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The purpose of this paper is to investigate the superior relationship between blood lipid- and cardiovascular disease (CVD)-related hematological parameters using superior grey relational analysis (GRA).

A total of 294 individuals who underwent simultaneous routine blood examination and blood lipid examination in the Physical Examination Center of the First Affiliated Hospital of Shantou University Medical College were included in this study. Superior GRA was performed to find out the superior factor in CVD-related hematological parameters and blood lipids. CVD-related hematological parameters included red blood cell distribution width, white cell count, and platelet count, platelet distribution width, mean platelet volume, as well as platelet crit. The indicators of blood lipids analyzed here consist of low-density lipoprotein, high-density lipoprotein, triglyceride and total cholesterol.

The results showed that all the grey relational degree of hematological parameters and blood lipids were over 0.8; the superior factor in hematological parameters was PLT, whereas TC was the superior factor in blood lipids.

Findings of this study suggested that hematological parameters are closely related to blood lipids and a potential role for hematological parameters in the prediction of dyslipidemia, which need further study; TC has the greatest influence on hematological parameters, whereas TG displays a minimal impact.

To the authors’ best knowledge, it was the first study to analyze the relationship between various CVD-related hematological parameters and blood lipids via superior GRA.

Electricity demand forecasting has always been a key issue, and inaccurate forecasts may mislead policymakers. To accurately predict China’s electricity demand up to 2030, this paper aims to establish a cross-validation-based linear model selection system, which can consider many factors to avoid missing useful information and select the best model according to estimated out-of-sample forecast performances.

With the nine identified influencing factors of electricity demand, this system first determines the parameters in four alternative fitting procedures, where for each procedure a lot of cross-validation is performed and the most frequently selected value is determined. Then, through comparing the out-of-sample performances of the traditional multiple linear regression and the four selected alternative fitting procedures, the best model is selected in view of forecast accuracy and stability and used for forecasting under four scenarios. Besides the baseline scenario, this paper investigates lower and higher economic growth and higher consumption share.

The results show the following: China will consume 7,120.49 TWh, 9,080.38 TWh and 11,649.73 TWh of electricity in 2020, 2025 and 2030, respectively; there is hardly any possibility of decoupling between economic development level and electricity demand; and shifting China from an investment-driven economy to a consumption-driven economy is greatly beneficial to save electricity.

Following insights are obtained: reasonable infrastructure construction plans should be made for increasing electricity demand; increasing electricity demand further challenges China’s greenhouse gas reduction target; and the fact of increasing electricity demand should be taken into account for China’s prompting electrification policies.

This paper aims to explore the promotion decisions of heterogeneous sellers on a decentralized platform under competitive conditions and analyze how seller behaviors impact platform profit, seller revenue, buyer surplus and social welfare.

This paper considers a Cournot model consisting of a platform charging a commission rate and two sellers with different conversion rates and browsing costs. Promotion efforts by sellers can increase traffic, but they also incur promotion costs for sellers. The sellers decide on promotion effort by weighing these two effects. The authors also explore the equilibrium when the platform charges a fixed usage fee.

The seller’s profit improves as its conversion rate increases and worsens as browsing costs increase. Also, increasing the commission rate charged by the platform makes the seller invest less in promotional efforts. Therefore, the platform must consider this trade-off to determine an optimal rate. The analysis shows that the seller with a high conversion rate and high browsing cost plays a greater role in generating more overall revenue. When the market favors such a seller, the platform tends to charge less in order not to impair its profitability.

This paper incorporates conversion rate, buyer’s browsing cost, unit promotion cost and the fee charged by the platform into the model to study sellers’ promotion decisions on decentralized platforms.

The online Question and Answer community is full of a large number of science and technology topics, the discussion and dissemination of which play an important role in promoting the popularization of new technologies and cultivating public enthusiasm for science. However, the spread of false information and rumors weakens the community's positive effect, making the community more difficult for people to obtain useful information on such topics. Research on the influencing factors and governance of the spread of false information on science and technology topics has become the key to the spread of popular science.

Therefore, this paper uses the Elaboration Likelihood Model as the theoretical framework to examine the role of the factors influencing the spread of false information on science and technology topics in Zhihu community on the information persuasion and the impact on public behavior attitude from the core path and the edge path. This paper compiles a crawler program to capture 12,893 response information under the “Metaverse” topic in Zhihu community as an empirical sample and uses text mining and conducts visual correlation analysis to explore the key factors affecting the persuasive transmission path of information on science and technology topics.

The research finds that the content specialization, content consistency and content coherence of science and technology topics affect personal judgment from the aspect of information content through the core path and have a positive correlation with information persuasion; the number of comments, the length of the text and the publishing authors' influence from the edge image characteristics through the edge path are positively correlated with the information persuasion. Then, from the perspective of topic platform, government and topic participants, this paper puts forward a general plan to improve the information persuasion of science and technology topics so as to deal with false information.

Compared with the small data set of the traditional questionnaire survey, the research based on community empirical big data is more reliable. The model takes into account the attitude and behavior of users and is more suitable for the research on the transmission path of scientific and technological information in the internet era. This research provides a direction for analyzing the text characteristics and development trends of information in the field of science and technology and is conducive to promoting the optimization of the network information environment and building a good ecology, with the spread of rumors about science and technology topics curbed and the governance of false information strengthened.

In the information commons (IC) space of library, it is very important to recognize the emotional state of users for better playing the role of IC. In view of this point, this paper aims to discuss the human expression of user emotion.

An emotional state recognition method based on body posture change under video monitoring is proposed. In this method, two parameters are proposed to represent the emotional state of users. Finally, the distribution of users’ overall emotional state is recognized.

It is found that the change of human posture reflects the emotional state of users to a certain extent. The spatial frequency of the user’s average body position change and per capita body position change can reflect the spatial distribution of individual and body position change, respectively.

The method in this paper can effectively overcome the inaccuracy of manual identification of video monitoring images, especially in the case of a large number of users and effectively help the construction of university library IC space and provide a basis for the setting of environmental parameters.

Binary classification on imbalanced data is a challenge; due to the imbalance of the classes, the minority class is easily masked by the majority class. However, most existing classifiers are better at identifying the majority class, thereby ignoring the minority class, which leads to classifier degradation. To address this, this paper proposes a twin-support vector machines for binary classification on imbalanced data.

In the proposed method, the authors construct two support vector machines to focus on majority classes and minority classes, respectively. In order to promote the learning ability of the two support vector machines, a new kernel is derived for them.

(1) A novel twin-support vector machine is proposed for binary classification on imbalanced data, and new kernels are derived. (2) For imbalanced data, the complexity of data distribution has negative effects on classification results; however, advanced classification results can be gained and desired boundaries are learned by using optimizing kernels. (3) Classifiers based on twin architectures have more advantages than those based on single architecture for binary classification on imbalanced data.

For imbalanced data, the complexity of data distribution has negative effects on classification results; however, advanced classification results can be gained and desired boundaries are learned through using optimizing kernels.

The purpose of this paper is to improve the dyeing effect of fast-growing fir wood dyed with reactive dyes.

In this study, five factors including temperature, the dosage of dye accelerator, dyeing time, the dosage of fixing agent and fixing time were investigated. Then, the color difference and light resistance of the wood surface after dyeing were used as the evaluation indicators; the best dyeing process under the two indicators was obtained through the range analysis. Finally, the two indicators were considered comprehensively, and the fuzzy comprehensive evaluation method was used to obtain the best dyeing process under the comprehensive indicators.

The results show that when the comprehensive index was used as the evaluation index, the optimal dyeing process for reactive red X-3B dyeing fast-growing fir veneer was that the dyeing temperature was 65°C; the amount of dye accelerator was 25 g L⁻¹; the dyeing time was 2 h; the amount of fixing agent was 15 g L⁻¹; and the fixing time was 35 min.

The technique of wood dyeing is an important method to increase the value of wood products. When using different kinds of dyes or dyeing substrates for wood dyeing, the dyeing process is different. This study determined the best process for reactive dye dyeing of fast-growing fir veneer and provided a solution for improving the value of fast-growing fir wood.

This paper constructs the uncertainty analysis model of prefabricated building supply chain risk. The model is designed to study the formation path of prefabricated building supply chain risk and is expected to be used by industry stakeholders for supply chain risk management.

Based on the uncertainty circle model, construct a configuration analysis framework for supply chain risks in prefabricated buildings. The fuzzy set qualitative comparative analysis (fsQCA) is used to study the configuration influence of five uncertain factors, including environment, plan-control, demand-supply, manufacturing and assembly-transportation, on the risk of the prefabricated building supply chain.

There are three paths to promote the high-risk generation of the prefabricated building supply chain: assembly-transportation-oriented, plan-control-oriented and manufacturing-oriented. There is a specific equivalent substitution relationship among the five causal conditions. Under specific conditions, different combinations of conditions have the same effect on promoting supply chain high-risk generation through equivalent substitution.

The multiple concurrent causal relationships of risk conditions in the assembly construction supply chain are studied under the grouping perspective, which helps to expand the research perspective of assembly construction supply chain risk and provides theoretical guidance for supply chain risk management of construction enterprises.

Development assurance level (DAL) is the measurement of the rigor of development assurance tasks performed to functions or items. The DAL assignment is an important process of aircraft system development that can make the reliability and safety of the system stay at acceptable levels. This paper aims to propose an optimization approach for the DAL assignments to minimize the development cost of aircraft systems.

The mathematical model for the DAL assignment optimization has been developed on the basis of the given expressions of objective function and constraints. In addition, a hybrid algorithm model synthesizing the advantages of genetic algorithm (GA) and Tabu search (TS) has been proposed to solve the optimization problem of the DAL assignment.

The results of the case study show that the proposed hybrid algorithm is more efficient and effective than the exhaustive method as well as the pure GA.

The proposed approach can be applied in the development of aircraft systems, and it has great significance in minimizing the development cost as well as keeping the system reliability and safety at an acceptable level.

The constrained optimization method has been applied in the DAL assignments, the corresponding mathematical model has been built and a hybrid evolutionary algorithm has been proposed to solve the optimization problem.

The purpose of this paper is to re-examine the effectiveness of price limits on stock volatilities in China over a more recent time period spanning from 2007 to 2012. The motivation stems from the fact that very high stock market volatilities are observed in China and we are sceptical of the volatility mitigating effect claimed by advocates of price limits.

The effectiveness of price limits on volatilities is examined using an event study methodology and within an expanded framework of volatility-volume relationships. The sample stocks include the 300 component stocks of the CSI300 Index.

Both event study and regression analysis suggest that price limits exaggerate market volatilities by causing volatility spillovers. The destabilising effect is much more pronounced for small firm stocks and when the market falls. In addition to the informational source of volatilities (represented by volume), price limits create another non-trivial frictional source of volatilities in China’s stock market.

This research is the first to re-examine the price limit effect in China’s stock market in an expanded framework of volatility-volume relationships. It identifies price limits, in addition to information, as another non-trivial frictional source of volatilities. The findings derived from a recent sample period confirm the conventional view of inefficiency of price limits raised by Fama (1989) and provide evidence in support of the pervasive trend of stock market deregulations.