To give play to the role of agricultural catastrophe risk fund in spreading agricultural catastrophe risk,we select natural conditions,economic conditions,social conditions,as the external vulnerability assessment ind...To give play to the role of agricultural catastrophe risk fund in spreading agricultural catastrophe risk,we select natural conditions,economic conditions,social conditions,as the external vulnerability assessment indicators;select commodity rate of agricultural products,substitutability of agricultural products,the extent of agricultural products being related to the national economy and the people's livelihood,as the internal vulnerability assessment indicators.We assign weight to indicators using Analytic Hierarchy Process,and establish assessment indicator system of agricultural catastrophe vulnerability,to analyze the compensation for losses of different agricultural products arising from agricultural catastrophe in different regions.And we take the case of rice in Sichuan Province,to demonstrate the role this indicator system.展开更多
The objective of the present study is to define two important aspects of the computer operating system concerning the number of its vulnerabilities behavior. We identify the Vulnerability Intensity Function (VIF), and...The objective of the present study is to define two important aspects of the computer operating system concerning the number of its vulnerabilities behavior. We identify the Vulnerability Intensity Function (VIF), and the Vulnerability Index Indicator (VII) of a computer operating network. Both of these functions, VIF and VII are entities of the stochastic process that we have identified, which characterizes the probabilistic behavior of the number of vulnerabilities of a computer operating network. The VIF identifies the rate at which the number of vulnerabilities changes with respect to time. The VII is an important index indicator that conveys the following information about the number of vulnerabilities of Desktop Operating Systems: the numbers are increasing, decreasing, or remaining the same at a particular time of interest. This decision type of index indicator is crucial in every strategic planning and decision-making. The proposed VIF and VII illustrate their importance by using real data for Microsoft Windows Operating Systems 10, 8, 7, and Apple MacOS. The results of the actual data attest to the importance of VIF and VII in the cybersecurity problem we are currently facing.展开更多
Land use is shaped by the interactions between nature and society, and these interactions can inhibit environmental sustainability and deplete the natural capital that provides the ecosystem services upon which humans...Land use is shaped by the interactions between nature and society, and these interactions can inhibit environmental sustainability and deplete the natural capital that provides the ecosystem services upon which humans depend. Urbanity Index and Landscape Vulnerability Indicator have been proposed to improve the impact interpretability of land use changes on sub-basin sustainability for local scenarios of biodiversity conservation. A time series of LandSat 5 Thematic Mapper remote sensing data from São Carlos municipality, Southeastern Brazil, for the years 1989, 2004 and 2014 revealed that land use changes in the sub-basins do not take place in a progressive and gradual way. Over the 25-year period, the main trends showed the loss and increase of forest cover so that it has remained quantitatively similar over time due to reduced agricultural land use. The aggregation of both indicators enabled the identification of greater naturalness and lower vulnerability, as well as lower naturalness and higher vulnerability under local sub-basin conditions, pointing the need for different strategies for sub-basin biodiversity conservation and sustainability. These preliminary scenarios provide a way to communicate problems of environmental sustainability at different landscape scales to the scientific community as well as to planners, policy makers and the broader public.展开更多
Potential of the Random Forest Model on mapping of different desertification processes was studied in Muttuma watershed of mid-Murrumbidgee river region of New South Wales,Australia.Desertification vulnerability index...Potential of the Random Forest Model on mapping of different desertification processes was studied in Muttuma watershed of mid-Murrumbidgee river region of New South Wales,Australia.Desertification vulnerability index was developed using climate,terrain,vegetation,soil and land quality indices to identify environmentally sensitive areas for desertification.Random Forest Model(RFM)was used to predict the different desertification processes such as soil erosion,salinization and waterlogging in the watershed and the information needed to train classification algorithms was obtained from satellite imagery interpretation and ground truth data.Climatic factors(evaporation,rainfall,temperature),terrain factors(aspect,slope,slope length,steepness,and wetness index),soil properties(pH,organic carbon,clay and sand content)and vulnerability indices were used as an explanatory variable.Classification accuracy and kappa index were calculated for training and testing datasets.We recorded an overall accuracy rate of 87.7%and 72.1%for training and testing sites,respectively.We found larger discrepancies between overall accuracy rate and kappa index for testing datasets(72.2%and 27.5%,respectively)suggesting that all the classes are not predicted well.The prediction of soil erosion and no desertification process was good and poor for salinization and water-logging process.Overall,the results observed give a new idea of using the knowledge of desertification process in training areas that can be used to predict the desertification processes at unvisited areas.展开更多
文摘To give play to the role of agricultural catastrophe risk fund in spreading agricultural catastrophe risk,we select natural conditions,economic conditions,social conditions,as the external vulnerability assessment indicators;select commodity rate of agricultural products,substitutability of agricultural products,the extent of agricultural products being related to the national economy and the people's livelihood,as the internal vulnerability assessment indicators.We assign weight to indicators using Analytic Hierarchy Process,and establish assessment indicator system of agricultural catastrophe vulnerability,to analyze the compensation for losses of different agricultural products arising from agricultural catastrophe in different regions.And we take the case of rice in Sichuan Province,to demonstrate the role this indicator system.
文摘The objective of the present study is to define two important aspects of the computer operating system concerning the number of its vulnerabilities behavior. We identify the Vulnerability Intensity Function (VIF), and the Vulnerability Index Indicator (VII) of a computer operating network. Both of these functions, VIF and VII are entities of the stochastic process that we have identified, which characterizes the probabilistic behavior of the number of vulnerabilities of a computer operating network. The VIF identifies the rate at which the number of vulnerabilities changes with respect to time. The VII is an important index indicator that conveys the following information about the number of vulnerabilities of Desktop Operating Systems: the numbers are increasing, decreasing, or remaining the same at a particular time of interest. This decision type of index indicator is crucial in every strategic planning and decision-making. The proposed VIF and VII illustrate their importance by using real data for Microsoft Windows Operating Systems 10, 8, 7, and Apple MacOS. The results of the actual data attest to the importance of VIF and VII in the cybersecurity problem we are currently facing.
文摘Land use is shaped by the interactions between nature and society, and these interactions can inhibit environmental sustainability and deplete the natural capital that provides the ecosystem services upon which humans depend. Urbanity Index and Landscape Vulnerability Indicator have been proposed to improve the impact interpretability of land use changes on sub-basin sustainability for local scenarios of biodiversity conservation. A time series of LandSat 5 Thematic Mapper remote sensing data from São Carlos municipality, Southeastern Brazil, for the years 1989, 2004 and 2014 revealed that land use changes in the sub-basins do not take place in a progressive and gradual way. Over the 25-year period, the main trends showed the loss and increase of forest cover so that it has remained quantitatively similar over time due to reduced agricultural land use. The aggregation of both indicators enabled the identification of greater naturalness and lower vulnerability, as well as lower naturalness and higher vulnerability under local sub-basin conditions, pointing the need for different strategies for sub-basin biodiversity conservation and sustainability. These preliminary scenarios provide a way to communicate problems of environmental sustainability at different landscape scales to the scientific community as well as to planners, policy makers and the broader public.
文摘Potential of the Random Forest Model on mapping of different desertification processes was studied in Muttuma watershed of mid-Murrumbidgee river region of New South Wales,Australia.Desertification vulnerability index was developed using climate,terrain,vegetation,soil and land quality indices to identify environmentally sensitive areas for desertification.Random Forest Model(RFM)was used to predict the different desertification processes such as soil erosion,salinization and waterlogging in the watershed and the information needed to train classification algorithms was obtained from satellite imagery interpretation and ground truth data.Climatic factors(evaporation,rainfall,temperature),terrain factors(aspect,slope,slope length,steepness,and wetness index),soil properties(pH,organic carbon,clay and sand content)and vulnerability indices were used as an explanatory variable.Classification accuracy and kappa index were calculated for training and testing datasets.We recorded an overall accuracy rate of 87.7%and 72.1%for training and testing sites,respectively.We found larger discrepancies between overall accuracy rate and kappa index for testing datasets(72.2%and 27.5%,respectively)suggesting that all the classes are not predicted well.The prediction of soil erosion and no desertification process was good and poor for salinization and water-logging process.Overall,the results observed give a new idea of using the knowledge of desertification process in training areas that can be used to predict the desertification processes at unvisited areas.
