Under the combined influence of climate change and human activities,vegetation ecosystem has undergone profound changes.It can be seen that there are obvious differences in the evolution patterns and driving mechanism...Under the combined influence of climate change and human activities,vegetation ecosystem has undergone profound changes.It can be seen that there are obvious differences in the evolution patterns and driving mechanisms of vegetation ecosystem in different historical periods.Therefore,it is urgent to identify and reveal the dominant factors and their contribution rates in the vegetation change cycle.Based on the data of climate elements(sunshine hours,precipitation and temperature),human activities(population intensity and GDP intensity)and other natural factors(altitude,slope and aspect),this study explored the spatial and temporal evolution patterns of vegetation NDVI in the Yellow River Basin of China from 1989 to 2019 through a residual method,a trend analysis,and a gravity center model,and quantitatively distinguished the relative actions of climate change and human activities on vegetation evolution based on Geodetector model.The results showed that the spatial distribution of vegetation NDVI in the Yellow River Basin showed a decreasing trend from southeast to northwest.During 1981-2019,the temporal variation of vegetation NDVI showed an overall increasing trend.The gravity centers of average vegetation NDVI during the study period was distributed in Zhenyuan County,Gansu Province,and the center moved northeastwards from 1981 to 2019.During 1981-2000 and 2001-2019,the proportion of vegetation restoration areas promoted by the combined action of climate change and human activities was the largest.During the study period(1981-2019),the dominant factors influencing vegetation NDVI shifted from natural factors to human activities.These results could provide decision support for the protection and restoration of vegetation ecosystem in the Yellow River Basin.展开更多
The development of artificial intelligence(AI)and smart home technologies has driven the need for speech recognition-based solutions.This demand stems from the quest for more intuitive and natural interaction between ...The development of artificial intelligence(AI)and smart home technologies has driven the need for speech recognition-based solutions.This demand stems from the quest for more intuitive and natural interaction between users and smart devices in their homes.Speech recognition allows users to control devices and perform everyday actions through spoken commands,eliminating the need for physical interfaces or touch screens and enabling specific tasks such as turning on or off the light,heating,or lowering the blinds.The purpose of this study is to develop a speech-based classification model for recognizing human actions in the smart home.It seeks to demonstrate the effectiveness and feasibility of using machine learning techniques in predicting categories,subcategories,and actions from sentences.A dataset labeled with relevant information about categories,subcategories,and actions related to human actions in the smart home is used.The methodology uses machine learning techniques implemented in Python,extracting features using CountVectorizer to convert sentences into numerical representations.The results show that the classification model is able to accurately predict categories,subcategories,and actions based on sentences,with 82.99%accuracy for category,76.19%accuracy for subcategory,and 90.28%accuracy for action.The study concludes that using machine learning techniques is effective for recognizing and classifying human actions in the smart home,supporting its feasibility in various scenarios and opening new possibilities for advanced natural language processing systems in the field of AI and smart homes.展开更多
A system for classifying four basic table tennis strokes using wearable devices and deep learning networks is proposed in this study.The wearable device consisted of a six-axis sensor,Raspberry Pi 3,and a power bank.M...A system for classifying four basic table tennis strokes using wearable devices and deep learning networks is proposed in this study.The wearable device consisted of a six-axis sensor,Raspberry Pi 3,and a power bank.Multiple kernel sizes were used in convolutional neural network(CNN)to evaluate their performance for extracting features.Moreover,a multiscale CNN with two kernel sizes was used to perform feature fusion at different scales in a concatenated manner.The CNN achieved recognition of the four table tennis strokes.Experimental data were obtained from20 research participants who wore sensors on the back of their hands while performing the four table tennis strokes in a laboratory environment.The data were collected to verify the performance of the proposed models for wearable devices.Finally,the sensor and multi-scale CNN designed in this study achieved accuracy and F1 scores of 99.58%and 99.16%,respectively,for the four strokes.The accuracy for five-fold cross validation was 99.87%.This result also shows that the multi-scale convolutional neural network has better robustness after fivefold cross validation.展开更多
Climate change,which is the result of human activities,has wide-ranging impact.It poses a serious threat to human rights.Environmental rights are where the protection of the ecological environment and the development ...Climate change,which is the result of human activities,has wide-ranging impact.It poses a serious threat to human rights.Environmental rights are where the protection of the ecological environment and the development of human rights intersect.In view of the close relationship between the actions for addressing climate change and environmental rights,China should integrate the protection of environmental rights into the actions for addressing climate change,so as to achieve simultaneous development of both.In the process of coping with climate change,the right to climate stability that mainly pursues a“harmless”environment and the right to a more livable climate that pursues a“beautiful eco-environment”are specific manifestations of environmental rights and should be the priority of protection efforts.However,there are still some obstacles to achieving the coordinated development of the efforts to address climate change and the protection of environmental rights because traditional rights protection methods mainly give individuals subjective rights with the power to claim and are thus difficult to meet the needs of environmental rights protection in the context of climate change,and there are inherent value differences between responding to climate change and the realization of other human rights.Building a multi-level national obligation system to address climate change,giving full play to the role of courts in responding to climate change through moderate judicial activism,and coordinating the efforts to cope with climate change and the development of human rights under the guidance of a holistic system view are effective ways to overcome the aforementioned difficulties.展开更多
This paper deals with the security of stock market transactions within financial markets, particularly that of the West African Economic and Monetary Union (UEMOA). The confidentiality and integrity of sensitive data ...This paper deals with the security of stock market transactions within financial markets, particularly that of the West African Economic and Monetary Union (UEMOA). The confidentiality and integrity of sensitive data in the stock market being crucial, the implementation of robust systems which guarantee trust between the different actors is essential. We therefore proposed, after analyzing the limits of several security approaches in the literature, an architecture based on blockchain technology making it possible to both identify and reduce the vulnerabilities linked to the design, implementation work or the use of web applications used for transactions. Our proposal makes it possible, thanks to two-factor authentication via the Blockchain, to strengthen the security of investors’ accounts and the automated recording of transactions in the Blockchain while guaranteeing the integrity of stock market operations. It also provides an application vulnerability report. To validate our approach, we compared our results to those of three other security tools, at the level of different metrics. Our approach achieved the best performance in each case.展开更多
基金This work was supported by grants from the National Natural Science Foundation of China(42101306,4217107)the Natural Science Foundation of Shandong Province(ZR2021MD047),the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA2002040203)+2 种基金the Open Fund of the Key Laboratory of National Geographic Census and Monitoring,Ministry of Natural Resources(MNR)(2020NGCM02)the Open Fund of the Key Laboratory of Urban Land Resources Monitoring and Simulation,Ministry of Natural Resources(KF-2020-05-001)the Major Project of the High Resolution Earth Observation System of China(GFZX0404130304).
文摘Under the combined influence of climate change and human activities,vegetation ecosystem has undergone profound changes.It can be seen that there are obvious differences in the evolution patterns and driving mechanisms of vegetation ecosystem in different historical periods.Therefore,it is urgent to identify and reveal the dominant factors and their contribution rates in the vegetation change cycle.Based on the data of climate elements(sunshine hours,precipitation and temperature),human activities(population intensity and GDP intensity)and other natural factors(altitude,slope and aspect),this study explored the spatial and temporal evolution patterns of vegetation NDVI in the Yellow River Basin of China from 1989 to 2019 through a residual method,a trend analysis,and a gravity center model,and quantitatively distinguished the relative actions of climate change and human activities on vegetation evolution based on Geodetector model.The results showed that the spatial distribution of vegetation NDVI in the Yellow River Basin showed a decreasing trend from southeast to northwest.During 1981-2019,the temporal variation of vegetation NDVI showed an overall increasing trend.The gravity centers of average vegetation NDVI during the study period was distributed in Zhenyuan County,Gansu Province,and the center moved northeastwards from 1981 to 2019.During 1981-2000 and 2001-2019,the proportion of vegetation restoration areas promoted by the combined action of climate change and human activities was the largest.During the study period(1981-2019),the dominant factors influencing vegetation NDVI shifted from natural factors to human activities.These results could provide decision support for the protection and restoration of vegetation ecosystem in the Yellow River Basin.
基金supported by Generalitat Valenciana with HAAS(CIAICO/2021/039)the Spanish Ministry of Science and Innovation under the Project AVANTIA PID2020-114480RB-I00.
文摘The development of artificial intelligence(AI)and smart home technologies has driven the need for speech recognition-based solutions.This demand stems from the quest for more intuitive and natural interaction between users and smart devices in their homes.Speech recognition allows users to control devices and perform everyday actions through spoken commands,eliminating the need for physical interfaces or touch screens and enabling specific tasks such as turning on or off the light,heating,or lowering the blinds.The purpose of this study is to develop a speech-based classification model for recognizing human actions in the smart home.It seeks to demonstrate the effectiveness and feasibility of using machine learning techniques in predicting categories,subcategories,and actions from sentences.A dataset labeled with relevant information about categories,subcategories,and actions related to human actions in the smart home is used.The methodology uses machine learning techniques implemented in Python,extracting features using CountVectorizer to convert sentences into numerical representations.The results show that the classification model is able to accurately predict categories,subcategories,and actions based on sentences,with 82.99%accuracy for category,76.19%accuracy for subcategory,and 90.28%accuracy for action.The study concludes that using machine learning techniques is effective for recognizing and classifying human actions in the smart home,supporting its feasibility in various scenarios and opening new possibilities for advanced natural language processing systems in the field of AI and smart homes.
基金supporting of the Ministry of Science and Technology MOST(Grant No.MOST 108–2221-E-150–022-MY3,MOST 110–2634-F-019–002)the National Taiwan Ocean University,China.
文摘A system for classifying four basic table tennis strokes using wearable devices and deep learning networks is proposed in this study.The wearable device consisted of a six-axis sensor,Raspberry Pi 3,and a power bank.Multiple kernel sizes were used in convolutional neural network(CNN)to evaluate their performance for extracting features.Moreover,a multiscale CNN with two kernel sizes was used to perform feature fusion at different scales in a concatenated manner.The CNN achieved recognition of the four table tennis strokes.Experimental data were obtained from20 research participants who wore sensors on the back of their hands while performing the four table tennis strokes in a laboratory environment.The data were collected to verify the performance of the proposed models for wearable devices.Finally,the sensor and multi-scale CNN designed in this study achieved accuracy and F1 scores of 99.58%and 99.16%,respectively,for the four strokes.The accuracy for five-fold cross validation was 99.87%.This result also shows that the multi-scale convolutional neural network has better robustness after fivefold cross validation.
基金This paper is a phased achievement of the 2022 judicial research major project of the Supreme People’s Court,“Research on Judicial Service to Guarantee Carbon Peak and Carbon Neutrality”(Project Approval No.ZGFYZDKT20220301)the major project of the National Social Science Foundation,“Research on Legal Regulations for Biodiversity Protection under the Overall System View”(Project Approval No.19ZDA162).
文摘Climate change,which is the result of human activities,has wide-ranging impact.It poses a serious threat to human rights.Environmental rights are where the protection of the ecological environment and the development of human rights intersect.In view of the close relationship between the actions for addressing climate change and environmental rights,China should integrate the protection of environmental rights into the actions for addressing climate change,so as to achieve simultaneous development of both.In the process of coping with climate change,the right to climate stability that mainly pursues a“harmless”environment and the right to a more livable climate that pursues a“beautiful eco-environment”are specific manifestations of environmental rights and should be the priority of protection efforts.However,there are still some obstacles to achieving the coordinated development of the efforts to address climate change and the protection of environmental rights because traditional rights protection methods mainly give individuals subjective rights with the power to claim and are thus difficult to meet the needs of environmental rights protection in the context of climate change,and there are inherent value differences between responding to climate change and the realization of other human rights.Building a multi-level national obligation system to address climate change,giving full play to the role of courts in responding to climate change through moderate judicial activism,and coordinating the efforts to cope with climate change and the development of human rights under the guidance of a holistic system view are effective ways to overcome the aforementioned difficulties.
文摘This paper deals with the security of stock market transactions within financial markets, particularly that of the West African Economic and Monetary Union (UEMOA). The confidentiality and integrity of sensitive data in the stock market being crucial, the implementation of robust systems which guarantee trust between the different actors is essential. We therefore proposed, after analyzing the limits of several security approaches in the literature, an architecture based on blockchain technology making it possible to both identify and reduce the vulnerabilities linked to the design, implementation work or the use of web applications used for transactions. Our proposal makes it possible, thanks to two-factor authentication via the Blockchain, to strengthen the security of investors’ accounts and the automated recording of transactions in the Blockchain while guaranteeing the integrity of stock market operations. It also provides an application vulnerability report. To validate our approach, we compared our results to those of three other security tools, at the level of different metrics. Our approach achieved the best performance in each case.
