Explaining Neural Network Predictions of Characteristics of Explosive Devices Using Functional Principal Components Analysis and Permutation Feature Importance.
Abstract not provided.
Goode, Katherine Jean↗
SEARCH · Engineering Papers
Results for “Function analysis”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Remote Radiation Sensing Using Aerial and Ground Platforms
Remote sensing of ionizing radiation has a significant role in waste management, nuclear material management and nonproliferation, and radiation safety. Robotic platforms can surpass the number of tasks that are achieved by humans. With this technique, the operator's radiation exposure can be decreased. Remote sensing allows for the evaluation and monitoring of radiological contamination. Gamma-ray and neutron sensors were integrated onto the robotic platforms. This approach allows for the radiation sensor data to be dynamically tracked and mapped thus enabling further analysis of the radiation flux in temporal and spatial domains. The goal is to complete scheduled tasks while the robot is being irradiated. To achieve this, electronic components must be shielded and radiation hardened. CZT Detector: Cadmium Zinc Telluride (CZT) detector technology has been a promising solution for gamma-ray and x-ray measurements. Detector data is transferred to the Odroid minicomputer that controls and powers the module via the USB. Robot Operating System (ROS) was utilized for data acquisition and data fusion. The Mariscotti method was employed for the spectrum analysis. A function was programmed in ROS for the automatic identification of photopeaks. CLYC Detector: A Cs{sub 2}LiYCl{sub 6}:Ce{sup 3+} (CLYC) detector was used for simultaneous medium-resolution gamma-ray measurements and neutron counting. A 2.54 cm diameter photomultiplier tube (PMT) was equipped with a high voltage supply and a miniature digitizer. Gamma-ray excitation: fast core-to-valence luminescence (CVL) with 1 ns decay constant, and prompt Ce{sup 3+} emission with 50 ns decay constant. Neutron excitation: slow cerium self-trapped excitation (Ce{sup 3+} STE), 1000 ns decay constant. Radiation Source Localization: Maximum Likelihood Estimation (MLE) and gradient-based methods were used to locate the position of a radiation source based on measured radiation intensities. Multi-Particle Transport Code FLUKA: Estimation of radiation damage of the electronic components is important in order to optimize the robot's operational time while it is irradiated. Displacement per atom (DPA) represents the radiation damage in materials exposed to the ionizing radiation. Various shielding layers of different thickness t were analyzed (< 5% statistical error). The model of the controller of the UAS was designed in FLUKA. Conclusion: CZT and CLYC detectors were integrated onto the robotic platforms. Radiation source localization and contour mapping using robotic platforms were studied. Functions for data analysis and fusion were developed in ROS. FLUKA code was utilized to analyze DPA values. Layers of low-density and high-density materials were used to shield the UAS electronics.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Discrete spherical harmonic functions for texture representation and analysis
A basis of discrete harmonic functions for efficient representation and analysis of crystallographic texture is presented. Discrete harmonics are a numerical representation of the harmonics on the sphere. A finite element formulation is utilized to calculate these orthonormal basis functions, which provides several advantageous features for quantitative texture analysis. These include high-precision numerical integration, a simple implementation of the non-negativity constraint and computational efficiency. Simple examples of pole figure and texture interpolation and of Fourier filtering using these basis sets are presented.
36 MATERIALS SCIENCE↗
Data-driven analysis of dipole strength functions using artificial neural networks
Here, we present a data-driven analysis of dipole strength functions across the nuclear chart, employing an artificial neural network to model nuclear dipole responses. We train the network on a dataset of experimentally measured dipole strength functions for 216 different nuclei. To assess its predictive capability, we test the trained model on an additional set of 10 new nuclei, where experimental data exist. We demonstrate that the artificial neural network not only accurately reproduces known data but also identifies potential inconsistencies in experimental datasets, indicating which results may warrant further review or possible rejection. For nuclei where experimental data are sparse or unavailable, the network confirms theoretical calculations, reinforcing its utility as a predictive tool in nuclear physics. Finally, utilizing the predicted electric dipole polarizability, we extract the value of the symmetry energy at saturation density and find it consistent with results from the literature.
artificial neural networks↗
Unraveling the Chemical Nature of the 3D “Hollow” Hybrid Halide Perovskites
The newly introduced class of 3D halide perovskites, termed “hollow” perovskites, has been recently demonstrated as light absorbing semiconductor materials for fabricating lead-free perovskite solar cells with enhanced efficiency and superior stability. Hollow perovskites derive from three-dimensional (3D) AMX 3 perovskites (A = methylammonium (MA), formamidinium (FA); M = Sn, Pb; X = Cl, Br, I), where small molecules such as ethylenediammonium cations (en) can be incorporated as the dication without altering the structure dimensionality. In this article, we present in this work the inherent structural properties of the hollow perovskites and expand this class of materials to the Pb-based analogues. Through a combination of physical and spectroscopic methods (XRD, gas pycnometry, 1 H NMR, TGA, SEM/EDX), we have assigned the general formula (A) 1–x (en) x (M) 1–0.7x (X) 3–0.4x to the hollow perovskites. The incorporation of en in the 3D perovskite structure leads to massive M and X vacancies in the 3D [MX 3 ] framework, thus the term hollow. The resulting materials are semiconductors with significantly blue-shifted direct band gaps from 1.25 to 1.51 eV for Sn-based perovskites and from 1.53 to 2.1 eV for the Pb-based analogues. The increased structural disorder and hollow nature were validated by single crystal X-ray diffraction analysis as well as pair distribution function (PDF) analysis. Density functional theory (DFT) calculations support the experimental trends and suggest that the observed widening of the band gap is attributed to the massive M and X vacancies, which create a less connected 3D hollow structure. The resulting materials have superior air stability, where in the case of Sn-based hollow perovskites it exceeds two orders of temporal magnitude compared to the conventional full perovskites of MASnI 3 and FASnI 3 . Lastly, the hollow perovskite compounds pose as a new platform of promising light absorbers that can be utilized in single junction or tandem solar cells.
14 SOLAR ENERGY↗
New parton distribution functions from a global analysis of quantum chromodynamics
Here, we present new parton distribution functions (PDFs) up to next-to-next-to-leading order (NNLO) from the CTEQ-TEA global analysis of quantum chromodynamics. These differ from previous CT PDFs in several respects, including the use of data from LHC experiments and the new D0 charged lepton rapidity asymmetry data, as well as the use of more flexible parametrization of PDFs that, in particular, allows a better fit to different combinations of quark flavors. Predictions for important LHC processes, especially Higgs boson production at 13 TeV, are presented. These CT14 PDFs include a central set and error sets in the Hessian representation. For completeness, we also present the CT14 PDFs determined at the leading order (LO) and the next-to-leading order (NLO) in QCD. Besides these general-purpose PDF sets, we provide a series of (N)NLO sets with various α s values and additional sets in general-mass variable flavor number (GM-VFN) schemes, to deal with heavy partons, with up to 3, 4, and 6 active flavors.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
THE EFFECT OF MAGNETIC FIELDS AND AMBIPOLAR DIFFUSION ON CORE MASS FUNCTIONS
Linear analysis of the formation of protostellar cores in planar magnetic interstellar clouds yields information about length scales involved in star formation. Combining these length scales with various distributions of other environmental variables (i.e., column density and mass-to-flux ratio) and applying Monte Carlo methods allow us to produce synthetic core mass functions (CMFs) for different environmental conditions. Our analysis shows that the shape of the CMF is directly dependent on the physical conditions of the cloud. Specifically, magnetic fields act to broaden the mass function and develop a high-mass tail while ambipolar diffusion will truncate this high-mass tail. In addition, we analyze the effect of small number statistics on the shape and high-mass slope of the synthetic CMFs. We find that observed CMFs are severely statistically limited, which has a profound effect on the derived slope for the high-mass tail.
79 ASTRONOMY AND ASTROPHYSICS↗
Uncertainty analysis of signal deconvolution using a measured instrument response function
A common analysis procedure minimizes the ln-likelihood that a set of experimental observables matches a parameterized model of the observation. The model includes a description of the underlying physical process as well as the instrument response function (IRF). Here, we investigate the National Ignition Facility (NIF) neutron time-of-flight (nTOF) spectrometers, the IRF is constructed from measurements and models. IRF measurements have a finite precision that can make significant contributions to the uncertainty estimate of the physical model’s parameters. Finally, we apply a Bayesian analysis to properly account for IRF uncertainties in calculating the ln-likelihood function used to find the optimum physical parameters.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Pr{sub 1.33}Pt{sub 4}Ga{sub 10}: Superstructure and magnetism
Pr{sub 1.33}Pt{sub 4}Ga{sub 10} crystals were prepared by Ga-flux method. The superstructure of this compound was studied by single-crystal X-ray diffraction (XRD), transmission electron microscopy (TEM), and diffuse X-ray scattering. Pr{sub 1.33}Pt{sub 4}Ga{sub 10} adopts the P6{sub 3}/mmc space group with a=b=4.3227(5) Å, c=16.485(3) Å: the structure features Pr{sub 2}Ga{sub 3} layers alternating with Pt{sub 2}Ga{sub 4} layers along the c-axis. TEM studies and pair distribution function (PDF) analysis of X-ray total scattering data show that Pr{sub 2}Ga{sub 3} layers possess an ordered superstructure (of dimension a′=a√(3)) in which Pr vacancies and Ga atoms are ordered within the ab-plane but disordered along the c-direction. PDF analysis also shows temperature-dependent structural features local to the Pr{sup 3+} ion. Magnetic measurements reveal that Pr{sup 3+} ions order ferrimagnetically below 12.5(2) K. - Graphical abstract: Left: Crystal structure of Pr{sub 1.33}Pt{sub 4}Ga{sub 10} showing Pr and Ga vacancies in the Pr{sub 2}Ga{sub 3} plane. Right: Tunneling electron microscopy (TEM) image of Pr{sub 1.33}Pt{sub 4}Ga{sub 10}. These vacancies have been studied using TEM and pair distribution function analysis. Magnetic measurements reveal that Pr{sup 3+} ions order ferrimagnetically below 7.8(2) K. - Highlights: • TEM studies indicate a superstructure from Pr and Ga vacancies. • Pair distribution function analyses may point to structural relaxation of vacancies. • Pr{sub 1.33}Pt{sub 4}Ga{sub 10} behaves as a ferromagnet and exhibits a metamagnetic transition.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Molecular Dissection of Induced Platinum Resistance through Functional and Gene Expression Analysis in a Cell Culture Model of Bladder Cancer
Here, we report the development, functional and molecular characterization of an isogenic, paired bladder cancer cell culture model system for studying platinum drug resistance. The 5637 human bladder cancer cell line was cultured over ten months with stepwise increases in oxaliplatin concentration to generate a drug resistant 5637R sub cell line. The MTT assay was used to measure the cytotoxicity of several bladder cancer drugs. Liquid scintillation counting allowed quantification of cellular drug uptake and efflux of radiolabeled oxaliplatin and carboplatin. The impact of intracellular drug inactivation was assessed by chemical modulation of glutathione levels. Oxaliplatin- and carboplatin-DNA adduct formation and repair was measured using accelerator mass spectrometry. Resistance factors including apoptosis, growth factor signaling and others were assessed with RNAseq of both cell lines and included confirmation of selected transcripts by RT-PCR. Oxaliplatin, carboplatin, cisplatin and gemcitabine were significantly less cytotoxic to 5637R cells compared to the 5637 cells. In contrast, doxorubicin, methotrexate and vinblastine had no cell line dependent difference in cytotoxicity. Upon exposure to therapeutically relevant doses of oxaliplatin, 5637R cells had lower drug-DNA adduct levels than 5637 cells. This difference was partially accounted for by pre-DNA damage mechanisms such as drug uptake and intracellular inactivation by glutathione, as well as faster oxaliplatin-DNA adduct repair. In contrast, both cell lines had no significant differences in carboplatin cell uptake, efflux and drug-DNA adduct formation and repair, suggesting distinct resistance mechanisms for these two closely related drugs. The functional studies were augmented by RNAseq analysis, which demonstrated a significant change in expression of 83 transcripts, including 50 known genes and 22 novel transcripts. Most of the transcripts were not previously associated with bladder cancer chemoresistance. This model system and the associated phenotypic and genotypic data has the potential to identify some novel details of resistance mechanisms of clinical importance to bladder cancer.
59 BASIC BIOLOGICAL SCIENCES↗
Local deuterium order in apparently disordered Laves phase deuteride YFe{sub 2}D{sub 4.2}
Deuterium short-range order in cubic Laves phase deuteride YFe{sub 2}D{sub 4.2} was studied by neutron (ToF) powder diffraction experiments and Pair Distribution Function (PDF) analysis between 290 and 400 K. The minimal allowed D-D distance of 2.1 A in a metal deuteride (Switendick rule) has been experimentally proved in the HT-disordered phase YFe{sub 2}D{sub 4.2}. It has been found that the distribution of deuterium atoms around the iron is not random, and cannot be explained only by applying the Switendick rule. The first coordination sphere of iron atoms in the high temperature (HT)-disordered phase resembles between 350 and 400 K the coordination observed in the low temperature (LT)-ordered phase. Reversed Monte Carlo modeling of the Pair Distribution Function of the HT-disordered phase prefers the coordination FeD{sub 5} and FeD{sub 4} in agreement with the LT-ordered phase. - Graphical abstract: Deuterium short-range order in cubic Laves phase deuteride YFe{sub 2}D{sub 4.2} was studied by ToF neutron powder diffraction experiments and Pair Distribution Function analysis between 290 and 400 K. It has been found that the distribution of deuterium atoms around the iron is not random, and cannot be explained only by applying the Switendick rule. The first coordination sphere of iron atoms in the HT-disordered phase resembles that of the LT-ordered phase. Highlights: > Switendick rule has been experimentally proved in YFe{sub 2}D{sub 4.2}. > The distribution of deuterium in YFe{sub 2}D{sub 4.2} cannot be explained only by Switendick rule. > The first coordination sphere of iron in the disordered phase resembles the ordered one.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
An open-source high-content analysis workflow for CFTR function measurements using the forskolin-induced swelling assay
Abstract Motivation The forskolin-induced swelling (FIS) assay has become the preferential assay to predict the efficacy of approved and investigational CFTR-modulating drugs for individuals with cystic fibrosis (CF). Currently, no standardized quantification method of FIS data exists thereby hampering inter-laboratory reproducibility. Results We developed a complete open-source workflow for standardized high-content analysis of CFTR function measurements in intestinal organoids using raw microscopy images as input. The workflow includes tools for (i) file and metadata handling; (ii) image quantification and (iii) statistical analysis. Our workflow reproduced results generated by published proprietary analysis protocols and enables standardized CFTR function measurements in CF organoids. Availability and implementation All workflow components are open-source and freely available: the htmrenamer R package for file handling https://github.com/hmbotelho/htmrenamer; CellProfiler and ImageJ analysis scripts/pipelines https://github.com/hmbotelho/FIS_image_analysis; the Organoid Analyst application for statistical analysis https://github.com/hmbotelho/organoid_analyst; detailed usage instructions and a demonstration dataset https://github.com/hmbotelho/FIS_analysis. Distributed under GPL v3.0. Supplementary information Supplementary data are available at Bioinformatics online.
Hagemeijer, Marne C.↗
Predicting fatigue from heart rate signatures using functional logistic regression
Physical fatigue can have adverse effects on humans in extreme environments. Therefore, being able to predict fatigue using easy to measure metrics such as heart rate (HR) signatures has potential to have an impact in real-life scenarios. We apply a functional logistic regression model that uses HR signatures to predict physical fatigue, where physical fatigue is defined in a data-driven manner. Data were collected using commercially available wearable devices on 47 participants hiking the 20.7-mile Grand Canyon rim-to-rim trail in a single day. Fitted model provides good predictions and interpretable parameters for real-life application.
60 APPLIED LIFE SCIENCES↗
Regression models using shapes of functions as predictors
Functional variables are often used as predictors in regression problems. A commonly used parametric approach, called scalar-on-function regression, uses the $\mathbb L^2$ inner product to map functional predictors into scalar responses. This method can perform poorly when predictor functions contain undesired phase variability, causing phases to have disproportionately large influence on the response variable. One past solution has been to perform phase–amplitude separation (as a pre-processing step) and then use only the amplitudes in the regression model. In this paper, we propose a more integrated approach, termed elastic functional regression model (EFRM), where phase-separation is performed inside the regression model, rather than as a pre-processing step. This approach generalizes the notion of phase in functional data, and is based on the norm-preserving time warping of predictors. Due to its invariance properties, this representation provides robustness to predictor phase variability and results in improved predictions of the response variable over traditional models. We demonstrate this framework using a number of datasets involving gait signals, NMR data, and stock market prices.
97 MATHEMATICS AND COMPUTING↗
An adaptive ANOVA-based PCKF for high-dimensional nonlinear inverse modeling
The probabilistic collocation-based Kalman filter (PCKF) is a recently developed approach for solving inverse problems. It resembles the ensemble Kalman filter (EnKF) in every aspect—except that it represents and propagates model uncertainty by polynomial chaos expansion (PCE) instead of an ensemble of model realizations. Previous studies have shown PCKF is a more efficient alternative to EnKF for many data assimilation problems. However, the accuracy and efficiency of PCKF depends on an appropriate truncation of the PCE series. Having more polynomial chaos basis functions in the expansion helps to capture uncertainty more accurately but increases computational cost. Selection of basis functions is particularly important for high-dimensional stochastic problems because the number of polynomial chaos basis functions required to represent model uncertainty grows dramatically as the number of input parameters (random dimensions) increases. In classic PCKF algorithms, the PCE basis functions are pre-set based on users' experience. Also, for sequential data assimilation problems, the basis functions kept in PCE expression remain unchanged in different Kalman filter loops, which could limit the accuracy and computational efficiency of classic PCKF algorithms. To address this issue, we present a new algorithm that adaptively selects PCE basis functions for different problems and automatically adjusts the number of basis functions in different Kalman filter loops. The algorithm is based on adaptive functional ANOVA (analysis of variance) decomposition, which approximates a high-dimensional function with the summation of a set of low-dimensional functions. Thus, instead of expanding the original model into PCE, we implement the PCE expansion on these low-dimensional functions, which is much less costly. We also propose a new adaptive criterion for ANOVA that is more suited for solving inverse problems. The new algorithm was tested with different examples and demonstrated great effectiveness in comparison with non-adaptive PCKF and EnKF algorithms.
97 MATHEMATICS AND COMPUTING↗
An ontology-based fault generation and fault propagation analysis approach for safety-critical computer systems at the design stage
Abstract Fault propagation analysis is a process used to determine the consequences of faults residing in a computer system. A typical computer system consists of diverse components (e.g., electronic and software components), thus, the faults contained in these components tend to possess diverse characteristics. How to describe and model such diverse faults, and further determine fault propagation through different components are challenging problems to be addressed in the fault propagation analysis. This paper proposes an ontology-based approach, which is an integrated method allowing for the generation, injection, and propagation through inference of diverse faults at an early stage of the design of a computer system. The results generated by the proposed framework can verify system robustness and identify safety and reliability risks with limited design level information. In this paper, we propose an ontological framework and its application to analyze an example safety-critical computer system. The analysis result shows that the proposed framework is capable of inferring fault propagation paths through software and hardware components and is effective in predicting the impact of faults.
97 MATHEMATICS AND COMPUTING↗
Integrated in silico analyses of regulatory and metabolic networks of Synechococcus sp. PCC 7002 reveal relationships between gene centrality and essentiality
Cyanobacteria dynamically relay environmental inputs to intracellular adaptations through a coordinated adjustment of photosynthetic efficiency and carbon processing rates. The output of such adaptations is reflected through changes in transcriptional patterns and metabolic flux distributions that ultimately define growth strategy. To address interrelationships between metabolism and regulation, we performed integrative analyses of metabolic and gene co-expression networks in a model cyanobacterium, Synechococcus sp. PCC 7002. Centrality analyses using the gene co-expression network identified a set of key genes, which were defined here as ‘topologically important.’ Parallel in silico gene knock-out simulations, using the genome-scale metabolic network, classified what we termed as ‘functionally important’ genes, deletion of which affected growth or metabolism. A strong positive correlation was observed between topologically and functionally important genes. Functionally important genes exhibited variable levels of topological centrality; however, the majority of topologically central genes were found to be functionally essential for growth. Subsequent functional enrichment analysis revealed that both functionally and topologically important genes in Synechococcus sp. PCC 7002 are predominantly associated with translation and energy metabolism, two cellular processes critical for growth. This research demonstrates how synergistic network-level analyses can be used for reconciliation of metabolic and gene expression data to uncover fundamental biological principles.
59 BASIC BIOLOGICAL SCIENCES↗