The water entry problem of an asymmetric wedge with roll motion was analyzed by the method of a modified Logvinovich model (MLM). The MLM is a kind of analytical model based on the Wagner method, which linearizes the ...The water entry problem of an asymmetric wedge with roll motion was analyzed by the method of a modified Logvinovich model (MLM). The MLM is a kind of analytical model based on the Wagner method, which linearizes the free surface condition and body boundary condition. The difference is that the MLM applies a nonlinear Bernoulli equation to obtain pressure distribution, which has been proven to be helpful to enhance the accuracy of hydrodynamic loads. The Wagner condition in this paper was generalized to solve the problem of the water entry of a wedge body with rotational velocity. The comparison of wet width between the MLM and a fully nonlinear numerical approach was given, and they agree well with each other. The effect of angular velocity on the hydrodynamic loads of a wedge body was investigated.展开更多
The U.S. EPA （Environmental Protection Agency） established the CASTNET （Clean Air Status and Trends Network） and its predecessor, the NDDN （national dry deposition network）, as national air quality and meteorolo...The U.S. EPA （Environmental Protection Agency） established the CASTNET （Clean Air Status and Trends Network） and its predecessor, the NDDN （national dry deposition network）, as national air quality and meteorological monitoring networks. Both CASTNET and NDDN were designed to measure concentrations of sulfur and nitrogen gases and particles. Both networks also estimate dry deposition using an inferential model. The design was based on the concept that atmospheric dry deposition flux could be estimated as the product of a measured air pollutant concentration and a modeled deposition velocity （Vd）. The MLM （multi-layer model）, the computer model used to simulate dry deposition, requires information on meteorological conditions and vegetative cover as model input. The MLM calculates hourly Fa for each pollutant, but any missing meteorological data for an hour renders Vd missing for that hour. Because of percent completeness requirements for aggregating data for long-term estimates, annual deposition rates for some sites are not always available primarily because of missing or invalid meteorological input data. In this work, three methods for replacing missing on-site measurements are investigated. These include （1） using historical values of deposition velocity or （2） historical meteorological measurements from the site being modeled or （3） current meteorological data from nearby sites to substitute for missing inputs and thereby improve data completeness for the network＇s dry deposition estimates. Results for a CASTNET site used to test the methods show promise for using historical measurements of weekly average meteorological parameters.展开更多
The performance of deep learning models is heavily reliant on the quality and quantity of train-ing data.Insufficient training data will lead to overfitting.However,in the task of alert-situation text classification,i...The performance of deep learning models is heavily reliant on the quality and quantity of train-ing data.Insufficient training data will lead to overfitting.However,in the task of alert-situation text classification,it is usually difficult to obtain a large amount of training data.This paper proposes a text data augmentation method based on masked language model(MLM),aiming to enhance the generalization capability of deep learning models by expanding the training data.The method em-ploys a Mask strategy to randomly conceal words in the text,effectively leveraging contextual infor-mation to predict and replace masked words based on MLM,thereby generating new training data.Three Mask strategies of character level,word level and N-gram are designed,and the performance of each Mask strategy under different Mask ratios is analyzed and studied.The experimental results show that the performance of the word-level Mask strategy is better than the traditional data augmen-tation method.展开更多
To find the quantitative trait loci associated with wood density in teak(Tectona grandis L.f.), 21 co-dominant markers including 13 site specific recombinase and 8 EST-based co-dominant markers designed from lignin bi...To find the quantitative trait loci associated with wood density in teak(Tectona grandis L.f.), 21 co-dominant markers including 13 site specific recombinase and 8 EST-based co-dominant markers designed from lignin biosynthesis genes were applied to 174 teak plus tree clones at the National Germplasm Bank, Chandrapur,India. The germplasm bank exhibited 10.6% coefficient of variation for wood densities with 84.5 ± 31.3 genetic polymorphism(%). The highly panmictic set of genotypes(FST= 0.035 ± 0.004) harbored 96.47 ± 0.40 genetic variability(%). The average allelic frequency of the 21 codominant markers was 0.65 ± 0.11 with 12.9% pairs of loci in significant LD(p\0.05, R^2 values [ 0.1), confirming their suitability for a strong marker-trait association study. The marker CCoAMT-1 was significantly(p\0.01) associated with wood density showing stability by both GLM and MLM models and explained 4.3% of the phenotypic effect. The marker from the EST representing CCoAMT can be further developed for gene-assisted selection of elite genotypes of teak with greater wood density. Therefore, we believe that the report will help accelerate the genetic improvement and advance the breeding program of the species.展开更多
Background and aims:Noninvasive predictors of choledocholithiasis have generally exhibited marginal performance characteristics.We aimed to identify noninvasive independent predictors of endoscopic retrograde cholangi...Background and aims:Noninvasive predictors of choledocholithiasis have generally exhibited marginal performance characteristics.We aimed to identify noninvasive independent predictors of endoscopic retrograde cholangiopancreatography(ERCP)-confirmed choledocholithiasis and accordingly developed predictive machine learning models(MLMs).Methods:Clinical data of consecutive patients undergoing first-ever ERCP for suspected chol-edocholithiasis from 2015 to 2019 were abstracted from a prospectively-maintained database.Multiple logistic regression was used to identify predictors of ERCP-confirmed choledocholithiasis.MLMs were then trained to predict ERCP-confirmed choledocholithiasis using pre-ERCP ultrasound(US)imaging only as well as using all available noninvasive imaging(US,computed tomography,and/or magnetic reso-nance cholangiopancreatography).The diagnostic performance of American Society for Gastrointestinal Endoscopy(ASGE)“high-likelihood”criteria was compared to MLMs.Results:We identified 270 patients(mean age 46 years,62.2%female,73.7%Hispanic/Latino,59%with noninvasive imaging positive for choledocholithiasis)with native papilla who underwent ERCP for suspected choledocholithiasis,of whom 230(85.2%)were found to have ERCP-confirmed chol-edocholithiasis.Logistic regression identified choledocholithiasis on noninvasive imaging(odds ratio(OR)¼3.045,P¼0.004)and common bile duct(CBD)diameter on noninvasive imaging(OR¼1.157,P¼0.011)as predictors of ERCP-confirmed choledocholithiasis.Among the various MLMs trained,the random forest-based MLM performed best;sensitivity was 61.4%and 77.3%and specificity was 100%and 75.0%,using US-only and using all available imaging,respectively.ASGE high-likelihood criteria demonstrated sensitivity of 90.9%and specificity of 25.0%;using cut-points achieving this specificity,MLMs achieved sensitivity up to 97.7%.Conclusions:MLMs using age,sex,race/ethnicity,presence of diabetes,fever,body mass index(BMI),total bilirubin,maximum CBD diameter,and choledocholithiasis on pre-ERCP noninvasive imaging predict ERCP-confirmed choledocholithiasis with good sensitivity and specificity and outperform the ASGE criteria for patients with suspected choledocholithiasis.展开更多
基金Supported by Supported by "111 Program" (B07019)
文摘The water entry problem of an asymmetric wedge with roll motion was analyzed by the method of a modified Logvinovich model (MLM). The MLM is a kind of analytical model based on the Wagner method, which linearizes the free surface condition and body boundary condition. The difference is that the MLM applies a nonlinear Bernoulli equation to obtain pressure distribution, which has been proven to be helpful to enhance the accuracy of hydrodynamic loads. The Wagner condition in this paper was generalized to solve the problem of the water entry of a wedge body with rotational velocity. The comparison of wet width between the MLM and a fully nonlinear numerical approach was given, and they agree well with each other. The effect of angular velocity on the hydrodynamic loads of a wedge body was investigated.
文摘The U.S. EPA （Environmental Protection Agency） established the CASTNET （Clean Air Status and Trends Network） and its predecessor, the NDDN （national dry deposition network）, as national air quality and meteorological monitoring networks. Both CASTNET and NDDN were designed to measure concentrations of sulfur and nitrogen gases and particles. Both networks also estimate dry deposition using an inferential model. The design was based on the concept that atmospheric dry deposition flux could be estimated as the product of a measured air pollutant concentration and a modeled deposition velocity （Vd）. The MLM （multi-layer model）, the computer model used to simulate dry deposition, requires information on meteorological conditions and vegetative cover as model input. The MLM calculates hourly Fa for each pollutant, but any missing meteorological data for an hour renders Vd missing for that hour. Because of percent completeness requirements for aggregating data for long-term estimates, annual deposition rates for some sites are not always available primarily because of missing or invalid meteorological input data. In this work, three methods for replacing missing on-site measurements are investigated. These include （1） using historical values of deposition velocity or （2） historical meteorological measurements from the site being modeled or （3） current meteorological data from nearby sites to substitute for missing inputs and thereby improve data completeness for the network＇s dry deposition estimates. Results for a CASTNET site used to test the methods show promise for using historical measurements of weekly average meteorological parameters.
基金Supported by the Humanities and Social Sciences Research Project of the Ministry of Education(No.22YJA840004).
文摘The performance of deep learning models is heavily reliant on the quality and quantity of train-ing data.Insufficient training data will lead to overfitting.However,in the task of alert-situation text classification,it is usually difficult to obtain a large amount of training data.This paper proposes a text data augmentation method based on masked language model(MLM),aiming to enhance the generalization capability of deep learning models by expanding the training data.The method em-ploys a Mask strategy to randomly conceal words in the text,effectively leveraging contextual infor-mation to predict and replace masked words based on MLM,thereby generating new training data.Three Mask strategies of character level,word level and N-gram are designed,and the performance of each Mask strategy under different Mask ratios is analyzed and studied.The experimental results show that the performance of the word-level Mask strategy is better than the traditional data augmen-tation method.
基金partially funded in the form of Senior Research Fellowship(vide No.09/1164(0001)/2016-EMR-I)awarded to the first author(Vivek Vaishnav)by Government of India Council of Scientific and Industrial Research,New Delhi,which is gratefully acknowledged
文摘To find the quantitative trait loci associated with wood density in teak(Tectona grandis L.f.), 21 co-dominant markers including 13 site specific recombinase and 8 EST-based co-dominant markers designed from lignin biosynthesis genes were applied to 174 teak plus tree clones at the National Germplasm Bank, Chandrapur,India. The germplasm bank exhibited 10.6% coefficient of variation for wood densities with 84.5 ± 31.3 genetic polymorphism(%). The highly panmictic set of genotypes(FST= 0.035 ± 0.004) harbored 96.47 ± 0.40 genetic variability(%). The average allelic frequency of the 21 codominant markers was 0.65 ± 0.11 with 12.9% pairs of loci in significant LD(p\0.05, R^2 values [ 0.1), confirming their suitability for a strong marker-trait association study. The marker CCoAMT-1 was significantly(p\0.01) associated with wood density showing stability by both GLM and MLM models and explained 4.3% of the phenotypic effect. The marker from the EST representing CCoAMT can be further developed for gene-assisted selection of elite genotypes of teak with greater wood density. Therefore, we believe that the report will help accelerate the genetic improvement and advance the breeding program of the species.
基金J.H.Tabibian was supported in part by the United States National Center for Advancing Translational Sciences grant UL1 TR000135.
文摘Background and aims:Noninvasive predictors of choledocholithiasis have generally exhibited marginal performance characteristics.We aimed to identify noninvasive independent predictors of endoscopic retrograde cholangiopancreatography(ERCP)-confirmed choledocholithiasis and accordingly developed predictive machine learning models(MLMs).Methods:Clinical data of consecutive patients undergoing first-ever ERCP for suspected chol-edocholithiasis from 2015 to 2019 were abstracted from a prospectively-maintained database.Multiple logistic regression was used to identify predictors of ERCP-confirmed choledocholithiasis.MLMs were then trained to predict ERCP-confirmed choledocholithiasis using pre-ERCP ultrasound(US)imaging only as well as using all available noninvasive imaging(US,computed tomography,and/or magnetic reso-nance cholangiopancreatography).The diagnostic performance of American Society for Gastrointestinal Endoscopy(ASGE)“high-likelihood”criteria was compared to MLMs.Results:We identified 270 patients(mean age 46 years,62.2%female,73.7%Hispanic/Latino,59%with noninvasive imaging positive for choledocholithiasis)with native papilla who underwent ERCP for suspected choledocholithiasis,of whom 230(85.2%)were found to have ERCP-confirmed chol-edocholithiasis.Logistic regression identified choledocholithiasis on noninvasive imaging(odds ratio(OR)¼3.045,P¼0.004)and common bile duct(CBD)diameter on noninvasive imaging(OR¼1.157,P¼0.011)as predictors of ERCP-confirmed choledocholithiasis.Among the various MLMs trained,the random forest-based MLM performed best;sensitivity was 61.4%and 77.3%and specificity was 100%and 75.0%,using US-only and using all available imaging,respectively.ASGE high-likelihood criteria demonstrated sensitivity of 90.9%and specificity of 25.0%;using cut-points achieving this specificity,MLMs achieved sensitivity up to 97.7%.Conclusions:MLMs using age,sex,race/ethnicity,presence of diabetes,fever,body mass index(BMI),total bilirubin,maximum CBD diameter,and choledocholithiasis on pre-ERCP noninvasive imaging predict ERCP-confirmed choledocholithiasis with good sensitivity and specificity and outperform the ASGE criteria for patients with suspected choledocholithiasis.
