To the Editor:We read with interest the article by Wang et al.,titled"Modeling the spread risk of dengue vector Aedes albopictus caused by environmental factors in Shanghai,China"[1].The use of ensemble ecol...To the Editor:We read with interest the article by Wang et al.,titled"Modeling the spread risk of dengue vector Aedes albopictus caused by environmental factors in Shanghai,China"[1].The use of ensemble ecological niche models to map Aedes albopictus distribution in urban Shanghai is both timely and methodologically sound.The identified drivers-vegetation index,temperature,and proximity to water-are well-known contributors to vector proliferation.However,one dimension remains notably underrepresented:human behavioral factors.展开更多
In this paper, from the viewpoint of the time value of money, we study the risk measures for portfolio vectors with discount factor. Cash subadditive risk measures for portfolio vectors are proposed. Representation re...In this paper, from the viewpoint of the time value of money, we study the risk measures for portfolio vectors with discount factor. Cash subadditive risk measures for portfolio vectors are proposed. Representation results are given by two different methods which are convex analysis and enlarging space. Especially, the method of convex analysis make the line of reasoning and the representation result be simpler. Meanwhile, spot and forward risk measures for portfolio vectors are also introduced, and the relationships between them are investigated.展开更多
Objective: To compile available data and to estimate the burden, characteristics and risks factors of cutaneous leishmaniasis(CL) in Mali. Methods: Articles in English and French were searched in Hinari, Google schola...Objective: To compile available data and to estimate the burden, characteristics and risks factors of cutaneous leishmaniasis(CL) in Mali. Methods: Articles in English and French were searched in Hinari, Google scholar and PubM ed. Unpublished studies were identified by searching in Google.com. Terms used were Cutaneous leishmaniasis Mali; Leishmaniasis Mali, Leishmania major Mali; or Phlebotomus Mali or Sergentomyia Mali. We select descriptive studies on CL and sandflies in Mali. Data were extracted and checked by the author, then analyzed by region, by study population and type of biological tests, meta-analysis approach with STATA software was used. Results: Nineteen published(n=19) and three unpublished were included. CL epidemiology was characterized by occurrence of clinical cases in different areas of Mali, outbreaks restricted to known areas of transmission and isolated cases diagnosed in travelers. In endemic areas, population at risk are young age persons, farmers, ranchers, housewives, teachers and military personnel. The annual incidence ranged from 290 to 580 cases of CL. Leishmania major is the main species encountered throughout the country(North Savanna, Sahel and Sub-Saharan areas), and Phlebotomus duboscqi has been identified as the vector and Sergentomyia(Spelaeomyia)darlingi as possible vector. The overall estimated prevalence of positive LST(Leishmanin Skin Test) was 22.1%. The overall frequency of CL disease among suspected cases was 40.3%. Conclusions: Although descriptive, hospital-based and cross-sectional studies are robust enough to determine the extent of CL in Mali; future well-designed eco-epidemiological studies at a nationwide scale are needed to fully characterize CL epidemiology and risk factors in Mali.展开更多
Risk management of projects is about the real time ev aluation and making of decisions proactively in order to maximize the probabilit y of achieving or surpassing the targets set for project objectives. Project objec...Risk management of projects is about the real time ev aluation and making of decisions proactively in order to maximize the probabilit y of achieving or surpassing the targets set for project objectives. Project objective generally includes three elements: time, cost, quality. Risk occurrin g in the projects will affect these three factors to some various degrees in the end. There are different emphases in each stage and integrated balanced goals b etween the three factors. A large complex engineering project generally consists of several stages each of which has variable objective combinations leading to variable important risks. In order to achieve strategic goals on the schedule under the restriction of lim ited resources, the paper gives the analysis of the so-called risk identificati on-assessment process on the basis of objective orientation. In this paper the set of involved mostly hazards is presented in terms of given objective weight v ector, and so is the model of risk ranking .By reducing the range of risk factor s step by step, risk manager could pay more attention to important ventures and effectively control of them. According to different objective combination at different stages, primary risk f actor sets at different stages are given. With the probability and their various effects to project objectives, evaluation of these sets is made aiming to r educing of the scope of risks and providing decision maker with a better decisio ns support. Successful projects are those, which focus on the relevant business objectives t hroughout the whole process and seek to information integration across project l ife cycle. This paper also introduces the idea of real time process of risk iden tification-assessment and presents a flow chart as a demonstration.展开更多
In order to improve the performance of support vector machine (SVM) applications in the field of credit risk evaluation, an adaptive Lq SVM model with Gauss kernel (ALqG-SVM) is proposed to evaluate credit risks. The ...In order to improve the performance of support vector machine (SVM) applications in the field of credit risk evaluation, an adaptive Lq SVM model with Gauss kernel (ALqG-SVM) is proposed to evaluate credit risks. The non-adaptive penalty of the object function is extended to (0, 2] to increase classification accuracy. To further improve the generalization performance of the proposed model, the Gauss kernel is introduced, thus the non-linear classification problem can be linearly separated in higher dimensional feature space. Two UCI credit datasets and a real life credit dataset from a US major commercial bank are used to check the efficiency of this model. Compared with other popular methods, satisfactory results are obtained through a novel method in the area of credit risk evaluation. So the new model is an excellent choice.展开更多
Rift Valley Fever (RVF) is an emerging, mosquito-borne disease with serious economical and negative implications on human and animal health. This study was conducted to verify the factors which influenced the spatial ...Rift Valley Fever (RVF) is an emerging, mosquito-borne disease with serious economical and negative implications on human and animal health. This study was conducted to verify the factors which influenced the spatial pattern of Rift Valley Fever occurrence and identified the high risk areas for the occurrence of the disease at Sinner State, Sudan. The normalized difference vegetation index (NDVI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite and rainfall data in addition to the point data of RVF clinical cases in humans were used in this study. In order to identify the RVF high risk areas, remote sensing data and rainfall data were integrated in a GIS with other information including, soil type, water body, DEM (Digital Elevation Model), and animal routes and analyzed using Spatial Analysis tools. The information on clinical cases was used for verification. The Normalized Difference Vegetation Index (NDVI) was used to describe vegetation patterns of the study area by calculating the mean NDVI. The results of the study showed that, RVF risk increased with the increase in vegetation cover (high NDVI values), and increase in rainfall, which both provided suitable conditions for disease vectors breeding and a good indicator for RVF epizootics. The study concluded that, identification of high risk area for RVF disease improved the understanding of the spatial distribution of the disease and helped in locating the areas where disease was likely to be endemic and therefore preparedness measures should be taken. The identification represents the first step of prospective predictions of RVF outbreaks and provides a baseline for improved early warning, control, response planning, and mitigation. Further detailed studies are recommended in this domain.展开更多
An approach which combines particle swarm optimization and support vector machine(PSO–SVM)is proposed to forecast large-scale goaf instability(LSGI).Firstly,influencing factors of goaf safety are analyzed,and followi...An approach which combines particle swarm optimization and support vector machine(PSO–SVM)is proposed to forecast large-scale goaf instability(LSGI).Firstly,influencing factors of goaf safety are analyzed,and following parameters were selected as evaluation indexes in the LSGI:uniaxial compressive strength(UCS)of rock,elastic modulus(E)of rock,rock quality designation(RQD),area ration of pillar(Sp),the ratio of width to height of the pillar(w/h),depth of ore body(H),volume of goaf(V),dip of ore body(a)and area of goaf(Sg).Then LSGI forecasting model by PSO-SVM was established according to the influencing factors.The performance of hybrid model(PSO+SVM=PSO–SVM)has been compared with the grid search method of support vector machine(GSM–SVM)model.The actual data of 40 goafs are applied to research the forecasting ability of the proposed method,and two cases of underground mine are also validated by the proposed model.The results indicated that the heuristic algorithm of PSO can speed up the SVM parameter optimization search,and the predictive ability of the PSO–SVM model with the RBF kernel function is acceptable and robust,which might hold a high potential to become a useful tool in goaf risky prediction research.展开更多
Using a geographic information system (GIS), the relations between a georeferenced data set of Culex pipiens s.l. collected in Portugal mainland during seven years (2006-2012) and meteorological and physiographic para...Using a geographic information system (GIS), the relations between a georeferenced data set of Culex pipiens s.l. collected in Portugal mainland during seven years (2006-2012) and meteorological and physiographic parameters are evaluated. This work is one of the results of a long-term surveillance program of pernicious insects that act as vectors of various diseases;its focus is on the possibility of prevention that can be achieved with abundance data. The focus on Culex pipiens is justified by its abundance and its competence as a vector for numerous health issues. The cumulative distribution of monthly captures by each meteorological parameter allows to compute thresholds corresponding to mosquito massive presence related to 90% of the captures. Using the weather parameters measured in the network of weather stations across the country, a monthly average of each parameter of interest (temperature, humidity, etc.) is computed and an interpolation of the results is made to produce raster maps corresponding to each month. The previously obtained thresholds are applied to each map, producing spatial masks with the relevant zones for each parameter. The intersection of the various masks for each month shows the most densely populated area of Culex, and the ensemble allows us to observe the evolution of mosquito presence through the critical season, which is from May to October at these latitudes. In parallel, mosquito abundance data are related to physiographic parameters. The relative distribution of female mosquitoes across land cover types in each month allows identifying which classes and seasons are most relevant. Orthometric altitude related to the presence of 90% of the catches shows the limits reached by mosquitoes in each month. The results are applied to the previously obtained climate envelopes, delimiting critical areas where the level of risk of transmission of the pathogens for which Culex pipiens is a competent vector is high and countermeasures should be concentrated, allowing its planning, and targeting on a monthly basis. The described procedure can be used with other relevant vectors in any region of the world, whenever abundance data is available.展开更多
Kernel-based methods work by embedding the data into a feature space and then searching linear hypothesis among the embedding data points. The performance is mostly affected by which kernel is used. A promising way is...Kernel-based methods work by embedding the data into a feature space and then searching linear hypothesis among the embedding data points. The performance is mostly affected by which kernel is used. A promising way is to learn the kernel from the data automatically. A general regularized risk functional （RRF） criterion for kernel matrix learning is proposed. Compared with the RRF criterion, general RRF criterion takes into account the geometric distributions of the embedding data points. It is proven that the distance between different geometric distdbutions can be estimated by their centroid distance in the reproducing kernel Hilbert space. Using this criterion for kernel matrix learning leads to a convex quadratically constrained quadratic programming （QCQP） problem. For several commonly used loss functions, their mathematical formulations are given. Experiment results on a collection of benchmark data sets demonstrate the effectiveness of the proposed method.展开更多
基金supported by Three-Year Initiative Plan for Strengthening Public Health System Construction in Shanghai(2023-2025)Key Discipline Project(No.GWVI-11.1-12).
文摘To the Editor:We read with interest the article by Wang et al.,titled"Modeling the spread risk of dengue vector Aedes albopictus caused by environmental factors in Shanghai,China"[1].The use of ensemble ecological niche models to map Aedes albopictus distribution in urban Shanghai is both timely and methodologically sound.The identified drivers-vegetation index,temperature,and proximity to water-are well-known contributors to vector proliferation.However,one dimension remains notably underrepresented:human behavioral factors.
基金Supported by the National Natural Science Foundation of China(11371284,11771343)
文摘In this paper, from the viewpoint of the time value of money, we study the risk measures for portfolio vectors with discount factor. Cash subadditive risk measures for portfolio vectors are proposed. Representation results are given by two different methods which are convex analysis and enlarging space. Especially, the method of convex analysis make the line of reasoning and the representation result be simpler. Meanwhile, spot and forward risk measures for portfolio vectors are also introduced, and the relationships between them are investigated.
基金supported by the UMI3189,FMERIEUX foundationFogarty International Center[grant number D43TW001589]
文摘Objective: To compile available data and to estimate the burden, characteristics and risks factors of cutaneous leishmaniasis(CL) in Mali. Methods: Articles in English and French were searched in Hinari, Google scholar and PubM ed. Unpublished studies were identified by searching in Google.com. Terms used were Cutaneous leishmaniasis Mali; Leishmaniasis Mali, Leishmania major Mali; or Phlebotomus Mali or Sergentomyia Mali. We select descriptive studies on CL and sandflies in Mali. Data were extracted and checked by the author, then analyzed by region, by study population and type of biological tests, meta-analysis approach with STATA software was used. Results: Nineteen published(n=19) and three unpublished were included. CL epidemiology was characterized by occurrence of clinical cases in different areas of Mali, outbreaks restricted to known areas of transmission and isolated cases diagnosed in travelers. In endemic areas, population at risk are young age persons, farmers, ranchers, housewives, teachers and military personnel. The annual incidence ranged from 290 to 580 cases of CL. Leishmania major is the main species encountered throughout the country(North Savanna, Sahel and Sub-Saharan areas), and Phlebotomus duboscqi has been identified as the vector and Sergentomyia(Spelaeomyia)darlingi as possible vector. The overall estimated prevalence of positive LST(Leishmanin Skin Test) was 22.1%. The overall frequency of CL disease among suspected cases was 40.3%. Conclusions: Although descriptive, hospital-based and cross-sectional studies are robust enough to determine the extent of CL in Mali; future well-designed eco-epidemiological studies at a nationwide scale are needed to fully characterize CL epidemiology and risk factors in Mali.
文摘Risk management of projects is about the real time ev aluation and making of decisions proactively in order to maximize the probabilit y of achieving or surpassing the targets set for project objectives. Project objective generally includes three elements: time, cost, quality. Risk occurrin g in the projects will affect these three factors to some various degrees in the end. There are different emphases in each stage and integrated balanced goals b etween the three factors. A large complex engineering project generally consists of several stages each of which has variable objective combinations leading to variable important risks. In order to achieve strategic goals on the schedule under the restriction of lim ited resources, the paper gives the analysis of the so-called risk identificati on-assessment process on the basis of objective orientation. In this paper the set of involved mostly hazards is presented in terms of given objective weight v ector, and so is the model of risk ranking .By reducing the range of risk factor s step by step, risk manager could pay more attention to important ventures and effectively control of them. According to different objective combination at different stages, primary risk f actor sets at different stages are given. With the probability and their various effects to project objectives, evaluation of these sets is made aiming to r educing of the scope of risks and providing decision maker with a better decisio ns support. Successful projects are those, which focus on the relevant business objectives t hroughout the whole process and seek to information integration across project l ife cycle. This paper also introduces the idea of real time process of risk iden tification-assessment and presents a flow chart as a demonstration.
基金The National Natural Science Foundation of China (No.70531040)the National Basic Research Program of China (973 Program) (No.2004CB720103)
文摘In order to improve the performance of support vector machine (SVM) applications in the field of credit risk evaluation, an adaptive Lq SVM model with Gauss kernel (ALqG-SVM) is proposed to evaluate credit risks. The non-adaptive penalty of the object function is extended to (0, 2] to increase classification accuracy. To further improve the generalization performance of the proposed model, the Gauss kernel is introduced, thus the non-linear classification problem can be linearly separated in higher dimensional feature space. Two UCI credit datasets and a real life credit dataset from a US major commercial bank are used to check the efficiency of this model. Compared with other popular methods, satisfactory results are obtained through a novel method in the area of credit risk evaluation. So the new model is an excellent choice.
文摘Rift Valley Fever (RVF) is an emerging, mosquito-borne disease with serious economical and negative implications on human and animal health. This study was conducted to verify the factors which influenced the spatial pattern of Rift Valley Fever occurrence and identified the high risk areas for the occurrence of the disease at Sinner State, Sudan. The normalized difference vegetation index (NDVI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite and rainfall data in addition to the point data of RVF clinical cases in humans were used in this study. In order to identify the RVF high risk areas, remote sensing data and rainfall data were integrated in a GIS with other information including, soil type, water body, DEM (Digital Elevation Model), and animal routes and analyzed using Spatial Analysis tools. The information on clinical cases was used for verification. The Normalized Difference Vegetation Index (NDVI) was used to describe vegetation patterns of the study area by calculating the mean NDVI. The results of the study showed that, RVF risk increased with the increase in vegetation cover (high NDVI values), and increase in rainfall, which both provided suitable conditions for disease vectors breeding and a good indicator for RVF epizootics. The study concluded that, identification of high risk area for RVF disease improved the understanding of the spatial distribution of the disease and helped in locating the areas where disease was likely to be endemic and therefore preparedness measures should be taken. The identification represents the first step of prospective predictions of RVF outbreaks and provides a baseline for improved early warning, control, response planning, and mitigation. Further detailed studies are recommended in this domain.
基金supported by the National Basic Research Program Project of China(No.2010CB732004)the National Natural Science Foundation Project of China(Nos.50934006 and41272304)+2 种基金the Graduated Students’ResearchInnovation Fund Project of Hunan Province of China(No.CX2011B119)the Scholarship Award for Excellent Doctoral Student of Ministry of Education of China and the Valuable Equipment Open Sharing Fund of Central South University(No.1343-76140000022)
文摘An approach which combines particle swarm optimization and support vector machine(PSO–SVM)is proposed to forecast large-scale goaf instability(LSGI).Firstly,influencing factors of goaf safety are analyzed,and following parameters were selected as evaluation indexes in the LSGI:uniaxial compressive strength(UCS)of rock,elastic modulus(E)of rock,rock quality designation(RQD),area ration of pillar(Sp),the ratio of width to height of the pillar(w/h),depth of ore body(H),volume of goaf(V),dip of ore body(a)and area of goaf(Sg).Then LSGI forecasting model by PSO-SVM was established according to the influencing factors.The performance of hybrid model(PSO+SVM=PSO–SVM)has been compared with the grid search method of support vector machine(GSM–SVM)model.The actual data of 40 goafs are applied to research the forecasting ability of the proposed method,and two cases of underground mine are also validated by the proposed model.The results indicated that the heuristic algorithm of PSO can speed up the SVM parameter optimization search,and the predictive ability of the PSO–SVM model with the RBF kernel function is acceptable and robust,which might hold a high potential to become a useful tool in goaf risky prediction research.
文摘Using a geographic information system (GIS), the relations between a georeferenced data set of Culex pipiens s.l. collected in Portugal mainland during seven years (2006-2012) and meteorological and physiographic parameters are evaluated. This work is one of the results of a long-term surveillance program of pernicious insects that act as vectors of various diseases;its focus is on the possibility of prevention that can be achieved with abundance data. The focus on Culex pipiens is justified by its abundance and its competence as a vector for numerous health issues. The cumulative distribution of monthly captures by each meteorological parameter allows to compute thresholds corresponding to mosquito massive presence related to 90% of the captures. Using the weather parameters measured in the network of weather stations across the country, a monthly average of each parameter of interest (temperature, humidity, etc.) is computed and an interpolation of the results is made to produce raster maps corresponding to each month. The previously obtained thresholds are applied to each map, producing spatial masks with the relevant zones for each parameter. The intersection of the various masks for each month shows the most densely populated area of Culex, and the ensemble allows us to observe the evolution of mosquito presence through the critical season, which is from May to October at these latitudes. In parallel, mosquito abundance data are related to physiographic parameters. The relative distribution of female mosquitoes across land cover types in each month allows identifying which classes and seasons are most relevant. Orthometric altitude related to the presence of 90% of the catches shows the limits reached by mosquitoes in each month. The results are applied to the previously obtained climate envelopes, delimiting critical areas where the level of risk of transmission of the pathogens for which Culex pipiens is a competent vector is high and countermeasures should be concentrated, allowing its planning, and targeting on a monthly basis. The described procedure can be used with other relevant vectors in any region of the world, whenever abundance data is available.
基金supported by the National Natural Science Fundation of China (60736021)the Joint Funds of NSFC-Guangdong Province(U0735003)
文摘Kernel-based methods work by embedding the data into a feature space and then searching linear hypothesis among the embedding data points. The performance is mostly affected by which kernel is used. A promising way is to learn the kernel from the data automatically. A general regularized risk functional （RRF） criterion for kernel matrix learning is proposed. Compared with the RRF criterion, general RRF criterion takes into account the geometric distributions of the embedding data points. It is proven that the distance between different geometric distdbutions can be estimated by their centroid distance in the reproducing kernel Hilbert space. Using this criterion for kernel matrix learning leads to a convex quadratically constrained quadratic programming （QCQP） problem. For several commonly used loss functions, their mathematical formulations are given. Experiment results on a collection of benchmark data sets demonstrate the effectiveness of the proposed method.
