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At present, coal accounts for more than 56% of China's primary energy consumption and will continue to dominate for a long time in the future. With the continuous expansion of the mining intensity and scale of Jurassic coal resources in Northwestern China, the problem of mine roof water disasters is becoming increasingly serious. The degree of harm is related to the hydrogeological structure of the overlying strata of the coal seam. Reasonable and effective prediction and evaluation of the water abundance of the coal seam roof aquifer is conducive to making scientific decisions on the prevention and control of roof water disasters, so as to achieve safe mining. In order to solve the problem of water abundance evaluation in mining areas lacking hydrological holes, taking the Hongliulin coal mine in Shennan mining area as an example, four main control factors for water abundance were selected: sandstone thickness, core recovery ratio, brittle rock thickness ratio, and flushing fluid consumption. Combined with unit water inflow and multiple factor comprehensive analysis, a back propagation (BP) artificial neural network and support vector machine regression (SVR) were introduced into water abundance evaluation. The reciprocal variance method was used to predict the measured unit water inflow. Finally, according to the "Detailed Rules for Coal Mine Water Prevention and Control", the water abundance of aquifers was classified to verify the accuracy of the model and partition the water abundance of the study area. The results indicate that, based on the predicted results of unit water inflow, out of 37 borehole data, 22 weak water abundance holes and 15 medium water abundance holes were evaluated correctly, verifying their applicability. The study area was generally weak in water abundance, with two grades of medium and weak. The medium water abundance area was mainly located in the north and south of the study area, and the weak water abundance area was mainly located in the east and west. It can be seen that this evaluation model has certain applicability for evaluating the water abundance of coal seam roofs. It is of great significance, especially for the evaluation of water abundance in mining areas where hydrological holes are lacking. [ABSTRACT FROM AUTHOR]

The focus of China's coal mining in recent years has shifted to western China, which has a fragile ecological environment. The Jurassic coal in this area is buried at shallow depths and is very thick. Currently, the two main issues are the inrush of underground water and the disruption to shallow surface water supplies brought on by mining. Accurately predicting the height of the water-conducting fractured zone (WCFZ) is the key to preventing roof water damage and attaining water conservation in this mining area. A nearest neighbor bootstrap regression (NNBR) model was established to predict the height of the WCFZ for working face 117 of the Jinjitan coal mine in western China, which uses fully mechanized top coal caving longwall mining. The model was verified based on the measured heights of the WCFZ (30 groups of measured data) using the actual mining thickness (> 8 m) and working face width as predictive factors. The mean error of the model's prediction was 1.5 m and the mean absolute percentage error was 13.6%, which was better than the result predicted using the established conventional empirical formula. The model was improved by applying a safety factor of 1.3, which met the 96.7% reliability requirements. The height of the WCFZ of working face 117 was predicted using this model, and the predicted value was compared with values measured by optical fiber monitoring and simple hydrological observations. The height of the WCFZ measured by optical fiber was 184.8 m, the predicted value was 196.0 m, and the relative error of that prediction was 6.0%. The hydrologically observed value was 221.0 m, the predicted value for that location was 225.7 m, and the relative error was 2.1%. Thus, the NNBR model is capable of reliably predicting the height of the WCFZ and it appears that it can be used to compute the safe water control height above mechanized top coal caving mines in western China's very thick Jurassic coal seams. [ABSTRACT FROM AUTHOR]

Objective: The School-based Education Program to Reduce Salt Intake in Children and Their Families study was a cluster randomized control trial among grade five students in 28 primary schools and their families in Changzhi, China. It achieved a significant effect in lowering systolic blood pressure (SBP) in all family adults by 2.3 mmHg and in elderlies (aged > = 60 years) by 9.5 mmHg. The aim of this study was to assess the cost-effectiveness of this salt reduction program. Methods: Costs of the intervention were assessed using an ingredients approach to identify resource use. A trial-based incremental cost-effectiveness ratio (ICER) was estimated based on the observed effectiveness in lowering SBP. A Markov model was used to estimate the long-term cost-effectiveness of the intervention, and then based on population data, extrapolated to a scenario where the program is scaled up nationwide. Findings were presented in terms of an incremental cost per quality-adjusted life year (QALY). The perspective was that of the health sector. Results: The intervention cost Int$19.04 per family and yielded an ICER of Int$2.74 (90% CI: 1.17–12.30) per mmHg reduction of SBP in all participants (combining children and adult participants together) compared with control group. If scaled up nationwide for 10 years and assumed deterioration in treatment effect of 50% over this period, it would reach 165 million families and estimated to avert 42,720 acute myocardial infarction deaths and 107,512 stroke deaths in China. This would represent a gain of 635,816 QALYs over 10-year time frame, translating into Int$1,358 per QALY gained. Conclusion: Based on WHO-CHOICE criteria, our analysis demonstrated that the proposed salt reduction strategy is highly cost-effective, and if scaled up nationwide, the benefits could be substantial. Trial registration: ClinicalTrials.gov [ABSTRACT FROM AUTHOR]