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This paper aims to analyse how both Lin’s birthplace identity and his Christian identity contributed to his fruitful public career and to ascertain which identity became the most significant.

Archival research is the main method used in this paper. The most important archives drawn from are the Daniel Tse Collection in the Special Collection and Archives of the Hong Kong Baptist University Library. Oral history has also been used in this paper to uncover more material that has not yet been discussed in existing scholarly works.

This paper argues that although Lin’s birthplace identity and social networks helped him to start his business career in Nam Pak Hong and develop into a leader in the local Chaozhou communities, these factors were insufficient to his becoming a respectable member of the Chinese elite in post-war Hong Kong. He became well known not because of his leading position in local Chaozhou communities or any great achievement he had obtained in business but because of his contribution to the development of Christian education. These achievements earned him a reputation as a “Christian educator”. Thus Lin’s Christian identity became more important than his birthplace identity in contributing to his successful public career.

This paper has value in showing how Christian influences interacted with various cultural factors in early Hong Kong. It also offers insights into Lin’s life and motivations as well as the history of the institutions he contributed to/founded. It not only furthers our understanding of the Chinese Christian business elite in early Hong Kong but also provides us with insights when further studying this group of people in other British colonies in Asia.

The purpose of this paper is to introduce the necessary background information and literature in order to make this special issue self‐contained.

The paper comprises: an Introduction; Section 2 outlines the origin of the yoyo structure by presenting a brief historical account and the concept of whole evolution of systems; Section 3 introduces the literature on applications of the systemic yoyo model in areas of natural science, social science, epistemology, and practical disastrous weather forecasts; and Section 4 outlines what is contained in this special issue.

A personal account of aspects of a career in systems research and description of a personal ambition to introduce laws for social science and laws that make both natural and social sciences exact at the same time.

With regard to the systemic yoyo model, this paper presents an introduction to what has been accomplished by using this model and what will be presented in this special issue in order to provide all interested colleagues with an overall picture in terms of where the paper stands in the relevant research activities.

The purpose of this paper is to summarize the most important quantitative representations of electric and magnetic yoyo fields obtained on the basis of the general systemic yoyo model.

The traditional calculus‐based methods are employed in reference with the qualitative analysis and reasoning of spinning yoyo flows.

This paper presents an opportunity for us to see how theoretical and experimental results obtained in the conventional electromagnetic theory can be potentially generalized to the study of the general systemic yoyo model.

This paper looks at the well‐established laws of electromagnetic theory from the angle of systems studies with an eye on the possibility of first generalizing these laws to the situations of general systems, and second, the potential to employ these generalized laws to social science and humanity areas.

The purpose of this paper is to establish the relevant quantitative methods for the investigation of the eddy and meridian fields of the general systemic yoyo model.

On the basis of established systemic yoyo models for electrons and positrons, flows of negative yoyo charges are naturally identified with electric currents so that use of the well‐developed quantitative methods can be made in electromagnetic theory to investigate the relationship between eddy and meridian fields of the general systemic yoyo model.

A general method is established on how to compute the intensity of the magnetic yoyo fields accompanying a moving yoyo charge or a canal of yoyo flow. A quantitative representation for magnetic yoyo fields is provided. And, the well‐known Ampere's law of electricity is generalized to the case of general yoyo flows.

By establishing an adequate quantitative method for the general systemic yoyo model, it should be possible to know more about the yoyo model and gain additional potential to successfully employ this model in various areas of learning, as proposed by Lin.

The purpose of this paper is to establish quantitative properties for the general systemic yoyo model so that this model can be readily employed to study problems from various disciplines of knowledge, be they from natural science or social science.

Figurative analysis combined with calculus‐based methods is applied in this research, in order to achieve the goal that whatever is obtained theoretically, here can be practically used in various applications.

The paper introduces the concepts of gravitation, potential energy, and yoyo potentials for general spinning yoyos and look at the structure of yoyo fields with relevant quantitative representations developed for practical computation purposes. The paper shows how the spin field of a yoyo can be decomposed into eddy and meridian fields and why spinning yoyos have the tendency to line up in such a way that their axes of spin are parallel to each other and their like polarities face the same direction. When looking at yoyo flows and resistances, the Kirchhoff Junction and Loop Laws are generalized to the most general case of systemic yoyos. By considering the concept of yoyo fluxes, it is possible to rewrite the Gauss's law in the relevant most general language.

This work is the first of its kind to develop quantitative methods for studying various properties of systems on a unified footing: the general systemic yoyo model. As shown in this special issue, the results of this work have been and will continue to be applied to address many difficult problems that have been encountering scholars in the areas of modern science and humanities.

The authors apply topic sentiment analysis (several relatively new text analysis methods) to the study of public opinion as expressed in social media by comparing reactions to the Trayvon Martin controversy in spring 2012 by commenters on the partisan news websites the Huffington Post and Daily Caller.

Topic sentiment analysis is a text analysis method that estimates the polarity of sentiments across units of text within large text corpora (Lin & He, 2009; Mei, Ling, Wondra, Su, & Zhai, 2007).

We apply topic sentiment analysis to public opinion as expressed in social media by comparing reactions to the Trayvon Martin controversy in spring 2012 by commenters on the partisan news websites the Huffington Post and Daily Caller. Based on studies that depict contemporary news media as an “outrage industry” that incentivizes media personalities to be controversial and polarizing (Berry & Sobieraj, 2014), we predict that high-profile commentators will be more polarizing than other news personalities and topics.

Results of the topic sentiment analysis support this prediction and in so doing provide partial validation of the application of topic sentiment analysis to online opinion.

Digital humanities database is one of the essential tools in digital humanities research area. Therefore, examining the usage of digital humanities database in academic papers is conducive to assessing the value of digital humanities database for scientific research activities and improving the construction of digital humanities infrastructure.

This paper constructs an evaluation system of digital humanities database from the perspective of academic influence and social influence, with mention frequency, usage motivation, platform access data, usage region and usage discipline as indicators and takes China Biographical Database Project as the empirical object to explore the usage of digital humanities database in China.

The data analysis result demonstrates that digital humanities databases are widely used and recognized in China. However, the problem of low actual usage remains.

This paper constructs the digital humanistic database's evaluation system and discusses applying the digital humanistic database in China, which provides a new perspective and method for the influence evaluation study of the digital humanistic database.

The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint.

A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint.

The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior.

The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation.

Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective.

The purpose of this paper is to evaluate the impact of scientific data in order to assess the reliability of data to support data curation, to establish trust between researchers to support reuse of digital data and encourage researchers to share more data.

The authors compared the correlations between usage counts of associated data in Dryad and citation counts of articles in Web of Science in different subject areas in order to assess the possibility of using altmetric indicators to evaluate scientific data.

There are high positive correlations between usage counts of data and citation counts of associated articles. The citation counts of article’s shared data are higher than the average citation counts in most of the subject areas examined by the authors.

The paper suggests that usage counts of data could be potentially used to evaluate scholarly impact of scientific data, especially for those subject areas without special data repositories.

The study examines the possibility to use usage counts to evaluate the impact of scientific data in a generic repository Dryad by different subject categories.

With the development of intelligent technology, deep learning has made significant progress and has been widely used in various fields. Deep learning is data-driven, and its training process requires a large amount of data to improve model performance. However, labeled data is expensive and not readily available.

To address the above problem, researchers have integrated semi-supervised and deep learning, using a limited number of labeled data and many unlabeled data to train models. In this paper, Generative Adversarial Networks (GANs) are analyzed as an entry point. Firstly, we discuss the current research on GANs in image super-resolution applications, including supervised, unsupervised, and semi-supervised learning approaches. Secondly, based on semi-supervised learning, different optimization methods are introduced as an example of image classification. Eventually, experimental comparisons and analyses of existing semi-supervised optimization methods based on GANs will be performed.

Following the analysis of the selected studies, we summarize the problems that existed during the research process and propose future research directions.

This paper reviews and analyzes research on generative adversarial networks for image super-resolution and classification from various learning approaches. The comparative analysis of experimental results on current semi-supervised GAN optimizations is performed to provide a reference for further research.