This study took small enterprises listed from 2010 to 2015 as the empirical object.The board size,CEO duality,and ratio of independent directors were independent variables.The return on total assets,return on sharehol...This study took small enterprises listed from 2010 to 2015 as the empirical object.The board size,CEO duality,and ratio of independent directors were independent variables.The return on total assets,return on shareholders’equity and earnings per share were taken as the dependent variables,and three hypotheses were tested with SPSS.It is found that the board size was positively correlated with firm performance,but was not significant.There was no significant correlation between the ratio of independent directors and CEO duality on firm performance.This study suggests that optimizing the leadership structure of the board of directors can help improve the firm performance of enterprises.展开更多
The coefficient of restitution(CoR)is an important parameter for designing vibration-harvesting machinery.There are three main types of fruit-to-fruit collisions during vibration harvesting:collision between fruits co...The coefficient of restitution(CoR)is an important parameter for designing vibration-harvesting machinery.There are three main types of fruit-to-fruit collisions during vibration harvesting:collision between fruits collected using a collection device and falling fruits,collision between fruits on branches before being removed,and collision of fruits in the air.The CoR for the first two types of collision was investigated separately using drop and pendulum methods.However,there have been few studies on CoR for the collision of fruits in the air.In this study,a platform was designed to simulate the collision of fruits in the air during vibration harvesting for the‘Gala’apple,where influences of collision velocity on CoR were studied.Images from a high-speed camera were processed based on RGB to Lab conversion to extract the bruise surface and calculate the bruise volume.Total bruise volume,the sum of two apple bruise volumes,was calculated and analyzed in relation to the CoR.Results showed that the CoR decreased with collision velocity increasing from 1.0 m/s to 1.4 m/s,where the CoR reached 0.93 or higher when collision velocity was 1.0 m/s,making fruits not bruise,while fruits began to bruise when collision velocity increased from 1.2 m/s.The CoR did not continue to decrease when collision velocity exceeded 1.4 m/s due to rotation.There was little correlation between total bruise volume and the CoR due to the composite motion of fruits in the air,indicating that the CoR may not be an indicator to determine the degree of fruit bruise when the fruit made a composite motion during the collision.Therefore,this research is expected to guide the establishment of a more accurate fruit model to design optimal vibration harvesting machinery.展开更多
Nondestructive and accurate estimation of walnut kernel protein content is important for food quality grading and profitability improvement of walnut packinghouses.Hyperspectral image technology provides potential sol...Nondestructive and accurate estimation of walnut kernel protein content is important for food quality grading and profitability improvement of walnut packinghouses.Hyperspectral image technology provides potential solutions for walnuts nutrients detection by obtaining both spectral and textural information.However,the redundancy and large computation of spectral data prevent the widespread application of hyperspectral technology for high throughput evaluation.For walnut kernel protein inversion from hyperspectral image,this study proposed a novel feature selection method,which is named as improved whale optimized algorithm(IWOA).In the IWOA,a comprehensive feature selection criterion was applied in the iterative process,which fully considered the relevance of spectra information with target variables,representative ability of the selected wavebands to entire spectra,and redundancy of the selected wavebands.Especially in the relevance with target variables,the amplitude and shape characteristics of the spectra were both taken into consideration.Eight wavelengths around 996,1225,1232,1377,1552,1600,1691 and 1700 nm were then selected as the sensitive wavelengths to walnut protein.These wavelengths showed good correlation with certain chemical compounds related to protein contents mechanistically.Then three protein prediction models were established.After analysis and comparison,the model based on the selected wavelengths got better results with the one based on the full spectrum.Compared to the models based on solely spectral information,the model that combine spectral and textural information outperformed and got the best prediction results.The R2 in the calibration group was 0.9047,and the root mean square errors(RMSE)was 11.1382 g/kg.In the validation group,the R2 was 0.8537,and the RMSE was 18.9288 g/kg.The results demonstrated that the combination of the selected wavelengths through the IWOA with the textural characteristics could effectively estimate walnut protein contents.And the proposed method can be extended to the detection and inversion of other nutritional variables of nuts.展开更多
Nondestructive and accurate estimation of walnut kernel protein content is important for food quality grading and profitability improvement of walnut packinghouses.Hyperspectral image technology provides potential sol...Nondestructive and accurate estimation of walnut kernel protein content is important for food quality grading and profitability improvement of walnut packinghouses.Hyperspectral image technology provides potential solutions for walnuts nutrients detection by obtaining both spectral and textural information.However,the redundancy and large computation of spectral data prevent the widespread application of hyperspectral technology for high throughput evaluation.For walnut kernel protein inversion from hyperspectral image,this study proposed a novel feature selection method,which is named as improved whale optimized algorithm(IWOA).In the IWOA,a comprehensive feature selection criterion was applied in the iterative process,which fully considered the relevance of spectra information with target variables,representative ability of the selected wavebands to entire spectra,and redundancy of the selected wavebands.Especially in the relevance with target variables,the amplitude and shape characteristics of the spectra were both taken into consideration.Eight wavelengths around 996,1225,1232,1377,1552,1600,1691 and 1700 nm were then selected as the sensitive wavelengths to walnut protein.These wavelengths showed good correlation with certain chemical compounds related to protein contents mechanistically.Then three protein prediction models were established.After analysis and comparison,the model based on the selected wavelengths got better results with the one based on the full spectrum.Compared to the models based on solely spectral information,the model that combine spectral and textural information outperformed and got the best prediction results.The R^(2)in the calibration group was 0.9047,and the root mean square errors(RMSE)was 11.1382 g/kg.In the validation group,the R^(2)was 0.8537,and the RMSE was 18.9288 g/kg.The results demonstrated that the combination of the selected wavelengths through the IWOA with the textural characteristics could effectively estimate walnut protein contents.And the proposed method can be extended to the detection and inversion of other nutritional variables of nuts.展开更多
文摘This study took small enterprises listed from 2010 to 2015 as the empirical object.The board size,CEO duality,and ratio of independent directors were independent variables.The return on total assets,return on shareholders’equity and earnings per share were taken as the dependent variables,and three hypotheses were tested with SPSS.It is found that the board size was positively correlated with firm performance,but was not significant.There was no significant correlation between the ratio of independent directors and CEO duality on firm performance.This study suggests that optimizing the leadership structure of the board of directors can help improve the firm performance of enterprises.
基金supported by the National Natural Science Foundation of China(Grant No.32171897)National Foreign Expert Project,Ministry of Human Resources and Social Security,China(Grant No.H20240238,No.Y20240046)Science and Technology Program of Yulin City,China(Grant No.2023-CXY-183).
文摘The coefficient of restitution(CoR)is an important parameter for designing vibration-harvesting machinery.There are three main types of fruit-to-fruit collisions during vibration harvesting:collision between fruits collected using a collection device and falling fruits,collision between fruits on branches before being removed,and collision of fruits in the air.The CoR for the first two types of collision was investigated separately using drop and pendulum methods.However,there have been few studies on CoR for the collision of fruits in the air.In this study,a platform was designed to simulate the collision of fruits in the air during vibration harvesting for the‘Gala’apple,where influences of collision velocity on CoR were studied.Images from a high-speed camera were processed based on RGB to Lab conversion to extract the bruise surface and calculate the bruise volume.Total bruise volume,the sum of two apple bruise volumes,was calculated and analyzed in relation to the CoR.Results showed that the CoR decreased with collision velocity increasing from 1.0 m/s to 1.4 m/s,where the CoR reached 0.93 or higher when collision velocity was 1.0 m/s,making fruits not bruise,while fruits began to bruise when collision velocity increased from 1.2 m/s.The CoR did not continue to decrease when collision velocity exceeded 1.4 m/s due to rotation.There was little correlation between total bruise volume and the CoR due to the composite motion of fruits in the air,indicating that the CoR may not be an indicator to determine the degree of fruit bruise when the fruit made a composite motion during the collision.Therefore,this research is expected to guide the establishment of a more accurate fruit model to design optimal vibration harvesting machinery.
基金This paper was supported by the Science and Technology Innovation Key Cultivation Project of Xinjiang Academy of Agricultural Sciences(xjkcpy-004).
文摘Nondestructive and accurate estimation of walnut kernel protein content is important for food quality grading and profitability improvement of walnut packinghouses.Hyperspectral image technology provides potential solutions for walnuts nutrients detection by obtaining both spectral and textural information.However,the redundancy and large computation of spectral data prevent the widespread application of hyperspectral technology for high throughput evaluation.For walnut kernel protein inversion from hyperspectral image,this study proposed a novel feature selection method,which is named as improved whale optimized algorithm(IWOA).In the IWOA,a comprehensive feature selection criterion was applied in the iterative process,which fully considered the relevance of spectra information with target variables,representative ability of the selected wavebands to entire spectra,and redundancy of the selected wavebands.Especially in the relevance with target variables,the amplitude and shape characteristics of the spectra were both taken into consideration.Eight wavelengths around 996,1225,1232,1377,1552,1600,1691 and 1700 nm were then selected as the sensitive wavelengths to walnut protein.These wavelengths showed good correlation with certain chemical compounds related to protein contents mechanistically.Then three protein prediction models were established.After analysis and comparison,the model based on the selected wavelengths got better results with the one based on the full spectrum.Compared to the models based on solely spectral information,the model that combine spectral and textural information outperformed and got the best prediction results.The R2 in the calibration group was 0.9047,and the root mean square errors(RMSE)was 11.1382 g/kg.In the validation group,the R2 was 0.8537,and the RMSE was 18.9288 g/kg.The results demonstrated that the combination of the selected wavelengths through the IWOA with the textural characteristics could effectively estimate walnut protein contents.And the proposed method can be extended to the detection and inversion of other nutritional variables of nuts.
基金supported by the Science and Technology Innovation Key Cultivation Project of Xinjiang Academy of Agricultural Sciences(Grant No.xjkcpy-004).
文摘Nondestructive and accurate estimation of walnut kernel protein content is important for food quality grading and profitability improvement of walnut packinghouses.Hyperspectral image technology provides potential solutions for walnuts nutrients detection by obtaining both spectral and textural information.However,the redundancy and large computation of spectral data prevent the widespread application of hyperspectral technology for high throughput evaluation.For walnut kernel protein inversion from hyperspectral image,this study proposed a novel feature selection method,which is named as improved whale optimized algorithm(IWOA).In the IWOA,a comprehensive feature selection criterion was applied in the iterative process,which fully considered the relevance of spectra information with target variables,representative ability of the selected wavebands to entire spectra,and redundancy of the selected wavebands.Especially in the relevance with target variables,the amplitude and shape characteristics of the spectra were both taken into consideration.Eight wavelengths around 996,1225,1232,1377,1552,1600,1691 and 1700 nm were then selected as the sensitive wavelengths to walnut protein.These wavelengths showed good correlation with certain chemical compounds related to protein contents mechanistically.Then three protein prediction models were established.After analysis and comparison,the model based on the selected wavelengths got better results with the one based on the full spectrum.Compared to the models based on solely spectral information,the model that combine spectral and textural information outperformed and got the best prediction results.The R^(2)in the calibration group was 0.9047,and the root mean square errors(RMSE)was 11.1382 g/kg.In the validation group,the R^(2)was 0.8537,and the RMSE was 18.9288 g/kg.The results demonstrated that the combination of the selected wavelengths through the IWOA with the textural characteristics could effectively estimate walnut protein contents.And the proposed method can be extended to the detection and inversion of other nutritional variables of nuts.
