Experimental Study on Coal Seam Gas Desorption Characteristics Caused by Moisture under Stepwise Depressurization

Expansion energy is the main factor of coal and gas outbursts, and the gas desorption around the outburst hole is developed in variable pressure conditions. While studying the impact of moisture on gas desorption characteristics, atmospheric pressure desorption is usually used, but its characteristics under variable pressure conditions have not been thoroughly investigated. In this study, typical outburst coal samples with different water contents from the Jincheng mining area of China were selected as research objects, and the effects of water on gas displacement, desorption, desorption rate, and gas desorption index (K1) of drilling cuttings under step-by-step depressurization were analyzed by means of stepwise depressurization and atmospheric desorption experiments. The research conclusions suggest that (1) the amount of gas replacement, which augments rapidly during the inception, increases with the growth of water content under the experimental conditions, and then the rate decreases; (2) the gas desorption falls gradually at different depressurization stages when the humidity is constant, while the total desorption and the drop amplitude taper with the increasing water content; (3) the additional water enhances the desorption rate significantly only at the initial stage, but scarcely has an impact later on; and (4) the value of the drilling cuttings’ gas desorption index (K1) shows a downward trend with the developing humidity in each stage of stepwise depressurization desorption. We take humidity as a variable to simulate the desorption process of coal gas around the hole when coal and gas outbursts occur in the laboratory and study the influence of water on the desorption characteristics under desorption conditions of stepwise depressurization. This provides a reference for the purpose of studying the mechanism of coal and gas outbursts from the perspective of energy

Flow of nitrous oxide in a venturi tube under conditions of a hybrid rocket motor

A Venturi tube is typically used as the flow control element of hybrid rocket motors (HRMs) for the precise control of the flow of a liquid oxidizer, e.g., nitrous oxide, which can efficiently realize the thrust throttling of motors. The flow rate of nitrous oxide in the venturi tube was calculated for a heterogeneous non-equilibrium flow by considering the influence of the variation in the compressibility temperature. In addition, a nitrous-oxide flow rate measurement system was established to calibrate the flow rate coefficient and obtain the corresponding calculation parameters. Results revealed that upstream pressure is not affected by back pressure when the throat pressure is less than a saturated vapor pressure of 4.2 MPa at the local temperature for the venturi tube with a throat diameter of 1.65 mm; however, cavitation disappeared at a back pressure of greater than 6.35 MPa. A flow rate evaluation error of less than 6 wt% was achieved by using the experimentally determined non-equilibrium parameter in the flow rate calculation method

Influence of Ventilation Duct Parameter Optimization on Pollutant Diffusion in Spiral Tunnels

The spiral tunnel could flexibly design the curvature and slope of the tunnel according to the geological structure, avoiding the adverse effect of complex terrain and topography, and had a strong advantage in engineering applications. Different from ordinary straight linear tunnels, spiral tunnels have a circular trend with obvious curvature and slope, which ensures certain differences between construction ventilation of the two types of tunnels, especially in terms of ventilation air flow field and pollutant diffusion. Relying on the actual spiral tunnel project, this paper studies the diffusion mechanism of ventilation pollutants in spiral tunnel construction. Optimization of the layout of the air duct based on numerical simulation and proposed pulsating ventilation method was performed to improve the ventilation and drainage effect of the tunnel. The time to reach the carbon monoxide concentration after blasting, and the dust concentration during the spraying process, were determined as indicators to measure the ventilation effect during the construction period. The hanging position of the air duct, the distance from the air duct outlet to the palm face, and the air speed were determined as the main factors affecting the ventilation effect. The main factors, the degree of influence and sensitivity of each influencing factor on the index, are studied, and the optimal air duct layout scheme suitable for the site is determined. The scheme can improve the ventilation effect and ventilation quality and provide effective reference for ventilation problems during the construction of other spiral tunnels. Meanwhile, more factors need to be examined to study the impact of pollutants in spiral tunnel construction, and physical models are needed to study the diffusion mechanism of pollutants in a spiral tunnel

Influence of Ventilation Duct Parameter Optimization on Pollutant Diffusion in Spiral Tunnels

The spiral tunnel could flexibly design the curvature and slope of the tunnel according to the geological structure, avoiding the adverse effect of complex terrain and topography, and had a strong advantage in engineering applications. Different from ordinary straight linear tunnels, spiral tunnels have a circular trend with obvious curvature and slope, which ensures certain differences between construction ventilation of the two types of tunnels, especially in terms of ventilation air flow field and pollutant diffusion. Relying on the actual spiral tunnel project, this paper studies the diffusion mechanism of ventilation pollutants in spiral tunnel construction. Optimization of the layout of the air duct based on numerical simulation and proposed pulsating ventilation method was performed to improve the ventilation and drainage effect of the tunnel. The time to reach the carbon monoxide concentration after blasting, and the dust concentration during the spraying process, were determined as indicators to measure the ventilation effect during the construction period. The hanging position of the air duct, the distance from the air duct outlet to the palm face, and the air speed were determined as the main factors affecting the ventilation effect. The main factors, the degree of influence and sensitivity of each influencing factor on the index, are studied, and the optimal air duct layout scheme suitable for the site is determined. The scheme can improve the ventilation effect and ventilation quality and provide effective reference for ventilation problems during the construction of other spiral tunnels. Meanwhile, more factors need to be examined to study the impact of pollutants in spiral tunnel construction, and physical models are needed to study the diffusion mechanism of pollutants in a spiral tunnel

Natural Deep Eutectic Solvent-Based Microwave-Assisted Extraction of Total Flavonoid Compounds from Spent Sweet Potato (<i>Ipomoea batatas</i> L.) Leaves: Optimization and Antioxidant and Bacteriostatic Activity

Natural deep eutectic solvents (NADESs) coupled with microwave-assisted extraction (MAE) were applied to extract total flavonoid compounds from spent sweet potato (Ipomoea batatas L.) leaves. In this study, ten different NADESs were successfully synthesized for the MAE. Based on single-factor experiments, the response surface methodology (RSM) was applied, and the microwave power, extraction temperature, extraction time, and solid–liquid ratio were further evaluated in order to optimize the yields of total flavonoid compounds. Besides, the extracts were recovered by macroporous resin for the biological activity detection of flavonoid compounds. As a result, NADES-2, synthesized by choline chloride and malic acid (molar ratio 1:2), exhibited the highest extraction yield. After that, the NADES-2-based MAE process was optimized and the optimal conditions were as follows: microwave power of 470 W, extraction temperature of 54 °C, extraction time of 21 min, and solid–liquid ratio of 70 mg/mL. The extraction yield (40.21 ± 0.23 mg rutin equivalents/g sweet potato leaves) of the model validation experiment was demonstrated to be in accordance with the predicted value (40.49 mg rutin equivalents/g sweet potato leaves). In addition, flavonoid compounds were efficiently recovered from NADES-extracts with a high recovery yield (>85%) using AB-8 macroporous resin. The bioactivity experiments in vitro confirmed that total flavonoid compounds had good DPPH and O2−· radical-scavenging activity, as well as inhibitory effects on E. coli, S. aureus, E. carotovora, and B. subtilis. In conclusion, this study provides a green and efficient method to extract flavonoid compounds from spent sweet potato leaves, providing technical support for the development and utilization of sweet potato leaves’ waste

Prediction of myopia development among Chinese school-aged children using refraction data from electronic medical records: A retrospective, multicentre machine learning study.

BackgroundElectronic medical records provide large-scale real-world clinical data for use in developing clinical decision systems. However, sophisticated methodology and analytical skills are required to handle the large-scale datasets necessary for the optimisation of prediction accuracy. Myopia is a common cause of vision loss. Current approaches to control myopia progression are effective but have significant side effects. Therefore, identifying those at greatest risk who should undergo targeted therapy is of great clinical importance. The objective of this study was to apply big data and machine learning technology to develop an algorithm that can predict the onset of high myopia, at specific future time points, among Chinese school-aged children.Methods and findingsReal-world clinical refraction data were derived from electronic medical record systems in 8 ophthalmic centres from January 1, 2005, to December 30, 2015. The variables of age, spherical equivalent (SE), and annual progression rate were used to develop an algorithm to predict SE and onset of high myopia (SE ≤ -6.0 dioptres) up to 10 years in the future. Random forest machine learning was used for algorithm training and validation. Electronic medical records from the Zhongshan Ophthalmic Centre (a major tertiary ophthalmic centre in China) were used as the training set. Ten-fold cross-validation and out-of-bag (OOB) methods were applied for internal validation. The remaining 7 independent datasets were used for external validation. Two population-based datasets, which had no participant overlap with the ophthalmic-centre-based datasets, were used for multi-resource validation testing. The main outcomes and measures were the area under the curve (AUC) values for predicting the onset of high myopia over 10 years and the presence of high myopia at 18 years of age. In total, 687,063 multiple visit records (≥3 records) of 129,242 individuals in the ophthalmic-centre-based electronic medical record databases and 17,113 follow-up records of 3,215 participants in population-based cohorts were included in the analysis. Our algorithm accurately predicted the presence of high myopia in internal validation (the AUC ranged from 0.903 to 0.986 for 3 years, 0.875 to 0.901 for 5 years, and 0.852 to 0.888 for 8 years), external validation (the AUC ranged from 0.874 to 0.976 for 3 years, 0.847 to 0.921 for 5 years, and 0.802 to 0.886 for 8 years), and multi-resource testing (the AUC ranged from 0.752 to 0.869 for 4 years). With respect to the prediction of high myopia development by 18 years of age, as a surrogate of high myopia in adulthood, the algorithm provided clinically acceptable accuracy over 3 years (the AUC ranged from 0.940 to 0.985), 5 years (the AUC ranged from 0.856 to 0.901), and even 8 years (the AUC ranged from 0.801 to 0.837). Meanwhile, our algorithm achieved clinically acceptable prediction of the actual refraction values at future time points, which is supported by the regressive performance and calibration curves. Although the algorithm achieved balanced and robust performance, concerns about the compromised quality of real-world clinical data and over-fitting issues should be cautiously considered.ConclusionsTo our knowledge, this study, for the first time, used large-scale data collected from electronic health records to demonstrate the contribution of big data and machine learning approaches to improved prediction of myopia prognosis in Chinese school-aged children. This work provides evidence for transforming clinical practice, health policy-making, and precise individualised interventions regarding the practical control of school-aged myopia

Additional file 1: of Construction and implications of structural equation modeling network for pediatric cataract: a data mining research of rare diseases

The detailed information for potential variables and evaluation indices are presented. (DOCX 16 kb