Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Principle components 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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Phase identification using statistical analysis

A device includes at least one processor configured to determine, based on (i) a first plurality of time-varying electrical measurements corresponding to a feeder head of a power distribution network having a plurality of phases and (ii) a second plurality of time-varying electrical measurements corresponding to a node in the power distribution network, and using statistical analysis, a predicted phase, from the plurality of phases, that corresponds to the node. The processor may be configured to use ranked correlation coefficients (such as the Kendall rank correlation coefficient) to determine the predicted phase and may use principle component analysis. The processor is also configured to cause at least one device of the power distribution network to modify operation based at least on part on the predicted phase.

Veda, Santosh Sambamoorthy↗
DOE OSTI2012

Polycyclic aromatic hydrocarbons as skin carcinogens: Comparison of benzo[a]pyrene, dibenzo[def,p]chrysene and three environmental mixtures in the FVB/N mouse

The polycyclic aromatic hydrocarbon (PAH), benzo[a]pyrene (BaP), was compared to dibenzo[def,p]chrysene (DBC) and combinations of three environmental PAH mixtures (coal tar, diesel particulate and cigarette smoke condensate) using a two stage, FVB/N mouse skin tumor model. DBC (4 nmol) was most potent, reaching 100% tumor incidence with a shorter latency to tumor formation, less than 20 weeks of 12-O-tetradecanoylphorbol-13-acetate (TPA) promotion compared to all other treatments. Multiplicity was 4 times greater than BaP (400 nmol). Both PAHs produced primarily papillomas followed by squamous cell carcinoma and carcinoma in situ. Diesel particulate extract (1 mg SRM 1650b; mix 1) did not differ from toluene controls and failed to elicit a carcinogenic response. Addition of coal tar extract (1 mg SRM 1597a; mix 2) produced a response similar to BaP. Further addition of 2 mg of cigarette smoke condensate (mix 3) did not alter the response with mix 2. PAH-DNA adducts measured in epidermis 12 h post initiation and analyzed by {sup 32}P post‐labeling, did not correlate with tumor incidence. PAH‐dependent alteration in transcriptome of skin 12 h post initiation was assessed by microarray. Principal component analysis (sum of all treatments) of the 922 significantly altered genes (p < 0.05), showed DBC and BaP to cluster distinct from PAH mixtures and each other. BaP and mixtures up-regulated phase 1 and phase 2 metabolizing enzymes while DBC did not. The carcinogenicity with DBC and two of the mixtures was much greater than would be predicted based on published Relative Potency Factors (RPFs). -- Highlights: ► Dibenzo[def,p]chrysene (DBC), 3 PAH mixtures, benzo[a]pyrene (BaP) were compared. ► DBC and 2 PAH mixtures were more potent than Relative Potency Factor estimates. ► Transcriptome profiles 12 hours post initiation were analyzed by microarray. ► Principle components analysis of alterations revealed treatment-based clustering. ► DBC gave a unique pattern of gene alterations compared to BaP and PAH mixtures.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

TU-EF-204-10: A Preliminary Study Evaluating Calculation Variations of Local Noise Power Spectra for CT Simulation in Radiation Therapy

Purpose: Local noise power spectrum (NPS) properties are significantly affected by calculation variables and CT acquisition and reconstruction parameters, but a thoughtful analysis of these effects is absent. In this study, we performed a complete analysis of the effects of calculation and imaging parameters on the NPS. Methods: The uniformity module of a Catphan phantom was scanned with a Philips Brilliance 64-slice CT simulator using various scanning protocols. Images were reconstructed using both FBP and iDose4 reconstruction algorithms. From these images, local NPS were calculated for regions of interest (ROI) of varying locations and sizes, using four image background removal methods. Additionally, using a predetermined ground truth, NPS calculation accuracy for various calculation parameters was compared for computer simulated ROIs. A complete analysis of the effects of calculation, acquisition, and reconstruction parameters on the NPS was conducted. Results: The local NPS varied with ROI size and image background removal method, particularly at low spatial frequencies. The image subtraction method was the most accurate according to the computer simulation study, and was also the most effective at removing low frequency background components in the acquired data. However, first-order polynomial fitting using residual sum of squares and principle component analysis provided comparable accuracy under certain situations. Similar general trends were observed when comparing the NPS for FBP to that of iDose4 while varying other calculation and scanning parameters. However, while iDose4 reduces the noise magnitude compared to FBP, this reduction is spatial-frequency dependent, further affecting NPS variations at low spatial frequencies. Conclusion: The local NPS varies significantly depending on calculation parameters, image acquisition parameters, and reconstruction techniques. Appropriate local NPS calculation should be performed to capture spatial variations of noise; calculation methodology should be selected with consideration of image reconstruction effects and the desired purpose of CT simulation for radiotherapy tasks.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Rotor blade imbalance fault detection for variable-speed marine current turbines via generator power signal analysis

Marine hydrokinetic (MHK) turbines extract renewable energy from oceanic environments. However, due to the harsh conditions that these turbines operate in, system performance naturally degrades over time. Thus, ensuring efficient condition-based maintenance is imperative towards guaranteeing reliable operation and reduced costs for marine hydrokinetic power. This work proposes a novel framework aimed at identifying and classifying the severity of rotor blade pitch imbalance faults experienced by marine current turbines (MCTs). In the framework, a Continuous Morlet Wavelet Transform (CMWT) is first utilized to acquire the wavelet coefficients encompassed within the 1P frequency range of the turbine's rotor shaft. From these coefficients, several statistical indices are tabulated into a six-dimensional feature space. Next, Principle Component Analysis (PCA) is employed on the resulting feature space for dimensionality reduction, and then the application of a K-Nearest Neighbor (KNN) machine learning algorithm is utilized for fault detection and severity classification. The effectiveness of the proposed framework is validated using a high-fidelity MCT numerical simulation platform, where results demonstrate that the presence of a pitch imbalance fault can be accurately detected 100% of the time and correctly classified based upon severity more than 97% of the time.

42 ENGINEERING↗
DOE OSTI2014

SU-E-J-98: Radiogenomics: Correspondence Between Imaging and Genetic Features Based On Clustering Analysis

Purpose: Radiogenomics aims to establish relationships between patient genotypes and imaging phenotypes. An open question remains on how best to integrate information from these distinct datasets. This work investigates if similarities in genetic features across patients correspond to similarities in PET-imaging features, assessed with various clustering algorithms. Methods: [{sup 18}F]FDG PET data was obtained for 26 NSCLC patients from a public database (TCIA). Tumors were contoured using an in-house segmentation algorithm combining gradient and region-growing techniques; resulting ROIs were used to extract 54 PET-based features. Corresponding genetic microarray data containing 48,778 elements were also obtained for each tumor. Given mismatch in feature sizes, two dimension reduction techniques were also applied to the genetic data: principle component analysis (PCA) and selective filtering of 25 NSCLC-associated genes-ofinterest (GOI). Gene datasets (full, PCA, and GOI) and PET feature datasets were independently clustered using K-means and hierarchical clustering using variable number of clusters (K). Jaccard Index (JI) was used to score similarity of cluster assignments across different datasets. Results: Patient clusters from imaging data showed poor similarity to clusters from gene datasets, regardless of clustering algorithms or number of clusters (JI{sub mean}= 0.3429±0.1623). Notably, we found clustering algorithms had different sensitivities to data reduction techniques. Using hierarchical clustering, the PCA dataset showed perfect cluster agreement to the full-gene set (JI =1) for all values of K, and the agreement between the GOI set and the full-gene set decreased as number of clusters increased (JI=0.9231 and 0.5769 for K=2 and 5, respectively). K-means clustering assignments were highly sensitive to data reduction and showed poor stability for different values of K (JI{sub range}: 0.2301–1). Conclusion: Using commonly-used clustering algorithms, we found poor agreement in patient characterization from genomic versus imaging data. Interpretation of radiogenomic associations was found to be dependent on clustering algorithms and sensitive to data reduction techniques.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

An Analysis Framework for Understanding the Origin of Nuclear Activity in Low-power Radio Galaxies

Using large samples containing nearly 2300 active galaxies of low radio luminosity (1.4 GHz luminosity between 2 × 10{sup 23} and 3 × 10{sup 25} W Hz{sup −1}, essentially low-excitation radio galaxies) at z ≲ 0.3, we present a self-contained analysis of the dependence of the nuclear radio activity on both intrinsic and extrinsic properties of galaxies, with the goal of identifying the best predictors of the nuclear radio activity. While confirming the established result that stellar mass must play a key role on the triggering of radio activities, we point out that for the central, most massive galaxies, the radio activity also shows a strong dependence on halo mass, which is not likely due to enhanced interaction rates in denser regions in massive, cluster-scale halos. We thus further investigate the effects of various properties of the intracluster medium (ICM) in massive clusters on the radio activities, employing two standard statistical tools, principle component analysis and logistic regression. It is found that ICM entropy, local cooling time, and pressure are the most effective in predicting the radio activity, pointing to the accretion of gas cooling out of a hot atmosphere to be the likely origin in triggering such activities in galaxies residing in massive dark matter halos. Our analysis framework enables us to logically discern the mechanisms responsible for the radio activity separately for central and satellite galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

IR Thermographic Analysis of 3D Printed CFRP Reference Samples with Back-Drilled and Embedded Defects

Carbon-fiber composite structures may demonstrate a defective behavior due to manufacturing induced anomalies (delamination, dis-bonds) or service related defectives (impact damage, water ingress). Thus, there is a need for a relatively fast and low cost non-intrusive testing schemes such as infrared thermography (IRT). Still, thermography testing requires calibrated samples and coupons to yield best results. The presented research demonstrates the novel use of 3D printing technology to generate IRT calibration samples. In this text, two carbon fiber reinforced polymer samples are 3D printed; the first mimics a 'back-drilled holes' type coupons, while the other is designed to embed air pockets similar to Teflon inserts. The generated samples are then tested using two IRT modalities; namely pulse thermography and lock-in thermography. Furthermore, the resulted thermograms are processed using a principle component analysis, to help highlight the variance of defectives in a consistent manner among the samples. This research findings offer insights on the variation of detectability between embedded and back-printed samples, which might be due to the inserts thickness.

36 MATERIALS SCIENCE↗
DOE OSTI2023

The Carnegie Supernova Project I: Spectroscopic analysis of stripped-envelope supernovae

An analysis leveraging 170 optical spectra of 35 stripped-envelope (SE) core-collapse supernovae (SNe) observed by the Carnegie Supernova Project I and published in a companion paper is presented. Mean template spectra were constructed for the SNe IIb, Ib, and Ic subtypes, and parent ions associated with designated spectral features are identified with the aid of the spectral synthesis code SYNAPPS. Our modeled mean spectra suggest the ~6150 Å feature in SNe IIb may have an underlying contribution due to silicon, while the same feature in some SNe Ib may have an underlying contribution due to hydrogen. Standard spectral line diagnostics consisting of pseudo-equivalent widths (pEWs) and blue-shifted Doppler velocity were measured for each of the spectral features. Correlation matrices and rolling mean values of both spectral diagnostics were constructed. A principle component analysis (PCA) was applied to various wavelength ranges of the entire dataset and suggests clear separation among the different SE SN subtypes, which follows from trends previously identified in the literature. In addition, our findings reveal the presence of two SNe IIb subtypes, a select number of SNe Ib displaying signatures of weak, high-velocity hydrogen, and a single SN Ic with evidence of weak helium features. Our PCA results can be leveraged to obtain robust subtyping of SE SNe based on a single spectrum taken during the so-called photospheric phase, separating SNe IIb from SNe Ib with ~80% completion.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

SU-F-BRD-04: Robustness Analysis of Proton Breast Treatments Using An Alpha-Stable Distribution Parameterization

Purpose: Currently, planning systems allow robustness calculations to be performed, but a generalized assessment methodology is not yet available. We introduce and evaluate a methodology to quantify the robustness of a plan on an individual patient basis. Methods: We introduce the notion of characterizing a treatment instance (i.e. one single fraction delivery) by describing the dose distribution within an organ as an alpha-stable distribution. The parameters of the distribution (shape(α), scale(γ), position(δ), and symmetry(β)), will vary continuously (in a mathematical sense) as the distributions change with the different positions. The rate of change of the parameters provides a measure of the robustness of the treatment. The methodology is tested in a planning study of 25 patients with known residual errors at each fraction. Each patient was planned using Eclipse with an IBA-proton beam model. The residual error space for every patient was sampled 30 times, yielding 31 treatment plans for each patient and dose distributions in 5 organs. The parameters’ change rate as a function of Euclidean distance from the original plan was analyzed. Results: More than 1,000 dose distributions were analyzed. For 4 of the 25 patients the change in scale rate (γ) was considerably higher than the lowest change rate, indicating a lack of robustness. The sign of the shape change rate (α) also seemed indicative but the experiment lacked the power to prove significance. Conclusion: There are indications that this robustness measure is a valuable tool to allow a more patient individualized approach to the determination of margins. In a further study we will also evaluate this robustness measure using photon treatments, and evaluate the impact of using breath hold techniques, and the a Monte Carlo based dose deposition calculation. A principle component analysis is also planned.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Identifying Important Ions and Positions in Mass Spectrometry Imaging Data Using CUR Matrix Decompositions

Mass spectrometry imaging enables label-free, high-resolution spatial mapping of the chemical composition of complex, biological samples. Typical experiments require selecting ions and/or positions from the images: ions for fragmentation studies to identify keystone compounds and positions for follow up validation measurements using microdissection or other orthogonal techniques. Unfortunately, with modern imaging machines, these must be selected from an overwhelming amount of raw data. Existing techniques to reduce the volume of data, the most popular of which are principle component analysis and non-negative matrix factorization, have the disadvantage that they return difficult-to-interpret linear combinations of actual data elements. In this work, we show that CX and CUR matrix decompositions can be used directly to address this selection need. CX and CUR matrix decompositions use empirical statistical leverage scores of the input data to provide provably good low-rank approximations of the measured data that are expressed in terms of actual ions and actual positions, as opposed to difficult-to-interpret eigenions and eigenpositions. We show that this leads to effective prioritization of information for both ions and positions. In particular, important ions can be found either by using the leverage scores as a ranking function and using a deterministic greedy selection algorithm or by using the leverage scores as an importance sampling distribution and using a random sampling algorithm; however, selection of important positions from the original matrix performed significantly better when they were chosen with the random sampling algorithm. Also, we show that 20 ions or 40 locations can be used to reconstruct the original matrix to a tolerance of 17% error for a widely studied image of brain lipids; and we provide a scalable implementation of this method that is applicable for analysis of the raw data where there are often more than a million rows and/or columns, which is larger than SVD-based low-rank approximation methods can handle. Lastly, these results introduce the concept of CX/CUR matrix factorizations to mass spectrometry imaging, describing their utility and illustrating principled algorithmic approaches to deal with the overwhelming amount of data generated by modern mass spectrometry imaging.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Substantial soil organic carbon retention along floodplains of mountain streams

Small, snowmelt–dominated mountain streams have the potential to store substantial organic carbon in floodplain sediment because of high inputs of particulate organic matter, relatively lower temperatures compared with lowland regions, and potential for increased moisture conditions. This work (i) quantifies mean soil organic carbon (OC) content along 24 study reaches in the Colorado Rocky Mountains using 660 soil samples, (ii) identifies potential controls of OC content based on soil properties and spatial position with respect to the channel, and (iii) and examines soil properties and OC across various floodplain geomorphic features in the study area. Stepwise multiple linear regression (adjusted r 2 = 0.48, p < 0.001) indicates that percentage of silt and clay, sample depth, percent sand, distance from the channel, and relative elevation from the channel are significant predictors of OC content in the study area. Principle component analysis indicates limited separation between geomorphic floodplain features based on predictors of OC content. A lack of significant differences among floodplain features suggests that the systematic random sampling employed in this study can capture the variability of OC across floodplains in the study area. Mean floodplain OC (6.3 ± 0.3%) is more variable but on average greater than values in uplands (1.5 ± 0.08% to 2.2 ± 0.14%) of the Colorado Front Range and higher than published values from floodplains in other regions, particularly those of larger rivers.

58 GEOSCIENCES↗
DOE OSTI2022

Evaluation of hydrograph separation techniques with uncertain end-member composition

Hydrograph separation is one of many approaches used to analyse shifts in source water contributions to stream flow resulting from climate change in remote watersheds. Understanding these shifts is vital, as shifts in source water contributions to a stream can shape water management decisions. Because remote watersheds are often inaccessible and have poorly characterized contributing water sources, or end-members, it is critical to understand the implications of using different hydrograph separation techniques in these data-limited environments. To explore the uncertainty associated with different techniques, results from two hydrograph separation techniques, mass balance and principle component analysis, were compared using 3 years of aqueous geochemical data from the East River watershed located in the Elk Mountains of Central Colorado. Solute concentrations of the end-members were characterized by both a limited set of direct chemical measurements of different sources and detailed seasonal instream chemistry to examine the influences of uncertain end-member compositions in a data-limited environment. Annual volumetric end-member contributions to stream flow had relatively good agreement across separation techniques. Large variations in time were observed in the hydrograph separations, depending on the end-member type, and estimated flow contributions varied between the selected solutes. End-member concentrations characterized by stream chemistry showed several limitations including a reduced number of distinguishable end-members and differences in timing of flow contributions. Here the results highlight the benefits of using multiple hydrograph separation techniques by providing a ‘weight-of-evidence’ approach to environments with limited end-member concentration data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Accelerated version of NUBEAM capabilities in DIII-D using neural networks

A neural network model of the effects of neutral beam injection on DIII-D has been developed. The training and testing data used by the model have been generated by the NUBEAM module of TRANSP for experimental discharges from the 2018 DIII-D campaign. Using a principle component analysis to reduce the dimensionality of profile data, the model has been shown to reproduce the results of the Monte Carlo code NUBEAM with a high level of accuracy and an execution time orders of magnitude faster than the execution time of NUBEAM. Furthermore, this makes the neural network model uniquely suited to applications in model-based scenario planning (off-line) and active control (on-line), where a large number of simulation runs are required by the associated optimization tasks that need to be performed before and during the discharge.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2012

Hepatic lipid profiling of deer mice fed ethanol using {sup 1}H and {sup 31}P NMR spectroscopy: A dose-dependent subchronic study

Chronic alcohol abuse is a 2nd major cause of liver disease resulting in significant morbidity and mortality. Alcoholic liver disease (ALD) is characterized by a wide spectrum of pathologies starting from fat accumulation (steatosis) in early reversible stage to inflammation with or without fibrosis and cirrhosis in later irreversible stages. Previously, we reported significant steatosis in the livers of hepatic alcohol dehydrogenase (ADH)-deficient (ADH{sup −}) vs. hepatic ADH-normal (ADH{sup +}) deer mice fed 4% ethanol daily for 2 months [Bhopale et al., 2006, Alcohol 39, 179–188]. However, ADH{sup −} deer mice fed 4% ethanol also showed a significant mortality. Therefore, a dose-dependent study was conducted to understand the mechanism and identify lipid(s) involved in the development of ethanol-induced fatty liver. ADH{sup −} and ADH{sup +} deer mice fed 1, 2 or 3.5% ethanol daily for 2 months and fatty infiltration in the livers were evaluated by histology and by measuring dry weights of extracted lipids. Lipid metabolomic changes in extracted lipids were determined by proton ({sup 1}H) and {sup 31}phosphorus ({sup 31}P) nuclear magnetic resonance (NMR) spectroscopy. The NMR data was analyzed by hierarchical clustering (HC) and principle component analysis (PCA) for pattern recognition. Extensive vacuolization by histology and significantly increased dry weights of total lipids found only in the livers of ADH{sup −} deer mice fed 3.5% ethanol vs. pair-fed controls suggest a dose-dependent formation of fatty liver in ADH{sup −} deer mouse model. Analysis of NMR data of ADH{sup −} deer mice fed 3.5% ethanol vs. pair-fed controls shows increases for total cholesterol, esterified cholesterol, fatty acid methyl esters (FAMEs), triacylglycerides and unsaturation, and decreases for free cholesterol, phospholipids and allylic and diallylic protons. Certain classes of neutral lipids (cholesterol esters, fatty acyl chain (-COCH{sub 2}-) and FAMEs) were also mildly increased in ADH{sup −} deer mice fed 1 or 2% ethanol. Only small increases were observed for allylic and diallylic protons, FAMEs and unsaturations in ADH{sup +} deer mice fed 3.5% ethanol vs. pair-fed controls. PCA of NMR data showed increased clustering by gradual separation of ethanol-fed ADH{sup −} deer mice groups from their respective pair-fed control groups and corresponding ethanol-fed ADH{sup +} deer mice groups. Our data indicate that dose of ethanol and hepatic ADH deficiency are two key factors involved in initiation and progression of alcoholic fatty liver disease. Further studies on characterization of individual lipid entities and associated metabolic pathways altered in our deer mouse model after different durations of ethanol feeding could be important to delineate mechanism(s) and identify potential biomarker candidate(s) of early stage ALD. -- Highlights: ► Dose-dependent ethanol-induced fatty liver was studied in deer mouse model. ► A NMR-based lipidomic approach with histology and dry lipid weights was used. ► We used principal component analysis (PCA) to analyze the NMR lipidomic data. ► Dose-dependent clustering patterns by PCA were compared among the groups.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Understanding Metal–Organic Framework Nucleation from a Solution with Evolving Graphs

A mechanistic understanding of metal–organic framework (MOF) synthesis and scale-up remains underexplored due to the complex nature of the interactions of their building blocks. In this work, we investigate the collective assembly of building units at the early stages of MOF nucleation, using MIL-101(Cr) as a prototypical example. Using large-scale molecular dynamics simulations, we observe that the choice of solvent (water and N,N-dimethylformamide), the introduction of ions (Na+ and F–) and the relative populations of MIL-101(Cr) half-secondary building unit (half-SBU) isomers have a strong influence on the cluster formation process. Additionally, the shape, size, nucleation and growth rates, crystallinity, and short and long-range order largely vary depending on the synthesis conditions. We evaluate these properties as they naturally emerge when interpreting the self-assembly of MOF nuclei as the time evolution of an undirected graph. Solution-induced conformational complexity and ionic concentration have a dramatic effect on the morphology of clusters emerging during assembly. While pure solvents lead to the rapid formation of a small number of large clusters, the presence of ions in aqueous solutions results in smaller clusters and slower nucleation. This diversity is captured by the key features of the graph representation. Principle component analysis on graph properties reveals that only a small number of molecular descriptors is needed to deconvolute MOF self-assembly. Furthermore, descriptors such as the average coordination number between half-SBUs and fractal dimension are of particular interest as they can be can be followed experimentally by techniques like by time-resolved spectroscopy. Ultimately, graph theory emerges as an approach that can be used to understand complex processes revealing molecular descriptors accessible by both simulation and experiment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Forensic tracers of exposure to produced water in freshwater mussels: a preliminary assessment of Ba, Sr, and cyclic hydrocarbons

Hydraulic fracturing is often criticized due in part to the potential degradation of ground and surface water quality by high-salinity produced water generated during well stimulation and production. This preliminary study evaluated the response of the freshwater mussel, Elliptio complanata, after exposure to produced water. A limited number of adult mussels were grown over an 8-week period in tanks dosed with produced water collected from a hydraulically fractured well. The fatty tissue and carbonate shells were assessed for accumulation of both inorganic and organic pollutants. Ba, Sr, and cyclic hydrocarbons indicated the potential to accumulate in the soft tissue of freshwater mussels following exposure to diluted oil and gas produced water. Exposed mussels showed accumulation of Ba in the soft tissue several hundred times above background water concentrations and increased concentrations of Sr. Cyclic hydrocarbons were detected in dosed mussels and principle component analysis of gas chromatograph time-of-flight mass spectrometer results could be a novel tool to help identify areas where aquatic organisms are impacted by oil and gas produced water, but larger studies with greater replication are necessary to confirm these results.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Nonlinear vs. linear biasing in Trp-cage folding simulations

Biased simulations have great potential for the study of slow processes, including protein folding. Atomic motions in molecules are nonlinear, which suggests that simulations with enhanced sampling of collective motions traced by nonlinear dimensionality reduction methods may perform better than linear ones. In this study, we compare an unbiased folding simulation of the Trp-cage miniprotein with metadynamics simulations using both linear (principle component analysis) and nonlinear (Isomap) low dimensional embeddings as collective variables. Folding of the mini-protein was successfully simulated in 200 ns simulation with linear biasing and non-linear motion biasing. The folded state was correctly predicted as the free energy minimum in both simulations. We found that the advantage of linear motion biasing is that it can sample a larger conformational space, whereas the advantage of nonlinear motion biasing lies in slightly better resolution of the resulting free energy surface. In terms of sampling efficiency, both methods are comparable.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Automated characterization of spatial and dynamical heterogeneity in supercooled liquids via implementation of machine learning

Abstract A computational approach by an implementation of the principle component analysis (PCA) with K -means and Gaussian mixture (GM) clustering methods from machine learning algorithms to identify structural and dynamical heterogeneities of supercooled liquids is developed. In this method, a collection of the average weighted coordination numbers ( W C N s ‾ ) of particles calculated from particles’ positions are used as an order parameter to build a low-dimensional representation of feature (structural) space for K -means clustering to sort the particles in the system into few meso-states using PCA. Nano-domains or aggregated clusters are also formed in configurational (real) space from a direct mapping using associated meso-states’ particle identities with some misclassified interfacial particles. These classification uncertainties can be improved by a co-learning strategy which utilizes the probabilistic GM clustering and the information transfer between the structural space and configurational space iteratively until convergence. A final classification of meso-states in structural space and domains in configurational space are stable over long times and measured to have dynamical heterogeneities. Armed with such a classification protocol, various studies over the thermodynamic and dynamical properties of these domains indicate that the observed heterogeneity is the result of liquid–liquid phase separation after quenching to a supercooled state.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗