In China, coal-to-liquid (CTL) lube base oils with ultrahigh viscosity index (VI) are very popular. Since it consists of chain alkanes only and can be precisely characterized by molecular structures alone, quantitative 13 C nuclear magnetic resonance (NMR) data are used to generate the average structural parameters (ASPs) of CTL base oil. In this work, the ASPs and bulk properties of CTL base oils were tested and compared with those of mineral base oils. Based on the test results, the correlation between the unique property of CTL base oil VI and ASPs was analyzed. To eliminate the effect of significant multicollinearity among the input variables, statistical methods such as ordinary least-squares (OLS), stepwise regression, and ridge regression methods were used to build the VI prediction model. The main findings are as follows: according to the 13 C NMR spectrum, CTL base oils had a significantly higher content of isomeric chain alkanes (including several branching structures) than mineral base oil, while the content of cycloalkanes was zero; among several branched structures, the one with the largest difference in content is structure S 67 , which has the highest percentage in the iso-paraffin structures, all above 25.5% in CTL base oils and below 21.39% in mineral oils; according to the distillation curve of the simulated distillation (SimDist) analysis, CTL base oils with similar carbon number distribution showed lower boiling points, narrower distillation ranges, and higher distillation efficiencies than mineral base oil; correlation analysis showed that the average chain length (ACL), normal paraffins (NPs), and structure S 67 caused the CTL base oil to exhibit a higher VI; and from 13 C NMR data, the ridge regression model was used to obtain regression coefficients consistent with reality, and the expected VI could be well predicted with a correlation coefficient of 0.935.

No abstract

No abstract

No abstract

Marketing in the social network environment integrates current advanced internet and information technologies. This marketing method not only broadens marketing channels and builds a network communication platform but also meets the purchase needs of customers in the entire market and shortens customer purchases. The process is also an inevitable product of the development of the times. However, when companies use social networks for product marketing, they usually face the impact of multiple realistic factors. This article takes the maximization of influence as the main idea to find seed users for product information dissemination and also considers the users’ interest preferences. The target users can influence the product, and the company should control marketing costs to obtain a larger marginal benefit. Based on this, this paper considers factors such as the scale of information diffusion, user interest preferences, and corporate budgets, takes the influence maximization model as a multiobjective optimization problem, and proposes a multiobjective maximization of influence (MOIM) model. To solve the NP-hard problem of maximizing influence, this paper uses Monte Carlo sampling to calculate high-influence users. Next, a seed user selection algorithm based on NSGA-II is proposed to optimize the above three objective functions and find the optimal solution. We use real social network data to verify the performance of models and methods. Experiments show that the proposed model can generate appropriate seed sets and can meet different purposes of information dissemination. Sensitivity analysis proves that our model is robust under different actual conditions.