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A new quantum watermark algorithm is presented by combining maximum pixel difference partitioning with the least significant bit substitution strategy. A quantum cover image with the novel enhanced quantum image representation is divided into non-overlapping blocks. And the difference between the maximal and the minimal pixel values in a block is calculated and compared with the predefined threshold. Then the current block is defined as an edge block or a smooth block according to the comparison result. Subsequently, an edge block could be embedded into more secret information than a smooth block due to the visual characteristic of human eyes. To enhance the security of the quantum watermark algorithm, the chaos matrix obtained by tent map is utilized in the scrambling and embedding process. The scrambled quantum watermark image is embedded into a quantum carrier image with the least significant bit substitution strategy. Quantum circuit analyses demonstrate that the complexity of embedding process is O(n2). Compared with other quantum watermark schemes, the proposed quantum watermark algorithm based on maximum pixel difference and tent map could achieve a better visual quality and a higher embedding capacity.

Eruption of solar flares is a complex nonlinear process, and the rays and high-energy particles generated by such an eruption are detrimental to the reliability of space-based or ground-based systems. So far, there are not reliable physical models to accurately account for the flare outburst mechanism, but a lot of data-driven models have been built to study a solar flare and forecast it. In the paper, the status of solar-flare forecasting is reviewed, with emphasis on the machine learning methods and data-processing techniques used in the models. At first, the essential forecast factors strongly relevant to solar flare outbursts, such as classification information of the sunspots and evolution pattern of the magnetic field, are reviewed and analyzed. Subsequently, methods of resampling for data preprocessing are introduced to solve the problems of class imbalance in the solar flare samples. Afterwards, typical model structures adopted for flare forecasting are reviewed from the aspects of the single and fusion models, and the forecast performances of the different models are analyzed. Finally, we herein summarize the current research on solar flare forecasting and outline its development trends.

The research of flare forecast based on the machine learning algorithm is an important content of space science. In order to improve the reliability of the data-driven model and weaken the impact of imbalanced data set on its forecast performance, we proposes a resampling method suitable for flare forecasting and a Particle Swarm Optimization (PSO)-based Support Vector Machine (SVM) regular term optimization method. Considering the problem of intra-class imbalance and inter-class imbalance in flare samples, we adopt the density clustering method combined with the Synthetic Minority Over-sampling Technique (SMOTE) oversampling method, and performs the interpolation operation based on Euclidean distance on the basis of analyzing the clustering space in the minority class. At the same time, for the problem that the objective function used for strong classification in SVM cannot adapt to the sample noise, In this research, on the basis of adding regularization parameters, the PSO algorithm is used to optimize the hyperparameters, which can maximize the performance of the classifier. Finally, through a comprehensive comparison test, it is proved that the method designed can be well applied to the flare forecast problem, and the effectiveness of the method is proved.

Predicting the activity of solar flares is of great significance for studying its physical mechanism and the impact on human production and life. Problems such as class imbalance, high time-series sensitivity, and over-localization of important features exist in the sample data used for flare forecasting. We design a solar flare fusion method based on resampling and the CNN-GRU algorithm to try to solve the above problems. In order to verify the effectiveness of this fusion method, first, we compared the forecast performance of different resampling methods by keeping the forecast model unchanged. Then, we used the resampling algorithm with high performance to combine some single forecast models and fusion forecast models respectively. We use the 2010–2017 sunspot data set to train and test the performance of the flare model in predicting flare events in the next 48 h. Through the conclusion of the above steps, we prove that the resampling method SMOTE and its variant SMOTE-ENN are more advantageous in class imbalance problem of flare samples. In addition, after the fusion of one-dimensional convolution and recurrent network with “forget-gate” , combined with the SMOTE-ENN to achieve TSS = 61%, HSS = 61%, TPRate = 77% and TNRate = 83%. This proves that the fusion model based on resampling and the CNN-GRU algorithm is more suitable for solar flare forecasting.

This paper investigates a simulation study on fuzzy state variable-model predictive control (MPC) of coal-fired power plants. Firstly, a fuzzy technology is used to approximate the nonlinear model of a boiler-turbine unit. Then, a state feedback controller is employed to enhance the dynamic performance of the unit. Finally, based on the obtained fuzzy augment model by considering the state feedback controller itself, an MPC controller is introduced to achieve the tracking control of load instruction and the reference value of throttle pressure generated by a sliding pressure curve. Simulations on a 330MW drum boiler unit show the effectiveness of the control strategy.

This study investigated whether the rotavirus infection rate in children is associated with temperature and air pollutants in Hangzhou, China. This study applied a distributed lag non-linear model (DLNM) to assess the effects of daily meteorological data and air pollutants on the rotavirus positive rate among outpatient children. There was a negative correlation between temperature and the rotavirus infection rate. The impact of temperature on the detection rate of rotavirus presented an evident lag effect, the temperature change shows the greatest impact on the detection rate of rotavirus approximate at lag one day, and the maximum relative risk (RR) was approximately 1.3. In 2015, the maximum cumulative RR due to the cumulative effect caused by the temperature drop was 2.5. Particulate matter (PM) 2.5 and PM10 were the primary air pollutants in Hangzhou. The highest RR of rotavirus infection occurred at lag 1–1.5 days after the increase in the concentration of these pollutants, and the RR increased gradually with the increase in concentration. Based on the average concentrations of PM2.5 of 53.9 μg/m 3 and PM10 of 80.6 μg/m 3 in Hangzhou in 2015, the cumulative RR caused by the cumulative effect was 2.5 and 2.2, respectively. The current study suggests that temperature is an important factor impacting the rotavirus infection rate of children in Hangzhou. Air pollutants significantly increased the risk of rotavirus infection, and dosage, lag and cumulative effects were observed.

A two-dimensional finite element mathematical model of rough rolling in "1+4" hot continuous rolling of 5052 aluminum alloy was developed by using finite element software Deform, The temperature and effective strain distribution of the strip in different process parameters has been investigated in by simulating the mathematical model in different simulation conditions. The process parameters such as rolling speed, initial temperature, contact heat transfer coefficient between work roll and strip have been considered. The simulation conditions were built by the means of orthogonal experiment. The process parameters, which can make the temperature and effective strain of the strip in a relatively uniformity distribution, were achieved by analyzing the simulation results under different simulation conditions. To judge the uniformity of the temperature and effective strain distribution of the strip quantitative in different simulation conditions, standard deviation has been used as a criterion.

As the Russian government has increased investment and management in the Global Navigation Satellite System (GLONASS), the paper improves the acquisition and tracking algorithms of GLONASS receiver based on the primary study of GPS receiver. On the platform of MATLAB, the Fast Fourier Transform (FFT) acquisition of the GLONASS intermediate frequency (IF) signal has achieved by GLONASS signal simulator and PC. At the same time the delay lock loop (that is early-late loop) and coast phase lock loop is used to achieve the code tracking and carrier tracking and decode the navigation data. Just as the experimental results shown, GLONASS IF signal can be acquired and tracked by these algorithms which are suggested in this paper. And it has laid a solid foundation for the study of a complete GLONASS software receiver in the future.

A numerical model is proposed to simulate the unsteady seepage flow through dam, with both saturation and water head as variables to describe the seepage domain. Experiments of seepage flow through dam with unsteady conditions were conducted in laboratory to verify the reliability and the accuracy of the numerical model. The performance of the model is also assessed by comparing with analytical solutions. The examples show that the accuracy and the reliability of the model are adequate to handle seepage flow problems with complex conditions. It is also demonstrated that the method of determining the seepage surface through the saturation description is natural, logical and practical in actual applications. The method provides a relatively simple computing scheme, as compared with traditional methods.

The objective is to investigate the relation between the levels of two serum adipocytokines (adiponectin and resistin) and non-alcoholic fatty liver disease (NAFLD) in obese children. In this study, 113 obese children were enrolled and divided into 3 groups. Obese group 1 was defined as obese children without any liver abnormality. Obese group 2 was defined as obese children just with fatty infiltration of the liver in ultrasonic appearance and obese group 3 was defined as obese children with liver function abnormality. The controls consisted of 37 nonobese children without endocrine, metabolic or kidney disease. The levels of serum adiponectin and resistin were measured by ELISA method. Insulin resistance by homeostasis model (HOMA-IR), area under curve of glucose (AUCG), serum total cholesterol, triglyceride, alanine aminotransferase, uric acid, HDL-cholesterol, LDL-cholesterol and body mass index (BMI) were measured as well. In obese children, NAFLD were found in 63 cases (55.75%). Serum adiponectin levels of obese children were significantly lower than that of controls (3.63 vs 5.79 microg/mL, P0.001) while serum resistin levels were not different (P = 0.876). Moreover, serum adiponectin levels in obese group 1 were significantly higher than that of group 2 and 3 (4.24 vs 3.37 and 3.12 microg/mL, all P0.05) and no difference was found between obese group 2 and obese group 3 (P0.05). Serum resistin levels among the three obese groups were 4.37 ng/mL, 3.72 ng/mL and 4.24 ng/mL without significant difference (P = 0.592). NAFLD, BMI, gender and HDL-cholesterol were independent determinants of serum adiponectin levels in children analyzed by multiple regression analysis, which explained 33% of the variance. Serum adiponectin levels were inversely associated with BMI, gender and NAFLD (all P0.05) and were positively associated with HDL-cholesterol levels (P = 0.033). These results suggest that adiponectin might be a protective factor in NAFLD occurrence in obese children, and that the measurement of adiponectin should be part of the standard evaluation of the obese child and may help to evaluate the occurrence of NAFLD.