China has resolved its overall regional poverty in 2020 by attaining moderate societal prosperity.The country has entered a new development stage designed to achieve its second centenary goal.However,ecological fragil...China has resolved its overall regional poverty in 2020 by attaining moderate societal prosperity.The country has entered a new development stage designed to achieve its second centenary goal.However,ecological fragility and risk susceptibility have increased the risk of returning to ecological poverty.In this paper,the Liupan Mountain Region of China was used as a case study,and the counties were used as the scale to reveal the spatiotempora differentiation and influcing factors of the risk of returning to poverty in study area.The indicator data for returning to ecological poverty from 2011-2020 were collected and summarized in three dimensions:ecological,economic and social.The autoregressive integrated moving average model(ARIMA)time series and exponential smoothing method(ES)were used to predict the multidimensional indicators of returning to ecological poverty for 61 counties(districts)in the Liupan Mountain Region for 2021-2030.The back propagation neural network(BPNN)and geographic information system(GIS)were used to generate the spatial distribution and time variation for the index of the risk of returning to ecological poverty(RREP index).The results show that 1)ecological factors were the main factors in the risk of returning to ecological poverty in Liupan Mountain Region.2)The RREP index for the 61 counties(districts)exhibited a downward trend from 2021-2030.The RREP index declined more in medium-and high-risk areas than in low-risk areas.From 2021 to 2025,the RREP index exhibited a slight downward trend.From 2026 to2030,the RREP index was expected to decline faster,especially from 2029-2030.3)Based on the RREP index,it can be roughly divided into three types,namely,the high-risk areas,the medium-risk areas,and the low-risk areas.The natural resource conditions in lowrisk areas of returning to ecological poverty,were better than those in medium-and high-risk areas.展开更多
Modern financial theory, commonly known as portfolio theory, provides an analytical framework for the investment decision to be made under uncertainty. It is a well-established proposition in portfolio theory that whe...Modern financial theory, commonly known as portfolio theory, provides an analytical framework for the investment decision to be made under uncertainty. It is a well-established proposition in portfolio theory that whenever there is an imperfect correlation between returns risk is reduced by maintaining only a portion of wealth in any asset, or by selecting a portfolio according to expected returns and correlations between returns. The major improvement of the portfolio approaches over prior received theory is the incorporation of 1) the riskiness of an asset and 2) the addition from investing in any asset. The theme of this paper is to discuss how to propose a new mathematical model like that provided by Markowitz, which helps in choosing a nearly perfect portfolio and an efficient input/output. Besides applying this model to reality, the researcher uses game theory, stochastic and linear programming to provide the model proposed and then uses this model to select a perfect portfolio in the Cairo Stock Exchange. The results are fruitful and the researcher considers this model a new contribution to previous models.展开更多
Quantification of a mineral prospectivity mapping(MPM)heavily relies on geological,geophysical and geochemical analysis,which combines various evidence layers into a single map.However,MPM is subject to considerable u...Quantification of a mineral prospectivity mapping(MPM)heavily relies on geological,geophysical and geochemical analysis,which combines various evidence layers into a single map.However,MPM is subject to considerable uncertainty due to lack of understanding of the metallogenesis and limited spatial data samples.In this paper,we provide a framework that addresses how uncertainty in the evidence layers can be quantified and how such uncertainty is propagated to the prediction of mineral potential.More specifically,we use Monte Carlo simulation to jointly quantify uncertainties on all uncertain evidence variables,categorized into geological,geochemical and geophysical.On stochastically simulated sets of the multiple input layers,logistic regression is employed to produce different quantifications of the mineral potential in terms of probability.Uncertainties we address lie in the downscaling of magnetic data to a scale that makes such data comparable with known mineral deposits.Additionally,we deal with the limited spatial sampling of geochemistry that leads to spatial uncertainty.Next,we deal with the conceptual geological uncertainty related to how the spatial extent of the influence of evidential geological features such as faults,granite intrusions and sedimentary formations.Finally,we provide a novel way to interpret the established uncertainty in a risk-return analysis to decide areas with high potential but at the same time low uncertainty on that potential.Our methods are illustrated and compared with traditional deterministic MPM on a real case study of prospecting skarn Fe deposition in southwestern Fujian,China.展开更多
Although widely used, both the Markowitz model and VAR (Value at Risk) model have some limitations in evaluating the risk and return of stock investnent.By the analysis of the conceptions of risk and return,together w...Although widely used, both the Markowitz model and VAR (Value at Risk) model have some limitations in evaluating the risk and return of stock investnent.By the analysis of the conceptions of risk and return,together with the three hypotheses of technological analysis, a novelty model of metering and evaluating the risk and return of stock investnent is established.The major indicator of this model , risk-return ratio K, combines the characteristic indicators of risk and return. Regardless of the form of the risk-return probability density functions, this indicator K can always reflect the risk-return performances of the invested stocks clearly and accurately. How to use the model to make optimum investment and how to make portfolio combined with clustering analysis is also explained.展开更多
Based on the meteorological and geographic information data, with statistical method and the FloodArea model, the extreme daily rainfall of the 100-year return period in Hunhe River basin was established, through the ...Based on the meteorological and geographic information data, with statistical method and the FloodArea model, the extreme daily rainfall of the 100-year return period in Hunhe River basin was established, through the simulation of rainstorm and flood disaster, characteristics of flood depth in warning spot Cangshi village in the upstream of the river were analysed, and possible effect on community economy was also evaluated. Results showed that, the precipitation of 100-year return period occurred, the flood depth has been below 1.0 meter in the most areas of Hunhe River basin, the depth was between 1.0 meter and 2.5 meters in the part areas of Hunhe River basin, and the flood depth has been exceed 2.5 meters in a small part of Hunhe River basin. After the beginning of precipitation, the flood was concentrated in the upper reaches of the river. With the accumulation of precipitation and the passage of time, the flood pools into midstream and downstream. Precipitation lasted for 24 hours, the warning spot was flooded in the beginning of precipitation. With the accumulation of precipitation, water level of the river increases gradually. The depth of warning spot has passed 1.0 meter at the 07 time of the whole process, and the maximum value of flood depth at warning spot was 1.083 meters that occurred at the 19 time. The flood depth of warning spot decreased gradually after the precipitation stopping, and the depth has been below 0.2 meters, the flood of upstream ended. Up to the end of the upstream flood process, in the whole river, about one million five hundred and sixty thousand people were affected by flooding, and thirty-eight billion and two hundred million RMB of gross domestic product were lost, in addition, dry land and paddy field were affected greatly, but woodland and grassland were less affected.展开更多
基金Under the auspices of National Natural Science Foundation of China(No.42071230)。
文摘China has resolved its overall regional poverty in 2020 by attaining moderate societal prosperity.The country has entered a new development stage designed to achieve its second centenary goal.However,ecological fragility and risk susceptibility have increased the risk of returning to ecological poverty.In this paper,the Liupan Mountain Region of China was used as a case study,and the counties were used as the scale to reveal the spatiotempora differentiation and influcing factors of the risk of returning to poverty in study area.The indicator data for returning to ecological poverty from 2011-2020 were collected and summarized in three dimensions:ecological,economic and social.The autoregressive integrated moving average model(ARIMA)time series and exponential smoothing method(ES)were used to predict the multidimensional indicators of returning to ecological poverty for 61 counties(districts)in the Liupan Mountain Region for 2021-2030.The back propagation neural network(BPNN)and geographic information system(GIS)were used to generate the spatial distribution and time variation for the index of the risk of returning to ecological poverty(RREP index).The results show that 1)ecological factors were the main factors in the risk of returning to ecological poverty in Liupan Mountain Region.2)The RREP index for the 61 counties(districts)exhibited a downward trend from 2021-2030.The RREP index declined more in medium-and high-risk areas than in low-risk areas.From 2021 to 2025,the RREP index exhibited a slight downward trend.From 2026 to2030,the RREP index was expected to decline faster,especially from 2029-2030.3)Based on the RREP index,it can be roughly divided into three types,namely,the high-risk areas,the medium-risk areas,and the low-risk areas.The natural resource conditions in lowrisk areas of returning to ecological poverty,were better than those in medium-and high-risk areas.
文摘Modern financial theory, commonly known as portfolio theory, provides an analytical framework for the investment decision to be made under uncertainty. It is a well-established proposition in portfolio theory that whenever there is an imperfect correlation between returns risk is reduced by maintaining only a portion of wealth in any asset, or by selecting a portfolio according to expected returns and correlations between returns. The major improvement of the portfolio approaches over prior received theory is the incorporation of 1) the riskiness of an asset and 2) the addition from investing in any asset. The theme of this paper is to discuss how to propose a new mathematical model like that provided by Markowitz, which helps in choosing a nearly perfect portfolio and an efficient input/output. Besides applying this model to reality, the researcher uses game theory, stochastic and linear programming to provide the model proposed and then uses this model to select a perfect portfolio in the Cairo Stock Exchange. The results are fruitful and the researcher considers this model a new contribution to previous models.
基金supported by the National Natural Science Foundation of China(Nos.41972303 and 41772344)the Stanford Center for Earth Resources Forecasting。
文摘Quantification of a mineral prospectivity mapping(MPM)heavily relies on geological,geophysical and geochemical analysis,which combines various evidence layers into a single map.However,MPM is subject to considerable uncertainty due to lack of understanding of the metallogenesis and limited spatial data samples.In this paper,we provide a framework that addresses how uncertainty in the evidence layers can be quantified and how such uncertainty is propagated to the prediction of mineral potential.More specifically,we use Monte Carlo simulation to jointly quantify uncertainties on all uncertain evidence variables,categorized into geological,geochemical and geophysical.On stochastically simulated sets of the multiple input layers,logistic regression is employed to produce different quantifications of the mineral potential in terms of probability.Uncertainties we address lie in the downscaling of magnetic data to a scale that makes such data comparable with known mineral deposits.Additionally,we deal with the limited spatial sampling of geochemistry that leads to spatial uncertainty.Next,we deal with the conceptual geological uncertainty related to how the spatial extent of the influence of evidential geological features such as faults,granite intrusions and sedimentary formations.Finally,we provide a novel way to interpret the established uncertainty in a risk-return analysis to decide areas with high potential but at the same time low uncertainty on that potential.Our methods are illustrated and compared with traditional deterministic MPM on a real case study of prospecting skarn Fe deposition in southwestern Fujian,China.
文摘Although widely used, both the Markowitz model and VAR (Value at Risk) model have some limitations in evaluating the risk and return of stock investnent.By the analysis of the conceptions of risk and return,together with the three hypotheses of technological analysis, a novelty model of metering and evaluating the risk and return of stock investnent is established.The major indicator of this model , risk-return ratio K, combines the characteristic indicators of risk and return. Regardless of the form of the risk-return probability density functions, this indicator K can always reflect the risk-return performances of the invested stocks clearly and accurately. How to use the model to make optimum investment and how to make portfolio combined with clustering analysis is also explained.
文摘Based on the meteorological and geographic information data, with statistical method and the FloodArea model, the extreme daily rainfall of the 100-year return period in Hunhe River basin was established, through the simulation of rainstorm and flood disaster, characteristics of flood depth in warning spot Cangshi village in the upstream of the river were analysed, and possible effect on community economy was also evaluated. Results showed that, the precipitation of 100-year return period occurred, the flood depth has been below 1.0 meter in the most areas of Hunhe River basin, the depth was between 1.0 meter and 2.5 meters in the part areas of Hunhe River basin, and the flood depth has been exceed 2.5 meters in a small part of Hunhe River basin. After the beginning of precipitation, the flood was concentrated in the upper reaches of the river. With the accumulation of precipitation and the passage of time, the flood pools into midstream and downstream. Precipitation lasted for 24 hours, the warning spot was flooded in the beginning of precipitation. With the accumulation of precipitation, water level of the river increases gradually. The depth of warning spot has passed 1.0 meter at the 07 time of the whole process, and the maximum value of flood depth at warning spot was 1.083 meters that occurred at the 19 time. The flood depth of warning spot decreased gradually after the precipitation stopping, and the depth has been below 0.2 meters, the flood of upstream ended. Up to the end of the upstream flood process, in the whole river, about one million five hundred and sixty thousand people were affected by flooding, and thirty-eight billion and two hundred million RMB of gross domestic product were lost, in addition, dry land and paddy field were affected greatly, but woodland and grassland were less affected.
