A Study on Economic Impact in the Context of American Election Based on AHP

To assess the economic impact of the different policies of the Trump and Biden candidates,we formulate metrics on five aspects:Covid-19 prevention and control measures,environmental protection policies,taxation,health care reform,foreign trade.Moreover,each metric is subdivided into several secondary metrics,making a three-tier hierarchical structure.Take environmental protection policy as an example:Without direct data under Biden’s policies,we collected data on U.S.CO2 emissions and U.S.oil consumption during Obama’s presidency as Biden’s legacy.First,use the analytic hierarchy process(AHP)to select indicators that can reflect the U.S.economy and determine the weight of each indicator.For the U.S.economy,Biden scored 2.6498,Trump 2.3502,suggesting that the election of Biden might make things better for the economy.For China’s economy,Biden scored 0.6810 and Trump 0.3245,meaning Biden could give the Chinese economy more room to grow.To reduce the influence of AHP subjectivity on the results,the Pearson correlation coefficient is introduced to establish the P-AHP model.Take the impact on China’s economy.Biden scored 0.5846 and Trump 0.4154.

Wolf is Coming-Dynamic Classification Prediction Model of Vespa Mandarinia

Given the threat of Vespa mandarinia invasion to ecological balance,ac­cording to the data and information provided,the dynamic reproduction model of Vespa mandarinia is established by using natural domain inter­polation,and the variation law of total bumblebee with time,latitude,and longitude is obtained.At the same time,we established the classification prediction model by using a neural network and established the mapping relationship between time and space to evaluation grade.We meshed the area provided by the title,assigned values to the location of Vespa mandarinia(VM),and established a VM diffusion model with natural neighborhood interpolation.Its propagation process is simulated by cellular automata.It is determined that VM spreads in a circular shape centered at(122.93174°W,48.93457°N)and(122.57376°W,49.07848°N)in the Washington area,with the farthest distance being 1184.4 km and 985 km respectively.We set up a classification prediction model for better classification.Ac­cording to the image upload time and location,SVM and neural network are used for classification prediction,and the classification accuracy is 74.26%and 97.60%,respectively,and the neural network has higher clas­sification accuracy.So we choose the neural network.