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This paper aims to provide a complete analysis framework and prediction method for the construction of the patent securitization (PS) basic asset pool.

This paper proposes an integrated classification method based on genetic algorithm and random forest algorithm. First, comprehensively consider the patent value evaluation model and SME credit evaluation model, determine 17 indicators to measure the patent value and SME credit; Secondly, establish the classification label of high-quality basic assets; Then, genetic algorithm and random forest model are used to predict and screen high-quality basic assets; Finally, the performance of the model is evaluated.

The machine learning model proposed in this study is mainly used to solve the screening problem of high-quality patents that constitute the underlying asset pool of PS. The empirical research shows that the integrated classification method based on genetic algorithm and random forest has good performance and prediction accuracy, and is superior to the single method that constitutes it.

The main contributions of the article are twofold: firstly, the machine learning model proposed in this article determines the standards for high-quality basic assets; Secondly, this article addresses the screening issue of basic assets in PS.

This paper aims to investigate the following issues: the mechanisms through which different types of top management team’s social capital influence the innovation quality of high-tech firms, and the moderating effect of organizational knowledge utilization on the relationship between top management team’s social capital and innovation quality in high-tech firms.

This study categorizes top management team’s social capital into political, business and academic dimensions, investigating their impact on innovation quality in high-tech firms. Furthermore, a research model is developed with organizational knowledge utilization as the moderating variable. Data from Chinese high-tech firms between 2010 and 2019 are collected as samples for analysis.

The innovation quality of high-tech firms shows an inverted U-shaped trend as the top management team’s political capital and business capital increase. The top management team’s academic capital has a significantly positive correlation with the innovation quality of high-tech firms. Moreover, organizational knowledge utilization plays a significant moderating role in the relationship between the top management team’s social capital and innovation quality in high-tech firms.

This study explores the relationship among different dimensions of top management team’s social capital, innovation quality and organizational knowledge utilization. It holds significant theoretical value in enriching and refining the interactions between top management team’s social capital, knowledge management theory and innovation management theory. In addition, it offers important practical implications for firms to rationally approach top management team’s social capital, emphasize top management team configuration management and establish a comprehensive and efficient organizational knowledge utilization mechanism.

Political factors play a crucial role in China's initial public offering (IPO) market due to its distinctive institutional context (i.e. “economic decentralization” and “political centralization”). Given the significant level of IPO underpricing in China, we examine the impact of local political uncertainty (measured by prefecture-level city official turnover rate) on IPO underpricing.

Using 2,259 IPOs of A-share listed companies from 2001 to 2019, we employ a structural equation model (SEM) to examine the channel (voluntarily lower the issuance price vs aftermarket trading) through which political uncertainty affects IPO underpricing. We check the robustness of the results using bootstrap tests, adopting alternative proxies for political uncertainty and IPO underpricing and employing subsample analysis.

Local official turnover-induced political uncertainty increases IPO underpricing by IPO firms voluntarily reducing the issuance price rather than by affecting investor sentiment in aftermarket trading. These relations are stronger in firms with pre-IPO political connections. The effect of political uncertainty on IPO underpricing is also contingent upon the industry and the growth phase of an IPO firm, more pronounced in politically sensitive industries and firms listed on the growth enterprise market board.

Local government officials in China usually have a short tenure and Chinese firms witness significantly severe IPO underpricing. By introducing the SEM model in studying China IPO underpricing, this study identifies the channel through which local government official turnover to political uncertainty on IPO underpricing.

The paper's purpose is to investigate the effects of digital finance on the risk of stock price crashes and the underlying transmission mechanisms, and to provide suggestions to inhibit the stock crash risk (CR).

This paper selects all companies that were listed on the Shanghai Stock Exchange and the Shenzhen Stock Exchange from 2011 to 2020. It then uses the two-way fixed effect model and the intermediary effect model to verify such effects.

The overall outcomes demonstrate such a result that the CR of listed companies in China can be significantly reduced by the development of digital finance, and the overall transparency of business financial information and the equity pledge of controlling shareholders are the two underlying transmission mechanisms that digital finance can cause effects on the CR of stocks.

The main limitations are that there may exist some problems in the method for evaluating the CR of stocks. And there may be a problem of endogeneity caused by the empirical model cannot control all correlation variables.

This paper would provide policy implications, for different roles, to inhibit the stock CR and to make the development of the economy more stabilize.

Digital finance can promote economic development while restraining financial risks at the same time. Therefore, although this study is based on the relevant data from China, it can also provide a reference for other economies with different basic conditions from China, to promote the overall development of the world economy.

The current academic research on digital finance or stock price CR has been relatively sufficient, but there are few papers that combined both. By combining digital finance with stock CR, this paper researches the influence of digital finance on the CR of stocks through empirical analysis. So, this paper would provide new research ideas and evidence for potential influence factors of the CR of stocks, fill the gap in this research field and provide certain help for subsequent scholars to conduct relevant research.

The purpose of this paper is to explore the features of health misinformation on social media sites (SMSs). The primary goal of the study is to investigate the salient features of health misinformation and to develop a tool of features to help users and social media companies identify health misinformation.

Empirical data include 1,168 pieces of health information that were collected from WeChat, a dominant SMS in China, and the obtained data were analyzed through a process of open coding, axial coding and selective coding. Then chi-square test and analysis of variance (ANOVA) were adopted to identify salient features of health misinformation.

The findings show that the features of health misinformation on SMSs involve surface features, semantic features and source features, and there are significant differences in the features of health misinformation between different topics. In addition, the list of features was developed to identify health misinformation on SMSs.

This study raises awareness of the key features of health misinformation on SMSs. It develops a list of features to help users distinguish health misinformation as well as help social media companies filter health misinformation.

Theoretically, this study contributes to the academic discourse on health misinformation on SMSs by exploring the features of health misinformation. Methodologically, the paper serves to enrich the literature around health misinformation and SMSs that have hitherto mostly drawn data from health websites.

The purpose of this paper is to develop a novel method for establishing the mathematical link between the restored Hogarth curve and the golden ratio, and to advance the potential application of the Hogarth curves in standardization of the garment pattern design by using this method.

The Hogarth curve was fully restored by using the plant design system (PDS) software package and divided the restored curve into two fragments based on the intersection point between the curve and the straight line joining the curve endpoints. The ratios between two fragments of curves and straight lines were calculated and compared with the golden ratio, respectively, which was set as a normalized value. The potential application of the restored Hogarth curves in the normalization of the garment pattern design was therefore studied by using the standard mannequins.

The ratio in the most beautiful curve, i.e., the fourth Hogarth curve, was found to be infinitely approaching to the golden ratio. Furthermore, the incorporation of the Hogarth curves into the garment pattern design demonstrated that the fourth Hogarth curve was the most aesthetic line.

This work offered a novel method for bridging the gaps between arts and maths, i.e., the mathematical relation between the Hogarth curves and the golden ratio. Such a method will provide the protocol to promote the potential applications of the Hogarth curves in the garment pattern design and the human body decoration.

Unhealthy diets are associated with an increased risk of non-communicable diseases and present a significant public health challenge. When developing effective interventions and policies, consideration must be given to the unique social culture in which food choice is embedded. Health vulnerabilities to poor nutrition exist throughout life but may be influenced by socio-cultural factors such as age. This study aimed to assess the attitudes of older or younger Chinese consumers towards healthy eating and explore the factors influencing their food choices.

Semi-structured interviews were conducted in Wuhan, China, with 20 consumers aged 18–25 (Group A) and 20 consumers aged 65 and over (Group B).

Thematic analysis revealed that the two groups had positive attitudes towards healthy eating, although Group A participants were more knowledgeable. Time pressure, food prices and social networks differentially influence healthy eating practices across age groups.

Given China’s economic and cultural context, healthy eating interventions should consider the individual characteristics and food preferences of the different age groups. This approach can optimize targeted healthy eating interventions, and media communications related to healthy eating.

This study aims to improve the accuracy of hyperspectral estimation of soil organic matter content.

Based on the uncertainty in spectral estimation, 76 soil samples collected in Zhangqiu District, Jinan City, Shandong Province, were studied in this paper. First, the spectral transformation of the spectral data after denoising was carried out by means of 11 transformation methods such as reciprocal and square, and the estimation factor was selected according to the principle of maximum correlation. Secondly, the grey weighted distance was used to calculate the grey relational degree between the samples to be estimated and the known patterns, and the local linear regression estimation model of soil organic matter content was established by using the pattern samples closest to the samples to be identified. Thirdly, the models were optimized by gradually increasing the number of modeling samples and adjusting the decision coefficient, and a comprehensive index was constructed to determine the optimal predicted value. Finally, the determination coefficient and average relative error are used to evaluate the validity of the model.

The results show that the maximum correlation coefficient of the seven estimated factors selected is 0.82; the estimation results of 14 test samples are of high accuracy, among which the determination coefficient R² = 0.924, and the average relative error is 6.608%.

Studies have shown that it is feasible and effective to estimate the content of soil organic matter by using grey correlation local linear regression model.

The paper succeeds in realizing both the soil organic matter hyperspectral grey relation estimating pattern based on the grey relational theory and the estimating pattern by using the local linear regression.

The purpose of this paper is to improve the properties of metal nanoparticles which are easy to agglomerate and hard to disperse evenly, thus limiting the application of metal nanoparticles in grease. A novel technology was proposed for modifying metal oxide to improve the dispersibility of nanoparticles.

SA-TiO2 nanoparticles were synthesized using an in-situ esterification method followed by surface modification with stearic acid. The microstructure of the nanoparticles was characterized by scanning electron microscope, transmission electron microscope and Fourier transform infrared spectroscopy and their thermal stability was evaluated by thermogravimetric analyzer. The tribological properties of the SA-TiO2 nanoparticles as additives in lithium grease were evaluated with a four-ball tester and TE77 reciprocating friction tester. The worn surfaces of the steel balls were investigated by EDS and XPS.

The prepared nanoparticles can be well dispersed in the lithium grease and possess much better tribological properties compared to traditional nanoparticles. The results indicated that the excellent tribological performance of SA-TiO2 was attributed to the chemical reaction film composing of Fe2O3, iron oxide and other organic compounds.

This paper provides a method to prevent the agglomeration of nano-TiO2 by surface modification with stearic acid. And the prepared nanoparticles can effectively improve the tribology performance of lithium grease.

The aim of this study is to understand thermal effects and surface topography of roller bearings with misaligned load under combination of multifactors by an experimental method.

A series of orthogonal experiments would need to be planned and performed. A ranking of impact degree of factors on edge effect and eccentric load effect can be learned with multivariate analysis of variance by Statistical Product and Service Solutions software. Influence rules of each individual factor can also be obtained through more experiments. A roller surface phase diagram both before and after test can be observed with metallographic microscope. An axial profile data of roller can be measured by PGI 3D Profiler, then a roller generatrix contour can be achieved through filtering measured signal with empirical mode decomposition method.

Slip fraction has most impact on edge effect, whereas tilting angle plays a key role in eccentric load effect. For the case of low temperature, skidding damage does not occur. Inversely, because of the high pressure in partial elastohydrodynamic lubrication caused by roller tilt, running-in occurs and micro asperity flattening is observed on a rough surface. And, the larger the tilting angle, the more obvious the micro-flattening and the greater the reduction of roller surface roughness after the test.

A lot of theoretical studies on thermal effect of roller bearings surface morphology have been published. However, there are little on relevant experimental study, especially on thermal effect with an integration of sliding, tilting and unbalance loading.