Based on the panel data, we analyze the US commercial banks＇ CRT. According to the study, we find that the introduction of CRT will increase the level of banks＇ liquid risk. The performance of bank mainly is that it...Based on the panel data, we analyze the US commercial banks＇ CRT. According to the study, we find that the introduction of CRT will increase the level of banks＇ liquid risk. The performance of bank mainly is that its supervision and review of risk will drop, based on the impact of asymmetric information, commercial Banks transfer the bad loans to investors. Through the analysis we can see that after the transfer of credit risk in commercial bank did not increase income and reduce risk. Because commercial Banks can extend more bad loans to expand its lending scale, and bad loans will increase the bank overall risk.展开更多
This study explores risk transfer options that precarious and marginal urban communities could use to protect themselves from future damages and losses generated by socio-natural hazards and disasters at the individua...This study explores risk transfer options that precarious and marginal urban communities could use to protect themselves from future damages and losses generated by socio-natural hazards and disasters at the individual and community levels. The design is framed within an evidence-based disaster risk reduction(DRR) strategy and follows the case study research approach. We analyze the2018 Neighborhood Approach for DRR programming evaluation carried out in four Latin American cities’ informal settlements and review relevant risk transfer experiences aimed at vulnerable populations. We calculate the pure risk premium for the four cases selected, using a previous catastrophe risk assessment for earthquakes and landslides. We propose three risk transfer options based on our analysis:(1) voluntary collective insurance;(2) structural reinforcement with a comprehensive housing insurance;and(3) hybrid parametric insurance. Risk transfer mechanisms conventionally focus on residual risk management. Here, due to the precariousness of the analyzed urban settings, the proposed alternatives go beyond the management of just residual risk to positively impact the beneficiaries’ quality of life and the reduction of the built environment’s physical vulnerability in the short and medium terms. Our study proposes a prospective estimation of future risk despite the limitations of data availability. This study opens a window to new approaches and proposes a systematic process to design DRR policy aimed at the poor and vulnerable strata of society.展开更多
Personal credit risk assessment is an important part of the development of financial enterprises. Big data credit investigation is an inevitable trend of personal credit risk assessment, but some data are missing and ...Personal credit risk assessment is an important part of the development of financial enterprises. Big data credit investigation is an inevitable trend of personal credit risk assessment, but some data are missing and the amount of data is small, so it is difficult to train. At the same time, for different financial platforms, we need to use different models to train according to the characteristics of the current samples, which is time-consuming. <span style="font-family:Verdana;">In view of</span><span style="font-family:Verdana;"> these two problems, this paper uses the idea of transfer learning to build a transferable personal credit risk model based on Instance-based Transfer Learning (Instance-based TL). The model balances the weight of the samples in the source domain, and migrates the existing large dataset samples to the target domain of small samples, and finds out the commonness between them. At the same time, we have done a lot of experiments on the selection of base learners, including traditional machine learning algorithms and ensemble learning algorithms, such as decision tree, logistic regression, </span><span style="font-family:Verdana;">xgboost</span> <span style="font-family:Verdana;">and</span><span style="font-family:Verdana;"> so on. The datasets are from P2P platform and bank, the results show that the AUC value of Instance-based TL is 24% higher than that of the traditional machine learning model, which fully proves that the model in this paper has good application value. The model’s evaluation uses AUC, prediction, recall, F1. These criteria prove that this model has good application value from many aspects. At present, we are trying to apply this model to more fields to improve the robustness and applicability of the model;on the other hand, we are trying to do more in-depth research on domain adaptation to enrich the model.</span>展开更多
The activity concentrations of radionuclides, absorbed dose rate, excess lifetime cancer risk, and soil-to-plant transfer factor have been evaluated in soil and crop samples from Jalingo and Wukari Local Government Ar...The activity concentrations of radionuclides, absorbed dose rate, excess lifetime cancer risk, and soil-to-plant transfer factor have been evaluated in soil and crop samples from Jalingo and Wukari Local Government Area of Taraba State, Nigeria. The activity concentrations were determined with the aid of High Purity Germanium detector. The absorbed dose and excess lifetime cancer risk were evaluated and forecasted for 60 years using the ResRad off-site model. The average activity concentration of <sup>40</sup>K, <sup>232</sup>Th, and <sup>238</sup>U in the soil samples were 633.13, 141.15, and 71.20 Bq·kg<sup>-1</sup> respectively, for the Jalingo study area, and while that of the Wukari study area was;199.21, 87.23, and 25.37 Bq·kg<sup>-1</sup> respectively. The average soil-to-plant transfer factors for <sup>40</sup>K, <sup>232</sup>Th, and <sup>238</sup>U were 0.51, 0.10, and 0.27 respectively for the Jalingo study area while that of Wukari are 0.40, 0.57, and 0.74 respectively. The mean annual effective dose equivalent for the study area is higher than the world average of 0.07 mS·vy<sup>-1</sup>. The excess lifetime cancer risk for the study areas has values that are higher than the safety limit. The ResRed model showed that direct radiation from the crops is the major contributor to excess cancer risk among other pathways. The radiological hazard indices reveal health risks to farmers, especially in the Jalingo area.展开更多
As a variant index, variation has an inherent shortcoming that it can only reflect the static fluctuation of the crop. This paper makes complementary analysis about it on the basis of the comment on Miranda＇s approac...As a variant index, variation has an inherent shortcoming that it can only reflect the static fluctuation of the crop. This paper makes complementary analysis about it on the basis of the comment on Miranda＇s approach of β index and goes on to analyze the β index approach under the condition of three kinds of crop insurance plans, β index approach has the advantage that it can dynamically reflect the risk transfer effect of crop insurance plan. At the same insurance level, the smaller the β index is, the better the corresponding risk transfer effect of crop insurance plan is; And vice versa.展开更多
The possible health risks of heavy metals contamination to local population through food chain were evaluated in Beijing and Tianjin city cluster, China, where have a long history of sewage irrigation. The transfer fa...The possible health risks of heavy metals contamination to local population through food chain were evaluated in Beijing and Tianjin city cluster, China, where have a long history of sewage irrigation. The transfer factors （TF） for heavy metals from soil to vegetables for six elements including Cu, Zn, Pb, Cr, As and Cd were calculated and the pollution load indexes （PLI） were also assessed. Results indicate that only Cd exceeded the maximum acceptable limit in these sites. So far, the heavy metal concentrations in soils and vegetables were all below the permissible limits set by the Ministry of Environmental Protection of China and World Health Organization. The transfer factors of six heavy metals showed the trend as Cd 〉 Zn 〉 Cu 〉 Pb 〉 As 〉 Cr, which were dependent on the vegetable species. The estimated dietary intakes of Cu, Zn, Pb, Cr, As and Cd were far below the tolerable limits and the target hazard quotient （THQ） values were less than 1, which suggested that the health risks of heavy metals exposure through consuming vegetables were generally assumed to be safe.展开更多
Contamination of soil and agricultural products by heavy metals resulting from rapid industrial development has caused major concern. In this study, we investigated heavy metal （Cu, Zn, Pb, Cr, Hg and Cd） concentrat...Contamination of soil and agricultural products by heavy metals resulting from rapid industrial development has caused major concern. In this study, we investigated heavy metal （Cu, Zn, Pb, Cr, Hg and Cd） concentrations in rice and garden vegetables, as well as in cultivated soils, in a rural-industrial developed region in southern Jiangsu, China, and estimated the potential health risks of metals to the inhabitants via consumption of locally produced rice and garden vegetables. A questionnaire-based survey on dietary consumption rates of foodstuffs showed that rice and vegetables accounted for 64% of total foodstuffs consumed, and over 60% of rice and vegetables were grown in the local region. Average concentrations of Cr, Cu, Zn, Cd, Hg and Pb were 0.75, 2.64, 12.00, 0.014, 0.006 and 0.054 mg/kg dw （dry weight） in rice and were 0.67, 1.18, 4.34, 0.011, 0.002 and 0.058 mg/kg fw （fresh weight） in garden vegetables, respectively. These values were all below the maximum allowable concentration in food in China except for Cr in vegetables. Leafy vegetables had higher metal concentrations than solanaceae vegetables. Average daily intake of Cr, Cu, Zn, Cd, Hg and Pb through the consumption of rice and garden vegetables were 5.66, 16.90, 74.21, 0.10, 0.04 and 0.43 μg/（kg·day）, respectively. Although Hazard Quotient values of individual metals were all lower than 1, when all six metal intakes via self-planted rice and garden vegetables were combined, the Hazard Index value was close to 1. Potential health risks from exposure to heavy metals in self-planted rice and garden vegetables need more attention.展开更多
Acute Kidney Injury (AKI) is one of the most common acute and critical illnesses in general wards and intensive care units. Its high morbidity and high fatality rate have become a major global public health problem. T...Acute Kidney Injury (AKI) is one of the most common acute and critical illnesses in general wards and intensive care units. Its high morbidity and high fatality rate have become a major global public health problem. There are often serious lags in clinical diagnosis of AKI. Early diagnosis and timely intervention and effective care become critical. The use of electronic medical record data to build an AKI risk prediction model has been proven to help prevent the occurrence of AKI. However, in actual clinical applications, the distribution of historical data and new data will continue to vary over time, resulting in a significant decrease in the performance of the model. How to solve the problem of model performance degradation over time will be a core challenge for the long-term use of predictive models in clinical applications. Aiming at the above problems, this paper studies the classic Transfer-Stacking model migration algorithm. Aiming at the lack of this algorithm, such as the loss of a large amount of feature information of the target domain and poor fit when integrating the model of the target domain, the Accumulate-Transfer-Stacking algorithm is proposed to improve it. Improvements include: 1) Optimize the input vector and model integration algorithm of Transfer-Stacking’s target domain model. 2) Optimize Transfer-Stacking from a single-source domain model to a multi-source domain model. The experimental results show that for the improved algorithm proposed in this paper when the data is sufficient and insufficient, the average AUC value of the model on the data of subsequent years is 0.89 and 0.87, and the average F1 Score value is 0.45 and 0.36. Moreover, this method is significantly better than the unimproved Transfer-Stacking algorithm and baseline method, and can effectively overcome the problem of data distribution heterogeneity caused by time factors.展开更多
文摘Based on the panel data, we analyze the US commercial banks＇ CRT. According to the study, we find that the introduction of CRT will increase the level of banks＇ liquid risk. The performance of bank mainly is that its supervision and review of risk will drop, based on the impact of asymmetric information, commercial Banks transfer the bad loans to investors. Through the analysis we can see that after the transfer of credit risk in commercial bank did not increase income and reduce risk. Because commercial Banks can extend more bad loans to expand its lending scale, and bad loans will increase the bank overall risk.
基金supported by USAID/OFDA under Cooperative Agreement # AID-OFDA-A-16-00019 with Florida International University.
文摘This study explores risk transfer options that precarious and marginal urban communities could use to protect themselves from future damages and losses generated by socio-natural hazards and disasters at the individual and community levels. The design is framed within an evidence-based disaster risk reduction(DRR) strategy and follows the case study research approach. We analyze the2018 Neighborhood Approach for DRR programming evaluation carried out in four Latin American cities’ informal settlements and review relevant risk transfer experiences aimed at vulnerable populations. We calculate the pure risk premium for the four cases selected, using a previous catastrophe risk assessment for earthquakes and landslides. We propose three risk transfer options based on our analysis:(1) voluntary collective insurance;(2) structural reinforcement with a comprehensive housing insurance;and(3) hybrid parametric insurance. Risk transfer mechanisms conventionally focus on residual risk management. Here, due to the precariousness of the analyzed urban settings, the proposed alternatives go beyond the management of just residual risk to positively impact the beneficiaries’ quality of life and the reduction of the built environment’s physical vulnerability in the short and medium terms. Our study proposes a prospective estimation of future risk despite the limitations of data availability. This study opens a window to new approaches and proposes a systematic process to design DRR policy aimed at the poor and vulnerable strata of society.
文摘Personal credit risk assessment is an important part of the development of financial enterprises. Big data credit investigation is an inevitable trend of personal credit risk assessment, but some data are missing and the amount of data is small, so it is difficult to train. At the same time, for different financial platforms, we need to use different models to train according to the characteristics of the current samples, which is time-consuming. <span style="font-family:Verdana;">In view of</span><span style="font-family:Verdana;"> these two problems, this paper uses the idea of transfer learning to build a transferable personal credit risk model based on Instance-based Transfer Learning (Instance-based TL). The model balances the weight of the samples in the source domain, and migrates the existing large dataset samples to the target domain of small samples, and finds out the commonness between them. At the same time, we have done a lot of experiments on the selection of base learners, including traditional machine learning algorithms and ensemble learning algorithms, such as decision tree, logistic regression, </span><span style="font-family:Verdana;">xgboost</span> <span style="font-family:Verdana;">and</span><span style="font-family:Verdana;"> so on. The datasets are from P2P platform and bank, the results show that the AUC value of Instance-based TL is 24% higher than that of the traditional machine learning model, which fully proves that the model in this paper has good application value. The model’s evaluation uses AUC, prediction, recall, F1. These criteria prove that this model has good application value from many aspects. At present, we are trying to apply this model to more fields to improve the robustness and applicability of the model;on the other hand, we are trying to do more in-depth research on domain adaptation to enrich the model.</span>
文摘The activity concentrations of radionuclides, absorbed dose rate, excess lifetime cancer risk, and soil-to-plant transfer factor have been evaluated in soil and crop samples from Jalingo and Wukari Local Government Area of Taraba State, Nigeria. The activity concentrations were determined with the aid of High Purity Germanium detector. The absorbed dose and excess lifetime cancer risk were evaluated and forecasted for 60 years using the ResRad off-site model. The average activity concentration of <sup>40</sup>K, <sup>232</sup>Th, and <sup>238</sup>U in the soil samples were 633.13, 141.15, and 71.20 Bq·kg<sup>-1</sup> respectively, for the Jalingo study area, and while that of the Wukari study area was;199.21, 87.23, and 25.37 Bq·kg<sup>-1</sup> respectively. The average soil-to-plant transfer factors for <sup>40</sup>K, <sup>232</sup>Th, and <sup>238</sup>U were 0.51, 0.10, and 0.27 respectively for the Jalingo study area while that of Wukari are 0.40, 0.57, and 0.74 respectively. The mean annual effective dose equivalent for the study area is higher than the world average of 0.07 mS·vy<sup>-1</sup>. The excess lifetime cancer risk for the study areas has values that are higher than the safety limit. The ResRed model showed that direct radiation from the crops is the major contributor to excess cancer risk among other pathways. The radiological hazard indices reveal health risks to farmers, especially in the Jalingo area.
文摘As a variant index, variation has an inherent shortcoming that it can only reflect the static fluctuation of the crop. This paper makes complementary analysis about it on the basis of the comment on Miranda＇s approach of β index and goes on to analyze the β index approach under the condition of three kinds of crop insurance plans, β index approach has the advantage that it can dynamically reflect the risk transfer effect of crop insurance plan. At the same insurance level, the smaller the β index is, the better the corresponding risk transfer effect of crop insurance plan is; And vice versa.
基金supported by the Major Projects of Knowledge Innovation Program of Chinese Academy of Sciences(No. KZCX2-YW-Q02-05)the Beijing Science and Technology Program (No. D101105046410004)
文摘The possible health risks of heavy metals contamination to local population through food chain were evaluated in Beijing and Tianjin city cluster, China, where have a long history of sewage irrigation. The transfer factors （TF） for heavy metals from soil to vegetables for six elements including Cu, Zn, Pb, Cr, As and Cd were calculated and the pollution load indexes （PLI） were also assessed. Results indicate that only Cd exceeded the maximum acceptable limit in these sites. So far, the heavy metal concentrations in soils and vegetables were all below the permissible limits set by the Ministry of Environmental Protection of China and World Health Organization. The transfer factors of six heavy metals showed the trend as Cd 〉 Zn 〉 Cu 〉 Pb 〉 As 〉 Cr, which were dependent on the vegetable species. The estimated dietary intakes of Cu, Zn, Pb, Cr, As and Cd were far below the tolerable limits and the target hazard quotient （THQ） values were less than 1, which suggested that the health risks of heavy metals exposure through consuming vegetables were generally assumed to be safe.
基金supported by the National Natural Science Foundation of China(No.40871231)the Key Technologies R&D Program for the 11th Five Year Plan(No.2006BAJ10B03)the Ministry of Science and Technology of People's Republic of China
文摘Contamination of soil and agricultural products by heavy metals resulting from rapid industrial development has caused major concern. In this study, we investigated heavy metal （Cu, Zn, Pb, Cr, Hg and Cd） concentrations in rice and garden vegetables, as well as in cultivated soils, in a rural-industrial developed region in southern Jiangsu, China, and estimated the potential health risks of metals to the inhabitants via consumption of locally produced rice and garden vegetables. A questionnaire-based survey on dietary consumption rates of foodstuffs showed that rice and vegetables accounted for 64% of total foodstuffs consumed, and over 60% of rice and vegetables were grown in the local region. Average concentrations of Cr, Cu, Zn, Cd, Hg and Pb were 0.75, 2.64, 12.00, 0.014, 0.006 and 0.054 mg/kg dw （dry weight） in rice and were 0.67, 1.18, 4.34, 0.011, 0.002 and 0.058 mg/kg fw （fresh weight） in garden vegetables, respectively. These values were all below the maximum allowable concentration in food in China except for Cr in vegetables. Leafy vegetables had higher metal concentrations than solanaceae vegetables. Average daily intake of Cr, Cu, Zn, Cd, Hg and Pb through the consumption of rice and garden vegetables were 5.66, 16.90, 74.21, 0.10, 0.04 and 0.43 μg/（kg·day）, respectively. Although Hazard Quotient values of individual metals were all lower than 1, when all six metal intakes via self-planted rice and garden vegetables were combined, the Hazard Index value was close to 1. Potential health risks from exposure to heavy metals in self-planted rice and garden vegetables need more attention.
文摘Acute Kidney Injury (AKI) is one of the most common acute and critical illnesses in general wards and intensive care units. Its high morbidity and high fatality rate have become a major global public health problem. There are often serious lags in clinical diagnosis of AKI. Early diagnosis and timely intervention and effective care become critical. The use of electronic medical record data to build an AKI risk prediction model has been proven to help prevent the occurrence of AKI. However, in actual clinical applications, the distribution of historical data and new data will continue to vary over time, resulting in a significant decrease in the performance of the model. How to solve the problem of model performance degradation over time will be a core challenge for the long-term use of predictive models in clinical applications. Aiming at the above problems, this paper studies the classic Transfer-Stacking model migration algorithm. Aiming at the lack of this algorithm, such as the loss of a large amount of feature information of the target domain and poor fit when integrating the model of the target domain, the Accumulate-Transfer-Stacking algorithm is proposed to improve it. Improvements include: 1) Optimize the input vector and model integration algorithm of Transfer-Stacking’s target domain model. 2) Optimize Transfer-Stacking from a single-source domain model to a multi-source domain model. The experimental results show that for the improved algorithm proposed in this paper when the data is sufficient and insufficient, the average AUC value of the model on the data of subsequent years is 0.89 and 0.87, and the average F1 Score value is 0.45 and 0.36. Moreover, this method is significantly better than the unimproved Transfer-Stacking algorithm and baseline method, and can effectively overcome the problem of data distribution heterogeneity caused by time factors.
