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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 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.

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.

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.

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.

This paper aims to investigate the preparation of AlN and Al₂O₃, as well as the effect of nano-AlN and nano-Al₂O₃, on friction and wear properties of copper-steel clad plate immersed in the lubricants.

Nano-AlN or nano-Al₂O₃ (0.1, 0.2, 0.3, 0.4 and 0.5 Wt.%) functional fluids were prepared. Their tribological properties were tested by an MRS-10A four-ball friction tester and a ball-on-plate configuration, and scanning electron microscope observed the worn surface of the plate.

An increase in nano-AlN and Al₂O₃ content enhances the extreme pressure and anti-wear performance of the lubricant. The best performance is achieved at 0.5 Wt.% of nano-AlN and 0.3 Wt.% of nano-Al₂O₃ with PB of 834 N and 883 N, a coefficient of friction (COF) of approximately 0.07 and 0.06, respectively. Furthermore, the inclusion of nano-AlN and nano-Al₂O₃ particles in the lubricant enhances its extreme pressure performance and reduces wear, leading to decreased wear spot depth. The lubricating effect of the nano-Al₂O₃ lubricant on the surface of the copper-steel composite plate is slightly superior to that of the nano-AlN lubricant, with a COF reaching 0.07. Both lubricants effectively fill and lubricate the holes on the surface of the copper-steel composite plate.

AlN and Al₂O₃ as water-based lubricants have excellent lubrication performance and can reduce the COF. It can provide some reference for the practical application of nano-water-based lubricants.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2023-0255/

The purpose of this study was to explicate how leaders’ knowledge hiding results in employees’ knowledge hiding. In addition, the study was intended to explore under what conditions leaders’ knowledge hiding affects employees’ moral disengagement more deleteriously.

Data were collected from 321 employees at three different times which were two months apart from each other. Structural equation modeling was used for data analysis.

The study found leaders’ knowledge hiding to be related to employee moral disengagement. In addition, the study found moral disengagement to affect employees’ knowledge-hiding behavior. Moral disengagement was found to mediate the relationship between leaders’ knowledge hiding and employees’ knowledge hiding. Finally, the study found that employees with high moral identity show more perseverance to preserve their moral engagement when led by knowledge-hiding leaders.

To the best of the authors’ knowledge, the study was first to establish a relationship between a leader’s knowledge hiding and employees’ moral disengagement. The study also established the mediating role of moral disengagement to work as a mediating mechanism linking leaders’ knowledge hiding to employees’ knowledge hiding. Finally, the study found that moral identity moderates the relationship between leaders’ knowledge hiding and employees’ moral disengagement.

Clinical pathways (CPs) are multidisciplinary care plans with essential care steps for patients with specific clinical problems. CPs were introduced in China in 2009 to assure quality, reduce risks, increase resource efficiency and control costs. The purpose of this paper is to present a Chinese public hospital case study where a CP pilot was undertaken to evaluate two main outcomes: length of stay and hospitalization costs for a tertiary hospital from 2010 to 2012 using a mixed-methods approach.

Data were drawn from hospital records and in-depth interviews with hospital staff in a Shanxi Province tertiary hospital, northern China.

The authors found that the main objectives: to standardize treatment procedures by reducing length of stay and containing costs, were not fully achieved. Staff implementing CPs clearly encountered several barriers; i.e., managers did not see the pilot as a useful managerial instrument but were still driven by revenue generation. Physicians, too, lacked incentive to follow the guidelines due to income concerns.

The authors point to the daunting challenges brought about by perverse incentives embedded in the country’s health system. The authors argue that concerted efforts are needed to undertake difficult health policy reforms in China.

The authors present the first empirical study in the English-language literature that examines China’s ongoing CP pilots from a micro perspective. The authors combine qualitative and quantitative methods and reveal the hospital-level dynamics in its implementation.

The purpose of this paper is to solve the problems existing in traditional grey incidence models and advance several new grey incidence models based on visual angle of similarity and nearness.

Based on the definition of traditional grey incidence models, two novel grey incidence models, grey similar incidence model and grey close incidence model, are studied in this paper. The interrelations and influence can be measured by the new models with different visual angle of similarity and/or nearness, respectively. The grey similar incidence model is used mainly to measure the similitude degree of the geometric patterns of sequence curves. The grey close incidence model is used mainly to measure the nearness of the sequence curves in space. The properties of the new models are discussed. It is proved that the proposed models are simplified methods to calculate the similitude degree and the close degree of grey incidence models.

The results show that the two novel grey incidence models satisfy the grey incidence axiom properly. It is useful to calculate the similitude degree and the close degree of two different sequences, and the process of calculating is easier than with traditional grey incidence models.

The method exposed in the paper can be used to calculate every two sequences. The similitude degree and the close degree of two different sequences can be given out. The method can also be used to rank sequences of more than two.

The paper succeeds in constructing two novel grey incidence models. The properties of novel model are studied and it is undoubtedly a new development in grey systems theory.

Environmental performance (EnPerf) needs to be critically studied so organizations can understand enhancing it. The purpose of this study is mainly to examine and explain the influence of beliefs and values of the human resources regarding religiosity (REL) and workplace spirituality (WS) on shaping an environmentally friendly work culture comprising environmental ethics (EE) and environmental passion (EP), to enhance EnPerf.

A survey methodology was used, and 316 responses were collected from the employees working in industries on the top list of polluting the environment using purposive sampling. Structural equation modeling was deployed to test the hypotheses.

This research is conducted to identify specific relationships of variables with the environment. It was discovered that WS affected EP and EE, positively affecting EnPerf.

This study guides organizations and their management to adopt WS, EE and EP, as these all increase EnPerf in the organization.

Not much work has been conducted on the environmental culture based on REL and WS, using the ability-motivation-opportunity theory. This research analyzes employees’ intrinsic factors, such as REL and WS, to develop EP and EE. Thus helping to comprehend how they can use to enhance EnPerf, which is the current priority for the organizations.