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As the most significant evaluation indicator in Journal Citation Report, Journal Impact Factor (JIF) plays a very important role in the process of assessing academic journals. However, the related study on JIF is only focused on bibliometrics and scientometrics. To remedy this issue, we proposed a social computing simulation model to analyze the correlation between JIF and quality of articles. In the model, a social problem can be evaluated from the perspective of simulation analysis and complex system, in which certain factors can be denoted by some parameters and agents. As an agent-based model, the JIF system consists of three agents (Author, Article and Journal). The simulation result and experimental analysis strongly demonstrated that the calculation of JIF can be modeled by the proposed model. Furthermore, it can be found that JIF can be reflected by the quality of the published articles.

The authority of journals is usually a key indicator in guiding people to choose important journals, and the journal ranking is a common way to distinguish the journals' authority. The common ranking methods or models are based on citation data, such as the impact factor (IF), the PageRank algorithm, and the hyperlink-induced topic search algorithm. In this paper, we present a new model, named the reputation analysis of citation behavior (RACB) model, which not only considers the number of citations but also considers the reputation of inter-citation behavior. First, the model fits the function relation of the citation desire index (CDI) based on the citation data of the most reputable journal (named the top journal in the remainder of this paper). Then, the CDI values of the target journals are calculated by using the function relation of the top journal. An improved gray correlation analysis is used to describe the deviation between the CDI and the actual citation rate, named the random citation rate, indicating the inter-citation reputation value of the target journals. Finally, a case study showed that the ranking result of the RACB model has a high similarity with the IF values of 2017, which indicates that the ranking results of the RACB model that considered journal reputation evaluations are more reasonable than those of the traditional PageRank algorithm. This paper proposes a new method of journal ranking from the perspective of reputation evaluation, which can rank the journals more reasonably. It encourages researchers to cite articles more fairly in order to avoid a situation in which the low reputation of the citation behavior affects the quality of the journal that published the article.

A range of empirical observations show that the right-skewed phenomenon emerges in the citation distributions of papers. The phenomenon also exists in the length distributions of learners’ time spent on viewing the videos of a massive open online course. The mechanisms underlying this phenomenon in both cases are the same, namely cumulative advantage, known as the Matthew effect and the Lindy effect respectively. These similarities make it possible to apply the theories and methods in scientometrics to attraction assessments of massive open online courses. Massive data of learning behaviors are recorded on the MOOC platforms with high measurability and wide coverage, setting the precondition for this study. Based on the data derived from the log records of viewing behavior, we showed how to utilize the ideas of typical scientometric indicators, such as the impact factor and $h$ -index, to assess courses’ attraction. Our work adumbrates not only the practicability but also the limitation of the provided indicators.

Reducing plastic at the source market and changing the way people dispose of plastic waste is critical to improve the environment. This paper aims to quantify the impacts of plastic waste on environment, determine managerial strategies for plastic market by adjusting human actions and study criterion to evaluate equity issues about regional benefit and contribution. This paper first develops the Human-Production-Environment(HPE) model based on the famous Pressure-State-Response(PSR) model. The HPE model has three index systems: human activity index, production index and environment performance index (EPI). After collection of indicators in World Bank website, the plastic production index(PI) was divided into three aspects: waste generation, recycling & recovery and alternative. Then, the Entropy Weight Method(EWM) is applied to calculate the weight of each PI indicator. This paper classifies the human activities index into economic response, policy response and human life response. To quantify the relations between plastic production and human activities, the impact coefficient of each response is introduced and regression method is utilized to obtain a combined impact factor. Aims to determine the minimal achievable level of global plastic waste, this paper determines the optimal proportion of types of human life. By using GM(1,1) prediction method, the plastic waste in the next five years is predicted to be reduced with management strategies. The green contribution coefficient is introduced to consider the regional equity issues in plastic management.

With the development of the new era, music is also developing. When artists create new music, many factors affect them. How to quantify the impact factor and predict the development trend of different genres during the evolution of music is a significant research topic in the current society. Based on the relationship between influencers and followers in the influence_data dataset provided by Integrative Collective Music (ICM), this paper establishes a directed network graph structure to express music influence and uses the PageRank algorithm to dynamically solve the influence degree of influencers on followers to quantify. Impact factor development captures the parameters of "music influence" in this network. And according to the aggregated influence_data dataset, the LSTM (Long Short-term Memory) neural network is established to predict different music genres' development trend. The three types of MAE predicted (Pop/Rock, Country, Jazz), MSE and R2 are MAE (6.675) ,7.843,8.306),MSE(71.879,108.297,103.521),R2(0.762,0.659 ,0.676). The results show that the model can quantify the impact factor very well and achieve good results under these three evaluation indicators.

As the outbreak of novel coronavirus in 2020 is a public crisis event sweeping across the globe, electronic posters as a kind of information transmission medium featuring outstanding visual effect and easy transmission are playing a significant role in timely issuing information, properly guiding the public opinion and alleviating the mental anxiety of the masses under the epidemic situation. In this paper, in order to optimize the transmission efficiency of electronic posters, special effort is made to screen main factors influencing the masses’ will of browsing electronic posters online under the epidemic situation from three aspects (Platform, Posters and Audiences) based on the literature research. Then, through the questionnaire survey and DEMATEL method, analysis is made on various factors and the relevance between factors. According to the research results, Information Visualization is the most important demand factor, the basic function of platform the core impact factor and the audiences’ anxiety the core impacted factor. Through comparing the result respectively concluded from the DEMATEL and the binary logistic regression analytical method, the research’s conclusions can be supplemented and extended. At last, based on the specific analysis and comparison result, suggestions on optimizing online anti-epidemic popular science posters are proposed to enhance the masses’ will of browsing and improve the transmission efficiency.

Free from the limitations of time and space, online education achieves a rapid development in spite of the attack by COVID-19 in 2020. How to improve the user experience through optimizing the product’s interactive design plays a significant role in enhancing the product quality. Based on UTAUT Model in combination with Flow Theory, this research examines the impact factor model and research hypotheses according to relevant questionnaire data from the perspective of interactive design. Corresponding results show that, Performance Expectancy and Flow Experience exert an obvious impact on users’ continuous usage behavior while Content Quality, Interactive Experience and Visual Design impose an indirect impact. In the conclusion part, corresponding interactive design suggestions are proposed in reference to such research results in this paper.

Collaborative research has become a viable and strategic engagement for universities and scholars. Researchers often take part in grants and share scarce resources while gaining insight from scholars regardless of their geographical disposition. International collaboration is widely believed to be the most beneficial of collaborative research and that developing countries stand the most to benefit from such engagement. In this work, we analyze the impact of collaborative research on an institution in a developing country. The selected institution is characterized by having no Ph.D. programs and a relatively low number of master’s degrees awarded annually. Accordingly, its research output is largely dependent on its faculty members and their collaborative networks largely comprised of developed countries. We use citation counts sourced from three different indices and Clarivate’s Journal Impact Factor rankings as proxies for impact and publishing venue quality, respectively. Our findings, partially, contest the claim that international collaboration is the most impactful collaboration type. Although articles co-authored with international collaborators are on average published in insubstantially higher ranked journals, the average number of citations per paper is comparable to institutional collaboration. The latter is further ratified when self-citations are excluded. In addition, we also consider viewership as an exposure metric where institutional collaborated articles garner almost double that of international.

The purpose of the current study is to develop a ranking of journals in fuzzy and intelligent systems upon the view of the experts of higher education level and then investigate the influential factors for their journal selection for reading and manuscript submission. The survey has been carried out based on the response of a sample of active experts. The main finding is that the researchers read the articles of journals more for the research oriented activities rather than the supplement to the teaching material. They also give merit to the journals for the quality of the peer review, the paper charge, impact factor, and the subject of the journal more than the other factors. The results will be beneficial to the researchers and students to know where to seek the high qualified articles and the administration of the research institutes for the effective evaluation and resource allocation.

The study proposed an adaptive cognitive diagnostic model based on Bi-LSTM which regards the model as a sequence prediction from participants’ responses to their knowledge state. With the merit of bidirectional long-sequence memory, Bi-LSTM is able to employ the whole sequence of response as the input of the adaptive cognitive diagnostic model. Comparing to traditional approaches with a maximum probability as the result of cognitive diagnosis, the model based on Bi-LSTM is superior in that it makes use of the whole response sequence to predict the result. The model was verified through the actual data from 1029 British middle school students who participated in the English reading test of the Program for International Student Assessment (PISA). It is found that the accuracy rate and the average attribute accuracy rate of the model make significant breakthroughs. Meanwhile, the order of the test items has some influence on participants’ responses, which may be considered as an impact factor in future test.

In this paper, the influence of the initial population into successive generations in Evolutionary Algorithms (EAs) is studied as a problem-independent approach. For this purpose, the Traceable Evolutionary Algorithm (T-EA) is proposed. This algorithm keeps track of the influence of the individuals from the initial population over the generations of the algorithm. The algorithm has been implemented for both bit-string and integer vector representations. In addition, in order to study the general influence of each individual, new impact factor metrics have been proposed. In this way, we aim to provide tools to measure the influence of initial individuals on the final solutions. As a proof of concept, three classical optimization problems (One Max, 0/1 Knapsack and Unbounded Knapsack problems) are used. We provide a framework that allows to explain why some individuals in the initial population work better than others in relation with the corresponding fitness values.

A novel method to cross impact matrix creation is presented in this paper to overcome the inherent challenges of cross impact analysis. The cross impact matrix is a strong prerequisite for numerous impact factor analysis methods which are used in future studies, military intelligence, game simulation and various business contexts. The method draws on the property of transitivity of preference as a key concept for the creation of the cross impact matrix from preference chains. A number of drawbacks associated with the traditional development and creation of the cross impact matrix are addressed and are overcome using this novel approach. An existing cross impact matrix is used to test the validity and reliability of the method developed.

Research is always based on previously done work. To acknowledge the worthy work of the predecessors of the field, researchers do citations. Citations are factors that are used for measuring the impact factor of journals, to rank the researchers, to find out latest research topics, for allocating research grants etc. In current epoch the research community has turned their focus towards citations and is of the view that all citations are not equally important. To find out important citations, researchers used different approaches such as context based, cue word based, metadata based, frequency based, textual based etc. Among proposed methodologies, frequency based approach was extensively used. The citation with high frequency was considered as important, but it is yet unclear that what should be the frequency cut off value of citation for considering it important. This research explored the significance of applying Threshold value over Frequency count for binary classification. We identified optimal threshold value of frequency count and further applied this to classify the citations into important and non-important ones. To evaluate the proposed approach a benchmark data set annotated by two domain experts was used that consisted of 465 citation pairs. The results were compared with state of the art precision value of 0.72. While the experiment showed increased value of 0.75 in terms of precision

"Virtual Work” has been a growing trend that defined as people work at different locations and times. The concept of virtual work is transforming a physical place into a virtual environment, and change people interacts via online communication technologies. How people work as a team and integrated progression in this virtual environment is a key issue to work effectively. Therefore, communication is a key process in the virtual work method to improve work performance. This paper aims at how to manage the communication process in a framework of virtual work, providing communication strategies for a startup entrepreneur to improve the effectiveness of work and reduce impact factors that affect to work performance of employees’ work-life in the virtual work method. This impact factor is about work-life balance, social relation, communication, and knowledge sharing. These communication strategies are combined both online communication technologies and face-to-face communication to reduce impact factors and providing objective of face-to-face communication to make productive meetings. This framework was approved that able to resolve various factors that are caused by digital transform as well.