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
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
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
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.
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>
