Applications of Spatial Distribution Maps for Advanced Atom Probe Reconstruction and Data Analysis

A real-space technique for finding structural information in atom probe tomographs, spatial distribution maps (SDM), is described. The mechanics of the technique are explained, and it is then applied to some test cases. Many applications of SDM in atom probe tomography are illustrated with examples including finding crystal lattices, correcting lattice strains in reconstructed images, quantifying trajectory aberrations, quantifying spatial resolution, quantifying chemical ordering, dark-field imaging, determining orientation relationships, extracting radial distribution functions, and measuring ion detection efficiency.

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Refining Spatial Distribution Maps for Atom Probe Tomography via Data Dimensionality Reduction Methods

Microscopy and Microanalysis ◽

10.1017/s1431927612001171 ◽

2012 ◽

Vol 18 (5) ◽

pp. 941-952 ◽

Cited By ~ 5

Author(s):

Santosh K. Suram ◽

Krishna Rajan

Keyword(s):

Spatial Distribution ◽

Atom Probe Tomography ◽

Structural Information ◽

Three Dimensional ◽

Relevant Information ◽

Atom Probe ◽

Mathematical Framework ◽

Frequency Distributions ◽

Distribution Maps ◽

Singular Vectors

AbstractA mathematical framework based on singular value decomposition is used to analyze the covariance among interatomic frequency distributions in spatial distribution maps (SDMs). Using this approach, singular vectors that capture the covariance within the SDM data are obtained. The structurally relevant singular vectors (SRSVs) are identified. Using the SRSVs, we extract information from z-SDMs that not only captures the offset between the atomic planes but also captures the covariance in the atomic structure among the neighborhood atomic planes. These refined z-SDMs classify the Δ(Δz) slices in the SDMs into structurally relevant information, noise, and aberrations. The SRSVs are used to construct refined xy-SDMs that provide enhanced structural information for three-dimensional atom probe tomography.

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Informatics for Quantitative Analysis of Atom Probe Tomography Images

MRS Proceedings ◽

10.1557/proc-1231-nn03-14 ◽

2009 ◽

Vol 1231 ◽

Cited By ~ 2

Author(s):

Santosh K Suram ◽

Krishna Rajan

Keyword(s):

Data Mining ◽

Spatial Distribution ◽

Quantitative Analysis ◽

Atom Probe Tomography ◽

Structural Information ◽

Three Dimensional ◽

Atomic Plane ◽

Atom Probe ◽

Distribution Maps

AbstractAn informatics based approach to extract further refinements on the crystallographic information embedded in the Spatial Distribution Maps (SDMs) has been developed. The data mining based methods to generate and interpret spectra that de-convolute the SDMs are discussed. This work has resulted in a method to generate SDMs that can map three-dimensional crystallographic information as opposed to existing methods that map structural information on only one atomic plane at a time. The broader implications of this work on enhancing the interpretation and resolution of structural information in atom probe tomography studies is also discussed.

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Extracting High-Volume Traffic Routes from AIS Spatial Distribution Maps

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2019.8900105 ◽

2019 ◽

Cited By ~ 1

Author(s):

T. L. Grobler ◽

W. Kleynhans

Keyword(s):

Spatial Distribution ◽

High Volume ◽

Distribution Maps

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Open Plot Project: an open-source toolkit for 3-D structural data analysis

Solid Earth ◽

10.5194/se-2-53-2011 ◽

2011 ◽

Vol 2 (1) ◽

pp. 53-63 ◽

Cited By ~ 18

Author(s):

S. Tavani ◽

P. Arbues ◽

M. Snidero ◽

N. Carrera ◽

J. A. Muñoz

Keyword(s):

Spatial Distribution ◽

Data Analysis ◽

Open Source ◽

Open Source Software ◽

Source Code ◽

Structural Data ◽

Geological Modelling ◽

Analysis Tools ◽

Transect Analysis ◽

Selection Of

Abstract. In this work we present the Open Plot Project, an open-source software for structural data analysis, including a 3-D environment. The software includes many classical functionalities of structural data analysis tools, like stereoplot, contouring, tensorial regression, scatterplots, histograms and transect analysis. In addition, efficient filtering tools are present allowing the selection of data according to their attributes, including spatial distribution and orientation. This first alpha release represents a stand-alone toolkit for structural data analysis. The presence of a 3-D environment with digitalising tools allows the integration of structural data with information extracted from georeferenced images to produce structurally validated dip domains. This, coupled with many import/export facilities, allows easy incorporation of structural analyses in workflows for 3-D geological modelling. Accordingly, Open Plot Project also candidates as a structural add-on for 3-D geological modelling software. The software (for both Windows and Linux O.S.), the User Manual, a set of example movies (complementary to the User Manual), and the source code are provided as Supplement. We intend the publication of the source code to set the foundation for free, public software that, hopefully, the structural geologists' community will use, modify, and implement. The creation of additional public controls/tools is strongly encouraged.

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Convolutional neural network-assisted recognition of nanoscale L12 ordered structures in face-centred cubic alloys

npj Computational Materials ◽

10.1038/s41524-020-00472-7 ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Yue Li ◽

Xuyang Zhou ◽

Timoteo Colnaghi ◽

Ye Wei ◽

Andreas Marek ◽

...

Keyword(s):

Three Dimensional ◽

Chemical Species ◽

Atom Probe ◽

Local Order ◽

Face Centered Cubic ◽

Ordered Structure ◽

Ordered Structures ◽

Distribution Maps ◽

Structure Recognition ◽

Fcc Alloys

AbstractNanoscale L12-type ordered structures are widely used in face-centered cubic (FCC) alloys to exploit their hardening capacity and thereby improve mechanical properties. These fine-scale particles are typically fully coherent with matrix with the same atomic configuration disregarding chemical species, which makes them challenging to be characterized. Spatial distribution maps (SDMs) are used to probe local order by interrogating the three-dimensional (3D) distribution of atoms within reconstructed atom probe tomography (APT) data. However, it is almost impossible to manually analyze the complete point cloud (>10 million) in search for the partial crystallographic information retained within the data. Here, we proposed an intelligent L12-ordered structure recognition method based on convolutional neural networks (CNNs). The SDMs of a simulated L12-ordered structure and the FCC matrix were firstly generated. These simulated images combined with a small amount of experimental data were used to train a CNN-based L12-ordered structure recognition model. Finally, the approach was successfully applied to reveal the 3D distribution of L12–type δ′–Al3(LiMg) nanoparticles with an average radius of 2.54 nm in a FCC Al-Li-Mg system. The minimum radius of detectable nanodomain is even down to 5 Å. The proposed CNN-APT method is promising to be extended to recognize other nanoscale ordered structures and even more-challenging short-range ordered phenomena in the near future.

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Distribution of fungi in a Triassic fern stem

Earth and Environmental Science Transactions of the Royal Society of Edinburgh ◽

10.1017/s175569101800021x ◽

2017 ◽

Vol 108 (4) ◽

pp. 387-398 ◽

Cited By ~ 2

Author(s):

Carla J. Harper ◽

Jean Galtier ◽

Thomas N. Taylor ◽

Edith L. Taylor ◽

Ronny Rößler ◽

...

Keyword(s):

Spatial Distribution ◽

Host Response ◽

Host Tissue ◽

Tissue Preservation ◽

Fossil Plants ◽

Fossil Plant ◽

Distribution Maps ◽

Different Types ◽

Fungal Distribution ◽

Fungal Associations

ABSTRACTDocumented evidence of fungi associated with Mesozoic ferns is exceedingly rare. Three different types of fungal remains occur in a portion of a small, permineralised fern stem of uncertain systematic affinities from the Triassic of Germany. Exquisite preservation of all internal tissues made it possible to map the spatial distribution of the fungi in several longitudinal and transverse sections. Narrow, intracellular hyphae extend through the entire cortex, while wide hyphae are concentrated in the cortical intercellular system adjacent to the stele and leaf traces. Hyphal swellings occur in the phloem and adjacent cortex, while moniliform hyphae (or chains of conidia) are present exclusively in parenchyma adjacent to the stele. No host response is recognisable, but host tissue preservation suggests that the fern was alive during fungal colonisation. The highest concentration of fungal remains occurs close to the stele and leaf traces, suggesting that the fungi either utilised the vascular tissues as an infection/colonisation pathway or extracted nutrients from these tissues. This study presents the first depiction of fungal distribution throughout a larger portion of a fossil plant. Although distribution maps are useful tools in assessing fungal associations in relatively small, fossil plants, preparing similar maps for larger and more complex fossils would certainly be difficult and extremely arduous.

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Spatial Distribution of Breast Cancer in Sudan 2010-2016

10.1101/516062 ◽

2019 ◽

Author(s):

Marwa Maweya Abdelbagi Elbasheer ◽

Ayah Galal Abdelrahman Alkhidir ◽

Siham Mohammed Awad Mohammed ◽

Areej Abuelgasim Hassan Abbas ◽

Aisha Osman Mohamed ◽

...

Keyword(s):

Breast Cancer ◽

Spatial Distribution ◽

Spatial Analysis ◽

High Risk ◽

Geographical Information ◽

Risk Level ◽

Histopathological Diagnosis ◽

Cross Sectional ◽

Distribution Maps ◽

Risk Areas

AbstractBackgroundBreast cancer is the most prevalent cancer among females worldwide including Sudan. The aim of this study was to determine the spatial distribution of breast cancer in Sudan.Materials and methodsA facility based cross-sectional study was implemented in eighteen histopathology laboratories distributed in the three localities of Khartoum State on a sample of 4630 Breast Cancer cases diagnosed during the period 2010-2016. A master database was developed through Epi Info™ 7.1.5.2 for computerizing the data collected: the facility name, type (public or private), and its geo- location (latitude and longitude). Personal data on patients were extracted from their respective medical records (name, age, marital status, ethnic group, State, locality, administrative unit, permanent address and phone number, histopathology diagnosis). The data was summarized through SPSS to generate frequency tables for estimating prevalence and the geographical information system (ArcGIS 10.3) was used to generate the epidemiological distribution maps. ArcGIS 10.3 spatial analysis features were used to develop risk maps based on the kriging method.ResultsBreast cancer prevalence was 3.9 cases per 100,000 female populations. Of the 4423 cases of breast cancer, invasive breast carcinoma of no special type (NST) was the most frequent (79.5%, 3517/4423) histopathological diagnosis. The spatial analysis indicated as high risk areas for breast cancer in Sudan the States of Nile River, Northern, Red Sea, White Nile, Northern and Southern Kordofan.ConclusionsThe attempt to develop a predictive map of breast cancer in Sudan revealed three levels of risk areas (risk, intermediate and high risk areas); regardless the risk level, appropriate preventive and curative health interventions with full support from decision makers are urgently needed.

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Epidemiological study and spatial modeling of peste des petits ruminants (PPR) in central area of Iran

Revista Mvz Córdoba ◽

10.21897/rmvz.1026 ◽

2017 ◽

pp. 5899-5909 ◽

Cited By ~ 1

Author(s):

Azam Mokhtari ◽

Zahra Azizi ◽

Soheila Rabiaee Fradonbeh

Keyword(s):

Information System ◽

Spatial Distribution ◽

Data Analysis ◽

Geographic Information System ◽

Disease Control ◽

Spatial Modeling ◽

Small Ruminant ◽

Statistical Tests ◽

Central Area ◽

Peste Des Petits Ruminants

Objective. Estimate the prevalence and spatial modeling of PPR in the small ruminant population of Chaharmahal and Bakhtiari, Iran, during 2009–2014. Materials and methods. Data were collected from veterinary organization and Offices in Chaharmahal and Bakhtiari province and data analysis was carried out using and IBM SPSS version 22 and Office 2010. For spatial modeling geographic information system (QGIS and PCI-Geomatic) was used. Results. This study showed that the overall prevalence of PPR during the years 2009 to 2014 was 1.37%. Koohrang, Ardal, Lordegan, Ben, Borougen, Shahrekord, Farsan and Kiar cities had the highest prevalence of PPR, respectively. The highest PPR infection rate was observed in the March and goat more affected rather than other ruminants. Conclusions. Our findings provide evidence of a rather common prevalence of PPR and its spatial distribution in Chaharmahal and Bakhtiari province. Using statistical tests for data analysis of PPR and its spatial modeling researchers can predict the incidence of disease in the future and could select appropriate measures of disease control.

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