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This study aims to investigate the mechanical and tribological behavior of 70Mn steel with different laser re-melted textured patterns.

Laser surface re-melting (LSR) was used to manufacture various textured patterns (i.e. line, grid and mixed) on both the original and heat-treated 70Mn steel plates. The micro-hardness, microstructure, tensile strength, yield strength, elongation, coefficients of friction (COF) and worn morphologies were characterized to evaluate the impact of different textured patterns on the overall performance.

The results show that re-melted unit exhibited the highest surface hardness on the subsurface. The increase in surface hardness of the re-melted unit for the heat-treated 70Mn steel samples was much lower than that of the original ones. The re-melted textured patterns did not improve the tensile strength, yield strength and elongation of either original or heat-treated 70Mn steel samples. The re-melted textured patterns effectively reduced the average COFs of heat-treated 70Mn steel samples, but increased friction of the non-heat-treated samples.

This study provides valuable insights into enhancing the mechanical properties and tribological characteristics of 70Mn steel, particularly in the automotive, heavy machinery and high-load application sectors. These industries have stringent requirements for durability and friction control, and the findings of this research are expected to effectively extend the lifespan of mechanical components.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2024-0443/

According to relevant research, non-uniform speed has a significant impact on the vehicle-track systems. Up to now, research work on it is still very limited. In this paper, the PEM is adopted to further transform it into a deterministic process to solve the vehicle’s problem of running at a non-uniform speed.

The multi-body vehicle model has 10 degrees of freedom and the track is regarded as a finite long beam supported by lumped sleepers and ballast blocks. They are connected via linear Hertz springs. The vertical track irregularity is a Gaussian stationary process in the space domain. It is transformed into a uniformly modulated nonstationary random process in the time domain with respect to the non-uniform vehicle speed. By solving the equation of motion of the coupled vehicle-track system with the pseudo-excitation method, the pseudo-response and consequently the power spectral density and the standard deviation of the structural response can be obtained.

Two kinds of vehicle braking programs are taken in the numerical example and some beneficial conclusions are drawn.

The pseudo-excitation method (PEM) was used to perform the random vibration analysis of a coupled non-uniform speed vehicle-track system. Transforming the track irregularity into a uniformly modulated nonstationary random process in time domain with respect to the non-uniform vehicle speed was undertaken. The pseudo-response of the coupled system is solved by applying the Newmark algorithm with constant space integral steps. The random vibration transfer mechanism of the coupled system is fully discussed.

In China, the clothing industry, featured by labor-intensive operation and low added value, is facing a major challenge, namely how to change the pattern of China's clothing industry by means of technology, innovation, originality and so on, and mitigate inventory pressure. The Red Collar Group presented in this case not only realizes zero inventory, but also achieves a year-on-year growth of more than 150% of annual sales in 2015. All of this can be attributed to the Internet zero inventory and customization model which took 10 years to build up: Using new information technology, collect personalized and fragmented customer needs, and design a production module involving business process reengineering and IT/IS integration.

This article was based on the information from The 5th International Conference of Institutes and Libraries for Chinese Overseas Studies held in the University of British Columbia in Vancouver, BC, Canada in which the author was a presenter in session 4.2.9a of the Early life of Yuan Shikai and the formation of Yuan family. The paper aims to include comprehensive analysis and development of the history of Chinese migration. An annotated bibliography of suggested readings was offered to highlight the subject knowledge for further research in this area.

The paper includes comprehensive analysis and development of the history of Chinese migration and the experiences and family histories of overseas Chinese in Canada. An annotated bibliography of suggested readings was offered to highlight the subject knowledge for further research in this area.

The paper offers full description and comprehensive analysis of the history of Chinese migration and overseas Chinese studies in Canada. A bbibliography of suggested readings was offered for further research in this area.

This research study has a strong subject focus on Chinese migration, overseas Chinese studies, and resource-sharing in the subject area. It is a specific field for research in Asian studies.

The result of this study will assist students, researchers, and the general public in the area of overseas Chinese studies and developing their interests in the social and historical value of Chinese migration history and resource-sharing in the area.

Very little research has been done in the area of Chinese migration and historical development. The paper would offer historians, sociologists, ethnologists, librarians, administrations, professors, as well as students in the fields of Asian history, anthropology, sociology, political science, geography, and other Asian-related interdisciplinary studies.

This paper investigates Chinese agricultural insurance agents willingness to offer (WTO) livestock insurance based on the variations of eight main attributes of livestock insurance.

This study implements discrete choice experiments (DCE) with actual insurance agents who design, sell and operate livestock insurance in China. The choice experiment of this study is based on the D-optimal approach, a six-block design, with 15 cards per block and two choices per card. The sample size was 211. Econometrics results are based on conditional and mixed logit models.

The authors find that the subsidy effect is enormous; a one level increase of subsidy leads to 3.166 times higher probability to offer. This subsidy effect is important as it confirms the endogenous structure between price and quantity in insurance offering, where subsidy does not only incentivize demand but also the supply. Another main factor of insurance investigated is the impact of different coverage types on agents' WTO. The authors find that agents prefer mortality insurance the most, followed by revenue insurance and profit insurance, while Index-Based Livestock Insurance (IBLI) is the least preferred to offer. Agents' knowledge about these newer types of insurance supports their WTO as well; thus, proper education is necessary to promote the more advanced types of livestock insurance.

A limitation is that in the presence of COVID 19, and administrative issues at the local level, the sample was not randomly drawn. Nonetheless, the authors believe that there is enough diversity across participants, insurers and provinces and have done sufficient robustness checks to support results and conclusions.

This study provides further validation for the DCE research method that could potentially be applied to different analyses: using choice experiments to study insurers and reveal their preferences, through combinations of various levels of core attributes for insurance products. The findings and contribution are critical to the reform and improvement of livestock insurance in China and for insurance markets more broadly. The authors find that insurers do not place equal weights or values on insurance product attributes and do not view types of insurance equally. In other words, while farmers may hold different preferences about the type of insurance they demand, the results suggest that insurers also hold preferences in the type of insurance they sell.

So far as the authors are aware, this is the first DCE designed around the supply of insurance products with the subjects being insurance agents, marketers and executives.

As an Internet fashion brand, HSTYLE has developed into an Internet enterprise with annual sales of 1.5 billion RMB within 10 years, establishing its position as the top industry performer in China. This case studies HSTYLES' innovation in business model and organizational management. HSTYLE's workgroups have achieved the balance of responsibilities and rights in a small team of three members at minimum, while mobilizing the enthusiasm and initiative of the line managers with the support of public service sector. At the same time, HSTYLE enriches its brand style, establishes a fashion cloud platform, and integrates individual and organizational consumers into its existing fashion design, manufacturing and sales system.

The modeling cost of the gradient-enhanced kriging (GEK) method is prohibitive for high-dimensional problems. This study aims to develop an efficient modeling strategy for the GEK method.

A two-step tuning strategy is proposed for the construction of the GEK model. First, an auxiliary kriging is built efficiently. Then, the hyperparameter of the kriging model is served as a good initial guess to that of the GEK model, and a local optimal search is further used to explore the search space of hyperparameter to guarantee the accuracy of the GEK model. In the construction of the auxiliary kriging, the maximal information coefficient is adopted to estimate the relative magnitude of the hyperparameter, which is used to transform the high-dimension maximum likelihood estimation problem into a one-dimensional optimization. The tuning problem of the auxiliary kriging becomes independent of the dimension. Therefore, the modeling efficiency can be improved significantly.

The performance of the proposed method is studied with analytic problems ranging from 10D to 50D and an 18D aerodynamic airfoil example. It is further compared with two efficient GEK modeling methods. The empirical experiments show that the proposed model can significantly improve the modeling efficiency without sacrificing accuracy compared with other efficient modeling methods.

This paper developed an efficient modeling strategy for GEK and demonstrated the effectiveness of the proposed method in modeling high-dimension problems.

The railway signal equipment failure diagnosis is a vital element to keep the railway system operating safely. One of the most difficulties in signal equipment failure diagnosis is the uncertainty of causality between the consequence and cause for the accident. The traditional method to solve this problem is based on Bayesian Network, which needs a rigid and independent assumption basis and prior probability knowledge but ignoring the semantic relationship in causality analysis. This paper aims to perform the uncertainty of causality in signal equipment failure diagnosis through a new way that emphasis on mining semantic relationships.

This study proposes a deterministic failure diagnosis (DFD) model based on the question answering system to implement railway signal equipment failure diagnosis. It includes the failure diagnosis module and deterministic diagnosis module. In the failure diagnosis module, this paper exploits the question answering system to recognise the cause of failure consequences. The question answering is composed of multi-layer neural networks, which extracts the position and part of speech features of text data from lower layers and acquires contextual features and interactive features of text data by Bi-LSTM and Match-LSTM, respectively, from high layers, subsequently generates the candidate failure cause set by proposed the enhanced boundary unit. In the second module, this study ranks the candidate failure cause set in the semantic matching mechanism (SMM), choosing the top 1st semantic matching degree as the deterministic failure causative factor.

Experiments on real data set railway maintenance signal equipment show that the proposed DFD model can implement the deterministic diagnosis of railway signal equipment failure. Comparing massive existing methods, the model achieves the state of art in the natural understanding semantic of railway signal equipment diagnosis domain.

It is the first time to use a question answering system executing signal equipment failure diagnoses, which makes failure diagnosis more intelligent than before. The EMU enables the DFD model to understand the natural semantic in long sequence contexture. Then, the SMM makes the DFD model acquire the certainty failure cause in the failure diagnosis of railway signal equipment.

This paper aims to investigate how institutional entrepreneurs in a mature field acquire different forms of legitimacy through various narrative strategies, as revealed by a historical analysis of the Late Qing Dynasty of China. Specifically, the study seeks to illuminate the narrative techniques employed by these entrepreneurs to build multi-level legitimacies for their new systems during the national crises of the late 19th century.

This study employs a qualitative approach based on three historical case studies of Westernization, namely advocates, reformists and revolutionaries during the Late Qing Dynasty of China. It delves into the institutional entrepreneurial activities of these groups, investigating how they navigated and influenced their sociopolitical context through narrative strategies.

The findings reveal that institutional entrepreneurs utilize various narrative techniques to build legitimacy. These include (1) modifying and disseminating narratives at the proper time, (2) ascribing their proposed system to cultural tradition by reinterpreting classical works and (3) offering understandable and concrete promises to stakeholders. The study also identifies an evolving pattern of narratives underpinned by mutual learning and continuous iteration among different factions of institutional entrepreneurs.

This paper adds new insights to the literature on institutional entrepreneurs and, more broadly, to institutional research literature by advancing our understanding of the discursive dynamics underlying institutional changes. It unveils the complex interplay of narrative techniques and legitimacy building in the context of societal and institutional upheaval, providing a nuanced understanding of the role of narratives in institutional entrepreneurship.

Vehicle fault diagnosis is a key factor in ensuring the safe and efficient operation of the railway system. Due to the numerous vehicle categories and different fault mechanisms, there is an unbalanced fault category problem. Most of the current methods to solve this problem have complex algorithm structures, low efficiency and require prior knowledge. This study aims to propose a new method which has a simple structure and does not require any prior knowledge to achieve a fast diagnosis of unbalanced vehicle faults.

This study proposes a novel K-means with feature learning based on the feature learning K-means-improved cluster-centers selection (FKM-ICS) method, which includes the ICS and the FKM. Specifically, this study defines cluster centers approximation to select the initialized cluster centers in the ICS. This study uses improved term frequency-inverse document frequency to measure and adjust the feature word weights in each cluster, retaining the top τ feature words with the highest weight in each cluster and perform the clustering process again in the FKM. With the FKM-ICS method, clustering performance for unbalanced vehicle fault diagnosis can be significantly enhanced.

This study finds that the FKM-ICS can achieve a fast diagnosis of vehicle faults on the vehicle fault text (VFT) data set from a railway station in the 2017 (VFT) data set. The experimental results on VFT indicate the proposed method in this paper, outperforms several state-of-the-art methods.

This is the first effort to address the vehicle fault diagnostic problem and the proposed method performs effectively and efficiently. The ICS enables the FKM-ICS method to exclude the effect of outliers, solves the disadvantages of the fault text data contained a certain amount of noisy data, which effectively enhanced the method stability. The FKM enhances the distribution of feature words that discriminate between different fault categories and reduces the number of feature words to make the FKM-ICS method faster and better cluster for unbalanced vehicle fault diagnostic.