Genomic prediction(GP)has become a valuable tool for predicting the performance of selection candidates for the next breeding cycle.A vast majority of statistical linear models on which GP is based rely on the assumpt...Genomic prediction(GP)has become a valuable tool for predicting the performance of selection candidates for the next breeding cycle.A vast majority of statistical linear models on which GP is based rely on the assumption of normality of the residuals and therefore on the response variable itself.In this study,we propose to use Bayesian regularized quantile regression(BRQR)in the context of GP;the model has been successfully used in other research areas.We evaluated the prediction ability of the proposed model and compared it with the Bayesian ridge regression(BRR;equivalent to genomic best linear unbiased predictor,GBLUP).In addition,BLUP can be used with pedigree information obtained from the coefficient of coancestry(ABLUP).We have found that the prediction ability of BRQR is comparable to that of BRR and,in some cases,better;it also has the potential to efficiently deal with outliers.A program written in the R statistical package is available as Supplementary material.展开更多
Objective This study aimed to investigate the potential relationship between urinary metals copper(Cu),arsenic(As),strontium(Sr),barium(Ba),iron(Fe),lead(Pb)and manganese(Mn)and grip strength.Methods We used linear re...Objective This study aimed to investigate the potential relationship between urinary metals copper(Cu),arsenic(As),strontium(Sr),barium(Ba),iron(Fe),lead(Pb)and manganese(Mn)and grip strength.Methods We used linear regression models,quantile g-computation and Bayesian kernel machine regression(BKMR)to assess the relationship between metals and grip strength.Results In the multimetal linear regression,Cu(β=−2.119),As(β=−1.318),Sr(β=−2.480),Ba(β=0.781),Fe(β=1.130)and Mn(β=−0.404)were significantly correlated with grip strength(P<0.05).The results of the quantile g-computation showed that the risk of occurrence of grip strength reduction was−1.007(95%confidence interval:−1.362,−0.652;P<0.001)when each quartile of the mixture of the seven metals was increased.Bayesian kernel function regression model analysis showed that mixtures of the seven metals had a negative overall effect on grip strength,with Cu,As and Sr being negatively associated with grip strength levels.In the total population,potential interactions were observed between As and Mn and between Cu and Mn(P_(interactions) of 0.003 and 0.018,respectively).Conclusion In summary,this study suggests that combined exposure to metal mixtures is negatively associated with grip strength.Cu,Sr and As were negatively correlated with grip strength levels,and there were potential interactions between As and Mn and between Cu and Mn.展开更多
In the engineering field,switching systems have been extensively studied,where sudden changes of parameter value and structural form have a significant impact on the operational performance of the system.Therefore,it ...In the engineering field,switching systems have been extensively studied,where sudden changes of parameter value and structural form have a significant impact on the operational performance of the system.Therefore,it is important to predict the behavior of the switching system,which includes the accurate detection of mutation points and rapid reidentification of the model.However,few efforts have been contributed to accurately locating the mutation points.In this paper,we propose a new measure of mutation detection—the threshold-based switching index by analogy with the Lyapunov exponent.We give the algorithm for selecting the optimal threshold,which greatly reduces the additional data collection and the relative error of mutation detection.In the system identification part,considering the small data amount available and noise in the data,the abrupt sparse Bayesian regression(abrupt-SBR)method is proposed.This method captures the model changes by updating the previously identified model,which requires less data and is more robust to noise than identifying the new model from scratch.With two representative dynamical systems,we illustrate the application and effectiveness of the proposed methods.Our research contributes to the accurate prediction and possible control of switching system behavior.展开更多
Caisson breakwaters are mainly constructed in deep waters to protect an area against waves.These breakwaters are con-ventionally designed based on the concept of the safety factor.However,the wave loads and resistance...Caisson breakwaters are mainly constructed in deep waters to protect an area against waves.These breakwaters are con-ventionally designed based on the concept of the safety factor.However,the wave loads and resistance of structures have epistemic or aleatory uncertainties.Furthermore,sliding failure is one of the most important failure modes of caisson breakwaters.In most previous studies,for assessment purposes,uncertainties,such as wave and wave period variation,were ignored.Therefore,in this study,Bayesian reliability analysis is implemented to assess the failure probability of the sliding of Tombak port breakwater in the Persian Gulf.The mean and standard deviations were taken as random variables to consider dismissed uncertainties.For this purpose,the frst-order reliability method(FORM)and the frst principal curvature cor-rection in FORM are used to calculate the reliability index.The performances of these methods are verifed by importance sampling through Monte Carlo simulation(MCS).In addition,the reliability index sensitivities of each random variable are calculated to evaluate the importance of diferent random variables while calculating the caisson sliding.The results show that the reliability index is most sensitive to the coefcients of friction,wave height,and caisson weight(or concrete density).The sensitivity of the failure probability of each of the random variables and their uncertainties are calculated by the derivative method.Finally,the Bayesian regression is implemented to predict the statistical properties of breakwater sliding with non-informative priors,which are compared to Goda’s formulation,used in breakwater design standards.The analysis shows that the model posterior for the sliding of a caisson breakwater has a mean and standard deviation of 0.039 and 0.022,respectively.A normal quantile analysis and residual analysis are also performed to evaluate the correctness of the model responses.展开更多
The accuracy of the statistical learning model depends on the learning technique used which in turn depends on the dataset’s values.In most research studies,the existence of missing values(MVs)is a vital problem.In a...The accuracy of the statistical learning model depends on the learning technique used which in turn depends on the dataset’s values.In most research studies,the existence of missing values(MVs)is a vital problem.In addition,any dataset with MVs cannot be used for further analysis or with any data driven tool especially when the percentage of MVs are high.In this paper,the authors propose a novel algorithm for dealing with MVs depending on the feature selec-tion(FS)of similarity classifier with fuzzy entropy measure.The proposed algo-rithm imputes MVs in cumulative order.The candidate feature to be manipulated is selected using similarity classifier with Parkash’s fuzzy entropy measure.The predictive model to predict MVs within the candidate feature is the Bayesian Ridge Regression(BRR)technique.Furthermore,any imputed features will be incorporated within the BRR equation to impute the MVs in the next chosen incomplete feature.The proposed algorithm was compared against some practical state-of-the-art imputation methods by conducting an experiment on four medical datasets which were gathered from several databases repository with MVs gener-ated from the three missingness mechanisms.The evaluation metrics of mean abso-lute error(MAE),root mean square error(RMSE)and coefficient of determination(R2 score)were used to measure the performance.The results exhibited that perfor-mance vary depending on the size of the dataset,amount of MVs and the missing-ness mechanism type.Moreover,compared to other methods,the results showed that the proposed method gives better accuracy and less error in most cases.展开更多
Growing studies have linked metal exposure to diabetes risk.However,these studies had inconsistent results.We used a multiple linear regression model to investigate the sexspecific and dose-response associations betwe...Growing studies have linked metal exposure to diabetes risk.However,these studies had inconsistent results.We used a multiple linear regression model to investigate the sexspecific and dose-response associations between urinary metals(cobalt(Co)and molybdenum(Mo))and diabetes-related indicators(fasting plasma glucose(FPG),hemoglobin A1c(HbA1c),homeostasis model assessment for insulin resistance(HOMA-IR),and insulin)in a cross-sectional study based on the United States National Health and Nutrition Examination Survey.The urinary metal concentrations of 1423 eligible individuals were stratified on the basis of the quartile distribution.Our results showed that the urinary Co level in males at the fourth quartile(Q4)was strongly correlated with increased FPG(β=0.61,95%CI:0.17–1.04),HbA1c(β=0.31,95%CI:0.09–0.54),insulin(β=8.18,95%CI:2.84–13.52),and HOMA–IR(β=3.42,95%CI:1.40–5.44)when compared with first quartile(Q1).High urinary Mo levels(Q4 vs.Q1)were associated with elevated FPG(β=0.46,95%CI:0.17–0.75)and HbA1c(β=0.27,95%CI:0.11–0.42)in the overall population.Positive linear dose-response associations were observed between urinary Co and insulin(Pnonlinear=0.513)and HOMA–IR(Pnonlinear=0.736)in males,as well as a positive linear dose-response relationship between urinary Mo and FPG(Pnonlinear=0.826)and HbA1c(Pnonlinear=0.376)in the overall population.Significant sex-specific and dose-response relationships were observed between urinary metals(Co and Mo)and diabetes-related indicators,and the potential mechanisms should be further investigated.展开更多
This study aims to divide traffic into meaningful clusters (regimes) and to investigate their impact on accident likelihood and accident severity. Furthermore, the likelihood of pow- ered-two-wheelers (PTWs) invol...This study aims to divide traffic into meaningful clusters (regimes) and to investigate their impact on accident likelihood and accident severity. Furthermore, the likelihood of pow- ered-two-wheelers (PTWs) involvement in an accident is examined. To achieve the aims of the study, traffic and accident data during the period 2006-2011 from two major arterials in Athens were collected and processed. Firstly, a finite mixture cluster analysis was imple- mented to classify traffic into clusters. Afterwards, discriminant analysis was carried out in order to correctly assign new cases to the existing regimes by using a training and a testing set. Lastly, Bayesian logistic regression models were developed to investigate the impact of traffic regimes on accident likelihood and severity. The findings of this study suggest that urban traffic can be divided into different regimes by using average traffic occupancy and its standard deviation, measured by nearby upstream and downstream loop detectors. The results revealed potential hazardous traffic conditions, which are discussed in the paper. In general, high occupancy values increase accident likelihood, but tend to lead slight acci- dents, while PTWs are more likely to be involved in an accident, when traffic occupancy is high. Transitions from high to low occupancy also increase accident likelihood.展开更多
Nonparametric stochastic volatility models,although providing great flexibility for modelling thevolatility equation,often fail to account for useful shape information.For example,a model maynot use the knowledge that...Nonparametric stochastic volatility models,although providing great flexibility for modelling thevolatility equation,often fail to account for useful shape information.For example,a model maynot use the knowledge that the autoregressive component of the volatility equation is monotonically increasing as the lagged volatility increases.We propose a class of additive stochasticvolatility models that allow for different shape constraints and can incorporate the leverageeffect–asymmetric impact of positive and negative return shocks on volatilities.We developa Bayesian fitting algorithm and demonstrate model performance on simulated and empiricaldatasets.Unlike general nonparametric models,our model sacrifices little when the true volatility equation is linear.In nonlinear situations we improve the model fit and the ability to estimatevolatilities over general,unconstrained,nonparametric models.展开更多
With continuous technology scaling, on-chip structures are becoming more and more susceptible to soft errors. Architectural vulnerability factor (AVF) has been introduced to quantify the architectural vulnerability ...With continuous technology scaling, on-chip structures are becoming more and more susceptible to soft errors. Architectural vulnerability factor (AVF) has been introduced to quantify the architectural vulnerability of on-chip structures to soft errors. Recent studies have found that designing soft error protection techniques with the awareness of AVF is greatly helpful to achieve a tradeoff between performance and reliability for several structures (i.e., issue queue, reorder buffer). Cache is one of the most susceptible components to soft errors and is commonly protected with error correcting codes (ECC). However, protecting caches closer to the processor (i.e., L1 data cache (LID)) using ECC could result in high overhead. Protecting caches without accurate knowledge of the vulnerability characteristics may lead to over-protection. Therefore, designing AVF-aware ECC is attractive for designers to balance among performance, power and reliability for cache, especially at early design stage. In this paper, we improve the methodology of cache AVF computation and develop a new AVF estimation framework, soft error reliability analysis based on SimpleScalar. Then we characterize dynamic vulnerability behavior of LID and detect the correlations between L1D AVF and various performance metrics. We propose to employ Bayesian additive regression trees to accurately model the variation of L1D AVF and to quantitatively explain the important effects of several key performance metrics on L1D AVF. Then, we employ bump hunting technique to reduce the complexity of L1D AVF prediction and extract some simple selecting rules based on several key performance metrics, thus enabling a simplified and fast estimation of L1D AVF. Based on the simplified and fast estimation of L1D AVF, intervals of high L1D AVF can be identified online, enabling us to develop the AVF-aware ECC technique to reduce the overhead of ECC. Experimental results show that compared with traditional ECC technique which provides complete ECC protection throughout the entire lifetime of a program, AVF-aware ECC technique reduces the L1D access latency by 35% and saves power consumption by 14% for SPEC2K benchmarks averagely.展开更多
基金The maize and wheat data set used in this study comes from the Drought Tolerance Maize for Africa Project and from CIMMYT's Global Wheat Program.We are thankful to everyone who generated the data used in this article.
文摘Genomic prediction(GP)has become a valuable tool for predicting the performance of selection candidates for the next breeding cycle.A vast majority of statistical linear models on which GP is based rely on the assumption of normality of the residuals and therefore on the response variable itself.In this study,we propose to use Bayesian regularized quantile regression(BRQR)in the context of GP;the model has been successfully used in other research areas.We evaluated the prediction ability of the proposed model and compared it with the Bayesian ridge regression(BRR;equivalent to genomic best linear unbiased predictor,GBLUP).In addition,BLUP can be used with pedigree information obtained from the coefficient of coancestry(ABLUP).We have found that the prediction ability of BRQR is comparable to that of BRR and,in some cases,better;it also has the potential to efficiently deal with outliers.A program written in the R statistical package is available as Supplementary material.
基金supported by the National Natural Science Foundation of China[rant Nos.81960583,81760577,81560523 and 82260629]Major Science and Technology Projects in Guangxi[GKAA22399 and AA22096026]+3 种基金the Guangxi Science and Technology Development Project[Grant Nos.AD 17129003 and 18050005]the Guangxi Natural Science Foundation for Innovation Research Team[2019GXNSFGA245002]the Innovation Platform and Talent Plan in Guilin[20220120-2]the Guangxi Scholarship Fund of Guangxi Education Department of China。
文摘Objective This study aimed to investigate the potential relationship between urinary metals copper(Cu),arsenic(As),strontium(Sr),barium(Ba),iron(Fe),lead(Pb)and manganese(Mn)and grip strength.Methods We used linear regression models,quantile g-computation and Bayesian kernel machine regression(BKMR)to assess the relationship between metals and grip strength.Results In the multimetal linear regression,Cu(β=−2.119),As(β=−1.318),Sr(β=−2.480),Ba(β=0.781),Fe(β=1.130)and Mn(β=−0.404)were significantly correlated with grip strength(P<0.05).The results of the quantile g-computation showed that the risk of occurrence of grip strength reduction was−1.007(95%confidence interval:−1.362,−0.652;P<0.001)when each quartile of the mixture of the seven metals was increased.Bayesian kernel function regression model analysis showed that mixtures of the seven metals had a negative overall effect on grip strength,with Cu,As and Sr being negatively associated with grip strength levels.In the total population,potential interactions were observed between As and Mn and between Cu and Mn(P_(interactions) of 0.003 and 0.018,respectively).Conclusion In summary,this study suggests that combined exposure to metal mixtures is negatively associated with grip strength.Cu,Sr and As were negatively correlated with grip strength levels,and there were potential interactions between As and Mn and between Cu and Mn.
基金the National Natural Science Foundation of China(Grant No.12072261)。
文摘In the engineering field,switching systems have been extensively studied,where sudden changes of parameter value and structural form have a significant impact on the operational performance of the system.Therefore,it is important to predict the behavior of the switching system,which includes the accurate detection of mutation points and rapid reidentification of the model.However,few efforts have been contributed to accurately locating the mutation points.In this paper,we propose a new measure of mutation detection—the threshold-based switching index by analogy with the Lyapunov exponent.We give the algorithm for selecting the optimal threshold,which greatly reduces the additional data collection and the relative error of mutation detection.In the system identification part,considering the small data amount available and noise in the data,the abrupt sparse Bayesian regression(abrupt-SBR)method is proposed.This method captures the model changes by updating the previously identified model,which requires less data and is more robust to noise than identifying the new model from scratch.With two representative dynamical systems,we illustrate the application and effectiveness of the proposed methods.Our research contributes to the accurate prediction and possible control of switching system behavior.
文摘Caisson breakwaters are mainly constructed in deep waters to protect an area against waves.These breakwaters are con-ventionally designed based on the concept of the safety factor.However,the wave loads and resistance of structures have epistemic or aleatory uncertainties.Furthermore,sliding failure is one of the most important failure modes of caisson breakwaters.In most previous studies,for assessment purposes,uncertainties,such as wave and wave period variation,were ignored.Therefore,in this study,Bayesian reliability analysis is implemented to assess the failure probability of the sliding of Tombak port breakwater in the Persian Gulf.The mean and standard deviations were taken as random variables to consider dismissed uncertainties.For this purpose,the frst-order reliability method(FORM)and the frst principal curvature cor-rection in FORM are used to calculate the reliability index.The performances of these methods are verifed by importance sampling through Monte Carlo simulation(MCS).In addition,the reliability index sensitivities of each random variable are calculated to evaluate the importance of diferent random variables while calculating the caisson sliding.The results show that the reliability index is most sensitive to the coefcients of friction,wave height,and caisson weight(or concrete density).The sensitivity of the failure probability of each of the random variables and their uncertainties are calculated by the derivative method.Finally,the Bayesian regression is implemented to predict the statistical properties of breakwater sliding with non-informative priors,which are compared to Goda’s formulation,used in breakwater design standards.The analysis shows that the model posterior for the sliding of a caisson breakwater has a mean and standard deviation of 0.039 and 0.022,respectively.A normal quantile analysis and residual analysis are also performed to evaluate the correctness of the model responses.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University(KAU)Jeddah,Saudi Arabia,under grant No.(PH:13-130-1442).
文摘The accuracy of the statistical learning model depends on the learning technique used which in turn depends on the dataset’s values.In most research studies,the existence of missing values(MVs)is a vital problem.In addition,any dataset with MVs cannot be used for further analysis or with any data driven tool especially when the percentage of MVs are high.In this paper,the authors propose a novel algorithm for dealing with MVs depending on the feature selec-tion(FS)of similarity classifier with fuzzy entropy measure.The proposed algo-rithm imputes MVs in cumulative order.The candidate feature to be manipulated is selected using similarity classifier with Parkash’s fuzzy entropy measure.The predictive model to predict MVs within the candidate feature is the Bayesian Ridge Regression(BRR)technique.Furthermore,any imputed features will be incorporated within the BRR equation to impute the MVs in the next chosen incomplete feature.The proposed algorithm was compared against some practical state-of-the-art imputation methods by conducting an experiment on four medical datasets which were gathered from several databases repository with MVs gener-ated from the three missingness mechanisms.The evaluation metrics of mean abso-lute error(MAE),root mean square error(RMSE)and coefficient of determination(R2 score)were used to measure the performance.The results exhibited that perfor-mance vary depending on the size of the dataset,amount of MVs and the missing-ness mechanism type.Moreover,compared to other methods,the results showed that the proposed method gives better accuracy and less error in most cases.
基金supported by the National Institutes of Health (U.S.)-(NIH Grant Number: 1R01ES029082)
文摘Growing studies have linked metal exposure to diabetes risk.However,these studies had inconsistent results.We used a multiple linear regression model to investigate the sexspecific and dose-response associations between urinary metals(cobalt(Co)and molybdenum(Mo))and diabetes-related indicators(fasting plasma glucose(FPG),hemoglobin A1c(HbA1c),homeostasis model assessment for insulin resistance(HOMA-IR),and insulin)in a cross-sectional study based on the United States National Health and Nutrition Examination Survey.The urinary metal concentrations of 1423 eligible individuals were stratified on the basis of the quartile distribution.Our results showed that the urinary Co level in males at the fourth quartile(Q4)was strongly correlated with increased FPG(β=0.61,95%CI:0.17–1.04),HbA1c(β=0.31,95%CI:0.09–0.54),insulin(β=8.18,95%CI:2.84–13.52),and HOMA–IR(β=3.42,95%CI:1.40–5.44)when compared with first quartile(Q1).High urinary Mo levels(Q4 vs.Q1)were associated with elevated FPG(β=0.46,95%CI:0.17–0.75)and HbA1c(β=0.27,95%CI:0.11–0.42)in the overall population.Positive linear dose-response associations were observed between urinary Co and insulin(Pnonlinear=0.513)and HOMA–IR(Pnonlinear=0.736)in males,as well as a positive linear dose-response relationship between urinary Mo and FPG(Pnonlinear=0.826)and HbA1c(Pnonlinear=0.376)in the overall population.Significant sex-specific and dose-response relationships were observed between urinary metals(Co and Mo)and diabetes-related indicators,and the potential mechanisms should be further investigated.
基金supported by the special fund for research grants of NTUA for PhD studies
文摘This study aims to divide traffic into meaningful clusters (regimes) and to investigate their impact on accident likelihood and accident severity. Furthermore, the likelihood of pow- ered-two-wheelers (PTWs) involvement in an accident is examined. To achieve the aims of the study, traffic and accident data during the period 2006-2011 from two major arterials in Athens were collected and processed. Firstly, a finite mixture cluster analysis was imple- mented to classify traffic into clusters. Afterwards, discriminant analysis was carried out in order to correctly assign new cases to the existing regimes by using a training and a testing set. Lastly, Bayesian logistic regression models were developed to investigate the impact of traffic regimes on accident likelihood and severity. The findings of this study suggest that urban traffic can be divided into different regimes by using average traffic occupancy and its standard deviation, measured by nearby upstream and downstream loop detectors. The results revealed potential hazardous traffic conditions, which are discussed in the paper. In general, high occupancy values increase accident likelihood, but tend to lead slight acci- dents, while PTWs are more likely to be involved in an accident, when traffic occupancy is high. Transitions from high to low occupancy also increase accident likelihood.
基金Peter Craigmile and Jiangyong Yin were supported in part by the National Science Foundation(NSF)under grant DMS-0906864Xinyi Xu,Jiangyong Yin and Steven MacEachern were supported in part by the NSF under grant DMS-1209194+2 种基金Peter Craigmile is additionally supported in part by the NSF under grants SES-1024709,DMS-1407604 and SES-1424481the National Cancer Institute of the National Institutes of Health under Award Number 1R21CA212308-01the project title is‘Evaluating how licensing-law strategies will change neighborhood disparities in tobacco retailer density’.Xinyi Xu and Steven MacEachern are supported under grant DMS-1613110.
文摘Nonparametric stochastic volatility models,although providing great flexibility for modelling thevolatility equation,often fail to account for useful shape information.For example,a model maynot use the knowledge that the autoregressive component of the volatility equation is monotonically increasing as the lagged volatility increases.We propose a class of additive stochasticvolatility models that allow for different shape constraints and can incorporate the leverageeffect–asymmetric impact of positive and negative return shocks on volatilities.We developa Bayesian fitting algorithm and demonstrate model performance on simulated and empiricaldatasets.Unlike general nonparametric models,our model sacrifices little when the true volatility equation is linear.In nonlinear situations we improve the model fit and the ability to estimatevolatilities over general,unconstrained,nonparametric models.
基金Supported by the National Natural Science Foundation of China under Grant Nos. 60970036 and 60873016the National High Technology Development 863 Program of China under Grant Nos. 2009AA01Z102 and 2009AA01Z124
文摘With continuous technology scaling, on-chip structures are becoming more and more susceptible to soft errors. Architectural vulnerability factor (AVF) has been introduced to quantify the architectural vulnerability of on-chip structures to soft errors. Recent studies have found that designing soft error protection techniques with the awareness of AVF is greatly helpful to achieve a tradeoff between performance and reliability for several structures (i.e., issue queue, reorder buffer). Cache is one of the most susceptible components to soft errors and is commonly protected with error correcting codes (ECC). However, protecting caches closer to the processor (i.e., L1 data cache (LID)) using ECC could result in high overhead. Protecting caches without accurate knowledge of the vulnerability characteristics may lead to over-protection. Therefore, designing AVF-aware ECC is attractive for designers to balance among performance, power and reliability for cache, especially at early design stage. In this paper, we improve the methodology of cache AVF computation and develop a new AVF estimation framework, soft error reliability analysis based on SimpleScalar. Then we characterize dynamic vulnerability behavior of LID and detect the correlations between L1D AVF and various performance metrics. We propose to employ Bayesian additive regression trees to accurately model the variation of L1D AVF and to quantitatively explain the important effects of several key performance metrics on L1D AVF. Then, we employ bump hunting technique to reduce the complexity of L1D AVF prediction and extract some simple selecting rules based on several key performance metrics, thus enabling a simplified and fast estimation of L1D AVF. Based on the simplified and fast estimation of L1D AVF, intervals of high L1D AVF can be identified online, enabling us to develop the AVF-aware ECC technique to reduce the overhead of ECC. Experimental results show that compared with traditional ECC technique which provides complete ECC protection throughout the entire lifetime of a program, AVF-aware ECC technique reduces the L1D access latency by 35% and saves power consumption by 14% for SPEC2K benchmarks averagely.