Accurately mapping urban vegetation carbon density is challenging because of complex landscapes and mixed pixels. In this study, a novel methodology was proposed that combines a linear spectral unmixing analysis (LSUA) with a linear stepwise regression (LSR), a logistic model-based stepwise regression (LMSR) and k-Nearest Neighbors (kNN), to map the forest carbon density of Shenzhen City of China, using Landsat 8 imagery and sample plot data collected in 2014. The independent variables that contributed to statistically significantly improving the fit of a model to data and reducing the sum of squared errors were first selected from a total of 284 spectral variables derived from the image bands. The vegetation fraction from LSUA was then added as an independent variable. The results obtained using cross-validation showed that: (1) Compared to the methods without the vegetation information, adding the vegetation fraction increased the accuracy of mapping carbon density by 1%-9.3%; (2) As the observed values increased, the LSR and kNN

The effect of solution treatment and intermediate heat treatment on the microstructure and properties of a new cast nickel-based high-Cr superalloy was investigated in this paper. The results indicate that the tensile strength and elongation at 900 °C increase when the solution temperature increases from 1160 °C to 1180 °C and then decrease when the solution temperature changes from 1180 °C to 1200 °C and 1220 °C. The stress rupture test results of the high-Cr superalloy under conditions of 900 °C/275 MPa shows that the rupture time, elongation, and reduction of area initially increased and then decreased with the increase in solution treatment temperatures. The results of stress rupture tests for the alloy after intermediate heat treatment followed by furnace-cooling, air-cooling, and water-cooling show that the morphology and distribution of γ’ phase have a great influence on the tensile test results at 900 °C of the alloy but no obvious influence on the test at 900 °C/275 MPa. The microstructure analysis of the superalloy after heat treatment shows that: when the solution treatment temperatures are at 1200 °C and 1220 °C, the incipient melting appears in the interdendritic region, which can severely deteriorate mechanical properties; the morphology of γ′ phase changes gradually from cube to spherical; and a large number of fine γ’ phase precipitates in the γ channel are found with increasing cooling rate after intermediate heat treatment.

With the development of the engineering construction industry, knowledge became an important strategic resource for construction enterprises, and knowledge graphs are an effective method for knowledge management. In the context of peak carbon dioxide emissions and carbon neutrality, low carbon emission became one of the important indicators for the selection of construction schemes, and knowledge management research related to low carbon construction must be performed. This study investigated a method of incorporating low-carbon construction knowledge into the bridge construction scheme knowledge graph construction process and proposed a bridge construction scheme recommendation method that considers carbon emission constraints based on the knowledge graph and similarity calculation. First, to solve the problem of the poor fitting effect of model parameters caused by less annotation of the corpus in the bridge construction field, an improved entity recognition model was proposed for low-resource conditions with limited data. A knowledge graph of low carbon construction schemes for bridges was constructed using a small sample dataset. Then, based on the construction of this knowledge graph, the entities and relationships related to construction schemes were obtained, and the comprehensive similarity of bridge construction schemes was calculated by combining the similarity calculation principle to realize the recommendation of bridge construction schemes under different constraints. Experiments on the constructed bridge low carbon construction scheme dataset showed that the proposed model achieved good accuracy with named entity recognition tasks. The comparative analysis with the construction scheme of the project verified the validity of the proposed construction scheme considering carbon emission constraints, which can provide support for the decision of the low-carbon construction scheme of bridges.

The mechanism of prediction of slope stability is formulated based on its material, geometrical and environmental situation, and the prediction of slope stability has been accepted as a tool for analyzing and predicting future structure stability based on geotechnical properties and failure mechanism. However, the study of slope instability is complex, which is usually difficult to be explained by mathematical methods. The number of slope cases limits the accuracy of slope stability prediction, and the soil or rock parameters of slope are variable, which poses a new challenge for prediction using traditional algorithms. To improve the accuracy of slope stability state prediction, this paper proposes an efficient slope stability state prediction method with the approach of a great robust convolutional neural network named the multi-scale multi-core one-dimensional convolutional neural network (MSC-1DCNN) and substantial Empirical information collected worldwide. Meanwhile, the collected dataset is amplified. Additionally, the probability of failure is calculated by considering the variability of soil or rock parameters. Compared with some state-of-the-art prediction methods, the MSC-1DCNN presents high prediction accuracy. Meanwhile, the proposed method is applied on a slope case, which indicates that our study provides a reliable slope stability state prediction method for homogeneity slope around the world.