Based on the simulation study of the publication delay control process [Yu & al., 2005], transfer function models of delay control processes by adjusting the accepted contribution flux and the published
contribution flux are identified using system identification. According to Cybernetics, the feedback control system of the
publication delay is designed and control processes are simulated and analyzed when the average publication delay are regarded
as the controlled object. On the basis of the relation between the average publication delay and the deposited contribution
quantity, another control method is proposed that the deposited contribution quantity is regarded as the controlled object
and the simulation result proves that the method is an excellent means and can help editors expediently manage their journals
and control publication delays.
The steady state solution of differential equations of periodical publication process is deduced, and based on this, the indicator of periodical publication delay, which reflects the degree of information ageing in editorial board of a periodical, is established. The indicator is proved to be the sum of two items: the pure publication delay, which reflects the editing rapidity of a periodical, and the ratio of deposited contribution quantity to the publishing quantity in one year, which reflects the waiting period of adopted papers deposited in editorial board. As a demonstration, the delay indicators of seven periodicals are calculated. Finally, the application of this indicator is discussed.
Summary According to the discrete model of periodical publication process, recurrence formulae of parameters of the process are gained and the initial conditions of control process parameters from one steady state to another are deduced. Using the variable separation approach, which is used generally to solve the partial differential equation, the recurrence computing formula of the publication probability function is deduced. First the publication delay increasing process caused by the accepted contribution flux increase is simulated, and then the publication delay decreasing processes under four different control means are simulated too. Finally it is demonstrated that the periodical publishing process is a strong inertia system and it is found that reducing the quantity of deposited contributions can shorten the publication delay.
In this paper, we discuss the application of the data mining tools to identify typical features for highly cited papers (HCPs). By integrating papers’ external features and quality features, the feature space used to model HCPs was established. Then, a series of predictor teams were extracted from the feature space with rough set reduction framework. Each predictor team was used to construct a base classifier. Then the five base classifiers with the highest classification performance and larger diversity on whole were selected to construct a multi-classifier system (MCS) for HCPs. The combination prediction model obtained better performance than models of a single predictor team. 11 typical prediction features for HCPs were extracted on the basis of the MCS. The findings show that both the papers’ inner quality and external features, mainly represented as the reputation of the authors and journals, contribute to generation of HCPs in future.
The inter-citation journal group is defined as a group of journals with inter-citation relations. In this paper, according
to the 2003 JCR, an inter-citation relation matrix of 10 medical journals is established. Based on the transfer function model
of the disturbed citing process, the calculation formula of journal impact factor disturbed by publication delays of certain
journal in the group is deduced and a changing process of every journal's impact factor caused by the increase of each journal's
average publication delay is simulated. In the inter-citation journal group, when a journal's publication delay increase,
impact factors of all journals will be decreased and rankings of journals according to the impact factor may be changed. The
closer a citation relation between two journals, the stronger the interaction of them and the larger the decrease of their
impact factors caused by the increase of their publication delays.
This study investigates the knowledge diffusion patterns of Nanoscience & Nanotechnology (N&N) by analyzing the overall research
interactions between N&N and nano-related subjects through citation analysis. Three perspectives were investigated to achieve
this purpose. Firstly, the overall research interactions were analyzed to identify the dominant driving forces in advancing
the development of N&N. Secondly, the knowledge diffusion intensity between N&N and nano-related subjects was investigated
to determine the areas most closely related to N&N. Thirdly, the diffusion speed was identified to detect the time distance
of knowledge diffusion between N&N and nano-related subjects. The analysis reveals that driving forces from the outside environment
rather than within N&N itself make the foremost contributions to the development of N&N. From 1998 to 2007, Material Science,
Physics, Chemistry, N&N, Electrical & Electronic and Metallurgy & Metallurgical Engineering are the key contributory and reference
subjects for N&N. Knowledge transfer within N&N itself is the quickest. And the speed of knowledge diffusion from other subjects
to N&N is slower than that from N&N to other subjects, demonstrating asymmetry of knowledge diffusion in the development of
N&N. The results indicate that N&N has matured into a relatively open, diffuse and dynamic system of interactive subjects.
Authors:Mingyang Wang, Guang Yu, Shuang An, and Daren Yu
In this paper, the machine learning tools were used to identify key features influencing citation impact. Both the papers’ external and quality information were considered in constructing papers’ feature space. Based on the feature space, the soft fuzzy rough set was used to generate a series of associated feature subsets. Then, the KNN classifier was used to find the feature subset with the best classification performance. The results show that citation impact could be predicted by objectively assessed factors. Both the papers’ quality and external features, mainly represented as the reputation of the first author, are contributed to future citation impact.
Based on the convolution formula of the disturbed aging distribution (Egghe&Rousseau, 2000) and the transfer function model
of the publishing delay process, we establish the transfer function model ofthe disturbed citing process. Using the model,
we make simulative investigations of disturbed citation distributions and impact factors according to different average publication
delays. These simulative results show that the bigger increment the average publication delays in a scientific field, the
larger shift backwards of the citation distribution curves and the more fall the impact factors of journals in the field.
Based on sometheoretical hypotheses, it is shown that there exists theoretically an approximate inverse linear relation between
the field (or discipline) average publication delay and the journal impact factor.