Salinity effects on microbial communities in saline soils is still unclear, and little is known about subsurface soil microbial communities especially in saline or hypersaline ecosystems. Here we presented the survey of the prokaryotic community in saline soils along a salinity gradient (17.3–148.3 dS/m) in surface (0–10 cm) and subsurface (15–30 cm) saline soils of Qarhan Salt Lake, China. Moreover, we compared them with three paired nonsaline normal soils. Using the high-throughput sequencing technology and several statistical methods, we observed no significant community difference between surface soils and subsurface soils. For environmental factors, we found that TOC was the primary driver of the prokaryotic community distribution in surface saline soils, so was pH in subsurface saline soils. Salinity had more effects on the prokaryotic community in subsurface saline soils than in surface saline soils and played a less important role in saline soils than in saline waters or saline sediments. Our research provided references for the prokaryotic community distribution along a salinity gradient in both surface and subsurface saline soils of arid playa areas.

High-throughput amplicon sequencing technology has been widely used in soil microbiome studies. Here, we estimated the bias of amplicon sequencing data affected by DNA extraction methods in a saline soil, and a non-saline normal soil was used as a control. Compared with the normal soil, several unique points were observed in the saline soil. The soil washing pretreatment can improve not only DNA quantity and quality but also microbial diversities in the saline soil; therefore, we recommend the soil washing pretreatment for saline soils especially hypersaline soils that cannot be achieved with detectable DNA amounts without the pretreatment. Also, evenness indices were more easily affected by DNA extraction methods than richness indices in the saline soil. Moreover, proportions of Gram-positive bacteria had significant positive correlations with the achieved microbial diversities within replicates of the saline soil. Though DNA extraction methods can bias the microbial diversity or community and relative abundances of some phyla/classes can vary by a factor of more than five, soil types were still the most important factor of the whole community. We confirmed good comparability in the whole community, but more attention should be paid when concentrating on an exact diversity value or the exact relative abundance of a certain taxon. Our study can provide references for the DNA extraction from saline and non-saline soils and comparing sequencing data across studies who may employ different DNA extraction methods.

The slope angle of a slope is one of the important parameters affecting the stability of rocky slopes. In this paper, a new method based on the random forest (RF) algorithm is proposed to study the slope angle of rocky slopes. Based on the international typical rocky slope actual measurement data, the RF model for predicting the foot of the rocky slope is constructed by determining ten influencing factors affecting the slope angle of the rocky slope, namely, rock strength, rock quality designation (RQD), joint spacing, continuity, openness, roughness, filling, weathering, groundwater, and engineering direction as independent variables. The experimental results show that (1) the RF model has the smallest out-of-bag error when the number of decision trees ntree is four and the number of features in the split feature set mtry is five hundred; (2) engineering direction, fill degree, RQD, groundwater, and joint spacing have a large influence on the foot of a rocky slope; (3) relative to artificial neural networks (BP), artificial neural networks optimized by genetic algorithm (GA-BP), support vector machine (SVM), and multiple linear regression (MLR), the RF regression model has obvious advantages in terms of prediction accuracy and model stability, which provides an effective method for achieving accurate prediction of slope angle of rocky slopes.

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In order to achieve resource conservation, protect the environment and realize the sustainable development of the construction industry, the low energy resource utilization of construction waste was explored. In this paper, the effect of air bubble swarm admixture, recycled brick powder admixture, water to material ratio, and HPMC content on the physical and mechanical properties of recycled brick powder foam concrete was investigated by conducting a 4-factor, 5-level orthogonal test with recycled brick powder as fine aggregate, and the effect of each factor on the physical and mechanical properties of recycled brick powder foam concrete was derived, and the optimum ratio of recycled brick powder foam concrete was determined by analysing the specific strength. Five machine learning models, namely, back propagation neural network improved by particle swarm optimization (PSO-BP), support vector machine (SVM), multiple linear regression (MLR), random forest (RF), and back propagation neural network (BP), were used to predict the compressive strength of recycled brick powder foam concrete, and the PSO-BP model was found to have obvious advantages in terms of prediction accuracy and model stability. The experimental results and prediction models can provide experimental and theoretical references for the research and application of recycled brick powder foam concrete.