Xiaoning Zhou’s scientific contributions

In this study, a compound method using modified Boeing Fuel Flow Method 2 (BFFM2) and an updated First Order Approximation V3.0 (FOA3.0) method deploying the ICAO Time-in-Mode (TIM) was used to produce a more reliable aircraft emission inventory for the Guangdong–Hong Kong–Macao Greater Bay Area (GBA). The results show that compared with the International Standard Atmosphere (ISA) conditions, the total emission of nitrogen oxides(NOx) decreased by 17.7%, while carbon monoxide(CO) and hydrocarbons(HC) emissions increased by 11.2%. We confirmed that taxiing is the phase in which an aircraft emits the most pollutants. These pollutant emissions will decrease by 0.3% to 3.9% if the taxiing time is reduced by 1 minute. Furthermore, the impact of reducing taxi-out time on emissions is more significant than that of reducing the taxi-in time. Taking the total aircraft emission factors as the main performance indicators, Hong Kong International Airport (VHHH) contributes the most to the total emissions of the GBA, while the Zhuhai airport(ZGSD) contributes the least. The contribution of an individual airport to the total emissions of the GBA is mainly determined by the proportion of Boeing B77L, B77W, and B744.

A machine learning model of handwritten digit recognition based on SVC is established in this paper. Then the influence of sample number, kernel function parameters, penalty coefficients and other parameters on the prediction model is analysed. This results show that training samples have a significant impact on the model. There is an acceptable training number. Different kernel functions have a different effect on the accuracy of the model. The radial basis function is the best in recognition model. The recognition rate increases continuously with C, while the recognition rate increases first with gamma increases, and when gamma increases to a certain value, precision begins to decline.