Maozhu Jin’s research while affiliated with Sichuan University and other places

The coronavirus disease (COVID-19) has impacted the hotel industry in all aspects. However, the changes in hotel customer satisfaction deserve additional attention. Using online hotel reviews, this study explores the difference between the influencing factors of customer satisfaction before and after the COVID-19 outbreak. By use of Latent Dirichlet Allocation and sentiment analysis, factors that affect customer satisfaction and their emotional strength before and after the COVID-19 outbreak are extracted. Then, multiple regression models are established to compare the differences of the impact of each factor on hotel customer satisfaction in different periods. According to the aforesaid study, hotel customer satisfaction and its influencing factors have changed significantly during the pandemic; hotel customer satisfaction during the pandemic is mainly influenced by service quality. Accordingly, strategies are proposed for hotel managers to improve their customer satisfaction during the COVID-19.

The purpose is to apply the fractal statistical model to analyze tourist flow feature of scenic area, and promote the effective performance of scenic area. Zhangjiajie scenic area is taken as an example. First, its natural resources characteristics, ecological environment and problems in the development of scenic area are analyzed in detail; moreover, fractal theory and fractal dimension calculation method are elaborated; finally, wavelet transform is proposed to analyze the nonlinear transformation characteristics of tourist flow. The results show that the changes of the high-frequency layer [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] all have cyclical fluctuations in the change of tourist flow. The change range of tourist flow in spring and autumn is relatively large, and the tourist flow in winter is low. Inbound tourism tourist flow has a certain seasonal regularity, with obvious characteristics of low and peak seasons. Wulingyuan scenic spot and Tianmen Mountain scenic spot of Zhangjiajie have three non-scale ranges, which are related to the scale and change of tourist flow. The concentration of inbound tourist flow in the peak season still exists in both scenic spots, and it is more and more serious. Tourist flow of inbound tourists from April to May and from September to October is large, accounting for more than half of the annual tourist flow, while most of the other periods are in the off-season.

In the process of task scheduling, due to the large search space of resources, it takes a long time to allocate appropriate resources for tasks, which results in the increase of execution time and execution cost of algorithms. For this reason, this paper proposes a cloud computing task fuzzy scheduling strategy based on hybrid search algorithm and differential evolution, which incorporates the classification based on normal distribution and a variety of mutation strategies on the basis of standard differential evolution algorithm. In mutation strategy, the individual difference vector assigns priority to each task and arranges tasks according to resource allocation rules. It improves the slow convergence speed and easy to fall into local optimum of standard difference algorithm, and can effectively solve the task scheduling problem of cloud computing. A cloud computing task scheduling algorithm with time and cost constraints is designed and tested in a simulation environment. The experimental results show that the algorithm can not only shorten the task processing time and reduce the execution cost, but also fully satisfy the users’ actual quality of service requirements by adjusting the weights of time and cost factors.

Background: The health risk assessment aims to describe and evaluate the possibility of a certain disease, hospitalization, or death. With the in-depth research of big data and machine learning technology, the health risk of individuals can be assessed by using the technology, and intervention measures can be taken in advance to reduce the risk. Objective: This study aims to accurately predict and evaluate the possible risks of the population and individuals caused by environmental factors, and constantly improve the medical implementation process. Methods: The relationship between air pollutants and health risk is analyzed from three dimensions of the respiratory system, circulatory system, and digestive system, the prediction method of health quantity related to environmental factors is explored, and a hybrid time series model HTSM (Heuristic Test Strategy Model) based on nonparametric regression and residual fitting is proposed. Results: Respiratory and circulatory diseases are pollutant-sensitive diseases, while the elderly (> 65 years old) are the high-risk population. The improved model can effectively predict the unplanned readmission data in the actual medical scene, and the accuracy of the improved model is 11.11%higher than that of the traditional prediction model. In contrast to the single prediction model, HTSM's error index for different systems is much lower. The mixed model HTSM is better than the single model in fitting the original data. Conclusion: HTSM model based on time series can effectively predict pollutant-sensitive diseases, which can provide an effective theoretical basis for assessing and predicting the population and individual health risks.

The latest “Guide to College English Teaching” lists cross-cultural education as an important part of college English courses. At the same time, ICAO ’s requirements for civil aviation pilots and controllers ’cross-cultural abilities increase year by year, which raises new requirements for the cultivation of cultural abilities. This article explores this subject from two aspects of cross-cultural psychology and cross-cultural behavior, using SPSS22.0 data analysis method and Delphi method to establish an evaluation model, which includes four first-level indicators: attitude, awareness, knowledge and skill. Attitude and consciousness evaluate cross-cultural psychology, and knowledge and skills evaluate cross-cultural behavior. Under the 4 first-level indicators, there are 6 second-level indicators and 17 third-level indicators. This paper uses the accuracy and convenience of language operators in the process of information conversion to transform fuzzy subjective qualitative analysis into quantitative analysis, and proposes a set of evaluation methods for cross-cultural competence based on language operators. At the same time, an empirical study was conducted with C University as an example. The statistical results show that the method has good applicability and scientificity for the evaluation of the cross-cultural competence of civil aviation college students.

Ridesharing has emerged as an alternative transportation mode along road networks around the world. Rideshare matching problem is vital to improve the sustainable development of ridesharing systems. This paper aims to address the stable two-sided satisfied matching problem considering the participants’ psychological perception. First of all, we investigate the elements that influence passengers’ and drivers’ ridesharing experience by means of semi-structured research interviews and questionnaire survey. Two ridesharing perception evaluation systems are originally established to get the preference orders of passengers and drivers separately. Then, rideshare matching-related definitions are stated and rideshare matching linguistic information processing is also elaborated in detail based on preference utility function, disappointment function, as well as elation function. Furthermore, we propose a stable two-sided satisfied matching model on account of fuzzy linguistic information processing about ridesharing, which is able to reflect participants’ psychological factors. To verify the validity of our model, we present a two-sided matching case based on hypothetical rideshare matching platform. The analytical results indicate that the use of stable two-sided satisfied matching method based on fuzzy linguistic information enables to substantially satisfy both drivers’ and passengers’ expectation and improve the sustainability of ridesharing systems.

With the development of science and technology, people also find that fractional-order models are more accurate than integer-order models in describing some phenomena and reflecting some properties of objects when studying practical problems. Since the fractional derivative is a quasi-differential operator, its memory-preserving non-locality, while beautifully portraying the real problem, also brings considerable difficulties to theoretical analysis and numerical calculation. This paper analyses the relationship between enterprise management and financial performance, analyses the mean and heterogeneity of the characteristics of enterprise management team, mathematically models its relationship, constructs fractional differential equations, and tests it through empirical research. The influence of the characteristics of enterprise management age, international experience, education level, team size and government background on the financial performance of the company.

In order to achieve the research from individual data to data system and from passive verification of data to active discovery, taking high dimensional data oriented data mining technology as the research object, an association rule redundancy processing algorithm based on hypergraph in data mining technology is studied according to the project requirements. The concepts of hypergraph and system are introduced to explore the construction of hypergraph on 3D matrix model. In view of the characteristics of big data, a new method of super edge definition is adopted, which improves the ability of dealing with problems. In the association rules redundancy and loop detection based on directed hypergraph, the association rules are transformed into directed hypergraph, and the adjacency matrix is redefined. The detection of redundancy and loop is transformed into the processing of connected blocks and circles in hypergraph, which provides a new idea and method for the redundant processing of association rules. The new method is applied to the data processing of practical projects. The experimental results show that the 3D matrix mathematical model and related data mining algorithms in this paper can find new high-quality knowledge from high-dimensional data.