This paper describes the development of a knowledgebased system (KBS) for determining whether or not, and under what conditions, a bank Ioan officer should grant a business loan to a company. The prototype system deve...This paper describes the development of a knowledgebased system (KBS) for determining whether or not, and under what conditions, a bank Ioan officer should grant a business loan to a company. The prototype system developed focuses on what is bank loans risks management, how to prevent risk by the analysis of the ability of paying back loans. The paper makes the structural analysis involved in the system's decision situation, the structured situation diagram or model, dependency diagram and the document needed by the KBS prototype system thus are developed. Through testing the samples from loan business, the quality for the analysis of the ability of paying back loans can be effectively evaluated by the KBS prototype system.展开更多
Intelligent Decision Support System (IISS) for Bank Loans Risk Classification (BLRC), based on the way of integration Artificial Neural Network (ANN) and Expert System (ES), is proposed. According to the feature of BL...Intelligent Decision Support System (IISS) for Bank Loans Risk Classification (BLRC), based on the way of integration Artificial Neural Network (ANN) and Expert System (ES), is proposed. According to the feature of BLRC, the key financial and non-financial factors are analyzed. Meanwhile, ES and Model Base (MB) which contain ANN are designed . The general framework,interaction and integration of the system are given. In addition, how the system realizes BLRC is elucidated in detail.展开更多
According to the index early warning method, a commercial bank loans risk early warning system based on BP neural networks is proposed. The warning signal is mainly involved with the financial situation signal of loan...According to the index early warning method, a commercial bank loans risk early warning system based on BP neural networks is proposed. The warning signal is mainly involved with the financial situation signal of loaning corporation. Except the structure description of the system structure the demonstration of attemptive designing is also elaborated.展开更多
During the financial crisis, the delayed recognition of credit losses on loans and other financial instruments was identified as a weakness in existing incurred loss model of impairment stated by International Account...During the financial crisis, the delayed recognition of credit losses on loans and other financial instruments was identified as a weakness in existing incurred loss model of impairment stated by International Accounting Standards (IAS) 39, because it is believed that this delay might generate pro-cyclical effects. In response to the recommendations of G20, Financial Crisis Advisory Group (FCAG), and other international bodies, the International Accounting Standards Board (IASB) has undertaken, since 2009, as a part of the project to replace IAS 39, a project (partially shared with Financial Accounting Standards Board (FASB)) aimed at introducing an expected loss model of impairment. Within the scope of this subset project, the IASB has previously issued two exposure documents proposing models to account for expected credit losses: an exposure draft (ED) Financial Instrument: Amortized Cost and Impairment, published in November 2009, and a supplementary document (SD) Financial Instrument: Impairment, published jointly with the FASB in January 2011. However, neither of the two proposals received strong support from interested parties. Recently, the IASB, after the FASB's decision to withdraw from the joint project and to develop a separate expected credit loss model based on a single measurement approach consisting in the sole recognition of lifetime expected credit losses, published a third proposal--Ahe so-called expected credit losses model (ED/2013/3 Financial Instruments: Expected Credit Losses).展开更多
There are problems of inadequate natural endowment and weak stamina in the development of micro-credit loans to farmers in China. Specifically,existing problems include narrow profit space,serious non-agricultural tre...There are problems of inadequate natural endowment and weak stamina in the development of micro-credit loans to farmers in China. Specifically,existing problems include narrow profit space,serious non-agricultural trend of funds,high dependence on government support,short life cycle,and constantly increasing operating risks. These problems are related to endogenous drawback in design,defect in operating procedure,lagging in relevant policies and measures,and vacancy in risk compensation mechanism.展开更多
The incompatibility of China's economy and finance has to some extent inhibited the development of rural economy. Taking Hubei Province for example,we measure the allocation efficiency of rural financial resources...The incompatibility of China's economy and finance has to some extent inhibited the development of rural economy. Taking Hubei Province for example,we measure the allocation efficiency of rural financial resources from the perspective of agricultural input and output,and use the modern rural financial development theory to set forth some policy recommendations on how to build a new rural financial resource allocation system. Studies have shown that the allocation efficiency of rural financial resources is low in China,and improving the allocation efficiency of rural financial resources is the key to perfecting rural financial environment while increasing financial support for agriculture.展开更多
Short-term(up to 30 days)predictions of Earth Rotation Parameters(ERPs)such as Polar Motion(PM:PMX and PMY)play an essential role in real-time applications related to high-precision reference frame conversion.Currentl...Short-term(up to 30 days)predictions of Earth Rotation Parameters(ERPs)such as Polar Motion(PM:PMX and PMY)play an essential role in real-time applications related to high-precision reference frame conversion.Currently,least squares(LS)+auto-regressive(AR)hybrid method is one of the main techniques of PM prediction.Besides,the weighted LS+AR hybrid method performs well for PM short-term prediction.However,the corresponding covariance information of LS fitting residuals deserves further exploration in the AR model.In this study,we have derived a modified stochastic model for the LS+AR hybrid method,namely the weighted LS+weighted AR hybrid method.By using the PM data products of IERS EOP 14 C04,the numerical results indicate that for PM short-term forecasting,the proposed weighted LS+weighted AR hybrid method shows an advantage over both the LS+AR hybrid method and the weighted LS+AR hybrid method.Compared to the mean absolute errors(MAEs)of PMX/PMY sho rt-term prediction of the LS+AR hybrid method and the weighted LS+AR hybrid method,the weighted LS+weighted AR hybrid method shows average improvements of 6.61%/12.08%and 0.24%/11.65%,respectively.Besides,for the slopes of the linear regression lines fitted to the errors of each method,the growth of the prediction error of the proposed method is slower than that of the other two methods.展开更多
The main purpose of this study is to ascertain the effect of bank-specific and macroeconomic factors on non-performing loans in systemically and non-systemically important commercial banks in Sri Lanka over 10 year’s...The main purpose of this study is to ascertain the effect of bank-specific and macroeconomic factors on non-performing loans in systemically and non-systemically important commercial banks in Sri Lanka over 10 year’s period from 2004 to 2013.Also,the study examines the impact of civil war that prevailed in the country for 30 years on the ex-post credit risk of the banking sector.The study employed panel data methodology to investigate the effect of bank-specific and macroeconomic factors on non-performing loans.Panel unit root test has been undertaken in order to test the stationary of the variables.Hausman test and Wald coefficient restriction test were used to select the appropriate model out of pooled,random,and fixed effect.A dummy variable panel regression model adopted to study the war effect,considering 2009 as the structural year.Findings revealed that return on assets as a proxy for bank efficiency has a significant negative influence,while non-interest income as a proxy for income diversity is positively correlated with non-performing loans of systemically important banks.Both real gross domestic products and lending rates were highly significant in both bank types.On contrary with literature,growth in bank branches is negatively correlated.Public banks do not account for higher level of non-performing loans compared to their private counterpart.Finally,it was identified that civil war had an effect on the level of non-performing loans in commercial banks.The research would have benefited if the analysis is carried out among classified types of loans offered by commercial banks.Future researchers should involve in identifying the most significant contributing loan type to the non-performing loans and its determinants.This study is one of the few studies which have investigated the causes of non-performing loans in the commercial banking industry in Sri Lanka.The analysis of civil war and its impact on non-performing loans is the first study of that nature to be conducted in the context.展开更多
With the implementation of the opening policy, the Bank of China(BOC) started lending to foreignfunded enterprises early in the 1980s. At the end of 1993, 50 percent of foreignfunded enterprise had opened accounts wit...With the implementation of the opening policy, the Bank of China(BOC) started lending to foreignfunded enterprises early in the 1980s. At the end of 1993, 50 percent of foreignfunded enterprise had opened accounts with the BOC. BOC has made US dollar loans worth $16.8 billion, and RMB ioans of ¥ 135.55 billion to展开更多
In this paper, through analyzing the necessity of the securitization of the non-performing loans of China's state-owned banks, the author proposes some tentative ideas for the securitization of the non-performing loa...In this paper, through analyzing the necessity of the securitization of the non-performing loans of China's state-owned banks, the author proposes some tentative ideas for the securitization of the non-performing loans and works out some problems that need to be solved in this process.展开更多
Central China’s power sup-ply will get a major boostfrom the expansion of Henan Province’sYanshi Thermal Power Plant --a project involving $180 million worth of WorldBank loans. Yanshi Plant is situated on thesouth ...Central China’s power sup-ply will get a major boostfrom the expansion of Henan Province’sYanshi Thermal Power Plant --a project involving $180 million worth of WorldBank loans. Yanshi Plant is situated on thesouth bank of Yellow River, about 30 kmeast of Luoyang Municipality. Once com-pleted at the end of 1995, the plant willboast an installed capacity of 1000 MW,展开更多
Home mortgage loan lending firms are exposed to many business risks.This paper focuses on the mortgage loan borrower risks and proposes a prospective loss analysis approach in regard to loan repayment defaults of borr...Home mortgage loan lending firms are exposed to many business risks.This paper focuses on the mortgage loan borrower risks and proposes a prospective loss analysis approach in regard to loan repayment defaults of borrowers.For this purpose,a predictive modeling is presented in three stages.In the first stage,occurrence of borrower defaults in a mortgage loans portfolio is modeled through the generalized linear models(GLMs)type regressions for which we specify a logistic distribution for default events.The second stage of modeling develops a survival analysis in order to estimate survival probability and hazard rate functions for individual loans.Ultimately,an expectable loss amount model is presented in the third stage as a function of conditional survival probabilities and corresponding hazard rates at loan levels.Throughout all modeling stages,a large and real data set is used as an empirical analysis case by which detailed interpretations and practical implications of the obtained results are stated.展开更多
In the 21st century, while the scope of banking activities has been expanding every day, collecting deposits and providing credit remain as their main and most important functions. They transfer the collected funds th...In the 21st century, while the scope of banking activities has been expanding every day, collecting deposits and providing credit remain as their main and most important functions. They transfer the collected funds thanks to the market confidence they create back to the market in terms of the credits they give. For the organizations operating in the banking sector, crediting is the highest revenue earning source. However, uncollected loans may disrupt the activities of banks and may reduce their effectiveness. Therefore, the control of bank credits has a particular importance in the bank balance sheets. In this study, the relationship between bank balance sheets and non-performing loans (NPL) will be analyzed using Granger causality test and vector autoregressive (VAR) method. This study aims to discuss the impact of NPL on balance sheets and contribute to making correct credit decisions. It also intends to assist to reduce the NPL ratios of banks and minimize the level of negativity in their financial statements.展开更多
Accurate load forecasting forms a crucial foundation for implementing household demand response plans andoptimizing load scheduling. When dealing with short-term load data characterized by substantial fluctuations,a s...Accurate load forecasting forms a crucial foundation for implementing household demand response plans andoptimizing load scheduling. When dealing with short-term load data characterized by substantial fluctuations,a single prediction model is hard to capture temporal features effectively, resulting in diminished predictionaccuracy. In this study, a hybrid deep learning framework that integrates attention mechanism, convolution neuralnetwork (CNN), improved chaotic particle swarm optimization (ICPSO), and long short-term memory (LSTM), isproposed for short-term household load forecasting. Firstly, the CNN model is employed to extract features fromthe original data, enhancing the quality of data features. Subsequently, the moving average method is used for datapreprocessing, followed by the application of the LSTM network to predict the processed data. Moreover, the ICPSOalgorithm is introduced to optimize the parameters of LSTM, aimed at boosting the model’s running speed andaccuracy. Finally, the attention mechanism is employed to optimize the output value of LSTM, effectively addressinginformation loss in LSTM induced by lengthy sequences and further elevating prediction accuracy. According tothe numerical analysis, the accuracy and effectiveness of the proposed hybrid model have been verified. It canexplore data features adeptly, achieving superior prediction accuracy compared to other forecasting methods forthe household load exhibiting significant fluctuations across different seasons.展开更多
Stress changes due to changes in fluid pressure and temperature in a faulted formation may lead to the opening/shearing of the fault.This can be due to subsurface(geo)engineering activities such as fluid injections an...Stress changes due to changes in fluid pressure and temperature in a faulted formation may lead to the opening/shearing of the fault.This can be due to subsurface(geo)engineering activities such as fluid injections and geologic disposal of nuclear waste.Such activities are expected to rise in the future making it necessary to assess their short-and long-term safety.Here,a new machine learning(ML)approach to model pore pressure and fault displacements in response to high-pressure fluid injection cycles is developed.The focus is on fault behavior near the injection borehole.To capture the temporal dependencies in the data,long short-term memory(LSTM)networks are utilized.To prevent error accumulation within the forecast window,four critical measures to train a robust LSTM model for predicting fault response are highlighted:(i)setting an appropriate value of LSTM lag,(ii)calibrating the LSTM cell dimension,(iii)learning rate reduction during weight optimization,and(iv)not adopting an independent injection cycle as a validation set.Several numerical experiments were conducted,which demonstrated that the ML model can capture peaks in pressure and associated fault displacement that accompany an increase in fluid injection.The model also captured the decay in pressure and displacement during the injection shut-in period.Further,the ability of an ML model to highlight key changes in fault hydromechanical activation processes was investigated,which shows that ML can be used to monitor risk of fault activation and leakage during high pressure fluid injections.展开更多
Accurate forecasting of time series is crucial across various domains.Many prediction tasks rely on effectively segmenting,matching,and time series data alignment.For instance,regardless of time series with the same g...Accurate forecasting of time series is crucial across various domains.Many prediction tasks rely on effectively segmenting,matching,and time series data alignment.For instance,regardless of time series with the same granularity,segmenting them into different granularity events can effectively mitigate the impact of varying time scales on prediction accuracy.However,these events of varying granularity frequently intersect with each other,which may possess unequal durations.Even minor differences can result in significant errors when matching time series with future trends.Besides,directly using matched events but unaligned events as state vectors in machine learning-based prediction models can lead to insufficient prediction accuracy.Therefore,this paper proposes a short-term forecasting method for time series based on a multi-granularity event,MGE-SP(multi-granularity event-based short-termprediction).First,amethodological framework for MGE-SP established guides the implementation steps.The framework consists of three key steps,including multi-granularity event matching based on the LTF(latest time first)strategy,multi-granularity event alignment using a piecewise aggregate approximation based on the compression ratio,and a short-term prediction model based on XGBoost.The data from a nationwide online car-hailing service in China ensures the method’s reliability.The average RMSE(root mean square error)and MAE(mean absolute error)of the proposed method are 3.204 and 2.360,lower than the respective values of 4.056 and 3.101 obtained using theARIMA(autoregressive integratedmoving average)method,as well as the values of 4.278 and 2.994 obtained using k-means-SVR(support vector regression)method.The other experiment is conducted on stock data froma public data set.The proposed method achieved an average RMSE and MAE of 0.836 and 0.696,lower than the respective values of 1.019 and 0.844 obtained using the ARIMA method,as well as the values of 1.350 and 1.172 obtained using the k-means-SVR method.展开更多
Accurate origin–destination(OD)demand prediction is crucial for the efficient operation and management of urban rail transit(URT)systems,particularly during a pandemic.However,this task faces several limitations,incl...Accurate origin–destination(OD)demand prediction is crucial for the efficient operation and management of urban rail transit(URT)systems,particularly during a pandemic.However,this task faces several limitations,including real-time availability,sparsity,and high-dimensionality issues,and the impact of the pandemic.Consequently,this study proposes a unified framework called the physics-guided adaptive graph spatial–temporal attention network(PAG-STAN)for metro OD demand prediction under pandemic conditions.Specifically,PAG-STAN introduces a real-time OD estimation module to estimate real-time complete OD demand matrices.Subsequently,a novel dynamic OD demand matrix compression module is proposed to generate dense real-time OD demand matrices.Thereafter,PAG-STAN leverages various heterogeneous data to learn the evolutionary trend of future OD ridership during the pandemic.Finally,a masked physics-guided loss function(MPG-loss function)incorporates the physical quantity information between the OD demand and inbound flow into the loss function to enhance model interpretability.PAG-STAN demonstrated favorable performance on two real-world metro OD demand datasets under the pandemic and conventional scenarios,highlighting its robustness and sensitivity for metro OD demand prediction.A series of ablation studies were conducted to verify the indispensability of each module in PAG-STAN.展开更多
BACKGROUND Previous studies have reported that low hematocrit levels indicate poor survival in patients with ovarian cancer and cervical cancer,the prognostic value of hematocrit for colorectal cancer(CRC)patients has...BACKGROUND Previous studies have reported that low hematocrit levels indicate poor survival in patients with ovarian cancer and cervical cancer,the prognostic value of hematocrit for colorectal cancer(CRC)patients has not been determined.The prognostic value of red blood cell distribution width(RDW)for CRC patients was controversial.AIM To investigate the impact of RDW and hematocrit on the short-term outcomes and long-term prognosis of CRC patients who underwent radical surgery.METHODS Patients who were diagnosed with CRC and underwent radical CRC resection between January 2011 and January 2020 at a single clinical center were included.The short-term outcomes,overall survival(OS)and disease-free survival(DFS)were compared among the different groups.Cox analysis was also conducted to identify independent risk factors for OS and DFS.RESULTS There were 4258 CRC patients who underwent radical surgery included in our study.A total of 1573 patients were in the lower RDW group and 2685 patients were in the higher RDW group.There were 2166 and 2092 patients in the higher hematocrit group and lower hematocrit group,respectively.Patients in the higher RDW group had more intraoperative blood loss(P<0.01)and more overall complications(P<0.01)than did those in the lower RDW group.Similarly,patients in the lower hematocrit group had more intraoperative blood loss(P=0.012),longer hospital stay(P=0.016)and overall complications(P<0.01)than did those in the higher hematocrit group.The higher RDW group had a worse OS and DFS than did the lower RDW group for tumor node metastasis(TNM)stage I(OS,P<0.05;DFS,P=0.001)and stage II(OS,P=0.004;DFS,P=0.01)than the lower RDW group;the lower hematocrit group had worse OS and DFS for TNM stage II(OS,P<0.05;DFS,P=0.001)and stage III(OS,P=0.001;DFS,P=0.001)than did the higher hematocrit group.Preoperative hematocrit was an independent risk factor for OS[P=0.017,hazard ratio(HR)=1.256,95%confidence interval(CI):1.041-1.515]and DFS(P=0.035,HR=1.194,95%CI:1.013-1.408).CONCLUSION A higher preoperative RDW and lower hematocrit were associated with more postoperative complications.However,only hematocrit was an independent risk factor for OS and DFS in CRC patients who underwent radical surgery,while RDW was not.展开更多
I. INTRODUCTIONPurpose1.1 The purpose of these Guidelines is to informthose carrying out a project that is financed in wholeor in part by a loan from the International Bank forReconstruction and Development (IBRD) or ...I. INTRODUCTIONPurpose1.1 The purpose of these Guidelines is to informthose carrying out a project that is financed in wholeor in part by a loan from the International Bank forReconstruction and Development (IBRD) or a creditfrom the International Development Association(IDA), of the arrangements to be made for procur-ing the goods and works (including relatedservices) required for the project. The Loan Agree-ment governs the legal relationships between the Bor-rower and the Bank, and the Guidelines are made ap-plicable to procurement of goods and works for theproject, as provided in the agreement. The rightsand obligations of the Borrower and the providers ofgoods and works for the project are governed by thebidding documents, and by the contracts signed bythe Borrower with the providers of goods and works,and not by these Guidelines or the Loan Agreements.No party other than the parties to the Loan Agree-ment shall derive any rights therefrom or have anyclaim to loan proceeds.展开更多
基金Supported by the National Science Foundation of China(No.7977086)
文摘This paper describes the development of a knowledgebased system (KBS) for determining whether or not, and under what conditions, a bank Ioan officer should grant a business loan to a company. The prototype system developed focuses on what is bank loans risks management, how to prevent risk by the analysis of the ability of paying back loans. The paper makes the structural analysis involved in the system's decision situation, the structured situation diagram or model, dependency diagram and the document needed by the KBS prototype system thus are developed. Through testing the samples from loan business, the quality for the analysis of the ability of paying back loans can be effectively evaluated by the KBS prototype system.
基金the National Natural Science Fund of China(Approved No.79779986)
文摘Intelligent Decision Support System (IISS) for Bank Loans Risk Classification (BLRC), based on the way of integration Artificial Neural Network (ANN) and Expert System (ES), is proposed. According to the feature of BLRC, the key financial and non-financial factors are analyzed. Meanwhile, ES and Model Base (MB) which contain ANN are designed . The general framework,interaction and integration of the system are given. In addition, how the system realizes BLRC is elucidated in detail.
基金Supported by the National Science Foundation of China(Approved NO.79770086)
文摘According to the index early warning method, a commercial bank loans risk early warning system based on BP neural networks is proposed. The warning signal is mainly involved with the financial situation signal of loaning corporation. Except the structure description of the system structure the demonstration of attemptive designing is also elaborated.
文摘During the financial crisis, the delayed recognition of credit losses on loans and other financial instruments was identified as a weakness in existing incurred loss model of impairment stated by International Accounting Standards (IAS) 39, because it is believed that this delay might generate pro-cyclical effects. In response to the recommendations of G20, Financial Crisis Advisory Group (FCAG), and other international bodies, the International Accounting Standards Board (IASB) has undertaken, since 2009, as a part of the project to replace IAS 39, a project (partially shared with Financial Accounting Standards Board (FASB)) aimed at introducing an expected loss model of impairment. Within the scope of this subset project, the IASB has previously issued two exposure documents proposing models to account for expected credit losses: an exposure draft (ED) Financial Instrument: Amortized Cost and Impairment, published in November 2009, and a supplementary document (SD) Financial Instrument: Impairment, published jointly with the FASB in January 2011. However, neither of the two proposals received strong support from interested parties. Recently, the IASB, after the FASB's decision to withdraw from the joint project and to develop a separate expected credit loss model based on a single measurement approach consisting in the sole recognition of lifetime expected credit losses, published a third proposal--Ahe so-called expected credit losses model (ED/2013/3 Financial Instruments: Expected Credit Losses).
文摘There are problems of inadequate natural endowment and weak stamina in the development of micro-credit loans to farmers in China. Specifically,existing problems include narrow profit space,serious non-agricultural trend of funds,high dependence on government support,short life cycle,and constantly increasing operating risks. These problems are related to endogenous drawback in design,defect in operating procedure,lagging in relevant policies and measures,and vacancy in risk compensation mechanism.
基金Supported by Humanities and Social Sciences Project of the Ministry of Education(10YJC790111)
文摘The incompatibility of China's economy and finance has to some extent inhibited the development of rural economy. Taking Hubei Province for example,we measure the allocation efficiency of rural financial resources from the perspective of agricultural input and output,and use the modern rural financial development theory to set forth some policy recommendations on how to build a new rural financial resource allocation system. Studies have shown that the allocation efficiency of rural financial resources is low in China,and improving the allocation efficiency of rural financial resources is the key to perfecting rural financial environment while increasing financial support for agriculture.
基金supported by National Natural Science Foundation of China,China(No.42004016)HuBei Natural Science Fund,China(No.2020CFB329)+1 种基金HuNan Natural Science Fund,China(No.2023JJ60559,2023JJ60560)the State Key Laboratory of Geodesy and Earth’s Dynamics self-deployment project,China(No.S21L6101)。
文摘Short-term(up to 30 days)predictions of Earth Rotation Parameters(ERPs)such as Polar Motion(PM:PMX and PMY)play an essential role in real-time applications related to high-precision reference frame conversion.Currently,least squares(LS)+auto-regressive(AR)hybrid method is one of the main techniques of PM prediction.Besides,the weighted LS+AR hybrid method performs well for PM short-term prediction.However,the corresponding covariance information of LS fitting residuals deserves further exploration in the AR model.In this study,we have derived a modified stochastic model for the LS+AR hybrid method,namely the weighted LS+weighted AR hybrid method.By using the PM data products of IERS EOP 14 C04,the numerical results indicate that for PM short-term forecasting,the proposed weighted LS+weighted AR hybrid method shows an advantage over both the LS+AR hybrid method and the weighted LS+AR hybrid method.Compared to the mean absolute errors(MAEs)of PMX/PMY sho rt-term prediction of the LS+AR hybrid method and the weighted LS+AR hybrid method,the weighted LS+weighted AR hybrid method shows average improvements of 6.61%/12.08%and 0.24%/11.65%,respectively.Besides,for the slopes of the linear regression lines fitted to the errors of each method,the growth of the prediction error of the proposed method is slower than that of the other two methods.
文摘The main purpose of this study is to ascertain the effect of bank-specific and macroeconomic factors on non-performing loans in systemically and non-systemically important commercial banks in Sri Lanka over 10 year’s period from 2004 to 2013.Also,the study examines the impact of civil war that prevailed in the country for 30 years on the ex-post credit risk of the banking sector.The study employed panel data methodology to investigate the effect of bank-specific and macroeconomic factors on non-performing loans.Panel unit root test has been undertaken in order to test the stationary of the variables.Hausman test and Wald coefficient restriction test were used to select the appropriate model out of pooled,random,and fixed effect.A dummy variable panel regression model adopted to study the war effect,considering 2009 as the structural year.Findings revealed that return on assets as a proxy for bank efficiency has a significant negative influence,while non-interest income as a proxy for income diversity is positively correlated with non-performing loans of systemically important banks.Both real gross domestic products and lending rates were highly significant in both bank types.On contrary with literature,growth in bank branches is negatively correlated.Public banks do not account for higher level of non-performing loans compared to their private counterpart.Finally,it was identified that civil war had an effect on the level of non-performing loans in commercial banks.The research would have benefited if the analysis is carried out among classified types of loans offered by commercial banks.Future researchers should involve in identifying the most significant contributing loan type to the non-performing loans and its determinants.This study is one of the few studies which have investigated the causes of non-performing loans in the commercial banking industry in Sri Lanka.The analysis of civil war and its impact on non-performing loans is the first study of that nature to be conducted in the context.
文摘With the implementation of the opening policy, the Bank of China(BOC) started lending to foreignfunded enterprises early in the 1980s. At the end of 1993, 50 percent of foreignfunded enterprise had opened accounts with the BOC. BOC has made US dollar loans worth $16.8 billion, and RMB ioans of ¥ 135.55 billion to
文摘In this paper, through analyzing the necessity of the securitization of the non-performing loans of China's state-owned banks, the author proposes some tentative ideas for the securitization of the non-performing loans and works out some problems that need to be solved in this process.
文摘Central China’s power sup-ply will get a major boostfrom the expansion of Henan Province’sYanshi Thermal Power Plant --a project involving $180 million worth of WorldBank loans. Yanshi Plant is situated on thesouth bank of Yellow River, about 30 kmeast of Luoyang Municipality. Once com-pleted at the end of 1995, the plant willboast an installed capacity of 1000 MW,
文摘Home mortgage loan lending firms are exposed to many business risks.This paper focuses on the mortgage loan borrower risks and proposes a prospective loss analysis approach in regard to loan repayment defaults of borrowers.For this purpose,a predictive modeling is presented in three stages.In the first stage,occurrence of borrower defaults in a mortgage loans portfolio is modeled through the generalized linear models(GLMs)type regressions for which we specify a logistic distribution for default events.The second stage of modeling develops a survival analysis in order to estimate survival probability and hazard rate functions for individual loans.Ultimately,an expectable loss amount model is presented in the third stage as a function of conditional survival probabilities and corresponding hazard rates at loan levels.Throughout all modeling stages,a large and real data set is used as an empirical analysis case by which detailed interpretations and practical implications of the obtained results are stated.
文摘In the 21st century, while the scope of banking activities has been expanding every day, collecting deposits and providing credit remain as their main and most important functions. They transfer the collected funds thanks to the market confidence they create back to the market in terms of the credits they give. For the organizations operating in the banking sector, crediting is the highest revenue earning source. However, uncollected loans may disrupt the activities of banks and may reduce their effectiveness. Therefore, the control of bank credits has a particular importance in the bank balance sheets. In this study, the relationship between bank balance sheets and non-performing loans (NPL) will be analyzed using Granger causality test and vector autoregressive (VAR) method. This study aims to discuss the impact of NPL on balance sheets and contribute to making correct credit decisions. It also intends to assist to reduce the NPL ratios of banks and minimize the level of negativity in their financial statements.
基金the Shanghai Rising-Star Program(No.22QA1403900)the National Natural Science Foundation of China(No.71804106)the Noncarbon Energy Conversion and Utilization Institute under the Shanghai Class IV Peak Disciplinary Development Program.
文摘Accurate load forecasting forms a crucial foundation for implementing household demand response plans andoptimizing load scheduling. When dealing with short-term load data characterized by substantial fluctuations,a single prediction model is hard to capture temporal features effectively, resulting in diminished predictionaccuracy. In this study, a hybrid deep learning framework that integrates attention mechanism, convolution neuralnetwork (CNN), improved chaotic particle swarm optimization (ICPSO), and long short-term memory (LSTM), isproposed for short-term household load forecasting. Firstly, the CNN model is employed to extract features fromthe original data, enhancing the quality of data features. Subsequently, the moving average method is used for datapreprocessing, followed by the application of the LSTM network to predict the processed data. Moreover, the ICPSOalgorithm is introduced to optimize the parameters of LSTM, aimed at boosting the model’s running speed andaccuracy. Finally, the attention mechanism is employed to optimize the output value of LSTM, effectively addressinginformation loss in LSTM induced by lengthy sequences and further elevating prediction accuracy. According tothe numerical analysis, the accuracy and effectiveness of the proposed hybrid model have been verified. It canexplore data features adeptly, achieving superior prediction accuracy compared to other forecasting methods forthe household load exhibiting significant fluctuations across different seasons.
基金supported by the US Department of Energy (DOE),the Office of Nuclear Energy,Spent Fuel and Waste Science and Technology Campaign,under Contract Number DE-AC02-05CH11231the National Energy Technology Laboratory under the award number FP00013650 at Lawrence Berkeley National Laboratory.
文摘Stress changes due to changes in fluid pressure and temperature in a faulted formation may lead to the opening/shearing of the fault.This can be due to subsurface(geo)engineering activities such as fluid injections and geologic disposal of nuclear waste.Such activities are expected to rise in the future making it necessary to assess their short-and long-term safety.Here,a new machine learning(ML)approach to model pore pressure and fault displacements in response to high-pressure fluid injection cycles is developed.The focus is on fault behavior near the injection borehole.To capture the temporal dependencies in the data,long short-term memory(LSTM)networks are utilized.To prevent error accumulation within the forecast window,four critical measures to train a robust LSTM model for predicting fault response are highlighted:(i)setting an appropriate value of LSTM lag,(ii)calibrating the LSTM cell dimension,(iii)learning rate reduction during weight optimization,and(iv)not adopting an independent injection cycle as a validation set.Several numerical experiments were conducted,which demonstrated that the ML model can capture peaks in pressure and associated fault displacement that accompany an increase in fluid injection.The model also captured the decay in pressure and displacement during the injection shut-in period.Further,the ability of an ML model to highlight key changes in fault hydromechanical activation processes was investigated,which shows that ML can be used to monitor risk of fault activation and leakage during high pressure fluid injections.
基金funded by the Fujian Province Science and Technology Plan,China(Grant Number 2019H0017).
文摘Accurate forecasting of time series is crucial across various domains.Many prediction tasks rely on effectively segmenting,matching,and time series data alignment.For instance,regardless of time series with the same granularity,segmenting them into different granularity events can effectively mitigate the impact of varying time scales on prediction accuracy.However,these events of varying granularity frequently intersect with each other,which may possess unequal durations.Even minor differences can result in significant errors when matching time series with future trends.Besides,directly using matched events but unaligned events as state vectors in machine learning-based prediction models can lead to insufficient prediction accuracy.Therefore,this paper proposes a short-term forecasting method for time series based on a multi-granularity event,MGE-SP(multi-granularity event-based short-termprediction).First,amethodological framework for MGE-SP established guides the implementation steps.The framework consists of three key steps,including multi-granularity event matching based on the LTF(latest time first)strategy,multi-granularity event alignment using a piecewise aggregate approximation based on the compression ratio,and a short-term prediction model based on XGBoost.The data from a nationwide online car-hailing service in China ensures the method’s reliability.The average RMSE(root mean square error)and MAE(mean absolute error)of the proposed method are 3.204 and 2.360,lower than the respective values of 4.056 and 3.101 obtained using theARIMA(autoregressive integratedmoving average)method,as well as the values of 4.278 and 2.994 obtained using k-means-SVR(support vector regression)method.The other experiment is conducted on stock data froma public data set.The proposed method achieved an average RMSE and MAE of 0.836 and 0.696,lower than the respective values of 1.019 and 0.844 obtained using the ARIMA method,as well as the values of 1.350 and 1.172 obtained using the k-means-SVR method.
基金supported by the National Natural Science Foundation of China(72288101,72201029,and 72322022).
文摘Accurate origin–destination(OD)demand prediction is crucial for the efficient operation and management of urban rail transit(URT)systems,particularly during a pandemic.However,this task faces several limitations,including real-time availability,sparsity,and high-dimensionality issues,and the impact of the pandemic.Consequently,this study proposes a unified framework called the physics-guided adaptive graph spatial–temporal attention network(PAG-STAN)for metro OD demand prediction under pandemic conditions.Specifically,PAG-STAN introduces a real-time OD estimation module to estimate real-time complete OD demand matrices.Subsequently,a novel dynamic OD demand matrix compression module is proposed to generate dense real-time OD demand matrices.Thereafter,PAG-STAN leverages various heterogeneous data to learn the evolutionary trend of future OD ridership during the pandemic.Finally,a masked physics-guided loss function(MPG-loss function)incorporates the physical quantity information between the OD demand and inbound flow into the loss function to enhance model interpretability.PAG-STAN demonstrated favorable performance on two real-world metro OD demand datasets under the pandemic and conventional scenarios,highlighting its robustness and sensitivity for metro OD demand prediction.A series of ablation studies were conducted to verify the indispensability of each module in PAG-STAN.
基金The study was approved by the ethics committee of the First Affiliated Hospital of Chongqing Medical University(2022-K205),this study was conducted in accordance with the World Medical Association Declaration of Helsinki as well。
文摘BACKGROUND Previous studies have reported that low hematocrit levels indicate poor survival in patients with ovarian cancer and cervical cancer,the prognostic value of hematocrit for colorectal cancer(CRC)patients has not been determined.The prognostic value of red blood cell distribution width(RDW)for CRC patients was controversial.AIM To investigate the impact of RDW and hematocrit on the short-term outcomes and long-term prognosis of CRC patients who underwent radical surgery.METHODS Patients who were diagnosed with CRC and underwent radical CRC resection between January 2011 and January 2020 at a single clinical center were included.The short-term outcomes,overall survival(OS)and disease-free survival(DFS)were compared among the different groups.Cox analysis was also conducted to identify independent risk factors for OS and DFS.RESULTS There were 4258 CRC patients who underwent radical surgery included in our study.A total of 1573 patients were in the lower RDW group and 2685 patients were in the higher RDW group.There were 2166 and 2092 patients in the higher hematocrit group and lower hematocrit group,respectively.Patients in the higher RDW group had more intraoperative blood loss(P<0.01)and more overall complications(P<0.01)than did those in the lower RDW group.Similarly,patients in the lower hematocrit group had more intraoperative blood loss(P=0.012),longer hospital stay(P=0.016)and overall complications(P<0.01)than did those in the higher hematocrit group.The higher RDW group had a worse OS and DFS than did the lower RDW group for tumor node metastasis(TNM)stage I(OS,P<0.05;DFS,P=0.001)and stage II(OS,P=0.004;DFS,P=0.01)than the lower RDW group;the lower hematocrit group had worse OS and DFS for TNM stage II(OS,P<0.05;DFS,P=0.001)and stage III(OS,P=0.001;DFS,P=0.001)than did the higher hematocrit group.Preoperative hematocrit was an independent risk factor for OS[P=0.017,hazard ratio(HR)=1.256,95%confidence interval(CI):1.041-1.515]and DFS(P=0.035,HR=1.194,95%CI:1.013-1.408).CONCLUSION A higher preoperative RDW and lower hematocrit were associated with more postoperative complications.However,only hematocrit was an independent risk factor for OS and DFS in CRC patients who underwent radical surgery,while RDW was not.
文摘I. INTRODUCTIONPurpose1.1 The purpose of these Guidelines is to informthose carrying out a project that is financed in wholeor in part by a loan from the International Bank forReconstruction and Development (IBRD) or a creditfrom the International Development Association(IDA), of the arrangements to be made for procur-ing the goods and works (including relatedservices) required for the project. The Loan Agree-ment governs the legal relationships between the Bor-rower and the Bank, and the Guidelines are made ap-plicable to procurement of goods and works for theproject, as provided in the agreement. The rightsand obligations of the Borrower and the providers ofgoods and works for the project are governed by thebidding documents, and by the contracts signed bythe Borrower with the providers of goods and works,and not by these Guidelines or the Loan Agreements.No party other than the parties to the Loan Agree-ment shall derive any rights therefrom or have anyclaim to loan proceeds.