Default Model Research Articles

Using a modified version of Airpacts model for estimating the damage posed by sulfur dioxide emission from power plants to urban and rural building façades (case study: Shahid Rajaee power plant, Qazvin Province, Iran).

This study aimed at upgrading the Air Indicator Report for Public Awareness and Community Tracking (Airpacts) model to evaluate the physical effects of sulfur dioxide (SO2) emissions from Shahid Rajaee power plant on the building façades in Qazvin, Iran in 2018. For this purpose, first, the methodological requirements of the model for the study area, including wind speed, relative humidity, and temperature, were analyzed. Then, the concentration of SO2 emission from the power plant stack was calculated using the Aeroqual Model 200. The obtained data were processed and loaded into the Airpacts model version 1.0. After localization of the costs for reconstruction of 1 m2 of façade, including the costs of purchasing materials, installation, and transport from the power plant, the damage posed by SO2 to the building façades in Qazvin was calculated. Outputs of the modified model suggested that the damage to the building façades included cleansing in contact with SO2 was 494,558,878.12 m2. Based on the calculations, the cost of damage caused by SO2 was estimated to be $0.25 per Megawatt-hour (MWh) electricity generation, reaching an amount of $29,422 annually. Based on the localized results obtained by the modified Airpacts, the external cost of damage to the façades was $0.3 per MWh electricity generation, reaching an amount of $35,720 annually. To overcome the constraints of the original model concerning the constant parameters of mixing height and wind speed in the default model algorithm, the two parameters were considered dynamic variables in the modified model.

Automatic Spline Knot Selection in Modeling Mortgage Loan Default Using Shape Constrained Regression

In mortgage default modeling, many of the key variables, such as loan age, FICO score, Debt-to-Income ratio (DTI), and Loan-to-House-Value ratio (LTV), have nonlinear relationships with the target default rates. Experienced modelers generally apply a spline transformation with knots to the individual variables. In this article, we introduce the Quantile-based Shape Constrained Maximum Likelihood Estimator (QSC-MLE), which features an automatic spline knot selection in a mortgage default model. QSC-MLE is an enhanced variant of SC-MLE (Chen and Samworth 2016) used in combination with a quantile-based knots set, to effectively process large datasets. QSC-MLE requires generic shape information of the inputs, for example, the monotonicity or convexity of the FICO score, DTI, and LTV, to capture any nonlinear effects. We show that the new default model considerably improves the accuracy of the out-of-sample prediction in comparison with the logistic regression and the Cox proportional hazards model. Moreover, the model conveniently generates component-wise spline functions, which facilitates the interpretation of the default rate response to the input variables. <b>Key Findings</b> ▪ A mortgage default model using the Quantile-based Shape Constrained Maximum Likelihood Estimator (QSC-MLE), which features automatic spline knot selection. ▪ QSC-MLE constructs shape-constrained spline functions to capture nonlinear effects of model inputs. ▪ The new default model considerably improves the accuracy of the out-of-sample prediction.

Considering the effect of belowground biomass on dune erosion volumes in coastal numerical modelling

This study aims at improving the XBeach model in predicting dune erosion in the presence of belowground (land-based) biomass under varying hydrodynamic conditions. In this regard, the XBeach model was extended by a literature-derived root model, which increases the critical velocity for erosion in user-defined areas due to additional root cohesion. The model was validated by using the results of a small-scale wave flume experiment as a basis, where the presence of belowground biomass reduced the measured dune erosion. Control runs considering an exclusively sand-based dune, which served as a comparison in the physical experiment, were used to setup and calibrate a one-dimensional (1D) XBeach model. Due to the application to small-scale, default parameters related to sediment transport were scaled within the scope of calibration. Results showed that the XBeach model is not capable to reproduce the observed control dune profiles satisfactorily for all hydrodynamic conditions. Subsequently, the calibrated model was applied to the physical model runs with belowground biomass. Regardless of the hydrodynamic conditions, applying the root model led to a decrease in the mean sediment concentrations and, in turn, the model-predicted erosion in the vegetated area was mainly driven by avalanching. As a result, erosion volumes were reduced in the vegetated area and a higher agreement with the measurements was achieved. At this stage, the root model is highly simplified and only validated for collision at small-scale. In this regard, the translation of the model setup to field scale would eliminate the need for parameter scaling and could result in an overall better performance of the default model. Further research should address the influence of belowground biomass on the time evolution of dune failure and avalanching.

An underestimated negative cloud feedback from cloud lifetime changes

As the atmosphere warms, part of the cloud population shifts from ice and mixed-phase (‘cold’) to liquid (‘warm’) clouds. Because warm clouds are more reflective and longer-lived, this phase change reduces the solar flux absorbed by the Earth and constitutes a negative radiative feedback. This cooling feedback is weaker in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) than in the fifth phase (CMIP5), contributing to greater greenhouse warming. Although this change is often attributed to improvements in the simulated cloud phase, another model bias persists: warm clouds precipitate too readily, potentially leading to underestimated negative lifetime feedbacks. In this study we modified a climate model to better simulate warm-rain probability and found that it exhibits a cloud lifetime feedback nearly three times larger than the default model. This suggests that model errors in cloud-precipitation processes may bias cloud feedbacks by as much as the CMIP5-to-CMIP6 climate sensitivity difference. Reliable climate model projections therefore require improved cloud process realism guided by process-oriented observations and observational constraints. CMIP6 models simulate higher and more accurate cloud liquid water fraction relative to CMIP5, but both ensembles overestimate warm cloud precipitation. Correcting these warm cloud processes in a model exposes compensating biases large enough to offset CMIP5–CMIP6 climate sensitivity differences.

Implications of Default Dependency on Portfolio Risk

We highlight important and specific characteristics of default risk and methodological implications. In a simulation contrasting independent, Gaussian and Clayton copulas, we also show that joint default probabilities might be a hidden source of risk in conventional portfolio models of default.

Strategic Default with an Exit Decision

In 2010 Greece became the first advanced country to enter a sovereign default crisis. Preceding the crisis, debt relative to GDP was high and counter-cyclical, and consumption was smoother than income. Debt spiked just prior to the crisis. These dynamics are counter to the predictions of the standard strategic default model, which was developed to explain crises emerging markets. To match this behavior, we extend the strategic default model by endogenizing the sovereign's decision to exit default and reenter capital markets. We assume that exit requires payment of a recovery value based ability to pay, consistent with IMF guidelines that a sovereign default negotiate in good faith. We assume that a sovereign default must choose whether to repay recovery value and exit, characterizing an excusable default, or to repay nothing and continue default with the standard punishments, a strategic default. We identify the Greek default as excusable, and match Greek debt behavior leading to the crisis and business-cycle moments.

Impact of COVID-19 on psychological wellbeing

Background/ObjectiveThis cross-sectional study aims to record post-traumatic stress (PTS) and post-traumatic growth (PTG) of the general population of China during the first wave of COVID-19 spread. Method: An online survey was distributed in China during February and March 2020 to record the general population's PTS (using the Post-traumatic Stress Disorder Checklist-Civilian Version, PCL-C) and PTG (using the Post-traumatic Growth Inventory, PTGI) due to COVID-19. Confirmatory Factor Analyses (CFAs) and a Two-Part Model (TPM) of regression analysis were conducted. Results: In total, 29,118 Chinese participants completed the survey (54.20% were in their 20s, 68% were males, and 60.30% had a university education). CFA results illustrated that bifactor models described the Chinese psychometric traits of PTS and PTG over the default models. Results of TPM suggested that female, low-educated, and middle-aged individuals were more vulnerable to PTS. Remarkably, mutual and positive correlations between the PTS and the PTG, though small in statistics, were observed through regression analyses. Conclusions: The current results presented new best-fit structural models, potential predictors, and valuable baseline information on the PTS and the PTG of the Chinese population in the context of COVID-19.

Data Assimilation of Steam Flow Through a Control Valve Using Ensemble Kalman Filter

Abstract The present work concentrates on the simulation enhancement of steam flow through a control valve using the data assimilation (DA) approach based on ensemble Kalman filter (EnKF). The k-ω shear stress transport (SST) model is used as the predictive model in which the model constants are optimized by DA. The selected measurement data at different operating conditions are used as observation, while the rest data are involved for validation. Before DA, four flow patterns which arise on their respective operating conditions are identified and analyzed to illustrate the basic characteristics of flow in the control valve. Then DA is performed based on the sample computation by perturbing the model constants and the EnKF process to determine the optimal model constants. These optimized constants are subsequently used for the precomputation of the valve flow with significant improvement on the flow rate prediction. The velocity and turbulent kinetic energy fields with default and DA-optimized model constants are also compared. The results show that the DA enhanced model constants can significantly reduce the predicted volume flow rate error at all opening ratios presently concerned. With the optimized model constants, the velocity and turbulent kinetic energy distributions are greatly modified in the valve seat between main valve and control valve.

Credit Scoring – General Approach in the IFRS 9 Context

With the coming into force of the standard IFRS 9 – Financial Instruments, in January 2018, financial institutions passed from an incurred loss model to a forward-looking model for the computation of impairment losses. As such, the IFRS 9 models use point-in-time, estimates of Probability of Default and Loss Given Default and provide a more faithful representation of the credit risk at a given as they are based on past experiences as well as the most recent and forecasted economic conditions. However, given the short-term fluctuations in the macroeconomic conditions, the final outcome of the Expected credit loss models is highly volatile due to their sensitivity to the business cycle. With regard to Probability of Default estimation under IFRS 9, the most commonly methods are: Markov Chains, Survival Analysis and single-factor models (Vasicek and Z-Shift). The development of the score-cards is still the same as in the case of the Internal Ratings Based Probability of Default models, encouraging institutions to use the already available credit rating systems and perform adjustment to the calibration. This paper outlines a non-exhaustive list of quantitative validation tests would satisfy the requirements of the IFRS 9 standard.

Enhanced PD-implied ratings by targeting the credit rating migration matrix

A high-quality and granular probability of default (PD) model is on many practical dimensions far superior to any categorical credit rating system. Business adoption of a PD model, however, needs to factor in the long-established business/regulatory conventions built around letter-based credit ratings. A mapping methodology that converts granular PDs into letter ratings via referencing the historical default experience of some credit rating agency exists in the literature. This paper improves the PD implied rating (PDiR) methodology by targeting the historical credit migration matrix instead of simply default rates. This enhanced PDiR methodology makes it possible to bypass the reliance on arbitrarily extrapolated target default rates for the AAA and AA+ categories, a necessity due to the fact that the historical realized default rates on these two top rating grades are typically zero.

Micro/nanoplastics effects on organisms: A review focusing on ‘dose’

Microplastics have become predominant contaminants, attracting much political and scientific attention. Despite the massively-increasing research on microplastics effects on organisms, the debate of whether environmental concentrations pose hazard and risk continues. This study critically reviews published literatures of microplastics effects on organisms within the context of “dose”. It provides substantial evidence of the common occurrence of threshold and hormesis dose responses of numerous aquatic and terrestrial organisms to microplastics. This finding along with accumulated evidence indicating the capacity of organisms for recovery suggests that the linear-no-threshold model is biologically irrelevant and should not serve as a default model for assessing the microplastics risks. The published literature does not provide sufficient evidence supporting the general conclusion that environmental doses of microplastics cause adverse effects on individual organisms. Instead, doses that are smaller than the dose of toxicological threshold and more likely to occur in the environment may even induce positive effects, although the ecological implications of these responses remain unknown. This study also shows that low doses of microplastics can reduce whereas high doses can increase the negative effects of other pollutants. The mechanisms explaining these findings are discussed, providing a novel perspective for evaluating the risks of microplastics in the environment.

Improved Convective Ice Microphysics Parameterization in the NCAR CAM Model

AbstractPartitioning deep convective cloud condensates into components that sediment and detrain, known to be a challenge for global climate models, is important for cloud vertical distribution and anvil cloud formation. In this study, we address this issue by improving the convective microphysics scheme in the National Center for Atmospheric Research Community Atmosphere Model version 5.3 (CAM5.3). The improvements include: (1) considering sedimentation for cloud ice crystals that do not fall in the original scheme, (2) applying a new terminal velocity parameterization that depends on the environmental conditions for convective snow, (3) adding a new hydrometeor category, “rimed ice,” to the original four‐class (cloud liquid, cloud ice, rain, and snow) scheme, and (4) allowing convective clouds to detrain snow particles into stratiform clouds. Results from the default and modified CAM5.3 models were evaluated against observations from the U.S. Department of Energy Tropical Warm Pool‐International Cloud Experiment (TWP‐ICE) field campaign. The default model overestimates ice amount, which is largely attributed to the underestimation of convective ice particle sedimentation. By considering cloud ice sedimentation and rimed ice particles and applying a new convective snow terminal velocity parameterization, the vertical distribution of ice amount is much improved in the midtroposphere and upper troposphere when compared to observations. The vertical distribution of ice condensate also agrees well with observational best estimates upon considering snow detrainment. Comparison with observed convective updrafts reveals that current bulk model fails to reproduce the observed updraft magnitude and occurrence frequency, suggesting spectral distributions be required to simulate the subgrid updraft heterogeneity.

Identificando os determinantes da inadimplência contratual no Programa Agroamigo Crescer

O objetivo do trabalho é identificar determinantes da inadimplência dos tomadores de financiamento do Programa Agroamigo Crescer Pecuária, no período de 2005 a 2017, atribuindo probabilidades a cada fator relacionado. Como metodologia, dentre as diversas variações dos modelos de atribuição de probabilidades, este trabalho utiliza o modelo LOGIT de resposta binária. Os resultados obtidos para o modelo de inadimplência estimado apontam para o sentido correto das relações empíricas percebidas. Temos que o aumento do valor do contrato aumenta a chance do tomador se tornar inadimplente e que o aumento do prazo reduzirá essa possibilidade. Enquanto que a finalidade direcionada a investimentos, essa a maior proporção dos contratos, aumentara o risco da inadimplência.

Self-fulfilling debt crises, fiscal policy and investment

This paper studies the circular relationship between sovereign credit risk, government fiscal and debt policy, and output. I consider a sovereign default model with fiscal policy and private capital accumulation. I show that, when fiscal policy responds to borrowing conditions in the sovereign debt market, multiple equilibria exist where the expectations of lenders are self-fulfilling. In the bad equilibrium, pessimistic beliefs make sovereign debt costly. The government substitutes borrowing with taxation, which depresses private investment and future output, increases default probabilities and verifies lenders' beliefs. This result is reminiscent of the European debt crisis of 2010–12: while recessionary, fiscal austerity may be the government best response to excessive borrowing costs during a confidence crisis. The extent of this type of crises can be reduced by the intervention of an external official lender committing to provide liquidity, or by policies aimed at sustaining private investment.

Improving CERES-Wheat Yield Forecasts by Assimilating Dynamic Landsat-Based Leaf Area Index: A Case Study in Iran

In this study, we tried to address the applicability of using dynamic remotely sensed data into a static crop model to capture the yield spatiotemporal variability at the field scale. Taking the example of the crop environment resource synthesis for wheat (CERES-wheat), the model was calibrated, improved, and validated using three years of winter wheat field measurement data (growing seasons of 2017–2019). We assimilated the Landsat-based leaf area index (LAI) into the model using the particle filter approach. Four vegetation indices, including NDVI, SAVI, EVI, and EVI-2, were evaluated to identify winter wheat LAI’s best estimator. A linear regression of Landsat-EVI-2 was found to be the most accurate representation of LAI (LAI = 10.08 × EVI-2 − 0.53) with R2 = 0.87, and mean bias error = − 2.04. The higher LAI accuracy from EVI-2 was attributed to the soil and canopy background noise reduction and accounting for certain atmospheric conditions. Assimilating the LAI based on Landsat-EVI-2 into the CERES model improved the model’s overall performance, particularly for grain yield and biomass simulations. The default model predicted LAImax, grain yield, and biomass at 5.1 cm2 cm−2, 8.3 Mg ha−1, and 14.9 Mg ha−1 with RMSE of 1.44, 0.91 Mg ha−1, and 1.2 Mg ha−1, respectively, while the modified model (using the Landsat-EVI-2 data) predicated these values at 6.6 cm2 cm−2, 9.9 Mg ha−1, and 16.6 Mg ha−1 with RMSE of 0.81, 0.54 Mg ha−1, and 0.62 Mg ha−1, respectively.

Modifications to the delivery of NHS face-to-face general practice consultations during the COVID-19 pandemic in England.

Background: To minimise transmission of SARS-CoV-2, the virus causing COVID-19, delivery of general practice consultations has been modified to enable the separation of diagnosed or suspected COVID-19 patients from others. Remote triage and consultations are currently the default model, with adapted face-to-face contact used when clinically necessary. This study aimed to identify the modified face-to-face delivery models used across England, and evidence for their effectiveness. Methods: In June 2020, a national survey was sent by email to the 135 Clinical Commissioning Groups (CCGs) in England to identify local organisation of face-to-face general practice consultations since March 2020. An email was sent to Public Health England (PHE) requesting information about COVID-19 outbreaks or clusters linked to general practice. Results: All CCGs responded. Separation of COVID-19 patients from others was achieved using combinations of the following models: zoned surgeries (used in 47% of CCGs), where COVID-19 and other patients are separated within their own practice;'hot' or 'cold' hubs (used in 90% of CCGs), separate sites where COVID-19 or other patients registered at one of several collaborating practices are seen;'hot' and 'cold' home visits (used in 70% of CCGs). One of seven model combinations was used across each CCG, with some flexibility according to changing need shown through hub availability. Concomitant PHE data showed less than 2% of COVID-19 outbreaks or clusters in England were linked to general practice. Conclusions: Varied, flexible ways of delivering face-to-face general practice consultations were identified. While COVID-19 outbreaks or clusters linked to general practice constituted a small proportion of totals, their investigation, together with evaluations of the modified delivery models in terms of management of COVID-19 and other conditions and impacts on staff and patients, may aid future management of the pandemic and identify aspects of adapted practice of benefit beyond this.

압축성 기저 유동에 대한 GEKO 모델 계수의 불확실성 정량화 연구

The present work, supersonic flow over an axisymmetric base is simulated using Generalized k-ω (GEKO) model which is proposed by Menter. GEKO two-equation model based on the k-ω formulation provides free model coefficients which can be adjusted by user depending on the specified flow type. Uncertainty Quantification analysis (UQ) is adopted to quantify the uncertainty of the model coefficients and to calibrate the coefficients for the flow. Latin Hypercube Sampling (LHS) method is used for sampling input parameters which are independent as a uniform distribution. Surrogate model is constructed by using ordinary least-squares (OLS). In order to characterize the posterior via Markov Chain Monte Carlo sampling, Affine Invariant Ensemble Algorithm (AIES) is selected. Through Forward problem, the most effective coefficient among the coefficients of GEKO model is figured out (Sensitivity analysis). Calibrated model coefficients are obtained through Bayesian inference. The results obtained using the calibrated coefficients by UQ to the base flow shows better agreement with available experimental measurements than the results obtained using default model coefficients.

Uncertainty studies on hydrogen source term with MAAP5 code

Abstract In the present study, a methodology is developed to quantify the uncertainties of special model parameters of the integral severe accident analysis code MAAP5. Here, the in-vessel hydrogen production during a core melt accident for Lungmen Nuclear Power Station of Taiwan Power Company, an advanced boiling water reactor, is analyzed. Sensitivity studies are performed to identify those parameters with an impact on the output parameter. For this, multiple calculations of MAAP5 are performed with input combinations generated from Latin Hypercube Sampling (LHS). The results are analyzed to determine the 95th percentile with 95% confidence level value of the amount of in-vessel hydrogen production. The calculations show that the default model options for IOXIDE and FGBYPA are recommended. The Pearson Correlation Coefficient (PCC) was used to determine the impact of model parameters on the target output parameters and showed that the three parameters TCLMAX, FCO, FOXBJ are highly influencing the in-vessel hydrogen generation. Suggestions of values of these three parameters are given.

Calibration and Validation of ArcGIS Solar Radiation Tool for Photovoltaic Potential Determination in the Netherlands

Geographic information system (GIS) based tools have become popular for solar photovoltaic (PV) potential estimations, especially in urban areas. There are readily available tools for the mapping and estimation of solar irradiation that give results with the click of a button. Although these tools capture the complexities of the urban environment, they often miss the more important atmospheric parameters that determine the irradiation and potential estimations. Therefore, validation of these models is necessary for accurate potential energy yield and capacity estimations. This paper demonstrates the calibration and validation of the solar radiation model developed by Fu and Rich, employed within ArcGIS, with a focus on the input atmospheric parameters, diffusivity and transmissivity for the Netherlands. In addition, factors affecting the model’s performance with respect to the resolution of the input data were studied. Data were calibrated using ground measurements from Royal Netherlands Meteorological Institute (KNMI) stations in the Netherlands and validated with the station data from Cabauw. The results show that the default model values of diffusivity and transmissivity lead to substantial underestimation or overestimation of solar insolation. In addition, this paper also shows that calibration can be performed at different time scales depending on the purpose and spatial resolution of the input data.

An analytic approach To network-based modelling for contagious defaults

This study proposes a contagious default model using a network-based approach. We design a cyclical structure for the liabilities held by financial entities under which an unexpected cash inflow occurs in the system. In this framework, we derive the probability of multiple defaults and expected recovery rate of the system in analytic form. This model allows us to quantify systemic risk – the likelihood of simultaneous defaults occurring and extent of the losses from default. Using a statistical test, we finally verify that the proposed formula provides stable and accurate results as well as performs faster than the existing method.