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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.

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 establish a piecewise Maxwell stress analytical model of bearingless switched reluctance motor (BSRM) for the full rotor angular positions. The proposed model varies from the existing models, which are only applicable to the partial-overlapping positions of stator and rotor poles. By extending the applicable rotor angular positions, this model provides a basic analytical model for the multi-phase excitation control of BSRM.

The full rotor angular positions are classified into the partial-overlapping positions and the non-overlapping positions. At first, two different air gap subdividing methods are proposed, respectively, for the two-position ranges. Then, different integration paths are selected accordingly. Furthermore, two approximate methods are presented to calculate the average flux density of each air gap subdivision. Finally, considering the mutual coupling between the two perpendicular radial suspension forces, a piecewise Maxwell stress analytical model is derived for the full rotor angular positions of BSRM.

A piecewise Maxwell stress analytical model of BSRM is built for the full rotor angular positions, and applicable to the multi-phase excitation mode of BSRM. For the partial-overlapping positions and the non-overlapping positions, two sets of air gap subdividing methods, integration paths and approximate calculation methods of air gap flux densities are proposed, respectively. The accuracy and reliability of the proposed model are verified by the finite element method.

The piecewise Maxwell stress analytical model of BSRM for the full rotor angular positions is proposed for the first time. The novel air gap subdividing methods, integration paths, approximate calculation methods of air gap flux densities and the coupling between the two radial suspension forces are adopted to improve the modeling accuracy. As the applicable range of rotor angular position is extended, this model overcomes the limitation of the existing models only for single-phase excitation mode and contributes to the accurate control of BSRM multi-phase excitation mode.

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.

Vehicle companion is one of the most common companion patterns in daily life, which has great value to accident investigation, group tracking, carpooling recommendation and road planning. Due to the complexity and large scale of vehicle sensor streaming data, existing work were difficult to ensure the efficiency and effectiveness of real-time vehicle companion discovery (VCD). This paper aims to provide a high-quality and low-cost method to discover vehicle companions in real time.

This paper provides a real-time VCD method based on pro-active data service collaboration. This study makes use of dynamic service collaboration to selectively process data produced by relative sensors, and relax the temporal and spatial constraints of vehicle companion pattern for discovering more potential companion vehicles.

Experiments based on real and simulated data show that the method can discover 67% more companion vehicles, with 62% less response time comparing with centralized method.

To reduce the amount of processing streaming data, this study provides a Service Collaboration-based Vehicle Companion Discovery method based on proactive data service model. And this study provides a new definition of vehicle companion through relaxing the temporal and spatial constraints for discover companion vehicles as many as possible.

The purpose of this study is to address a significant gap in the understanding of entrepreneurship at the microfoundation level. It focuses on how individual entrepreneurs, specifically Hongbang entrepreneurs in China from 1896 to 1949, shape and transform their contexts. The aim is to provide a deeper understanding of the mechanisms that facilitate entrepreneurial success.

The study adopts a microhistorical approach, investigating the case of Hongbang entrepreneurs in China during 1896-1949. It involves an in-depth examination of historical records to explore the strategic interactions between these entrepreneurs and core stakeholders such as consumers, financial intermediaries, government regulators, and human resources. The research methodology emphasizes a process-oriented view, examining the evolution of personalized networks into extensive connections.

The research reveals that Hongbang entrepreneurs successfully reshaped their unfavorable embedded contexts by strategically collaborating with key stakeholders. They influenced consumer tastes, allied with financial intermediaries, negotiated with governments on regulation policies, and developed human resource stocks. The transformation was facilitated by the evolution of their networks from personalized to extensive connections. These findings highlight the localized strategies such as cronyism in resource acquisition within China’s private property development industry.

This study contributes to the field by offering insights into entrepreneurial contextualization and networking. It sheds light on the complex interplay between entrepreneurs and their contexts, providing a nuanced understanding of localized strategies in the Chinese context. The findings add value to the discourse on entrepreneurship by elucidating the strategic and processual acts through which entrepreneurs engage with stakeholders and reshape their environments.

The purpose of this study is to explore the relationship between environmental, social and governance (ESG) performance and tone management in the annual report. This is based on the notion that managers, driven by personal interests, may use their ESG accomplishments by using an abnormal positive tone to enhance their reputation or career prospects.

Using panel data from Chinese listed companies from 2010 to 2022, this study first investigates the relationship between ESG performance and abnormal tone management. The study then uncovers this relationship is mediated through the mechanisms of equity-based incentive and analyst coverage. The conclusions of this paper hold even after a series of robustness tests, such as propensity score matching, Heckman two-stage method and two-stage least squares with instrumental variables.

This study finds a positive correlation between ESG performance and the presence of abnormal positive tone in annual reports. Furthermore, the mechanistic analysis reveals that managers in companies with strong ESG performance are motivated to use an overly positive tone, largely due to their vested interests in equity-based compensation. Moreover, in an effort to alleviate the pressure stemming from heightened financial analyst coverage and enhance the impression conveyed through analysts' reports, managers with superior ESG performance also tend to inflate the tone within their annual reports.

This study provides significant insights into the ongoing dialogue surrounding ESG-related equity incentives, which incentivize managerial manipulation of stock prices through the use of abnormal positive tone. The findings call upon investors to exercise greater vigilance in examining narrative information in annual reports, as abnormally positive tones may not always faithfully represent performance but rather reflect managerial self-interest.

There is an emphasis on the importance of robust oversight mechanisms within corporate governance bodies to curb the manipulation of tone for managers’ personal gain.

This study enhances the theoretical foundation of ESG studies, offering a holistic perspective on the intricate interplay among ESG performance, managerial behavior and financial markets, with potential implications for researchers, investors and regulators.

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.

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.