Cohesion is an important soil strength parameter for the overall structure and quality of building foundations as well as slope stability. For a civil engineering project, cohesion (c) can be determined directly from ...Cohesion is an important soil strength parameter for the overall structure and quality of building foundations as well as slope stability. For a civil engineering project, cohesion (c) can be determined directly from mainly unconfined compression tests, direct shear tests, and triaxial tests of soil. However, it’s quite challenging to collect soil samples as there are time and cost constraints, as well as a diversity of soil deposits. Hence, this research aims to demonstrate a simplified method in order to determine the strength parameter of cohesive soil. Here, we propose an alternative solution adopting statistical correlations and machine learning techniques to establish correlations between the liquid limit, plastic limit, moisture content and %fine of soil with the strength parameter. In laboratory testing, these parameters can be obtained easily and these tests are relatively simple, quick to perform and also comparatively inexpensive. Hence, several test results were used from 100 boreholes which were soft soil or silty clay-type soil. Using the collected in-situ and lab test results of soil samples, a Multiple Linear Regression (MLR), Random Forest Regression (RFR) and Machine Learning (ML) model will be developed to establish a relationship between cohesion and the available test results. In order to assess the performances of both models, several performance indicators like: correlation coefficient (R<sup>2</sup>), mean squared error (MSE), root mean square error (RMSE), and mean average error (MAE) will be used. These correlation coefficients will be used to demonstrate the prediction capacity and accuracy of both models. It should be noted that this approach will substitute the conventional testing required for strength parameters, which is both expensive and time-consuming.展开更多
Excessive accumulation of zinc(Zn)in urban soil can lead to environmental pollution and pose a potential threat to human health and the ecosystem.How to quickly and accurately monitor the urban soil zinc content on a ...Excessive accumulation of zinc(Zn)in urban soil can lead to environmental pollution and pose a potential threat to human health and the ecosystem.How to quickly and accurately monitor the urban soil zinc content on a large scale in real time and dynamically is crucial.Hyperspectral remote sensing technology provides a new method for rapid and nondestructive soil property detection.The main goal of this study is to find an optimal combination of spectral transformation and a hyperspectral estimation model to predict the Zn content in urban soil.A total of 88 soil samples were collected to obtain the Zn contents and related hyperspectral data,and perform 18 transformations on the original spectral data.Then,select important wavelengths by Pearson’s correlation coefficient analysis(PCC)and CARS.Finally,establish a partial least squares regression model(PLSR)and random forest regression model(RFR)with soil Zn content and important wavelengths.The results indicated that the average Zn content of the collected soil samples is 60.88 mg/kg.Pearson’s correlation coefficient analysis(PCC)and CARS for the original and transformed wavelengths can effectively improve the correlations between the spectral data and soil Zn content.The number of important wavelengths selected by CARS is less than the important wavelengths selected by PCC.Partial least squares regression model based on first-order differentiation of the reciprocal by CARS(CARS-RTFD-PLSR)is more stable 2 and has the highest prediction ability(R=0.937,RMSE=8.914,MAE=2.735,RPD=3.985).The CARS-RTFD-PLSR method can be used as a means of prediction of Zn content in soil in oasis cities.The results of the study can provide technical support for the hyperspectral estimation of the soil Zn content.展开更多
Among 174 patients with Wolff-Parkinson-White syndrome, 13(17.5%) had multiple atrioventricular(AV) accessory' pathway. Twenty-one of the 29 pathways' were identified by baseline electrophysiologic study and e...Among 174 patients with Wolff-Parkinson-White syndrome, 13(17.5%) had multiple atrioventricular(AV) accessory' pathway. Twenty-one of the 29 pathways' were identified by baseline electrophysiologic study and eight were found after the radiofrequency ablation of the initial ones. Bilateral ablation approach was tried in four patients and unilateral approach in nine. Twenty-seven of the pathways (93.1%) were eliminated permanently after a mean of 32±14 pulses of current delivery. There were no complications. The pulses was more and the procedure took more time (3.6±0.8hr) in the group compared to those in single pathway ablation (14±11 and 2.1±0.8 hr respectively, P<0.05). The success rate was identical in the two patient populations (93.1% vs 94.0%, P>0.05). However, the recurrence in multiple pathway ablation(7.6%) was more than that in single pathway ablation (1.9%, P<0.01). Conclusion: radiofrequency catheter ablation is a safe and effective modality for tachyarrhythmias mediated by multiple accessory pathways.展开更多
目的:建立注射用苦碟子HPLC数字化指纹图谱,为注射用苦碟子质量控制提供依据。方法:采用反相HPLC法分析注射用苦碟子水溶液,以Century SIL BDS柱(250mm×4.6mm,5μm),流动相为1%醋酸水-1%醋酸乙腈低压梯度洗脱,检测波长265nm,柱温(3...目的:建立注射用苦碟子HPLC数字化指纹图谱,为注射用苦碟子质量控制提供依据。方法:采用反相HPLC法分析注射用苦碟子水溶液,以Century SIL BDS柱(250mm×4.6mm,5μm),流动相为1%醋酸水-1%醋酸乙腈低压梯度洗脱,检测波长265nm,柱温(30±0.15)℃,进样量20μL。用"中药色谱指纹图谱超信息特征数字化评价系统3.0"软件计算不同批次的注射用苦碟子HPLC指纹图谱的色谱指纹图谱指数F和色谱指纹图分离量指数RF等42个参数进行潜信息特征数字化评价。以双参照物体系结合定量结构-性质相关(QSPR)原理标定指纹峰的表观分子量、峰位、洗脱动量数δ、折合相对积分φ。结果:以咖啡酸峰为参照物峰,确定21个共有峰,建立了注射用苦碟子HPLC数字化指纹图谱,获得了判别注射用苦碟子质量的重要数字化信息。以双定性双定量相似度法评价注射用苦碟子批间质量稳定。结论:所建立HPLC数字化指纹图谱具有较好的精密度和重现性,适用于注射用苦碟子的质量控制。数字化指纹图谱是数字中药的核心技术,双定性双定量相似度法是宏观定性定量评价中药质量的最准确和最佳技术。展开更多
文摘Cohesion is an important soil strength parameter for the overall structure and quality of building foundations as well as slope stability. For a civil engineering project, cohesion (c) can be determined directly from mainly unconfined compression tests, direct shear tests, and triaxial tests of soil. However, it’s quite challenging to collect soil samples as there are time and cost constraints, as well as a diversity of soil deposits. Hence, this research aims to demonstrate a simplified method in order to determine the strength parameter of cohesive soil. Here, we propose an alternative solution adopting statistical correlations and machine learning techniques to establish correlations between the liquid limit, plastic limit, moisture content and %fine of soil with the strength parameter. In laboratory testing, these parameters can be obtained easily and these tests are relatively simple, quick to perform and also comparatively inexpensive. Hence, several test results were used from 100 boreholes which were soft soil or silty clay-type soil. Using the collected in-situ and lab test results of soil samples, a Multiple Linear Regression (MLR), Random Forest Regression (RFR) and Machine Learning (ML) model will be developed to establish a relationship between cohesion and the available test results. In order to assess the performances of both models, several performance indicators like: correlation coefficient (R<sup>2</sup>), mean squared error (MSE), root mean square error (RMSE), and mean average error (MAE) will be used. These correlation coefficients will be used to demonstrate the prediction capacity and accuracy of both models. It should be noted that this approach will substitute the conventional testing required for strength parameters, which is both expensive and time-consuming.
基金funded by the National Natural Science Foundation of China(No.U2003301)the Tianshan Talent Training Project of Xinjiang.
文摘Excessive accumulation of zinc(Zn)in urban soil can lead to environmental pollution and pose a potential threat to human health and the ecosystem.How to quickly and accurately monitor the urban soil zinc content on a large scale in real time and dynamically is crucial.Hyperspectral remote sensing technology provides a new method for rapid and nondestructive soil property detection.The main goal of this study is to find an optimal combination of spectral transformation and a hyperspectral estimation model to predict the Zn content in urban soil.A total of 88 soil samples were collected to obtain the Zn contents and related hyperspectral data,and perform 18 transformations on the original spectral data.Then,select important wavelengths by Pearson’s correlation coefficient analysis(PCC)and CARS.Finally,establish a partial least squares regression model(PLSR)and random forest regression model(RFR)with soil Zn content and important wavelengths.The results indicated that the average Zn content of the collected soil samples is 60.88 mg/kg.Pearson’s correlation coefficient analysis(PCC)and CARS for the original and transformed wavelengths can effectively improve the correlations between the spectral data and soil Zn content.The number of important wavelengths selected by CARS is less than the important wavelengths selected by PCC.Partial least squares regression model based on first-order differentiation of the reciprocal by CARS(CARS-RTFD-PLSR)is more stable 2 and has the highest prediction ability(R=0.937,RMSE=8.914,MAE=2.735,RPD=3.985).The CARS-RTFD-PLSR method can be used as a means of prediction of Zn content in soil in oasis cities.The results of the study can provide technical support for the hyperspectral estimation of the soil Zn content.
文摘Among 174 patients with Wolff-Parkinson-White syndrome, 13(17.5%) had multiple atrioventricular(AV) accessory' pathway. Twenty-one of the 29 pathways' were identified by baseline electrophysiologic study and eight were found after the radiofrequency ablation of the initial ones. Bilateral ablation approach was tried in four patients and unilateral approach in nine. Twenty-seven of the pathways (93.1%) were eliminated permanently after a mean of 32±14 pulses of current delivery. There were no complications. The pulses was more and the procedure took more time (3.6±0.8hr) in the group compared to those in single pathway ablation (14±11 and 2.1±0.8 hr respectively, P<0.05). The success rate was identical in the two patient populations (93.1% vs 94.0%, P>0.05). However, the recurrence in multiple pathway ablation(7.6%) was more than that in single pathway ablation (1.9%, P<0.01). Conclusion: radiofrequency catheter ablation is a safe and effective modality for tachyarrhythmias mediated by multiple accessory pathways.
文摘目的:建立注射用苦碟子HPLC数字化指纹图谱,为注射用苦碟子质量控制提供依据。方法:采用反相HPLC法分析注射用苦碟子水溶液,以Century SIL BDS柱(250mm×4.6mm,5μm),流动相为1%醋酸水-1%醋酸乙腈低压梯度洗脱,检测波长265nm,柱温(30±0.15)℃,进样量20μL。用"中药色谱指纹图谱超信息特征数字化评价系统3.0"软件计算不同批次的注射用苦碟子HPLC指纹图谱的色谱指纹图谱指数F和色谱指纹图分离量指数RF等42个参数进行潜信息特征数字化评价。以双参照物体系结合定量结构-性质相关(QSPR)原理标定指纹峰的表观分子量、峰位、洗脱动量数δ、折合相对积分φ。结果:以咖啡酸峰为参照物峰,确定21个共有峰,建立了注射用苦碟子HPLC数字化指纹图谱,获得了判别注射用苦碟子质量的重要数字化信息。以双定性双定量相似度法评价注射用苦碟子批间质量稳定。结论:所建立HPLC数字化指纹图谱具有较好的精密度和重现性,适用于注射用苦碟子的质量控制。数字化指纹图谱是数字中药的核心技术,双定性双定量相似度法是宏观定性定量评价中药质量的最准确和最佳技术。
