Calibration of an agricultural-hydrological model (RZWQM2) using surrogate global optimization

DOE PAGES

Xi, Maolong; Lu, Dan; Gui, Dongwei; ...

2016-11-27

Robust calibration of an agricultural-hydrological model is critical for simulating crop yield and water quality and making reasonable agricultural management. However, calibration of the agricultural-hydrological system models is challenging because of model complexity, the existence of strong parameter correlation, and significant computational requirements. Therefore, only a limited number of simulations can be allowed in any attempt to find a near-optimal solution within an affordable time, which greatly restricts the successful application of the model. The goal of this study is to locate the optimal solution of the Root Zone Water Quality Model (RZWQM2) given a limited simulation time, so asmore » to improve the model simulation and help make rational and effective agricultural-hydrological decisions. To this end, we propose a computationally efficient global optimization procedure using sparse-grid based surrogates. We first used advanced sparse grid (SG) interpolation to construct a surrogate system of the actual RZWQM2, and then we calibrate the surrogate model using the global optimization algorithm, Quantum-behaved Particle Swarm Optimization (QPSO). As the surrogate model is a polynomial with fast evaluation, it can be efficiently evaluated with a sufficiently large number of times during the optimization, which facilitates the global search. We calibrate seven model parameters against five years of yield, drain flow, and NO 3-N loss data from a subsurface-drained corn-soybean field in Iowa. Results indicate that an accurate surrogate model can be created for the RZWQM2 with a relatively small number of SG points (i.e., RZWQM2 runs). Compared to the conventional QPSO algorithm, our surrogate-based optimization method can achieve a smaller objective function value and better calibration performance using a fewer number of expensive RZWQM2 executions, which greatly improves computational efficiency.« less

Calibration of an agricultural-hydrological model (RZWQM2) using surrogate global optimization

NASA Astrophysics Data System (ADS)

Xi, Maolong; Lu, Dan; Gui, Dongwei; Qi, Zhiming; Zhang, Guannan

2017-01-01

Robust calibration of an agricultural-hydrological model is critical for simulating crop yield and water quality and making reasonable agricultural management. However, calibration of the agricultural-hydrological system models is challenging because of model complexity, the existence of strong parameter correlation, and significant computational requirements. Therefore, only a limited number of simulations can be allowed in any attempt to find a near-optimal solution within an affordable time, which greatly restricts the successful application of the model. The goal of this study is to locate the optimal solution of the Root Zone Water Quality Model (RZWQM2) given a limited simulation time, so as to improve the model simulation and help make rational and effective agricultural-hydrological decisions. To this end, we propose a computationally efficient global optimization procedure using sparse-grid based surrogates. We first used advanced sparse grid (SG) interpolation to construct a surrogate system of the actual RZWQM2, and then we calibrate the surrogate model using the global optimization algorithm, Quantum-behaved Particle Swarm Optimization (QPSO). As the surrogate model is a polynomial with fast evaluation, it can be efficiently evaluated with a sufficiently large number of times during the optimization, which facilitates the global search. We calibrate seven model parameters against five years of yield, drain flow, and NO3-N loss data from a subsurface-drained corn-soybean field in Iowa. Results indicate that an accurate surrogate model can be created for the RZWQM2 with a relatively small number of SG points (i.e., RZWQM2 runs). Compared to the conventional QPSO algorithm, our surrogate-based optimization method can achieve a smaller objective function value and better calibration performance using a fewer number of expensive RZWQM2 executions, which greatly improves computational efficiency.

Calibration of an agricultural-hydrological model (RZWQM2) using surrogate global optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xi, Maolong; Lu, Dan; Gui, Dongwei

Robust calibration of an agricultural-hydrological model is critical for simulating crop yield and water quality and making reasonable agricultural management. However, calibration of the agricultural-hydrological system models is challenging because of model complexity, the existence of strong parameter correlation, and significant computational requirements. Therefore, only a limited number of simulations can be allowed in any attempt to find a near-optimal solution within an affordable time, which greatly restricts the successful application of the model. The goal of this study is to locate the optimal solution of the Root Zone Water Quality Model (RZWQM2) given a limited simulation time, so asmore » to improve the model simulation and help make rational and effective agricultural-hydrological decisions. To this end, we propose a computationally efficient global optimization procedure using sparse-grid based surrogates. We first used advanced sparse grid (SG) interpolation to construct a surrogate system of the actual RZWQM2, and then we calibrate the surrogate model using the global optimization algorithm, Quantum-behaved Particle Swarm Optimization (QPSO). As the surrogate model is a polynomial with fast evaluation, it can be efficiently evaluated with a sufficiently large number of times during the optimization, which facilitates the global search. We calibrate seven model parameters against five years of yield, drain flow, and NO 3-N loss data from a subsurface-drained corn-soybean field in Iowa. Results indicate that an accurate surrogate model can be created for the RZWQM2 with a relatively small number of SG points (i.e., RZWQM2 runs). Compared to the conventional QPSO algorithm, our surrogate-based optimization method can achieve a smaller objective function value and better calibration performance using a fewer number of expensive RZWQM2 executions, which greatly improves computational efficiency.« less

Global Parameter Optimization of CLM4.5 Using Sparse-Grid Based Surrogates

NASA Astrophysics Data System (ADS)

Lu, D.; Ricciuto, D. M.; Gu, L.

2016-12-01

Calibration of the Community Land Model (CLM) is challenging because of its model complexity, large parameter sets, and significant computational requirements. Therefore, only a limited number of simulations can be allowed in any attempt to find a near-optimal solution within an affordable time. The goal of this study is to calibrate some of the CLM parameters in order to improve model projection of carbon fluxes. To this end, we propose a computationally efficient global optimization procedure using sparse-grid based surrogates. We first use advanced sparse grid (SG) interpolation to construct a surrogate system of the actual CLM model, and then we calibrate the surrogate model in the optimization process. As the surrogate model is a polynomial whose evaluation is fast, it can be efficiently evaluated with sufficiently large number of times in the optimization, which facilitates the global search. We calibrate five parameters against 12 months of GPP, NEP, and TLAI data from the U.S. Missouri Ozark (US-MOz) tower. The results indicate that an accurate surrogate model can be created for the CLM4.5 with a relatively small number of SG points (i.e., CLM4.5 simulations), and the application of the optimized parameters leads to a higher predictive capacity than the default parameter values in the CLM4.5 for the US-MOz site.

Surrogate assisted multidisciplinary design optimization for an all-electric GEO satellite

NASA Astrophysics Data System (ADS)

Shi, Renhe; Liu, Li; Long, Teng; Liu, Jian; Yuan, Bin

2017-09-01

State-of-the-art all-electric geostationary earth orbit (GEO) satellites use electric thrusters to execute all propulsive duties, which significantly differ from the traditional all-chemical ones in orbit-raising, station-keeping, radiation damage protection, and power budget, etc. Design optimization task of an all-electric GEO satellite is therefore a complex multidisciplinary design optimization (MDO) problem involving unique design considerations. However, solving the all-electric GEO satellite MDO problem faces big challenges in disciplinary modeling techniques and efficient optimization strategy. To address these challenges, we presents a surrogate assisted MDO framework consisting of several modules, i.e., MDO problem definition, multidisciplinary modeling, multidisciplinary analysis (MDA), and surrogate assisted optimizer. Based on the proposed framework, the all-electric GEO satellite MDO problem is formulated to minimize the total mass of the satellite system under a number of practical constraints. Then considerable efforts are spent on multidisciplinary modeling involving geosynchronous transfer, GEO station-keeping, power, thermal control, attitude control, and structure disciplines. Since orbit dynamics models and finite element structural model are computationally expensive, an adaptive response surface surrogate based optimizer is incorporated in the proposed framework to solve the satellite MDO problem with moderate computational cost, where a response surface surrogate is gradually refined to represent the computationally expensive MDA process. After optimization, the total mass of the studied GEO satellite is decreased by 185.3 kg (i.e., 7.3% of the total mass). Finally, the optimal design is further discussed to demonstrate the effectiveness of our proposed framework to cope with the all-electric GEO satellite system design optimization problems. This proposed surrogate assisted MDO framework can also provide valuable references for other all-electric spacecraft system design.

Shape Optimization by Bayesian-Validated Computer-Simulation Surrogates

NASA Technical Reports Server (NTRS)

Patera, Anthony T.

1997-01-01

A nonparametric-validated, surrogate approach to optimization has been applied to the computational optimization of eddy-promoter heat exchangers and to the experimental optimization of a multielement airfoil. In addition to the baseline surrogate framework, a surrogate-Pareto framework has been applied to the two-criteria, eddy-promoter design problem. The Pareto analysis improves the predictability of the surrogate results, preserves generality, and provides a means to rapidly determine design trade-offs. Significant contributions have been made in the geometric description used for the eddy-promoter inclusions as well as to the surrogate framework itself. A level-set based, geometric description has been developed to define the shape of the eddy-promoter inclusions. The level-set technique allows for topology changes (from single-body,eddy-promoter configurations to two-body configurations) without requiring any additional logic. The continuity of the output responses for input variations that cross the boundary between topologies has been demonstrated. Input-output continuity is required for the straightforward application of surrogate techniques in which simplified, interpolative models are fitted through a construction set of data. The surrogate framework developed previously has been extended in a number of ways. First, the formulation for a general, two-output, two-performance metric problem is presented. Surrogates are constructed and validated for the outputs. The performance metrics can be functions of both outputs, as well as explicitly of the inputs, and serve to characterize the design preferences. By segregating the outputs and the performance metrics, an additional level of flexibility is provided to the designer. The validated outputs can be used in future design studies and the error estimates provided by the output validation step still apply, and require no additional appeals to the expensive analysis. Second, a candidate-based a posteriori error analysis capability has been developed which provides probabilistic error estimates on the true performance for a design randomly selected near the surrogate-predicted optimal design.

Surrogate-based optimization of hydraulic fracturing in pre-existing fracture networks

NASA Astrophysics Data System (ADS)

Chen, Mingjie; Sun, Yunwei; Fu, Pengcheng; Carrigan, Charles R.; Lu, Zhiming; Tong, Charles H.; Buscheck, Thomas A.

2013-08-01

Hydraulic fracturing has been used widely to stimulate production of oil, natural gas, and geothermal energy in formations with low natural permeability. Numerical optimization of fracture stimulation often requires a large number of evaluations of objective functions and constraints from forward hydraulic fracturing models, which are computationally expensive and even prohibitive in some situations. Moreover, there are a variety of uncertainties associated with the pre-existing fracture distributions and rock mechanical properties, which affect the optimized decisions for hydraulic fracturing. In this study, a surrogate-based approach is developed for efficient optimization of hydraulic fracturing well design in the presence of natural-system uncertainties. The fractal dimension is derived from the simulated fracturing network as the objective for maximizing energy recovery sweep efficiency. The surrogate model, which is constructed using training data from high-fidelity fracturing models for mapping the relationship between uncertain input parameters and the fractal dimension, provides fast approximation of the objective functions and constraints. A suite of surrogate models constructed using different fitting methods is evaluated and validated for fast predictions. Global sensitivity analysis is conducted to gain insights into the impact of the input variables on the output of interest, and further used for parameter screening. The high efficiency of the surrogate-based approach is demonstrated for three optimization scenarios with different and uncertain ambient conditions. Our results suggest the critical importance of considering uncertain pre-existing fracture networks in optimization studies of hydraulic fracturing.

The effect of framing on surrogate optimism bias: A simulation study.

PubMed

Patel, Dev; Cohen, Elan D; Barnato, Amber E

2016-04-01

To explore the effect of emotion priming and physician communication behaviors on optimism bias. We conducted a 5 × 2 between-subject randomized factorial experiment using a Web-based interactive video designed to simulate a family meeting for a critically ill spouse/parent. Eligibility included age at least 35 years and self-identifying as the surrogate for a spouse/parent. The primary outcome was the surrogate's election of code status. We defined optimism bias as the surrogate's estimate of prognosis with cardiopulmonary resuscitation (CPR) > their recollection of the physician's estimate. Of 373 respondents, 256 (69%) logged in and were randomized and 220 (86%) had nonmissing data for prognosis. Sixty-seven (30%) of 220 overall and 56 of (32%) 173 with an accurate recollection of the physician's estimate had optimism bias. Optimism bias correlated with choosing CPR (P < .001). Emotion priming (P = .397), physician attention to emotion (P = .537), and framing of CPR as the social norm (P = .884) did not affect optimism bias. Framing the decision as the patient's vs the surrogate's (25% vs 36%, P = .066) and describing the alternative to CPR as "allow natural death" instead of "do not resuscitate" (25% vs 37%, P = .035) decreased optimism bias. Framing of CPR choice during code status conversations may influence surrogates' optimism bias. Copyright © 2015 Elsevier Inc. All rights reserved.

Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis :

DOE Office of Scientific and Technical Information (OSTI.GOV)

Adams, Brian M.; Ebeida, Mohamed Salah; Eldred, Michael S.

The Dakota (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a exible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quanti cation with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components requiredmore » for iterative systems analyses, the Dakota toolkit provides a exible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user's manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.« less

Rapid Optimization of External Quantum Efficiency of Thin Film Solar Cells Using Surrogate Modeling of Absorptivity.

PubMed

Kaya, Mine; Hajimirza, Shima

2018-05-25

This paper uses surrogate modeling for very fast design of thin film solar cells with improved solar-to-electricity conversion efficiency. We demonstrate that the wavelength-specific optical absorptivity of a thin film multi-layered amorphous-silicon-based solar cell can be modeled accurately with Neural Networks and can be efficiently approximated as a function of cell geometry and wavelength. Consequently, the external quantum efficiency can be computed by averaging surrogate absorption and carrier recombination contributions over the entire irradiance spectrum in an efficient way. Using this framework, we optimize a multi-layer structure consisting of ITO front coating, metallic back-reflector and oxide layers for achieving maximum efficiency. Our required computation time for an entire model fitting and optimization is 5 to 20 times less than the best previous optimization results based on direct Finite Difference Time Domain (FDTD) simulations, therefore proving the value of surrogate modeling. The resulting optimization solution suggests at least 50% improvement in the external quantum efficiency compared to bare silicon, and 25% improvement compared to a random design.

The Effect of Framing on Surrogate Optimism Bias: A Simulation Study

PubMed Central

Patel, Dev; Cohen, Elan D.; Barnato, Amber E.

2016-01-01

Purpose To explore the effect of emotion priming and physician communication behaviors on optimism bias. Materials and Methods We conducted a 5 × 2 between-subject randomized factorial experiment using a web-based interactive video designed to simulate a family meeting for a critically ill spouse/parent. Eligibility included age ≥ 35 and self-identifying as the surrogate for a spouse/parent. The primary outcome was the surrogate's election of code status. We defined optimism bias as the surrogate's estimate of prognosis with CPR > their recollection of the physician's estimate. Results 256/373 respondents (69%) logged-in and were randomized and 220 (86%) had non-missing data for prognosis. 67/220 (30%) overall, and 56/173 (32%) of those with an accurate recollection of the physician's estimate had optimism bias. Optimism bias correlated with choosing CPR (p<.001). Emotion priming (p=.397), physician attention to emotion (p=.537), and framing of CPR as the social norm (p=.884) did not affect optimism bias. Framing the decision as the patient's vs. the surrogate's (25% vs. 36%, p=.066) and describing the alternative to CPR as “allow natural death” instead of “do not resuscitate” (25% vs. 37%, p =.035) decreased optimism bias. Conclusions Framing of CPR choice during code status conversations may influence surrogates’ optimism bias. PMID:26796950

Methodology for Formulating Diesel Surrogate Fuels with Accurate Compositional, Ignition-Quality, and Volatility Characteristics

DOE PAGES

Mueller, Charles J.; Cannella, William J.; Bruno, Thomas J.; ...

2012-05-22

In this study, a novel approach was developed to formulate surrogate fuels having characteristics that are representative of diesel fuels produced from real-world refinery streams. Because diesel fuels typically consist of hundreds of compounds, it is difficult to conclusively determine the effects of fuel composition on combustion properties. Surrogate fuels, being simpler representations of these practical fuels, are of interest because they can provide a better understanding of fundamental fuel-composition and property effects on combustion and emissions-formation processes in internal-combustion engines. In addition, the application of surrogate fuels in numerical simulations with accurate vaporization, mixing, and combustion models could revolutionizemore » future engine designs by enabling computational optimization for evolving real fuels. Dependable computational design would not only improve engine function, it would do so at significant cost savings relative to current optimization strategies that rely on physical testing of hardware prototypes. The approach in this study utilized the state-of-the-art techniques of 13C and 1H nuclear magnetic resonance spectroscopy and the advanced distillation curve to characterize fuel composition and volatility, respectively. The ignition quality was quantified by the derived cetane number. Two well-characterized, ultra-low-sulfur #2 diesel reference fuels produced from refinery streams were used as target fuels: a 2007 emissions certification fuel and a Coordinating Research Council (CRC) Fuels for Advanced Combustion Engines (FACE) diesel fuel. A surrogate was created for each target fuel by blending eight pure compounds. The known carbon bond types within the pure compounds, as well as models for the ignition qualities and volatilities of their mixtures, were used in a multiproperty regression algorithm to determine optimal surrogate formulations. The predicted and measured surrogate-fuel properties were quantitatively compared to the measured target-fuel properties, and good agreement was found.« less

Impact of competitor species composition on predicting diameter growth and survival rates of Douglas-fir trees in southwestern Oregon

USGS Publications Warehouse

Bravo, Felipe; Hann, D.W.; Maguire, Douglas A.

2001-01-01

Mixed conifer and hardwood stands in southwestern Oregon were studied to explore the hypothesis that competition effects on individual-tree growth and survival will differ according to the species comprising the competition measure. Likewise, it was hypothesized that competition measures should extrapolate best if crown-based surrogates are given preference over diameter-based (basal area based) surrogates. Diameter growth and probability of survival were modeled for individual Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) trees growing in pure stands. Alternative models expressing one-sided and two-sided competition as a function of either basal area or crown structure were then applied to other plots in which Douglas-fir was mixed with other conifers and (or) hardwood species. Crown-based variables outperformed basal area based variables as surrogates for one-sided competition in both diameter growth and survival probability, regardless of species composition. In contrast, two-sided competition was best represented by total basal area of competing trees. Surrogates reflecting differences in crown morphology among species relate more closely to the mechanics of competition for light and, hence, facilitate extrapolation to species combinations for which no observations are available.

A Surrogate Approach to the Experimental Optimization of Multielement Airfoils

NASA Technical Reports Server (NTRS)

Otto, John C.; Landman, Drew; Patera, Anthony T.

1996-01-01

The incorporation of experimental test data into the optimization process is accomplished through the use of Bayesian-validated surrogates. In the surrogate approach, a surrogate for the experiment (e.g., a response surface) serves in the optimization process. The validation step of the framework provides a qualitative assessment of the surrogate quality, and bounds the surrogate-for-experiment error on designs "near" surrogate-predicted optimal designs. The utility of the framework is demonstrated through its application to the experimental selection of the trailing edge ap position to achieve a design lift coefficient for a three-element airfoil.

Scramjet Fuel Injection Array Optimization Utilizing Mixed Variable Pattern Search With Kriging Surrogates

DTIC Science & Technology

2008-03-01

injector con- figurations for Scramjet applications.” International Journal of Heat and Mass Transfer 49: 3634–3644 (2006). 8. Anderson, C.D...Experimental Attainment of Optimal Conditions,” Journal of the Royal Statistical Society, B(13): 1–38, 1951. 19. Brewer, K.M. Exergy Methods for the Mission...second applies mvps to a new scramjet design in support of the Hypersonic International Flight Re- search Experimentation (hifire). The results

An efficient surrogate-based simulation-optimization method for calibrating a regional MODFLOW model

NASA Astrophysics Data System (ADS)

Chen, Mingjie; Izady, Azizallah; Abdalla, Osman A.

2017-01-01

Simulation-optimization method entails a large number of model simulations, which is computationally intensive or even prohibitive if the model simulation is extremely time-consuming. Statistical models have been examined as a surrogate of the high-fidelity physical model during simulation-optimization process to tackle this problem. Among them, Multivariate Adaptive Regression Splines (MARS), a non-parametric adaptive regression method, is superior in overcoming problems of high-dimensions and discontinuities of the data. Furthermore, the stability and accuracy of MARS model can be improved by bootstrap aggregating methods, namely, bagging. In this paper, Bagging MARS (BMARS) method is integrated to a surrogate-based simulation-optimization framework to calibrate a three-dimensional MODFLOW model, which is developed to simulate the groundwater flow in an arid hardrock-alluvium region in northwestern Oman. The physical MODFLOW model is surrogated by the statistical model developed using BMARS algorithm. The surrogate model, which is fitted and validated using training dataset generated by the physical model, can approximate solutions rapidly. An efficient Sobol' method is employed to calculate global sensitivities of head outputs to input parameters, which are used to analyze their importance for the model outputs spatiotemporally. Only sensitive parameters are included in the calibration process to further improve the computational efficiency. Normalized root mean square error (NRMSE) between measured and simulated heads at observation wells is used as the objective function to be minimized during optimization. The reasonable history match between the simulated and observed heads demonstrated feasibility of this high-efficient calibration framework.

SOP: parallel surrogate global optimization with Pareto center selection for computationally expensive single objective problems

DOE PAGES

Krityakierne, Tipaluck; Akhtar, Taimoor; Shoemaker, Christine A.

2016-02-02

This paper presents a parallel surrogate-based global optimization method for computationally expensive objective functions that is more effective for larger numbers of processors. To reach this goal, we integrated concepts from multi-objective optimization and tabu search into, single objective, surrogate optimization. Our proposed derivative-free algorithm, called SOP, uses non-dominated sorting of points for which the expensive function has been previously evaluated. The two objectives are the expensive function value of the point and the minimum distance of the point to previously evaluated points. Based on the results of non-dominated sorting, P points from the sorted fronts are selected as centersmore » from which many candidate points are generated by random perturbations. Based on surrogate approximation, the best candidate point is subsequently selected for expensive evaluation for each of the P centers, with simultaneous computation on P processors. Centers that previously did not generate good solutions are tabu with a given tenure. We show almost sure convergence of this algorithm under some conditions. The performance of SOP is compared with two RBF based methods. The test results show that SOP is an efficient method that can reduce time required to find a good near optimal solution. In a number of cases the efficiency of SOP is so good that SOP with 8 processors found an accurate answer in less wall-clock time than the other algorithms did with 32 processors.« less

Parametric geometric model and hydrodynamic shape optimization of a flying-wing structure underwater glider

NASA Astrophysics Data System (ADS)

Wang, Zhen-yu; Yu, Jian-cheng; Zhang, Ai-qun; Wang, Ya-xing; Zhao, Wen-tao

2017-12-01

Combining high precision numerical analysis methods with optimization algorithms to make a systematic exploration of a design space has become an important topic in the modern design methods. During the design process of an underwater glider's flying-wing structure, a surrogate model is introduced to decrease the computation time for a high precision analysis. By these means, the contradiction between precision and efficiency is solved effectively. Based on the parametric geometry modeling, mesh generation and computational fluid dynamics analysis, a surrogate model is constructed by adopting the design of experiment (DOE) theory to solve the multi-objects design optimization problem of the underwater glider. The procedure of a surrogate model construction is presented, and the Gaussian kernel function is specifically discussed. The Particle Swarm Optimization (PSO) algorithm is applied to hydrodynamic design optimization. The hydrodynamic performance of the optimized flying-wing structure underwater glider increases by 9.1%.

Optimization and Control of Agent-Based Models in Biology: A Perspective.

PubMed

An, G; Fitzpatrick, B G; Christley, S; Federico, P; Kanarek, A; Neilan, R Miller; Oremland, M; Salinas, R; Laubenbacher, R; Lenhart, S

2017-01-01

Agent-based models (ABMs) have become an increasingly important mode of inquiry for the life sciences. They are particularly valuable for systems that are not understood well enough to build an equation-based model. These advantages, however, are counterbalanced by the difficulty of analyzing and using ABMs, due to the lack of the type of mathematical tools available for more traditional models, which leaves simulation as the primary approach. As models become large, simulation becomes challenging. This paper proposes a novel approach to two mathematical aspects of ABMs, optimization and control, and it presents a few first steps outlining how one might carry out this approach. Rather than viewing the ABM as a model, it is to be viewed as a surrogate for the actual system. For a given optimization or control problem (which may change over time), the surrogate system is modeled instead, using data from the ABM and a modeling framework for which ready-made mathematical tools exist, such as differential equations, or for which control strategies can explored more easily. Once the optimization problem is solved for the model of the surrogate, it is then lifted to the surrogate and tested. The final step is to lift the optimization solution from the surrogate system to the actual system. This program is illustrated with published work, using two relatively simple ABMs as a demonstration, Sugarscape and a consumer-resource ABM. Specific techniques discussed include dimension reduction and approximation of an ABM by difference equations as well systems of PDEs, related to certain specific control objectives. This demonstration illustrates the very challenging mathematical problems that need to be solved before this approach can be realistically applied to complex and large ABMs, current and future. The paper outlines a research program to address them.

A computational framework for simultaneous estimation of muscle and joint contact forces and body motion using optimization and surrogate modeling.

PubMed

Eskinazi, Ilan; Fregly, Benjamin J

2018-04-01

Concurrent estimation of muscle activations, joint contact forces, and joint kinematics by means of gradient-based optimization of musculoskeletal models is hindered by computationally expensive and non-smooth joint contact and muscle wrapping algorithms. We present a framework that simultaneously speeds up computation and removes sources of non-smoothness from muscle force optimizations using a combination of parallelization and surrogate modeling, with special emphasis on a novel method for modeling joint contact as a surrogate model of a static analysis. The approach allows one to efficiently introduce elastic joint contact models within static and dynamic optimizations of human motion. We demonstrate the approach by performing two optimizations, one static and one dynamic, using a pelvis-leg musculoskeletal model undergoing a gait cycle. We observed convergence on the order of seconds for a static optimization time frame and on the order of minutes for an entire dynamic optimization. The presented framework may facilitate model-based efforts to predict how planned surgical or rehabilitation interventions will affect post-treatment joint and muscle function. Copyright © 2018 IPEM. Published by Elsevier Ltd. All rights reserved.

Committee-Based Active Learning for Surrogate-Assisted Particle Swarm Optimization of Expensive Problems.

PubMed

Wang, Handing; Jin, Yaochu; Doherty, John

2017-09-01

Function evaluations (FEs) of many real-world optimization problems are time or resource consuming, posing a serious challenge to the application of evolutionary algorithms (EAs) to solve these problems. To address this challenge, the research on surrogate-assisted EAs has attracted increasing attention from both academia and industry over the past decades. However, most existing surrogate-assisted EAs (SAEAs) either still require thousands of expensive FEs to obtain acceptable solutions, or are only applied to very low-dimensional problems. In this paper, a novel surrogate-assisted particle swarm optimization (PSO) inspired from committee-based active learning (CAL) is proposed. In the proposed algorithm, a global model management strategy inspired from CAL is developed, which searches for the best and most uncertain solutions according to a surrogate ensemble using a PSO algorithm and evaluates these solutions using the expensive objective function. In addition, a local surrogate model is built around the best solution obtained so far. Then, a PSO algorithm searches on the local surrogate to find its optimum and evaluates it. The evolutionary search using the global model management strategy switches to the local search once no further improvement can be observed, and vice versa. This iterative search process continues until the computational budget is exhausted. Experimental results comparing the proposed algorithm with a few state-of-the-art SAEAs on both benchmark problems up to 30 decision variables as well as an airfoil design problem demonstrate that the proposed algorithm is able to achieve better or competitive solutions with a limited budget of hundreds of exact FEs.

Optimizing water resources management in large river basins with integrated surface water-groundwater modeling: A surrogate-based approach

NASA Astrophysics Data System (ADS)

Wu, Bin; Zheng, Yi; Wu, Xin; Tian, Yong; Han, Feng; Liu, Jie; Zheng, Chunmiao

2015-04-01

Integrated surface water-groundwater modeling can provide a comprehensive and coherent understanding on basin-scale water cycle, but its high computational cost has impeded its application in real-world management. This study developed a new surrogate-based approach, SOIM (Surrogate-based Optimization for Integrated surface water-groundwater Modeling), to incorporate the integrated modeling into water management optimization. Its applicability and advantages were evaluated and validated through an optimization research on the conjunctive use of surface water (SW) and groundwater (GW) for irrigation in a semiarid region in northwest China. GSFLOW, an integrated SW-GW model developed by USGS, was employed. The study results show that, due to the strong and complicated SW-GW interactions, basin-scale water saving could be achieved by spatially optimizing the ratios of groundwater use in different irrigation districts. The water-saving potential essentially stems from the reduction of nonbeneficial evapotranspiration from the aqueduct system and shallow groundwater, and its magnitude largely depends on both water management schemes and hydrological conditions. Important implications for water resources management in general include: first, environmental flow regulation needs to take into account interannual variation of hydrological conditions, as well as spatial complexity of SW-GW interactions; and second, to resolve water use conflicts between upper stream and lower stream, a system approach is highly desired to reflect ecological, economic, and social concerns in water management decisions. Overall, this study highlights that surrogate-based approaches like SOIM represent a promising solution to filling the gap between complex environmental modeling and real-world management decision-making.

Implications of optimization cost for balancing exploration and exploitation in global search and for experimental optimization

NASA Astrophysics Data System (ADS)

Chaudhuri, Anirban

Global optimization based on expensive and time consuming simulations or experiments usually cannot be carried out to convergence, but must be stopped because of time constraints, or because the cost of the additional function evaluations exceeds the benefits of improving the objective(s). This dissertation sets to explore the implications of such budget and time constraints on the balance between exploration and exploitation and the decision of when to stop. Three different aspects are considered in terms of their effects on the balance between exploration and exploitation: 1) history of optimization, 2) fixed evaluation budget, and 3) cost as a part of objective function. To this end, this research develops modifications to the surrogate-based optimization technique, Efficient Global Optimization algorithm, that controls better the balance between exploration and exploitation, and stopping criteria facilitated by these modifications. Then the focus shifts to examining experimental optimization, which shares the issues of cost and time constraints. Through a study on optimization of thrust and power for a small flapping wing for micro air vehicles, important differences and similarities between experimental and simulation-based optimization are identified. The most important difference is that reduction of noise in experiments becomes a major time and cost issue, and a second difference is that parallelism as a way to cut cost is more challenging. The experimental optimization reveals the tendency of the surrogate to display optimistic bias near the surrogate optimum, and this tendency is then verified to also occur in simulation based optimization.

Effective use of integrated hydrological models in basin-scale water resources management: surrogate modeling approaches

NASA Astrophysics Data System (ADS)

Zheng, Y.; Wu, B.; Wu, X.

2015-12-01

Integrated hydrological models (IHMs) consider surface water and subsurface water as a unified system, and have been widely adopted in basin-scale water resources studies. However, due to IHMs' mathematical complexity and high computational cost, it is difficult to implement them in an iterative model evaluation process (e.g., Monte Carlo Simulation, simulation-optimization analysis, etc.), which diminishes their applicability for supporting decision-making in real-world situations. Our studies investigated how to effectively use complex IHMs to address real-world water issues via surrogate modeling. Three surrogate modeling approaches were considered, including 1) DYCORS (DYnamic COordinate search using Response Surface models), a well-established response surface-based optimization algorithm; 2) SOIM (Surrogate-based Optimization for Integrated surface water-groundwater Modeling), a response surface-based optimization algorithm that we developed specifically for IHMs; and 3) Probabilistic Collocation Method (PCM), a stochastic response surface approach. Our investigation was based on a modeling case study in the Heihe River Basin (HRB), China's second largest endorheic river basin. The GSFLOW (Coupled Ground-Water and Surface-Water Flow Model) model was employed. Two decision problems were discussed. One is to optimize, both in time and in space, the conjunctive use of surface water and groundwater for agricultural irrigation in the middle HRB region; and the other is to cost-effectively collect hydrological data based on a data-worth evaluation. Overall, our study results highlight the value of incorporating an IHM in making decisions of water resources management and hydrological data collection. An IHM like GSFLOW can provide great flexibility to formulating proper objective functions and constraints for various optimization problems. On the other hand, it has been demonstrated that surrogate modeling approaches can pave the path for such incorporation in real-world situations, since they can dramatically reduce the computational cost of using IHMs in an iterative model evaluation process. In addition, our studies generated insights into the human-nature water conflicts in the specific study area and suggested potential solutions to address them.

A surrogate model for thermal characteristics of stratospheric airship

NASA Astrophysics Data System (ADS)

Zhao, Da; Liu, Dongxu; Zhu, Ming

2018-06-01

A simple and accurate surrogate model is extremely needed to reduce the analysis complexity of thermal characteristics for a stratospheric airship. In this paper, a surrogate model based on the Least Squares Support Vector Regression (LSSVR) is proposed. The Gravitational Search Algorithm (GSA) is used to optimize hyper parameters. A novel framework consisting of a preprocessing classifier and two regression models is designed to train the surrogate model. Various temperature datasets of the airship envelope and the internal gas are obtained by a three-dimensional transient model for thermal characteristics. Using these thermal datasets, two-factor and multi-factor surrogate models are trained and several comparison simulations are conducted. Results illustrate that the surrogate models based on LSSVR-GSA have good fitting and generalization abilities. The pre-treated classification strategy proposed in this paper plays a significant role in improving the accuracy of the surrogate model.

Uncertainty Quantification in CO 2 Sequestration Using Surrogate Models from Polynomial Chaos Expansion

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Yan; Sahinidis, Nikolaos V.

2013-03-06

In this paper, surrogate models are iteratively built using polynomial chaos expansion (PCE) and detailed numerical simulations of a carbon sequestration system. Output variables from a numerical simulator are approximated as polynomial functions of uncertain parameters. Once generated, PCE representations can be used in place of the numerical simulator and often decrease simulation times by several orders of magnitude. However, PCE models are expensive to derive unless the number of terms in the expansion is moderate, which requires a relatively small number of uncertain variables and a low degree of expansion. To cope with this limitation, instead of using amore » classical full expansion at each step of an iterative PCE construction method, we introduce a mixed-integer programming (MIP) formulation to identify the best subset of basis terms in the expansion. This approach makes it possible to keep the number of terms small in the expansion. Monte Carlo (MC) simulation is then performed by substituting the values of the uncertain parameters into the closed-form polynomial functions. Based on the results of MC simulation, the uncertainties of injecting CO{sub 2} underground are quantified for a saline aquifer. Moreover, based on the PCE model, we formulate an optimization problem to determine the optimal CO{sub 2} injection rate so as to maximize the gas saturation (residual trapping) during injection, and thereby minimize the chance of leakage.« less

An Integrated Optimization Design Method Based on Surrogate Modeling Applied to Diverging Duct Design

NASA Astrophysics Data System (ADS)

Hanan, Lu; Qiushi, Li; Shaobin, Li

2016-12-01

This paper presents an integrated optimization design method in which uniform design, response surface methodology and genetic algorithm are used in combination. In detail, uniform design is used to select the experimental sampling points in the experimental domain and the system performance is evaluated by means of computational fluid dynamics to construct a database. After that, response surface methodology is employed to generate a surrogate mathematical model relating the optimization objective and the design variables. Subsequently, genetic algorithm is adopted and applied to the surrogate model to acquire the optimal solution in the case of satisfying some constraints. The method has been applied to the optimization design of an axisymmetric diverging duct, dealing with three design variables including one qualitative variable and two quantitative variables. The method of modeling and optimization design performs well in improving the duct aerodynamic performance and can be also applied to wider fields of mechanical design and seen as a useful tool for engineering designers, by reducing the design time and computation consumption.

DAKOTA Design Analysis Kit for Optimization and Terascale

DOE Office of Scientific and Technical Information (OSTI.GOV)

Adams, Brian M.; Dalbey, Keith R.; Eldred, Michael S.

2010-02-24

The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes (computational models) and iterative analysis methods. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and analysis of computational models on high performance computers.A user provides a set of DAKOTA commands in an input file and launches DAKOTA. DAKOTA invokes instances of the computational models, collects their results, and performs systems analyses. DAKOTA contains algorithms for optimization with gradient and nongradient-basedmore » methods; uncertainty quantification with sampling, reliability, polynomial chaos, stochastic collocation, and epistemic methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as hybrid optimization, surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. Services for parallel computing, simulation interfacing, approximation modeling, fault tolerance, restart, and graphics are also included.« less

Scalability of surrogate-assisted multi-objective optimization of antenna structures exploiting variable-fidelity electromagnetic simulation models

NASA Astrophysics Data System (ADS)

Koziel, Slawomir; Bekasiewicz, Adrian

2016-10-01

Multi-objective optimization of antenna structures is a challenging task owing to the high computational cost of evaluating the design objectives as well as the large number of adjustable parameters. Design speed-up can be achieved by means of surrogate-based optimization techniques. In particular, a combination of variable-fidelity electromagnetic (EM) simulations, design space reduction techniques, response surface approximation models and design refinement methods permits identification of the Pareto-optimal set of designs within a reasonable timeframe. Here, a study concerning the scalability of surrogate-assisted multi-objective antenna design is carried out based on a set of benchmark problems, with the dimensionality of the design space ranging from six to 24 and a CPU cost of the EM antenna model from 10 to 20 min per simulation. Numerical results indicate that the computational overhead of the design process increases more or less quadratically with the number of adjustable geometric parameters of the antenna structure at hand, which is a promising result from the point of view of handling even more complex problems.

Multi-Scale Modeling, Surrogate-Based Analysis, and Optimization of Lithium-Ion Batteries for Vehicle Applications

NASA Astrophysics Data System (ADS)

Du, Wenbo

A common attribute of electric-powered aerospace vehicles and systems such as unmanned aerial vehicles, hybrid- and fully-electric aircraft, and satellites is that their performance is usually limited by the energy density of their batteries. Although lithium-ion batteries offer distinct advantages such as high voltage and low weight over other battery technologies, they are a relatively new development, and thus significant gaps in the understanding of the physical phenomena that govern battery performance remain. As a result of this limited understanding, batteries must often undergo a cumbersome design process involving many manual iterations based on rules of thumb and ad-hoc design principles. A systematic study of the relationship between operational, geometric, morphological, and material-dependent properties and performance metrics such as energy and power density is non-trivial due to the multiphysics, multiphase, and multiscale nature of the battery system. To address these challenges, two numerical frameworks are established in this dissertation: a process for analyzing and optimizing several key design variables using surrogate modeling tools and gradient-based optimizers, and a multi-scale model that incorporates more detailed microstructural information into the computationally efficient but limited macro-homogeneous model. In the surrogate modeling process, multi-dimensional maps for the cell energy density with respect to design variables such as the particle size, ion diffusivity, and electron conductivity of the porous cathode material are created. A combined surrogate- and gradient-based approach is employed to identify optimal values for cathode thickness and porosity under various operating conditions, and quantify the uncertainty in the surrogate model. The performance of multiple cathode materials is also compared by defining dimensionless transport parameters. The multi-scale model makes use of detailed 3-D FEM simulations conducted at the particle-level. A monodisperse system of ellipsoidal particles is used to simulate the effective transport coefficients and interfacial reaction current density within the porous microstructure. Microscopic simulation results are shown to match well with experimental measurements, while differing significantly from homogenization approximations used in the macroscopic model. Global sensitivity analysis and surrogate modeling tools are applied to couple the two length scales and complete the multi-scale model.

A multi-damages identification method for cantilever beam based on mode shape curvatures and Kriging surrogate model

NASA Astrophysics Data System (ADS)

Xie, Fengle; Jiang, Zhansi; Jiang, Hui

2018-05-01

This paper presents a multi-damages identification method for Cantilever Beam. First, the damage location is identified by using the mode shape curvatures. Second, samples of varying damage severities at the damage location and their corresponding natural frequencies are used to construct the initial Kriging surrogate model. Then a particle swarm optimization (PSO) algorithm is employed to identify the damage severities based on Kriging surrogate model. The simulation study of a double-damaged cantilever beam demonstrated that the proposed method is effective.

A Method To Determine the Kinetics of Solute Mixing in Liquid/Liquid Formulation Dual-Chamber Syringes.

PubMed

Werk, Tobias; Mahler, Hanns-Christian; Ludwig, Imke Sonja; Luemkemann, Joerg; Huwyler, Joerg; Hafner, Mathias

Dual-chamber syringes were originally designed to separate a solid substance and its diluent. However, they can also be used to separate liquid formulations of two individual drug products, which cannot be co-formulated due to technical or regulatory issues. A liquid/liquid dual-chamber syringe can be designed to achieve homogenization and mixing of both solutions prior to administration, or it can be used to sequentially inject both solutions. While sequential injection can be easily achieved by a dual-chamber syringe with a bypass located at the needle end of the syringe barrel, mixing of the two fluids may provide more challenges. Within this study, the mixing behavior of surrogate solutions in different dual-chamber syringes is assessed. Furthermore, the influence of parameters such as injection angle, injection speed, agitation, and sample viscosity were studied. It was noted that mixing was poor for the commercial dual-chamber syringes (with a bypass designed as a longitudinal ridge) when the two liquids significantly differ in their physical properties (viscosity, density). However, an optimized dual-chamber syringe design with multiple bypass channels resulted in improved mixing of liquids. Dual-chamber syringes were originally designed to separate a solid substance and its diluent. However, they can also be used to separate liquid formulations of two individual drug products. A liquid/liquid dual-chamber syringe can be designed to achieve homogenization and mixing of both solutions prior to administration, or it can be used to sequentially inject both solutions. While sequential injection can be easily achieved by a dual-chamber syringe with a bypass located at the needle end of the syringe barrel, mixing of the two fluids may provide more challenges. Within this study, the mixing behavior of surrogate solutions in different dual-chamber syringes is assessed. Furthermore, the influence of parameters such as injection angle, injection speed, agitation, and sample viscosity were studied. It was noted that mixing was poor for the commercially available dual-chamber syringes when the two liquids significantly differ in viscosity and density. However, an optimized dual-chamber syringe design resulted in improved mixing of liquids. © PDA, Inc. 2017.

Efficient SRAM yield optimization with mixture surrogate modeling

NASA Astrophysics Data System (ADS)

Zhongjian, Jiang; Zuochang, Ye; Yan, Wang

2016-12-01

Largely repeated cells such as SRAM cells usually require extremely low failure-rate to ensure a moderate chi yield. Though fast Monte Carlo methods such as importance sampling and its variants can be used for yield estimation, they are still very expensive if one needs to perform optimization based on such estimations. Typically the process of yield calculation requires a lot of SPICE simulation. The circuit SPICE simulation analysis accounted for the largest proportion of time in the process yield calculation. In the paper, a new method is proposed to address this issue. The key idea is to establish an efficient mixture surrogate model. The surrogate model is based on the design variables and process variables. This model construction method is based on the SPICE simulation to get a certain amount of sample points, these points are trained for mixture surrogate model by the lasso algorithm. Experimental results show that the proposed model is able to calculate accurate yield successfully and it brings significant speed ups to the calculation of failure rate. Based on the model, we made a further accelerated algorithm to further enhance the speed of the yield calculation. It is suitable for high-dimensional process variables and multi-performance applications.

A novel surrogate-based approach for optimal design of electromagnetic-based circuits

NASA Astrophysics Data System (ADS)

Hassan, Abdel-Karim S. O.; Mohamed, Ahmed S. A.; Rabie, Azza A.; Etman, Ahmed S.

2016-02-01

A new geometric design centring approach for optimal design of central processing unit-intensive electromagnetic (EM)-based circuits is introduced. The approach uses norms related to the probability distribution of the circuit parameters to find distances from a point to the feasible region boundaries by solving nonlinear optimization problems. Based on these normed distances, the design centring problem is formulated as a max-min optimization problem. A convergent iterative boundary search technique is exploited to find the normed distances. To alleviate the computation cost associated with the EM-based circuits design cycle, space-mapping (SM) surrogates are used to create a sequence of iteratively updated feasible region approximations. In each SM feasible region approximation, the centring process using normed distances is implemented, leading to a better centre point. The process is repeated until a final design centre is attained. Practical examples are given to show the effectiveness of the new design centring method for EM-based circuits.

Sobol‧ sensitivity analysis of NAPL-contaminated aquifer remediation process based on multiple surrogates

NASA Astrophysics Data System (ADS)

Luo, Jiannan; Lu, Wenxi

2014-06-01

Sobol‧ sensitivity analyses based on different surrogates were performed on a trichloroethylene (TCE)-contaminated aquifer to assess the sensitivity of the design variables of remediation duration, surfactant concentration and injection rates at four wells to remediation efficiency First, the surrogate models of a multi-phase flow simulation model were constructed by applying radial basis function artificial neural network (RBFANN) and Kriging methods, and the two models were then compared. Based on the developed surrogate models, the Sobol‧ method was used to calculate the sensitivity indices of the design variables which affect the remediation efficiency. The coefficient of determination (R2) and the mean square error (MSE) of these two surrogate models demonstrated that both models had acceptable approximation accuracy, furthermore, the approximation accuracy of the Kriging model was slightly better than that of the RBFANN model. Sobol‧ sensitivity analysis results demonstrated that the remediation duration was the most important variable influencing remediation efficiency, followed by rates of injection at wells 1 and 3, while rates of injection at wells 2 and 4 and the surfactant concentration had negligible influence on remediation efficiency. In addition, high-order sensitivity indices were all smaller than 0.01, which indicates that interaction effects of these six factors were practically insignificant. The proposed Sobol‧ sensitivity analysis based on surrogate is an effective tool for calculating sensitivity indices, because it shows the relative contribution of the design variables (individuals and interactions) to the output performance variability with a limited number of runs of a computationally expensive simulation model. The sensitivity analysis results lay a foundation for the optimal groundwater remediation process optimization.

Methodology for Formulating Diesel Surrogate Fuels with Accurate Compositional, Ignition-Quality, and Volatility Characteristics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mueller, Charles J.; Cannella, William J.; Bruno, Thomas J.

In this study, a novel approach was developed to formulate surrogate fuels having characteristics that are representative of diesel fuels produced from real-world refinery streams. Because diesel fuels typically consist of hundreds of compounds, it is difficult to conclusively determine the effects of fuel composition on combustion properties. Surrogate fuels, being simpler representations of these practical fuels, are of interest because they can provide a better understanding of fundamental fuel-composition and property effects on combustion and emissions-formation processes in internal-combustion engines. In addition, the application of surrogate fuels in numerical simulations with accurate vaporization, mixing, and combustion models could revolutionizemore » future engine designs by enabling computational optimization for evolving real fuels. Dependable computational design would not only improve engine function, it would do so at significant cost savings relative to current optimization strategies that rely on physical testing of hardware prototypes. The approach in this study utilized the stateof- the-art techniques of 13C and 1H nuclear magnetic resonance spectroscopy and the advanced distillation curve to characterize fuel composition and volatility, respectively. The ignition quality was quantified by the derived cetane number. Two wellcharacterized, ultra-low-sulfur #2 diesel reference fuels produced from refinery streams were used as target fuels: a 2007 emissions certification fuel and a Coordinating Research Council (CRC) Fuels for Advanced Combustion Engines (FACE) diesel fuel. A surrogate was created for each target fuel by blending eight pure compounds. The known carbon bond types within the pure compounds, as well as models for the ignition qualities and volatilities of their mixtures, were used in a multiproperty regression algorithm to determine optimal surrogate formulations. The predicted and measured surrogate-fuel properties were quantitatively compared to the measured target-fuel properties, and good agreement was found. This paper is dedicated to the memory of our friend and colleague Jim Franz. Funding for this research was provided by the U.S. Department of Energy (U.S. DOE) Office of Vehicle Technologies, and by the Coordinating Research Council (CRC) and the companies that employ the CRC members. The study was conducted under the auspices of CRC. The authors thank U.S. DOE program manager Kevin Stork for supporting the participation of the U.S. national laboratories in this study.« less

Hybrid surrogate-model-based multi-fidelity efficient global optimization applied to helicopter blade design

NASA Astrophysics Data System (ADS)

Ariyarit, Atthaphon; Sugiura, Masahiko; Tanabe, Yasutada; Kanazaki, Masahiro

2018-06-01

A multi-fidelity optimization technique by an efficient global optimization process using a hybrid surrogate model is investigated for solving real-world design problems. The model constructs the local deviation using the kriging method and the global model using a radial basis function. The expected improvement is computed to decide additional samples that can improve the model. The approach was first investigated by solving mathematical test problems. The results were compared with optimization results from an ordinary kriging method and a co-kriging method, and the proposed method produced the best solution. The proposed method was also applied to aerodynamic design optimization of helicopter blades to obtain the maximum blade efficiency. The optimal shape obtained by the proposed method achieved performance almost equivalent to that obtained using the high-fidelity, evaluation-based single-fidelity optimization. Comparing all three methods, the proposed method required the lowest total number of high-fidelity evaluation runs to obtain a converged solution.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Krityakierne, Tipaluck; Akhtar, Taimoor; Shoemaker, Christine A.

This paper presents a parallel surrogate-based global optimization method for computationally expensive objective functions that is more effective for larger numbers of processors. To reach this goal, we integrated concepts from multi-objective optimization and tabu search into, single objective, surrogate optimization. Our proposed derivative-free algorithm, called SOP, uses non-dominated sorting of points for which the expensive function has been previously evaluated. The two objectives are the expensive function value of the point and the minimum distance of the point to previously evaluated points. Based on the results of non-dominated sorting, P points from the sorted fronts are selected as centersmore » from which many candidate points are generated by random perturbations. Based on surrogate approximation, the best candidate point is subsequently selected for expensive evaluation for each of the P centers, with simultaneous computation on P processors. Centers that previously did not generate good solutions are tabu with a given tenure. We show almost sure convergence of this algorithm under some conditions. The performance of SOP is compared with two RBF based methods. The test results show that SOP is an efficient method that can reduce time required to find a good near optimal solution. In a number of cases the efficiency of SOP is so good that SOP with 8 processors found an accurate answer in less wall-clock time than the other algorithms did with 32 processors.« less

Reliability based design including future tests and multiagent approaches

NASA Astrophysics Data System (ADS)

Villanueva, Diane

The initial stages of reliability-based design optimization involve the formulation of objective functions and constraints, and building a model to estimate the reliability of the design with quantified uncertainties. However, even experienced hands often overlook important objective functions and constraints that affect the design. In addition, uncertainty reduction measures, such as tests and redesign, are often not considered in reliability calculations during the initial stages. This research considers two areas that concern the design of engineering systems: 1) the trade-off of the effect of a test and post-test redesign on reliability and cost and 2) the search for multiple candidate designs as insurance against unforeseen faults in some designs. In this research, a methodology was developed to estimate the effect of a single future test and post-test redesign on reliability and cost. The methodology uses assumed distributions of computational and experimental errors with re-design rules to simulate alternative future test and redesign outcomes to form a probabilistic estimate of the reliability and cost for a given design. Further, it was explored how modeling a future test and redesign provides a company an opportunity to balance development costs versus performance by simultaneously designing the design and the post-test redesign rules during the initial design stage. The second area of this research considers the use of dynamic local surrogates, or surrogate-based agents, to locate multiple candidate designs. Surrogate-based global optimization algorithms often require search in multiple candidate regions of design space, expending most of the computation needed to define multiple alternate designs. Thus, focusing on solely locating the best design may be wasteful. We extended adaptive sampling surrogate techniques to locate multiple optima by building local surrogates in sub-regions of the design space to identify optima. The efficiency of this method was studied, and the method was compared to other surrogate-based optimization methods that aim to locate the global optimum using two two-dimensional test functions, a six-dimensional test function, and a five-dimensional engineering example.

Surrogate modeling of deformable joint contact using artificial neural networks.

PubMed

Eskinazi, Ilan; Fregly, Benjamin J

2015-09-01

Deformable joint contact models can be used to estimate loading conditions for cartilage-cartilage, implant-implant, human-orthotic, and foot-ground interactions. However, contact evaluations are often so expensive computationally that they can be prohibitive for simulations or optimizations requiring thousands or even millions of contact evaluations. To overcome this limitation, we developed a novel surrogate contact modeling method based on artificial neural networks (ANNs). The method uses special sampling techniques to gather input-output data points from an original (slow) contact model in multiple domains of input space, where each domain represents a different physical situation likely to be encountered. For each contact force and torque output by the original contact model, a multi-layer feed-forward ANN is defined, trained, and incorporated into a surrogate contact model. As an evaluation problem, we created an ANN-based surrogate contact model of an artificial tibiofemoral joint using over 75,000 evaluations of a fine-grid elastic foundation (EF) contact model. The surrogate contact model computed contact forces and torques about 1000 times faster than a less accurate coarse grid EF contact model. Furthermore, the surrogate contact model was seven times more accurate than the coarse grid EF contact model within the input domain of a walking motion. For larger input domains, the surrogate contact model showed the expected trend of increasing error with increasing domain size. In addition, the surrogate contact model was able to identify out-of-contact situations with high accuracy. Computational contact models created using our proposed ANN approach may remove an important computational bottleneck from musculoskeletal simulations or optimizations incorporating deformable joint contact models. Copyright © 2015 IPEM. Published by Elsevier Ltd. All rights reserved.

Surrogate Modeling of Deformable Joint Contact using Artificial Neural Networks

PubMed Central

Eskinazi, Ilan; Fregly, Benjamin J.

2016-01-01

Deformable joint contact models can be used to estimate loading conditions for cartilage-cartilage, implant-implant, human-orthotic, and foot-ground interactions. However, contact evaluations are often so expensive computationally that they can be prohibitive for simulations or optimizations requiring thousands or even millions of contact evaluations. To overcome this limitation, we developed a novel surrogate contact modeling method based on artificial neural networks (ANNs). The method uses special sampling techniques to gather input-output data points from an original (slow) contact model in multiple domains of input space, where each domain represents a different physical situation likely to be encountered. For each contact force and torque output by the original contact model, a multi-layer feed-forward ANN is defined, trained, and incorporated into a surrogate contact model. As an evaluation problem, we created an ANN-based surrogate contact model of an artificial tibiofemoral joint using over 75,000 evaluations of a fine-grid elastic foundation (EF) contact model. The surrogate contact model computed contact forces and torques about 1000 times faster than a less accurate coarse grid EF contact model. Furthermore, the surrogate contact model was seven times more accurate than the coarse grid EF contact model within the input domain of a walking motion. For larger input domains, the surrogate contact model showed the expected trend of increasing error with increasing domain size. In addition, the surrogate contact model was able to identify out-of-contact situations with high accuracy. Computational contact models created using our proposed ANN approach may remove an important computational bottleneck from musculoskeletal simulations or optimizations incorporating deformable joint contact models. PMID:26220591

Surrogates for numerical simulations; optimization of eddy-promoter heat exchangers

NASA Technical Reports Server (NTRS)

Patera, Anthony T.; Patera, Anthony

1993-01-01

Although the advent of fast and inexpensive parallel computers has rendered numerous previously intractable calculations feasible, many numerical simulations remain too resource-intensive to be directly inserted in engineering optimization efforts. An attractive alternative to direct insertion considers models for computational systems: the expensive simulation is evoked only to construct and validate a simplified, input-output model; this simplified input-output model then serves as a simulation surrogate in subsequent engineering optimization studies. A simple 'Bayesian-validated' statistical framework for the construction, validation, and purposive application of static computer simulation surrogates is presented. As an example, dissipation-transport optimization of laminar-flow eddy-promoter heat exchangers are considered: parallel spectral element Navier-Stokes calculations serve to construct and validate surrogates for the flowrate and Nusselt number; these surrogates then represent the originating Navier-Stokes equations in the ensuing design process.

Investigation of Navier-Stokes Code Verification and Design Optimization

NASA Technical Reports Server (NTRS)

Vaidyanathan, Rajkumar

2004-01-01

With rapid progress made in employing computational techniques for various complex Navier-Stokes fluid flow problems, design optimization problems traditionally based on empirical formulations and experiments are now being addressed with the aid of computational fluid dynamics (CFD). To be able to carry out an effective CFD-based optimization study, it is essential that the uncertainty and appropriate confidence limits of the CFD solutions be quantified over the chosen design space. The present dissertation investigates the issues related to code verification, surrogate model-based optimization and sensitivity evaluation. For Navier-Stokes (NS) CFD code verification a least square extrapolation (LSE) method is assessed. This method projects numerically computed NS solutions from multiple, coarser base grids onto a freer grid and improves solution accuracy by minimizing the residual of the discretized NS equations over the projected grid. In this dissertation, the finite volume (FV) formulation is focused on. The interplay between the xi concepts and the outcome of LSE, and the effects of solution gradients and singularities, nonlinear physics, and coupling of flow variables on the effectiveness of LSE are investigated. A CFD-based design optimization of a single element liquid rocket injector is conducted with surrogate models developed using response surface methodology (RSM) based on CFD solutions. The computational model consists of the NS equations, finite rate chemistry, and the k-6 turbulence closure. With the aid of these surrogate models, sensitivity and trade-off analyses are carried out for the injector design whose geometry (hydrogen flow angle, hydrogen and oxygen flow areas and oxygen post tip thickness) is optimized to attain desirable goals in performance (combustion length) and life/survivability (the maximum temperatures on the oxidizer post tip and injector face and a combustion chamber wall temperature). A preliminary multi-objective optimization study is carried out using a geometric mean approach. Following this, sensitivity analyses with the aid of variance-based non-parametric approach and partial correlation coefficients are conducted using data available from surrogate models of the objectives and the multi-objective optima to identify the contribution of the design variables to the objective variability and to analyze the variability of the design variables and the objectives. In summary the present dissertation offers insight into an improved coarse to fine grid extrapolation technique for Navier-Stokes computations and also suggests tools for a designer to conduct design optimization study and related sensitivity analyses for a given design problem.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhao, T; Ruan, D

Purpose: The growing size and heterogeneity in training atlas necessitates sophisticated schemes to identify only the most relevant atlases for the specific multi-atlas-based image segmentation problem. This study aims to develop a model to infer the inaccessible oracle geometric relevance metric from surrogate image similarity metrics, and based on such model, provide guidance to atlas selection in multi-atlas-based image segmentation. Methods: We relate the oracle geometric relevance metric in label space to the surrogate metric in image space, by a monotonically non-decreasing function with additive random perturbations. Subsequently, a surrogate’s ability to prognosticate the oracle order for atlas subset selectionmore » is quantified probabilistically. Finally, important insights and guidance are provided for the design of fusion set size, balancing the competing demands to include the most relevant atlases and to exclude the most irrelevant ones. A systematic solution is derived based on an optimization framework. Model verification and performance assessment is performed based on clinical prostate MR images. Results: The proposed surrogate model was exemplified by a linear map with normally distributed perturbation, and verified with several commonly-used surrogates, including MSD, NCC and (N)MI. The derived behaviors of different surrogates in atlas selection and their corresponding performance in ultimate label estimate were validated. The performance of NCC and (N)MI was similarly superior to MSD, with a 10% higher atlas selection probability and a segmentation performance increase in DSC by 0.10 with the first and third quartiles of (0.83, 0.89), compared to (0.81, 0.89). The derived optimal fusion set size, valued at 7/8/8/7 for MSD/NCC/MI/NMI, agreed well with the appropriate range [4, 9] from empirical observation. Conclusion: This work has developed an efficacious probabilistic model to characterize the image-based surrogate metric on atlas selection. Analytical insights lead to valid guiding principles on fusion set size design.« less

Assessing the potential of surrogate EPS to mimic natural biofilm mechanical properties

NASA Astrophysics Data System (ADS)

Thom, Moritz; Schimmels, Stefan

2017-04-01

Biofilms growing on benthic sediments may increase the resistance towards erosion considerably by the sticky nature of extracellular polymeric substances (EPS). The EPS is a biopolymer which is secreted by the microorganisms inhabiting the biofilm matrix and may be regarded as natural glue. However, laboratory studies on the biostabilization effect mediated by biofilms are often hampered by the unavailability of "environmental" flumes in which light intensities, water temperature and nutrient content can be controlled. To allow investigations on biostabilization in "traditional" flume settings the use of surrogate materials is studied. Another advantage of using appropriate surrogates is the potential to reduce the experimental time, as compared to cultivating natural biofilms, the surrogates can readily be designed to mimic biofilms at different growth stages. Furthermore, the use of surrogates which are expected to have more homogeneous mechanical properties could facilitate fundamental studies to improve our knowledge on biostabilization. Even though a number of studies have already utilized EPS surrogates it is not clear how to mix them to correctly mimic natural EPS mechanical properties. In this study the adhesiveness (a measure of stickiness) on the surface of several EPS surrogates (e.g. Xanthan Gum, sodium alginate) is measured. These surrogates which are originally used in the food industry as rheology modifiers are mixed by adding water to a powder at a desired concentration (C). The measured surface adhesion of different surrogates at different concentrations ranged from 0.5 to 6.7 N/m2, which is well in line with values found for laboratory cultured biofilms. We found that the surrogate characteristics differed largely especially in regard to a) the response of the adhesiveness to increased concentrations (powder to water) and b) in their rheological characteristics. A seemingly promising surrogate for the use in biostabilization studies is Xanthan Gum (XG) which can be easily mixed to achieve natural-like adhesion values. A comparison of XG characteristics to natural biofilms cultivated under different environmental conditions and at different seasons will be provided at the conference along with more details on the different surrogates. These findings will help designing laboratory experiments on biofilm-stabilization by providing a first guideline for adequately mixing the surrogates to enable e.g. investigations on fundamental aspects of biostabilization or speeding up experiments with long cultivation times.

Robust optimization of supersonic ORC nozzle guide vanes

NASA Astrophysics Data System (ADS)

Bufi, Elio A.; Cinnella, Paola

2017-03-01

An efficient Robust Optimization (RO) strategy is developed for the design of 2D supersonic Organic Rankine Cycle turbine expanders. The dense gas effects are not-negligible for this application and they are taken into account describing the thermodynamics by means of the Peng-Robinson-Stryjek-Vera equation of state. The design methodology combines an Uncertainty Quantification (UQ) loop based on a Bayesian kriging model of the system response to the uncertain parameters, used to approximate statistics (mean and variance) of the uncertain system output, a CFD solver, and a multi-objective non-dominated sorting algorithm (NSGA), also based on a Kriging surrogate of the multi-objective fitness function, along with an adaptive infill strategy for surrogate enrichment at each generation of the NSGA. The objective functions are the average and variance of the isentropic efficiency. The blade shape is parametrized by means of a Free Form Deformation (FFD) approach. The robust optimal blades are compared to the baseline design (based on the Method of Characteristics) and to a blade obtained by means of a deterministic CFD-based optimization.

A surrogate-based metaheuristic global search method for beam angle selection in radiation treatment planning.

PubMed

Zhang, H H; Gao, S; Chen, W; Shi, L; D'Souza, W D; Meyer, R R

2013-03-21

An important element of radiation treatment planning for cancer therapy is the selection of beam angles (out of all possible coplanar and non-coplanar angles in relation to the patient) in order to maximize the delivery of radiation to the tumor site and minimize radiation damage to nearby organs-at-risk. This category of combinatorial optimization problem is particularly difficult because direct evaluation of the quality of treatment corresponding to any proposed selection of beams requires the solution of a large-scale dose optimization problem involving many thousands of variables that represent doses delivered to volume elements (voxels) in the patient. However, if the quality of angle sets can be accurately estimated without expensive computation, a large number of angle sets can be considered, increasing the likelihood of identifying a very high quality set. Using a computationally efficient surrogate beam set evaluation procedure based on single-beam data extracted from plans employing equallyspaced beams (eplans), we have developed a global search metaheuristic process based on the nested partitions framework for this combinatorial optimization problem. The surrogate scoring mechanism allows us to assess thousands of beam set samples within a clinically acceptable time frame. Tests on difficult clinical cases demonstrate that the beam sets obtained via our method are of superior quality.

A surrogate-based metaheuristic global search method for beam angle selection in radiation treatment planning

PubMed Central

Zhang, H H; Gao, S; Chen, W; Shi, L; D’Souza, W D; Meyer, R R

2013-01-01

An important element of radiation treatment planning for cancer therapy is the selection of beam angles (out of all possible coplanar and non-coplanar angles in relation to the patient) in order to maximize the delivery of radiation to the tumor site and minimize radiation damage to nearby organs-at-risk. This category of combinatorial optimization problem is particularly difficult because direct evaluation of the quality of treatment corresponding to any proposed selection of beams requires the solution of a large-scale dose optimization problem involving many thousands of variables that represent doses delivered to volume elements (voxels) in the patient. However, if the quality of angle sets can be accurately estimated without expensive computation, a large number of angle sets can be considered, increasing the likelihood of identifying a very high quality set. Using a computationally efficient surrogate beam set evaluation procedure based on single-beam data extracted from plans employing equally-spaced beams (eplans), we have developed a global search metaheuristic process based on the Nested Partitions framework for this combinatorial optimization problem. The surrogate scoring mechanism allows us to assess thousands of beam set samples within a clinically acceptable time frame. Tests on difficult clinical cases demonstrate that the beam sets obtained via our method are superior quality. PMID:23459411

Hamiltonian Monte Carlo acceleration using surrogate functions with random bases.

PubMed

Zhang, Cheng; Shahbaba, Babak; Zhao, Hongkai

2017-11-01

For big data analysis, high computational cost for Bayesian methods often limits their applications in practice. In recent years, there have been many attempts to improve computational efficiency of Bayesian inference. Here we propose an efficient and scalable computational technique for a state-of-the-art Markov chain Monte Carlo methods, namely, Hamiltonian Monte Carlo. The key idea is to explore and exploit the structure and regularity in parameter space for the underlying probabilistic model to construct an effective approximation of its geometric properties. To this end, we build a surrogate function to approximate the target distribution using properly chosen random bases and an efficient optimization process. The resulting method provides a flexible, scalable, and efficient sampling algorithm, which converges to the correct target distribution. We show that by choosing the basis functions and optimization process differently, our method can be related to other approaches for the construction of surrogate functions such as generalized additive models or Gaussian process models. Experiments based on simulated and real data show that our approach leads to substantially more efficient sampling algorithms compared to existing state-of-the-art methods.

Combining endangered plants and animals as surrogates to identify priority conservation areas in Yunnan, China

NASA Astrophysics Data System (ADS)

Yang, Feiling; Hu, Jinming; Wu, Ruidong

2016-08-01

Suitable surrogates are critical for identifying optimal priority conservation areas (PCAs) to protect regional biodiversity. This study explored the efficiency of using endangered plants and animals as surrogates for identifying PCAs at the county level in Yunnan, southwest China. We ran the Dobson algorithm under three surrogate scenarios at 75% and 100% conservation levels and identified four types of PCAs. Assessment of the protection efficiencies of the four types of PCAs showed that endangered plants had higher surrogacy values than endangered animals but that the two were not substitutable; coupled endangered plants and animals as surrogates yielded a higher surrogacy value than endangered plants or animals as surrogates; the plant-animal priority areas (PAPAs) was the optimal among the four types of PCAs for conserving both endangered plants and animals in Yunnan. PAPAs could well represent overall species diversity distribution patterns and overlap with critical biogeographical regions in Yunnan. Fourteen priority units in PAPAs should be urgently considered as optimizing Yunnan’s protected area system. The spatial pattern of PAPAs at the 100% conservation level could be conceptualized into three connected conservation belts, providing a valuable reference for optimizing the layout of the in situ protected area system in Yunnan.

An Efficient Variable Screening Method for Effective Surrogate Models for Reliability-Based Design Optimization

DTIC Science & Technology

2014-04-01

surrogate model generation is difficult for high -dimensional problems, due to the curse of dimensionality. Variable screening methods have been...a variable screening model was developed for the quasi-molecular treatment of ion-atom collision [16]. In engineering, a confidence interval of...for high -level radioactive waste [18]. Moreover, the design sensitivity method can be extended to the variable screening method because vital

Four-dimensional MRI using an internal respiratory surrogate derived by dimensionality reduction

NASA Astrophysics Data System (ADS)

Uh, Jinsoo; Ayaz Khan, M.; Hua, Chiaho

2016-11-01

This study aimed to develop a practical and accurate 4-dimensional (4D) magnetic resonance imaging (MRI) method using a non-navigator, image-based internal respiratory surrogate derived by dimensionality reduction (DR). The use of DR has been previously suggested but not implemented for reconstructing 4D MRI, despite its practical advantages. We compared multiple image-acquisition schemes and refined a retrospective-sorting process to optimally implement a DR-derived surrogate. The comparison included an unconventional scheme that acquires paired slices alternately to mitigate the internal surrogate’s dependency on a specific slice location. We introduced ‘target-oriented sorting’, as opposed to conventional binning, to quantify the coherence in retrospectively sorted images, thereby determining the minimal scan time needed for sufficient coherence. This study focused on evaluating the proposed method using digital phantoms which provided unequivocal gold standard. The evaluation indicated that the DR-based respiratory surrogate is highly accurate: the error in amplitude percentile of the surrogate signal was less than 5% with the optimal scheme. Acquiring alternating paired slices was superior to the conventional scheme of acquiring individual slices; the advantage of the unconventional scheme was more pronounced when a substantial phase shift occurred across slice locations. The analysis of coherence across sorted images confirmed the advantage of higher sampling efficiencies in non-navigator respiratory surrogates. We determined that a scan time of 20 s per imaging slice was sufficient to achieve a mean coherence error of less than 1% for the tested respiratory patterns. The clinical applicability of the proposed 4D MRI has been demonstrated with volunteers and patients. The diaphragm motion in 4D MRI was consistent with that in dynamic 2D imaging which was regarded as the gold standard (difference within 1.8 mm on average).

Developing a particle tracking surrogate model to improve inversion of ground water - Surface water models

NASA Astrophysics Data System (ADS)

Cousquer, Yohann; Pryet, Alexandre; Atteia, Olivier; Ferré, Ty P. A.; Delbart, Célestine; Valois, Rémi; Dupuy, Alain

2018-03-01

The inverse problem of groundwater models is often ill-posed and model parameters are likely to be poorly constrained. Identifiability is improved if diverse data types are used for parameter estimation. However, some models, including detailed solute transport models, are further limited by prohibitive computation times. This often precludes the use of concentration data for parameter estimation, even if those data are available. In the case of surface water-groundwater (SW-GW) models, concentration data can provide SW-GW mixing ratios, which efficiently constrain the estimate of exchange flow, but are rarely used. We propose to reduce computational limits by simulating SW-GW exchange at a sink (well or drain) based on particle tracking under steady state flow conditions. Particle tracking is used to simulate advective transport. A comparison between the particle tracking surrogate model and an advective-dispersive model shows that dispersion can often be neglected when the mixing ratio is computed for a sink, allowing for use of the particle tracking surrogate model. The surrogate model was implemented to solve the inverse problem for a real SW-GW transport problem with heads and concentrations combined in a weighted hybrid objective function. The resulting inversion showed markedly reduced uncertainty in the transmissivity field compared to calibration on head data alone.

Computational study of engine external aerodynamics as a part of multidisciplinary optimization procedure

NASA Astrophysics Data System (ADS)

Savelyev, Andrey; Anisimov, Kirill; Kazhan, Egor; Kursakov, Innocentiy; Lysenkov, Alexandr

2016-10-01

The paper is devoted to the development of methodology to optimize external aerodynamics of the engine. Optimization procedure is based on numerical solution of the Reynolds-averaged Navier-Stokes equations. As a method of optimization the surrogate based method is used. As a test problem optimal shape design of turbofan nacelle is considered. The results of the first stage, which investigates classic airplane configuration with engine located under the wing, are presented. Described optimization procedure is considered in the context of multidisciplinary optimization of the 3rd generation, developed in the project AGILE.

Comparative study of surrogate models for groundwater contamination source identification at DNAPL-contaminated sites

NASA Astrophysics Data System (ADS)

Hou, Zeyu; Lu, Wenxi

2018-05-01

Knowledge of groundwater contamination sources is critical for effectively protecting groundwater resources, estimating risks, mitigating disaster, and designing remediation strategies. Many methods for groundwater contamination source identification (GCSI) have been developed in recent years, including the simulation-optimization technique. This study proposes utilizing a support vector regression (SVR) model and a kernel extreme learning machine (KELM) model to enrich the content of the surrogate model. The surrogate model was itself key in replacing the simulation model, reducing the huge computational burden of iterations in the simulation-optimization technique to solve GCSI problems, especially in GCSI problems of aquifers contaminated by dense nonaqueous phase liquids (DNAPLs). A comparative study between the Kriging, SVR, and KELM models is reported. Additionally, there is analysis of the influence of parameter optimization and the structure of the training sample dataset on the approximation accuracy of the surrogate model. It was found that the KELM model was the most accurate surrogate model, and its performance was significantly improved after parameter optimization. The approximation accuracy of the surrogate model to the simulation model did not always improve with increasing numbers of training samples. Using the appropriate number of training samples was critical for improving the performance of the surrogate model and avoiding unnecessary computational workload. It was concluded that the KELM model developed in this work could reasonably predict system responses in given operation conditions. Replacing the simulation model with a KELM model considerably reduced the computational burden of the simulation-optimization process and also maintained high computation accuracy.

Optimization of the Alkyl Linker of TO Base Surrogate in Triplex-Forming PNA for Enhanced Binding to Double-Stranded RNA.

PubMed

Sato, Takaya; Sato, Yusuke; Nishizawa, Seiichi

2017-03-23

A series of triplex-forming peptide nucleic acid (TFP) probes carrying a thiazole orange (TO) base surrogate through an alkyl linker was synthesized, and the interactions between these so-called tFIT probes and purine-rich sequences within double-stranded RNA (dsRNA) were examined. We found that the TO base surrogate linker significantly affected both the binding affinity and the fluorescence response upon triplex formation with the target dsRNA. Among the probes examined, the TO base surrogate connected through the propyl linker in the tFIT probes increased the binding affinity by a factor of ten while maintaining its function as the fluorescent universal base. Isothermal titration calorimetry experiments revealed that the increased binding affinity resulted from the gain in the binding enthalpy, which could be explained by the enhanced π-stacking interaction between the TO base surrogate and the dsRNA part of the triplex. We expect that these results will provide a molecular basis for designing strong binding tFIT probes for fluorescence sensing of various kinds of purine-rich dsRNAs sequences including those carrying a pyrimidine-purine inversion. The obtained data also offers a new insight into further development of the universal bases incorporated in TFP. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Comparison of bedside screening methods for frailty assessment in older adult trauma patients in the emergency department.

PubMed

Shah, Sachita P; Penn, Kevin; Kaplan, Stephen J; Vrablik, Michael; Jablonowski, Karl; Pham, Tam N; Reed, May J

2018-04-14

Frailty is linked to poor outcomes in older patients. We prospectively compared the utility of the picture-based Clinical Frailty Scale (CFS9), clinical assessments, and ultrasound muscle measurements against the reference FRAIL scale in older adult trauma patients in the emergency department (ED). We recruited a convenience sample of adults 65 yrs. or older with blunt trauma and injury severity scores <9. We queried subjects (or surrogates) on the FRAIL scale, and compared this to: physician-based and subject/surrogate-based CFS9; mid-upper arm circumference (MUAC) and grip strength; and ultrasound (US) measures of muscle thickness (limbs and abdominal wall). We derived optimal diagnostic thresholds and calculated performance metrics for each comparison using sensitivity, specificity, predictive values, and area under receiver operating characteristic curves (AUROC). Fifteen of 65 patients were frail by FRAIL scale (23%). CFS9 performed well when assessed by subject/surrogate (AUROC 0.91 [95% CI 0.84-0.98] or physician (AUROC 0.77 [95% CI 0.63-0.91]. Optimal thresholds for both physician and subject/surrogate were CFS9 of 4 or greater. If both physician and subject/surrogate provided scores <4, sensitivity and negative predictive value were 90.0% (54.1-99.5%) and 95.0% (73.1-99.7%). Grip strength and MUAC were not predictors. US measures that combined biceps and quadriceps thickness showed an AUROC of 0.75 compared to the reference standard. The ED needs rapid, validated tools to screen for frailty. The CFS9 has excellent negative predictive value in ruling out frailty. Ultrasound of combined biceps and quadriceps has modest concordance as an alternative in trauma patients who cannot provide a history. Copyright © 2017 Elsevier Inc. All rights reserved.

Combining endangered plants and animals as surrogates to identify priority conservation areas in Yunnan, China

PubMed Central

Yang, Feiling; Hu, Jinming; Wu, Ruidong

2016-01-01

Suitable surrogates are critical for identifying optimal priority conservation areas (PCAs) to protect regional biodiversity. This study explored the efficiency of using endangered plants and animals as surrogates for identifying PCAs at the county level in Yunnan, southwest China. We ran the Dobson algorithm under three surrogate scenarios at 75% and 100% conservation levels and identified four types of PCAs. Assessment of the protection efficiencies of the four types of PCAs showed that endangered plants had higher surrogacy values than endangered animals but that the two were not substitutable; coupled endangered plants and animals as surrogates yielded a higher surrogacy value than endangered plants or animals as surrogates; the plant-animal priority areas (PAPAs) was the optimal among the four types of PCAs for conserving both endangered plants and animals in Yunnan. PAPAs could well represent overall species diversity distribution patterns and overlap with critical biogeographical regions in Yunnan. Fourteen priority units in PAPAs should be urgently considered as optimizing Yunnan’s protected area system. The spatial pattern of PAPAs at the 100% conservation level could be conceptualized into three connected conservation belts, providing a valuable reference for optimizing the layout of the in situ protected area system in Yunnan. PMID:27538537

A Multi-Fidelity Surrogate Model for the Equation of State for Mixtures of Real Gases

NASA Astrophysics Data System (ADS)

Ouellet, Frederick; Park, Chanyoung; Koneru, Rahul; Balachandar, S.; Rollin, Bertrand

2017-11-01

The explosive dispersal of particles is a complex multiphase and multi-species fluid flow problem. In these flows, the products of detonated explosives must be treated as real gases while the ideal gas equation of state is used for the ambient air. As the products expand outward, they mix with the air and create a region where both state equations must be satisfied. One of the most accurate, yet expensive, methods to handle this problem is an algorithm that iterates between both state equations until both pressure and thermal equilibrium are achieved inside of each computational cell. This work creates a multi-fidelity surrogate model to replace this process. This is achieved by using a Kriging model to produce a curve fit which interpolates selected data from the iterative algorithm. The surrogate is optimized for computing speed and model accuracy by varying the number of sampling points chosen to construct the model. The performance of the surrogate with respect to the iterative method is tested in simulations using a finite volume code. The model's computational speed and accuracy are analyzed to show the benefits of this novel approach. This work was supported by the U.S. Department of Energy, National Nuclear Security Administration, Advanced Simulation and Computing Program, as a Cooperative Agreement under the Predictive Science Academic Alliance Program, under Contract No. DE-NA00023.

Co-Optimization of CO 2-EOR and Storage Processes in Mature Oil Reservoirs

DOE PAGES

Ampomah, William; Balch, Robert S.; Grigg, Reid B.; ...

2016-08-02

This article presents an optimization methodology for CO 2 enhanced oil recovery in partially depleted reservoirs. A field-scale compositional reservoir flow model was developed for assessing the performance history of an active CO 2 flood and for optimizing both oil production and CO 2 storage in the Farnsworth Unit (FWU), Ochiltree County, Texas. A geological framework model constructed from geophysical, geological, and engineering data acquired from the FWU was the basis for all reservoir simulations and the optimization method. An equation of state was calibrated with laboratory fluid analyses and subsequently used to predict the thermodynamic minimum miscible pressure (MMP).more » Initial history calibrations of primary, secondary and tertiary recovery were conducted as the basis for the study. After a good match was achieved, an optimization approach consisting of a proxy or surrogate model was constructed with a polynomial response surface method (PRSM). The PRSM utilized an objective function that maximized both oil recovery and CO 2 storage. Experimental design was used to link uncertain parameters to the objective function. Control variables considered in this study included: water alternating gas cycle and ratio, production rates and bottom-hole pressure of injectors and producers. Other key parameters considered in the modeling process were CO 2 purchase, gas recycle and addition of infill wells and/or patterns. The PRSM proxy model was ‘trained’ or calibrated with a series of training simulations. This involved an iterative process until the surrogate model reached a specific validation criterion. A sensitivity analysis was first conducted to ascertain which of these control variables to retain in the surrogate model. A genetic algorithm with a mixed-integer capability optimization approach was employed to determine the optimum developmental strategy to maximize both oil recovery and CO 2 storage. The proxy model reduced the computational cost significantly. The validation criteria of the reduced order model ensured accuracy in the dynamic modeling results. The prediction outcome suggested robustness and reliability of the genetic algorithm for optimizing both oil recovery and CO 2 storage. The reservoir modeling approach used in this study illustrates an improved approach to optimizing oil production and CO 2 storage within partially depleted oil reservoirs such as FWU. Lastly, this study may serve as a benchmark for potential CO 2–EOR projects in the Anadarko basin and/or geologically similar basins throughout the world.« less

Co-Optimization of CO 2-EOR and Storage Processes in Mature Oil Reservoirs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ampomah, William; Balch, Robert S.; Grigg, Reid B.

This article presents an optimization methodology for CO 2 enhanced oil recovery in partially depleted reservoirs. A field-scale compositional reservoir flow model was developed for assessing the performance history of an active CO 2 flood and for optimizing both oil production and CO 2 storage in the Farnsworth Unit (FWU), Ochiltree County, Texas. A geological framework model constructed from geophysical, geological, and engineering data acquired from the FWU was the basis for all reservoir simulations and the optimization method. An equation of state was calibrated with laboratory fluid analyses and subsequently used to predict the thermodynamic minimum miscible pressure (MMP).more » Initial history calibrations of primary, secondary and tertiary recovery were conducted as the basis for the study. After a good match was achieved, an optimization approach consisting of a proxy or surrogate model was constructed with a polynomial response surface method (PRSM). The PRSM utilized an objective function that maximized both oil recovery and CO 2 storage. Experimental design was used to link uncertain parameters to the objective function. Control variables considered in this study included: water alternating gas cycle and ratio, production rates and bottom-hole pressure of injectors and producers. Other key parameters considered in the modeling process were CO 2 purchase, gas recycle and addition of infill wells and/or patterns. The PRSM proxy model was ‘trained’ or calibrated with a series of training simulations. This involved an iterative process until the surrogate model reached a specific validation criterion. A sensitivity analysis was first conducted to ascertain which of these control variables to retain in the surrogate model. A genetic algorithm with a mixed-integer capability optimization approach was employed to determine the optimum developmental strategy to maximize both oil recovery and CO 2 storage. The proxy model reduced the computational cost significantly. The validation criteria of the reduced order model ensured accuracy in the dynamic modeling results. The prediction outcome suggested robustness and reliability of the genetic algorithm for optimizing both oil recovery and CO 2 storage. The reservoir modeling approach used in this study illustrates an improved approach to optimizing oil production and CO 2 storage within partially depleted oil reservoirs such as FWU. Lastly, this study may serve as a benchmark for potential CO 2–EOR projects in the Anadarko basin and/or geologically similar basins throughout the world.« less

Optimal two-stage enrichment design correcting for biomarker misclassification.

PubMed

Zang, Yong; Guo, Beibei

2018-01-01

The enrichment design is an important clinical trial design to detect the treatment effect of the molecularly targeted agent (MTA) in personalized medicine. Under this design, patients are stratified into marker-positive and marker-negative subgroups based on their biomarker statuses and only the marker-positive patients are enrolled into the trial and randomized to receive either the MTA or a standard treatment. As the biomarker plays a key role in determining the enrollment of the trial, a misclassification of the biomarker can induce substantial bias, undermine the integrity of the trial, and seriously affect the treatment evaluation. In this paper, we propose a two-stage optimal enrichment design that utilizes the surrogate marker to correct for the biomarker misclassification. The proposed design is optimal in the sense that it maximizes the probability of correctly classifying each patient's biomarker status based on the surrogate marker information. In addition, after analytically deriving the bias caused by the biomarker misclassification, we develop a likelihood ratio test based on the EM algorithm to correct for such bias. We conduct comprehensive simulation studies to investigate the operating characteristics of the optimal design and the results confirm the desirable performance of the proposed design.

The use of surrogates for an optimal management of coupled groundwater-agriculture hydrosystems

NASA Astrophysics Data System (ADS)

Grundmann, J.; Schütze, N.; Brettschneider, M.; Schmitz, G. H.; Lennartz, F.

2012-04-01

For ensuring an optimal sustainable water resources management in arid coastal environments, we develop a new simulation based integrated water management system. It aims at achieving best possible solutions for groundwater withdrawals for agricultural and municipal water use including saline water management together with a substantial increase of the water use efficiency in irrigated agriculture. To achieve a robust and fast operation of the management system regarding water quality and water quantity we develop appropriate surrogate models by combining physically based process modelling with methods of artificial intelligence. Thereby we use an artificial neural network for modelling the aquifer response, inclusive the seawater interface, which was trained on a scenario database generated by a numerical density depended groundwater flow model. For simulating the behaviour of high productive agricultural farms crop water production functions are generated by means of soil-vegetation-atmosphere-transport (SVAT)-models, adapted to the regional climate conditions, and a novel evolutionary optimisation algorithm for optimal irrigation scheduling and control. We apply both surrogates exemplarily within a simulation based optimisation environment using the characteristics of the south Batinah region in the Sultanate of Oman which is affected by saltwater intrusion into the coastal aquifer due to excessive groundwater withdrawal for irrigated agriculture. We demonstrate the effectiveness of our methodology for the evaluation and optimisation of different irrigation practices, cropping pattern and resulting abstraction scenarios. Due to contradicting objectives like profit-oriented agriculture vs. aquifer sustainability a multi-criterial optimisation is performed.

Applications of New Surrogate Global Optimization Algorithms including Efficient Synchronous and Asynchronous Parallelism for Calibration of Expensive Nonlinear Geophysical Simulation Models.

NASA Astrophysics Data System (ADS)

Shoemaker, C. A.; Pang, M.; Akhtar, T.; Bindel, D.

2016-12-01

New parallel surrogate global optimization algorithms are developed and applied to objective functions that are expensive simulations (possibly with multiple local minima). The algorithms can be applied to most geophysical simulations, including those with nonlinear partial differential equations. The optimization does not require simulations be parallelized. Asynchronous (and synchronous) parallel execution is available in the optimization toolbox "pySOT". The parallel algorithms are modified from serial to eliminate fine grained parallelism. The optimization is computed with open source software pySOT, a Surrogate Global Optimization Toolbox that allows user to pick the type of surrogate (or ensembles), the search procedure on surrogate, and the type of parallelism (synchronous or asynchronous). pySOT also allows the user to develop new algorithms by modifying parts of the code. In the applications here, the objective function takes up to 30 minutes for one simulation, and serial optimization can take over 200 hours. Results from Yellowstone (NSF) and NCSS (Singapore) supercomputers are given for groundwater contaminant hydrology simulations with applications to model parameter estimation and decontamination management. All results are compared with alternatives. The first results are for optimization of pumping at many wells to reduce cost for decontamination of groundwater at a superfund site. The optimization runs with up to 128 processors. Superlinear speed up is obtained for up to 16 processors, and efficiency with 64 processors is over 80%. Each evaluation of the objective function requires the solution of nonlinear partial differential equations to describe the impact of spatially distributed pumping and model parameters on model predictions for the spatial and temporal distribution of groundwater contaminants. The second application uses an asynchronous parallel global optimization for groundwater quality model calibration. The time for a single objective function evaluation varies unpredictably, so efficiency is improved with asynchronous parallel calculations to improve load balancing. The third application (done at NCSS) incorporates new global surrogate multi-objective parallel search algorithms into pySOT and applies it to a large watershed calibration problem.

Constrained optimization by radial basis function interpolation for high-dimensional expensive black-box problems with infeasible initial points

NASA Astrophysics Data System (ADS)

Regis, Rommel G.

2014-02-01

This article develops two new algorithms for constrained expensive black-box optimization that use radial basis function surrogates for the objective and constraint functions. These algorithms are called COBRA and Extended ConstrLMSRBF and, unlike previous surrogate-based approaches, they can be used for high-dimensional problems where all initial points are infeasible. They both follow a two-phase approach where the first phase finds a feasible point while the second phase improves this feasible point. COBRA and Extended ConstrLMSRBF are compared with alternative methods on 20 test problems and on the MOPTA08 benchmark automotive problem (D.R. Jones, Presented at MOPTA 2008), which has 124 decision variables and 68 black-box inequality constraints. The alternatives include a sequential penalty derivative-free algorithm, a direct search method with kriging surrogates, and two multistart methods. Numerical results show that COBRA algorithms are competitive with Extended ConstrLMSRBF and they generally outperform the alternatives on the MOPTA08 problem and most of the test problems.

Fire performance in traditional silvicultural and fire and fire surrogate treatments in Sierran mixed-conifer forests: a brief summary

Treesearch

Jason J. Moghaddas; Scott L. Stephens

2007-01-01

Mixed conifer forests cover 7.9 million acres of California’s total land base. Forest structure in these forests has been influenced by harvest practices and silvicultural systems implemented since the beginning of the California Gold Rush in 1849. Today, the role of fire in coniferous forests, both in shaping past stand structure and its ability to shape future...

Optimal Reference Strain Structure for Studying Dynamic Responses of Flexible Rockets

NASA Technical Reports Server (NTRS)

Tsushima, Natsuki; Su, Weihua; Wolf, Michael G.; Griffin, Edwin D.; Dumoulin, Marie P.

2017-01-01

In the proposed paper, the optimal design of reference strain structures (RSS) will be performed targeting for the accurate observation of the dynamic bending and torsion deformation of a flexible rocket. It will provide the detailed description of the finite-element (FE) model of a notional flexible rocket created in MSC.Patran. The RSS will be attached longitudinally along the side of the rocket and to track the deformation of the thin-walled structure under external loads. An integrated surrogate-based multi-objective optimization approach will be developed to find the optimal design of the RSS using the FE model. The Kriging method will be used to construct the surrogate model. For the data sampling and the performance evaluation, static/transient analyses will be performed with MSC.Natran/Patran. The multi-objective optimization will be solved with NSGA-II to minimize the difference between the strains of the launch vehicle and RSS. Finally, the performance of the optimal RSS will be evaluated by checking its strain-tracking capability in different numerical simulations of the flexible rocket.

Fire and fire surrogate treatments in mixed-oak forests: Effects on herbaceous layer vegetation

Treesearch

Ross Phillips; Todd Hutchinson; Lucy Brudnak; Thomas Waldrop

2007-01-01

Herbaceous layer vegetation responses to prescribed fire and fire surrogate treatments (thinning and understory removal) were examined. Results from 3 to 4 years following treatment are presented for the Ohio Hills Country and the Southern Appalachian Mountain sites of the National Fire and Fire Surrogate Study. At the Ohio Hills site, changes in forest structure were...

Relationships between perceptual attributes and rheology in over-the-counter vaginal products: a potential tool for microbicide development.

PubMed

Mahan, Ellen D; Zaveri, Toral; Ziegler, Gregory R; Hayes, John E

2014-01-01

Vaginal microbicides are believed to have substantial potential to empower women to protect themselves from HIV, although clinical trials to date have had mixed results at best. Issues with patient adherence in these trials suggest additional emphasis should be placed on optimizing acceptability. Acceptability is driven, in part, by the sensory properties of the microbicide, so better understanding of the relationships between sensory properties and the physical and rheological properties of microbicides should facilitate the simultaneous optimization of sensory properties in parallel with the biophysical properties required for drug deployment. Recently, we have applied standard methods to assess the potential acceptability of microbicide prototypes ex vivo and to quantify the sensory properties of microbicide surrogates. Here, we link quantitative perceptual data to the rheological properties of 6 over-the counter (OTC) vaginal products used as ex vivo microbicide surrogates. Shear-thinning behavior (n) and tan δ (10 rad/s) showed no relationship with any perceptual attributes while shear storage modulus, G' (10 rad/s) was correlated with some attributes, but did not appear to be a strong predictor of sensory properties. Conversely, the storage loss modulus, G" (10 rad/s) and the consistency coefficient, K, were correlated with several sensory attributes: stickiness, rubberiness, and uniform thickness for G'' and stickiness, rubberiness, and peaking for K. Although these relationships merit confirmation in later studies, this pilot study suggests rheological principles can be used to understand the sensory properties evoked by microbicide surrogates assessed ex vivo. Additional work is needed to determine if these findings would apply for microbicides in vivo.

An integrated simulation and optimization approach for managing human health risks of atmospheric pollutants by coal-fired power plants.

PubMed

Dai, C; Cai, X H; Cai, Y P; Guo, H C; Sun, W; Tan, Q; Huang, G H

2014-06-01

This research developed a simulation-aided nonlinear programming model (SNPM). This model incorporated the consideration of pollutant dispersion modeling, and the management of coal blending and the related human health risks within a general modeling framework In SNPM, the simulation effort (i.e., California puff [CALPUFF]) was used to forecast the fate of air pollutants for quantifying the health risk under various conditions, while the optimization studies were to identify the optimal coal blending strategies from a number of alternatives. To solve the model, a surrogate-based indirect search approach was proposed, where the support vector regression (SVR) was used to create a set of easy-to-use and rapid-response surrogates for identifying the function relationships between coal-blending operating conditions and health risks. Through replacing the CALPUFF and the corresponding hazard quotient equation with the surrogates, the computation efficiency could be improved. The developed SNPM was applied to minimize the human health risk associated with air pollutants discharged from Gaojing and Shijingshan power plants in the west of Beijing. Solution results indicated that it could be used for reducing the health risk of the public in the vicinity of the two power plants, identifying desired coal blending strategies for decision makers, and considering a proper balance between coal purchase cost and human health risk. A simulation-aided nonlinear programming model (SNPM) is developed. It integrates the advantages of CALPUFF and nonlinear programming model. To solve the model, a surrogate-based indirect search approach based on the combination of support vector regression and genetic algorithm is proposed. SNPM is applied to reduce the health risk caused by air pollutants discharged from Gaojing and Shijingshan power plants in the west of Beijing. Solution results indicate that it is useful for generating coal blending schemes, reducing the health risk of the public, reflecting the trade-offbetween coal purchase cost and health risk.

A Poisson approach to the validation of failure time surrogate endpoints in individual patient data meta-analyses.

PubMed

Rotolo, Federico; Paoletti, Xavier; Burzykowski, Tomasz; Buyse, Marc; Michiels, Stefan

2017-01-01

Surrogate endpoints are often used in clinical trials instead of well-established hard endpoints for practical convenience. The meta-analytic approach relies on two measures of surrogacy: one at the individual level and one at the trial level. In the survival data setting, a two-step model based on copulas is commonly used. We present a new approach which employs a bivariate survival model with an individual random effect shared between the two endpoints and correlated treatment-by-trial interactions. We fit this model using auxiliary mixed Poisson models. We study via simulations the operating characteristics of this mixed Poisson approach as compared to the two-step copula approach. We illustrate the application of the methods on two individual patient data meta-analyses in gastric cancer, in the advanced setting (4069 patients from 20 randomized trials) and in the adjuvant setting (3288 patients from 14 randomized trials).

Multi-fidelity Gaussian process regression for prediction of random fields

DOE Office of Scientific and Technical Information (OSTI.GOV)

Parussini, L.; Venturi, D., E-mail: venturi@ucsc.edu; Perdikaris, P.

We propose a new multi-fidelity Gaussian process regression (GPR) approach for prediction of random fields based on observations of surrogate models or hierarchies of surrogate models. Our method builds upon recent work on recursive Bayesian techniques, in particular recursive co-kriging, and extends it to vector-valued fields and various types of covariances, including separable and non-separable ones. The framework we propose is general and can be used to perform uncertainty propagation and quantification in model-based simulations, multi-fidelity data fusion, and surrogate-based optimization. We demonstrate the effectiveness of the proposed recursive GPR techniques through various examples. Specifically, we study the stochastic Burgersmore » equation and the stochastic Oberbeck–Boussinesq equations describing natural convection within a square enclosure. In both cases we find that the standard deviation of the Gaussian predictors as well as the absolute errors relative to benchmark stochastic solutions are very small, suggesting that the proposed multi-fidelity GPR approaches can yield highly accurate results.« less

Design of Aminobenzothiazole Inhibitors of Rho Kinases 1 and 2 by Using Protein Kinase A as a Structure Surrogate.

PubMed

Judge, Russell A; Vasudevan, Anil; Scott, Victoria E; Simler, Gricelda H; Pratt, Steve D; Namovic, Marian T; Putman, C Brent; Aguirre, Ana; Stoll, Vincent S; Mamo, Mulugeta; Swann, Steven I; Cassar, Steven C; Faltynek, Connie R; Kage, Karen L; Boyce-Rustay, Janel M; Hobson, Adrian D

2018-03-16

We describe the design, synthesis, and structure-activity relationships (SARs) of a series of 2-aminobenzothiazole inhibitors of Rho kinases (ROCKs) 1 and 2, which were optimized to low nanomolar potencies by use of protein kinase A (PKA) as a structure surrogate to guide compound design. A subset of these molecules also showed robust activity in a cell-based myosin phosphatase assay and in a mechanical hyperalgesia in vivo pain model. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Using Approximations to Accelerate Engineering Design Optimization

NASA Technical Reports Server (NTRS)

Torczon, Virginia; Trosset, Michael W.

1998-01-01

Optimization problems that arise in engineering design are often characterized by several features that hinder the use of standard nonlinear optimization techniques. Foremost among these features is that the functions used to define the engineering optimization problem often are computationally intensive. Within a standard nonlinear optimization algorithm, the computational expense of evaluating the functions that define the problem would necessarily be incurred for each iteration of the optimization algorithm. Faced with such prohibitive computational costs, an attractive alternative is to make use of surrogates within an optimization context since surrogates can be chosen or constructed so that they are typically much less expensive to compute. For the purposes of this paper, we will focus on the use of algebraic approximations as surrogates for the objective. In this paper we introduce the use of so-called merit functions that explicitly recognize the desirability of improving the current approximation to the objective during the course of the optimization. We define and experiment with the use of merit functions chosen to simultaneously improve both the solution to the optimization problem (the objective) and the quality of the approximation. Our goal is to further improve the effectiveness of our general approach without sacrificing any of its rigor.

Magnetic Resonance Imaging–Guided versus Surrogate-Based Motion Tracking in Liver Radiation Therapy: A Prospective Comparative Study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Paganelli, Chiara, E-mail: chiara.paganelli@polimi.it; Seregni, Matteo; Fattori, Giovanni

Purpose: This study applied automatic feature detection on cine–magnetic resonance imaging (MRI) liver images in order to provide a prospective comparison between MRI-guided and surrogate-based tracking methods for motion-compensated liver radiation therapy. Methods and Materials: In a population of 30 subjects (5 volunteers plus 25 patients), 2 oblique sagittal slices were acquired across the liver at high temporal resolution. An algorithm based on scale invariant feature transform (SIFT) was used to extract and track multiple features throughout the image sequence. The position of abdominal markers was also measured directly from the image series, and the internal motion of each featuremore » was quantified through multiparametric analysis. Surrogate-based tumor tracking with a state-of-the-art external/internal correlation model was simulated. The geometrical tracking error was measured, and its correlation with external motion parameters was also investigated. Finally, the potential gain in tracking accuracy relying on MRI guidance was quantified as a function of the maximum allowed tracking error. Results: An average of 45 features was extracted for each subject across the whole liver. The multi-parametric motion analysis reported relevant inter- and intrasubject variability, highlighting the value of patient-specific and spatially-distributed measurements. Surrogate-based tracking errors (relative to the motion amplitude) were were in the range 7% to 23% (1.02-3.57mm) and were significantly influenced by external motion parameters. The gain of MRI guidance compared to surrogate-based motion tracking was larger than 30% in 50% of the subjects when considering a 1.5-mm tracking error tolerance. Conclusions: Automatic feature detection applied to cine-MRI allows detailed liver motion description to be obtained. Such information was used to quantify the performance of surrogate-based tracking methods and to provide a prospective comparison with respect to MRI-guided radiation therapy, which could support the definition of patient-specific optimal treatment strategies.« less

Relationships between Perceptual Attributes and Rheology in Over-the-Counter Vaginal Products: A Potential Tool for Microbicide Development

PubMed Central

Mahan, Ellen D.; Zaveri, Toral; Ziegler, Gregory R.; Hayes, John E.

2014-01-01

Vaginal microbicides are believed to have substantial potential to empower women to protect themselves from HIV, although clinical trials to date have had mixed results at best. Issues with patient adherence in these trials suggest additional emphasis should be placed on optimizing acceptability. Acceptability is driven, in part, by the sensory properties of the microbicide, so better understanding of the relationships between sensory properties and the physical and rheological properties of microbicides should facilitate the simultaneous optimization of sensory properties in parallel with the biophysical properties required for drug deployment. Recently, we have applied standard methods to assess the potential acceptability of microbicide prototypes ex vivo and to quantify the sensory properties of microbicide surrogates. Here, we link quantitative perceptual data to the rheological properties of 6 over-the counter (OTC) vaginal products used as ex vivo microbicide surrogates. Shear-thinning behavior (n) and tan δ (10 rad/s) showed no relationship with any perceptual attributes while shear storage modulus, G’ (10 rad/s) was correlated with some attributes, but did not appear to be a strong predictor of sensory properties. Conversely, the storage loss modulus, G” (10 rad/s) and the consistency coefficient, K, were correlated with several sensory attributes: stickiness, rubberiness, and uniform thickness for G’’ and stickiness, rubberiness, and peaking for K. Although these relationships merit confirmation in later studies, this pilot study suggests rheological principles can be used to understand the sensory properties evoked by microbicide surrogates assessed ex vivo. Additional work is needed to determine if these findings would apply for microbicides in vivo. PMID:25188244

Analysis of a Multi-Fidelity Surrogate for Handling Real Gas Equations of State

NASA Astrophysics Data System (ADS)

Ouellet, Frederick; Park, Chanyoung; Rollin, Bertrand; Balachandar, S.

2017-06-01

The explosive dispersal of particles is a complex multiphase and multi-species fluid flow problem. In these flows, the detonation products of the explosive must be treated as real gas while the ideal gas equation of state is used for the surrounding air. As the products expand outward from the detonation point, they mix with ambient air and create a mixing region where both state equations must be satisfied. One of the most accurate, yet computationally expensive, methods to handle this problem is an algorithm that iterates between both equations of state until pressure and thermal equilibrium are achieved inside of each computational cell. This work aims to use a multi-fidelity surrogate model to replace this process. A Kriging model is used to produce a curve fit which interpolates selected data from the iterative algorithm using Bayesian statistics. We study the model performance with respect to the iterative method in simulations using a finite volume code. The model's (i) computational speed, (ii) memory requirements and (iii) computational accuracy are analyzed to show the benefits of this novel approach. Also, optimizing the combination of model accuracy and computational speed through the choice of sampling points is explained. This work was supported by the U.S. Department of Energy, National Nuclear Security Administration, Advanced Simulation and Computing Program as a Cooperative Agreement under the Predictive Science Academic Alliance Program under Contract No. DE-NA0002378.

A liquid metal-based structurally embedded vascular antenna: II. Multiobjective and parameterized design exploration

NASA Astrophysics Data System (ADS)

Hartl, D. J.; Frank, G. J.; Malak, R. J.; Baur, J. W.

2017-02-01

Research on the structurally embedded vascular antenna concept leverages past efforts on liquid metal (LM) reconfigurable electronics, microvascular composites, and structurally integrated and reconfigurable antennas. Such a concept has potential for reducing system weight or volume while simultaneously allowing in situ adjustment of resonant frequencies and/or changes in antenna directivity. This work considers a microvascular pattern embedded in a laminated composite and filled with LM. The conductive liquid provides radio frequency (RF) functionality while also allowing self-cooling. Models describing RF propagation and heat transfer, in addition to the structural effects of both the inclusion of channels and changes in temperature, were described in part 1 of this two-part work. In this part 2, the engineering models developed and demonstrated in part 1 toward the initial exploration of design trends are implemented into multiple optimization frameworks for more detailed design studies, one of which being novel and particularly applicable to this class of problem. The computational expense associated with the coupled multiphysical analysis of the structurally embedded LM transmitting antenna motivates the consideration of surrogate-based optimization methods. Both static and adaptive approaches are explored; it is shown that iteratively correcting the surrogate leads to more accurate optimized design predictions. The expected strong dependence of antenna performance on thermal environment motivates the consideration of a novel ‘parameterized’ optimization approach that simultaneously calculates whole families of optimal designs based on changes in design or operational variables generally beyond the control of the designer. The change in Pareto-optimal response with evolution in operating conditions is clearly demonstrated.

Generation of optimal artificial neural networks using a pattern search algorithm: application to approximation of chemical systems.

PubMed

Ihme, Matthias; Marsden, Alison L; Pitsch, Heinz

2008-02-01

A pattern search optimization method is applied to the generation of optimal artificial neural networks (ANNs). Optimization is performed using a mixed variable extension to the generalized pattern search method. This method offers the advantage that categorical variables, such as neural transfer functions and nodal connectivities, can be used as parameters in optimization. When used together with a surrogate, the resulting algorithm is highly efficient for expensive objective functions. Results demonstrate the effectiveness of this method in optimizing an ANN for the number of neurons, the type of transfer function, and the connectivity among neurons. The optimization method is applied to a chemistry approximation of practical relevance. In this application, temperature and a chemical source term are approximated as functions of two independent parameters using optimal ANNs. Comparison of the performance of optimal ANNs with conventional tabulation methods demonstrates equivalent accuracy by considerable savings in memory storage. The architecture of the optimal ANN for the approximation of the chemical source term consists of a fully connected feedforward network having four nonlinear hidden layers and 117 synaptic weights. An equivalent representation of the chemical source term using tabulation techniques would require a 500 x 500 grid point discretization of the parameter space.

A review on design of experiments and surrogate models in aircraft real-time and many-query aerodynamic analyses

NASA Astrophysics Data System (ADS)

Yondo, Raul; Andrés, Esther; Valero, Eusebio

2018-01-01

Full scale aerodynamic wind tunnel testing, numerical simulation of high dimensional (full-order) aerodynamic models or flight testing are some of the fundamental but complex steps in the various design phases of recent civil transport aircrafts. Current aircraft aerodynamic designs have increase in complexity (multidisciplinary, multi-objective or multi-fidelity) and need to address the challenges posed by the nonlinearity of the objective functions and constraints, uncertainty quantification in aerodynamic problems or the restrained computational budgets. With the aim to reduce the computational burden and generate low-cost but accurate models that mimic those full order models at different values of the design variables, Recent progresses have witnessed the introduction, in real-time and many-query analyses, of surrogate-based approaches as rapid and cheaper to simulate models. In this paper, a comprehensive and state-of-the art survey on common surrogate modeling techniques and surrogate-based optimization methods is given, with an emphasis on models selection and validation, dimensionality reduction, sensitivity analyses, constraints handling or infill and stopping criteria. Benefits, drawbacks and comparative discussions in applying those methods are described. Furthermore, the paper familiarizes the readers with surrogate models that have been successfully applied to the general field of fluid dynamics, but not yet in the aerospace industry. Additionally, the review revisits the most popular sampling strategies used in conducting physical and simulation-based experiments in aircraft aerodynamic design. Attractive or smart designs infrequently used in the field and discussions on advanced sampling methodologies are presented, to give a glance on the various efficient possibilities to a priori sample the parameter space. Closing remarks foster on future perspectives, challenges and shortcomings associated with the use of surrogate models by aircraft industrial aerodynamicists, despite their increased interest among the research communities.

Reduced cost mission design using surrogate models

NASA Astrophysics Data System (ADS)

Feldhacker, Juliana D.; Jones, Brandon A.; Doostan, Alireza; Hampton, Jerrad

2016-01-01

This paper uses surrogate models to reduce the computational cost associated with spacecraft mission design in three-body dynamical systems. Sampling-based least squares regression is used to project the system response onto a set of orthogonal bases, providing a representation of the ΔV required for rendezvous as a reduced-order surrogate model. Models are presented for mid-field rendezvous of spacecraft in orbits in the Earth-Moon circular restricted three-body problem, including a halo orbit about the Earth-Moon L2 libration point (EML-2) and a distant retrograde orbit (DRO) about the Moon. In each case, the initial position of the spacecraft, the time of flight, and the separation between the chaser and the target vehicles are all considered as design inputs. The results show that sample sizes on the order of 102 are sufficient to produce accurate surrogates, with RMS errors reaching 0.2 m/s for the halo orbit and falling below 0.01 m/s for the DRO. A single function call to the resulting surrogate is up to two orders of magnitude faster than computing the same solution using full fidelity propagators. The expansion coefficients solved for in the surrogates are then used to conduct a global sensitivity analysis of the ΔV on each of the input parameters, which identifies the separation between the spacecraft as the primary contributor to the ΔV cost. Finally, the models are demonstrated to be useful for cheap evaluation of the cost function in constrained optimization problems seeking to minimize the ΔV required for rendezvous. These surrogate models show significant advantages for mission design in three-body systems, in terms of both computational cost and capabilities, over traditional Monte Carlo methods.

Deterministic and reliability based optimization of integrated thermal protection system composite panel using adaptive sampling techniques

NASA Astrophysics Data System (ADS)

Ravishankar, Bharani

Conventional space vehicles have thermal protection systems (TPS) that provide protection to an underlying structure that carries the flight loads. In an attempt to save weight, there is interest in an integrated TPS (ITPS) that combines the structural function and the TPS function. This has weight saving potential, but complicates the design of the ITPS that now has both thermal and structural failure modes. The main objectives of this dissertation was to optimally design the ITPS subjected to thermal and mechanical loads through deterministic and reliability based optimization. The optimization of the ITPS structure requires computationally expensive finite element analyses of 3D ITPS (solid) model. To reduce the computational expenses involved in the structural analysis, finite element based homogenization method was employed, homogenizing the 3D ITPS model to a 2D orthotropic plate. However it was found that homogenization was applicable only for panels that are much larger than the characteristic dimensions of the repeating unit cell in the ITPS panel. Hence a single unit cell was used for the optimization process to reduce the computational cost. Deterministic and probabilistic optimization of the ITPS panel required evaluation of failure constraints at various design points. This further demands computationally expensive finite element analyses which was replaced by efficient, low fidelity surrogate models. In an optimization process, it is important to represent the constraints accurately to find the optimum design. Instead of building global surrogate models using large number of designs, the computational resources were directed towards target regions near constraint boundaries for accurate representation of constraints using adaptive sampling strategies. Efficient Global Reliability Analyses (EGRA) facilitates sequentially sampling of design points around the region of interest in the design space. EGRA was applied to the response surface construction of the failure constraints in the deterministic and reliability based optimization of the ITPS panel. It was shown that using adaptive sampling, the number of designs required to find the optimum were reduced drastically, while improving the accuracy. System reliability of ITPS was estimated using Monte Carlo Simulation (MCS) based method. Separable Monte Carlo method was employed that allowed separable sampling of the random variables to predict the probability of failure accurately. The reliability analysis considered uncertainties in the geometry, material properties, loading conditions of the panel and error in finite element modeling. These uncertainties further increased the computational cost of MCS techniques which was also reduced by employing surrogate models. In order to estimate the error in the probability of failure estimate, bootstrapping method was applied. This research work thus demonstrates optimization of the ITPS composite panel with multiple failure modes and large number of uncertainties using adaptive sampling techniques.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kadoura, Ahmad, E-mail: ahmad.kadoura@kaust.edu.sa, E-mail: adil.siripatana@kaust.edu.sa, E-mail: shuyu.sun@kaust.edu.sa, E-mail: omar.knio@kaust.edu.sa; Sun, Shuyu, E-mail: ahmad.kadoura@kaust.edu.sa, E-mail: adil.siripatana@kaust.edu.sa, E-mail: shuyu.sun@kaust.edu.sa, E-mail: omar.knio@kaust.edu.sa; Siripatana, Adil, E-mail: ahmad.kadoura@kaust.edu.sa, E-mail: adil.siripatana@kaust.edu.sa, E-mail: shuyu.sun@kaust.edu.sa, E-mail: omar.knio@kaust.edu.sa

In this work, two Polynomial Chaos (PC) surrogates were generated to reproduce Monte Carlo (MC) molecular simulation results of the canonical (single-phase) and the NVT-Gibbs (two-phase) ensembles for a system of normalized structureless Lennard-Jones (LJ) particles. The main advantage of such surrogates, once generated, is the capability of accurately computing the needed thermodynamic quantities in a few seconds, thus efficiently replacing the computationally expensive MC molecular simulations. Benefiting from the tremendous computational time reduction, the PC surrogates were used to conduct large-scale optimization in order to propose single-site LJ models for several simple molecules. Experimental data, a set of supercriticalmore » isotherms, and part of the two-phase envelope, of several pure components were used for tuning the LJ parameters (ε, σ). Based on the conducted optimization, excellent fit was obtained for different noble gases (Ar, Kr, and Xe) and other small molecules (CH{sub 4}, N{sub 2}, and CO). On the other hand, due to the simplicity of the LJ model used, dramatic deviations between simulation and experimental data were observed, especially in the two-phase region, for more complex molecules such as CO{sub 2} and C{sub 2} H{sub 6}.« less

A Rigorous Framework for Optimization of Expensive Functions by Surrogates

NASA Technical Reports Server (NTRS)

Booker, Andrew J.; Dennis, J. E., Jr.; Frank, Paul D.; Serafini, David B.; Torczon, Virginia; Trosset, Michael W.

1998-01-01

The goal of the research reported here is to develop rigorous optimization algorithms to apply to some engineering design problems for which design application of traditional optimization approaches is not practical. This paper presents and analyzes a framework for generating a sequence of approximations to the objective function and managing the use of these approximations as surrogates for optimization. The result is to obtain convergence to a minimizer of an expensive objective function subject to simple constraints. The approach is widely applicable because it does not require, or even explicitly approximate, derivatives of the objective. Numerical results are presented for a 31-variable helicopter rotor blade design example and for a standard optimization test example.

An Empirical Assessment and Comparison of Species-Based and Habitat-Based Surrogates: A Case Study of Forest Vertebrates and Large Old Trees

PubMed Central

Lindenmayer, David B.; Barton, Philip S.; Lane, Peter W.; Westgate, Martin J.; McBurney, Lachlan; Blair, David; Gibbons, Philip; Likens, Gene E.

2014-01-01

A holy grail of conservation is to find simple but reliable measures of environmental change to guide management. For example, particular species or particular habitat attributes are often used as proxies for the abundance or diversity of a subset of other taxa. However, the efficacy of such kinds of species-based surrogates and habitat-based surrogates is rarely assessed, nor are different kinds of surrogates compared in terms of their relative effectiveness. We use 30-year datasets on arboreal marsupials and vegetation structure to quantify the effectiveness of: (1) the abundance of a particular species of arboreal marsupial as a species-based surrogate for other arboreal marsupial taxa, (2) hollow-bearing tree abundance as a habitat-based surrogate for arboreal marsupial abundance, and (3) a combination of species- and habitat-based surrogates. We also quantify the robustness of species-based and habitat-based surrogates over time. We then use the same approach to model overall species richness of arboreal marsupials. We show that a species-based surrogate can appear to be a valid surrogate until a habitat-based surrogate is co-examined, after which the effectiveness of the former is lost. The addition of a species-based surrogate to a habitat-based surrogate made little difference in explaining arboreal marsupial abundance, but altered the co-occurrence relationship between species. Hence, there was limited value in simultaneously using a combination of kinds of surrogates. The habitat-based surrogate also generally performed significantly better and was easier and less costly to gather than the species-based surrogate. We found that over 30 years of study, the relationships which underpinned the habitat-based surrogate generally remained positive but variable over time. Our work highlights why it is important to compare the effectiveness of different broad classes of surrogates and identify situations when either species- or habitat-based surrogates are likely to be superior. PMID:24587050

An empirical assessment and comparison of species-based and habitat-based surrogates: a case study of forest vertebrates and large old trees.

PubMed

Lindenmayer, David B; Barton, Philip S; Lane, Peter W; Westgate, Martin J; McBurney, Lachlan; Blair, David; Gibbons, Philip; Likens, Gene E

2014-01-01

A holy grail of conservation is to find simple but reliable measures of environmental change to guide management. For example, particular species or particular habitat attributes are often used as proxies for the abundance or diversity of a subset of other taxa. However, the efficacy of such kinds of species-based surrogates and habitat-based surrogates is rarely assessed, nor are different kinds of surrogates compared in terms of their relative effectiveness. We use 30-year datasets on arboreal marsupials and vegetation structure to quantify the effectiveness of: (1) the abundance of a particular species of arboreal marsupial as a species-based surrogate for other arboreal marsupial taxa, (2) hollow-bearing tree abundance as a habitat-based surrogate for arboreal marsupial abundance, and (3) a combination of species- and habitat-based surrogates. We also quantify the robustness of species-based and habitat-based surrogates over time. We then use the same approach to model overall species richness of arboreal marsupials. We show that a species-based surrogate can appear to be a valid surrogate until a habitat-based surrogate is co-examined, after which the effectiveness of the former is lost. The addition of a species-based surrogate to a habitat-based surrogate made little difference in explaining arboreal marsupial abundance, but altered the co-occurrence relationship between species. Hence, there was limited value in simultaneously using a combination of kinds of surrogates. The habitat-based surrogate also generally performed significantly better and was easier and less costly to gather than the species-based surrogate. We found that over 30 years of study, the relationships which underpinned the habitat-based surrogate generally remained positive but variable over time. Our work highlights why it is important to compare the effectiveness of different broad classes of surrogates and identify situations when either species- or habitat-based surrogates are likely to be superior.

Suborbital spaceplane optimization using non-stationary Gaussian processes

NASA Astrophysics Data System (ADS)

Dufour, Robin; de Muelenaere, Julien; Elham, Ali

2014-10-01

This paper presents multidisciplinary design optimization of a sub-orbital spaceplane. The optimization includes three disciplines: the aerodynamics, the structure and the trajectory. An Adjoint Euler code is used to calculate the aerodynamic lift and drag of the vehicle as well as their derivatives with respect to the design variables. A new surrogate model has been developed based on a non-stationary Gaussian process. That model was used to estimate the aerodynamic characteristics of the vehicle during the trajectory optimization. The trajectory of thevehicle has been optimized together with its geometry in order to maximize the amount of payload that can be carried by the spaceplane.

Accelerated iterative beam angle selection in IMRT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bangert, Mark, E-mail: m.bangert@dkfz.de; Unkelbach, Jan

2016-03-15

Purpose: Iterative methods for beam angle selection (BAS) for intensity-modulated radiation therapy (IMRT) planning sequentially construct a beneficial ensemble of beam directions. In a naïve implementation, the nth beam is selected by adding beam orientations one-by-one from a discrete set of candidates to an existing ensemble of (n − 1) beams. The best beam orientation is identified in a time consuming process by solving the fluence map optimization (FMO) problem for every candidate beam and selecting the beam that yields the largest improvement to the objective function value. This paper evaluates two alternative methods to accelerate iterative BAS based onmore » surrogates for the FMO objective function value. Methods: We suggest to select candidate beams not based on the FMO objective function value after convergence but (1) based on the objective function value after five FMO iterations of a gradient based algorithm and (2) based on a projected gradient of the FMO problem in the first iteration. The performance of the objective function surrogates is evaluated based on the resulting objective function values and dose statistics in a treatment planning study comprising three intracranial, three pancreas, and three prostate cases. Furthermore, iterative BAS is evaluated for an application in which a small number of noncoplanar beams complement a set of coplanar beam orientations. This scenario is of practical interest as noncoplanar setups may require additional attention of the treatment personnel for every couch rotation. Results: Iterative BAS relying on objective function surrogates yields similar results compared to naïve BAS with regard to the objective function values and dose statistics. At the same time, early stopping of the FMO and using the projected gradient during the first iteration enable reductions in computation time by approximately one to two orders of magnitude. With regard to the clinical delivery of noncoplanar IMRT treatments, we could show that optimized beam ensembles using only a few noncoplanar beam orientations often approach the plan quality of fully noncoplanar ensembles. Conclusions: We conclude that iterative BAS in combination with objective function surrogates can be a viable option to implement automated BAS at clinically acceptable computation times.« less

Accelerated iterative beam angle selection in IMRT.

PubMed

Bangert, Mark; Unkelbach, Jan

2016-03-01

Iterative methods for beam angle selection (BAS) for intensity-modulated radiation therapy (IMRT) planning sequentially construct a beneficial ensemble of beam directions. In a naïve implementation, the nth beam is selected by adding beam orientations one-by-one from a discrete set of candidates to an existing ensemble of (n - 1) beams. The best beam orientation is identified in a time consuming process by solving the fluence map optimization (FMO) problem for every candidate beam and selecting the beam that yields the largest improvement to the objective function value. This paper evaluates two alternative methods to accelerate iterative BAS based on surrogates for the FMO objective function value. We suggest to select candidate beams not based on the FMO objective function value after convergence but (1) based on the objective function value after five FMO iterations of a gradient based algorithm and (2) based on a projected gradient of the FMO problem in the first iteration. The performance of the objective function surrogates is evaluated based on the resulting objective function values and dose statistics in a treatment planning study comprising three intracranial, three pancreas, and three prostate cases. Furthermore, iterative BAS is evaluated for an application in which a small number of noncoplanar beams complement a set of coplanar beam orientations. This scenario is of practical interest as noncoplanar setups may require additional attention of the treatment personnel for every couch rotation. Iterative BAS relying on objective function surrogates yields similar results compared to naïve BAS with regard to the objective function values and dose statistics. At the same time, early stopping of the FMO and using the projected gradient during the first iteration enable reductions in computation time by approximately one to two orders of magnitude. With regard to the clinical delivery of noncoplanar IMRT treatments, we could show that optimized beam ensembles using only a few noncoplanar beam orientations often approach the plan quality of fully noncoplanar ensembles. We conclude that iterative BAS in combination with objective function surrogates can be a viable option to implement automated BAS at clinically acceptable computation times.

Gradient-based optimization with B-splines on sparse grids for solving forward-dynamics simulations of three-dimensional, continuum-mechanical musculoskeletal system models.

PubMed

Valentin, J; Sprenger, M; Pflüger, D; Röhrle, O

2018-05-01

Investigating the interplay between muscular activity and motion is the basis to improve our understanding of healthy or diseased musculoskeletal systems. To be able to analyze the musculoskeletal systems, computational models are used. Albeit some severe modeling assumptions, almost all existing musculoskeletal system simulations appeal to multibody simulation frameworks. Although continuum-mechanical musculoskeletal system models can compensate for some of these limitations, they are essentially not considered because of their computational complexity and cost. The proposed framework is the first activation-driven musculoskeletal system model, in which the exerted skeletal muscle forces are computed using 3-dimensional, continuum-mechanical skeletal muscle models and in which muscle activations are determined based on a constraint optimization problem. Numerical feasibility is achieved by computing sparse grid surrogates with hierarchical B-splines, and adaptive sparse grid refinement further reduces the computational effort. The choice of B-splines allows the use of all existing gradient-based optimization techniques without further numerical approximation. This paper demonstrates that the resulting surrogates have low relative errors (less than 0.76%) and can be used within forward simulations that are subject to constraint optimization. To demonstrate this, we set up several different test scenarios in which an upper limb model consisting of the elbow joint, the biceps and triceps brachii, and an external load is subjected to different optimization criteria. Even though this novel method has only been demonstrated for a 2-muscle system, it can easily be extended to musculoskeletal systems with 3 or more muscles. Copyright © 2018 John Wiley & Sons, Ltd.

A framework for optimization and quantification of uncertainty and sensitivity for developing carbon capture systems

DOE PAGES

Eslick, John C.; Ng, Brenda; Gao, Qianwen; ...

2014-12-31

Under the auspices of the U.S. Department of Energy’s Carbon Capture Simulation Initiative (CCSI), a Framework for Optimization and Quantification of Uncertainty and Sensitivity (FOQUS) has been developed. This tool enables carbon capture systems to be rapidly synthesized and rigorously optimized, in an environment that accounts for and propagates uncertainties in parameters and models. FOQUS currently enables (1) the development of surrogate algebraic models utilizing the ALAMO algorithm, which can be used for superstructure optimization to identify optimal process configurations, (2) simulation-based optimization utilizing derivative free optimization (DFO) algorithms with detailed black-box process models, and (3) rigorous uncertainty quantification throughmore » PSUADE. FOQUS utilizes another CCSI technology, the Turbine Science Gateway, to manage the thousands of simulated runs necessary for optimization and UQ. Thus, this computational framework has been demonstrated for the design and analysis of a solid sorbent based carbon capture system.« less

Simulation and optimization of pressure swing adsorption systmes using reduced-order modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Agarwal, A.; Biegler, L.; Zitney, S.

2009-01-01

Over the past three decades, pressure swing adsorption (PSA) processes have been widely used as energyefficient gas separation techniques, especially for high purity hydrogen purification from refinery gases. Models for PSA processes are multiple instances of partial differential equations (PDEs) in time and space with periodic boundary conditions that link the processing steps together. The solution of this coupled stiff PDE system is governed by steep fronts moving with time. As a result, the optimization of such systems represents a significant computational challenge to current differential algebraic equation (DAE) optimization techniques and nonlinear programming algorithms. Model reduction is one approachmore » to generate cost-efficient low-order models which can be used as surrogate models in the optimization problems. This study develops a reducedorder model (ROM) based on proper orthogonal decomposition (POD), which is a low-dimensional approximation to a dynamic PDE-based model. The proposed method leads to a DAE system of significantly lower order, thus replacing the one obtained from spatial discretization and making the optimization problem computationally efficient. The method has been applied to the dynamic coupled PDE-based model of a twobed four-step PSA process for separation of hydrogen from methane. Separate ROMs have been developed for each operating step with different POD modes for each of them. A significant reduction in the order of the number of states has been achieved. The reduced-order model has been successfully used to maximize hydrogen recovery by manipulating operating pressures, step times and feed and regeneration velocities, while meeting product purity and tight bounds on these parameters. Current results indicate the proposed ROM methodology as a promising surrogate modeling technique for cost-effective optimization purposes.« less

On global optimization using an estimate of Lipschitz constant and simplicial partition

NASA Astrophysics Data System (ADS)

Gimbutas, Albertas; Žilinskas, Antanas

2016-10-01

A new algorithm is proposed for finding the global minimum of a multi-variate black-box Lipschitz function with an unknown Lipschitz constant. The feasible region is initially partitioned into simplices; in the subsequent iteration, the most suitable simplices are selected and bisected via the middle point of the longest edge. The suitability of a simplex for bisection is evaluated by minimizing of a surrogate function which mimics the lower bound for the considered objective function over that simplex. The surrogate function is defined using an estimate of the Lipschitz constant and the objective function values at the vertices of a simplex. The novelty of the algorithm is the sophisticated method of estimating the Lipschitz constant, and the appropriate method to minimize the surrogate function. The proposed algorithm was tested using 600 random test problems of different complexity, showing competitive results with two popular advanced algorithms which are based on similar assumptions.

Optimizing Parameters of Axial Pressure-Compounded Ultra-Low Power Impulse Turbines at Preliminary Design

NASA Astrophysics Data System (ADS)

Kalabukhov, D. S.; Radko, V. M.; Grigoriev, V. A.

2018-01-01

Ultra-low power turbine drives are used as energy sources in auxiliary power systems, energy units, terrestrial, marine, air and space transport within the confines of shaft power N td = 0.01…10 kW. In this paper we propose a new approach to the development of surrogate models for evaluating the integrated efficiency of multistage ultra-low power impulse turbine with pressure stages. This method is based on the use of existing mathematical models of ultra-low power turbine stage efficiency and mass. It has been used in a method for selecting the rational parameters of two-stage axial ultra-low power turbine. The article describes the basic features of an algorithm for two-stage turbine parameters optimization and for efficiency criteria evaluating. Pledged mathematical models are intended for use at the preliminary design of turbine drive. The optimization method was tested at preliminary design of an air starter turbine. Validation was carried out by comparing the results of optimization calculations and numerical gas-dynamic simulation in the Ansys CFX package. The results indicate a sufficient accuracy of used surrogate models for axial two-stage turbine parameters selection

Surrogate decision making: do we have to trade off accuracy and procedural satisfaction?

PubMed

Frey, Renato; Hertwig, Ralph; Herzog, Stefan M

2014-02-01

Making surrogate decisions on behalf of incapacitated patients can raise difficult questions for relatives, physicians, and society. Previous research has focused on the accuracy of surrogate decisions (i.e., the proportion of correctly inferred preferences). Less attention has been paid to the procedural satisfaction that patients' surrogates and patients attribute to specific approaches to making surrogate decisions. The objective was to investigate hypothetical patients' and surrogates' procedural satisfaction with specific approaches to making surrogate decisions and whether implementing these preferences would lead to tradeoffs between procedural satisfaction and accuracy. Study 1 investigated procedural satisfaction by assigning participants (618 in a mixed-age but relatively young online sample and 50 in an older offline sample) to the roles of hypothetical surrogates or patients. Study 2 (involving 64 real multigenerational families with a total of 253 participants) investigated accuracy using 24 medical scenarios. Hypothetical patients and surrogates had closely aligned preferences: Procedural satisfaction was highest with a patient-designated surrogate, followed by shared surrogate decision-making approaches and legally assigned surrogates. These approaches did not differ substantially in accuracy. Limitations are that participants' preferences regarding existing and novel approaches to making surrogate decisions can only be elicited under hypothetical conditions. Next to decision making by patient-designated surrogates, shared surrogate decision making is the preferred approach among patients and surrogates alike. This approach appears to impose no tradeoff between procedural satisfaction and accuracy. Therefore, shared decision making should be further studied in representative samples of the general population, and if people's preferences prove to be robust, they deserve to be weighted more strongly in legal frameworks in addition to patient-designated surrogates.

GAMBIT: A Parameterless Model-Based Evolutionary Algorithm for Mixed-Integer Problems.

PubMed

Sadowski, Krzysztof L; Thierens, Dirk; Bosman, Peter A N

2018-01-01

Learning and exploiting problem structure is one of the key challenges in optimization. This is especially important for black-box optimization (BBO) where prior structural knowledge of a problem is not available. Existing model-based Evolutionary Algorithms (EAs) are very efficient at learning structure in both the discrete, and in the continuous domain. In this article, discrete and continuous model-building mechanisms are integrated for the Mixed-Integer (MI) domain, comprising discrete and continuous variables. We revisit a recently introduced model-based evolutionary algorithm for the MI domain, the Genetic Algorithm for Model-Based mixed-Integer opTimization (GAMBIT). We extend GAMBIT with a parameterless scheme that allows for practical use of the algorithm without the need to explicitly specify any parameters. We furthermore contrast GAMBIT with other model-based alternatives. The ultimate goal of processing mixed dependences explicitly in GAMBIT is also addressed by introducing a new mechanism for the explicit exploitation of mixed dependences. We find that processing mixed dependences with this novel mechanism allows for more efficient optimization. We further contrast the parameterless GAMBIT with Mixed-Integer Evolution Strategies (MIES) and other state-of-the-art MI optimization algorithms from the General Algebraic Modeling System (GAMS) commercial algorithm suite on problems with and without constraints, and show that GAMBIT is capable of solving problems where variable dependences prevent many algorithms from successfully optimizing them.

Combined Economic and Hydrologic Modeling to Support Collaborative Decision Making Processes

NASA Astrophysics Data System (ADS)

Sheer, D. P.

2008-12-01

For more than a decade, the core concept of the author's efforts in support of collaborative decision making has been a combination of hydrologic simulation and multi-objective optimization. The modeling has generally been used to support collaborative decision making processes. The OASIS model developed by HydroLogics Inc. solves a multi-objective optimization at each time step using a mixed integer linear program (MILP). The MILP can be configured to include any user defined objective, including but not limited too economic objectives. For example, an estimated marginal value for water for crops and M&I use were included in the objective function to drive trades in a model of the lower Rio Grande. The formulation of the MILP, constraints and objectives, in any time step is conditional: it changes based on the value of state variables and dynamic external forcing functions, such as rainfall, hydrology, market prices, arrival of migratory fish, water temperature, etc. It therefore acts as a dynamic short term multi-objective economic optimization for each time step. MILP is capable of solving a general problem that includes a very realistic representation of the physical system characteristics in addition to the normal multi-objective optimization objectives and constraints included in economic models. In all of these models, the short term objective function is a surrogate for achieving long term multi-objective results. The long term performance for any alternative (especially including operating strategies) is evaluated by simulation. An operating rule is the combination of conditions, parameters, constraints and objectives used to determine the formulation of the short term optimization in each time step. Heuristic wrappers for the simulation program have been developed improve the parameters of an operating rule, and are initiating research on a wrapper that will allow us to employ a genetic algorithm to improve the form of the rule (conditions, constraints, and short term objectives) as well. In the models operating rules represent different models of human behavior, and the objective of the modeling is to find rules for human behavior that perform well in terms of long term human objectives. The conceptual model used to represent human behavior incorporates economic multi-objective optimization for surrogate objectives, and rules that set those objectives based on current conditions and accounting for uncertainty, at least implicitly. The author asserts that real world operating rules follow this form and have evolved because they have been perceived as successful in the past. Thus, the modeling efforts focus on human behavior in much the same way that economic models focus on human behavior. This paper illustrates the above concepts with real world examples.

Optimizing 4-Dimensional Magnetic Resonance Imaging Data Sampling for Respiratory Motion Analysis of Pancreatic Tumors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stemkens, Bjorn, E-mail: b.stemkens@umcutrecht.nl; Tijssen, Rob H.N.; Senneville, Baudouin D. de

2015-03-01

Purpose: To determine the optimum sampling strategy for retrospective reconstruction of 4-dimensional (4D) MR data for nonrigid motion characterization of tumor and organs at risk for radiation therapy purposes. Methods and Materials: For optimization, we compared 2 surrogate signals (external respiratory bellows and internal MRI navigators) and 2 MR sampling strategies (Cartesian and radial) in terms of image quality and robustness. Using the optimized protocol, 6 pancreatic cancer patients were scanned to calculate the 4D motion. Region of interest analysis was performed to characterize the respiratory-induced motion of the tumor and organs at risk simultaneously. Results: The MRI navigator was foundmore » to be a more reliable surrogate for pancreatic motion than the respiratory bellows signal. Radial sampling is most benign for undersampling artifacts and intraview motion. Motion characterization revealed interorgan and interpatient variation, as well as heterogeneity within the tumor. Conclusions: A robust 4D-MRI method, based on clinically available protocols, is presented and successfully applied to characterize the abdominal motion in a small number of pancreatic cancer patients.« less

Chance-constrained multi-objective optimization of groundwater remediation design at DNAPLs-contaminated sites using a multi-algorithm genetically adaptive method

NASA Astrophysics Data System (ADS)

Ouyang, Qi; Lu, Wenxi; Hou, Zeyu; Zhang, Yu; Li, Shuai; Luo, Jiannan

2017-05-01

In this paper, a multi-algorithm genetically adaptive multi-objective (AMALGAM) method is proposed as a multi-objective optimization solver. It was implemented in the multi-objective optimization of a groundwater remediation design at sites contaminated by dense non-aqueous phase liquids. In this study, there were two objectives: minimization of the total remediation cost, and minimization of the remediation time. A non-dominated sorting genetic algorithm II (NSGA-II) was adopted to compare with the proposed method. For efficiency, the time-consuming surfactant-enhanced aquifer remediation simulation model was replaced by a surrogate model constructed by a multi-gene genetic programming (MGGP) technique. Similarly, two other surrogate modeling methods-support vector regression (SVR) and Kriging (KRG)-were employed to make comparisons with MGGP. In addition, the surrogate-modeling uncertainty was incorporated in the optimization model by chance-constrained programming (CCP). The results showed that, for the problem considered in this study, (1) the solutions obtained by AMALGAM incurred less remediation cost and required less time than those of NSGA-II, indicating that AMALGAM outperformed NSGA-II. It was additionally shown that (2) the MGGP surrogate model was more accurate than SVR and KRG; and (3) the remediation cost and time increased with the confidence level, which can enable decision makers to make a suitable choice by considering the given budget, remediation time, and reliability.

Efficient Bayesian experimental design for contaminant source identification

NASA Astrophysics Data System (ADS)

Zhang, Jiangjiang; Zeng, Lingzao; Chen, Cheng; Chen, Dingjiang; Wu, Laosheng

2015-01-01

In this study, an efficient full Bayesian approach is developed for the optimal sampling well location design and source parameters identification of groundwater contaminants. An information measure, i.e., the relative entropy, is employed to quantify the information gain from concentration measurements in identifying unknown parameters. In this approach, the sampling locations that give the maximum expected relative entropy are selected as the optimal design. After the sampling locations are determined, a Bayesian approach based on Markov Chain Monte Carlo (MCMC) is used to estimate unknown parameters. In both the design and estimation, the contaminant transport equation is required to be solved many times to evaluate the likelihood. To reduce the computational burden, an interpolation method based on the adaptive sparse grid is utilized to construct a surrogate for the contaminant transport equation. The approximated likelihood can be evaluated directly from the surrogate, which greatly accelerates the design and estimation process. The accuracy and efficiency of our approach are demonstrated through numerical case studies. It is shown that the methods can be used to assist in both single sampling location and monitoring network design for contaminant source identifications in groundwater.

redNumerical modelling of a peripheral arterial stenosis using dimensionally reduced models and kernel methods.

PubMed

Köppl, Tobias; Santin, Gabriele; Haasdonk, Bernard; Helmig, Rainer

2018-05-06

In this work, we consider two kinds of model reduction techniques to simulate blood flow through the largest systemic arteries, where a stenosis is located in a peripheral artery i.e. in an artery that is located far away from the heart. For our simulations we place the stenosis in one of the tibial arteries belonging to the right lower leg (right post tibial artery). The model reduction techniques that are used are on the one hand dimensionally reduced models (1-D and 0-D models, the so-called mixed-dimension model) and on the other hand surrogate models produced by kernel methods. Both methods are combined in such a way that the mixed-dimension models yield training data for the surrogate model, where the surrogate model is parametrised by the degree of narrowing of the peripheral stenosis. By means of a well-trained surrogate model, we show that simulation data can be reproduced with a satisfactory accuracy and that parameter optimisation or state estimation problems can be solved in a very efficient way. Furthermore it is demonstrated that a surrogate model enables us to present after a very short simulation time the impact of a varying degree of stenosis on blood flow, obtaining a speedup of several orders over the full model. This article is protected by copyright. All rights reserved.

Shape optimization of pulsatile ventricular assist devices using FSI to minimize thrombotic risk

NASA Astrophysics Data System (ADS)

Long, C. C.; Marsden, A. L.; Bazilevs, Y.

2014-10-01

In this paper we perform shape optimization of a pediatric pulsatile ventricular assist device (PVAD). The device simulation is carried out using fluid-structure interaction (FSI) modeling techniques within a computational framework that combines FEM for fluid mechanics and isogeometric analysis for structural mechanics modeling. The PVAD FSI simulations are performed under realistic conditions (i.e., flow speeds, pressure levels, boundary conditions, etc.), and account for the interaction of air, blood, and a thin structural membrane separating the two fluid subdomains. The shape optimization study is designed to reduce thrombotic risk, a major clinical problem in PVADs. Thrombotic risk is quantified in terms of particle residence time in the device blood chamber. Methods to compute particle residence time in the context of moving spatial domains are presented in a companion paper published in the same issue (Comput Mech, doi: 10.1007/s00466-013-0931-y, 2013). The surrogate management framework, a derivative-free pattern search optimization method that relies on surrogates for increased efficiency, is employed in this work. For the optimization study shown here, particle residence time is used to define a suitable cost or objective function, while four adjustable design optimization parameters are used to define the device geometry. The FSI-based optimization framework is implemented in a parallel computing environment, and deployed with minimal user intervention. Using five SEARCH/ POLL steps the optimization scheme identifies a PVAD design with significantly better throughput efficiency than the original device.

Informed consent in medical decision-making in commercial gestational surrogacy: a mixed methods study in New Delhi, India.

PubMed

Tanderup, Malene; Reddy, Sunita; Patel, Tulsi; Nielsen, Birgitte Bruun

2015-05-01

To investigate ethical issues in informed consent for decisions regarding embryo transfer and fetal reduction in commercial gestational surrogacy. Mixed methods study employing observations, an interview-guide and semi-structured interviews. Fertility clinics and agencies in Delhi, India, between December 2011 and December 2012. Doctors providing conceptive technologies to commissioning couples and carrying out surrogacy procedures; surrogate mothers; agents functioning as links for surrogacy. Interviews using semi-structured interview guides were carried out among 20 doctors in 18 fertility clinics, five agents from four agencies and 14 surrogate mothers. Surrogate mothers were interviewed both individually and in the presence of doctors and agents. Data on socio-economic context and experiences among and between various actors in the surrogacy process were coded to identify categories of ethical concern. Numerical and grounded theory-oriented analyses were used. Informed consent, number of embryos transferred, fetal reduction, conflict of interest among the involved parties. None of the 14 surrogate mothers were able to explain the risks involved in embryo transfer and fetal reduction. The majority of the doctors took unilateral decisions about embryo transfer and fetal reduction. The commissioning parents were usually only indirectly involved. In the qualitative analysis, difficulties in explaining procedures, autonomy, self-payment of fertility treatment and conflicts of interest were the main themes. Clinical procedural decisions were primarily made by the doctors. Surrogate mothers were not adequately informed. There is a need for regulation on decision-making procedures to safeguard the interests of surrogate mothers. © 2015 Nordic Federation of Societies of Obstetrics and Gynecology.

Atomic Radius and Charge Parameter Uncertainty in Biomolecular Solvation Energy Calculations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Xiu; Lei, Huan; Gao, Peiyuan

Atomic radii and charges are two major parameters used in implicit solvent electrostatics and energy calculations. The optimization problem for charges and radii is under-determined, leading to uncertainty in the values of these parameters and in the results of solvation energy calculations using these parameters. This paper presents a method for quantifying this uncertainty in solvation energies using surrogate models based on generalized polynomial chaos (gPC) expansions. There are relatively few atom types used to specify radii parameters in implicit solvation calculations; therefore, surrogate models for these low-dimensional spaces could be constructed using least-squares fitting. However, there are many moremore » types of atomic charges; therefore, construction of surrogate models for the charge parameter space required compressed sensing combined with an iterative rotation method to enhance problem sparsity. We present results for the uncertainty in small molecule solvation energies based on these approaches. Additionally, we explore the correlation between uncertainties due to radii and charges which motivates the need for future work in uncertainty quantification methods for high-dimensional parameter spaces.« less

A polynomial chaos expansion based molecular dynamics study for probabilistic strength analysis of nano-twinned copper

NASA Astrophysics Data System (ADS)

Mahata, Avik; Mukhopadhyay, Tanmoy; Adhikari, Sondipon

2016-03-01

Nano-twinned structures are mechanically stronger, ductile and stable than its non-twinned form. We have investigated the effect of varying twin spacing and twin boundary width (TBW) on the yield strength of the nano-twinned copper in a probabilistic framework. An efficient surrogate modelling approach based on polynomial chaos expansion has been proposed for the analysis. Effectively utilising 15 sets of expensive molecular dynamics simulations, thousands of outputs have been obtained corresponding to different sets of twin spacing and twin width using virtual experiments based on the surrogates. One of the major outcomes of this work is that there exists an optimal combination of twin boundary spacing and twin width until which the strength can be increased and after that critical point the nanowires weaken. This study also reveals that the yield strength of nano-twinned copper is more sensitive to TBW than twin spacing. Such robust inferences have been possible to be drawn only because of applying the surrogate modelling approach, which makes it feasible to obtain results corresponding to 40 000 combinations of different twin boundary spacing and twin width in a computationally efficient framework.

Bench scale demonstration and conceptual engineering for DETOX{sup SM} catalyzed wet oxidation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Moslander, J.; Bell, R.; Robertson, D.

1994-06-01

Laboratory and bench scale studies of the DETOX{sup SM} catalyzed wet oxidation process have been performed with the object of developing the process for treatment of hazardous and mixed wastes. Reaction orders, apparent rates, and activation energies have been determined for a range of organic waste surrogates. Reaction intermediates and products have been analyzed. Metals` fates have been determined. Bench scale units have been designed, fabricated, and tested with solid and liquid organic waste surrogates. Results from the laboratory and bench scale studies have been used to develop conceptual designs for application of the process to hazardous and mixed wastes.

Hybrid optimal scheduling for intermittent androgen suppression of prostate cancer

NASA Astrophysics Data System (ADS)

Hirata, Yoshito; di Bernardo, Mario; Bruchovsky, Nicholas; Aihara, Kazuyuki

2010-12-01

We propose a method for achieving an optimal protocol of intermittent androgen suppression for the treatment of prostate cancer. Since the model that reproduces the dynamical behavior of the surrogate tumor marker, prostate specific antigen, is piecewise linear, we can obtain an analytical solution for the model. Based on this, we derive conditions for either stopping or delaying recurrent disease. The solution also provides a design principle for the most favorable schedule of treatment that minimizes the rate of expansion of the malignant cell population.

Gating based on internal/external signals with dynamic correlation updates.

PubMed

Wu, Huanmei; Zhao, Qingya; Berbeco, Ross I; Nishioka, Seiko; Shirato, Hiroki; Jiang, Steve B

2008-12-21

Precise localization of mobile tumor positions in real time is critical to the success of gated radiotherapy. Tumor positions are usually derived from either internal or external surrogates. Fluoroscopic gating based on internal surrogates, such as implanted fiducial markers, is accurate however requiring a large amount of imaging dose. Gating based on external surrogates, such as patient abdominal surface motion, is non-invasive however less accurate due to the uncertainty in the correlation between tumor location and external surrogates. To address these complications, we propose to investigate an approach based on hybrid gating with dynamic internal/external correlation updates. In this approach, the external signal is acquired at high frequency (such as 30 Hz) while the internal signal is sparsely acquired (such as 0.5 Hz or less). The internal signal is used to validate and update the internal/external correlation during treatment. Tumor positions are derived from the external signal based on the newly updated correlation. Two dynamic correlation updating algorithms are introduced. One is based on the motion amplitude and the other is based on the motion phase. Nine patients with synchronized internal/external motion signals are simulated retrospectively to evaluate the effectiveness of hybrid gating. The influences of different clinical conditions on hybrid gating, such as the size of gating windows, the optimal timing for internal signal acquisition and the acquisition frequency are investigated. The results demonstrate that dynamically updating the internal/external correlation in or around the gating window will reduce false positive with relatively diminished treatment efficiency. This improvement will benefit patients with mobile tumors, especially greater for early stage lung cancers, for which the tumors are less attached or freely floating in the lung.

Revisiting photodynamic therapy dosimetry: reductionist & surrogate approaches to facilitate clinical success

NASA Astrophysics Data System (ADS)

Pogue, Brian W.; Elliott, Jonathan T.; Kanick, Stephen C.; Davis, Scott C.; Samkoe, Kimberley S.; Maytin, Edward V.; Pereira, Stephen P.; Hasan, Tayyaba

2016-04-01

Photodynamic therapy (PDT) can be a highly complex treatment, with many parameters influencing treatment efficacy. The extent to which dosimetry is used to monitor and standardize treatment delivery varies widely, ranging from measurement of a single surrogate marker to comprehensive approaches that aim to measure or estimate as many relevant parameters as possible. Today, most clinical PDT treatments are still administered with little more than application of a prescribed drug dose and timed light delivery, and thus the role of patient-specific dosimetry has not reached widespread clinical adoption. This disconnect is at least partly due to the inherent conflict between the need to measure and understand multiple parameters in vivo in order to optimize treatment, and the need for expedience in the clinic and in the regulatory and commercialization process. Thus, a methodical approach to selecting primary dosimetry metrics is required at each stage of translation of a treatment procedure, moving from complex measurements to understand PDT mechanisms in pre-clinical and early phase I trials, towards the identification and application of essential dose-limiting and/or surrogate measurements in phase II/III trials. If successful, identifying the essential and/or reliable surrogate dosimetry measurements should help facilitate increased adoption of clinical PDT. In this paper, examples of essential dosimetry points and surrogate dosimetry tools that may be implemented in phase II/III trials are discussed. For example, the treatment efficacy as limited by light penetration in interstitial PDT may be predicted by the amount of contrast uptake in CT, and so this could be utilized as a surrogate dosimetry measurement to prescribe light doses based upon pre-treatment contrast. Success of clinical ALA-based skin lesion treatment is predicted almost uniquely by the explicit or implicit measurements of photosensitizer and photobleaching, yet the individualization of treatment based upon each patients measured bleaching needs to be attempted. In the case of ALA, lack of PpIX is more likely an indicator that alternative PpIX production methods must be implemented. Parsimonious dosimetry, using surrogate measurements that are clinically acceptable, might strategically help to advance PDT in a medical world that is increasingly cost and time sensitive. Careful attention to methodologies that can identify and advance the most critical dosimetric measurements, either direct or surrogate, are needed to ensure successful incorporation of PDT into niche clinical procedures.

The overlay tester (OT) : comparison with other crack test methods and recommendations for surrogate crack tests.

DOT National Transportation Integrated Search

2012-10-01

Presently, one of the principal performance concerns of hot-mix asphalt (HMA) pavements is premature : cracking, particularly of HMA surfacing mixes. Regrettably, however, while many USA transportation agencies have : implemented design-level tests t...

Chance-constrained multi-objective optimization of groundwater remediation design at DNAPLs-contaminated sites using a multi-algorithm genetically adaptive method.

PubMed

Ouyang, Qi; Lu, Wenxi; Hou, Zeyu; Zhang, Yu; Li, Shuai; Luo, Jiannan

2017-05-01

In this paper, a multi-algorithm genetically adaptive multi-objective (AMALGAM) method is proposed as a multi-objective optimization solver. It was implemented in the multi-objective optimization of a groundwater remediation design at sites contaminated by dense non-aqueous phase liquids. In this study, there were two objectives: minimization of the total remediation cost, and minimization of the remediation time. A non-dominated sorting genetic algorithm II (NSGA-II) was adopted to compare with the proposed method. For efficiency, the time-consuming surfactant-enhanced aquifer remediation simulation model was replaced by a surrogate model constructed by a multi-gene genetic programming (MGGP) technique. Similarly, two other surrogate modeling methods-support vector regression (SVR) and Kriging (KRG)-were employed to make comparisons with MGGP. In addition, the surrogate-modeling uncertainty was incorporated in the optimization model by chance-constrained programming (CCP). The results showed that, for the problem considered in this study, (1) the solutions obtained by AMALGAM incurred less remediation cost and required less time than those of NSGA-II, indicating that AMALGAM outperformed NSGA-II. It was additionally shown that (2) the MGGP surrogate model was more accurate than SVR and KRG; and (3) the remediation cost and time increased with the confidence level, which can enable decision makers to make a suitable choice by considering the given budget, remediation time, and reliability. Copyright © 2017 Elsevier B.V. All rights reserved.

Recommendations for Filler Material Composition and Delivery Method for Bench-Scale Testing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hardin, Ernest; Brady, Patrick Vane

This report supplements Joint Workplan on Filler Investigations for DPCs (SNL 2017) providing new and some corrected information for use in planning Phase 1 laboratory testing of slurry cements as possible DPC fillers. The scope description is to "Describe a complete laboratory testing program for filler composition, delivery, emplacement in surrogate canisters, and post-test examination. To the extent possible specify filler material and equipment sources." This report includes results from an independent expert review (Dr. Arun Wagh, retired from Argonne National Laboratory and contracted by Sandia) that helped to narrow the range of cement types for consideration, and to providemore » further guidance on mix variations to optimize injectability, durability, and other aspects of filler performance.« less

Optimizing Discharge Capacity of Li-O 2 Batteries by Design of Air-Electrode Porous Structure: Multifidelity Modeling and Optimization

DOE PAGES

Pan, Wenxiao; Yang, Xiu; Bao, Jie; ...

2017-01-01

We develop a new mathematical framework to study the optimal design of air electrode microstructures for lithium-oxygen (Li-O2) batteries. It can eectively reduce the number of expensive experiments for testing dierent air-electrodes, thereby minimizing the cost in the design of Li-O2 batteries. The design parameters to characterize an air-electrode microstructure include the porosity, surface-to-volume ratio, and parameters associated with the pore-size distribution. A surrogate model (also known as response surface) for discharge capacity is rst constructed as a function of these design parameters. The surrogate model is accurate and easy to evaluate such that an optimization can be performed basedmore » on it. In particular, a Gaussian process regression method, co-kriging, is employed due to its accuracy and eciency in predicting high-dimensional responses from a combination of multidelity data. Specically, a small amount of data from high-delity simulations are combined with a large number of data obtained from computationally ecient low-delity simulations. The high-delity simulation is based on a multiscale modeling approach that couples the microscale (pore-scale) and macroscale (device-scale) models. Whereas, the low-delity simulation is based on an empirical macroscale model. The constructed response surface provides quantitative understanding and prediction about how air electrode microstructures aect the discharge performance of Li-O2 batteries. The succeeding sensitivity analysis via Sobol indices and optimization via genetic algorithm ultimately oer a reliable guidance on the optimal design of air electrode microstructures. The proposed mathematical framework can be generalized to investigate other new energy storage techniques and materials.« less

Optimizing Discharge Capacity of Li-O 2 Batteries by Design of Air-Electrode Porous Structure: Multifidelity Modeling and Optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pan, Wenxiao; Yang, Xiu; Bao, Jie

We develop a new mathematical framework to study the optimal design of air electrode microstructures for lithium-oxygen (Li-O2) batteries. It can eectively reduce the number of expensive experiments for testing dierent air-electrodes, thereby minimizing the cost in the design of Li-O2 batteries. The design parameters to characterize an air-electrode microstructure include the porosity, surface-to-volume ratio, and parameters associated with the pore-size distribution. A surrogate model (also known as response surface) for discharge capacity is rst constructed as a function of these design parameters. The surrogate model is accurate and easy to evaluate such that an optimization can be performed basedmore » on it. In particular, a Gaussian process regression method, co-kriging, is employed due to its accuracy and eciency in predicting high-dimensional responses from a combination of multidelity data. Specically, a small amount of data from high-delity simulations are combined with a large number of data obtained from computationally ecient low-delity simulations. The high-delity simulation is based on a multiscale modeling approach that couples the microscale (pore-scale) and macroscale (device-scale) models. Whereas, the low-delity simulation is based on an empirical macroscale model. The constructed response surface provides quantitative understanding and prediction about how air electrode microstructures aect the discharge performance of Li-O2 batteries. The succeeding sensitivity analysis via Sobol indices and optimization via genetic algorithm ultimately oer a reliable guidance on the optimal design of air electrode microstructures. The proposed mathematical framework can be generalized to investigate other new energy storage techniques and materials.« less

A unified procedure for meta-analytic evaluation of surrogate end points in randomized clinical trials

PubMed Central

Dai, James Y.; Hughes, James P.

2012-01-01

The meta-analytic approach to evaluating surrogate end points assesses the predictiveness of treatment effect on the surrogate toward treatment effect on the clinical end point based on multiple clinical trials. Definition and estimation of the correlation of treatment effects were developed in linear mixed models and later extended to binary or failure time outcomes on a case-by-case basis. In a general regression setting that covers nonnormal outcomes, we discuss in this paper several metrics that are useful in the meta-analytic evaluation of surrogacy. We propose a unified 3-step procedure to assess these metrics in settings with binary end points, time-to-event outcomes, or repeated measures. First, the joint distribution of estimated treatment effects is ascertained by an estimating equation approach; second, the restricted maximum likelihood method is used to estimate the means and the variance components of the random treatment effects; finally, confidence intervals are constructed by a parametric bootstrap procedure. The proposed method is evaluated by simulations and applications to 2 clinical trials. PMID:22394448

Design optimization of single mixed refrigerant LNG process using a hybrid modified coordinate descent algorithm

NASA Astrophysics Data System (ADS)

Qyyum, Muhammad Abdul; Long, Nguyen Van Duc; Minh, Le Quang; Lee, Moonyong

2018-01-01

Design optimization of the single mixed refrigerant (SMR) natural gas liquefaction (LNG) process involves highly non-linear interactions between decision variables, constraints, and the objective function. These non-linear interactions lead to an irreversibility, which deteriorates the energy efficiency of the LNG process. In this study, a simple and highly efficient hybrid modified coordinate descent (HMCD) algorithm was proposed to cope with the optimization of the natural gas liquefaction process. The single mixed refrigerant process was modeled in Aspen Hysys® and then connected to a Microsoft Visual Studio environment. The proposed optimization algorithm provided an improved result compared to the other existing methodologies to find the optimal condition of the complex mixed refrigerant natural gas liquefaction process. By applying the proposed optimization algorithm, the SMR process can be designed with the 0.2555 kW specific compression power which is equivalent to 44.3% energy saving as compared to the base case. Furthermore, in terms of coefficient of performance (COP), it can be enhanced up to 34.7% as compared to the base case. The proposed optimization algorithm provides a deep understanding of the optimization of the liquefaction process in both technical and numerical perspectives. In addition, the HMCD algorithm can be employed to any mixed refrigerant based liquefaction process in the natural gas industry.

Application of Design of Experiments and Surrogate Modeling within the NASA Advanced Concepts Office, Earth-to-Orbit Design Process

NASA Technical Reports Server (NTRS)

Zwack, Mathew R.; Dees, Patrick D.; Holt, James B.

2016-01-01

Decisions made during early conceptual design have a large impact upon the expected life-cycle cost (LCC) of a new program. It is widely accepted that up to 80% of such cost is committed during these early design phases. Therefore, to help minimize LCC, decisions made during conceptual design must be based upon as much information as possible. To aid in the decision making for new launch vehicle programs, the Advanced Concepts Office (ACO) at NASA Marshall Space Flight Center (MSFC) provides rapid turnaround pre-phase A and phase A concept definition studies. The ACO team utilizes a proven set of tools to provide customers with a full vehicle mass breakdown to tertiary subsystems, preliminary structural sizing based upon worst-case flight loads, and trajectory optimization to quantify integrated vehicle performance for a given mission. Although the team provides rapid turnaround for single vehicle concepts, the scope of the trade space can be limited due to analyst availability and the manpower requirements for manual execution of the analysis tools. In order to enable exploration of a broader design space, the ACO team has implemented an advanced design methods (ADM) based approach. This approach applies the concepts of design of experiments (DOE) and surrogate modeling to more exhaustively explore the trade space and provide the customer with additional design information to inform decision making. This paper will first discuss the automation of the ACO tool set, which represents a majority of the development effort. In order to fit a surrogate model within tolerable error bounds a number of DOE cases are needed. This number will scale with the number of variable parameters desired and the complexity of the system's response to those variables. For all but the smallest design spaces, the number of cases required cannot be produced within an acceptable timeframe using a manual process. Therefore, automation of the tools was a key enabler for the successful application of an ADM approach to an ACO design study. Following the overview of the tool set automation, an example problem will be given to illustrate the implementation of the ADM approach. The example problem will first cover the inclusion of ground rules and assumptions (GR&A) for a study. The GR&A are very important to the study as they determine the constraints within which a trade study can be conducted. These trades must ultimately reconcile with the customer's desired output and any anticipated "what if" questions. The example problem will then illustrate the setup and execution of a DOE through the automated ACO tools. This process is accomplished more efficiently in this work by splitting the tools into two separate environments. The first environment encompasses the structural optimization and mass estimation tools, while the second is focused on trajectory optimization. Surrogate models are fit to the outputs of each environment and are "integrated" via connection of the surrogate equations. Throughout this process, checks are implemented to compare the output of the surrogates to the output of manually run cases to ensure that the error of the final surrogates is at an acceptable level. The conclusion of the example problem demonstrates the utility of the ADM based approach. Using surrogate models gives the ACO team the ability to visualize vehicle sensitivities to various design parameters and identify regions of interest within the design space. The ADM approach can thus be used to inform concept down selection and isolate promising vehicle configurations to be explored in more detail through the manual design process. In addition it provides the customer with an almost instantaneous turnaround on any ''what if" questions that may arise within the bounds of the surrogate model. This approach ultimately expands the ability of the ACO team to provide its customer with broad and rapid turnaround trade studies for launch vehicle conceptual design. The ability to identify a selection of designs which can meet the customer requirements will help ensure lower LCC of launch vehicle designs originating from ACO.

Application of Design of Experiments and Surrogate Modeling within the NASA Advanced Concepts Office, Earth-to-Orbit Design Process

NASA Technical Reports Server (NTRS)

Zwack, Mathew R.; Dees, Patrick D.; Holt, James B.

2016-01-01

Decisions made during early conceptual design have a large impact upon the expected life-cycle cost (LCC) of a new program. It is widely accepted that up to 80% of such cost is committed during these early design phases.1 Therefore, to help minimize LCC, decisions made during conceptual design must be based upon as much information as possible. To aid in the decision making for new launch vehicle programs, the Advanced Concepts Office (ACO) at NASA Marshall Space Flight Center (MSFC) provides rapid turnaround pre-phase A and phase A concept definition studies. The ACO team utilizes a proven set of tools to provide customers with a full vehicle mass breakdown to tertiary subsystems, preliminary structural sizing based upon worst-case flight loads, and trajectory optimization to quantify integrated vehicle performance for a given mission.2 Although the team provides rapid turnaround for single vehicle concepts, the scope of the trade space can be limited due to analyst availability and the manpower requirements for manual execution of the analysis tools. In order to enable exploration of a broader design space, the ACO team has implemented an Advanced Design Methods (ADM) based approach. This approach applies the concepts of Design of Experiments (DOE) and surrogate modeling to more exhaustively explore the trade space and provide the customer with additional design information to inform decision making. This paper will first discuss the automation of the ACO tool set, which represents a majority of the development e ort. In order to t a surrogate model within tolerable error bounds a number of DOE cases are needed. This number will scale with the number of variable parameters desired and the complexity of the system's response to those variables. For all but the smallest design spaces, the number of cases required cannot be produced within an acceptable timeframe using a manual process. Therefore, automation of the tools was a key enabler for the successful application of an ADM approach to an ACO design study. Following the overview of the tool set automation, an example problem will be given to illustrate the implementation of the ADM approach. The example problem will first cover the inclusion of Ground Rules and Assumptions (GR&A) for a study. The GR&A are very important to the study as they determine the constraints within which a trade study can be conducted. These trades must ultimately reconcile with the customer's desired output and any anticipated \\what if" questions. The example problem will then illustrate the setup and execution of a DOE through the automated ACO tools. This process is accomplished more efficiently in this work by splitting the tools into two separate environments. The first environment encompasses the structural optimization and mass estimation tools, while the second is focused on trajectory optimization. Surrogate models are t to the outputs of each environment and are integrated via connection of the surrogate equations. Throughout this process, checks are implemented to compare the output of the surrogates to the output of manually run cases to ensure that the error of the final surrogates is at an acceptable level. The conclusion of the example problem demonstrates the utility of the ADM based approach. Using surrogate models gives the ACO team the ability to visualize vehicle sensitivities to various design parameters and identify regions of interest within the design space. The ADM approach can thus be used to inform concept down selection and isolate promising vehicle configurations to be explored in more detail through the manual design process. In addition it provides the customer with an almost instantaneous turnaround on any \\what if" questions that may arise within the bounds of the surrogate model. This approach ultimately expands the ability of the ACO team to provide its customer with broad and rapid turnaround trade studies for launch vehicle conceptual design. The ability to identify a selection of designs which can meet the customer requirements will have the potential to lower LCC of launch vehicle designs originating from ACO.

Rapid and Efficient Filtration-Based Procedure for Separation and Safe Analysis of CBRN Mixed Samples

PubMed Central

Bentahir, Mostafa; Laduron, Frederic; Irenge, Leonid; Ambroise, Jérôme; Gala, Jean-Luc

2014-01-01

Separating CBRN mixed samples that contain both chemical and biological warfare agents (CB mixed sample) in liquid and solid matrices remains a very challenging issue. Parameters were set up to assess the performance of a simple filtration-based method first optimized on separate C- and B-agents, and then assessed on a model of CB mixed sample. In this model, MS2 bacteriophage, Autographa californica nuclear polyhedrosis baculovirus (AcNPV), Bacillus atrophaeus and Bacillus subtilis spores were used as biological agent simulants whereas ethyl methylphosphonic acid (EMPA) and pinacolyl methylphophonic acid (PMPA) were used as VX and soman (GD) nerve agent surrogates, respectively. Nanoseparation centrifugal devices with various pore size cut-off (30 kD up to 0.45 µm) and three RNA extraction methods (Invisorb, EZ1 and Nuclisens) were compared. RNA (MS2) and DNA (AcNPV) quantification was carried out by means of specific and sensitive quantitative real-time PCRs (qPCR). Liquid chromatography coupled to time-of-flight mass spectrometry (LC/TOFMS) methods was used for quantifying EMPA and PMPA. Culture methods and qPCR demonstrated that membranes with a 30 kD cut-off retain more than 99.99% of biological agents (MS2, AcNPV, Bacillus Atrophaeus and Bacillus subtilis spores) tested separately. A rapid and reliable separation of CB mixed sample models (MS2/PEG-400 and MS2/EMPA/PMPA) contained in simple liquid or complex matrices such as sand and soil was also successfully achieved on a 30 kD filter with more than 99.99% retention of MS2 on the filter membrane, and up to 99% of PEG-400, EMPA and PMPA recovery in the filtrate. The whole separation process turnaround-time (TAT) was less than 10 minutes. The filtration method appears to be rapid, versatile and extremely efficient. The separation method developed in this work constitutes therefore a useful model for further evaluating and comparing additional separation alternative procedures for a safe handling and preparation of CB mixed samples. PMID:24505375

Optimal design and installation of ultra high bypass ratio turbofan nacelle

NASA Astrophysics Data System (ADS)

Savelyev, Andrey; Zlenko, Nikolay; Matyash, Evgeniy; Mikhaylov, Sergey; Shenkin, Andrey

2016-10-01

The paper is devoted to the problem of designing and optimizing the nacelle of turbojet bypass engine with high bypass ratio and high thrust. An optimization algorithm EGO based on development of surrogate models and the method for maximizing the probability of improving the objective function has been used. The designing methodology has been based on the numerical solution of the Reynolds equations system. Spalart-Allmaras turbulence model has been chosen for RANS closure. The effective thrust losses has been uses as an objective function in optimizing the engine nacelle. As a result of optimization, effective thrust has been increased by 1.5 %. The Blended wing body aircraft configuration has been studied as a possible application. Two variants of the engine layout arrangement have been considered. It has been shown that the power plant changes the pressure distribution on the aircraft surface. It results in essential diminishing the configuration lift-drag ratio.

Sequential experimental design based generalised ANOVA

NASA Astrophysics Data System (ADS)

Chakraborty, Souvik; Chowdhury, Rajib

2016-07-01

Over the last decade, surrogate modelling technique has gained wide popularity in the field of uncertainty quantification, optimization, model exploration and sensitivity analysis. This approach relies on experimental design to generate training points and regression/interpolation for generating the surrogate. In this work, it is argued that conventional experimental design may render a surrogate model inefficient. In order to address this issue, this paper presents a novel distribution adaptive sequential experimental design (DA-SED). The proposed DA-SED has been coupled with a variant of generalised analysis of variance (G-ANOVA), developed by representing the component function using the generalised polynomial chaos expansion. Moreover, generalised analytical expressions for calculating the first two statistical moments of the response, which are utilized in predicting the probability of failure, have also been developed. The proposed approach has been utilized in predicting probability of failure of three structural mechanics problems. It is observed that the proposed approach yields accurate and computationally efficient estimate of the failure probability.

Sequential experimental design based generalised ANOVA

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chakraborty, Souvik, E-mail: csouvik41@gmail.com; Chowdhury, Rajib, E-mail: rajibfce@iitr.ac.in

Over the last decade, surrogate modelling technique has gained wide popularity in the field of uncertainty quantification, optimization, model exploration and sensitivity analysis. This approach relies on experimental design to generate training points and regression/interpolation for generating the surrogate. In this work, it is argued that conventional experimental design may render a surrogate model inefficient. In order to address this issue, this paper presents a novel distribution adaptive sequential experimental design (DA-SED). The proposed DA-SED has been coupled with a variant of generalised analysis of variance (G-ANOVA), developed by representing the component function using the generalised polynomial chaos expansion. Moreover,more » generalised analytical expressions for calculating the first two statistical moments of the response, which are utilized in predicting the probability of failure, have also been developed. The proposed approach has been utilized in predicting probability of failure of three structural mechanics problems. It is observed that the proposed approach yields accurate and computationally efficient estimate of the failure probability.« less

To address surface reaction network complexity using scaling relations machine learning and DFT calculations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ulissi, Zachary W.; Medford, Andrew J.; Bligaard, Thomas

Surface reaction networks involving hydrocarbons exhibit enormous complexity with thousands of species and reactions for all but the very simplest of chemistries. We present a framework for optimization under uncertainty for heterogeneous catalysis reaction networks using surrogate models that are trained on the fly. The surrogate model is constructed by teaching a Gaussian process adsorption energies based on group additivity fingerprints, combined with transition-state scaling relations and a simple classifier for determining the rate-limiting step. The surrogate model is iteratively used to predict the most important reaction step to be calculated explicitly with computationally demanding electronic structure theory. Applying thesemore » methods to the reaction of syngas on rhodium(111), we identify the most likely reaction mechanism. Lastly, propagating uncertainty throughout this process yields the likelihood that the final mechanism is complete given measurements on only a subset of the entire network and uncertainty in the underlying density functional theory calculations.« less

To address surface reaction network complexity using scaling relations machine learning and DFT calculations

DOE PAGES

Ulissi, Zachary W.; Medford, Andrew J.; Bligaard, Thomas; ...

2017-03-06

Surface reaction networks involving hydrocarbons exhibit enormous complexity with thousands of species and reactions for all but the very simplest of chemistries. We present a framework for optimization under uncertainty for heterogeneous catalysis reaction networks using surrogate models that are trained on the fly. The surrogate model is constructed by teaching a Gaussian process adsorption energies based on group additivity fingerprints, combined with transition-state scaling relations and a simple classifier for determining the rate-limiting step. The surrogate model is iteratively used to predict the most important reaction step to be calculated explicitly with computationally demanding electronic structure theory. Applying thesemore » methods to the reaction of syngas on rhodium(111), we identify the most likely reaction mechanism. Lastly, propagating uncertainty throughout this process yields the likelihood that the final mechanism is complete given measurements on only a subset of the entire network and uncertainty in the underlying density functional theory calculations.« less

Effects of fire and fire surrogate treatments on bark beetle-caused tree mortality in the Southern Cascades, California

Treesearch

C.J. Fettig; R.R. Borys; C.P. and Dabney

2010-01-01

We examined bark beetle responses to fire and fire surrogate treatments 2 and 4 years after the application of prescribed fire in a mixed-conifer forest in northern California. Treatments included an untreated control (C), thinning from below (T), and applications of prescribed fire (B) and T + B replicated three times in 10-ha experimental units. A total of 1,822...

Different interest group views of fuels treatments: survey results from fire and fire surrogate treatments in a Sierran mixed conifer forest, California, USA

Treesearch

Sarah McCaffrey; Jason J. Moghaddas; Scott L. Stephens

2008-01-01

The present paper discusses results from a survey about the acceptance of and preferences for fuels treatments of participants following a field tour of the University of California Blodgett Forest Fire and Fire Surrogate Study Site. Although original expectations were that tours would be composed of general members of the public, individual tour groups ultimately were...

On the control of riverbed incision induced by run-of-river power plant

NASA Astrophysics Data System (ADS)

Bizzi, Simone; Dinh, Quang; Bernardi, Dario; Denaro, Simona; Schippa, Leonardo; Soncini-Sessa, Rodolfo

2015-07-01

Water resource management (WRM) through dams or reservoirs is worldwide necessary to support key human-related activities, ranging from hydropower production to water allocation and flood risk mitigation. Designing of reservoir operations aims primarily to fulfill the main purpose (or purposes) for which the structure has been built. However, it is well known that reservoirs strongly influence river geomorphic processes, causing sediment deficits downstream, altering water, and sediment fluxes, leading to riverbed incision and causing infrastructure instability and ecological degradation. We propose a framework that, by combining physically based modeling, surrogate modeling techniques, and multiobjective (MO) optimization, allows to include fluvial geomorphology into MO optimization whose main objectives are the maximization of hydropower revenue and the minimization of riverbed degradation. The case study is a run-of-the-river power plant on the River Po (Italy). A 1-D mobile-bed hydro-morphological model simulated the riverbed evolution over a 10 year horizon for alternatives operation rules of the power plant. The knowledge provided by such a physically based model is integrated into a MO optimization routine via surrogate modeling using the response surface methodology. Hence, this framework overcomes the high computational costs that so far hindered the integration of river geomorphology into WRM. We provided numerical proof that river morphologic processes and hydropower production are indeed in conflict but that the conflict may be mitigated with appropriate control strategies.

Assay Development Process | Office of Cancer Clinical Proteomics Research

Cancer.gov

Typical steps involved in the development of a  mass spectrometry-based targeted assay include: (1) selection of surrogate or signature peptides corresponding to the targeted protein or modification of interest; (2) iterative optimization of instrument and method parameters for optimal detection of the selected peptide; (3) method development for protein extraction from biological matrices such as tissue, whole cell lysates, or blood plasma/serum and proteolytic digestion of proteins (usually with trypsin); (4) evaluation of the assay in the intended biological matrix to determine if e

Aerodynamic Optimization of Rocket Control Surface Geometry Using Cartesian Methods and CAD Geometry

NASA Technical Reports Server (NTRS)

Nelson, Andrea; Aftosmis, Michael J.; Nemec, Marian; Pulliam, Thomas H.

2004-01-01

Aerodynamic design is an iterative process involving geometry manipulation and complex computational analysis subject to physical constraints and aerodynamic objectives. A design cycle consists of first establishing the performance of a baseline design, which is usually created with low-fidelity engineering tools, and then progressively optimizing the design to maximize its performance. Optimization techniques have evolved from relying exclusively on designer intuition and insight in traditional trial and error methods, to sophisticated local and global search methods. Recent attempts at automating the search through a large design space with formal optimization methods include both database driven and direct evaluation schemes. Databases are being used in conjunction with surrogate and neural network models as a basis on which to run optimization algorithms. Optimization algorithms are also being driven by the direct evaluation of objectives and constraints using high-fidelity simulations. Surrogate methods use data points obtained from simulations, and possibly gradients evaluated at the data points, to create mathematical approximations of a database. Neural network models work in a similar fashion, using a number of high-fidelity database calculations as training iterations to create a database model. Optimal designs are obtained by coupling an optimization algorithm to the database model. Evaluation of the current best design then gives either a new local optima and/or increases the fidelity of the approximation model for the next iteration. Surrogate methods have also been developed that iterate on the selection of data points to decrease the uncertainty of the approximation model prior to searching for an optimal design. The database approximation models for each of these cases, however, become computationally expensive with increase in dimensionality. Thus the method of using optimization algorithms to search a database model becomes problematic as the number of design variables is increased.

Methylene blue as a lignin surrogate in manganese peroxidase reaction systems.

PubMed

Goby, Jeffrey D; Penner, Michael H; Lajoie, Curtis A; Kelly, Christine J

2017-11-15

Manganese peroxidase (MnP) is associated with lignin degradation and is thus relevant to lignocellulosic-utilization technologies. Technological applications require reaction mixture optimization. A surrogate substrate can facilitate this if its susceptibility to degradation is easily monitored and mirrors that of lignin. The dye methylene blue (MB) was evaluated in these respects as a surrogate substrate by testing its reactivity in reaction mixtures containing relevant redox mediators (dicarboxylic acids, fatty acids). Relative rates of MB degradation were compared to available literature reports of lignin degradation under similar conditions, and suggest that MB can be a useful lignin surrogate in MnP systems. Copyright © 2017 Elsevier Inc. All rights reserved.

Subpixel Mapping of Hyperspectral Image Based on Linear Subpixel Feature Detection and Object Optimization

NASA Astrophysics Data System (ADS)

Liu, Zhaoxin; Zhao, Liaoying; Li, Xiaorun; Chen, Shuhan

2018-04-01

Owing to the limitation of spatial resolution of the imaging sensor and the variability of ground surfaces, mixed pixels are widesperead in hyperspectral imagery. The traditional subpixel mapping algorithms treat all mixed pixels as boundary-mixed pixels while ignoring the existence of linear subpixels. To solve this question, this paper proposed a new subpixel mapping method based on linear subpixel feature detection and object optimization. Firstly, the fraction value of each class is obtained by spectral unmixing. Secondly, the linear subpixel features are pre-determined based on the hyperspectral characteristics and the linear subpixel feature; the remaining mixed pixels are detected based on maximum linearization index analysis. The classes of linear subpixels are determined by using template matching method. Finally, the whole subpixel mapping results are iteratively optimized by binary particle swarm optimization algorithm. The performance of the proposed subpixel mapping method is evaluated via experiments based on simulated and real hyperspectral data sets. The experimental results demonstrate that the proposed method can improve the accuracy of subpixel mapping.

Sparse Polynomial Chaos Surrogate for ACME Land Model via Iterative Bayesian Compressive Sensing

NASA Astrophysics Data System (ADS)

Sargsyan, K.; Ricciuto, D. M.; Safta, C.; Debusschere, B.; Najm, H. N.; Thornton, P. E.

2015-12-01

For computationally expensive climate models, Monte-Carlo approaches of exploring the input parameter space are often prohibitive due to slow convergence with respect to ensemble size. To alleviate this, we build inexpensive surrogates using uncertainty quantification (UQ) methods employing Polynomial Chaos (PC) expansions that approximate the input-output relationships using as few model evaluations as possible. However, when many uncertain input parameters are present, such UQ studies suffer from the curse of dimensionality. In particular, for 50-100 input parameters non-adaptive PC representations have infeasible numbers of basis terms. To this end, we develop and employ Weighted Iterative Bayesian Compressive Sensing to learn the most important input parameter relationships for efficient, sparse PC surrogate construction with posterior uncertainty quantified due to insufficient data. Besides drastic dimensionality reduction, the uncertain surrogate can efficiently replace the model in computationally intensive studies such as forward uncertainty propagation and variance-based sensitivity analysis, as well as design optimization and parameter estimation using observational data. We applied the surrogate construction and variance-based uncertainty decomposition to Accelerated Climate Model for Energy (ACME) Land Model for several output QoIs at nearly 100 FLUXNET sites covering multiple plant functional types and climates, varying 65 input parameters over broad ranges of possible values. This work is supported by the U.S. Department of Energy, Office of Science, Biological and Environmental Research, Accelerated Climate Modeling for Energy (ACME) project. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000.

Laser dimpling process parameters selection and optimization using surrogate-driven process capability space

NASA Astrophysics Data System (ADS)

Ozkat, Erkan Caner; Franciosa, Pasquale; Ceglarek, Dariusz

2017-08-01

Remote laser welding technology offers opportunities for high production throughput at a competitive cost. However, the remote laser welding process of zinc-coated sheet metal parts in lap joint configuration poses a challenge due to the difference between the melting temperature of the steel (∼1500 °C) and the vapourizing temperature of the zinc (∼907 °C). In fact, the zinc layer at the faying surface is vapourized and the vapour might be trapped within the melting pool leading to weld defects. Various solutions have been proposed to overcome this problem over the years. Among them, laser dimpling has been adopted by manufacturers because of its flexibility and effectiveness along with its cost advantages. In essence, the dimple works as a spacer between the two sheets in lap joint and allows the zinc vapour escape during welding process, thereby preventing weld defects. However, there is a lack of comprehensive characterization of dimpling process for effective implementation in real manufacturing system taking into consideration inherent changes in variability of process parameters. This paper introduces a methodology to develop (i) surrogate model for dimpling process characterization considering multiple-inputs (i.e. key control characteristics) and multiple-outputs (i.e. key performance indicators) system by conducting physical experimentation and using multivariate adaptive regression splines; (ii) process capability space (Cp-Space) based on the developed surrogate model that allows the estimation of a desired process fallout rate in the case of violation of process requirements in the presence of stochastic variation; and, (iii) selection and optimization of the process parameters based on the process capability space. The proposed methodology provides a unique capability to: (i) simulate the effect of process variation as generated by manufacturing process; (ii) model quality requirements with multiple and coupled quality requirements; and (iii) optimize process parameters under competing quality requirements such as maximizing the dimple height while minimizing the dimple lower surface area.

Efficient Bayesian experimental design for contaminant source identification

NASA Astrophysics Data System (ADS)

Zhang, J.; Zeng, L.

2013-12-01

In this study, an efficient full Bayesian approach is developed for the optimal sampling well location design and source parameter identification of groundwater contaminants. An information measure, i.e., the relative entropy, is employed to quantify the information gain from indirect concentration measurements in identifying unknown source parameters such as the release time, strength and location. In this approach, the sampling location that gives the maximum relative entropy is selected as the optimal one. Once the sampling location is determined, a Bayesian approach based on Markov Chain Monte Carlo (MCMC) is used to estimate unknown source parameters. In both the design and estimation, the contaminant transport equation is required to be solved many times to evaluate the likelihood. To reduce the computational burden, an interpolation method based on the adaptive sparse grid is utilized to construct a surrogate for the contaminant transport. The approximated likelihood can be evaluated directly from the surrogate, which greatly accelerates the design and estimation process. The accuracy and efficiency of our approach are demonstrated through numerical case studies. Compared with the traditional optimal design, which is based on the Gaussian linear assumption, the method developed in this study can cope with arbitrary nonlinearity. It can be used to assist in groundwater monitor network design and identification of unknown contaminant sources. Contours of the expected information gain. The optimal observing location corresponds to the maximum value. Posterior marginal probability densities of unknown parameters, the thick solid black lines are for the designed location. For comparison, other 7 lines are for randomly chosen locations. The true values are denoted by vertical lines. It is obvious that the unknown parameters are estimated better with the desinged location.

Sequential Designs Based on Bayesian Uncertainty Quantification in Sparse Representation Surrogate Modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Ray -Bing; Wang, Weichung; Jeff Wu, C. F.

A numerical method, called OBSM, was recently proposed which employs overcomplete basis functions to achieve sparse representations. While the method can handle non-stationary response without the need of inverting large covariance matrices, it lacks the capability to quantify uncertainty in predictions. We address this issue by proposing a Bayesian approach which first imposes a normal prior on the large space of linear coefficients, then applies the MCMC algorithm to generate posterior samples for predictions. From these samples, Bayesian credible intervals can then be obtained to assess prediction uncertainty. A key application for the proposed method is the efficient construction ofmore » sequential designs. Several sequential design procedures with different infill criteria are proposed based on the generated posterior samples. As a result, numerical studies show that the proposed schemes are capable of solving problems of positive point identification, optimization, and surrogate fitting.« less

Sequential Designs Based on Bayesian Uncertainty Quantification in Sparse Representation Surrogate Modeling

DOE PAGES

Chen, Ray -Bing; Wang, Weichung; Jeff Wu, C. F.

2017-04-12

A numerical method, called OBSM, was recently proposed which employs overcomplete basis functions to achieve sparse representations. While the method can handle non-stationary response without the need of inverting large covariance matrices, it lacks the capability to quantify uncertainty in predictions. We address this issue by proposing a Bayesian approach which first imposes a normal prior on the large space of linear coefficients, then applies the MCMC algorithm to generate posterior samples for predictions. From these samples, Bayesian credible intervals can then be obtained to assess prediction uncertainty. A key application for the proposed method is the efficient construction ofmore » sequential designs. Several sequential design procedures with different infill criteria are proposed based on the generated posterior samples. As a result, numerical studies show that the proposed schemes are capable of solving problems of positive point identification, optimization, and surrogate fitting.« less

A general framework to learn surrogate relevance criterion for atlas based image segmentation

NASA Astrophysics Data System (ADS)

Zhao, Tingting; Ruan, Dan

2016-09-01

Multi-atlas based image segmentation sees great opportunities in the big data era but also faces unprecedented challenges in identifying positive contributors from extensive heterogeneous data. To assess data relevance, image similarity criteria based on various image features widely serve as surrogates for the inaccessible geometric agreement criteria. This paper proposes a general framework to learn image based surrogate relevance criteria to better mimic the behaviors of segmentation based oracle geometric relevance. The validity of its general rationale is verified in the specific context of fusion set selection for image segmentation. More specifically, we first present a unified formulation for surrogate relevance criteria and model the neighborhood relationship among atlases based on the oracle relevance knowledge. Surrogates are then trained to be small for geometrically relevant neighbors and large for irrelevant remotes to the given targets. The proposed surrogate learning framework is verified in corpus callosum segmentation. The learned surrogates demonstrate superiority in inferring the underlying oracle value and selecting relevant fusion set, compared to benchmark surrogates.

Outcomes for Gestational Carriers Versus Traditional Surrogates in the United States.

PubMed

Fuchs, Erika L; Berenson, Abbey B

2018-05-01

Little is known about the obstetric and procedural outcomes of traditional surrogates and gestational carriers. Participants included 222 women living in the United States who completed a brief online survey between November 2015 and February 2016. Differences between gestational carriers (n = 204) and traditional surrogates (n = 18) in demographic characteristics, pregnancy outcomes, and procedural outcomes were examined using chi-squared tests, Fisher's exact tests, and t-tests. Out of 248 eligible respondents, 222 surveys were complete, for a response rate of 89.5%. Overall, obstetric outcomes were similar among gestational carriers and traditional surrogates. Traditional surrogates were more likely than gestational carriers to have a Center for Epidemiologic Studies Depression Scale Revised score of 16 or higher (37.5% vs. 4.0%). Gestational carriers reported higher mean compensation ($27,162.80 vs. $17,070.07) and were more likely to travel over 400 miles (46.0% vs. 0.0%) than traditional surrogates. Procedural differences, but not differences in obstetric outcomes, emerged between gestational carriers and traditional surrogates. To ensure that both traditional surrogates and gestational carriers receive optimal medical care, it may be necessary to extend practice guidelines to ensure that traditional surrogates are offered the same level of care offered to gestational carriers.

Strain gage based determination of mixed mode SIFs

NASA Astrophysics Data System (ADS)

Murthy, K. S. R. K.; Sarangi, H.; Chakraborty, D.

2018-05-01

Accurate determination of mixed mode stress intensity factors (SIFs) is essential in understanding and analysis of mixed mode fracture of engineering components. Only a few strain gage determination of mixed mode SIFs are reported in literatures and those also do not provide any prescription for radial locations of strain gages to ensure accuracy of measurement. The present investigation experimentally demonstrates the efficacy of a proposed methodology for the accurate determination of mixed mode I/II SIFs using strain gages. The proposed approach is based on the modified Dally and Berger's mixed mode technique. Using the proposed methodology appropriate gage locations (optimal locations) for a given configuration have also been suggested ensuring accurate determination of mixed mode SIFs. Experiments have been conducted by locating the gages at optimal and non-optimal locations to study the efficacy of the proposed approach. The experimental results from the present investigation show that highly accurate SIFs (0.064%) can be determined using the proposed approach if the gages are located at the suggested optimal locations. On the other hand, results also show the very high errors (212.22%) in measured SIFs possible if the gages are located at non-optimal locations. The present work thus clearly substantiates the importance of knowing the optimal locations of the strain gages apriori in accurate determination of SIFs.

Improving the Hydrodynamic Performance of Diffuser Vanes via Shape Optimization

NASA Technical Reports Server (NTRS)

Goel, Tushar; Dorney, Daniel J.; Haftka, Raphael T.; Shyy, Wei

2007-01-01

The performance of a diffuser in a pump stage depends on its configuration and placement within the stage. The influence of vane shape on the hydrodynamic performance of a diffuser has been studied. The goal of this effort has been to improve the performance of a pump stage by optimizing the shape of the diffuser vanes. The shape of the vanes was defined using Bezier curves and circular arcs. Surrogate model based tools were used to identify regions of the vane that have a strong influence on its performance. Optimization of the vane shape, in the absence of manufacturing, and stress constraints, led to a nearly nine percent reduction in the total pressure losses compared to the baseline design by reducing the extent of the base separation.

An efficient adaptive sampling strategy for global surrogate modeling with applications in multiphase flow simulation

NASA Astrophysics Data System (ADS)

Mo, S.; Lu, D.; Shi, X.; Zhang, G.; Ye, M.; Wu, J.

2016-12-01

Surrogate models have shown remarkable computational efficiency in hydrological simulations involving design space exploration, sensitivity analysis, uncertainty quantification, etc. The central task of constructing a global surrogate models is to achieve a prescribed approximation accuracy with as few original model executions as possible, which requires a good design strategy to optimize the distribution of data points in the parameter domains and an effective stopping criterion to automatically terminate the design process when desired approximation accuracy is achieved. This study proposes a novel adaptive sampling strategy, which starts from a small number of initial samples and adaptively selects additional samples by balancing the collection in unexplored regions and refinement in interesting areas. We define an efficient and effective evaluation metric basing on Taylor expansion to select the most promising potential samples from candidate points, and propose a robust stopping criterion basing on the approximation accuracy at new points to guarantee the achievement of desired accuracy. The numerical results of several benchmark analytical functions indicate that the proposed approach is more computationally efficient and robust than the widely used maximin distance design and two other well-known adaptive sampling strategies. The application to two complicated multiphase flow problems further demonstrates the efficiency and effectiveness of our method in constructing global surrogate models for high-dimensional and highly nonlinear problems. Acknowledgements: This work was financially supported by the National Nature Science Foundation of China grants No. 41030746 and 41172206.

Aerodynamic design applying automatic differentiation and using robust variable fidelity optimization

NASA Astrophysics Data System (ADS)

Takemiya, Tetsushi

In modern aerospace engineering, the physics-based computational design method is becoming more important, as it is more efficient than experiments and because it is more suitable in designing new types of aircraft (e.g., unmanned aerial vehicles or supersonic business jets) than the conventional design method, which heavily relies on historical data. To enhance the reliability of the physics-based computational design method, researchers have made tremendous efforts to improve the fidelity of models. However, high-fidelity models require longer computational time, so the advantage of efficiency is partially lost. This problem has been overcome with the development of variable fidelity optimization (VFO). In VFO, different fidelity models are simultaneously employed in order to improve the speed and the accuracy of convergence in an optimization process. Among the various types of VFO methods, one of the most promising methods is the approximation management framework (AMF). In the AMF, objective and constraint functions of a low-fidelity model are scaled at a design point so that the scaled functions, which are referred to as "surrogate functions," match those of a high-fidelity model. Since scaling functions and the low-fidelity model constitutes surrogate functions, evaluating the surrogate functions is faster than evaluating the high-fidelity model. Therefore, in the optimization process, in which gradient-based optimization is implemented and thus many function calls are required, the surrogate functions are used instead of the high-fidelity model to obtain a new design point. The best feature of the AMF is that it may converge to a local optimum of the high-fidelity model in much less computational time than the high-fidelity model. However, through literature surveys and implementations of the AMF, the author xx found that (1) the AMF is very vulnerable when the computational analysis models have numerical noise, which is very common in high-fidelity models, and that (2) the AMF terminates optimization erroneously when the optimization problems have constraints. The first problem is due to inaccuracy in computing derivatives in the AMF, and the second problem is due to erroneous treatment of the trust region ratio, which sets the size of the domain for an optimization in the AMF. In order to solve the first problem of the AMF, automatic differentiation (AD) technique, which reads the codes of analysis models and automatically generates new derivative codes based on some mathematical rules, is applied. If derivatives are computed with the generated derivative code, they are analytical, and the required computational time is independent of the number of design variables, which is very advantageous for realistic aerospace engineering problems. However, if analysis models implement iterative computations such as computational fluid dynamics (CFD), which solves system partial differential equations iteratively, computing derivatives through the AD requires a massive memory size. The author solved this deficiency by modifying the AD approach and developing a more efficient implementation with CFD, and successfully applied the AD to general CFD software. In order to solve the second problem of the AMF, the governing equation of the trust region ratio, which is very strict against the violation of constraints, is modified so that it can accept the violation of constraints within some tolerance. By accepting violations of constraints during the optimization process, the AMF can continue optimization without terminating immaturely and eventually find the true optimum design point. With these modifications, the AMF is referred to as "Robust AMF," and it is applied to airfoil and wing aerodynamic design problems using Euler CFD software. The former problem has 21 design variables, and the latter 64. In both problems, derivatives computed with the proposed AD method are first compared with those computed with the finite differentiation (FD) method, and then, the Robust AMF is implemented along with the sequential quadratic programming (SQP) optimization method with only high-fidelity models. The proposed AD method computes derivatives more accurately and faster than the FD method, and the Robust AMF successfully optimizes shapes of the airfoil and the wing in a much shorter time than SQP with only high-fidelity models. These results clearly show the effectiveness of the Robust AMF. Finally, the feasibility of reducing computational time for calculating derivatives and the necessity of AMF with an optimum design point always in the feasible region are discussed as future work.

Learning Incoherent Sparse and Low-Rank Patterns from Multiple Tasks

PubMed Central

Chen, Jianhui; Liu, Ji; Ye, Jieping

2013-01-01

We consider the problem of learning incoherent sparse and low-rank patterns from multiple tasks. Our approach is based on a linear multi-task learning formulation, in which the sparse and low-rank patterns are induced by a cardinality regularization term and a low-rank constraint, respectively. This formulation is non-convex; we convert it into its convex surrogate, which can be routinely solved via semidefinite programming for small-size problems. We propose to employ the general projected gradient scheme to efficiently solve such a convex surrogate; however, in the optimization formulation, the objective function is non-differentiable and the feasible domain is non-trivial. We present the procedures for computing the projected gradient and ensuring the global convergence of the projected gradient scheme. The computation of projected gradient involves a constrained optimization problem; we show that the optimal solution to such a problem can be obtained via solving an unconstrained optimization subproblem and an Euclidean projection subproblem. We also present two projected gradient algorithms and analyze their rates of convergence in details. In addition, we illustrate the use of the presented projected gradient algorithms for the proposed multi-task learning formulation using the least squares loss. Experimental results on a collection of real-world data sets demonstrate the effectiveness of the proposed multi-task learning formulation and the efficiency of the proposed projected gradient algorithms. PMID:24077658

Learning Incoherent Sparse and Low-Rank Patterns from Multiple Tasks.

PubMed

Chen, Jianhui; Liu, Ji; Ye, Jieping

2012-02-01

We consider the problem of learning incoherent sparse and low-rank patterns from multiple tasks. Our approach is based on a linear multi-task learning formulation, in which the sparse and low-rank patterns are induced by a cardinality regularization term and a low-rank constraint, respectively. This formulation is non-convex; we convert it into its convex surrogate, which can be routinely solved via semidefinite programming for small-size problems. We propose to employ the general projected gradient scheme to efficiently solve such a convex surrogate; however, in the optimization formulation, the objective function is non-differentiable and the feasible domain is non-trivial. We present the procedures for computing the projected gradient and ensuring the global convergence of the projected gradient scheme. The computation of projected gradient involves a constrained optimization problem; we show that the optimal solution to such a problem can be obtained via solving an unconstrained optimization subproblem and an Euclidean projection subproblem. We also present two projected gradient algorithms and analyze their rates of convergence in details. In addition, we illustrate the use of the presented projected gradient algorithms for the proposed multi-task learning formulation using the least squares loss. Experimental results on a collection of real-world data sets demonstrate the effectiveness of the proposed multi-task learning formulation and the efficiency of the proposed projected gradient algorithms.

ARES Simulations of a Double Shell Surrogate Target

NASA Astrophysics Data System (ADS)

Sacks, Ryan; Tipton, Robert; Graziani, Frank

2015-11-01

Double shell targets provide an alternative path to ignition that allows for a less robust laser profile and non-cryogenic initial temperatures. The target designs call for a high-Z material to abut the gas/liquid DT fuel which is cause for concern due to possible mix of the inner shell with the fuel. This research concentrates on developing a surrogate target for a double shell capsule that can be fielded in a current NIF two-shock hohlraum. Through pressure-density scaling the hydrodynamic behavior of the high-Z pusher of a double shell can be approximated allowing for studies of performance and mix. Use of the ARES code allows for investigation of mix in one and two dimensions and analysis of instabilities in two dimensions. Development of a shell material that will allow for experiments similar to CD Mix is also discussed. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under contract DE-AC52-07NA27344, Lawrence Livermore National Security, LLC. Information Management release number LLNL-ABS-675098.

Seasonal Avifauna Reponses to Fuel Reduction Treatments in the Upper Piedmont of South Carolina: Results From Phase 1 of the National Fire and Fire Surrogate Study

Treesearch

Laura A. Zebehazy; J. Drew Lanham; Thomas A. Waldrop

2004-01-01

We examined avian species and assemblage responses to prescribed burns and thinning in a southeastern Piedmont pine and mixed pine-hardwood forest as part of the National Fire and Fire Surrogate Study (NFFS) examining the effects of fuel reduction on forest health. Point counts conducted during the non-breeding and breeding seasons of 2000-2002 showed that winter bird...

Real-time tumor motion estimation using respiratory surrogate via memory-based learning

NASA Astrophysics Data System (ADS)

Li, Ruijiang; Lewis, John H.; Berbeco, Ross I.; Xing, Lei

2012-08-01

Respiratory tumor motion is a major challenge in radiation therapy for thoracic and abdominal cancers. Effective motion management requires an accurate knowledge of the real-time tumor motion. External respiration monitoring devices (optical, etc) provide a noninvasive, non-ionizing, low-cost and practical approach to obtain the respiratory signal. Due to the highly complex and nonlinear relations between tumor and surrogate motion, its ultimate success hinges on the ability to accurately infer the tumor motion from respiratory surrogates. Given their widespread use in the clinic, such a method is critically needed. We propose to use a powerful memory-based learning method to find the complex relations between tumor motion and respiratory surrogates. The method first stores the training data in memory and then finds relevant data to answer a particular query. Nearby data points are assigned high relevance (or weights) and conversely distant data are assigned low relevance. By fitting relatively simple models to local patches instead of fitting one single global model, it is able to capture highly nonlinear and complex relations between the internal tumor motion and external surrogates accurately. Due to the local nature of weighting functions, the method is inherently robust to outliers in the training data. Moreover, both training and adapting to new data are performed almost instantaneously with memory-based learning, making it suitable for dynamically following variable internal/external relations. We evaluated the method using respiratory motion data from 11 patients. The data set consists of simultaneous measurement of 3D tumor motion and 1D abdominal surface (used as the surrogate signal in this study). There are a total of 171 respiratory traces, with an average peak-to-peak amplitude of ∼15 mm and average duration of ∼115 s per trace. Given only 5 s (roughly one breath) pretreatment training data, the method achieved an average 3D error of 1.5 mm and 95th percentile error of 3.4 mm on unseen test data. The average 3D error was further reduced to 1.4 mm when the model was tuned to its optimal setting for each respiratory trace. In one trace where a few outliers are present in the training data, the proposed method achieved an error reduction of as much as ∼50% compared with the best linear model (1.0 mm versus 2.1 mm). The memory-based learning technique is able to accurately capture the highly complex and nonlinear relations between tumor and surrogate motion in an efficient manner (a few milliseconds per estimate). Furthermore, the algorithm is particularly suitable to handle situations where the training data are contaminated by large errors or outliers. These desirable properties make it an ideal candidate for accurate and robust tumor gating/tracking using respiratory surrogates.

Measuring bed load discharge in rivers: bedload-surrogate monitoring workshop Minneapolis, Minnesota, 11-14 April 2007

USGS Publications Warehouse

Gray, John R.; Laronne, Jonathan B.; Marr, Jeffrey D.G.

2007-01-01

The International Bedload-Surrogate Monitoring Workshop (http://www.nced.umn.edu/BRIC_2007.html), organized by the Bedload Research International Cooperative (BRIC; www.bedloadresearch.org), was held to assess and abet progress in continuous, semiautomated, or fully automated (surrogate) technologies for monitoring bed load discharge in gravel-, sand-, and mixed gravel-sand-bedded rivers. Direct bed load measurements, particularly at medium and high flows, during which most bed load occurs, tend to be time-consuming, expensive, and potentially hazardous. Surrogate technologies developed largely over the past decade and used at a number of research sites around the world show considerable promise toward providing relatively dense, robust, and quantifiably reliable bed load data sets. However, information on the efficacy of selected technologies for use in monitoring programs is needed, as is identification of the ways and means for bringing the most promising and practical of the technologies to fruition.

Multi-Modal Surrogates for Retrieving and Making Sense of Videos: Is Synchronization between the Multiple Modalities Optimal?

ERIC Educational Resources Information Center

Song, Yaxiao

2010-01-01

Video surrogates can help people quickly make sense of the content of a video before downloading or seeking more detailed information. Visual and audio features of a video are primary information carriers and might become important components of video retrieval and video sense-making. In the past decades, most research and development efforts on…

Virucides in apiculture: persistence of surrogate enterovirus under simulated field conditions.

PubMed

Prodělalová, Jana; Malenovská, Hana; Moutelíková, Romana; Titěra, Dalibor

2017-12-01

Honeybee viruses have been recognized as being among the most important factors leading to colony losses worldwide. Colony food and faeces are regarded as possible sources of infectious viruses able to contaminate the environment and equipment of apiaries. Thus, methods for elimination of viruses are required. No cell culture assay for testing the effect of disinfectants on honeybee viruses is yet available. Therefore, surrogate virus was employed for testing of the efficacy of iodophor- and peracetic acid-based disinfectants in combination with six organic contaminants at +6 °C and +22 °C. Moreover, we evaluated the persistence of the surrogate in honey at +6 °C, +22 °C, and +50 °C. Iodophor-based disinfectant showed a maximum reduction of virus titre of 3.4 log 10 . Peracetic acid reduced the titre (≥4 log 10 ) only at 22 °C and without yeast extract/bovine serum albumin. After 25 days of incubation of the virus - honey mix, no decrease of virus titre was observed at +6 °C, whereas a significant reduction (3.5 log 10 ) was found at +50 °C already after 1 day. Both tested disinfectants can serve as appropriate virucides in apiaries. The effect of peracetic acid significantly depended on temperature and organic contaminants. The iodophor-based disinfectant showed a stable antiviral effect at different temperatures and with different contaminants. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

A Kind of Nonlinear Programming Problem Based on Mixed Fuzzy Relation Equations Constraints

NASA Astrophysics Data System (ADS)

Li, Jinquan; Feng, Shuang; Mi, Honghai

In this work, a kind of nonlinear programming problem with non-differential objective function and under the constraints expressed by a system of mixed fuzzy relation equations is investigated. First, some properties of this kind of optimization problem are obtained. Then, a polynomial-time algorithm for this kind of optimization problem is proposed based on these properties. Furthermore, we show that this algorithm is optimal for the considered optimization problem in this paper. Finally, numerical examples are provided to illustrate our algorithms.

Mixed H(2)/H(sub infinity): Control with output feedback compensators using parameter optimization

NASA Technical Reports Server (NTRS)

Schoemig, Ewald; Ly, Uy-Loi

1992-01-01

Among the many possible norm-based optimization methods, the concept of H-infinity optimal control has gained enormous attention in the past few years. Here the H-infinity framework, based on the Small Gain Theorem and the Youla Parameterization, effectively treats system uncertainties in the control law synthesis. A design approach involving a mixed H(sub 2)/H-infinity norm strives to combine the advantages of both methods. This advantage motivates researchers toward finding solutions to the mixed H(sub 2)/H-infinity control problem. The approach developed in this research is based on a finite time cost functional that depicts an H-infinity bound control problem in a H(sub 2)-optimization setting. The goal is to define a time-domain cost function that optimizes the H(sub 2)-norm of a system with an H-infinity-constraint function.

Mixed H2/H(infinity)-Control with an output-feedback compensator using parameter optimization

NASA Technical Reports Server (NTRS)

Schoemig, Ewald; Ly, Uy-Loi

1992-01-01

Among the many possible norm-based optimization methods, the concept of H-infinity optimal control has gained enormous attention in the past few years. Here the H-infinity framework, based on the Small Gain Theorem and the Youla Parameterization, effectively treats system uncertainties in the control law synthesis. A design approach involving a mixed H(sub 2)/H-infinity norm strives to combine the advantages of both methods. This advantage motivates researchers toward finding solutions to the mixed H(sub 2)/H-infinity control problem. The approach developed in this research is based on a finite time cost functional that depicts an H-infinity bound control problem in a H(sub 2)-optimization setting. The goal is to define a time-domain cost function that optimizes the H(sub 2)-norm of a system with an H-infinity-constraint function.

CFD-based optimization in plastics extrusion

NASA Astrophysics Data System (ADS)

Eusterholz, Sebastian; Elgeti, Stefanie

2018-05-01

This paper presents novel ideas in numerical design of mixing elements in single-screw extruders. The actual design process is reformulated as a shape optimization problem, given some functional, but possibly inefficient initial design. Thereby automatic optimization can be incorporated and the design process is advanced, beyond the simulation-supported, but still experience-based approach. This paper proposes concepts to extend a method which has been developed and validated for die design to the design of mixing-elements. For simplicity, it focuses on single-phase flows only. The developed method conducts forward-simulations to predict the quasi-steady melt behavior in the relevant part of the extruder. The result of each simulation is used in a black-box optimization procedure based on an efficient low-order parameterization of the geometry. To minimize user interaction, an objective function is formulated that quantifies the products' quality based on the forward simulation. This paper covers two aspects: (1) It reviews the set-up of the optimization framework as discussed in [1], and (2) it details the necessary extensions for the optimization of mixing elements in single-screw extruders. It concludes with a presentation of first advances in the unsteady flow simulation of a metering and mixing section with the SSMUM [2] using the Carreau material model.

NURSE-LED INTERVENTION TO IMPROVE SURROGATE DECISION MAKING FOR PATIENTS WITH ADVANCED CRITICAL ILLNESS

PubMed Central

White, Douglas B.; Cua, Sarah Martin; Walk, Roberta; Pollice, Laura; Weissfeld, Lisa; Hong, Seoyeon; Landefeld, C. Seth; Arnold, Robert M.

2013-01-01

Background Problems persist with surrogate decision making in intensive care units, leading to distress for surrogates and treatment that may not reflect patients’ values. Objectives To assess the feasibility, acceptability, and perceived effectiveness of a multifaceted, nurse-led intervention to improve surrogate decision making in intensive care units. Study Design A single-center, single-arm, interventional study in which 35 surrogates and 15 physicians received the Four Supports Intervention, which involved incorporating a family support specialist into the intensive care team. That specialist maintained a longitudinal relationship with surrogates and provided emotional support, communication support, decision support, and anticipatory grief support. A mixed-methods approach was used to evaluate the intervention. Results The intervention was implemented successfully in all 15 patients, with a high level of completion of each component of the intervention. The family support specialist devoted a mean of 48 (SD 36) minutes per day to each clinician-patient-family triad. All participants reported that they would recommend the intervention to others. At least 90% of physicians and surrogates reported that the intervention (1) improved the quality and timeliness of communication, (2) facilitated discussion of the patient’s values and treatment preferences, and (3) improved the patient-centeredness of care. Conclusions The Four Supports Intervention is feasible, acceptable, and was perceived by physicians and surrogates to improve the quality of decision making and the patient-centeredness of care. A randomized trial is warranted to determine whether the intervention improves patient, family, and health system outcomes. PMID:23117903

Collaboration pathway(s) using new tools for optimizing operational climate monitoring from space

NASA Astrophysics Data System (ADS)

Helmuth, Douglas B.; Selva, Daniel; Dwyer, Morgan M.

2014-10-01

Consistently collecting the earth's climate signatures remains a priority for world governments and international scientific organizations. Architecting a solution requires transforming scientific missions into an optimized robust `operational' constellation that addresses the needs of decision makers, scientific investigators and global users for trusted data. The application of new tools offers pathways for global architecture collaboration. Recent (2014) rulebased decision engine modeling runs that targeted optimizing the intended NPOESS architecture, becomes a surrogate for global operational climate monitoring architecture(s). This rule-based systems tools provide valuable insight for Global climate architectures, through the comparison and evaluation of alternatives considered and the exhaustive range of trade space explored. A representative optimization of Global ECV's (essential climate variables) climate monitoring architecture(s) is explored and described in some detail with thoughts on appropriate rule-based valuations. The optimization tools(s) suggest and support global collaboration pathways and hopefully elicit responses from the audience and climate science shareholders.

Chemical degradation of trimethyl phosphate as surrogate for organo-phosporus pesticides on nanostructured metal oxides

DOE Office of Scientific and Technical Information (OSTI.GOV)

Štengl, Václav, E-mail: stengl@iic.cas.cz; Henych, Jiří; Grygar, Tomáš

Nanostructured TiO{sub 2} and mixed oxides of Ti and Fe, Hf, In, Mn or Zr -were prepared by homogeneous hydrolysis of aqueous solution of metal sulphates with urea. The oxides were characterised by X-ray powder diffraction (XRD), scanning electron microscopy, particle size distribution, surface area and porosity. The oxide materials consists of a few nanometre primary crystals (mainly anatase) arranged in a few micrometre regular spherical agglomerates with specific surface area 133–511 m{sup 2} g{sup −1}. The FTIR diffuse spectroscopy was used for monitoring chemical degradation of trimethylphosphate (TMP) as a surrogate for organo-phosphorus pesticides under ambient and higher temperatures.more » Undoped TiO{sub 2} and Ti,Mn-mixed oxide were most active in cleavage (hydrolysis) of CH{sub 3}O from TMP at room temperature and 100 °C. Cleavage of CH{sub 3}O in the other studied mixed oxides was not complete until temperature exceeds the boiling point of TMP.« less

The Effects of Prescribed Burning and Thinning on Herpetofauna and Small Mammals in the Upper Piedmont of South Carolina: Preliminary Results of the National Fire and Fire Surrogate Study

Treesearch

Eran S. Kilpatrick; Dean B. Kubacz; David C. Guynn; J. Drew Lanham; Thomas A. Waldrop

2004-01-01

Due to heavy fuel loads resulting from years of fire suppression, upland pine and mixed pine hardwood forests in the Upper Piedmont of South Carolina are at risk of severe wildfire. The National Fire and Fire Surrogate Study (NFFS) was conducted on the Clemson Experimental Forest to study the effects of prescribed burning and thinning on a multitude of factors,...

An improved NSGA - II algorithm for mixed model assembly line balancing

NASA Astrophysics Data System (ADS)

Wu, Yongming; Xu, Yanxia; Luo, Lifei; Zhang, Han; Zhao, Xudong

2018-05-01

Aiming at the problems of assembly line balancing and path optimization for material vehicles in mixed model manufacturing system, a multi-objective mixed model assembly line (MMAL), which is based on optimization objectives, influencing factors and constraints, is established. According to the specific situation, an improved NSGA-II algorithm based on ecological evolution strategy is designed. An environment self-detecting operator, which is used to detect whether the environment changes, is adopted in the algorithm. Finally, the effectiveness of proposed model and algorithm is verified by examples in a concrete mixing system.

Optimal control of open quantum systems: A combined surrogate Hamiltonian optimal control theory approach applied to photochemistry on surfaces

NASA Astrophysics Data System (ADS)

Asplund, Erik; Klüner, Thorsten

2012-03-01

In this paper, control of open quantum systems with emphasis on the control of surface photochemical reactions is presented. A quantum system in a condensed phase undergoes strong dissipative processes. From a theoretical viewpoint, it is important to model such processes in a rigorous way. In this work, the description of open quantum systems is realized within the surrogate Hamiltonian approach [R. Baer and R. Kosloff, J. Chem. Phys. 106, 8862 (1997)], 10.1063/1.473950. An efficient and accurate method to find control fields is optimal control theory (OCT) [W. Zhu, J. Botina, and H. Rabitz, J. Chem. Phys. 108, 1953 (1998), 10.1063/1.475576; Y. Ohtsuki, G. Turinici, and H. Rabitz, J. Chem. Phys. 120, 5509 (2004)], 10.1063/1.1650297. To gain control of open quantum systems, the surrogate Hamiltonian approach and OCT, with time-dependent targets, are combined. Three open quantum systems are investigated by the combined method, a harmonic oscillator immersed in an ohmic bath, CO adsorbed on a platinum surface, and NO adsorbed on a nickel oxide surface. Throughout this paper, atomic units, i.e., ℏ = me = e = a0 = 1, have been used unless otherwise stated.

21 CFR 314.510 - Approval based on a surrogate endpoint or on an effect on a clinical endpoint other than survival...

Code of Federal Regulations, 2011 CFR

2011-04-01

... 21 Food and Drugs 5 2011-04-01 2011-04-01 false Approval based on a surrogate endpoint or on an... Serious or Life-Threatening Illnesses § 314.510 Approval based on a surrogate endpoint or on an effect on... the drug product has an effect on a surrogate endpoint that is reasonably likely, based on...

21 CFR 314.510 - Approval based on a surrogate endpoint or on an effect on a clinical endpoint other than survival...

Code of Federal Regulations, 2014 CFR

2014-04-01

... 21 Food and Drugs 5 2014-04-01 2014-04-01 false Approval based on a surrogate endpoint or on an... Serious or Life-Threatening Illnesses § 314.510 Approval based on a surrogate endpoint or on an effect on... the drug product has an effect on a surrogate endpoint that is reasonably likely, based on...

21 CFR 314.510 - Approval based on a surrogate endpoint or on an effect on a clinical endpoint other than survival...

Code of Federal Regulations, 2013 CFR

2013-04-01

... 21 Food and Drugs 5 2013-04-01 2013-04-01 false Approval based on a surrogate endpoint or on an... Serious or Life-Threatening Illnesses § 314.510 Approval based on a surrogate endpoint or on an effect on... the drug product has an effect on a surrogate endpoint that is reasonably likely, based on...

21 CFR 314.510 - Approval based on a surrogate endpoint or on an effect on a clinical endpoint other than survival...

Code of Federal Regulations, 2012 CFR

2012-04-01

... 21 Food and Drugs 5 2012-04-01 2012-04-01 false Approval based on a surrogate endpoint or on an... Serious or Life-Threatening Illnesses § 314.510 Approval based on a surrogate endpoint or on an effect on... the drug product has an effect on a surrogate endpoint that is reasonably likely, based on...

Surrogate alcohol containing methanol, social deprivation and public health in Novosibirsk, Russia.

PubMed

Neufeld, Maria; Lachenmeier, Dirk; Hausler, Thomas; Rehm, Jürgen

2016-11-01

Surrogate alcohol, i.e. alcohol not intended or not officially intended for human consumption, continues to play an important role in alcohol consumption in Russia, especially for people with alcohol dependence. Among the different types of surrogate alcohol, there are windshield washer antifreeze liquids; these products are the cheapest kinds of non-beverage alcohol available and thus likely to be used by the most deprived and marginalised groups such as homeless people with alcohol dependence. Although it is well known, that non-beverage alcohol is used for consumption by various groups in Russia, and although there are laws to prohibit the use of methanol as part of windshield washer antifreeze liquids for the very reason that such products could be used as surrogate alcohol, we detected products in retail sale which were a mix of water and methanol only. Methanol poses serious health threats including blindness and death, and there had been repeated methanol deaths from surrogate alcohol in Russia over the last years. If law-enforcement does not change for surrogate products, we can expect more methanol-resulting deaths in the most deprived and marginalized groups of people with alcohol dependence in Russia. In addition, ingredients with questionable safety profiles such as formic acid should also be prohibited in non-beverage alcohol products that are likely to be consumed as surrogate alcohol. Copyright © 2016. Published by Elsevier B.V.

Low-rank separated representation surrogates of high-dimensional stochastic functions: Application in Bayesian inference

NASA Astrophysics Data System (ADS)

Validi, AbdoulAhad

2014-03-01

This study introduces a non-intrusive approach in the context of low-rank separated representation to construct a surrogate of high-dimensional stochastic functions, e.g., PDEs/ODEs, in order to decrease the computational cost of Markov Chain Monte Carlo simulations in Bayesian inference. The surrogate model is constructed via a regularized alternative least-square regression with Tikhonov regularization using a roughening matrix computing the gradient of the solution, in conjunction with a perturbation-based error indicator to detect optimal model complexities. The model approximates a vector of a continuous solution at discrete values of a physical variable. The required number of random realizations to achieve a successful approximation linearly depends on the function dimensionality. The computational cost of the model construction is quadratic in the number of random inputs, which potentially tackles the curse of dimensionality in high-dimensional stochastic functions. Furthermore, this vector-valued separated representation-based model, in comparison to the available scalar-valued case, leads to a significant reduction in the cost of approximation by an order of magnitude equal to the vector size. The performance of the method is studied through its application to three numerical examples including a 41-dimensional elliptic PDE and a 21-dimensional cavity flow.

Active Learning to Understand Infectious Disease Models and Improve Policy Making

PubMed Central

Vladislavleva, Ekaterina; Broeckhove, Jan; Beutels, Philippe; Hens, Niel

2014-01-01

Modeling plays a major role in policy making, especially for infectious disease interventions but such models can be complex and computationally intensive. A more systematic exploration is needed to gain a thorough systems understanding. We present an active learning approach based on machine learning techniques as iterative surrogate modeling and model-guided experimentation to systematically analyze both common and edge manifestations of complex model runs. Symbolic regression is used for nonlinear response surface modeling with automatic feature selection. First, we illustrate our approach using an individual-based model for influenza vaccination. After optimizing the parameter space, we observe an inverse relationship between vaccination coverage and cumulative attack rate reinforced by herd immunity. Second, we demonstrate the use of surrogate modeling techniques on input-response data from a deterministic dynamic model, which was designed to explore the cost-effectiveness of varicella-zoster virus vaccination. We use symbolic regression to handle high dimensionality and correlated inputs and to identify the most influential variables. Provided insight is used to focus research, reduce dimensionality and decrease decision uncertainty. We conclude that active learning is needed to fully understand complex systems behavior. Surrogate models can be readily explored at no computational expense, and can also be used as emulator to improve rapid policy making in various settings. PMID:24743387

Active learning to understand infectious disease models and improve policy making.

PubMed

Willem, Lander; Stijven, Sean; Vladislavleva, Ekaterina; Broeckhove, Jan; Beutels, Philippe; Hens, Niel

2014-04-01

Modeling plays a major role in policy making, especially for infectious disease interventions but such models can be complex and computationally intensive. A more systematic exploration is needed to gain a thorough systems understanding. We present an active learning approach based on machine learning techniques as iterative surrogate modeling and model-guided experimentation to systematically analyze both common and edge manifestations of complex model runs. Symbolic regression is used for nonlinear response surface modeling with automatic feature selection. First, we illustrate our approach using an individual-based model for influenza vaccination. After optimizing the parameter space, we observe an inverse relationship between vaccination coverage and cumulative attack rate reinforced by herd immunity. Second, we demonstrate the use of surrogate modeling techniques on input-response data from a deterministic dynamic model, which was designed to explore the cost-effectiveness of varicella-zoster virus vaccination. We use symbolic regression to handle high dimensionality and correlated inputs and to identify the most influential variables. Provided insight is used to focus research, reduce dimensionality and decrease decision uncertainty. We conclude that active learning is needed to fully understand complex systems behavior. Surrogate models can be readily explored at no computational expense, and can also be used as emulator to improve rapid policy making in various settings.

Estimating and communicating prognosis in advanced neurologic disease

PubMed Central

Gramling, Robert; Kelly, Adam G.

2013-01-01

Prognosis can no longer be relegated behind diagnosis and therapy in high-quality neurologic care. High-stakes decisions that patients (or their surrogates) make often rest upon perceptions and beliefs about prognosis, many of which are poorly informed. The new science of prognostication—the estimating and communication “what to expect”—is in its infancy and the evidence base to support “best practices” is lacking. We propose a framework for formulating a prediction and communicating “what to expect” with patients, families, and surrogates in the context of common neurologic illnesses. Because neurologic disease affects function as much as survival, we specifically address 2 important prognostic questions: “How long?” and “How well?” We provide a summary of prognostic information and highlight key points when tailoring a prognosis for common neurologic diseases. We discuss the challenges of managing prognostic uncertainty, balancing hope and realism, and ways to effectively engage surrogate decision-makers. We also describe what is known about the nocebo effects and the self-fulfilling prophecy when communicating prognoses. There is an urgent need to establish research and educational priorities to build a credible evidence base to support best practices, improve communication skills, and optimize decision-making. Confronting the challenges of prognosis is necessary to fulfill the promise of delivering high-quality, patient-centered care. PMID:23420894

Modified Bayesian Kriging for Noisy Response Problems for Reliability Analysis

DTIC Science & Technology

2015-01-01

52242, USA nicholas-gaul@uiowa.edu Mary Kathryn Cowles Department of Statistics & Actuarial Science College of Liberal Arts and Sciences , The...Forrester, A. I. J., & Keane, A. J. (2009). Recent advances in surrogate-based optimization. Progress in Aerospace Sciences , 45(1–3), 50-79. doi...Wiley. [27] Sacks, J., Welch, W. J., Toby J. Mitchell, & Wynn, H. P. (1989). Design and analysis of computer experiments. Statistical Science , 4

Facial recognition of heroin vaccine opiates: type 1 cross-reactivities of antibodies induced by hydrolytically stable haptenic surrogates of heroin, 6-acetylmorphine, and morphine.

PubMed

Matyas, Gary R; Rice, Kenner C; Cheng, Kejun; Li, Fuying; Antoline, Joshua F G; Iyer, Malliga R; Jacobson, Arthur E; Mayorov, Alexander V; Beck, Zoltan; Torres, Oscar B; Alving, Carl R

2014-03-14

Novel synthetic compounds similar to heroin and its major active metabolites, 6-acetylmorphine and morphine, were examined as potential surrogate haptens for the ability to interface with the immune system for a heroin vaccine. Recent studies have suggested that heroin-like haptens must degrade hydrolytically to induce independent immune responses both to heroin and to the metabolites, resulting in antisera containing mixtures of antibodies (type 2 cross-reactivity). To test this concept, two unique hydrolytically stable haptens were created based on presumed structural facial similarities to heroin or to its active metabolites. After conjugation of a heroin-like hapten (DiAmHap) to tetanus toxoid and mixing with liposomes containing monophosphoryl lipid A, high titers of antibodies after two injections in mice had complementary binding sites that exhibited strong type 1 ("true") specific cross-reactivity with heroin and with both of its physiologically active metabolites. Mice immunized with each surrogate hapten exhibited reduced antinociceptive effects caused by injection of heroin. This approach obviates the need to create hydrolytically unstable synthetic heroin-like compounds to induce independent immune responses to heroin and its active metabolites for vaccine development. Facial recognition of hydrolytically stable surrogate haptens by antibodies together with type 1 cross-reactivities with heroin and its metabolites can help to guide synthetic chemical strategies for efficient development of a heroin vaccine. Copyright © 2014. Published by Elsevier Ltd.

Facial recognition of heroin vaccine opiates: Type 1 cross-reactivities of antibodies induced by hydrolytically stable haptenic surrogates of heroin, 6-acetylmorphine, and morphine

PubMed Central

Matyas, Gary R.; Rice, Kenner C.; Cheng, Kejun; Li, Fuying; Antoline, Joshua F. G.; Iyer, Malliga R.; Jacobson, Arthur E.; Mayorov, Alexander V.; Beck, Zoltan; Torres, Oscar; Alving, Carl R.

2014-01-01

Novel synthetic compounds similar to heroin and its major active metabolites, 6-acetylmorphine and morphine, were examined as potential surrogate haptens for the ability to interface with the immune system for a heroin vaccine. Recent studies have suggested that heroin-like haptens must degrade hydrolytically to induce independent immune responses both to heroin and to the metabolites, resulting in antisera containing mixtures of antibodies (type 2 cross-reactivity). To test this concept, two unique hydrolytically stable haptens were created based on presumed structural facial similarities to heroin or to its active metabolites. After conjugation of a heroin-like hapten (DiAmHap) to tetanus toxoid and mixing with liposomes containing monophosphoryl lipid A, high titers of antibodies after two injections in mice had complementary binding sites that exhibited strong type 1 (“true”) specific cross-reactivity with heroin and with both of its physiologically active metabolites. Mice immunized with each surrogate hapten exhibited reduced antinociceptive effects caused by injection of heroin. This approach obviates the need to create hydrolytically unstable synthetic heroin-like compounds to induce independent immune responses to heroin and its active metabolites for vaccine development. Facial recognition of hydrolytically stable surrogate haptens by antibodies together with type 1 cross-reactivities with heroin and its metabolites can help to guide synthetic chemical strategies for efficient development of a heroin vaccine. PMID:24486371

Surrogate Plant Data Base : Volume 1. Introduction, Appendix A : The Development of Surrogate Plant Data ; Appendix B : Application of the Surrogate .

DOT National Transportation Integrated Search

1983-03-01

This four volume report consists of a data base describing "surrogate" automobile and truck manufacturing plants developed as part of a methodology for evaluating capital investment requirements in new manufacturing facilities to build new fleets of ...

A weak Hamiltonian finite element method for optimal control problems

NASA Technical Reports Server (NTRS)

Hodges, Dewey H.; Bless, Robert R.

1989-01-01

A temporal finite element method based on a mixed form of the Hamiltonian weak principle is developed for dynamics and optimal control problems. The mixed form of Hamilton's weak principle contains both displacements and momenta as primary variables that are expanded in terms of nodal values and simple polynomial shape functions. Unlike other forms of Hamilton's principle, however, time derivatives of the momenta and displacements do not appear therein; instead, only the virtual momenta and virtual displacements are differentiated with respect to time. Based on the duality that is observed to exist between the mixed form of Hamilton's weak principle and variational principles governing classical optimal control problems, a temporal finite element formulation of the latter can be developed in a rather straightforward manner. Several well-known problems in dynamics and optimal control are illustrated. The example dynamics problem involves a time-marching problem. As optimal control examples, elementary trajectory optimization problems are treated.

A weak Hamiltonian finite element method for optimal control problems

NASA Technical Reports Server (NTRS)

Hodges, Dewey H.; Bless, Robert R.

1990-01-01

A temporal finite element method based on a mixed form of the Hamiltonian weak principle is developed for dynamics and optimal control problems. The mixed form of Hamilton's weak principle contains both displacements and momenta as primary variables that are expanded in terms of nodal values and simple polynomial shape functions. Unlike other forms of Hamilton's principle, however, time derivatives of the momenta and displacements do not appear therein; instead, only the virtual momenta and virtual displacements are differentiated with respect to time. Based on the duality that is observed to exist between the mixed form of Hamilton's weak principle and variational principles governing classical optimal control problems, a temporal finite element formulation of the latter can be developed in a rather straightforward manner. Several well-known problems in dynamics and optimal control are illustrated. The example dynamics problem involves a time-marching problem. As optimal control examples, elementary trajectory optimization problems are treated.

Weak Hamiltonian finite element method for optimal control problems

NASA Technical Reports Server (NTRS)

Hodges, Dewey H.; Bless, Robert R.

1991-01-01

A temporal finite element method based on a mixed form of the Hamiltonian weak principle is developed for dynamics and optimal control problems. The mixed form of Hamilton's weak principle contains both displacements and momenta as primary variables that are expanded in terms of nodal values and simple polynomial shape functions. Unlike other forms of Hamilton's principle, however, time derivatives of the momenta and displacements do not appear therein; instead, only the virtual momenta and virtual displacements are differentiated with respect to time. Based on the duality that is observed to exist between the mixed form of Hamilton's weak principle and variational principles governing classical optimal control problems, a temporal finite element formulation of the latter can be developed in a rather straightforward manner. Several well-known problems in dynamics and optimal control are illustrated. The example dynamics problem involves a time-marching problem. As optimal control examples, elementary trajectory optimization problems are treated.

Computational benefits using artificial intelligent methodologies for the solution of an environmental design problem: saltwater intrusion.

PubMed

Papadopoulou, Maria P; Nikolos, Ioannis K; Karatzas, George P

2010-01-01

Artificial Neural Networks (ANNs) comprise a powerful tool to approximate the complicated behavior and response of physical systems allowing considerable reduction in computation time during time-consuming optimization runs. In this work, a Radial Basis Function Artificial Neural Network (RBFN) is combined with a Differential Evolution (DE) algorithm to solve a water resources management problem, using an optimization procedure. The objective of the optimization scheme is to cover the daily water demand on the coastal aquifer east of the city of Heraklion, Crete, without reducing the subsurface water quality due to seawater intrusion. The RBFN is utilized as an on-line surrogate model to approximate the behavior of the aquifer and to replace some of the costly evaluations of an accurate numerical simulation model which solves the subsurface water flow differential equations. The RBFN is used as a local approximation model in such a way as to maintain the robustness of the DE algorithm. The results of this procedure are compared to the corresponding results obtained by using the Simplex method and by using the DE procedure without the surrogate model. As it is demonstrated, the use of the surrogate model accelerates the convergence of the DE optimization procedure and additionally provides a better solution at the same number of exact evaluations, compared to the original DE algorithm.

Modeling of the radiation belt megnetosphere in decisional timeframes

DOEpatents

Koller, Josef; Reeves, Geoffrey D; Friedel, Reiner H.W.

2013-04-23

Systems and methods for calculating L* in the magnetosphere with essentially the same accuracy as with a physics based model at many times the speed by developing a surrogate trained to be a surrogate for the physics-based model. The trained model can then beneficially process input data falling within the training range of the surrogate model. The surrogate model can be a feedforward neural network and the physics-based model can be the TSK03 model. Operatively, the surrogate model can use parameters on which the physics-based model was based, and/or spatial data for the location where L* is to be calculated. Surrogate models should be provided for each of a plurality of pitch angles. Accordingly, a surrogate model having a closed drift shell can be used from the plurality of models. The feedforward neural network can have a plurality of input-layer units, there being at least one input-layer unit for each physics-based model parameter, a plurality of hidden layer units and at least one output unit for the value of L*.

Measuring the quality of renal care: things to keep in mind when selecting and using quality indicators.

PubMed

van der Veer, Sabine N; van Biesen, Wim; Couchoud, Cécile; Tomson, Charles R V; Jager, Kitty J

2014-08-01

This educational paper discusses a variety of indicators that can be used to measure the quality of care in renal medicine. Based on what aspect of care they reflect, indicators can be grouped into four main categories: structure, process, surrogate outcome and outcome indicators. Each category has its own advantages and disadvantages, and we give some pointers on how to balance these pros and cons while taking into account the aim of the measurement initiative. Especially within initiatives that link payment or reputation to indicator measurement, this balancing should be done with utmost care to avoid potential, unintended consequences. Furthermore, we suggest consideration of (i) a causal chain-i.e. subsequent aspects of care connected by evidence-based links-as a starting point for composing a performance indicator set and (ii) adequate case-mix adjustment, not only of (surrogate) outcomes, but also of process indicators in order to obtain fair comparisons between facilities and within facilities over time. © The Author 2013. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.

Hypothesis test for synchronization: twin surrogates revisited.

PubMed

Romano, M Carmen; Thiel, Marco; Kurths, Jürgen; Mergenthaler, Konstantin; Engbert, Ralf

2009-03-01

The method of twin surrogates has been introduced to test for phase synchronization of complex systems in the case of passive experiments. In this paper we derive new analytical expressions for the number of twins depending on the size of the neighborhood, as well as on the length of the trajectory. This allows us to determine the optimal parameters for the generation of twin surrogates. Furthermore, we determine the quality of the twin surrogates with respect to several linear and nonlinear statistics depending on the parameters of the method. In the second part of the paper we perform a hypothesis test for phase synchronization in the case of experimental data from fixational eye movements. These miniature eye movements have been shown to play a central role in neural information processing underlying the perception of static visual scenes. The high number of data sets (21 subjects and 30 trials per person) allows us to compare the generated twin surrogates with the "natural" surrogates that correspond to the different trials. We show that the generated twin surrogates reproduce very well all linear and nonlinear characteristics of the underlying experimental system. The synchronization analysis of fixational eye movements by means of twin surrogates reveals that the synchronization between the left and right eye is significant, indicating that either the centers in the brain stem generating fixational eye movements are closely linked, or, alternatively that there is only one center controlling both eyes.

21 CFR 601.41 - Approval based on a surrogate endpoint or on an effect on a clinical endpoint other than survival...

Code of Federal Regulations, 2014 CFR

2014-04-01

... 21 Food and Drugs 7 2014-04-01 2014-04-01 false Approval based on a surrogate endpoint or on an... Approval based on a surrogate endpoint or on an effect on a clinical endpoint other than survival or... uncertainty as to the relation of the surrogate endpoint to clinical benefit, or of the observed clinical...

21 CFR 601.41 - Approval based on a surrogate endpoint or on an effect on a clinical endpoint other than survival...

Code of Federal Regulations, 2013 CFR

2013-04-01

... 21 Food and Drugs 7 2013-04-01 2013-04-01 false Approval based on a surrogate endpoint or on an... Approval based on a surrogate endpoint or on an effect on a clinical endpoint other than survival or... uncertainty as to the relation of the surrogate endpoint to clinical benefit, or of the observed clinical...

21 CFR 601.41 - Approval based on a surrogate endpoint or on an effect on a clinical endpoint other than survival...

Code of Federal Regulations, 2012 CFR

2012-04-01

... 21 Food and Drugs 7 2012-04-01 2012-04-01 false Approval based on a surrogate endpoint or on an... Approval based on a surrogate endpoint or on an effect on a clinical endpoint other than survival or... uncertainty as to the relation of the surrogate endpoint to clinical benefit, or of the observed clinical...

21 CFR 601.41 - Approval based on a surrogate endpoint or on an effect on a clinical endpoint other than survival...

Code of Federal Regulations, 2011 CFR

2011-04-01

... 21 Food and Drugs 7 2011-04-01 2010-04-01 true Approval based on a surrogate endpoint or on an... Approval based on a surrogate endpoint or on an effect on a clinical endpoint other than survival or... uncertainty as to the relation of the surrogate endpoint to clinical benefit, or of the observed clinical...

21 CFR 601.41 - Approval based on a surrogate endpoint or on an effect on a clinical endpoint other than survival...

Code of Federal Regulations, 2010 CFR

2010-04-01

... 21 Food and Drugs 7 2010-04-01 2010-04-01 false Approval based on a surrogate endpoint or on an... Approval based on a surrogate endpoint or on an effect on a clinical endpoint other than survival or... uncertainty as to the relation of the surrogate endpoint to clinical benefit, or of the observed clinical...

Optimal husbandry of hatchling Eastern Indigo Snakes (Drymarchon couperi) during a captive head-start program.

PubMed

Wines, Michael P; Johnson, Valerie M; Lock, Brad; Antonio, Fred; Godwin, James C; Rush, Elizabeth M; Guyer, Craig

2015-01-01

Optimal husbandry techniques are desirable for any headstart program, but frequently are unknown for rare species. Here we describe key reproductive variables and determine optimal incubation temperature and diet diversity for Eastern Indigo Snakes (Drymarchon couperi) grown in laboratory settings. Optimal incubation temperature was estimated from two variables dependent on temperature, shell dimpling, a surrogate for death from fungal infection, and deviation of an egg from an ovoid shape, a surrogate for death from developmental anomalies. Based on these relationships and size at hatching we determined optimal incubation temperature to be 26°C. Additionally, we used incubation data to assess the effect of temperature on duration of incubation and size of hatchlings. We also examined hatchling diets necessary to achieve optimal growth over a 21-month period. These snakes exhibited a positive linear relationship between total mass eaten and growth rate, when individuals were fed less than 1711 g of prey, and displayed constant growth for individuals exceeding 1711 g of prey. Similarly, growth rate increased linearly with increasing diet diversity up to a moderately diverse diet, followed by constant growth for higher levels of diet diversity. Of the two components of diet diversity, diet evenness played a stronger role than diet richness in explaining variance in hatchling growth. These patterns document that our goal of satiating snakes was achieved for some individuals but not others and that diets in which total grams consumed over the first 21 months of life is distributed equivalently among at least three prey genera yielded the fastest growth rates for hatchling snakes. © 2015 Wiley Periodicals, Inc.

An integrated, multi-sensing approach to describe the dynamic relations between turbulence, fluid-forces, and reconfiguration of a submerged plant model in steady flows.

NASA Astrophysics Data System (ADS)

Henry, Pierre-Yves; Aberle, Jochen; Dijkstra, Jasper; Myrhaug, Dag

2016-04-01

Aquatic vegetation plays a vital role in ecohydrological systems regulating many physical, chemical, and biological processes across a wide range of spatial and temporal scales. As a consequence, plant-flow interactions are of particular interest to a wide range of disciplines. While early studies of the interactions between vegetation and flowing water employed simplified and non-flexible structures such as rigid cylinders, recent studies have included flexible plants to identify the main characteristics of the hydrodynamics of vegetated flows. However, the description of plant reconfiguration has often been based on a static approach, i.e. considering the plant's deformation under a static load and neglecting turbulent fluctuations. Correlations between drag fluctuations, plant movements, and upstream turbulence were recently established showing that shear layer turbulence at the surface of the different plant elements (such as blades or stems) can contribute significantly to the dynamic behaviour of the plant. However, the relations between plant movement and force fluctuations might change under varying flow velocities, and although this point is crucial for mixing processes and plant dislodgement by fatigue, these aspects of fluid-structure interactions applied to aquatic vegetation remain largely unexplored. Using an innovative combination of sensing techniques in one set of experiments, this study investigates the relations between turbulence, fluctuating fluid forces and movements of a flexible cylindrical plant surrogate. A silicone-based flexible cylinder was attached at the bottom of a 1m wide flume in fully-developed uniform flow. The lower 22 cm of the plant surrogate were made of plain flexible silicone, while the higher 13cm included a casted rigid sensor, measuring accelerations at the tip of the surrogate. Forces were sampled at high frequencies at the surrogate's base by a 6-degrees-of-freedom force/torque sensor measuring down to the gram-force. Point measurements of turbulence were realized by two ADVs which were located upstream and downstream of the surrogate. Detailed motions of the surrogate were recorded by two cameras above and next to the flume. Image processing allowed for the characterization of the mean deformation and the different modes of horizontal and vertical 'vibration' of the surrogate. The experimental results were compared to numerical simulations obtained from an updated version of the Dynveg code developed by Deltares. The results showed a clear correlation between the cylinder's movements and the (drag) force fluctuations. Due to the swaying motion of the surrogate, the turbulence spectrum is significantly affected when the flow passes the plant model. The succession of several motion modes are observed as the velocity increases, affecting the dominant frequencies in the drag force spectrum and the overall drag. These preliminary results emphasise the importance of the dynamics of the plant flow interactions, and provide an example of the use of new methodologies to provide deeper insights into the physics of complex flows.

Robust Constrained Blackbox Optimization with Surrogates

DTIC Science & Technology

2015-05-21

algorithms with OPAL . Mathematical Programming Computation, 6(3):233–254, 2014. 6. M.S. Ouali, H. Aoudjit, and C. Audet. Replacement scheduling of a fleet of...Orban. Optimization of Algorithms with OPAL . Mathematical Programming Computation, 6(3), 233-254, September 2014. DISTRIBUTION A: Distribution

The natural logarithm of zinc-α2-glycoprotein/HOMA-IR is a better predictor of insulin sensitivity than the product of triglycerides and glucose and the other lipid ratios.

PubMed

Qu, Chunmei; Zhou, Xiaoxin; Yang, Gangyi; Li, Ling; Liu, Hua; Liang, Zerong

2016-03-01

The euglycemic-hyperinsulinemic clamp (EHC) is not available in most clinical settings and is costly, time consuming and invasive, and requires trained staff. Therefore, an accessible and inexpensive test to identify insulin resistance (IR) is needed. The aim of this study is to assess whether zinc-α2-glycoprotein (ZAG) index [Ln ZAG/homeostasis model assessment of IR (HOMA-IR)] is a better surrogate index for estimating IR or metabolic syndrome (MetS) compared with other surrogate indices. We performed a population-based cross-sectional study. Two hundred healthy subjects, 102 polycystic ovary syndrome (PCOS) patients, 97 newly diagnosed type 2 diabetes mellitus (nT2DM) and 84 impaired glucose tolerance (IGT) subjects were enrolled. The EHC was performed to identify IR. Circulating ZAG and adiponectin levels were determined by ELISA. The ZAG index was significantly lower in participants with IR including IGT, nT2DM and PCOS than in those without IR. In addition, subjects with MetS had lower ZAG indices and higher the product of fasting triglycerides and glucose (TyG) indices than those without MetS. The ZAG index showed a significantly stronger association with M values than the other surrogate indices, whereas the TyG index showed a stronger association with MetS. The optimal cutoff value of the ZAG index for detection of IR was 2.97 with a sensitivity of 88% and a specificity of 91%, whereas the optimal cutoff value of TyG index for detection of MetS was 4.90 with a sensitivity of 82% and a specificity of 86%. The ZAG index is a better marker than the other surrogate indices for identifying IR, whereas the TyG index has high sensitivity and specificity for identifying MetS. Copyright © 2016 Elsevier Ltd. All rights reserved.

On-orbit servicing system assessment and optimization methods based on lifecycle simulation under mixed aleatory and epistemic uncertainties

NASA Astrophysics Data System (ADS)

Yao, Wen; Chen, Xiaoqian; Huang, Yiyong; van Tooren, Michel

2013-06-01

To assess the on-orbit servicing (OOS) paradigm and optimize its utilities by taking advantage of its inherent flexibility and responsiveness, the OOS system assessment and optimization methods based on lifecycle simulation under uncertainties are studied. The uncertainty sources considered in this paper include both the aleatory (random launch/OOS operation failure and on-orbit component failure) and the epistemic (the unknown trend of the end-used market price) types. Firstly, the lifecycle simulation under uncertainties is discussed. The chronological flowchart is presented. The cost and benefit models are established, and the uncertainties thereof are modeled. The dynamic programming method to make optimal decision in face of the uncertain events is introduced. Secondly, the method to analyze the propagation effects of the uncertainties on the OOS utilities is studied. With combined probability and evidence theory, a Monte Carlo lifecycle Simulation based Unified Uncertainty Analysis (MCS-UUA) approach is proposed, based on which the OOS utility assessment tool under mixed uncertainties is developed. Thirdly, to further optimize the OOS system under mixed uncertainties, the reliability-based optimization (RBO) method is studied. To alleviate the computational burden of the traditional RBO method which involves nested optimum search and uncertainty analysis, the framework of Sequential Optimization and Mixed Uncertainty Analysis (SOMUA) is employed to integrate MCS-UUA, and the RBO algorithm SOMUA-MCS is developed. Fourthly, a case study on the OOS system for a hypothetical GEO commercial communication satellite is investigated with the proposed assessment tool. Furthermore, the OOS system is optimized with SOMUA-MCS. Lastly, some conclusions are given and future research prospects are highlighted.

Optimization and Analysis of Centrifugal Pump considering Fluid-Structure Interaction

PubMed Central

Hu, Sanbao

2014-01-01

This paper presents the optimization of vibrations of centrifugal pump considering fluid-structure interaction (FSI). A set of centrifugal pumps with various blade shapes were studied using FSI method, in order to investigate the transient vibration performance. The Kriging model, based on the results of the FSI simulations, was established to approximate the relationship between the geometrical parameters of pump impeller and the root mean square (RMS) values of the displacement response at the pump bearing block. Hence, multi-island genetic algorithm (MIGA) has been implemented to minimize the RMS value of the impeller displacement. A prototype of centrifugal pump has been manufactured and an experimental validation of the optimization results has been carried out. The comparison among results of Kriging surrogate model, FSI simulation, and experimental test showed a good consistency of the three approaches. Finally, the transient mechanical behavior of pump impeller has been investigated using FSI method based on the optimized geometry parameters of pump impeller. PMID:25197690

Parameter estimation of a nonlinear Burger's model using nanoindentation and finite element-based inverse analysis

NASA Astrophysics Data System (ADS)

Hamim, Salah Uddin Ahmed

Nanoindentation involves probing a hard diamond tip into a material, where the load and the displacement experienced by the tip is recorded continuously. This load-displacement data is a direct function of material's innate stress-strain behavior. Thus, theoretically it is possible to extract mechanical properties of a material through nanoindentation. However, due to various nonlinearities associated with nanoindentation the process of interpreting load-displacement data into material properties is difficult. Although, simple elastic behavior can be characterized easily, a method to characterize complicated material behavior such as nonlinear viscoelasticity is still lacking. In this study, a nanoindentation-based material characterization technique is developed to characterize soft materials exhibiting nonlinear viscoelasticity. Nanoindentation experiment was modeled in finite element analysis software (ABAQUS), where a nonlinear viscoelastic behavior was incorporated using user-defined subroutine (UMAT). The model parameters were calibrated using a process called inverse analysis. In this study, a surrogate model-based approach was used for the inverse analysis. The different factors affecting the surrogate model performance are analyzed in order to optimize the performance with respect to the computational cost.

Intelligent Space Tube Optimization for speeding ground water remedial design.

PubMed

Kalwij, Ineke M; Peralta, Richard C

2008-01-01

An innovative Intelligent Space Tube Optimization (ISTO) two-stage approach facilitates solving complex nonlinear flow and contaminant transport management problems. It reduces computational effort of designing optimal ground water remediation systems and strategies for an assumed set of wells. ISTO's stage 1 defines an adaptive mobile space tube that lengthens toward the optimal solution. The space tube has overlapping multidimensional subspaces. Stage 1 generates several strategies within the space tube, trains neural surrogate simulators (NSS) using the limited space tube data, and optimizes using an advanced genetic algorithm (AGA) with NSS. Stage 1 speeds evaluating assumed well locations and combinations. For a large complex plume of solvents and explosives, ISTO stage 1 reaches within 10% of the optimal solution 25% faster than an efficient AGA coupled with comprehensive tabu search (AGCT) does by itself. ISTO input parameters include space tube radius and number of strategies used to train NSS per cycle. Larger radii can speed convergence to optimality for optimizations that achieve it but might increase the number of optimizations reaching it. ISTO stage 2 automatically refines the NSS-AGA stage 1 optimal strategy using heuristic optimization (we used AGCT), without using NSS surrogates. Stage 2 explores the entire solution space. ISTO is applicable for many heuristic optimization settings in which the numerical simulator is computationally intensive, and one would like to reduce that burden.

Multidisciplinary Multiobjective Optimal Design for Turbomachinery Using Evolutionary Algorithm

NASA Technical Reports Server (NTRS)

2005-01-01

This report summarizes Dr. Lian s efforts toward developing a robust and efficient tool for multidisciplinary and multi-objective optimal design for turbomachinery using evolutionary algorithms. This work consisted of two stages. The first stage (from July 2003 to June 2004) Dr. Lian focused on building essential capabilities required for the project. More specifically, Dr. Lian worked on two subjects: an enhanced genetic algorithm (GA) and an integrated optimization system with a GA and a surrogate model. The second stage (from July 2004 to February 2005) Dr. Lian formulated aerodynamic optimization and structural optimization into a multi-objective optimization problem and performed multidisciplinary and multi-objective optimizations on a transonic compressor blade based on the proposed model. Dr. Lian s numerical results showed that the proposed approach can effectively reduce the blade weight and increase the stage pressure ratio in an efficient manner. In addition, the new design was structurally safer than the original design. Five conference papers and three journal papers were published on this topic by Dr. Lian.

Considerations for development of surrogate endpoints for antifracture efficacy of new treatments in osteoporosis: a perspective.

PubMed

Bouxsein, Mary L; Delmas, Pierre D

2008-08-01

Because of the broad availability of efficacious osteoporosis therapies, conduct of placebo-controlled trials in subjects at high risk for fracture is becoming increasing difficult. Alternative trial designs include placebo-controlled trials in patients at low risk for fracture or active comparator studies, both of which would require enormous sample sizes and associated financial resources. Another more attractive alternative is to develop and validate surrogate endpoints for fracture. In this perspective, we review the concept of surrogate endpoints as it has been developed in other fields of medicine and discuss how it could be applied in clinical trials of osteoporosis. We outline a stepwise approach and possible study designs to qualify a biomarker as a surrogate endpoint in osteoporosis and review the existing data for several potential surrogate endpoints to assess their success in meeting the proposed criteria. Finally, we suggest a research agenda needed to advance the development of biomarkers as surrogate endpoints for fracture in osteoporosis trials. To ensure optimal development and best use of biomarkers to accelerate drug development, continuous dialog among the health professionals, industry, and regulators is of paramount importance.

Evolutionary Optimization of Centrifugal Nozzles for Organic Vapours

NASA Astrophysics Data System (ADS)

Persico, Giacomo

2017-03-01

This paper discusses the shape-optimization of non-conventional centrifugal turbine nozzles for Organic Rankine Cycle applications. The optimal aerodynamic design is supported by the use of a non-intrusive, gradient-free technique specifically developed for shape optimization of turbomachinery profiles. The method is constructed as a combination of a geometrical parametrization technique based on B-Splines, a high-fidelity and experimentally validated Computational Fluid Dynamic solver, and a surrogate-based evolutionary algorithm. The non-ideal gas behaviour featuring the flow of organic fluids in the cascades of interest is introduced via a look-up-table approach, which is rigorously applied throughout the whole optimization process. Two transonic centrifugal nozzles are considered, featuring very different loading and radial extension. The use of a systematic and automatic design method to such a non-conventional configuration highlights the character of centrifugal cascades; the blades require a specific and non-trivial definition of the shape, especially in the rear part, to avoid the onset of shock waves. It is shown that the optimization acts in similar way for the two cascades, identifying an optimal curvature of the blade that both provides a relevant increase of cascade performance and a reduction of downstream gradients.

An adaptive sparse-grid high-order stochastic collocation method for Bayesian inference in groundwater reactive transport modeling

NASA Astrophysics Data System (ADS)

Zhang, Guannan; Lu, Dan; Ye, Ming; Gunzburger, Max; Webster, Clayton

2013-10-01

Bayesian analysis has become vital to uncertainty quantification in groundwater modeling, but its application has been hindered by the computational cost associated with numerous model executions required by exploring the posterior probability density function (PPDF) of model parameters. This is particularly the case when the PPDF is estimated using Markov Chain Monte Carlo (MCMC) sampling. In this study, a new approach is developed to improve the computational efficiency of Bayesian inference by constructing a surrogate of the PPDF, using an adaptive sparse-grid high-order stochastic collocation (aSG-hSC) method. Unlike previous works using first-order hierarchical basis, this paper utilizes a compactly supported higher-order hierarchical basis to construct the surrogate system, resulting in a significant reduction in the number of required model executions. In addition, using the hierarchical surplus as an error indicator allows locally adaptive refinement of sparse grids in the parameter space, which further improves computational efficiency. To efficiently build the surrogate system for the PPDF with multiple significant modes, optimization techniques are used to identify the modes, for which high-probability regions are defined and components of the aSG-hSC approximation are constructed. After the surrogate is determined, the PPDF can be evaluated by sampling the surrogate system directly without model execution, resulting in improved efficiency of the surrogate-based MCMC compared with conventional MCMC. The developed method is evaluated using two synthetic groundwater reactive transport models. The first example involves coupled linear reactions and demonstrates the accuracy of our high-order hierarchical basis approach in approximating high-dimensional posteriori distribution. The second example is highly nonlinear because of the reactions of uranium surface complexation, and demonstrates how the iterative aSG-hSC method is able to capture multimodal and non-Gaussian features of PPDF caused by model nonlinearity. Both experiments show that aSG-hSC is an effective and efficient tool for Bayesian inference.

Comparative Virucidal Efficacy of Seven Disinfectants Against Murine Norovirus and Feline Calicivirus, Surrogates of Human Norovirus.

PubMed

Zonta, William; Mauroy, Axel; Farnir, Frederic; Thiry, Etienne

2016-03-01

Human noroviruses (HuNoV) are the leading cause of acute non-bacterial gastroenteritis in humans and can be transmitted either by person-to-person contact or by consumption of contaminated food. A knowledge of an efficient disinfection for both hands and food-contact surfaces is helpful for the food sector and provides precious information for public health. The aim of this study was to evaluate the effect of seven disinfectants belonging to different groups of biocides (alcohol, halogen, oxidizing agents, quaternary ammonium compounds, aldehyde and biguanide) on infectious viral titre and on genomic copy number. Due to the absence of a cell culture system for HuNoV, two HuNoV surrogates, such as murine norovirus and feline calicivirus, were used and the tests were performed in suspension, on gloves and on stainless steel discs. When, as criteria of efficacy, a log reduction >3 of the infectious viral titre on both surrogates and in the three tests is used, the most efficacious disinfectants in this study appear to be biocidal products B, C and D, representing the halogens, the oxidizing agents group and a mix of QAC, alcohol and aldehyde, respectively. In addition, these three disinfectants also elicited a significant effect on genomic copy number for both surrogate viruses and in all three tests. The results of this study demonstrate that a halogen compound, oxidizing agents and a mix of QAC, alcohol and aldehyde are advisable for HuNoV disinfection of either potentially contaminated surfaces or materials in contact with foodstuffs.

Optimization of the formulation and technology of pearl millet based 'ready-to-reconstitute' kheer mix powder.

PubMed

Bunkar, Durga Shankar; Jha, Alok; Mahajan, Ankur

2014-10-01

The objective of this study was to optimize the process of manufacturing instant kheer mix based on pearl millet instead of rice. Dairy whitener, pearl millet and powdered sugar were the responses studied by employing the 3-factor Central Composite Rotatable Design. The formulation with 15 g sugar, 30 g dairy whitener and 20 g pearl millet was found suitable for obtaining dry kheer mix. The analyses were based on scores of consistency, cohesiveness, viscosity and overall acceptability. The reconstituted product from the formulated kheer mix had an overall acceptability score of 7.66 and desirability index of 0.7663. The moisture, fat, protein, carbohydrate and ash contents of the dry mix product were 2.8, 4.38, 5.84, 85.88 and 1.1 %, respectively.

Effective Condition for Whole Testis Cryopreservation of Endangered Miho Spine Loach (Cobitis choii) Through the Optimization of Mud Loach (Misgurnus mizolepis) Whole Testis Cryopreservation Condition.

PubMed

Kim, J J; Nam, Y K; Bang, I C; Gong, S P

　　BACKGROUND: Miho spine loach (Cobitis choii) is an endangered Korean endemic fish. Whole testis cryopreservation is a good way for species preservation, but needs to the sacrifice of a large number of fish to optimize the freezing condition. Considering this limitation, a surrogate fish species was used for the protocol development. This study was to establish the effective condition for Miho spine loach whole testis cryopreservation by optimizing the conditions for whole testis cryopreservation in an allied species, mud loach (Misgurnus mizolepis). The condition for whole testis cryopreservation was optimized in mud loach first, and then the optimal condition was applied to Miho spine loach testes. The optimal condition for mud loach testis cryopreservation consists of the freezing medium containing 1.3 M dimethyl sulfoxide, 6% fetal bovine serum and 0.3 M trehalose, -1 C/min cooling rate and 26 degree C thawing temperature, which also permits effective cryopreservation of Miho spine loach testes. An effective cryopreservation condition for whole testis of the endangered Miho spine loach has been established by using mud loach as a surrogate fish.

Structural reliability analysis under evidence theory using the active learning kriging model

NASA Astrophysics Data System (ADS)

Yang, Xufeng; Liu, Yongshou; Ma, Panke

2017-11-01

Structural reliability analysis under evidence theory is investigated. It is rigorously proved that a surrogate model providing only correct sign prediction of the performance function can meet the accuracy requirement of evidence-theory-based reliability analysis. Accordingly, a method based on the active learning kriging model which only correctly predicts the sign of the performance function is proposed. Interval Monte Carlo simulation and a modified optimization method based on Karush-Kuhn-Tucker conditions are introduced to make the method more efficient in estimating the bounds of failure probability based on the kriging model. Four examples are investigated to demonstrate the efficiency and accuracy of the proposed method.

Assessment and Reduction of Model Parametric Uncertainties: A Case Study with A Distributed Hydrological Model

NASA Astrophysics Data System (ADS)

Gan, Y.; Liang, X. Z.; Duan, Q.; Xu, J.; Zhao, P.; Hong, Y.

2017-12-01

The uncertainties associated with the parameters of a hydrological model need to be quantified and reduced for it to be useful for operational hydrological forecasting and decision support. An uncertainty quantification framework is presented to facilitate practical assessment and reduction of model parametric uncertainties. A case study, using the distributed hydrological model CREST for daily streamflow simulation during the period 2008-2010 over ten watershed, was used to demonstrate the performance of this new framework. Model behaviors across watersheds were analyzed by a two-stage stepwise sensitivity analysis procedure, using LH-OAT method for screening out insensitive parameters, followed by MARS-based Sobol' sensitivity indices for quantifying each parameter's contribution to the response variance due to its first-order and higher-order effects. Pareto optimal sets of the influential parameters were then found by the adaptive surrogate-based multi-objective optimization procedure, using MARS model for approximating the parameter-response relationship and SCE-UA algorithm for searching the optimal parameter sets of the adaptively updated surrogate model. The final optimal parameter sets were validated against the daily streamflow simulation of the same watersheds during the period 2011-2012. The stepwise sensitivity analysis procedure efficiently reduced the number of parameters that need to be calibrated from twelve to seven, which helps to limit the dimensionality of calibration problem and serves to enhance the efficiency of parameter calibration. The adaptive MARS-based multi-objective calibration exercise provided satisfactory solutions to the reproduction of the observed streamflow for all watersheds. The final optimal solutions showed significant improvement when compared to the default solutions, with about 65-90% reduction in 1-NSE and 60-95% reduction in |RB|. The validation exercise indicated a large improvement in model performance with about 40-85% reduction in 1-NSE, and 35-90% reduction in |RB|. Overall, this uncertainty quantification framework is robust, effective and efficient for parametric uncertainty analysis, the results of which provide useful information that helps to understand the model behaviors and improve the model simulations.

Stable isotope signatures and trophic-step fractionation factors of fish tissues collected as non-lethal surrogates of dorsal muscle.

PubMed

Busst, Georgina M A; Bašić, Tea; Britton, J Robert

2015-08-30

Dorsal white muscle is the standard tissue analysed in fish trophic studies using stable isotope analyses. As muscle is usually collected destructively, fin tissues and scales are often used as non-lethal surrogates; we examined the utility of scales and fin tissue as muscle surrogates. The muscle, fin and scale δ(15) N and δ(13) C values from 10 cyprinid fish species determined with an elemental analyser coupled with an isotope ratio mass spectrometer were compared. The fish comprised (1) samples from the wild, and (2) samples from tank aquaria, using six species held for 120 days and fed a single food resource. Relationships between muscle, fin and scale isotope ratios were examined for each species and for the entire dataset, with the efficacy of four methods of predicting muscle isotope ratios from fin and scale values being tested. The fractionation factors between the three tissues of the laboratory fishes and their food resource were then calculated and applied to Bayesian mixing models to assess their effect on fish diet predictions. The isotopic data of the three tissues per species were distinct, but were significantly related, enabling estimations of muscle values from the two surrogates. Species-specific equations provided the least erroneous corrections of scale and fin isotope ratios (errors < 0.6‰). The fractionation factors for δ(15) N values were in the range obtained for other species, but were often higher for δ(13) C values. Their application to data from two fish populations in the mixing models resulted in significant alterations in diet predictions. Scales and fin tissue are strong surrogates of dorsal muscle in food web studies as they can provide estimates of muscle values within an acceptable level of error when species-specific methods are used. Their derived fractionation factors can also be applied to models predicting fish diet composition from δ(15) N and δ(13) C values. Copyright © 2015 John Wiley & Sons, Ltd.

Experiments on Induction Times of Diesel-Fuels and its Surrogates

NASA Astrophysics Data System (ADS)

Eigenbrod, Christian; Reimert, Manfredo; Marks, Guenther; Rickmers, Peter; Klinkov, Konstantin; Moriue, Osamu

Aiming for as low polluting combustion control as possible in Diesel-engines or gas-turbines, pre-vaporized and pre-mixed combustion at low mean temperature levels marks the goal. Low-est emissions of nitric-oxides are achievable at combustion temperatures associated to mixture ratios close to the lean flammability limit. In order to prevent local mixture ratios to be below the flammability limit (resulting in flame extinction or generation of unburned hydrocarbons and carbon-monoxide) or to be richer than required (resulting in more nitric-oxide than possi-ble), well-stirred conditioning is required. The time needed for spray generation, vaporization and turbulent mixing is limited through the induction time to self-ignition in a hot high-pressure ambiance. Therefore, detailed knowledge about the autoignition of fuels is a pre-requisit. Experiments were performed at the Bremen drop tower to investigate the self-ignition behavior of single droplets of fossil-Diesel oil, rapeseed-oil, Gas-to-Liquid (GTL) synthetic Diesel-oil and the fossil Diesel surrogates n-heptane, n-tetradecane, 50 n-tetradecane/ 50 1-methylnaphthalene as well as on the GTL-surrogates n-tetradecane / bicyclohexyl and n-tetradecane / 2,2,4,4,6,8,8-heptamethylnonane (iso-cetane). The rules for selection of the above fuels and the experimental results are presented and dis-cussed.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bostick, W.D.; Hoffmann, D.P.; Stevenson, R.J.

The category of sludges, filter cakes, and other waste processing residuals represent the largest volume of low-level mixed (hazardous and radioactive) wastes within the US Department of Energy (DOE) complex. Treatment of these wastes to minimize the mobility of contaminants, and to eliminate the presence of free water, is required under the Federal Facility Compliance Act agreements between DOE and the Environmental Protection Agency. In the text, we summarize the currently available data for several of the high priority mixed-waste sludge inventories within DOE. Los Alamos National Laboratory TA-50 Sludge and Rocky Flats Plant By-Pass Sludge are transuranic (TRU)-contaminated sludgesmore » that were isolated with the use of silica-based filter aids. The Oak Ridge Y-12 Plant West End Treatment Facility Sludge is predominantly calcium carbonate and biomass. The Oak Ridge K-25 Site Pond Waste is a large-volume waste stream, containing clay, silt, and other debris in addition to precipitated metal hydroxides. We formulate ``simulants`` for the waste streams described above, using cerium oxide as a surrogate for the uranium or plutonium present in the authentic material. Use of nonradiological surrogates greatly simplifies material handling requirements for initial treatability studies. The use of synthetic mixtures for initial treatability testing will facilitate compositional variation for use in conjunction with statistical design experiments; this approach may help to identify any ``operating window`` limitations. The initial treatability testing demonstrations utilizing these ``simulants`` will be based upon vitrification, although the materials are also amenable to testing grout-based and other stabilization procedures. After the feasibility of treatment and the initial evaluation of treatment performance has been demonstrated, performance must be verified using authentic samples of the candidate waste stream.« less

Nitric Oxide PLIF Visualization of Simulated Fuel-Air Mixing in a Dual-Mode Scramjet

NASA Technical Reports Server (NTRS)

Cantu, Luca M. L.; Gallo, Emanuela C. A.; Cutler, Andrew D.; Bathel, Brett F.; Danehy, Paul M.; Rockwell, Robert D.; Goyne, Christopher P.; McDaniel, James C.

2015-01-01

Nitric oxide (NO) planar induced laser fluorescence (PLIF) measurements have been performed in a small scale scramjet combustor at the University of Virginia Aerospace Research Laboratory at nominal simulated Mach 5 flight. A mixture of NO and N2 was injected at the upstream end of the inlet isolator as a surrogate for ethylene fuel, and the mixing of this fuel simulant was studied with and without a shock train. The shock train was produced by an air throttle, which simulated the blockage effects of combustion downstream of the cavity flame holder. NO PLIF signal was imaged in a plane orthogonal to the freestream at the leading edge of the cavity. Instantaneous planar images were recorded and analyzed to identify the most uniform cases, which were achieved by varying the location of the fuel injection and shock train. This method was used to screen different possible fueling configurations to provide optimized test conditions for follow-on combustion measurements using ethylene fuel. A theoretical study of the selected NO rotational transitions was performed to obtain a LIF signal that is linear with NO mole fraction and approximately independent of pressure and temperature.

Optimization of Heat Exchangers with Dimpled Surfaces to Improve the Performance in Thermoelectric Generators Using a Kriging Model

NASA Astrophysics Data System (ADS)

Li, Shuai; Wang, Yiping; Wang, Tao; Yang, Xue; Deng, Yadong; Su, Chuqi

2017-05-01

Thermoelectric generators (TEGs) have become a topic of interest for vehicle exhaust energy recovery. Electrical power generation is deeply influenced by temperature differences, temperature uniformity and topological structures of TEGs. When the dimpled surfaces are adopted in heat exchangers, the heat transfer rates can be augmented with a minimal pressure drop. However, the temperature distribution shows a large gradient along the flow direction which has adverse effects on the power generation. In the current study, the heat exchanger performance was studied in a computational fluid dynamics (CFD) model. The dimple depth, dimple print diameter, and channel height were chosen as design variables. The objective function was defined as a combination of average temperature, temperature uniformity and pressure loss. The optimal Latin hypercube method was used to determine the experiment points as a method of design of the experiment in order to analyze the sensitivity of the design variables. A Kriging surrogate model was built and verified according to the database resulting from the CFD simulation. A multi-island genetic algorithm was used to optimize the structure in the heat exchanger based on the surrogate model. The results showed that the average temperature of the heat exchanger was most sensitive to the dimple depth. The pressure loss and temperature uniformity were most sensitive to the parameter of channel rear height, h 2. With an optimal design of channel structure, the temperature uniformity can be greatly improved compared with the initial exchanger, and the additional pressure loss also increased.

A perfect correlate does not a surrogate make

PubMed Central

Baker, Stuart G; Kramer, Barnett S

2003-01-01

Background There is common belief among some medical researchers that if a potential surrogate endpoint is highly correlated with a true endpoint, then a positive (or negative) difference in potential surrogate endpoints between randomization groups would imply a positive (or negative) difference in unobserved true endpoints between randomization groups. We investigate this belief when the potential surrogate and unobserved true endpoints are perfectly correlated within each randomization group. Methods We use a graphical approach. The vertical axis is the unobserved true endpoint and the horizontal axis is the potential surrogate endpoint. Perfect correlation within each randomization group implies that, for each randomization group, potential surrogate and true endpoints are related by a straight line. In this scenario the investigator does not know the slopes or intercepts. We consider a plausible example where the slope of the line is higher for the experimental group than for the control group. Results In our example with unknown lines, a decrease in mean potential surrogate endpoints from control to experimental groups corresponds to an increase in mean true endpoint from control to experimental groups. Thus the potential surrogate endpoints give the wrong inference. Similar results hold for binary potential surrogate and true outcomes (although the notion of correlation does not apply). The potential surrogate endpointwould give the correct inference if either (i) the unknown lines for the two group coincided, which means that the distribution of true endpoint conditional on potential surrogate endpoint does not depend on treatment group, which is called the Prentice Criterion or (ii) if one could accurately predict the lines based on data from prior studies. Conclusion Perfect correlation between potential surrogate and unobserved true outcomes within randomized groups does not guarantee correct inference based on a potential surrogate endpoint. Even in early phase trials, investigators should not base conclusions on potential surrogate endpoints in which the only validation is high correlation with the true endpoint within a group. PMID:12962545

Dynamic ADMM for Real-Time Optimal Power Flow

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dall-Anese, Emiliano; Zhang, Yijian; Hong, Mingyi

This paper considers distribution networks featuring distributed energy resources (DERs), and develops a dynamic optimization method to maximize given operational objectives in real time while adhering to relevant network constraints. The design of the dynamic algorithm is based on suitable linearization of the AC power flow equations, and it leverages the so-called alternating direction method of multipliers (ADMM). The steps of the ADMM, however, are suitably modified to accommodate appropriate measurements from the distribution network and the DERs. With the aid of these measurements, the resultant algorithm can enforce given operational constraints in spite of inaccuracies in the representation ofmore » the AC power flows, and it avoids ubiquitous metering to gather the state of noncontrollable resources. Optimality and convergence of the proposed algorithm are established in terms of tracking of the solution of a convex surrogate of the AC optimal power flow problem.« less

Dynamic ADMM for Real-Time Optimal Power Flow: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dall-Anese, Emiliano; Zhang, Yijian; Hong, Mingyi

This paper considers distribution networks featuring distributed energy resources (DERs), and develops a dynamic optimization method to maximize given operational objectives in real time while adhering to relevant network constraints. The design of the dynamic algorithm is based on suitable linearizations of the AC power flow equations, and it leverages the so-called alternating direction method of multipliers (ADMM). The steps of the ADMM, however, are suitably modified to accommodate appropriate measurements from the distribution network and the DERs. With the aid of these measurements, the resultant algorithm can enforce given operational constraints in spite of inaccuracies in the representation ofmore » the AC power flows, and it avoids ubiquitous metering to gather the state of non-controllable resources. Optimality and convergence of the propose algorithm are established in terms of tracking of the solution of a convex surrogate of the AC optimal power flow problem.« less

Fuel Injector Design Optimization for an Annular Scramjet Geometry

NASA Technical Reports Server (NTRS)

Steffen, Christopher J., Jr.

2003-01-01

A four-parameter, three-level, central composite experiment design has been used to optimize the configuration of an annular scramjet injector geometry using computational fluid dynamics. The computational fluid dynamic solutions played the role of computer experiments, and response surface methodology was used to capture the simulation results for mixing efficiency and total pressure recovery within the scramjet flowpath. An optimization procedure, based upon the response surface results of mixing efficiency, was used to compare the optimal design configuration against the target efficiency value of 92.5%. The results of three different optimization procedures are presented and all point to the need to look outside the current design space for different injector geometries that can meet or exceed the stated mixing efficiency target.

Optimization on the impeller of a low-specific-speed centrifugal pump for hydraulic performance improvement

NASA Astrophysics Data System (ADS)

Pei, Ji; Wang, Wenjie; Yuan, Shouqi; Zhang, Jinfeng

2016-09-01

In order to widen the high-efficiency operating range of a low-specific-speed centrifugal pump, an optimization process for considering efficiencies under 1.0 Q d and 1.4 Q d is proposed. Three parameters, namely, the blade outlet width b 2, blade outlet angle β 2, and blade wrap angle φ, are selected as design variables. Impellers are generated using the optimal Latin hypercube sampling method. The pump efficiencies are calculated using the software CFX 14.5 at two operating points selected as objectives. Surrogate models are also constructed to analyze the relationship between the objectives and the design variables. Finally, the particle swarm optimization algorithm is applied to calculate the surrogate model to determine the best combination of the impeller parameters. The results show that the performance curve predicted by numerical simulation has a good agreement with the experimental results. Compared with the efficiencies of the original impeller, the hydraulic efficiencies of the optimized impeller are increased by 4.18% and 0.62% under 1.0 Q d and 1.4Qd, respectively. The comparison of inner flow between the original pump and optimized one illustrates the improvement of performance. The optimization process can provide a useful reference on performance improvement of other pumps, even on reduction of pressure fluctuations.

Improving the mixing performance of side channel type micromixers using an optimal voltage control model.

PubMed

Wu, Chien-Hsien; Yang, Ruey-Jen

2006-06-01

Electroosmotic flow in microchannels is restricted to low Reynolds number regimes. Since the inertia forces are extremely weak in such regimes, turbulent conditions do not readily develop, and hence species mixing occurs primarily as a result of diffusion. Consequently, achieving a thorough species mixing generally relies upon the use of extended mixing channels. This paper aims to improve the mixing performance of conventional side channel type micromixers by specifying the optimal driving voltages to be applied to each channel. In the proposed approach, the driving voltages are identified by constructing a simple theoretical scheme based on a 'flow-rate-ratio' model and Kirchhoff's law. The numerical and experimental results confirm that the optimal voltage control approach provides a better mixing performance than the use of a single driving voltage gradient.

Probabilistic Fatigue Damage Prognosis Using a Surrogate Model Trained Via 3D Finite Element Analysis

NASA Technical Reports Server (NTRS)

Leser, Patrick E.; Hochhalter, Jacob D.; Newman, John A.; Leser, William P.; Warner, James E.; Wawrzynek, Paul A.; Yuan, Fuh-Gwo

2015-01-01

Utilizing inverse uncertainty quantification techniques, structural health monitoring can be integrated with damage progression models to form probabilistic predictions of a structure's remaining useful life. However, damage evolution in realistic structures is physically complex. Accurately representing this behavior requires high-fidelity models which are typically computationally prohibitive. In the present work, a high-fidelity finite element model is represented by a surrogate model, reducing computation times. The new approach is used with damage diagnosis data to form a probabilistic prediction of remaining useful life for a test specimen under mixed-mode conditions.

Improvement in surrogate endpoints by a multidisciplinary team in a mobile clinic serving a low-income, immigrant minority population in South Florida.

PubMed

Singh-Franco, Devada; Perez, Alexandra; Wolowich, William R

2013-02-01

To determine effect on surrogate endpoints for cardiovascular disease (CVD), we performed a retrospective chart review of 114 patients seen by a multidisciplinary team that provided primary care services in a mobile clinic over 12 months. Eligible patients had outcomes available for at least six months. Mixed effect modeling examined variation in surrogate markers for CVD: blood pressure (BP), heart rate, and body mass index. Repeated measures ANOVA compared lipids, hemoglobin A1c, and medication use from baseline and throughout study. Most patients were female (75%), Haitian (76%), and low-income ($747/month) with average age 63 years. Common diagnoses were hypertension (82%) and hyperlipidemia (63%). Significant reduction in systolic BP, total- and LDL-cholesterol, and hemoglobin A1c were found (p<.05). Use of ACE-inhibitors, beta-blockers, diuretics, aspirin, metformin, and statins increased significantly (p<.05). Mobile clinic with a multidisciplinary team improved surrogate endpoints over 12 months in underserved, low-income, mostly foreign-born, Haitian population in U.S.

The Role of Model Complexity in Determining Patterns of Chlorophyll Variability in the Coastal Northwest North Atlantic

NASA Astrophysics Data System (ADS)

Kuhn, A. M.; Fennel, K.; Bianucci, L.

2016-02-01

A key feature of the North Atlantic Ocean's biological dynamics is the annual phytoplankton spring bloom. In the region comprising the continental shelf and adjacent deep ocean of the northwest North Atlantic, we identified two patterns of bloom development: 1) locations with cold temperatures and deep winter mixed layers, where the spring bloom peaks around April and the annual chlorophyll cycle has a large amplitude, and 2) locations with warmer temperatures and shallow winter mixed layers, where the spring bloom peaks earlier in the year, sometimes indiscernible from the fall bloom. These patterns result from a combination of limiting environmental factors and interactions among planktonic groups with different optimal requirements. Simple models that represent the ecosystem with a single phytoplankton (P) and a single zooplankton (Z) group are challenged to reproduce these ecological interactions. Here we investigate the effect that added complexity has on determining spatio-temporal chlorophyll. We compare two ecosystem models, one that contains one P and one Z group, and one with two P and three Z groups. We consider three types of changes in complexity: 1) added dependencies among variables (e.g., temperature dependent rates), 2) modified structural pathways, and 3) added pathways. Subsets of the most sensitive parameters are optimized in each model to replicate observations in the region. For computational efficiency, the parameter optimization is performed using 1D surrogates of a 3D model. We evaluate how model complexity affects model skill, and whether the optimized parameter sets found for each model modify the interpretation of ecosystem functioning. Spatial differences in the parameter sets that best represent different areas hint at the existence of different ecological communities or at physical-biological interactions that are not represented in the simplest model. Our methodology emphasizes the combined use of observations, 1D models to help identifying patterns, and 3D models able to simulate the environment modre realistically, as a means to acquire predictive understanding of the ocean's ecology.

Biologically Inspired, Anisoptropic Flexible Wing for Optimal Flapping Flight

DTIC Science & Technology

2013-01-31

Anisotropic Flexible Wing for Optimal Flapping Flight FA9550-07-1-0547 Sb. GRANT NUMBER Sc. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Sd. PROJECT NUMBER...anisotropic structural flexibility ; c) Conducted coordinated experimental and computational modeling to determine the roles of aerodynamic loading, wing inertia...and structural flexibility and elasticity; and d) Developed surrogate tools for flapping wing MA V design and optimization. Detailed research

WE-H-207A-06: Hypoxia Quantification in Static PET Images: The Signal in the Noise

DOE Office of Scientific and Technical Information (OSTI.GOV)

Keller, H; Yeung, I; Milosevic, M

2016-06-15

Purpose: Quantification of hypoxia from PET images is of considerable clinical interest. In the absence of dynamic PET imaging the hypoxic fraction (HF) of a tumor has to be estimated from voxel values of activity concentration of a radioactive hypoxia tracer. This work is part of an effort to standardize quantification of tumor hypoxic fraction from PET images. Methods: A simple hypoxia imaging model in the tumor was developed. The distribution of the tracer activity was described as the sum of two different probability distributions, one for the normoxic (and necrotic), the other for the hypoxic voxels. The widths ofmore » the distributions arise due to variability of the transport, tumor tissue inhomogeneity, tracer binding kinetics, and due to PET image noise. Quantification of HF was performed for various levels of variability using two different methodologies: a) classification thresholds between normoxic and hypoxic voxels based on a non-hypoxic surrogate (muscle), and b) estimation of the (posterior) probability distributions based on maximizing likelihood optimization that does not require a surrogate. Data from the hypoxia imaging model and from 27 cervical cancer patients enrolled in a FAZA PET study were analyzed. Results: In the model, where the true value of HF is known, thresholds usually underestimate the value for large variability. For the patients, a significant uncertainty of the HF values (an average intra-patient range of 17%) was caused by spatial non-uniformity of image noise which is a hallmark of all PET images. Maximum likelihood estimation (MLE) is able to directly optimize for the weights of both distributions, however, may suffer from poor optimization convergence. For some patients, MLE-based HF values showed significant differences to threshold-based HF-values. Conclusion: HF-values depend critically on the magnitude of the different sources of tracer uptake variability. A measure of confidence should also be reported.« less

Multi-Objective Aerodynamic Optimization of the Streamlined Shape of High-Speed Trains Based on the Kriging Model.

PubMed

Xu, Gang; Liang, Xifeng; Yao, Shuanbao; Chen, Dawei; Li, Zhiwei

2017-01-01

Minimizing the aerodynamic drag and the lift of the train coach remains a key issue for high-speed trains. With the development of computing technology and computational fluid dynamics (CFD) in the engineering field, CFD has been successfully applied to the design process of high-speed trains. However, developing a new streamlined shape for high-speed trains with excellent aerodynamic performance requires huge computational costs. Furthermore, relationships between multiple design variables and the aerodynamic loads are seldom obtained. In the present study, the Kriging surrogate model is used to perform a multi-objective optimization of the streamlined shape of high-speed trains, where the drag and the lift of the train coach are the optimization objectives. To improve the prediction accuracy of the Kriging model, the cross-validation method is used to construct the optimal Kriging model. The optimization results show that the two objectives are efficiently optimized, indicating that the optimization strategy used in the present study can greatly improve the optimization efficiency and meet the engineering requirements.

Optimization of an electrokinetic mixer for microfluidic applications.

PubMed

Bockelmann, Hendryk; Heuveline, Vincent; Barz, Dominik P J

2012-06-01

This work is concerned with the investigation of the concentration fields in an electrokinetic micromixer and its optimization in order to achieve high mixing rates. The mixing concept is based on the combination of an alternating electrical excitation applied to a pressure-driven base flow in a meandering microchannel geometry. The electrical excitation induces a secondary electrokinetic velocity component, which results in a complex flow field within the meander bends. A mathematical model describing the physicochemical phenomena present within the micromixer is implemented in an in-house finite-element-method code. We first perform simulations comparable to experiments concerned with the investigation of the flow field in the bends. The comparison of the complex flow topology found in simulation and experiment reveals excellent agreement. Hence, the validated model and numerical schemes are employed for a numerical optimization of the micromixer performance. In detail, we optimize the secondary electrokinetic flow by finding the best electrical excitation parameters, i.e., frequency and amplitude, for a given waveform. Two optimized electrical excitations featuring a discrete and a continuous waveform are discussed with respect to characteristic time scales of our mixing problem. The results demonstrate that the micromixer is able to achieve high mixing degrees very rapidly.

Optimization of an electrokinetic mixer for microfluidic applications

PubMed Central

Bockelmann, Hendryk; Heuveline, Vincent; Barz, Dominik P. J.

2012-01-01

This work is concerned with the investigation of the concentration fields in an electrokinetic micromixer and its optimization in order to achieve high mixing rates. The mixing concept is based on the combination of an alternating electrical excitation applied to a pressure-driven base flow in a meandering microchannel geometry. The electrical excitation induces a secondary electrokinetic velocity component, which results in a complex flow field within the meander bends. A mathematical model describing the physicochemical phenomena present within the micromixer is implemented in an in-house finite-element-method code. We first perform simulations comparable to experiments concerned with the investigation of the flow field in the bends. The comparison of the complex flow topology found in simulation and experiment reveals excellent agreement. Hence, the validated model and numerical schemes are employed for a numerical optimization of the micromixer performance. In detail, we optimize the secondary electrokinetic flow by finding the best electrical excitation parameters, i.e., frequency and amplitude, for a given waveform. Two optimized electrical excitations featuring a discrete and a continuous waveform are discussed with respect to characteristic time scales of our mixing problem. The results demonstrate that the micromixer is able to achieve high mixing degrees very rapidly. PMID:22712034

System Reliability-Based Design Optimization Under Input and Model Uncertainties

DTIC Science & Technology

2014-02-02

Standard Form 298 (Rev 8/98) Prescribed by ANSI Std. Z39.18 W911NF-09- 1 -0250 319-335-5684 Final Report 56025-NS.40 a. REPORT 14. ABSTRACT 16...resource. In such cases the inaccuracy and uncertainty of the surrogate model needs to 1 . REPORT DATE (DD-MM-YYYY) 4. TITLE AND SUBTITLE 13...estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the

Cost of Achieving Squad Overmatch by Training Resilience and Situational Awareness Skills that Optimize Human Performance and Discourage PTS and Suicide

DTIC Science & Technology

2015-12-01

actors. The study team canvassed commercial, academic, and research institutions for a possible surrogate solution and introduced wall based avatars ...barrel, tent, or market stand); other technologies such as wall-projected avatars are not used (instead role players are used). CACTF/MOUT must be...sites at each home station are fitted with mounts or docks to enable enhanced site technology (MILES-enabled wall- projected avatars , non-pyro effects

Measures of Residual Risk with Connections to Regression, Risk Tracking, Surrogate Models, and Ambiguity

DTIC Science & Technology

2015-05-20

original variable. Residual risk can be exempli ed as a quanti cation of the improved situation faced by a hedging investor compared to that of a...distributional information about Yx for every x as well as the computational cost of evaluating R(Yx) for numerous x, for example within an optimization...Still, when g is costly to evaluate , it might be desirable to develop an approximation of R(Yx), x ∈ IRn through regression based on observations {xj

Development of Optimized Combustors and Thermoelectric Generators for Palm Power Generation

DTIC Science & Technology

2004-10-26

manufacturing techniques and microfabrication, on the chemical kinetics of JP-8 surrogates and on the development of advanced laser diagnostics for JP-8...takes the shape of a cone from the tip of which a thin liquid thread emerges, in the so-called cone-jet mode [1]. This microjet breaks into a stream of...combustion systems. 2. The development of a diagnostic technique based on two-color laser induced fluorescence from fluorescence tags added to the fuel

Surrogate mobility and orientation affect the early neurobehavioral development of infant rhesus macaques (Macaca mulatta).

PubMed

Dettmer, Amanda M; Ruggiero, Angela M; Novak, Melinda A; Meyer, Jerrold S; Suomi, Stephen J

2008-05-01

A biological mother's movement appears necessary for optimal development in infant monkeys. However, nursery-reared monkeys are typically provided with inanimate surrogate mothers that move very little. The purpose of this study was to evaluate the effects of a novel, highly mobile surrogate mother on motor development, exploration, and reactions to novelty. Six infant rhesus macaques (Macaca mulatta) were reared on mobile hanging surrogates (MS) and compared to six infants reared on standard stationary rocking surrogates (RS) and to 9-15 infants reared with their biological mothers (MR) for early developmental outcome. We predicted that MS infants would develop more similarly to MR infants than RS infants. In neonatal assessments conducted at Day 30, both MS and MR infants showed more highly developed motor activity than RS infants on measures of grasping (p = .009), coordination (p = .038), spontaneous crawl (p = .009), and balance (p = .003). At 2-3 months of age, both MS and MR infants displayed higher levels of exploration in the home cage than RS infants (p = .016). In a novel situation in which only MS and RS infants were tested, MS infants spent less time near their surrogates in the first five minutes of the test session than RS infants (p = .05), indicating a higher level of comfort. Collectively, these results suggest that when nursery-rearing of infant monkeys is necessary, a mobile hanging surrogate may encourage more normative development of gross motor skills and exploratory behavior and may serve as a useful alternative to stationary or rocking surrogates.

An adjoint-based framework for maximizing mixing in binary fluids

NASA Astrophysics Data System (ADS)

Eggl, Maximilian; Schmid, Peter

2017-11-01

Mixing in the inertial, but laminar parameter regime is a common application in a wide range of industries. Enhancing the efficiency of mixing processes thus has a fundamental effect on product quality, material homogeneity and, last but not least, production costs. In this project, we address mixing efficiency in the above mentioned regime (Reynolds number Re = 1000 , Peclet number Pe = 1000) by developing and demonstrating an algorithm based on nonlinear adjoint looping that minimizes the variance of a passive scalar field which models our binary Newtonian fluids. The numerical method is based on the FLUSI code (Engels et al. 2016), a Fourier pseudo-spectral code, which we modified and augmented by scalar transport and adjoint equations. Mixing is accomplished by moving stirrers which are numerically modeled using a penalization approach. In our two-dimensional simulations we consider rotating circular and elliptic stirrers and extract optimal mixing strategies from the iterative scheme. The case of optimizing shape and rotational speed of the stirrers will be demonstrated.

Advanced topics in evidence-based urologic oncology: surrogate endpoints.

PubMed

Lavallée, Luke T; Montori, Victor M; Canfield, Stephen E; Breau, Rodney H

2011-01-01

Clinical trials often report surrogate endpoint data. A surrogate endpoint is a biological marker or clinical sign that can be substituted for a patient-important outcome. Using surrogate endpoints correctly may facilitate and expedite clinical trials and may improve medical decisions. However, rigorous criteria must be met for an endpoint to be considered a valid surrogate. The purpose of this article is to review the topic of surrogate endpoints in the context of a urologic encounter. Copyright © 2011 Elsevier Inc. All rights reserved.

Predictive uncertainty analysis of plume distribution for geological carbon sequestration using sparse-grid Bayesian method

NASA Astrophysics Data System (ADS)

Shi, X.; Zhang, G.

2013-12-01

Because of the extensive computational burden, parametric uncertainty analyses are rarely conducted for geological carbon sequestration (GCS) process based multi-phase models. The difficulty of predictive uncertainty analysis for the CO2 plume migration in realistic GCS models is not only due to the spatial distribution of the caprock and reservoir (i.e. heterogeneous model parameters), but also because the GCS optimization estimation problem has multiple local minima due to the complex nonlinear multi-phase (gas and aqueous), and multi-component (water, CO2, salt) transport equations. The geological model built by Doughty and Pruess (2004) for the Frio pilot site (Texas) was selected and assumed to represent the 'true' system, which was composed of seven different facies (geological units) distributed among 10 layers. We chose to calibrate the permeabilities of these facies. Pressure and gas saturation values from this true model were then extracted and used as observations for subsequent model calibration. Random noise was added to the observations to approximate realistic field conditions. Each simulation of the model lasts about 2 hours. In this study, we develop a new approach that improves computational efficiency of Bayesian inference by constructing a surrogate system based on an adaptive sparse-grid stochastic collocation method. This surrogate response surface global optimization algorithm is firstly used to calibrate the model parameters, then prediction uncertainty of the CO2 plume position is quantified due to the propagation from parametric uncertainty in the numerical experiments, which is also compared to the actual plume from the 'true' model. Results prove that the approach is computationally efficient for multi-modal optimization and prediction uncertainty quantification for computationally expensive simulation models. Both our inverse methodology and findings can be broadly applicable to GCS in heterogeneous storage formations.

Correlations between prefrontal neurons form a small-world network that optimizes the generation of multineuron sequences of activity

PubMed Central

Luongo, Francisco J.; Zimmerman, Chris A.; Horn, Meryl E.

2016-01-01

Sequential patterns of prefrontal activity are believed to mediate important behaviors, e.g., working memory, but it remains unclear exactly how they are generated. In accordance with previous studies of cortical circuits, we found that prefrontal microcircuits in young adult mice spontaneously generate many more stereotyped sequences of activity than expected by chance. However, the key question of whether these sequences depend on a specific functional organization within the cortical microcircuit, or emerge simply as a by-product of random interactions between neurons, remains unanswered. We observed that correlations between prefrontal neurons do follow a specific functional organization—they have a small-world topology. However, until now it has not been possible to directly link small-world topologies to specific circuit functions, e.g., sequence generation. Therefore, we developed a novel analysis to address this issue. Specifically, we constructed surrogate data sets that have identical levels of network activity at every point in time but nevertheless represent various network topologies. We call this method shuffling activity to rearrange correlations (SHARC). We found that only surrogate data sets based on the actual small-world functional organization of prefrontal microcircuits were able to reproduce the levels of sequences observed in actual data. As expected, small-world data sets contained many more sequences than surrogate data sets with randomly arranged correlations. Surprisingly, small-world data sets also outperformed data sets in which correlations were maximally clustered. Thus the small-world functional organization of cortical microcircuits, which effectively balances the random and maximally clustered regimes, is optimal for producing stereotyped sequential patterns of activity. PMID:26888108

Mixed integer simulation optimization for optimal hydraulic fracturing and production of shale gas fields

NASA Astrophysics Data System (ADS)

Li, J. C.; Gong, B.; Wang, H. G.

2016-08-01

Optimal development of shale gas fields involves designing a most productive fracturing network for hydraulic stimulation processes and operating wells appropriately throughout the production time. A hydraulic fracturing network design-determining well placement, number of fracturing stages, and fracture lengths-is defined by specifying a set of integer ordered blocks to drill wells and create fractures in a discrete shale gas reservoir model. The well control variables such as bottom hole pressures or production rates for well operations are real valued. Shale gas development problems, therefore, can be mathematically formulated with mixed-integer optimization models. A shale gas reservoir simulator is used to evaluate the production performance for a hydraulic fracturing and well control plan. To find the optimal fracturing design and well operation is challenging because the problem is a mixed integer optimization problem and entails computationally expensive reservoir simulation. A dynamic simplex interpolation-based alternate subspace (DSIAS) search method is applied for mixed integer optimization problems associated with shale gas development projects. The optimization performance is demonstrated with the example case of the development of the Barnett Shale field. The optimization results of DSIAS are compared with those of a pattern search algorithm.

A Study on the Optimal Generation Mix Based on Portfolio Theory with Considering the Basic Condition for Power Supply

NASA Astrophysics Data System (ADS)

Kato, Moritoshi; Zhou, Yicheng

This paper presents a novel method to analyze the optimal generation mix based on portfolio theory with considering the basic condition for power supply, which means that electricity generation corresponds with load curve. The optimization of portfolio is integrated with the calculation of a capacity factor of each generation in order to satisfy the basic condition for power supply. Besides, each generation is considered to be an asset, and risks of the generation asset both in its operation period and construction period are considered. Environmental measures are evaluated through restriction of CO2 emissions, which are indicated by CO2 price. Numerical examples show the optimal generation mix according to risks such as the deviation of capacity factor of nuclear power or restriction of CO2 emissions, the possibility of introduction of clean coal technology (IGCC, CCS) or renewable energy, and so on. The results of this work will be possibly applied as setting the target of the generation mix for the future according to prospects of risks of each generation and restrictions of CO2 emissions.

Microreactor-based mixing strategy suppresses product inhibition to enhance sugar yields in enzymatic hydrolysis for cellulosic biofuel production.

PubMed

Chakraborty, Saikat; Singh, Prasun Kumar; Paramashetti, Pawan

2017-08-01

A novel microreactor-based energy-efficient process of using complete convective mixing in a macroreactor till an optimal mixing time followed by no mixing in 200-400μl microreactors enhances glucose and reducing sugar yields by upto 35% and 29%, respectively, while saving 72-90% of the energy incurred on reactor mixing in the enzymatic hydrolysis of cellulose. Empirical exponential relations are provided for determining the optimal mixing time, during which convective mixing in the macroreactor promotes mass transport of the cellulase enzyme to the solid Avicel substrate, while the latter phase of no mixing in the microreactor suppresses product inhibition by preventing the inhibitors (glucose and cellobiose) from homogenizing across the reactor. Sugar yield increases linearly with liquid to solid height ratio (r h ), irrespective of substrate loading and microreactor size, since large r h allows the inhibitors to diffuse in the liquid away from the solids, thus reducing product inhibition. Copyright © 2017 Elsevier Ltd. All rights reserved.

Simultaneous Thermal Analysis of Remediated Nitrate Salt Surrogates

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wayne, David Matthew

The actinide engineering and science group (MET-1) have completed simultaneous thermal analysis and offgas analysis by mass spectrometry (STA-MS) of remediated nitrate salt (RNS) surrogates formulated by the high explosives science and technology group (M-7). The 1.0 to 1.5g surrogate samples were first analyzed as received, then a new set was analyzed with 100-200mL 10M HNO 3 +0.3 MHF added, and a third set was analyzed after 200 mL of a concentrated Pu-AM spike (in 10M HNO 3 +0.3 MHF) was added. The acid and spike solutions were formulated by the actinide analytical chemistry group (C-AAC) using reagent-grade HNO 3more » and HF, which was also used to dissolve a small quantity of mixed, high-fired PuO 2/ AmO 2 oxide.« less

Comparison of treatment effect sizes from pivotal and postapproval trials of novel therapeutics approved by the FDA based on surrogate markers of disease: a meta-epidemiological study.

PubMed

Wallach, Joshua D; Ciani, Oriana; Pease, Alison M; Gonsalves, Gregg S; Krumholz, Harlan M; Taylor, Rod S; Ross, Joseph S

2018-03-21

The U.S. Food and Drug Administration (FDA) often approves new drugs based on trials that use surrogate markers for endpoints, which involve certain trade-offs and may risk making erroneous inferences about the medical product's actual clinical effect. This study aims to compare the treatment effects among pivotal trials supporting FDA approval of novel therapeutics based on surrogate markers of disease with those observed among postapproval trials for the same indication. We searched Drugs@FDA and PubMed to identify published randomized superiority design pivotal trials for all novel drugs initially approved by the FDA between 2005 and 2012 based on surrogate markers as primary endpoints and published postapproval trials using the same surrogate markers or patient-relevant outcomes as endpoints. Summary ratio of odds ratios (RORs) and difference between standardized mean differences (dSMDs) were used to quantify the average difference in treatment effects between pivotal and matched postapproval trials. Between 2005 and 2012, the FDA approved 88 novel drugs for 90 indications based on one or multiple pivotal trials using surrogate markers of disease. Of these, 27 novel drugs for 27 indications were approved based on pivotal trials using surrogate markers as primary endpoints that could be matched to at least one postapproval trial, for a total of 43 matches. For nine (75.0%) of the 12 matches using the same non-continuous surrogate markers as trial endpoints, pivotal trials had larger treatment effects than postapproval trials. On average, treatment effects were 50% higher (more beneficial) in the pivotal than the postapproval trials (ROR 1.5; 95% confidence interval CI 1.01-2.23). For 17 (54.8%) of the 31 matches using the same continuous surrogate markers as trial endpoints, pivotal trials had larger treatment effects than the postapproval trials. On average, there was no difference in treatment effects between pivotal and postapproval trials (dSMDs 0.01; 95% CI -0.15-0.16). Many postapproval drug trials are not directly comparable to previously published pivotal trials, particularly with respect to endpoint selection. Although treatment effects from pivotal trials supporting FDA approval of novel therapeutics based on non-continuous surrogate markers of disease are often larger than those observed among postapproval trials using surrogate markers as trial endpoints, there is no evidence of difference between pivotal and postapproval trials using continuous surrogate markers.

Optimal mix of renewable power generation in the MENA region as a basis for an efficient electricity supply to europe

NASA Astrophysics Data System (ADS)

Alhamwi, Alaa; Kleinhans, David; Weitemeyer, Stefan; Vogt, Thomas

2014-12-01

Renewable Energy sources are gaining importance in the Middle East and North Africa (MENA) region. The purpose of this study is to quantify the optimal mix of renewable power generation in the MENA region, taking Morocco as a case study. Based on hourly meteorological data and load data, a 100% solar-plus-wind only scenario for Morocco is investigated. For the optimal mix analyses, a mismatch energy modelling approach is adopted with the objective to minimise the required storage capacities. For a hypothetical Moroccan energy supply system which is entirely based on renewable energy sources, our results show that the minimum storage capacity is achieved at a share of 63% solar and 37% wind power generations.

surrosurv: An R package for the evaluation of failure time surrogate endpoints in individual patient data meta-analyses of randomized clinical trials.

PubMed

Rotolo, Federico; Paoletti, Xavier; Michiels, Stefan

2018-03-01

Surrogate endpoints are attractive for use in clinical trials instead of well-established endpoints because of practical convenience. To validate a surrogate endpoint, two important measures can be estimated in a meta-analytic context when individual patient data are available: the R indiv 2 or the Kendall's τ at the individual level, and the R trial 2 at the trial level. We aimed at providing an R implementation of classical and well-established as well as more recent statistical methods for surrogacy assessment with failure time endpoints. We also intended incorporating utilities for model checking and visualization and data generating methods described in the literature to date. In the case of failure time endpoints, the classical approach is based on two steps. First, a Kendall's τ is estimated as measure of individual level surrogacy using a copula model. Then, the R trial 2 is computed via a linear regression of the estimated treatment effects; at this second step, the estimation uncertainty can be accounted for via measurement-error model or via weights. In addition to the classical approach, we recently developed an approach based on bivariate auxiliary Poisson models with individual random effects to measure the Kendall's τ and treatment-by-trial interactions to measure the R trial 2 . The most common data simulation models described in the literature are based on: copula models, mixed proportional hazard models, and mixture of half-normal and exponential random variables. The R package surrosurv implements the classical two-step method with Clayton, Plackett, and Hougaard copulas. It also allows to optionally adjusting the second-step linear regression for measurement-error. The mixed Poisson approach is implemented with different reduced models in addition to the full model. We present the package functions for estimating the surrogacy models, for checking their convergence, for performing leave-one-trial-out cross-validation, and for plotting the results. We illustrate their use in practice on individual patient data from a meta-analysis of 4069 patients with advanced gastric cancer from 20 trials of chemotherapy. The surrosurv package provides an R implementation of classical and recent statistical methods for surrogacy assessment of failure time endpoints. Flexible simulation functions are available to generate data according to the methods described in the literature. Copyright © 2017 Elsevier B.V. All rights reserved.

A rank test for bivariate time-to-event outcomes when one event is a surrogate

PubMed Central

Shaw, Pamela A.; Fay, Michael P.

2016-01-01

In many clinical settings, improving patient survival is of interest but a practical surrogate, such as time to disease progression, is instead used as a clinical trial’s primary endpoint. A time-to-first endpoint (e.g. death or disease progression) is commonly analyzed but may not be adequate to summarize patient outcomes if a subsequent event contains important additional information. We consider a surrogate outcome very generally, as one correlated with the true endpoint of interest. Settings of interest include those where the surrogate indicates a beneficial outcome so that the usual time-to-first endpoint of death or surrogate event is nonsensical. We present a new two-sample test for bivariate, interval-censored time-to-event data, where one endpoint is a surrogate for the second, less frequently observed endpoint of true interest. This test examines whether patient groups have equal clinical severity. If the true endpoint rarely occurs, the proposed test acts like a weighted logrank test on the surrogate; if it occurs for most individuals, then our test acts like a weighted logrank test on the true endpoint. If the surrogate is a useful statistical surrogate, our test can have better power than tests based on the surrogate that naively handle the true endpoint. In settings where the surrogate is not valid (treatment affects the surrogate but not the true endpoint), our test incorporates the information regarding the lack of treatment effect from the observed true endpoints and hence is expected to have a dampened treatment effect compared to tests based on the surrogate alone. PMID:27059817

Towards inverse modeling of turbidity currents: The inverse lock-exchange problem

NASA Astrophysics Data System (ADS)

Lesshafft, Lutz; Meiburg, Eckart; Kneller, Ben; Marsden, Alison

2011-04-01

A new approach is introduced for turbidite modeling, leveraging the potential of computational fluid dynamics methods to simulate the flow processes that led to turbidite formation. The practical use of numerical flow simulation for the purpose of turbidite modeling so far is hindered by the need to specify parameters and initial flow conditions that are a priori unknown. The present study proposes a method to determine optimal simulation parameters via an automated optimization process. An iterative procedure matches deposit predictions from successive flow simulations against available localized reference data, as in practice may be obtained from well logs, and aims at convergence towards the best-fit scenario. The final result is a prediction of the entire deposit thickness and local grain size distribution. The optimization strategy is based on a derivative-free, surrogate-based technique. Direct numerical simulations are performed to compute the flow dynamics. A proof of concept is successfully conducted for the simple test case of a two-dimensional lock-exchange turbidity current. The optimization approach is demonstrated to accurately retrieve the initial conditions used in a reference calculation.

Accelerating global optimization of aerodynamic shapes using a new surrogate-assisted parallel genetic algorithm

NASA Astrophysics Data System (ADS)

Ebrahimi, Mehdi; Jahangirian, Alireza

2017-12-01

An efficient strategy is presented for global shape optimization of wing sections with a parallel genetic algorithm. Several computational techniques are applied to increase the convergence rate and the efficiency of the method. A variable fidelity computational evaluation method is applied in which the expensive Navier-Stokes flow solver is complemented by an inexpensive multi-layer perceptron neural network for the objective function evaluations. A population dispersion method that consists of two phases, of exploration and refinement, is developed to improve the convergence rate and the robustness of the genetic algorithm. Owing to the nature of the optimization problem, a parallel framework based on the master/slave approach is used. The outcomes indicate that the method is able to find the global optimum with significantly lower computational time in comparison to the conventional genetic algorithm.

Search for a test for fracture potential of asphalt mixes : close out meeting.

DOT National Transportation Integrated Search

2012-08-01

Presentation Outline : 1)Part A Introduction : 2)Part B The OT test method : 3)Part C Surrogate crack tests (6 No.) : 4)Part D Comparison of the crack test methods : 5)Part E Summary & recommendations : 6)Miscellaneous & discussions

Ionic force field optimization based on single-ion and ion-pair solvation properties: Going beyond standard mixing rules

NASA Astrophysics Data System (ADS)

Fyta, Maria; Netz, Roland R.

2012-03-01

Using molecular dynamics (MD) simulations in conjunction with the SPC/E water model, we optimize ionic force-field parameters for seven different halide and alkali ions, considering a total of eight ion-pairs. Our strategy is based on simultaneous optimizing single-ion and ion-pair properties, i.e., we first fix ion-water parameters based on single-ion solvation free energies, and in a second step determine the cation-anion interaction parameters (traditionally given by mixing or combination rules) based on the Kirkwood-Buff theory without modification of the ion-water interaction parameters. In doing so, we have introduced scaling factors for the cation-anion Lennard-Jones (LJ) interaction that quantify deviations from the standard mixing rules. For the rather size-symmetric salt solutions involving bromide and chloride ions, the standard mixing rules work fine. On the other hand, for the iodide and fluoride solutions, corresponding to the largest and smallest anion considered in this work, a rescaling of the mixing rules was necessary. For iodide, the experimental activities suggest more tightly bound ion pairing than given by the standard mixing rules, which is achieved in simulations by reducing the scaling factor of the cation-anion LJ energy. For fluoride, the situation is different and the simulations show too large attraction between fluoride and cations when compared with experimental data. For NaF, the situation can be rectified by increasing the cation-anion LJ energy. For KF, it proves necessary to increase the effective cation-anion Lennard-Jones diameter. The optimization strategy outlined in this work can be easily adapted to different kinds of ions.

Shape-optimization of round-to-slot holes for improving film cooling effectiveness on a flat surface

NASA Astrophysics Data System (ADS)

Huang, Ying; Zhang, Jing-zhou; Wang, Chun-hua

2018-01-01

Single-objective optimization for improving adiabatic film cooling effectiveness is performed for single row of round-to-slot film cooling holes on a flat surface by using CFD analysis and surrogate approximation methods. Among the main geometric parameters, dimensionless hole-to-hole pitch (P/d) and slot length-to-diameter (l/d) are fixed as 2.4 and 2 respectively, and the other parameters (hole height-to-diameter ratio, slot width-to-diameter and inclination angle) are chosen as the design variables. Given a wide range of possible geometric variables, the geometric optimization of round-to-slot holes is carried out under two typical blowing ratios of M = 0.5 and M = 1.5 by selecting a spatially-averaged adiabatic film cooling effectiveness between x/d = 2 and x/d = 12 as the objective function to be maximized. Radial basis function neural network is applied for constructing the surrogate model and then the optimal design point is searched by a genetic algorithm. It is revealed that the optimal round-to-slot hole is of converging feature under a low blowing ratio but of diffusing feature under a high blowing ratio. Further, the influence principle of optimal round-to-slot geometry on film cooling performance is illustrated according to the detailed flow and thermal behaviors.

Shape-optimization of round-to-slot holes for improving film cooling effectiveness on a flat surface

NASA Astrophysics Data System (ADS)

Huang, Ying; Zhang, Jing-zhou; Wang, Chun-hua

2018-06-01

Single-objective optimization for improving adiabatic film cooling effectiveness is performed for single row of round-to-slot film cooling holes on a flat surface by using CFD analysis and surrogate approximation methods. Among the main geometric parameters, dimensionless hole-to-hole pitch ( P/ d) and slot length-to-diameter ( l/ d) are fixed as 2.4 and 2 respectively, and the other parameters (hole height-to-diameter ratio, slot width-to-diameter and inclination angle) are chosen as the design variables. Given a wide range of possible geometric variables, the geometric optimization of round-to-slot holes is carried out under two typical blowing ratios of M = 0.5 and M = 1.5 by selecting a spatially-averaged adiabatic film cooling effectiveness between x/ d = 2 and x/ d = 12 as the objective function to be maximized. Radial basis function neural network is applied for constructing the surrogate model and then the optimal design point is searched by a genetic algorithm. It is revealed that the optimal round-to-slot hole is of converging feature under a low blowing ratio but of diffusing feature under a high blowing ratio. Further, the influence principle of optimal round-to-slot geometry on film cooling performance is illustrated according to the detailed flow and thermal behaviors.

Uncertainty quantification-based robust aerodynamic optimization of laminar flow nacelle

NASA Astrophysics Data System (ADS)

Xiong, Neng; Tao, Yang; Liu, Zhiyong; Lin, Jun

2018-05-01

The aerodynamic performance of laminar flow nacelle is highly sensitive to uncertain working conditions, especially the surface roughness. An efficient robust aerodynamic optimization method on the basis of non-deterministic computational fluid dynamic (CFD) simulation and Efficient Global Optimization (EGO)algorithm was employed. A non-intrusive polynomial chaos method is used in conjunction with an existing well-verified CFD module to quantify the uncertainty propagation in the flow field. This paper investigates the roughness modeling behavior with the γ-Ret shear stress transport model including modeling flow transition and surface roughness effects. The roughness effects are modeled to simulate sand grain roughness. A Class-Shape Transformation-based parametrical description of the nacelle contour as part of an automatic design evaluation process is presented. A Design-of-Experiments (DoE) was performed and surrogate model by Kriging method was built. The new design nacelle process demonstrates that significant improvements of both mean and variance of the efficiency are achieved and the proposed method can be applied to laminar flow nacelle design successfully.

Mixed-source reintroductions lead to outbreeding depression in second-generation descendents of a native North American fish

USGS Publications Warehouse

Huff, D.D.; Miller, L.M.; Chizinski, C.J.; Vondracek, B.

2011-01-01

Reintroductions are commonly employed to preserve intraspecific biodiversity in fragmented landscapes. However, reintroduced populations are frequently smaller and more geographically isolated than native populations. Mixing genetically, divergent sources are often proposed to attenuate potentially low genetic diversity in reintroduced populations that may result from small effective population sizes. However, a possible negative tradeoff for mixing sources is outbreeding depression in hybrid offspring. We examined the consequences of mixed-source reintroductions on several fitness surrogates at nine slimy sculpin (Cottus cognatus) reintroduction sites in south-east Minnesota. We inferred the relative fitness of each crosstype in the reintroduced populations by comparing their growth rate, length, weight, body condition and persistence in reintroduced populations. Pure strain descendents from a single source population persisted in a greater proportion than expected in the reintroduced populations, whereas all other crosstypes occurred in a lesser proportion. Length, weight and growth rate were lower for second-generation intra-population hybrid descendents than for pure strain and first-generation hybrids. In the predominant pure strain, young-of the-year size was significantly greater than any other crosstype. Our results suggested that differences in fitness surrogates among crosstypes were consistent with disrupted co-adapted gene complexes associated with beneficial adaptations in these reintroduced populations. Future reintroductions may be improved by evaluating the potential for local adaptation in source populations or by avoiding the use of mixed sources by default when information on local adaptations or other genetic characteristics is lacking. ?? 2011 Blackwell Publishing Ltd.

Pull out strength calculator for pedicle screws using a surrogate ensemble approach.

PubMed

Varghese, Vicky; Ramu, Palaniappan; Krishnan, Venkatesh; Saravana Kumar, Gurunathan

2016-12-01

Pedicle screw instrumentation is widely used in the treatment of spinal disorders and deformities. Currently, the surgeon decides the holding power of instrumentation based on the perioperative feeling which is subjective in nature. The objective of the paper is to develop a surrogate model which will predict the pullout strength of pedicle screw based on density, insertion angle, insertion depth and reinsertion. A Taguchi's orthogonal array was used to design an experiment to find the factors effecting pullout strength of pedicle screw. The pullout studies were carried using polyaxial pedicle screw on rigid polyurethane foam block according to American society for testing of materials (ASTM F543). Analysis of variance (ANOVA) and Tukey's honestly significant difference multiple comparison tests were done to find factor effect. Based on the experimental results, surrogate models based on Krigging, polynomial response surface and radial basis function were developed for predicting the pullout strength for different combination of factors. An ensemble of these surrogates based on weighted average surrogate model was also evaluated for prediction. Density, insertion depth, insertion angle and reinsertion have a significant effect (p <0.05) on pullout strength of pedicle screw. Weighted average surrogate performed the best in predicting the pull out strength amongst the surrogate models considered in this study and acted as insurance against bad prediction. A predictive model for pullout strength of pedicle screw was developed using experimental values and surrogate models. This can be used in pre-surgical planning and decision support system for spine surgeon. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Airfoil Shape Optimization based on Surrogate Model

NASA Astrophysics Data System (ADS)

Mukesh, R.; Lingadurai, K.; Selvakumar, U.

2018-02-01

Engineering design problems always require enormous amount of real-time experiments and computational simulations in order to assess and ensure the design objectives of the problems subject to various constraints. In most of the cases, the computational resources and time required per simulation are large. In certain cases like sensitivity analysis, design optimisation etc where thousands and millions of simulations have to be carried out, it leads to have a life time of difficulty for designers. Nowadays approximation models, otherwise called as surrogate models (SM), are more widely employed in order to reduce the requirement of computational resources and time in analysing various engineering systems. Various approaches such as Kriging, neural networks, polynomials, Gaussian processes etc are used to construct the approximation models. The primary intention of this work is to employ the k-fold cross validation approach to study and evaluate the influence of various theoretical variogram models on the accuracy of the surrogate model construction. Ordinary Kriging and design of experiments (DOE) approaches are used to construct the SMs by approximating panel and viscous solution algorithms which are primarily used to solve the flow around airfoils and aircraft wings. The method of coupling the SMs with a suitable optimisation scheme to carryout an aerodynamic design optimisation process for airfoil shapes is also discussed.

Optimization Properties of Environmentally Friendly Paper Coating Based Starch-Polyethylene glycol (PEG) Mixture

NASA Astrophysics Data System (ADS)

Galih Saputri, Diani; Khairuddin; Dwi Nurhayati, Nanik; Pham, Trinh

2017-11-01

The use of starch as biodegradable base material for packaging application was of great interest as an environmentally friendly alternative to the present use of polyethylene and polyvinyl chloride. However, starch tended to be brittle and had a lack of stability due to exposure to water. Several aproaches have been done to improve shellac properties including through chemical modification, mixing with polymers, clays, and plasticizers. The present study related to optimization of starch properties when mixing with polyethylene glycol (PEG) coated on the paper. The aim was to obtain the temperature and mixing time between starch and PEG so produced composites with optimal barrier properties. The composites of PEG/starch 10 % w/w were prepared using solvent casting and coated on paper surface, and dried in the oven for 12 hours at 40°C. Water Vapour Transmitter Rate (WVTR) (Payne cup method) showed that 70°C was the optimum temperature when mixing time was 30 minutes. Moreover, it showed that the optimum mixing time was 30 minutes when mixing temperature was 80 and 70 °C. Fourier Transform Infra Red (FTIR) showed a strong interaction between PEG400 and starch.

Multi-objective shape optimization of plate structure under stress criteria based on sub-structured mixed FEM and genetic algorithms

NASA Astrophysics Data System (ADS)

Garambois, Pierre; Besset, Sebastien; Jézéquel, Louis

2015-07-01

This paper presents a methodology for the multi-objective (MO) shape optimization of plate structure under stress criteria, based on a mixed Finite Element Model (FEM) enhanced with a sub-structuring method. The optimization is performed with a classical Genetic Algorithm (GA) method based on Pareto-optimal solutions and considers thickness distributions parameters and antagonist objectives among them stress criteria. We implement a displacement-stress Dynamic Mixed FEM (DM-FEM) for plate structure vibrations analysis. Such a model gives a privileged access to the stress within the plate structure compared to primal classical FEM, and features a linear dependence to the thickness parameters. A sub-structuring reduction method is also computed in order to reduce the size of the mixed FEM and split the given structure into smaller ones with their own thickness parameters. Those methods combined enable a fast and stress-wise efficient structure analysis, and improve the performance of the repetitive GA. A few cases of minimizing the mass and the maximum Von Mises stress within a plate structure under a dynamic load put forward the relevance of our method with promising results. It is able to satisfy multiple damage criteria with different thickness distributions, and use a smaller FEM.

Control of wavepacket dynamics in mixed alkali metal clusters by optimally shaped fs pulses

NASA Astrophysics Data System (ADS)

Bartelt, A.; Minemoto, S.; Lupulescu, C.; Vajda, Š.; Wöste, L.

We have performed adaptive feedback optimization of phase-shaped femtosecond laser pulses to control the wavepacket dynamics of small mixed alkali-metal clusters. An optimization algorithm based on Evolutionary Strategies was used to maximize the ion intensities. The optimized pulses for NaK and Na2K converged to pulse trains consisting of numerous peaks. The timing of the elements of the pulse trains corresponds to integer and half integer numbers of the vibrational periods of the molecules, reflecting the wavepacket dynamics in their excited states.

Geometric modeling of space-optimal unit-cell-based tissue engineering scaffolds

NASA Astrophysics Data System (ADS)

Rajagopalan, Srinivasan; Lu, Lichun; Yaszemski, Michael J.; Robb, Richard A.

2005-04-01

Tissue engineering involves regenerating damaged or malfunctioning organs using cells, biomolecules, and synthetic or natural scaffolds. Based on their intended roles, scaffolds can be injected as space-fillers or be preformed and implanted to provide mechanical support. Preformed scaffolds are biomimetic "trellis-like" structures which, on implantation and integration, act as tissue/organ surrogates. Customized, computer controlled, and reproducible preformed scaffolds can be fabricated using Computer Aided Design (CAD) techniques and rapid prototyping devices. A curved, monolithic construct with minimal surface area constitutes an efficient substrate geometry that promotes cell attachment, migration and proliferation. However, current CAD approaches do not provide such a biomorphic construct. We address this critical issue by presenting one of the very first physical realizations of minimal surfaces towards the construction of efficient unit-cell based tissue engineering scaffolds. Mask programmability, and optimal packing density of triply periodic minimal surfaces are used to construct the optimal pore geometry. Budgeted polygonization, and progressive minimal surface refinement facilitate the machinability of these surfaces. The efficient stress distributions, as deduced from the Finite Element simulations, favor the use of these scaffolds for orthopedic applications.

21 CFR 314.510 - Approval based on a surrogate endpoint or on an effect on a clinical endpoint other than survival...

Code of Federal Regulations, 2010 CFR

2010-04-01

... 21 Food and Drugs 5 2010-04-01 2010-04-01 false Approval based on a surrogate endpoint or on an effect on a clinical endpoint other than survival or irreversible morbidity. 314.510 Section 314.510 Food... Serious or Life-Threatening Illnesses § 314.510 Approval based on a surrogate endpoint or on an effect on...

The Role of Nuclear Power in Reducing Risk of the Fossil Fuel Prices and Diversity of Electricity Generation in Tunisia: A Portfolio Approach

NASA Astrophysics Data System (ADS)

Abdelhamid, Mohamed Ben; Aloui, Chaker; Chaton, Corinne; Souissi, Jomâa

2010-04-01

This paper applies real options and mean-variance portfolio theories to analyze the electricity generation planning into presence of nuclear power plant for the Tunisian case. First, we analyze the choice between fossil fuel and nuclear production. A dynamic model is presented to illustrate the impact of fossil fuel cost uncertainty on the optimal timing to switch from gas to nuclear. Next, we use the portfolio theory to manage risk of the electricity generation portfolio and to determine the optimal fuel mix with the nuclear alternative. Based on portfolio theory, the results show that there is other optimal mix than the mix fixed for the Tunisian mix for the horizon 2010-2020, with lower cost for the same risk degree. In the presence of nuclear technology, we found that the optimal generating portfolio must include 13% of nuclear power technology share.

Efficiency of porcine somatic cell nuclear transfer – a retrospective study of factors related to embryo recipient and embryos transferred

PubMed Central

Huang, Yongye; Ouyang, Hongsheng; Yu, Hao; Lai, Liangxue; Pang, Daxin; Li, Zhanjun

2013-01-01

Summary The successful generation of pigs via somatic cell nuclear transfer depends on reducing risk factors in several aspects. To provide an overview of some influencing factors related to embryo transfer, the follow-up data related to cloned pig production collected in our laboratory was examined. (i) Spring showed a higher full-term pregnancy rate compared with winter (33.6% vs 18.6%, P = 0.006). Furthermore, a regression equation can be drawn between full-term pregnancy numbers and pregnancy numbers in different months (y = 0.692x−3.326). (ii) There were no significant differences detected in the number of transferred embryos between surrogate sows exhibiting full-term development compared to those that did not. (iii) Non-ovulating surrogate sows presented a higher percentage of full-term pregnancies compared with ovulating sows (32.0% vs 17.5%, P = 0.004; respectively). (iv) Abortion was most likely to take place between Day 27 to Day 34. (v) Based on Life Table Survival Analysis, delivery in normally fertilized and surrogate sows is expected to be completed before Day 117 or Day 125, respectively. Additionally, the length of pregnancy in surrogate sows was negatively correlated with the average litter size, which was not found for normally fertilized sows. In conclusion, performing embryo transfer in appropriate seasons, improving the quality of embryos transferred, optimizing the timing of embryo transfer, limiting the occurrence of abortion, combined with ameliorating the management of delivery, is expected to result in the harvest of a great number of surviving cloned piglets. PMID:24244859

Surrogate Mobility and Orientation Affect the Early Neurobehavioral Development of Infant Rhesus Macaques (Macaca mulatta)

PubMed Central

Dettmer, Amanda M.; Ruggerio, Angela M.; Novak, Melinda A.; Meyer, Jerrold S.; Suomi, Stephen J.

2008-01-01

A biological mother’s movement appears necessary for optimal development in infant monkeys. However, nursery-reared monkeys are typically provided with inanimate surrogate mothers that move very little. The purpose of this study was to evaluate the effects of a novel, highly mobile surrogate mother on motor development, exploration, and reactions to novelty. Six infant rhesus macaques (Macaca mulatta) were reared on mobile hanging surrogates (MS) and compared to six infants reared on standard stationary rocking surrogates (RS) and to 9-15 infants reared with their biological mothers (MR) for early developmental outcome. We predicted that MS infants would develop more similarly to MR infants than RS infants. In neonatal assessments conducted at day 30, both MS and MR infants showed more highly developed motor activity than RS infants on measures of grasping (p=.009), coordination (p=.038), spontaneous crawl (p=.009), and balance (p=.003). At 2-3 months of age, both MS and MR infants displayed higher levels of exploration in the home cage than RS infants (p=.016). In a novel situation in which only MS and RS infants were tested, MS infants showed less of a stress response, spending less time near their surrogates in the first five minutes of the test session than RS infants (p=.05) and exhibiting a significantly lower rise in salivary cortisol after the test than RS infants (p=.018). Collectively, these results suggest that when nursery-rearing of infant monkeys is necessary, a mobile hanging surrogate may encourage more normative development of gross motor skills and exploratory behavior and may serve as a useful alternative to stationary or rocking surrogates. PMID:19810188

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, Bo, E-mail: luboufl@gmail.com; Park, Justin C.; Fan, Qiyong

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 sparsemore » 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.« less

238Pu recovery and salt disposition from the molten salt oxidation process

NASA Astrophysics Data System (ADS)

Remerowski, M. L.; Stimmel, Jay J.; Wong, Amy S.; Ramsey, Kevin B.

2000-07-01

We have begun designing and optimizing our recovery and recycling processes by experimenting with samples of "spent salt" produced by MSO treatment of surrogate waste in the reaction vessel at the Naval Surface Warfare Center-Indian Head. One salt was produced by treating surrogate waste containing pyrolysis ash spiked with cerium. The other salt contains residues from MSO treatment of materials similar to those used in 238Pu processing, e.g., Tygon tubing, PVC bagout bags, HDPE bottles. Using these two salt samples, we will present results from our investigations.

Dynamic response due to behind helmet blunt trauma measured with a human head surrogate.

PubMed

Freitas, Christopher J; Mathis, James T; Scott, Nikki; Bigger, Rory P; Mackiewicz, James

2014-01-01

A Human Head Surrogate has been developed for use in behind helmet blunt trauma experiments. This human head surrogate fills the void between Post-Mortem Human Subject testing (with biofidelity but handling restrictions) and commercial ballistic head forms (with no biofidelity but ease of use). This unique human head surrogate is based on refreshed human craniums and surrogate materials representing human head soft tissues such as the skin, dura, and brain. A methodology for refreshing the craniums is developed and verified through material testing. A test methodology utilizing these unique human head surrogates is also developed and then demonstrated in a series of experiments in which non-perforating ballistic impact of combat helmets is performed with and without supplemental ceramic appliques for protecting against larger caliber threats. Sensors embedded in the human head surrogates allow for direct measurement of intracranial pressure, cranial strain, and head and helmet acceleration. Over seventy (70) fully instrumented experiments have been executed using this unique surrogate. Examples of the data collected are presented. Based on these series of tests, the Southwest Research Institute (SwRI) Human Head Surrogate has demonstrated great potential for providing insights in to injury mechanics resulting from non-perforating ballistic impact on combat helmets, and directly supports behind helmet blunt trauma studies.

Dynamic Response Due to Behind Helmet Blunt Trauma Measured with a Human Head Surrogate

PubMed Central

Freitas, Christopher J.; Mathis, James T.; Scott, Nikki; Bigger, Rory P.; MacKiewicz, James

2014-01-01

A Human Head Surrogate has been developed for use in behind helmet blunt trauma experiments. This human head surrogate fills the void between Post-Mortem Human Subject testing (with biofidelity but handling restrictions) and commercial ballistic head forms (with no biofidelity but ease of use). This unique human head surrogate is based on refreshed human craniums and surrogate materials representing human head soft tissues such as the skin, dura, and brain. A methodology for refreshing the craniums is developed and verified through material testing. A test methodology utilizing these unique human head surrogates is also developed and then demonstrated in a series of experiments in which non-perforating ballistic impact of combat helmets is performed with and without supplemental ceramic appliques for protecting against larger caliber threats. Sensors embedded in the human head surrogates allow for direct measurement of intracranial pressure, cranial strain, and head and helmet acceleration. Over seventy (70) fully instrumented experiments have been executed using this unique surrogate. Examples of the data collected are presented. Based on these series of tests, the Southwest Research Institute (SwRI) Human Head Surrogate has demonstrated great potential for providing insights in to injury mechanics resulting from non-perforating ballistic impact on combat helmets, and directly supports behind helmet blunt trauma studies. PMID:24688303

Evolutionary algorithm based optimization of hydraulic machines utilizing a state-of-the-art block coupled CFD solver and parametric geometry and mesh generation tools

NASA Astrophysics Data System (ADS)

S, Kyriacou; E, Kontoleontos; S, Weissenberger; L, Mangani; E, Casartelli; I, Skouteropoulou; M, Gattringer; A, Gehrer; M, Buchmayr

2014-03-01

An efficient hydraulic optimization procedure, suitable for industrial use, requires an advanced optimization tool (EASY software), a fast solver (block coupled CFD) and a flexible geometry generation tool. EASY optimization software is a PCA-driven metamodel-assisted Evolutionary Algorithm (MAEA (PCA)) that can be used in both single- (SOO) and multiobjective optimization (MOO) problems. In MAEAs, low cost surrogate evaluation models are used to screen out non-promising individuals during the evolution and exclude them from the expensive, problem specific evaluation, here the solution of Navier-Stokes equations. For additional reduction of the optimization CPU cost, the PCA technique is used to identify dependences among the design variables and to exploit them in order to efficiently drive the application of the evolution operators. To further enhance the hydraulic optimization procedure, a very robust and fast Navier-Stokes solver has been developed. This incompressible CFD solver employs a pressure-based block-coupled approach, solving the governing equations simultaneously. This method, apart from being robust and fast, also provides a big gain in terms of computational cost. In order to optimize the geometry of hydraulic machines, an automatic geometry and mesh generation tool is necessary. The geometry generation tool used in this work is entirely based on b-spline curves and surfaces. In what follows, the components of the tool chain are outlined in some detail and the optimization results of hydraulic machine components are shown in order to demonstrate the performance of the presented optimization procedure.

Optimal clinical trial design based on a dichotomous Markov-chain mixed-effect sleep model.

PubMed

Steven Ernest, C; Nyberg, Joakim; Karlsson, Mats O; Hooker, Andrew C

2014-12-01

D-optimal designs for discrete-type responses have been derived using generalized linear mixed models, simulation based methods and analytical approximations for computing the fisher information matrix (FIM) of non-linear mixed effect models with homogeneous probabilities over time. In this work, D-optimal designs using an analytical approximation of the FIM for a dichotomous, non-homogeneous, Markov-chain phase advanced sleep non-linear mixed effect model was investigated. The non-linear mixed effect model consisted of transition probabilities of dichotomous sleep data estimated as logistic functions using piecewise linear functions. Theoretical linear and nonlinear dose effects were added to the transition probabilities to modify the probability of being in either sleep stage. D-optimal designs were computed by determining an analytical approximation the FIM for each Markov component (one where the previous state was awake and another where the previous state was asleep). Each Markov component FIM was weighted either equally or by the average probability of response being awake or asleep over the night and summed to derive the total FIM (FIM(total)). The reference designs were placebo, 0.1, 1-, 6-, 10- and 20-mg dosing for a 2- to 6-way crossover study in six dosing groups. Optimized design variables were dose and number of subjects in each dose group. The designs were validated using stochastic simulation/re-estimation (SSE). Contrary to expectations, the predicted parameter uncertainty obtained via FIM(total) was larger than the uncertainty in parameter estimates computed by SSE. Nevertheless, the D-optimal designs decreased the uncertainty of parameter estimates relative to the reference designs. Additionally, the improvement for the D-optimal designs were more pronounced using SSE than predicted via FIM(total). Through the use of an approximate analytic solution and weighting schemes, the FIM(total) for a non-homogeneous, dichotomous Markov-chain phase advanced sleep model was computed and provided more efficient trial designs and increased nonlinear mixed-effects modeling parameter precision.

New numerical methods for open-loop and feedback solutions to dynamic optimization problems

NASA Astrophysics Data System (ADS)

Ghosh, Pradipto

The topic of the first part of this research is trajectory optimization of dynamical systems via computational swarm intelligence. Particle swarm optimization is a nature-inspired heuristic search method that relies on a group of potential solutions to explore the fitness landscape. Conceptually, each particle in the swarm uses its own memory as well as the knowledge accumulated by the entire swarm to iteratively converge on an optimal or near-optimal solution. It is relatively straightforward to implement and unlike gradient-based solvers, does not require an initial guess or continuity in the problem definition. Although particle swarm optimization has been successfully employed in solving static optimization problems, its application in dynamic optimization, as posed in optimal control theory, is still relatively new. In the first half of this thesis particle swarm optimization is used to generate near-optimal solutions to several nontrivial trajectory optimization problems including thrust programming for minimum fuel, multi-burn spacecraft orbit transfer, and computing minimum-time rest-to-rest trajectories for a robotic manipulator. A distinct feature of the particle swarm optimization implementation in this work is the runtime selection of the optimal solution structure. Optimal trajectories are generated by solving instances of constrained nonlinear mixed-integer programming problems with the swarming technique. For each solved optimal programming problem, the particle swarm optimization result is compared with a nearly exact solution found via a direct method using nonlinear programming. Numerical experiments indicate that swarm search can locate solutions to very great accuracy. The second half of this research develops a new extremal-field approach for synthesizing nearly optimal feedback controllers for optimal control and two-player pursuit-evasion games described by general nonlinear differential equations. A notable revelation from this development is that the resulting control law has an algebraic closed-form structure. The proposed method uses an optimal spatial statistical predictor called universal kriging to construct the surrogate model of a feedback controller, which is capable of quickly predicting an optimal control estimate based on current state (and time) information. With universal kriging, an approximation to the optimal feedback map is computed by conceptualizing a set of state-control samples from pre-computed extremals to be a particular realization of a jointly Gaussian spatial process. Feedback policies are computed for a variety of example dynamic optimization problems in order to evaluate the effectiveness of this methodology. This feedback synthesis approach is found to combine good numerical accuracy with low computational overhead, making it a suitable candidate for real-time applications. Particle swarm and universal kriging are combined for a capstone example, a near optimal, near-admissible, full-state feedback control law is computed and tested for the heat-load-limited atmospheric-turn guidance of an aeroassisted transfer vehicle. The performance of this explicit guidance scheme is found to be very promising; initial errors in atmospheric entry due to simulated thruster misfirings are found to be accurately corrected while closely respecting the algebraic state-inequality constraint.

Surrogate outcomes: experiences at the Common Drug Review

PubMed Central

2013-01-01

Background Surrogate outcomes are a significant challenge in drug evaluation for health technology assessment (HTA) agencies. The research objectives were to: identify factors associated with surrogate use and acceptability in Canada’s Common Drug Review (CDR) recommendations, and compare the CDR with other HTA or regulatory agencies regarding surrogate concerns. Methods Final recommendations were identified from CDR inception (September 2003) to December 31, 2010. Recommendations were classified by type of outcome (surrogate, final, other) and acceptability of surrogates (determined by the presence/absence of statements of concern regarding surrogates). Descriptive and statistical analyses examined factors related to surrogate use and acceptability. For thirteen surrogate-based submissions, recommendations from international HTA and regulatory agencies were reviewed for statements about surrogate acceptability. Results Of 156 final recommendations, 68 (44%) involved surrogates. The overall ‘do not list’ (DNL) rate was 48%; the DNL rate for surrogates was 41% (p = 0.175). The DNL rate was 64% for non-accepted surrogates (n = 28) versus 25% for accepted surrogates (odds ratio 5.4, p = 0.002). Clinical uncertainty, use of economic evidence over price alone, and a premium price were significantly associated with non-accepted surrogates. Surrogates were used most commonly for HIV, diabetes, rare diseases, cardiovascular disease and cancer. For the subset of drugs studied, other HTA agencies did not express concerns for most recommendations, while regulatory agencies frequently stated surrogate acceptance. Conclusions The majority of surrogates were accepted at the CDR. Non-accepted surrogates were significantly associated with clinical uncertainty and a DNL recommendation. There was inconsistency of surrogate acceptability across several international agencies. Stakeholders should consider collaboratively establishing guidelines on the use, validation, and acceptability of surrogates. PMID:24341379

An acoustofluidic micromixer based on oscillating sidewall sharp-edges.

PubMed

Huang, Po-Hsun; Xie, Yuliang; Ahmed, Daniel; Rufo, Joseph; Nama, Nitesh; Chen, Yuchao; Chan, Chung Yu; Huang, Tony Jun

2013-10-07

Rapid and homogeneous mixing inside a microfluidic channel is demonstrated via the acoustic streaming phenomenon induced by the oscillation of sidewall sharp-edges. By optimizing the design of the sharp-edges, excellent mixing performance and fast mixing speed can be achieved in a simple device, making our sharp-edge-based acoustic micromixer a promising candidate for a wide variety of applications.

A review of selection-based tests of abiotic surrogates for species representation.

PubMed

Beier, Paul; Sutcliffe, Patricia; Hjort, Jan; Faith, Daniel P; Pressey, Robert L; Albuquerque, Fabio

2015-06-01

Because conservation planners typically lack data on where species occur, environmental surrogates--including geophysical settings and climate types--have been used to prioritize sites within a planning area. We reviewed 622 evaluations of the effectiveness of abiotic surrogates in representing species in 19 study areas. Sites selected using abiotic surrogates represented more species than an equal number of randomly selected sites in 43% of tests (55% for plants) and on average improved on random selection of sites by about 8% (21% for plants). Environmental diversity (ED) (42% median improvement on random selection) and biotically informed clusters showed promising results and merit additional testing. We suggest 4 ways to improve performance of abiotic surrogates. First, analysts should consider a broad spectrum of candidate variables to define surrogates, including rarely used variables related to geographic separation, distance from coast, hydrology, and within-site abiotic diversity. Second, abiotic surrogates should be defined at fine thematic resolution. Third, sites (the landscape units prioritized within a planning area) should be small enough to ensure that surrogates reflect species' environments and to produce prioritizations that match the spatial resolution of conservation decisions. Fourth, if species inventories are available for some planning units, planners should define surrogates based on the abiotic variables that most influence species turnover in the planning area. Although species inventories increase the cost of using abiotic surrogates, a modest number of inventories could provide the data needed to select variables and evaluate surrogates. Additional tests of nonclimate abiotic surrogates are needed to evaluate the utility of conserving nature's stage as a strategy for conservation planning in the face of climate change. © 2015 Society for Conservation Biology.

Simultaneous Detection of Human C-Terminal p53 Isoforms by Single Template Molecularly Imprinted Polymers (MIPs) Coupled with Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS)-Based Targeted Proteomics.

PubMed

Jiang, Wenting; Liu, Liang; Chen, Yun

2018-03-06

Abnormal expression of C-terminal p53 isoforms α, β, and γ can cause the development of cancers including breast cancer. To date, much evidence has demonstrated that these isoforms can differentially regulate target genes and modulate their expression. Thus, quantification of individual isoforms may help to link clinical outcome to p53 status and to improve cancer patient treatment. However, there are few studies on accurate determination of p53 isoforms, probably due to sequence homology of these isoforms and also their low abundance. In this study, a targeted proteomics assay combining molecularly imprinted polymers (MIPs) and liquid chromatography-tandem mass spectrometry (LC-MS/MS) was developed for simultaneous quantification of C-terminal p53 isoforms. Isoform-specific surrogate peptides (i.e., KPLDGEYFTLQIR (peptide-α) for isoform α, KPLDGEYFTLQDQTSFQK (peptide-β) for isoform β, and KPLDGEYFTLQMLLDLR (peptide-γ) for isoform γ) were first selected and used in both MIPs enrichment and mass spectrometric detection. The common sequence KPLDGEYFTLQ of these three surrogate peptides was used as single template in MIPs. In addition to optimization of imprinting conditions and characterization of the prepared MIPs, binding affinity and cross-reactivity of the MIPs for each surrogate peptide were also evaluated. As a result, a LOQ of 5 nM was achieved, which was >15-fold more sensitive than that without MIPs. Finally, the assay was validated and applied to simultaneous quantitative analysis of C-terminal p53 isoforms α, β, and γ in several human breast cell lines (i.e., MCF-10A normal cells, MCF-7 and MDA-MB-231 cancer cells, and drug-resistant MCF-7/ADR cancer cells). This study is among the first to employ single template MIPs and cross-reactivity phenomenon to select isoform-specific surrogate peptides and enable simultaneous quantification of protein isoforms in LC-MS/MS-based targeted proteomics.

Tracing binding modes in hit-to-lead optimization: chameleon-like poses of aspartic protease inhibitors.

PubMed

Kuhnert, Maren; Köster, Helene; Bartholomäus, Ruben; Park, Ah Young; Shahim, Amir; Heine, Andreas; Steuber, Holger; Klebe, Gerhard; Diederich, Wibke E

2015-02-23

Successful lead optimization in structure-based drug discovery depends on the correct deduction and interpretation of the underlying structure-activity relationships (SAR) to facilitate efficient decision-making on the next candidates to be synthesized. Consequently, the question arises, how frequently a binding mode (re)-validation is required, to ensure not to be misled by invalid assumptions on the binding geometry. We present an example in which minor chemical modifications within one inhibitor series lead to surprisingly different binding modes. X-ray structure determination of eight inhibitors derived from one core scaffold resulted in four different binding modes in the aspartic protease endothiapepsin, a well-established surrogate for e.g. renin and β-secretase. In addition, we suggest an empirical metrics that might serve as an indicator during lead optimization to qualify compounds as candidates for structural revalidation. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Detox{sup SM} wet oxidation system studies for engineering scale up

DOE Office of Scientific and Technical Information (OSTI.GOV)

Robertson, D.T.; Moslander, J.E.; Zigmond, J.A.

1995-12-31

Catalyzed wet oxidation utilizing iron(III) has been shown to have promise for treating many hazardous and mixed wastes. The reaction occurs at the surface of contact between an aqueous iron(III) solution and organic material. Studies with liquid- and vapor-phase organic waste surrogates have established reaction kinetics and the limits of reaction rate based on organic concentration and iron(III) diffusion. Continuing engineering studies have concentrated on reaction vessel agitator and solids feed configurations, an improved bench scale reflux condenser and reflux condenser calculations, sparging of organic compounds from the process condensate water, filtration of solids from the process solution, and flammabilitymore » limits for volatile organic compounds in the headspace of the reaction vessel under the reaction conditions. Detailed engineering design and fabrication of a demonstration unit for treatment of mixed waste is in progress.« less

Assessing the validity of surrogate endpoints in the context of a controversy about the measurement of effectiveness of hepatitis C virus treatment.

PubMed

Dobler, Claudia C; Morgan, Rebecca L; Falck-Ytter, Yngve; Montori, Victor M; Murad, M Hassan

2018-04-01

Surrogate endpoints are often used in clinical trials, as they allow for indirect measures of outcomes (eg, shorter trials with less participants). Improvements in surrogate endpoints (eg, reduction in low density lipoprotein cholesterol, normalisation of glycated haemoglobin) achieved with an intervention are, however, not always associated with improvements in patient-important outcomes. The common tendency in evidence-based medicine is to view results based on surrogate endpoints as less certain than results based on long term, final patient-important outcomes and rate them as 'lower quality evidence'. However, careful appraisal of the validity of a surrogate endpoint as a measure of the final, patient-important outcome is more useful than an automatic judgement. In this guide, we use a contemporary and currently highly debated example of the surrogate endpoint 'sustained viral response' (ie, viral eradication considered to represent successful treatment) in patients treated for chronic hepatitis C virus. We demonstrate how the validity of a surrogate endpoint can be critically appraised to assess the quality of the evidence (ie, the certainty in estimates) and the implications for decision-making. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

A Taylor Expansion-Based Adaptive Design Strategy for Global Surrogate Modeling With Applications in Groundwater Modeling

NASA Astrophysics Data System (ADS)

Mo, Shaoxing; Lu, Dan; Shi, Xiaoqing; Zhang, Guannan; Ye, Ming; Wu, Jianfeng; Wu, Jichun

2017-12-01

Global sensitivity analysis (GSA) and uncertainty quantification (UQ) for groundwater modeling are challenging because of the model complexity and significant computational requirements. To reduce the massive computational cost, a cheap-to-evaluate surrogate model is usually constructed to approximate and replace the expensive groundwater models in the GSA and UQ. Constructing an accurate surrogate requires actual model simulations on a number of parameter samples. Thus, a robust experimental design strategy is desired to locate informative samples so as to reduce the computational cost in surrogate construction and consequently to improve the efficiency in the GSA and UQ. In this study, we develop a Taylor expansion-based adaptive design (TEAD) that aims to build an accurate global surrogate model with a small training sample size. TEAD defines a novel hybrid score function to search informative samples, and a robust stopping criterion to terminate the sample search that guarantees the resulted approximation errors satisfy the desired accuracy. The good performance of TEAD in building global surrogate models is demonstrated in seven analytical functions with different dimensionality and complexity in comparison to two widely used experimental design methods. The application of the TEAD-based surrogate method in two groundwater models shows that the TEAD design can effectively improve the computational efficiency of GSA and UQ for groundwater modeling.

Multi-objective optimization of a low specific speed centrifugal pump using an evolutionary algorithm

NASA Astrophysics Data System (ADS)

An, Zhao; Zhounian, Lai; Peng, Wu; Linlin, Cao; Dazhuan, Wu

2016-07-01

This paper describes the shape optimization of a low specific speed centrifugal pump at the design point. The target pump has already been manually modified on the basis of empirical knowledge. A genetic algorithm (NSGA-II) with certain enhancements is adopted to improve its performance further with respect to two goals. In order to limit the number of design variables without losing geometric information, the impeller is parametrized using the Bézier curve and a B-spline. Numerical simulation based on a Reynolds averaged Navier-Stokes (RANS) turbulent model is done in parallel to evaluate the flow field. A back-propagating neural network is constructed as a surrogate for performance prediction to save computing time, while initial samples are selected according to an orthogonal array. Then global Pareto-optimal solutions are obtained and analysed. The results manifest that unexpected flow structures, such as the secondary flow on the meridian plane, have diminished or vanished in the optimized pump.

Surrogate outcomes are associated with low methodological quality of studies of rheumatoid arthritis treated with antitumour necrosis factor agents: a systematic review.

PubMed

Nobre, Moacyr Roberto Cuce; da Costa, Frnanda Marques

2012-02-01

Surrogate endpoints may be used as substitutes for, but often do not predict clinically relevant events. Objective To assess the methodological quality of articles that present their conclusions based on clinically relevant or surrogate outcomes in a systematic review of randomised trials and cohort studies of patients with rheumatoid arthritis treated with antitumour necrosis factor (TNF) agents. PubMed, Embase and Cochrane databases were searched. The Jadad score, the percentage of Consolidated Standards Of Reporting Trials (CONSORT) statement items adequately reported and levels-of-evidence (Center for Evidence-based Medicine, Oxford) were used in a descriptive synthesis. Among 88 articles appraised, 27 had surrogate endpoints, mainly radiographic, and 44 were duplicate publications; 74% of articles with surrogate and 39% of articles with clinical endpoints (p=0.006). Fewer articles with surrogate endpoints represented a high level of evidence (Level 1b, 33% vs 62%, p=0.037) and the mean percentage of CONSORT statement items met was also lower for articles with surrogate endpoints (62.5 vs 70.7, p=0.026). Although fewer articles with surrogate endpoints were randomised trials (63% vs 74%, p=0.307) and articles with surrogate endpoints had lower Jadad scores (3.0 vs 3.2, p=0.538), these differences were not statistically significant. Studies of anti-TNF agents that report surrogate outcomes are of lesser methodological quality. As such, inclusion of such studies in evidence syntheses may bias results.

Numerical Optimization Using Computer Experiments

NASA Technical Reports Server (NTRS)

Trosset, Michael W.; Torczon, Virginia

1997-01-01

Engineering design optimization often gives rise to problems in which expensive objective functions are minimized by derivative-free methods. We propose a method for solving such problems that synthesizes ideas from the numerical optimization and computer experiment literatures. Our approach relies on kriging known function values to construct a sequence of surrogate models of the objective function that are used to guide a grid search for a minimizer. Results from numerical experiments on a standard test problem are presented.

Reduced-order model for dynamic optimization of pressure swing adsorption processes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Agarwal, A.; Biegler, L.; Zitney, S.

2007-01-01

Over the past decades, pressure swing adsorption (PSA) processes have been widely used as energy-efficient gas and liquid separation techniques, especially for high purity hydrogen purification from refinery gases. The separation processes are based on solid-gas equilibrium and operate under periodic transient conditions. Models for PSA processes are therefore multiple instances of partial differential equations (PDEs) in time and space with periodic boundary conditions that link the processing steps together. The solution of this coupled stiff PDE system is governed by steep concentrations and temperature fronts moving with time. As a result, the optimization of such systems for either designmore » or operation represents a significant computational challenge to current differential algebraic equation (DAE) optimization techniques and nonlinear programming algorithms. Model reduction is one approach to generate cost-efficient low-order models which can be used as surrogate models in the optimization problems. The study develops a reduced-order model (ROM) based on proper orthogonal decomposition (POD), which is a low-dimensional approximation to a dynamic PDE-based model. Initially, a representative ensemble of solutions of the dynamic PDE system is constructed by solving a higher-order discretization of the model using the method of lines, a two-stage approach that discretizes the PDEs in space and then integrates the resulting DAEs over time. Next, the ROM method applies the Karhunen-Loeve expansion to derive a small set of empirical eigenfunctions (POD modes) which are used as basis functions within a Galerkin's projection framework to derive a low-order DAE system that accurately describes the dominant dynamics of the PDE system. The proposed method leads to a DAE system of significantly lower order, thus replacing the one obtained from spatial discretization before and making optimization problem computationally-efficient. The method has been applied to the dynamic coupled PDE-based model of a two-bed four-step PSA process for separation of hydrogen from methane. Separate ROMs have been developed for each operating step with different POD modes for each of them. A significant reduction in the order of the number of states has been achieved. The gas-phase mole fraction, solid-state loading and temperature profiles from the low-order ROM and from the high-order simulations have been compared. Moreover, the profiles for a different set of inputs and parameter values fed to the same ROM were compared with the accurate profiles from the high-order simulations. Current results indicate the proposed ROM methodology as a promising surrogate modeling technique for cost-effective optimization purposes. Moreover, deviations from the ROM for different set of inputs and parameters suggest that a recalibration of the model is required for the optimization studies. Results for these will also be presented with the aforementioned results.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen Lin; Chen Yixin

We show that no universal quantum cloning machine exists that can broadcast an arbitrary mixed qubit with a constant fidelity. Based on this result, we investigate the dependent quantum cloner in the sense that some parameter of the input qubit {rho}{sub s}({theta},{omega},{lambda}) is regarded as constant in the fidelity. For the case of constant {omega}, we establish the 1{yields}2 optimal symmetric dependent cloner with a fidelity 1/2. It is also shown that the 1{yields}M optimal quantum cloning machine for pure qubits is also optimal for mixed qubits, when {lambda} is the unique parameter in the fidelity. For general N{yields}M broadcastingmore » of mixed qubits, the situation is very different.« less

Shaken, Not Stirred: How Tidal Advection and Dispersion Mechanisms Rather Than Turbulent Mixing Impact the Movement and Fate of Aquatic Constituents and Fish in the California Central Valley

NASA Astrophysics Data System (ADS)

Sridharan, V. K.; Fong, D.; Monismith, S. G.; Jackson, D.; Russel, P.; Pope, A.; Danner, E.; Lindley, S. T.

2016-12-01

River deltas worldwide - home to nearly a billion people, thousands of species of flora and fauna, and economies worth trillions of dollars - have experienced massive ecosystem decline caused by urbanization, pollution, and water withdrawals. Habitat restoration in these systems is imperative not only for preserving endangered biomes, but also in sustaining human demand for freshwater and long term commercial viability. The sustainable management of heavily engineered, multi-use, branched tidal estuaries such as the Sacramento-San Joaquin Delta (henceforth, the Delta) requires utilizing physical transport and mixing process models. These inform us about the movement and fate of water quality constituents and aquatic organisms. This study identifies and quantifies the effects of various hydrodynamic mechanisms in the Delta across multiple spatio-temporal scales. A particle tracking model with accurate channel junction physics and an agent based model with realistic biological hypotheses of fish behavior were developed to study the movement and fate of tracers (surrogates for water quality constituents) and fish in the Delta. Simulations performed with these models were used to (1) determine the transport pathways through the Delta, (2) quantify the magnitude of transport and mixing processes along those pathways, and (3) describe the effects of physical stressors on fates of juvenile salmon. The Delta is largely dominated by large spatial scale advection by river flows, tidal pumping, and significantly increased dispersion through chaos due to the interaction of tidal flows with channel junctions. The movement and fate of simulated tracers and juvenile salmon are governed largely by the water diversion and pumping operations, transport pathways and chaotic tidal mixing mechanisms along those pathways. There is also a significant effect of predation on fish. These transport pathway and mechanistic dependencies indicate that restoration efforts which are harmonious with human needs can be undertaken along the identified transport pathways by optimizing land and water use along these pathways, while facilitating transport of substances such as sediments and nutrients, and biota, between spatial regions to sustain populations of desired aquatic organisms.

The evaporatively driven cloud-top mixing layer

NASA Astrophysics Data System (ADS)

Mellado, Juan Pedro

2010-11-01

Turbulent mixing caused by the local evaporative cooling at the top cloud-boundary of stratocumuli will be discussed. This research is motivated by the lack of a complete understanding of several phenomena in that important region, which translates into an unacceptable variability of order one in current models, including those employed in climate research. The cloud-top mixing layer is a simplified surrogate to investigate, locally, particular aspects of the fluid dynamics at the boundary between the stratocumulus clouds and the upper cloud-free air. In this work, direct numerical simulations have been used to study latent heat effects. The problem is the following: When the cloud mixes with the upper cloud-free layer, relatively warm and dry, evaporation tends to cool the mixture and, if strong enough, the buoyancy reversal instability develops. This instability leads to a turbulent convection layer growing next to the upper boundary of the cloud, which is, in several aspects, similar to free convection below a cold horizontal surface. In particular, results show an approximately self-preserving behavior that is characterized by the molecular buoyancy flux at the inversion base, fact that helps to explain the difficulties found when doing large-eddy simulations of this problem using classical subgrid closures.

Uncertainty propagation through an aeroelastic wind turbine model using polynomial surrogates

DOE Office of Scientific and Technical Information (OSTI.GOV)

Murcia, Juan Pablo; Réthoré, Pierre-Elouan; Dimitrov, Nikolay

Polynomial surrogates are used to characterize the energy production and lifetime equivalent fatigue loads for different components of the DTU 10 MW reference wind turbine under realistic atmospheric conditions. The variability caused by different turbulent inflow fields are captured by creating independent surrogates for the mean and standard deviation of each output with respect to the inflow realizations. A global sensitivity analysis shows that the turbulent inflow realization has a bigger impact on the total distribution of equivalent fatigue loads than the shear coefficient or yaw miss-alignment. The methodology presented extends the deterministic power and thrust coefficient curves to uncertaintymore » models and adds new variables like damage equivalent fatigue loads in different components of the turbine. These surrogate models can then be implemented inside other work-flows such as: estimation of the uncertainty in annual energy production due to wind resource variability and/or robust wind power plant layout optimization. It can be concluded that it is possible to capture the global behavior of a modern wind turbine and its uncertainty under realistic inflow conditions using polynomial response surfaces. In conclusion, the surrogates are a way to obtain power and load estimation under site specific characteristics without sharing the proprietary aeroelastic design.« less

Uncertainty propagation through an aeroelastic wind turbine model using polynomial surrogates

DOE PAGES

Murcia, Juan Pablo; Réthoré, Pierre-Elouan; Dimitrov, Nikolay; ...

2017-07-17

Polynomial surrogates are used to characterize the energy production and lifetime equivalent fatigue loads for different components of the DTU 10 MW reference wind turbine under realistic atmospheric conditions. The variability caused by different turbulent inflow fields are captured by creating independent surrogates for the mean and standard deviation of each output with respect to the inflow realizations. A global sensitivity analysis shows that the turbulent inflow realization has a bigger impact on the total distribution of equivalent fatigue loads than the shear coefficient or yaw miss-alignment. The methodology presented extends the deterministic power and thrust coefficient curves to uncertaintymore » models and adds new variables like damage equivalent fatigue loads in different components of the turbine. These surrogate models can then be implemented inside other work-flows such as: estimation of the uncertainty in annual energy production due to wind resource variability and/or robust wind power plant layout optimization. It can be concluded that it is possible to capture the global behavior of a modern wind turbine and its uncertainty under realistic inflow conditions using polynomial response surfaces. In conclusion, the surrogates are a way to obtain power and load estimation under site specific characteristics without sharing the proprietary aeroelastic design.« less

An acoustofluidic micromixer based on oscillating sidewall sharp-edges†

PubMed Central

Huang, Po-Hsun; Xie, Yuliang; Ahmed, Daniel; Rufo, Joseph; Nama, Nitesh; Chen, Yuchao; Chan, Chung Yu; Huang, Tony Jun

2014-01-01

Rapid and homogeneous mixing inside a microfluidic channel is demonstrated via the acoustic streaming phenomenon induced by the oscillation of sidewall sharp-edges. By optimizing the design of the sharp-edges, excellent mixing performance and fast mixing speed can be achieved in a simple device, making our sharp-edge-based acoustic micromixer a promising candidate for a wide variety of applications. PMID:23896797

Partnership for Edge Physics (EPSI), University of Texas Final Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Moser, Robert; Carey, Varis; Michoski, Craig

Simulations of tokamak plasmas require a number of inputs whose values are uncertain. The effects of these input uncertainties on the reliability of model predictions is of great importance when validating predictions by comparison to experimental observations, and when using the predictions for design and operation of devices. However, high fidelity simulation of tokamak plasmas, particular those aimed at characterization of the edge plasma physics, are computationally expensive, so lower cost surrogates are required to enable practical uncertainty estimates. Two surrogate modeling techniques have been explored in the context of tokamak plasma simulations using the XGC family of plasma simulationmore » codes. The first is a response surface surrogate, and the second is an augmented surrogate relying on scenario extrapolation. In addition, to reduce the costs of the XGC simulations, a particle resampling algorithm was developed, which allows marker particle distributions to be adjusted to maintain optimal importance sampling. This means that the total number of particles in and therefore the cost of a simulation can be reduced while maintaining the same accuracy.« less

Combining conversation analysis and event sequencing to study health communication.

PubMed

Pecanac, Kristen E

2018-06-01

Good communication is essential in patient-centered care. The purpose of this paper is to describe conversation analysis and event sequencing and explain how integrating these methods strengthened the analysis in a study of communication between clinicians and surrogate decision makers in an intensive care unit. Conversation analysis was first used to determine how clinicians introduced the need for decision-making regarding life-sustaining treatment and how surrogate decision makers responded. Event sequence analysis then was used to determine the transitional probability (probability of one event leading to another in the interaction) that a given type of clinician introduction would lead to surrogate resistance or alignment. Conversation analysis provides a detailed analysis of the interaction between participants in a conversation. When combined with a quantitative analysis of the patterns of communication in an interaction, these data add information on the communication strategies that produce positive outcomes. Researchers can apply this mixed-methods approach to identify beneficial conversational practices and design interventions to improve health communication. © 2018 Wiley Periodicals, Inc.

Three Dimensional CFD Analysis of the GTX Combustor

NASA Technical Reports Server (NTRS)

Steffen, C. J., Jr.; Bond, R. B.; Edwards, J. R.

2002-01-01

The annular combustor geometry of a combined-cycle engine has been analyzed with three-dimensional computational fluid dynamics. Both subsonic combustion and supersonic combustion flowfields have been simulated. The subsonic combustion analysis was executed in conjunction with a direct-connect test rig. Two cold-flow and one hot-flow results are presented. The simulations compare favorably with the test data for the two cold flow calculations; the hot-flow data was not yet available. The hot-flow simulation indicates that the conventional ejector-ramjet cycle would not provide adequate mixing at the conditions tested. The supersonic combustion ramjet flowfield was simulated with frozen chemistry model. A five-parameter test matrix was specified, according to statistical design-of-experiments theory. Twenty-seven separate simulations were used to assemble surrogate models for combustor mixing efficiency and total pressure recovery. ScramJet injector design parameters (injector angle, location, and fuel split) as well as mission variables (total fuel massflow and freestream Mach number) were included in the analysis. A promising injector design has been identified that provides good mixing characteristics with low total pressure losses. The surrogate models can be used to develop performance maps of different injector designs. Several complex three-way variable interactions appear within the dataset that are not adequately resolved with the current statistical analysis.

Robust estimation of the proportion of treatment effect explained by surrogate marker information.

PubMed

Parast, Layla; McDermott, Mary M; Tian, Lu

2016-05-10

In randomized treatment studies where the primary outcome requires long follow-up of patients and/or expensive or invasive obtainment procedures, the availability of a surrogate marker that could be used to estimate the treatment effect and could potentially be observed earlier than the primary outcome would allow researchers to make conclusions regarding the treatment effect with less required follow-up time and resources. The Prentice criterion for a valid surrogate marker requires that a test for treatment effect on the surrogate marker also be a valid test for treatment effect on the primary outcome of interest. Based on this criterion, methods have been developed to define and estimate the proportion of treatment effect on the primary outcome that is explained by the treatment effect on the surrogate marker. These methods aim to identify useful statistical surrogates that capture a large proportion of the treatment effect. However, current methods to estimate this proportion usually require restrictive model assumptions that may not hold in practice and thus may lead to biased estimates of this quantity. In this paper, we propose a nonparametric procedure to estimate the proportion of treatment effect on the primary outcome that is explained by the treatment effect on a potential surrogate marker and extend this procedure to a setting with multiple surrogate markers. We compare our approach with previously proposed model-based approaches and propose a variance estimation procedure based on a perturbation-resampling method. Simulation studies demonstrate that the procedure performs well in finite samples and outperforms model-based procedures when the specified models are not correct. We illustrate our proposed procedure using a data set from a randomized study investigating a group-mediated cognitive behavioral intervention for peripheral artery disease participants. Copyright © 2015 John Wiley & Sons, Ltd.

Optimal perturbations for nonlinear systems using graph-based optimal transport

NASA Astrophysics Data System (ADS)

Grover, Piyush; Elamvazhuthi, Karthik

2018-06-01

We formulate and solve a class of finite-time transport and mixing problems in the set-oriented framework. The aim is to obtain optimal discrete-time perturbations in nonlinear dynamical systems to transport a specified initial measure on the phase space to a final measure in finite time. The measure is propagated under system dynamics in between the perturbations via the associated transfer operator. Each perturbation is described by a deterministic map in the measure space that implements a version of Monge-Kantorovich optimal transport with quadratic cost. Hence, the optimal solution minimizes a sum of quadratic costs on phase space transport due to the perturbations applied at specified times. The action of the transport map is approximated by a continuous pseudo-time flow on a graph, resulting in a tractable convex optimization problem. This problem is solved via state-of-the-art solvers to global optimality. We apply this algorithm to a problem of transport between measures supported on two disjoint almost-invariant sets in a chaotic fluid system, and to a finite-time optimal mixing problem by choosing the final measure to be uniform. In both cases, the optimal perturbations are found to exploit the phase space structures, such as lobe dynamics, leading to efficient global transport. As the time-horizon of the problem is increased, the optimal perturbations become increasingly localized. Hence, by combining the transfer operator approach with ideas from the theory of optimal mass transportation, we obtain a discrete-time graph-based algorithm for optimal transport and mixing in nonlinear systems.

Maintaining tumor targeting accuracy in real-time motion compensation systems for respiration-induced tumor motion.

PubMed

Malinowski, Kathleen; McAvoy, Thomas J; George, Rohini; Dieterich, Sonja; D'Souza, Warren D

2013-07-01

To determine how best to time respiratory surrogate-based tumor motion model updates by comparing a novel technique based on external measurements alone to three direct measurement methods. Concurrently measured tumor and respiratory surrogate positions from 166 treatment fractions for lung or pancreas lesions were analyzed. Partial-least-squares regression models of tumor position from marker motion were created from the first six measurements in each dataset. Successive tumor localizations were obtained at a rate of once per minute on average. Model updates were timed according to four methods: never, respiratory surrogate-based (when metrics based on respiratory surrogate measurements exceeded confidence limits), error-based (when localization error ≥ 3 mm), and always (approximately once per minute). Radial tumor displacement prediction errors (mean ± standard deviation) for the four schema described above were 2.4 ± 1.2, 1.9 ± 0.9, 1.9 ± 0.8, and 1.7 ± 0.8 mm, respectively. The never-update error was significantly larger than errors of the other methods. Mean update counts over 20 min were 0, 4, 9, and 24, respectively. The same improvement in tumor localization accuracy could be achieved through any of the three update methods, but significantly fewer updates were required when the respiratory surrogate method was utilized. This study establishes the feasibility of timing image acquisitions for updating respiratory surrogate models without direct tumor localization.

Gaussian process surrogates for failure detection: A Bayesian experimental design approach

NASA Astrophysics Data System (ADS)

Wang, Hongqiao; Lin, Guang; Li, Jinglai

2016-05-01

An important task of uncertainty quantification is to identify the probability of undesired events, in particular, system failures, caused by various sources of uncertainties. In this work we consider the construction of Gaussian process surrogates for failure detection and failure probability estimation. In particular, we consider the situation that the underlying computer models are extremely expensive, and in this setting, determining the sampling points in the state space is of essential importance. We formulate the problem as an optimal experimental design for Bayesian inferences of the limit state (i.e., the failure boundary) and propose an efficient numerical scheme to solve the resulting optimization problem. In particular, the proposed limit-state inference method is capable of determining multiple sampling points at a time, and thus it is well suited for problems where multiple computer simulations can be performed in parallel. The accuracy and performance of the proposed method is demonstrated by both academic and practical examples.

Discovering variable fractional orders of advection-dispersion equations from field data using multi-fidelity Bayesian optimization

NASA Astrophysics Data System (ADS)

Pang, Guofei; Perdikaris, Paris; Cai, Wei; Karniadakis, George Em

2017-11-01

The fractional advection-dispersion equation (FADE) can describe accurately the solute transport in groundwater but its fractional order has to be determined a priori. Here, we employ multi-fidelity Bayesian optimization to obtain the fractional order under various conditions, and we obtain more accurate results compared to previously published data. Moreover, the present method is very efficient as we use different levels of resolution to construct a stochastic surrogate model and quantify its uncertainty. We consider two different problem set ups. In the first set up, we obtain variable fractional orders of one-dimensional FADE, considering both synthetic and field data. In the second set up, we identify constant fractional orders of two-dimensional FADE using synthetic data. We employ multi-resolution simulations using two-level and three-level Gaussian process regression models to construct the surrogates.

Identifying taxonomic and functional surrogates for spring biodiversity conservation.

PubMed

Jyväsjärvi, Jussi; Virtanen, Risto; Ilmonen, Jari; Paasivirta, Lauri; Muotka, Timo

2018-02-27

Surrogate approaches are widely used to estimate overall taxonomic diversity for conservation planning. Surrogate taxa are frequently selected based on rarity or charisma, whereas selection through statistical modeling has been applied rarely. We used boosted-regression-tree models (BRT) fitted to biological data from 165 springs to identify bryophyte and invertebrate surrogates for taxonomic and functional diversity of boreal springs. We focused on these 2 groups because they are well known and abundant in most boreal springs. The best indicators of taxonomic versus functional diversity differed. The bryophyte Bryum weigelii and the chironomid larva Paratrichocladius skirwithensis best indicated taxonomic diversity, whereas the isopod Asellus aquaticus and the chironomid Macropelopia spp. were the best surrogates of functional diversity. In a scoring algorithm for priority-site selection, taxonomic surrogates performed only slightly better than random selection for all spring-dwelling taxa, but they were very effective in representing spring specialists, providing a distinct improvement over random solutions. However, the surrogates for taxonomic diversity represented functional diversity poorly and vice versa. When combined with cross-taxon complementarity analyses, surrogate selection based on statistical modeling provides a promising approach for identifying groundwater-dependent ecosystems of special conservation value, a key requirement of the EU Water Framework Directive. © 2018 Society for Conservation Biology.

GLOBAL SOLUTIONS TO FOLDED CONCAVE PENALIZED NONCONVEX LEARNING

PubMed Central

Liu, Hongcheng; Yao, Tao; Li, Runze

2015-01-01

This paper is concerned with solving nonconvex learning problems with folded concave penalty. Despite that their global solutions entail desirable statistical properties, there lack optimization techniques that guarantee global optimality in a general setting. In this paper, we show that a class of nonconvex learning problems are equivalent to general quadratic programs. This equivalence facilitates us in developing mixed integer linear programming reformulations, which admit finite algorithms that find a provably global optimal solution. We refer to this reformulation-based technique as the mixed integer programming-based global optimization (MIPGO). To our knowledge, this is the first global optimization scheme with a theoretical guarantee for folded concave penalized nonconvex learning with the SCAD penalty (Fan and Li, 2001) and the MCP penalty (Zhang, 2010). Numerical results indicate a significant outperformance of MIPGO over the state-of-the-art solution scheme, local linear approximation, and other alternative solution techniques in literature in terms of solution quality. PMID:27141126

A systematic optimization for graphene-based supercapacitors

NASA Astrophysics Data System (ADS)

Deuk Lee, Sung; Lee, Han Sung; Kim, Jin Young; Jeong, Jaesik; Kahng, Yung Ho

2017-08-01

Increasing the energy-storage density for supercapacitors is critical for their applications. Many researchers have attempted to identify optimal candidate component materials to achieve this goal, but investigations into systematically optimizing their mixing rate for maximizing the performance of each candidate material have been insufficient, which hinders the progress in their technology. In this study, we employ a statistically systematic method to determine the optimum mixing ratio of three components that constitute graphene-based supercapacitor electrodes: reduced graphene oxide (rGO), acetylene black (AB), and polyvinylidene fluoride (PVDF). By using the extreme-vertices design, the optimized proportion is determined to be (rGO: AB: PVDF  =  0.95: 0.00: 0.05). The corresponding energy-storage density increases by a factor of 2 compared with that of non-optimized electrodes. Electrochemical and microscopic analyses are performed to determine the reason for the performance improvements.

A hybrid Jaya algorithm for reliability-redundancy allocation problems

NASA Astrophysics Data System (ADS)

Ghavidel, Sahand; Azizivahed, Ali; Li, Li

2018-04-01

This article proposes an efficient improved hybrid Jaya algorithm based on time-varying acceleration coefficients (TVACs) and the learning phase introduced in teaching-learning-based optimization (TLBO), named the LJaya-TVAC algorithm, for solving various types of nonlinear mixed-integer reliability-redundancy allocation problems (RRAPs) and standard real-parameter test functions. RRAPs include series, series-parallel, complex (bridge) and overspeed protection systems. The search power of the proposed LJaya-TVAC algorithm for finding the optimal solutions is first tested on the standard real-parameter unimodal and multi-modal functions with dimensions of 30-100, and then tested on various types of nonlinear mixed-integer RRAPs. The results are compared with the original Jaya algorithm and the best results reported in the recent literature. The optimal results obtained with the proposed LJaya-TVAC algorithm provide evidence for its better and acceptable optimization performance compared to the original Jaya algorithm and other reported optimal results.

Image segmentation with a novel regularized composite shape prior based on surrogate study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhao, Tingting, E-mail: tingtingzhao@mednet.ucla.edu; Ruan, Dan, E-mail: druan@mednet.ucla.edu

Purpose: Incorporating training into image segmentation is a good approach to achieve additional robustness. This work aims to develop an effective strategy to utilize shape prior knowledge, so that the segmentation label evolution can be driven toward the desired global optimum. Methods: In the variational image segmentation framework, a regularization for the composite shape prior is designed to incorporate the geometric relevance of individual training data to the target, which is inferred by an image-based surrogate relevance metric. Specifically, this regularization is imposed on the linear weights of composite shapes and serves as a hyperprior. The overall problem is formulatedmore » in a unified optimization setting and a variational block-descent algorithm is derived. Results: The performance of the proposed scheme is assessed in both corpus callosum segmentation from an MR image set and clavicle segmentation based on CT images. The resulted shape composition provides a proper preference for the geometrically relevant training data. A paired Wilcoxon signed rank test demonstrates statistically significant improvement of image segmentation accuracy, when compared to multiatlas label fusion method and three other benchmark active contour schemes. Conclusions: This work has developed a novel composite shape prior regularization, which achieves superior segmentation performance than typical benchmark schemes.« less

Surrogate inaccuracy in predicting older adults' desire for life-sustaining interventions in the event of decisional incapacity: is it due in part to erroneous quality-of-life assessments?

PubMed

Bravo, Gina; Sene, Modou; Arcand, Marcel

2017-07-01

Family members are often called upon to make decisions for an incapacitated relative. Yet they have difficulty predicting a loved one's desire to receive treatments in hypothetical situations. We tested the hypothesis that this difficulty could in part be explained by discrepant quality-of-life assessments. The data come from 235 community-dwelling adults aged 70 years and over who rated their quality of life and desire for specified interventions in four health states (current state, mild to moderate stroke, incurable brain cancer, and severe dementia). All ratings were made on Likert-type scales. Using identical rating scales, a surrogate chosen by the older adult was asked to predict the latter's responses. Linear mixed models were fitted to determine whether differences in quality-of-life ratings between the older adult and surrogate were associated with surrogates' inaccuracy in predicting desire for treatment. The difference in quality-of-life ratings was a significant predictor of prediction inaccuracy for the three hypothetical health states (p < 0.01) and nearly significant for the current health state (p = 0.077). All regression coefficients were negative, implying that the more the surrogate overestimated quality of life compared to the older adult, the more he or she overestimated the older adult's desire to be treated. Discrepant quality-of-life ratings are associated with surrogates' difficulty in predicting desire for life-sustaining interventions in hypothetical situations. This finding underscores the importance of discussing anticipated quality of life in states of cognitive decline, to better prepare family members for making difficult decisions for their loved ones. ISRCTN89993391.

TOXICITY OF PENTACHLOROPHENOL TO ENDANGERED AND SURROGATE FISH SPECIES

EPA Science Inventory

Water quality criteria (WQC) generally are based on the responses of easily cultured and tested surrogate species. Little is known about the relative sensitivity of surrogate and endangered species. The objective of this study was to compare acute and chronic (early life-stage) ...

Determination of mixed mode (I/II) SIFs of cracked orthotropic materials

NASA Astrophysics Data System (ADS)

Chakraborty, D.; Chakraborty, Debaleena; Murthy, K. S. R. K.

2018-05-01

Strain gage techniques have been successfully but sparsely used for the determination of stress intensity factors (SIFs) of orthotropic materials. For mode I cases, few works have been reported on the strain gage based determination of mode I SIF of orthotropic materials. However, for mixed mode (I/II) cases, neither a theoretical development of a strain gage based technique nor any recommended guidelines for minimum number of strain gages and their locations were reported in the literature for determination of mixed mode SIFs. The authors for the first time came up with a theoretical proposition to successfully use strain gages for determination of mixed mode SIFs of orthotropic materials [1]. Based on these formulations, the present paper discusses a finite element (FE) based numerical simulation of the proposed strain gage technique employing [902/0]10S carbon-epoxy laminates with a slant edge crack. An FE based procedure has also been presented for determination of the optimal radial locations of the strain gages apriori to actual experiments. To substantiate the efficacy of the proposed technique, numerical simulations for strain gage based determination of mixed mode SIFs have been conducted. Results show that it is possible to accurately determine the mixed mode SIFs of orthotropic laminates when the strain gages are placed within the optimal radial locations estimated using the present formulation.

Assessing and optimising flood control options along the Arachthos river floodplain (Epirus, Greece)

NASA Astrophysics Data System (ADS)

Drosou, Athina; Dimitriadis, Panayiotis; Lykou, Archontia; Kossieris, Panagiotis; Tsoukalas, Ioannis; Efstratiadis, Andreas; Mamassis, Nikos

2015-04-01

We present a multi-criteria simulation-optimization framework for the optimal design and setting of flood protection structures along river banks. The methodology is tested in the lower course of the Arachthos River (Epirus, Greece), downstream of the hydroelectric dam of Pournari. The entire study area is very sensitive, particularly because the river crosses the urban area of Arta, which is located just after the dam. Moreover, extended agricultural areas that are crucial for the local economy are prone to floods. In the proposed methodology we investigate two conflicting criteria, i.e. the minimization of flood hazards (due to damages to urban infrastructures, crops, etc.) and the minimization of construction costs of the essential hydraulic structures (e.g. dikes). For the hydraulic simulation we examine two flood routing models, named 1D HEC-RAS and quasi-2D LISFLOOD, whereas the optimization is carried out through the Surrogate-Enhanced Evolutionary Annealing-Simplex (SE-EAS) algorithm that couples the strengths of surrogate modeling with the effectiveness and efficiency of the EAS method.

Delamination detection using methods of computational intelligence

NASA Astrophysics Data System (ADS)

Ihesiulor, Obinna K.; Shankar, Krishna; Zhang, Zhifang; Ray, Tapabrata

2012-11-01

Abstract Reliable delamination prediction scheme is indispensable in order to prevent potential risks of catastrophic failures in composite structures. The existence of delaminations changes the vibration characteristics of composite laminates and hence such indicators can be used to quantify the health characteristics of laminates. An approach for online health monitoring of in-service composite laminates is presented in this paper that relies on methods based on computational intelligence. Typical changes in the observed vibration characteristics (i.e. change in natural frequencies) are considered as inputs to identify the existence, location and magnitude of delaminations. The performance of the proposed approach is demonstrated using numerical models of composite laminates. Since this identification problem essentially involves the solution of an optimization problem, the use of finite element (FE) methods as the underlying tool for analysis turns out to be computationally expensive. A surrogate assisted optimization approach is hence introduced to contain the computational time within affordable limits. An artificial neural network (ANN) model with Bayesian regularization is used as the underlying approximation scheme while an improved rate of convergence is achieved using a memetic algorithm. However, building of ANN surrogate models usually requires large training datasets. K-means clustering is effectively employed to reduce the size of datasets. ANN is also used via inverse modeling to determine the position, size and location of delaminations using changes in measured natural frequencies. The results clearly highlight the efficiency and the robustness of the approach.

Multi-fidelity and multi-disciplinary design optimization of supersonic business jets

NASA Astrophysics Data System (ADS)

Choi, Seongim

Supersonic jets have been drawing great attention after the end of service for the Concorde was announced on April of 2003. It is believed, however, that civilian supersonic aircraft may make a viable return in the business jet market. This thesis focuses on the design optimization of feasible supersonic business jet configurations. Preliminary design techniques for mitigation of ground sonic boom are investigated while ensuring that all relevant disciplinary constraints are satisfied (including aerodynamic performance, propulsion, stability & control and structures.) In order to achieve reasonable confidence in the resulting designs, high-fidelity simulations are required, making the entire design process both expensive and complex. In order to minimize the computational cost, surrogate/approximate models are constructed using a hierarchy of different fidelity analysis tools including PASS, A502/Panair and Euler/NS codes. Direct search methods such as Genetic Algorithms (GAs) and a nonlinear SIMPLEX are employed to designs in searches of large and noisy design spaces. A local gradient-based search method can be combined with these global search methods for small modifications of candidate optimum designs. The Mesh Adaptive Direct Search (MADS) method can also be used to explore the design space using a solution-adaptive grid refinement approach. These hybrid approaches, both in search methodology and surrogate model construction, are shown to result in designs with reductions in sonic boom and improved aerodynamic performance.

A diameter-sensitive flow entropy method for reliability consideration in water distribution system design

NASA Astrophysics Data System (ADS)

Liu, Haixing; Savić, Dragan; Kapelan, Zoran; Zhao, Ming; Yuan, Yixing; Zhao, Hongbin

2014-07-01

Flow entropy is a measure of uniformity of pipe flows in water distribution systems. By maximizing flow entropy one can identify reliable layouts or connectivity in networks. In order to overcome the disadvantage of the common definition of flow entropy that does not consider the impact of pipe diameter on reliability, an extended definition of flow entropy, termed as diameter-sensitive flow entropy, is proposed. This new methodology is then assessed by using other reliability methods, including Monte Carlo Simulation, a pipe failure probability model, and a surrogate measure (resilience index) integrated with water demand and pipe failure uncertainty. The reliability assessment is based on a sample of WDS designs derived from an optimization process for each of the two benchmark networks. Correlation analysis is used to evaluate quantitatively the relationship between entropy and reliability. To ensure reliability, a comparative analysis between the flow entropy and the new method is conducted. The results demonstrate that the diameter-sensitive flow entropy shows consistently much stronger correlation with the three reliability measures than simple flow entropy. Therefore, the new flow entropy method can be taken as a better surrogate measure for reliability and could be potentially integrated into the optimal design problem of WDSs. Sensitivity analysis results show that the velocity parameters used in the new flow entropy has no significant impact on the relationship between diameter-sensitive flow entropy and reliability.

Bridging Scales: A Model-Based Assessment of the Technical Tidal-Stream Energy Resource off Massachusetts, USA

NASA Astrophysics Data System (ADS)

Cowles, G. W.; Hakim, A.; Churchill, J. H.

2016-02-01

Tidal in-stream energy conversion (TISEC) facilities provide a highly predictable and dependable source of energy. Given the economic and social incentives to migrate towards renewable energy sources there has been tremendous interest in the technology. Key challenges to the design process stem from the wide range of problem scales extending from device to array. In the present approach we apply a multi-model approach to bridge the scales of interest and select optimal device geometries to estimate the technical resource for several realistic sites in the coastal waters of Massachusetts, USA. The approach links two computational models. To establish flow conditions at site scales ( 10m), a barotropic setup of the unstructured grid ocean model FVCOM is employed. The model is validated using shipboard and fixed ADCP as well as pressure data. For device scale, the structured multiblock flow solver SUmb is selected. A large ensemble of simulations of 2D cross-flow tidal turbines is used to construct a surrogate design model. The surrogate model is then queried using velocity profiles extracted from the tidal model to determine the optimal geometry for the conditions at each site. After device selection, the annual technical yield of the array is evaluated with FVCOM using a linear momentum actuator disk approach to model the turbines. Results for several key Massachusetts sites including comparison with theoretical approaches will be presented.

A Taylor Expansion-Based Adaptive Design Strategy for Global Surrogate Modeling With Applications in Groundwater Modeling

DOE PAGES

Mo, Shaoxing; Lu, Dan; Shi, Xiaoqing; ...

2017-12-27

Global sensitivity analysis (GSA) and uncertainty quantification (UQ) for groundwater modeling are challenging because of the model complexity and significant computational requirements. To reduce the massive computational cost, a cheap-to-evaluate surrogate model is usually constructed to approximate and replace the expensive groundwater models in the GSA and UQ. Constructing an accurate surrogate requires actual model simulations on a number of parameter samples. Thus, a robust experimental design strategy is desired to locate informative samples so as to reduce the computational cost in surrogate construction and consequently to improve the efficiency in the GSA and UQ. In this study, we developmore » a Taylor expansion-based adaptive design (TEAD) that aims to build an accurate global surrogate model with a small training sample size. TEAD defines a novel hybrid score function to search informative samples, and a robust stopping criterion to terminate the sample search that guarantees the resulted approximation errors satisfy the desired accuracy. The good performance of TEAD in building global surrogate models is demonstrated in seven analytical functions with different dimensionality and complexity in comparison to two widely used experimental design methods. The application of the TEAD-based surrogate method in two groundwater models shows that the TEAD design can effectively improve the computational efficiency of GSA and UQ for groundwater modeling.« less

A Taylor Expansion-Based Adaptive Design Strategy for Global Surrogate Modeling With Applications in Groundwater Modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mo, Shaoxing; Lu, Dan; Shi, Xiaoqing

Global sensitivity analysis (GSA) and uncertainty quantification (UQ) for groundwater modeling are challenging because of the model complexity and significant computational requirements. To reduce the massive computational cost, a cheap-to-evaluate surrogate model is usually constructed to approximate and replace the expensive groundwater models in the GSA and UQ. Constructing an accurate surrogate requires actual model simulations on a number of parameter samples. Thus, a robust experimental design strategy is desired to locate informative samples so as to reduce the computational cost in surrogate construction and consequently to improve the efficiency in the GSA and UQ. In this study, we developmore » a Taylor expansion-based adaptive design (TEAD) that aims to build an accurate global surrogate model with a small training sample size. TEAD defines a novel hybrid score function to search informative samples, and a robust stopping criterion to terminate the sample search that guarantees the resulted approximation errors satisfy the desired accuracy. The good performance of TEAD in building global surrogate models is demonstrated in seven analytical functions with different dimensionality and complexity in comparison to two widely used experimental design methods. The application of the TEAD-based surrogate method in two groundwater models shows that the TEAD design can effectively improve the computational efficiency of GSA and UQ for groundwater modeling.« less

Development of an ELISA for evaluation of swab recovery efficiencies of bovine serum albumin.

PubMed

Sparding, Nadja; Slotved, Hans-Christian; Nicolaisen, Gert M; Giese, Steen B; Elmlund, Jón; Steenhard, Nina R

2014-01-01

After a potential biological incident the sampling strategy and sample analysis are crucial for the outcome of the investigation and identification. In this study, we have developed a simple sandwich ELISA based on commercial components to quantify BSA (used as a surrogate for ricin) with a detection range of 1.32-80 ng/mL. We used the ELISA to evaluate different protein swabbing procedures (swabbing techniques and after-swabbing treatments) for two swab types: a cotton gauze swab and a flocked nylon swab. The optimal swabbing procedure for each swab type was used to obtain recovery efficiencies from different surface materials. The surface recoveries using the optimal swabbing procedure ranged from 0-60% and were significantly higher from nonporous surfaces compared to porous surfaces. In conclusion, this study presents a swabbing procedure evaluation and a simple BSA ELISA based on commercial components, which are easy to perform in a laboratory with basic facilities. The data indicate that different swabbing procedures were optimal for each of the tested swab types, and the particular swab preference depends on the surface material to be swabbed.

Hygroscopicity of organic surrogate compounds from biomass burning and their effect on the efflorescence of ammonium sulfate in mixed aerosol particles

NASA Astrophysics Data System (ADS)

Lei, Ting; Zuend, Andreas; Cheng, Yafang; Su, Hang; Wang, Weigang; Ge, Maofa

2018-01-01

Hygroscopic growth factors of organic surrogate compounds representing biomass burning and mixed organic-inorganic aerosol particles exhibit variability during dehydration experiments depending on their chemical composition, which we observed using a hygroscopicity tandem differential mobility analyzer (HTDMA). We observed that levoglucosan and humic acid aerosol particles release water upon dehumidification in the range from 90 to 5 % relative humidity (RH). However, 4-Hydroxybenzoic acid aerosol particles remain in the solid state upon dehumidification and exhibit a small shrinking in size at higher RH compared to the dry size. For example, the measured growth factor of 4-hyroxybenzoic acid aerosol particles is ˜ 0.96 at 90 % RH. The measurements were accompanied by RH-dependent thermodynamic equilibrium calculations using the Aerosol Inorganic-Organic Mixtures Functional groups Activity Coefficients (AIOMFAC) model and Extended Aerosol Inorganics Model (E-AIM), the Zdanovskii-Stokes-Robinson (ZSR) relation, and a fitted hygroscopicity expression. We observed several effects of organic components on the hygroscopicity behavior of mixtures containing ammonium sulfate (AS) in relation to the different mass fractions of organic compounds: (1) a shift of efflorescence relative humidity (ERH) of ammonium sulfate to higher RH due to the presence of 25 wt % levoglucosan in the mixture. (2) There is a distinct efflorescence transition at 25 % RH for mixtures consisting of 25 wt % of 4-hydroxybenzoic acid compared to the ERH at 35 % for organic-free AS particles. (3) There is indication for a liquid-to-solid phase transition of 4-hydroxybenzoic acid in the mixed particles during dehydration. (4) A humic acid component shows no significant effect on the efflorescence of AS in mixed aerosol particles. In addition, consideration of a composition-dependent degree of dissolution of crystallization AS (solid-liquid equilibrium) in the AIOMFAC and E-AIM models leads to a relatively good agreement between models and observed growth factors, as well as ERH of AS in the mixed system. The use of the ZSR relation leads to good agreement with measured diameter growth factors of aerosol particles containing humic acid and ammonium sulfate. Lastly, two distinct mixtures of organic surrogate compounds, including levoglucosan, 4-hydroxybenzoic acid, and humic acid, were used to represent the average water-soluble organic carbon (WSOC) fractions observed during the wet and dry seasons in the central Amazon Basin. A comparison of the organic fraction's hygroscopicity parameter for the simple mixtures, e.g., κ ≈ 0.12 to 0.15 for the wet-season mixture in the 90 to 40 % RH range, shows good agreement with field data for the wet season in the Amazon Basin (WSOC κ ≈ 0.14±0.06 at 90 % RH). This suggests that laboratory-generated mixtures containing organic surrogate compounds and ammonium sulfate can be used to mimic, in a simplified manner, the chemical composition of ambient aerosols from the Amazon Basin for the purpose of RH-dependent hygroscopicity studies.

Comparing biomarkers as principal surrogate endpoints.

PubMed

Huang, Ying; Gilbert, Peter B

2011-12-01

Recently a new definition of surrogate endpoint, the "principal surrogate," was proposed based on causal associations between treatment effects on the biomarker and on the clinical endpoint. Despite its appealing interpretation, limited research has been conducted to evaluate principal surrogates, and existing methods focus on risk models that consider a single biomarker. How to compare principal surrogate value of biomarkers or general risk models that consider multiple biomarkers remains an open research question. We propose to characterize a marker or risk model's principal surrogate value based on the distribution of risk difference between interventions. In addition, we propose a novel summary measure (the standardized total gain) that can be used to compare markers and to assess the incremental value of a new marker. We develop a semiparametric estimated-likelihood method to estimate the joint surrogate value of multiple biomarkers. This method accommodates two-phase sampling of biomarkers and is more widely applicable than existing nonparametric methods by incorporating continuous baseline covariates to predict the biomarker(s), and is more robust than existing parametric methods by leaving the error distribution of markers unspecified. The methodology is illustrated using a simulated example set and a real data set in the context of HIV vaccine trials. © 2011, The International Biometric Society.

A new sparse optimization scheme for simultaneous beam angle and fluence map optimization in radiotherapy planning

NASA Astrophysics Data System (ADS)

Liu, Hongcheng; Dong, Peng; Xing, Lei

2017-08-01

{{\\ell }2,1} -minimization-based sparse optimization was employed to solve the beam angle optimization (BAO) in intensity-modulated radiation therapy (IMRT) planning. The technique approximates the exact BAO formulation with efficiently computable convex surrogates, leading to plans that are inferior to those attainable with recently proposed gradient-based greedy schemes. In this paper, we alleviate/reduce the nontrivial inconsistencies between the {{\\ell }2,1} -based formulations and the exact BAO model by proposing a new sparse optimization framework based on the most recent developments in group variable selection. We propose the incorporation of the group-folded concave penalty (gFCP) as a substitution to the {{\\ell }2,1} -minimization framework. The new formulation is then solved by a variation of an existing gradient method. The performance of the proposed scheme is evaluated by both plan quality and the computational efficiency using three IMRT cases: a coplanar prostate case, a coplanar head-and-neck case, and a noncoplanar liver case. Involved in the evaluation are two alternative schemes: the {{\\ell }2,1} -minimization approach and the gradient norm method (GNM). The gFCP-based scheme outperforms both counterpart approaches. In particular, gFCP generates better plans than those obtained using the {{\\ell }2,1} -minimization for all three cases with a comparable computation time. As compared to the GNM, the gFCP improves both the plan quality and computational efficiency. The proposed gFCP-based scheme provides a promising framework for BAO and promises to improve both planning time and plan quality.

A new sparse optimization scheme for simultaneous beam angle and fluence map optimization in radiotherapy planning.

PubMed

Liu, Hongcheng; Dong, Peng; Xing, Lei

2017-07-20

[Formula: see text]-minimization-based sparse optimization was employed to solve the beam angle optimization (BAO) in intensity-modulated radiation therapy (IMRT) planning. The technique approximates the exact BAO formulation with efficiently computable convex surrogates, leading to plans that are inferior to those attainable with recently proposed gradient-based greedy schemes. In this paper, we alleviate/reduce the nontrivial inconsistencies between the [Formula: see text]-based formulations and the exact BAO model by proposing a new sparse optimization framework based on the most recent developments in group variable selection. We propose the incorporation of the group-folded concave penalty (gFCP) as a substitution to the [Formula: see text]-minimization framework. The new formulation is then solved by a variation of an existing gradient method. The performance of the proposed scheme is evaluated by both plan quality and the computational efficiency using three IMRT cases: a coplanar prostate case, a coplanar head-and-neck case, and a noncoplanar liver case. Involved in the evaluation are two alternative schemes: the [Formula: see text]-minimization approach and the gradient norm method (GNM). The gFCP-based scheme outperforms both counterpart approaches. In particular, gFCP generates better plans than those obtained using the [Formula: see text]-minimization for all three cases with a comparable computation time. As compared to the GNM, the gFCP improves both the plan quality and computational efficiency. The proposed gFCP-based scheme provides a promising framework for BAO and promises to improve both planning time and plan quality.

Use of surrogate outcomes in US FDA drug approvals, 2003-2012: a survey.

PubMed

Yu, Tsung; Hsu, Yea-Jen; Fain, Kevin M; Boyd, Cynthia M; Holbrook, Janet T; Puhan, Milo A

2015-11-27

To evaluate, across a spectrum of diseases, how often surrogate outcomes are used as a basis for drug approvals by the US Food and Drug Administration (FDA), and whether and how the rationale for using treatment effects on surrogates as predictors of treatment effects on patient-centred outcomes is discussed. We used the Drugs@FDA website to identify drug approvals produced from 2003 to 2012 by the FDA. We focused on four diseases (chronic obstructive pulmonary disease (COPD), type 1 or 2 diabetes, glaucoma and osteoporosis) for which surrogates are commonly used in trials. We reviewed the drug labels and medical reviews to provide empirical evidence on how surrogate outcomes are handled by the FDA. Of 1043 approvals screened, 58 (6%) were for the four diseases of interest. Most drugs for COPD (7/9, 78%), diabetes (26/26, 100%) and glaucoma (9/9, 100%) were approved based on surrogates while for osteoporosis, most drugs (10/14, 71%) were also approved for patient-centred outcomes (fractures). The rationale for using surrogates was discussed in 11 of the 43 (26%) drug approvals based on surrogates. In these drug approvals, we found drug approvals for diabetes are more likely than the other examined conditions to contain a discussion of trial evidence demonstrating that treatment effects on surrogate outcomes predict treatment effects on patient-centred outcomes. Our results suggest that the FDA did not use a consistent approach to address surrogates in assessing the benefits and harms of drugs for COPD, type 1 or 2 diabetes, glaucoma and osteoporosis. For evaluating new drugs, patient-centred outcomes should be chosen whenever possible. If the use of surrogate outcomes is necessary, then a consistent approach is important to review the evidence for surrogacy and consider surrogate's usage in the treatment and population under study. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

A PILOT-SCALE STUDY OF THE PRECURSORS LEADING TO THE FORMATION OF MIXED BROMO-CHLORO DIOXINS AND FURANS

EPA Science Inventory

The paper gives results of experiments in a pilot-scale rotary kiln incinerator simulator where a mixture of chlorinated and brominated surrogate waste was burned in the presence of injected fly-ash from a coal-fired utility boiler. Measurements were made of semivolatile products...

An Artificial Turf-Based Surrogate Surface Collector for the Direct Measurement of Atmospheric Mercury Dry Deposition

EPA Science Inventory

This paper describes the development of a new artificial turf surrogate surface (ATSS) sampler for use in the measurement of mercury (Hg) dry deposition. In contrast to many existing surrogate surface designs, the ATSS utilizes a three-dimensional deposition surface that may more...

Relationship between time post-ovulation and progesterone on oocyte maturation and pregnancy in canine cloning.

PubMed

Kim, Joung Joo; Park, Kang Bae; Choi, Eun Ji; Hyun, Sang Hwan; Kim, Nam-Hyung; Jeong, Yeon Woo; Hwang, Woo Suk

2017-10-01

Canine oocytes ovulated at prophase complete meiosis and continue to develop in presence of a high progesterone concentration in the oviduct. Considering that meiotic competence of canine oocyte is accomplished in the oviductal environment, we postulate that hormonal milieu resulting from the circulating progesterone concentration may affect oocyte maturation and early development of embryos. From 237 oocyte donors, 2620 oocytes were collected and their meiotic status and morphology were determined. To determine optimal characteristics of the mature oocytes subjected to nuclear transfer, a proportion of the meiotic status of the oocytes were classified in reference to time post-ovulation as well as progesterone (P4) level. A high proportion of matured oocytes were collected from >126h (55.5%) post-ovulation or 40-50ngmL -1 (46.4%) group compared to the other groups. Of the oocyte donors that provided mature oocytes in vivo, there was no correlation between serum progesterone of donors and time post ovulation, however, time post-ovulation were significantly shorter for <30ng/mL group (P<0.05). Using mature oocytes, 1161 cloned embryos were reconstructed and transferred into 77 surrogates. In order to determine the relationship between pregnancy performance and serum progesterone level, embryos were transferred into surrogates showing various P4 serum levels. The highest pregnancy (31.8%) and live birth cloning efficacy (2.2%) rates were observed when the embryos were transferred into surrogates with circulating P4 levels were from 40 to 50ngmL -1 . In conclusion, measurement of circulating progesterone of female dog could be a suitable an indicator of the optimal time to collect quality oocyte and to select surrogates for cloning. Copyright © 2017 Elsevier B.V. All rights reserved.

Maintaining tumor targeting accuracy in real-time motion compensation systems for respiration-induced tumor motion

PubMed Central

Malinowski, Kathleen; McAvoy, Thomas J.; George, Rohini; Dieterich, Sonja; D’Souza, Warren D.

2013-01-01

Purpose: To determine how best to time respiratory surrogate-based tumor motion model updates by comparing a novel technique based on external measurements alone to three direct measurement methods. Methods: Concurrently measured tumor and respiratory surrogate positions from 166 treatment fractions for lung or pancreas lesions were analyzed. Partial-least-squares regression models of tumor position from marker motion were created from the first six measurements in each dataset. Successive tumor localizations were obtained at a rate of once per minute on average. Model updates were timed according to four methods: never, respiratory surrogate-based (when metrics based on respiratory surrogate measurements exceeded confidence limits), error-based (when localization error ≥3 mm), and always (approximately once per minute). Results: Radial tumor displacement prediction errors (mean ± standard deviation) for the four schema described above were 2.4 ± 1.2, 1.9 ± 0.9, 1.9 ± 0.8, and 1.7 ± 0.8 mm, respectively. The never-update error was significantly larger than errors of the other methods. Mean update counts over 20 min were 0, 4, 9, and 24, respectively. Conclusions: The same improvement in tumor localization accuracy could be achieved through any of the three update methods, but significantly fewer updates were required when the respiratory surrogate method was utilized. This study establishes the feasibility of timing image acquisitions for updating respiratory surrogate models without direct tumor localization. PMID:23822413

High-Performance Mixed Models Based Genome-Wide Association Analysis with omicABEL software

PubMed Central

Fabregat-Traver, Diego; Sharapov, Sodbo Zh.; Hayward, Caroline; Rudan, Igor; Campbell, Harry; Aulchenko, Yurii; Bientinesi, Paolo

2014-01-01

To raise the power of genome-wide association studies (GWAS) and avoid false-positive results in structured populations, one can rely on mixed model based tests. When large samples are used, and when multiple traits are to be studied in the ’omics’ context, this approach becomes computationally challenging. Here we consider the problem of mixed-model based GWAS for arbitrary number of traits, and demonstrate that for the analysis of single-trait and multiple-trait scenarios different computational algorithms are optimal. We implement these optimal algorithms in a high-performance computing framework that uses state-of-the-art linear algebra kernels, incorporates optimizations, and avoids redundant computations, increasing throughput while reducing memory usage and energy consumption. We show that, compared to existing libraries, our algorithms and software achieve considerable speed-ups. The OmicABEL software described in this manuscript is available under the GNU GPL v. 3 license as part of the GenABEL project for statistical genomics at http: //www.genabel.org/packages/OmicABEL. PMID:25717363

High-Performance Mixed Models Based Genome-Wide Association Analysis with omicABEL software.

PubMed

Fabregat-Traver, Diego; Sharapov, Sodbo Zh; Hayward, Caroline; Rudan, Igor; Campbell, Harry; Aulchenko, Yurii; Bientinesi, Paolo

2014-01-01

To raise the power of genome-wide association studies (GWAS) and avoid false-positive results in structured populations, one can rely on mixed model based tests. When large samples are used, and when multiple traits are to be studied in the 'omics' context, this approach becomes computationally challenging. Here we consider the problem of mixed-model based GWAS for arbitrary number of traits, and demonstrate that for the analysis of single-trait and multiple-trait scenarios different computational algorithms are optimal. We implement these optimal algorithms in a high-performance computing framework that uses state-of-the-art linear algebra kernels, incorporates optimizations, and avoids redundant computations, increasing throughput while reducing memory usage and energy consumption. We show that, compared to existing libraries, our algorithms and software achieve considerable speed-ups. The OmicABEL software described in this manuscript is available under the GNU GPL v. 3 license as part of the GenABEL project for statistical genomics at http: //www.genabel.org/packages/OmicABEL.

Evaluation of CNN as anthropomorphic model observer

NASA Astrophysics Data System (ADS)

Massanes, Francesc; Brankov, Jovan G.

2017-03-01

Model observers (MO) are widely used in medical imaging to act as surrogates of human observers in task-based image quality evaluation, frequently towards optimization of reconstruction algorithms. In this paper, we explore the use of convolutional neural networks (CNN) to be used as MO. We will compare CNN MO to alternative MO currently being proposed and used such as the relevance vector machine based MO and channelized Hotelling observer (CHO). As the success of the CNN, and other deep learning approaches, is rooted in large data sets availability, which is rarely the case in medical imaging systems task-performance evaluation, we will evaluate CNN performance on both large and small training data sets.

Assessing the applicability of WRF optimal parameters under the different precipitation simulations in the Greater Beijing Area

NASA Astrophysics Data System (ADS)

Di, Zhenhua; Duan, Qingyun; Wang, Chen; Ye, Aizhong; Miao, Chiyuan; Gong, Wei

2018-03-01

Forecasting skills of the complex weather and climate models have been improved by tuning the sensitive parameters that exert the greatest impact on simulated results based on more effective optimization methods. However, whether the optimal parameter values are still work when the model simulation conditions vary, which is a scientific problem deserving of study. In this study, a highly-effective optimization method, adaptive surrogate model-based optimization (ASMO), was firstly used to tune nine sensitive parameters from four physical parameterization schemes of the Weather Research and Forecasting (WRF) model to obtain better summer precipitation forecasting over the Greater Beijing Area in China. Then, to assess the applicability of the optimal parameter values, simulation results from the WRF model with default and optimal parameter values were compared across precipitation events, boundary conditions, spatial scales, and physical processes in the Greater Beijing Area. The summer precipitation events from 6 years were used to calibrate and evaluate the optimal parameter values of WRF model. Three boundary data and two spatial resolutions were adopted to evaluate the superiority of the calibrated optimal parameters to default parameters under the WRF simulations with different boundary conditions and spatial resolutions, respectively. Physical interpretations of the optimal parameters indicating how to improve precipitation simulation results were also examined. All the results showed that the optimal parameters obtained by ASMO are superior to the default parameters for WRF simulations for predicting summer precipitation in the Greater Beijing Area because the optimal parameters are not constrained by specific precipitation events, boundary conditions, and spatial resolutions. The optimal values of the nine parameters were determined from 127 parameter samples using the ASMO method, which showed that the ASMO method is very highly-efficient for optimizing WRF model parameters.

The association of funding source on effect size in randomized controlled trials: 2013-2015 - a cross-sectional survey and meta-analysis.

PubMed

Falk Delgado, Alberto; Falk Delgado, Anna

2017-03-14

Trials financed by for-profit organizations have been associated with favorable outcomes of new treatments, although the effect size of funding source impact on outcome is unknown. The aim of this study was to estimate the effect size for a favorable outcome in randomized controlled trials (RCTs), stratified by funding source, that have been published in general medical journals. Parallel-group RCTs published in The Lancet, New England Journal of Medicine, and JAMA between 2013 and 2015 were identified. RCTs with binary primary endpoints were included. The primary outcome was the OR of patients' having a favorable outcome in the intervention group compared with the control group. The OR of a favorable outcome in each trial was calculated by the number of positive events that occurred in the intervention and control groups. A meta-analytic technique with random effects model was used to calculate summary OR. Data were stratified by funding source as for-profit, mixed, and nonprofit. Prespecified sensitivity, subgroup, and metaregression analyses were performed. Five hundred nine trials were included. The OR for a favorable outcome in for-profit-funded RCTs was 1.92 (95% CI 1.72-2.14), which was higher than mixed source-funded RCTs (OR 1.34, 95% CI 1.25-1.43) and nonprofit-funded RCTs (OR 1.32, 95% CI 1.26-1.39). The OR for a favorable outcome was higher for both clinical and surrogate endpoints in for-profit-funded trials than in RCTs with other funding sources. Excluding drug trials lowered the OR for a favorable outcome in for-profit-funded RCTs. The OR for a favorable surrogate outcome in drug trials was higher in for-profit-funded trials than in nonprofit-funded trials. For-profit-funded RCTs have a higher OR for a favorable outcome than nonprofit- and mixed source-funded RCTs. This difference is associated mainly with the use of surrogate endpoints in for-profit-financed drug trials.

The role of imperfect surrogate endpoint information in drug approval and reimbursement decisions.

PubMed

Bognar, Katalin; Romley, John A; Bae, Jay P; Murray, James; Chou, Jacquelyn W; Lakdawalla, Darius N

2017-01-01

Approval of new drugs is increasingly reliant on "surrogate endpoints," which correlate with but imperfectly predict clinical benefits. Proponents argue surrogate endpoints allow for faster approval, but critics charge they provide inadequate evidence. We develop an economic framework that addresses the value of improvement in the predictive power, or "quality," of surrogate endpoints, and clarifies how quality can influence decisions by regulators, payers, and manufacturers. For example, the framework shows how lower-quality surrogates lead to greater misalignment of incentives between payers and regulators, resulting in more drugs that are approved for use but not covered by payers. Efficient price-negotiation in the marketplace can help align payer incentives for granting access based on surrogates. Higher-quality surrogates increase manufacturer profits and social surplus from early access to new drugs. Since the return on better quality is shared between manufacturers and payers, private incentives to invest in higher-quality surrogates are inefficiently low. Copyright © 2016 The Author(s). Published by Elsevier B.V. All rights reserved.

Persistent high-risk human papillomavirus (HPV) infections as surrogate endpoints of progressive cervical disease. Potential new endpoint for efficacy studies with new-generation (non-HPV 16/18) prophylactic HPV vaccines.

PubMed

Syrjänen, K

2011-01-01

Recent data indicate that persistent HR-HPV infections represent a significantly increased risk of developing incident high-grade CIN and cervical cancer. Accordingly, 6-month (6M+) or 12-month (12M+) type-specific persistence of HR-HPV have been proposed as powerful surrogates of progressive disease. Because of substantial practical impact in future HPV vaccine trials using non-HPV 16/18 vaccines, studies on HR-HPV persistence as a surrogate endpoint of progressive CIN have been subject to a comprehensive meta-analyses recently. The present communication was solicited to bring this important and timely topic to the awareness of the readers, in a format consisting of a review of the recent literature, supplemented with the author's own experience from different studies. Based on a large number of relevant studies, there remains little doubt that persistence of HR-HPV for 6+ or 12+ months is associated with a significantly increased risk of developing incident high-grade CIN. However, some data also disclosed several important issues that need to be carefully considered and/or adequately resolved before adopting 6M+ or 12M+ HR-HPV persistence as a surrogate of progressive disease. These include i) definitions of HPV persistence, ii) HPV detection techniques and iii) testing intervals and iv) length of follow-up, as well as v) diagnosis of the surrogate endpoints, and vi) other study characteristics, including vii) the type of reference category used in calculating the risk estimates. All these issues are critically discussed in the present communication. Of major impact seems to be the reference category used to calculate these risk estimates, as evident from the NIS-LAMS cohort. Taken together, it is suggested that in all future studies using the 6M+ or 12M+ HR-HPV persistence as a surrogate endpoint of progressive disease, a "gold standard" should be used in calculating the risk estimates. In addition to deciding, 1) whether to use 6M+ or 12M+ persistence criteria, and 2) cytological, histological or combined surrogate endpoints (SIL, CIN1, CIN2, CIN/SIL), one should 3) use exclusively the HPV negative reference group in calculating the risk estimates for viral persistence endpoints. This is supported by the data from the recent meta-analysis as well as from the author's combined NIS-LAMS cohort, both implicating that the most consistent association to progressive disease is obtained when women with persistent HR-HPV are compared with HPV-negative women. It is the conviction of this author that the two other reference categories (HPV transient and HPV mixed outcome) are far too heterogeneous and subject to potential misclassifications to give consistent and reproducible risk estimates for HR-HPV persistence as a surrogate endpoint of progressive CIN.

The numerical modelling of mixing phenomena of nanofluids in passive micromixers

NASA Astrophysics Data System (ADS)

Milotin, R.; Lelea, D.

2018-01-01

The paper deals with the rapid mixing phenomena in micro-mixing devices with four tangential injections and converging tube, considering nanoparticles and water as the base fluid. Several parameters like Reynolds number (Re = 6 - 284) or fluid temperature are considered in order to optimize the process and obtain fundamental insight in mixing phenomena. The set of partial differential equations is considered based on conservation of momentum and species. Commercial package software Ansys-Fluent is used for solution of differential equations, based on a finite volume method. The results reveal that mixing index and mixing process is strongly dependent both on Reynolds number and heat flux. Moreover there is a certain Reynolds number when flow instabilities are generated that intensify the mixing process due to the tangential injections of the fluids.

Design and optimization of mixed flow pump impeller blades by varying semi-cone angle

NASA Astrophysics Data System (ADS)

Dash, Nehal; Roy, Apurba Kumar; Kumar, Kaushik

2018-03-01

The mixed flow pump is a cross between the axial and radial flow pump. These pumps are used in a large number of applications in modern fields. For the designing of these mixed flow pump impeller blades, a lot number of design parameters are needed to be considered which makes this a tedious task for which fundamentals of turbo-machinery and fluid mechanics are always prerequisites. The semi-cone angle of mixed flow pump impeller blade has a specified range of variations generally between 45o to 60o. From the literature review done related to this topic researchers have considered only a particular semi-cone angle and all the calculations are based on this very same semi-cone angle. By varying this semi-cone angle in the specified range, it can be verified if that affects the designing of the impeller blades for a mixed flow pump. Although a lot of methods are available for designing of mixed flow pump impeller blades like inverse time marching method, the pseudo-stream function method, Fourier expansion singularity method, free vortex method, mean stream line theory method etc. still the optimized design of the mixed flow pump impeller blade has been a cumbersome work. As stated above since all the available research works suggest or propose the blade designs with constant semi-cone angle, here the authors have designed the impeller blades by varying the semi-cone angle in a particular range with regular intervals for a Mixed-Flow pump. Henceforth several relevant impeller blade designs are obtained and optimization is carried out to obtain the optimized design (blade with optimal geometry) of impeller blade.

Diesel surrogate fuels for engine testing and chemical-kinetic modeling: Compositions and properties

DOE PAGES

Mueller, Charles J.; Cannella, William J.; Bays, J. Timothy; ...

2016-01-07

The primary objectives of this work were to formulate, blend, and characterize a set of four ultralow-sulfur diesel surrogate fuels in quantities sufficient to enable their study in single-cylinder-engine and combustion-vessel experiments. The surrogate fuels feature increasing levels of compositional accuracy (i.e., increasing exactness in matching hydrocarbon structural characteristics) relative to the single target diesel fuel upon which the surrogate fuels are based. This approach was taken to assist in determining the minimum level of surrogate-fuel compositional accuracy that is required to adequately emulate the performance characteristics of the target fuel under different combustion modes. For each of the fourmore » surrogate fuels, an approximately 30 L batch was blended, and a number of the physical and chemical properties were measured. In conclusion, this work documents the surrogate-fuel creation process and the results of the property measurements.« less

Diesel Surrogate Fuels for Engine Testing and Chemical-Kinetic Modeling: Compositions and Properties

PubMed Central

Mueller, Charles J.; Cannella, William J.; Bays, J. Timothy; Bruno, Thomas J.; DeFabio, Kathy; Dettman, Heather D.; Gieleciak, Rafal M.; Huber, Marcia L.; Kweon, Chol-Bum; McConnell, Steven S.; Pitz, William J.; Ratcliff, Matthew A.

2016-01-01

The primary objectives of this work were to formulate, blend, and characterize a set of four ultralow-sulfur diesel surrogate fuels in quantities sufficient to enable their study in single-cylinder-engine and combustion-vessel experiments. The surrogate fuels feature increasing levels of compositional accuracy (i.e., increasing exactness in matching hydrocarbon structural characteristics) relative to the single target diesel fuel upon which the surrogate fuels are based. This approach was taken to assist in determining the minimum level of surrogate-fuel compositional accuracy that is required to adequately emulate the performance characteristics of the target fuel under different combustion modes. For each of the four surrogate fuels, an approximately 30 L batch was blended, and a number of the physical and chemical properties were measured. This work documents the surrogate-fuel creation process and the results of the property measurements. PMID:27330248

Hydropower Optimization Using Artificial Neural Network Surrogate Models of a High-Fidelity Hydrodynamics and Water Quality Model

NASA Astrophysics Data System (ADS)

Shaw, Amelia R.; Smith Sawyer, Heather; LeBoeuf, Eugene J.; McDonald, Mark P.; Hadjerioua, Boualem

2017-11-01

Hydropower operations optimization subject to environmental constraints is limited by challenges associated with dimensionality and spatial and temporal resolution. The need for high-fidelity hydrodynamic and water quality models within optimization schemes is driven by improved computational capabilities, increased requirements to meet specific points of compliance with greater resolution, and the need to optimize operations of not just single reservoirs but systems of reservoirs. This study describes an important advancement for computing hourly power generation schemes for a hydropower reservoir using high-fidelity models, surrogate modeling techniques, and optimization methods. The predictive power of the high-fidelity hydrodynamic and water quality model CE-QUAL-W2 is successfully emulated by an artificial neural network, then integrated into a genetic algorithm optimization approach to maximize hydropower generation subject to constraints on dam operations and water quality. This methodology is applied to a multipurpose reservoir near Nashville, Tennessee, USA. The model successfully reproduced high-fidelity reservoir information while enabling 6.8% and 6.6% increases in hydropower production value relative to actual operations for dissolved oxygen (DO) limits of 5 and 6 mg/L, respectively, while witnessing an expected decrease in power generation at more restrictive DO constraints. Exploration of simultaneous temperature and DO constraints revealed capability to address multiple water quality constraints at specified locations. The reduced computational requirements of the new modeling approach demonstrated an ability to provide decision support for reservoir operations scheduling while maintaining high-fidelity hydrodynamic and water quality information as part of the optimization decision support routines.

Hydropower Optimization Using Artificial Neural Network Surrogate Models of a High-Fidelity Hydrodynamics and Water Quality Model

DOE PAGES

Shaw, Amelia R.; Sawyer, Heather Smith; LeBoeuf, Eugene J.; ...

2017-10-24

Hydropower operations optimization subject to environmental constraints is limited by challenges associated with dimensionality and spatial and temporal resolution. The need for high-fidelity hydrodynamic and water quality models within optimization schemes is driven by improved computational capabilities, increased requirements to meet specific points of compliance with greater resolution, and the need to optimize operations of not just single reservoirs but systems of reservoirs. This study describes an important advancement for computing hourly power generation schemes for a hydropower reservoir using high-fidelity models, surrogate modeling techniques, and optimization methods. The predictive power of the high-fidelity hydrodynamic and water quality model CE-QUAL-W2more » is successfully emulated by an artificial neural network, then integrated into a genetic algorithm optimization approach to maximize hydropower generation subject to constraints on dam operations and water quality. This methodology is applied to a multipurpose reservoir near Nashville, Tennessee, USA. The model successfully reproduced high-fidelity reservoir information while enabling 6.8% and 6.6% increases in hydropower production value relative to actual operations for dissolved oxygen (DO) limits of 5 and 6 mg/L, respectively, while witnessing an expected decrease in power generation at more restrictive DO constraints. Exploration of simultaneous temperature and DO constraints revealed capability to address multiple water quality constraints at specified locations. Here, the reduced computational requirements of the new modeling approach demonstrated an ability to provide decision support for reservoir operations scheduling while maintaining high-fidelity hydrodynamic and water quality information as part of the optimization decision support routines.« less

Hydropower Optimization Using Artificial Neural Network Surrogate Models of a High-Fidelity Hydrodynamics and Water Quality Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shaw, Amelia R.; Sawyer, Heather Smith; LeBoeuf, Eugene J.

Hydropower operations optimization subject to environmental constraints is limited by challenges associated with dimensionality and spatial and temporal resolution. The need for high-fidelity hydrodynamic and water quality models within optimization schemes is driven by improved computational capabilities, increased requirements to meet specific points of compliance with greater resolution, and the need to optimize operations of not just single reservoirs but systems of reservoirs. This study describes an important advancement for computing hourly power generation schemes for a hydropower reservoir using high-fidelity models, surrogate modeling techniques, and optimization methods. The predictive power of the high-fidelity hydrodynamic and water quality model CE-QUAL-W2more » is successfully emulated by an artificial neural network, then integrated into a genetic algorithm optimization approach to maximize hydropower generation subject to constraints on dam operations and water quality. This methodology is applied to a multipurpose reservoir near Nashville, Tennessee, USA. The model successfully reproduced high-fidelity reservoir information while enabling 6.8% and 6.6% increases in hydropower production value relative to actual operations for dissolved oxygen (DO) limits of 5 and 6 mg/L, respectively, while witnessing an expected decrease in power generation at more restrictive DO constraints. Exploration of simultaneous temperature and DO constraints revealed capability to address multiple water quality constraints at specified locations. Here, the reduced computational requirements of the new modeling approach demonstrated an ability to provide decision support for reservoir operations scheduling while maintaining high-fidelity hydrodynamic and water quality information as part of the optimization decision support routines.« less

Nearly ideal binary communication in squeezed channels

NASA Astrophysics Data System (ADS)

Paris, Matteo G.

2001-07-01

We analyze the effect of squeezing the channel in binary communication based on Gaussian states. We show that for coding on pure states, squeezing increases the detection probability at fixed size of the strategy, actually saturating the optimal bound already for moderate signal energy. Using Neyman-Pearson lemma for fuzzy hypothesis testing we are able to analyze also the case of mixed states, and to find the optimal amount of squeezing that can be effectively employed. It results that optimally squeezed channels are robust against signal mixing, and largely improve the strategy power by comparison with coherent ones.

Review of meta-analyses evaluating surrogate endpoints for overall survival in oncology.

PubMed

Sherrill, Beth; Kaye, James A; Sandin, Rickard; Cappelleri, Joseph C; Chen, Connie

2012-01-01

Overall survival (OS) is the gold standard in measuring the treatment effect of new drug therapies for cancer. However, practical factors may preclude the collection of unconfounded OS data, and surrogate endpoints are often used instead. Meta-analyses have been widely used for the validation of surrogate endpoints, specifically in oncology. This research reviewed published meta-analyses on the types of surrogate measures used in oncology studies and examined the extent of correlation between surrogate endpoints and OS for different cancer types. A search was conducted in October 2010 to compile available published evidence in the English language for the validation of disease progression-related endpoints as surrogates of OS, based on meta-analyses. We summarize published meta-analyses that quantified the correlation between progression-based endpoints and OS for multiple advanced solid-tumor types. We also discuss issues that affect the interpretation of these findings. Progression-free survival is the most commonly used surrogate measure in studies of advanced solid tumors, and correlation with OS is reported for a limited number of cancer types. Given the increased use of crossover in trials and the availability of second-/third-line treatment options available to patients after progression, it will become increasingly more difficult to establish correlation between effects on progression-free survival and OS in additional tumor types.

Numerical investigation on layout optimization of obstacles in a three-dimensional passive micromixer.

PubMed

Chen, Xueye; Zhao, Zhongyi

2017-04-29

This paper aims at layout optimization design of obstacles in a three-dimensional T-type micromixer. Numerical analysis shows that the direction of flow velocity change constantly due to the obstacles blocking, which produces the chaotic convection and increases species mixing effectively. The orthogonal experiment method was applied for determining the effects of some key parameters on mixing efficiency. The weights in the order are: height of obstacles > geometric shape > symmetry = number of obstacles. Based on the optimized results, a multi-units obstacle micromixer was designed. Compared with T-type micromixer, the multi-units obstacle micromixer is more efficient, and more than 90% mixing efficiency were obtained for a wide range of peclet numbers. It can be demonstrated that the presented optimal design method of obstacles layout in three-dimensional microchannels is a simple and effective technology to improve species mixing in microfluidic devices. The obstacles layout methodology has the potential for applications in chemical engineering and bioengineering. Copyright © 2017 Elsevier B.V. All rights reserved.

Surrogate Plant Data Base : Volume 2. Appendix C : Facilities Planning Baseline Data

DOT National Transportation Integrated Search

1983-05-01

This four volume report consists of a data base describing "surrogate" automobile and truck manufacturing plants developed as part of a methodology for evaluating capital investment requirements in new manufacturing facilities to build new fleets of ...

Surrogate Plant Data Base : Volume 4. Appendix E : Medium and Heavy Truck Manufacturing

DOT National Transportation Integrated Search

1983-05-01

This four volume report consists of a data base describing "surrogate" automobile and truck manufacturing plants developed as part of a methodology for evaluating capital investment requirements in new manufacturing facilities to build new fleets of ...

The establishment of surrogates and correlates of protection: Useful tools for the licensure of effective influenza vaccines?

PubMed Central

Ward, Brian J.; Pillet, Stephane; Charland, Nathalie; Trepanier, Sonia; Couillard, Julie; Landry, Nathalie

2018-01-01

ABSTRACT The search for a test that can predict vaccine efficacy is an important part of any vaccine development program. Although regulators hesitate to acknowledge any test as a true ‘correlate of protection’, there are many precedents for defining ‘surrogate’ assays. Surrogates can be powerful tools for vaccine optimization, licensure, comparisons between products and development of improved products. When such tests achieve ‘reference’ status however, they can inadvertently become barriers to new technologies that do not work the same way as existing vaccines. This is particularly true when these tests are based upon circularly-defined ‘reference’ or, even worse, proprietary reagents. The situation with inactivated influenza vaccines is a good example of this phenomenon. The most frequently used tests to define vaccine-induced immunity are all serologic assays: hemagglutination inhibition (HI), single radial hemolysis (SRH) and microneutralization (MN). The first two, and particularly the HI assay, have achieved reference status and criteria have been established in many jurisdictions for their use in licensing new vaccines and to compare the performance of different vaccines. However, all of these assays are based on biological reagents that are notoriously difficult to standardize and can vary substantially by geography, by chance (i.e. developing reagents in eggs that may not antigenitically match wild-type viruses) and by intention (ie: choosing reagents that yield the most favorable results). This review describes attempts to standardize these assays to improve their performance as surrogates, the dangers of over-reliance on ‘reference’ serologic assays, the ways that manufacturers can exploit the existing regulatory framework to make their products ‘look good’ and the implications of this long-established system for the introduction of novel influenza vaccines. PMID:29252098

Constrained minimization problems for the reproduction number in meta-population models.

PubMed

Poghotanyan, Gayane; Feng, Zhilan; Glasser, John W; Hill, Andrew N

2018-02-14

The basic reproduction number ([Formula: see text]) can be considerably higher in an SIR model with heterogeneous mixing compared to that from a corresponding model with homogeneous mixing. For example, in the case of measles, mumps and rubella in San Diego, CA, Glasser et al. (Lancet Infect Dis 16(5):599-605, 2016. https://doi.org/10.1016/S1473-3099(16)00004-9 ), reported an increase of 70% in [Formula: see text] when heterogeneity was accounted for. Meta-population models with simple heterogeneous mixing functions, e.g., proportionate mixing, have been employed to identify optimal vaccination strategies using an approach based on the gradient of the effective reproduction number ([Formula: see text]), which consists of partial derivatives of [Formula: see text] with respect to the proportions immune [Formula: see text] in sub-groups i (Feng et al. in J Theor Biol 386:177-187, 2015.  https://doi.org/10.1016/j.jtbi.2015.09.006 ; Math Biosci 287:93-104, 2017.  https://doi.org/10.1016/j.mbs.2016.09.013 ). These papers consider cases in which an optimal vaccination strategy exists. However, in general, the optimal solution identified using the gradient may not be feasible for some parameter values (i.e., vaccination coverages outside the unit interval). In this paper, we derive the analytic conditions under which the optimal solution is feasible. Explicit expressions for the optimal solutions in the case of [Formula: see text] sub-populations are obtained, and the bounds for optimal solutions are derived for [Formula: see text] sub-populations. This is done for general mixing functions and examples of proportionate and preferential mixing are presented. Of special significance is the result that for general mixing schemes, both [Formula: see text] and [Formula: see text] are bounded below and above by their corresponding expressions when mixing is proportionate and isolated, respectively.

Optimal morphologic response to preoperative chemotherapy: an alternate outcome end point before resection of hepatic colorectal metastases.

PubMed

Shindoh, Junichi; Loyer, Evelyne M; Kopetz, Scott; Boonsirikamchai, Piyaporn; Maru, Dipen M; Chun, Yun Shin; Zimmitti, Giuseppe; Curley, Steven A; Charnsangavej, Chusilp; Aloia, Thomas A; Vauthey, Jean-Nicolas

2012-12-20

The purposes of this study were to confirm the prognostic value of an optimal morphologic response to preoperative chemotherapy in patients undergoing chemotherapy with or without bevacizumab before resection of colorectal liver metastases (CLM) and to identify predictors of the optimal morphologic response. The study included 209 patients who underwent resection of CLM after preoperative chemotherapy with oxaliplatin- or irinotecan-based regimens with or without bevacizumab. Radiologic responses were classified as optimal or suboptimal according to the morphologic response criteria. Overall survival (OS) was determined, and prognostic factors associated with an optimal response were identified in multivariate analysis. An optimal morphologic response was observed in 47% of patients treated with bevacizumab and 12% of patients treated without bevacizumab (P < .001). The 3- and 5-year OS rates were higher in the optimal response group (82% and 74%, respectively) compared with the suboptimal response group (60% and 45%, respectively; P < .001). On multivariate analysis, suboptimal morphologic response was an independent predictor of worse OS (hazard ratio, 2.09; P = .007). Receipt of bevacizumab (odds ratio, 6.71; P < .001) and largest metastasis before chemotherapy of ≤ 3 cm (odds ratio, 2.12; P = .025) were significantly associated with optimal morphologic response. The morphologic response showed no specific correlation with conventional size-based RECIST criteria, and it was superior to RECIST in predicting major pathologic response. Independent of preoperative chemotherapy regimen, optimal morphologic response is sufficiently correlated with OS to be considered a surrogate therapeutic end point for patients with CLM.

Optimal Morphologic Response to Preoperative Chemotherapy: An Alternate Outcome End Point Before Resection of Hepatic Colorectal Metastases

PubMed Central

Shindoh, Junichi; Loyer, Evelyne M.; Kopetz, Scott; Boonsirikamchai, Piyaporn; Maru, Dipen M.; Chun, Yun Shin; Zimmitti, Giuseppe; Curley, Steven A.; Charnsangavej, Chusilp; Aloia, Thomas A.; Vauthey, Jean-Nicolas

2012-01-01

Purpose The purposes of this study were to confirm the prognostic value of an optimal morphologic response to preoperative chemotherapy in patients undergoing chemotherapy with or without bevacizumab before resection of colorectal liver metastases (CLM) and to identify predictors of the optimal morphologic response. Patients and Methods The study included 209 patients who underwent resection of CLM after preoperative chemotherapy with oxaliplatin- or irinotecan-based regimens with or without bevacizumab. Radiologic responses were classified as optimal or suboptimal according to the morphologic response criteria. Overall survival (OS) was determined, and prognostic factors associated with an optimal response were identified in multivariate analysis. Results An optimal morphologic response was observed in 47% of patients treated with bevacizumab and 12% of patients treated without bevacizumab (P < .001). The 3- and 5-year OS rates were higher in the optimal response group (82% and 74%, respectively) compared with the suboptimal response group (60% and 45%, respectively; P < .001). On multivariate analysis, suboptimal morphologic response was an independent predictor of worse OS (hazard ratio, 2.09; P = .007). Receipt of bevacizumab (odds ratio, 6.71; P < .001) and largest metastasis before chemotherapy of ≤ 3 cm (odds ratio, 2.12; P = .025) were significantly associated with optimal morphologic response. The morphologic response showed no specific correlation with conventional size-based RECIST criteria, and it was superior to RECIST in predicting major pathologic response. Conclusion Independent of preoperative chemotherapy regimen, optimal morphologic response is sufficiently correlated with OS to be considered a surrogate therapeutic end point for patients with CLM. PMID:23150701

Mix Proportion Design of Asphalt Concrete

NASA Astrophysics Data System (ADS)

Wu, Xianhu; Gao, Lingling; Du, Shoujun

2017-12-01

Based on the gradation of AC and SMA, this paper designs a new type of anti slide mixture with two types of advantages. Chapter introduces the material selection, ratio of ore mixture ratio design calculation, and determine the optimal asphalt content test and proportioning design of asphalt concrete mix. This paper introduces the new technology of mix proportion.

Experimental optimal maximum-confidence discrimination and optimal unambiguous discrimination of two mixed single-photon states

DOE Office of Scientific and Technical Information (OSTI.GOV)

Steudle, Gesine A.; Knauer, Sebastian; Herzog, Ulrike

2011-05-15

We present an experimental implementation of optimum measurements for quantum state discrimination. Optimum maximum-confidence discrimination and optimum unambiguous discrimination of two mixed single-photon polarization states were performed. For the latter the states of rank 2 in a four-dimensional Hilbert space are prepared using both path and polarization encoding. Linear optics and single photons from a true single-photon source based on a semiconductor quantum dot are utilized.

Surrogate matrix and surrogate analyte approaches for definitive quantitation of endogenous biomolecules.

PubMed

Jones, Barry R; Schultz, Gary A; Eckstein, James A; Ackermann, Bradley L

2012-10-01

Quantitation of biomarkers by LC-MS/MS is complicated by the presence of endogenous analytes. This challenge is most commonly overcome by calibration using an authentic standard spiked into a surrogate matrix devoid of the target analyte. A second approach involves use of a stable-isotope-labeled standard as a surrogate analyte to allow calibration in the actual biological matrix. For both methods, parallelism between calibration standards and the target analyte in biological matrix must be demonstrated in order to ensure accurate quantitation. In this communication, the surrogate matrix and surrogate analyte approaches are compared for the analysis of five amino acids in human plasma: alanine, valine, methionine, leucine and isoleucine. In addition, methodology based on standard addition is introduced, which enables a robust examination of parallelism in both surrogate analyte and surrogate matrix methods prior to formal validation. Results from additional assays are presented to introduce the standard-addition methodology and to highlight the strengths and weaknesses of each approach. For the analysis of amino acids in human plasma, comparable precision and accuracy were obtained by the surrogate matrix and surrogate analyte methods. Both assays were well within tolerances prescribed by regulatory guidance for validation of xenobiotic assays. When stable-isotope-labeled standards are readily available, the surrogate analyte approach allows for facile method development. By comparison, the surrogate matrix method requires greater up-front method development; however, this deficit is offset by the long-term advantage of simplified sample analysis.

Optimized Reduction of Unsteady Radial Forces in a Singlechannel Pump for Wastewater Treatment

NASA Astrophysics Data System (ADS)

Kim, Jin-Hyuk; Cho, Bo-Min; Choi, Young-Seok; Lee, Kyoung-Yong; Peck, Jong-Hyeon; Kim, Seon-Chang

2016-11-01

A single-channel pump for wastewater treatment was optimized to reduce unsteady radial force sources caused by impeller-volute interactions. The steady and unsteady Reynolds- averaged Navier-Stokes equations using the shear-stress transport turbulence model were discretized by finite volume approximations and solved on tetrahedral grids to analyze the flow in the single-channel pump. The sweep area of radial force during one revolution and the distance of the sweep-area center of mass from the origin were selected as the objective functions; the two design variables were related to the internal flow cross-sectional area of the volute. These objective functions were integrated into one objective function by applying the weighting factor for optimization. Latin hypercube sampling was employed to generate twelve design points within the design space. A response-surface approximation model was constructed as a surrogate model for the objectives, based on the objective function values at the generated design points. The optimized results showed considerable reduction in the unsteady radial force sources in the optimum design, relative to those of the reference design.

Surrogate Parents and Children with Disabilities: State-Level Approaches. inForum

ERIC Educational Resources Information Center

Muller, Eve

2009-01-01

Based on a survey of states, this document summarizes state-level approaches to using surrogate parents in order to meet the needs of children with disabilities. Most respondents noted that their states had issued policy or formal guidance pertaining to surrogate parents and children with disabilities, and most also described efforts to ensure…

An entropy-based nonparametric test for the validation of surrogate endpoints.

PubMed

Miao, Xiaopeng; Wang, Yong-Cheng; Gangopadhyay, Ashis

2012-06-30

We present a nonparametric test to validate surrogate endpoints based on measure of divergence and random permutation. This test is a proposal to directly verify the Prentice statistical definition of surrogacy. The test does not impose distributional assumptions on the endpoints, and it is robust to model misspecification. Our simulation study shows that the proposed nonparametric test outperforms the practical test of the Prentice criterion in terms of both robustness of size and power. We also evaluate the performance of three leading methods that attempt to quantify the effect of surrogate endpoints. The proposed method is applied to validate magnetic resonance imaging lesions as the surrogate endpoint for clinical relapses in a multiple sclerosis trial. Copyright © 2012 John Wiley & Sons, Ltd.

Surrogate outcomes in health technology assessment: an international comparison.

PubMed

Velasco Garrido, Marcial; Mangiapane, Sandra

2009-07-01

Our aim was to review the recommendations given by health technology assessment (HTA) institutions in their methodological guidelines concerning the use of surrogate outcomes in their assessments. In a second step, we aimed at quantifying the role surrogate parameters take in assessment reports. We analyzed methodological papers and guidelines from HTA agencies with International Network of Agencies for Health Technology Assessment membership as well as from institutions related to pharmaceutical regulation (i.e., reimbursement, pricing). We analyzed the use of surrogate outcomes in a sample of HTA reports randomly drawn from the HTA database. We checked methods, results (including evidence tables), and conclusions sections and extracted the outcomes reported. We report descriptive statistics on the presence of surrogate outcomes in the reports. We identified thirty-four methodological guidelines, twenty of them addressing the issue of outcome parameter choice and the problematic of surrogate outcomes. Overall HTA agencies call on caution regarding the reliance on surrogate outcomes. None of the agencies has provided a list or catalog of acceptable and validated surrogate outcomes. We extracted the outcome parameter of 140 HTA reports. Only around half of the reports determined the outcomes for the assessment prospectively. Surrogate outcomes had been used in 62 percent of the reports. However, only 3.6 percent were based upon surrogate outcomes exclusively. All of them assessed diagnostic or screening technologies and the surrogate outcomes were predominantly test characteristics. HTA institutions seem to agree on a cautious approach to the use of surrogate outcomes in technology assessment. Thorough assessment of health technologies should not rely exclusively on surrogate outcomes.

Moderate reagent mixing on an orbital shaker reduces the incubation time of enzyme-linked immunosorbent assay.

PubMed

Kumar, Saroj; Ahirwar, Rajesh; Rehman, Ishita; Nahar, Pradip

2017-07-01

Rapid diagnostic tests can be developed using ELISA for detection of diseases in emergency conditions. Conventional ELISA takes 1-2 days, making it unsuitable for rapid diagnostics. Here, we report the effect of reagents mixing via shaking or vortexing on the assay timing of ELISA. A 48-min protocol of ELISA involving 12-min incubations with reagent mixing at 750 rpm for every step was optimized. Contrary to this, time-optimized control ELISA performed without mixing produced similar results in 8 h, leaving a time gain of 7 h using the developed protocol. Collectively, the findings suggest the development of ELISA-based rapid diagnostics. Copyright © 2017 Elsevier Inc. All rights reserved.

Graph-cut based discrete-valued image reconstruction.

PubMed

Tuysuzoglu, Ahmet; Karl, W Clem; Stojanovic, Ivana; Castañòn, David; Ünlü, M Selim

2015-05-01

Efficient graph-cut methods have been used with great success for labeling and denoising problems occurring in computer vision. Unfortunately, the presence of linear image mappings has prevented the use of these techniques in most discrete-amplitude image reconstruction problems. In this paper, we develop a graph-cut based framework for the direct solution of discrete amplitude linear image reconstruction problems cast as regularized energy function minimizations. We first analyze the structure of discrete linear inverse problem cost functions to show that the obstacle to the application of graph-cut methods to their solution is the variable mixing caused by the presence of the linear sensing operator. We then propose to use a surrogate energy functional that overcomes the challenges imposed by the sensing operator yet can be utilized efficiently in existing graph-cut frameworks. We use this surrogate energy functional to devise a monotonic iterative algorithm for the solution of discrete valued inverse problems. We first provide experiments using local convolutional operators and show the robustness of the proposed technique to noise and stability to changes in regularization parameter. Then we focus on nonlocal, tomographic examples where we consider limited-angle data problems. We compare our technique with state-of-the-art discrete and continuous image reconstruction techniques. Experiments show that the proposed method outperforms state-of-the-art techniques in challenging scenarios involving discrete valued unknowns.

Fluid mixing in droplet-based microfluidics with T junction and convergent-divergent sinusoidal microchannels.

PubMed

Yang, Li; Li, Shanshan; Liu, Jixiao; Cheng, Jingmeng

2018-02-01

To explore and utilize the advantages of droplet-based microfluidics, hydrodynamics, and mixing process within droplets traveling though the T junction channel and convergent-divergent sinusoidal microchannels are studied by numerical simulations and experiments, respectively. In the T junction channel, the mixing efficiency is significantly influenced by the twirling effect, which controls the initial distributions of the mixture during the droplet formation stage. Therefore, the internal recirculating flow can create a convection mechanism, thus improving mixing. The twirling effect is noticeably influenced by the velocity of the continuous phase; in the sinusoidal channel, the Dean vortices and droplet deformation are induced by centrifugal force and alternative velocity gradient, thus enhancing the mixing efficiency. The best mixing occurred when the droplet size is comparable with the channel width. Finally, we propose a unique optimized structure, which includes a T junction inlet joined to a sinusoidal channel. In this structure, the mixing of fluids in the droplets follows two routes: One is the twirling effect and symmetric recirculation flow in the straight channel. The other is the asymmetric recirculation and droplet deformation in the winding and variable cross-section. Among the three structures, the optimized structure has the best mixing efficiency at the shortest mixing time (0.25 ms). The combination of the twirling effect, variable cross-section effect, and Dean vortices greatly intensifies the chaotic flow. This study provides the insight of the mixing process and may benefit the design and operations of droplet-based microfluidics. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Incorporating approximation error in surrogate based Bayesian inversion

NASA Astrophysics Data System (ADS)

Zhang, J.; Zeng, L.; Li, W.; Wu, L.

2015-12-01

There are increasing interests in applying surrogates for inverse Bayesian modeling to reduce repetitive evaluations of original model. In this way, the computational cost is expected to be saved. However, the approximation error of surrogate model is usually overlooked. This is partly because that it is difficult to evaluate the approximation error for many surrogates. Previous studies have shown that, the direct combination of surrogates and Bayesian methods (e.g., Markov Chain Monte Carlo, MCMC) may lead to biased estimations when the surrogate cannot emulate the highly nonlinear original system. This problem can be alleviated by implementing MCMC in a two-stage manner. However, the computational cost is still high since a relatively large number of original model simulations are required. In this study, we illustrate the importance of incorporating approximation error in inverse Bayesian modeling. Gaussian process (GP) is chosen to construct the surrogate for its convenience in approximation error evaluation. Numerical cases of Bayesian experimental design and parameter estimation for contaminant source identification are used to illustrate this idea. It is shown that, once the surrogate approximation error is well incorporated into Bayesian framework, promising results can be obtained even when the surrogate is directly used, and no further original model simulations are required.

Beauty and the beast: Some perspectives on efficient model analysis, surrogate models, and the future of modeling

NASA Astrophysics Data System (ADS)

Hill, M. C.; Jakeman, J.; Razavi, S.; Tolson, B.

2015-12-01

For many environmental systems model runtimes have remained very long as more capable computers have been used to add more processes and more time and space discretization. Scientists have also added more parameters and kinds of observations, and many model runs are needed to explore the models. Computational demand equals run time multiplied by number of model runs divided by parallelization opportunities. Model exploration is conducted using sensitivity analysis, optimization, and uncertainty quantification. Sensitivity analysis is used to reveal consequences of what may be very complex simulated relations, optimization is used to identify parameter values that fit the data best, or at least better, and uncertainty quantification is used to evaluate the precision of simulated results. The long execution times make such analyses a challenge. Methods for addressing this challenges include computationally frugal analysis of the demanding original model and a number of ingenious surrogate modeling methods. Both commonly use about 50-100 runs of the demanding original model. In this talk we consider the tradeoffs between (1) original model development decisions, (2) computationally frugal analysis of the original model, and (3) using many model runs of the fast surrogate model. Some questions of interest are as follows. If the added processes and discretization invested in (1) are compared with the restrictions and approximations in model analysis produced by long model execution times, is there a net benefit related of the goals of the model? Are there changes to the numerical methods that could reduce the computational demands while giving up less fidelity than is compromised by using computationally frugal methods or surrogate models for model analysis? Both the computationally frugal methods and surrogate models require that the solution of interest be a smooth function of the parameters or interest. How does the information obtained from the local methods typical of (2) and the global averaged methods typical of (3) compare for typical systems? The discussion will use examples of response of the Greenland glacier to global warming and surface and groundwater modeling.

Machine Learning Techniques for Global Sensitivity Analysis in Climate Models

NASA Astrophysics Data System (ADS)

Safta, C.; Sargsyan, K.; Ricciuto, D. M.

2017-12-01

Climate models studies are not only challenged by the compute intensive nature of these models but also by the high-dimensionality of the input parameter space. In our previous work with the land model components (Sargsyan et al., 2014) we identified subsets of 10 to 20 parameters relevant for each QoI via Bayesian compressive sensing and variance-based decomposition. Nevertheless the algorithms were challenged by the nonlinear input-output dependencies for some of the relevant QoIs. In this work we will explore a combination of techniques to extract relevant parameters for each QoI and subsequently construct surrogate models with quantified uncertainty necessary to future developments, e.g. model calibration and prediction studies. In the first step, we will compare the skill of machine-learning models (e.g. neural networks, support vector machine) to identify the optimal number of classes in selected QoIs and construct robust multi-class classifiers that will partition the parameter space in regions with smooth input-output dependencies. These classifiers will be coupled with techniques aimed at building sparse and/or low-rank surrogate models tailored to each class. Specifically we will explore and compare sparse learning techniques with low-rank tensor decompositions. These models will be used to identify parameters that are important for each QoI. Surrogate accuracy requirements are higher for subsequent model calibration studies and we will ascertain the performance of this workflow for multi-site ALM simulation ensembles.

Preliminary Therapy Evaluation of 225Ac-DOTA-c(RGDyK) Demonstrates that Cerenkov Radiation Derived from 225Ac Daughter Decay Can Be Detected by Optical Imaging for In Vivo Tumor Visualization

PubMed Central

Pandya, Darpan N.; Hantgan, Roy; Budzevich, Mikalai M.; Kock, Nancy D.; Morse, David L.; Batista, Izadora; Mintz, Akiva; Li, King C.; Wadas, Thaddeus J.

2016-01-01

The theranostic potential of 225Ac-based radiopharmaceuticals continues to increase as researchers seek innovative ways to harness the nuclear decay of this radioisotope for therapeutic and imaging applications. This communication describes the evaluation of 225Ac-DOTA-c(RGDyK) in both biodistribution and Cerenkov luminescence imaging (CLI) studies. Initially, La-DOTA-c(RGDyK) was prepared as a non-radioactive surrogate to evaluate methodologies that would contribute to an optimized radiochemical synthetic strategy and estimate the radioactive conjugate's affinity for αvβ3, using surface plasmon resonance spectroscopy. Surface plasmon resonance spectroscopy studies revealed the IC50 and Ki of La-DOTA-c(RGDyK) to be 33 ± 13 nM and 26 ± 11 nM, respectively, and suggest that the complexation of the La3+ ion to the conjugate did not significantly alter integrin binding. Furthermore, use of this surrogate allowed optimization of radiochemical synthesis strategies to prepare 225Ac-DOTA-c(RGDyK) with high radiochemical purity and specific activity similar to other 225Ac-based radiopharmaceuticals. This radiopharmaceutical was highly stable in vitro. In vivo biodistribution studies confirmed the radiotracer's ability to target αvβ3 integrin with specificity; specificity was detected in tumor-bearing animals using Cerenkov luminescence imaging. Furthermore, tumor growth control was achieved using non-toxic doses of the radiopharmaceutical in U87mg tumor-bearing nude mice. To our knowledge, this is the first report to describe the CLI of αvβ3+ tumors in live animals using the daughter products derived from 225Ac decay in situ. This concept holds promise to further enhance development of targeted alpha particle therapy. PMID:27022417

Preliminary Therapy Evaluation of (225)Ac-DOTA-c(RGDyK) Demonstrates that Cerenkov Radiation Derived from (225)Ac Daughter Decay Can Be Detected by Optical Imaging for In Vivo Tumor Visualization.

PubMed

Pandya, Darpan N; Hantgan, Roy; Budzevich, Mikalai M; Kock, Nancy D; Morse, David L; Batista, Izadora; Mintz, Akiva; Li, King C; Wadas, Thaddeus J

2016-01-01

The theranostic potential of (225)Ac-based radiopharmaceuticals continues to increase as researchers seek innovative ways to harness the nuclear decay of this radioisotope for therapeutic and imaging applications. This communication describes the evaluation of (225)Ac-DOTA-c(RGDyK) in both biodistribution and Cerenkov luminescence imaging (CLI) studies. Initially, La-DOTA-c(RGDyK) was prepared as a non-radioactive surrogate to evaluate methodologies that would contribute to an optimized radiochemical synthetic strategy and estimate the radioactive conjugate's affinity for αvβ3, using surface plasmon resonance spectroscopy. Surface plasmon resonance spectroscopy studies revealed the IC50 and Ki of La-DOTA-c(RGDyK) to be 33 ± 13 nM and 26 ± 11 nM, respectively, and suggest that the complexation of the La(3+) ion to the conjugate did not significantly alter integrin binding. Furthermore, use of this surrogate allowed optimization of radiochemical synthesis strategies to prepare (225)Ac-DOTA-c(RGDyK) with high radiochemical purity and specific activity similar to other (225)Ac-based radiopharmaceuticals. This radiopharmaceutical was highly stable in vitro. In vivo biodistribution studies confirmed the radiotracer's ability to target αvβ3 integrin with specificity; specificity was detected in tumor-bearing animals using Cerenkov luminescence imaging. Furthermore, tumor growth control was achieved using non-toxic doses of the radiopharmaceutical in U87mg tumor-bearing nude mice. To our knowledge, this is the first report to describe the CLI of αvβ3 (+) tumors in live animals using the daughter products derived from (225)Ac decay in situ. This concept holds promise to further enhance development of targeted alpha particle therapy.

Optimization of Maghemite (γ-Fe2O3) Nano-Powder Mixed micro-EDM of CoCrMo with Multiple Responses Using Gray Relational Analysis (GRA)

NASA Astrophysics Data System (ADS)

Mejid Elsiti, Nagwa; Noordin, M. Y.; Idris, Ani; Saed Majeed, Faraj

2017-10-01

This paper presents an optimization of process parameters of Micro-Electrical Discharge Machining (EDM) process with (γ-Fe2O3) nano-powder mixed dielectric using multi-response optimization Grey Relational Analysis (GRA) method instead of single response optimization. These parameters were optimized based on 2-Level factorial design combined with Grey Relational Analysis. The machining parameters such as peak current, gap voltage, and pulse on time were chosen for experimentation. The performance characteristics chosen for this study are material removal rate (MRR), tool wear rate (TWR), Taper and Overcut. Experiments were conducted using electrolyte copper as the tool and CoCrMo as the workpiece. Experimental results have been improved through this approach.

Physiology-Based Modeling May Predict Surgical Treatment Outcome for Obstructive Sleep Apnea

PubMed Central

Li, Yanru; Ye, Jingying; Han, Demin; Cao, Xin; Ding, Xiu; Zhang, Yuhuan; Xu, Wen; Orr, Jeremy; Jen, Rachel; Sands, Scott; Malhotra, Atul; Owens, Robert

2017-01-01

Study Objectives: To test whether the integration of both anatomical and nonanatomical parameters (ventilatory control, arousal threshold, muscle responsiveness) in a physiology-based model will improve the ability to predict outcomes after upper airway surgery for obstructive sleep apnea (OSA). Methods: In 31 patients who underwent upper airway surgery for OSA, loop gain and arousal threshold were calculated from preoperative polysomnography (PSG). Three models were compared: (1) a multiple regression based on an extensive list of PSG parameters alone; (2) a multivariate regression using PSG parameters plus PSG-derived estimates of loop gain, arousal threshold, and other trait surrogates; (3) a physiological model incorporating selected variables as surrogates of anatomical and nonanatomical traits important for OSA pathogenesis. Results: Although preoperative loop gain was positively correlated with postoperative apnea-hypopnea index (AHI) (P = .008) and arousal threshold was negatively correlated (P = .011), in both model 1 and 2, the only significant variable was preoperative AHI, which explained 42% of the variance in postoperative AHI. In contrast, the physiological model (model 3), which included AHIREM (anatomy term), fraction of events that were hypopnea (arousal term), the ratio of AHIREM and AHINREM (muscle responsiveness term), loop gain, and central/mixed apnea index (control of breathing terms), was able to explain 61% of the variance in postoperative AHI. Conclusions: Although loop gain and arousal threshold are associated with residual AHI after surgery, only preoperative AHI was predictive using multivariate regression modeling. Instead, incorporating selected surrogates of physiological traits on the basis of OSA pathophysiology created a model that has more association with actual residual AHI. Commentary: A commentary on this article appears in this issue on page 1023. Clinical Trial Registration: ClinicalTrials.Gov; Title: The Impact of Sleep Apnea Treatment on Physiology Traits in Chinese Patients With Obstructive Sleep Apnea; Identifier: NCT02696629; URL: https://clinicaltrials.gov/show/NCT02696629 Citation: Li Y, Ye J, Han D, Cao X, Ding X, Zhang Y, Xu W, Orr J, Jen R, Sands S, Malhotra A, Owens R. Physiology-based modeling may predict surgical treatment outcome for obstructive sleep apnea. J Clin Sleep Med. 2017;13(9):1029–1037. PMID:28818154

Model's sparse representation based on reduced mixed GMsFE basis methods

NASA Astrophysics Data System (ADS)

Jiang, Lijian; Li, Qiuqi

2017-06-01

In this paper, we propose a model's sparse representation based on reduced mixed generalized multiscale finite element (GMsFE) basis methods for elliptic PDEs with random inputs. A typical application for the elliptic PDEs is the flow in heterogeneous random porous media. Mixed generalized multiscale finite element method (GMsFEM) is one of the accurate and efficient approaches to solve the flow problem in a coarse grid and obtain the velocity with local mass conservation. When the inputs of the PDEs are parameterized by the random variables, the GMsFE basis functions usually depend on the random parameters. This leads to a large number degree of freedoms for the mixed GMsFEM and substantially impacts on the computation efficiency. In order to overcome the difficulty, we develop reduced mixed GMsFE basis methods such that the multiscale basis functions are independent of the random parameters and span a low-dimensional space. To this end, a greedy algorithm is used to find a set of optimal samples from a training set scattered in the parameter space. Reduced mixed GMsFE basis functions are constructed based on the optimal samples using two optimal sampling strategies: basis-oriented cross-validation and proper orthogonal decomposition. Although the dimension of the space spanned by the reduced mixed GMsFE basis functions is much smaller than the dimension of the original full order model, the online computation still depends on the number of coarse degree of freedoms. To significantly improve the online computation, we integrate the reduced mixed GMsFE basis methods with sparse tensor approximation and obtain a sparse representation for the model's outputs. The sparse representation is very efficient for evaluating the model's outputs for many instances of parameters. To illustrate the efficacy of the proposed methods, we present a few numerical examples for elliptic PDEs with multiscale and random inputs. In particular, a two-phase flow model in random porous media is simulated by the proposed sparse representation method.

Model's sparse representation based on reduced mixed GMsFE basis methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, Lijian, E-mail: ljjiang@hnu.edu.cn; Li, Qiuqi, E-mail: qiuqili@hnu.edu.cn

2017-06-01

In this paper, we propose a model's sparse representation based on reduced mixed generalized multiscale finite element (GMsFE) basis methods for elliptic PDEs with random inputs. A typical application for the elliptic PDEs is the flow in heterogeneous random porous media. Mixed generalized multiscale finite element method (GMsFEM) is one of the accurate and efficient approaches to solve the flow problem in a coarse grid and obtain the velocity with local mass conservation. When the inputs of the PDEs are parameterized by the random variables, the GMsFE basis functions usually depend on the random parameters. This leads to a largemore » number degree of freedoms for the mixed GMsFEM and substantially impacts on the computation efficiency. In order to overcome the difficulty, we develop reduced mixed GMsFE basis methods such that the multiscale basis functions are independent of the random parameters and span a low-dimensional space. To this end, a greedy algorithm is used to find a set of optimal samples from a training set scattered in the parameter space. Reduced mixed GMsFE basis functions are constructed based on the optimal samples using two optimal sampling strategies: basis-oriented cross-validation and proper orthogonal decomposition. Although the dimension of the space spanned by the reduced mixed GMsFE basis functions is much smaller than the dimension of the original full order model, the online computation still depends on the number of coarse degree of freedoms. To significantly improve the online computation, we integrate the reduced mixed GMsFE basis methods with sparse tensor approximation and obtain a sparse representation for the model's outputs. The sparse representation is very efficient for evaluating the model's outputs for many instances of parameters. To illustrate the efficacy of the proposed methods, we present a few numerical examples for elliptic PDEs with multiscale and random inputs. In particular, a two-phase flow model in random porous media is simulated by the proposed sparse representation method.« less

4D CT sorting based on patient internal anatomy

NASA Astrophysics Data System (ADS)

Li, Ruijiang; Lewis, John H.; Cerviño, Laura I.; Jiang, Steve B.

2009-08-01

Respiratory motion during free-breathing computed tomography (CT) scan may cause significant errors in target definition for tumors in the thorax and upper abdomen. A four-dimensional (4D) CT technique has been widely used for treatment simulation of thoracic and abdominal cancer radiotherapy. The current 4D CT techniques require retrospective sorting of the reconstructed CT slices oversampled at the same couch position. Most sorting methods depend on external surrogates of respiratory motion recorded by extra instruments. However, respiratory signals obtained from these external surrogates may not always accurately represent the internal target motion, especially when irregular breathing patterns occur. We have proposed a new sorting method based on multiple internal anatomical features for multi-slice CT scan acquired in the cine mode. Four features are analyzed in this study, including the air content, lung area, lung density and body area. We use a measure called spatial coherence to select the optimal internal feature at each couch position and to generate the respiratory signals for 4D CT sorting. The proposed method has been evaluated for ten cancer patients (eight with thoracic cancer and two with abdominal cancer). For nine patients, the respiratory signals generated from the combined internal features are well correlated to those from external surrogates recorded by the real-time position management (RPM) system (average correlation: 0.95 ± 0.02), which is better than any individual internal measures at 95% confidence level. For these nine patients, the 4D CT images sorted by the combined internal features are almost identical to those sorted by the RPM signal. For one patient with an irregular breathing pattern, the respiratory signals given by the combined internal features do not correlate well with those from RPM (correlation: 0.68 ± 0.42). In this case, the 4D CT image sorted by our method presents fewer artifacts than that from the RPM signal. Our 4D CT internal sorting method eliminates the need of externally recorded surrogates of respiratory motion. It is an automatic, accurate, robust, cost efficient and yet simple method and therefore can be readily implemented in clinical settings.

The Biomarker-Surrogacy Evaluation Schema: a review of the biomarker-surrogate literature and a proposal for a criterion-based, quantitative, multidimensional hierarchical levels of evidence schema for evaluating the status of biomarkers as surrogate endpoints.

PubMed

Lassere, Marissa N

2008-06-01

There are clear advantages to using biomarkers and surrogate endpoints, but concerns about clinical and statistical validity and systematic methods to evaluate these aspects hinder their efficient application. Section 2 is a systematic, historical review of the biomarker-surrogate endpoint literature with special reference to the nomenclature, the systems of classification and statistical methods developed for their evaluation. In Section 3 an explicit, criterion-based, quantitative, multidimensional hierarchical levels of evidence schema - Biomarker-Surrogacy Evaluation Schema - is proposed to evaluate and co-ordinate the multiple dimensions (biological, epidemiological, statistical, clinical trial and risk-benefit evidence) of the biomarker clinical endpoint relationships. The schema systematically evaluates and ranks the surrogacy status of biomarkers and surrogate endpoints using defined levels of evidence. The schema incorporates the three independent domains: Study Design, Target Outcome and Statistical Evaluation. Each domain has items ranked from zero to five. An additional category called Penalties incorporates additional considerations of biological plausibility, risk-benefit and generalizability. The total score (0-15) determines the level of evidence, with Level 1 the strongest and Level 5 the weakest. The term ;surrogate' is restricted to markers attaining Levels 1 or 2 only. Surrogacy status of markers can then be directly compared within and across different areas of medicine to guide individual, trial-based or drug-development decisions. This schema would facilitate communication between clinical, researcher, regulatory, industry and consumer participants necessary for evaluation of the biomarker-surrogate-clinical endpoint relationship in their different settings.

Weighted Iterative Bayesian Compressive Sensing (WIBCS) for High Dimensional Polynomial Surrogate Construction

NASA Astrophysics Data System (ADS)

Sargsyan, K.; Ricciuto, D. M.; Safta, C.; Debusschere, B.; Najm, H. N.; Thornton, P. E.

2016-12-01

Surrogate construction has become a routine procedure when facing computationally intensive studies requiring multiple evaluations of complex models. In particular, surrogate models, otherwise called emulators or response surfaces, replace complex models in uncertainty quantification (UQ) studies, including uncertainty propagation (forward UQ) and parameter estimation (inverse UQ). Further, surrogates based on Polynomial Chaos (PC) expansions are especially convenient for forward UQ and global sensitivity analysis, also known as variance-based decomposition. However, the PC surrogate construction strongly suffers from the curse of dimensionality. With a large number of input parameters, the number of model simulations required for accurate surrogate construction is prohibitively large. Relatedly, non-adaptive PC expansions typically include infeasibly large number of basis terms far exceeding the number of available model evaluations. We develop Weighted Iterative Bayesian Compressive Sensing (WIBCS) algorithm for adaptive basis growth and PC surrogate construction leading to a sparse, high-dimensional PC surrogate with a very few model evaluations. The surrogate is then readily employed for global sensitivity analysis leading to further dimensionality reduction. Besides numerical tests, we demonstrate the construction on the example of Accelerated Climate Model for Energy (ACME) Land Model for several output QoIs at nearly 100 FLUXNET sites covering multiple plant functional types and climates, varying 65 input parameters over broad ranges of possible values. This work is supported by the U.S. Department of Energy, Office of Science, Biological and Environmental Research, Accelerated Climate Modeling for Energy (ACME) project. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000.

Development of a Post-Intensive Care Unit Storytelling Intervention for Surrogates Involved in Decisions to Limit Life-Sustaining Treatment

PubMed Central

Schenker, Yael; Dew, Mary Amanda; Reynolds, Charles F.; Arnold, Robert M.; Tiver, Greer A.; Barnato, Amber E.

2014-01-01

Objectives Surrogates involved in decisions to limit life-sustaining treatment for a loved one in the intensive care unit (ICU) are at increased risk for adverse psychological outcomes lasting months to years after the ICU experience. Post-ICU interventions to reduce surrogate distress have not been developed. We sought to 1) describe a conceptual framework underlying the beneficial mental health effects of storytelling and 2) present formative work developing a storytelling intervention to reduce distress for recently bereaved surrogates. Methods An interdisciplinary team conceived the idea for a storytelling intervention based upon evidence from narrative theory that storytelling reduces distress from traumatic events through emotional disclosure, cognitive processing, and social connections. We developed an initial storytelling guide based upon this theory and the clinical perspectives of team members. We then conducted a case series with recently bereaved surrogates to iteratively test and modify the guide. Results The storytelling guide covered three key domains of the surrogate's experience of the patient's illness and death: antecedents, ICU experience, and aftermath. The facilitator focused on parts of the story that appeared to generate strong emotions and used non-judgmental statements to attend to these emotions. Between September 2012 and May 2013 we identified 28 eligible surrogates from 1 medical ICU and consented 20 for medical record review and recontact; 10 became eligible of whom 6 consented and completed the storytelling intervention. The single-session storytelling intervention lasted 40-92 minutes. All storytelling participants endorsed the intervention as acceptable, and 5 of 6 reported that it was helpful. Significance of Results Surrogate storytelling is an innovative and acceptable post-ICU intervention for recently bereaved surrogates and should be evaluated further. PMID:24524736

Development of a post-intensive care unit storytelling intervention for surrogates involved in decisions to limit life-sustaining treatment.

PubMed

Schenker, Yael; Dew, Mary Amanda; Reynolds, Charles F; Arnold, Robert M; Tiver, Greer A; Barnato, Amber E

2015-06-01

Surrogates involved in decisions to limit life-sustaining treatment for a loved one in the intensive care unit (ICU) are at increased risk for adverse psychological outcomes that can last for months to years after the ICU experience. Post-ICU interventions to reduce surrogate distress have not yet been developed. We sought to (1) describe a conceptual framework underlying the beneficial mental health effects of storytelling, and (2) present formative work developing a storytelling intervention to reduce distress for recently bereaved surrogates. An interdisciplinary team conceived the idea for a storytelling intervention based on evidence from narrative theory that storytelling reduces distress from traumatic events through emotional disclosure, cognitive processing, and social connection. We developed an initial storytelling guide based on this theory and the clinical perspectives of team members. We then conducted a case series with recently bereaved surrogates to iteratively test and modify the guide. The storytelling guide covered three key domains of the surrogate's experience of the patient's illness and death: antecedents, ICU experience, and aftermath. The facilitator focused on the parts of a story that appeared to generate strong emotions and used nonjudgmental statements to attend to these emotions. Between September 2012 and May 2013, we identified 28 eligible surrogates from a medical ICU and consented 20 for medical record review and recontact; 10 became eligible, of whom 6 consented and completed the storytelling intervention. The single-session storytelling intervention lasted from 40 to 92 minutes. All storytelling participants endorsed the intervention as acceptable, and five of six reported it as helpful. Surrogate storytelling is an innovative and acceptable post-ICU intervention for recently bereaved surrogates and should be evaluated further.

Northeastern Salt Marshes: Elevation Capital and Resilience to Sea Level Rise

EPA Science Inventory

Stable tidal salt marshes exist at an elevation that is supra-optimal relative to peak biomass production, which for Spartina alterniflora, and other marsh macrophytes, follows a parabolic distribution as a function of elevation, as a surrogate for inundation frequency. In order...

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mueller, Charles J.; Cannella, William J.; Bays, J. Timothy

The primary objectives of this work were to formulate, blend, and characterize a set of four ultralow-sulfur diesel surrogate fuels in quantities sufficient to enable their study in single-cylinder-engine and combustion-vessel experiments. The surrogate fuels feature increasing levels of compositional accuracy (i.e., increasing exactness in matching hydrocarbon structural characteristics) relative to the single target diesel fuel upon which the surrogate fuels are based. This approach was taken to assist in determining the minimum level of surrogate-fuel compositional accuracy that is required to adequately emulate the performance characteristics of the target fuel under different combustion modes. For each of the fourmore » surrogate fuels, an approximately 30 L batch was blended, and a number of the physical and chemical properties were measured. In conclusion, this work documents the surrogate-fuel creation process and the results of the property measurements.« less

Fast neural network surrogates for very high dimensional physics-based models in computational oceanography.

PubMed

van der Merwe, Rudolph; Leen, Todd K; Lu, Zhengdong; Frolov, Sergey; Baptista, Antonio M

2007-05-01

We present neural network surrogates that provide extremely fast and accurate emulation of a large-scale circulation model for the coupled Columbia River, its estuary and near ocean regions. The circulation model has O(10(7)) degrees of freedom, is highly nonlinear and is driven by ocean, atmospheric and river influences at its boundaries. The surrogates provide accurate emulation of the full circulation code and run over 1000 times faster. Such fast dynamic surrogates will enable significant advances in ensemble forecasts in oceanography and weather.

Evaluation of two new surrogate indices including parameters not using insulin to assess insulin sensitivity/resistance in non-diabetic postmenopausal women: a MONET group study.

PubMed

Bastard, J-P; Lavoie, M-E; Messier, V; Prud'homme, D; Rabasa-Lhoret, R

2012-06-01

The study evaluated and compared, with other surrogate indices of insulin sensitivity/resistance (IS/R), the relevance of the TyG index, a product of fasting glucose and triglyceride (TG) levels, and the EGIR index, which includes TG, high-density lipoprotein cholesterol (HDL-c) and waist circumference in its formula to estimate IS/R, in non-diabetic postmenopausal women. A secondary analysis was performed using the baseline data for 163 non-diabetic postmenopausal women from the Montreal-Ottawa New Emerging Team (MONET) population database. The subjects participated in hyperinsulinaemic-euglycaemic (HIEG) clamp and oral glucose tolerance (OGTT) tests. Correlations and comparisons between surrogate indices were performed in addition to inter-rater agreement tests. The optimal value of surrogate indices for diagnosis of IS/R was established on a receiver operating characteristic (ROC) scatter plot. A significant correlation was found between the HIEG clamp and all IS/R surrogate indices tested [r=-0.370 (TyG index) to 0.608 (SIisOGTT index); P<0.001]. On ROC curve analysis, a higher AUROC was found for SIisOGTT (0.791) than for TyG and EGIR (0.706 and 0.675, respectively; P=0.07 and P<0.05, respectively). The TyG and EGIR IS/R indices were only relatively modestly related to the HIEG clamp. In contrast, both fasting- and OGTT-derived IS/R surrogate indices, which include insulin values in their formulae, appeared to be more accurate in estimating IS/R in our study population. Thus, the TyG and EGIR IS/R indices need to be tested and validated more extensively in different populations before being put to large-scale clinical use. Copyright © 2012 Elsevier Masson SAS. All rights reserved.

Summary Report of Comprehensive Laboratory Testing to Establish the Effectiveness of Proposed Treatment Methods for Unremediated and Remediated Nitrate Salt Waste Streams

DOE Office of Scientific and Technical Information (OSTI.GOV)

Anast, Kurt Roy; Funk, David John; Hargis, Kenneth Marshall

The inadvertent creation of transuranic waste carrying hazardous waste codes D001 and D002 requires the treatment of the material to eliminate the hazardous characteristics and allow its eventual shipment and disposal at the Waste Isolation Pilot Plant (WIPP). This report documents the effectiveness of two treatment methods proposed to stabilize both the unremediated and remediated nitrate salt waste streams (UNS and RNS, respectively) at Los Alamos National Laboratory (LANL). The two technologies include the addition of zeolite (with and without the addition of water as a processing aid) and cementation. Surrogates were developed to evaluate both the solid and liquidmore » fractions expected from parent waste containers, and both the solid and liquid fractions were tested. Both technologies are shown to be effective at eliminating the characteristic of ignitability (D001), and the addition of zeolite was determined to be effective at eliminating corrosivity (D002), with the preferred option1 of adding zeolite currently planned for implementation at LANL’s Waste Characterization, Reduction, and Repackaging Facility (WCRRF). The course of this work verified the need to evaluate and demonstrate the effectiveness of the proposed remedy for debris material, if required. The evaluation determined that WypAlls, cheesecloth, and Celotex absorbed with saturated nitrate salt solutions exhibit the ignitability characteristic (all other expected debris is not classified as ignitable). Finally, liquid surrogates containing saturated nitrate salts did not exhibit the characteristic of ignitability in their pure form (those neutralized with Kolorsafe and mixed with sWheat did exhibit D001). Sensitivity testing and an analysis were conducted to evaluate the waste form for reactivity. Tests included subjecting surrogate material to mechanical impact, friction, electrostatic discharge and thermal insults. The testing confirmed that the waste does not exhibit the characteristic of reactivity (D003). Follow-on testing was conducted to demonstrate the effectiveness of zeolite stabilization for ignitable WypAll and cheesecloth debris and additional nitrate salt solutions (those exhibiting the oxidizer characteristic) to demonstrate the effectiveness of the remedy. Follow-on testing also included testing of surrogate materials containing Waste Lock 770, which is present in four of the RNS containers, and potential items of debris such as plywood and Celotex material. Testing to evaluate the effectiveness of the remedy was performed using the specific remediation processes that are planned for use at the WCRRF. Finally, testing was also performed to evaluate the holding capacity of zeolite using a highly acidic surrogate solution and to characterize the composition of gases generated during mixing of zeolite with surrogate solutions. All these tests demonstrated the effectiveness of adding zeolite as the planned remedy.« less

Patient Preferences and Surrogate Decision Making in Neuroscience Intensive Care Units

PubMed Central

Cai, Xuemei; Robinson, Jennifer; Muehlschlegel, Susanne; White, Douglas B.; Holloway, Robert G.; Sheth, Kevin N.; Fraenkel, Liana; Hwang, David Y.

2016-01-01

In the neuroscience intensive care unit (NICU), most patients lack the capacity to make their own preferences known. This fact leads to situations where surrogate decision makers must fill the role of the patient in terms of making preference-based treatment decisions, oftentimes in challenging situations where prognosis is uncertain. The neurointensivist has a large responsibility and role to play in this shared decision making process. This review covers how NICU patient preferences are determined through existing advance care documentation or surrogate decision makers and how the optimum roles of the physician and surrogate decision maker are addressed. We outline the process of reaching a shared decision between family and care team and describe a practice for conducting optimum family meetings based on studies of ICU families in crisis. We review challenges in the decision making process between surrogate decision makers and medical teams in neurocritical care settings, as well as methods to ameliorate conflicts. Ultimately, the goal of shared decision making is to increase knowledge amongst surrogates and care providers, decrease decisional conflict, promote realistic expectations and preference-centered treatment strategies, and lift the emotional burden on families of neurocritical care patients. PMID:25990137

Evaluating Candidate Principal Surrogate Endpoints

PubMed Central

Gilbert, Peter B.; Hudgens, Michael G.

2009-01-01

Summary Frangakis and Rubin (2002, Biometrics 58, 21–29) proposed a new definition of a surrogate endpoint (a “principal” surrogate) based on causal effects. We introduce an estimand for evaluating a principal surrogate, the causal effect predictiveness (CEP) surface, which quantifies how well causal treatment effects on the biomarker predict causal treatment effects on the clinical endpoint. Although the CEP surface is not identifiable due to missing potential outcomes, it can be identified by incorporating a baseline covariate(s) that predicts the biomarker. Given case–cohort sampling of such a baseline predictor and the biomarker in a large blinded randomized clinical trial, we develop an estimated likelihood method for estimating the CEP surface. This estimation assesses the “surrogate value” of the biomarker for reliably predicting clinical treatment effects for the same or similar setting as the trial. A CEP surface plot provides a way to compare the surrogate value of multiple biomarkers. The approach is illustrated by the problem of assessing an immune response to a vaccine as a surrogate endpoint for infection. PMID:18363776

Arm span and ulnar length are reliable and accurate estimates of recumbent length and height in a multiethnic population of infants and children under 6 years of age.

PubMed

Forman, Michele R; Zhu, Yeyi; Hernandez, Ladia M; Himes, John H; Dong, Yongquan; Danish, Robert K; James, Kyla E; Caulfield, Laura E; Kerver, Jean M; Arab, Lenore; Voss, Paula; Hale, Daniel E; Kanafani, Nadim; Hirschfeld, Steven

2014-09-01

Surrogate measures are needed when recumbent length or height is unobtainable or unreliable. Arm span has been used as a surrogate but is not feasible in children with shoulder or arm contractures. Ulnar length is not usually impaired by joint deformities, yet its utility as a surrogate has not been adequately studied. In this cross-sectional study, we aimed to examine the accuracy and reliability of ulnar length measured by different tools as a surrogate measure of recumbent length and height. Anthropometrics [recumbent length, height, arm span, and ulnar length by caliper (ULC), ruler (ULR), and grid (ULG)] were measured in 1479 healthy infants and children aged <6 y across 8 study centers in the United States. Multivariate mixed-effects linear regression models for recumbent length and height were developed by using ulnar length and arm span as surrogate measures. The agreement between the measured length or height and the predicted values by ULC, ULR, ULG, and arm span were examined by Bland-Altman plots. All 3 measures of ulnar length and arm span were highly correlated with length and height. The degree of precision of prediction equations for length by ULC, ULR, and ULG (R(2) = 0.95, 0.95, and 0.92, respectively) was comparable with that by arm span (R(2) = 0.97) using age, sex, and ethnicity as covariates; however, height prediction by ULC (R(2) = 0.87), ULR (R(2) = 0.85), and ULG (R(2) = 0.88) was less comparable with arm span (R(2) = 0.94). Our study demonstrates that arm span and ULC, ULR, or ULG can serve as accurate and reliable surrogate measures of recumbent length and height in healthy children; however, ULC, ULR, and ULG tend to slightly overestimate length and height in young infants and children. Further testing of ulnar length as a surrogate is warranted in physically impaired or nonambulatory children. © 2014 American Society for Nutrition.

An adaptive Gaussian process-based method for efficient Bayesian experimental design in groundwater contaminant source identification problems: ADAPTIVE GAUSSIAN PROCESS-BASED INVERSION

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Jiangjiang; Li, Weixuan; Zeng, Lingzao

Surrogate models are commonly used in Bayesian approaches such as Markov Chain Monte Carlo (MCMC) to avoid repetitive CPU-demanding model evaluations. However, the approximation error of a surrogate may lead to biased estimations of the posterior distribution. This bias can be corrected by constructing a very accurate surrogate or implementing MCMC in a two-stage manner. Since the two-stage MCMC requires extra original model evaluations, the computational cost is still high. If the information of measurement is incorporated, a locally accurate approximation of the original model can be adaptively constructed with low computational cost. Based on this idea, we propose amore » Gaussian process (GP) surrogate-based Bayesian experimental design and parameter estimation approach for groundwater contaminant source identification problems. A major advantage of the GP surrogate is that it provides a convenient estimation of the approximation error, which can be incorporated in the Bayesian formula to avoid over-confident estimation of the posterior distribution. The proposed approach is tested with a numerical case study. Without sacrificing the estimation accuracy, the new approach achieves about 200 times of speed-up compared to our previous work using two-stage MCMC.« less

Instruction via Web-Based Modules in Early Childhood Personnel Preparation: A Mixed-Methods Study of Effectiveness and Learner Perspectives

ERIC Educational Resources Information Center

Hollingsworth, Heidi L.; Lim, Chih-Ing

2015-01-01

Effective personnel preparation is critical to the development of a high quality early childhood workforce that provides optimal care and education for young children. This mixed-methods study examined the effectiveness of, and learner perspectives on, instruction via web-based modules within face-to-face early childhood personnel preparation…

Error modeling for surrogates of dynamical systems using machine learning: Machine-learning-based error model for surrogates of dynamical systems

DOE PAGES

Trehan, Sumeet; Carlberg, Kevin T.; Durlofsky, Louis J.

2017-07-14

A machine learning–based framework for modeling the error introduced by surrogate models of parameterized dynamical systems is proposed. The framework entails the use of high-dimensional regression techniques (eg, random forests, and LASSO) to map a large set of inexpensively computed “error indicators” (ie, features) produced by the surrogate model at a given time instance to a prediction of the surrogate-model error in a quantity of interest (QoI). This eliminates the need for the user to hand-select a small number of informative features. The methodology requires a training set of parameter instances at which the time-dependent surrogate-model error is computed bymore » simulating both the high-fidelity and surrogate models. Using these training data, the method first determines regression-model locality (via classification or clustering) and subsequently constructs a “local” regression model to predict the time-instantaneous error within each identified region of feature space. We consider 2 uses for the resulting error model: (1) as a correction to the surrogate-model QoI prediction at each time instance and (2) as a way to statistically model arbitrary functions of the time-dependent surrogate-model error (eg, time-integrated errors). We then apply the proposed framework to model errors in reduced-order models of nonlinear oil-water subsurface flow simulations, with time-varying well-control (bottom-hole pressure) parameters. The reduced-order models used in this work entail application of trajectory piecewise linearization in conjunction with proper orthogonal decomposition. Moreover, when the first use of the method is considered, numerical experiments demonstrate consistent improvement in accuracy in the time-instantaneous QoI prediction relative to the original surrogate model, across a large number of test cases. When the second use is considered, results show that the proposed method provides accurate statistical predictions of the time- and well-averaged errors.« less

Error modeling for surrogates of dynamical systems using machine learning: Machine-learning-based error model for surrogates of dynamical systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Trehan, Sumeet; Carlberg, Kevin T.; Durlofsky, Louis J.

A machine learning–based framework for modeling the error introduced by surrogate models of parameterized dynamical systems is proposed. The framework entails the use of high-dimensional regression techniques (eg, random forests, and LASSO) to map a large set of inexpensively computed “error indicators” (ie, features) produced by the surrogate model at a given time instance to a prediction of the surrogate-model error in a quantity of interest (QoI). This eliminates the need for the user to hand-select a small number of informative features. The methodology requires a training set of parameter instances at which the time-dependent surrogate-model error is computed bymore » simulating both the high-fidelity and surrogate models. Using these training data, the method first determines regression-model locality (via classification or clustering) and subsequently constructs a “local” regression model to predict the time-instantaneous error within each identified region of feature space. We consider 2 uses for the resulting error model: (1) as a correction to the surrogate-model QoI prediction at each time instance and (2) as a way to statistically model arbitrary functions of the time-dependent surrogate-model error (eg, time-integrated errors). We then apply the proposed framework to model errors in reduced-order models of nonlinear oil-water subsurface flow simulations, with time-varying well-control (bottom-hole pressure) parameters. The reduced-order models used in this work entail application of trajectory piecewise linearization in conjunction with proper orthogonal decomposition. Moreover, when the first use of the method is considered, numerical experiments demonstrate consistent improvement in accuracy in the time-instantaneous QoI prediction relative to the original surrogate model, across a large number of test cases. When the second use is considered, results show that the proposed method provides accurate statistical predictions of the time- and well-averaged errors.« less

Comparison of penalty functions on a penalty approach to mixed-integer optimization

NASA Astrophysics Data System (ADS)

Francisco, Rogério B.; Costa, M. Fernanda P.; Rocha, Ana Maria A. C.; Fernandes, Edite M. G. P.

2016-06-01

In this paper, we present a comparative study involving several penalty functions that can be used in a penalty approach for globally solving bound mixed-integer nonlinear programming (bMIMLP) problems. The penalty approach relies on a continuous reformulation of the bMINLP problem by adding a particular penalty term to the objective function. A penalty function based on the `erf' function is proposed. The continuous nonlinear optimization problems are sequentially solved by the population-based firefly algorithm. Preliminary numerical experiments are carried out in order to analyze the quality of the produced solutions, when compared with other penalty functions available in the literature.

TU-AB-303-06: Does Online Adaptive Radiation Therapy Mean Zero Margin for Intermediate-Risk Prostate Cancer? An Intra-Fractional Seminal Vesicles Motion Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sheng, Y; Li, T; Lee, W

Purpose: To provide benchmark for seminal vesicles (SVs) margin selection to account for intra-fractional motion; and to investigate the effectiveness of two motion surrogates in predicting intra-fractional SV underdosage. Methods: 9 prostate SBRT patients were studied; each has five pairs of pre-treatment and post-treatment cone-beam CTs (CBCTs). Each pair of CBCTs was registered based on fiducial markers in the prostate. To provide “ground truth” for coverage evaluation, all pre-treatment SVs were expanded with isotropic margin of 1,2,3,5 and 8mm, and their overlap with post-treatment SVs were used to quantify intra-fractional coverage. Two commonly used motion surrogates, the center-of-mass (COM) andmore » the border of contour (the most distal points in SI/AP/LR directions) were evaluated using Receiver-Operating Characteristic (ROC) analyses for predicting SV underdosage due to intra-fractional motion. Action threshold of determining underdosage for each surrogate was calculated by selecting the optimal balancing between sensitivity and specificity. For comparison, margin for each surrogate was also calculated based on traditional margin recipe. Results: 90% post-treatment SV coverage can be achieved in 47%, 82%, 91%, 98% and 98% fractions for 1,2,3,5 and 8mm margins. 3mm margin ensured the 90% intra-fractional SV coverage in 90% fractions when prostate was aligned. The ROC analysis indicated the AUC for COM and border were 0.88 and 0.72. The underdosage threshold was 2.9mm for COM and 4.1mm for border. The Van Herk’s margin recipe recommended 0.5, 0 and 1.8mm margin in LR, AP and SI direction based on COM and for border, the corresponding margin was 2.1, 4.5 and 3mm. Conclusion: 3mm isotropic margin is the minimum required to mitigate the intra-fractional SV motion when prostate is aligned. ROC analysis reveals that both COM and border are acceptable predictors for SV underdosage with 2.9mm and 4.1mm action threshold. Traditional margin calculation is less reliable for this application. This work is partially supported a master research grant from Varian Medical Systems.« less

Review of meta-analyses evaluating surrogate endpoints for overall survival in oncology

PubMed Central

Sherrill, Beth; Kaye, James A; Sandin, Rickard; Cappelleri, Joseph C; Chen, Connie

2012-01-01

Overall survival (OS) is the gold standard in measuring the treatment effect of new drug therapies for cancer. However, practical factors may preclude the collection of unconfounded OS data, and surrogate endpoints are often used instead. Meta-analyses have been widely used for the validation of surrogate endpoints, specifically in oncology. This research reviewed published meta-analyses on the types of surrogate measures used in oncology studies and examined the extent of correlation between surrogate endpoints and OS for different cancer types. A search was conducted in October 2010 to compile available published evidence in the English language for the validation of disease progression-related endpoints as surrogates of OS, based on meta-analyses. We summarize published meta-analyses that quantified the correlation between progression-based endpoints and OS for multiple advanced solid-tumor types. We also discuss issues that affect the interpretation of these findings. Progression-free survival is the most commonly used surrogate measure in studies of advanced solid tumors, and correlation with OS is reported for a limited number of cancer types. Given the increased use of crossover in trials and the availability of second-/third-line treatment options available to patients after progression, it will become increasingly more difficult to establish correlation between effects on progression-free survival and OS in additional tumor types. PMID:23109809

A review of surrogate models and their application to groundwater modeling

NASA Astrophysics Data System (ADS)

Asher, M. J.; Croke, B. F. W.; Jakeman, A. J.; Peeters, L. J. M.

2015-08-01

The spatially and temporally variable parameters and inputs to complex groundwater models typically result in long runtimes which hinder comprehensive calibration, sensitivity, and uncertainty analysis. Surrogate modeling aims to provide a simpler, and hence faster, model which emulates the specified output of a more complex model in function of its inputs and parameters. In this review paper, we summarize surrogate modeling techniques in three categories: data-driven, projection, and hierarchical-based approaches. Data-driven surrogates approximate a groundwater model through an empirical model that captures the input-output mapping of the original model. Projection-based models reduce the dimensionality of the parameter space by projecting the governing equations onto a basis of orthonormal vectors. In hierarchical or multifidelity methods the surrogate is created by simplifying the representation of the physical system, such as by ignoring certain processes, or reducing the numerical resolution. In discussing the application to groundwater modeling of these methods, we note several imbalances in the existing literature: a large body of work on data-driven approaches seemingly ignores major drawbacks to the methods; only a fraction of the literature focuses on creating surrogates to reproduce outputs of fully distributed groundwater models, despite these being ubiquitous in practice; and a number of the more advanced surrogate modeling methods are yet to be fully applied in a groundwater modeling context.

Rheology and Extrusion of Cement-Fly Ashes Pastes

NASA Astrophysics Data System (ADS)

Micaelli, F.; Lanos, C.; Levita, G.

2008-07-01

The addition of fly ashes in cement pastes is tested to optimize the forming of cement based material by extrusion. Two sizes of fly ashes grains are examinated. The rheology of concentrated suspensions of ashes mixes is studied with a parallel plates rheometer. In stationary flow state, tested suspensions viscosities are satisfactorily described by the Krieger-Dougherty model. An "overlapped grain" suspensions model able to describe the bimodal suspensions behaviour is proposed. For higher values of solid volume fraction, Bingham viscoplastic behaviour is identified. Results showed that the plastic viscosity and plastic yield values present minimal values for the same optimal formulation of bimodal mixes. The rheological study is extended to more concentrated systems using an extruder. Finally it is observed that the addition of 30% vol. of optimized ashes mix determined a significant reduction of required extrusion load.

Surrogate Plant Data Base : Volume 3. Appendix D : Facilities Planning Data ; Operating Manpower, Manufacturing Budgets and Pre-Production Launch ...

DOT National Transportation Integrated Search

1983-05-01

This four volume report consists of a data base describing "surrogate" automobile and truck manufacturing plants developed as part of a methodology for evaluating capital investment requirements in new manufacturing facilities to build new fleets of ...

Creation of an Upper Stage Trajectory Capability Boundary to Enable Booster System Trade Space Exploration

NASA Technical Reports Server (NTRS)

Walsh, Ptrick; Coulon, Adam; Edwards, Stephen; Mavris, Dimitri N.

2012-01-01

The problem of trajectory optimization is important in all space missions. The solution of this problem enables one to specify the optimum thrust steering program which should be followed to achieve a specified mission objective, simultaneously satisfying the constraints.1 It is well known that whether or not the ascent trajectory is optimal can have a significant impact on propellant usage for a given payload, or on payload weight for the same gross vehicle weight.2 Consequently, ascent guidance commands are usually optimized in some fashion. Multi-stage vehicles add complexity to this analysis process as changes in vehicle properties in one stage propagate to the other stages through gear ratios and changes in the optimal trajectory. These effects can cause an increase in analysis time as more variables are added and convergence of the optimizer to system closure requires more analysis iterations. In this paper, an approach to simplifying this multi-stage problem through the creation of an upper stage capability boundary is presented. This work was completed as part of a larger study focused on trade space exploration for the advanced booster system that will eventually form a part of NASA s new Space Launch System.3 The approach developed leverages Design of Experiments and Surrogate Modeling4 techniques to create a predictive model of the SLS upper stage performance. The design of the SLS core stages is considered fixed for the purposes of this study, which results in trajectory parameters such as staging conditions being the only variables relevant to the upper stage. Through the creation of a surrogate model, which takes staging conditions as inputs and predicts the payload mass delivered by the SLS upper stage to a reference orbit as the response, it is possible to identify a "surface" of staging conditions which all satisfy the SLS requirement of placing 130 metric tons into low-Earth orbit (LEO).3 This identified surface represents the 130 metric ton capability boundary for the upper stage, such that if the combined first stage and boosters can achieve any one staging point on that surface, then the design is identified as feasible. With the surrogate model created, design and analysis of advanced booster concepts is streamlined, as optimization of the upper stage trajectory is no longer required in every design loop.

Risk estimates for persistent high-risk human papillomavirus infections as surrogate endpoints of progressive cervical disease critically depend on reference category: analysis of the combined prospective cohort of the New Independent States of the Former Soviet Union and Latin American Screening studies.

PubMed

Syrjänen, K; Shabalova, I; Naud, P; Kozachenko, V; Derchain, S; Zakharchenko, S; Roteli-Martins, C; Nerovjna, R; Longatto-Filho, A; Kljukina, L; Tatti, S; Branovskaja, M; Hammes, L S; Branca, M; Grunjberga, V; Eržen, M; Juschenko, A; Costa, S; Sarian, L; Podistov, J; Syrjänen, S

2011-06-01

To make feasible future clinical trials with new-generation human papillomavirus (HPV) vaccines, novel virological surrogate endpoints of progressive disease have been proposed, including high-risk HPV (HR-HPV) persistence for six months (6M+) or 12 months (12M+). The risk estimates (relative risks [RRs]) of these 'virological endpoints' are influenced by several variables, not yet validated adequately. We compared the impact of three referent groups: (i) HPV-negative, (ii) HPV-transient, (iii) HPV-mixed outcome on the risk estimates for 6M+ or 12M+ HR-HPV persistence as predictors of progressive disease. Generalized estimating equation models were used to estimate the strength of 6M+ and 12M+ HR-HPV persistence with disease progression to squamous intraepithelial lesions (SILs), cervical intraepithelial neoplasia (CIN) grade 1+, CIN2+, CIN/SIL endpoints, comparing three optional reference categories (i)-(iii) in a prospective sub-cohort of 1865 women from the combined New Independent States of the Former Soviet Union (NIS) and Latin American Screening (LAMS) studies cohort (n = 15,301). The RRs of these viral endpoints as predictors of progressive disease are affected by the length of viral persistence (6M+ or 12M+) and the surrogate endpoint (SIL, CIN1, CIN2, CIN/SIL). Most dramatic is the effect of the referent group used in risk estimates, with the HPV-negative referent group giving the highest and most consistent RRs for both 6M+ and 12M+ viral persistence, irrespective of which surrogate is used. In addition to deciding on whether to use 6M+ or 12M+ persistence criteria, and cytological, histological or combined surrogate endpoints, one should adopt the HPV-negative referent group as the gold standard in all future studies using viral persistence as the surrogate endpoint of progressive disease.

Robust control of systems with real parameter uncertainty and unmodelled dynamics

NASA Technical Reports Server (NTRS)

Chang, Bor-Chin; Fischl, Robert

1991-01-01

During this research period we have made significant progress in the four proposed areas: (1) design of robust controllers via H infinity optimization; (2) design of robust controllers via mixed H2/H infinity optimization; (3) M-delta structure and robust stability analysis for structured uncertainties; and (4) a study on controllability and observability of perturbed plant. It is well known now that the two-Riccati-equation solution to the H infinity control problem can be used to characterize all possible stabilizing optimal or suboptimal H infinity controllers if the optimal H infinity norm or gamma, an upper bound of a suboptimal H infinity norm, is given. In this research, we discovered some useful properties of these H infinity Riccati solutions. Among them, the most prominent one is that the spectral radius of the product of these two Riccati solutions is a continuous, nonincreasing, convex function of gamma in the domain of interest. Based on these properties, quadratically convergent algorithms are developed to compute the optimal H infinity norm. We also set up a detailed procedure for applying the H infinity theory to robust control systems design. The desire to design controllers with H infinity robustness but H(exp 2) performance has recently resulted in mixed H(exp 2) and H infinity control problem formulation. The mixed H(exp 2)/H infinity problem have drawn the attention of many investigators. However, solution is only available for special cases of this problem. We formulated a relatively realistic control problem with H(exp 2) performance index and H infinity robustness constraint into a more general mixed H(exp 2)/H infinity problem. No optimal solution yet is available for this more general mixed H(exp 2)/H infinity problem. Although the optimal solution for this mixed H(exp 2)/H infinity control has not yet been found, we proposed a design approach which can be used through proper choice of the available design parameters to influence both robustness and performance. For a large class of linear time-invariant systems with real parametric perturbations, the coefficient vector of the characteristic polynomial is a multilinear function of the real parameter vector. Based on this multilinear mapping relationship together with the recent developments for polytopic polynomials and parameter domain partition technique, we proposed an iterative algorithm for coupling the real structured singular value.

Mission Operations Planning with Preferences: An Empirical Study

NASA Technical Reports Server (NTRS)

Bresina, John L.; Khatib, Lina; McGann, Conor

2006-01-01

This paper presents an empirical study of some nonexhaustive approaches to optimizing preferences within the context of constraint-based, mixed-initiative planning for mission operations. This work is motivated by the experience of deploying and operating the MAPGEN (Mixed-initiative Activity Plan GENerator) system for the Mars Exploration Rover Mission. Responsiveness to the user is one of the important requirements for MAPGEN, hence, the additional computation time needed to optimize preferences must be kept within reasonabble bounds. This was the primary motivation for studying non-exhaustive optimization approaches. The specific goals of rhe empirical study are to assess the impact on solution quality of two greedy heuristics used in MAPGEN and to assess the improvement gained by applying a linear programming optimization technique to the final solution.

Soil nitrogen mineralization and enzymatic activities in fire and fire surrogate treatments in California

Treesearch

J. R. Miesel; R. E. J. Boerner; C. N. Skinner

2011-01-01

Forest thinning and prescribed fire are management strategies used to reduce hazardous fuel loads and catastrophic wildfires in western mixed-conifer forests. We evaluated effects of thinning (Thin) and prescribed fire (Burn), alone and in combination (Thin+Burn), on N transformations and microbial enzyme activities relative to an untreated control (Control) at 1 and 3...

The rationale for microcirculatory guided fluid therapy.

PubMed

Ince, Can

2014-06-01

The ultimate purpose of fluid administration in states of hypovolemia is to correct cardiac output to improve microcirculatory perfusion and tissue oxygenation. Observation of the microcirculation using handheld microscopes gives insight into the nature of convective and diffusive defect in hypovolemia. The purpose of this article is to introduce a new platform for hemodynamic-targeted fluid therapy based on the correction of tissue and microcirculatory perfusion assumed to be at risk during hypovolemia. Targeting systemic hemodynamic targets and/or clinical surrogates of hypovolemia gives inadequate guarantee for the correction of tissue perfusion by fluid therapy especially in conditions of distributive shock as occur in inflammation and sepsis. Findings are presented, which support the idea that only clinical signs of hypovolemia associated with low microcirculatory flow can be expected to benefit from fluid therapy and that fluid overload causes a defect in the diffusion of oxygen transport. We hypothesized that the optimal amount of fluid needed for correction of hypovolemia is defined by a physiologically based functional microcirculatory hemodynamic platform where convection and diffusion need to be optimized. Future clinical trials using handheld microscopes able to automatically evaluate the microcirculation at the bedside will show whether such a platform will indeed optimize fluid therapy.

OPTIMIZING POTENTIAL GREEN REPLACEMENT CHEMICALS – BALANCING FUNCTION AND RISK

EPA Science Inventory

An important focus of green chemistry is the design of new chemicals that are inherently less toxic than the ones they might replace, but still retain required functional properties. A variety of methods exist to measure or model both functional and toxicity surrogates that could...

Optimal design of piezoelectric transformers: a rational approach based on an analytical model and a deterministic global optimization.

PubMed

Pigache, Francois; Messine, Frédéric; Nogarede, Bertrand

2007-07-01

This paper deals with a deterministic and rational way to design piezoelectric transformers in radial mode. The proposed approach is based on the study of the inverse problem of design and on its reformulation as a mixed constrained global optimization problem. The methodology relies on the association of the analytical models for describing the corresponding optimization problem and on an exact global optimization software, named IBBA and developed by the second author to solve it. Numerical experiments are presented and compared in order to validate the proposed approach.

Robust optimization of a tandem grating solar thermal absorber

NASA Astrophysics Data System (ADS)

Choi, Jongin; Kim, Mingeon; Kang, Kyeonghwan; Lee, Ikjin; Lee, Bong Jae

2018-04-01

Ideal solar thermal absorbers need to have a high value of the spectral absorptance in the broad solar spectrum to utilize the solar radiation effectively. Majority of recent studies about solar thermal absorbers focus on achieving nearly perfect absorption using nanostructures, whose characteristic dimension is smaller than the wavelength of sunlight. However, precise fabrication of such nanostructures is not easy in reality; that is, unavoidable errors always occur to some extent in the dimension of fabricated nanostructures, causing an undesirable deviation of the absorption performance between the designed structure and the actually fabricated one. In order to minimize the variation in the solar absorptance due to the fabrication error, the robust optimization can be performed during the design process. However, the optimization of solar thermal absorber considering all design variables often requires tremendous computational costs to find an optimum combination of design variables with the robustness as well as the high performance. To achieve this goal, we apply the robust optimization using the Kriging method and the genetic algorithm for designing a tandem grating solar absorber. By constructing a surrogate model through the Kriging method, computational cost can be substantially reduced because exact calculation of the performance for every combination of variables is not necessary. Using the surrogate model and the genetic algorithm, we successfully design an effective solar thermal absorber exhibiting a low-level of performance degradation due to the fabrication uncertainty of design variables.

Responding to surrogate requests that seem inconsistent with a patient's living will.

PubMed

Vig, Elizabeth K; Sudore, Rebecca L; Berg, Karina M; Fromme, Erik K; Arnold, Robert M

2011-11-01

Clinicians may feel conflicted when a patient's legal decision maker is making decisions that seem inconsistent with a patient's living will. We provide evidence-based information to help clinicians consider whether a surrogate's inconsistent decisions are ethically appropriate. Surrogates are not flawless translators of their loved one's preferences; they are influenced by their own hopes and the current clinical context. Patients may be aware of this, are often concerned about burdening their loved ones, and often grant their surrogates leeway in interpreting their wishes. When appropriate, clinicians should respect surrogates' interpretations of patient values and take steps to decrease surrogate stress during the decision-making process. Finally, if clinicians are cognizant of their own values and preferences, they may recognize how these may affect their responses to certain clinical cases. Copyright © 2011 U.S. Cancer Pain Relief Committee. All rights reserved.

On Using Surrogates with Genetic Programming.

PubMed

Hildebrandt, Torsten; Branke, Jürgen

2015-01-01

One way to accelerate evolutionary algorithms with expensive fitness evaluations is to combine them with surrogate models. Surrogate models are efficiently computable approximations of the fitness function, derived by means of statistical or machine learning techniques from samples of fully evaluated solutions. But these models usually require a numerical representation, and therefore cannot be used with the tree representation of genetic programming (GP). In this paper, we present a new way to use surrogate models with GP. Rather than using the genotype directly as input to the surrogate model, we propose using a phenotypic characterization. This phenotypic characterization can be computed efficiently and allows us to define approximate measures of equivalence and similarity. Using a stochastic, dynamic job shop scenario as an example of simulation-based GP with an expensive fitness evaluation, we show how these ideas can be used to construct surrogate models and improve the convergence speed and solution quality of GP.

The Surgeon Volume-outcome Relationship: Not Yet Ready for Policy.

PubMed

Modrall, J Gregory; Minter, Rebecca M; Minhajuddin, Abu; Eslava-Schmalbach, Javier; Joshi, Girish P; Patel, Shivani; Rosero, Eric B

2018-05-01

Increasing surgeon volume may improve outcomes for index operations. We hypothesized that there may be surrogate operative experiences that yield similar outcomes for surgeons with a low-volume experience with a specific index operation, such as esophagectomy. The relationship between surgeon volume and outcomes has potential implications for credentialing of surgeons. Restrictions of privileges based on surgeon volume are only reasonable if there is no substitute for direct experience with the index operation. This study was aimed at determining whether there are valid surrogates for direct experience with a sample index operation-open esophagectomy. The Nationwide Inpatient Sample (2003-2009) was utilized. Surgeons were stratified into low and high-volume groups based on annual volume of esophagectomy. Surrogate volume was defined as the aggregate annual volume per surgeon of upper gastrointestinal operations including excision of esophageal diverticulum, gastrectomy, gastroduodenectomy, and repair of diaphragmatic hernia. In all, 26,795 esophagectomies were performed nationwide (2003-2009), with a crude inhospital mortality rate of 5.2%. Inhospital mortality decreased with increasing volume of esophagectomies performed annually: 7.7% and 3.8% for low and high-volume surgeons, respectively (P < 0.0001). Among surgeons with a low-volume esophagectomy experience, increasing volume of surrogate operations improved the outcomes observed for esophagectomy: 9.7%, 7.1%, and 4.3% for low, medium, and high-surrogate-volume surgeons, respectively (P = 0.016). Both operation-specific volume and surrogate volume are significant predictors of inhospital mortality for esophagectomy. Based on these observations, it would be premature to limit hospital privileges based solely on operation-specific surgeon volume criteria.

Surrogate Endpoint Evaluation: Principal Stratification Criteria and the Prentice Definition.

PubMed

Gilbert, Peter B; Gabriel, Erin E; Huang, Ying; Chan, Ivan S F

2015-09-01

A common problem of interest within a randomized clinical trial is the evaluation of an inexpensive response endpoint as a valid surrogate endpoint for a clinical endpoint, where a chief purpose of a valid surrogate is to provide a way to make correct inferences on clinical treatment effects in future studies without needing to collect the clinical endpoint data. Within the principal stratification framework for addressing this problem based on data from a single randomized clinical efficacy trial, a variety of definitions and criteria for a good surrogate endpoint have been proposed, all based on or closely related to the "principal effects" or "causal effect predictiveness (CEP)" surface. We discuss CEP-based criteria for a useful surrogate endpoint, including (1) the meaning and relative importance of proposed criteria including average causal necessity (ACN), average causal sufficiency (ACS), and large clinical effect modification; (2) the relationship between these criteria and the Prentice definition of a valid surrogate endpoint; and (3) the relationship between these criteria and the consistency criterion (i.e., assurance against the "surrogate paradox"). This includes the result that ACN plus a strong version of ACS generally do not imply the Prentice definition nor the consistency criterion, but they do have these implications in special cases. Moreover, the converse does not hold except in a special case with a binary candidate surrogate. The results highlight that assumptions about the treatment effect on the clinical endpoint before the candidate surrogate is measured are influential for the ability to draw conclusions about the Prentice definition or consistency. In addition, we emphasize that in some scenarios that occur commonly in practice, the principal strata sub-populations for inference are identifiable from the observable data, in which cases the principal stratification framework has relatively high utility for the purpose of effect modification analysis, and is closely connected to the treatment marker selection problem. The results are illustrated with application to a vaccine efficacy trial, where ACN and ACS for an antibody marker are found to be consistent with the data and hence support the Prentice definition and consistency.

Surrogate Endpoint Evaluation: Principal Stratification Criteria and the Prentice Definition

PubMed Central

Gilbert, Peter B.; Gabriel, Erin E.; Huang, Ying; Chan, Ivan S.F.

2015-01-01

A common problem of interest within a randomized clinical trial is the evaluation of an inexpensive response endpoint as a valid surrogate endpoint for a clinical endpoint, where a chief purpose of a valid surrogate is to provide a way to make correct inferences on clinical treatment effects in future studies without needing to collect the clinical endpoint data. Within the principal stratification framework for addressing this problem based on data from a single randomized clinical efficacy trial, a variety of definitions and criteria for a good surrogate endpoint have been proposed, all based on or closely related to the “principal effects” or “causal effect predictiveness (CEP)” surface. We discuss CEP-based criteria for a useful surrogate endpoint, including (1) the meaning and relative importance of proposed criteria including average causal necessity (ACN), average causal sufficiency (ACS), and large clinical effect modification; (2) the relationship between these criteria and the Prentice definition of a valid surrogate endpoint; and (3) the relationship between these criteria and the consistency criterion (i.e., assurance against the “surrogate paradox”). This includes the result that ACN plus a strong version of ACS generally do not imply the Prentice definition nor the consistency criterion, but they do have these implications in special cases. Moreover, the converse does not hold except in a special case with a binary candidate surrogate. The results highlight that assumptions about the treatment effect on the clinical endpoint before the candidate surrogate is measured are influential for the ability to draw conclusions about the Prentice definition or consistency. In addition, we emphasize that in some scenarios that occur commonly in practice, the principal strata sub-populations for inference are identifiable from the observable data, in which cases the principal stratification framework has relatively high utility for the purpose of effect modification analysis, and is closely connected to the treatment marker selection problem. The results are illustrated with application to a vaccine efficacy trial, where ACN and ACS for an antibody marker are found to be consistent with the data and hence support the Prentice definition and consistency. PMID:26722639

Reduction of nutrients, microbes, and personal care products in domestic wastewater by a benchtop electrocoagulation unit

PubMed Central

Symonds, E. M.; Cook, M. M.; McQuaig, S. M.; Ulrich, R. M.; Schenck, R. O.; Lukasik, J. O.; Van Vleet, E. S.; Breitbart, M.

2015-01-01

To preserve environmental and human health, improved treatment processes are needed to reduce nutrients, microbes, and emerging chemical contaminants from domestic wastewater prior to discharge into the environment. Electrocoagulation (EC) treatment is increasingly used to treat industrial wastewater; however, this technology has not yet been thoroughly assessed for its potential to reduce concentrations of nutrients, a variety of microbial surrogates, and personal care products found in domestic wastewater. This investigation's objective was to determine the efficiency of a benchtop EC unit with aluminum sacrificial electrodes to reduce concentrations of the aforementioned biological and chemical pollutants from raw and tertiary-treated domestic wastewater. EC treatment resulted in significant reductions (p < 0.05, α = 0.05) in phosphate, all microbial surrogates, and several personal care products from raw and tertiary-treated domestic wastewater. When wastewater was augmented with microbial surrogates representing bacterial, viral, and protozoan pathogens to measure the extent of reduction, EC treatment resulted in up to 7-log10 reduction of microbial surrogates. Future pilot and full-scale investigations are needed to optimize EC treatment for the following: reducing nitrogen species, personal care products, and energy consumption; elucidating the mechanisms behind microbial reductions; and performing life cycle analyses to determine the appropriateness of implementation. PMID:25797885

Reduction of nutrients, microbes, and personal care products in domestic wastewater by a benchtop electrocoagulation unit.

PubMed

Symonds, E M; Cook, M M; McQuaig, S M; Ulrich, R M; Schenck, R O; Lukasik, J O; Van Vleet, E S; Breitbart, M

2015-03-23

To preserve environmental and human health, improved treatment processes are needed to reduce nutrients, microbes, and emerging chemical contaminants from domestic wastewater prior to discharge into the environment. Electrocoagulation (EC) treatment is increasingly used to treat industrial wastewater; however, this technology has not yet been thoroughly assessed for its potential to reduce concentrations of nutrients, a variety of microbial surrogates, and personal care products found in domestic wastewater. This investigation's objective was to determine the efficiency of a benchtop EC unit with aluminum sacrificial electrodes to reduce concentrations of the aforementioned biological and chemical pollutants from raw and tertiary-treated domestic wastewater. EC treatment resulted in significant reductions (p < 0.05, α = 0.05) in phosphate, all microbial surrogates, and several personal care products from raw and tertiary-treated domestic wastewater. When wastewater was augmented with microbial surrogates representing bacterial, viral, and protozoan pathogens to measure the extent of reduction, EC treatment resulted in up to 7-log10 reduction of microbial surrogates. Future pilot and full-scale investigations are needed to optimize EC treatment for the following: reducing nitrogen species, personal care products, and energy consumption; elucidating the mechanisms behind microbial reductions; and performing life cycle analyses to determine the appropriateness of implementation.

Reduction of nutrients, microbes, and personal care products in domestic wastewater by a benchtop electrocoagulation unit

NASA Astrophysics Data System (ADS)

Symonds, E. M.; Cook, M. M.; McQuaig, S. M.; Ulrich, R. M.; Schenck, R. O.; Lukasik, J. O.; van Vleet, E. S.; Breitbart, M.

2015-03-01

To preserve environmental and human health, improved treatment processes are needed to reduce nutrients, microbes, and emerging chemical contaminants from domestic wastewater prior to discharge into the environment. Electrocoagulation (EC) treatment is increasingly used to treat industrial wastewater; however, this technology has not yet been thoroughly assessed for its potential to reduce concentrations of nutrients, a variety of microbial surrogates, and personal care products found in domestic wastewater. This investigation's objective was to determine the efficiency of a benchtop EC unit with aluminum sacrificial electrodes to reduce concentrations of the aforementioned biological and chemical pollutants from raw and tertiary-treated domestic wastewater. EC treatment resulted in significant reductions (p < 0.05, α = 0.05) in phosphate, all microbial surrogates, and several personal care products from raw and tertiary-treated domestic wastewater. When wastewater was augmented with microbial surrogates representing bacterial, viral, and protozoan pathogens to measure the extent of reduction, EC treatment resulted in up to 7-log10 reduction of microbial surrogates. Future pilot and full-scale investigations are needed to optimize EC treatment for the following: reducing nitrogen species, personal care products, and energy consumption; elucidating the mechanisms behind microbial reductions; and performing life cycle analyses to determine the appropriateness of implementation.

Flume Computer-Aided Design (CAD): Integrated CAD for Microflume Components and Systems

DTIC Science & Technology

2002-04-01

31 3.3: Matching the Mix Ratio (Part B...sizes) will be optimized based on the required flow rates and mixing ratios of the different species. The influence of etch depth is investigated on a...Inhibition Study In this network, the target protein is mixed with protease (i.e. enzyme that cleaves the target protein) and the protease inhibitor (the

Reliability evaluation of high-performance, low-power FinFET standard cells based on mixed RBB/FBB technique

NASA Astrophysics Data System (ADS)

Wang, Tian; Cui, Xiaoxin; Ni, Yewen; Liao, Kai; Liao, Nan; Yu, Dunshan; Cui, Xiaole

2017-04-01

With shrinking transistor feature size, the fin-type field-effect transistor (FinFET) has become the most promising option in low-power circuit design due to its superior capability to suppress leakage. To support the VLSI digital system flow based on logic synthesis, we have designed an optimized high-performance low-power FinFET standard cell library based on employing the mixed FBB/RBB technique in the existing stacked structure of each cell. This paper presents the reliability evaluation of the optimized cells under process and operating environment variations based on Monte Carlo analysis. The variations are modelled with Gaussian distribution of the device parameters and 10000 sweeps are conducted in the simulation to obtain the statistical properties of the worst-case delay and input-dependent leakage for each cell. For comparison, a set of non-optimal cells that adopt the same topology without employing the mixed biasing technique is also generated. Experimental results show that the optimized cells achieve standard deviation reduction of 39.1% and 30.7% at most in worst-case delay and input-dependent leakage respectively while the normalized deviation shrinking in worst-case delay and input-dependent leakage can be up to 98.37% and 24.13%, respectively, which demonstrates that our optimized cells are less sensitive to variability and exhibit more reliability. Project supported by the National Natural Science Foundation of China (No. 61306040), the State Key Development Program for Basic Research of China (No. 2015CB057201), the Beijing Natural Science Foundation (No. 4152020), and Natural Science Foundation of Guangdong Province, China (No. 2015A030313147).

Efficient prediction designs for random fields.

PubMed

Müller, Werner G; Pronzato, Luc; Rendas, Joao; Waldl, Helmut

2015-03-01

For estimation and predictions of random fields, it is increasingly acknowledged that the kriging variance may be a poor representative of true uncertainty. Experimental designs based on more elaborate criteria that are appropriate for empirical kriging (EK) are then often non-space-filling and very costly to determine. In this paper, we investigate the possibility of using a compound criterion inspired by an equivalence theorem type relation to build designs quasi-optimal for the EK variance when space-filling designs become unsuitable. Two algorithms are proposed, one relying on stochastic optimization to explicitly identify the Pareto front, whereas the second uses the surrogate criteria as local heuristic to choose the points at which the (costly) true EK variance is effectively computed. We illustrate the performance of the algorithms presented on both a simple simulated example and a real oceanographic dataset. © 2014 The Authors. Applied Stochastic Models in Business and Industry published by John Wiley & Sons, Ltd.

Multi-objective robust design of energy-absorbing components using coupled process-performance simulations

NASA Astrophysics Data System (ADS)

Najafi, Ali; Acar, Erdem; Rais-Rohani, Masoud

2014-02-01

The stochastic uncertainties associated with the material, process and product are represented and propagated to process and performance responses. A finite element-based sequential coupled process-performance framework is used to simulate the forming and energy absorption responses of a thin-walled tube in a manner that both material properties and component geometry can evolve from one stage to the next for better prediction of the structural performance measures. Metamodelling techniques are used to develop surrogate models for manufacturing and performance responses. One set of metamodels relates the responses to the random variables whereas the other relates the mean and standard deviation of the responses to the selected design variables. A multi-objective robust design optimization problem is formulated and solved to illustrate the methodology and the influence of uncertainties on manufacturability and energy absorption of a metallic double-hat tube. The results are compared with those of deterministic and augmented robust optimization problems.

Comparison of SIRT and SQS for Regularized Weighted Least Squares Image Reconstruction

PubMed Central

Gregor, Jens; Fessler, Jeffrey A.

2015-01-01

Tomographic image reconstruction is often formulated as a regularized weighted least squares (RWLS) problem optimized by iterative algorithms that are either inherently algebraic or derived from a statistical point of view. This paper compares a modified version of SIRT (Simultaneous Iterative Reconstruction Technique), which is of the former type, with a version of SQS (Separable Quadratic Surrogates), which is of the latter type. We show that the two algorithms minimize the same criterion function using similar forms of preconditioned gradient descent. We present near-optimal relaxation for both based on eigenvalue bounds and include a heuristic extension for use with ordered subsets. We provide empirical evidence that SIRT and SQS converge at the same rate for all intents and purposes. For context, we compare their performance with an implementation of preconditioned conjugate gradient. The illustrative application is X-ray CT of luggage for aviation security. PMID:26478906

Surrogate endpoints in oncology: when are they acceptable for regulatory and clinical decisions, and are they currently overused?

PubMed

Kemp, Robert; Prasad, Vinay

2017-07-21

Surrogate outcomes are not intrinsically beneficial to patients, but are designed to be easier and faster to measure than clinically meaningful outcomes. The use of surrogates as an endpoint in clinical trials and basis for regulatory approval is common, and frequently exceeds the guidance given by regulatory bodies. In this article, we demonstrate that the use of surrogates in oncology is widespread and increasing. At the same time, the strength of association between the surrogates used and clinically meaningful outcomes is often unknown or weak. Attempts to validate surrogates are rarely undertaken. When this is done, validation relies on only a fraction of available data, and often concludes that the surrogate is poor. Post-marketing studies, designed to ensure drugs have meaningful benefits, are often not performed. Alternatively, if a drug fails to improve quality of life or overall survival, market authorization is rarely revoked. We suggest this reliance on surrogates, and the imprecision surrounding their acceptable use, means that numerous drugs are now approved based on small yet statistically significant increases in surrogates of questionable reliability. In turn, this means the benefits of many approved drugs are uncertain. This is an unacceptable situation for patients and professionals, as prior experience has shown that such uncertainty can be associated with significant harm. The use of surrogate outcomes should be limited to situations where a surrogate has demonstrated robust ability to predict meaningful benefits, or where cases are dire, rare or with few treatment options. In both cases, surrogates must be used only when continuing studies examining hard endpoints have been fully recruited.

Synergy of iron and copper oxides in the catalytic formation of PCDD/Fs from 2-monochlorophenol.

PubMed

Potter, Phillip M; Guan, Xia; Lomnicki, Slawomir M

2018-07-01

Transition metal oxides present in waste incineration systems have the ability to catalyze the formation of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) through surface reactions involving organic dioxin precursors. However, studies have concentrated on the catalytic effects of individual transition metal oxides, while the complex elemental composition of fly ash introduces the possibility of synergistic or inhibiting effects between multiple, catalytically active components. In this study, we have tested fly ash surrogates containing different ratios (by weight) of iron (III) oxide and copper (II) oxide. Such Fe 2 O 3 /CuO mixed-oxide surrogates (in the Fe:Cu ratio of 3.5, 0.9 and 0.2 ) were used to study the cooperative effects between two transition metals that are present in high concentrations in most combustion systems and are known to individually catalyze the formation of PCDD/Fs. The presence of both iron and copper oxides increased the oxidative power of the fly ash surrogates in oxygen rich conditions and led to extremely high PCDD/F yields under pyrolytic conditions (up to >5% yield) from 2-monochlorophenol precursor. PCDD/F congener profiles from the mixed oxide samples are similar to results obtained from only CuO, however the total PCDD/F yield increases with increasing Fe 2 O 3 content. Careful analysis of the reaction products and changes to the oxidation states of active metals indicate the CuO surface sites are centers for reaction while the Fe 2 O 3 is affecting the bonds in CuO and increasing the ability of copper centers to form surface-bound radicals that are precursors to PCDD/Fs. Copyright © 2018 Elsevier Ltd. All rights reserved.

Quality of Documentation as a Surrogate Marker for Awareness and Training Effectiveness of PHTLS-Courses. Part of the Prospective Longitudinal Mixed-Methods EPPTC-Trial.

PubMed

Häske, David; Beckers, Stefan K; Hofmann, Marzellus; Lefering, Rolf; Gliwitzky, Bernhard; Wölfl, Christoph C; Grützner, Paul; Stöckle, Ulrich; Dieroff, Marc; Münzberg, Matthias

2017-01-01

Care for severely injured patients requires multidisciplinary teamwork. A decrease in the number of accident victims ultimately affects the routine and skills. PHTLS ("Pre-Hospital Trauma Life Support") courses are established two-day courses for medical and non-medical rescue service personnel, aimed at improving the pre-hospital care of trauma patients worldwide. The study aims the examination of the quality of documentation before and after PHTLS courses as a surrogate endpoint of training effectiveness and awareness. This was a prospective pre-post intervention trial and was part of the mixed-method longitudinal EPPTC (Effect of Paramedic Training on Pre-Hospital Trauma Care) study, evaluating subjective and objective changes among participants and real patient care, as a result of PHTLS courses. The courses provide an overview of the SAMPLE approach for interrogation of anamnestic information, which is believed to be responsible for patient safety as relevant, among others, "Allergies," "Medication," and "Patient History" (AMP). The focus of the course is not the documentation. In total, 320 protocols were analyzed before and after the training. The PHTLS course led to a significant increase (p < 0.001) in the "AMP" information in the documentation. The subgroups analysis of "allergies" (+47.2%), "drugs" (+38.1%), and "medical history" (+27.8%) before and after the PHTLS course showed a significant increase in the information content. In summary, we showed that PHTLS training improves documentation quality, which we used as a surrogate endpoint for learning effectiveness and awareness. In this regard, we demonstrated that participants use certain parts of training in real life, thereby suggesting that the learning methods of PHTLS training are effective. These results, however, do not indicate whether patient care has changed.

Utilizing Adjoint-Based Error Estimates for Surrogate Models to Accurately Predict Probabilities of Events

DOE PAGES

Butler, Troy; Wildey, Timothy

2018-01-01

In thist study, we develop a procedure to utilize error estimates for samples of a surrogate model to compute robust upper and lower bounds on estimates of probabilities of events. We show that these error estimates can also be used in an adaptive algorithm to simultaneously reduce the computational cost and increase the accuracy in estimating probabilities of events using computationally expensive high-fidelity models. Specifically, we introduce the notion of reliability of a sample of a surrogate model, and we prove that utilizing the surrogate model for the reliable samples and the high-fidelity model for the unreliable samples gives preciselymore » the same estimate of the probability of the output event as would be obtained by evaluation of the original model for each sample. The adaptive algorithm uses the additional evaluations of the high-fidelity model for the unreliable samples to locally improve the surrogate model near the limit state, which significantly reduces the number of high-fidelity model evaluations as the limit state is resolved. Numerical results based on a recently developed adjoint-based approach for estimating the error in samples of a surrogate are provided to demonstrate (1) the robustness of the bounds on the probability of an event, and (2) that the adaptive enhancement algorithm provides a more accurate estimate of the probability of the QoI event than standard response surface approximation methods at a lower computational cost.« less

Uncertainty quantification for accident management using ACE surrogates

DOE Office of Scientific and Technical Information (OSTI.GOV)

Varuttamaseni, A.; Lee, J. C.; Youngblood, R. W.

The alternating conditional expectation (ACE) regression method is used to generate RELAP5 surrogates which are then used to determine the distribution of the peak clad temperature (PCT) during the loss of feedwater accident coupled with a subsequent initiation of the feed and bleed (F and B) operation in the Zion-1 nuclear power plant. The construction of the surrogates assumes conditional independence relations among key reactor parameters. The choice of parameters to model is based on the macroscopic balance statements governing the behavior of the reactor. The peak clad temperature is calculated based on the independent variables that are known tomore » be important in determining the success of the F and B operation. The relationship between these independent variables and the plant parameters such as coolant pressure and temperature is represented by surrogates that are constructed based on 45 RELAP5 cases. The time-dependent PCT for different values of F and B parameters is calculated by sampling the independent variables from their probability distributions and propagating the information through two layers of surrogates. The results of our analysis show that the ACE surrogates are able to satisfactorily reproduce the behavior of the plant parameters even though a quasi-static assumption is primarily used in their construction. The PCT is found to be lower in cases where the F and B operation is initiated, compared to the case without F and B, regardless of the F and B parameters used. (authors)« less

Utilizing Adjoint-Based Error Estimates for Surrogate Models to Accurately Predict Probabilities of Events

DOE Office of Scientific and Technical Information (OSTI.GOV)

Butler, Troy; Wildey, Timothy

In thist study, we develop a procedure to utilize error estimates for samples of a surrogate model to compute robust upper and lower bounds on estimates of probabilities of events. We show that these error estimates can also be used in an adaptive algorithm to simultaneously reduce the computational cost and increase the accuracy in estimating probabilities of events using computationally expensive high-fidelity models. Specifically, we introduce the notion of reliability of a sample of a surrogate model, and we prove that utilizing the surrogate model for the reliable samples and the high-fidelity model for the unreliable samples gives preciselymore » the same estimate of the probability of the output event as would be obtained by evaluation of the original model for each sample. The adaptive algorithm uses the additional evaluations of the high-fidelity model for the unreliable samples to locally improve the surrogate model near the limit state, which significantly reduces the number of high-fidelity model evaluations as the limit state is resolved. Numerical results based on a recently developed adjoint-based approach for estimating the error in samples of a surrogate are provided to demonstrate (1) the robustness of the bounds on the probability of an event, and (2) that the adaptive enhancement algorithm provides a more accurate estimate of the probability of the QoI event than standard response surface approximation methods at a lower computational cost.« less

Bayesian Adaptive Trial Design for a Newly Validated Surrogate Endpoint

PubMed Central

Renfro, Lindsay A.; Carlin, Bradley P.; Sargent, Daniel J.

2011-01-01

Summary The evaluation of surrogate endpoints for primary use in future clinical trials is an increasingly important research area, due to demands for more efficient trials coupled with recent regulatory acceptance of some surrogates as ‘valid.’ However, little consideration has been given to how a trial which utilizes a newly-validated surrogate endpoint as its primary endpoint might be appropriately designed. We propose a novel Bayesian adaptive trial design that allows the new surrogate endpoint to play a dominant role in assessing the effect of an intervention, while remaining realistically cautious about its use. By incorporating multi-trial historical information on the validated relationship between the surrogate and clinical endpoints, then subsequently evaluating accumulating data against this relationship as the new trial progresses, we adaptively guard against an erroneous assessment of treatment based upon a truly invalid surrogate. When the joint outcomes in the new trial seem plausible given similar historical trials, we proceed with the surrogate endpoint as the primary endpoint, and do so adaptively–perhaps stopping the trial for early success or inferiority of the experimental treatment, or for futility. Otherwise, we discard the surrogate and switch adaptive determinations to the original primary endpoint. We use simulation to test the operating characteristics of this new design compared to a standard O’Brien-Fleming approach, as well as the ability of our design to discriminate trustworthy from untrustworthy surrogates in hypothetical future trials. Furthermore, we investigate possible benefits using patient-level data from 18 adjuvant therapy trials in colon cancer, where disease-free survival is considered a newly-validated surrogate endpoint for overall survival. PMID:21838811

Response Surface Methodology for the Optimization of Preparation of Biocomposites Based on Poly(lactic acid) and Durian Peel Cellulose

PubMed Central

Penjumras, Patpen; Abdul Rahman, Russly; Talib, Rosnita A.; Abdan, Khalina

2015-01-01

Response surface methodology was used to optimize preparation of biocomposites based on poly(lactic acid) and durian peel cellulose. The effects of cellulose loading, mixing temperature, and mixing time on tensile strength and impact strength were investigated. A central composite design was employed to determine the optimum preparation condition of the biocomposites to obtain the highest tensile strength and impact strength. A second-order polynomial model was developed for predicting the tensile strength and impact strength based on the composite design. It was found that composites were best fit by a quadratic regression model with high coefficient of determination (R 2) value. The selected optimum condition was 35 wt.% cellulose loading at 165°C and 15 min of mixing, leading to a desirability of 94.6%. Under the optimum condition, the tensile strength and impact strength of the biocomposites were 46.207 MPa and 2.931 kJ/m2, respectively. PMID:26167523

Runoff load estimation of particulate and dissolved nitrogen in Lake Inba watershed using continuous monitoring data on turbidity and electric conductivity.

PubMed

Kim, J; Nagano, Y; Furumai, H

2012-01-01

Easy-to-measure surrogate parameters for water quality indicators are needed for real time monitoring as well as for generating data for model calibration and validation. In this study, a novel linear regression model for estimating total nitrogen (TN) based on two surrogate parameters is proposed based on evaluation of pollutant loads flowing into a eutrophic lake. Based on their runoff characteristics during wet weather, electric conductivity (EC) and turbidity were selected as surrogates for particulate nitrogen (PN) and dissolved nitrogen (DN), respectively. Strong linear relationships were established between PN and turbidity and DN and EC, and both models subsequently combined for estimation of TN. This model was evaluated by comparison of estimated and observed TN runoff loads during rainfall events. This analysis showed that turbidity and EC are viable surrogates for PN and DN, respectively, and that the linear regression model for TN concentration was successful in estimating TN runoff loads during rainfall events and also under dry weather conditions.

Biomarkers and Surrogate Markers: An FDA Perspective

PubMed Central

Katz, Russell

2004-01-01

Summary: Interest is increasing rapidly in the use of surrogate markers as primary measures of the effectiveness of investigational drugs in definitive drug trials. Many such surrogate markers have been proposed as potential candidates for use in definitive effectiveness trials of agents to treat neurologic or psychiatric disease, but as of this date, there are no such markers that have been adequately “validated,” that is, shown to predict the effect of the treatment on the clinical outcome of interest. While the current law and regulations permit the United States Food and Drug Administration to base the approval of a drug product on a determination the effect of the drug on an unvalidated surrogate marker (that is, one for which it is not known that an effect on the surrogate actually predicts the desired clinical benefit), there are a number of difficulties in interpreting trials that use surrogate markers as primary measures of drug effect. In this article, the relevant regulatory context will be discussed, as well as the epistemological problems related to the interpretation of clinical trials in which unvalidated surrogate markers are used as primary outcomes. PMID:15717019

The Different Moral Bases of Patient and Surrogate Decision-Making.

PubMed

Brudney, Daniel

2018-01-01

My topic is a problem with our practice of surrogate decision-making in health care, namely, the problem of the surrogate who is not doing her job-the surrogate who cannot be reached or the surrogate who seems to refuse to understand or to be unable to understand the clinical situation. The analysis raises a question about the surrogate who simply disagrees with the medical team. One might think that such a surrogate is doing her job-the team just doesn't like how she is doing it. My analysis raises the question of whether (or perhaps when) she should be overridden. In approaching this problem, I focus not on the range of difficulties in practice but on the underlying moral conceptual issue. My concern will be to show that the moral values that underpin patient decision-making are fundamentally different from those that underpin surrogate decision-making. Identifying the distinctions will set parameters for any successful solution to the "Who should decide?" A patient has a specific kind of moral right to make her own medical decisions. A surrogate has no analogous moral right to decide for someone else. We want the surrogate to make the decision because we believe that she has a relevant epistemological advantage over anyone else on the scene. If and when she has no such advantage or if she refuses or is unable to use it, then there might not be sufficient reason to let her be the decision-maker. © 2018 The Hastings Center.

The Use of Surrogate Endpoints in Regulating Medicines for Cardio-Renal Disease: Opinions of Stakeholders

PubMed Central

Schievink, Bauke; Lambers Heerspink, Hiddo; Leufkens, Hubert; De Zeeuw, Dick; Hoekman, Jarno

2014-01-01

Aim There is discussion whether medicines can be authorized on the market based on evidence from surrogate endpoints. We assessed opinions of different stakeholders on this topic. Methods We conducted an online questionnaire that targeted various stakeholder groups (regulatory agencies, pharmaceutical industry, academia, relevant public sector organisations) and medical specialties (cardiology or nephrology vs. other). Participants were enrolled through purposeful sampling. We inquired for conditions under which surrogate endpoints can be used, the validity of various cardio-renal biomarkers and new approaches for biomarker use. Results Participants agreed that surrogate endpoints can be used when the surrogate is scientifically valid (5-point Likert response format, mean score: 4.3, SD: 0.9) or when there is an unmet clinical need (mean score: 3.8, SD: 1.2). Industry participants agreed to a greater extent than regulators and academics. However, out of four proposed surrogates (blood pressure (BP), HbA1c, albuminuria, CRP) for cardiovascular outcomes or end-stage renal disease, only use of BP for cardiovascular outcomes was deemed moderately accurate (mean: 3.6, SD: 1.1). Specialists in cardiology or nephrology tended to be more positive about the use of surrogate endpoints. Conclusion Stakeholders in drug development do not oppose to the use of surrogate endpoints in drug marketing authorization, but most surrogates are not considered valid. To solve this impasse, increased efforts are required to validate surrogate endpoints and to explore alternative ways to use them. PMID:25268242

The use of surrogate endpoints in regulating medicines for cardio-renal disease: opinions of stakeholders.

PubMed

Schievink, Bauke; Lambers Heerspink, Hiddo; Leufkens, Hubert; De Zeeuw, Dick; Hoekman, Jarno

2014-01-01

There is discussion whether medicines can be authorized on the market based on evidence from surrogate endpoints. We assessed opinions of different stakeholders on this topic. We conducted an online questionnaire that targeted various stakeholder groups (regulatory agencies, pharmaceutical industry, academia, relevant public sector organisations) and medical specialties (cardiology or nephrology vs. other). Participants were enrolled through purposeful sampling. We inquired for conditions under which surrogate endpoints can be used, the validity of various cardio-renal biomarkers and new approaches for biomarker use. Participants agreed that surrogate endpoints can be used when the surrogate is scientifically valid (5-point Likert response format, mean score: 4.3, SD: 0.9) or when there is an unmet clinical need (mean score: 3.8, SD: 1.2). Industry participants agreed to a greater extent than regulators and academics. However, out of four proposed surrogates (blood pressure (BP), HbA1c, albuminuria, CRP) for cardiovascular outcomes or end-stage renal disease, only use of BP for cardiovascular outcomes was deemed moderately accurate (mean: 3.6, SD: 1.1). Specialists in cardiology or nephrology tended to be more positive about the use of surrogate endpoints. Stakeholders in drug development do not oppose to the use of surrogate endpoints in drug marketing authorization, but most surrogates are not considered valid. To solve this impasse, increased efforts are required to validate surrogate endpoints and to explore alternative ways to use them.

Energy configuration optimization of submerged propeller in oxidation ditch based on CFD

NASA Astrophysics Data System (ADS)

Wu, S. Y.; Zhou, D. Q.; Zheng, Y.

2012-11-01

The submerged propeller is presented as an important dynamic source in oxidation ditch. In order to guarantee the activated sludge not deposit, it is necessary to own adequate drive power. Otherwise, it will cause many problems such as the awful mixed flow and the great consuming of energy. At present, carrying on the installation optimization of submerged propeller in oxidation ditch mostly depends on experience. So it is necessary to use modern design method to optimize the installation position and number of submerged propeller, and to research submerged propeller flow field characteristics. The submerged propeller internal flow is simulated by using CFD software FLUENT6.3. Based on Navier-Stokes equations and standard k - ɛ turbulence model, the flow was simulated by using a SIMPLE algorithm. The results indicate that the submerged propeller installation position change could avoid the condition of back mixing, which caused by the strong drive. Besides, the problem of sludge deposit and the low velocity in the bend which caused by the drive power attenuation could be solved. By adjusting the submerged propeller number, the least power density that the mixing drive needed could be determined and saving energy purpose could be achieved. The study can provide theoretical guidance for optimize the submerged propeller installation position and determine submerged propeller number.

76 FR 1620 - Trials to Verify and Describe Clinical Benefit of Midodrine Hydrochloride; Establishment of...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-01-11

... a surrogate endpoint that is reasonably likely to predict clinical benefit or based on a clinical endpoint other than survival or irreversible morbidity. Approval of PROAMATINE was based on trials... surrogate endpoints are ``subject to the requirement that the applicant study the drug further to verify and...

Improvements to surrogate data methods for nonstationary time series.

PubMed

Lucio, J H; Valdés, R; Rodríguez, L R

2012-05-01

The method of surrogate data has been extensively applied to hypothesis testing of system linearity, when only one realization of the system, a time series, is known. Normally, surrogate data should preserve the linear stochastic structure and the amplitude distribution of the original series. Classical surrogate data methods (such as random permutation, amplitude adjusted Fourier transform, or iterative amplitude adjusted Fourier transform) are successful at preserving one or both of these features in stationary cases. However, they always produce stationary surrogates, hence existing nonstationarity could be interpreted as dynamic nonlinearity. Certain modifications have been proposed that additionally preserve some nonstationarity, at the expense of reproducing a great deal of nonlinearity. However, even those methods generally fail to preserve the trend (i.e., global nonstationarity in the mean) of the original series. This is the case of time series with unit roots in their autoregressive structure. Additionally, those methods, based on Fourier transform, either need first and last values in the original series to match, or they need to select a piece of the original series with matching ends. These conditions are often inapplicable and the resulting surrogates are adversely affected by the well-known artefact problem. In this study, we propose a simple technique that, applied within existing Fourier-transform-based methods, generates surrogate data that jointly preserve the aforementioned characteristics of the original series, including (even strong) trends. Moreover, our technique avoids the negative effects of end mismatch. Several artificial and real, stationary and nonstationary, linear and nonlinear time series are examined, in order to demonstrate the advantages of the methods. Corresponding surrogate data are produced with the classical and with the proposed methods, and the results are compared.

The effect of emotion and physician communication behaviors on surrogates' life-sustaining treatment decisions: a randomized simulation experiment.

PubMed

Barnato, Amber E; Arnold, Robert M

2013-07-01

Surrogate decision makers for critically ill patients experience strong negative emotional states. Emotions influence risk perception, risk preferences, and decision making. We sought to explore the effect of emotional state and physician communication behaviors on surrogates' life-sustaining treatment decisions. 5 × 2 between-subject randomized factorial experiment. Web-based simulated interactive video meeting with an intensivist to discuss code status. Community-based participants 35 and older who self-identified as the surrogate for a parent or spouse recruited from eight U.S. cities through public advertisements. Block random assignment to emotion arousal manipulation and each of the four physician communication behaviors. Surrogate's code status decision (cardiopulmonary resuscitation vs do not resuscitate/allow natural death). Two hundred fifty-six of 373 respondents (69%) logged-in and were randomized: average age was 50; 70% were surrogates for a parent; 63.5% were women; 76% were white, 11% black, and 9% Asian; and 81% were college educated. When asked about code status, 56% chose cardiopulmonary resuscitation. The emotion arousal manipulation increased the score on depression-dejection scale (β = 1.76 [0.58 - 2.94]) but did not influence cardiopulmonary resuscitation choice. Physician attending to emotion and framing the decision as the patient's rather than the surrogate's did not influence cardiopulmonary resuscitation choice. Framing no cardiopulmonary resuscitation as the norm rather than cardiopulmonary resuscitation resulted in fewer surrogates choosing cardiopulmonary resuscitation (48% vs 64%, odds ratio, 0.52 [95% CI, 0.32-0.87]), as did framing the alternative to cardiopulmonary resuscitation as "allow natural death" rather than do not resuscitate (49% vs 61%, odds ratio, 0.58 [95% CI, 0.35-0.96]). Experimentally induced emotional state did not influence code status decisions, although small changes in physician communication behaviors substantially influenced this decision.

A unified framework for the evaluation of surrogate endpoints in mental-health clinical trials.

PubMed

Molenberghs, Geert; Burzykowski, Tomasz; Alonso, Ariel; Assam, Pryseley; Tilahun, Abel; Buyse, Marc

2010-06-01

For a number of reasons, surrogate endpoints are considered instead of the so-called true endpoint in clinical studies, especially when such endpoints can be measured earlier, and/or with less burden for patient and experimenter. Surrogate endpoints may occur more frequently than their standard counterparts. For these reasons, it is not surprising that the use of surrogate endpoints in clinical practice is increasing. Building on the seminal work of Prentice(1) and Freedman et al.,(2) Buyse et al. (3) framed the evaluation exercise within a meta-analytic setting, in an effort to overcome difficulties that necessarily surround evaluation efforts based on a single trial. In this article, we review the meta-analytic approach for continuous outcomes, discuss extensions to non-normal and longitudinal settings, as well as proposals to unify the somewhat disparate collection of validation measures currently on the market. Implications for design and for predicting the effect of treatment in a new trial, based on the surrogate, are discussed. A case study in schizophrenia is analysed.

Biomarkers and Surrogate Endpoints in Drug Development: A European Regulatory View.

PubMed

Wickström, Kerstin; Moseley, Jane

2017-05-01

To give a European regulatory overview of the requirements on and the use of biomarkers or surrogate endpoints in the development of drugs for ocular disease. Definitions, methods to validate new markers, and circumstances where surrogate endpoints can be appropriate are summarized. The key endpoints that have been used in registration studies so far are based on visual acuity, signs, and symptoms, or on surrogate endpoints. In some ocular conditions, established outcome measures such as those based on visual acuity or visual field are not feasible (as with slowly progressing diseases), or lack relevance (e.g., when central visual acuity may be preserved even though the patient is legally blind owing to a severely restricted visual field, or vice versa). There are several ocular conditions for which there is an unmet medical need. In some of these conditions, surrogate endpoints as well as new clinical endpoints are needed to help speed up patient access to new medicines. Interaction with European regulators through the pathway specific for the development of biomarkers or novel methods is encouraged.

Significance of the model considering mixed grain-size for inverse analysis of turbidites

NASA Astrophysics Data System (ADS)

Nakao, K.; Naruse, H.; Tokuhashi, S., Sr.

2016-12-01

A method for inverse analysis of turbidity currents is proposed for application to field observations. Estimation of initial condition of the catastrophic events from field observations has been important for sedimentological researches. For instance, there are various inverse analyses to estimate hydraulic conditions from topography observations of pyroclastic flows (Rossano et al., 1996), real-time monitored debris-flow events (Fraccarollo and Papa, 2000), tsunami deposits (Jaffe and Gelfenbaum, 2007) and ancient turbidites (Falcini et al., 2009). These inverse analyses need forward models and the most turbidity current models employ uniform grain-size particles. The turbidity currents, however, are the best characterized by variation of grain-size distribution. Though there are numerical models of mixed grain-sized particles, the models have difficulty in feasibility of application to natural examples because of calculating costs (Lesshaft et al., 2011). Here we expand the turbidity current model based on the non-steady 1D shallow-water equation at low calculation costs for mixed grain-size particles and applied the model to the inverse analysis. In this study, we compared two forward models considering uniform and mixed grain-size particles respectively. We adopted inverse analysis based on the Simplex method that optimizes the initial conditions (thickness, depth-averaged velocity and depth-averaged volumetric concentration of a turbidity current) with multi-point start and employed the result of the forward model [h: 2.0 m, U: 5.0 m/s, C: 0.01%] as reference data. The result shows that inverse analysis using the mixed grain-size model found the known initial condition of reference data even if the condition where the optimization started is deviated from the true solution, whereas the inverse analysis using the uniform grain-size model requires the condition in which the starting parameters for optimization must be in quite narrow range near the solution. The uniform grain-size model often reaches to local optimum condition that is significantly different from true solution. In conclusion, we propose a method of optimization based on the model considering mixed grain-size particles, and show its application to examples of turbidites in the Kiyosumi Formation, Boso Peninsula, Japan.

Model-assisted probability of detection of flaws in aluminum blocks using polynomial chaos expansions

NASA Astrophysics Data System (ADS)

Du, Xiaosong; Leifsson, Leifur; Grandin, Robert; Meeker, William; Roberts, Ronald; Song, Jiming

2018-04-01

Probability of detection (POD) is widely used for measuring reliability of nondestructive testing (NDT) systems. Typically, POD is determined experimentally, while it can be enhanced by utilizing physics-based computational models in combination with model-assisted POD (MAPOD) methods. With the development of advanced physics-based methods, such as ultrasonic NDT testing, the empirical information, needed for POD methods, can be reduced. However, performing accurate numerical simulations can be prohibitively time-consuming, especially as part of stochastic analysis. In this work, stochastic surrogate models for computational physics-based measurement simulations are developed for cost savings of MAPOD methods while simultaneously ensuring sufficient accuracy. The stochastic surrogate is used to propagate the random input variables through the physics-based simulation model to obtain the joint probability distribution of the output. The POD curves are then generated based on those results. Here, the stochastic surrogates are constructed using non-intrusive polynomial chaos (NIPC) expansions. In particular, the NIPC methods used are the quadrature, ordinary least-squares (OLS), and least-angle regression sparse (LARS) techniques. The proposed approach is demonstrated on the ultrasonic testing simulation of a flat bottom hole flaw in an aluminum block. The results show that the stochastic surrogates have at least two orders of magnitude faster convergence on the statistics than direct Monte Carlo sampling (MCS). Moreover, the evaluation of the stochastic surrogate models is over three orders of magnitude faster than the underlying simulation model for this case, which is the UTSim2 model.

The operating room case-mix problem under uncertainty and nurses capacity constraints.

PubMed

Yahia, Zakaria; Eltawil, Amr B; Harraz, Nermine A

2016-12-01

Surgery is one of the key functions in hospitals; it generates significant revenue and admissions to hospitals. In this paper we address the decision of choosing a case-mix for a surgery department. The objective of this study is to generate an optimal case-mix plan of surgery patients with uncertain surgery operations, which includes uncertainty in surgery durations, length of stay, surgery demand and the availability of nurses. In order to obtain an optimal case-mix plan, a stochastic optimization model is proposed and the sample average approximation method is applied. The proposed model is used to determine the number of surgery cases to be weekly served, the amount of operating rooms' time dedicated to each specialty and the number of ward beds dedicated to each specialty. The optimal case-mix selection criterion is based upon a weighted score taking into account both the waiting list and the historical demand of each patient category. The score aims to maximizing the service level of the operating rooms by increasing the total number of surgery cases that could be served. A computational experiment is presented to demonstrate the performance of the proposed method. The results show that the stochastic model solution outperforms the expected value problem solution. Additional analysis is conducted to study the effect of varying the number of ORs and nurses capacity on the overall ORs' performance.

Validation of surrogate endpoints in cancer clinical trials via principal stratification with an application to a prostate cancer trial.

PubMed

Tanaka, Shiro; Matsuyama, Yutaka; Ohashi, Yasuo

2017-08-30

Increasing attention has been focused on the use and validation of surrogate endpoints in cancer clinical trials. Previous literature on validation of surrogate endpoints are classified into four approaches: the proportion explained approach; the indirect effects approach; the meta-analytic approach; and the principal stratification approach. The mainstream in cancer research has seen the application of a meta-analytic approach. However, VanderWeele (2013) showed that all four of these approaches potentially suffer from the surrogate paradox. It was also shown that, if a principal surrogate satisfies additional criteria called one-sided average causal sufficiency, the surrogate cannot exhibit a surrogate paradox. Here, we propose a method for estimating principal effects under a monotonicity assumption. Specifically, we consider cancer clinical trials which compare a binary surrogate endpoint and a time-to-event clinical endpoint under two naturally ordered treatments (e.g. combined therapy vs. monotherapy). Estimation based on a mean score estimating equation will be implemented by the expectation-maximization algorithm. We will also apply the proposed method as well as other surrogacy criteria to evaluate the surrogacy of prostate-specific antigen using data from a phase III advanced prostate cancer trial, clarifying the complementary roles of both the principal stratification and meta-analytic approaches in the evaluation of surrogate endpoints in cancer. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fogg, P; Aland, T; West, M

Purpose: To investigate the effects of external surrogate and tumour motion by observing the reconstructed phases and AveCT in an Amplitude and Time based 4DCT. Methods: Based on patient motion studies, Cos6 and sinusoidal motions were simulated as external surrogate and tumour motions in a motion phantom. The diaphragm and tumour motions may or may not display the same waveform therefore the same and different waveforms were programmed into the phantom, scanned and reconstructed based on Amplitude and Time. The AveCT and phases were investigated with these different scenarios. The AveCT phantom images were also compared with CBCT phantom imagesmore » programmed with the same motions. Results: For the same surrogate and tumour sin motions, the phases (Amplitude and Time) and AveCT indicated similar motions based on the position of the BB at the slice and displayed contrast values respectively. For cos6 motions, due to the varied time the tumour spends at each position, the Amplitude and Time based phases differed. The AveCT images represented the actual tumour motions and the Time and Amplitude based phases were represented by the surrogate with varied times. Conclusion: Different external surrogate and tumour motions may result in different displayed image motions when observing the AveCT and reconstructed phases. During the 4DCT, the surrogate motion is readily available for observation of the amplitude and time of the diaphragm position. Following image reconstruction, the user may need to observe the AveCT in addition to the reconstructed phases to comprehend the time weightings of the tumour motion during the scan. This may also apply to 3D CBCT images where the displayed tumour position in the images is influenced by the long duration of the CBCT. Knowledge of the tumour motion represented by the greyscale of the AveCT may also assist in CBCT treatment beam verification matching.« less

Mitigating Errors in External Respiratory Surrogate-Based Models of Tumor Position

DOE Office of Scientific and Technical Information (OSTI.GOV)

Malinowski, Kathleen T.; Fischell Department of Bioengineering, University of Maryland, College Park, MD; McAvoy, Thomas J.

2012-04-01

Purpose: To investigate the effect of tumor site, measurement precision, tumor-surrogate correlation, training data selection, model design, and interpatient and interfraction variations on the accuracy of external marker-based models of tumor position. Methods and Materials: Cyberknife Synchrony system log files comprising synchronously acquired positions of external markers and the tumor from 167 treatment fractions were analyzed. The accuracy of Synchrony, ordinary-least-squares regression, and partial-least-squares regression models for predicting the tumor position from the external markers was evaluated. The quantity and timing of the data used to build the predictive model were varied. The effects of tumor-surrogate correlation and the precisionmore » in both the tumor and the external surrogate position measurements were explored by adding noise to the data. Results: The tumor position prediction errors increased during the duration of a fraction. Increasing the training data quantities did not always lead to more accurate models. Adding uncorrelated noise to the external marker-based inputs degraded the tumor-surrogate correlation models by 16% for partial-least-squares and 57% for ordinary-least-squares. External marker and tumor position measurement errors led to tumor position prediction changes 0.3-3.6 times the magnitude of the measurement errors, varying widely with model algorithm. The tumor position prediction errors were significantly associated with the patient index but not with the fraction index or tumor site. Partial-least-squares was as accurate as Synchrony and more accurate than ordinary-least-squares. Conclusions: The accuracy of surrogate-based inferential models of tumor position was affected by all the investigated factors, except for the tumor site and fraction index.« less

A Systematic Review of Cardiovascular Outcomes-Based Cost-Effectiveness Analyses of Lipid-Lowering Therapies.

PubMed

Wei, Ching-Yun; Quek, Ruben G W; Villa, Guillermo; Gandra, Shravanthi R; Forbes, Carol A; Ryder, Steve; Armstrong, Nigel; Deshpande, Sohan; Duffy, Steven; Kleijnen, Jos; Lindgren, Peter

2017-03-01

Previous reviews have evaluated economic analyses of lipid-lowering therapies using lipid levels as surrogate markers for cardiovascular disease. However, drug approval and health technology assessment agencies have stressed that surrogates should only be used in the absence of clinical endpoints. The aim of this systematic review was to identify and summarise the methodologies, weaknesses and strengths of economic models based on atherosclerotic cardiovascular disease event rates. Cost-effectiveness evaluations of lipid-lowering therapies using cardiovascular event rates in adults with hyperlipidaemia were sought in Medline, Embase, Medline In-Process, PubMed and NHS EED and conference proceedings. Search results were independently screened, extracted and quality checked by two reviewers. Searches until February 2016 retrieved 3443 records, from which 26 studies (29 publications) were selected. Twenty-two studies evaluated secondary prevention (four also assessed primary prevention), two considered only primary prevention and two included mixed primary and secondary prevention populations. Most studies (18) based treatment-effect estimates on single trials, although more recent evaluations deployed meta-analyses (5/10 over the last 10 years). Markov models (14 studies) were most commonly used and only one study employed discrete event simulation. Models varied particularly in terms of health states and treatment-effect duration. No studies used a systematic review to obtain utilities. Most studies took a healthcare perspective (21/26) and sourced resource use from key trials instead of local data. Overall, reporting quality was suboptimal. This review reveals methodological changes over time, but reporting weaknesses remain, particularly with respect to transparency of model reporting.

Correlates of protection for inactivated enterovirus 71 vaccine: the analysis of immunological surrogate endpoints.

PubMed

Zhu, Wenbo; Jin, Pengfei; Li, Jing-Xin; Zhu, Feng-Cai; Liu, Pei

2017-09-01

Inactivated Enterovirus 71 (EV71) vaccines showed significant efficacy against the diseases associated with EV71 and a neutralizing antibody (NTAb) titer of 1:16-1:32 was suggested as the correlates of the vaccine protection. This paper aims to further estimate the immunological surrogate endpoints for the protection of inactivated EV71 vaccines and the effect factors. Pre-vaccination NTAb against EV71 at baseline (day 0), post-vaccination NTAb against EV71 at day 56, and the occurrence of laboratory-confirmed EV71-associated diseases during a 24-months follow-up period were collected from a phase 3 efficacy trial of an inactivated EV71 vaccine. We used the mixed-scaled logit model and the absolute sigmoid function by some extensions in continuous models to estimate the immunological surrogate endpoint for the EV71 vaccine protection, respectively. For children with a negative baseline of EV71 NTAb titers, an antibody level of 26.6 U/ml (1:30) was estimated to provide at least a 50% protection for 12 months, and an antibody level of 36.2 U/ml (1:42) may be needed to achieve a 50% protective level of the population for 24 months. Both the pre-vaccination NTAb level and the vaccine protective period could affect the estimation of the immunological surrogate for EV71 vaccine. A post-vaccination NTAb titer of 1:42 or more may be needed for long-term protection. NCT01508247.

SOA formation from photooxidation of naphthalene and methylnaphthalenes with m-xylene and surrogate mixtures

NASA Astrophysics Data System (ADS)

Chen, Chia-Li; Li, Lijie; Tang, Ping; Cocker, David R.

2018-05-01

SOA formation is not well predicted in current models in urban area. The interaction among multiple anthropogenic volatile organic compounds is essential for the SOA formation in the complex urban atmosphere. Secondary organic aerosol (SOA) from the photooxidation of naphthalene, 1-methylnaphthalene, and 2-methylnaphthalene as well as individual polycyclic aromatic hydrocarbons (PAHs) mixed with m-xylene or an atmospheric surrogate mixture was explored in the UCR CE-CERT environmental chamber under urban relevant low NOx and extremely low NOx (H2O2) conditions. Addition of m-xylene suppressed SOA formation from the individual PAH precursor. A similar suppression effect on SOA formation was observed during the surrogate mixture photooxidation suggesting the importance of gas-phase chemical reactivity to SOA formation. The SOA growth rate for different PAH-m-xylene mixtures was strongly correlated with initial [HO2]/[RO2] ratio but negatively correlated with initial m-xylene/NO ratio. Decreasing SOA formation was observed for increasing m-xylene/PAHs ratios and increasing initial m-xylene/NO ratio. The SOA chemical composition characteristics such as f44 versus f43, H/C ratio, O/C ratio, and the oxidation state of the carbon OSbarc were consistent with a continuously aging with the SOA exhibiting characteristics of both individual precursors. SOA formation from PAHs was also suppressed within an atmospheric surrogate mixture compared to the SOA formed from individual PAHs, indicating that atmospheric reactivity directly influences SOA formation from PAHs.

Optimization and uncertainty assessment of strongly nonlinear groundwater models with high parameter dimensionality

NASA Astrophysics Data System (ADS)

Keating, Elizabeth H.; Doherty, John; Vrugt, Jasper A.; Kang, Qinjun

2010-10-01

Highly parameterized and CPU-intensive groundwater models are increasingly being used to understand and predict flow and transport through aquifers. Despite their frequent use, these models pose significant challenges for parameter estimation and predictive uncertainty analysis algorithms, particularly global methods which usually require very large numbers of forward runs. Here we present a general methodology for parameter estimation and uncertainty analysis that can be utilized in these situations. Our proposed method includes extraction of a surrogate model that mimics key characteristics of a full process model, followed by testing and implementation of a pragmatic uncertainty analysis technique, called null-space Monte Carlo (NSMC), that merges the strengths of gradient-based search and parameter dimensionality reduction. As part of the surrogate model analysis, the results of NSMC are compared with a formal Bayesian approach using the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm. Such a comparison has never been accomplished before, especially in the context of high parameter dimensionality. Despite the highly nonlinear nature of the inverse problem, the existence of multiple local minima, and the relatively large parameter dimensionality, both methods performed well and results compare favorably with each other. Experiences gained from the surrogate model analysis are then transferred to calibrate the full highly parameterized and CPU intensive groundwater model and to explore predictive uncertainty of predictions made by that model. The methodology presented here is generally applicable to any highly parameterized and CPU-intensive environmental model, where efficient methods such as NSMC provide the only practical means for conducting predictive uncertainty analysis.

Optimal Control of Evolution Mixed Variational Inclusions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Alduncin, Gonzalo, E-mail: alduncin@geofisica.unam.mx

2013-12-15

Optimal control problems of primal and dual evolution mixed variational inclusions, in reflexive Banach spaces, are studied. The solvability analysis of the mixed state systems is established via duality principles. The optimality analysis is performed in terms of perturbation conjugate duality methods, and proximation penalty-duality algorithms to mixed optimality conditions are further presented. Applications to nonlinear diffusion constrained problems as well as quasistatic elastoviscoplastic bilateral contact problems exemplify the theory.

Hawks, doves, and mixed-symmetry games.

PubMed

Crowley, P H

2000-06-21

The hawk-dove game has proved to be an important tool for understanding the role of aggression in social interactions. Here, the game is presented in a more general form (GHD) to facilitate analyses of interactions between individuals that may differ in "size", where size is interpreted as a surrogate for resource holding power. Three different situations are considered, based on the availability and use of information that interacting individuals have about their sizes: the classical symmetric case, in which no information about sizes is used, the asymmetric case, in which the individuals know their relative sizes and thus their chances of prevailing in combat, and a mixed-symmetry case, in which each individual only knows its own size (or only knows its opponent's size). I describe and use some recently developed methods for multitype games-evolutionary games involving two or more categories of players. With these methods and others, the evolutionarily stable strategies (ESSs) that emerge for the three different cases are identified and compared. A proof of the form and uniqueness of the ESS for the mixed-symmetry case is presented. In this situation, one size category at most can play a mixed strategy; larger individuals are aggressive and smaller individuals are not. As the number of size categories approaches infinity and the size distribution becomes continuous, there is a threshold size, above which all individuals are aggressive, and below which they are not. Copyright 2000 Academic Press.

Optimization Research of Generation Investment Based on Linear Programming Model

NASA Astrophysics Data System (ADS)

Wu, Juan; Ge, Xueqian

Linear programming is an important branch of operational research and it is a mathematical method to assist the people to carry out scientific management. GAMS is an advanced simulation and optimization modeling language and it will combine a large number of complex mathematical programming, such as linear programming LP, nonlinear programming NLP, MIP and other mixed-integer programming with the system simulation. In this paper, based on the linear programming model, the optimized investment decision-making of generation is simulated and analyzed. At last, the optimal installed capacity of power plants and the final total cost are got, which provides the rational decision-making basis for optimized investments.

Assessing the weighted multi-objective adaptive surrogate model optimization to derive large-scale reservoir operating rules with sensitivity analysis

NASA Astrophysics Data System (ADS)

Zhang, Jingwen; Wang, Xu; Liu, Pan; Lei, Xiaohui; Li, Zejun; Gong, Wei; Duan, Qingyun; Wang, Hao

2017-01-01

The optimization of large-scale reservoir system is time-consuming due to its intrinsic characteristics of non-commensurable objectives and high dimensionality. One way to solve the problem is to employ an efficient multi-objective optimization algorithm in the derivation of large-scale reservoir operating rules. In this study, the Weighted Multi-Objective Adaptive Surrogate Model Optimization (WMO-ASMO) algorithm is used. It consists of three steps: (1) simplifying the large-scale reservoir operating rules by the aggregation-decomposition model, (2) identifying the most sensitive parameters through multivariate adaptive regression splines (MARS) for dimensional reduction, and (3) reducing computational cost and speeding the searching process by WMO-ASMO, embedded with weighted non-dominated sorting genetic algorithm II (WNSGAII). The intercomparison of non-dominated sorting genetic algorithm (NSGAII), WNSGAII and WMO-ASMO are conducted in the large-scale reservoir system of Xijiang river basin in China. Results indicate that: (1) WNSGAII surpasses NSGAII in the median of annual power generation, increased by 1.03% (from 523.29 to 528.67 billion kW h), and the median of ecological index, optimized by 3.87% (from 1.879 to 1.809) with 500 simulations, because of the weighted crowding distance and (2) WMO-ASMO outperforms NSGAII and WNSGAII in terms of better solutions (annual power generation (530.032 billion kW h) and ecological index (1.675)) with 1000 simulations and computational time reduced by 25% (from 10 h to 8 h) with 500 simulations. Therefore, the proposed method is proved to be more efficient and could provide better Pareto frontier.

A Bell-Curved Based Algorithm for Mixed Continuous and Discrete Structural Optimization

NASA Technical Reports Server (NTRS)

Kincaid, Rex K.; Weber, Michael; Sobieszczanski-Sobieski, Jaroslaw

2001-01-01

An evolutionary based strategy utilizing two normal distributions to generate children is developed to solve mixed integer nonlinear programming problems. This Bell-Curve Based (BCB) evolutionary algorithm is similar in spirit to (mu + mu) evolutionary strategies and evolutionary programs but with fewer parameters to adjust and no mechanism for self adaptation. First, a new version of BCB to solve purely discrete optimization problems is described and its performance tested against a tabu search code for an actuator placement problem. Next, the performance of a combined version of discrete and continuous BCB is tested on 2-dimensional shape problems and on a minimum weight hub design problem. In the latter case the discrete portion is the choice of the underlying beam shape (I, triangular, circular, rectangular, or U).

A coupling strategy for nonlocal and local diffusion models with mixed volume constraints and boundary conditions

DOE PAGES

D'Elia, Marta; Perego, Mauro; Bochev, Pavel B.; ...

2015-12-21

We develop and analyze an optimization-based method for the coupling of nonlocal and local diffusion problems with mixed volume constraints and boundary conditions. The approach formulates the coupling as a control problem where the states are the solutions of the nonlocal and local equations, the objective is to minimize their mismatch on the overlap of the nonlocal and local domains, and the controls are virtual volume constraints and boundary conditions. When some assumptions on the kernel functions hold, we prove that the resulting optimization problem is well-posed and discuss its implementation using Sandia’s agile software components toolkit. As a result,more » the latter provides the groundwork for the development of engineering analysis tools, while numerical results for nonlocal diffusion in three-dimensions illustrate key properties of the optimization-based coupling method.« less

Composite Sampling of a Bacillus anthracis Surrogate with ...

EPA Pesticide Factsheets

Journal Article A series of experiments were conducted to explore the utility of composite-based collection of surface samples for the detection of a Bacillus anthracis surrogate using cellulose sponge samplers on a stainless steel surface.

Surrogate Modeling of High-Fidelity Fracture Simulations for Real-Time Residual Strength Predictions

NASA Technical Reports Server (NTRS)

Spear, Ashley D.; Priest, Amanda R.; Veilleux, Michael G.; Ingraffea, Anthony R.; Hochhalter, Jacob D.

2011-01-01

A surrogate model methodology is described for predicting in real time the residual strength of flight structures with discrete-source damage. Starting with design of experiment, an artificial neural network is developed that takes as input discrete-source damage parameters and outputs a prediction of the structural residual strength. Target residual strength values used to train the artificial neural network are derived from 3D finite element-based fracture simulations. A residual strength test of a metallic, integrally-stiffened panel is simulated to show that crack growth and residual strength are determined more accurately in discrete-source damage cases by using an elastic-plastic fracture framework rather than a linear-elastic fracture mechanics-based method. Improving accuracy of the residual strength training data would, in turn, improve accuracy of the surrogate model. When combined, the surrogate model methodology and high-fidelity fracture simulation framework provide useful tools for adaptive flight technology.

Surrogate Modeling of High-Fidelity Fracture Simulations for Real-Time Residual Strength Predictions

NASA Technical Reports Server (NTRS)

Spear, Ashley D.; Priest, Amanda R.; Veilleux, Michael G.; Ingraffea, Anthony R.; Hochhalter, Jacob D.

2011-01-01

A surrogate model methodology is described for predicting, during flight, the residual strength of aircraft structures that sustain discrete-source damage. Starting with design of experiment, an artificial neural network is developed that takes as input discrete-source damage parameters and outputs a prediction of the structural residual strength. Target residual strength values used to train the artificial neural network are derived from 3D finite element-based fracture simulations. Two ductile fracture simulations are presented to show that crack growth and residual strength are determined more accurately in discrete-source damage cases by using an elastic-plastic fracture framework rather than a linear-elastic fracture mechanics-based method. Improving accuracy of the residual strength training data does, in turn, improve accuracy of the surrogate model. When combined, the surrogate model methodology and high fidelity fracture simulation framework provide useful tools for adaptive flight technology.

Licensing Surrogate Decision-Makers.

PubMed

Rosoff, Philip M

2017-06-01

As medical technology continues to improve, more people will live longer lives with multiple chronic illnesses with increasing cumulative debilitation, including cognitive dysfunction. Combined with the aging of society in most developed countries, an ever-growing number of patients will require surrogate decision-makers. While advance care planning by patients still capable of expressing their preferences about medical interventions and end-of-life care can improve the quality and accuracy of surrogate decisions, this is often not the case, not infrequently leading to demands for ineffective, inappropriate and prolonged interventions. In 1980 LaFollette called for the licensing of prospective parents, basing his argument on the harm they can do to vulnerable people (children). In this paper, I apply his arguments to surrogate decision-makers for cognitively incapacitated patients, rhetorically suggesting that we require potential surrogates to qualify for this position by demonstrating their ability to make reasonable and rational decisions for others. I employ this theoretical approach to argue that the loose criteria by which we authorize surrogates' generally unchallenged power should be reconsidered.

A system methodology for optimization design of the structural crashworthiness of a vehicle subjected to a high-speed frontal crash

NASA Astrophysics Data System (ADS)

Xia, Liang; Liu, Weiguo; Lv, Xiaojiang; Gu, Xianguang

2018-04-01

The structural crashworthiness design of vehicles has become an important research direction to ensure the safety of the occupants. To effectively improve the structural safety of a vehicle in a frontal crash, a system methodology is presented in this study. The surrogate model of Online support vector regression (Online-SVR) is adopted to approximate crashworthiness criteria and different kernel functions are selected to enhance the accuracy of the model. The Online-SVR model is demonstrated to have the advantages of solving highly nonlinear problems and saving training costs, and can effectively be applied for vehicle structural crashworthiness design. By combining the non-dominated sorting genetic algorithm II and Monte Carlo simulation, both deterministic optimization and reliability-based design optimization (RBDO) are conducted. The optimization solutions are further validated by finite element analysis, which shows the effectiveness of the RBDO solution in the structural crashworthiness design process. The results demonstrate the advantages of using RBDO, resulting in not only increased energy absorption and decreased structural weight from a baseline design, but also a significant improvement in the reliability of the design.

Prospective treatment planning to improve locoregional hyperthermia for oesophageal cancer.

PubMed

Kok, H P; van Haaren, P M A; van de Kamer, J B; Zum Vörde Sive Vörding, P J; Wiersma, J; Hulshof, M C C M; Geijsen, E D; van Lanschot, J J B; Crezee, J

2006-08-01

In the Academic Medical Center (AMC) Amsterdam, locoregional hyperthermia for oesophageal tumours is applied using the 70 MHz AMC-4 phased array system. Due to the occurrence of treatment-limiting hot spots in normal tissue and systemic stress at high power, the thermal dose achieved in the tumour can be sub-optimal. The large number of degrees of freedom of the heating device, i.e. the amplitudes and phases of the antennae, makes it difficult to avoid treatment-limiting hot spots by intuitive amplitude/phase steering. Prospective hyperthermia treatment planning combined with high resolution temperature-based optimization was applied to improve hyperthermia treatment of patients with oesophageal cancer. All hyperthermia treatments were performed with 'standard' clinical settings. Temperatures were measured systemically, at the location of the tumour and near the spinal cord, which is an organ at risk. For 16 patients numerically optimized settings were obtained from treatment planning with temperature-based optimization. Steady state tumour temperatures were maximized, subject to constraints to normal tissue temperatures. At the start of 48 hyperthermia treatments in these 16 patients temperature rise (DeltaT) measurements were performed by applying a short power pulse with the numerically optimized amplitude/phase settings, with the clinical settings and with mixed settings, i.e. numerically optimized amplitudes combined with clinical phases. The heating efficiency of the three settings was determined by the measured DeltaT values and the DeltaT-ratio between the DeltaT in the tumour (DeltaToes) and near the spinal cord (DeltaTcord). For a single patient the steady state temperature distribution was computed retrospectively for all three settings, since the temperature distributions may be quite different. To illustrate that the choice of the optimization strategy is decisive for the obtained settings, a numerical optimization on DeltaT-ratio was performed for this patient and the steady state temperature distribution for the obtained settings was computed. A higher DeltaToes was measured with the mixed settings compared to the calculated and clinical settings; DeltaTcord was higher with the mixed settings compared to the clinical settings. The DeltaT-ratio was approximately 1.5 for all three settings. These results indicate that the most effective tumour heating can be achieved with the mixed settings. DeltaT is proportional to the Specific Absorption Rate (SAR) and a higher SAR results in a higher steady state temperature, which implies that mixed settings are likely to provide the most effective heating at steady state as well. The steady state temperature distributions for the clinical and mixed settings, computed for the single patient, showed some locations where temperatures exceeded the normal tissue constraints used in the optimization. This demonstrates that the numerical optimization did not prescribe the mixed settings, because it had to comply with the constraints set to the normal tissue temperatures. However, the predicted hot spots are not necessarily clinically relevant. Numerical optimization on DeltaT-ratio for this patient yielded a very high DeltaT-ratio ( approximately 380), albeit at the cost of excessive heating of normal tissue and lower steady state tumour temperatures compared to the conventional optimization. Treatment planning can be valuable to improve hyperthermia treatments. A thorough discussion on clinically relevant objectives and constraints is essential.

Propellers And Fans Based On The Moebius Strip

NASA Technical Reports Server (NTRS)

Seiner, John Milton; Gilinsky, Mikhail Markovich

1996-01-01

Moebius strip proposed as basis for optimally shaped airplane and boat propellers, fans, helicopter rotors, mixing screws, coffee grinders, and concrete mixers. Basic idea of optimal shaping of such device to increase working efficiency by increasing area for capture of still medium without increasing power needed for rotation.

Reduced order surrogate modelling (ROSM) of high dimensional deterministic simulations

NASA Astrophysics Data System (ADS)

Mitry, Mina

Often, computationally expensive engineering simulations can prohibit the engineering design process. As a result, designers may turn to a less computationally demanding approximate, or surrogate, model to facilitate their design process. However, owing to the the curse of dimensionality, classical surrogate models become too computationally expensive for high dimensional data. To address this limitation of classical methods, we develop linear and non-linear Reduced Order Surrogate Modelling (ROSM) techniques. Two algorithms are presented, which are based on a combination of linear/kernel principal component analysis and radial basis functions. These algorithms are applied to subsonic and transonic aerodynamic data, as well as a model for a chemical spill in a channel. The results of this thesis show that ROSM can provide a significant computational benefit over classical surrogate modelling, sometimes at the expense of a minor loss in accuracy.

Synthesis and monitored selection of nucleotide surrogates for binding T:A base pairs in homopurine-homopyrimidine DNA triple helices.

PubMed

Mokhir, A A; Connors, W H; Richert, C

2001-09-01

A total of 16 oligodeoxyribonucleotides of general sequence 5'-TCTTCTZTCTTTCT-3', where Z denotes an N-acyl-N-(2-hydroxyethyl)glycine residue, were prepared via solid phase synthesis. The ability of these oligonucleotides to form triplexes with the duplex 5'-AGAAGATAGAAAGA-HEG-TCTTTCTATCTTCT-3', where HEG is a hexaethylene glycol linker, was tested. In these triplexes, an 'interrupting' T:A base pair faces the Z residue in the third strand. Among the acyl moieties of Z tested, an anthraquinone carboxylic acid residue linked via a glycinyl group gave the most stable triplex, whose UV melting point was 8.4 degrees C higher than that of the triplex with 5'-TCTTCTGTCTTTCT-3' as the third strand. The results from exploratory nuclease selection experiments suggest that a combinatorial search for strands capable of recognizing mixed sequences by triple helix formation is feasible.

Classification-Assisted Memetic Algorithms for Equality-Constrained Optimization Problems

NASA Astrophysics Data System (ADS)

Handoko, Stephanus Daniel; Kwoh, Chee Keong; Ong, Yew Soon

Regressions has successfully been incorporated into memetic algorithm (MA) to build surrogate models for the objective or constraint landscape of optimization problems. This helps to alleviate the needs for expensive fitness function evaluations by performing local refinements on the approximated landscape. Classifications can alternatively be used to assist MA on the choice of individuals that would experience refinements. Support-vector-assisted MA were recently proposed to alleviate needs for function evaluations in the inequality-constrained optimization problems by distinguishing regions of feasible solutions from those of the infeasible ones based on some past solutions such that search efforts can be focussed on some potential regions only. For problems having equality constraints, however, the feasible space would obviously be extremely small. It is thus extremely difficult for the global search component of the MA to produce feasible solutions. Hence, the classification of feasible and infeasible space would become ineffective. In this paper, a novel strategy to overcome such limitation is proposed, particularly for problems having one and only one equality constraint. The raw constraint value of an individual, instead of its feasibility class, is utilized in this work.

Model inversion via multi-fidelity Bayesian optimization: a new paradigm for parameter estimation in haemodynamics, and beyond.

PubMed

Perdikaris, Paris; Karniadakis, George Em

2016-05-01

We present a computational framework for model inversion based on multi-fidelity information fusion and Bayesian optimization. The proposed methodology targets the accurate construction of response surfaces in parameter space, and the efficient pursuit to identify global optima while keeping the number of expensive function evaluations at a minimum. We train families of correlated surrogates on available data using Gaussian processes and auto-regressive stochastic schemes, and exploit the resulting predictive posterior distributions within a Bayesian optimization setting. This enables a smart adaptive sampling procedure that uses the predictive posterior variance to balance the exploration versus exploitation trade-off, and is a key enabler for practical computations under limited budgets. The effectiveness of the proposed framework is tested on three parameter estimation problems. The first two involve the calibration of outflow boundary conditions of blood flow simulations in arterial bifurcations using multi-fidelity realizations of one- and three-dimensional models, whereas the last one aims to identify the forcing term that generated a particular solution to an elliptic partial differential equation. © 2016 The Author(s).

Model inversion via multi-fidelity Bayesian optimization: a new paradigm for parameter estimation in haemodynamics, and beyond

PubMed Central

Perdikaris, Paris; Karniadakis, George Em

2016-01-01

We present a computational framework for model inversion based on multi-fidelity information fusion and Bayesian optimization. The proposed methodology targets the accurate construction of response surfaces in parameter space, and the efficient pursuit to identify global optima while keeping the number of expensive function evaluations at a minimum. We train families of correlated surrogates on available data using Gaussian processes and auto-regressive stochastic schemes, and exploit the resulting predictive posterior distributions within a Bayesian optimization setting. This enables a smart adaptive sampling procedure that uses the predictive posterior variance to balance the exploration versus exploitation trade-off, and is a key enabler for practical computations under limited budgets. The effectiveness of the proposed framework is tested on three parameter estimation problems. The first two involve the calibration of outflow boundary conditions of blood flow simulations in arterial bifurcations using multi-fidelity realizations of one- and three-dimensional models, whereas the last one aims to identify the forcing term that generated a particular solution to an elliptic partial differential equation. PMID:27194481

Lagrangian Approach to Jet Mixing and Optimization of the Reactor for Production of Carbon Nanotubes

NASA Technical Reports Server (NTRS)

Povitsky, Alex; Salas, Manuel D.

2001-01-01

This study was motivated by an attempt to optimize the High Pressure carbon oxide (HiPco) process for the production of carbon nanotubes from gaseous carbon oxide, The goal is to achieve rapid and uniform heating of catalyst particles by an optimal arrangement of jets. A mixed Eulerian and Lagrangian approach is implemented to track the temperature of catalyst particles along their trajectories as a function of time. The FLUENT CFD software with second-order upwind approximation of convective terms and an algebraic multigrid-based solver is used. The poor performance of the original reactor configuration is explained in terms of features of particle trajectories. The trajectories most exposed to the hot jets appear to be the most problematic for heating because they either bend towards the cold jet interior or rotate upwind of the mixing zone. To reduce undesirable slow and/or oscillatory heating of catalyst particles, a reactor configuration with three central jets is proposed and the optimal location of the central and peripheral nozzles is determined.

Accurate quantification of PGE2 in the polyposis in rat colon (Pirc) model by surrogate analyte-based UPLC-MS/MS.

PubMed

Yun, Changhong; Dashwood, Wan-Mohaiza; Kwong, Lawrence N; Gao, Song; Yin, Taijun; Ling, Qinglan; Singh, Rashim; Dashwood, Roderick H; Hu, Ming

2018-01-30

An accurate and reliable UPLC-MS/MS method is reported for the quantification of endogenous Prostaglandin E2 (PGE 2 ) in rat colonic mucosa and polyps. This method adopted the "surrogate analyte plus authentic bio-matrix" approach, using two different stable isotopic labeled analogs - PGE 2 -d9 as the surrogate analyte and PGE 2 -d4 as the internal standard. A quantitative standard curve was constructed with the surrogate analyte in colonic mucosa homogenate, and the method was successfully validated with the authentic bio-matrix. Concentrations of endogenous PGE 2 in both normal and inflammatory tissue homogenates were back-calculated based on the regression equation. Because of no endogenous interference on the surrogate analyte determination, the specificity was particularly good. By using authentic bio-matrix for validation, the matrix effect and exaction recovery are identically same for the quantitative standard curve and actual samples - this notably increased the assay accuracy. The method is easy, fast, robust and reliable for colon PGE 2 determination. This "surrogate analyte" approach was applied to measure the Pirc (an Apc-mutant rat kindred that models human FAP) mucosa and polyps PGE 2 , one of the strong biomarkers of colorectal cancer. A similar concept could be applied to endogenous biomarkers in other tissues. Copyright © 2017 Elsevier B.V. All rights reserved.

Magnetic resonance imaging as a surrogate outcome for multiple sclerosis relapses

PubMed Central

Petkau, J; Reingold, SC; Held, U; Cutter, GR; Fleming, TR; Hughes, MD; Miller, DH; McFarland, HF; Wolinsky, JS

2009-01-01

Background Magnetic resonance imaging (MRI) of lesions in the brain may be the best current candidate for a surrogate biological marker of clinical outcomes in relapsing remitting multiple sclerosis (MS), based on its role as an objective indicator of disease pathology. No biological surrogate marker has yet been validated for MS clinical outcomes. Objective The objective of this study was to use a multi-phased study to determine if a valid surrogate relationship could be demonstrated between counts of contrast enhancing lesions (CELs) and occurrence of relapses in MS. Methods We examined correlations for the concurrent and predictive relationship between CELs over 6 months and MS relapses over the same 6 months and an additional 6 months (total: 12 months), using available data on untreated patients from a large clinical trial and natural history database. Results Concurrent and predictive correlations were inadequate to justify continuation of this study to the planned additional phases required to demonstrate a surrogate relationship between CELs and MS relapses. Conclusions Confidence intervals for correlations between CELs and MS relapses exclude the possibility that CELs can be a good surrogate for relapses over the time scales we investigated. Further exploration of surrogacy between MRI measures and MS clinical outcomes may require improved datasets, the development of MRI techniques that couple better to clinical disease, and the ability to test a wide range of imaging- and clinically-based hypotheses for surrogacy. PMID:18535021

Statistical characteristics of surrogate data based on geophysical measurements

NASA Astrophysics Data System (ADS)

Venema, V.; Bachner, S.; Rust, H. W.; Simmer, C.

2006-09-01

In this study, the statistical properties of a range of measurements are compared with those of their surrogate time series. Seven different records are studied, amongst others, historical time series of mean daily temperature, daily rain sums and runoff from two rivers, and cloud measurements. Seven different algorithms are used to generate the surrogate time series. The best-known method is the iterative amplitude adjusted Fourier transform (IAAFT) algorithm, which is able to reproduce the measured distribution as well as the power spectrum. Using this setup, the measurements and their surrogates are compared with respect to their power spectrum, increment distribution, structure functions, annual percentiles and return values. It is found that the surrogates that reproduce the power spectrum and the distribution of the measurements are able to closely match the increment distributions and the structure functions of the measurements, but this often does not hold for surrogates that only mimic the power spectrum of the measurement. However, even the best performing surrogates do not have asymmetric increment distributions, i.e., they cannot reproduce nonlinear dynamical processes that are asymmetric in time. Furthermore, we have found deviations of the structure functions on small scales.

A traits-based approach for prioritizing species for monitoring and surrogacy selection

DOE PAGES

Pracheil, Brenda M.; McManamay, Ryan A.; Bevelhimer, Mark S.; ...

2016-11-28

The bar for justifying the use of vertebrate animals for study is being increasingly raised, thus requiring increased rigor for species selection and study design. Although we have power analyses to provide quantitative backing for the numbers of organisms used, quantitative backing for selection of study species is not frequently employed. This can be especially important when measuring the impacts of ecosystem alteration, when study species must be chosen that are both sensitive to the alteration and of sufficient abundance for study. Just as important is providing justification for designation of surrogate species for study, especially when the species ofmore » interest is rare or of conservation concern and selection of an appropriate surrogate can have legal implications. In this study, we use a combination of GIS, a fish traits database and multivariate statistical analyses to quantitatively prioritize species for study and to determine potential study surrogate species. We provide two case studies to illustrate our quantitative, traits-based approach for designating study species and surrogate species. In the first case study, we select broadly representative fish species to understand the effects of turbine passage on adult fishes based on traits that suggest sensitivity to turbine passage. In our second case study, we present a framework for selecting a surrogate species for an endangered species. Lastly, we suggest that our traits-based framework can provide quantitative backing and added justification to selection of study species while expanding the inference space of study results.« less

Convergence analysis of surrogate-based methods for Bayesian inverse problems

NASA Astrophysics Data System (ADS)

Yan, Liang; Zhang, Yuan-Xiang

2017-12-01

The major challenges in the Bayesian inverse problems arise from the need for repeated evaluations of the forward model, as required by Markov chain Monte Carlo (MCMC) methods for posterior sampling. Many attempts at accelerating Bayesian inference have relied on surrogates for the forward model, typically constructed through repeated forward simulations that are performed in an offline phase. Although such approaches can be quite effective at reducing computation cost, there has been little analysis of the approximation on posterior inference. In this work, we prove error bounds on the Kullback-Leibler (KL) distance between the true posterior distribution and the approximation based on surrogate models. Our rigorous error analysis show that if the forward model approximation converges at certain rate in the prior-weighted L 2 norm, then the posterior distribution generated by the approximation converges to the true posterior at least two times faster in the KL sense. The error bound on the Hellinger distance is also provided. To provide concrete examples focusing on the use of the surrogate model based methods, we present an efficient technique for constructing stochastic surrogate models to accelerate the Bayesian inference approach. The Christoffel least squares algorithms, based on generalized polynomial chaos, are used to construct a polynomial approximation of the forward solution over the support of the prior distribution. The numerical strategy and the predicted convergence rates are then demonstrated on the nonlinear inverse problems, involving the inference of parameters appearing in partial differential equations.

A traits-based approach for prioritizing species for monitoring and surrogacy selection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pracheil, Brenda M.; McManamay, Ryan A.; Bevelhimer, Mark S.

The bar for justifying the use of vertebrate animals for study is being increasingly raised, thus requiring increased rigor for species selection and study design. Although we have power analyses to provide quantitative backing for the numbers of organisms used, quantitative backing for selection of study species is not frequently employed. This can be especially important when measuring the impacts of ecosystem alteration, when study species must be chosen that are both sensitive to the alteration and of sufficient abundance for study. Just as important is providing justification for designation of surrogate species for study, especially when the species ofmore » interest is rare or of conservation concern and selection of an appropriate surrogate can have legal implications. In this study, we use a combination of GIS, a fish traits database and multivariate statistical analyses to quantitatively prioritize species for study and to determine potential study surrogate species. We provide two case studies to illustrate our quantitative, traits-based approach for designating study species and surrogate species. In the first case study, we select broadly representative fish species to understand the effects of turbine passage on adult fishes based on traits that suggest sensitivity to turbine passage. In our second case study, we present a framework for selecting a surrogate species for an endangered species. Lastly, we suggest that our traits-based framework can provide quantitative backing and added justification to selection of study species while expanding the inference space of study results.« less

Design optimization and uncertainty quantification for aeromechanics forced response of a turbomachinery blade

NASA Astrophysics Data System (ADS)

Modgil, Girish A.

Gas turbine engines for aerospace applications have evolved dramatically over the last 50 years through the constant pursuit for better specific fuel consumption, higher thrust-to-weight ratio, lower noise and emissions all while maintaining reliability and affordability. An important step in enabling these improvements is a forced response aeromechanics analysis involving structural dynamics and aerodynamics of the turbine. It is well documented that forced response vibration is a very critical problem in aircraft engine design, causing High Cycle Fatigue (HCF). Pushing the envelope on engine design has led to increased forced response problems and subsequently an increased risk of HCF failure. Forced response analysis is used to assess design feasibility of turbine blades for HCF using a material limit boundary set by the Goodman Diagram envelope that combines the effects of steady and vibratory stresses. Forced response analysis is computationally expensive, time consuming and requires multi-domain experts to finalize a result. As a consequence, high-fidelity aeromechanics analysis is performed deterministically and is usually done at the end of the blade design process when it is very costly to make significant changes to geometry or aerodynamic design. To address uncertainties in the system (engine operating point, temperature distribution, mistuning, etc.) and variability in material properties, designers apply conservative safety factors in the traditional deterministic approach, which leads to bulky designs. Moreover, using a deterministic approach does not provide a calculated risk of HCF failure. This thesis describes a process that begins with the optimal aerodynamic design of a turbomachinery blade developed using surrogate models of high-fidelity analyses. The resulting optimal blade undergoes probabilistic evaluation to generate aeromechanics results that provide a calculated likelihood of failure from HCF. An existing Rolls-Royce High Work Single Stage (HWSS) turbine blisk provides a baseline to demonstrate the process. The generalized polynomial chaos (gPC) toolbox which was developed includes sampling methods and constructs polynomial approximations. The toolbox provides not only the means for uncertainty quantification of the final blade design, but also facilitates construction of the surrogate models used for the blade optimization. This paper shows that gPC , with a small number of samples, achieves very fast rates of convergence and high accuracy in describing probability distributions without loss of detail in the tails . First, an optimization problem maximizes stage efficiency using turbine aerodynamic design rules as constraints; the function evaluations for this optimization are surrogate models from detailed 3D steady Computational Fluid Dynamics (CFD) analyses. The resulting optimal shape provides a starting point for the 3D high-fidelity aeromechanics (unsteady CFD and 3D Finite Element Analysis (FEA)) UQ study assuming three uncertain input parameters. This investigation seeks to find the steady and vibratory stresses associated with the first torsion mode for the HWSS turbine blisk near maximum operating speed of the engine. Using gPC to provide uncertainty estimates of the steady and vibratory stresses enables the creation of a Probabilistic Goodman Diagram, which - to the authors' best knowledge - is the first of its kind using high fidelity aeromechanics for turbomachinery blades. The Probabilistic Goodman Diagram enables turbine blade designers to make more informed design decisions and it allows the aeromechanics expert to assess quantitatively the risk associated with HCF for any mode crossing based on high fidelity simulations.

3-D phononic crystals with ultra-wide band gaps

PubMed Central

Lu, Yan; Yang, Yang; Guest, James K.; Srivastava, Ankit

2017-01-01

In this paper gradient based topology optimization (TO) is used to discover 3-D phononic structures that exhibit ultra-wide normalized all-angle all-mode band gaps. The challenging computational task of repeated 3-D phononic band-structure evaluations is accomplished by a combination of a fast mixed variational eigenvalue solver and distributed Graphic Processing Unit (GPU) parallel computations. The TO algorithm utilizes the material distribution-based approach and a gradient-based optimizer. The design sensitivity for the mixed variational eigenvalue problem is derived using the adjoint method and is implemented through highly efficient vectorization techniques. We present optimized results for two-material simple cubic (SC), body centered cubic (BCC), and face centered cubic (FCC) crystal structures and show that in each of these cases different initial designs converge to single inclusion network topologies within their corresponding primitive cells. The optimized results show that large phononic stop bands for bulk wave propagation can be achieved at lower than close packed spherical configurations leading to lighter unit cells. For tungsten carbide - epoxy crystals we identify all angle all mode normalized stop bands exceeding 100%, which is larger than what is possible with only spherical inclusions. PMID:28233812

3-D phononic crystals with ultra-wide band gaps.

PubMed

Lu, Yan; Yang, Yang; Guest, James K; Srivastava, Ankit

2017-02-24

In this paper gradient based topology optimization (TO) is used to discover 3-D phononic structures that exhibit ultra-wide normalized all-angle all-mode band gaps. The challenging computational task of repeated 3-D phononic band-structure evaluations is accomplished by a combination of a fast mixed variational eigenvalue solver and distributed Graphic Processing Unit (GPU) parallel computations. The TO algorithm utilizes the material distribution-based approach and a gradient-based optimizer. The design sensitivity for the mixed variational eigenvalue problem is derived using the adjoint method and is implemented through highly efficient vectorization techniques. We present optimized results for two-material simple cubic (SC), body centered cubic (BCC), and face centered cubic (FCC) crystal structures and show that in each of these cases different initial designs converge to single inclusion network topologies within their corresponding primitive cells. The optimized results show that large phononic stop bands for bulk wave propagation can be achieved at lower than close packed spherical configurations leading to lighter unit cells. For tungsten carbide - epoxy crystals we identify all angle all mode normalized stop bands exceeding 100%, which is larger than what is possible with only spherical inclusions.

Dual door entry to exciplex emission in a chimeric DNA duplex containing non-nucleoside-nucleoside pair.

PubMed

Bag, Subhendu Sekhar; Talukdar, Sangita; Kundu, Rajen; Saito, Isao; Jana, Subhashis

2014-01-25

Dual door entry to exciplex formation was established in a chimeric DNA duplex wherein a fluorescent non-nucleosidic base surrogate () is paired against a fluorescent nucleosidic base surrogate (). Packing of the nucleobases via intercalative stacking interactions led to an exciplex emission either via FRET from the donor or direct excitation of the FRET acceptor .

From Surrogacy to Contested Adoption: What Went Wrong?

ERIC Educational Resources Information Center

Greenfield, Joanna; Jennings, Seamus

1995-01-01

Notes the complex and emotive nature of surrogate motherhood. Describes a surrogacy arrangement that was apparently based initially on a clear agreement and partnership, but developed into a disputed adoption application when the surrogate mother became dissatisfied with contact arrangements. (HTH)

Mixing Enhancement in a Lobed Injector

NASA Technical Reports Server (NTRS)

Smith, L. L.; Majamaki, A. J.; Lam, I. T.; Delabroy, O.; Karagozian, A. R.; Marble, F. E.; Smith, O. I.

1997-01-01

An experimental investigation of the non-reactive mixing processes associated with a lobed fuel injector in a coflowing air stream is presented. The lobed fuel injector is a device which generates streamwise vorticity, producing high strain rates which can enhance the mixing of reactants while delaying ignition in a controlled manner. The lobed injectors examined in the present study consist of two corrugated plates between which a fuel surrogate, CO2, is injected into coflowing air. Acetone is seeded in the CO2 supply as a fuel marker. Comparison of two alternative lobed injector geometries is made with a straight fuel injector to determine net differences in mixing and strain fields due to streamwise vorticity generation. Planar laser-induced fluorescence (PLIF) of the seeded acetone yields two-dimensional images of the scalar concentration field at various downstream locations, from which local mixing and scalar dissipation rates are computed. It is found that the lobed injector geometry can enhance molecular mixing and create a highly strained flowfield, and that the strain rates generated by scalar energy dissipation can potentially delay ignition in a reacting flowfield.

Testing Pairwise Association between Spatially Autocorrelated Variables: A New Approach Using Surrogate Lattice Data

PubMed Central

Deblauwe, Vincent; Kennel, Pol; Couteron, Pierre

2012-01-01

Background Independence between observations is a standard prerequisite of traditional statistical tests of association. This condition is, however, violated when autocorrelation is present within the data. In the case of variables that are regularly sampled in space (i.e. lattice data or images), such as those provided by remote-sensing or geographical databases, this problem is particularly acute. Because analytic derivation of the null probability distribution of the test statistic (e.g. Pearson's r) is not always possible when autocorrelation is present, we propose instead the use of a Monte Carlo simulation with surrogate data. Methodology/Principal Findings The null hypothesis that two observed mapped variables are the result of independent pattern generating processes is tested here by generating sets of random image data while preserving the autocorrelation function of the original images. Surrogates are generated by matching the dual-tree complex wavelet spectra (and hence the autocorrelation functions) of white noise images with the spectra of the original images. The generated images can then be used to build the probability distribution function of any statistic of association under the null hypothesis. We demonstrate the validity of a statistical test of association based on these surrogates with both actual and synthetic data and compare it with a corrected parametric test and three existing methods that generate surrogates (randomization, random rotations and shifts, and iterative amplitude adjusted Fourier transform). Type I error control was excellent, even with strong and long-range autocorrelation, which is not the case for alternative methods. Conclusions/Significance The wavelet-based surrogates are particularly appropriate in cases where autocorrelation appears at all scales or is direction-dependent (anisotropy). We explore the potential of the method for association tests involving a lattice of binary data and discuss its potential for validation of species distribution models. An implementation of the method in Java for the generation of wavelet-based surrogates is available online as supporting material. PMID:23144961

[Selection of "surrogate" and "endpoints" evaluation of the efficacy of medical interventions].

PubMed

Lazebnik, L B; Gusein-Zade, M G; Efremov, L I

2011-01-01

With the advent of new medical technologies and medicines, as well as due to changes in disease patterns and demographic problems rises the need for continued increases in health spending. Increased costs can be totally inadequate, if it has been done without studying the effectiveness of medical interventions, based on the results of evidence-based medicine and economic of their feasibility. To evaluate the clinical effectiveness of medical interventions have been recently used specific criteria, so called points of clinical efficacy (surrogate and endpoints), that allow to conclude feasibility or harmfulness of the introduction or application of the intervention in clinical practice. The endpoint is reliable indicator the effectiveness of medical intervention. Surrogate point--is a biomarker that is intended to replace the endpoint and is a predictor of the effectiveness of medical intervention. The use of surrogate points has several advantages such as simple in identification and measurement, as well as more higher in compare with endpoints the vents frequency, that can significantly reduce the size of the selection and duration and cost of clinical trials, respectively. Finally, the surrogate points allow to evaluate treatment effect in situations where the use of endpoints is difficult or is unethical.

Design Mining Interacting Wind Turbines.

PubMed

Preen, Richard J; Bull, Larry

2016-01-01

An initial study has recently been presented of surrogate-assisted evolutionary algorithms used to design vertical-axis wind turbines wherein candidate prototypes are evaluated under fan-generated wind conditions after being physically instantiated by a 3D printer. Unlike other approaches, such as computational fluid dynamics simulations, no mathematical formulations were used and no model assumptions were made. This paper extends that work by exploring alternative surrogate modelling and evolutionary techniques. The accuracy of various modelling algorithms used to estimate the fitness of evaluated individuals from the initial experiments is compared. The effect of temporally windowing surrogate model training samples is explored. A surrogate-assisted approach based on an enhanced local search is introduced; and alternative coevolution collaboration schemes are examined.

Key stakeholders' perceptions of the acceptability and usefulness of a tablet-based tool to improve communication and shared decision making in ICUs.

PubMed

Ernecoff, Natalie C; Witteman, Holly O; Chon, Kristen; Chen, Yanquan Iris; Buddadhumaruk, Praewpannarai; Chiarchiaro, Jared; Shotsberger, Kaitlin J; Shields, Anne-Marie; Myers, Brad A; Hough, Catherine L; Carson, Shannon S; Lo, Bernard; Matthay, Michael A; Anderson, Wendy G; Peterson, Michael W; Steingrub, Jay S; Arnold, Robert M; White, Douglas B

2016-06-01

Although barriers to shared decision making in intensive care units are well documented, there are currently no easily scaled interventions to overcome these problems. We sought to assess stakeholders' perceptions of the acceptability, usefulness, and design suggestions for a tablet-based tool to support communication and shared decision making in ICUs. We conducted in-depth semi-structured interviews with 58 key stakeholders (30 surrogates and 28 ICU care providers). Interviews explored stakeholders' perceptions about the acceptability of a tablet-based tool to support communication and shared decision making, including the usefulness of modules focused on orienting families to the ICU, educating them about the surrogate's role, completing a question prompt list, eliciting patient values, educating about treatment options, eliciting perceptions about prognosis, and providing psychosocial support resources. The interviewer also elicited stakeholders' design suggestions for such a tool. We used constant comparative methods to identify key themes that arose during the interviews. Overall, 95% (55/58) of participants perceived the proposed tool to be acceptable, with 98% (57/58) of interviewees finding six or more of the seven content domains acceptable. Stakeholders identified several potential benefits of the tool including that it would help families prepare for the surrogate role and for family meetings as well as give surrogates time and a framework to think about the patient's values and treatment options. Key design suggestions included: conceptualize the tool as a supplement to rather than a substitute for surrogate-clinician communication; make the tool flexible with respect to how, where, and when surrogates can access the tool; incorporate interactive exercises; use video and narration to minimize the cognitive load of the intervention; and build an extremely simple user interface to maximize usefulness for individuals with low computer literacy. There is broad support among stakeholders for the use of a tablet-based tool to improve communication and shared decision making in ICUs. Eliciting the perspectives of key stakeholders early in the design process yielded important insights to create a tool tailored to the needs of surrogates and care providers in ICUs. Copyright © 2016 Elsevier Inc. All rights reserved.

Searching the optimal PTH target range in children undergoing peritoneal dialysis: new insights from international cohort studies.

PubMed

Haffner, Dieter; Schaefer, Franz

2013-04-01

The treatment of the mineral and bone disorder associated with chronic kidney disease (CKD-MBD) remains a major challenge in pediatric patients. The principal aims of therapeutic measures are not only to prevent the debilitating skeletal complications and to achieve normal growth but also to preserve long-term cardiovascular health. Serum parathyroid hormone (PTH) levels are used as a surrogate parameter of bone turnover. Whereas it is generally accepted that serum calcium and phosphate levels should be kept within the range for age, current pediatric consensus guidelines differ markedly with respect to the optimal PTH target range and operate on a limited evidence base. Recently, the International Pediatric Dialysis Network (IPPN) established a global registry collecting detailed clinical and biochemical information, including data relevant to CKD-MBD in children on chronic peritoneal dialysis (PD). This review highlights the current evidence basis regarding the optimal PTH target range in pediatric CKD patients, and re-assesses the current guidelines in view of the outcome data collected by the IPPN registry. Based on a comprehensive evaluation of CKD-MBD outcome measures in this global patient cohort, a PTH target range of 1.7-3 times the upper limit of normal (i.e. 100-200 pg/ml) appears reasonable in children undergoing chronic PD.

Breast Radiotherapy with Mixed Energy Photons; a Model for Optimal Beam Weighting.

PubMed

Birgani, Mohammadjavad Tahmasebi; Fatahiasl, Jafar; Hosseini, Seyed Mohammad; Bagheri, Ali; Behrooz, Mohammad Ali; Zabiehzadeh, Mansour; Meskani, Reza; Gomari, Maryam Talaei

2015-01-01

Utilization of high energy photons (>10 MV) with an optimal weight using a mixed energy technique is a practical way to generate a homogenous dose distribution while maintaining adequate target coverage in intact breast radiotherapy. This study represents a model for estimation of this optimal weight for day to day clinical usage. For this purpose, treatment planning computed tomography scans of thirty-three consecutive early stage breast cancer patients following breast conservation surgery were analyzed. After delineation of the breast clinical target volume (CTV) and placing opposed wedge paired isocenteric tangential portals, dosimeteric calculations were conducted and dose volume histograms (DVHs) were generated, first with pure 6 MV photons and then these calculations were repeated ten times with incorporating 18 MV photons (ten percent increase in weight per step) in each individual patient. For each calculation two indexes including maximum dose in the breast CTV (Dmax) and the volume of CTV which covered with 95% Isodose line (VCTV, 95%IDL) were measured according to the DVH data and then normalized values were plotted in a graph. The optimal weight of 18 MV photons was defined as the intersection point of Dmax and VCTV, 95%IDL graphs. For creating a model to predict this optimal weight multiple linear regression analysis was used based on some of the breast and tangential field parameters. The best fitting model for prediction of 18 MV photons optimal weight in breast radiotherapy using mixed energy technique, incorporated chest wall separation plus central lung distance (Adjusted R2=0.776). In conclusion, this study represents a model for the estimation of optimal beam weighting in breast radiotherapy using mixed photon energy technique for routine day to day clinical usage.

PeptidePicker: a scientific workflow with web interface for selecting appropriate peptides for targeted proteomics experiments.

PubMed

Mohammed, Yassene; Domański, Dominik; Jackson, Angela M; Smith, Derek S; Deelder, André M; Palmblad, Magnus; Borchers, Christoph H

2014-06-25

One challenge in Multiple Reaction Monitoring (MRM)-based proteomics is to select the most appropriate surrogate peptides to represent a target protein. We present here a software package to automatically generate these most appropriate surrogate peptides for an LC/MRM-MS analysis. Our method integrates information about the proteins, their tryptic peptides, and the suitability of these peptides for MRM which is available online in UniProtKB, NCBI's dbSNP, ExPASy, PeptideAtlas, PRIDE, and GPMDB. The scoring algorithm reflects our knowledge in choosing the best candidate peptides for MRM, based on the uniqueness of the peptide in the targeted proteome, its physiochemical properties, and whether it previously has been observed. The modularity of the workflow allows further extension and additional selection criteria to be incorporated. We have developed a simple Web interface where the researcher provides the protein accession number, the subject organism, and peptide-specific options. Currently, the software is designed for human and mouse proteomes, but additional species can be easily be added. Our software improved the peptide selection by eliminating human error, considering multiple data sources and all of the isoforms of the protein, and resulted in faster peptide selection - approximately 50 proteins per hour compared to 8 per day. Compiling a list of optimal surrogate peptides for target proteins to be analyzed by LC/MRM-MS has been a cumbersome process, in which expert researchers retrieved information from different online repositories and used their own reasoning to find the most appropriate peptides. Our scientific workflow automates this process by integrating information from different data sources including UniProt, Global Proteome Machine, NCBI's dbSNP, and PeptideAtlas, simulating the researchers' reasoning, and incorporating their knowledge of how to select the best proteotypic peptides for an MRM analysis. The developed software can help to standardize the selection of peptides, eliminate human error, and increase productivity. Copyright © 2014 Elsevier B.V. All rights reserved.

A toxin-free enzyme-linked immunosorbent assay for the analysis of aflatoxins based on a VHH surrogate standard.

PubMed

Wang, Yanru; Li, Peiwu; Zhang, Qi; Hu, Xiaofeng; Zhang, Wen

2016-09-01

A toxin-free enzyme-linked immunosorbent assay (ELISA) for aflatoxins was developed using an anti-idiotype nanobody VHH 2-5 as surrogate standard. Anti-idiotype nanobody VHH 2-5 was generated by immunizing an alpaca with anti-aflatoxin monoclonal antibody 1C11. This assay was used to detect aflatoxins in agro-products after a simple extraction with 75 % methanol/H2O. Aflatoxin concentration was calculated by a two-step approach: the concentration of VHH 2-5 was first obtained by a four-parameter logistic regression from the detected absorbance value at 450 nm, and then converted to aflatoxin concentration by a linear equation. The assay exhibits a limit of detection (LOD) of 0.015 ng mL(-1), which is better than or comparable with conventional immunoassays. The performance of our VHH surrogate-based ELISA was further validated with a high-performance liquid chromatography (HPLC) method for total aflatoxins determination in 20 naturally contaminated peanut samples, displaying a good correlation (R (2) = 0.988). In conclusion, the proposed assay represents a first example applying an anti-idiotype VHH antibody as a standard surrogate in ELISA. With the advantages of high stability and ease of production, the VHH antibody-based standard surrogate can be extended in the future to immunoassays for other highly toxic compounds. Graphical Abstract ᅟ.

Effects of cost metric on cost-effectiveness of protected-area network design in urban landscapes.

PubMed

Burkhalter, J C; Lockwood, J L; Maslo, B; Fenn, K H; Leu, K

2016-04-01

A common goal in conservation planning is to acquire areas that are critical to realizing biodiversity goals in the most cost-effective manner. The way monetary acquisition costs are represented in such planning is an understudied but vital component to realizing cost efficiencies. We sought to design a protected-area network within a forested urban region that would protect 17 birds of conservation concern. We compared the total costs and spatial structure of the optimal protected-area networks produced using three acquisition-cost surrogates (area, agricultural land value, and tax-assessed land value). Using the tax-assessed land values there was a 73% and 78% cost savings relative to networks derived using area or agricultural land value, respectively. This cost reduction was due to the considerable heterogeneity in acquisition costs revealed in tax-assessed land values, especially for small land parcels, and the corresponding ability of the optimization algorithm to identify lower-cost parcels for inclusion that had equal value to our target species. Tax-assessed land values also reflected the strong spatial differences in acquisition costs (US$0.33/m(2)-$55/m(2)) and thus allowed the algorithm to avoid inclusion of high-cost parcels when possible. Our results add to a nascent but growing literature that suggests conservation planners must consider the cost surrogate they use when designing protected-area networks. We suggest that choosing cost surrogates that capture spatial- and size-dependent heterogeneity in acquisition costs may be relevant to establishing protected areas in urbanizing ecosystems. © 2015 Society for Conservation Biology.

Construction and characterization of stable, constitutively expressed, chromosomal green and red fluorescent transcriptional fusions in the select agents, Bacillus anthracis, Yersinia pestis, Burkholderia mallei, and Burkholderia pseudomallei

PubMed Central

Su, Shengchang; Bangar, Hansraj; Saldanha, Roland; Pemberton, Adin; Aronow, Bruce; Dean, Gary E; Lamkin, Thomas J; Hassett, Daniel J

2014-01-01

Here, we constructed stable, chromosomal, constitutively expressed, green and red fluorescent protein (GFP and RFP) as reporters in the select agents, Bacillus anthracis, Yersinia pestis, Burkholderia mallei, and Burkholderia pseudomallei. Using bioinformatic approaches and other experimental analyses, we identified P0253 and P1 as potent promoters that drive the optimal expression of fluorescent reporters in single copy in B. anthracis and Burkholderia spp. as well as their surrogate strains, respectively. In comparison, Y. pestis and its surrogate strain need two chromosomal copies of cysZK promoter (P2cysZK) for optimal fluorescence. The P0253-, P2cysZK-, and P1-driven GFP and RFP fusions were first cloned into the vectors pRP1028, pUC18R6KT-mini-Tn7T-Km, pmini-Tn7-gat, or their derivatives. The resultant constructs were delivered into the respective surrogates and subsequently into the select agent strains. The chromosomal GFP- and RFP-tagged strains exhibited bright fluorescence at an exposure time of less than 200 msec and displayed the same virulence traits as their wild-type parental strains. The utility of the tagged strains was proven by the macrophage infection assays and lactate dehydrogenase release analysis. Such strains will be extremely useful in high-throughput screens for novel compounds that could either kill these organisms, or interfere with critical virulence processes in these important bioweapon agents and during infection of alveolar macrophages. PMID:25044501

Construction and characterization of stable, constitutively expressed, chromosomal green and red fluorescent transcriptional fusions in the select agents, Bacillus anthracis, Yersinia pestis, Burkholderia mallei, and Burkholderia pseudomallei.

PubMed

Su, Shengchang; Bangar, Hansraj; Saldanha, Roland; Pemberton, Adin; Aronow, Bruce; Dean, Gary E; Lamkin, Thomas J; Hassett, Daniel J

2014-10-01

Here, we constructed stable, chromosomal, constitutively expressed, green and red fluorescent protein (GFP and RFP) as reporters in the select agents, Bacillus anthracis, Yersinia pestis, Burkholderia mallei, and Burkholderia pseudomallei. Using bioinformatic approaches and other experimental analyses, we identified P0253 and P1 as potent promoters that drive the optimal expression of fluorescent reporters in single copy in B. anthracis and Burkholderia spp. as well as their surrogate strains, respectively. In comparison, Y. pestis and its surrogate strain need two chromosomal copies of cysZK promoter (P2cysZK) for optimal fluorescence. The P0253-, P2cysZK-, and P1-driven GFP and RFP fusions were first cloned into the vectors pRP1028, pUC18R6KT-mini-Tn7T-Km, pmini-Tn7-gat, or their derivatives. The resultant constructs were delivered into the respective surrogates and subsequently into the select agent strains. The chromosomal GFP- and RFP-tagged strains exhibited bright fluorescence at an exposure time of less than 200 msec and displayed the same virulence traits as their wild-type parental strains. The utility of the tagged strains was proven by the macrophage infection assays and lactate dehydrogenase release analysis. Such strains will be extremely useful in high-throughput screens for novel compounds that could either kill these organisms, or interfere with critical virulence processes in these important bioweapon agents and during infection of alveolar macrophages. © 2014 The Authors. MicrobiologyOpen published by John Wiley & Sons Ltd.

MIXOPTIM: A tool for the evaluation and the optimization of the electricity mix in a territory

NASA Astrophysics Data System (ADS)

Bonin, Bernard; Safa, Henri; Laureau, Axel; Merle-Lucotte, Elsa; Miss, Joachim; Richet, Yann

2014-09-01

This article presents a method of calculation of the generation cost of a mixture of electricity sources, by means of a Monte Carlo simulation of the production output taking into account the fluctuations of the demand and the stochastic nature of the availability of the various power sources that compose the mix. This evaluation shows that for a given electricity mix, the cost has a non-linear dependence on the demand level. In the second part of the paper, we develop some considerations on the management of intermittence. We develop a method based on spectral decomposition of the imposed power fluctuations to calculate the minimal amount of the controlled power sources needed to follow these fluctuations. This can be converted into a viability criterion of the mix included in the MIXOPTIM software. In the third part of the paper, the MIXOPTIM cost evaluation method is applied to the multi-criteria optimization of the mix, according to three main criteria: the cost of the mix; its impact on climate in terms of CO2 production; and the security of supply.

Bioaccessibility of metals in alloys: Evaluation of three surrogate biofluids

PubMed Central

Hillwalker, Wendy E.; Anderson, Kim A.

2014-01-01

Bioaccessibility in vitro tests measure the solubility of materials in surrogate biofluids. However, the lack of uniform methods and the effects of variable test parameters on material solubility limit interpretation. One aim of this study was to measure and compare bioaccessibility of selected economically important alloys and metals in surrogate physiologically based biofluids representing oral, inhalation and dermal exposures. A second aim was to experimentally test different biofluid formulations and residence times in vitro. A third aim was evaluation of dissolution behavior of alloys with in vitro lung and dermal biofluid surrogates. This study evaluated the bioaccessibility of sixteen elements in six alloys and 3 elemental/metal powders. We found that the alloys/metals, the chemical properties of the surrogate fluid, and residence time all had major impacts on metal solubility. The large variability of bioaccessibility indicates the relevancy of assessing alloys as toxicologically distinct relative to individual metals. PMID:24212234

The virucidal effects against murine norovirus and feline calicivirus F4 as surrogates for human norovirus by the different additive concentrations of ethanol-based sanitizers.

PubMed

Akasaka, Tempei; Shimizu-Onda, Yuko; Hayakawa, Satoshi; Ushijima, Hiroshi

2016-03-01

Since human norovirus is non-cultivable, murine norovirus and feline calicivirus have been used as surrogates. In this study, the virucidal effects of ethanol-based sanitizers with different concentrations of additives (malic acid/sodium malate, glycerin-fatty acid ester) against murine norovirus and feline calicivirus F4 were examined. The ethanol-based sanitizers at pH 7 showed sufficient virucidal effects, but glycerin-fatty acid ester included in ethanol-based sanitizers at pH 4 or 6 reduced the virucidal effects against murine norovirus. The ethanol-based sanitizers containing malic acid/sodium malate inactivated feline calicivirus F4 in shorter time, but there is no difference between ethanol-based sanitizers with and without glycerin-fatty acid ester. Traditionally, feline calicivirus has been used for long time as a surrogate virus for human norovirus. However, this study suggested that murine norovirus and feline calicivirus F4 had different sensitivity with the additive components of ethanol-based sanitizers. Therefore, using feline calicivirus alone as a surrogate for human norovirus may not be sufficient to evaluate the virucidal effect of sanitizers on food-borne infections caused by human norovirus. Sanitizers having virucidal effects against at least both murine norovirus and feline calicivirus may be more suitable to inactivate human norovirus. Copyright © 2015. Published by Elsevier Ltd.

Nanodosimetry-Based Plan Optimization for Particle Therapy

PubMed Central

Schulte, Reinhard W.

2015-01-01

Treatment planning for particle therapy is currently an active field of research due uncertainty in how to modify physical dose in order to create a uniform biological dose response in the target. A novel treatment plan optimization strategy based on measurable nanodosimetric quantities rather than biophysical models is proposed in this work. Simplified proton and carbon treatment plans were simulated in a water phantom to investigate the optimization feasibility. Track structures of the mixed radiation field produced at different depths in the target volume were simulated with Geant4-DNA and nanodosimetric descriptors were calculated. The fluences of the treatment field pencil beams were optimized in order to create a mixed field with equal nanodosimetric descriptors at each of the multiple positions in spread-out particle Bragg peaks. For both proton and carbon ion plans, a uniform spatial distribution of nanodosimetric descriptors could be obtained by optimizing opposing-field but not single-field plans. The results obtained indicate that uniform nanodosimetrically weighted plans, which may also be radiobiologically uniform, can be obtained with this approach. Future investigations need to demonstrate that this approach is also feasible for more complicated beam arrangements and that it leads to biologically uniform response in tumor cells and tissues. PMID:26167202

Advance Care Planning Beyond Advance Directives: Perspectives from Patients and Surrogates

PubMed Central

McMahan, Ryan; Knight, Sara J.; Fried, Terri R.; Sudore, Rebecca L.

2014-01-01

Context Advance care planning (ACP) has focused on documenting life-sustaining treatment preferences in advance directives (ADs). ADs alone may be insufficient to prepare diverse patients and surrogates for complex medical decisions. Objectives To understand what steps best prepare patients and surrogates for decision making. Methods We conducted 13 English/Spanish focus groups with participants from a Veterans Affairs and county hospital and the community. Seven groups included patients (n=38) aged ≥65 years, who reported making serious medical decisions. Six separate groups included surrogates (n=31), aged ≥18 years, who made decisions for others. Semi-structured focus groups asked what activities best prepared participants for decision making. Two investigators independently coded data and performed thematic content analysis. Disputes were resolved by consensus. Results Mean±SD patient age was 78±8 years and 61% were non-white. Mean±SD surrogate age was 57±10 years and 91% were non-white. Qualitative analysis identified four overarching themes about how to best prepare for decision making: 1) identify values based on past experiences and quality of life, 2) choose surrogates wisely and verify they understand their role, 3) decide whether to grant leeway in surrogate decision making, and 4) inform other family and friends of one's wishes to prevent conflict. Conclusion Beyond ADs, patients and surrogates recommend several additional steps to prepare for medical decision making including using past experiences to identify values, verifying the surrogate understands their role, deciding whether to grant surrogates leeway, and informing other family and friends of one's wishes. Future ACP interventions should consider incorporating these additional ACP activities. PMID:23200188

Is there a role for physician involvement in introducing research to surrogate decision makers in the intensive care unit? (The Approach trial: a pilot mixed methods study).

PubMed

Burns, K E A; Rizvi, L; Smith, O M; Lee, Y; Lee, J; Wang, M; Brown, M; Parker, M; Premji, A; Leung, D; Hammond Mobilio, M; Gotlib-Conn, L; Nisenbaum, R; Santos, M; Li, Y; Mehta, S

2015-01-01

To assess the feasibility of conducting a randomized trial comparing two strategies [physician (MD) vs. non-physician (non-MD)] for approaching substitute decision makers (SDMs) for research and to evaluate SDMs' experiences in being approached for consent. A pilot mixed methods study of first encounters with SDMs. Of 137 SDMs (162 eligibility events), 67 and 70 were randomized to MD and non-MD introductions, respectively. Eighty SDMs (98 events) provided consent and 21 SDMs (24 events) declined consent for studies, including 2 SDMs who provided and declined consent. We identified few missed introductions [4/52 (7.7 %)] and protocol violations [6/117 (5.1 %)], high comfort, satisfaction and acceptance scores and similar consent rates in both arms. SDMs provided consent significantly more often when a patient update was provided in the MD arm. Most SDMs (85.7 %) felt that physician involvement was inconsequential and preferred physician time to be dedicated to patient care; however, SDM experiences were closely related to their recall of being approached and recall was poor. SDMs highlighted 7 themes of importance to them in research surrogate decision-making. SDMs prioritized the personal attributes of the person approaching them over professional designation and preferred physician time to be dedicated to patient care. A mixed methods design evaluated intervention fidelity and provided the rationale for not proceeding to a larger trial, despite achieving all feasibility metrics in the pilot trial. NCT01232621.

Measurement of health outcomes.

PubMed

Thavorncharoensap, Montarat

2014-05-01

Health outcomes are one of the most important components of health technology assessments (HTAs). All HTA outcomes should be measured from a relevant sample using a properly designed study and method. A number of recommendations on health outcome measurements are made in this second edition of Thailand's HTA guidelines. In particular the use of final outcomes, rather than surrogate outcomes, in HTAs is stressed. Where surrogate outcomes are used, strong justification and evidence must be provided. Effectiveness is preferred over efficacy. The relative treatment effect (the difference between health outcome that would be experienced by patients receiving the technology and that experienced by the same group were they to receive an alternative technology) should be derived from a systematic review of head-to-head RCTs. Mixed treatment comparison (MTC) should be used only to provide supplementary data that cannot be obtained from a head-to-head comparison. Where no direct comparison evidence exists, indirect comparison and observational study data can be used.

High-Temperature Oxidation of Plutonium Surrogate Metals and Alloys

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sparks, Joshua C.; Krantz, Kelsie E.; Christian, Jonathan H.

The Plutonium Management and Disposition Agreement (PMDA) is a nuclear non-proliferation agreement designed to remove 34 tons of weapons-grade plutonium from Russia and the United States. While several removal options have been proposed since the agreement was first signed in 2000, processing the weapons-grade plutonium to mixed-oxide (MOX) fuel has remained the leading candidate for achieving the goals of the PMDA. However, the MOX program has received its share of criticisms, which causes its future to be uncertain. One alternative pathway for plutonium disposition would involve oxidizing the metal followed by impurity down blending and burial in the Waste Isolationmore » Pilot Plant (WIPP) in Carlsbad, New Mexico. This pathway was investigated by use of a hybrid microwave and a muffle furnace with Fe and Al as surrogate materials. Oxidation occurred similarly in the microwave and muffle furnace; however, the microwave process time was significantly faster.« less