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In order to correct the monitoring data of the miniature air quality detector, an air quality prediction model fusing Principal Component Regression (PCR), Support Vector Regression (SVR) machine, and Autoregressive Moving Average (ARMA) model was proposed to improve the prediction accuracy of the six types of pollutants in the air. First, the main information of factors affecting air quality is extracted by principal component analysis, and then principal component regression is used to give the predicted values of six types of pollutants. Second, the support vector regression machine is used to regress the predicted value of principal component regression and various influencing factors. Finally, the autoregressive moving average model is used to correct the residual items, and finally the predicted values of six types of pollutants are obtained. The experimental results showed that the proposed combination prediction model of PCR–SVR–ARMA had a better prediction effect than the artificial neural network, the standard support vector regression machine, the principal component regression, and PCR–SVR method. The Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and relative Mean Absolute Percent Error (MAPE) are used as evaluation indicators to evaluate the PCR–SVR–ARMA model. This model can increase the accuracy of self-built points by 72.6% to 93.2%, and the model has excellent prediction effects in the training set and detection set, indicating that the model has good generalization ability. This model can play an active role scientific arrangement and promotion of miniature air quality detectors and grid-based monitoring of the concentration of various pollutants.

In this paper, six types of air pollutant concentrations are taken as the research object, and the data monitored by the micro air quality detector are calibrated by the national control point measurement data. We use correlation analysis to find out the main factors affecting air quality, and then build a stepwise regression model for six types of pollutants based on 8 months of data. Taking the stepwise regression fitting value and the data monitored by the miniature air quality detector as input variables, combined with the multilayer perceptron neural network, the SRA-MLP model was obtained to correct the pollutant data. We compared the stepwise regression model, the standard multilayer perceptron neural network and the SRA-MLP model by three indicators. Whether it is root mean square error, average absolute error or average relative error, SRA-MLP model is the best model. Using the SRA-MLP model to correct the data can increase the accuracy of the self-built point data by 42.5% to 86.5%. The SRA-MLP model has excellent prediction effects on both the training set and the test set, indicating that it has good generalization ability. This model plays a positive role in scientific arrangement and promotion of miniature air quality detectors. It can be applied not only to air quality monitoring, but also to the monitoring of other environmental indicators.

Grid monitoring is the current development direction of atmospheric monitoring. The micro air quality detector is of great help to the grid monitoring of the atmosphere, so higher requirements are put forward for the accuracy of the micro air quality detector. This paper presents a model to calibrate the measurement data of the micro air quality detector using the monitoring data of the air quality monitoring station. The concentration of six types of air pollutants is the research object of this study to establish a calibration model for the measurement data of the micro air quality detector. The first step is to use correlation analysis to find out the main factors affecting the concentration of the six types of pollutants. The second step uses Ridge Regression (RR) to select variables, find out the factors that have significant effects on the concentration of pollutants, and give the quantitative relationship between these factors and the pollutants. Finally, the predicted value of the ridge regression model and the measurement data of the micro air quality detector are used as input variables, and the Extreme Gradient Boosting (XGBoost) algorithm is used to give the final pollutant concentration prediction model. We named the combined model of ridge regression and XGBoost algorithm RR-XGBoost model. Relative Mean Absolute Percent Error (MAPE), Mean Absolute Error (MAE), goodness of fit ( R 2 ), and Root Mean Square Error (RMSE) were used to evaluate the prediction accuracy of the RR-XGBoost model. The results show that the model is superior to some commonly used pollutant prediction methods such as random forest, support vector machine, and multilayer perceptron neural network in the evaluation of various indicators. The model not only has a good prediction effect on the training set but also on the test set, indicating that the model has good generalization ability. Using the RR-XGBoost model to calibrate the data of the micro air quality detector can make up for the shortcomings of the data monitoring accuracy of the micro air quality detector. The model plays an active role in the deployment of micro air quality detectors and grid monitoring of the atmosphere.

Studies have shown that there is a certain correlation between air pollution and various human diseases, especially lung diseases, so it is very meaningful to monitor the concentration of pollutants in the air. Compared with the national air quality monitoring station (national control point), the micro air quality detector has the advantage that it can monitor the concentration of pollutants in real time and grid, but its measurement accuracy needs to be improved. This paper proposes a model combining the least absolute selection and shrinkage operator (LASSO) regression and nonlinear autoregressive models with exogenous inputs (NARX) to calibrate the data measured by the micro air quality detector. Before establishing the LASSO-NARX model, correlation analysis is used to test whether the correlation between the concentration of air pollutants and its influencing factors is significant, and to find out the main factors that affect the concentration of pollutants. Due to the multicollinearity between various influencing factors, LASSO regression is used to further screen the influencing factors and give the quantitative relationship between the pollutant concentration and various influencing factors. In order to improve the prediction accuracy of pollutant concentration, the predicted value of each pollutant concentration in the LASSO regression model and the measurement data of the micro air quality detector are used as input variables, and the LASSO-NARX model is constructed using the NARX neural network. Several indicators such as goodness of fit, root mean square error, mean absolute error and relative mean absolute percent error are used to compare various air quality models. The results show that the prediction results of the LASSO-NARX model are not only better than the LASSO model alone and the NARX model alone, but also better than the commonly used multilayer perceptron and radial basis function neural network. Using this model to calibrate the measurement data of the micro air quality detector can increase the accuracy by 61.3–91.7%.

Infrared spectroscopy can quickly and non-destructively extract analytical information from samples. It can be applied to the authenticity identification of various Chinese herbal medicines, the prediction of the mixing amount of defective products, and the analysis of the origin. In this paper, the spectral information of Cornus officinalis from 11 origins was used as the research object, and the origin identification model of Cornus officinalis based on mid-infrared spectroscopy was established. First, principal component analysis was used to extract the absorbance data of Cornus officinalis in the wavenumber range of 551~3998 cm –1 . The extracted principal components contain more than 99.8% of the information of the original data. Second, the extracted principal component information was used as input, and the origin category was used as output, and the origin identification model was trained with the help of support vector machine. In this paper, this combined model is called PCA-SVM combined model. Finally, the generalization ability of the PCA-SVM model is evaluated through an external test set. The three indicators of Accuracy, F1-Score, and Kappa coefficient are used to compare this model with other commonly used classification models such as naive Bayes model, decision trees, linear discriminant analysis, radial basis function neural network and partial least square discriminant analysis. The results show that PCA-SVM model is superior to other commonly used models in accuracy, F1 score and Kappa coefficient. In addition, compared with the SVM model with full spectrum data, the PCA-SVM model not only reduces the redundant variables in the model, but also has higher accuracy. Using this model to identify the origin of Cornus officinalis , the accuracy rate is 84.8%.

The uncertain production-inventory problem with deteriorating items is investigated and an optimal control model is developed in the present paper. The uncertain production-inventory problem is perturbed by an uncertain canonical process. Based on uncertainty theory, an optimistic-value optimal-based control model is established. The present study aims to find the optimistic value of revenue at a certain confidence level. The uncertainty theory is used to obtain the equation of optimality. Using the Hamilton–Jacobi–Bellman principle, a nonlinear partial differential equation that has to be satisfied by a value function is obtained. Assuming a specific form of the solution, backsubstituting the partial differential equation to find functions of time is conducted, and the functions are then used to solve the partial differential equation. Numerical experiments with different demand functions are used to assess the feasibility of this model and this method.

In this study, we present an optimal control model for an uncertain production inventory problem with deteriorating items. The dynamics of the model includes perturbation by an uncertain canonical process. An expected value optimal control model is established based on the uncertainty theory. The aim of this study is to apply the optimal control theory to solve a production inventory problem with deteriorating items and derive an optimal inventory level and production rate that would maximize the expected revenue. The uncertainty theory is used to obtain the equation of optimality. The Hamilton–Jacobi–Bellman (HJB) principle is used to solve the equation of optimality. The results are discussed using numerical experiments for different demand functions.

Infrared spectroscopy can quickly and non-destructively extract analytical information from samples. It can be applied to the authenticity identification of various Chinese herbal medicines, the prediction of the mixing amount of defective products, and the analysis of the origin. In this paper, the spectral information of Cornus officinalis from 11 origins was used as the research object, and the origin identification model of Cornus officinalis based on mid-infrared spectroscopy was established. First, principal component analysis was used to extract the absorbance data of Cornus officinalis in the wavenumber range of 551 3998 cm-1. The extracted principal components contain more than 99.8% of the information of the original data. Second, the extracted principal component information was used as input, and the origin category was used as output, and the origin identification model was trained with the help of support vector machine. In this paper, this combined model is called PCA-SVM combined model. Finally, the generalization ability of the PCA-SVM model is evaluated through an external test set. The three indicators of Accuracy, F1-Score, and Kappa coefficient are used to compare this model with other commonly used classification models such as naive Bayes model, decision trees, linear discriminant analysis, radial basis function neural network and partial least square discriminant analysis. The results show that PCA-SVM model is superior to other commonly used models in accuracy, F1 score and Kappa coefficient. In addition, compared with the SVM model with full spectrum data, the PCA-SVM model not only reduces the redundant variables in the model, but also has higher accuracy. Using this model to identify the origin of Cornus officinalis, the accuracy rate is 84.8%.

Geometric factor is the key parameter for inversion of particle spectrum in space particle detection. Traditional geometric factor is obtained through the method of numerical calculation with the actual structure of the detector as the input condition. The degree of accuracy for data inversion is reduced since traditional geometric factor fails to take into account the physical process of interaction between the particle and substance as well as the influence of factors such as the particle interference between different energy channels on the measurement result. Here we propose an improved geometrical factor calculation method, the concept of which is to conduct actual structural modelling of the detector in the GEANT4 program, consider the process of interaction between the particle and substance, obtain the response function of the detector to particles of different energy channels through the method of Monte Carlo simulation, calculate the influence of contaminated particle on the geometrical factor, and finally get the geometrical factors for different energy channels of the detector. The imrpoved geometrical factor obtained through the method has carried out inversion for the data of high energy protons detector on China＇s FY-3 satellite, the energy spectrum after which is more in line with the power law distribution recognized by space physics. The comparison with the measured result of POES satellite indicates that the FY-3 satellite data are in good accordance with the satellite data, which shows the method may effectively improve the quality of data inversion.

The ascomycete fungus Fusarium oxysporum f.sp. cucumerinum causes vascular wilt diseases in cucumber. However, few genes related to morphogenesis and pathogenicity of this fungal pathogen have been functionally characterized. BLASTp searches of the Aspergillus fumigatus UgmA and galatofuranosyltransferases (Gal f -transferases) sequences in the F . oxysporum genome identified two genes encoding putative UDP-galactopyranose mutase (UGM), ugmA and ugmB , and six genes encoding putative Gal f -transferase homologs. In this study, the single and double mutants of the ugmA , ugmB and gfsB were obtained. The roles of UGMs and GfsB were investigated by analyzing the phenotypes of the mutants. Our results showed that deletion of the ugmA gene led to a reduced production of galactofuranose-containing sugar chains, reduced growth and impaired conidiation of F . oxysporum f.sp. cucumerinum . Most importantly, the ugmA deletion mutant lost the pathogenicity in cucumber plantlets. Although deletion of the ugmB gene did not cause any visible phenotype, deletion of both ugmA and ugmB genes caused more severe phenotypes as compared with the Δ ugmA , suggesting that UgmA and UgmB are redundant and they can both contribute to synthesis of UDP-Gal f . Furthermore, the Δ gfsB exhibited an attenuated virulence although no other phenotype was observed. Our results demonstrate that the galactofuranose (Gal f ) synthesis contributes to the cell wall integrity, germination, hyphal growth, conidiation and virulence in Fusarium oxysporum f.sp. cucumerinum and an ideal target for the development of new anti- Fusarium agents.