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MgO-ZrO2nanosized powders have been synthesized via the co-precipitation route using ammonia, ammonium carbonate and ammonium hydrogen carbonate as precipitant, respectively. The three different powders were characterized by TG-DSC, XRD and SEM. And the sinterability behavior of each powder also has been investigated. The results indicated that uniformly dispersed and much looser MgO-ZrO2nanosized powders could be obtained when the ammonium carbonate, especially the ammonium hydrogen carbonate were used. When ammonia was used, some agglomeration could be observed. The crystalline grain size of the three powders is closed to each other. Due to the existing of agglomeration, the sintered samples prepared by using ammonia as precipitant exhibited poorer sinterability than that of using carbonates as precipitant.

When reading texts for text classification tasks, a large number of words are irrelevant, and in text classification tasks, the traditional self-attention mechanism has the problem of weight distribution limitations. Therefore, a text classification model that combines an improved self-attention mechanism with a Skip-GRU (Skip-grate recurrent unit) network (SA-SGRU) is proposed in this paper. Firstly, Skip-GRU, the enhanced model of GRU (Grate Recurrent Unit), is used to skip the content that is not important for text classification when reading texts and only capture effective global information. Then, the improved self-attention mechanism is introduced to redistribute the weight of the deep text sequences. Secondly, the optimized CNN (convolutional neural network) is combined to bring up the local features of texts. Finally, a Softmax classifier is used to obtain the classification results of sample labels. Experimental results show that the proposed method can achieve better performance on three public datasets compared with other baseline methods. The ablation experiments also demonstrate the effectiveness of each module in the proposed model.

To enhance the security of the digital image information transmitted through Internet, this paper proposes an image scrambling algorithm based on spiral filling of bits. This algorithm first reads the pixel values of all the odd-number lines in the images successively from top to bottom and then does the same to the even-number lines. All results will be saved in an onedimensional array. All the elements in the array are transformed into binary digits and saved in a one-dimensional array that is 8 times longer than the previous one. In this new array every element only saves one binary digit. The bit sequences corresponding to the image oddnumber lines in this array are rearranged in an inverted order. The arrays disposed into an inverted order successively fill a matrix whose line number is the same as the old image and column number is 8 times of the old image in a direction of heliciform. Then a new matrix is generated. Lastly every 8 elements in this new matrix are considered as 8 binary digits and are transformed into decimal digits. That is how image scrambling is conducted. This algorithm is simple and easy to be realized. Abundant experimental results have shown that the algorithm has well scrambling effect and is able to recover image without distortion.

The operation of the system greatly affects our work efficiency. We need to improve our system construction management. Therefore, we will use the eight-way detection method to study how to manage the minimum system with the water temperature detector. The research results show that the design and construction of the minimum system has passed the temperature detection of DS18B20 in eight, the system is stable, and the effect of the minimum system of process management is achieved.

In order to determine whether the existing in water resources system sustainable management solution, we need to develop a tool to provide reasonable and reliable information to decision makers. RESCON project's goal is to use data for reservoir and dam, to provide a toolkit in policy formulation used in decision making. At the same time, it also prompt policymakers stand at the national level to recognize the importance of reservoir protection. Sediment transport rate is an important parameter, which determines the amount of sediment flushed of the reservoir. So its calculating formula based on RESCON Model is revised and validated that it can be used to evaluate sediment management using the data of the Sanmenxia Reservoir in 2003. Among all the five-sediment management solutions RESCON model recommends that, the Flushing is with the highest economic benefit. Also, Flushing as the current sediment management strategy of the Sanmenxia reservoir shows that it is reasonable and effective. Among 15 Flushing solutions, the best one is that while the water level is at 290 m, flushing discharge rate is 1188 m 3 /s, dredging every two years with a flushing period of 67 days each time and long-term reservoir storage volume is 16.05 billion m 3 below 323 m water level.