Search

Microseismic data are widely employed for assessing rockburst risks; however, significant disparities exist in the monitoring capabilities of seismic networks across different mines, and none can capture a complete dataset of microseismic events. Such differences introduce unfairness when applying the same methodologies to evaluate rockburst risks in various mines. This paper proposes a method for assessing the monitoring capability of seismic networks applicable to heterogeneous media in mines. It achieves this by integrating three gradient boosting algorithms: Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost). Initially, the isolation forest algorithm is utilized for preliminary data cleansing, and feature engineering is constructed based on the relative locations of event occurrences to monitoring stations and the working face. Subsequently, the optimal hyperparameters for three models are searched for using 8508 microseismic events from the a Coal Mine in eastern China as samples, and 18 sub-models are trained. Model weights are then determined based on the performance metrics of different algorithms, and an ensemble model is created to predict the monitoring capability of the network. The model demonstrated excellent performance on the training and test sets, achieving log loss, accuracy, and recall scores of 7.13, 0.81, and 0.76 and 6.99, 0.80, and 0.77, respectively. Finally, the method proposed in this study was compared with traditional approaches. The results indicated that, under the same conditions, the proposed method calculated the monitoring capability of the key areas to be 11% lower than that of the traditional methods. The reasons for the differences between these methods were identified and partially explained. [ABSTRACT FROM AUTHOR]

Conventional plasma electrolytic oxidation treatments produce oxide coatings with micron-scale discharge pores, resulting in insulation and wear and corrosion resistance far below that expected of highly dense Al2O3 coatings. The introduction of cathodic polarization during the plasma electrolytic oxidation process, especially when the applied cathode-to-anode current ratio (Rpn) is greater than 1, triggers a unique plasma discharge phenomenon known as "soft sparking". The soft spark discharge mode significantly improves the densification of the anode ceramic layer and facilitates the formation of the high-temperature α-Al2O3 phase within the coating. Although the soft spark discharge phenomenon has been known for a long time, the growth behavior of the coating under its discharge mode still needs to be studied and improved. In this paper, the growth behavior of the coating before and after soft spark discharge is investigated with the help of the micro-morphology, phase composition and element distribution of a homemade fixture. The results show that the ceramic layer grows mainly along the oxide–electrolyte direction before the soft spark discharge transformation; after the soft spark discharge, the ceramic layer grows along the oxide–substrate direction. It was also unexpectedly found that, under soft spark discharge, the silicon element only exists on the outside of the coating, which is caused by the large size and slow migration of SiO32−, which can only enter the ceramic layer and participate in the reaction through the discharge channel generated by the strong discharge. In addition, it was also found that the relative phase content of α-Al2O3 in the coating increased from 0.487 to 0.634 after 10 min of rotary spark discharge, which is an increase of 30.2% compared with that before the soft spark discharge transition. On the other hand, the relative phase content of α-Al2O3 in the coating decreased from 0.487 to 0.313 after 20 min of transfer spark discharge, which was a 55.6% decrease compared to that before the soft spark discharge transformation. [ABSTRACT FROM AUTHOR]

Vegetation plays an essential role in the atmospheric and hydrological processes, and vegetation responds differently to climate change in various regions, especially in extreme climates. Therefore, the use of static prescribed vegetation information from past years in numerical models can be a source of biases in hydrological simulations. However, previous studies have mainly focused on the effects of vegetation dynamics on hydrological processes in arid and semi‐arid regions. It remains unclear how static or dynamic vegetation affects hydrological simulations in humid regions, especially under drought conditions. In this study, the Weather Research and Forecasting (WRF) model coupled with Noah‐MP was used to assess the impact of vegetation dynamics on hydrological simulations in the East River basin (ERb) of China, which is a major water source for several major cities in the Pearl River Delta. The model was run with prescribed and dynamic vegetation conditions, respectively. Our model validation based on observed 2‐m temperature (T2) and Leaf Area Index (LAI) showed that the model performance was improved when vegetation dynamics were considered. Our simulations with static or dynamic vegetation showed the impacts of vegetation dynamics on hydrological simulations under droughts. The model with vegetation dynamics simulated a wetter condition with higher soil moisture and runoff and lower T2, compared with the simulations of static vegetation. The results suggested that ignoring vegetation dynamics may overestimate the severity of drought in this humid basin, unlike arid and semi‐arid regions. Therefore, consideration of vegetation dynamics in this humid basin will deepen our research on different types of zones and serve as a reference for other humid regions. Unrealistic LAI (vegetation information) likely causes significant biases in regional climate simulations. Dramatic changes in vegetation were observed in the East River basin. Our results showed that the performance of the WRF model was significantly improved with a more appropriate representation of vegetation, that is, dynamic vegetation. Furthermore, the drought severity was highly overestimated (higher temperature and lower LAI, drier soil moisture, less runoff, etc.) when the vegetation dynamics were ignored. Therefore, the vegetation dynamics should be considered in this humid region, especially during extreme drought conditions. [ABSTRACT FROM AUTHOR]

40CrNiMoA steel was nitrided by arc plasma-assisted nitriding (PAN) process at 430 °C, 1 Pa for 3 h with different N2/Ar ratios. Their structures, micrographs, microhardness profiles and tribological properties were investigated by X-ray diffraction (XRD), optical microscope, SEM, Vickers microhardness tester and pin-on-disk tribometer, respectively. All of the nitrided layers were primarily consisted of the γ′-Fe4N and ε-Fe3N. The thickness of compound layers increased with the N2/Ar ratio increasing. The maximum thickness of the compound layer was about 6 μm after nitriding, while the thickness of nitrided layer reached about 230 μm. The surface microhardness and wear resistance of the steel were significantly improved. Its maximum surface microhardness enhanced as 3 times (approx.) as that of the substrate. The wear mechanism changed from adhesive wear to abrasive wear. In addition, the nitrided sample treated at N2/Ar ratio of 75%:25% had the optimum abrasion resistance and lowest wear rate. [ABSTRACT FROM AUTHOR]

In this study, white thermal control coatings were produced on a 6061 Al alloy using plasma electrolytic oxidation (PEO). The coatings were mainly formed by incorporating K2ZrF6. The phase composition, microstructure, thickness, and roughness of the coatings were characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), a surface roughness tester, and an eddy current thickness meter, respectively. The solar absorbance and infrared emissivity of the PEO coatings were measured using a UV–Vis–NIR spectrophotometer and FTIR spectrometer, respectively. The addition of K2ZrF6 to the trisodium phosphate electrolyte was found to significantly enhance the thickness of the white PEO coating on the Al alloy, with the coating thickness increasing in proportion to the concentration of K2ZrF6. Meanwhile, the surface roughness was observed to stabilize at a certain level as the K2ZrF6 concentration increased. At the same time, the addition of K2ZrF6 altered the growth mechanism of the coating. In the absence of K2ZrF6 in the electrolyte, the PEO coating on the Al alloy surface predominantly developed outwards. However, with the introduction of K2ZrF6, the coating's growth mode transitioned to a combination of outward and inward growth, with the proportion of inward growth progressively increasing in proportion to the concentration of K2ZrF6. The addition of K2ZrF6 substantially enhanced the adhesion of the coating to the substrate and endowed it with exceptional thermal shock resistance, as the inward growth of the coating was facilitated by the presence of K2ZrF6. In addition, the phase composition of the aluminum alloy PEO coating in the electrolyte containing K2ZrF6 was dominated by tetragonal zirconia (t-ZrO2) and monoclinic zirconia (m-ZrO2). With the increase in K2ZrF6 concentration, the L* value of the coating increased from 71.69 to 90.53. Moreover, the coating absorbance α decreased, while the emissivity ε increased. Notably, at a K2ZrF6 concentration of 15 g/L, the coating exhibited the lowest absorbance (0.16) and the highest emissivity (0.72), which are attributed to the enhanced roughness resulting from the substantial increase in coating thickness caused by the addition of K2ZrF6, as well as the presence of ZrO2 with higher emissivity within the coating. [ABSTRACT FROM AUTHOR]