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Integrating artificial intelligence and quantitative investment has given birth to various agricultural futures price prediction models suitable for nonlinear and non-stationary data. However, traditional models have limitations in testing the spatial transmission relationship in time series, and the actual prediction effect is restricted by the inability to obtain the prices of other variable factors in the future.

To explore the impact of spatiotemporal factors on agricultural prices and achieve the best prediction effect, the authors innovatively propose a price prediction method for China's soybean and palm oil futures prices. First, an improved Granger Causality Test was adopted to explore the spatial transmission relationship in the data; second, the Seasonal and Trend decomposition using Loess model (STL) was employed to decompose the price; then, the Apriori algorithm was applied to test the time spillover effect between data, and CRITIC was used to extract essential features; finally, the N-Beats model was selected as the prediction model for futures prices.

Using the Apriori and STL algorithms, the authors found a spillover effect in agricultural prices, and past trends and seasonal data will impact future prices. Using the improved Granger causality test method to analyze the unidirectional causality relationship between the prices, the authors obtained a spatial effect among the agricultural product prices. By comparison, the N-Beats model based on the spatiotemporal factors shows excellent prediction effects on different prices.

This paper addressed the problem that traditional models can only predict the current prices of different agricultural products on the same date, and traditional spatial models cannot test the characteristics of time series. This result is beneficial to the sustainable development of agriculture and provides necessary numerical and technical support to ensure national agricultural security.

This study empirically examines the curvilinear relationship between top management team task-related demographic faultlines and over-investment, as well as how biodemographic faultlines and industrial environment moderate the curvilinear relationship between task-related demographic faultlines and over-investment.

The study designs the panel data from the listed companies of China's growth enterprises board (GEB) (set up by Shenzhen Stock Exchange in 2009) in the period 2011–2016 and uses hierarchical regression analysis and grouping regression analysis in exploring the curvilinear relationship with the variables involved.

The study provides empirical insights into the relationship on top management team (TMT) task-related demographic faultlines and over-investment, as well as how biodemographic faultlines and industrial environment moderate the relationship between task-related demographic faultlines and over-investment. It suggests that the relationship between task-related demographic faultlines and over-investment is significantly inverted-U. Furthermore, biodemographic faultlines and industrial environment can strengthen the inverted-U relationship between TMT task-related demographic faultlines and over-investment.

The study investigates the influence of task-related demographic faultlines on firm over-investment. The sample is restricted to the listed companies on GEM in China and limited in size. It is also not concerned with the cross-culture contrastive analysis between the Chinese- and Western-listed companies.

The findings suggest that strong/weak TMT task-related demographic faultlines is beneficial in promoting rational investment, but medium TMT task-related demographic faultlines may lead to over-investment.

The study within the crossed-categorization theory, the study provides a contemporary research path by moderating biodemographic faultlines and industrial environments to explain the long-ignored impact of TMT faultlines within a new perspective of firm investment efficiency with a recent significant sample of new emerging countries (e.g. China).

This study aims to ensure the operation safety of high speed trains, it is necessary to carry out nondestructive monitoring of the tensile damage of the gearbox housing material in rail time, yet the traditional tests of mechanical property can hardly meet this requirement.

In this study the acoustic emission (AE) technology is applied in the tensile tests of the gearbox housing material of an high-speed rail (HSR) train, during which the acoustic signatures are acquired for parameter analysis. Afterward, the support vector machine (SVM) classifier is introduced to identify and classify the characteristic parameters extracted, on which basis the SVM is improved and the weighted support vector machine (WSVM) method is applied to effectively reduce the misidentification of the SVM classifier. Through the study of the law of relations between the characteristic values and the tensile life, a degradation model of the gearbox housing material amid tensile is built.

The results show that the growth rate of the logarithmic hit count of AE signals and that of logarithmic amplitude can well characterize the stage of the material tensile process, and the WSVM method can improve the classification accuracy of the imbalanced data to above 94%. The degradation model built can identify the damage occurred to the HSR gearbox housing material amid the tensile process and predict the service life remains.

The results of this study provide new concepts for the life prediction of tensile samples, and more further tests should be conducted to verify the conclusion of this research.

The purpose of this paper is to develop a novel multivariate fractional grey model termed GM(a, n) based on the classical GM(1, n) model. The new model can provide accurate prediction with more freedom, and enrich the content of grey theory.

The GM(α, n) model is systematically studied by using the grey modelling technique and the forward difference method. The optimal fractional order a is computed by the genetic algorithm. Meanwhile, a stochastic testing scheme is presented to verify the accuracy of the new GM(a, n) model.

The recursive expressions of the time response function and the restored values of the presented model are deduced. The GM(1, n), GM(a, 1) and GM(1, 1) models are special cases of the model. Computational results illustrate that the GM(a, n) model provides accurate prediction.

The GM(a, n) model is used to predict China’s total energy consumption with the raw data from 2006 to 2016. The superiority of the GM(a, n) model is more freedom and better modelling by fractional derivative, which implies its high potential to be used in energy field.

It is the first time to investigate the multivariate fractional grey GM(α, n) model, apply it to study the effects of China’s economic growth and urbanization on energy consumption.

The exposure of recruitment discrimination often leads to negative publicity, which can substantially affect organizational attractiveness and the behavioral intentions of stakeholders. Therefore, this study aims to examine the relationship between negative publicity and intention to pursue a job, with organizational attractiveness as a mediator and perceived corporate social responsibility (CSR) and corporate product quality (CPQ) as two moderators.

A survey completed by 382 college students in three universities in Nanjing, China, was used to empirically test the research hypotheses. The respondents were asked their intention to apply for a job at Meituan after the exposure of its recruitment discrimination scandal in 2017.

Perceived negative publicity negatively influences intention to pursue employment via organizational attractiveness. In addition, moderated path analysis indicated that perceived CSR strengthens and perceived CPQ weakens the direct effect of perceived negative publicity on organizational attractiveness and its indirect effect on intention to pursue employment.

Future research should seek to replicate these findings in other contexts and populations, including people who are not new to the job market and to control additional firm-level and contextual variables.

This research confirms a moderated mediation model positioning organizational attractiveness as a mediator of negative publicity's effects on intention to pursue employment and organizational image as a moderator of such effects. This study also contributes to the debate concerning the effect of corporate image by demonstrating opposing effects of its different dimensions amid a discrimination scandal.

The purpose of this paper is to study a fractional grey model FAGM(1,1,t^α) based on the GM(1,1,t^α) model and the fractional accumulated generating operation, and then predict the national health expenditure, the government health expenditure and the out-of-pocket health expenditure of China.

The presented univariate grey model is systematically studied by using the grey modelling technique, the fractional accumulated generating operation and the trapezoid approximation formula of definite integral. The optimal system parameters r and α are evaluated by the particle swarm optimisation algorithm.

The expressions of the time response function and the restored values of this model are derived. The GM(1,1), NGM(1,1,k,c) and GM(1,1,t^α) models are particular cases of the FAGM(1,1,t^α) model with deterministic r and α. Compared with other forecasting models, the results of the FAGM(1,1,t^α) model have higher precision.

The superiority of the new model has high potential to be used in the medicine and health fields and others. Results can provide a guideline for government decision making.

The univariate fractional grey model FAGM (1,1,t^α) successfully studies the China’s health expenditure.

Tingting Qi's chapter titled, “Moving toward Decentralization? Changing Education Governance in China After 1985,” provides the historical and policy context for the volume. This chapter integrates the post-1978 Chinese educational reforms into the socioeconomic context of China. The special contribution of this chapter is that it explores the complexity of educational decentralization in China through an in-depth analysis of changes in education finance, administration, and curriculum. Qi reviews prior studies, government documents, laws, and regulations related to Chinese education reform since 1978 within the context of education decentralization in China. Qi also demonstrates that China's educational policy reforms are moving China toward “centralized decentralization” because decentralization is driven by a common, centralized national goal of economic modernization. The chapter presents evidence that “centralized decentralization” is a strategic maneuver that maintains centralized control while providing the reform legitimacy of decentralization. By focusing on decentralization as the core of Chinese educational policy reforms, this chapter situates the following chapters within the social, cultural, and political context of post-1978 China.

This study aims to examine how tourism impacts on local community, managers’ attachment to the community and their identification with the value of heritage resources influence managers’ corporate social responsibility (CSR) attitudes, utilizing the value identification and agency theories.

The proposed hypotheses were tested utilizing cross-sectional data collected from 228 managers of a plethora of travel and tourism companies that operate at a UNESCO World Heritage site in China. A structured questionnaire was administered in person in managers’ offices by a team of trained research assistants. A total of 202 valid surveys were included in the data analysis. A two-step structural equation modeling (SEM) approach was used to first examine the psychometric properties of the measurement model, and then test the causal relationships proposed in the structural model.

The findings indicate that managers’ place attachment, their heritage value identification and their perceptions of positive tourism impacts affect their CSR attitudes. However, the negative effects of tourism do not significantly influence CSR attitudes. Data collected through open-ended questions incorporated in the structured survey have provided justification for the insignificant relationship.

CSR perceptions of managers, especially those at heritage sites, have not received much attention from tourism scholars. Because travel and tourism companies at heritage sites are integral in the preservation and conservation of heritage sites while managers of those companies are the ones who initiate and implement socially responsible policies and practices, it is important to understand the factors that may influence those managers’ CSR attitudes and behaviors.

This study aims to follow a rigorous approach to identify, critically analyze and synthesize 75 papers published from 2000 to 2022.

The study presents a systematic literature review on identity expressiveness (IE), clarifying and expanding what is currently known about the concept.

To synthesize current knowledge on IE, the study uses the overarching framework of antecedents-phenomenon-consequences, using this same framework to identify gaps and future research directions. The findings show individual and brand-related factors such as the need for uniqueness and anthropomorphism as antecedents of IE, and eWOM/WOM, impulse purchases and upgrading to more exclusive lines as consequences of IE.

The study contributes to theory by synthesizing and mapping current understanding of the state of knowledge on the concept of IE while highlighting gaps in the extant literature and paving future research directions for scholars in the field.

The study offers useful insights for practitioners, broadening marketers’ actionable options in identity-based marketing. Marketers can use insights from this study to inform marketing strategy and communication campaigns for different types of brands.

To the best of the authors’ knowledge, this study is the first of its kind and offers an integrative review of the current literature on IE, thus enhancing understanding of the concept, its antecedents and consequences. The study also contributes to knowledge by highlighting future research priorities for researchers in this field of enquiry.

The intelligent Central Traffic Control (CTC) system plays a vital role in establishing an intelligent high-speed railway (HSR) system. As the core of HSR transportation command, the intelligent CTC system is a new HSR dispatching command system that integrates the widely used CTC in China with the practical service requirements of intelligent dispatching. This paper aims to propose key technologies and applications for intelligent dispatching command in HSR in China.

This paper first briefly introduces the functions and configuration of the intelligent CTC system. Some new servers, terminals and interfaces are introduced, which are plan adjustment server/terminal, interface for automatic train operation (ATO), interface for Dynamic Monitoring System of Train Control Equipment (DMS), interface for Power Supervisory Control and Data Acquisition (PSCADA), interface for Disaster Monitoring, etc.

The key technologies applied in the intelligent CTC system include automatic adjustment of train operation plans, safety control of train routes and commands, traffic information data platform, integrated simulation of traffic dispatching and ATO function. These technologies have been applied in the Beijing-Zhangjiakou HSR, which commenced operations at the end of 2019. Implementing these key intelligent functions has improved the train dispatching command capacity, ensured the safe operation of intelligent HSR, reduced the labor intensity of dispatching operators and enhanced the intelligence level of China's dispatching system.

This paper provides further challenges and research directions for the intelligent dispatching command of HSR. To achieve the objectives, new measures need to be conducted, including the development of advanced technologies for intelligent dispatching command, coping with new requirements with the development of China's railway signaling system, the integration of traffic dispatching and train control and the application of AI and data-driven modeling and methods.