In order to explore the impact of imported solid waste ban on China's economy and environment, a grey prediction model was established in this paper. GDP, comprehensive income from the utilization of waste resources, the number of imported solid waste treatment enterprises, and the discharge of polluting waste gas and waste water were selected as the indicators to measure the economy and environment. The model was used to predict the data of the unreal garbage ban and compare it with the data after the actual implementation, and get the proportion of the impact on China's economy and environment after the introduction of the policy. The results showed that before and after the promulgation of the ban on foreign waste, China's environmental pollution had improved significantly, the total exhaust gas emissions have been reduced by 34.1%, and the number of imported waste enterprises had decreased by 66.1%, stimulating the rise of the domestic waste utilization industry, and the overall domestic economy had grown steadily.
This paper solved the problem of how to manage the distribution of airport taxis and balance the revenue of long-and shorthaul passenger taxis. In this research, we established a multiobjective programming model, which was solved using genetic algorithms to obtain a reasonable distribution scheme in airport with the highest riding efficiency: set up a pick-up location in the middle of the pick-up area, requiring all cars to leave uniformly when fully loaded, and release an average of 78 taxis per batch in every single boarding location. In addition, with the queuing theory we set the basic parameters of the road. Taking the income balance difference as the objective function, we used the VISSIM software to simulate the simulation. Then the short-term "priority" arrangement plan was: Calculate the ratio of the travel time of the short-distance taxi to the distance from the airport to the city center. If the ratio is less than 0.0659, the taxis that meet the conditions are allowed to be given priority after return. The results have some guidance and strong practical significance.
ABSTRACTHttps://escipub.com/american-journal-of-computer-sciences-and-applications/ 1Boying Lv et al., AJCSA, 2020; 3:23 AJCSA: https://escipub.com/american-journal-of-computer-sciences-and-applications/ 2
Research Article AJERR (2020) 3:25 Discussion on the relationship between construction safety accident and time based on the correspondence analysis model Based on the correspondence analysis model and the method of mining the cause and time of construction accidents. Using Python crawler technology to crawl 3896 data of the causes of construction safety accidents in China Construction Safety Supervision Information System from 2012 to 2018, a contingency table of various types and times of accidents in construction safety accidents was established. By using factor analysis, the relationship between Q-factor analysis and R-factor analysis in the corresponding factor analysis model was established. The model was analyzed and optimized by a special point removal method, and the convergence of the model was strengthened. Finally, the causes of construction safety accidents in the model and the time of one day and one year were calculated and drawn in the corresponding analysis chart of the two-dimensional model. Mining the relationship between them according to their relationship in the graph based on the data collection of 3896 building safety line accidents, this paper put forward the method of mining the main causes of accidents, and used SPSS platform technology to mine the accident data.The results showed that the occurrence of electric shock and drowning was closely related to August, fire and explosion were closely related to February, and poisoning and asphyxiation were closely related to January. 5-6 a.m. was closely related to fire
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