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The economy of China has developed rapidly, and its international status has quickly risen in the past 30 years. China is shifting from a major exporter into a major consumer, becoming an essential part of the international market. However, some global brands make wrong market decisions because of a lack of understanding of Eastern consumer culture.

Based on consumer xenocentrism, this paper helps global enterprises better understand Chinese consumers' psychology in foreign goods, which is conducive to the development of transnational trade. This study proposes a hypothesis model based on previous literature. The authors collected 300 questionnaires from China and tested the model by SPSS24 and AMOS24.

The findings show that curiosity and country of origin positively affect consumer xenocentrism, consumer xenocentrism has a positive impact on word-of-mouth and purchase intention, word-of-mouth plays a mediating role in xenocentrism and purchase intention and social comparison tendency plays a moderating role in consumer xenocentrism and purchase intention. Simultaneously, this paper develops a scale measuring consumer xenocentrism to provide some quantitative support for this research.

The authors propose some suggestions basing on the research of consumer xenocentrism and provide some further directions.

Current models of transaction credit in the e-commerce network face many problems, such as the one-sided measurement, low accuracy and insufficient anti-aggression solutions. This paper aims to address these problems by studying the transaction credit problem in the crowd transaction network.

This study divides the transaction credit into two parts, direct transaction credit and recommended transaction credit, and it proposes a model based on the crowd transaction network. The direct transaction credit comprehensively includes various factors influencing the transaction credit, including transaction evaluation, transaction time, transaction status, transaction amount and transaction times. The recommendation transaction credit introduces two types of recommendation nodes and constructs the recommendation credibility for each type. This paper also proposes a “buyer + circle of friends” method to store and update the transaction credit data.

The simulation results show that this model is superior with high accuracy and anti-aggression.

The direct transaction credit improves the accuracy of the transaction credit data. The recommendation transaction credit strengthens the anti-aggression of the transaction credit data. In addition, the “buyer + circle of friends” method fully uses the computing of the storage ability of the internet, and it also solves the failure problem of using a single node.

There is a certain error in the satellite positioning of the vehicle. The error will cause the drift point of the positioning point, which makes the vehicle trajectory shift to the real road. This paper aims to solve this problem.

The key technology to solve the problem is map matching (MM). The low sampling frequency of the vehicle is far from the distance between adjacent points, which weakens the correlation between the points, making MM more difficult. In this paper, an MM algorithm based on priority rules is designed for vehicle trajectory characteristics at low sampling frequencies.

The experimental results show that the MM based on priority rule algorithm can effectively match the trajectory data of low sampling frequency with the actual road, and the matching accuracy is better than other similar algorithms, the processing speed reaches 73 per second.

In the algorithm verification of this paper, although the algorithm design and experimental verification are considered considering the diversity of GPS data sampling frequency, the experimental data used are still a single source.

Based on the GPS trajectory data of the Ministry of Transport, the experimental results show that the accuracy of the priority-based weight-based algorithm is higher. The accuracy of this algorithm is over 98.1 per cent, which is better than other similar algorithms.

The purpose of this paper is to obtain the characteristic parameters of dynamic trend for hydro turbine governors, and take these as the inputs of the neural network to realize the diagnosis of the system.

Computer simulation technique has become an important tool in the science research and development. Because of the different degree defaults of the traditional modeling method, a distributed bond graph modeling (BGD) method is presented in this paper. Fault diagnosis and identification have been widely developed in recent years, while many kinds of diagnosis methods have been used in this area. Since it is difficult to get the characteristic parameters of dynamic trend, a new methodology is proposed which integrates BGD and neural network. It gets the characteristic parameters by simulation to the system, and takes these as the inputs of the neural network to realize the diagnosis of the system.

The paper presents the need for application of two models – conventional procedure and bond graph model approaches.

The paper is a very useful diagnosis tool for operators.

A new methodology is proposed which integrates BGD and neural network. The paper is aimed at operational researches and engineers, the results from simulations are reported and commented in the paper.

The purpose of this paper is to assess the serious corrosion problems of the water injection system on the offshore oil field and to study the type, and effect factors and mechanisms of corrosion on the offshore oil field in order to develop an effective corrosion inhibitor for the sea water injection system.

The corrosion of metal in a water injection system was studied by weight‐loss and electrochemical methods.The effect factors and mechanisms of corrosion on the offshore oil field were proposed from the trend of corrosion.

FeCO₃ is the main corrosion product in the water injection system of the Chengdao Offshore Oil Field. The corrosion rate of coupons in sea water injection systems reaches a maximum peak at a temperature of 50‐60°C. The corrosion rate of coupons exposed in all three water samples increased with an increase in the dissolved oxygen concentration. When the mixed ratio of sea water and produced water and well water is 1:3:1 or 1:2:2, the corrosion rate of carbon steel is lower than is the case in pure water. The electrochemical mechanism of corrosion indicates that corrosion in the well water, produced water, and sea water samples were all controlled by the oxygen absorption process, which controlled the cathodic reaction. The corrosion rate of coupons followed the ranking order: well water; produced water; sea water.

This paper provides the main corrosion product in the water injection system of the Chengdao Offshore Oil Field, and provides new information on the effect factors and mechanisms of corrosion on the offshore oil field.

In this paper, an intelligent information assisted communication transportation framework (II-CTF) has been introduced to reduce congestion, data reliability in transportation and the environmental effects.

The main concern of II-CTF is to mitigate public congestion using current transport services, which helps to improve data reliability under hazardous circumstances and to avoid accidents when the driver cannot respond reasonably. The program uses machine learning assistance to predict optimal routes based on movement patterns and categorization of vehicles, which helps to minimize congestion of traffic.

In II-CTF, scheduling traffic optimization helps to reduce the energy and many challenges faced by traffic managers in terms of optimization of the route, average waiting time and congestion of traffic, travel, and environmental impact due to heavy traffic collision.

The II-CTF definition is supposed to attempt to overcome some of the problems of the transportation environment that pose difficulties and make the carriage simpler, safer, more efficient and green for all.