Dynamic response characteristics and damage rule of graphite ore rock under different strain rates.

In the process of mining graphite mine, rock mass is often subjected to dynamic loads such as blasting or mechanical crushing, which involves dynamic responses of different strain rates, and blasting and crushing effect are affected by the rock dynamic properties and damage specials. The dynamic response characteristics and damage rule of graphite ore rock under different strain rates are very important but rarely studied in the past. To study these issues and provide support for graphite ore rock mining, the dynamic compression tests of graphite ore rock under five kinds of impact pressures were designed and carried out by using the Split Hopkinson Pressure Bar (SHPB) test system, combining with the high-speed photography system and crushing screening tests. The dynamic characteristics, crushing process, crushing mode, crushing form and fragmentation distribution of the graphite ore rock under different strain rates were analyzed. The results show that the dynamic characteristics of the graphite ore rock have obvious strain rate effect. The hardening coefficient (DIF) is positively correlated with the cubic root of strain rate, and the softening factor (K) is negatively correlated with the cubic root of strain rate. Shear failure mainly occurs in the graphite ore rock under impact load, and the crushing process can be divided into five stages, they are compaction, crack initiation, crack expansion and penetration, fragmentation collision and fragmentation fall. In addition, the crushed blocks are mainly triangular pyramid (or cone-like) fine granular and powder. The broken fragments of the graphite ore rock are in accord with the fractal geometry characteristics. That is, the average broken particle size (dS) decreases linearly with the increase of strain rate, and the fractal dimension (Da) increases weakly with the increase of strain rate. Based on D-P fracture criterion and Weibull distribution model, the dynamic damage constitutive model of the graphite ore rock was established, and the correlation between strain rate and Weibull distribution parameters (m and F0) was used to reasonably modify the damage constitutive model. The modified damage constitutive model curve is in good agreement with the experimental curve, which can basically reflect the strain rate effect of the dynamic characteristics of the graphite ore rock and the evolution characteristics of the dynamic stress-strain curve at different stage.

Using LSTM and ARIMA to Simulate and Predict Limestone Price Variations.

There have been many improvements and advancements in the application of neural networks in the mining industry. In this study, two advanced deep learning neural networks called recurrent neural network (RNN) and autoregressive integrated moving average (ARIMA) were implemented in the simulation and prediction of limestone price variation. The RNN uses long short-term memory layers (LSTM), dropout regularization, activation functions, mean square error (MSE), and the Adam optimizer to simulate the predictions. The LSTM stores previous data over time and uses it in simulating future prices based on defined parameters and algorithms. The ARIMA model is a statistical method that captures different time series based on the level, trend, and seasonality of the data. The auto ARIMA function searches for the best parameters that fit the model. Different layers and parameters are added to the model to simulate the price prediction. The performance of both network models is remarkable in terms of trend variability and factors affecting limestone price. The ARIMA model has an accuracy of 95.7% while RNN has an accuracy of 91.8%. This shows that the ARIMA model outperforms the RNN model. In addition, the time required to train the ARIMA is than that of the RNN. Predicting limestone prices may help both investors and industries in making economical and technical decisions, for example, when to invest, buy, sell, increase, and decrease production.

Effect of Positive End-Expiratory Pressure on Overlap Between Internal Jugular Vein and Carotid Artery in Mechanically Ventilated Patients with Laryngeal Mask Airway (LMA) Insertion - A Prospective Randomized Trial.

BACKGROUND In the present study, we aimed to determine the effect of different positive end-expiratory pressure (PEEP) levels on the cross-sectional area (CSA) of the right internal jugular vein (RIJV) and the overlap index between the RIJV and the right common carotid artery (RCCA) in mechanically ventilated patients with laryngeal mask airway (LMA) insertion. MATERIAL AND METHODS A total of 60 patients who were scheduled for elective surgery under general anesthesia with LMA insertion were enrolled. After LMA insertion, the image of RIJV and RCCA were taken after applying 4 different PEEPs in a random order: 0 (P0), 5 (P5), 10 (P10), and 15 (P15) cm H2O. The CSA, transverse and anteroposterior (AP) diameters of the RIJV, and the overlap index were measured. RESULTS Compared to group P0, the overlap indexes of P10 (P=0.0032) and P15 (P<0.001) were significantly increased, but without a significant trend toward further increases in group P15. PEEP at 10 and 15 cm H2O increased CSA, transverse and AP diameter of the RIJV in comparison to group P0 (all P<0.001). There was a statistically significant increase in CSA of the RIJV in P15 compared with P10 by 12.2% (P<0.001), but did not reach the relevant cut-off value (ΔCSA ≥15%). CONCLUSIONS The application of PEEP at 10 cm and 15 cm H2O in patients receiving mechanical ventilation with LMA insertion significantly increases the size of the RIJV. However, the overlap index between the RIJV and the RCCA increased as well.