Ganoderma lucidum spore powder was used as raw material,  ethanol  with a volume fraction of  70%  as extractant.  We adopted a combination of enzymatic  hydrolysis and ultrasound assisted extraction method,  set different liquid-solid ratios,  ultrasound time,  enzymatic hydrolysis time,  and enzyme dosage  as  four factors for a one-way test and designed a response surface experiment to  determine the optimal extraction method and its influencing factors. The   Ganoderma triterpene were separated and purified by using macroporous resin chromatography. By optimizing the  separation and purification process, the optimal elution resin,   eluent volume fraction,  flow rate of the upper sample solution and the mass ratio of the upper sample solution were determined. The compositional differences of the total  Ganoderma triterpenes were analysed by high performance liquid chromatography (HPLC). Though the pre-experimental analysis, the results show that the enzyme + ultrasound assisted extraction is more efficient compared to the single extraction method. Ethanol is used as an extractant to extract triterpenoids from Ganoderma lucidum can enhance the purity of triterpenoids. Under optimal conditions, the  rapid and accurate determination of the triterpene content can be achieved, providing a theoretical basis for the separation and purification of Ganoderma triterpenes.

We considered a  class of tumor-immune model, discussed the existence  conditions  of their equilibrium points, and used characteristic equations to analyze the local kinetic stability of each equilibrium point,  proving that the model underwent Hopf bifurcation under the corresponding conditions. By calculating the first Lyapunov coefficient, it can be concluded that if the coefficient is not zero, the model undergoes Hopf bifurcation,  the bifurcation is supercritical if the coefficient is less than zero, and the bifurcation is subcritical if the coefficient is greater than zero. Finally, numerical simulations are used to validate the theoretical analysis results.

Aiming at the shortcomings of forensic technology based on Docker container, we proposed an investigation and forensics model based on Docker host, and gave a targeted data forensics method according to the different states of the Docker host in 
the forensic model. The experimental results show that the forensics  can obtain relevant electronic evidence more specifically by using the model.

We have applied partial least squares (PLS) to establish a calibration model, the precision of which is verified by cross-validation. Via adopting optimum wavelength to calibrate, a satisfactory model has been established. In this model, the correlation coefficient in methanol determination is 0.999 91, the root mean square error of cross-validation (RMSECV) is 0.431; in ethanol de termination, the correlation coefficient is up to 0.999　98, and the RMSECV is 0.193. Compared with that of gas chromatography, the relative error of the sample determination results is not more than 3.505%.

Firstly, aiming at the problems that the existing technology could not automatically extract software repository, mark data, build quality analysis model and analyze software quality, we proposed a just-in-time quality analysis framework GIF for Python projects. Secondly, based on GIF, the top 10 most popular Python projects on GitHub were extracted and labeled, and three classical classifiers (logistic regression, naive Bayes and random forest) were used for experimental verification on the evaluation indexes of AUC and F₁ values. The experimental results show that GIF framework can identify software defects in Python projects immediately and effectively, it is an easy-to-use just-in-time software quality analysis framework.

Aiming at the problems existing in few-shot learning at present, we designed a new network structure and its training method to improve the few-shot learning. The  convolution network and multi-scale slide pooling method were used to enhance feature extraction in the feature embedding part of the network. The main structure  of the networks was the Siamese network  to facilitate learning semantics from small sample data through comparison between samples. The training method  of the framework adopted nested level parameter updating to ensure the stability of convergence. Compared with the common visual model and 
few-shot learning methods, the experimental results  on two classical few-shot learning datasets show that the method significantly improves the  accuracy of  few-shot learning, and  can be used as a solution  under the condition of insufficient sample.

Based on the characteristics of polyvinylidene fluoride (PVDF) piezoelectric materials, a pressure sensor was designed, which consists of probe, charge amplifier circuit, filter circuit and voltage amplifier circuit.  The vibrating elastic iron slice was deformed by the contact point displacement. The measurement of pressure can be obtained by measuring the change of piezoelectric signal of the PVDF material on the iron slice. The  sensitivity of sensor is 61.22 mV/N and may be applied to various pressure measurement.