outside China—29% compared to 21% within China. Building on China’s lake management experience and recognizing the transboundary nature of GLOFs, the comprehensive framework we propose for region-wide glacial lake risk reduction across the Himalaya integrates engineering measures, early warning systems, and community responses. This framework addresses the urgent need for proactive and coordinated mitigation strategies in densely populated high-mountain regions
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