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Near-Infrared (NIR) spectroscopic analyses can be applied in waste textile recycling as a rapid and non-invasive method to provide both qualitative and quantitative results. However, it has been a challenge to enhance the accuracy rate of NIR-based waste textile sorting due to the major influences from water contexts in the samples. Orthogonalization of External Parameters (EPO) has been introduced to reduce the interference from water absorption in NIR spectral signals for better accuracy and reliability in modeling. Here we explore the feasibility of applying EPO strategy with varieties of algorithms, including partial least squares regression (PLS), artificial neural network (ANN), decision tree (DT), random forest (RF), gradient boosting decision tree (GBDT), extreme random tree (Extra-tree), decision tree model based on AdaBoost algorithm (AdaBoost-tree), support Vector machine (SVM), one-dimensional convolutional neural network (1D-CNN), and one-dimensional convolutional neural network with improved Inception structure (1D-Inception-CNN). 216 waste textiles samples from Xinjiang, China, were studied with different moisture levels. Among them, 80 samples were used to develop the EPO algorithm, 112 were used to establish the prediction models, and 24 were used as test datasets. Then, the samples were scanned using a near-infrared spectrometer at different moisture regain rates. Our results showed that the moisture content of waste textiles had strong absorption peaks near 1150 and 1450 nm, leading to a decrease in the near-infrared reflectance of waste textiles. To verify the effectiveness of the EPO algorithm, the decision coefficients (R2 score) and other indicators of the model without the EPO process and the model with EPO process are systematically compared. Our results show that the EPO algorithm preprocessing improves the accuracy of the NIR model (The average decision coefficient (R2 score) of the models was increased by 0.83), especially when the moisture interference level is significant. Therefore, the EPO integrated modeling method is a reliable approach for better accuracy in NIR-based waste textile sorting. [ABSTRACT FROM AUTHOR]

A 6.0-magnitude (Mw) earthquake was reported to have occurred in Jiashi County, Xinjiang at 21:27 on 19 January 2020, Beijing time (13:27 UTC). This study applied a stochastic finite-fault approach based on the dynamic corner frequency (EXSIM) to simulate 23 near-field stations within a distance of 120 km from the epicenter. The stochastic finite-fault approach is the simplest and most effective method for simulating high-frequency ground motions. According to studies and empirical relationships, we estimated a region-specific parameter (K0) for use in simulations. In this study, two different slip models, namely, random and inverted slip models, were used to explain their influence on the simulation results. For most stations, the simulated seismic characteristics, such as the peak ground acceleration (PGA), pseudo spectral acceleration, and Fourier acceleration spectrum, are highly compatible with the observed values in the frequency range (f > 1 Hz), indicating that the stochastic finite-fault approach is not highly sensitive to input slip distributions and fault dimensions. The simulation results indicated no significance between the two slip models under high-frequency conditions. Finally, based on the predicted PGA values of 1891 field points selected near the fault, the acceleration field of the Jiashi earthquake is plotted. The results show that the use of the acceleration time history predicted by modified EXSIM is feasible and practical in the high-frequency band above 1 Hz. [ABSTRACT FROM AUTHOR]