Purpose-The Covid 19 prediction process is more indispensable to handle the spread and deathocurred rate because of Covid-19.However early and precise prediction of Covid-19 is more difcult because of different sizes ...Purpose-The Covid 19 prediction process is more indispensable to handle the spread and deathocurred rate because of Covid-19.However early and precise prediction of Covid-19 is more difcult because of different sizes and resolutions of input image Thus these challenges and problems experienced by traditional Covid-19 detection methods are considered as major motivation to develop JHBO-based DNFN.Design/methodology/approach-The major contribution of this research is to desigm an ffectualCovid-19 detection model using devised JHBObased DNFN,Here,the audio signal is considered as input for detecting Covid-19.The Gaussian filter is applied to input signal for removing the noises and then feature extraction is performed.The substantial features,like spectral rlloff.spectral bandwidth,Mel-frequency,cepstral coefficients (MFCC),spectral flatness,zero crossing rate,spectral centroid,mean square energy and spectral contract are extracted for further processing.Finally,DNFN is applied for detecting Covid 19 and the deep leaning model is trained by designed JHBO algorithm.Accordingly.the developed JHBO method is newly desigmed by inoorporating Honey Badger optimization Algorithm(HBA)and.Jaya algorithm.Findings-The performance of proposed hybrid optimization-based deep learming algorithm is estimated by meansof twoperformance metrics,namely testing accuracy,sensitivity and speificity of 09176,09218 and 09219.Research limitations/implications-The JHBO-based DNFN approach is developed for Covid-19 detection.The developed approach can be extended by including other hybrid optimization algorithms as well as other features can be extracted for further improving the detection performance.Practical implications-The proposed Covid-19 detection method is useful in various applications,like medical and so on,Originality/value-Developed JHBO-enabled DNFN for Covid-19 detection:An effective Covid-19 detection technique is introduced based on hybrid optimization-driven deep learning model The DNFN is used for detecting Covid-19,which cla展开更多
In the structural design of tall buildings, peak factors have been widely used to predict mean extreme responses of tall buildings under wind excitations. Vanmarcke＇s peak factor is directly related to an explicit me...In the structural design of tall buildings, peak factors have been widely used to predict mean extreme responses of tall buildings under wind excitations. Vanmarcke＇s peak factor is directly related to an explicit measure of structural reliability against a Gaussian response process. We review the use of this factor for time-variant reliability design by comparing it to the conven- tional Davenport＇s peak factor. Based on the asymptotic theory of statistical extremes, a new closed-form peak factor, the so-called Gamma peak factor, can be obtained for a non-Gaussian resultant response characterized by a Rayleigh distribution process. Using the Gamma peak factor, a combined peak factor method was developed for predicting the expected maximum resultant responses of a building undergoing lateral-torsional vibration. The effects of the standard deviation ratio of two sway components and the inter-component correlation on the evaluation of peak resultant response were also investigated. Utilizing wind tunnel data derived from synchronous multi-pressure measurements, we carried out a wind-induced time history response analysis of the Common- wealth Advisory Aeronautical Research Council （CAARC） standard tall building to validate the applicability of the Gamma peak factor to the prediction of the peak resultant acceleration. Results from the building example indicated that the use of the Gamma peak factor enables accurate predictions to be made of the mean extreme resultant acceleration responses for dynamic service- ability performance design of modem tall buildings.展开更多
Genetic segregation analysis for flag leaf angle was conducted using six generations of P1, P2, F1, B1, B2 and F2 derived from a cross of 863B （a maintainer line of japonica rice） and A7444 （a germplasm with large ...Genetic segregation analysis for flag leaf angle was conducted using six generations of P1, P2, F1, B1, B2 and F2 derived from a cross of 863B （a maintainer line of japonica rice） and A7444 （a germplasm with large flag leaf angle）. Genotypes and phenotypes of flag leaf angle were investigated in 863B （P1）, A7444 （P2） and 141 plants in BC^F~ （863BIA744411863B） population. An SSR genetic linkage map was constructed and QTLs for flag leaf angle were detected. The genetic map containing 79 information loci was constructed, which covers a total distance of 441.6 cM, averaging 5.6 cM between two neighboring loci. Results showed that the trait was controlled by two major genes plus polygene and the major genes were more important. Fifteen markers showed highly significant correlations with flag leaf angle based on single marker regression analysis. Two QTLs （qFLA2 and qFLA8） for flag leaf angle were detected by both composite interval method in software WinQTLCart 2.5 and composite interval method based on mixed linear model in QTL Network 2.0. The qFLA2 explained 10.50% and 13.28% of phenotypic variation, respectively, and was located at the interval of RM300 and RM145 on the short arm of chromosome 2. The qFLA8 explained 9.59% and 7.64% of phenotypic variation, respectively, and was located at the interval flanking RM6215 and RM8265 on the long arm of chromosome 8. The positive alleles at the two QTLs were both contributed from A7444.展开更多
文摘Purpose-The Covid 19 prediction process is more indispensable to handle the spread and deathocurred rate because of Covid-19.However early and precise prediction of Covid-19 is more difcult because of different sizes and resolutions of input image Thus these challenges and problems experienced by traditional Covid-19 detection methods are considered as major motivation to develop JHBO-based DNFN.Design/methodology/approach-The major contribution of this research is to desigm an ffectualCovid-19 detection model using devised JHBObased DNFN,Here,the audio signal is considered as input for detecting Covid-19.The Gaussian filter is applied to input signal for removing the noises and then feature extraction is performed.The substantial features,like spectral rlloff.spectral bandwidth,Mel-frequency,cepstral coefficients (MFCC),spectral flatness,zero crossing rate,spectral centroid,mean square energy and spectral contract are extracted for further processing.Finally,DNFN is applied for detecting Covid 19 and the deep leaning model is trained by designed JHBO algorithm.Accordingly.the developed JHBO method is newly desigmed by inoorporating Honey Badger optimization Algorithm(HBA)and.Jaya algorithm.Findings-The performance of proposed hybrid optimization-based deep learming algorithm is estimated by meansof twoperformance metrics,namely testing accuracy,sensitivity and speificity of 09176,09218 and 09219.Research limitations/implications-The JHBO-based DNFN approach is developed for Covid-19 detection.The developed approach can be extended by including other hybrid optimization algorithms as well as other features can be extracted for further improving the detection performance.Practical implications-The proposed Covid-19 detection method is useful in various applications,like medical and so on,Originality/value-Developed JHBO-enabled DNFN for Covid-19 detection:An effective Covid-19 detection technique is introduced based on hybrid optimization-driven deep learning model The DNFN is used for detecting Covid-19,which cla
基金Project supported by the National Natural Science Foundation of China (No. 51008275)the China Postdoctoral Science Foundation (No.201104736)
文摘In the structural design of tall buildings, peak factors have been widely used to predict mean extreme responses of tall buildings under wind excitations. Vanmarcke＇s peak factor is directly related to an explicit measure of structural reliability against a Gaussian response process. We review the use of this factor for time-variant reliability design by comparing it to the conven- tional Davenport＇s peak factor. Based on the asymptotic theory of statistical extremes, a new closed-form peak factor, the so-called Gamma peak factor, can be obtained for a non-Gaussian resultant response characterized by a Rayleigh distribution process. Using the Gamma peak factor, a combined peak factor method was developed for predicting the expected maximum resultant responses of a building undergoing lateral-torsional vibration. The effects of the standard deviation ratio of two sway components and the inter-component correlation on the evaluation of peak resultant response were also investigated. Utilizing wind tunnel data derived from synchronous multi-pressure measurements, we carried out a wind-induced time history response analysis of the Common- wealth Advisory Aeronautical Research Council （CAARC） standard tall building to validate the applicability of the Gamma peak factor to the prediction of the peak resultant acceleration. Results from the building example indicated that the use of the Gamma peak factor enables accurate predictions to be made of the mean extreme resultant acceleration responses for dynamic service- ability performance design of modem tall buildings.
基金supported by the National High Technology Research and Development Program of China(Grant No. 2010AA101300)the Platform Construction for Science and Technology Basic Condition from Science and Technology Ministry,China (Grant No.505005)
文摘Genetic segregation analysis for flag leaf angle was conducted using six generations of P1, P2, F1, B1, B2 and F2 derived from a cross of 863B （a maintainer line of japonica rice） and A7444 （a germplasm with large flag leaf angle）. Genotypes and phenotypes of flag leaf angle were investigated in 863B （P1）, A7444 （P2） and 141 plants in BC^F~ （863BIA744411863B） population. An SSR genetic linkage map was constructed and QTLs for flag leaf angle were detected. The genetic map containing 79 information loci was constructed, which covers a total distance of 441.6 cM, averaging 5.6 cM between two neighboring loci. Results showed that the trait was controlled by two major genes plus polygene and the major genes were more important. Fifteen markers showed highly significant correlations with flag leaf angle based on single marker regression analysis. Two QTLs （qFLA2 and qFLA8） for flag leaf angle were detected by both composite interval method in software WinQTLCart 2.5 and composite interval method based on mixed linear model in QTL Network 2.0. The qFLA2 explained 10.50% and 13.28% of phenotypic variation, respectively, and was located at the interval of RM300 and RM145 on the short arm of chromosome 2. The qFLA8 explained 9.59% and 7.64% of phenotypic variation, respectively, and was located at the interval flanking RM6215 and RM8265 on the long arm of chromosome 8. The positive alleles at the two QTLs were both contributed from A7444.
