Lignin plays an important role in the formation of stone cells in pears. However, the accumulation of lignin had adverse impact on the flavor and quality of the fruit. A rapid and accurate method for measuring the lig...
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Lignin plays an important role in the formation of stone cells in pears. However, the accumulation of lignin had adverse impact on the flavor and quality of the fruit. A rapid and accurate method for measuring the lignin content of pears is therefore required. An improved variables selection method called 'the bootstrapping soft shrinkage combined with frequency and regression coefficient of variables (FRCBOSS)' was therefore developed based on 'the bootstrapping soft shrinkage (BOSS)'technique, to identify the characteristic wavelengths of near-infrared (NIR) spectra for non-destructive and rapid analysis of lignin. Sub-models were generated by weighted bootstrap sampling (WBS) in the FRCBOSS method. For the BOSS method, the new weights of variables were determined only as the absolute values of regression coefficients of variables in each iteration. In contrast, the FRCBOSS method also considers the frequency of variables in variable space. Moreover, the FRCBOSS algorithm overcomes the disadvantage of BOSS in selecting variables, which could incorporate useful wavelengths that would otherwise be removed by the BOSS method. In addition, a range of different pre-treatment methods were used for comparison in the detection of lignin in the Snow pears. These include Savitzky-Golay Smoothing (SG), Normalization (NORM), Standard Normal Variate (SNV), and 1st Derivative (D1), as well as a combination of these methods and the different variables selection method (SiPLS, SiPLS-SPA, SiPLS-CARS, SiPLS-BOSS, and SiPLS-FRCBOSS). The number of variables selected by FRCBOSS was a little larger than that selected by BOSS. The partial least square regression (PLSR) model based on the 19 variables selected by SiPLS-FRCBOSS method had the best prediction ability, with a Rp value of prediction of 0.880 and a RMSEP value of 1.004%. We conclude that NIR diffuse reflectance spectroscopy technology combined with FRC-BOSS is an accurate and useful tool for the non-destructive and rapid det
Theheat injection during depressurization is a crucial technique for sustaining the long-term gas production rate of natural gas hydrates. To analyze the production patterns and mechanisms of hydrate dissociation und...
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In the process of extracting naturally-occurring oceanic gas hydrates, the dissociation of hydrates can causea reduction in soil strength. This reduction has the potential to trigger slope failure and submarine landsl...
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ISBN:
(纸本)9781959025610
In the process of extracting naturally-occurring oceanic gas hydrates, the dissociation of hydrates can causea reduction in soil strength. This reduction has the potential to trigger slope failure and submarine landslides,which present a catastrophic threat to offshore facilities and hydrate production. This research aims to create a robust and accurate machine-learning model that can efficiently predict stability of submarine continental slopes where gas hydrates are widespread. By collecting and analyzing 144 relevant cases, a comprehensive dataset was constructed, incorporating slope basic data, overlying and underlying layer data, and geological parameters of the hydrate layer. After conducting a correlation coefficient analysis between the characteristic parameters of the dataset and the output, the key characteristic parameters were determined. To model the dataset and assess its performance, four machine learning techniques were employed: Random Forest(RF), XGBoost, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). Formation physical parameters, geotechnical parameters, and NGH parameters were taken as input parameters, and the stability of NGH slopes was taken as prediction indicator. Evaluation metrics such as the ROC and confusion matrix were employed to comprehensively evaluate these models’ classification ability. Among these algorithms,the RF algorithm achieves the best prediction accuracy and AUC value, demonstrating its potential in submarine continental slopes stability prediction of natural gas hydrates. Additionally, sensitive analysis using Gini impurity calculations revealed that hydrate decomposition degree is the most significant factor affecting slope stability, followed by the burial depth and thickness of the hydrate layer. The slope angle,cohesion, and internal friction angle also have significant impacts. This study provides a new perspective for predicting submarine continental slopes stability with NGH and offers a scientific
In this present study, a series of 5-phenyl-2-furan and 4-phenyl-2-oxazole derivatives were designed and synthesized as phosphodiesterase type 4 (PDE4) inhibitors. In vitro results showed that the synthesized compound...
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In this present study, a series of 5-phenyl-2-furan and 4-phenyl-2-oxazole derivatives were designed and synthesized as phosphodiesterase type 4 (PDE4) inhibitors. In vitro results showed that the synthesized compounds exhibited considerable inhibitory activity against PDE4B and blockade of LPS-induced TNF-alpha release. Among the designed compounds, Compound 5j exhibited lower IC50 value (1.4 mu M) against PDE4 than parent rolipram (2.0 mu M) in in vitro enzyme assay, which also displayed good in vivo activity in animal models of asthma/COPD and sepsis induced by LPS. Docking results suggested that introduction of methoxy group at para-position of phenyl ring, demonstrated good interaction with metal binding pocket domain of PDE4B, which was helpful to enhance inhibitory activity. (C) 2020 Elsevier Masson SAS. All rights reserved.
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