The Solid-State Synthesis of BiOIO3 Nanoplates with Boosted Photocatalytic Degradation Ability for Organic Contaminants.

BiOIO3 exhibits excellent oxidation capacity in the photocatalytic degradation of contaminants thanks to its unique polarized electric and internal electrostatic field. However, the synthetic method of BiOIO3 nanomaterials is mainly focused on hydrothermal technology, owing to its high energy consumption and time-consuming nature. In this work, a BiOIO3 nanosheet was prepared by a simple solid-state chemical reaction, which was identified by XRD, EDS, XPS, and HRTEM. Benefiting from the strong oxidation ability of the valence band maximum, the distinctive layer structure, and the promoted generation of ·O2-, the BiOIO3 nanosheet exhibits excellent photo-degradation activity for methyl orange (MO) and its apparent rate constant is 0.2179 min-1, which is about 3.02, 8.60, and 10.26 times higher than that of P25, BiOCl, and Bi2O2CO3, respectively. Interestingly, the BiOIO3 nanosheet also has good photocatalytic degradation performance for phenolic compounds; in particular, the degradation rate of BPA can reach 96.5% after 16 min, mainly due to hydroxylation reaction.

MEEMD Decomposition-Prediction-Reconstruction Model of Precipitation Time Series.

To address the problem of low prediction accuracy of precipitation time series data, an improved overall mean empirical modal decomposition-prediction-reconstruction model (MDPRM) is constructed in this paper. First, the non-stationary precipitation time series are decomposed into multiple decomposition terms by the improved overall mean empirical modal decomposition (MEEMD). Then, a particle swarm optimization support vector machine (PSO-SVM) and convolutional neural network (CNN) and recurrent neural network (RNN) models are used to make predictions according to the characteristics of different decomposition terms. Finally, the prediction results of each decomposition term are superimposed and reconstructed to form the final prediction results. In addition, the application is carried out with the summer precipitation in the Wujiang River basin of Guizhou Province from 1961 to 2018, using the first 38 years of data to train MDPRM and the last 20 years of data to test MDPRM, and comparing with a feedback neural network (BP), a support vector machine (SVM), a particle swarm optimization support vector machine (PSO-SVM), a convolutional neural network (CNN), and a recurrent neural network (RNN), etc. The results show that the mean relative error (MAPE) of the proposed MDPRM is reduced from 0.31 to 0.09, the root mean square error (RMSE) is reduced from 0.56 to 0.30, and the consistency index (α) is significantly improved from 0.33 to 0.86, which has a higher prediction accuracy. Finally, the trained MDPRM predicts the average summer precipitation in the Wujiang River basin from 2019 to 2028 to be 466.42 mm, the minimum precipitation in 2020 to be 440.94 mm, and the maximum precipitation in 2024 to be 497.94 mm. Based on the prediction results, the agricultural drought level is evaluated using the Z index, which indicates that the summer is normal in the 10-year period. The study provides technical support for the effective guidance of regional water resources' allocation and scheduling and drought mitigation.

Effect of early continuously intravenous tranexamic acid on perioperative blood loss in thoracolumbar burst fractures with neurological symptoms.

There is still a conflict between early surgical decompression and increased bleeding resulting from early surgery for thoracolumbar burst fractures (TBF) with neurological symptoms. The aim of this study is to investigate the effect of early continuously intravenous tranexamic acid (TXA) on perioperative blood loss in TBF with neurological symptoms who underwent early surgery. A retrospective comparative analysis was performed. The patients in study group were treated with intravenous TXA 15 mg/kg every 24 hours after admission besides intravenous TXA 15 mg/kg before skin incision and patients in control group were treated with intravenous TXA 15 mg/kg before skin incision only. Perioperative blood loss was compared between the 2 groups. The hemoglobin at admission, before surgery, 1 day and 3 days after surgery, the operation time, drainage time, blood transfusion and volume, incidence of complications and length of hospital stay were also compared. The operation time, preoperative, intraoperative, total, hidden amounts of blood loss in TXA group were significantly lower than those in control group (P < .001). The hemoglobin level in the TXA group was significantly higher than that in the control group before and 1 day after surgery (P < .05). The remove drainage time, hospitalization time, blood transfusion rate and volume in the TXA group were significantly lower than those in the control group (P < .001). There was no significant difference in the incidence of lower limb thrombosis between the 2 groups (P > .05). Early continuously intravenous TXA reduces the perioperative blood loss of patients with TBF who underwent early posterior fracture reduction, nerve decompression and pedicle screw fixation.

Application of PSO-BPNN-PID Controller in Nutrient Solution EC Precise Control System: Applied Research.

In this paper, we present a nutrient solution control system, designing a nutrient solution electrical conductivity (EC) sensing system composed of multiple long-range radio (LoRa) slave nodes, narrow-band Internet of Things (NB-IoT) master nodes, and a host computer, building a nutrient solution EC control model and using the particle swarm optimization (PSO) algorithm to optimize the initial weights of a back-propagation neural network (BPNN). In addition, the optimized best weights are put into the BPNN to adjust the proportional-integral-derivative (PID) control parameters Kp, Ki, and Kd so that the system performance index can be optimized. Under the same initial conditions, we input EC = 2 mS/cm and use the particle swarm optimization BP neural network PID (PSO-BPNN-PID) to control the EC target value of the nutrient solution. The optimized scale factors were Kp = 81, Ki = 0.095, and Kd = 0.044; the steady state time was about 43 s, the overshoot was about 0.14%, and the EC value was stable at 1.9997 mS/cm-2.0027 mS/cm. Compared with the BP neural network PID (BPNN-PID) and the traditional PID control approach, the results show that PSO-BPNN-PID had a faster response speed and higher accuracy. Furthermore, we input 1 mS/cm, 1.5 mS/cm, 2 mS/cm, and 2.5 mS/cm, respectively, and simulated and verified the PSO-BPNN-PID system model. The results showed that the fluctuation range of EC was 0.003 mS/cm~0.119 mS/cm, the steady-state time was 40 s~60 s, and the overshoot was 0.3%~0.14%, which can meet the requirements of the rapid and accurate integration of water and fertilizer in agricultural production.

Visual method for measuring the roughness of a grinding piece based on color indices.

The existing machine-vision surface roughness measurement technique extracts relevant evaluation indices from grayscale images without using the strong sensitivity of color information. In addition, most of these measurements use a micro-vision imaging method to measure a small area and cannot make an overall assessment of the workpiece's surface. To address these issues, a method of measuring surface roughness that uses an ordinary light source and a macro-vision perspective to generate a red and green color index for each pixel is proposed in the present study. A comparison test is conducted on a set of test samples before and after surface contamination using the color index and gray-level algebraic averaging, the square of the main component of the Fourier transform in the frequency domain, and the entropy. A strong correlation between the color index and the surface roughness is established; this correlation is not only higher than that of other indices but also present despite contamination and very robust. Verification using a regression model based on a support vector machine proves that the proposed method not only has a simple apparatus and makes measurement easy but also provides high precision and is suitable over a wide measurement range. The impact of the red and green color blocks, the lighting, and the direction of the surface texture on the correlation between the color index and the roughness are also assessed and discussed in this paper.