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The purpose of this paper is to propose a method for selecting the position and attitude trajectory of error measurement to improve the kinematic calibration efficiency of a one translational and two rotational (1T2R) parallel power head and to improve the error compensation effect by improving the properties of the error identification matrix.

First, a general mapping model between the endpoint synthesis error is established and each geometric error source. Second, a model for optimizing the position and attitude trajectory of error measurement based on sensitivity analysis results is proposed, providing a basis for optimizing the error measurement trajectory of the mechanism in the working space. Finally, distance error measurement information and principal component analysis (PCA) ideas are used to construct an error identification matrix. The robustness and compensation effect of the identification algorithm were verified by simulation and through experiments.

Through sensitivity analysis, it is found that the distribution of the sensitivity coefficient of each error source in the plane of the workspace can approximately represent its distribution in the workspace, and when the end of the mechanism moves in a circle with a large nutation angle, the comprehensive influence coefficient of each sensitivity is the largest. Residual analysis shows that the robustness of the identification algorithm with the idea of PCA is improved. Through experiments, it is found that the compensation effect is improved.

A model for optimizing the position and attitude trajectory of error measurement is proposed, which can effectively improve the error measurement efficiency of the 1T2R parallel mechanism. In addition, the PCA idea is introduced. A least-squares PCA error identification algorithm that improves the robustness of the identification algorithm by improving the property of the identification matrix is proposed, and the compensation effect is improved. This method has been verified by experiments on 1T2R parallel mechanism and can be extended to other similar parallel mechanisms.

Conversion of the correlated random variables into independent variables, especially into independent standard normal variables, is the common technology for estimating the statistical moments of response and evaluating reliability of random system, in which calculating the equivalent correlation coefficient is an important component. The purpose of this paper is to investigate an accurate, efficient and easy to implement estimation method for the equivalent correlation coefficient of various incomplete probability systems.

First, an approach based on the Mehler’s formula for evaluating the equivalent correlation coefficient is introduced, then, by combining with polynomial normal transformations, this approach is improved to be valid for various incomplete probability systems, which is named as the direct method. Next, with the convenient linear reference variables for eight frequently used random variables and the approximation of the Rosenblatt transformation introduced, a further improved implementation without iteration process is developed, which is named as the simplified method. Finally, several examples are investigated to verify the characteristics of the proposed methods.

The results of the examples in this paper show that both the proposed two methods are of high accuracy, by comparison, the proposed simplified method is more effective and convenient.

Based on the Mehler’s formula, two practical implementations for evaluating the equivalent correlation coefficient are proposed, which are accurate, efficient, easy to implement and valid for various incomplete probability systems.

The purpose of this paper is to design the novel oil–air distributor (N-OAD). Its structure design, oil feeding reliability, service life and viscosity properties of air bubble (AB) oil were analyzed. Meanwhile, the formation mechanism of AB oil was established based on Kelvin–Helmholtz instability.

First, oil–air distributor (OAD) and N-OAD were randomly selected for testing when the air pressure was 0.25 MPa and oil feeding was 100 times per hour. Then, the bubbles were found in the lubricant during the experiment, and the void fraction and viscosity properties of AB oil were tested by image processing method and the MARS 40 rheometer, respectively.

N-OAD has longer service life and higher working reliability than OAD. The key factors of AB oil formation were air pressure and oil feeding. And the void fraction of AB oil has different results on the viscosity at high and low shear rates.

The outcome of this research paper gives an insight to improve the reliability of oil–air lubrication systems and the safety factor of machine tool spindle operation.

The aim of this study was to examine the early stages of the COVID-19 outbreak and the international communication management of Chinese diplomats as a case for extending the definition of intermestic public diplomacy. The goal was to reveal how Beijing subtly used both domestic and foreign social media to organize a network for communication about COVID-19 and purposefully soften the highly centralized and hierarchical political propaganda of the Communist Party of China (CPC).

Based on the literature on digital public diplomacy, the authors applied the existing concept of intermestic to Chinese politics in order to demonstrate the digitalization of public diplomacy, along with its forms and strategies under an authoritarian regime. A hybrid methodology combining quantitative network analysis and qualitative discourse analysis permits examination of China's intermestic online communication network dynamics, shedding light on how such an intermestic practice promoted Chinese values and power to international publics in the early stages of the COVID-19 crisis.

The authors’ findings extend the implications of intermestic public diplomacy from a democratic context to an authoritarian one. By analyzing the content of public diplomacy and para-diplomatic social media accounts in China and abroad at the beginning of the COVID-19 crisis, the authors outlined China's early crisis management, explaining its intermestic public diplomacy transmission modes and strategies. Moreover, the authors identified changes in the narrative strategies of Chinese diplomats and journalists during this process.

The findings of this study underline that Beijing established a narrative-making virtual communication structure for disseminating favorable Chinese strategic narratives and voices through differentiated communication on domestic and foreign social media platforms. Such intermestic communication strategies were particularly evident and even further weaponized by Beijing in its large-scale Wolf Warrior diplomacy in the spring of 2020. Thus, the study’s findings help readers understand how China digitalized its public diplomacy, its digital communication patterns and strategies.

On the one hand, geopolitical uncertainty and the popularity of social media have contributed to the evolution of the intermestic model of public diplomacy. This model allows actors to coordinate homogenous and differentiated communication practices to deploy their influence. On the other hand, the authors did not examine how intermestic audiences perceive and receive public diplomacy practices. In future studies, scholars should measure the agenda-setting capacity of diplomatic actors by examining the effects of such intermestic communication efforts.

The polynomial dimensional decomposition (PDD) method is a popular tool to establish a surrogate model in several scientific areas and engineering disciplines. The selection of appropriate truncated polynomials is the main topic in the PDD. In this paper, an easy-to-implement adaptive PDD method with a better balance between precision and efficiency is proposed.

First, the original random variables are transformed into corresponding independent reference variables according to the statistical information of variables. Second, the performance function is decomposed as a summation of component functions that can be approximated through a series of orthogonal polynomials. Third, the truncated maximum order of the orthogonal polynomial functions is determined through the nonlinear judgment method. The corresponding expansion coefficients are calculated through the point estimation method. Subsequently, the performance function is reconstructed through appropriate orthogonal polynomials and known expansion coefficients.

Several examples are investigated to illustrate the accuracy and efficiency of the proposed method compared with the other methods in reliability analysis.

The number of unknown coefficients is significantly reduced, and the computational burden for reliability analysis is eased accordingly. The coefficient evaluation for the multivariate component function is decoupled with the order judgment of the variable. The proposed method achieves a good trade-off of efficiency and accuracy for reliability analysis.

The manufacturability of extremely fine porous structures in the SLM process has rarely been investigated, leading to unpredicted manufacturing results and preventing steady medical or industrial application. The research objective is to find out the process limitation and key processing parameters for printing fine porous structures so as to give reference for design and manufacturing planning.

In metallic AM processes, the difficulty of geometric modeling and manufacturing of structures with pore sizes less than 350 μm exists. The manufacturability of porous structures in selective laser melting (SLM) has rarely been investigated, leading to unpredicted manufacturing results and preventing steady medical or industrial application. To solve this problem, a comprehensive experimental study was conducted to benchmark the manufacturability of the SLM process for extremely fine porous structures (less than 350 um and near a limitation of 100 um) and propose a manufacturing result evaluation method. Numerous porous structure samples were printed to help collect critical datasets for manufacturability analysis.

The results show that the SLM process can achieve an extreme fine feature with a diameter of 90 μm in stable process control, and the process parameters with their control strategies as well as the printing process planning have an important impact on the printing results. A statistical analysis reveals the implicit complex relations between the porous structure geometries and the SLM process parameter settings.

It is the first time to investigate the manufacturability of extremely fine porous structures of SLM. The method for manufacturability analysis and printing parameter control of fine porous structure are discussed.

The purpose of this paper is to find an accurate, efficient and easy-to-implement point estimate method (PEM) for the statistical moments of random systems.

First, by the theoretical and numerical analysis, the approximate reference variables for the frequently used nine types of random variables are obtained; then by combining with the dimension-reduction method (DRM), a new method which consists of four sub-methods is proposed; and finally, several examples are investigated to verify the characteristics of the proposed method.

Two types of reference variables for the frequently used nine types of variables are proposed, and four sub-methods for estimating the moments of responses are presented by combining with the univariate and bivariate DRM.

In this paper, the number of nodes of one-dimensional integrals is determined subjectively and empirically; therefore, determining the number of nodes rationally is still a challenge.

Through the linear transformation, the optimal reference variables of random variables are presented, and a PEM based on the linear transformation is proposed which is efficient and easy to implement. By the numerical method, the quasi-optimal reference variables are given, which is the basis of the proposed PEM based on the quasi-optimal reference variables, together with high efficiency and ease of implementation.

This paper analyzes Fang Fang's 2020 Wuhan Diary‐Dispatches from a Quarantined City, to show how the author communicates the coronavirus crisis in Wuhan in a global information ecosystem. The success of the diary showcases how the actual health emergency has been transformed into a communication issue.

This study is exploratory and qualitative in nature. The authors conducted a thematic analysis (TA) of Wuhan Diary, in which we decided to focus on the aspects of sousveillance and solidarity. For the purposes of our paper, we used the English translation of the text by Dr. Michael Berry.

The authors focus on two major themes in their exploration of the corona crisis as a global communication issue: sousveillance and solidarity. The authors argue that the diary's ways of seeing perform a version of “sousveillance” or “undersight” in juxtaposition to surveillance or “oversight” (Mann, 2017). Fang Fang calls for solidarity as an effective measure for individuals, communities and societies to contain the pandemic and a potential misinfodemic.

Since Wuhan was the early epicenter of the COVID-19 pandemic, Wuhan Diary presents an unprecedented narrative account of life under quarantine that could function as a litmus test for other cities and countries. Fang Fang's diary provides a countermeasure to official accounts of the pandemic in Wuhan, which has resonated both with people in China and abroad.

The paper aims to explore multilingual thesauri automation construction based on the freely available digital library resources. The key methods and study results are presented in the paper. It also proposes a way that terms are automatically extracted from multilingual parallel corpus.

The study adopted the technology of natural language processing to analyze the linguistics characteristics of terms, and combined this with statistical analyses to extract the terms from technological documents. The methods consist of automatically extracting and filtering terms, judging and building relationship among terms, building the multilingual parallel corpus, and extracting term pairs between Chinese and foreign languages through calculating their associated probability. The experiments run on the Java test platform.

The study obtains the following conclusions: finding the similarities and differences between the Chinese thesaurus standard and international thesaurus standard. The methods for automatically extracting terms and building relationships among them are presented. Eventually the multilingual terms' translation sets are generated based on real corpora. The results of the study show that the proposed methods can obtain better performance. The effect of automatic terms' translation alignment method is better than that of traditional IBM model method.

The study results can provide references for further study and application of multilingual thesauri automation construction using Chinese as a pivot.

The paper proposes new ideas on thesaurus automation construction in the digital age. The presented method based on linguistics and statistics is a new attempt. According to the experimental results, this exploration and study is innovative and valuable. In addition, these ideas and methods give a good start for improving information services of the PRC's National Science and Technology Digital Library.

A novel asynchronous network-based model is proposed in this paper for the sentiment analysis of online public opinions. This new model provides a new approach to analyze the evolution characteristics of online public opinion sentiments in complex environment.

Firstly, a new sentiment analysis model is proposed based on the asynchronous network theory. Then the graphical evaluation and review technique is employed and extended to design the model-based sentiment analysis algorithms. Finally, simulations and real-world case studies are given to show the effectiveness of the proposed model.

The dynamics of online public opinion sentiments are determined by both personal preferences to certain topics and the complex interactive influences of environmental factors. The application of appropriate quantitative models can improve the prediction of public opinion sentiment.

The proposed model-based algorithms provide simple but effective ways to explore the complex dynamics of online public opinions. Case studies highlight the role of government agencies in shaping sentiments of public opinions on social topics.

This paper proposes a new asynchronous network model for the dynamic sentiment analysis of online public opinions. It extends the previous static models and provides a new way to extract opinion evolution patterns in complex environment. Applications of the proposed model provide some new insights into the online public opinion management.