Legumes constitute a major component of sustainable cropping systems due to their biological nitrogen fixing potential. A field study was conducted in 2020 and 2021 at Ashanti-Mampong in the forest transition zone of ...Legumes constitute a major component of sustainable cropping systems due to their biological nitrogen fixing potential. A field study was conducted in 2020 and 2021 at Ashanti-Mampong in the forest transition zone of Ghana to quantify nitrogen credits to carrot from early (70 - 75 days) and medium maturing (80 - 85 days) cowpea varieties (Asetenapa and Soronko) respectively, and Obatanpa maize variety as a reference crop. The experimental design was a split plot with five Nitrogen levels (0, 30, 45, 60 and 90 N kg/ha) applied to carrot as sub-plots following the legumes and the maize variety as main plots. NPK (15:15:15) was applied at the rate of 250 kg/ha to provide the nitrogen. The sub-plot treatments (0, 30, 45, 60 and 90 N kg/ha) were planted following the two cowpea varieties and the maize variety as a reference crop. Soronko had the highest number of nodules (176) while Asetenapa had the lowest nodules (55). Nitrogen credit to carrot from the early-maturing cowpea (Asetenapa) was 32 N kg/ha in the first year of incorporation and 18 N kg/ha in the second year after incorporation. N-credit from the medium-maturing cowpea (Soronko) was 18 N kg/ha and 29 N kg/ha in the first and second year after incorporation respectively. Obatanpa maize variety with 0 kg N/ha fertilizer level produced the lowest carrot yield, indicating that the soil amendment increased yields. The species and maturity of legumes are important determinants of their N credit contribution to crops in rotation.展开更多
Credit Card Fraud Detection(CCFD)is an essential technology for banking institutions to control fraud risks and safeguard their reputation.Class imbalance and insufficient representation of feature data relating to cr...Credit Card Fraud Detection(CCFD)is an essential technology for banking institutions to control fraud risks and safeguard their reputation.Class imbalance and insufficient representation of feature data relating to credit card transactions are two prevalent issues in the current study field of CCFD,which significantly impact classification models’performance.To address these issues,this research proposes a novel CCFD model based on Multifeature Fusion and Generative Adversarial Networks(MFGAN).The MFGAN model consists of two modules:a multi-feature fusion module for integrating static and dynamic behavior data of cardholders into a unified highdimensional feature space,and a balance module based on the generative adversarial network to decrease the class imbalance ratio.The effectiveness of theMFGAN model is validated on two actual credit card datasets.The impacts of different class balance ratios on the performance of the four resamplingmodels are analyzed,and the contribution of the two different modules to the performance of the MFGAN model is investigated via ablation experiments.Experimental results demonstrate that the proposed model does better than state-of-the-art models in terms of recall,F1,and Area Under the Curve(AUC)metrics,which means that the MFGAN model can help banks find more fraudulent transactions and reduce fraud losses.展开更多
Average credit scores for people in the United States （US） differ from state to state. Some states have high, and some states have low average credit scores. Since lenders and employers use credit scores to make loa...Average credit scores for people in the United States （US） differ from state to state. Some states have high, and some states have low average credit scores. Since lenders and employers use credit scores to make loan and employment decisions, people living in states where average credit scores are high should experience the benefits of living where credit scores tend to allow more favorable loan and employment decisions. Although credit scores are the direct result of credit histories, credit histories may be impacted by demographic factors. If the demographic factors that impact credit histories are identified, ways to improve credit scores are likely to be discovered and available to people and state government policymakers. This study looks for demographic factors to indirectly explain the average credit scores for people living in each state of the US. The methodology includes statistical analyses and geographic information systems （GIS） mapping. Statistical analyses provide evidence to suggest that state average credit scores are explained by the demographic factors of education, family, income, and health. GIS mapping reveals clusters of states with similar demographics and credit scores.展开更多
China＇s robust economy is bringing unprecedented growth and prosperity to the country. However, there is ample evidence to suggest that weaknesses in the banking system and the slow progress in reforming those weakne...China＇s robust economy is bringing unprecedented growth and prosperity to the country. However, there is ample evidence to suggest that weaknesses in the banking system and the slow progress in reforming those weaknesses may cause disruption to continued economic growth. Bad loan portfolios as well as inadequate capital ratios point to the lack of proper governance and credit cultures at many banks. Unless these issues are quickly and properly addressed, the economic engine that drives the country will be at risk.展开更多
文摘Legumes constitute a major component of sustainable cropping systems due to their biological nitrogen fixing potential. A field study was conducted in 2020 and 2021 at Ashanti-Mampong in the forest transition zone of Ghana to quantify nitrogen credits to carrot from early (70 - 75 days) and medium maturing (80 - 85 days) cowpea varieties (Asetenapa and Soronko) respectively, and Obatanpa maize variety as a reference crop. The experimental design was a split plot with five Nitrogen levels (0, 30, 45, 60 and 90 N kg/ha) applied to carrot as sub-plots following the legumes and the maize variety as main plots. NPK (15:15:15) was applied at the rate of 250 kg/ha to provide the nitrogen. The sub-plot treatments (0, 30, 45, 60 and 90 N kg/ha) were planted following the two cowpea varieties and the maize variety as a reference crop. Soronko had the highest number of nodules (176) while Asetenapa had the lowest nodules (55). Nitrogen credit to carrot from the early-maturing cowpea (Asetenapa) was 32 N kg/ha in the first year of incorporation and 18 N kg/ha in the second year after incorporation. N-credit from the medium-maturing cowpea (Soronko) was 18 N kg/ha and 29 N kg/ha in the first and second year after incorporation respectively. Obatanpa maize variety with 0 kg N/ha fertilizer level produced the lowest carrot yield, indicating that the soil amendment increased yields. The species and maturity of legumes are important determinants of their N credit contribution to crops in rotation.
基金supported by the National Key R&D Program of China(Nos.2022YFB3104103,and 2019QY1406)the National Natural Science Foundation of China(Nos.61732022,61732004,61672020,and 62072131).
文摘Credit Card Fraud Detection(CCFD)is an essential technology for banking institutions to control fraud risks and safeguard their reputation.Class imbalance and insufficient representation of feature data relating to credit card transactions are two prevalent issues in the current study field of CCFD,which significantly impact classification models’performance.To address these issues,this research proposes a novel CCFD model based on Multifeature Fusion and Generative Adversarial Networks(MFGAN).The MFGAN model consists of two modules:a multi-feature fusion module for integrating static and dynamic behavior data of cardholders into a unified highdimensional feature space,and a balance module based on the generative adversarial network to decrease the class imbalance ratio.The effectiveness of theMFGAN model is validated on two actual credit card datasets.The impacts of different class balance ratios on the performance of the four resamplingmodels are analyzed,and the contribution of the two different modules to the performance of the MFGAN model is investigated via ablation experiments.Experimental results demonstrate that the proposed model does better than state-of-the-art models in terms of recall,F1,and Area Under the Curve(AUC)metrics,which means that the MFGAN model can help banks find more fraudulent transactions and reduce fraud losses.
文摘Average credit scores for people in the United States （US） differ from state to state. Some states have high, and some states have low average credit scores. Since lenders and employers use credit scores to make loan and employment decisions, people living in states where average credit scores are high should experience the benefits of living where credit scores tend to allow more favorable loan and employment decisions. Although credit scores are the direct result of credit histories, credit histories may be impacted by demographic factors. If the demographic factors that impact credit histories are identified, ways to improve credit scores are likely to be discovered and available to people and state government policymakers. This study looks for demographic factors to indirectly explain the average credit scores for people living in each state of the US. The methodology includes statistical analyses and geographic information systems （GIS） mapping. Statistical analyses provide evidence to suggest that state average credit scores are explained by the demographic factors of education, family, income, and health. GIS mapping reveals clusters of states with similar demographics and credit scores.
文摘China＇s robust economy is bringing unprecedented growth and prosperity to the country. However, there is ample evidence to suggest that weaknesses in the banking system and the slow progress in reforming those weaknesses may cause disruption to continued economic growth. Bad loan portfolios as well as inadequate capital ratios point to the lack of proper governance and credit cultures at many banks. Unless these issues are quickly and properly addressed, the economic engine that drives the country will be at risk.
