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DOE OSTI2012

AGES AND METALLICITIES OF CLUSTER GALAXIES IN A779 USING MODIFIED STROeMGREN PHOTOMETRY

In the quest for the formation and evolution of galaxy clusters, Rakos and co-workers introduced a spectrophotometric method using modified Stroemgren photometry, but with the considerable debate toward the project's abilities, we re-introduce the system by testing for the repeatability of the modified Stroemgren colors and compare them with the Stroemgren colors, and check for the reproducibility of the ages and metallicities (using the Principle Component Analysis (PCA) technique and the GALEV models) for the six common galaxies in all three A779 data sets. As a result, a fair agreement between two filter systems was found to produce similar colors (with a precision of 0.09 mag in (uz - vz), 0.02 mag in (bz - yz), and 0.03 mag in (vz - vz)) and the generated ages and metallicities are also similar (with an uncertainty of 0.36 Gyr and 0.04 dex from PCA and 0.44 Gyr and 0.2 dex using the GALEV models). We infer that the technique is able to relieve the age-metallicity degeneracy by separating the age effects from the metallicity effects, but it is still unable to completely eliminate it. We further extend this paper to re-study the evolution of galaxies in the low mass, dynamically poor A779 cluster (as it was not elaborately analyzed by Rakos and co-workers in their previous work) by correlating the luminosity (mass), density, and radial distance with the estimated age, metallicity, and the star formation history. Our results distinctly show the bimodality of the young, low-mass, metal-poor population with a mean age of 6.7 Gyr ({+-} 0.5 Gyr) and the old, high-mass, metal-rich galaxies with a mean age of 9 Gyr ({+-} 0.5 Gyr). The method also observes the color evolution of the blue cluster galaxies to red (Butcher-Oemler phenomenon), and the downsizing phenomenon. Our analysis shows that modified Stroemgren photometry is very well suited for studying low- and intermediate-z clusters, as it is capable of observing deeper with better spatial resolution at spectroscopic redshift limits, and the narrowband filters estimate the age and metallicity with fewer uncertainties compared to other methods that study stellar population scenarios.

79 ASTRONOMY AND ASTROPHYSICS↗
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↗
DOE OSTI2011

Uncorrelated estimates of the primordial power spectrum

We use the localized principle component analysis to detect deviations from scale invariance of the primordial power spectrum of curvature perturbations. With the technique we make uncorrelated estimates of the primordial power spectrum with five wavenumber bins. In the framework of a minimal ΛCDM model, using the latest cosmic microwave background data from the WMAP and ACT experiments we find that more than 95% of the preferred models are incompatible with the assumption of scale-invariance, but still compatible with a power-law primordial spectrum. We also forecast the sensitivity and constraints achievable by the Planck experiment by performing Monte Carlo studies on simulated data. Planck could significantly improve the constraints on the primordial power spectrum, especially at small scales by roughly a factor of 4.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Quantum decimation in Hilbert space: Coarse graining without structure

We introduce a technique to coarse grain quantum states in a finite-dimensional Hilbert space. Our method is distinguished from other approaches by not relying on structures such as a preferred factorization of Hilbert space or a preferred set of operators (local or otherwise) in an associated algebra. Rather, we use the data corresponding to a given set of states, either specified independently or constructed from a single state evolving in time. Our method is based on principle component analysis (PCA), and the resulting coarse-grained quantum states live in a lower-dimensional Hilbert space whose basis is defined using the underlying (isometric embedding) transformation of the set of fine-grained states we wish to coarse grain. Physically, the transformation can be interpreted to be an “entanglement coarse-graining” scheme that retains most of the global, useful entanglement structure of each state, while needing fewer degrees of freedom for its reconstruction. This scheme could be useful for efficiently describing collections of states whose number is much smaller than the dimension of Hilbert space, or a single state evolving over time.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Reconstruction of the remote dipole and quadrupole fields from the kinetic Sunyaev Zel’dovich and polarized Sunyaev Zel’dovich effects

The kinetic Sunyaev Zel’dovich (kSZ) and polarized Sunyaev Zel’dovich (pSZ) effects are temperature and polarization anisotropies induced by the scattering of cosmic microwave background (CMB) photons from structure in the post-reionization universe. In the case of the kSZ effect, small angular scale anisotropies in the optical depth are modulated by the CMB dipole field, i.e., the CMB dipole observed at each spacetime point, which is sourced by the primordial dipole and especially the local peculiar velocity. In the case of the pSZ effect, similar small-scale anisotropies are modulated by the CMB quadrupole field, which receives contributions from both scalar and tensor modes. Statistical anisotropies in the cross-correlations of CMB temperature and polarization with tracers of the inhomogeneous distribution of electrons provide a means of isolating and reconstructing the dipole and quadrupole fields. In this paper, we present a set of unbiased minimum variance quadratic estimators for the reconstruction of the dipole and quadrupole fields, and forecast the ability of future CMB experiments and large-scale structure surveys to perform this reconstruction. Furthermore, consistent with previous work, we find that a high fidelity reconstruction of the dipole and quadrupole fields over a variety of scales is indeed possible, and demonstrate the sensitivity of the pSZ effect to primordial tensor modes. Using a principle component analysis, we estimate how many independent modes could be accessed in such a reconstruction. We also comment on a few first applications of a detection of the dipole and quadrupole fields, including a reconstruction of the primordial contribution to our locally observed CMB dipole, a test of statistical homogeneity on large scales from the first modes of the quadrupole field, and a reconstruction technique for the primordial potential on the largest scales.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Data-driven Event Identification in the U.S. Power Systems Based on 2D-OLPP and RUSBoosting Trees

Accurate event identification is an essential part of situation awareness ability for power system operators. Therefore, this work proposes an integrated event identification algorithm for power systems. First, to obtain and filter suitable inputs for event identification, an event detection trigger based on the rate of change of frequency (RoCoF) is presented. Then, the wave arrival time difference-based triangulation method considering the anisotropy of wave propagation speed is utilized to estimate the location of the detected event. Next, the two-dimensional orthogonal locality preserving projection (2D-OLPP)-based method, which is suitable for multiple types of measured data, is employed to achieve higher effectiveness in extracting the event features compared with traditional one-dimensional projection and principle component analysis (PCA). Finally, the random undersampling boosted (RUSBoosted) trees-based classifier, which can mitigate the data sample imbalance issue, is utilized to identify the type of the detected event. Furthermore, the proposed approach is demonstrated using the actual measurement data of U.S. power systems from FNET/GridEye. Comparison results show that the proposed event identification algorithm can achieve better performance than existing approaches.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Data-Driven Event Detection of Power Systems Based on Unequal-Interval Reduction of PMU Data and Local Outlier Factor

With the deployment of phasor measurement units (PMU) and wide area measurement system (WAMS), it is feasible to have an insight into the events occurred in power systems based on measured data. Thus, a novel data-driven algorithm based on local outlier factor (LOF) is proposed in this work to detect and locate events in power systems using reduced PMU data. First, the unequal-interval reduction method is presented to reduce the scale of PMU data in sub-stations and reconstruct it in master station of WAMS, which can relieve the burden of communication systems. Then, principle component analysis (PCA)-based similarity search method is proposed to measure the differences of operation state between any two buses. Next, LOF is presented to detect the abnormal events in power systems, and employed to determine the region of the event source. Finally, six cases from the Western electricity coordinating council (WECC) 179-bus power system, a case from the South China power system (SCPS), and a case from the Guangdong power system (GDPS) are utilized to demonstrate the effectiveness of the proposed algorithm. Overall, the results show that proposed algorithm is effective and can be applied to event detection, event location, and online monitoring, which can enhance the situation awareness ability of power system operators.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Cy-Phy ADS: Cyber Physical Anomaly Detection Framework for EV Charging Systems

Today’s large-scale Electric Vehicle (EV) infrastructures are heavily dependent on information communication technologies to maintain their operation and to support communication within sub-system components as well as the outside world. These technologies are vulnerable to various cyber and physical threats. Timely identification and mitigation of these threats are critical for improving human safety, avoiding economic losses, and preventing catastrophic system failures. By addressing this, our work presents a ResNet Autoencoder (AE) based Cyber-Physical Anomaly Detection System (Cy-Phy ADS) for detecting anomalies in EV Controller Area Network (CAN) protocol communication. It consists of four main components: Cyber-Physical Feature Extractor, ResNet AE-based Anomaly Detection Framework, Cyber-Physical Health Metric (CPHM), and Visualization Dashboard. The presented framework was trained and tested using CAN data collected from the EV charging system testbed at the Idaho National Laboratory. The presented Cy-Phy ADS compared against six widely used unsupervised anomaly detection algorithms: One Class Support Vector Machine (OCSVM), Variational Autoencoder (VAE), LSTM Autoencoder (LSTM AE), Isolation Forest (IForest), Principle Component Analysis (PCA) and Local Outlier Factor (LOF). Here the presented approach showed the highest accuracy among the compared methods. Further, the proposed approach showed comparable performance in terms of precision, F1, and False positive rate. It also showed the lowest training and inference time compared to the neural network-based baseline algorithms compared against with. Additionally, the Cy-Phy ADS has advantages such as unsupervised training, the ability to provide a holistic metric for system health characterization, and non-linear feature extraction.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2017

Characterization of Syntrophic Geobacter Communities Using ToF-SIMS

The aggregation of syntrophic Geobacter metallireducens and Geobacter sulfurreducens is known to be beneficial for enhancing direct interspecies electron transfer (DIET). However, the chemical speciation of such co-cultured communities was unclear. Time-of-flight secondary ion mass spectrometry (ToF-SIMS) has been used to characterize biological samples relevant to aggregates of Geobacter, including G. metallireducens and G. sulfurreducens single cultures in planktonic, biofilms formed from either the single bacteria or both, and co-cultures of these two strains in planktonic and aggregate forms. By using principle component analysis (PCA), the syntrophic Geobacter communities were significantly distinguished from their planktonic cells by a series of characteristic peaks such as amino acids, fatty acids/lipids, and quorum sensing signal molecules, indicating a different chemical composition and structure formed on their surfaces. Additionally, insights of co-culture community dynamics were obtained from a new perspective . These first SIMS results demonstrate that proteins played an important role in microbial communications.

ToF-SIMS, Geobacter, DIET, syntrophic↗
DOE OSTI2012

Organ sample generator for expected treatment dose construction and adaptive inverse planning optimization

Purpose: To create an organ sample generator (OSG) for expected treatment dose construction and adaptive inverse planning optimization. The OSG generates random samples of organs of interest from a distribution obeying the patient specific organ variation probability density function (PDF) during the course of adaptive radiotherapy. Methods: Principle component analysis (PCA) and a time-varying least-squares regression (LSR) method were used on patient specific geometric variations of organs of interest manifested on multiple daily volumetric images obtained during the treatment course. The construction of the OSG includes the determination of eigenvectors of the organ variation using PCA, and the determination of the corresponding coefficients using time-varying LSR. The coefficients can be either random variables or random functions of the elapsed treatment days depending on the characteristics of organ variation as a stationary or a nonstationary random process. The LSR method with time-varying weighting parameters was applied to the precollected daily volumetric images to determine the function form of the coefficients. Eleven h and n cancer patients with 30 daily cone beam CT images each were included in the evaluation of the OSG. The evaluation was performed using a total of 18 organs of interest, including 15 organs at risk and 3 targets. Results: Geometric variations of organs of interest during h and n cancer radiotherapy can be represented using the first 3 {approx} 4 eigenvectors. These eigenvectors were variable during treatment, and need to be updated using new daily images obtained during the treatment course. The OSG generates random samples of organs of interest from the estimated organ variation PDF of the individual. The accuracy of the estimated PDF can be improved recursively using extra daily image feedback during the treatment course. The average deviations in the estimation of the mean and standard deviation of the organ variation PDF for h and n cancer radiotherapy were less than 2 and 1 mm, respectively, for most organs after the second week of treatment. After the first three weeks of treatment, the mean discrepancy of the dose estimation accuracy was within 1% for most of organs, the corresponding standard deviation was within 2.5% for parotids, the brain stem and the cochleae, and within 1% for other organs. Conclusions: A patient specific OSG is feasible and can be used to generate random samples of organs of interest for the expected treatment dose construction and adaptive inverse planning. The accuracy of the OSG can be improved continuously and recursively during the adaptive treatment course using daily volumetric image feedback.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

SU-D-16A-04: Accuracy of Treatment Plan TCP and NTCP Values as Determined Via Treatment Course Delivery Simulations

Purpose: To to determine if tumor control probability (TCP) and normal tissue control probability (NTCP) values computed on the treatment planning image are representative of TCP/NTCP distributions resulting from probable positioning variations encountered during external-beam radiotherapy. Methods: We compare TCP/NTCP as typically computed on the planning PTV/OARs with distributions of those parameters computed for CTV/OARs via treatment delivery simulations which include the effect of patient organ deformations for a group of 19 prostate IMRT pseudocases. Planning objectives specified 78 Gy to PTV1=prostate CTV+5 mm margin, 66 Gy to PTV2=seminal vesicles+8 mm margin, and multiple bladder/rectum OAR objectives to achieve typical clinical OAR sparing. TCP were computed using the Poisson Model while NTCPs used the Lyman-Kutcher-Bruman model. For each patient, 1000 30-fraction virtual treatment courses were simulated with each fractional pseudo- time-oftreatment anatomy sampled from a principle component analysis patient deformation model. Dose for each virtual treatment-course was determined via deformable summation of dose from the individual fractions. CTVTCP/ OAR-NTCP values were computed for each treatment course, statistically analyzed, and compared with the planning PTV-TCP/OARNTCP values. Results: Mean TCP from the simulations differed by <1% from planned TCP for 18/19 patients; 1/19 differed by 1.7%. Mean bladder NTCP differed from the planned NTCP by >5% for 12/19 patients and >10% for 4/19 patients. Similarly, mean rectum NTCP differed by >5% for 12/19 patients, >10% for 4/19 patients. Both mean bladder and mean rectum NTCP differed by >5% for 10/19 patients and by >10% for 2/19 patients. For several patients, planned NTCP was less than the minimum or more than the maximum from the treatment course simulations. Conclusion: Treatment course simulations yield TCP values that are similar to planned values, while OAR NTCPs differ significantly, indicating the need for probabilistic methods or PRVs for OAR risk assessment. Presenting author receives support from Philips Medical Systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

A method of surface marker location optimization for tumor motion estimation in lung stereotactic body radiation therapy

Purpose: Accurately localizing lung tumor localization is essential for high-precision radiation therapy techniques such as stereotactic body radiation therapy (SBRT). Since direct monitoring of tumor motion is not always achievable due to the limitation of imaging modalities for treatment guidance, placement of fiducial markers on the patient’s body surface to act as a surrogate for tumor position prediction is a practical alternative for tracking lung tumor motion during SBRT treatments. In this work, the authors propose an innovative and robust model to solve the multimarker position optimization problem. The model is able to overcome the major drawbacks of the sparse optimization approach (SOA) model. Methods: The principle-component-analysis (PCA) method was employed as the framework to build the authors’ statistical prediction model. The method can be divided into two stages. The first stage is to build the surrogate tumor matrix and calculate its eigenvalues and associated eigenvectors. The second stage is to determine the “best represented” columns of the eigenvector matrix obtained from stage one and subsequently acquire the optimal marker positions as well as numbers. Using 4-dimensional CT (4DCT) and breath hold CT imaging data, the PCA method was compared to the SOA method with respect to calculation time, average prediction accuracy, prediction stability, noise resistance, marker position consistency, and marker distribution. Results: The PCA and SOA methods which were both tested were on all 11 patients for a total of 130 cases including 4DCT and breath-hold CT scenarios. The maximum calculation time for the PCA method was less than 1 s with 64 752 surface points, whereas the average calculation time for the SOA method was over 12 min with 400 surface points. Overall, the tumor center position prediction errors were comparable between the two methods, and all were less than 1.5 mm. However, for the extreme scenarios (breath hold), the prediction errors for the PCA method were not only smaller, but were also more stable than for the SOA method. Results obtained by imposing a series of random noises to the surrogates indicated that the PCA method was much more noise resistant than the SOA method. The marker position consistency tests using various combinations of 4DCT phases to construct the surrogates suggested that the marker position predictions of the PCA method were more consistent than those of the SOA method, in spite of surrogate construction. Marker distribution tests indicated that greater than 80% of the calculated marker positions fell into the high cross correlation and high motion magnitude regions for both of the algorithms. Conclusions: The PCA model is an accurate, efficient, robust, and practical model for solving the multimarker position optimization problem to predict lung tumor motion during SBRT treatments. Due to its generality, PCA model can also be applied to other imaging guidance system whichever using surface motion as the surrogates.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-J-26: A Novel Technique for Markerless Self-Sorted 4D-CBCT Using Patient Motion Modeling: A Feasibility Study

Purpose: To develop an automatic markerless 4D-CBCT projection sorting technique by using a patient respiratory motion model extracted from the planning 4D-CT images. Methods: Each phase of onboard 4D-CBCT is considered as a deformation of one phase of the prior planning 4D-CT. The deformation field map (DFM) is represented as a linear combination of three major deformation patterns extracted from the planning 4D-CT using principle component analysis (PCA). The coefficients of the PCA deformation patterns are solved by matching the digitally reconstructed radiograph (DRR) of the deformed volume to the onboard projection acquired. The PCA coefficients are solved for each single projection, and are used for phase sorting. Projections at the peaks of the Z direction coefficient are sorted as phase 1 and other projections are assigned into 10 phase bins by dividing phases equally between peaks. The 4D digital extended-cardiac-torso (XCAT) phantom was used to evaluate the proposed technique. Three scenarios were simulated, with different tumor motion amplitude (3cm to 2cm), tumor spatial shift (8mm SI), and tumor body motion phase shift (2 phases) from prior to on-board images. Projections were simulated over 180 degree scan-angle for the 4D-XCAT. The percentage of accurately binned projections across entire dataset was calculated to represent the phase sorting accuracy. Results: With a changed tumor motion amplitude from 3cm to 2cm, markerless phase sorting accuracy was 100%. With a tumor phase shift of 2 phases w.r.t. body motion, the phase sorting accuracy was 100%. With a tumor spatial shift of 8mm in SI direction, phase sorting accuracy was 86.1%. Conclusion: The XCAT phantom simulation results demonstrated that it is feasible to use prior knowledge and motion modeling technique to achieve markerless 4D-CBCT phase sorting. National Institutes of Health Grant No. R01-CA184173 Varian Medical System.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-AB-209-05: Development of an Ultra-Fast High Quality Whole Breast Radiotherapy Treatment Planning System

Purpose: To enable near-real-time (<20sec) and interactive planning without compromising quality for whole breast RT treatment planning using tangential fields. Methods: Whole breast RT plans from 20 patients treated with single energy (SE, 6MV, 10 patients) or mixed energy (ME, 6/15MV, 10 patients) were randomly selected for model training. Additional 20 cases were used as validation cohort. The planning process for a new case consists of three fully automated steps:1. Energy Selection. A classification model automatically selects energy level. To build the energy selection model, principle component analysis (PCA) was applied to the digital reconstructed radiographs (DRRs) of training cases to extract anatomy-energy relationship.2. Fluence Estimation. Once energy is selected, a random forest (RF) model generates the initial fluence. This model summarizes the relationship between patient anatomy’s shape based features and the output fluence. 3. Fluence Fine-tuning. This step balances the overall dose contribution throughout the whole breast tissue by automatically selecting reference points and applying centrality correction. Fine-tuning works at beamlet-level until the dose distribution meets clinical objectives. Prior to finalization, physicians can also make patient-specific trade-offs between target coverage and high-dose volumes.The proposed method was validated by comparing auto-plans with manually generated clinical-plans using Wilcoxon Signed-Rank test. Results: In 19/20 cases the model suggested the same energy combination as clinical-plans. The target volume coverage V100% was 78.1±4.7% for auto-plans, and 79.3±4.8% for clinical-plans (p=0.12). Volumes receiving 105% Rx were 69.2±78.0cc for auto-plans compared to 83.9±87.2cc for clinical-plans (p=0.13). The mean V10Gy, V20Gy of the ipsilateral lung was 24.4±6.7%, 18.6±6.0% for auto plans and 24.6±6.7%, 18.9±6.1% for clinical-plans (p=0.04, <0.001). Total computational time for auto-plans was < 20s. Conclusion: We developed an automated method that generates breast radiotherapy plans with accurate energy selection, similar target volume coverage, reduced hotspot volumes, and significant reduction in planning time, allowing for near-real-time planning.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TH-CD-202-07: A Methodology for Generating Numerical Phantoms for Radiation Therapy Using Geometric Attribute Distribution Models

Purpose: A persistent challenge for the quality assessment of radiation therapy treatments (e.g. contouring accuracy) is the absence of the known, ground truth for patient data. Moreover, assessment results are often patient-dependent. Computer simulation studies utilizing numerical phantoms can be performed for quality assessment with a known ground truth. However, previously reported numerical phantoms do not include the statistical properties of inter-patient variations, as their models are based on only one patient. In addition, these models do not incorporate tumor data. In this study, a methodology was developed for generating numerical phantoms which encapsulate the statistical variations of patients within radiation therapy, including tumors. Methods: Based on previous work in contouring assessment, geometric attribute distribution (GAD) models were employed to model both the deterministic and stochastic properties of individual organs via principle component analysis. Using pre-existing radiation therapy contour data, the GAD models are trained to model the shape and centroid distributions of each organ. Then, organs with different shapes and positions can be generated by assigning statistically sound weights to the GAD model parameters. Organ contour data from 20 retrospective prostate patient cases were manually extracted and utilized to train the GAD models. As a demonstration, computer-simulated CT images of generated numerical phantoms were calculated and assessed subjectively and objectively for realism. Results: A cohort of numerical phantoms of the male human pelvis was generated. CT images were deemed realistic both subjectively and objectively in terms of image noise power spectrum. Conclusion: A methodology has been developed to generate realistic numerical anthropomorphic phantoms using pre-existing radiation therapy data. The GAD models guarantee that generated organs span the statistical distribution of observed radiation therapy patients, according to the training dataset. The methodology enables radiation therapy treatment assessment with multi-modality imaging and a known ground truth, and without patient-dependent bias.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TH-CD-207A-03: A Surface Deformation Driven Respiratory Model for Organ Motion Tracking in Lung Cancer Radiotherapy

Purpose: To propose and validate a novel real-time surface-mesh-based internal organ-external surface motion and deformation tracking method for lung cancer radiotherapy. Methods: Deformation vector fields (DVFs) which characterizes the internal and external motion are obtained by registering the internal organ and tumor contours and external surface meshes to a reference phase in the 4D CT images using a recent developed local topology preserved non-rigid point matching algorithm (TOP). A composite matrix is constructed by combing the estimated internal and external DVFs. Principle component analysis (PCA) is then applied on the composite matrix to extract principal motion characteristics and finally yield the respiratory motion model parameters which correlates the internal and external motion and deformation. The accuracy of the respiratory motion model is evaluated using a 4D NURBS-based cardiac-torso (NCAT) synthetic phantom and three lung cancer cases. The center of mass (COM) difference is used to measure the tumor motion tracking accuracy, and the Dice’s coefficient (DC), percent error (PE) and Housdourf’s distance (HD) are used to measure the agreement between the predicted and ground truth tumor shape. Results: The mean COM is 0.84±0.49mm and 0.50±0.47mm for the phantom and patient data respectively. The mean DC, PE and HD are 0.93±0.01, 0.13±0.03 and 1.24±0.34 voxels for the phantom, and 0.91±0.04, 0.17±0.07 and 3.93±2.12 voxels for the three lung cancer patients, respectively. Conclusions: We have proposed and validate a real-time surface-mesh-based organ motion and deformation tracking method with an internal-external motion modeling. The preliminary results conducted on a synthetic 4D NCAT phantom and 4D CT images from three lung cancer cases show that the proposed method is reliable and accurate in tracking both the tumor motion trajectory and deformation, which can serve as a potential tool for real-time organ motion and deformation monitoring in lung cancer radiotherapy. This work is supported in part by grant from VARIAN MEDICAL SYSTEMS INC, the National Natural Science Foundation of China (no 81428019 and no 81301940), the Guangdong Natural Science Foundation (2015A030313302)and the 2015 Pearl River S&T Nova Program of Guangzhou (201506010096).

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Comparing residential contamination in a Houston environmental justice neighborhood before and after Hurricane Harvey

Polycyclic aromatic hydrocarbons (PAHs) are complex environmental toxicants; exposure to them has been linked to adverse health outcomes including cancer, as well as diseases of the skin, liver, and immune system. Based on an ongoing community engagement partnership with stakeholder groups and residents, we conducted a small longitudinal study to assess domestic exposure to PAHs among residents of Manchester, an environmental justice neighborhood located in the East End of Houston, TX. In December, 2016, we used fiber wipes to collect samples of household dust from 25 homes in Manchester. Following Hurricane Harvey, in September 2017, we revisited 24 of the 25 homes to collect soil samples from the front yards of the same homes. Wipes and soil were analyzed for the presence of PAHs using gas chromatography–mass spectrometry (GC-MS) methods. Principle component analysis plots, heatmaps, and PAH ratios were used to compare pre- and post-Hurricane Harvey samples. While direct comparison is difficult, we present three methods for comparing PAHs found in pre-hurricane fiber wipes and post-hurricane soil samples. The methods demonstrate that the PAHs found before and after Hurricane Harvey are likely from similar sources and that those sources are most likely to be associated with combustion. We also found evidence of redistribution of PAHs due to extreme flooding associated with Hurricane Harvey. Residents of the Manchester neighborhood of Houston, TX, are exposed to a range of PAHs in household dust and outdoor soil. While difficult to compare directly, we were able to use several methods to assess detected concentrations, changes in site-specific PAH allocations, and PAH origination. Additional research is needed to identify specific sources of domestic PAH exposure in these communities and continued work involving community members and policy makers should aim to develop interventions to reduce domestic exposure to and prevent negative health outcomes from PAHs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Structural Health Monitoring of Epoxy Bond between Dissimilar Materials

Structures composed of adhesively bonded materials face many challenges when subjected to thermal loading, as adherend material(s) behave differently and can induce stresses in the glue bond. Current methods to test bond strength are limited to destructive tests, which increase costs to produce adhesively bonded structures. Strain gages are proposed to monitor the expansion profile of adhesive bonds as structures undergo thermal loading. Because resulting signals are DC in nature, no spectral quantities can be observed. Principle component analysis (PCA) is proposed to differentiate between damaged and undamaged structures. Training data are taken for various damage states and the minimum Mahalanobis distance is calculated to categorize testing data into damage state. The results indicate the potential for a strain-gage based monitoring system with PCA based decision-making, with further research necessary prior to implementation of the system.

36 MATERIALS SCIENCE↗