SIAM undergraduate research online最新文献_第3页

Unraveling the Complexity of Nano-Dispersoids in the Oxide Dispersion Strengthened Alloy 617. 揭示氧化物分散强化合金 617 中纳米分散体的复杂性。

IF 2.8

SIAM undergraduate research online Pub Date : 2022-05-26 DOI: 10.1017/S143192762200071X

Shyam Kanta Sinha, Arup Dasgupta, M Sivakumar, Chanchal Ghosh, S Raju

{"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}

引用次数: 0

A Probabilistic Analysis of Shotgun Sequencing for Metagenomics 宏基因组学Shotgun测序的概率分析

SIAM undergraduate research online Pub Date : 2022-01-13 DOI: 10.1137/22s1472437

Marlee Herring

{"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}

引用次数: 0

A Mathematical Analysis of Reconstruction Artifacts in Radar Limited Data Tomography 雷达有限数据层析成像重建伪影的数学分析

SIAM undergraduate research online Pub Date : 2022-01-01 DOI: 10.1137/21s1468759

Elena Martinez

引用次数: 0

hp Gauss-Legendre Quadrature for Layer Functions 层函数的高斯-勒让德正交

SIAM undergraduate research online Pub Date : 2022-01-01 DOI: 10.1137/22s1514866

Kleio Liotati

引用次数: 0

Neural Network Approach to NFL Position Classification 神经网络在NFL位置分类中的应用

SIAM undergraduate research online Pub Date : 2022-01-01 DOI: 10.1137/21s1444485

Sithija Manage

引用次数: 0

The Effects of Seasonality on Competition for a Limiting Resource 季节性对有限资源竞争的影响

SIAM undergraduate research online Pub Date : 2022-01-01 DOI: 10.1137/21s1458132

Lluc Briganti Wiprachtiger

引用次数: 0

An Agent-Based Model of COVID-19 on the Diamond Princess Cruise Ship 基于agent的钻石公主号邮轮COVID-19模型

SIAM undergraduate research online Pub Date : 2022-01-01 DOI: 10.1137/21s1462520

Naomi A. Rankin

引用次数: 0

Accelerating Parameter Inference in Diffusion-Reaction Models of Glioblastoma Using Physics-Informed Neural Networks 利用物理信息神经网络加速胶质母细胞瘤扩散反应模型的参数推断

SIAM undergraduate research online Pub Date : 2022-01-01 DOI: 10.1137/22s1472814

Andy Zhu

{"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}

引用次数: 3

The Effect of Academic Performance on Athletic Success in Collegiate Athletic Programs 大学体育项目中学业成绩对运动成功的影响

SIAM undergraduate research online Pub Date : 2022-01-01 DOI: 10.1137/22s1491216

Derek Brickley

引用次数: 0

Characterising Dark Matter Substructure in Gravitational Lens Galaxies with Deep Learning 用深度学习表征引力透镜星系中的暗物质子结构

SIAM undergraduate research online Pub Date : 2022-01-01 DOI: 10.1137/22s1478033

Owen J. Scutt

{"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}

引用次数: 0