Application of deep learning classification model for regional evaluation of roof pressure support evolution effects over time in coal mining face.

Hydraulic support leg pressure serves as a crucial indicator for assessing work face support quality. Current evaluation methods for support quality primarily concentrate on static analyses-like inadequate initial support force, pressure overrun, and uneven bracket force-while neglecting dynamic column pressure changes. This paper introduces a model for assessing hydraulic support quality using deep learning techniques. Real-time data is preprocessed into a spatio-temporal pressure sub-matrix sample, which is then inputted into the model. This process assesses the support quality type and characterizes its dynamic evolution within the area. The model facilitates the identification of dynamic support quality effects in the working face area, aiding operators in making targeted adjustments to hydraulic support status. Experimental results revealed that the optimized LeNet-5 network-adjusting parameters like convolutional layer count, kernel size, and ReLU activation function-achieved the highest classification accuracy of 85.25 % for support quality, surpassed other networks. Furthermore, the improved LeNet-5 network outperformed other networks in both F1 score and recall. Additionally, the improved LeNet-5 network achieved faster convergence to the optimal solution, accelerated training speed. This highlighted its advantages in evaluating the spatio-temporal support quality of hydraulic supports in smart mining operations.

Spinal cord stimulation and deep brain stimulation for disorders of consciousness: a systematic review and individual patient data analysis of 608 cases.

The application of spinal cord stimulation (SCS) and deep brain stimulation (DBS) for disorders of consciousness (DoC) has been increasingly reported. However, there is no sufficient evidence to determine how effective and safe SCS and DBS are for DoC owing to various methodological limitations. We conducted a systematic review to elucidate the safety and efficacy of SCS and DBS for DoC by systematically reviewing related literature by searching PubMed, EMBASE, Medline, and Cochrane Library. Twenty eligible studies with 608 patients were included in this study. Ten studies with 508 patients reported the efficacy of SCS for DoC, and the estimated overall effectiveness rate was 37%. Five studies with 343 patients reported the efficacy of SCS for VS, and the estimated effectiveness rate was 30%. Three studies with 53 patients reported the efficacy of SCS for MCS, and the estimated effectiveness rate was 63%. Five studies with 92 patients reported the efficacy of DBS for DoC, and the estimated overall effectiveness rate was 40%. Four studies with 63 patients reported the efficacy of DBS for VS, and the estimated effectiveness rate was 26%. Three studies with 19 patients reported the efficacy of DBS for MCS, and the estimated effectiveness rate was 74%. The adverse event rate of DoC was 8.1% and 18.2% after SCS and DBS, respectively. These results suggest that SCS and DBS can be considered reasonable treatments for DoC with considerable efficacy and safety.

Residual concentrations and ecological risks of neonicotinoid insecticides in the soils of tomato and cucumber greenhouses in Shouguang, Shandong Province, East China.

Neonicotinoid insecticides (NNIs) are the most widely used insecticides in China and worldwide. Continuous use of NNIs can lead to their accumulation in soil, causing potential ecological risks due to their relatively long half-life. We used liquid chromatography-tandem mass spectrometry (LC-MS/MS) to investigate the residual levels of nine neonicotinoids in greenhouse soils in Shouguang, East China, at different soil depths and with different crops (tomato and cucumber) after varying periods of cultivation. Seven neonicotinoids were detected in the soils of the tomato greenhouses and six were detected in the soils of the cucumber greenhouses, with total concentrations ranging from 0.731 to 11.383 µg kg-1 and 0.363 to 19.224 µg kg-1, respectively. In all samples, the neonicotinoid residues in the soils cultivated for 8-9 years were lower than in those cultivated for 2 years and 14-17 years. In the tomato greenhouse soils, the residual levels of NNIs were highest in the topsoil, with progressively lower concentrations found with depth. Under cucumber cultivation, the NNI residue levels were also highest in the topsoil but there was little difference between the middle and lower soil layers. Total organic carbon (TOC) decreased with soil depth while pH showed the opposite trend, showing a significant negative correlation in both types of soils (tomato soils ρ = -0.900, p = .001; cucumber soils ρ = -0.883, p = .002). Furthermore, TOC was significantly positively correlated, and pH was negatively correlated, with total NNI concentrations in both types of soils (TOC: tomato soils ρ = 0.800, p = .010; cucumber soils ρ = 0.881, p = .004; pH: tomato soils ρ = -0.850, p = .004; cucumber soils ρ = -0.643, p = .086). The results of an ecological risk analysis showed that acetamiprid represents a particularly high toxicity risk in these soils. Based on our analysis, NNI residues in the soils of tomato greenhouses and their associated ecological risks deserve more attention than those of cucumber greenhouse soils.

Effects of pesticide residues on bacterial community diversity and structure in typical greenhouse soils with increasing cultivation years in Northern China.

The understanding of soil microbiome is important for sustainable cultivation, especially under greenhouse conditions. Here, we investigated the changes in soil pesticide residues and microbial diversity and community structure at different cultivation years under a greenhouse system. The 9-to-14 years sites were found to have the least diversity/rich microbial population as compared to sites under 8 years and over 16 years, as analyzed with alpha diversity index. In total, 42 bacterial phyla were identified across soils with different pesticide residues and cultivation ages. Proteobacteria, Acidobacteria, and Bacteroidetes represented the dominant phyla, that accounted for 34.2-43.4%, 9.7-19.3% and 9.2-16.5% of the total population, respectively. Our data prove that certain pesticides contribute to variation in soil microbial community and that soil bacteria respond differently to cultivation years under greenhouse conditions. Thus, this study provides an insight into microbial community structure changes by pesticides under greenhouse systems and natural biodegradation may have an important part in pesticides soil decontamination.