The rural region of the municipality of Bananal (SP, Brazil) experiences recurrent flooding events associated with rising water levels in tributaries of the Bananal River, especially during periods of intense rainfall. This study aimed to compare the performance of different Digital Elevation Models (DEMs), one derived from NASA orbital data and another generated from drone-based aerophotogrammetric surveys, in identifying and mapping flood-prone areas. The objective was to assess whether drone field campaigns are essential for this type of analysis or whether orbital DEMs are sufficient for the hydrodynamic characterization of the area. Hydrodynamic models were developed using the software QGIS, HidroFlu—for watershed parametrization and inflow estimation, and MODCEL—for hydrodynamic simulation, with spatial resolutions of 10 m, 30 m, and 50 m, in order to analyze the impact of topographic detail on simulation results. Two approaches were tested for defining boundary conditions: one based on precipitation data with a 25-year return period, and another based on the Bananal River discharge estimated from the watershed. The results indicated that the model based on the drone-derived DEM, with 10 m resolution and boundary conditions defined by river discharge, showed the best performance in representing floodable areas. However, the findings also highlight that high-resolution DEMs entail higher operational costs, due to the need for field activities and greater computational capacity to run the simulations.
Stereolithography 3D printing technology is widely used in aerospace, automotive, medical, weapons, and other fields because of its high processing accuracy, low cost, simple operation, and flexible manufacturing. The photocuring 3D printing ceramic slurry is a key part of the photocuring 3D printing ceramic technology. The preparation techniques of photocurable 3D printing ceramic slurry mainly include the mechanical mixing method, sol-gel method, ultrasonic dispersion method, and in-situ polymerization method. This paper summarizes the preparation methods and research progress of photocuring 3D printing ceramic slurry, expounds the essence of photocuring and the composition and function of ceramic slurry, and analyzes the influence of various properties of photocuring 3D printing ceramic slurry on the properties of final products, such as rheological properties, solid content, curing thickness, and stability. Finally, the existing problems and future development potential of photocuring 3D printing ceramic slurry preparation technology are summarized.
Porous ceramic filters exhibit excellent prospects for application in the field of high-temperature flue gas filtration. In this study, the MgAl2O4 porous ceramics were prepared using α-Al2O3, MgO, and EDTA-MgNa2 as raw materials by the in-situ decomposition method. The effect of the introduction of EDTA-MgNa2 on phase composition and microstructure, as well as the correlation between the content of EDTA-MgNa2 and ceramic properties, was investigated using XRD, SEM, and EDS. The results revealed that the introduction of EDTA-MgNa2 formed pores, thereby improving gas permeability. Additionally, the addition of EDTA-MgNa2 was beneficial for the formation of a transitional liquid and promoted sintering, thereby slowing the decrease in compressive strength. The optimal specimen is the ceramic with 10 wt% EDTA-MgNa2, which exhibits a high porosity of 56.28%, a compressive strength of 10.93 MPa, and a high gas permeability coefficient (8.84 × 10−9 m2).
Radiation-induced brain injury (RIBI), a common adverse effect of cranial radiotherapy for head malignancies, causes severe complications, including blood-brain barrier (BBB) disruption, neuroinflammation, cognitive decline, and radiation necrosis (RN) that impair patients’ quality of life. The pathophysiology of RIBI involves intricate crosstalk between various central nervous system (CNS) cell types, with astrocytes, the principal CNS glial cells, serving as key mediators. Under physiological conditions, they sustain brain homeostasis, but their transition to reactive phenotypes and subsequent dysfunction propel RIBI development. This review summarizes recent advances in astrocytes’ pathophysiological roles in RIBI, focusing on mechanisms like reactive astrocyte polarization, neuroinflammation, BBB impairment, radiation-induced senescence, astrocyte-mediated RN progression, and pathological crosstalk with other CNS cells. It also outlines astrocyte-targeted therapeutic strategies with preclinical efficacy, including anti-inflammatory therapies, anti-vascular endothelial growth factor A (VEGFA) interventions, TSPO ligands, RAS blockers, apolipoprotein E (ApoE) regulation, Δ133p53, and microRNAs (miRNAs), which alleviate RIBI by targeting these pathological processes. A comprehensive understanding of astrocyte-mediated mechanisms and preclinical evidence will lay the foundation for developing targeted, low-toxicity therapies to mitigate RIBI in cranial radiotherapy patients.
Semi-enclosed coastal systems are highly dynamic environments where benthic organisms are exposed to strong hydrographic gradients and increasing anthropogenic pressures. This study assessed the habitat suitability of the pearl oyster Pinctada radiata in two contrasting Mediterranean gulfs of Central Greece, the Maliakos and the South Evoikos, by integrating Copernicus Earth Observation (EO) products with an Artificial Intelligence (AI) modeling framework. Environmental variables, including sea surface temperature, salinity, chlorophyll-a concentration, current velocity, and dissolved oxygen, were derived from satellite and marine datasets and used to train a multi-algorithm ensemble combining Maximum Entropy (MaxEnt), Extreme Gradient Boosting (XGBoost), and a Convolutional Neural Network (CNN). The ensemble model showed strong predictive skill (AUC = 0.94; TSS = 0.80) and identified temperature, dissolved oxygen, and substrate type as the main drivers of habitat suitability. Spatial projections indicated that roughly two-thirds of the study area currently supports favorable conditions for P. radiata, particularly in shallow, low-energy, mesotrophic zones. Under a simulated +2 °C warming scenario, highly suitable habitats declined by about 20%, highlighting the species’ sensitivity to future thermal stress and subsequent oxygen depletion, demonstrating the value of EO-driven AI approaches for anticipating ecological change in vulnerable coastal systems.
