44 research outputs found
Securitization and mortgage default
No full textThe academic literature, the popular press, and policymakers have all debated securitization's contribution to the poor performance of mortgages originated in the run-up to the recent crisis. Theoretical arguments have been advanced on both sides, but the lack of suitable data has made it difficult to assess them empirically. The author examines this issue by using a loan-level data set from LPS Analytics, covering approximately two-thirds of the mortgages originated in 2005 and 2006, and including both securitized and nonsecuritized loans. ; The author finds evidence that privately securitized loans do indeed perform worse than observably similar, nonsecuritized loans. Moreover, this effect is strongest in prime mortgage markets, which have not been studied in the previous literature. For example, a typical prime loan becomes delinquent at a 20 percent higher rate if it is privately securitized, ceteris paribus. This is consistent with the existence of adverse selection; that is, that lenders used information not available to investors to securitize loans that were riskier than they otherwise appeared. By contrast, for subprime mortgages, the impact of private securitization is concentrated in low or no-documentation loans; this latter result is consistent with previous work such as Keys et al. (2009).Mortgage-backed securities ; Default (Finance)
Role of Risk Stratification and Genetics in Sudden Cardiac Death
No full textSudden cardiac death (SCD) is a major public health issue due to its increasing incidence in the general population and the difficulty in identifying high-risk individuals. Nearly 300,000-350,000 patients in the United States and 4- to 5 million patients in the world die from SCD. Coronary artery disease and advanced heart failure are the main etiology for SCD. Ischemia of any cause precipitates lethal arrhythmias, and ventricular tachycardia and ventricular fibrillation are the most common lethal arrhythmias precipitating SCD. Pulse-less electrical activity, brady-arrhythmia and electromechanical dissociation also result in SCD. Most sudden cardiac deaths occur out-of-the-hospital setting, so it is difficult to estimate the public burden, which results in overestimating the incidence of SCD. The insufficiency and limited predictive value of various indicators and criteria for SCD result in the increasing incidences. As a result, there is a need to develop better risk stratification criteria and find modifiable variables to decrease the incidence. Primary and secondary prevention and treatment of SCD need further research. This critical review is focused on the etiology, risk factors, prognostic factors and importance of risk stratification of SCD.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author
Physics-informed machine learning for nonlinear deformations in soft solids
No full textMild traumatic brain injury (mTBI), often referred to as concussion, is a significant public health concern, particularly in contact sports, vehicular collisions and similar scenarios. The key mechanisms behind mTBI involve the transmission of shear shock waves through the brain tissue following an impact. These shock waves induce rapid, nonlinear deformations within the soft, heterogeneous structure of the brain, disrupting the integrity of the brain. The complexity of these injuries necessitates highly accurate biomechanical models that can capture the intricate response of brain tissue under loading. Traditionally, hyperelastic models such as the Neo-Hookean (NH) and Mooney-Rivlin (MR) have been employed to simulate brain tissue mechanics. However, these models fall short of accurately describing the highly nonlinear and rate-dependent behaviour observed in mTBI scenarios. A fourth-order Landau (LA) hyperelastic model offers a more suitable alternative, as it can capture the material stiffening effects and large strain behaviours intrinsic to brain tissue, making it particularly well-suited for modelling shear shock wave propagation.
Finite Element Methods (FEM) have been the standard for solving the partial differential equations (PDEs) that govern such biomechanical systems. These solvers provide highly accurate predictions of tissue deformation and stress distributions. However, their computational intensity, especially when incorporating spatial resolution and nonlinear material behaviour, makes them impractical for real-time applications such as injury prediction in sports helmets or real-time diagnostics in clinical settings. To overcome this limitation, Physics-Informed Neural Networks (PINNs) have emerged as a compelling alternative. PINNs offer the potential to simulate real-time brain deformation under impact conditions with far lower computational costs than traditional FEM based solvers. PINNs are a class of deep learning models that embed the governing PDEs of physical systems directly into the training process of a neural network. Instead of relying on labelled data, PINNs minimise the residuals of the PDEs and enforce initial and boundary conditions during training. This allows the neural network to learn solutions that are consistent with physical laws, even with sparse or noisy data. PINNs are being used to solve partial differential equations in various physical systems.
PINNs have garnered significant interest due to their applications in solving partial differential equations that govern various physical phenomena. In 2019, Raissi et al. proposed the concept of PINN, which has since led to hundreds of publications with applications in various disciplines. However, baseline PINN face several challenges, leading to inaccurate solutions in many scenarios. This work proposes a mesh-free Causal Marching Physics-Informed Neural Networks (CMPINN) model for various hyperelastic models to capture their nonlinear mechanical response of higher-order hyperelastic materials. It marks the first attempt to develop physics-informed neural networks to capture complex fourth-order deformation behaviours in soft biological tissues such as the brain. The developed CMPINN framework introduces a ”multinet” architecture, which is designed to handle multimaterial domains effectively. This structure enables the simultaneous modelling of different materials within a single computational domain, which is crucial for realistic simulations involving heterogeneous tissues. Enforcing material incompressibility can be numerically challenging. Another key advancement of CMPINN is the tailored enforcement of incompressibility to address floating-point errors, ensuring more stable and accurate training. Additionally, CMPINN introduces an automated model selection strategy across temporal or load-stepping iterations. The CMPINN framework autonomously selects the most accurate model for each test case by continuously monitoring training loss during these steps. To further refine performance, the study introduces a hyperparameter tuning strategy designed to efficiently identify the optimal set of training parameters.
The proposed CMPINN framework is developed for a cube undergoing homogeneous isotropic, incompressible and canonical deformations: uniaxial tension/compression, simple shear, biaxial tension/compression, and pure shear. Three other tests for scenarios involving spatially varying material properties and inhomogeneous deformations are performed and benchmarked with numerical solutions. The approach systematically identifies optimal values for network depth, learning rate, loss weighting, and optimiser training epochs. The developed CMPINN model is mesh-free and accurately captures the mechanical deformation without labelled data. Once trained, the model can rapidly respond to any spatial coordinate within the physical domain. We develop the CMPINN framework to describe the propagation of nonlinear shear waves in soft solids, inspired by Tripathi et al.. The training of the CMPINN was performed without any labelled data to incorporate the causality of the wave propagation. Different tests involving linear and nonlinear shear propagation were performed, and the results were benchmarked against the numerical solution. In summary, this work advances the state of the art in PINN-based modelling by creating a stable and adaptive framework capable of handling the fourth-order hyperelastic deformation in soft solids. It opens new avenues for efficient and accurate simulation in biomechanics, particularly for applications such as real-time brain injury prediction
Hyperparameter optimization for causal marching physics informed neural network for hyperelasticity
No full textThis study presents an approach for hyperparameter optimization in the Causal Marching Physics-Informed Neural Networks (CMPINNs) framework, specifically designed to model hyperelasticity. Physics-Informed Neural Networks (PINNs) are powerful tools for solv ing governing partial differential equations (PDEs) in physical systems. The CMPINNs model proposed in this work enhances the PINN framework by minimizing the residuals of the gov erning PDEs while enforcing the boundary conditions for the nonlinear mechanical responses of hyperelasticity. We study the accuracy of using CMPINNs to solve the Neo-Hookean hy perelastic model using soft and hard constrained boundary conditions. Additionally, the study presented a hyperparameter optimization for CMPINNs to identify the best suitable set of hy perparameters for deformation like biaxial compression. This optimization process ensures that the CMPINN effectively captures the complex stress-strain relationships in hyperelastic mate rials under deformation. This research advances the development of robust, physics-informed computational models for hyperelastic materials, reducing reliance on labelled or synthetic data.This work is done under the project, “DigiBrain”, funded by the EU Commission Recovery and Resilience Facility under the Science Foundation Ireland Future Digital Challenge Grant Number 22/NCF/FD/11007
Iterative enhancement fusion-based cascaded model for detection and localization of multiple disease from CXR-Images
No full textAhmadian, Ali/0000-0002-0106-7050; Innab, Nisreen/0000-0003-4412-7727; Taniar, David/0000-0002-8862-3960; Sharma, Vikrant/0000-0003-3178-8657; Vats, Satvik/0000-0002-9422-4915The lungs are a vital organ of the human body. Malfunctioning of the lungs caused a direct threat to life. In recent years the world has witnessed massive medical insufficiency to handle the lung diseases caused by numerous agents including COVID-19. According to the recommended course of treatment, medical imaging tests including X-rays and CT scans have been very helpful in identifying multiple chest infections. Automatic detection of chest disease is the need of the modern time as it will speed up patient care and reduce doctors' workload. An Iterative Enhancement Fusion-based Cascaded (IEFCM) model to identify multiple diseases from chest X-ray images is suggested in the present paper. If a chest infection is discovered in the imaging, the suggested model additionally localizes the precise infected area on the CXR image. Experimental outcome clearly demonstrates that the performance of suggested model is significantly superior to the pre-trained model, that is the Golden standard dataset and data from the local population. In terms of sensitivity and specificity, IEFCM achieved 95.62 % sensitivity, which indicates an accurate diagnosis of lung disease, reducing the risk of missing any instances. Similarly, the specificity is 96.23 %, which denotes, the IEFCM model correctly identified the healthy people. It resulted decrease of misdiagnosis and unnecessary follow-up testings.Deanship of Research and Graduate Studies at King Khalid University [RGP2/210/45]; Almaarefa University, Riyadh, Saudi Arabia [MHIRSP2024013]The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under grant number RGP2/210/45. Also, the article is supported by Researchers Supporting Project number (MHIRSP2024013), Almaarefa University, Riyadh, Saudi Arabia.Science Citation Index Expande
Hyperparameter Optimization for Causal Marching Physics Informed Neural Network for Hyperelasticity
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A novel approach to MP-PIC: Continuum particle model for dense particle flows in fluidized beds
No full textA novel approach to Multiphase-Particle-in-Cell (MP-PIC), called Continuum Particle Model (CPM), is developed for dense gas-particle flows. CPM has high computational speed, comparable to that of MP-PIC, but a robustness and accuracy closer to that of a Discrete Element Model (DEM). The gas phase is treated as a continuum phase and particles are tracked discretely, but particle collisions are modelled by considering the divergence of the continuum particle stress tensor. Details on efficient solution to the model are presented. For comparison, a parametric study is performed for quasi-2D fluidized beds. Comparison of CFD-CPM is made with MP-PIC and CFD-DEM. The particle stress models by Harris and Crighton, and by Srivastava and Sundaresan are tested in our CFD-CPM. Results from CFD-CPM based on the Srivastava and Sundaresan particle stress model show good agreement with CFD-DEM results. We validate our model by comparison with experimental benchmark results from Gopalan et. al. (2016).Complex Fluid Processin
Extraction of Phenol from Aqueous Effluents using Supported Liquid Membrane
No full textThis Dissertation / Report is the outcome of investigation carried out by the creator(s) / author(s) at the department/division of Central Food Technological Research Institute (CFTRI), Mysore mentioned below in this page
Prolonging the lifetime of wireless sensor networks using prediction based data reduction scheme
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