Pub Date : 2022-05-26DOI: 10.1017/S143192762200071X
Shyam Kanta Sinha, Arup Dasgupta, M Sivakumar, Chanchal Ghosh, S Raju
Nanocrystalline oxides are mainly responsible for Ni-base oxide dispersion strengthened (ODS) superalloys excellent thermo-mechanical properties. To establish the microstructural correlations between the metallic matrix and various oxide dispersoids, we report here the atomic-scale structure and chemistry of the complex nano-oxide dispersoids. Ultrahigh-resolution Cs-aberration-corrected scanning transmission electron microscopy (STEM) based techniques have been used to resolve nano-dispersoids in the Alloy 617 ODS. These nano-oxides, interestingly, possess a variety of high-angle annular dark-field (HAADF) contrasts, that is, bright, dark, and bi-phases. Both the light and heavy atoms have been found to be present in Y–Al–O complex-oxide nanostructures in varying quantities and forming a characteristic interface with the metallic matrix. In overcoming the limitation of conventional STEM-HAADF imaging, the integrated differential phase-contrast imaging technique was employed to investigate the oxygen atoms along with other elements in the dispersoids and its interface with the matrix. The most intriguing aspect of the study is the discovery of a few atoms thick Al2O3 interlayer (shell) around a monoclinic Y–Al–O core in the Ni-matrix. On the other hand, when the dispersoid is a hexagonal type Y–Al–O complex, the interface energy is already low, maintaining a semi-coherent interface and it was devoid of a shell.
{"title":"Unraveling the Complexity of Nano-Dispersoids in the Oxide Dispersion Strengthened Alloy 617.","authors":"Shyam Kanta Sinha, Arup Dasgupta, M Sivakumar, Chanchal Ghosh, S Raju","doi":"10.1017/S143192762200071X","DOIUrl":"10.1017/S143192762200071X","url":null,"abstract":"<p><p>Nanocrystalline oxides are mainly responsible for Ni-base oxide dispersion strengthened (ODS) superalloys excellent thermo-mechanical properties. To establish the microstructural correlations between the metallic matrix and various oxide dispersoids, we report here the atomic-scale structure and chemistry of the complex nano-oxide dispersoids. Ultrahigh-resolution Cs-aberration-corrected scanning transmission electron microscopy (STEM) based techniques have been used to resolve nano-dispersoids in the Alloy 617 ODS. These nano-oxides, interestingly, possess a variety of high-angle annular dark-field (HAADF) contrasts, that is, bright, dark, and bi-phases. Both the light and heavy atoms have been found to be present in Y–Al–O complex-oxide nanostructures in varying quantities and forming a characteristic interface with the metallic matrix. In overcoming the limitation of conventional STEM-HAADF imaging, the integrated differential phase-contrast imaging technique was employed to investigate the oxygen atoms along with other elements in the dispersoids and its interface with the matrix. The most intriguing aspect of the study is the discovery of a few atoms thick Al2O3 interlayer (shell) around a monoclinic Y–Al–O core in the Ni-matrix. On the other hand, when the dispersoid is a hexagonal type Y–Al–O complex, the interface energy is already low, maintaining a semi-coherent interface and it was devoid of a shell.</p>","PeriodicalId":93373,"journal":{"name":"SIAM undergraduate research online","volume":"1 1","pages":"1-9"},"PeriodicalIF":2.8,"publicationDate":"2022-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86369776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Genome sequencing is the basis for many modern biological and medicinal studies. With recent technological advances, metagenomics has become a problem of interest. This problem entails the analysis and reconstruction of multiple DNA sequences from different sources. Shotgun genome sequencing works by breaking up long DNA sequences into shorter segments called reads. Given this collection of reads, one would like to reconstruct the original collection of DNA sequences. For experimental design in metagenomics, it is important to understand how the minimal read length necessary for reliable reconstruction depends on the number and characteristics of the genomes involved. Utilizing simple probabilistic models for each DNA sequence, we analyze the identifiability of collections of M genomes of length N in an asymptotic regime in which N tends to infinity and M may grow with N. Our first main result provides a threshold in terms of M and N so that if the read length exceeds the threshold, then a simple greedy algorithm successfully reconstructs the full collection of genomes with probability tending to one. Our second main result establishes a lower threshold in terms of M and N such that if the read length is shorter than the threshold, then reconstruction of the full collection of genomes is impossible with probability tending to one.
{"title":"A Probabilistic Analysis of Shotgun Sequencing for Metagenomics","authors":"Marlee Herring","doi":"10.1137/22s1472437","DOIUrl":"https://doi.org/10.1137/22s1472437","url":null,"abstract":"Genome sequencing is the basis for many modern biological and medicinal studies. With recent technological advances, metagenomics has become a problem of interest. This problem entails the analysis and reconstruction of multiple DNA sequences from different sources. Shotgun genome sequencing works by breaking up long DNA sequences into shorter segments called reads. Given this collection of reads, one would like to reconstruct the original collection of DNA sequences. For experimental design in metagenomics, it is important to understand how the minimal read length necessary for reliable reconstruction depends on the number and characteristics of the genomes involved. Utilizing simple probabilistic models for each DNA sequence, we analyze the identifiability of collections of M genomes of length N in an asymptotic regime in which N tends to infinity and M may grow with N. Our first main result provides a threshold in terms of M and N so that if the read length exceeds the threshold, then a simple greedy algorithm successfully reconstructs the full collection of genomes with probability tending to one. Our second main result establishes a lower threshold in terms of M and N such that if the read length is shorter than the threshold, then reconstruction of the full collection of genomes is impossible with probability tending to one.","PeriodicalId":93373,"journal":{"name":"SIAM undergraduate research online","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43116182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
. In the study of tomography, there are often missing data values. This 4 leads artifacts to present themselves in data reconstructions. We investigate this 5 problem in a bistatic radar system that has a radio transmitter in a fixed location 6 and a receiver flying around the transmitter in a circular path. Our data is collected 7 by integrating over all ellipses in a given space that have the transmitter and receiver 8 as foci. We reconstruct this numerical data and analyze the artifacts that present 9 themselves when we place objects within and outside of the receiver’s path. Our 10 research demonstrates how objects outside the receiver’s path can create artifacts 11 inside the receiver’s path and vice versa. This shows an intrinsic limitation to a 12 method that works well when the scanned region outside the receiver’s path is clear.
{"title":"A Mathematical Analysis of Reconstruction Artifacts in Radar Limited Data Tomography","authors":"Elena Martinez","doi":"10.1137/21s1468759","DOIUrl":"https://doi.org/10.1137/21s1468759","url":null,"abstract":". In the study of tomography, there are often missing data values. This 4 leads artifacts to present themselves in data reconstructions. We investigate this 5 problem in a bistatic radar system that has a radio transmitter in a fixed location 6 and a receiver flying around the transmitter in a circular path. Our data is collected 7 by integrating over all ellipses in a given space that have the transmitter and receiver 8 as foci. We reconstruct this numerical data and analyze the artifacts that present 9 themselves when we place objects within and outside of the receiver’s path. Our 10 research demonstrates how objects outside the receiver’s path can create artifacts 11 inside the receiver’s path and vice versa. This shows an intrinsic limitation to a 12 method that works well when the scanned region outside the receiver’s path is clear.","PeriodicalId":93373,"journal":{"name":"SIAM undergraduate research online","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64315491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
. We consider the numerical approximation of integrals involving layer functions, which appear as components in the solution of singularly perturbed boundary value problems. The hp version of the Gauss-Legendre composite quadrature, from [1], is utilized in conjunction with the Spectral Boundary Layer mesh from [2]. We show that the error goes to zero exponentially fast, as the number of Gauss points increases, independently of the singular perturbation parameter. Numerical examples illustrating the theory are also presented.
{"title":"hp Gauss-Legendre Quadrature for Layer Functions","authors":"Kleio Liotati","doi":"10.1137/22s1514866","DOIUrl":"https://doi.org/10.1137/22s1514866","url":null,"abstract":". We consider the numerical approximation of integrals involving layer functions, which appear as components in the solution of singularly perturbed boundary value problems. The hp version of the Gauss-Legendre composite quadrature, from [1], is utilized in conjunction with the Spectral Boundary Layer mesh from [2]. We show that the error goes to zero exponentially fast, as the number of Gauss points increases, independently of the singular perturbation parameter. Numerical examples illustrating the theory are also presented.","PeriodicalId":93373,"journal":{"name":"SIAM undergraduate research online","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64317445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With an ever-increasing captivation of the United States sports-viewing audience, the National Football League continues to produce some of the world’s most capable, physical athletes. In this work, athletes’ positions C, OG, OT, DE, and DT were categorized as on the line , while the remaining positions were categorized as not on the line . In this work, a predictive neural network is applied to classify 2,022 National Football League players into the two classifications using scouting combine data of height, weight, and 40-Yard dash time, outperforming the current standard logistic regression. The two measures utilized to compare the strength of the methods were total accuracy and area under ROC curve, with the neural network yielding a slightly higher average in both. In terms of total accuracy, the neural network had an accuracy of 0.9134 to the logistic model’s 0.9065, and in terms of area under ROC curve, the neural network had an area of 0.9578 compared to the logistic model’s 0.9567. As a head-to-head iteration-wise comparison, the neural network had a winning Win-Loss-Tie ratio of 7-2-1 and 5-5-0 in the two measures respectively.
{"title":"Neural Network Approach to NFL Position Classification","authors":"Sithija Manage","doi":"10.1137/21s1444485","DOIUrl":"https://doi.org/10.1137/21s1444485","url":null,"abstract":"With an ever-increasing captivation of the United States sports-viewing audience, the National Football League continues to produce some of the world’s most capable, physical athletes. In this work, athletes’ positions C, OG, OT, DE, and DT were categorized as on the line , while the remaining positions were categorized as not on the line . In this work, a predictive neural network is applied to classify 2,022 National Football League players into the two classifications using scouting combine data of height, weight, and 40-Yard dash time, outperforming the current standard logistic regression. The two measures utilized to compare the strength of the methods were total accuracy and area under ROC curve, with the neural network yielding a slightly higher average in both. In terms of total accuracy, the neural network had an accuracy of 0.9134 to the logistic model’s 0.9065, and in terms of area under ROC curve, the neural network had an area of 0.9578 compared to the logistic model’s 0.9567. As a head-to-head iteration-wise comparison, the neural network had a winning Win-Loss-Tie ratio of 7-2-1 and 5-5-0 in the two measures respectively.","PeriodicalId":93373,"journal":{"name":"SIAM undergraduate research online","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64315223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The Effects of Seasonality on Competition for a Limiting Resource","authors":"Lluc Briganti Wiprachtiger","doi":"10.1137/21s1458132","DOIUrl":"https://doi.org/10.1137/21s1458132","url":null,"abstract":"","PeriodicalId":93373,"journal":{"name":"SIAM undergraduate research online","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64315640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
. We model the COVID-19 outbreak and shipboard quarantine with a 3-D agent-based simulation of a SEIR model which preserves the ratios of crew, passengers, and shipboard space. The stochastic model captures the movement patterns of passengers and crew members on-board the ship, as well as how this movement changed once quarantine is established. The study includes the derivation of the basic reproduction number based on contact numbers and transmission rates. We capture the number of contacts between two people when they remain within the model equivalent of a 3-foot radius for 60 minutes and the transmission probability per contact. We show that, based on the measured reproduction number, an outbreak is bound to occur in the majority of simulations even with quarantine imposed on the ship. We also show that most infection on board occurs by others of the same group (passenger or crew), with passengers causing the majority of infections.
{"title":"An Agent-Based Model of COVID-19 on the Diamond Princess Cruise Ship","authors":"Naomi A. Rankin","doi":"10.1137/21s1462520","DOIUrl":"https://doi.org/10.1137/21s1462520","url":null,"abstract":". We model the COVID-19 outbreak and shipboard quarantine with a 3-D agent-based simulation of a SEIR model which preserves the ratios of crew, passengers, and shipboard space. The stochastic model captures the movement patterns of passengers and crew members on-board the ship, as well as how this movement changed once quarantine is established. The study includes the derivation of the basic reproduction number based on contact numbers and transmission rates. We capture the number of contacts between two people when they remain within the model equivalent of a 3-foot radius for 60 minutes and the transmission probability per contact. We show that, based on the measured reproduction number, an outbreak is bound to occur in the majority of simulations even with quarantine imposed on the ship. We also show that most infection on board occurs by others of the same group (passenger or crew), with passengers causing the majority of infections.","PeriodicalId":93373,"journal":{"name":"SIAM undergraduate research online","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64315808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Glioblastoma is an aggressive brain tumor with cells that infiltrate and proliferate rapidly into surrounding brain tissue. Current mathematical models of glioblastoma growth capture this behavior using partial differential equations (PDEs) that are simulated via numerical solvers—a highly-efficient im-plementation can take about 80 seconds to complete a single forward evaluation. However, clinical applications of tumor modeling are often framed as inverse problems that require sophisticated numerical methods and, if implemented naively, can lead to prohibitively long runtimes that render them inadequate for clinical settings. Recently, physics-informed neural networks (PINNs) have emerged as a novel method in scientific machine learning for solving nonlinear PDEs. Compared to traditional solvers, PINNs leverage unsupervised deep learning methods to minimize residuals across mesh-free domains, enabling greater flexibility while avoiding the need for complex grid constructions. Here, we describe and implement a general method for solving time-dependent diffusion-reaction PDE models of glioblastoma and inferring biophysical parameters from numerical data via PINNs. We evaluate the PINNs over patient-specific geometries, accounting for individual variations with diffusion mobilities derived from pre-operative MRI scans. Using synthetic data, we demonstrate the performance of our algorithm in patient-specific geometries. We show that PINNs are capable of solving parameter inference inverse problems in approximately one hour, expediting previous approaches by 20–40 times owing to the robust interpolation capabilities of machine learning algorithms. We anticipate this method may be sufficiently accurate and efficient for clinical usage, potentially rendering personalized treatments more accessible in standard-of-care medical protocols.
{"title":"Accelerating Parameter Inference in Diffusion-Reaction Models of Glioblastoma Using Physics-Informed Neural Networks","authors":"Andy Zhu","doi":"10.1137/22s1472814","DOIUrl":"https://doi.org/10.1137/22s1472814","url":null,"abstract":"Glioblastoma is an aggressive brain tumor with cells that infiltrate and proliferate rapidly into surrounding brain tissue. Current mathematical models of glioblastoma growth capture this behavior using partial differential equations (PDEs) that are simulated via numerical solvers—a highly-efficient im-plementation can take about 80 seconds to complete a single forward evaluation. However, clinical applications of tumor modeling are often framed as inverse problems that require sophisticated numerical methods and, if implemented naively, can lead to prohibitively long runtimes that render them inadequate for clinical settings. Recently, physics-informed neural networks (PINNs) have emerged as a novel method in scientific machine learning for solving nonlinear PDEs. Compared to traditional solvers, PINNs leverage unsupervised deep learning methods to minimize residuals across mesh-free domains, enabling greater flexibility while avoiding the need for complex grid constructions. Here, we describe and implement a general method for solving time-dependent diffusion-reaction PDE models of glioblastoma and inferring biophysical parameters from numerical data via PINNs. We evaluate the PINNs over patient-specific geometries, accounting for individual variations with diffusion mobilities derived from pre-operative MRI scans. Using synthetic data, we demonstrate the performance of our algorithm in patient-specific geometries. We show that PINNs are capable of solving parameter inference inverse problems in approximately one hour, expediting previous approaches by 20–40 times owing to the robust interpolation capabilities of machine learning algorithms. We anticipate this method may be sufficiently accurate and efficient for clinical usage, potentially rendering personalized treatments more accessible in standard-of-care medical protocols.","PeriodicalId":93373,"journal":{"name":"SIAM undergraduate research online","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64316524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The Effect of Academic Performance on Athletic Success in Collegiate Athletic Programs","authors":"Derek Brickley","doi":"10.1137/22s1491216","DOIUrl":"https://doi.org/10.1137/22s1491216","url":null,"abstract":"","PeriodicalId":93373,"journal":{"name":"SIAM undergraduate research online","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64317001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
. We investigate the novel application of two sequential convolutional neural networks (CNNs) for the char-acterisation of dark matter substructure in lensing galaxies from galaxy-galaxy strong gravitational lensing images. In our configuration, an initial CNN predicts the number of substructures from a gravitationally lensed image and then this number, along with the same image, is input to a second CNN which predicts the power-law slope of the substructure mass distribution function. We have trained and tested the CNNs on simulated images created by lensing a galaxy-like light distribution with a foreground galaxy mass. We find that training and testing the CNNs on images created with a fixed lens geometry allows the number of substructures and the mass function power-law slope to be retrieved well. We then explore the effect of reducing the resolution of images such that the image pixel scale is halved finding that the accuracy of the number of predicted substructures decreases by only 7% while the accuracy of the predicted mass function slope decreases by 25%. When we allow variation in lens geometry between images in the test set, to mimic more physically motivated lens samples, we observe a decrease in accuracy of the number of predicted substructures and the mass function slope of 57% and 81% respectively. We attribute this significant degradation in predicting the mass function power-law slope to the degradation in the performance of the number-predicting CNN by comparing with predictions of the slope that are made when the CNN is given the true number of substructures. We discuss future possible improvements and the impact of the computing hardware available for this work.
{"title":"Characterising Dark Matter Substructure in Gravitational Lens Galaxies with Deep Learning","authors":"Owen J. Scutt","doi":"10.1137/22s1478033","DOIUrl":"https://doi.org/10.1137/22s1478033","url":null,"abstract":". We investigate the novel application of two sequential convolutional neural networks (CNNs) for the char-acterisation of dark matter substructure in lensing galaxies from galaxy-galaxy strong gravitational lensing images. In our configuration, an initial CNN predicts the number of substructures from a gravitationally lensed image and then this number, along with the same image, is input to a second CNN which predicts the power-law slope of the substructure mass distribution function. We have trained and tested the CNNs on simulated images created by lensing a galaxy-like light distribution with a foreground galaxy mass. We find that training and testing the CNNs on images created with a fixed lens geometry allows the number of substructures and the mass function power-law slope to be retrieved well. We then explore the effect of reducing the resolution of images such that the image pixel scale is halved finding that the accuracy of the number of predicted substructures decreases by only 7% while the accuracy of the predicted mass function slope decreases by 25%. When we allow variation in lens geometry between images in the test set, to mimic more physically motivated lens samples, we observe a decrease in accuracy of the number of predicted substructures and the mass function slope of 57% and 81% respectively. We attribute this significant degradation in predicting the mass function power-law slope to the degradation in the performance of the number-predicting CNN by comparing with predictions of the slope that are made when the CNN is given the true number of substructures. We discuss future possible improvements and the impact of the computing hardware available for this work.","PeriodicalId":93373,"journal":{"name":"SIAM undergraduate research online","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64317218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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