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This study aims to achieve long-term prediction on a specific monotonic data series of atmospheric corrosion rate vs time.

This paper presents a new method, used to the collected corrosion data of carbon steel provided by the China Gateway to Corrosion and Protection, that combines non-linear gray Bernoulli model (NGBM(1,1) with genetic algorithm to attain the purpose of this study.

Results of the experiments showed that the present study’s method is more accurate than other algorithms. In particular, the mean absolute percentage error (MAPE) and the root mean square error (RMSE) of the proposed method in data sets are 9.15 per cent and 1.23 µm/a, respectively. Furthermore, this study illustrates that model parameter can be used to evaluate the similarity of curve tendency between two carbon steel data sets.

Corrosion data are part of a typical small-sample data set, and these also belong to a gray system because corrosion has a clear outcome and an uncertainly occurrence mechanism. In this work, a new gray forecast model was proposed to achieve the goal of long-term prediction of carbon steel in China.

An electrochemical method based on the open circuit potential (OCP) fluctuations was put forward. It can be used to optimize the alloy compositions for improving the corrosion resistance of rust layer.

The potential trends and potential fluctuations of carbon steels in seawater were separated by Hodrick–Prescott filter. The Spearman correlation coefficient and max information coefficient were used to explore the correlation of alloy compositions and potential fluctuations.

After long-term immersion, potential fluctuation resistance (PFR) can be used to characterize the corrosion resistance of metals and its rust layers. In the 1,500 to 2,500 h exposure period, Fe, C and S compositions have strong negative correlations, whereas PFR and P composition have weak negative correlations. Mn, Cu and Ti alloy compositions help the rust layer of carbon steels have higher PFRs. These elements that exhibit higher PFRs in this period have been confirmed to have the effect on improving the corrosion resistance of rust layer.

A new computing method for alloy composition optimization of carbon steels based on the OCP fluctuations was put forward. This method combines electrochemical monitoring with the long-term actual seawater environmental tests of various carbon steels.

The purpose of this paper is to present a new model which combines the non-equidistant GM(1,1) model with GCHM_WBO (generalized contra-harmonic mean (GCHM); weakening buffer operator (WBO)). The authors use the model to solve the deadlock that for a large number of non-equidistant corrosion rate, it is difficult to establish a reasonable prediction model and improve the prediction accuracy.

This research consists of three parts: non-equidistant GM(1,1) model, GCHM_WBO operator, and the optimization of morphing parameter (contained in GCHM, control the intensity of the weakening operator). The methodology is explained as follows. First, the authors built a non-equidistant GM(1,1) model with GCHM_WBO weakened data, of which morphing parameter was randomly selected. Next, the authors calculated the error between prediction data of model and the real data, and adjusted the morphing parameter according to the error and property of GCHM. Then, the authors generated a new non-equidistant GM(1,1) based on new morphing parameter, and repeated the previous step until the termination condition was satisfied. Finally, the model with appropriate morphing parameter was used to implement the prediction of new data.

This paper finds a property of GCHM, which is a monotonic increasing function of morphing parameter in some specific conditions. Based on the property and the fixed point axiom of WBO, an algorithm was designed to search an appropriate morphing parameter. The appropriate morphing parameter was implemented for the purpose of improving the accuracy of the model. The model was applied to predict the corrosion rate of six steels at Guangzhou experimental station. The results showed that the proposed method can get more accuracy in prediction capability compared to the models with the original data and AWBO weakened data. The method is applicable to long-term forecasts in case of data scarcity.

Corrosion will cause huge economic loss to a country; therefore, it is important to judge the remaining useful life of a material or equipment; the foundation for judgement of which is the prediction of material corrosion rate. However, the prediction of corrosion rate is very difficult because of corrosion data’s features, such as small sample size, non-equidistant, etc. The proposed method can be used to implement long-term forecast of corrosion data with only one sample and non-equidistant samples.

This paper presented a model which combines the non-equidistant GM(1,1) model with GCHM_WBO to handle the problem of long-term forecasting of corrosion data. In the modelling process, the proposed morphing parameter searched through algorithm can improve the prediction accuracy of the model. Therefore, the model can provide effective and reliable result when data are of a small sample size and non-equidistant.

This paper aims to discover the mathematical model for Q235 carbon steel corrosion date acquired in the initial stage of atmospheric corrosion using electrical resistance probe.

In this paper, mathematical approaches are used to construct a classification model for atmospheric environmental elements and material corrosion rates.

Results of the experiment show that the corrosion data can be converted into corrosion depth for calculating corrosion rate to obtain corrosion kinetics model and conform corrosion acceleration phase. Combined with corresponding atmospheric environmental elements, a real time grade subdivision model for corrosion rate can be constructed.

These mathematical models constructed by real time corrosion data can be well used to research the characteristics about initial atmospheric corrosion of Q235 carbon steel.

The purpose of this paper is to verify that improved immune network can be used to design new controller for engineering.

First, the definition of artificial immune controller is given out. Second, the disadvantage of Varela immune network which is not fit for control system is pointed out. Third, based on the analysis, the Varela immune network is modified for the purpose of designing controller with the mechanisms of immune network. And an immune controller based on improved Varela immune network (improved Varela immune network model (IVINM)‐AIC) is designed out. Its theoretic background is described in detail.

Based on the theoretic analysis and experiment of motor speed control, it is found that Varela immune work can be used to design immune controller. The experiments results show that IVINM‐AIC is much more robust, stable and anti‐delay and less overshoot than classical proportion, integration, and differentiation controller. It is good at controlling nonlinear system which is single input single output (SISO) system. The limitation of IVINM‐AIC is that it is used for simple SISO system.

The theoretic analysis of improved Varela immune network controller is original and it is useful for the analysis and design of new and complex immune controller. The experiment design is useful for comparison of new test in future.

The purpose of this study is to examine the effects of executives’ overseas education and work experience on enterprise digital as executives’ overseas background is critical to the development of enterprises. It also explored the mediating role of enterprise digital transformation on the relationship between executives’ overseas background and enterprise growth.

Chinese A-share companies listed on the Shanghai and Shenzhen Stock Exchanges for the period 2018–2020 were analyzed using regression analysis and bootstrapping to verify hypothesized relationships.

Executives’ overseas study and work experience both enhanced enterprise digital transformation significantly, thus improving enterprise growth. The level of employee education moderated the mediating role proposed in the theoretical model. Moreover, the promoting effect of executives’ overseas background on enterprise digital transformation was more significant for non-state-owned enterprises and those in eastern China.

The findings provide reference for the formulation and optimization of companies’ human resource structure and have implications on the improvement of enterprise digital transformation and enterprise growth.

This study explored the factors influencing enterprise digital transformation at the microlevel of corporate human capital, thereby providing microlevel empirical evidence for research on the factors influencing enterprise digital transformation. Its findings shed light on the mechanism and context under which executives with overseas backgrounds may enhance enterprise digital transformation and growth.

The purpose of this study is to construct a scale that is contextually suitable for measuring early childhood leadership in China.

Following a standard scale development procedure, both qualitative and quantitative research approaches were addressed. Qualitative data was collected from 21 semistructured interviews with formal and informal teacher leaders in Xiamen City, China. Using survey data of 120 respondents and 305 respondents, an exploratory factor analysis was conducted twice to determine the underlying factorial structure of the scale. A further sample of 317 respondents were used to test the latent structure and validity of the scale using confirmatory factor analysis.

Based on the results from reliability and validity tests, this study indicates that the scale demonstrates sound psychometric properties. A three-factor model was determined, including staff management and development, peer learning and support and communication with parents.

The scale is the first of its kind for measuring early childhood leadership in China.

The paper sets out to understand the key issues that the various functions and optimal allocation of NGOs (non-governmental organizations) in the circular economy that provide public services depend not only on external quantities or densities but also on their internal size of human resources.

The paper uses different data samples and models to study the influence mechanism of optimal NGO size of human resources and its differentiated effects on governance quality of entrepreneurship.

The authors find that a reduction in transaction costs and an increase in the aggregation degree of public demand lead to increased human capital and lower financial capital intensity. In addition, the authors find that NGO size of human resources has a relationship that is approximately U-shaped (or inverse U-shaped) with the governance quality of entrepreneurship.

The paper discusses the implications for programs that encourage NGOs to optimally determine their internal size of human resources and further improve the governance quality of entrepreneurship in the circular economy.

The paper reveals the significant nonmonotonic relationship between local governance quality and NGO financial size, even after controlling for other NGO, city and provincial characteristics.

In this study, the research question posed was: What are the defining characteristics, limitations, and potential opportunities in the research on heterogeneity within ASD?

This scoping review used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology to address the research question: “What are the defining characteristics, limitations, and potential opportunities in the research on heterogeneity within ASD?” A comprehensive literature search was conducted across databases including MEDLINE/PubMed, SciVerse Scopus and Springer Link, with keywords such as autism, autism spectrum disorder (ASD), heterogeneity and neurodevelopment. Inclusion criteria covered original research, reviews and protocols published since 1990, while irrelevant or out-of-date works were excluded. Thematic analysis was applied to collected data to identify common patterns, trends and key characteristics, leading to a narrative synthesis. Ethical review board approval was not required due to the nature of the review.

The scoping review underscored the multifaceted nature of ASD, emphasizing its clinical, methodological and investigational complexities. ASD’s diverse behavioral, social and biological characteristics challenged its classification as a uniform entity. To address this, the review examined strategies like stricter clinical criteria, categorization into functional subgroups, and larger, diverse sample sizes. Moreover, it highlighted the transformative role of Big Data and machine learning in advancing the comprehension of ASD’s manifold manifestations. This research contributed valuable insights and innovative approaches for addressing the intrinsic heterogeneity of ASD, reshaping the understanding of this complex condition.

One limitation of this scoping review is that it primarily relied on existing literature and did not involve primary data collection. While the review synthesized and analyzed a substantial body of research, the absence of original data collection may limit the depth of insights into specific aspects of ASD heterogeneity. Future research could benefit from incorporating primary data collection methods, such as surveys or interviews with individuals with ASD and their families, to gain more nuanced perspectives on the condition’s heterogeneity.

The reliance on existing literature in this scoping review highlights the need for further empirical studies exploring ASD’s heterogeneity. Researchers should consider conducting primary data collection to capture real-world experiences and variations within the ASD population. This approach could provide more comprehensive and context-specific insights, ultimately informing the development of tailored interventions and support strategies for individuals with ASD and their families.

This paper offers a fresh perspective on understanding ASD by examining its clinical, methodological and investigational implications in light of its inherent heterogeneity. Rather than viewing ASD as a uniform condition, this study explores strategies such as stricter clinical criteria, subcategorization based on functionality and diverse sample sizes to address its complexity. In addition, this study highlights the innovative use of Big Data and machine learning to gain deeper insights into ASD’s diverse manifestations. This approach contributes new insights and promising directions for future research, challenging the conventional understanding of ASD as a singular entity.