Existing research of extreme rainfall impact on transport networks primarily examines the effect of waterlogging. Although the other two main factors-reduced visibility and traffic-signal power outages-have been shown to significantly affect road operation, their contributions at the network scale remain underexplored. Taking a macroscopic approach, this study gauges the impacts of these three factors on the road network connectivity and efficiency during extreme rainfall through a case study of 26 Local Government Areas in and around Greater London. The result shows that focusing solely on waterlogging while disregarding reduced visibility and traffic signal power failures overestimates road capacities by 15-30% and underestimates network efficiency impacts by 1–23% under different rainfall scenarios. Particularly, the largest impact underestimation is observed for 1-in-30-year rainfall risk, where waterlogging is less dominant, while poor visibility considerably contributes to the impacts. The analysis also suggests that signal power failures during rainfall have limited, localised effects at the network level.

Structural health monitoring of underground tunnels increasingly uses advanced sensing and data-driven methods. Laser-scanned 3D point clouds capture spatially rich measurements of segmental tunnel linings and require segmentation as a prerequisite for downstream analysis. Deep learning (DL) is effective for point-cloud segmentation, but scarce datasets and costly annotation limit practical use. This paper presents Tunnel Scanner , a high-fidelity simulator that synthesises realistic tunnel point clouds with automatic annotation. The plug-and-play module Hybrid Position–Normal Local Spatial Encoding embeds geometric priors into DL backbones and combines with transfer learning (TL) to exploit synthetic data for domain adaptation. Models trained only on synthetic data achieved 71.9% mean Intersection-over-Union (mIoU) and 86.6% Overall Accuracy (OA), and TL increased performance to at least 78.8% mIoU and 90.9% OA with limited real data. This paper highlights geometry-informed data synthesis as a viable augmentation approach for digital inspection and asset management of large-scale tunnels. • Develop a high-fidelity simulator to address the scarcity of tunnel point clouds. • Propose a plug-and-play module encoding geometric features into DL backbones. • Investigate transfer learning to enhance 3D Sim-to-Real domain adaptation. • The end-to-end framework yields ＋24% segmentation accuracy with limited real data.

This study evaluates the feasibility and performance of uranium-zirconium hydride (U-ZrH x ) fuel in a graphite-free AGR-like Fluoride Salt-Cooled High-Temperature Reactor (AGR-like FHR). This reactor concept combines the pin-type geometry of Advanced Gas-cooled Reactors (AGR) with the low-pressure, high-efficiency characteristics of molten salt coolants and Hastelloy-N as cladding, aiming to improve thermal efficiency and passive safety. While conventional UO 2 fuel has been previously considered for this configuration, issues such as excessive fission gas release (FGR), swelling, and high fuel temperatures limit its viability. To address these challenges, a promising solution relies on using U-ZrH x fuel. The study explores its potential benefits, particularly its intrinsic neutron moderation, and includes an analysis of hydrogen transport accounting for diffusion, thermal gradients, and permeation across cladding. A new graphite-free 4-ring fuel pins cluster geometry was also introduced, increasing pin count while maintaining the core average power density; this increase from 36 to 60 pins reduces the pin-average power and linear heat rate proportionally (36/60 ≈ 0.60), thereby flattening the power distribution and reducing peak temperatures. The analyses were carried out using the OFFBEAT multidimensional fuel performance code and the Serpent Monte Carlo code for neutronics analyses. Simulation results show that U-ZrH 1.6 45 wt% U fuel used in the AGR-like FHR can potentially reduce peak fuel temperatures and azimuthal temperature variation, while maintaining a relatively constant temperature drop across the gap and internal pressure. Hydrogen migration to the gap and through the cladding can be mitigated through an inner liner coating. Verification against MARVEL fuel computational benchmark cases shows the accuracy of the hydrogen transport model. These findings are promising to support the viability of U-ZrH x fuel for the AGR-like FHR design, offering improved safety margins, mechanical stability, and simplified core geometry. • Extension of the OFFBEAT code to model U-ZrH x hydride fuel behaviour. • Development of a hydrogen transport model including diffusion and boundary loss. • Introduction of a graphite-free 4-ring AGR-like FHR core geometry. • U-ZrH x fuel reduces peak temperature by ∼ 250 ℃ and improves temperature uniformity. • Hydrogen migration identified as main pressure driver; liner coating mitigates leakage.

We present a method for absolute quantification of deuterated metabolites in vivo at 7T. We describe acquisition protocols and an analysis pipeline compatible with 7T Terra MRIs. We apply them using a 2H/1H receive array in healthy volunteers and glioblastoma patients. B1 +/B1 - maps from multiple CSI scans in a uniform phantom were used to derive calibrated coil weights for absolute quantification. We validated this in phantoms with known deuterated compounds. 2H MRSI was performed in twelve healthy volunteers (two post-[6,6-2H2]-glucose) and five treatment-naive glioblastoma patients (all post-[6,6-2H2]-glucose). Spectra were fitted with OXSA. We compared Glx/Lac between tumor and normal-appearing brain using linear mixed-effects models. Measured B1 + maps were 0.82 ± 0.19 μT/√W across the whole phantom. Natural abundance deuterium in water was 8.96 ± 0.7 mmol/L. Absolute maps of HDO, Glc, Glx, and Lac were acquired following [6,6-2H2]glucose. Rate maps showed higher Lac production in tumors (2.3 μmol/L/min, SE = 0.87) compared with normal-appearing regions (1.0 μmol/L/min, SE = 0.36; p < 0.01) and healthy brain (0.5 μmol/L/min, SE = 0.17; p < 0.01). Glx production was lower in tumors (3.8 μmol/L/min, SE = 0.44) relative to normal-appearing contralateral regions (6.0 μmol/L/min, SE = 0.36; p < 0.001) and healthy brain (9.2 μmol/L/min, SE = 0.61; p < 0.001). We demonstrate robust absolute quantification for human 7T DMI. Glioblastoma tumors showed elevated Lac and reduced Glx labeling relative to normal brain, with inter-patient heterogeneity consistent with an existence of different metabolic subtypes.