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This paper aims to develop a novel grey Bernoulli model with memory characteristics, which is designed to dynamically choose the optimal memory kernel function and the length of memory dependence period, ultimately enhancing the model's predictive accuracy.

This paper enhances the traditional grey Bernoulli model by introducing memory-dependent derivatives, resulting in a novel memory-dependent derivative grey model. Additionally, fractional-order accumulation is employed for preprocessing the original data. The length of the memory dependence period for memory-dependent derivatives is determined through grey correlation analysis. Furthermore, the whale optimization algorithm is utilized to optimize the cumulative order, power index and memory kernel function index of the model, enabling adaptability to diverse scenarios.

The selection of appropriate memory kernel functions and memory dependency lengths will improve model prediction performance. The model can adaptively select the memory kernel function and memory dependence length, and the performance of the model is better than other comparison models.

The model presented in this article has some limitations. The grey model is itself suitable for small sample data, and memory-dependent derivatives mainly consider the memory effect on a fixed length. Therefore, this model is mainly applicable to data prediction with short-term memory effect and has certain limitations on time series of long-term memory.

In practical systems, memory effects typically exhibit a decaying pattern, which is effectively characterized by the memory kernel function. The model in this study skillfully determines the appropriate kernel functions and memory dependency lengths to capture these memory effects, enhancing its alignment with real-world scenarios.

Based on the memory-dependent derivative method, a memory-dependent derivative grey Bernoulli model that more accurately reflects the actual memory effect is constructed and applied to power generation forecasting in China, South Korea and India.

This paper aims to study the impact of external fairness of executive compensation on M&A premium, and examine the moderate role of institutional investors. The high M&A premium is the main factors that induce the huge impairment of listed companies’ goodwill and the plummeting performance. Executives are the decision-makers of M&As, and their decision-making process is inevitably affected by the psychological factors. In recent years, institutional investors have become an important external force that can affect the governance of listed companies.

The authors use M&A data of listed companies from 2008 to 2018 and use OLS regression to test the relationship between executive compensation fairness and M&A premium.

The results show that the lower the external fairness of executive compensation, the greater the M&A premium. Institutional investors can effectively reduce the impact of external compensation unfairness on M&A premiums. The mechanism tests show that executives' psychological perception of fairness induced by external unfairness reduces their motivation to work and prompts them to use high premium to seek alternative compensation incentives. Further examinations of executive characteristics and corporate characteristics show that the role of external unfairness in executive compensation in driving M&A premiums is more pronounced in companies with longer executive tenure, weaker executive reputation incentives and private property.

This paper enriches the research on the pre-factors of M&A premiums from the perspective of executives’ psychological perception of fairness, provides evidence that institutional investors play a positive governance role and provides decision-making references for companies to take corresponding measures to reduce M&A premium risks.

Yi Jing is one of the most archaic works among the Chinese classics. Various schools of thought developed their philosophies from the philosophical perspectives in this book and its influence is wide ranging and far reaching. This paper attempts to show how leaders can approach the Yi Jing beneficially by identifying the principles, values and virtues in each of the 64 scenarios, deriving insights about change and leadership. It also aims to show how the Yi Jing compares with Western management literature.

This paper provides an analysis of Qian Gua of Yi Jing as an example of how leaders can draw insights from Yi Jing about change and leadership.

Qian Gua, which is the first scenario in the Yi Jing, offers us powerful insights about change and leadership by offering: a framework of understanding change and human enterprises, considering the aspects of beginning, process, benefit and sustainability; a framework to understand the leadership development process; and leadership principles of conscientious self‐cultivation, transformational leadership through virtues and riding the tides of change.

A change and leadership model inspired by the Yi Jing should interest business leaders both in the east and the west, and help Westerners gain success in doing business in China or collaborating with the Chinese on a joint venture.

This is the first paper to draw insights about change and leadership through an analysis of Qian Gua of Yi Jing.

This study aims to let researchers sort out the past, look to the future and boost further management research.

This paper takes the exploration of the application of AR in the field of management research as the starting point. Through the combing and induction of AR literature to accurately grasp the essential characteristics of AR, this paper analyzes the application prospects and possible difficulties of AR in the field of management research in China, puts forward specific suggestions and explores the specific path of the transformation of management academic research to practice from the perspective of research methods.

In research methods, empirical research and case studies have become the mainstream research methods, but there is a passive research limitation of “study what happens.” Action research is different from the standpoint of empirical research that is independent of the research object. It no longer simply treats managers as the “object of information acquisition,” but as the subject of participating in the research. Management should be oriented towards the transformation of practice, and action research (AR) that can organically integrate researchers and practitioners is just a “bridge” to break the gap between academia and business circles to establish a cooperative relationship.

This paper takes the exploration of the application of AR in the field of management research as the starting point. Through the combing and induction of AR literature to accurately grasp the essential characteristics of AR, this paper analyzes the application prospects and possible difficulties of AR in the field of management research in China, puts forward specific suggestions and explores the specific path of the transformation of management academic research to practice from the perspective of research methods.

Given the rapid growth in efforts on misinformation correction, the study aims to test how evidence type and veracity interact with news agreement on the effectiveness of fact-checking on how well a corrective message discount a false news information.

Experimental participants (N = 511) in Hong Kong were exposed to the same news article and then to a piece of corrective information debunking the news article with variation in the types of evidence (numerical vs narrative) and veracity (no verdict vs half false vs entirely false) in 2019.

Among the participants who disagreed with the news article, numerical fact-checking was more effective than narrative fact-checking in discounting the news article. Some evidence of the backfire effect was found among participants for whom the article was attitude incongruent.

When debunking false information with people exposed to attitude-incongruent news, a milder verdict presented in the form of a half-false scale can prompt a more positive perception of the issue at stake than an entirely false scale, implying that a less certain verdict can help in mitigating the backfire effect compared to a certain verdict.

Starting from corporate entrepreneurship, spin-off entrepreneurship and business model theory, this paper aims to examine the key influential factors and inherent mechanism in the process of business model transfer from parent enterprise to spin-off start-ups.

Grounded theory method is fit for constructing theoretical models, which can discover and interpret phenomenon and activities. According to the guidance of theoretical sampling and other core principles in grounded theory, this study extracts two parent enterprises named Haier and Phnix and lots of spin-offs derived from them.

This paper presents the theoretical framework that business models transfer from parents to spin-offs and probes into the embedding logic and connection relationship between factors and categories in this process, such as preconditions, incubation veins, business model elements, stripping mechanism and independent operations.

Although this study is focused on the manufacturing industry, the main characteristics, comparative advantages, governance rules summarized from transfer activities of business model in spin-off entrepreneurship can also bring inspirations to both parent enterprises and spin-off start-ups.

Excavates process and mechanism of business model transfer from internal to external, extends the theoretical perspectives about existing related theories such as corporate entrepreneurship, spin-off entrepreneurship and business model theory and reveals new approaches and methods on business model design, which is different from the past way.

Density-based spatial clustering of applications with noise (DBSCAN) is the most commonly used density-based clustering algorithm, while it cannot be directly applied to the railway investment risk assessment. To overcome the shortcomings of calculation method and parameter limits of DBSCAN, this paper proposes a new algorithm called Improved Multiple Density-based Spatial clustering of Applications with Noise (IM-DBSCAN) based on the DBSCAN and rough set theory.

First, the authors develop an improved affinity propagation (AP) algorithm, which is then combined with the DBSCAN (hereinafter referred to as AP-DBSCAN for short) to improve the parameter setting and efficiency of the DBSCAN. Second, the IM-DBSCAN algorithm, which consists of the AP-DBSCAN and a modified rough set, is designed to investigate the railway investment risk. Finally, the IM-DBSCAN algorithm is tested on the China–Laos railway's investment risk assessment, and its performance is compared with other related algorithms.

The IM-DBSCAN algorithm is implemented on China–Laos railway's investment risk assessment and compares with other related algorithms. The clustering results validate that the AP-DBSCAN algorithm is feasible and efficient in terms of clustering accuracy and operating time. In addition, the experimental results also indicate that the IM-DBSCAN algorithm can be used as an effective method for the prospective risk assessment in railway investment.

This study proposes IM-DBSCAN algorithm that consists of the AP-DBSCAN and a modified rough set to study the railway investment risk. Different from the existing clustering algorithms, AP-DBSCAN put forward the density calculation method to simplify the process of optimizing DBSCAN parameters. Instead of using Euclidean distance approach, the cutoff distance method is introduced to improve the similarity measure for optimizing the parameters. The developed AP-DBSCAN is used to classify the China–Laos railway's investment risk indicators more accurately. Combined with a modified rough set, the IM-DBSCAN algorithm is proposed to analyze the railway investment risk assessment. The contributions of this study can be summarized as follows: (1) Based on AP, DBSCAN, an integrated methodology AP-DBSCAN, which considers improving the parameter setting and efficiency, is proposed to classify railway risk indicators. (2) As AP-DBSCAN is a risk classification model rather than a risk calculation model, an IM-DBSCAN algorithm that consists of the AP-DBSCAN and a modified rough set is proposed to assess the railway investment risk. (3) Taking the China–Laos railway as a real-life case study, the effectiveness and superiority of the proposed IM-DBSCAN algorithm are verified through a set of experiments compared with other state-of-the-art algorithms.

This paper aims to manufacture Ti6Al4V/TiC functionally graded material (FGM) by direct laser deposition (DLD) using Ti6Al4V and TiC powder. The objective is to investigate the effect of process parameters and TiC composition on microstructure, Vickers hardness and mechanical properties.

Powder blends with three different volume percentages of Ti6Al4V and TiC were used as feed material for DLD process. Five experiments with different values of laser power and scan speed were conducted to investigate the effect on microstructure and Vickers hardness for different compositions of feed material. Mini-tensile tests were performed to evaluate the mechanical properties of the FGM samples. Digital image correlation (DIC) was applied to estimate Young’s modulus and ultimate tensile stress (UTS) of heterogeneous material.

This paper indicates that primary carbide, eutectic carbide and un-melted carbide phases are formed in the FGM deposit. As the energy density was increased, the primary and secondary dendrite arm spacing was found to increase. As TiC composition was increased, Young’s modulus increased and UTS decreased. The dendritic morphology of primary TiC growth was expected to cause low resistance for crack propagation, causing lower UTS values. Tensile specimens cut in vertical orientation were observed to possess higher values of Young’s modulus in comparison with specimens cut horizontally at low carbon content.

Current work presents unique and original contributions from the study of miniature FGM tensile specimens using DIC method. It investigates the effect of specimen orientation and TiC content on Young’s modulus and UTS. The relationship between energy density and dendritic arm spacing was evaluated. The relationship between laser power and scan speed with microstructure and Vickers hardness was investigated.

Suspension is a significantly important component for automotive and railway vehicles. Regenerative hydraulic-electric shock absorbers (RHSA) have been proposed for the purpose of attenuating vibration of vehicle suspension, and also recover kinetic energy originated from vehicle vibration that is conventionally dissipated by hydraulic dampers. To advance the technology, the paper aims to present an RHSA system for heavy-duty and railway vehicles and create a dynamic modelling to discuss on the development process of RHSA model.

First, the development of RHSA dynamic model can be resolved into three stage models (an ideal one, a second one with an added accumulator and a third one that considers both accumulator and system losses) to comprehensively evaluate the RHSA's characterisation. Second, a prototype is fabricated for testing and the results meet desired agreements between simulation and measurement. Finally, the study of key parameters is carried out to investigate the influences of hydraulic-cylinder size, hydraulic-motor displacement and accumulator pre-charged pressure on the RHSA system.

The findings of sensitivity analysis indicate that the component design can satisfy the damping characteristics and power performance required for heavy-duty vehicle, freight wagon and typical passenger train. The results also show that reducing the losses is highly beneficial for saving suspension energy, improving system reliability and increasing power-conversion efficiency.

The paper presents a more detailed method for the development and analysis of a RHSA. Compared with the typical shock absorbers, RHSA can also recover the vibration energy dissipated by suspension.

The purpose of this paper is to reveal the transient thermo-elasto-hydrodynamic lubrication mechanism of a bidirectional thrust bearing in a pumped-storage unit, and to propose the transient simulation method of two-way fluid-solid-thermal interaction of thrust bearing.

The transient fluid-solid-thermal interaction method is used to simulate the three-dimensional lubrication of the thrust bearing, during the start-up and shutdown process of a pumped storage unit. A pad including an oil hole is modelled to analyze the temporal variation of lubrication characteristics, such as the film pressure, thickness and temperature, during the transient operation process.

The injection of the high-pressure oil sufficiently affects the lubrication characteristics on film, in which the hysteresis phenomena were found between the start-up and shutdown possess.

This paper reveals the transient lubrication mechanism of tilting pad in a thrust bearing, by means of transient fluid-solid-thermal interaction method. Lubrication characteristics are simulated without assuming the temperature relationship between the oil film inlet and the outlet and the heat transfer on the pad free surface. This paper provides a theoretical basis for the safe design and stable operation of thrust bearings.