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IntroductionThe ecological balance of the plant microbiome, as a barrier against pathogens, is very important for host health. Coptis chinensis is one of the important medicinal plants in China. In recent years, Illumina Miseq high-throughput sequencing technology was frequently used to analyze root rot pathogens and the effects of root rot on rhizosphere microorganisms of C. chinensis. But the effects of root rot infection on rhizosphere microecological balance of C. chinensis have received little attention.MethodsIn this study, Illumina Miseq high-throughput sequencing technology was applied to analyze the impact on microbial composition and diversity of C. chinensis by root rot.ResultsThe results showed that root rot infection had significant impact on bacterial α-diversity in rhizome samples, but had no significant effect on that in leaf samples and rhizosphere soil samples, while root rot infection exhibited significant impact on the fungal α-diversity in leaf samples and rhizosphere soil samples, and no significant impact on that in rhizome samples. PCoA analysis showed that the root rot infection had a greater impact on the fungal community structure in the rhizosphere soil, rhizome, and leaf samples of C. chinensis than on the bacterial community structure. Root rot infection destroyed the microecological balance of the original microbiomes in the rhizosphere soil, rhizome, and leaf samples of C. chinensis, which may also be one of the reasons for the serious root rot of C. chinensis.DiscussionIn conclusion, our findings suggested that root rot infection with C. chinensis disrupts microecological balance of rhizosphere soil and endophytic microbiomes. The results of this study can provide theoretical basis for the prevention and control of C. chinensis root rot by microecological regulation.

Deep neural networks are suffering from over parameterized high storage and high consumption problems. Pruning can effectively reduce storage and computation costs of deep neural networks by eliminating their redundant parameters. In existing pruning methods, filter pruning achieves more efficient inference, while element-wise pruning maintains better accuracy. To make a trade-off between the two endpoints, a variety of pruning patterns has been proposed. This study analyzes the performance characteristics of sparse DNNs pruned by different patterns, including element-wise, vector-wise, block-wise, and group-wise. Based on the analysis, we propose an efficient implementation of group-wise sparse DNN inference, which can make better use of GPUs. Experimental results on VGG, ResNet, BERT and ViT show that our optimized group-wise pruning pattern achieves much lower inference latency on GPU than other sparse patterns and the existing group-wise pattern implementation.

As an important part of renewable energy, wind power is crucial to the realization of carbon neutrality. It is worth studying on how to accurately predict the wind output so that it can be integrated into the power grid as much as possible to enhance its utilization rate. In this article, a data enhancement method and a framework are proposed to assist wind power forecasting. The proposed method uses the super-resolution perception technology to first detect the completeness and correctness of historical meteorological and wind power data collected by industrial devices. Then, the detected errors are corrected and the missing data are recovered to make the data complete. The frequency of the data is then increased using the proposed method so that the data become complete high-frequency data. Based on the enhanced complete high-frequency data with more detailed characteristics, more accurate forecasts of wind power can be achieved, thereby improving the utilization rate of wind power. Experiments based on public datasets are used to demonstrate the effectiveness of the proposed method and framework. With the proposed method and framework, higher frequency data with more detailed information can be achieved, thereby providing support for accurate wind power prediction that was not possible before.

The smart grid is an evolving critical infrastructure, which combines renewable energy and the most advanced information and communication technologies to provide more economic and secure power supply services. To cope with the intermittency of ever-increasing renewable energy and ensure the security of the smart grid, state estimation, which serves as a basic tool for understanding the true states of a smart grid, should be performed with high frequency. More complete system state data are needed to support high-frequency state estimation. The data completeness problem for smart grid state estimation is therefore studied in this paper. The problem of improving data completeness by recovering high-frequency data from low-frequency data is formulated as a super resolution perception (SRP) problem in this paper. A novel machine-learning-based SRP approach is thereafter proposed. The proposed method, namely the Super Resolution Perception Net for State Estimation (SRPNSE), consists of three steps: feature extraction, information completion, and data reconstruction. Case studies have demonstrated the effectiveness and value of the proposed SRPNSE approach in recovering high-frequency data from low-frequency data for the state estimation.

The overlying strata of the Yima coalfield are ultrathick conglomerate. Aiming at the problem of frequent occurrence of rockburst events in the central Yima coalfield during 2006–2015, the characteristics of rockburst events, microseismic (MS) monitoring, and rockburst event-inducing factors were analyzed through data mining and field investigation methods. The results showed that the rockburst events in roadways mainly occurred during mining of the working face, and they occurred at a large buried depth and were within the influence of mining stress, accompanied by an abrupt energy release. The occurrence of rockburst in roadways was accompanied by a sudden release of energy. The ultrathick strata and the fault nearby were the key influence factors of rockburst events. The stress field of roadway surrounding rocks was changed because of the mining disturbance, roadway repair and maintenance, and blasting, which would change the regional stress fields in the surrounding rocks and induce roadway rockburst events. The characteristics of rockburst events were floor heave, sharp convergence of two side walls, severe damage of the supporting body, and even closure of the roadway. The occurrence of rockburst can be prevented by reducing the mining speed and injecting water into coal seam.

The huge storage and computation cost of convolutional neural networks (CNN) make them challenging to meet the real-time inference requirement in many applications. Existing channel pruning methods mainly focus on removing unimportant channels in a CNN model based on rule-of-thumb designs, using reduced floating-point operations (FLOPs) and parameter numbers to measure the pruning quality. The inference latency of pruned models is often overlooked. In this paper, we propose a latency-aware automatic CNN channel pruning method (LACP), which aims to search low latency and accurate pruned network structure automatically. We evaluate the inaccuracy of measuring pruning quality by FLOPs and the number of parameters, and use the model inference latency as the direct optimization metric. To bridge model pruning and inference acceleration, we analyze the inference latency of convolutional layers on GPU. Results show that the inference latency of convolutional layers exhibits a staircase pattern along with channel number due to the GPU tail effect. Based on that observation, we greatly shrink the search space of network structures. Then we apply an evolutionary procedure to search a computationally efficient pruned network structure, which reduces the inference latency and maintains the model accuracy. Experiments and comparisons with state-of-the-art methods on three image classification datasets show that our method can achieve better inference acceleration with less accuracy loss.

Drilling of large-diameter boreholes is regarded as an effective measure for rockburst prevention. By investigating the morphological characteristic and evolution of plastic zone in borehole surrounding rock, the pressure-relief mechanism of large-diameter borehole was ascertained, and the engineering application of large-diameter boreholes was assessed in the 13230 working face of Gengcun Coal Mine, Henan Province, China. The results showed that (1) the plastic zone in surrounding rock of borehole appear as circular, elliptical, and butterfly shapes, in which the maximum size of the butterfly wings of the plastic zone is several times larger than the borehole diameter; (2) under certain stress conditions, multiple large-diameter boreholes distributed in coal seam with rockburst risk lead to the generation and coalescence of large-range butterfly-shape plastic zone. They reduce the stress concentration and capacity for storing elastic energy of coal seam, thus reducing the rockburst risk of the coal seam; (3) large-diameter boreholes significantly decrease the stress concentration in front of the 13230 working face and improve the stress environment in the head entry, promoting the safe mining of the working face.

Aiming at the complex and changeful engineering geological conditions of the intake tunnel of Taishan nuclear power station, such as large diameter, large water pressure and long tunnel length, the proportion of slurry material is studied to ensure the grouting effect of shield. The controlling factors of grouting material, such as process performance, water dispersibility, preventing floatation and durability of segments, are analyzed. The performance indexes of high performance synchronous materials are discussed, and reasonable grouting materials and proportioning experimental instruments and equipment are selected and determined. Through the proportioning test of grouting material, the synchronous grouting material formulation, slurry configuration method and suitable grouting process are studied to improve the synchronous grouting effect and achieve the purpose of tunnel reinforcement and water stop. The engineering application shows that through the proportioning test, the mixture ratio of 1 and the mixture ratio of 9 grouting material can meet the needs of on- site construction, and the filling effect is good, so it has wide application value.