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Tantalum is a kind of metal material with moderate hardness, high ductility, small thermal expansion coefficient, excellent corrosion resistance and outstanding biocompatibility. The purpose of this study is that its tribological performance could be tested and analyzed so as to use it in different fields.

The friction resistance of a-Ta under dry friction conditions was tested at different roads. The relationships between load and friction coefficient, wear rate and two-dimensional shape of wear scars were studied.

The stable Ta₂O₅ film with lubrication effect was generated in the process of friction. And, the larger the test load, the more Ta₂O₅ would be generated.

This work lays a theoretical foundation for tantalum as an excellent wear-resistant material.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-02-2023-0047/

The purposes of this paper are to analyze the path and speed of rural transformation (RT) and explore the relationship between farmer's income and RT as well as structural transformation (ST) and typology of RT in the past four decades in China.

Based on the major indicators of RT and ST, graphic illustration is used to analyze the relationships between these indicators and farmer's income using the time-series and cross-provincial data in 1978–2017.

While China has experienced significant RT and ST, the levels and speeds of these transformations differed largely among provinces. Higher and faster RT and ST are often positively associated with the higher and faster growth of rural income. Based on this study, a general typology of rural and structural transformations and rural income is developed. The likely impacts of institutions, policies and investments (IPIs) on RT are discussed.

The authors believe that the findings of this study provide the insights on regional RT and ST and policy implications to increase farmer's income through facilitating and speeding up RT and ST with appropriate IPIs during the rural transformation.

The impact of network centrality on innovation performance is inconclusive. The purpose of this paper is to examine how formal and informal institutions affect the influence of network centrality on firms’ innovation performance in emerging economies by integrating social network theory and institutional theory.

Multisource and lagged data from 234 technology-based entrepreneurial firms listed on the Chinese Growth Enterprise Market were leveraged to test a proposed research model.

Results suggest that formal institutions (marketization) positively moderate the relationship between network centrality and innovation performance, whereas informal institutions (social cohesion) negatively moderate this relationship. Moreover, formal and informal institutions have a strong joint impact on such relationship, that is, the effect of network centrality on innovation performance is most positive when marketization is high and social cohesion is low.

This empirical research provides new insights into whether and how firms can grasp the innovation benefits of network centrality by exploring institutional contingencies. It further sheds on light the scope of the network centrality–innovation issue by extending its research context to Chinese entrepreneurial firms.

In the pursuit of competitive advantage, an increasing number of firms are adopting open innovation (OI) strategies. However, previous studies have often overlooked the role of strategic human resource management (SHRM) in promoting OI. This study aims to fill this gap by examining how SHRM impacts OI through the mediating factors of intellectual capital (IC) and supply chain integration (SCI). This research sheds light on the critical interplay between SHRM, IC and SCI in driving OI success. The findings underscore the importance of adopting a comprehensive and integrated approach to OI that encompasses both resources and dynamic capabilities.

By integrating resource-based view with the dynamic capability perspective, the hypotheses were tested with a survey sample of 136 Chinese manufacture firms using hierarchical regression and bootstrap method.

The results show that SHRM has a positive effect on OI, and both IC and SCI are partial mediators of the relationship between SHRM and OI. In addition, the chain mediation effect of “SHRM-IC-SCI-OI” has further been verified.

This study uncovers the “black box” between SHRM and OI, and responds to the call for strengthening research on the relationship between SHRM and OI. The study indicates that firms should implement HR practices, including extensive training, team reward and internal promotion to promote the implementation of OI strategy.

This paper aims to unveil the greenwashing intention of green bonds issuing in Chinese enterprises through the lens of stock pricing efficiency.

Drawing on data of Chinese listed companies during 2012–2021, this study uses a difference-in-differences method to study how and through what mechanisms issuing green bonds impacts stock pricing efficiency.

Issuing green bonds lowers stock pricing efficiency, verifying the greenwashing intention of green bonds in China. Potential mechanisms underlie the increased investor attention and sentiment resulting from the information disclosures about corporate green and low-carbon development. This greenwashing issue is more pronounced in firms facing lower financing constraints, having stronger relations with the government, and located in highly marketized regions. In the context of uncertainty surrounding economic policies, especially trade policies, issuing green bonds can signal a weakening of the greenwashing effect.

The quality of information disclosure should be emphasized to ensure a substantive commitment to environmental responsibility signaled by green bond issuance, thereby mitigating greenwashing concerns.

Regulators and standard-setters should improve the issuance system for green bonds and promote the sustainable development of the green bond market through formulating unified certification criteria for green bonds and implementing a stringently periodic reporting system.

First, to the best of the authors’ knowledge, it is the first study to draw on the quality of information disclosure and the perspective of stock pricing efficiency to identify whether firms issuing green bonds engage in greenwashing. Second, the study uncovers the black-box underlying this greenwashing issue through investor attention and sentiment and examines further the moderating role of economic policy uncertainties.

Surveillance is the emerging concept in the current technology, as it plays a vital role in monitoring keen activities at the nooks and corner of the world. Among which moving object identifying and tracking by means of computer vision techniques is the major part in surveillance. If we consider moving object detection in video analysis is the initial step among the various computer applications. The main drawbacks of the existing object tracking method is a time-consuming approach if the video contains a high volume of information. There arise certain issues in choosing the optimum tracking technique for this huge volume of data. Further, the situation becomes worse when the tracked object varies orientation over time and also it is difficult to predict multiple objects at the same time. In order to overcome these issues here, we have intended to propose an effective method for object detection and movement tracking. In this paper, we proposed robust video object detection and tracking technique. The proposed technique is divided into three phases namely detection phase, tracking phase and evaluation phase in which detection phase contains Foreground segmentation and Noise reduction. Mixture of Adaptive Gaussian (MoAG) model is proposed to achieve the efficient foreground segmentation. In addition to it the fuzzy morphological filter model is implemented for removing the noise present in the foreground segmented frames. Moving object tracking is achieved by the blob detection which comes under tracking phase. Finally, the evaluation phase has feature extraction and classification. Texture based and quality based features are extracted from the processed frames which is given for classification. For classification we are using J48 ie, decision tree based classifier. The performance of the proposed technique is analyzed with existing techniques k-NN and MLP in terms of precision, recall, f-measure and ROC.

This study aims to analyze the relevance of the city spatial structure for smart city innovation from the perspective of agglomeration externalities, and discusses whether there is heterogeneity in innovation across different geographical areas and population scales of cities.

The authors construct the centralization and concentration indexes to conceptualize the city spatial structure of 286 cities (prefecture-level) in China based on the LandScan Global Population Dataset from 2001 to 2016. A fixed-effects panel data model is employed to analyze the relationship between the spatial structure and the innovation ability of smart cities; the results were validated through robustness tests and heterogeneity analyses.

The study found that the more concentrated and more evenly the distribution of urban population, namely the more city spatial structure tends to be weak-monocentricity, the higher the level of innovation in smart cities. The relevance of the weak-monocentricity structure and smart city innovation varies significantly depending on their geographical location and the size of the city. This result is more applicable to cities in the eastern and central regions, as well as to cities with smaller populations.

The adjustment and optimization of the city spatial structure is important for enhancing smart city construction. Unlike previous studies, which mostly use a single dimension of “the proportion of population in sub-centres to the population of all central areas” to measure city spatial structure, the authors employed the spatial centralization and spatial concentration. It is hoped that this study can guide smart city construction from the perspective of the development model of city spatial structure.

The purpose of the paper is to propose a new optimization algorithm to realize a synchronous optimization of sensor array and classifier, to improve the performance of E-nose in the detection of wound infection. When an electronic nose (E-nose) is used to detect the wound infection, sensor array’s optimization and parameters’ setting of classifier have a strong impact on the classification accuracy.

An enhanced quantum-behaved particle swarm optimization based on genetic algorithm, genetic quantum-behaved particle swarm optimization (G-QPSO), is proposed to realize a synchronous optimization of sensor array and classifier. The importance-factor (I-F) method is used to weight the sensors of E-nose by its degree of importance in classification. Both radical basis function network and support vector machine are used for classification.

The classification accuracy of E-nose is the highest when the weighting coefficients of the I-F method and classifier’s parameters are optimized by G-QPSO. All results make it clear that the proposed method is an ideal optimization method of E-nose in the detection of wound infection.

To make the proposed optimization method more effective, the key point of further research is to enhance the classifier of E-nose.

In this paper, E-nose is used to distinguish the class of wound infection; meanwhile, G-QPSO is used to realize a synchronous optimization of sensor array and classifier of E-nose. These are all important for E-nose to realize its clinical application in wound monitoring.

The innovative concept improves the performance of E-nose in wound monitoring and paves the way for the clinical detection of E-nose.

Abnormal changes in temperature directly affect the stability and reliability of a gyroscope. Predicting the temperature and detecting the abnormal change is great value for timely understanding of the working state of the gyroscope. Considering that the actual collected gyroscope shell temperature data have strong non-linearity and are accompanied by random noise pollution, the prediction accuracy and convergence speed of the traditional method need to be improved. The purpose of this paper is to use a predictive model with strong nonlinear mapping ability to predict the temperature of the gyroscope to improve the prediction accuracy and detect the abnormal change.

In this paper, an double hidden layer long-short term memory (LSTM) is presented to predict temperature data for the gyroscope (including single point and period prediction), and the evaluation index of the prediction effect is also proposed, and the prediction effects of shell temperature data are compared by BP network, support vector machine (SVM) and LSTM network. Using the estimated value detects the abnormal change of the gyroscope.

By combined simulation calculation with the gyroscope measured data, the effect of different network hyperparameters on shell temperature prediction of the gyroscope is analyzed, and the LSTM network can be used to predict the temperature (time series data). By comparing the performance indicators of different prediction methods, the accuracy of the shell temperature estimation by LSTM is better, which can meet the requirements of abnormal change detection. Quick and accurate diagnosis of different types of gyroscope faults (steps and drifts) can be achieved by setting reasonable data window lengths and thresholds.

The LSTM model is a deep neural network model with multiple non-linear mapping levels, and can abstract the input signal layer by layer and extract features to discover deeper underlying laws. The improved method has been used to solve the problem of strong non-linearity and random noise pollution in time series, and the estimated value can detect the abnormal change of the gyroscope.

In this paper, based on the LSTM network, an double hidden layer LSTM is presented to predict temperature data for the gyroscope (including single point and period prediction), and validate the effectiveness and feasibility of the algorithm by using shell temperature measurement data. The prediction effects of shell temperature data are compared by BP network, SVM and LSTM network. The LSTM network has the best prediction effect, and is used to predict the temperature of the gyroscope to improve the prediction accuracy and detect the abnormal change.

The tribological properties of muscovite and its thermal-treated products as lubricant additives in lithium grease were investigated. The effect of thermal temperature on the crystal structure and tribological properties of muscovite was studied. This study aims to explore the tribological mechanism of muscovite and optimize a proper thermal activation temperature, thus further improving the tribological properties.

The crystal structure of muscovite samples was characterized by SEM, TG-DSC, XRD and FTIR. The tribological properties of grease samples were investigated using a four-ball tribotester and the worn surface was analyzed by SEM and EDS.

The excellent tribological properties of muscovite can be ascribed to the layer structure and lubricant film formed on the worn surface. Thermal temperature at 500-600°C increases the surface activity and oxygen releasing capability, and thus favors the formation of lubricant film and accordingly further improves the tribological properties. However, the layer structure is destroyed and hard phases such as alumina and amorphous appear after thermal temperature activated beyond 1000°C, as it results in the aggravation of friction and wear.

To the authors’ knowledge, it is the first to study the effect of thermal temperature on the crystal structure and tribological properties of muscovite. The tribological mechanism of muscovite particle and its thermal-treated products was disclosed.