Microstructure reconstruction of 2D/3D random materials via diffusion-based deep generative models

Microstructure reconstruction serves as a crucial foundation for establishing Process-Structure-Property (PSP) relationship in material design. Confronting the limitations of variational autoencoder and generative adversarial network within generative modeling, this study adopted the denoising diffusion probability model (DDPM) to learn the probability distribution of high-dimensional raw data and successfully reconstructed the microstructures of various composite materials, such as inclusion materials, spinodal decomposition materials, chessboard materials, fractal noise materials, and so on. The quality of generated microstructure was evaluated using quantitative measures like spatial correlation functions and Fourier descriptor. On this basis, this study also successfully achieved the regulation of microstructure randomness and the generation of gradient materials through continuous interpolation in latent space using denoising diffusion implicit model (DDIM). Furthermore, the two-dimensional microstructure reconstruction is extended to three-dimensional framework and integrates permeability as a feature encoding embedding. This enables the conditional generation of three-dimensional microstructures for random porous materials within a defined permeability range. The permeabilities of these generated microstructures were further validated through the application of the Boltzmann method

Robust Nuclear Spin Polarization via Ground-State Level Anti-Crossing of Boron Vacancy Defects in Hexagonal Boron Nitride

Nuclear spin polarization plays a crucial role in quantum information processing and quantum sensing. In this work, we demonstrate a robust and efficient method for nuclear spin polarization with boron vacancy ($\mathrm{V_B^-}$) defects in hexagonal boron nitride (h-BN) using ground-state level anti-crossing (GSLAC). We show that GSLAC-assisted nuclear polarization can be achieved with significantly lower laser power than excited-state level anti-crossing, making the process experimentally more viable. Furthermore, we have demonstrated direct optical readout of nuclear spins for $\mathrm{V_B^-}$ in h-BN. Our findings suggest that GSLAC is a promising technique for the precise control and manipulation of nuclear spins in $\mathrm{V_B^-}$ defects in h-BN.Comment: 6 pages, 4 figure

Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge

Airway-related quantitative imaging biomarkers are crucial for examination, diagnosis, and prognosis in pulmonary diseases. However, the manual delineation of airway structures remains prohibitively time-consuming. While significant efforts have been made towards enhancing automatic airway modelling, current public-available datasets predominantly concentrate on lung diseases with moderate morphological variations. The intricate honeycombing patterns present in the lung tissues of fibrotic lung disease patients exacerbate the challenges, often leading to various prediction errors. To address this issue, the 'Airway-Informed Quantitative CT Imaging Biomarker for Fibrotic Lung Disease 2023' (AIIB23) competition was organized in conjunction with the official 2023 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). The airway structures were meticulously annotated by three experienced radiologists. Competitors were encouraged to develop automatic airway segmentation models with high robustness and generalization abilities, followed by exploring the most correlated QIB of mortality prediction. A training set of 120 high-resolution computerised tomography (HRCT) scans were publicly released with expert annotations and mortality status. The online validation set incorporated 52 HRCT scans from patients with fibrotic lung disease and the offline test set included 140 cases from fibrosis and COVID-19 patients. The results have shown that the capacity of extracting airway trees from patients with fibrotic lung disease could be enhanced by introducing voxel-wise weighted general union loss and continuity loss. In addition to the competitive image biomarkers for mortality prediction, a strong airway-derived biomarker (Hazard ratio>1.5, p < 0.0001) was revealed for survival prognostication compared with existing clinical measurements, clinician assessment and AI-based biomarkers

Small area prediction and big data visualization: Analysis of soil losses from sheet and rill erosion on cropland

Assessment of soil erosion benefits both the well-being of people and agricultural production. Sustainable and environmentally friendly agriculture needs to balance short-time production, long-term capabilities, and environmental quality. The overarching applications related to the works in this dissertation are related to the National Resources Inventory (NRI) program. The ongoing NRI surveys collect a wealth of sample data describing natural resources conditions and trends to support national policy-making and enterprise-level landowner decision making on resource conservation practices. Among those natural resources issues, soil erosion assessment is of primary interest to prioritize future soil conservation needs and measure past soil conservation impact. Our effort is aimed at estimation of land use and soil erosion rates, especially sheet and rill erosion, through combined techniques of small area estimation and big data visualization. Small area estimation (SAE) techniques are used to construct model-based estimators when direct survey estimators cannot achieve desired statistical reliability. To account for the zero-contamination and right-skew of the sheet and rill erosion data in our case study, we consider a zero-inflated log-normal model framework and extend the two-part model of Chandra and Chambers (2016) by including an additional parameter to account for significant correlation between the pair of random effects for an area. We develop an empirical Bayes predictor of the area mean that replaces the unknown model parameters in the best predictor, which is guaranteed to be unbiased and have the minimum mean squared error, with consistent parameter estimates. We address the analytic challenges associated with parameter estimation under this model framework by using a maximum likelihood method. Maximum likelihood estimation is challenging because of a need to integrate over a bivariate distribution of the pair of random effects for a county. We transform the bivariate integral to a univariate integral to facilitate numerical integration through a computationally efficient Gauss-Hermite approximation. Computationally efficiency in terms of assessing statistical uncertainty in the estimates is further enhanced by using the one-step MSE estimator, an estimator we propose that does not require resampling. The reliable county-level erosion estimates that are not obtainable from the NRI sample data can be used to prioritize conservation resource allocation at a more granular level. To help practitioners implement our SAE methodology, we develop an R package saezero, available at https://github.com/XiaodanLyu/saezero. Besides the characteristic of reliability, there are many other dimensions of data quality, such as accuracy, consistency, timeliness, usability, accessibility, and relevance, which are featured in the quality assurance (QA) process of NRI. The QA process is operationally complex as the involved databases are large in scale. Effective visualization techniques, under the help of well-managed databases, can facilitate the QA process by alleviating the cognitive load and enabling user-data interactions. By using the reactive framework of R shiny, we built three web-based graphical tools intended to be used by NRI. The first tool iNtr , whose public version is available at https://lyux.shinyapps.io/table_review/, is designed to help with the labor-intensive NRI table review process so that data accuracy and consistency can be checked as much as possible without sacrificing the timeliness of the NRI releases. The second tool VISCOVER , available at https://lyux.shinyapps.io/viscover/, is developed to check the accuracy of the auxiliary variables, i.e., public soil and crop-cover data, used in the case study of our SAE methodology. An R package viscover, available at https://github.com/XiaodanLyu/viscover, has also been developed by us for practitioners to query the two databases easily. The third tool SREM , available at https://lyux.shinyapps.io/srem/, presents an interactive sheet and rill erosion map at a 30-meter spatial resolution to enhance the usability and accessibility of NRI in that the NRI erosion estimates used to be available only at national and state level in the form of printed figures and tables. SREM is built upon five databases --- one sheet-and-rill-erosion and four soil-erosion-factor databases we created by assembling the NRI Database and several other public databases by data linkage and statistical modeling

Quantum sensing based on nitrogen vacancy centers in diamond and boron vacancy centers in hexagonal boron nitride

Solid-state spins provide a promising platform for quantum sensing. Hexagonal boron nitride (h-BN) is not only a promising functional material for the development of 2-dimensional (2D) optoelectronic devices but also a good candidate for quantum sensing thanks to the presence of quantum emitters in the form of atom-like defects. Their exploitation in quantum technologies necessitates understanding their coherence properties as well as their sensitivity to external stimuli. In this work, we probe the strain configuration of boron vacancy centers (VB−) created by ion implantation in h-BN flakes thanks to wide field spatially-resolved optically detected magnetic resonance and sub-micro Raman spectroscopy. Our experiments demonstrate the ability of VB− for quantum sensing of strain and, given the omnipresence of h-BN in 2D-based devices, open the door for in-situ imaging of strain under working conditions. To further investigate the symmetry of h-BN, we probe the nonlinear response of h-BN flakes on the gold film, where the giant enhancement is observed. We demonstrate that the enhancement is parity independent, inspiring the nonlinear optical properties investigation for those flakes with even layers. We also verified that this enhancement is layer-dependent, which implies the change of distribution of the electric field on the surface of the film. The enhancement is broadband which means that the nonlinear optical devices can be applied for different wavelengths. We also observed an extra enhancement on the h-BN homostructures, demonstrating the probability of realizing nonlinear devices with complicated structures. This study paves the way to the ultra-elaborate definition of lattice orientation of h-BN flakes with various thicknesses, giving the possibility of strain or electric field sensing with well-defined directions of fields applied. Studies on Nitrogen-Vacancy (NV) color centers have been continued for several decades using a variety of spectroscopic techniques. The most recent renewed interest in the NV center is to explore it as a solid-state physical system for quantum sensing. Current technologies suffer from lacking high-spatial-resolution information on the target in the sample. Our proposed wide-field nuclear magnetic resonance (NMR) microscopy provides a promising solution to address the drawbacks of current technologies. By performing the dynamical decoupling and correlations measurements, we observe the time evolution of 13C in the vicinity of nitrogen-vacancy centers. The sensitivity of the wide-field microscopy is proved to reach √50nT/ Hz, which reaches the highest sensitivity reported. Last, we demonstrate the impact of 13C on the iQdyne measurement, providing a new strategy to detect nuclear spin dynamics. A more robust, rapid, and high throughput screening with high accuracy and high throughput is led by the development of the wide-field NMR system. Our study opens the door for its future usage as biosensors for disease diagnosis.Doctor of Philosoph

Small area prediction and big data visualization: Analysis of soil losses from sheet and rill erosion on cropland

Assessment of soil erosion benefits both the well-being of people and agricultural production. Sustainable and environmentally friendly agriculture needs to balance short-time production, long-term capabilities, and environmental quality. The overarching applications related to the works in this dissertation are related to the National Resources Inventory (NRI) program. The ongoing NRI surveys collect a wealth of sample data describing natural resources conditions and trends to support national policy-making and enterprise-level landowner decision making on resource conservation practices. Among those natural resources issues, soil erosion assessment is of primary interest to prioritize future soil conservation needs and measure past soil conservation impact. Our effort is aimed at estimation of land use and soil erosion rates, especially sheet and rill erosion, through combined techniques of small area estimation and "big" data visualization. Small area estimation (SAE) techniques are used to construct model-based estimators when direct survey estimators cannot achieve desired statistical reliability. To account for the zero-contamination and right-skew of the sheet and rill erosion data in our case study, we consider a zero-inflated log-normal model framework and extend the two-part model of Chandra and Chambers (2016) by including an additional parameter to account for significant correlation between the pair of random effects for an area. We develop an empirical Bayes predictor of the area mean that replaces the unknown model parameters in the best predictor, which is guaranteed to be unbiased and have the minimum mean squared error, with consistent parameter estimates. We address the analytic challenges associated with parameter estimation under this model framework by using a maximum likelihood method. Maximum likelihood estimation is challenging because of a need to integrate over a bivariate distribution of the pair of random effects for a county. We transform the bivariate integral to a univariate integral to facilitate numerical integration through a computationally efficient Gauss-Hermite approximation. Computationally efficiency in terms of assessing statistical uncertainty in the estimates is further enhanced by using the "one-step" MSE estimator, an estimator we propose that does not require resampling. The reliable county-level erosion estimates that are not obtainable from the NRI sample data can be used to prioritize conservation resource allocation at a more granular level. To help practitioners implement our SAE methodology, we develop an R package saezero, available at https://github.com/XiaodanLyu/saezero. Besides the characteristic of reliability, there are many other dimensions of data quality, such as accuracy, consistency, timeliness, usability, accessibility, and relevance, which are featured in the quality assurance (QA) process of NRI. The QA process is operationally complex as the involved databases are large in scale. Effective visualization techniques, under the help of well-managed databases, can facilitate the QA process by alleviating the cognitive load and enabling user-data interactions. By using the reactive framework of R shiny, we built three web-based graphical tools intended to be used by NRI. The first tool "iNtr", whose public version is available at https://lyux.shinyapps.io/table_review/, is designed to help with the labor-intensive NRI table review process so that data accuracy and consistency can be checked as much as possible without sacrificing the timeliness of the NRI releases. The second tool "VISCOVER", available at https://lyux.shinyapps.io/viscover/, is developed to check the accuracy of the auxiliary variables, i.e., public soil and crop-cover data, used in the case study of our SAE methodology. An R package viscover, available at https://github.com/XiaodanLyu/viscover, has also been developed by us for practitioners to query the two databases easily. The third tool "SREM", available at https://lyux.shinyapps.io/srem/, presents an interactive sheet and rill erosion map at a 30-meter spatial resolution to enhance the usability and accessibility of NRI in that the NRI erosion estimates used to be available only at national and state level in the form of printed figures and tables. "SREM" is built upon five databases --- one sheet-and-rill-erosion and four soil-erosion-factor databases we created by assembling the NRI Database and several other public databases by data linkage and statistical modeling.</p

Using landscape habitat associations to prioritize areas of conservation action for terrestrial birds

Predicting species distributions has long been a valuable tool to plan and focus efforts for biodiversity conservation, particularly because such an approach allows researchers and managers to evaluate species distribution changes in response to various threats. Utilizing data from a long-term monitoring program and land cover data sets, we modeled the probability of occupancy and colonization for 38 bird Species of Greatest Conservation Need (SGCN) in the robust design occupancy modeling framework, and used results from the best models to predict occupancy and colonization on the Iowa landscape. Bird surveys were conducted at 292 properties from April to October, 2006–2014. We calculated landscape habitat characteristics at multiple spatial scales surrounding each of our surveyed properties to be used in our models and then used kriging in ArcGIS to create predictive maps of species distributions. We validated models with data from 2013 using the area under the receiver operating characteristic curve (AUC). Probability of occupancy ranged from 0.001 (SE 0.70). The most important predictor for occupancy of grassland birds was percentage of the landscape in grassland habitat, and the most important predictor for woodland birds was percentage of the landscape in woodland habitat. This emphasizes the need for managers to restore specific habitats on the landscape. In an era during which funding continues to decrease for conservation agencies, our approach aids in determining where to focus limited resources to best conserve bird species of conservation concern.This article is published as Harms TM, Murphy KT, Lyu X, Patterson SS, Kinkead KE, Dinsmore SJ, et al. (2017) Using landscape habitat associations to prioritize areas of conservation action for terrestrial birds. PLoS ONE 12(3): e0173041. Doi: 10.1371/journal.pone.0173041. </p

A Comprehensive Evaluation of Tomato Fruit Quality and Identification of Volatile Compounds

Tomatoes (Lycopersicon esculentum) are the most valuable vegetable crop in the world. This study identified the morphological characteristics, vitamin content, etc., from 15 tomato varieties in total, that included five each from the three experimental types, during the commercial ripening period. The results showed that the hardness with peel and the moisture content of tasty tomatoes were 157.81% and 54.50%, and 3.16% and 1.90% lower than those of regular tomatoes and cherry tomatoes, respectively, while the soluble solids were 60.25% and 20.79% higher than those of the latter two types. In addition, the contents of vitamin C, lycopene, fructose, glucose, and total organic acids of tasty tomatoes were higher than those of regular tomatoes and cherry tomatoes. A total of 110 volatile compounds were detected in the 15 tomato varieties. The average volatile compound content of tasty tomatoes was 57.94% higher than that of regular tomatoes and 15.24% higher than that of cherry tomatoes. Twenty of the 34 characteristic tomato aroma components were identified in tasty tomatoes, with fruity and green being the main odor types. Ten characteristic aroma components in regular tomatoes were similar to those of tasty tomatoes; ten types of cherry tomatoes had floral and woody aromas as the main odor types. The flavor sensory score was significantly positively correlated with the content of soluble solids, fructose, glucose, citric acid, fumaric acid, and β-ionone (p < 0.01), and significantly negatively correlated with water content and firmness without peel. Regular, tasty, and cherry tomatoes were separated using principal component analysis, and the quality of tasty tomatoes was found to be better than cherry tomatoes, followed by regular tomatoes. These results provide valuable information for a comprehensive evaluation of fruit quality among tomato varieties to develop consumer guidelines

Using landscape habitat associations to prioritize areas of conservation action for terrestrial birds.

Predicting species distributions has long been a valuable tool to plan and focus efforts for biodiversity conservation, particularly because such an approach allows researchers and managers to evaluate species distribution changes in response to various threats. Utilizing data from a long-term monitoring program and land cover data sets, we modeled the probability of occupancy and colonization for 38 bird Species of Greatest Conservation Need (SGCN) in the robust design occupancy modeling framework, and used results from the best models to predict occupancy and colonization on the Iowa landscape. Bird surveys were conducted at 292 properties from April to October, 2006-2014. We calculated landscape habitat characteristics at multiple spatial scales surrounding each of our surveyed properties to be used in our models and then used kriging in ArcGIS to create predictive maps of species distributions. We validated models with data from 2013 using the area under the receiver operating characteristic curve (AUC). Probability of occupancy ranged from 0.001 (SE 0.70). The most important predictor for occupancy of grassland birds was percentage of the landscape in grassland habitat, and the most important predictor for woodland birds was percentage of the landscape in woodland habitat. This emphasizes the need for managers to restore specific habitats on the landscape. In an era during which funding continues to decrease for conservation agencies, our approach aids in determining where to focus limited resources to best conserve bird species of conservation concern