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At least 1,423 records · Page 79

Robust system size reduction of discrete fracture networks: a multi-fidelity method that preserves transport characteristics

In this paper, we propose a multi-fidelity system reduction technique that uses weighted graphs paired with three-dimensional discrete fracture network (DFN) modelling for efficient simulation of subsurface flow and transport in fractured media. DFN models are used to simulate flow and transport in subsurface fractured rock with low-permeability. One method to alleviate the heavy computational overhead associated with these simulations is to reduce the size of the DFN using a graph representation of it to identify the primary flow sub-network and only simulate flow and transport thereon. The first of these methods used unweighted graphs constructed solely on DFN topology and could be used for accurate predictions of first-passage times. However, these techniques perform poorly when predicting later stages of the mass breakthrough. We utilize a weighted-graph representation of the DFN where edge weights are based on hydrological parameters in the DFN that allows us to exploit the kinematic quantities derivable a posteriori from the flow solution obtained on the graph representation of the DFN to perform system reduction and predict the later stages of the breakthrough curve with high fidelity. We also propose and demonstrate the use of an adaptive pruning algorithm with error control that produces a pruned DFN sub-network whose predicted mass breakthrough agrees with the original DFN within a user-specified tolerance. Lastly, the method allows for the level of accuracy to be a user-controlled parameter.

58 GEOSCIENCES↗

ISEE System Technical Report

The Immersive Simulations and Engineering Environment (ISEE) Laboratory at Kennedy Space Center is a tool used to assist with operation simulation, for Human Factors operation activities, assisting future missions. The simulations created are important because they ensure that simulated operations are performed for customers, to assist with requirements. Simultaneously, each simulation will allow stakeholders to determine any and all risk associated with the Human Factors involved in a specific assignment. My internship consists of multiple project elements. The first of which, was to assist the Integrations branch chief in developing a growth rate algorithm proof. The integral part of this project, involved learning to powerup and perform the operational procedures of the ISEE lab, capturing and creating characters used in the CAD model virtual environment. In doing so, this led to use the software to model and 3D print screw cap structures for mock-up. The structures will be used to manipulate weight change out used in mock-up design and construction. These structures will also be used in future mock-up models constructed in the lab.

Darryl A J Minor↗

Joint PP and PS plane-wave wave-equation migration-velocity analysis

Conventional joint PP and PS velocity analysis is based on ray tomography. We develop a joint PP and PS wave-equation migration-velocity-analysis method using plane-wave common-image gathers (CIGs) to produce accurate P- and S-wave velocity models. The objective function of our new method consists of three terms: The first and second terms penalize the moveout residuals computed from PP and PS plane-wave CIGs, respectively, and the third term constrains the nonzero relative depth shifts between the PP and PS migration images. The moveout of plane-wave CIGs is automatically picked using a semblance analysis method, and the relative depth shifts between the PP and PS images are automatically computed using dynamic warping or manually picking the depths of certain primary reflectors. The moveout residuals and the relative depth shifts are transformed into weighted image perturbations, and they are then projected into the velocity models to update the P- and S-wave velocity models using the scalar-wave equations and their linearized forms. Here, numerical tests with synthetic and multicomponent field data demonstrate that our method can simultaneously invert for accurate P- and S-wave velocity models for elastic migration.

58 GEOSCIENCES↗

Probabilistic partition of unity networks for high–dimensional regression problems

We explore the probabilistic partition of unity network (PPOU-Net) model in the context of high-dimensional regression problems and propose a general framework focusing on adaptive dimensionality reduction. With the proposed framework, the target function is approximated by a mixture of experts model on a low-dimensional manifold, where each cluster is associated with a fixed-degree polynomial. We present a training strategy that leverages the expectation maximization (EM) algorithm. During the training, we alternate between (i) applying gradient descent to update the DNN coefficients; and (ii) using closed-form formulae derived from the EM algorithm to update the mixture of experts model parameters. Under the probabilistic formulation, step (ii) admits the form of embarrassingly paralleliazable weighted least-squares solves. The PPOU-Nets consistently outperform the baseline fully-connected neural networks of comparable sizes in numerical experiments of various data dimensions. Here, we also explore the proposed model in applications of quantum computing, where the PPOU-Nets act as surrogate models for cost landscapes associated with variational quantum circuits.

97 MATHEMATICS AND COMPUTING↗

Active control of propeller induced noise fields inside a flexible cylinder

An active noise control model has been evaluated for reducing aircraft interior noise. The structural noise transmission properties of an aircraft fuselage were modelled as a flexible cylinder excited by external acoustic dipoles simulating the noise produced by twin propellers. The amplitudes of an internal distribution of monopole control sources were determined such that the area-weighted mean square acoustic pressure was minimized in the propeller plane. The noise control model was evaluated at low frequencies corresponding to the blade passage frequency and first few harmonics of a typical turbo-prop aircraft. Interior noise reductions of 20 25 dB were achieved, over a substantial region of the cylindrical cross-section, with just a few monopole control sources. The most favorable interior noise reductions were achieved when the active noise control model was used in combination with propeller source phasing.

Lester, H. C.↗

Accounting for partiality in serial crystallography using ray-tracing principles

Serial crystallography generates partial reflections from still diffraction images. Partialities are estimated with EVAL ray-tracing simulations, thereby improving merged reflection data to a similar quality as conventional rotation data. Serial crystallography generates ‘still’ diffraction data sets that are composed of single diffraction images obtained from a large number of crystals arbitrarily oriented in the X-ray beam. Estimation of the reflection partialities, which accounts for the expected observed fractions of diffraction intensities, has so far been problematic. In this paper, a method is derived for modelling the partialities by making use of the ray-tracing diffraction-integration method EVAL. The method estimates partialities based on crystal mosaicity, beam divergence, wavelength dispersion, crystal size and the interference function, accounting for crystallite size. It is shown that modelling of each reflection by a distribution of interference-function weighted rays yields a ‘still’ Lorentz factor. Still data are compared with a conventional rotation data set collected from a single lysozyme crystal. Overall, the presented still integration method improves the data quality markedly. The R factor of the still data compared with the rotation data decreases from 26% using a Monte Carlo approach to 12% after applying the Lorentz correction, to 5.3% when estimating partialities by EVAL and finally to 4.7% after post-refinement. The merging R{sub int} factor of the still data improves from 105 to 56% but remains high. This suggests that the accuracy of the model parameters could be further improved. However, with a multiplicity of around 40 and an R{sub int} of ∼50% the merged still data approximate the quality of the rotation data. The presented integration method suitably accounts for the partiality of the observed intensities in still diffraction data, which is a critical step to improve data quality in serial crystallography.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Godiva IV Thermal Neutron Dosimetry Modeling and Variance Reduction

The transfer of the Godiva IV experiment from the Los Alamos Critical Experiments Facility (LACEF) to the National Critical Experiments Research Center (NCERC) introduced a vastly different experiment room return to the neutron flux. The contribution of the background to the burst neutron energy spectrum is significant in the thermal and epithermal neutron energies. Target materials may be placed in various locations in the Godiva room, or outside of the room, for thermal neutron activation. Modeling of this dosimetry problem in Monte Carlo N-Particle (MCNP) presented a novel challenge compared to previous Godiva IV glory hole irradiation simulations. An advanced dosimetry modeling framework for high efficiency calculations in locations far from the Godiva IV fission source was desired. The mesh-based weight windows and point detector advanced variance reduction techniques in MCNP were implemented and tested using adaptations of the critical experiment benchmark model of the Godiva IV problem. The models were validated against measured activations of Nickel, Indium, Scandium, and Cobalt foils at locations 2 meters from the Godiva IV core. Dosimetry measurements were performed in collaboration with Sandia National Laboratory. The weight windows and point detector variance reduction coupled method resulted in the highest problem efficiency.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

A novel methodology to integrate outcomes regarding perioperative pain experience into a composite score: Prediction model development and validation

Abstract Background An integrated score that globally assesses perioperative pain experience and rationally weights each component has not yet been developed. Methods A development dataset specific to adult Chinese patients undergoing orthopaedic surgery was obtained from PAIN OUT (1985 qualified patients of 2244). A more recent validation dataset obeying the same conditions was obtained from the Chinese Anaesthesia Shared‐database Platform (1004 qualified patients of 1032). Outcomes were assessed using the International Pain Outcomes Questionnaire (IPO‐Q), which comprises key patient‐level outcomes of perioperative pain management, including pain experience and perceptions of care. Using principal component analysis and regression models, a composite score (CS) was inferred to integrate pain experience. The discrimination of the CS for dissatisfaction and desire for more pain treatment was compared with that of the worst pain score. Results A CS was developed from the 12 items of the IPO‐Q regarding pain experience. The weight for calculating the CS was worst pain 11, least pain 17, time spent in severe pain 11, interference with activity in bed 9, interference with breathing deeply or coughing 10, interference with sleep 9, anxiety 12, helplessness 12, nausea 0, drowsiness 2, itch 5 and dizziness 2. In external validation, the CS indicated superior discrimination to the worst pain in predicting dissatisfaction ( p < 0.001) and desire for more pain treatment ( p < 0.001). Conclusions This study introduced a methodology to integrate outcomes regarding perioperative pain experience into a CS, which was based on the weight of each item. Significance This novel methodology sheds additional light on the riveting issue of carefully integrating several measures into a composite endpoint, which may be useful for quality improvement purposes when addressing the impact of a change in clinical practice.

Jiang, Bailin↗

A data-driven weighting scheme for multivariate phenotypic endpoints recapitulates zebrafish developmental cascades

Zebrafish have become a key alternative model for studying health effects of environmental stressors, partly due to their genetic similarity to humans, fast generation time, and the efficiency of generating high-dimensional systematic data. Studies aiming to characterize adverse health effects in zebrafish typically include several phenotypic measurements (endpoints). While there is a solid biomedical basis for capturing a comprehensive set of endpoints, making summary judgments regarding health effects requires thoughtful integration across endpoints. Here, we introduce a Bayesian method to quantify the informativeness of 17 distinct zebrafish endpoints as a data-driven weighting scheme for a multi-endpoint summary measure, called weighted Aggregate Entropy (wAggE). We implement wAggE using high-throughput screening (HTS) data from zebrafish exposed to five concentrations of all 1060 ToxCast chemicals. Our results show that our empirical weighting scheme provides better performance in terms of the Receiver Operating Characteristic (ROC) curve for identifying significant morphological effects and improves robustness over traditional curve-fitting approaches. From a biological perspective, our results suggest that developmental cascade effects triggered by chemical exposure can be recapitulated by analyzing the relationships among endpoints. Thus, wAggE offers a powerful approach for analysis of multivariate phenotypes that can reveal underlying etiological processes. - Highlights: • Introduced a data-driven weighting scheme for multiple phenotypic endpoints. • Weighted Aggregate Entropy (wAggE) implies differential importance of endpoints. • Endpoint relationships reveal developmental cascade effects triggered by exposure. • wAggE is generalizable to multi-endpoint data of different shapes and scales.

60 APPLIED LIFE SCIENCES↗

Anti-diabetic and hypolipidemic effects of Sargassum yezoense in db/db mice

Highlights: Black-Right-Pointing-Pointer Sargassum yezoense (SY) treatment improved glucose and lipid impairment in vivo. Black-Right-Pointing-Pointer This pharmacological action is associated with PPAR{alpha}/{gamma} dual activation. Black-Right-Pointing-Pointer It decreases the expression of G6Pase for gluconeogenesis in liver. Black-Right-Pointing-Pointer It increases the expression of UCP3 for lipid metabolism in adipose tissue. Black-Right-Pointing-Pointer There are no significant side effects such as body weight gain and hepatomegaly. -- Abstract: Peroxisome proliferator-activated receptors (PPARs) have been considered to be desirable targets for metabolic syndrome, even though their specific agonists have several side effects including body weight gain, edema and tissue failure. Previously, we have reported in vitro effects of Sargassum yezoense (SY) and its ingredients, sargaquinoic acid (SQA) and sargahydroquinoic acid (SHQA), on PPAR{alpha}/{gamma} dual transcriptional activation. In this study, we describe in vivo pharmacological property of SY on metabolic disorders. SY treatment significantly improved glucose and lipid impairment in db/db mice model. More importantly, there are no significant side effects such as body weight gain and hepatomegaly in SY-treated animals, indicating little side effects of SY in liver and lipid metabolism. In addition, SY led to a decrease in the expression of G6Pase for gluconeogenesis in liver responsible for lowering blood glucose level and an increase in the expression of UCP3 in adipose tissue for the reduction of total and LDL-cholesterol level. Altogether, our data suggest that SY would be a potential therapeutic agent against type 2 diabetes and related metabolic disorders by ameliorating the glucose and lipid metabolism.

60 APPLIED LIFE SCIENCES↗

Calculating the bidirectional reflectance of natural vegetation covers using Boolean models and geometric optics

The bidirectional radiance or reflectance of a forest or woodland can be modeled using principles of geometric optics and Boolean models for random sets in a three dimensional space. This model may be defined at two levels, the scene includes four components; sunlight and shadowed canopy, and sunlit and shadowed background. The reflectance of the scene is modeled as the sum of the reflectances of the individual components as weighted by their areal proportions in the field of view. At the leaf level, the canopy envelope is an assemblage of leaves, and thus the reflectance is a function of the areal proportions of sunlit and shadowed leaf, and sunlit and shadowed background. Because the proportions of scene components are dependent upon the directions of irradiance and exitance, the model accounts for the hotspot that is well known in leaf and tree canopies.

Strahler, Alan H.↗

SU-C-BRD-01: A Statistical Modeling Method for Quality Control of Intensity- Modulated Radiation Therapy Planning

Purpose: To apply a statistical modeling approach, threshold modeling (TM), for quality control of intensity-modulated radiation therapy (IMRT) treatment plans. Methods: A quantitative measure, which was the weighted sum of violations of dose/dose-volume constraints, was first developed to represent the quality of each IMRT plan. Threshold modeling approach, which is is an extension of extreme value theory in statistics and is an effect way to model extreme values, was then applied to analyze the quality of the plans summarized by our quantitative measures. Our approach modeled the plans generated by planners as a series of independent and identically distributed random variables and described the behaviors of them if the plan quality was controlled below certain threshold. We tested our approach with five locally advanced head and neck cancer patients retrospectively. Two statistics were incorporated for numerical analysis: probability of quality improvement (PQI) of the plans and expected amount of improvement on the quantitative measure (EQI). Results: After clinical planners generated 15 plans for each patient, we applied our approach to obtain the PQI and EQI as if planners would generate additional 15 plans. For two of the patients, the PQI was significantly higher than the other three (0.17 and 0.18 comparing to 0.08, 0.01 and 0.01). The actual percentage of the additional 15 plans that outperformed the best of initial 15 plans was 20% and 27% comparing to 11%, 0% and 0%. EQI for the two potential patients were 34.5 and 32.9 and the rest of three patients were 9.9, 1.4 and 6.6. The actual improvements obtained were 28.3 and 20.5 comparing to 6.2, 0 and 0. Conclusion: TM is capable of reliably identifying the potential quality improvement of IMRT plans. It provides clinicians an effective tool to assess the trade-off between extra planning effort and achievable plan quality. This work was supported in part by NIH/NCI grant CA130814.

60 APPLIED LIFE SCIENCES↗

Fluorescence-labeled liposome accumulation in injured colon of a mouse model of T-cell transfer-mediated inflammatory bowel disease

Drug delivery systems maximize the efficacy of drugs by improving their pharmacokinetic profiles, pharmacodynamic effects, or both and reducing their adverse effects. One of the most advanced, clinically available formulations are liposome-encapsulated drugs. In this study, we aimed to determine if liposomes can selectively deliver compounds in gastrointestinal diseases. Initially, we evaluated the correlation between the diarrhea score and accumulation of fluorescence (FL)-labeled liposome using in vivo imaging systems in various disease states of an inflammatory bowel disease mouse model. The result showed that FL-labeled liposome accumulation and colon tissue weight, which reflect the disease state were highly and positively correlated. Then, to confirm the accumulation of liposomes at injured sites of the colon, we administered both FL-labeled liposomes and luminescence probes for detecting reactive oxygen species (ROS) to the mouse model. The imaging data showed that liposome accumulation tended to coincide with ROS detected sites and the correlation coefficient indicated a significantly positive correlation between liposome accumulation and ROS detection levels. Finally, we evaluated the involvement of macrophages in the uptake mechanism of the liposomes by analyzing the relationship between FL-labeled liposome accumulation and macrophage marker gene expression levels. The result showed that the expression of each macrophage marker gene and liposome accumulation showed a significant positive correlation. Therefore, the macrophages considerably contributed to the uptake mechanism of the liposomes. These data suggest that liposomes could be an attractive delivery tool for enhancing the accumulation of drug candidates through macrophages in injured colonic tissues. This approach is expected to provide new treatment options for patients with colitis.

60 APPLIED LIFE SCIENCES↗

Development and cross‐validation of a circumference‐based predictive equation to estimate body fat in an active population

Abstract Objective The U.S. Army uses sex‐specific circumference‐based prediction equations to estimate percent body fat (%BF) to evaluate adherence to body composition standards. The equations are periodically evaluated to ensure that they continue to accurately assess %BF in a diverse population. The objective of this study was to develop and validate alternative field expedient equations that may improve upon the current Army Regulation (AR) body fat (%BF) equations. Methods Body size and composition were evaluated in a representatively sampled cohort of 1904 active‐duty Soldiers (1261 Males, 643 Females), using dual‐energy X‐ray absorptiometry (%BF DXA ), and circumferences obtained with 3D imaging and manual measurements. Sex stratified linear prediction equations for %BF were constructed using internal cross validation with %BF DXA as the criterion measure. Prediction equations were evaluated for accuracy and precision using root mean squared error, bias, and intraclass correlations. Equations were externally validated in a convenient sample of 1073 Soldiers. Results Three new equations were developed using one to three circumference sites. The predictive values of waist, abdomen, hip circumference, weight and height were evaluated. Changing from a 3‐site model to a 1‐site model had minimal impact on measurements of model accuracy and performance. Male‐specific equations demonstrated larger gains in accuracy, whereas female‐specific equations resulted in minor improvements in accuracy compared to existing AR equations. Equations performed similarly in the second external validation cohort. Conclusions The equations developed improved upon the current AR equation while demonstrating robust and consistent results within an external population. The 1‐site waist circumference‐based equation utilized the abdominal measurement, which aligns with associated obesity related health outcomes. This could be used to identify individuals at risk for negative health outcomes for earlier intervention.

Taylor, Kathryn M.↗

Prenatal dichlorodiphenyldichloroethylene (DDE) exposure and child growth during the first year of life

Background: Due to its long-term persistence in the environment and its ability to cross the placental barrier, prenatal p,p Prime -dichlorodiphenyldichloroethene (DDE) exposure continues to be a public health concern. This study aimed to evaluate the association between prenatal DDE exposure and child growth, at birth and during the first year of life. Methods: 253 pregnant women were recruited between January 2001 and June 2005 in a prospective cohort in Morelos, Mexico. Serum levels of DDE were measured during each trimester of pregnancy by gas chromatography with an electron capture detector. Using the generalized mixed-effects models, the association between DDE and child growth parameters (weight-for-age, length-for-age, weight-for-length, BMI-for-age and head circumference-for-age Z-scores) from birth to 1 year of age was assessed. Maternal dietary intake was considered as covariable among others. Results: DDE levels were 6.3{+-}2.8 ng/mL (first trimester), 6.6{+-}2.9 ng/mL (second trimester), and 7.6{+-}2.9 ng/mL (third trimester). After adjusting for potential confounder variables, no significant associations were observed with prenatal DDE exposure and each of the selected parameters. Conclusions: Our results show no evidence of an association between prenatal DDE exposure and child growth during the first year of life.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Multiresolution molecular mechanics: Surface effects in nanoscale materials

Surface effects have been observed to contribute significantly to the mechanical response of nanoscale structures. The newly proposed energy-based coarse-grained atomistic method Multiresolution Molecular Mechanics (MMM) (Yang, To (2015), ) is applied to capture surface effect for nanosized structures by designing a surface summation rule SR{sup S} within the framework of MMM. Combined with previously proposed bulk summation rule SR{sup B}, the MMM summation rule SR{sup MMM} is completed. SR{sup S} and SR{sup B} are consistently formed within SR{sup MMM} for general finite element shape functions. Analogous to quadrature rules in finite element method (FEM), the key idea to the good performance of SR{sup MMM} lies in that the order or distribution of energy for coarse-grained atomistic model is mathematically derived such that the number, position and weight of quadrature-type (sampling) atoms can be determined. Mathematically, the derived energy distribution of surface area is different from that of bulk region. Physically, the difference is due to the fact that surface atoms lack neighboring bonding. As such, SR{sup S} and SR{sup B} are employed for surface and bulk domains, respectively. Two- and three-dimensional numerical examples using the respective 4-node bilinear quadrilateral, 8-node quadratic quadrilateral and 8-node hexahedral meshes are employed to verify and validate the proposed approach. It is shown that MMM with SR{sup MMM} accurately captures corner, edge and surface effects with less 0.3% degrees of freedom of the original atomistic system, compared against full atomistic simulation. The effectiveness of SR{sup MMM} with respect to high order element is also demonstrated by employing the 8-node quadratic quadrilateral to solve a beam bending problem considering surface effect. In addition, the introduced sampling error with SR{sup MMM} that is analogous to numerical integration error with quadrature rule in FEM is very small. - Highlights: • Surface effect captured by Multiresolution Molecular Mechanics (MMM) is presented. • A novel surface summation rule within the framework of MMM is proposed. • Surface, corner and edges effects are accuterly captured in two and three dimension. • MMM with less 0.3% degrees of freedom of atomistics reproduces atomistic results.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

Explicitly integrating parameter, input, and structure uncertainties into Bayesian Neural Networks for probabilistic hydrologic forecasting

Estimating uncertainty of hydrologic forecasting is valuable to water resources and other relevant decision making processes. Recently, Bayesian Neural Networks (BNNs) have been proved powerful tools for quantifying uncertainty of streamflow forecasting. In this study, we propose a Markov Chain Monte Carlo (MCMC) framework to incorporate the uncertainties associated with input, model structure, and parameter into BNNs. This framework allows the structure of the neural networks to change by removing or adding connections between neurons and enables scaling of input data by using rainfall multipliers. The results show that the new BNNs outperform the BNNs that only consider uncertainties associated with parameter and model structure. Critical evaluation of posterior distribution of neural network weights, number of effective connections, rainfall multipliers, and hyper-parameters show that the assumptions held in our BNNs are not well supported. Further understanding of characteristics of different uncertainty sources and including output error into the MCMC framework are expected to enhance the application of neural networks for uncertainty analysis of hydrologic forecasting.

58 GEOSCIENCES↗

Measurements of photo-nuclear jet production in Pb + Pb collisions with ATLAS

Ultra-peripheral heavy ion collisions provide a unique opportunity to study the parton distributions in the colliding nuclei via the measurement of photo-nuclear jet production. An analysis of jet production in ultra-peripheral Pb+Pb collisions at √ s NN = 5.02 TeV performed using data collected with the ATLAS detector in 2015 is described. The data set corresponds to a total Pb+Pb integrated luminosity of 0.38 nb -1 . The ultra-peripheral collisions are selected using a combination of forward neutron and rapidity gap requirements. The cross-sections, not unfolded for detector response, are compared to results from Pythia Monte Carlo simulations re-weighted to match a photon spectrum obtained from the STARlight model. As a result, qualitative agreement between data and these simulations is observed over a broad kinematic range suggesting that using these collisions to measure nuclear parton distributions is experimentally realisable.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗