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Satellite remote sensing has become an important means of forest fire monitoring because it has the advantages of wide coverage, few ground constraints and high dynamics. When utilizing satellites for forest fire hotspot monitoring, two types of ground hotspots, agricultural and other fire hotspots can be ruled out through ground object features. False forest fire hotspots within forested areas must be excluded for a more accurate distinction between forest fires and non-forest fires. This study utilizes spatio-temporal data along with time-series classification to excavate false forest fire hotspots exhibiting temporal characteristics within forested areas and construct a dataset of such false forest fire hotspots, thereby achieving a more realistic forest fire dataset. Taking Hunan Province as the research object, this study takes the satellite ground hotspots in the forests of Hunan Province as the suspected forest fire hotspot dataset and excludes the satellite ground hotspots in the forests such as fixed heat sources, periodic heat sources and recurring heat sources which are excavated. The validity of these methods and results was then analyzed. False forest fire hotspots, from satellite ground hotspots extracted from 2019 to 2023 Himawari-8/9 satellite images, closely resemble the official release of actual forest fires data and the accuracy rate in the actual forest fire monitoring is 95.12%. This validates that the method employed in this study can improve the accuracy of satellite-based forest fire monitoring. [ABSTRACT FROM AUTHOR]

The atmospheric weighted mean temperature (Tm) is a key parameter in global navigation satellite system (GNSS) water vapor retrieval and can convert the zenith wet delay (ZWD) into precipitable water vapor (PWV). However, there are some shortcomings in the existing Tm models, such as the detailed time-varying lapse rate not being considered. Additionally, the spatiotemporal characteristics of Tm need to be further refined. Therefore, we developed a new global high-precision and high-spatiotemporal-resolution Tm model considering time-varying lapse rate using the latest European Centre for Medium-Range Weather Forecasts ReAnalysis 5 (ERA5) atmospheric reanalysis data. Firstly, a global multidimensional Tm lapse rate model (NGGTm-H model) was developed using the sliding window algorithm. Secondly, the daily variation characteristics of Tm and its relationships with geographical situation were investigated. Finally, a hybrid-grid global Tm model considering time-varying lapse rate (NGGTm model) was developed. To verify the effectiveness of the proposed model, the NGGTm model was compared with the Bevis and GPT3 models using the Tm data recorded at 378 radiosonde stations in 2017 and the surface grid Tm data calculated from the ERA5 reanalysis data. The results show that taking the surface grid Tm data of ERA5 as reference values, the average root mean square error (RMSE) value predicted by the NGGTm model was 2.84 K, which was higher with 0.50 K, 0.18 K and 0.06 K than those of the Bevis, GPT3-5 and GPT3-1 models, respectively. Meanwhile, taking the Tm data from the radiosonde stations as the reference values, the mean bias and RMSE of the NGGTm model were 0.10 K and 3.30 K, respectively, which exhibit the best accuracy and stability among the Bevis, GPT3-5 and GPT3-1 models. [ABSTRACT FROM AUTHOR]

Gold mineralization in the Jiaojia gold belt was formed in a structurally- dominant hydrothermal mineral system showing a close spatial association with the Jiaojia detachment fault. This study delves into the Jiaojia gold belt from the perspective of coupled spatial association and ore-forming processes by employing spatial analysis of three-dimensional (3D) models, 3D ore-forming numerical modeling, and 3D prospectivity modeling using machine learning techniques (random forest (RF) and multilayer perceptron (MLP)). The overarching goal is to gain insight into the structural-hydrothermal gold system and pinpoint potential areas of deep-seated gold deposits for future exploration endeavors. The spatial analysis of ore-controlling faults uncovers a close correlation between gold enrichment and specific fault geometrical attributes, including a dip angle ranging from 20° to 40°, minimal variations in dip angle (less than 5°), and convex topographical features. These attributes likely stem from the influence of fault morphology on the flow and pooling of fluids. In conjunction with this, 3D ore-forming numerical modeling of structural deformation and fluid flow reveals that gold mineralization is intertwined with moderate volumetric strain and shear strain of rock and fluid divergence. This interaction seems particularly pronounced in areas characterized by channel-like or gentle features. Consequently, it is plausible that gold distribution in the Jiaojia region is the outcome of a comprehensive coupling process involving strain localization, rock deformation, fluid flow, heat transfer and/or interaction. The deep gold prospectivity models of RF and MLP for the Jiaojia district jointly using the predictive variables of fault geometry features and ore-forming simulation data (volume strain, shear strain, temperature variation, and fluid flux) exhibit higher AUC (area under the curve) values compared to models employing individual predictor variable datasets. This improvement underscores their enhanced predictive capability. The prospectivity results thus were used for identifying gold potential within the Jiaojia region, where five promising gold targets at depth were ultimately determined. [ABSTRACT FROM AUTHOR]

In order to solve the power quality problems and eliminate the neutral sections in the railway, the implementation of the continuous co‐phase traction power supply system (CCTPSS) is the development trend of the future. However, various parameters of the compensator in CCTPSS have a huge impact on the resonance, which will possibly change the original resonance characteristic of the railway. Therefore, the parameters design of the compensator has become a difficult problem to be solved. An optimization mode method is proposed in this paper to obtain the influence of the compensator on the resonance characteristic of CCTPSS and the optimal solution of each parameter is obtained with the goal of suppressing resonance and minimizing the cost. Firstly, the impedance analysis and transfer function analysis are used to establish models for CCTPSS. It is found that the equivalent harmonic impedance of the traction substation decreases after adding the compensator, which will inevitably cause the resonance frequency to increase. By comparing the resonance points of the original traction power supply system (TPSS) and CCTPSS, the results show that the parameters of the compensator have a greater influence on the resonance point in CCTPSS. At last, taking the CCTPSS based on LCL‐type reactive power comprehensive compensator (RPCC) as an example to carry out the optimization mode method, the result proves that this method is not only beneficial to the suppression of harmonic resonance, but also helpful for reducing the cost of the RPCC. [ABSTRACT FROM AUTHOR]

This paper proposes a novel glass fiber‐reinforced polymer (GFRP)‐timber‐steel tube‐concrete composite column (GTSC) that combines an outer steel tube with GFRP fabric reinforcement, an inner timber core, and concrete for the rest of the space. The mechanical behavior of eight specimens of novel composite columns was investigated considering diameters of timber cores, layer numbers, and angles of GFRP laminates. The results showed that as the size of the timber core increased from 0 to 50 mm, the ductility and the energy dissipation capacity of the composite column maximum increased by 61.5% and 63.4%, respectively. Conversely, the ultimate load carrying capacity and stiffness decreased. It was also found that the timber core weakened the strength of the confined concrete. However, timber core reduced the weight of columns, which slowed down the reduction of the ultimate load carrying capacity of per unit GTSCs, with the stiffness remains unchanged. GFRP laminates with an angle of 60° provide the best stiffness and ductility, and the layer numbers of GFRP has a positive effect on GTSC's mechanical performance. In addition, an analytical model was proposed based on the ultimate equilibrium method, which accounts for the ultimate bearing capacity of GTSC based on the influence of timber. Analyses revealed a good correlation between analytical and experimental results. [ABSTRACT FROM AUTHOR]

In order to evaluate the performance of precipitable water vapor (PWV) inversion from reanalysis data in specific areas and under specific weather conditions, an atmospheric weighted mean temperature model was established based on the observation data of Guilin radiosonde stations from 2015 to 2017. The continuous operating reference stations (CORS) precipitable water vapor (CORS-PWV) calculated by the model was used as the reference value, and was compared with the PWV extracted from the two reanalysis data ERAS and MERRA-2. The results show that compared with Bevis model, Guangxi model and Eastern China model, the accuracy of the new model is improved by 8.1%, 13.1% and 17.1%, respectively. In the course of a rainstorm, taking the CORS-PWV of day of year from 178-183 in 2017 as reference value, the mean bias and RMSE of PWV extracted by ERAS are 1.55 mm and 3.38 mm respectively, while the mean bias and RMSE of PWV extracted by MERRA-2 are -1.60 mm and 6.18 mm. The PWV extracted by ERAS has a better consistency with the PWV retrieved by CORS station, which is consistent with the actual ground precipitation situation. [ABSTRACT FROM AUTHOR]

In recent years, geographically weighted regression (GWR) models have been widely used to address the spatial heterogeneity and spatial autocorrelation of PM2.5, but these studies have not fully considered the effects of all potential variables on PM2.5 variation and have rarely optimized the models for residuals. Therefore, we first propose a modified GWR model based on principal component analysis (PCA-GWR), then introduce five different spatial interpolation methods of radial basis functions to correct the residuals of the PCA-GWR model, and finally construct five combinations of residual correction models to estimate regional PM2.5 concentrations. The results show that (1) the PCA-GWR model can fully consider the contributions of all potential explanatory variables to estimate PM2.5 concentrations and minimize the multicollinearity among explanatory variables, and the PM2.5 estimation accuracy and the fitting effect of the PCA-GWR model are better than the original GWR model. (2) All five residual correction combination models can better achieve the residual correction optimization of the PCA-GWR model, among which the PCA-GWR model corrected by Multiquadric Spline (MS) residual interpolation (PCA-GWRMS) has the most obvious accuracy improvement and more stable generalizability at different time scales. Therefore, the residual correction of PCA-GWR models using spatial interpolation methods is effective and feasible, and the results can provide references for regional PM2.5 spatial estimation and spatiotemporal mapping. (3) The PM2.5 concentrations in the study area are high in winter months (January, February, December) and low in summer months (June, July, August), and spatially, PM2.5 concentrations show a distribution of high north and low south. [ABSTRACT FROM AUTHOR]