As the goal of"low carbon"sustainable development becomes more salient,the corporations'environmental,social,and governance(ESG)practices are under higher visibility.How to promote the ESG performance of...As the goal of"low carbon"sustainable development becomes more salient,the corporations'environmental,social,and governance(ESG)practices are under higher visibility.How to promote the ESG performance of corporations has become a big challenge that needs to be solved.Spatial econometrics methods based on panel data on listed corporations in the period from 2018 to 2023 are used in the paper to empirically analyze the peer effect of a corporation from the view of strategic interaction.The results found relatively positive industry peer effects as well as regional peer effects.However,the latter is much weaker.Industry-wise results indicate that most sectors demonstrate positive peer competition on ESG issues,with only a few,like the special equipment manufacturing industry,not showing significant peer effects.The empirical results in this paper support the mode of cooperative interaction among firms,help broaden the scope of understanding factors that encourage ESG practices,and suggest relevant policies for boosting corporate social responsibilities and sustainable development through encouraging factors.展开更多
Smart Agriculture,also known as Agricultural 5.0,is expected to be an integral part of our human lives to reduce the cost of agricultural inputs,increasing productivity and improving the quality of the final product.I...Smart Agriculture,also known as Agricultural 5.0,is expected to be an integral part of our human lives to reduce the cost of agricultural inputs,increasing productivity and improving the quality of the final product.Indeed,the safety and ongoing maintenance of Smart Agriculture from cyber-attacks are vitally important.To provide more comprehensive protection against potential cyber-attacks,this paper proposes a new deep learning-based intrusion detection system for securing Smart Agriculture.The proposed Intrusion Detection System IDS,namely GMLPIDS,combines the feedforward neural network Multilayer Perceptron(MLP)and the Gaussian Mixture Model(GMM)that can better protect the Smart Agriculture system.GMLP-IDS is evaluated with the CIC-DDoS2019 dataset,which contains various Distributed Denial-of-Service(DDoS)attacks.The paper first uses the Pearson’s correlation coefficient approach to determine the correlation between the CIC-DDoS2019 dataset characteristics and their corresponding class labels.Then,the CIC-DDoS2019 dataset is divided randomly into two parts,i.e.,training and testing.75%of the data is used for training,and 25%is employed for testing.The performance of the newly proposed IDS has been compared to the traditional MLP model in terms of accuracy rating,loss rating,recall,and F1 score.Comparisons are handled on both binary and multi-class classification problems.The results revealed that the proposed GMLP-IDS system achieved more than 99.99%detection accuracy and a loss of 0.02%compared to traditional MLP.Furthermore,evaluation performance demonstrates that the proposed approach covers a more comprehensive range of security properties for Smart Agriculture and can be a promising solution for detecting unknown DDoS attacks.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.72073045).
文摘As the goal of"low carbon"sustainable development becomes more salient,the corporations'environmental,social,and governance(ESG)practices are under higher visibility.How to promote the ESG performance of corporations has become a big challenge that needs to be solved.Spatial econometrics methods based on panel data on listed corporations in the period from 2018 to 2023 are used in the paper to empirically analyze the peer effect of a corporation from the view of strategic interaction.The results found relatively positive industry peer effects as well as regional peer effects.However,the latter is much weaker.Industry-wise results indicate that most sectors demonstrate positive peer competition on ESG issues,with only a few,like the special equipment manufacturing industry,not showing significant peer effects.The empirical results in this paper support the mode of cooperative interaction among firms,help broaden the scope of understanding factors that encourage ESG practices,and suggest relevant policies for boosting corporate social responsibilities and sustainable development through encouraging factors.
基金funded by the Deanship of Scientific Research in Cooperation with Olive Research Center at Jouf University under Grant Number(DSR2022-RG-0163).
文摘Smart Agriculture,also known as Agricultural 5.0,is expected to be an integral part of our human lives to reduce the cost of agricultural inputs,increasing productivity and improving the quality of the final product.Indeed,the safety and ongoing maintenance of Smart Agriculture from cyber-attacks are vitally important.To provide more comprehensive protection against potential cyber-attacks,this paper proposes a new deep learning-based intrusion detection system for securing Smart Agriculture.The proposed Intrusion Detection System IDS,namely GMLPIDS,combines the feedforward neural network Multilayer Perceptron(MLP)and the Gaussian Mixture Model(GMM)that can better protect the Smart Agriculture system.GMLP-IDS is evaluated with the CIC-DDoS2019 dataset,which contains various Distributed Denial-of-Service(DDoS)attacks.The paper first uses the Pearson’s correlation coefficient approach to determine the correlation between the CIC-DDoS2019 dataset characteristics and their corresponding class labels.Then,the CIC-DDoS2019 dataset is divided randomly into two parts,i.e.,training and testing.75%of the data is used for training,and 25%is employed for testing.The performance of the newly proposed IDS has been compared to the traditional MLP model in terms of accuracy rating,loss rating,recall,and F1 score.Comparisons are handled on both binary and multi-class classification problems.The results revealed that the proposed GMLP-IDS system achieved more than 99.99%detection accuracy and a loss of 0.02%compared to traditional MLP.Furthermore,evaluation performance demonstrates that the proposed approach covers a more comprehensive range of security properties for Smart Agriculture and can be a promising solution for detecting unknown DDoS attacks.
