Rationale:This study aims to contribute to settling the lack of consensus regarding the determinants of bank performance,not only by considering bank governance,but also by including factors such as CEO compensation a...Rationale:This study aims to contribute to settling the lack of consensus regarding the determinants of bank performance,not only by considering bank governance,but also by including factors such as CEO compensation and risk management committee.Previous literature has included bank governance and considered only large banks in their surveys.The exclusion of other factors such as small-and medium-size banks may render the findings of these studies limited in applicability.Objective:The objective of this paper is to examine the impact of internal governance on bank performance.Methodology:To achieve this goal,we used annual data of a sample of ten Tunisian commercial banks listed in the Tunisian Stock Exchange observed during the period 1998–2015.We use the Generalized Method of Moments(GMM)to estimate the parameters of our econometric model.Results:Our study finds that the correlation between the size of the board of directors,the state’s inclusion,and the presence of independent directors is positive and significant.On the other hand,we have found that CEO compensation,as well as foreign and institutional investors negatively affect the performance of the banks.Conclusions and implications:Tunisian banks are invited to broaden their size through appropriate restructuring,adopt new remuneration policies and define the optimal number of directors representing the state within the board of directors.Our results suggest managerial implications that can be of great value to ensuring the success of Tunisian banks.The latter should favor a higher presence of independent directors to reduce the bank control ineffectiveness caused by having a significant number of foreign and institutional investors in the board of directors.展开更多
Although a number of studies have been published in the general area on various factors affecting the ecologicalization of urban industrial structure,little work has been carried out for empirical studies quantitative...Although a number of studies have been published in the general area on various factors affecting the ecologicalization of urban industrial structure,little work has been carried out for empirical studies quantitatively analyzing the relevance between green finance development and the ecologicalization of urban industrial structure.Therefore,based on a comprehensive index of green finance development,this research employs panel data of target cities1 for the period 2012–2020 to explore the influence of green finance on the ecologicalization of urban industrial structure.The empirical results show that green finance development significantly improves the ecologicalization level of urban industrial structure.In addition,it is found that green finance plays a stronger role in promoting the ecologicalization of industrial structure in economically developed regions than in economically underdeveloped regions2.The research results can provide a valuable policy reference for urban green financial market planning and green product innovation.展开更多
Based on the principle of FBG sensing mechanism,an alternating current sensor system,which uses a FBG attached on a magnetostrictive rod as the probe,was developed.A dynamic interrogation method was proposed based on ...Based on the principle of FBG sensing mechanism,an alternating current sensor system,which uses a FBG attached on a magnetostrictive rod as the probe,was developed.A dynamic interrogation method was proposed based on wide band light source,by making use of the linear relationship between its power intensity and wavelength as spectrum filter.A 50 Hz AC signal is interrogated successfully through the developed system and the system transfer function was established.The amplitude and frequency of the AC signal to be measured can be well deduced through the transfer function of the system, so the alternating current measurement is accomplished.展开更多
This paper improves and presents an advanced method of the voice conversion system based on Gaussian Mixture Models(GMM) models by changing the time-scale of speech.The Speech Transformation and Representation using A...This paper improves and presents an advanced method of the voice conversion system based on Gaussian Mixture Models(GMM) models by changing the time-scale of speech.The Speech Transformation and Representation using Adaptive Interpolation of weiGHTed spectrum(STRAIGHT) model is adopted to extract the spectrum features,and the GMM models are trained to generate the conversion function.The spectrum features of a source speech will be converted by the conversion function.The time-scale of speech is changed by extracting the converted features and adding to the spectrum.The conversion voice was evaluated by subjective and objective measurements.The results confirm that the transformed speech not only approximates the characteristics of the target speaker,but also more natural and more intelligible.展开更多
Machine learning algorithms are considered as effective methods for improving the effectiveness of neutron-gamma(n-γ)discrimination.This study proposed an intelligent discrimination method that combined a Gaussian mi...Machine learning algorithms are considered as effective methods for improving the effectiveness of neutron-gamma(n-γ)discrimination.This study proposed an intelligent discrimination method that combined a Gaussian mixture model(GMM)with the K-nearest neighbor(KNN)algorithm,referred to as GMM-KNN.First,the unlabeled training and test data were categorized into three energy ranges:0–25 keV,25–100 keV,and 100–2100 keV.Second,GMM-KNN achieved small-batch clustering in three energy intervals with only the tail integral Q_(tail) and total integral Q_(total) as the pulse features.Subsequently,we selected the pulses with a probability greater than 99%from the GMM clustering results to construct the training set.Finally,we improved the KNN algorithm such that GMM-KNN realized the classification and regression algorithms through the LabVIEW language.The outputs of GMM-KNN were the category or regression predictions.The proposed GMM-KNN constructed the training set using unlabeled real pulse data and realized n-γdiscrimination of ^(241)Am-Be pulses using the LabVIEW program.The experimental results demonstrated the high robustness and flexibility of GMM-KNN.Even when using only 1/4 of the training set,the execution time of GMM-KNN was only 2021 ms,with a difference of only 0.13%compared with the results obtained on the full training set.Furthermore,GMM-KNN outperformed the charge comparison method in terms of accuracy,and correctly classified 5.52%of the ambiguous pulses.In addition,the GMM-KNN regressor achieved a higher figure of merit(FOM),with FOM values of 0.877,1.262,and 1.020,corresponding to the three energy ranges,with a 32.08%improvement in 0–25 keV.In conclusion,the GMM-KNN algorithm demonstrates accurate and readily deployable real-time n-γdiscrimination performance,rendering it suitable for on-site analysis.展开更多
文摘Rationale:This study aims to contribute to settling the lack of consensus regarding the determinants of bank performance,not only by considering bank governance,but also by including factors such as CEO compensation and risk management committee.Previous literature has included bank governance and considered only large banks in their surveys.The exclusion of other factors such as small-and medium-size banks may render the findings of these studies limited in applicability.Objective:The objective of this paper is to examine the impact of internal governance on bank performance.Methodology:To achieve this goal,we used annual data of a sample of ten Tunisian commercial banks listed in the Tunisian Stock Exchange observed during the period 1998–2015.We use the Generalized Method of Moments(GMM)to estimate the parameters of our econometric model.Results:Our study finds that the correlation between the size of the board of directors,the state’s inclusion,and the presence of independent directors is positive and significant.On the other hand,we have found that CEO compensation,as well as foreign and institutional investors negatively affect the performance of the banks.Conclusions and implications:Tunisian banks are invited to broaden their size through appropriate restructuring,adopt new remuneration policies and define the optimal number of directors representing the state within the board of directors.Our results suggest managerial implications that can be of great value to ensuring the success of Tunisian banks.The latter should favor a higher presence of independent directors to reduce the bank control ineffectiveness caused by having a significant number of foreign and institutional investors in the board of directors.
基金supported by Shandong Province Key Research and Development Program(Soft Science Project)(No.2021RKY01007).
文摘Although a number of studies have been published in the general area on various factors affecting the ecologicalization of urban industrial structure,little work has been carried out for empirical studies quantitatively analyzing the relevance between green finance development and the ecologicalization of urban industrial structure.Therefore,based on a comprehensive index of green finance development,this research employs panel data of target cities1 for the period 2012–2020 to explore the influence of green finance on the ecologicalization of urban industrial structure.The empirical results show that green finance development significantly improves the ecologicalization level of urban industrial structure.In addition,it is found that green finance plays a stronger role in promoting the ecologicalization of industrial structure in economically developed regions than in economically underdeveloped regions2.The research results can provide a valuable policy reference for urban green financial market planning and green product innovation.
文摘Based on the principle of FBG sensing mechanism,an alternating current sensor system,which uses a FBG attached on a magnetostrictive rod as the probe,was developed.A dynamic interrogation method was proposed based on wide band light source,by making use of the linear relationship between its power intensity and wavelength as spectrum filter.A 50 Hz AC signal is interrogated successfully through the developed system and the system transfer function was established.The amplitude and frequency of the AC signal to be measured can be well deduced through the transfer function of the system, so the alternating current measurement is accomplished.
基金Supported by the National Natural Science Foundation of China (No. 60872105)the Program for Science & Technology Innovative Research Team of Qing Lan Project in Higher Educational Institutions of Jiangsuthe Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)
文摘This paper improves and presents an advanced method of the voice conversion system based on Gaussian Mixture Models(GMM) models by changing the time-scale of speech.The Speech Transformation and Representation using Adaptive Interpolation of weiGHTed spectrum(STRAIGHT) model is adopted to extract the spectrum features,and the GMM models are trained to generate the conversion function.The spectrum features of a source speech will be converted by the conversion function.The time-scale of speech is changed by extracting the converted features and adding to the spectrum.The conversion voice was evaluated by subjective and objective measurements.The results confirm that the transformed speech not only approximates the characteristics of the target speaker,but also more natural and more intelligible.
基金supported by National Science Fund for Distinguished Young Scholars of China(No.12205062).
文摘Machine learning algorithms are considered as effective methods for improving the effectiveness of neutron-gamma(n-γ)discrimination.This study proposed an intelligent discrimination method that combined a Gaussian mixture model(GMM)with the K-nearest neighbor(KNN)algorithm,referred to as GMM-KNN.First,the unlabeled training and test data were categorized into three energy ranges:0–25 keV,25–100 keV,and 100–2100 keV.Second,GMM-KNN achieved small-batch clustering in three energy intervals with only the tail integral Q_(tail) and total integral Q_(total) as the pulse features.Subsequently,we selected the pulses with a probability greater than 99%from the GMM clustering results to construct the training set.Finally,we improved the KNN algorithm such that GMM-KNN realized the classification and regression algorithms through the LabVIEW language.The outputs of GMM-KNN were the category or regression predictions.The proposed GMM-KNN constructed the training set using unlabeled real pulse data and realized n-γdiscrimination of ^(241)Am-Be pulses using the LabVIEW program.The experimental results demonstrated the high robustness and flexibility of GMM-KNN.Even when using only 1/4 of the training set,the execution time of GMM-KNN was only 2021 ms,with a difference of only 0.13%compared with the results obtained on the full training set.Furthermore,GMM-KNN outperformed the charge comparison method in terms of accuracy,and correctly classified 5.52%of the ambiguous pulses.In addition,the GMM-KNN regressor achieved a higher figure of merit(FOM),with FOM values of 0.877,1.262,and 1.020,corresponding to the three energy ranges,with a 32.08%improvement in 0–25 keV.In conclusion,the GMM-KNN algorithm demonstrates accurate and readily deployable real-time n-γdiscrimination performance,rendering it suitable for on-site analysis.
