Rock classification plays an important role in rock mechanics, petrology, mining engineering, magmatic processes, and numerous other fields pertaining to geosciences. This study proposes a concatenated convolutional neural network (Con-CNN) method for classifying the geologic rock type based on petrographic thin sections. Herein, plane polarized light (PPL) and crossed polarized light (XPL) were used to acquire thin section images as the fundamental data. After conducting the necessary pre-processing analyses, the PPL and XPL images as well as their comprehensive image (CI) were incorporated in three convolutional neural networks (CNNs) comprising the same structure for achieving a preliminary classification; these images were developed by employing the fused principal component analysis (PCA). Subsequently, the results of the CNNs were concatenated by using the maximum likelihood detection to obtain a comprehensive classification result. Finally, a statistical revision was applied to fix the misclassification due to the proportion difference of minerals that were similar in appearance. In this study, 13 types of 92 rock samples, 196 petrographic thin sections, 588 images, and 63504 image patches were fabricated for the training and validation of the Con-CNN. The five-folds cross validation shows that the method proposed provides an overall accuracy of 89.97%, which facilitates the automation of rock classification in petrographic thin sections.
Government funding is a key scientific research resource, and it has made a concrete contribution to the world's science and technology development. But these funds come from common taxpayers, so we need to evaluate the effectiveness of these funds. Generally speaking, policymakers adopt the method of peer review to make assessments. Compared to kinds of shortcomings of peer review, the paper here proposed the benchmarking evaluation method based on the academic publication outputs of supporting funds, mainly guiding indicators from scientometrics. At first, with the academic publication output extracted from the concluding report project manager submitted to the government after the fund finished, we designed the analysis framework to search and define the research field the fund belonged to. And then from the following three perspectives, including quantity, quality and relative influence, we compared the research fund output to the field output. Later, we took one fund program from national program on key basic research project of China (973 Program) in the field of quantum physics as an example to make an empirical analysis to demonstrate its effectiveness. At last, we found that the funded program performance was superior to the field, and even about 11.65% of the research achievement reaches the top 1/1000 of the world, but the research was lacking in remarkable papers, so it needs further improvement.
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