Authors:Guang-Heng Dong, Ziliang Wang, Haohao Dong, Min Wang, Yanbin Zheng, Shuer Ye, Jialin Zhang, and Marc N. Potenza
that can be identified using machinelearning using neurobiological data. Second, although MVPA can differentiate IGD from RGU subjects at a more stringent diagnostic threshold, the accuracy rates were approximately 60%, which are higher than those
In this Section of the journal, the literature on continuous flow synthesis (primarily organic synthesis and functional materials) from the period of October — December 2016 is presented. All the publications are listed ordered by journal name, with two Review articles appearing at the end. In this quarter the number of papers on continuous flow organic synthesis is relatively less as a few special issues are planned in the coming months. Two contributions on machine learning for optimization in flow synthesis and the scale-up of continuous flow reactors from Eli Lilly are the real highlights of this quarter!
Authors:Ádám Pintér, Balázs Schmuck, and Sándor Szénási
The aim of this paper is to present a technique, which uses machine learning to process the short text answers with Hungarian language. The processing is based on a special neural network, the convolutional neural network, which can efficiently process short text answer. To achieve precise classification for training and recall grammatically consistent answers and the conversion of the text to the input are inevitable. To convert the input, continuous bag of words and Skip-Gram models will be used, resulting in a model that will be able to evaluate the Hungarian short text answers.
Authors:Shlomo Argamon, Jeff Dodick, and Paul Chase
Recently, philosophers of science have argued that the epistemological requirements of different scientific fields lead necessarily
to differences in scientific method. In this paper, we examine possible variation in how language is used in peer-reviewed
journal articles from various fields to see if features of such variation may help to elucidate and support claims of methodological
variation among the sciences. We hypothesize that significant methodological differences will be reflected in related differences
in scientists’ language style.
This paper reports a corpus-based study of peer-reviewed articles from twelve separate journals in six fields of experimental
and historical sciences. Machine learning methods were applied to compare the discourse styles of articles in different fields,
based on easily-extracted linguistic features of the text. Features included function word frequencies, as used often in computational
stylistics, as well as lexical features based on systemic functional linguistics, which affords rich resources for comparative
textual analysis. We found that indeed the style of writing in the historical sciences is readily distinguishable from that
of the experimental sciences. Furthermore, the most significant linguistic features of these distinctive styles are directly
related to the methodological differences posited by philosophers of science between historical and experimental sciences,
lending empirical weight to their contentions.
During the production of gas one of the major problems is the formation of hydrate crystals in the pipeline. The growing hydrate crystals can form hydrate plugs in the pipeline. The hydrate plug effect lengthens production outages and results in the loss of money of the maintainer, because the removal of the plug is a time consuming procedure. One of the solutions used to prevent hydrate formation is the addition of modern compositions to the gas flow. The modern compositions help to dehydrate the gas, thus, the size of hydrate crystal does not increase. The substances, used in low concentrations, have to be locally injected at the gas well sites. Thus, an injector unit is required for this purpose. The production-related aspect that the consumers expect much more flexibility from gas provider cannot be neglected because of the habits of the users and the appearance of energy-saving technologies are different. The first part of the article a newly developed injection system is introduced. To achieve optimal dosage, not only the hardware of injection system is important, but also the software. In addition to the traditional control, a preventive inhibitor dosing system can be developed, based on model driven system. The nature of the model highly influences the quality of control system. In the second part of the article a machine learning based predictive detection system is introduced