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(1) Background: Digital Elevation Models (DEMs) encompass digital bare earth surface representations that are essential for spatial data analysis, such as hydrological and geological modelling, as well as for other applications, such as agriculture and environmental management. However, available bare-earth DEMs can have limited coverage or accessibility. Moreover, the majority of available global DEMs have lower spatial resolutions (∼30–90 m) and contain errors introduced by surface features such as buildings and vegetation. (2) Methods: This research presents an innovative method to convert global DEMs to bare-earth DEMs while enhancing their spatial resolution as measured by the improved vertical accuracy of each pixel, combined with reduced pixel size. We propose the Joint Spatial Propagation Super-Resolution network (JSPSR), which integrates Guided Image Filtering (GIF) and Spatial Propagation Network (SPN). By leveraging guidance features extracted from remote sensing images with or without auxiliary spatial data, our method can correct elevation errors and enhance the spatial resolution of DEMs. We developed a dataset for real-world bare-earth DEM Super-Resolution (SR) problems in low-relief areas utilising open-access data. Experiments were conducted on the dataset using JSPSR and other methods to predict 3 m and 8 m spatial resolution DEMs from 30 m spatial resolution Copernicus GLO-30 DEMs. (3) Results: JSPSR improved prediction accuracy by 71.74% on Root Mean Squared Error (RMSE) and reconstruction quality by 22.9% on Peak Signal-to-Noise Ratio (PSNR) compared to bicubic interpolated GLO-30 DEMs, and achieves 56.03% and 13.8% improvement on the same items against a baseline Single Image Super Resolution (SISR) method. Overall RMSE was 1.06 m at 8 m spatial resolution and 1.1 m at 3 m, compared to 3.8 m for GLO-30, 1.8 m for FABDEM and 1.3 m for FathomDEM, at either resolution. (4) Conclusions: JSPSR outperforms other methods in bare-earth DEM super-resolution tasks, with improved elevation accuracy compared to other state-of-the-art globally available datasets.

Understanding the correlation between chemical and microstructural properties is critical for unraveling the fundamental relationship between materials chemistry and physical structures that can benefit materials science and engineering. Here, we demonstrate novel in situ correlative imaging of the X‐ray Compton scattering computed tomography (XCS‐CT) technique for studying this fundamental relationship. XCS‐CT can image light elements that do not usually exhibit strong signals using other X‐ray characterization techniques. This paper describes the XCS‐CT setup and data analysis method for calculating the valence electron momentum density and lithium‐ion concentration, and provides two examples of spatially and temporally resolved chemical properties inside batteries in 3D. XCS‐CT was applied to study two types of rechargeable lithium batteries in standard coin cell casings: (1) a lithium‐ion battery containing a cathode of bespoke microstructure and liquid electrolyte, and (2) a solid‐state battery containing a solid‐polymer electrolyte. The XCS‐CT technique is beneficial to a wide variety of materials and systems to map chemical composition changes in 3D structures. A novel in situ correlative imaging X‐ray Compton scattering computed tomography technique has been developed to unravel the fundamental relationship between the materials chemistry of light elements and their 3D structures. Here, this technique is applied to two types of batteries to map the internal lithium‐ion concentrations.

Abstract Rationale Acute kidney injury may contribute to distant organ dysfunction. Few studies have examined kidney injury as a risk factor for delirium and coma. Objectives To examine whether acute kidney injury is associated with delirium and coma in critically ill adults. Methods In a prospective cohort study of intensive care unit patients with respiratory failure and/or shock, we examined the association between acute kidney injury and daily mental status using multinomial transition models adjusting for demographics, nonrenal organ failure, sepsis, prior mental status, and sedative exposure. Acute kidney injury was characterized daily using the difference between baseline and peak serum creatinine and staged according to Kidney Disease Improving Global Outcomes criteria. Mental status (normal vs. delirium vs. coma) was assessed daily with the Confusion Assessment Method for the ICU and Richmond Agitation-Sedation Scale. Measurements and Main Results Among 466 patients, stage 2 acute kidney injury was a risk factor for delirium (odds ratio [OR], 1.55; 95% confidence interval [CI], 1.07–2.26) and coma (OR, 2.04; 95% CI, 1.25–3.34) as was stage 3 injury (OR for delirium, 2.56; 95% CI, 1.57–4.16) (OR for coma, 3.34; 95% CI, 1.85–6.03). Daily peak serum creatinine (adjusted for baseline) values were also associated with delirium (OR, 1.35; 95% CI, 1.18–1.55) and coma (OR, 1.44; 95% CI, 1.20–1.74). Renal replacement therapy modified the association between stage 3 acute kidney injury and daily peak serum creatinine and both delirium and coma. Conclusions Acute kidney injury is a risk factor for delirium and coma during critical illness.

In this study, we developed a XGBoost-based algorithm to downscale 2 km-resolution land surface temperature (LST) data from the GOES satellite to a finer 70 m resolution, using ancillary variables including NDVI, NDBI, and DEM. This method demonstrated a superior performance over the conventional TsHARP technique, achieving a reduced RMSE of 1.90 °C, compared to 2.51 °C with TsHARP. Our approach utilizes the geostationary GOES satellite data alongside high-resolution ECOSTRESS data, enabling hourly LST downscaling to 70 m—a significant advancement over previous methodologies that typically measure LST only once daily. Applying these high-resolution LST data, we examined the hottest days in Chicago and their correlation with ethnic inequality. Our analysis indicated that Hispanic/Latino communities endure the highest LSTs, with a maximum LST that is 1.5 °C higher in blocks predominantly inhabited by Hispanic/Latino residents compared to those predominantly occupied by White residents. This study highlights the intersection of urban development, ethnic inequality, and environmental inequities, emphasizing the need for targeted urban planning to mitigate these disparities. The enhanced spatial and temporal resolution of our LST data provides deeper insights into diurnal temperature variations, crucial for understanding and addressing the urban heat distribution and its impact on vulnerable communities.

River bathymetry is important for accurate flood inundation modelling but is often unavailable due to the time-intensive and expensive nature of its acquisition. This leads to several proposed and implemented approaches for its estimation. However, the errors in estimations inherent in these methods and how they affect the accuracy of the flood inundation modelling outputs, has not been extensively researched. Hence, to contribute, we investigate the sensitivity of flood predictions to the errors in river slope, width, and bank-full flow used in two formulas – the Uniform Flow and the Conceptual Multivariate Regression – for estimating river bathymetry. In this study, we employed a Monte Carlo framework to introduce random errors into these parameters drawn from a normal distribution with zero mean and a standard deviation set to 10 % of their best estimates. Using this process, we generated 50 simulated river bathymetries for each parameter along with an additional 50 where the errors were applied to all parameters simultaneously. The riverbeds generated from these bathymetries were combined with topographic LiDAR data to create model grids. Each grid was used in the hydrodynamic model LISFLOOD-FP to simulate the 2005 flood event in the Waikanae River area of New Zealand. We assessed the resulting flood inundation predictions for their variability and sensitivity. The results indicate that between two methods, the errors in the parameters in the Uniform Flow formula are associated with greater uncertainty in flood inundation depths and extents compared to the Conceptual Multivariate Regression. Among the parameters, the width errors correspond to the highest uncertainty, while the slope errors correspond to the lowest.

(1) Background: Wetlands are ecologically significant ecosystems that support biodiversity and contribute to essential environmental functions such as water purification, carbon storage and flood regulation. However, these ecosystems face increasing pressures from land-use change and degradation, prompting the need for scalable and accurate classification methods to support conservation and policy efforts. In this research, our motivation was to test whether high-spatial-resolution PlanetScope imagery can be used with pixel-based machine learning to support the mapping and monitoring of wetlands at a national scale. (2) Methods: This study compared four machine learning classification models—Random Forest (RF), XGBoost (XGB), Histogram-Based Gradient Boosting (HGB) and a Multi-Layer Perceptron Classifier (MLPC)—to detect and map wetland areas across New Zealand. All models were trained using eight-band SuperDove satellite imagery from PlanetScope, with a spatial resolution of ~3 m, and ancillary geospatial datasets representing topography and soil drainage characteristics, each of which is available globally. (3) Results: All four machine learning models performed well in detecting wetlands from SuperDove imagery and environmental covariates, with varying strengths. The highest accuracy was achieved using all eight image bands alongside features created from supporting geospatial data. For binary wetland classification, the highest F1 scores were recorded by XGB (0.73) and RF/HGB (both 0.72) when including all covariates. MLPC also showed competitive performance (wetland F1 score of 0.71), despite its relatively lower spatial consistency. However, each model over-predicts total wetland area at a national level, an issue which was able to be reduced by increasing the classification probability threshold and spatial filtering. (4) Conclusions: The comparative analysis highlights the strengths and trade-offs of RF, XGB, HGB and MLPC models for wetland classification. While all four methods are viable, RF offers some key advantages, including ease of deployment and transferability, positioning it as a promising candidate for scalable, high-resolution wetland monitoring across diverse ecological settings. Further work is required for verification of small-scale wetlands (<~0.5 ha) and the addition of fine-spatial-scale covariates.

Cadmium zinc telluride (CdZnTe) detectors are known to suffer from polarization effects under high photon flux due to poor hole transport in the crystal material. This has led to the development of a high-flux capable CdZnTe material (HF-CdZnTe). Detectors with the HF-CdZnTe material have shown promising results at mitigating the onset of the polarization phenomenon, likely linked to improved crystal quality and hole carrier transport. Better hole transport will have an impact on charge collection, particularly in pixelated detector designs and thick sensors (>1 mm). In this paper, the presence of charge sharing and the magnitude of charge loss were calculated for a 2 mm thick pixelated HF-CdZnTe detector with 250 μm pixel pitch and 25 μm pixel gaps, bonded to the STFC HEXITEC ASIC. Results are compared with a CdTe detector as a reference point and supported with simulations from a Monte-Carlo detector model. Charge sharing events showed minimal charge loss in the HF-CdZnTe, resulting in a spectral resolution of 1.63 ± 0.08 keV Full Width at Half Maximum (FWHM) for bipixel charge sharing events at 59.5 keV. Depth of interaction effects were shown to influence charge loss in shared events. The performance is discussed in relation to the improved hole transport of HF-CdZnTe and comparison with simulated results provided evidence of a uniform electric field.

Floodplain channels are important components of river‐floodplain systems and are known to play a key role in hydrodynamic exchange and sediment transport. The Amazon floodplain exhibits complex networks of these channels, and despite their potential importance to this globally important wetland system, these floodplain channels are relatively unstudied. The research presented here is the first systematic and detailed study of the network and morphologic characteristics of a large number of these channels in the middle reach of the central Amazon River using analysis of data derived from Landsat Enhanced Thematic Mapper Plus (ETM+) mosaic and field survey. Our findings show that the channels are ubiquitous, their width varies widely, and some of their characteristics can be fitted using power laws, potentially much like the self‐similar or fractal‐like behavior hypothesized for other types of fluvial networks. In all, 96% of the floodplain channels are not wide enough to be represented well, or at all, in the ∼90 m Shuttle Radar Topography Mission data. Channel depths are tied closely to the local amplitude of the passing main river flood wave (p value of 0.75), except where there are local runoff inputs, which results in substantially deeper channels which provide preferential flow paths across the floodplain. Channel networks imply that areas of the floodplain function for large parts of the flood cycle as separate hydrogeomorphic land units, here termed floodplain hydrological units (FHUs). These hypothesized FHUs also have distinct spatial and pattern characteristics, and it is suggested here that their differences could provide the beginnings of a framework for understanding the detailed hydrodynamics of the floodplain. In particular, different types of FHUs have differences in flood water source, which will have important implications for biogeochemical studies of the wetlands. Key Points Channel depths are related to river flood wave amplitude and any extra runoff Channels are poorly represented in the Shuttle Radar Topography Mission data Floodplain can be subdivided into functionally separate areas by channel network

Synchrotron x-rays are a powerful tool to probe real-time changes in the microstructure of materials as they respond to an external stimulus, such as phase transformations that take place in response to a change in temperature. X-ray imaging techniques include radiography and tomography, and have been steadily improved over the last decades so that they can now resolve micrometer-scale or even finer structural changes in bulk specimens over time scales of a second or less. Under certain conditions, these imaging approaches can also give spatially resolved chemical information. In this article, we focus on the liquid to solid transformation of metallic alloys and the temporal and spatial resolution of the accompanying segregation of alloying elements. The solidification of alloys provides an excellent case study for x-ray imaging because it is usually accompanied by the progressive, preferential segregation of one or more of the alloying elements to either the solid or the liquid, and gives rise to surprisingly complex chemical segregation patterns. We describe chemical mapping investigations of binary and quasi-binary alloys using radiography and tomography, and recent developments in x-ray fluorescence imaging that offer the prospect of a more general, multielement mapping technique. Future developments for synchrotron-based chemical mapping are also considered.

Urban green spaces are important nature-based solutions to mitigate climate change. While the distribution of green spaces within cities is well documented, few studies assess whether inequities in green space quantity (i.e., percent cover) are mirrored by inequities in green space quality (i.e., vegetation health or sensitivity to stressors). Green space quality is important to measure alongside green space quantity because vegetation that is healthier and less sensitive to stressors such as climatic fluctuations sustain critical ecosystem services through stressful environmental conditions, especially as the climate changes. We use a 40-year remote sensing dataset to examine the spatial patterns and underlying drivers of vegetation sensitivity to short-term (monthly) climate fluctuations in Chicago. Our results show that although vegetation cover was not equitably distributed between racially and ethnically segregated census tracts, socio-demographic composition was not a key driver of spatial variation in short-term vegetation sensitivity to climate. Instead, we found that vegetation growth form was a strong predictor of differences in vegetation sensitivity among communities. At the census tract level, higher herbaceous/shrub cover was associated with increased sensitivity to climate, while higher tree cover was associated with decreased sensitivity. These results suggest that urban green spaces comprising trees will be less sensitive (i.e., more resistant) to short-term climate fluctuations than those comprising predominately herbaceous or shrub cover. Our findings highlight that urban green space quality can vary spatially within cities; however, more work is needed to understand how the drivers of vegetation sensitivity vary among cities, especially those experiencing different climatic regimes. This work is key to planning and planting high-quality, climate change-resilient and equitable urban green spaces.