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BackgroundInert matrix fuel (IMF) can efficiently convert plutonium and long-lived minor actinides used for preventing the proliferation of nuclear weapons and improving spent fuel disposal, hence has been becoming a hot research topic in recent years. The sol-gel method has the advantage of uniform elemental distribution of the products and the wet operation process is less likely to produce radioactive dust, therefore, it has been used to prepare zirconium-based IMF in the research.PurposeThis study aims to prepare a colloidal solution with good dispersive properties and to obtain IMF microspheres with good sphericity, uniform size, and homogeneous elemental distribution.MethodsFirst of all, ThxZr1-xO2 inert matrix fuel was prepared by an external gelation process, and the sol-gel viscosity was used as the main gelation index. Then, the variation tendency of sol viscosity with c(NH4+)/c(NO3-) was investigated for different metal ions concentrations and different temperatures. Finally, the statistical distributions of colloidal particle sizes were obtained for different metal ions and reaction temperatures by laser particle sizing tests, and the X-ray diffraction (XRD) was used to study the structure of IMF after heat treatment at different temperatures.ResultsThe results showed that the complex gelation parameters and properties can be categorized and quantified using gelation field diagrams. In addition, ThxZr1-xO2 IMF kernels with uniform element distribution, good sphericity, and integral appearance were obtained by optimizing the process parameters. Zirconia showed low solubility behavior in the thorium-oxide system, leading to the generation of a biphasic structure.ConclusionsThe results of this study indicate that zirconium-based spherical IMF microspheres with good performance can be prepared by external gelation method.

This study aims to provide analyses of the levels of airborne particulate matter (PM) using a two-pronged approach that combines data from in situ Internet of Things (IoT) sensor networks with remotely sensed aerosol optical depth (AOD). Our approach involved setting up a network of custom-designed PM sensors that could be powered by the electrical grid or solar panels. These sensors were strategically placed throughout the densely populated areas of North Texas to collect data on PM levels, weather conditions, and other gases from September 2021 to June 2023. The collected data were then used to create models that predict PM concentrations in different size categories, demonstrating high accuracy with correlation coefficients greater than 0.9. This highlights the importance of collecting hyperlocal data with precise geographic and temporal alignment for PM analysis. Furthermore, we expanded our analysis to a national scale by developing machine learning models that estimate hourly PM   2.5 levels throughout the continental United States. These models used high-resolution data from the Geostationary Operational Environmental Satellites (GOES-16) Aerosol Optical Depth (AOD) dataset, along with meteorological data from the European Center for Medium-Range Weather Forecasting (ECMWF), AOD reanalysis, and air pollutant information from the MERRA-2 database, covering the period from January 2020 to June 2023. Our models were refined using ground truth data from our IoT sensor network, the OpenAQ network, and the National Environmental Protection Agency (EPA) network, enhancing the accuracy of our remote sensing PM estimates. The findings demonstrate that the combination of AOD data with meteorological analyses and additional datasets can effectively model PM   2.5 concentrations, achieving a significant correlation coefficient of 0.849. The reconstructed PM   2.5 surfaces created in this study are invaluable for monitoring pollution events and performing detailed PM   2.5 analyses. These results were further validated through real-world observations from two in situ MINTS sensors located in Joppa (South Dallas) and Austin, confirming the effectiveness of our comprehensive approach to PM analysis. The US Environmental Protection Agency (EPA) recently updated the national standard for PM   2.5 to 9 μ g/m   3 , a move aimed at significantly reducing air pollution and protecting public health by lowering the allowable concentration of harmful fine particles in the air. Using our analysis approach to reconstruct the fine-time resolution PM   2.5 distribution across the entire United States for our study period, we found that the entire nation encountered PM   2.5 levels that exceeded 9 μ g/m   3 for more than 20% of the time of our analysis period, with the eastern United States and California experiencing concentrations exceeding 9 μ g/m   3 for over 50% of the time, highlighting the importance of regulatory efforts to maintain annual PM   2.5 concentrations below 9 μ g/m   3 . [ABSTRACT FROM AUTHOR]

Background: To investigate the association between sex and neonatal respiratory distress syndrome (NRDS). Methods: Neonates born at our hospital and transferred to the neonatal department within 1 h were retrospectively analyzed. Depending on whether they developed NRDS during their hospital stay, the neonates was divided into NRDS and non-NRDS groups. There were 142 neonates in the NRDS group (95 males and 47 females) and 310 neonates in the non-NRDS group (180 males and 140 females). The neonates' data on gestational age (GA), sex, birth weight, white blood cell count (WBC), platelet count (PLT), C-reactive protein (CRP), total immunoglobulin M (total IgM), gestational diabetes mellitus(GDM), antenatal steroids use, meconium-stained amniotic fluid, and preterm premature rupture of membranes(PPROM) were gathered. Results: 452 neonates (265 males and 187 females) were involved for the purpose of collecting basic characteristic. Multivariate analysis, males had a 1.87 times higher risk of NRDS than females (P < 0.05) after controlling for the confounding effects of GA, birth weight, WBC, PLT, CRP, total IgM, GDM, antenatal steroids use, meconium-stained amniotic fluid, and PPROM. Conclusions: Sex was associated with NRDS; males had a considerably higher risk of NRDS than females. [ABSTRACT FROM AUTHOR]

BackgroundThe Uranium dioxide (UO2) is currently the most widely used nuclear fuel for commercial nuclear reactors. However, the Fukushima Daiichi nuclear disaster revealed the primary safety risks of this fuel in an accident, so various international programs were launched to develop accident tolerant fuel (ATF), a new generation of fuel system developed to enhance the capability of nuclear fuel assemblies in severe accidents.PurposeThis study aims to improve the thermal conductivity of fuel pellets by adding a second material to the UO2 matrix, an important research direction for ATF.MethodsFirst of all, large-grain UO2 particles were used as raw materials, and the high-density large grain UO2-SiC composite fuel pellets were obtained by Spark Plasma Sintering (SPS) sintering process at lower sintering temperature. Then, the properties, such as microstructure and chemical composition, of the composite fuel pellets were characterized by using metallographic microscope (MM), scanning electron microscope (SEM), X-ray diffraction (XRD) and energy dispersive spectrometer (EDS). Finally, the high-temperature oxidation resistance in air environment was studied.ResultsThe results show that the UO2-SiC interfacial reaction can be avoided by the SPS sintering at lower temperature, and the density of the prepared pellets is more than 95% theoretical density (TD). Compared with traditional UO2 fuel pellets and SPS sintered UO2-SiC pellets using conventional UO2 powders, the thermal conductivity of large-grain composite fuel pellets is significantly improved. Oxidation tests results indicate that the oxidation weight gain of the composite fuel pellets is significantly weaker than that of traditional pellets when the temperature is lower than 350 ℃. However, when the temperature reaches 350 ℃, the oxidation of UO2 cannot be further prevented by SiC.ConclusionsThis study provides reference for improving the thermal conductivity of UO2 matrix by adding a second phase with high thermal conductivity.

When using non-destructive testing equipment to evaluate the quality of asphalt pavement, the asphalt pavement mixtures will exhibit different dielectric properties under dissimilar temperature conditions since the external electric field is affected by ambient temperature, which interferes with the accuracy of non-destructive testing. To improve the accuracy of non-destructive testing and quantify the influence of temperature on the dielectric properties of asphalt mixtures, a dielectric model suitable for describing the relationship between the relative dielectric constants of asphalt mixtures and temperature is established. The relative dielectric constant and dielectric loss of the two types of asphalt mixtures are measured at different temperatures to verify the accuracy of the dielectric model, and the goodness of model fit verifies wide applicability of the model. This temperature-based asphalt mixture dielectric model has great significance for quantifying the influence of temperature on the dielectric properties. Furthermore, the model shows that the dielectric properties of different asphalts and aggregates have different temperature sensitivity, and the concept of conductance activation energy proposed in this research provides innovative approaches for the optimisation of asphalt mixtures and raw materials. [ABSTRACT FROM AUTHOR]

Ground penetrating radar (GPR) is widely used in nondestructive tests of asphalt pavements, but the characteristics of its detection data need to be extracted manually, and it is very difficult to determine the hidden distress efficiently and accurately inside the asphalt pavement. Therefore, to recognize hidden distress inside asphalt pavement intelligently and improve the efficiency and accuracy of GPR for asphalt pavement detection, this research used the convolutional neural network (CNN) algorithm combined with GPR detection technology. Significantly, the image of the dielectric properties distribution of the road was obtained by processing the GPR detection data, and the GPR detection image data set was established. Moreover, this research dealt with the imbalance of the data set by weighting the loss value. Next, this research designed a CNN model with a simple structure, called visual GPR (VGPR), and used the GPR image data set to train, validate, and test the VGPR. Finally, the model was fine-tuned according to the results, and the optimal hyperparameters were selected. The results show that VGPR converges fast in the training process, and no obvious overfitting phenomenon exists. During the test, the weighted average F1 score in the comprehensive metrics reached 99.626%, indicating that VGPR has excellent generalization performance. When VGPR uses a graphics processing unit for computing, the image recognition efficiency can meet the engineering requirements. A comparison of VGPR with other CNN models reveals that when using the same GPR detection image data set, VGPR has the best recognition effect. In summary, the method detailed herein can quickly and accurately identify the hidden distress of the asphalt pavement and provide a guarantee for the maintenance and operation of the expressway. [ABSTRACT FROM AUTHOR]

This second article in a series of two also addresses the conjectures posited in Part I, asserting that many georeferenced attribute random variables (RVs) contain a positive–negative spatial autocorrelation (SA) mixture, and that georeferenced socio‐economic/demographic attribute RVs have roughly the same magnitude of positive SA as that frequently computed for remotely sensed pixel data, an extent suppressed in its net amount by the negative SA in a variable's mixture. Rather than a single RV across a multitude of geographic landscapes, this article summarizes research exploring these two propositions for a wide range of attribute RVs. Findings from both Parts I and II corroborate this mixture's commonplace existence, as well as the tendency for pronounced positive SA to characterize both socio‐economic/demographic and remotely sensed variates, with moderate net positive SA typifying measurements for these former georeferenced data. [ABSTRACT FROM AUTHOR]

PM 2.5 , a type of fine particulate with a diameter equal to or less than 2.5 micrometers, has been identified as a major source of air pollution, and is associated with many health issues. Research on utilizing various data sources, such as remote sensing and in situ sensors, for PM 2.5 concentrations modeling remains a hot topic. In this study, the Next Generation Weather Radar (NEXRAD) is used as a supplementary weather data source, along with European Centre for Medium-Range Weather Forecasts (ECMWF), solar angles, and Geostationary Operational Environmental Satellite (GOES16) Aerosol Optical Depth (AOD) to model high spatial-temporal PM 2.5 concentrations. PM 2.5 concentrations as well as in situ weather condition variables are collected from the 31 sensors that are deployed in the Dallas Metropolitan area. Four machine learning models with different predictor variables are developed based on an ensemble approach. Since in situ weather observations are not widely available, ECMWF is used as an alternative data source for weather conditions in studies. Hence, the four established models are compared in three groups. Both models in this first group use weather variables collected from deployed sensors, but one uses NEXRAD and the other does not. In the second group, the two models use weather variables retrieved from ECMWF, one using NEXRAD and one without. In the third group, one model uses weather variables from ECMWF, and the other uses in situ weather variables, both without NEXRAD. The first two environmental groups investigate how NEXRAD can enhance model performances with weather variables collected from in situ observations and ECMWF, respectively. The third group explores how effective using ECMWF as an alternative source of weather conditions. Based on the results, the incorporation of NEXRAD achieves an R 2 score of 0.86 and 0.83 for groups 1 and 2, respectively, for an improvement of 2.8% and 9.6% over those models without NEXRAD. For group three, the use of ECMWF as an alternative source of in situ weather observations results in a 0.13 R 2 drop. For PM 2.5 estimation, weather variables including precipitation, temperature, pressure, and surface pressure from ECMWF and deployed sensors, as well as NEXRAD velocity, are shown to be significant factors. [ABSTRACT FROM AUTHOR]

In this study, we present a nationwide machine learning model for hourly PM 2.5 estimation for the continental United States (US) using high temporal resolution Geostationary Operational Environmental Satellites (GOES-16) Aerosol Optical Depth (AOD) data, meteorological variables from the European Center for Medium Range Weather Forecasting (ECMWF) and ancillary data collected between May 2017 and December 2020. A model sensitivity analysis was conducted on predictor variables to determine the optimal model. It turns out that GOES16 AOD, variables from ECMWF, and ancillary data are effective variables in PM 2.5 estimation and historical reconstruction, which achieves an average mean absolute error (MAE) of 3.0 μ g/m 3 , and a root mean square error (RMSE) of 5.8 μ g/m 3 . This study also found that the model performance as well as the site measured PM 2.5 concentrations demonstrate strong spatial and temporal patterns. Specifically, in the temporal scale, the model performed best between 8:00 p.m. and 11:00 p.m. (UTC TIME) and had the highest coefficient of determination (R 2 ) in Autumn and the lowest MAE and RMSE in Spring. In the spatial scale, the analysis results based on ancillary data show that the R 2 scores correlate positively with the mean measured PM 2.5 concentration at monitoring sites. Mean measured PM 2.5 concentrations are positively correlated with population density and negatively correlated with elevation. Water, forests, and wetlands are associated with low PM 2.5 concentrations, whereas developed, cultivated crops, shrubs, and grass are associated with high PM 2.5 concentrations. In addition, the reconstructed PM 2.5 surfaces serve as an important data source for pollution event tracking and PM 2.5 analysis. For this purpose, from May 2017 to December 2020, hourly PM 2.5 estimates were made for 10 km by 10 km and the PM 2.5 estimates from August through November 2020 during the period of California Santa Clara Unite (SCU) Lightning Complex fires are presented. Based on the quantitative and visualization results, this study reveals that a number of large wildfires in California had a profound impact on the value and spatial-temporal distributions of PM 2.5 concentrations. [ABSTRACT FROM AUTHOR]