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An English-Bengali machine translation (MT) application can convert an English text into a corresponding Bengali translation. To build a better model for this task, we can optimize English-Bengali MT. MT for languages with rich resources, like English-German, started decades ago. However, MT for languages lacking many parallel corpora remains challenging. In our study, we employed back-translation to improve the translation accuracy. With back-translation, we can have a pseudo-parallel corpus, and the generated (pseudo) corpus can be added to the original dataset to obtain an augmented dataset. However, the new data can be regarded as noisy data because they are generated by models that may not be trained very well or not evaluated well, like human translators. Since the original output of a translation model is a probability distribution of candidate words, to make the model more robust, different decoding methods are used, such as beam search, top-k random sampling and random sampling with temperature T, and others. Notably, top-k random sampling and random sampling with temperature T are more commonly used and more optimal decoding methods than the beam search. To this end, our study compares LSTM (Long-Short Term Memory, as a baseline) and Transformer. Our results show that Transformer (BLEU: 27.80 in validation, 1.33 in test) outperforms LSTM ( 3.62 in validation, 0.00 in test) by a large margin in the English-Bengali translation task. (Evaluating LSTM and Transformer without any augmented data is our baseline study.) We also incorporate two decoding methods, top-k random sampling and random sampling with temperature T, for back-translation that help improve the translation accuracy of the model. The results show that data generated by back-translation without top-k or temperature sampling ("no strategy") help improve the accuracy (BLEU 38.22 , + 10.42 on validation, 2.07 , + 0.74 on test). Specifically, back-translation with top-k sampling is less effective ( k = 10 , BLEU 29.43 , + 1.83 on validation, 1.36 , + 0.03 on test), while sampling with a proper value of T, T = 0.5 makes the model achieve a higher score ( T = 0.5 , BLEU 35.02 , + 7.22 on validation, 2.35 , + 1.02 on test). This implies that in English-Bengali MT, we can augment the training set through back-translation using random sampling with a proper temperature T. [ABSTRACT FROM AUTHOR]

Salvia miltiorrhiza is one of the famous traditional Chinese medicines for treating cardiovascular and cerebrovascular diseases. Tanshinone and phenolic acids are the main active compounds of Salvia miltiorrhiza, whereas the distribution patterns of the two kinds of components are still unclear among Salvia spp. In this work, high-performance liquid chromatography was applied to analyze the distribution patterns of major components in the roots and leaves of 58 Salvia spp. The results showed that the distribution patterns of tanshinone and phenolic acids in Salvia spp. varied significantly. Phenolic acid components such as rosmarinus acid, caffeic acid, and danshensu are widely distributed in the roots and leaves, and phenolic acids in the leaves of Salvia spp. are generally higher than that in roots. Tanshinones are mainly detected in the roots of Salvia przewalskii, Salvia trijuga, Salvia castanea, and Salvia yunnanensis. The content of major components of the different species varied significantly. The content of phenolic acids in most Salvia spp. generally followed the pattern of salvianolic acid B > rosmarinic acid > danshensu > caffeic acid both in the roots and leaves. Tanshinone IIA and cryptotanshinone were the main lipophilic components of Salvia spp. distributed in southwest China. A correlation between the distribution pattern of tanshinone and the genetic relationship of species was indicated in the work. This research systematically reveals the distribution patterns of tanshinone and phenolic acids in Salvia spp., providing a theoretical basis for the development and utilization of medicinal resources of Salvia. [ABSTRACT FROM AUTHOR]

Coal mine is a complex system engineering, technical links are increasingly complex, variables are numerous, specialized division of labor is refined. Technical management is the key to solve the hidden danger and technical bottleneck in the production process of coal enterprises, which directly affects the safe development of enterprises. We expound the technology of coal mine based on the perspective of business and its decision-making purview division, management system, and put forward the organization system, business system, process, system, standard system and examination system, the architecture of the proposed business and technology management system principle, control technology for coal mine management provides a way to construct architecture clear and responsibilities clear.