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The comprehensive wastewater from a pharmaceutical chemical plant in Shandong Province consists of concentrated wastewater，clean wastewater and domestic wastewater. The concentrated wastewater has the characteristics of high COD and high NH3-N，and therefore requires pretreatment after quality separation to meet the water quality standards for the receiving body of the wastewater treatment plant. Firstly，the concentrated wastewater was pretreated with micro-electrolysis. The pretreated concentrated wastewater was mixed with the clean and domestic wastewaters after removing the suspended solids before undergoing subsequent treatment through an UASB-improved two stage A/O-sequence batch precipitation combination process. Engineering practice showed that this process had a good treatment effect on the comprehensive wastewater，resulting in stable operation and a high capacity to handle impact loads. After treatment，the COD，NH3-N and TN of the effluent were reduced to below 200，3 and 20 mg/L，respectively，meeting the secondary standards in Table 4 of the Comprehensive Sewage Discharge Standard （GB 8978-1996） and meeting the water quality requirements of the receiving water body of the sewage treatment plant.

Precipitation is a vital physical parameter of the earth surface system, and accurate estimation of spatiotemporal patterns of precipitation is essential for flood disaster monitoring, drought monitoring, and water management. However, regional precipitation, which is derived solely from rain gauges, remote sensing, and weather radar, is subject to large uncertainties, especially for topographically complex mountain areas. Multi-source precipitation data fusion is a practical method for achieving high-accuracy and high-resolution precipitation information. This study proposes an XGBoost-based geostatistical fusion method (XGBoost) for combining information from ground-based measurements, radar precipitation, and other auxiliary parameters, to improve the accuracy of the spatiotemporal distribution of precipitation in geographically complex mountain areas. In the XGBoost-based geostatistical fusion model, radar precipitation and terrestrial parameters, which include longitude, latitude, digital elevation model data, aspect, slope, enhanced vegetation index, and distance from the coastline, are considered as the independent variables. The XGBoost-based geostatistical fusion model was applied to a regional rainstorm event that lasted from August 26th to 30th, 2018, in northern Guangdong using daily measurements from 206 rain gauges and 51 stations for model training and validation. The fused results were further compared with the results obtained from the multiple linear regression kriging method (LM). Validation using ground-based precipitation measurements was applied for different data fusion methods based on the coefficient of determination (R2), Mean Absolute Error (MAE), and Root-Mean-Square Error (RMSE). The experimental results indicated that: (1) The ground-based precipitation data were positively associated with radar precipitation, and the correlation coefficient between the ground-based precipitation data and the terrestrial parameters varied significantly with measurement time over the regional rainstorm event. (2) The XGBoost produced 1 km precipitation prediction with higher accuracy than the LM before residual correction. (3) The accuracy of fused precipitation with the XGBoost-based geostatistical method was reduced after residual correction, but the accuracy of the LM was increased. The XGBoost-based geostatistical method produced 1-km precipitation with lower accuracy than the TsHARP utility on August 27th and 29th; however, in general, the XGBoost-based geostatistical method outperformed the LM because the nonlinear relationships between the ground-based precipitation data and the independent variables were considered in XGBoost. (4) The XGBoost-based geostatistical method captured the differences in precipitation for different land cover patterns and produced the spatial details of fused precipitation over the complex mountain areas.

Aiming at the current peanut pickup combine harvester seedling vines crushing mechanism operation qualified seedling length rate is low, the impurity rate is high, and the seedling vines crushing mechanism is optimized design. On the basis of the material characteristics of peanut seedling vines, taking the crushing mechanism as the research carrier, the interaction mechanism of the seedling vine mechanism was analyzed. Establish the mechanical model and motion trajectory model in the crushing process, simulate the peanut seedling vines crushing process based on the discrete element method, analyze the influence of the cutter shaft rotational speed, movement speed, and bending angle on the crushing of peanut seedling vines, and establish the regression model, and the results of the research show that the influence factors in the main order of priority are: knife shaft speed > movement speed > bending angle The optimal parameter combination is knife shaft speed 2171.94 r/min, movement speed 0.79 m/s, and bending angle 45°. According to the field test conditions, it is determined that the knife shaft rotational speed of 2170 r/min, movement speed of 0.8 m/s, and bending angle of 45° can make the peanut seedling vines crushing mechanism of the work performance to achieve a qualified seedling length rate of 97.292%, the rate of impurity content of 2.746%, and the error with the predicted value is less than 2%, which indicates that this study can provide a reference for the optimal design of the seedling vines crushing mechanism of the peanut pickup combine harvester. [ABSTRACT FROM AUTHOR]

In the era of 6G, the rapid increase in communication data volume poses higher demands on traditional channel estimation techniques and those based on deep learning, especially when processing large-scale data as their computational load and real-time performance often fail to meet practical requirements. To overcome this bottleneck, this paper introduces quantum computing techniques, exploring for the first time the application of Quantum Generative Adversarial Networks (QGAN) to broadband channel estimation challenges. Although generative adversarial technology has been applied to channel estimation, obtaining instantaneous channel information remains a significant challenge. To address the issue of instantaneous channel estimation, this paper proposes an innovative QGAN with a dualmodule design in the generator. The adversarial loss function and the Mean Squared Error (MSE) loss function are separately applied for the parameter updates of these two modules, facilitating the learning of statistical channel information and the generation of instantaneous channel details. Experimental results demonstrate the efficiency and accuracy of the proposed dual-module QGAN technique in channel estimation on the Pennylane quantum computing simulation platform. This research opens a new direction for physical layer techniques in wireless communication and offers expanded possibilities for the future development of wireless communication technologies. [ABSTRACT FROM AUTHOR]

Precipitation nowcasting has important implications for urban operation and flood prevention. Radar echo extrapolation is a common method in precipitation nowcasting. Using deep learning models to extrapolate radar echo data has great potential. The increase of lead time leads to a weaker correlation between the real rainfall evolution and the generated images. The evolution information is easily lost during extrapolation, which is reflected as echo attenuation. Existing models, including generative adversarial network (GAN)-based models, have difficulty curbing attenuation, resulting in insufficient accuracy in rainfall prediction. To solve this issue, a spatiotemporal process enhancement network (GAN-argcPredNet v2.0) based on GAN-argcPredNet v1.0 has been designed. GAN-argcPredNet v2.0 curbs attenuation by avoiding blurring or maintaining the intensity. A spatiotemporal image correlation (STIC) prediction network is designed as the generator. By suppressing the blurring effect of rain distribution and reducing the negative bias by STIC attention, the generator generates more accurate images. Furthermore, the discriminator is a channel–spatial (CS) convolution network. The discriminator enhances the discrimination of echo information and provides better guidance to the generator in image generation by CS attention. The experiments are based on the radar dataset of southern China. The results show that GAN-argcPredNet v2.0 performs better than other models. In heavy rainfall prediction, compared with the baseline, the probability of detection (POD), the critical success index (CSI), the Heidke skill score (HSS) and bias score increase by 18.8 %, 17.0 %, 17.2 % and 26.3 %, respectively. The false alarm ratio (FAR) decreases by 3.0 %. [ABSTRACT FROM AUTHOR]