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A compositing process for selecting spatial data collected over a period of time, creating temporal data cubes from the spatial data, and processing and/or analyzing the data using temporal mapping algebra functions. In some embodiments, the temporal data cube is creating a masked cube using the data cubes, and computing a composite from the masked cube by using temporal mapping algebra.
BEST-CMS (boundary element solution technology - composite modeling system) is an advanced engineering system for the micro-analysis of fiber composite structures. BEST-CMS is based upon the boundary element program BEST3D which was developed for NASA by Pratt and Whitney Aircraft and the State University of New York at Buffalo under contract NAS3-23697. BEST-CMS presently has the capabilities for elastostatic analysis, steady-state and transient heat transfer analysis, steady-state and transient concurrent thermoelastic analysis, and elastoplastic and creep analysis. The fibers are assumed to be perfectly bonded to the composite matrix, or in the case of static or steady-state analysis, the fibers may be assumed to have spring connections, thermal resistance, and/or frictional sliding between the fibers and the composite matrix. The primary objective of this user's manual is to provide an overview of all BEST-CMS capabilities, along with detailed descriptions of the input data requirements. In the next chapter, a brief review of the theoretical background is presented for each analysis category. Then, chapter three discusses the key aspects of the numerical implementation, while chapter four provides a tutorial for the beginning BEST-CMS user. The heart of the manual, however, is in chapter five, where a complete description of all data input items is provided. Within this chapter, the individual entries are grouped on a functional basis for a more coherent presentation. Chapter six includes sample problems and should be of considerable assistance to the novice. Chapter seven includes capsules of a number of fiber-composite analysis problems that have been solved using BEST-CMS. This chapter is primarily descriptive in nature and is intended merely to illustrate the level of analysis that is possible within the present BEST-CMS system. Chapter eight contains a detail description of the BEST-CMS Neutral File which is helpful in writing an interface between BEST-CMS and any graphic post-processor program. Finally, all pertinent references are listed in chapter nine.
BEST-CMS (Boundary Element Solution Technology - Composite Modeling System) is an advanced engineering system for the micro-analysis of fiber composite structures. BEST-CMS is based upon the boundary element program BEST3D which was developed for NASA by Pratt and Whitney Aircraft and the State University of New York at Buffalo under contract NAS3-23697. BEST-CMS presently has the capabilities for elastostatic analysis, steady-state and transient heat transfer analysis, steady-state and transient concurrent thermoelastic analysis and elastoplastic and creep analysis. The fibers are assumed to be perfectly bonded to the composite matrix, or in the case of static or steady-state analysis, the fibers may be assumed to have spring connections, thermal resistance, and/or frictional sliding between the fibers and the composite matrix. The primary objective of this User's Manual is to provide an overview of all BEST-CMS capabilities, along with detailed descriptions of the input data requirements. A brief review of the theoretical background is presented for each analysis category. Then, Chapter 3 discusses the key aspects of the numerical implementation, while Chapter 4 provides a tutorial for the beginning BEST-CMS user. The heart of the manual, however, is in Chapter 5, where a complete description of all data input items is provided. Within this chapter, the individual entries are grouped on a functional basis for a more coherent presentation. Chapter 6 includes sample problems and should be of considerable assistance to the novice. Chapter 7 includes capsules of a number of fiber-composite analysis problems that have been solved using BEST-CMS. This chapter is primarily descriptive in nature and is intended merely to illustrate the level of analysis that is possible within the present BEST-CMS system. Chapter 8 contains a detailed description of the BEST-CMS Neutral File which is helpful in writing an interface between BEST- CMS and any graphic post-processor program. Finally, all pertinent references are listed in Chapter 9.
Proper scaling in turbulent planar plumes is investigated in this study using a scaling patch approach. Based on the scaled boundary conditions, a proper velocity scale for the mean axial flow is the plume centerline velocity U ref = U ctr , and a proper temperature scale for the temperature excess is Θ ref = T ctr – T ∞ , where T ctr is the plume centerline temperature and T ∞ is the ambient fluid temperature. By seeking an admissible scaling, a key concept in the scaling patch approach, for the mean continuity, mean momentum, and mean energy equations, respectively, the following is found: (1) a proper scale for the mean transverse flow is V ref = ( d δ / d x ) U ctr , where d δ / d x is the growth rate of the plume width. (2) A proper scale for the Reynolds shear stress is R vu , ref = U ctr V ref = ( d δ / d x ) U ctr 2 , a mix of the scales for the mean axial and transverse flows. (3) A proper scale for the turbulent heat flux is R v θ , ref = V ref Θ ctr , a mix of the scales for the mean transverse flow and mean temperature excess. The mean transverse flow thus plays a critical role in the scaling of turbulent planar plumes. Approximate functions are developed for the scaled mean transverse flow, Reynolds shear stress, and turbulent temperature flux, and are found to agree favorably with experimental and numerical simulation data. The integral analysis of the mean momentum equation yields a Richardson number Ri, which remains invariant in the axial direction. The Richardson number is defined as Ri = def g β Θ ctr δ t / ( U ctr V ref ) ≈ 1 / 2 , where g is the gravitational acceleration, β is the thermal expansion coefficient, and δ t is the plume half-width based on the mean temperature profile. This Richardson number arises directly from the scaling patch analysis of the mean momentum equation, including both the streamwise and transverse velocity scales.
The exchange of angular momentum between the solid earth and the atmosphere from January 1976 through March 1982 is investigated using estimates of the earth's rotation from optical astrometry and lunar laser ranging and meteorological estimates of the atmospheric angular momentum M(atm). The physics of the earth's angular momentum budget is described, and earth rotation measurements are related to changes in the angular momentum of the fluid parts of the earth. The availability and reliability of earth rotation and M(atm) data are reported, and the possibility of estimating the exchange of angular momentum with the oceans and with the core is examined. Estimates of the power spectrum, cospectral coherence, and linear transfer functions and an analysis of the unmodeled part of the angular momentum budget are presented and discussed. The amplitude and phase of the semiannual, monthly, and fortnightly tidal variations in the length of day are estimated after removing observed atmospheric excitation.
We construct cosmic void catalogues with the dive void finder upon SDSS BOSS DR12 and eBOSS DR16 galaxy samples with BAO reconstruction, and perform a joint BAO analysis using different types of galaxies and the corresponding voids. The BAO peak is evident for the galaxy–galaxy, galaxy–void, and void–void correlation functions of all data sets, including the ones cross-correlating LRG and ELG samples. Two multitracer BAO fitting schemes are tested, one combining the galaxy and void correlation functions with a weight applied to voids, and the other using a single BAO dilation parameter for all clustering measurements. Both methods produce consistent results with mock catalogues, and on average ~10 per cent improvements of the BAO statistical uncertainties are observed for all samples, compared to the results from galaxies alone. By combining the clustering of galaxies and voids, the uncertainties of BAO measurements from the SDSS data are reduced by 5–15 per cent, yielding 0.9 per cent, 0.8 per cent, 1.1 per cent, 2.3 per cent, and 2.9 per cent constraints on the distance $D_{_{\rm V}}(z)$, at effective redshifts 0.38, 0.51, 0.70, 0.77, and 0.85, respectively. When combined with BAO measurements from SDSS MGS, QSO, and Lyα samples, as well as the BBN results, we obtain $H_0 = 67.58 \pm 0.91\, {\rm km}\, {\rm s}^{-1}\, {\rm Mpc}^{-1}$, Ω m = 0.290 ± 0.015, and $\Omega _\Lambda h^2 = 0.3241 \pm 0.0079$ in the flat-ΛCDM framework, where the 1σ uncertainties are around 6 per cent, 6 per cent, and 17 per cent smaller respectively, compared to constraints from the corresponding anisotropic BAO measurements without voids and LRG–ELG cross-correlations.
Accurate knowledge of the thermophysical properties of TPS (thermal protection system) materials is necessary for pre-flight design and post-flight data analysis. Thermal properties, such as thermal conductivity and the volumetric specific heat, can be estimated from transient temperature measurements using non-linear parameter estimation methods. Property values are derived by minimizing a functional of the differences between measured and calculated temperatures. High temperature thermal response testing of TPS materials is usually done in arc-jet or radiant heating facilities which provide a quasi one-dimensional heating environment. Last year, under the NASA-ASEE-Stanford Fellowship Program, my work focused on developing a radiant heating apparatus. This year, I have worked on increasing the fidelity of the experimental measurements, optimizing the experimental procedures and interpreting the data.
CSPlib is an open source software library for analyzing general ordinary differential equation (ODE) systems and detailed chemical kinetic ODE systems. It relies on the computational singular perturbation (CSP) method for the analysis of these systems. The software provides support for: General ODE models (gODE model class) for computing source terms and Jacobians for a generic ODE system; TChem model (ChemElemODETChem model class) for computing source term, Jacobian, other necessary chemical reaction data, as well as the rates of progress for a homogenous batch reactor using an elementary step detailed chemical kinetic reaction mechanism. This class relies on the TChem [2] library; A set of functions to compute essential elements of CSP analysis (Kernel class). This includes computations of the eigensolution of the Jacobian matrix, CSP basis vectors and co-vectors, time scales (reciprocals of the magnitudes of the Jacobian eigenvalues), mode amplitudes, CSP pointers, and the number of exhausted modes. This class relies on the Tines library; A set of functions to compute the eigensolution of the Jacobian matrix using Tines library GPU eigensolver; A set of functions to compute CSP indices (Index Class). This includes participation indices and both slow and fast importance indices.
Low latitude magnetic field variations (magnetic storms) caused by large fluctuations in the equatorial ring current were derived from magnetic field magnitude data obtained by OGO 2, 4, and 6 satellites over an almost 5 year period. Analysis procedures consisted of (1) separating the disturbance field into internal and external parts relative to the surface of the Earth; (2) estimating the response function which related to the internally generated magnetic field variations to the external variations due to the ring current; and (3) interpreting the estimated response function using theoretical response functions for known conductivity profiles. Special consideration is given to possible ocean effects. A temperature profile is proposed using conductivity temperature data for single crystal olivine. The resulting temperature profile is reasonable for depths below 150-200 km, but is too high for shallower depths. Apparently, conductivity is not controlled solely by olivine at shallow depths.
NOvA is a long-baseline accelerator neutrino oscillation experiment using the NuMIneutrino beam from Fermilab. Its main physics goals are to probe the 3-flavour oscillationparameters: neutrino mass hierarchy, CP-violating phase dcp and octant of .23 mixingangle by observing electron neutrino appearance and muon neutrino disappearance. Twofunctionally identical detectors are placed off-axis from the centre of the NuMI beam.The near detector at Fermilab is 100 m underground, and the far detector is locatedon the surface at Ash River, 810 km away from the beam source. The initial neutrinobeam spectra are measured using the near detector data and the oscillation parametersare extracted by fitting the observed data to the predicted neutrino spectrum in the fardetector.This thesis is centered around how to improve the sensitivity of |.m232| and .23 measurementsin the muon neutrino disappearance analysis. NOvA will take data for about12 years. The operation of the NOvA experiment for each year costs tens of millions ofdollars, thus it is valuable to maximise the sensitivity of the analysis. Three samples ofmuon neutrino events are studied in this thesis to improve the analysis sensitivities. First,higher energy muon neutrinos are investigated by extending the energy range in NOvA’scurrent standard analysis. Second, for the sample of events used in NOvA’s existing analysis,a new energy estimator which has been developed to improve the neutrino energy resolution is considered. Furthermore, in addition to binning the events as function ofenergy and hadronic energy fraction, three particle identifiers are introduced to separateneutrino events by signal purity to reduce the effects from backgrounds. Third, an additionallower purity sample of muon neutrino charged current (CC) events that look similarto neutral current events and have not been included in NOvA’s existing analyses havebeen studied.This thesis reanalyses NOvA’s data used in the 2020 analysis, corresponding to anexposure of 13.60×1020 protons on target (POT) in the neutrino beam mode recordedfrom February 6, 2014 to March 20, 2020, and 12.50×1020 protons on target in theantineutrino beam mode recorded between June 29, 2016 to February 26, 2019. Thisthesis has implemented a fit to Asimov fake data, generated where sin2 .23 = 0.59 and.m232 = 2.5 × 10-3 eV2. These sensitivity studies show that the uncertainty range of|.m232| at 1 s in the new analysis is reduced by 5.5% and the significance of maximaldisappearance rejection improves by 7.7%, compared to the standard analysis. This isequivalent to adding 11-16% more data. The best fit values of the oscillation parametersfrom fitting to the far detector (FD) data with the new analysis are found to besin2 .23 = 0.568+0.025-0.043 (sin2 .23 = 0.454+0.046-0.026) and .m232 = 2.399+0.055-0.070 × 10-3 eV2 (.m232= -2.427+0.055-0.067 × 10-3 eV2) for the normal (inverted) hierarchy. The difference in thebest fit for sin2 .23 (.m232) between the new analysis and NOvA’s 2020 analysis is around2% (1.4%). The uncertainty range at 1 s for .m232 decrease by 8% (4%) for the normalhierarchy (inverted hierarchy) compared to the standard analysis. The uncertainty rangefor sin2 .23 is close to the standard analysis. This thesis also implements the fit from combiningelectron neutrino appearance and muon neutrino disappearance. The combinedanalysis shows that the best fit values are very close to the standard analysis. However,the uncertainty range of .m232 at 1 s is reduced by 3.7% using the new analysis. The maximaldisappearance significance is not improved in the new analysis, but the new analysisslightly improves the rejection of the disfavoured octant.
The cosmological evolution and the Luminosity Function (LF) of x-ray selected Active Galactic Nuclei (AGN) are discussed. The sample used is extracted from the Einstein Observatory Extended Medium Sensitivity Survey (EMSS) and consists of more than 420 objects. Preliminary results from the Rosat all sky survey data confirm the correctness of the optical identification of the EMSS sources, thus giving confidence to the results obtained from the analysis of the AGN sample. The XLF observed at different redshifts gives direct evidence of cosmological evolution. Data were analyzed within the framework of luminosity evolution models and the two most common evolutionary forms were considered. Luminosity dependent evolution is required if the evolution function has the exponential form, whereas the simpler pure luminosity evolution model is still acceptable if the evolution function has the power law form. Using the whole sample of objects the number counts and the devolved XLF were derived. A comparison of the EMSS Data with preliminary Rosat results indicates an overall agreement.
This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors near Trail Creek. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format TR-X-Y, where TR refers to Trail Creek, X is the treatment block identifier, and Y is the location identifier. Specifically, TR-ASCC1 is the control treatment block under the Adaptive Silviculture for Climate Change (ASCC) project, and TR-ASCC2 is the clear-cut treatment block. TR-ASCC-EHSn is associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and TR-ASCC-ERTn (upslope n=1) are ecohydrology sites along the electrical resistivity tomography transects. The sample and location information can be found in metadata.csv, and the data from the soil sensors will be included in a future data version when the observation period becomes sufficiently long for data analysis. Sampling and Measurements Each sample falls into one of the two sampling methods – (1) intact cores or (2) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. The intact cores were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all files. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.
Designing alternative fuels for advanced compression ignition engines necessitates a predictive model for cetane number (CN). In this study, the physics-informed graph neural networks are introduced for a reliable CN prediction by considering molecular features pertinent to the physical properties of molecules that affect CN. The reliability of measured data is another key factor to consider for improving the predictive model. Various experimental instruments for measuring CN exist, including standard and non-standard methods. In this regard, a systematic data quality analysis was carried out for the total 630 CNs collected from literature and new measurements in this study using Advanced Fuel Ignition Delay Analyzer (AFIDA). The results from this data curation process were reflected in the model by imposing lower sample weights on the data coming from less reliable measurement techniques. This approach effectively maximized the prediction accuracy while incorporating data from all available sources. Using the sample weights decreased the mean absolute error (MAE) up to 0.8 CN units. The accuracy was also improved by introducing the CN-related physical properties (the number of hydrogen bond donors and acceptors); the test set MAE is 5.74 and 7.01 for the model with and without such properties, respectively. Investigating molecular structural effects on CN was also carried out to gain chemical insights into factors used to design new fuel candidates. The dimensionality reduction analysis of feature vectors showed a clear clustering in terms of functional groups and CN and the structural effect derived from the model was consistent with the physicochemical insights. Finally, this physics-informed model and data curation would be helpful for accurate CN prediction and inform rational fuel design.
An investigation of the data system needs as driven by users of space acquired Earth observation data is documented. Two major categories of users, operational and research, are identified. Limiting data acquisition alleviates some of the delays in processing thus improving timeliness of the delivered product. Trade offs occur between timeliness and data distribution costs, and between data storage and reprocessing. The complexity of the data system requirements to apply space data to users' needs is such that no single analysis suffices to design and implement the optimum system. A series of iterations is required with analyses of the salient problems in a general way, followed by a limited implementation of benefit to some users with a continual upgrade in system capacity, functions, and applications served. The resulting most important requirement for the data system is flexibility to accommodate changing requirements as the system is implemented.
An equation for the thermal conductivity of steam at pressures of 1 atmosphere and below is presented. Sources of the experimental data and the actual data used in the analysis are presented. Also included is low density data below 100 C.
Computational science, data management and analysis have been key factors in the success of Brookhaven National Laboratory's scientific programs at the Relativistic Heavy Ion Collider (RHIC), the National Synchrotron Light Source II (NSLS-II), the Center for Functional Nanomaterials (CFN), and in biological, atmospheric, and energy systems science, Lattice Quantum Chromodynamics (LQCD) and Materials Science, as well as our participation in international research collaborations, such as the ATLAS experiment at Europe's Large Hadron Collider (LHC) at CERN (Switzerland) and the Belle II experiment at KEK (Japan). The construction of a new data center is an acknowledgement of the increasing demand for computing and storage services at BNL in the near term and enable the Lab to address the needs of the future experiments at the High-Luminosity LHC at CERN and the Electron-Ion Collider (EIC) at BNL in the long term.
Three data analysis methods were compared for gamma ray spectra collected by planar and coaxial high-purity germanium detectors (HPGe) on thick-walled uranium hexafluoride cylinders. The cylinder contents range from natural to low-enriched uranium (4.95% 235 U). Each spectrum was analyzed with the traditional 185.7 keV enrichment meter method, a 185.7/1001 keV ratio method, and the Fixed Energy Response Function Analysis with Multiple Efficiency (FRAM) software that utilizes low- and high-energy gamma rays and X-rays. When results for all cylinders were compared, the enrichment meter method and the 185.7/1001 keV peak ratio method consistently provided the most accurate data for all detector types. The error on the enrichment calculations analyzed with FRAM did not meet the international target value whereas those calculated with the enrichment meter and ratio method did. Enrichments calculated by FRAM were found to skew to higher values on natural uranium and to lower values on low-enriched uranium. FRAM calculated the least-precise enrichments; replicate analyses of single cylinders resulted in 235 U values that varied by as much as 19%. FRAM does have the significant advantages in that it does not require the use of calibration cylinders, infinite thickness criteria are not necessary, and it can be applied to any HPGe detector. Finally, if calibration cylinders are available, the enrichment meter and 185.7/1001 peak ratio are the recommended methods as they are the most accurate and precise.
Abstract Analysis of the temperature‐ and stimulus‐dependent imaging data toward elucidation of the physical transformations is an ubiquitous problem in multiple fields. Here, temperature‐induced phase transition in BaTiO 3 is explored using the machine learning analysis of domain morphologies visualized via variable‐temperature scanning transmission electron microscopy (STEM) imaging data. This approach is based on the multivariate statistical analysis of the time or temperature dependence of the statistical descriptors of the system, derived in turn from the categorical classification of observed domain structures or projection on the continuous parameter space of the feature extraction‐dimensionality reduction transform. The proposed workflow offers a powerful tool for the exploration of the dynamic data based on the statistics of image representation as a function of the external control variable to visualize the transformation pathways during phase transitions and chemical reactions. This can include the mesoscopic STEM data as demonstrated here, but also optical, chemical imaging, etc., data. It can further be extended to the higher dimensional spaces, for example, analysis of the combinatorial libraries of materials compositions.