有组织科研团队建设有赖于对科研合作现象和规律的科学认识。常用于科研合作模式研究的合著者网络默认同一成果的合作者间贡献均等,但这通常与科研合作实践相左。作者贡献声明数据的出现为揭示更细粒度的合作实践提供了重要素材。为此,...有组织科研团队建设有赖于对科研合作现象和规律的科学认识。常用于科研合作模式研究的合著者网络默认同一成果的合作者间贡献均等,但这通常与科研合作实践相左。作者贡献声明数据的出现为揭示更细粒度的合作实践提供了重要素材。为此,本研究提出一种利用贡献声明数据构建的新型合作网络——合贡献者网络,为深入研究科研合作问题提供新工具。本研究以PLoS(Public Library of Science)上的药学论文数据为例,以合著者网络为基准,从合贡献者网络的网络结构特征入手,认识此新型合作网络的物理性质;选取当前重要研究方向之一的“合作群体识别”为切入点,进一步认识合贡献者网络的应用价值。研究结果表明:①在网络结构形态上,合贡献者网络比合著者网络更稀疏;②在合作群体识别上,两种网络的群体识别结果部分一致,重合度约为57%;约32%的合作群体在合贡献者网络上发生了重组;③合贡献者网络中的合作群体发文主题比合著者网络更为聚焦,但检验结果并不显著。总体来看,在本研究的数据集上,合贡献者网络较之合著者网络显示出更良好的社区结构;合贡献者网络有助于识别出更细粒度的合作群体,且在所识别的合作群体上发文主题的一致性更高。展开更多
Accurate estimation of liquid thermal conductivity is highly necessary to appropriately design equipments in different industries. Respect to this necessity, in the current investigation a feed-forward artificial neur...Accurate estimation of liquid thermal conductivity is highly necessary to appropriately design equipments in different industries. Respect to this necessity, in the current investigation a feed-forward artificial neural network(ANN) model is examined to correlate the liquid thermal conductivity of normal and aromatic hydrocarbons at the temperatures range of 257–338 K and atmospheric pressure. For this purpose, 956 experimental thermal conductivities for normal and aromatic hydrocarbons are collected from different previously published literature.During the modeling stage, to discriminate different substances, critical temperature(Tc), critical pressure(Pc)and acentric factor(ω) are utilized as the network inputs besides the temperature. During the examination, effects of different transfer functions and number of neurons in hidden layer are investigated to find the optimum network architecture. Besides, statistical error analysis considering the results obtained from available correlations and group contribution methods and proposed neural network is performed to reliably check the feasibility and accuracy of the proposed method. Respect to the obtained results, it can be concluded that the proposed neural network consisted of three layers namely, input, hidden and output layers with 22 neurons in hidden layer was the optimum ANN model. Generally, the proposed model enables to correlate the thermal conductivity of normal and aromatic hydrocarbons with absolute average relative deviation percent(AARD), mean square error(MSE), and correlation coefficient(R^2) of lower than 0.2%, 1.05 × 10^(-7) and 0.9994, respectively.展开更多
文摘有组织科研团队建设有赖于对科研合作现象和规律的科学认识。常用于科研合作模式研究的合著者网络默认同一成果的合作者间贡献均等,但这通常与科研合作实践相左。作者贡献声明数据的出现为揭示更细粒度的合作实践提供了重要素材。为此,本研究提出一种利用贡献声明数据构建的新型合作网络——合贡献者网络,为深入研究科研合作问题提供新工具。本研究以PLoS(Public Library of Science)上的药学论文数据为例,以合著者网络为基准,从合贡献者网络的网络结构特征入手,认识此新型合作网络的物理性质;选取当前重要研究方向之一的“合作群体识别”为切入点,进一步认识合贡献者网络的应用价值。研究结果表明:①在网络结构形态上,合贡献者网络比合著者网络更稀疏;②在合作群体识别上,两种网络的群体识别结果部分一致,重合度约为57%;约32%的合作群体在合贡献者网络上发生了重组;③合贡献者网络中的合作群体发文主题比合著者网络更为聚焦,但检验结果并不显著。总体来看,在本研究的数据集上,合贡献者网络较之合著者网络显示出更良好的社区结构;合贡献者网络有助于识别出更细粒度的合作群体,且在所识别的合作群体上发文主题的一致性更高。
文摘Accurate estimation of liquid thermal conductivity is highly necessary to appropriately design equipments in different industries. Respect to this necessity, in the current investigation a feed-forward artificial neural network(ANN) model is examined to correlate the liquid thermal conductivity of normal and aromatic hydrocarbons at the temperatures range of 257–338 K and atmospheric pressure. For this purpose, 956 experimental thermal conductivities for normal and aromatic hydrocarbons are collected from different previously published literature.During the modeling stage, to discriminate different substances, critical temperature(Tc), critical pressure(Pc)and acentric factor(ω) are utilized as the network inputs besides the temperature. During the examination, effects of different transfer functions and number of neurons in hidden layer are investigated to find the optimum network architecture. Besides, statistical error analysis considering the results obtained from available correlations and group contribution methods and proposed neural network is performed to reliably check the feasibility and accuracy of the proposed method. Respect to the obtained results, it can be concluded that the proposed neural network consisted of three layers namely, input, hidden and output layers with 22 neurons in hidden layer was the optimum ANN model. Generally, the proposed model enables to correlate the thermal conductivity of normal and aromatic hydrocarbons with absolute average relative deviation percent(AARD), mean square error(MSE), and correlation coefficient(R^2) of lower than 0.2%, 1.05 × 10^(-7) and 0.9994, respectively.
