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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.

Crop production management has advanced into the stage of smart agriculture, which is driven by state-of-the-art agricultural information technology, intelligent equipment and massive data resources. Smart agriculture inherits ideas from precision agriculture and brings agricultural production and management from mechanization and informalization to intelligentization with automatization. Precision agriculture has been developed from strategic monitoring operations in the 1980s to tactical monitoring and control operations in the 2010s. In its development, agricultural aviation has played a key role in serving systems for spray application of crop protection and production materials for precision agriculture with the guidance of global navigation through geospatial prescription mapping derived from remotely-sensed data. With the development of modernized agriculture, agricultural aviation is even more important for advancing precision agricultural practices with more efficient soil and plant health sensing and more prompt and effective system actuation and action. This paper overviewed the status of agricultural aviation for precision agriculture to move toward smart agriculture, especially in the Mississippi Delta region, one of the most important agricultural areas in the U.S. The research and development by scientists associated with the Mississippi Delta region are reported. The issues, challenges and opportunities were identified and discussed for further research and development of agricultural aviation technology for next-generation precision agriculture and smart agriculture.

Sediment load in rivers is recognized as both a carrier and a potential source of contaminants. Sediment deposition significantly changes river flow and morphology, thereby affecting stream hydrology and aquatic life. We projected sediment load from the Pearl River basin (PRB), Mississippi into the northern Gulf of Mexico under a future climate with afforestation using the SWAT (Soil and Water Assessment Tool)-based HAWQS (Hydrologic and Water Quality System) model. Three simulation scenarios were developed in this study: (1) the past scenario for estimating the 40-year sediment load from 1981 to 2020; (2) the future scenario for projecting the 40-year sediment load from 2025 to 2064, and (3) the future afforestation scenario that was the same as the future scenario, except for converting the rangeland located in the middle section of the Pearl River watershed of the PRB into the mixed forest land cover. Simulations showed a 16% decrease in sediment load for the future scenario in comparison to the past scenario due to the decrease in future surface runoff. Over both the past and future 40 years, the monthly maximum and minimum sediment loads occurred, respectively, in April and August; whereas the seasonal sediment load followed the order: spring > winter > summer > fall. Among the four seasons, winter and spring accounted for about 86% of sediment load for both scenarios. Under the future 40-year climate conditions, a 10% reduction in annual average sediment load with afforestation was observed in comparison to without afforestation. This study provides new insights into how a future climate with afforestation would affect sediment load into the northern Gulf of Mexico. [ABSTRACT FROM AUTHOR]

Aim: This study aimed to examine the relationship between social support and quality of life in urostomy patients and identify the mediating role of resilience in that relationship. Design: A cross‐sectional design. Methods: Participants included 232 patients who were recruited from a tertiary hospital in Beijing during March 2020 and August 2020. They completed questionnaires about perceived social support, resilience and ostomy‐related quality of life. Structural equation modelling was performed to analyse the data. Results: The mean age of patients was 65.79 (SD = 8.67) years, and the mean length of time after surgery was 42.14 (SD = 15.76) months. Urostomy patients' quality of life, social support and resilience were all above moderate. Social support had a positive direct effect on the quality of life and a positive indirect effect on the quality of life through the mediating role of resilience. [ABSTRACT FROM AUTHOR]

B4C ceramics were prepared by conventional hot pressing (HP) and hot oscillatory pressing (HOP). The results show that at the temperature and pressure studied here, the main densification mechanism of HP is grain‐boundary sliding, whereas that of HOP is plastic deformation caused by dislocation gliding, indicating that the oscillatory pressure promoted plastic deformation of the material. The hardness of B4C prepared by HOP reaches 39.5 GPa, which is much higher than the hardness of sample prepared by HP. The current study suggests that HOP is promising for preparing high‐performance B4C ceramics. [ABSTRACT FROM AUTHOR]