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Purpose: Predicting highly cited papers can enable an evaluation of the potential of papers and the early detection and determination of academic achievement value. However, most highly cited paper prediction studies consider early citation information, so predicting highly cited papers by publication is challenging. Therefore, the authors propose a method for predicting early highly cited papers based on their own features. Design/methodology/approach: This research analyzed academic papers published in the Journal of the Association for Computing Machinery (ACM) from 2000 to 2013. Five types of features were extracted: paper features, journal features, author features, reference features and semantic features. Subsequently, the authors applied a deep neural network (DNN), support vector machine (SVM), decision tree (DT) and logistic regression (LGR), and they predicted highly cited papers 1–3 years after publication. Findings: Experimental results showed that early highly cited academic papers are predictable when they are first published. The authors' prediction models showed considerable performance. This study further confirmed that the features of references and authors play an important role in predicting early highly cited papers. In addition, the proportion of high-quality journal references has a more significant impact on prediction. Originality/value: Based on the available information at the time of publication, this study proposed an effective early highly cited paper prediction model. This study facilitates the early discovery and realization of the value of scientific and technological achievements. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to predict a suitable paper substrate which has high capillary pressure with the tendency of subsequent fluid wrenching in onward direction for the fabrication of microfluidics device application. Design/methodology/approach: The experiment has been done on the WhatmanTM grade 1, WhatmanTM chromatography and nitrocellulose paper samples which are made by GE Healthcare Life Sciences. The structural characterization of paper samples for surface properties has been done by scanning electron microscope and ImageJ software. Identification of functional groups on the surface of samples has been done by Fourier transform infrared analysis. A finite elemental analysis has also been performed by using the "Multiphase Flow in Porous Media" module of the COMSOL Multiphysics tool which combines Darcy's law and Phase Transport in Porous Media interface. Findings: Experimentally, it has been concluded that the paper substrate for flexible microfluidic device application must have large number of internal (intra- and interfiber) pores with fewer void spaces (external pores) that have high capillary pressure to propel the fluid in onward direction with narrow paper fiber channel. Originality/value: Surface structure has a dynamic impact in paper substrate utilization in multiple applications such as paper manufacturing, printing process and microfluidics applications. [ABSTRACT FROM AUTHOR]