Homotopy optimization methods for global optimization.
Dunlavy, Daniel M.; O'Leary, Dianne P. (University of Maryland, College Park, MD)
2005-12-01
We define a new method for global optimization, the Homotopy Optimization Method (HOM). This method differs from previous homotopy and continuation methods in that its aim is to find a minimizer for each of a set of values of the homotopy parameter, rather than to follow a path of minimizers. We define a second method, called HOPE, by allowing HOM to follow an ensemble of points obtained by perturbation of previous ones. We relate this new method to standard methods such as simulated annealing and show under what circumstances it is superior. We present results of extensive numerical experiments demonstrating performance of HOM and HOPE.
Enhancing Polyhedral Relaxations for Global Optimization
ERIC Educational Resources Information Center
Bao, Xiaowei
2009-01-01
During the last decade, global optimization has attracted a lot of attention due to the increased practical need for obtaining global solutions and the success in solving many global optimization problems that were previously considered intractable. In general, the central question of global optimization is to find an optimal solution to a given…
Global optimality of extremals: An example
NASA Technical Reports Server (NTRS)
Kreindler, E.; Newman, F.
1980-01-01
The question of the existence and location of Darboux points is crucial for minimally sufficient conditions for global optimality and for computation of optimal trajectories. A numerical investigation is presented of the Darboux points and their relationship with conjugate points for a problem of minimum fuel, constant velocity, and horizontal aircraft turns to capture a line. This simple second order optimal control problem shows that ignoring the possible existence of Darboux points may play havoc with the computation of optimal trajectories.
Global optimization of digital circuits
NASA Astrophysics Data System (ADS)
Flandera, Richard
1991-12-01
This thesis was divided into two tasks. The first task involved developing a parser which could translate a behavioral specification in Very High-Speed Integrated Circuits (VHSIC) Hardware Description Language (VHDL) into the format used by an existing digital circuit optimization tool, Boolean Reasoning In Scheme (BORIS). Since this tool is written in Scheme, a dialect of Lisp, the parser was also written in Scheme. The parser was implemented is Artez's modification of Earley's Algorithm. Additionally, a VHDL tokenizer was implemented in Scheme and a portion of the VHDL grammar was converted into the format which the parser uses. The second task was the incorporation of intermediate functions into BORIS. The existing BORIS contains a recursive optimization system that optimizes digital circuits by using circuit outputs as inputs into other circuits. Intermediate functions provide a greater selection of functions to be used as circuits inputs. Using both intermediate functions and output functions, the costs of the circuits in the test set were reduced by 43 percent. This is a 10 percent reduction when compared to the existing recursive optimization system. Incorporating intermediate functions into BORIS required the development of an intermediate-function generator and a set of control methods to keep the computation time from increasing exponentially.
FPSO global strength and hull optimization
NASA Astrophysics Data System (ADS)
Ma, Junyuan; Xiao, Jianhua; Ma, Rui; Cao, Kai
2014-03-01
Global strength is a significant item for floating production storage and offloading (FPSO) design, and steel weight plays an important role in the building costs of FPSO. It is the main task to consider and combine these two aspects by optimizing hull dimensions. There are many optional methods for the global strength analysis. A common method is to use the ABS FPSO Eagle software to analyze the global strength including the rule check and direct strength analysis. And the same method can be adopted for the FPSO hull optimization by changing the depth. After calculation and optimization, the results are compared and analyzed. The results can be used as a reference for the future design or quotation purpose.
Globally optimal impulsive transfers via Green's theorem
NASA Astrophysics Data System (ADS)
Hazelrigg, G. A., Jr.
1984-08-01
For certain classes of trajectories the cost function (characteristic velocity) can be written as a 'quasilinear' function of the change in state. In the case presented, impulsive transfers between coplanar, coaxial orbits with transfer time and angle unrestricted, Green's theorem can be used to determine the optimal transfer between given terminal states. This is done in a manner which places no restrictions on the number of impulses used and leads to globally optimal results. These results are used to show that the Hohmann transfer and the biparabolic transfer provide global minima in their respective regions. The regions in which monoelliptic and biparabolic trajectories are globally optimal are also defined for elliptic terminal states. The results are applicable to the case in which restrictions are placed on the radius of closest approach or greatest recession from the center of the force field.
Global Design Optimization for Fluid Machinery Applications
NASA Technical Reports Server (NTRS)
Shyy, Wei; Papila, Nilay; Tucker, Kevin; Vaidyanathan, Raj; Griffin, Lisa
2000-01-01
Recent experiences in utilizing the global optimization methodology, based on polynomial and neural network techniques for fluid machinery design are summarized. Global optimization methods can utilize the information collected from various sources and by different tools. These methods offer multi-criterion optimization, handle the existence of multiple design points and trade-offs via insight into the entire design space can easily perform tasks in parallel, and are often effective in filtering the noise intrinsic to numerical and experimental data. Another advantage is that these methods do not need to calculate the sensitivity of each design variable locally. However, a successful application of the global optimization method needs to address issues related to data requirements with an increase in the number of design variables and methods for predicting the model performance. Examples of applications selected from rocket propulsion components including a supersonic turbine and an injector element and a turbulent flow diffuser are used to illustrate the usefulness of the global optimization method.
Intervals in evolutionary algorithms for global optimization
Patil, R.B.
1995-05-01
Optimization is of central concern to a number of disciplines. Interval Arithmetic methods for global optimization provide us with (guaranteed) verified results. These methods are mainly restricted to the classes of objective functions that are twice differentiable and use a simple strategy of eliminating a splitting larger regions of search space in the global optimization process. An efficient approach that combines the efficient strategy from Interval Global Optimization Methods and robustness of the Evolutionary Algorithms is proposed. In the proposed approach, search begins with randomly created interval vectors with interval widths equal to the whole domain. Before the beginning of the evolutionary process, fitness of these interval parameter vectors is defined by evaluating the objective function at the center of the initial interval vectors. In the subsequent evolutionary process the local optimization process returns an estimate of the bounds of the objective function over the interval vectors. Though these bounds may not be correct at the beginning due to large interval widths and complicated function properties, the process of reducing interval widths over time and a selection approach similar to simulated annealing helps in estimating reasonably correct bounds as the population evolves. The interval parameter vectors at these estimated bounds (local optima) are then subjected to crossover and mutation operators. This evolutionary process continues for predetermined number of generations in the search of the global optimum.
Electronic neural networks for global optimization
NASA Technical Reports Server (NTRS)
Thakoor, A. P.; Moopenn, A. W.; Eberhardt, S.
1990-01-01
An electronic neural network with feedback architecture, implemented in analog custom VLSI is described. Its application to problems of global optimization for dynamic assignment is discussed. The convergence properties of the neural network hardware are compared with computer simulation results. The neural network's ability to provide optimal or near optimal solutions within only a few neuron time constants, a speed enhancement of several orders of magnitude over conventional search methods, is demonstrated. The effect of noise on the circuit dynamics and the convergence behavior of the neural network hardware is also examined.
Global Optimality of the Successive Maxbet Algorithm.
ERIC Educational Resources Information Center
Hanafi, Mohamed; ten Berge, Jos M. F.
2003-01-01
It is known that the Maxbet algorithm, which is an alternative to the method of generalized canonical correlation analysis and Procrustes analysis, may converge to local maxima. Discusses an eigenvalue criterion that is sufficient, but not necessary, for global optimality of the successive Maxbet algorithm. (SLD)
A Novel Particle Swarm Optimization Algorithm for Global Optimization
Wang, Chun-Feng; Liu, Kui
2016-01-01
Particle Swarm Optimization (PSO) is a recently developed optimization method, which has attracted interest of researchers in various areas due to its simplicity and effectiveness, and many variants have been proposed. In this paper, a novel Particle Swarm Optimization algorithm is presented, in which the information of the best neighbor of each particle and the best particle of the entire population in the current iteration is considered. Meanwhile, to avoid premature, an abandoned mechanism is used. Furthermore, for improving the global convergence speed of our algorithm, a chaotic search is adopted in the best solution of the current iteration. To verify the performance of our algorithm, standard test functions have been employed. The experimental results show that the algorithm is much more robust and efficient than some existing Particle Swarm Optimization algorithms. PMID:26955387
Global optimization of bilinear engineering design models
Grossmann, I.; Quesada, I.
1994-12-31
Recently Quesada and Grossmann have proposed a global optimization algorithm for solving NLP problems involving linear fractional and bilinear terms. This model has been motivated by a number of applications in process design. The proposed method relies on the derivation of a convex NLP underestimator problem that is used within a spatial branch and bound search. This paper explores the use of alternative bounding approximations for constructing the underestimator problem. These are applied in the global optimization of problems arising in different engineering areas and for which different relaxations are proposed depending on the mathematical structure of the models. These relaxations include linear and nonlinear underestimator problems. Reformulations that generate additional estimator functions are also employed. Examples from process design, structural design, portfolio investment and layout design are presented.
Global optimization algorithm for heat exchanger networks
Quesada, I.; Grossmann, I.E. )
1993-03-01
This paper deals with the global optimization of heat exchanger networks with fixed topology. It is shown that if linear area cost functions are assumed, as well as arithmetic mean driving force temperature differences in networks with isothermal mixing, the corresponding nonlinear programming (NLP) optimization problem involves linear constraints and a sum of linear fractional functions in the objective which are nonconvex. A rigorous algorithm is proposed that is based on a convex NLP underestimator that involves linear and nonlinear estimators for fractional and bilinear terms which provide a tight lower bound to the global optimum. This NLP problem is used within a spatial branch and bound method for which branching rules are given. Basic properties of the proposed method are presented, and its application is illustrated with several example problems. The results show that the proposed method only requires few nodes in the branch and bound search.
Solving global optimization problems on GPU cluster
NASA Astrophysics Data System (ADS)
Barkalov, Konstantin; Gergel, Victor; Lebedev, Ilya
2016-06-01
The paper contains the results of investigation of a parallel global optimization algorithm combined with a dimension reduction scheme. This allows solving multidimensional problems by means of reducing to data-independent subproblems with smaller dimension solved in parallel. The new element implemented in the research consists in using several graphic accelerators at different computing nodes. The paper also includes results of solving problems of well-known multiextremal test class GKLS on Lobachevsky supercomputer using tens of thousands of GPU cores.
On Global Optimal Sailplane Flight Strategy
NASA Technical Reports Server (NTRS)
Sander, G. J.; Litt, F. X.
1979-01-01
The derivation and interpretation of the necessary conditions that a sailplane cross-country flight has to satisfy to achieve the maximum global flight speed is considered. Simple rules are obtained for two specific meteorological models. The first one uses concentrated lifts of various strengths and unequal distance. The second one takes into account finite, nonuniform space amplitudes for the lifts and allows, therefore, for dolphin style flight. In both models, altitude constraints consisting of upper and lower limits are shown to be essential to model realistic problems. Numerical examples illustrate the difference with existing techniques based on local optimality conditions.
LDRD Final Report: Global Optimization for Engineering Science Problems
HART,WILLIAM E.
1999-12-01
For a wide variety of scientific and engineering problems the desired solution corresponds to an optimal set of objective function parameters, where the objective function measures a solution's quality. The main goal of the LDRD ''Global Optimization for Engineering Science Problems'' was the development of new robust and efficient optimization algorithms that can be used to find globally optimal solutions to complex optimization problems. This SAND report summarizes the technical accomplishments of this LDRD, discusses lessons learned and describes open research issues.
An approximation based global optimization strategy for structural synthesis
NASA Technical Reports Server (NTRS)
Sepulveda, A. E.; Schmit, L. A.
1991-01-01
A global optimization strategy for structural synthesis based on approximation concepts is presented. The methodology involves the solution of a sequence of highly accurate approximate problems using a global optimization algorithm. The global optimization algorithm implemented consists of a branch and bound strategy based on the interval evaluation of the objective function and constraint functions, combined with a local feasible directions algorithm. The approximate design optimization problems are constructed using first order approximations of selected intermediate response quantities in terms of intermediate design variables. Some numerical results for example problems are presented to illustrate the efficacy of the design procedure setforth.
Global smoothing and continuation for large-scale molecular optimization
More, J.J.; Wu, Zhijun
1995-10-01
We discuss the formulation of optimization problems that arise in the study of distance geometry, ionic systems, and molecular clusters. We show that continuation techniques based on global smoothing are applicable to these molecular optimization problems, and we outline the issues that must be resolved in the solution of large-scale molecular optimization problems.
Strategies for Global Optimization of Temporal Preferences
NASA Technical Reports Server (NTRS)
Morris, Paul; Morris, Robert; Khatib, Lina; Ramakrishnan, Sailesh
2004-01-01
A temporal reasoning problem can often be naturally characterized as a collection of constraints with associated local preferences for times that make up the admissible values for those constraints. Globally preferred solutions to such problems emerge as a result of well-defined operations that compose and order temporal assignments. The overall objective of this work is a characterization of different notions of global preference, and to identify tractable sub-classes of temporal reasoning problems incorporating these notions. This paper extends previous results by refining the class of useful notions of global temporal preference that are associated with problems that admit of tractable solution techniques. This paper also answers the hitherto open question of whether problems that seek solutions that are globally preferred from a Utilitarian criterion for global preference can be found tractably.
Applications of parallel global optimization to mechanics problems
NASA Astrophysics Data System (ADS)
Schutte, Jaco Francois
Global optimization of complex engineering problems, with a high number of variables and local minima, requires sophisticated algorithms with global search capabilities and high computational efficiency. With the growing availability of parallel processing, it makes sense to address these requirements by increasing the parallelism in optimization strategies. This study proposes three methods of concurrent processing. The first method entails exploiting the structure of population-based global algorithms such as the stochastic Particle Swarm Optimization (PSO) algorithm and the Genetic Algorithm (GA). As a demonstration of how such an algorithm may be adapted for concurrent processing we modify and apply the PSO to several mechanical optimization problems on a parallel processing machine. Desirable PSO algorithm features such as insensitivity to design variable scaling and modest sensitivity to algorithm parameters are demonstrated. A second approach to parallelism and improving algorithm efficiency is by utilizing multiple optimizations. With this method a budget of fitness evaluations is distributed among several independent sub-optimizations in place of a single extended optimization. Under certain conditions this strategy obtains a higher combined probability of converging to the global optimum than a single optimization which utilizes the full budget of fitness evaluations. The third and final method of parallelism addressed in this study is the use of quasiseparable decomposition, which is applied to decompose loosely coupled problems. This yields several sub-problems of lesser dimensionality which may be concurrently optimized with reduced effort.
Hybrid and adaptive meta-model-based global optimization
NASA Astrophysics Data System (ADS)
Gu, J.; Li, G. Y.; Dong, Z.
2012-01-01
As an efficient and robust technique for global optimization, meta-model-based search methods have been increasingly used in solving complex and computation intensive design optimization problems. In this work, a hybrid and adaptive meta-model-based global optimization method that can automatically select appropriate meta-modelling techniques during the search process to improve search efficiency is introduced. The search initially applies three representative meta-models concurrently. Progress towards a better performing model is then introduced by selecting sample data points adaptively according to the calculated values of the three meta-models to improve modelling accuracy and search efficiency. To demonstrate the superior performance of the new algorithm over existing search methods, the new method is tested using various benchmark global optimization problems and applied to a real industrial design optimization example involving vehicle crash simulation. The method is particularly suitable for design problems involving computation intensive, black-box analyses and simulations.
Interior search algorithm (ISA): a novel approach for global optimization.
Gandomi, Amir H
2014-07-01
This paper presents the interior search algorithm (ISA) as a novel method for solving optimization tasks. The proposed ISA is inspired by interior design and decoration. The algorithm is different from other metaheuristic algorithms and provides new insight for global optimization. The proposed method is verified using some benchmark mathematical and engineering problems commonly used in the area of optimization. ISA results are further compared with well-known optimization algorithms. The results show that the ISA is efficiently capable of solving optimization problems. The proposed algorithm can outperform the other well-known algorithms. Further, the proposed algorithm is very simple and it only has one parameter to tune.
Nonlinear Global Optimization Using Curdling Algorithm
1996-03-01
An algorithm for performing curdling optimization which is a derivative-free, grid-refinement approach to nonlinear optimization was developed and implemented in software. This approach overcomes a number of deficiencies in existing approaches. Most notably, it finds extremal regions rather than only single external extremal points. The program is interactive and collects information on control parameters and constraints using menus. For up to four dimensions, function convergence is displayed graphically. Because the algorithm does not compute derivatives,more » gradients or vectors, it is numerically stable. It can find all the roots of a polynomial in one pass. It is an inherently parallel algorithm. Constraints are handled as being initially fuzzy, but become tighter with each iteration.« less
Neural network training with global optimization techniques.
Yamazaki, Akio; Ludermir, Teresa B
2003-04-01
This paper presents an approach of using Simulated Annealing and Tabu Search for the simultaneous optimization of neural network architectures and weights. The problem considered is the odor recognition in an artificial nose. Both methods have produced networks with high classification performance and low complexity. Generalization has been improved by using the backpropagation algorithm for fine tuning. The combination of simple and traditional search methods has shown to be very suitable for generating compact and efficient networks.
Global nonlinear optimization of spacecraft protective structures design
NASA Technical Reports Server (NTRS)
Mog, R. A.; Lovett, J. N., Jr.; Avans, S. L.
1990-01-01
The global optimization of protective structural designs for spacecraft subject to hypervelocity meteoroid and space debris impacts is presented. This nonlinear problem is first formulated for weight minimization of the space station core module configuration using the Nysmith impact predictor. Next, the equivalence and uniqueness of local and global optima is shown using properties of convexity. This analysis results in a new feasibility condition for this problem. The solution existence is then shown, followed by a comparison of optimization techniques. Finally, a sensitivity analysis is presented to determine the effects of variations in the systemic parameters on optimal design. The results show that global optimization of this problem is unique and may be achieved by a number of methods, provided the feasibility condition is satisfied. Furthermore, module structural design thicknesses and weight increase with increasing projectile velocity and diameter and decrease with increasing separation between bumper and wall for the Nysmith predictor.
Dispositional optimism and terminal decline in global quality of life.
Zaslavsky, Oleg; Palgi, Yuval; Rillamas-Sun, Eileen; LaCroix, Andrea Z; Schnall, Eliezer; Woods, Nancy F; Cochrane, Barbara B; Garcia, Lorena; Hingle, Melanie; Post, Stephen; Seguin, Rebecca; Tindle, Hilary; Shrira, Amit
2015-06-01
We examined whether dispositional optimism relates to change in global quality of life (QOL) as a function of either chronological age or years to impending death. We used a sample of 2,096 deceased postmenopausal women from the Women's Health Initiative clinical trials who were enrolled in the 2005-2010 Extension Study and for whom at least 1 global QOL and optimism measure were analyzed. Growth curve models were examined. Competing models were contrasted using model fit criteria. On average, levels of global QOL decreased with both higher age and closer proximity to death (e.g., M(score) = 7.7 eight years prior to death vs. M(score) = 6.1 one year prior to death). A decline in global QOL was better modeled as a function of distance to death (DtD) than as a function of chronological age (Bayesian information criterion [BIC](DtD) = 22,964.8 vs. BIC(age) = 23,322.6). Optimism was a significant correlate of both linear (estimate(DtD) = -0.01, SE(DtD) = 0.005; ρ = 0.004) and quadratic (estimate(DtD) = -0.006, SE(DtD) = 0.002; ρ = 0.004) terminal decline in global QOL so that death-related decline in global QOL was steeper among those with a high level of optimism than those with a low level of optimism. We found that dispositional optimism helps to maintain positive psychological perspective in the face of age-related decline. Optimists maintain higher QOL compared with pessimists when death-related trajectories were considered; however, the gap between those with high optimism and those with low optimism progressively attenuated with closer proximity to death, to the point that is became nonsignificant at the time of death.
Optimizing human activity patterns using global sensitivity analysis
Hickmann, Kyle S.; Mniszewski, Susan M.; Del Valle, Sara Y.; Hyman, James M.
2014-01-01
Implementing realistic activity patterns for a population is crucial for modeling, for example, disease spread, supply and demand, and disaster response. Using the dynamic activity simulation engine, DASim, we generate schedules for a population that capture regular (e.g., working, eating, and sleeping) and irregular activities (e.g., shopping or going to the doctor). We use the sample entropy (SampEn) statistic to quantify a schedule’s regularity for a population. We show how to tune an activity’s regularity by adjusting SampEn, thereby making it possible to realistically design activities when creating a schedule. The tuning process sets up a computationally intractable high-dimensional optimization problem. To reduce the computational demand, we use Bayesian Gaussian process regression to compute global sensitivity indices and identify the parameters that have the greatest effect on the variance of SampEn. We use the harmony search (HS) global optimization algorithm to locate global optima. Our results show that HS combined with global sensitivity analysis can efficiently tune the SampEn statistic with few search iterations. We demonstrate how global sensitivity analysis can guide statistical emulation and global optimization algorithms to efficiently tune activities and generate realistic activity patterns. Though our tuning methods are applied to dynamic activity schedule generation, they are general and represent a significant step in the direction of automated tuning and optimization of high-dimensional computer simulations. PMID:25580080
Optimizing human activity patterns using global sensitivity analysis
Fairchild, Geoffrey; Hickmann, Kyle S.; Mniszewski, Susan M.; Del Valle, Sara Y.; Hyman, James M.
2013-12-10
Implementing realistic activity patterns for a population is crucial for modeling, for example, disease spread, supply and demand, and disaster response. Using the dynamic activity simulation engine, DASim, we generate schedules for a population that capture regular (e.g., working, eating, and sleeping) and irregular activities (e.g., shopping or going to the doctor). We use the sample entropy (SampEn) statistic to quantify a schedule’s regularity for a population. We show how to tune an activity’s regularity by adjusting SampEn, thereby making it possible to realistically design activities when creating a schedule. The tuning process sets up a computationally intractable high-dimensional optimization problem. To reduce the computational demand, we use Bayesian Gaussian process regression to compute global sensitivity indices and identify the parameters that have the greatest effect on the variance of SampEn. Here we use the harmony search (HS) global optimization algorithm to locate global optima. Our results show that HS combined with global sensitivity analysis can efficiently tune the SampEn statistic with few search iterations. We demonstrate how global sensitivity analysis can guide statistical emulation and global optimization algorithms to efficiently tune activities and generate realistic activity patterns. Finally, though our tuning methods are applied to dynamic activity schedule generation, they are general and represent a significant step in the direction of automated tuning and optimization of high-dimensional computer simulations.
Nallasivam, Ulaganathan; Shah, Vishesh H.; Shenvi, Anirudh A.; Huff, Joshua; Tawarmalani, Mohit; Agrawal, Rakesh
2016-02-10
We present a general Global Minimization Algorithm (GMA) to identify basic or thermally coupled distillation configurations that require the least vapor duty under minimum reflux conditions for separating any ideal or near-ideal multicomponent mixture into a desired number of product streams. In this algorithm, global optimality is guaranteed by modeling the system using Underwood equations and reformulating the resulting constraints to bilinear inequalities. The speed of convergence to the globally optimal solution is increased by using appropriate feasibility and optimality based variable-range reduction techniques and by developing valid inequalities. As a result, the GMA can be coupled with already developedmore » techniques that enumerate basic and thermally coupled distillation configurations, to provide for the first time, a global optimization based rank-list of distillation configurations.« less
Communication: Optimal parameters for basin-hopping global optimization based on Tsallis statistics
NASA Astrophysics Data System (ADS)
Shang, C.; Wales, D. J.
2014-08-01
A fundamental problem associated with global optimization is the large free energy barrier for the corresponding solid-solid phase transitions for systems with multi-funnel energy landscapes. To address this issue we consider the Tsallis weight instead of the Boltzmann weight to define the acceptance ratio for basin-hopping global optimization. Benchmarks for atomic clusters show that using the optimal Tsallis weight can improve the efficiency by roughly a factor of two. We present a theory that connects the optimal parameters for the Tsallis weighting, and demonstrate that the predictions are verified for each of the test cases.
Communication: Optimal parameters for basin-hopping global optimization based on Tsallis statistics
Shang, C. Wales, D. J.
2014-08-21
A fundamental problem associated with global optimization is the large free energy barrier for the corresponding solid-solid phase transitions for systems with multi-funnel energy landscapes. To address this issue we consider the Tsallis weight instead of the Boltzmann weight to define the acceptance ratio for basin-hopping global optimization. Benchmarks for atomic clusters show that using the optimal Tsallis weight can improve the efficiency by roughly a factor of two. We present a theory that connects the optimal parameters for the Tsallis weighting, and demonstrate that the predictions are verified for each of the test cases.
Similarity-based global optimization of buildings in urban scene
NASA Astrophysics Data System (ADS)
Zhu, Quansheng; Zhang, Jing; Jiang, Wanshou
2013-10-01
In this paper, an approach for the similarity-based global optimization of buildings in urban scene is presented. In the past, most researches concentrated on single building reconstruction, making it difficult to reconstruct reliable models from noisy or incomplete point clouds. To obtain a better result, a new trend is to utilize the similarity among the buildings. Therefore, a new similarity detection and global optimization strategy is adopted to modify local-fitting geometric errors. Firstly, the hierarchical structure that consists of geometric, topological and semantic features is constructed to represent complex roof models. Secondly, similar roof models can be detected by combining primitive structure and connection similarities. At last, the global optimization strategy is applied to preserve the consistency and precision of similar roof structures. Moreover, non-local consolidation is adapted to detect small roof parts. The experiments reveal that the proposed method can obtain convincing roof models and promote the reconstruction quality of 3D buildings in urban scene.
A deterministic global approach for mixed-discrete structural optimization
NASA Astrophysics Data System (ADS)
Lin, Ming-Hua; Tsai, Jung-Fa
2014-07-01
This study proposes a novel approach for finding the exact global optimum of a mixed-discrete structural optimization problem. Although many approaches have been developed to solve the mixed-discrete structural optimization problem, they cannot guarantee finding a global solution or they adopt too many extra binary variables and constraints in reformulating the problem. The proposed deterministic method uses convexification strategies and linearization techniques to convert a structural optimization problem into a convex mixed-integer nonlinear programming problem solvable to obtain a global optimum. To enhance the computational efficiency in treating complicated problems, the range reduction technique is also applied to tighten variable bounds. Several numerical experiments drawn from practical structural design problems are presented to demonstrate the effectiveness of the proposed method.
Global search acceleration in the nested optimization scheme
NASA Astrophysics Data System (ADS)
Grishagin, Vladimir A.; Israfilov, Ruslan A.
2016-06-01
Multidimensional unconstrained global optimization problem with objective function under Lipschitz condition is considered. For solving this problem the dimensionality reduction approach on the base of the nested optimization scheme is used. This scheme reduces initial multidimensional problem to a family of one-dimensional subproblems being Lipschitzian as well and thus allows applying univariate methods for the execution of multidimensional optimization. For two well-known one-dimensional methods of Lipschitz optimization the modifications providing the acceleration of the search process in the situation when the objective function is continuously differentiable in a vicinity of the global minimum are considered and compared. Results of computational experiments on conventional test class of multiextremal functions confirm efficiency of the modified methods.
Optimizing human activity patterns using global sensitivity analysis
Fairchild, Geoffrey; Hickmann, Kyle S.; Mniszewski, Susan M.; Del Valle, Sara Y.; Hyman, James M.
2013-12-10
Implementing realistic activity patterns for a population is crucial for modeling, for example, disease spread, supply and demand, and disaster response. Using the dynamic activity simulation engine, DASim, we generate schedules for a population that capture regular (e.g., working, eating, and sleeping) and irregular activities (e.g., shopping or going to the doctor). We use the sample entropy (SampEn) statistic to quantify a schedule’s regularity for a population. We show how to tune an activity’s regularity by adjusting SampEn, thereby making it possible to realistically design activities when creating a schedule. The tuning process sets up a computationally intractable high-dimensional optimizationmore » problem. To reduce the computational demand, we use Bayesian Gaussian process regression to compute global sensitivity indices and identify the parameters that have the greatest effect on the variance of SampEn. Here we use the harmony search (HS) global optimization algorithm to locate global optima. Our results show that HS combined with global sensitivity analysis can efficiently tune the SampEn statistic with few search iterations. We demonstrate how global sensitivity analysis can guide statistical emulation and global optimization algorithms to efficiently tune activities and generate realistic activity patterns. Finally, though our tuning methods are applied to dynamic activity schedule generation, they are general and represent a significant step in the direction of automated tuning and optimization of high-dimensional computer simulations.« less
Orbit design and optimization based on global telecommunication performance metrics
NASA Technical Reports Server (NTRS)
Lee, Seungwon; Lee, Charles H.; Kerridge, Stuart; Cheung, Kar-Ming; Edwards, Charles D.
2006-01-01
The orbit selection of telecommunications orbiters is one of the critical design processes and should be guided by global telecom performance metrics and mission-specific constraints. In order to aid the orbit selection, we have coupled the Telecom Orbit Analysis and Simulation Tool (TOAST) with genetic optimization algorithms. As a demonstration, we have applied the developed tool to select an optimal orbit for general Mars telecommunications orbiters with the constraint of being a frozen orbit. While a typical optimization goal is to minimize tele-communications down time, several relevant performance metrics are examined: 1) area-weighted average gap time, 2) global maximum of local maximum gap time, 3) global maximum of local minimum gap time. Optimal solutions are found with each of the metrics. Common and different features among the optimal solutions as well as the advantage and disadvantage of each metric are presented. The optimal solutions are compared with several candidate orbits that were considered during the development of Mars Telecommunications Orbiter.
Application of clustering global optimization to thin film design problems.
Lemarchand, Fabien
2014-03-10
Refinement techniques usually calculate an optimized local solution, which is strongly dependent on the initial formula used for the thin film design. In the present study, a clustering global optimization method is used which can iteratively change this initial formula, thereby progressing further than in the case of local optimization techniques. A wide panel of local solutions is found using this procedure, resulting in a large range of optical thicknesses. The efficiency of this technique is illustrated by two thin film design problems, in particular an infrared antireflection coating, and a solar-selective absorber coating. PMID:24663856
Application of clustering global optimization to thin film design problems.
Lemarchand, Fabien
2014-03-10
Refinement techniques usually calculate an optimized local solution, which is strongly dependent on the initial formula used for the thin film design. In the present study, a clustering global optimization method is used which can iteratively change this initial formula, thereby progressing further than in the case of local optimization techniques. A wide panel of local solutions is found using this procedure, resulting in a large range of optical thicknesses. The efficiency of this technique is illustrated by two thin film design problems, in particular an infrared antireflection coating, and a solar-selective absorber coating.
A global optimization paradigm based on change of measures
Sarkar, Saikat; Roy, Debasish; Vasu, Ram Mohan
2015-01-01
A global optimization framework, COMBEO (Change Of Measure Based Evolutionary Optimization), is proposed. An important aspect in the development is a set of derivative-free additive directional terms, obtainable through a change of measures en route to the imposition of any stipulated conditions aimed at driving the realized design variables (particles) to the global optimum. The generalized setting offered by the new approach also enables several basic ideas, used with other global search methods such as the particle swarm or the differential evolution, to be rationally incorporated in the proposed set-up via a change of measures. The global search may be further aided by imparting to the directional update terms additional layers of random perturbations such as ‘scrambling’ and ‘selection’. Depending on the precise choice of the optimality conditions and the extent of random perturbation, the search can be readily rendered either greedy or more exploratory. As numerically demonstrated, the new proposal appears to provide for a more rational, more accurate and, in some cases, a faster alternative to many available evolutionary optimization schemes. PMID:26587268
Global Optimal Trajectory in Chaos and NP-Hardness
NASA Astrophysics Data System (ADS)
Latorre, Vittorio; Gao, David Yang
This paper presents an unconventional theory and method for solving general nonlinear dynamical systems. Instead of the direct iterative methods, the discretized nonlinear system is first formulated as a global optimization problem via the least squares method. A newly developed canonical duality theory shows that this nonconvex minimization problem can be solved deterministically in polynomial time if a global optimality condition is satisfied. The so-called pseudo-chaos produced by linear iterative methods are mainly due to the intrinsic numerical error accumulations. Otherwise, the global optimization problem could be NP-hard and the nonlinear system can be really chaotic. A conjecture is proposed, which reveals the connection between chaos in nonlinear dynamics and NP-hardness in computer science. The methodology and the conjecture are verified by applications to the well-known logistic equation, a forced memristive circuit and the Lorenz system. Computational results show that the canonical duality theory can be used to identify chaotic systems and to obtain realistic global optimal solutions in nonlinear dynamical systems. The method and results presented in this paper should bring some new insights into nonlinear dynamical systems and NP-hardness in computational complexity theory.
Simple proof of the global optimality of the Hohmann transfer
NASA Technical Reports Server (NTRS)
Prussing, John E.
1992-01-01
The case of two-impulse transfer between coplanar circular orbits is considered. The global optimality of the Hohmann transfer among the class of two-impulse transfers is proved via ordinary calculus by using the familiar orbital elements, eccentricity e and parameter (semilatus rectum) p. It is noted that this proof is simpler than existing proofs in the literature.
Simple proof of the global optimality of the Hohmann transfer
NASA Astrophysics Data System (ADS)
Prussing, John E.
1992-08-01
The case of two-impulse transfer between coplanar circular orbits is considered. The global optimality of the Hohmann transfer among the class of two-impulse transfers is proved via ordinary calculus by using the familiar orbital elements, eccentricity e and parameter (semilatus rectum) p. It is noted that this proof is simpler than existing proofs in the literature.
Obstetricians’ Opinions of the Optimal Caesarean Rate: A Global Survey
Cavallaro, Francesca L.; Cresswell, Jenny A.; Ronsmans, Carine
2016-01-01
Background The debate surrounding the optimal caesarean rate has been ongoing for several decades, with the WHO recommending an “acceptable” rate of 5–15% since 1997, despite a weak evidence base. Global expert opinion from obstetric care providers on the optimal caesarean rate has not been documented. The objective of this study was to examine providers’ opinions of the optimal caesarean rate worldwide, among all deliveries and within specific sub-groups of deliveries. Methods A global online survey of medical doctors who had performed at least one caesarean in the last five years was conducted between August 2013 and January 2014. Respondents were asked to report their opinion of the optimal caesarean rate—defined as the caesarean rate that would minimise poor maternal and perinatal outcomes—at the population level and within specific sub-groups of deliveries (including women with demographic and clinical risk factors for caesareans). Median reported optimal rates and corresponding inter-quartile ranges (IQRs) were calculated for the sample, and stratified according to national caesarean rate, institutional caesarean rate, facility level, and respondent characteristics. Results Responses were collected from 1,057 medical doctors from 96 countries. The median reported optimal caesarean rate was 20% (IQR: 15–30%) for all deliveries. Providers in private for-profit facilities and in facilities with high institutional rates reported optimal rates of 30% or above, while those in Europe, in public facilities and in facilities with low institutional rates reported rates of 15% or less. Reported optimal rates were lowest among low-risk deliveries and highest for Absolute Maternal Indications (AMIs), with wide IQRs observed for most categories other than AMIs. Conclusions Three-quarters of respondents reported an optimal caesarean rate above the WHO 15% upper threshold. There was substantial variation in responses, highlighting a lack of consensus around
Automated parameterization of intermolecular pair potentials using global optimization techniques
NASA Astrophysics Data System (ADS)
Krämer, Andreas; Hülsmann, Marco; Köddermann, Thorsten; Reith, Dirk
2014-12-01
In this work, different global optimization techniques are assessed for the automated development of molecular force fields, as used in molecular dynamics and Monte Carlo simulations. The quest of finding suitable force field parameters is treated as a mathematical minimization problem. Intricate problem characteristics such as extremely costly and even abortive simulations, noisy simulation results, and especially multiple local minima naturally lead to the use of sophisticated global optimization algorithms. Five diverse algorithms (pure random search, recursive random search, CMA-ES, differential evolution, and taboo search) are compared to our own tailor-made solution named CoSMoS. CoSMoS is an automated workflow. It models the parameters' influence on the simulation observables to detect a globally optimal set of parameters. It is shown how and why this approach is superior to other algorithms. Applied to suitable test functions and simulations for phosgene, CoSMoS effectively reduces the number of required simulations and real time for the optimization task.
Local and global strategies for optimal selective mass scaling
NASA Astrophysics Data System (ADS)
Tkachuk, Anton; Bischoff, Manfred
2014-06-01
The problem of optimal selective mass scaling for linearized elasto-dynamics is discussed. Optimal selective mass scaling should provide solutions for dynamical problems that are close to the ones obtained with a lumped mass matrix, but at much smaller computational costs. It should be equally applicable to all structurally relevant load cases. The three main optimality criteria, namely eigenmode preservation, small number of non-zero entries and good conditioning of the mass matrix are explicitly formulated in the article. An example of optimal mass scaling which relies on redistribution of mass on a global system level is constructed. Alternative local mass scaling strategies are proposed and compared with existing methods using one modal and two transient numerical examples.
Global optimization for multisensor fusion in seismic imaging
Barhen, J.; Protopopescu, V.; Reister, D.
1997-06-01
The accurate imaging of subsurface structures requires the fusion of data collected from large arrays of seismic sensors. The fusion process is formulated as an optimization problem and yields an extremely complex energy surface. Due to the very large number of local minima to be explored and escaped from, the seismic imaging problem has typically been tackled with stochastic optimization methods based on Monte Carlo techniques. Unfortunately, these algorithms are very cumbersome and computationally intensive. Here, the authors present TRUST--a novel deterministic algorithm for global optimization that they apply to seismic imaging. The excellent results demonstrate that TRUST may provide the necessary breakthrough to address major scientific and technological challenges in fields as diverse as seismic modeling, process optimization, and protein engineering.
Globally Optimal Segmentation of Permanent-Magnet Systems
NASA Astrophysics Data System (ADS)
Insinga, A. R.; Bjørk, R.; Smith, A.; Bahl, C. R. H.
2016-06-01
Permanent-magnet systems are widely used for generation of magnetic fields with specific properties. The reciprocity theorem, an energy-equivalence principle in magnetostatics, can be employed to calculate the optimal remanent flux density of the permanent-magnet system, given any objective functional that is linear in the magnetic field. This approach, however, yields a continuously varying remanent flux density, while in practical applications, magnetic assemblies are realized by combining uniformly magnetized segments. The problem of determining the optimal shape of each of these segments remains unsolved. We show that the problem of optimal segmentation of a two-dimensional permanent-magnet assembly with respect to a linear objective functional can be reduced to the problem of piecewise linear approximation of a plane curve by perimeter maximization. Once the problem has been cast into this form, the globally optimal solution can be easily computed employing dynamic programming.
A global optimization approach to multi-polarity sentiment analysis.
Li, Xinmiao; Li, Jing; Wu, Yukeng
2015-01-01
Following the rapid development of social media, sentiment analysis has become an important social media mining technique. The performance of automatic sentiment analysis primarily depends on feature selection and sentiment classification. While information gain (IG) and support vector machines (SVM) are two important techniques, few studies have optimized both approaches in sentiment analysis. The effectiveness of applying a global optimization approach to sentiment analysis remains unclear. We propose a global optimization-based sentiment analysis (PSOGO-Senti) approach to improve sentiment analysis with IG for feature selection and SVM as the learning engine. The PSOGO-Senti approach utilizes a particle swarm optimization algorithm to obtain a global optimal combination of feature dimensions and parameters in the SVM. We evaluate the PSOGO-Senti model on two datasets from different fields. The experimental results showed that the PSOGO-Senti model can improve binary and multi-polarity Chinese sentiment analysis. We compared the optimal feature subset selected by PSOGO-Senti with the features in the sentiment dictionary. The results of this comparison indicated that PSOGO-Senti can effectively remove redundant and noisy features and can select a domain-specific feature subset with a higher-explanatory power for a particular sentiment analysis task. The experimental results showed that the PSOGO-Senti approach is effective and robust for sentiment analysis tasks in different domains. By comparing the improvements of two-polarity, three-polarity and five-polarity sentiment analysis results, we found that the five-polarity sentiment analysis delivered the largest improvement. The improvement of the two-polarity sentiment analysis was the smallest. We conclude that the PSOGO-Senti achieves higher improvement for a more complicated sentiment analysis task. We also compared the results of PSOGO-Senti with those of the genetic algorithm (GA) and grid search method. From
Global Design Optimization for Aerodynamics and Rocket Propulsion Components
NASA Technical Reports Server (NTRS)
Shyy, Wei; Papila, Nilay; Vaidyanathan, Rajkumar; Tucker, Kevin; Turner, James E. (Technical Monitor)
2000-01-01
Modern computational and experimental tools for aerodynamics and propulsion applications have matured to a stage where they can provide substantial insight into engineering processes involving fluid flows, and can be fruitfully utilized to help improve the design of practical devices. In particular, rapid and continuous development in aerospace engineering demands that new design concepts be regularly proposed to meet goals for increased performance, robustness and safety while concurrently decreasing cost. To date, the majority of the effort in design optimization of fluid dynamics has relied on gradient-based search algorithms. Global optimization methods can utilize the information collected from various sources and by different tools. These methods offer multi-criterion optimization, handle the existence of multiple design points and trade-offs via insight into the entire design space, can easily perform tasks in parallel, and are often effective in filtering the noise intrinsic to numerical and experimental data. However, a successful application of the global optimization method needs to address issues related to data requirements with an increase in the number of design variables, and methods for predicting the model performance. In this article, we review recent progress made in establishing suitable global optimization techniques employing neural network and polynomial-based response surface methodologies. Issues addressed include techniques for construction of the response surface, design of experiment techniques for supplying information in an economical manner, optimization procedures and multi-level techniques, and assessment of relative performance between polynomials and neural networks. Examples drawn from wing aerodynamics, turbulent diffuser flows, gas-gas injectors, and supersonic turbines are employed to help demonstrate the issues involved in an engineering design context. Both the usefulness of the existing knowledge to aid current design
p-MEMPSODE: Parallel and irregular memetic global optimization
NASA Astrophysics Data System (ADS)
Voglis, C.; Hadjidoukas, P. E.; Parsopoulos, K. E.; Papageorgiou, D. G.; Lagaris, I. E.; Vrahatis, M. N.
2015-12-01
A parallel memetic global optimization algorithm suitable for shared memory multicore systems is proposed and analyzed. The considered algorithm combines two well-known and widely used population-based stochastic algorithms, namely Particle Swarm Optimization and Differential Evolution, with two efficient and parallelizable local search procedures. The sequential version of the algorithm was first introduced as MEMPSODE (MEMetic Particle Swarm Optimization and Differential Evolution) and published in the CPC program library. We exploit the inherent and highly irregular parallelism of the memetic global optimization algorithm by means of a dynamic and multilevel approach based on the OpenMP tasking model. In our case, tasks correspond to local optimization procedures or simple function evaluations. Parallelization occurs at each iteration step of the memetic algorithm without affecting its searching efficiency. The proposed implementation, for the same random seed, reaches the same solution irrespectively of being executed sequentially or in parallel. Extensive experimental evaluation has been performed in order to illustrate the speedup achieved on a shared-memory multicore server.
A deterministic global optimization using smooth diagonal auxiliary functions
NASA Astrophysics Data System (ADS)
Sergeyev, Yaroslav D.; Kvasov, Dmitri E.
2015-04-01
In many practical decision-making problems it happens that functions involved in optimization process are black-box with unknown analytical representations and hard to evaluate. In this paper, a global optimization problem is considered where both the goal function f (x) and its gradient f‧ (x) are black-box functions. It is supposed that f‧ (x) satisfies the Lipschitz condition over the search hyperinterval with an unknown Lipschitz constant K. A new deterministic 'Divide-the-Best' algorithm based on efficient diagonal partitions and smooth auxiliary functions is proposed in its basic version, its convergence conditions are studied and numerical experiments executed on eight hundred test functions are presented.
Comments upon the usage of derivatives in Lipschitz global optimization
NASA Astrophysics Data System (ADS)
Sergeyev, Yaroslav D.; Kvasov, Dmitri E.; Mukhametzhanov, Marat S.
2016-06-01
An optimization problem is considered where the objective function f (x) is black-box and multiextremal and the information about its gradient ∇ f (x) is available during the search. It is supposed that ∇ f (x) satisfies the Lipschitz condition over the admissible hyperinterval with an unknown Lipschitz constant K. Some numerical Lipschitz global optimization methods based on geometric ideas with the usage of different estimates of the Lipschitz constant K are presented. Results of their systematic experimental investigation are reported and commented on.
Global Optimization Methods for Gravitational Lens Systems with Regularized Sources
NASA Astrophysics Data System (ADS)
Rogers, Adam; Fiege, Jason D.
2012-11-01
Several approaches exist to model gravitational lens systems. In this study, we apply global optimization methods to find the optimal set of lens parameters using a genetic algorithm. We treat the full optimization procedure as a two-step process: an analytical description of the source plane intensity distribution is used to find an initial approximation to the optimal lens parameters; the second stage of the optimization uses a pixelated source plane with the semilinear method to determine an optimal source. Regularization is handled by means of an iterative method and the generalized cross validation (GCV) and unbiased predictive risk estimator (UPRE) functions that are commonly used in standard image deconvolution problems. This approach simultaneously estimates the optimal regularization parameter and the number of degrees of freedom in the source. Using the GCV and UPRE functions, we are able to justify an estimation of the number of source degrees of freedom found in previous work. We test our approach by applying our code to a subset of the lens systems included in the SLACS survey.
Global optimization using the y-ybar diagram
NASA Astrophysics Data System (ADS)
Brown, Daniel M.
1991-12-01
Software is under development at Teledyne Brown Engineering to represent a lens configuration as a y-ybar or Delano diagram. The program determines third-order Seidel and chromatic aberrations for each configuration. It performs a global search through all valid permutations of configuration space and determines, to within a step increment of the space, the configuration with smallest third-order aberrations. The program was developed to generate first-order optical layouts which promised to reach global minima during subsequent conventional optimization. Other operations allowed by the program are: add or delete surfaces, couple surfaces (for Mangin mirrors), shift the stop position, and display first-order properties and the optical layout (surface radii and thicknesses) for subsequent entry into a conventional lens-design program with automatic optimization. Algorithms for performing some of the key functions, not covered by previous authors, are discussed in this paper.
Multi-fidelity global design optimization including parallelization potential
NASA Astrophysics Data System (ADS)
Cox, Steven Edward
The DIRECT global optimization algorithm is a relatively new space partitioning algorithm designed to determine the globally optimal design within a designated design space. This dissertation examines the applicability of the DIRECT algorithm to two classes of design problems: unimodal functions where small amplitude, high frequency fluctuations in the objective function make optimization difficult; and multimodal functions where multiple local optima are formed by the underlying physics of the problem (as opposed to minor fluctuations in the analysis code). DIRECT is compared with two other multistart local optimization techniques on two polynomial test problems and one engineering conceptual design problem. Three modifications to the DIRECT algorithm are proposed to increase the effectiveness of the algorithm. The DIRECT-BP algorithm is presented which alters the way DIRECT searches the neighborhood of the current best point as optimization progresses. The algorithm reprioritizes which points to analyze at each iteration. This is to encourage analysis of points that surround the best point but that are farther away than the points selected by the DIRECT algorithm. This increases the robustness of the DIRECT search and provides more information on the characteristics of the neighborhood of the point selected as the global optimum. A multifidelity version of the DIRECT algorithm is proposed to reduce the cost of optimization using DIRECT. By augmenting expensive high-fidelity analysis with cheap low-fidelity analysis, the optimization can be performed with fewer high-fidelity analyses. Two correction schemes are examined using high- and low-fidelity results at one point to correct the low-fidelity result at a nearby point. This corrected value is then used in place of a high-fidelity analysis by the DIRECT algorithm. In this way the number of high-fidelity analyses required is reduced and the optimization became less expensive. Finally the DIRECT algorithm is
Asynchronous global optimization techniques for medium and large inversion problems
Pereyra, V.; Koshy, M.; Meza, J.C.
1995-04-01
We discuss global optimization procedures adequate for seismic inversion problems. We explain how to save function evaluations (which may involve large scale ray tracing or other expensive operations) by creating a data base of information on what parts of parameter space have already been inspected. It is also shown how a correct parallel implementation using PVM speeds up the process almost linearly with respect to the number of processors, provided that the function evaluations are expensive enough to offset the communication overhead.
Multidisciplinary optimization of controlled space structures with global sensitivity equations
NASA Technical Reports Server (NTRS)
Padula, Sharon L.; James, Benjamin B.; Graves, Philip C.; Woodard, Stanley E.
1991-01-01
A new method for the preliminary design of controlled space structures is presented. The method coordinates standard finite element structural analysis, multivariable controls, and nonlinear programming codes and allows simultaneous optimization of the structures and control systems of a spacecraft. Global sensitivity equations are a key feature of this method. The preliminary design of a generic geostationary platform is used to demonstrate the multidisciplinary optimization method. Fifteen design variables are used to optimize truss member sizes and feedback gain values. The goal is to reduce the total mass of the structure and the vibration control system while satisfying constraints on vibration decay rate. Incorporating the nonnegligible mass of actuators causes an essential coupling between structural design variables and control design variables. The solution of the demonstration problem is an important step toward a comprehensive preliminary design capability for structures and control systems. Use of global sensitivity equations helps solve optimization problems that have a large number of design variables and a high degree of coupling between disciplines.
Methods for accurate homology modeling by global optimization.
Joo, Keehyoung; Lee, Jinwoo; Lee, Jooyoung
2012-01-01
High accuracy protein modeling from its sequence information is an important step toward revealing the sequence-structure-function relationship of proteins and nowadays it becomes increasingly more useful for practical purposes such as in drug discovery and in protein design. We have developed a protocol for protein structure prediction that can generate highly accurate protein models in terms of backbone structure, side-chain orientation, hydrogen bonding, and binding sites of ligands. To obtain accurate protein models, we have combined a powerful global optimization method with traditional homology modeling procedures such as multiple sequence alignment, chain building, and side-chain remodeling. We have built a series of specific score functions for these steps, and optimized them by utilizing conformational space annealing, which is one of the most successful combinatorial optimization algorithms currently available.
Wu, Zong-Sheng; Fu, Wei-Ping; Xue, Ru
2015-01-01
Teaching-learning-based optimization (TLBO) algorithm is proposed in recent years that simulates the teaching-learning phenomenon of a classroom to effectively solve global optimization of multidimensional, linear, and nonlinear problems over continuous spaces. In this paper, an improved teaching-learning-based optimization algorithm is presented, which is called nonlinear inertia weighted teaching-learning-based optimization (NIWTLBO) algorithm. This algorithm introduces a nonlinear inertia weighted factor into the basic TLBO to control the memory rate of learners and uses a dynamic inertia weighted factor to replace the original random number in teacher phase and learner phase. The proposed algorithm is tested on a number of benchmark functions, and its performance comparisons are provided against the basic TLBO and some other well-known optimization algorithms. The experiment results show that the proposed algorithm has a faster convergence rate and better performance than the basic TLBO and some other algorithms as well.
Wu, Zong-Sheng; Fu, Wei-Ping; Xue, Ru
2015-01-01
Teaching-learning-based optimization (TLBO) algorithm is proposed in recent years that simulates the teaching-learning phenomenon of a classroom to effectively solve global optimization of multidimensional, linear, and nonlinear problems over continuous spaces. In this paper, an improved teaching-learning-based optimization algorithm is presented, which is called nonlinear inertia weighted teaching-learning-based optimization (NIWTLBO) algorithm. This algorithm introduces a nonlinear inertia weighted factor into the basic TLBO to control the memory rate of learners and uses a dynamic inertia weighted factor to replace the original random number in teacher phase and learner phase. The proposed algorithm is tested on a number of benchmark functions, and its performance comparisons are provided against the basic TLBO and some other well-known optimization algorithms. The experiment results show that the proposed algorithm has a faster convergence rate and better performance than the basic TLBO and some other algorithms as well. PMID:26421005
Global structual optimizations of surface systems with a genetic algorithm
Chuang, Feng-Chuan
2005-01-01
Global structural optimizations with a genetic algorithm were performed for atomic cluster and surface systems including aluminum atomic clusters, Si magic clusters on the Si(111) 7 x 7 surface, silicon high-index surfaces, and Ag-induced Si(111) reconstructions. First, the global structural optimizations of neutral aluminum clusters Al_{n} algorithm in combination with tight-binding and first-principles calculations were performed to study the structures of magic clusters on the Si(111) 7 x 7 surface. Extensive calculations show that the magic cluster observed in scanning tunneling microscopy (STM) experiments consist of eight Si atoms. Simulated STM images of the Si magic cluster exhibit a ring-like feature similar to STM experiments. Third, a genetic algorithm coupled with a highly optimized empirical potential were used to determine the lowest energy structure of high-index semiconductor surfaces. The lowest energy structures of Si(105) and Si(114) were determined successfully. The results of Si(105) and Si(114) are reported within the framework of highly optimized empirical potential and first-principles calculations. Finally, a genetic algorithm coupled with Si and Ag tight-binding potentials were used to search for Ag-induced Si(111) reconstructions at various Ag and Si coverages. The optimized structural models of √3 x √3, 3 x 1, and 5 x 2 phases were reported using first-principles calculations. A novel model is found to have lower surface energy than the proposed double-honeycomb chained (DHC) model both for Au/Si(111) 5 x 2 and Ag/Si(111) 5 x 2 systems.
A Novel Hybrid Firefly Algorithm for Global Optimization
Zhang, Lina; Liu, Liqiang; Yang, Xin-She; Dai, Yuntao
2016-01-01
Global optimization is challenging to solve due to its nonlinearity and multimodality. Traditional algorithms such as the gradient-based methods often struggle to deal with such problems and one of the current trends is to use metaheuristic algorithms. In this paper, a novel hybrid population-based global optimization algorithm, called hybrid firefly algorithm (HFA), is proposed by combining the advantages of both the firefly algorithm (FA) and differential evolution (DE). FA and DE are executed in parallel to promote information sharing among the population and thus enhance searching efficiency. In order to evaluate the performance and efficiency of the proposed algorithm, a diverse set of selected benchmark functions are employed and these functions fall into two groups: unimodal and multimodal. The experimental results show better performance of the proposed algorithm compared to the original version of the firefly algorithm (FA), differential evolution (DE) and particle swarm optimization (PSO) in the sense of avoiding local minima and increasing the convergence rate. PMID:27685869
STP: A Stochastic Tunneling Algorithm for Global Optimization
Oblow, E.M.
1999-05-20
A stochastic approach to solving continuous function global optimization problems is presented. It builds on the tunneling approach to deterministic optimization presented by Barhen et al, by combining a series of local descents with stochastic searches. The method uses a rejection-based stochastic procedure to locate new local minima descent regions and a fixed Lipschitz-like constant to reject unpromising regions in the search space, thereby increasing the efficiency of the tunneling process. The algorithm is easily implemented in low-dimensional problems and scales easily to large problems. It is less effective without further heuristics in these latter cases, however. Several improvements to the basic algorithm which make use of approximate estimates of the algorithms parameters for implementation in high-dimensional problems are also discussed. Benchmark results are presented, which show that the algorithm is competitive with the best previously reported global optimization techniques. A successful application of the approach to a large-scale seismology problem of substantial computational complexity using a low-dimensional approximation scheme is also reported.
A Global Optimization Approach to Multi-Polarity Sentiment Analysis
Li, Xinmiao; Li, Jing; Wu, Yukeng
2015-01-01
Following the rapid development of social media, sentiment analysis has become an important social media mining technique. The performance of automatic sentiment analysis primarily depends on feature selection and sentiment classification. While information gain (IG) and support vector machines (SVM) are two important techniques, few studies have optimized both approaches in sentiment analysis. The effectiveness of applying a global optimization approach to sentiment analysis remains unclear. We propose a global optimization-based sentiment analysis (PSOGO-Senti) approach to improve sentiment analysis with IG for feature selection and SVM as the learning engine. The PSOGO-Senti approach utilizes a particle swarm optimization algorithm to obtain a global optimal combination of feature dimensions and parameters in the SVM. We evaluate the PSOGO-Senti model on two datasets from different fields. The experimental results showed that the PSOGO-Senti model can improve binary and multi-polarity Chinese sentiment analysis. We compared the optimal feature subset selected by PSOGO-Senti with the features in the sentiment dictionary. The results of this comparison indicated that PSOGO-Senti can effectively remove redundant and noisy features and can select a domain-specific feature subset with a higher-explanatory power for a particular sentiment analysis task. The experimental results showed that the PSOGO-Senti approach is effective and robust for sentiment analysis tasks in different domains. By comparing the improvements of two-polarity, three-polarity and five-polarity sentiment analysis results, we found that the five-polarity sentiment analysis delivered the largest improvement. The improvement of the two-polarity sentiment analysis was the smallest. We conclude that the PSOGO-Senti achieves higher improvement for a more complicated sentiment analysis task. We also compared the results of PSOGO-Senti with those of the genetic algorithm (GA) and grid search method. From
NASA Astrophysics Data System (ADS)
Paasche, H.; Tronicke, J.
2012-04-01
In many near surface geophysical applications multiple tomographic data sets are routinely acquired to explore subsurface structures and parameters. Linking the model generation process of multi-method geophysical data sets can significantly reduce ambiguities in geophysical data analysis and model interpretation. Most geophysical inversion approaches rely on local search optimization methods used to find an optimal model in the vicinity of a user-given starting model. The final solution may critically depend on the initial model. Alternatively, global optimization (GO) methods have been used to invert geophysical data. They explore the solution space in more detail and determine the optimal model independently from the starting model. Additionally, they can be used to find sets of optimal models allowing a further analysis of model parameter uncertainties. Here we employ particle swarm optimization (PSO) to realize the global optimization of tomographic data. PSO is an emergent methods based on swarm intelligence characterized by fast and robust convergence towards optimal solutions. The fundamental principle of PSO is inspired by nature, since the algorithm mimics the behavior of a flock of birds searching food in a search space. In PSO, a number of particles cruise a multi-dimensional solution space striving to find optimal model solutions explaining the acquired data. The particles communicate their positions and success and direct their movement according to the position of the currently most successful particle of the swarm. The success of a particle, i.e. the quality of the currently found model by a particle, must be uniquely quantifiable to identify the swarm leader. When jointly inverting disparate data sets, the optimization solution has to satisfy multiple optimization objectives, at least one for each data set. Unique determination of the most successful particle currently leading the swarm is not possible. Instead, only statements about the Pareto
PROSPECT: A Computer System for Globally-Optimal Threading
Xu, D.; Xu, Y.
1999-08-06
This paper presents a new computer system, PROSPECT, for protein threading. PROSPECT employs an energy function that consists of three additive terms: (1) a singleton fitness term, (2) a distance-dependent pairwise-interaction preference term, and (3) alignment gap penalty; and currently uses FSSP as its threading template database. PROSPECT uses a divide-and-conquer algorithm to find an alignment between a query protein sequence and a protein fold template, which is guaranteed to be globally optimal for its energy function. The threading algorithm presented here significantly improves the computational efficiency of our previously-published algorithm, which makes PROSPECT a practical tool even for large protein threading problems. Mathematically, PROSPECT finds a globally-optimal threading between a query sequence of n residues and a fold template of m residues and M core secondary structures in O(nm + MnN{sup 1.5C{minus}1}) time and O(nm + nN{sup C{minus}1}) space, where C, the topological complexity of the template fold as we term, is a value which characterizes the overall structure of the considered pairwise interactions in the fold; and N represents the maximum number of possible alignments between an individual core of the fold and the query sequence when its neighboring cores are already aligned. PROSPECT allows a user to incorporate known biological constraints about the query sequence during the threading process. For given constraints, the system finds a globally-optimal threading which satisfies the constraints. Currently PROSPECT can deal with constraints which reflect geometrical relationships among residues of disulfide bonds, active sites, or determined by the NOE constraints of (low-resolution) NMR spectral data.
Practical strategy for global optimization of zoom lenses
NASA Astrophysics Data System (ADS)
Kuper, Thomas G.; Harris, Thomas I.
1998-09-01
The effectiveness of global optimizers for non-zoomed lenses has been steadily improving, but until recently their application to zoom lens design has been less successful. Although some methods have been able to make minor improvements to initial design forms, the algorithms have not consistently discovered new solutions with different group power distributions in a single run. In many cases, the difficulty appears related to how effective focal length (EFL) is controlled across zoom positions. Improvements made to the Global SynthesisTM (GS) algorithm in Code VTM, together with a revised strategy for controlling the EFL via weighted constraints, have significantly improved the ability of GS to discover distinct zoom lens solutions, including those with different group powers. We offer a plausible explanation for the success of these changes, and we discuss an example zoom lens design problem based on a 2-group, 7-element patent design.
An Adaptive Unified Differential Evolution Algorithm for Global Optimization
Qiang, Ji; Mitchell, Chad
2014-11-03
In this paper, we propose a new adaptive unified differential evolution algorithm for single-objective global optimization. Instead of the multiple mutation strate- gies proposed in conventional differential evolution algorithms, this algorithm employs a single equation unifying multiple strategies into one expression. It has the virtue of mathematical simplicity and also provides users the flexibility for broader exploration of the space of mutation operators. By making all control parameters in the proposed algorithm self-adaptively evolve during the process of optimization, it frees the application users from the burden of choosing appro- priate control parameters and also improves the performance of the algorithm. In numerical tests using thirteen basic unimodal and multimodal functions, the proposed adaptive unified algorithm shows promising performance in compari- son to several conventional differential evolution algorithms.
Design and global optimization of high-efficiency thermophotovoltaic systems.
Bermel, Peter; Ghebrebrhan, Michael; Chan, Walker; Yeng, Yi Xiang; Araghchini, Mohammad; Hamam, Rafif; Marton, Christopher H; Jensen, Klavs F; Soljačić, Marin; Joannopoulos, John D; Johnson, Steven G; Celanovic, Ivan
2010-09-13
Despite their great promise, small experimental thermophotovoltaic (TPV) systems at 1000 K generally exhibit extremely low power conversion efficiencies (approximately 1%), due to heat losses such as thermal emission of undesirable mid-wavelength infrared radiation. Photonic crystals (PhC) have the potential to strongly suppress such losses. However, PhC-based designs present a set of non-convex optimization problems requiring efficient objective function evaluation and global optimization algorithms. Both are applied to two example systems: improved micro-TPV generators and solar thermal TPV systems. Micro-TPV reactors experience up to a 27-fold increase in their efficiency and power output; solar thermal TPV systems see an even greater 45-fold increase in their efficiency (exceeding the Shockley-Quiesser limit for a single-junction photovoltaic cell).
A Unified Differential Evolution Algorithm for Global Optimization
Qiang, Ji; Mitchell, Chad
2014-06-24
Abstract?In this paper, we propose a new unified differential evolution (uDE) algorithm for single objective global optimization. Instead of selecting among multiple mutation strategies as in the conventional differential evolution algorithm, this algorithm employs a single equation as the mutation strategy. It has the virtue of mathematical simplicity and also provides users the flexbility for broader exploration of different mutation strategies. Numerical tests using twelve basic unimodal and multimodal functions show promising performance of the proposed algorithm in comparison to convential differential evolution algorithms.
A self-learning particle swarm optimizer for global optimization problems.
Li, Changhe; Yang, Shengxiang; Nguyen, Trung Thanh
2012-06-01
Particle swarm optimization (PSO) has been shown as an effective tool for solving global optimization problems. So far, most PSO algorithms use a single learning pattern for all particles, which means that all particles in a swarm use the same strategy. This monotonic learning pattern may cause the lack of intelligence for a particular particle, which makes it unable to deal with different complex situations. This paper presents a novel algorithm, called self-learning particle swarm optimizer (SLPSO), for global optimization problems. In SLPSO, each particle has a set of four strategies to cope with different situations in the search space. The cooperation of the four strategies is implemented by an adaptive learning framework at the individual level, which can enable a particle to choose the optimal strategy according to its own local fitness landscape. The experimental study on a set of 45 test functions and two real-world problems show that SLPSO has a superior performance in comparison with several other peer algorithms.
A global optimization algorithm for protein surface alignment
2010-01-01
Background A relevant problem in drug design is the comparison and recognition of protein binding sites. Binding sites recognition is generally based on geometry often combined with physico-chemical properties of the site since the conformation, size and chemical composition of the protein surface are all relevant for the interaction with a specific ligand. Several matching strategies have been designed for the recognition of protein-ligand binding sites and of protein-protein interfaces but the problem cannot be considered solved. Results In this paper we propose a new method for local structural alignment of protein surfaces based on continuous global optimization techniques. Given the three-dimensional structures of two proteins, the method finds the isometric transformation (rotation plus translation) that best superimposes active regions of two structures. We draw our inspiration from the well-known Iterative Closest Point (ICP) method for three-dimensional (3D) shapes registration. Our main contribution is in the adoption of a controlled random search as a more efficient global optimization approach along with a new dissimilarity measure. The reported computational experience and comparison show viability of the proposed approach. Conclusions Our method performs well to detect similarity in binding sites when this in fact exists. In the future we plan to do a more comprehensive evaluation of the method by considering large datasets of non-redundant proteins and applying a clustering technique to the results of all comparisons to classify binding sites. PMID:20920230
New Algorithms for Global Optimization and Reaction Path Determination.
Weber, D; Bellinger, D; Engels, B
2016-01-01
We present new schemes to improve the convergence of an important global optimization problem and to determine reaction pathways (RPs) between identified minima. Those methods have been implemented into the CAST program (Conformational Analysis and Search Tool). The first part of this chapter shows how to improve convergence of the Monte Carlo with minimization (MCM, also known as Basin Hopping) method when applied to optimize water clusters or aqueous solvation shells using a simple model. Since the random movement on the potential energy surface (PES) is an integral part of MCM, we propose to employ a hydrogen bonding-based algorithm for its improvement. We show comparisons of the results obtained for random dihedral and for the proposed random, rigid-body water molecule movement, giving evidence that a specific adaption of the distortion process greatly improves the convergence of the method. The second part is about the determination of RPs in clusters between conformational arrangements and for reactions. Besides standard approaches like the nudged elastic band method, we want to focus on a new algorithm developed especially for global reaction path search called Pathopt. We started with argon clusters, a typical benchmark system, which possess a flat PES, then stepwise increase the magnitude and directionality of interactions. Therefore, we calculated pathways for a water cluster and characterize them by frequency calculations. Within our calculations, we were able to show that beneath local pathways also additional pathways can be found which possess additional features. PMID:27497166
Optimizing a global alignment of protein interaction networks
Chindelevitch, Leonid; Ma, Cheng-Yu; Liao, Chung-Shou; Berger, Bonnie
2013-01-01
Motivation: The global alignment of protein interaction networks is a widely studied problem. It is an important first step in understanding the relationship between the proteins in different species and identifying functional orthologs. Furthermore, it can provide useful insights into the species’ evolution. Results: We propose a novel algorithm, PISwap, for optimizing global pairwise alignments of protein interaction networks, based on a local optimization heuristic that has previously demonstrated its effectiveness for a variety of other intractable problems. PISwap can begin with different types of network alignment approaches and then iteratively adjust the initial alignments by incorporating network topology information, trading it off for sequence information. In practice, our algorithm efficiently refines other well-studied alignment techniques with almost no additional time cost. We also show the robustness of the algorithm to noise in protein interaction data. In addition, the flexible nature of this algorithm makes it suitable for different applications of network alignment. This algorithm can yield interesting insights into the evolutionary dynamics of related species. Availability: Our software is freely available for non-commercial purposes from our Web site, http://piswap.csail.mit.edu/. Contact: bab@csail.mit.edu or csliao@ie.nthu.edu.tw Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24048352
GenMin: An enhanced genetic algorithm for global optimization
NASA Astrophysics Data System (ADS)
Tsoulos, Ioannis G.; Lagaris, I. E.
2008-06-01
A new method that employs grammatical evolution and a stopping rule for finding the global minimum of a continuous multidimensional, multimodal function is considered. The genetic algorithm used is a hybrid genetic algorithm in conjunction with a local search procedure. We list results from numerical experiments with a series of test functions and we compare with other established global optimization methods. The accompanying software accepts objective functions coded either in Fortran 77 or in C++. Program summaryProgram title: GenMin Catalogue identifier: AEAR_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEAR_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 35 810 No. of bytes in distributed program, including test data, etc.: 436 613 Distribution format: tar.gz Programming language: GNU-C++, GNU-C, GNU Fortran 77 Computer: The tool is designed to be portable in all systems running the GNU C++ compiler Operating system: The tool is designed to be portable in all systems running the GNU C++ compiler RAM: 200 KB Word size: 32 bits Classification: 4.9 Nature of problem: A multitude of problems in science and engineering are often reduced to minimizing a function of many variables. There are instances that a local optimum does not correspond to the desired physical solution and hence the search for a better solution is required. Local optimization techniques are frequently trapped in local minima. Global optimization is hence the appropriate tool. For example, solving a nonlinear system of equations via optimization, employing a least squares type of objective, one may encounter many local minima that do not correspond to solutions (i.e. they are far from zero). Solution method: Grammatical evolution and a stopping rule. Running time: Depending on the
GMG: A Guaranteed, Efficient Global Optimization Algorithm for Remote Sensing.
D'Helon, CD
2004-08-18
The monocular passive ranging (MPR) problem in remote sensing consists of identifying the precise range of an airborne target (missile, plane, etc.) from its observed radiance. This inverse problem may be set as a global optimization problem (GOP) whereby the difference between the observed and model predicted radiances is minimized over the possible ranges and atmospheric conditions. Using additional information about the error function between the predicted and observed radiances of the target, we developed GMG, a new algorithm to find the Global Minimum with a Guarantee. The new algorithm transforms the original continuous GOP into a discrete search problem, thereby guaranteeing to find the position of the global minimum in a reasonably short time. The algorithm is first applied to the golf course problem, which serves as a litmus test for its performance in the presence of both complete and degraded additional information. GMG is further assessed on a set of standard benchmark functions and then applied to various realizations of the MPR problem.
GMG - A guaranteed global optimization algorithm: Application to remote sensing
D'Helon, Cassius; Protopopescu, Vladimir A; Wells, Jack C; Barhen, Jacob
2007-01-01
We investigate the role of additional information in reducing the computational complexity of the global optimization problem (GOP). Following this approach, we develop GMG -- an algorithm to find the Global Minimum with a Guarantee. The new algorithm breaks up an originally continuous GOP into a discrete (grid) search problem followed by a descent problem. The discrete search identifies the basin of attraction of the global minimum after which the actual location of the minimizer is found upon applying a descent algorithm. The algorithm is first applied to the golf course problem, which serves as a litmus test for its performance in the presence of both complete and degraded additional information. GMG is further assessed on a set of standard benchmark functions. We then illustrate the performance of the the validated algorithm on a simple realization of the monocular passive ranging (MPR) problem in remote sensing, which consists of identifying the range of an airborne target (missile, plane, etc.) from its observed radiance. This inverse problem is set as a GOP whereby the difference between the observed and model predicted radiances is minimized over the possible ranges and atmospheric conditions. We solve the GOP using GMG and report on the performance of the algorithm.
Parallel global optimization with the particle swarm algorithm.
Schutte, J F; Reinbolt, J A; Fregly, B J; Haftka, R T; George, A D
2004-12-01
Present day engineering optimization problems often impose large computational demands, resulting in long solution times even on a modern high-end processor. To obtain enhanced computational throughput and global search capability, we detail the coarse-grained parallelization of an increasingly popular global search method, the particle swarm optimization (PSO) algorithm. Parallel PSO performance was evaluated using two categories of optimization problems possessing multiple local minima-large-scale analytical test problems with computationally cheap function evaluations and medium-scale biomechanical system identification problems with computationally expensive function evaluations. For load-balanced analytical test problems formulated using 128 design variables, speedup was close to ideal and parallel efficiency above 95% for up to 32 nodes on a Beowulf cluster. In contrast, for load-imbalanced biomechanical system identification problems with 12 design variables, speedup plateaued and parallel efficiency decreased almost linearly with increasing number of nodes. The primary factor affecting parallel performance was the synchronization requirement of the parallel algorithm, which dictated that each iteration must wait for completion of the slowest fitness evaluation. When the analytical problems were solved using a fixed number of swarm iterations, a single population of 128 particles produced a better convergence rate than did multiple independent runs performed using sub-populations (8 runs with 16 particles, 4 runs with 32 particles, or 2 runs with 64 particles). These results suggest that (1) parallel PSO exhibits excellent parallel performance under load-balanced conditions, (2) an asynchronous implementation would be valuable for real-life problems subject to load imbalance, and (3) larger population sizes should be considered when multiple processors are available.
Parallel global optimization with the particle swarm algorithm
Schutte, J. F.; Reinbolt, J. A.; Fregly, B. J.; Haftka, R. T.; George, A. D.
2007-01-01
SUMMARY Present day engineering optimization problems often impose large computational demands, resulting in long solution times even on a modern high-end processor. To obtain enhanced computational throughput and global search capability, we detail the coarse-grained parallelization of an increasingly popular global search method, the particle swarm optimization (PSO) algorithm. Parallel PSO performance was evaluated using two categories of optimization problems possessing multiple local minima—large-scale analytical test problems with computationally cheap function evaluations and medium-scale biomechanical system identification problems with computationally expensive function evaluations. For load-balanced analytical test problems formulated using 128 design variables, speedup was close to ideal and parallel efficiency above 95% for up to 32 nodes on a Beowulf cluster. In contrast, for load-imbalanced biomechanical system identification problems with 12 design variables, speedup plateaued and parallel efficiency decreased almost linearly with increasing number of nodes. The primary factor affecting parallel performance was the synchronization requirement of the parallel algorithm, which dictated that each iteration must wait for completion of the slowest fitness evaluation. When the analytical problems were solved using a fixed number of swarm iterations, a single population of 128 particles produced a better convergence rate than did multiple independent runs performed using sub-populations (8 runs with 16 particles, 4 runs with 32 particles, or 2 runs with 64 particles). These results suggest that (1) parallel PSO exhibits excellent parallel performance under load-balanced conditions, (2) an asynchronous implementation would be valuable for real-life problems subject to load imbalance, and (3) larger population sizes should be considered when multiple processors are available. PMID:17891226
Global Optimization and Broadband Analysis Software for Interstellar Chemistry (GOBASIC)
NASA Astrophysics Data System (ADS)
Rad, Mary L.; Zou, Luyao; Sanders, James L.; Widicus Weaver, Susanna L.
2016-01-01
Context. Broadband receivers that operate at millimeter and submillimeter frequencies necessitate the development of new tools for spectral analysis and interpretation. Simultaneous, global, multimolecule, multicomponent analysis is necessary to accurately determine the physical and chemical conditions from line-rich spectra that arise from sources like hot cores. Aims: We aim to provide a robust and efficient automated analysis program to meet the challenges presented with the large spectral datasets produced by radio telescopes. Methods: We have written a program in the MATLAB numerical computing environment for simultaneous global analysis of broadband line surveys. The Global Optimization and Broadband Analysis Software for Interstellar Chemistry (GOBASIC) program uses the simplifying assumption of local thermodynamic equilibrium (LTE) for spectral analysis to determine molecular column density, temperature, and velocity information. Results: GOBASIC achieves simultaneous, multimolecule, multicomponent fitting for broadband spectra. The number of components that can be analyzed at once is only limited by the available computational resources. Analysis of subsequent sets of molecules or components is performed iteratively while taking the previous fits into account. All features of a given molecule across the entire window are fitted at once, which is preferable to the rotation diagram approach because global analysis is less sensitive to blended features and noise features in the spectra. In addition, the fitting method used in GOBASIC is insensitive to the initial conditions chosen, the fitting is automated, and fitting can be performed in a parallel computing environment. These features make GOBASIC a valuable improvement over previously available LTE analysis methods. A copy of the sofware is available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (ftp://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/585/A23
Gradient gravitational search: An efficient metaheuristic algorithm for global optimization.
Dash, Tirtharaj; Sahu, Prabhat K
2015-05-30
The adaptation of novel techniques developed in the field of computational chemistry to solve the concerned problems for large and flexible molecules is taking the center stage with regard to efficient algorithm, computational cost and accuracy. In this article, the gradient-based gravitational search (GGS) algorithm, using analytical gradients for a fast minimization to the next local minimum has been reported. Its efficiency as metaheuristic approach has also been compared with Gradient Tabu Search and others like: Gravitational Search, Cuckoo Search, and Back Tracking Search algorithms for global optimization. Moreover, the GGS approach has also been applied to computational chemistry problems for finding the minimal value potential energy of two-dimensional and three-dimensional off-lattice protein models. The simulation results reveal the relative stability and physical accuracy of protein models with efficient computational cost. PMID:25779670
WFH: closing the global gap--achieving optimal care.
Skinner, Mark W
2012-07-01
For 50 years, the World Federation of Hemophilia (WFH) has been working globally to close the gap in care and to achieve Treatment for All patients, men and women, with haemophilia and other inherited bleeding disorders, regardless of where they might live. The WFH estimates that more than one in 1000 men and women has a bleeding disorder equating to 6,900,000 worldwide. To close the gap in care between developed and developing nations a continued focus on the successful strategies deployed heretofore will be required. However, in response to the rapid advances in treatment and emerging therapeutic advances on the horizon it will also require fresh approaches and renewed strategic thinking. It is difficult to predict what each therapeutic advance on the horizon will mean for the future, but there is no doubt that we are in a golden age of research and development, which has the prospect of revolutionizing treatment once again. An improved understanding of "optimal" treatment is fundamental to the continued evolution of global care. The challenges of answering government and payer demands for evidence-based medicine, and cost justification for the introduction and enhancement of treatment, are ever-present and growing. To sustain and improve care it is critical to build the body of outcome data for individual patients, within haemophilia treatment centers (HTCs), nationally, regionally and globally. Emerging therapeutic advances (longer half-life therapies and gene transfer) should not be justified or brought to market based only on the notion that they will be economically more affordable, although that may be the case, but rather more importantly that they will be therapeutically more advantageous. Improvements in treatment adherence, reductions in bleeding frequency (including microhemorrhages), better management of trough levels, and improved health outcomes (including quality of life) should be the foremost considerations. As part of a new WFH strategic plan
WFH: closing the global gap--achieving optimal care.
Skinner, Mark W
2012-07-01
For 50 years, the World Federation of Hemophilia (WFH) has been working globally to close the gap in care and to achieve Treatment for All patients, men and women, with haemophilia and other inherited bleeding disorders, regardless of where they might live. The WFH estimates that more than one in 1000 men and women has a bleeding disorder equating to 6,900,000 worldwide. To close the gap in care between developed and developing nations a continued focus on the successful strategies deployed heretofore will be required. However, in response to the rapid advances in treatment and emerging therapeutic advances on the horizon it will also require fresh approaches and renewed strategic thinking. It is difficult to predict what each therapeutic advance on the horizon will mean for the future, but there is no doubt that we are in a golden age of research and development, which has the prospect of revolutionizing treatment once again. An improved understanding of "optimal" treatment is fundamental to the continued evolution of global care. The challenges of answering government and payer demands for evidence-based medicine, and cost justification for the introduction and enhancement of treatment, are ever-present and growing. To sustain and improve care it is critical to build the body of outcome data for individual patients, within haemophilia treatment centers (HTCs), nationally, regionally and globally. Emerging therapeutic advances (longer half-life therapies and gene transfer) should not be justified or brought to market based only on the notion that they will be economically more affordable, although that may be the case, but rather more importantly that they will be therapeutically more advantageous. Improvements in treatment adherence, reductions in bleeding frequency (including microhemorrhages), better management of trough levels, and improved health outcomes (including quality of life) should be the foremost considerations. As part of a new WFH strategic plan
Quantum-inspired immune clonal algorithm for global optimization.
Jiao, Licheng; Li, Yangyang; Gong, Maoguo; Zhang, Xiangrong
2008-10-01
Based on the concepts and principles of quantum computing, a novel immune clonal algorithm, called a quantum-inspired immune clonal algorithm (QICA), is proposed to deal with the problem of global optimization. In QICA, the antibody is proliferated and divided into a set of subpopulation groups. The antibodies in a subpopulation group are represented by multistate gene quantum bits. In the antibody's updating, the general quantum rotation gate strategy and the dynamic adjusting angle mechanism are applied to accelerate convergence. The quantum not gate is used to realize quantum mutation to avoid premature convergences. The proposed quantum recombination realizes the information communication between subpopulation groups to improve the search efficiency. Theoretical analysis proves that QICA converges to the global optimum. In the first part of the experiments, 10 unconstrained and 13 constrained benchmark functions are used to test the performance of QICA. The results show that QICA performs much better than the other improved genetic algorithms in terms of the quality of solution and computational cost. In the second part of the experiments, QICA is applied to a practical problem (i.e., multiuser detection in direct-sequence code-division multiple-access systems) with a satisfying result.
ABCluster: the artificial bee colony algorithm for cluster global optimization.
Zhang, Jun; Dolg, Michael
2015-10-01
Global optimization of cluster geometries is of fundamental importance in chemistry and an interesting problem in applied mathematics. In this work, we introduce a relatively new swarm intelligence algorithm, i.e. the artificial bee colony (ABC) algorithm proposed in 2005, to this field. It is inspired by the foraging behavior of a bee colony, and only three parameters are needed to control it. We applied it to several potential functions of quite different nature, i.e., the Coulomb-Born-Mayer, Lennard-Jones, Morse, Z and Gupta potentials. The benchmarks reveal that for long-ranged potentials the ABC algorithm is very efficient in locating the global minimum, while for short-ranged ones it is sometimes trapped into a local minimum funnel on a potential energy surface of large clusters. We have released an efficient, user-friendly, and free program "ABCluster" to realize the ABC algorithm. It is a black-box program for non-experts as well as experts and might become a useful tool for chemists to study clusters. PMID:26327507
Optimizing global liver function in radiation therapy treatment planning
NASA Astrophysics Data System (ADS)
Wu, Victor W.; Epelman, Marina A.; Wang, Hesheng; Romeijn, H. Edwin; Feng, Mary; Cao, Yue; Ten Haken, Randall K.; Matuszak, Martha M.
2016-09-01
Liver stereotactic body radiation therapy (SBRT) patients differ in both pre-treatment liver function (e.g. due to degree of cirrhosis and/or prior treatment) and radiosensitivity, leading to high variability in potential liver toxicity with similar doses. This work investigates three treatment planning optimization models that minimize risk of toxicity: two consider both voxel-based pre-treatment liver function and local-function-based radiosensitivity with dose; one considers only dose. Each model optimizes different objective functions (varying in complexity of capturing the influence of dose on liver function) subject to the same dose constraints and are tested on 2D synthesized and 3D clinical cases. The normal-liver-based objective functions are the linearized equivalent uniform dose (\\ell \\text{EUD} ) (conventional ‘\\ell \\text{EUD} model’), the so-called perfusion-weighted \\ell \\text{EUD} (\\text{fEUD} ) (proposed ‘fEUD model’), and post-treatment global liver function (GLF) (proposed ‘GLF model’), predicted by a new liver-perfusion-based dose-response model. The resulting \\ell \\text{EUD} , fEUD, and GLF plans delivering the same target \\ell \\text{EUD} are compared with respect to their post-treatment function and various dose-based metrics. Voxel-based portal venous liver perfusion, used as a measure of local function, is computed using DCE-MRI. In cases used in our experiments, the GLF plan preserves up to 4.6 % ≤ft(7.5 % \\right) more liver function than the fEUD (\\ell \\text{EUD} ) plan does in 2D cases, and up to 4.5 % ≤ft(5.6 % \\right) in 3D cases. The GLF and fEUD plans worsen in \\ell \\text{EUD} of functional liver on average by 1.0 Gy and 0.5 Gy in 2D and 3D cases, respectively. Liver perfusion information can be used during treatment planning to minimize the risk of toxicity by improving expected GLF; the degree of benefit varies with perfusion pattern. Although fEUD model optimization is computationally inexpensive and
Geophysical Inversion With Multi-Objective Global Optimization Methods
NASA Astrophysics Data System (ADS)
Lelièvre, Peter; Bijani, Rodrigo; Farquharson, Colin
2016-04-01
We are investigating the use of Pareto multi-objective global optimization (PMOGO) methods to solve numerically complicated geophysical inverse problems. PMOGO methods can be applied to highly nonlinear inverse problems, to those where derivatives are discontinuous or simply not obtainable, and to those were multiple minima exist in the problem space. PMOGO methods generate a suite of solutions that minimize multiple objectives (e.g. data misfits and regularization terms) in a Pareto-optimal sense. This allows a more complete assessment of the possibilities and provides opportunities to calculate statistics regarding the likelihood of particular model features. We are applying PMOGO methods to four classes of inverse problems. The first are discrete-body problems where the inversion determines values of several parameters that define the location, orientation, size and physical properties of an anomalous body represented by a simple shape, for example a sphere, ellipsoid, cylinder or cuboid. A PMOGO approach can determine not only the optimal shape parameters for the anomalous body but also the optimal shape itself. Furthermore, when one expects several anomalous bodies in the subsurface, a PMOGO inversion approach can determine an optimal number of parameterized bodies. The second class of inverse problems are standard mesh-based problems where the physical property values in each cell are treated as continuous variables. The third class of problems are lithological inversions, which are also mesh-based but cells can only take discrete physical property values corresponding to known or assumed rock units. In the fourth class, surface geometry inversions, we consider a fundamentally different type of problem in which a model comprises wireframe surfaces representing contacts between rock units. The physical properties of each rock unit remain fixed while the inversion controls the position of the contact surfaces via control nodes. Surface geometry inversion can be
Arasomwan, Martins Akugbe; Adewumi, Aderemi Oluyinka
2013-01-01
Linear decreasing inertia weight (LDIW) strategy was introduced to improve on the performance of the original particle swarm optimization (PSO). However, linear decreasing inertia weight PSO (LDIW-PSO) algorithm is known to have the shortcoming of premature convergence in solving complex (multipeak) optimization problems due to lack of enough momentum for particles to do exploitation as the algorithm approaches its terminal point. Researchers have tried to address this shortcoming by modifying LDIW-PSO or proposing new PSO variants. Some of these variants have been claimed to outperform LDIW-PSO. The major goal of this paper is to experimentally establish the fact that LDIW-PSO is very much efficient if its parameters are properly set. First, an experiment was conducted to acquire a percentage value of the search space limits to compute the particle velocity limits in LDIW-PSO based on commonly used benchmark global optimization problems. Second, using the experimentally obtained values, five well-known benchmark optimization problems were used to show the outstanding performance of LDIW-PSO over some of its competitors which have in the past claimed superiority over it. Two other recent PSO variants with different inertia weight strategies were also compared with LDIW-PSO with the latter outperforming both in the simulation experiments conducted. PMID:24324383
Joint Geophysical Inversion With Multi-Objective Global Optimization Methods
NASA Astrophysics Data System (ADS)
Lelievre, P. G.; Bijani, R.; Farquharson, C. G.
2015-12-01
Pareto multi-objective global optimization (PMOGO) methods generate a suite of solutions that minimize multiple objectives (e.g. data misfits and regularization terms) in a Pareto-optimal sense. Providing a suite of models, as opposed to a single model that minimizes a weighted sum of objectives, allows a more complete assessment of the possibilities and avoids the often difficult choice of how to weight each objective. We are applying PMOGO methods to three classes of inverse problems. The first class are standard mesh-based problems where the physical property values in each cell are treated as continuous variables. The second class of problems are also mesh-based but cells can only take discrete physical property values corresponding to known or assumed rock units. In the third class we consider a fundamentally different type of inversion in which a model comprises wireframe surfaces representing contacts between rock units; the physical properties of each rock unit remain fixed while the inversion controls the position of the contact surfaces via control nodes. This third class of problem is essentially a geometry inversion, which can be used to recover the unknown geometry of a target body or to investigate the viability of a proposed Earth model. Joint inversion is greatly simplified for the latter two problem classes because no additional mathematical coupling measure is required in the objective function. PMOGO methods can solve numerically complicated problems that could not be solved with standard descent-based local minimization methods. This includes the latter two classes of problems mentioned above. There are significant increases in the computational requirements when PMOGO methods are used but these can be ameliorated using parallelization and problem dimension reduction strategies.
Zou, Feng; Chen, Debao; Wang, Jiangtao
2016-01-01
An improved teaching-learning-based optimization with combining of the social character of PSO (TLBO-PSO), which is considering the teacher's behavior influence on the students and the mean grade of the class, is proposed in the paper to find the global solutions of function optimization problems. In this method, the teacher phase of TLBO is modified; the new position of the individual is determined by the old position, the mean position, and the best position of current generation. The method overcomes disadvantage that the evolution of the original TLBO might stop when the mean position of students equals the position of the teacher. To decrease the computation cost of the algorithm, the process of removing the duplicate individual in original TLBO is not adopted in the improved algorithm. Moreover, the probability of local convergence of the improved method is decreased by the mutation operator. The effectiveness of the proposed method is tested on some benchmark functions, and the results are competitive with respect to some other methods. PMID:27057157
Zou, Feng; Chen, Debao; Wang, Jiangtao
2016-01-01
An improved teaching-learning-based optimization with combining of the social character of PSO (TLBO-PSO), which is considering the teacher's behavior influence on the students and the mean grade of the class, is proposed in the paper to find the global solutions of function optimization problems. In this method, the teacher phase of TLBO is modified; the new position of the individual is determined by the old position, the mean position, and the best position of current generation. The method overcomes disadvantage that the evolution of the original TLBO might stop when the mean position of students equals the position of the teacher. To decrease the computation cost of the algorithm, the process of removing the duplicate individual in original TLBO is not adopted in the improved algorithm. Moreover, the probability of local convergence of the improved method is decreased by the mutation operator. The effectiveness of the proposed method is tested on some benchmark functions, and the results are competitive with respect to some other methods. PMID:27057157
Global optimization of fuel consumption in rendezvous scenarios by the method of interval analysis
NASA Astrophysics Data System (ADS)
Ma, Hongliang; Xu, Shijie
2015-03-01
To reduce the optimal but large Δv of the fixed-short-time two impulse Lambert rendezvous between two spacecrafts along two coplanar circular orbits, the three-impulse Lambert rendezvous optimized via the optimization algorithm-interval analysis (IA) is proposed in this paper. The purpose of optimization is to minimize the velocity increment of the fixed-short-time three-impulse Lambert rendezvous. The optimization algorithm IA is given for solving the rendezvous optimization problem with multiple uncertain variables, and strong nonlinearity and nonconvexity. Numerical examples of the time-open, coplanar-circular-orbit, multiple-revolution Lambert rendezvous with a parking time optimized via the optimization algorithm IA are firstly undertaken to validate the feasibility of the optimization algorithm IA by comparing the optimization results with those of the globally optimal Hohmann transfer. The results indicate that the globally optimal parameters of the time-open coplanar-circular-orbit multiple-revolution Lambert rendezvous can be obtained by the optimization algorithm IA, and the initial separation angle of two spacecrafts with different orbit radius can be adjusted to obtain the globally optimal and small Δv by distributing an optimal parking time. After that, for the fixed-short-time two-impulse Lambert rendezvous problem without sufficient time to adjust the separation angle by distributing a parking time like the open-time Lambert rendezvous problem, three-impulse Lambert rendezvous involving multiple optimization variables is given and the variables are optimized by the optimization algorithm IA to obtain an optimal and small Δv. Numerical simulation indicates that the optimal and small Δv of the fixed short time, three-impulse Lambert rendezvous can be obtained using the optimization algorithm IA.
Fournier, René; Mohareb, Amir
2016-01-14
We devised a global optimization (GO) strategy for optimizing molecular properties with respect to both geometry and chemical composition. A relative index of thermodynamic stability (RITS) is introduced to allow meaningful energy comparisons between different chemical species. We use the RITS by itself, or in combination with another calculated property, to create an objective function F to be minimized. Including the RITS in the definition of F ensures that the solutions have some degree of thermodynamic stability. We illustrate how the GO strategy works with three test applications, with F calculated in the framework of Kohn-Sham Density Functional Theory (KS-DFT) with the Perdew-Burke-Ernzerhof exchange-correlation. First, we searched the composition and configuration space of CmHnNpOq (m = 0-4, n = 0-10, p = 0-2, q = 0-2, and 2 ≤ m + n + p + q ≤ 12) for stable molecules. The GO discovered familiar molecules like N2, CO2, acetic acid, acetonitrile, ethane, and many others, after a small number (5000) of KS-DFT energy evaluations. Second, we carried out a GO of the geometry of CumSnn (+) (m = 1, 2 and n = 9-12). A single GO run produced the same low-energy structures found in an earlier study where each CumSnn (+) species had been optimized separately. Finally, we searched bimetallic clusters AmBn (3 ≤ m + n ≤ 6, A,B= Li, Na, Al, Cu, Ag, In, Sn, Pb) for species and configurations having a low RITS and large highest occupied Molecular Orbital (MO) to lowest unoccupied MO energy gap (Eg). We found seven bimetallic clusters with Eg > 1.5 eV. PMID:26772561
NASA Astrophysics Data System (ADS)
Fournier, René; Mohareb, Amir
2016-01-01
We devised a global optimization (GO) strategy for optimizing molecular properties with respect to both geometry and chemical composition. A relative index of thermodynamic stability (RITS) is introduced to allow meaningful energy comparisons between different chemical species. We use the RITS by itself, or in combination with another calculated property, to create an objective function F to be minimized. Including the RITS in the definition of F ensures that the solutions have some degree of thermodynamic stability. We illustrate how the GO strategy works with three test applications, with F calculated in the framework of Kohn-Sham Density Functional Theory (KS-DFT) with the Perdew-Burke-Ernzerhof exchange-correlation. First, we searched the composition and configuration space of CmHnNpOq (m = 0-4, n = 0-10, p = 0-2, q = 0-2, and 2 ≤ m + n + p + q ≤ 12) for stable molecules. The GO discovered familiar molecules like N2, CO2, acetic acid, acetonitrile, ethane, and many others, after a small number (5000) of KS-DFT energy evaluations. Second, we carried out a GO of the geometry of Cu m Snn + (m = 1, 2 and n = 9-12). A single GO run produced the same low-energy structures found in an earlier study where each Cu m S nn + species had been optimized separately. Finally, we searched bimetallic clusters AmBn (3 ≤ m + n ≤ 6, A,B= Li, Na, Al, Cu, Ag, In, Sn, Pb) for species and configurations having a low RITS and large highest occupied Molecular Orbital (MO) to lowest unoccupied MO energy gap (Eg). We found seven bimetallic clusters with Eg > 1.5 eV.
Global Optimization of Low-Thrust Interplanetary Trajectories Subject to Operational Constraints
NASA Technical Reports Server (NTRS)
Englander, Jacob A.; Vavrina, Matthew A.; Hinckley, David
2016-01-01
Low-thrust interplanetary space missions are highly complex and there can be many locally optimal solutions. While several techniques exist to search for globally optimal solutions to low-thrust trajectory design problems, they are typically limited to unconstrained trajectories. The operational design community in turn has largely avoided using such techniques and has primarily focused on accurate constrained local optimization combined with grid searches and intuitive design processes at the expense of efficient exploration of the global design space. This work is an attempt to bridge the gap between the global optimization and operational design communities by presenting a mathematical framework for global optimization of low-thrust trajectories subject to complex constraints including the targeting of planetary landing sites, a solar range constraint to simplify the thermal design of the spacecraft, and a real-world multi-thruster electric propulsion system that must switch thrusters on and off as available power changes over the course of a mission.
Nonlinear Global Optimization Using Curdling Algorithm in Mathematica Environmet
Craig Loehle, Ph. D.
1997-08-05
An algorithm for performing optimization which is a derivative-free, grid-refinement approach to nonlinear optimization was developed and implemented in software as OPTIMIZE. This approach overcomes a number of deficiencies in existing approaches. Most notably, it finds extremal regions rather than only single extremal points. the program is interactive and collects information on control parameters and constraints using menus. For up to two (and potentially three) dimensions, function convergence is displayed graphically. Because the algorithm does not compute derivatives, gradients, or vectors, it is numerically stable. It can find all the roots of a polynomial in one pass. It is an inherently parallel algorithm. OPTIMIZE-M is a modification of OPTIMIZE designed for use within the Mathematica environment created by Wolfram Research.
Nonlinear Global Optimization Using Curdling Algorithm in Mathematica Environmet
1997-08-05
An algorithm for performing optimization which is a derivative-free, grid-refinement approach to nonlinear optimization was developed and implemented in software as OPTIMIZE. This approach overcomes a number of deficiencies in existing approaches. Most notably, it finds extremal regions rather than only single extremal points. the program is interactive and collects information on control parameters and constraints using menus. For up to two (and potentially three) dimensions, function convergence is displayed graphically. Because the algorithm doesmore » not compute derivatives, gradients, or vectors, it is numerically stable. It can find all the roots of a polynomial in one pass. It is an inherently parallel algorithm. OPTIMIZE-M is a modification of OPTIMIZE designed for use within the Mathematica environment created by Wolfram Research.« less
Optimal Detection of Global Warming using Temperature Profiles
NASA Technical Reports Server (NTRS)
Leroy, Stephen S.
1997-01-01
Optimal fingerprinting is applied to estimate the amount of time it would take to detect warming by increased concentrations of carbon dioxide in monthly averages of temperature profiles over the Indian Ocean.
NASA Astrophysics Data System (ADS)
Younis, Adel; Dong, Zuomin
2012-07-01
Surrogate-based modeling is an effective search method for global design optimization over well-defined areas using complex and computationally intensive analysis and simulation tools. However, indentifying the appreciate surrogate models and their suitable areas remains a challenge that requires extensive human intervention. In this work, a new global optimization algorithm, namely Mixed Surrogate and Space Elimination (MSSE) method, is introduced. Representative surrogate models, including Quadratic Response Surface, Radial Basis function, and Kriging, are mixed with different weight ratios to form an adaptive metamodel with best tested performance. The approach divides the field of interest into several unimodal regions; identifies and ranks the regions that likely contain the global minimum; fits the weighted surrogate models over each promising region using additional design experiment data points from Latin Hypercube Designs and adjusts the weights according to the performance of each model; identifies its minimum and removes the processed region; and moves to the next most promising region until all regions are processed and the global optimum is identified. The proposed algorithm was tested using several benchmark problems for global optimization and compared with several widely used space exploration global optimization algorithms, showing reduced computation efforts, robust performance and comparable search accuracy, making the proposed method an excellent tool for computationally intensive global design optimization problems.
Avoiding spurious submovement decompositions : a globally optimal algorithm.
Rohrer, Brandon Robinson; Hogan, Neville
2003-07-01
Evidence for the existence of discrete submovements underlying continuous human movement has motivated many attempts to extract them. Although they produce visually convincing results, all of the methodologies that have been employed are prone to produce spurious decompositions. Examples of potential failures are given. A branch-and-bound algorithm for submovement extraction, capable of global nonlinear minimization (and hence capable of avoiding spurious decompositions), is developed and demonstrated.
Global stability and optimal control of an SIRS epidemic model on heterogeneous networks
NASA Astrophysics Data System (ADS)
Chen, Lijuan; Sun, Jitao
2014-09-01
In this paper, we consider an SIRS epidemic model with vaccination on heterogeneous networks. By constructing suitable Lyapunov functions, global stability of the disease-free equilibrium and the endemic equilibrium of the model is investigated. Also we firstly study an optimally controlled SIRS epidemic model on complex networks. We show that an optimal control exists for the control problem. Finally some examples are presented to show the global stability and the efficiency of this optimal control. These results can help in adopting pragmatic treatment upon diseases in structured populations.
Global optimization for future gravitational wave detector sites
NASA Astrophysics Data System (ADS)
Hu, Yi-Ming; Raffai, Péter; Gondán, László; Heng, Ik Siong; Kelecsényi, Nándor; Hendry, Martin; Márka, Zsuzsa; Márka, Szabolcs
2015-05-01
We consider the optimal site selection of future generations of gravitational wave (GW) detectors. Previously, Raffai et al optimized a two-detector network with a combined figure of merit (FoM). This optimization was extended to networks with more than two detectors in a limited way by first fixing the parameters of all other component detectors. In this work we now present a more general optimization that allows the locations of all detectors to be simultaneously chosen. We follow the definition of Raffai et al on the metric that defines the suitability of a certain detector network. Given the locations of the component detectors in the network, we compute a measure of the network's ability to distinguish the polarization, constrain the sky localization and reconstruct the parameters of a GW source. We further define the ‘flexibility index’ for a possible site location, by counting the number of multi-detector networks with a sufficiently high FoM that include that site location. We confirm the conclusion of Raffai et al, that in terms of the flexibility index as defined in this work, Australia hosts the best candidate site to build a future generation GW detector. This conclusion is valid for either a three-detector network or a five-detector network. For a three-detector network, site locations in Northern Europe display a comparable flexibility index to sites in Australia. However, for a five-detector network, Australia is found to be a clearly better candidate than any other location.
An evolutionary algorithm for global optimization based on self-organizing maps
NASA Astrophysics Data System (ADS)
Barmada, Sami; Raugi, Marco; Tucci, Mauro
2016-10-01
In this article, a new population-based algorithm for real-parameter global optimization is presented, which is denoted as self-organizing centroids optimization (SOC-opt). The proposed method uses a stochastic approach which is based on the sequential learning paradigm for self-organizing maps (SOMs). A modified version of the SOM is proposed where each cell contains an individual, which performs a search for a locally optimal solution and it is affected by the search for a global optimum. The movement of the individuals in the search space is based on a discrete-time dynamic filter, and various choices of this filter are possible to obtain different dynamics of the centroids. In this way, a general framework is defined where well-known algorithms represent a particular case. The proposed algorithm is validated through a set of problems, which include non-separable problems, and compared with state-of-the-art algorithms for global optimization.
Fast globally optimal segmentation of cells in fluorescence microscopy images.
Bergeest, Jan-Philip; Rohr, Karl
2011-01-01
Accurate and efficient segmentation of cells in fluorescence microscopy images is of central importance for the quantification of protein expression in high-throughput screening applications. We propose a new approach for segmenting cell nuclei which is based on active contours and convex energy functionals. Compared to previous work, our approach determines the global solution. Thus, the approach does not suffer from local minima and the segmentation result does not depend on the initialization. We also suggest a numeric approach for efficiently computing the solution. The performance of our approach has been evaluated using fluorescence microscopy images of different cell types. We have also performed a quantitative comparison with previous segmentation approaches.
Global Optimization, Local Adaptation, and the Role of Growth in Distribution Networks
NASA Astrophysics Data System (ADS)
Ronellenfitsch, Henrik; Katifori, Eleni
2016-09-01
Highly optimized complex transport networks serve crucial functions in many man-made and natural systems such as power grids and plant or animal vasculature. Often, the relevant optimization functional is nonconvex and characterized by many local extrema. In general, finding the global, or nearly global optimum is difficult. In biological systems, it is believed that such an optimal state is slowly achieved through natural selection. However, general coarse grained models for flow networks with local positive feedback rules for the vessel conductivity typically get trapped in low efficiency, local minima. In this work we show how the growth of the underlying tissue, coupled to the dynamical equations for network development, can drive the system to a dramatically improved optimal state. This general model provides a surprisingly simple explanation for the appearance of highly optimized transport networks in biology such as leaf and animal vasculature.
Handling inequality constraints in continuous nonlinear global optimization
Wang, Tao; Wah, B.W.
1996-12-31
In this paper, we present a new method to handle inequality constraints and apply it in NOVEL (Nonlinear Optimization via External Lead), a system we have developed for solving constrained continuous nonlinear optimization problems. In general, in applying Lagrange-multiplier methods to solve these problems, inequality constraints are first converted into equivalent equality constraints. One such conversion method adds a slack variable to each inequality constraint in order to convert it into an equality constraint. The disadvantage of this conversion is that when the search is inside a feasible region, some satisfied constraints may still pose a non-zero weight in the Lagrangian function, leading to possible oscillations and divergence when a local optimum lies on the boundary of a feasible region. We propose a new conversion method called the MaxQ method such that all satisfied constraints in a feasible region always carry zero weight in the Lagrange function; hence, minimizing the Lagrange function in a feasible region always leads to local minima of the objective function. We demonstrate that oscillations do not happen in our method. We also propose methods to speed up convergence when a local optimum lies on the boundary of a feasible region. Finally, we show improved experimental results in applying our proposed method in NOVEL on some existing benchmark problems and compare them to those obtained by applying the method based on slack variables.
OPTIMIZE-M. Nonlinear Global Optimization Using Curdling Algorithm in Mathematica Environmet
Loehle, C.
1997-07-01
An algorithm for performing optimization which is a derivative-free, grid-refinement approach to nonlinear optimization was developed and implemented in software as OPTIMIZE. This approach overcomes a number of deficiencies in existing approaches. Most notably, it finds extremal regions rather than only single extremal points. the program is interactive and collects information on control parameters and constraints using menus. For up to two (and potentially three) dimensions, function convergence is displayed graphically. Because the algorithm does not compute derivatives, gradients, or vectors, it is numerically stable. It can find all the roots of a polynomial in one pass. It is an inherently parallel algorithm. OPTIMIZE-M is a modification of OPTIMIZE designed for use within the Mathematica environment created by Wolfram Research.
A Global Optimization Methodology for Rocket Propulsion Applications
NASA Technical Reports Server (NTRS)
2001-01-01
While the response surface method is an effective method in engineering optimization, its accuracy is often affected by the use of limited amount of data points for model construction. In this chapter, the issues related to the accuracy of the RS approximations and possible ways of improving the RS model using appropriate treatments, including the iteratively re-weighted least square (IRLS) technique and the radial-basis neural networks, are investigated. A main interest is to identify ways to offer added capabilities for the RS method to be able to at least selectively improve the accuracy in regions of importance. An example is to target the high efficiency region of a fluid machinery design space so that the predictive power of the RS can be maximized when it matters most. Analytical models based on polynomials, with controlled level of noise, are used to assess the performance of these techniques.
Metamodel-based global optimization using fuzzy clustering for design space reduction
NASA Astrophysics Data System (ADS)
Li, Yulin; Liu, Li; Long, Teng; Dong, Weili
2013-09-01
High fidelity analysis are utilized in modern engineering design optimization problems which involve expensive black-box models. For computation-intensive engineering design problems, efficient global optimization methods must be developed to relieve the computational burden. A new metamodel-based global optimization method using fuzzy clustering for design space reduction (MGO-FCR) is presented. The uniformly distributed initial sample points are generated by Latin hypercube design to construct the radial basis function metamodel, whose accuracy is improved with increasing number of sample points gradually. Fuzzy c-mean method and Gath-Geva clustering method are applied to divide the design space into several small interesting cluster spaces for low and high dimensional problems respectively. Modeling efficiency and accuracy are directly related to the design space, so unconcerned spaces are eliminated by the proposed reduction principle and two pseudo reduction algorithms. The reduction principle is developed to determine whether the current design space should be reduced and which space is eliminated. The first pseudo reduction algorithm improves the speed of clustering, while the second pseudo reduction algorithm ensures the design space to be reduced. Through several numerical benchmark functions, comparative studies with adaptive response surface method, approximated unimodal region elimination method and mode-pursuing sampling are carried out. The optimization results reveal that this method captures the real global optimum for all the numerical benchmark functions. And the number of function evaluations show that the efficiency of this method is favorable especially for high dimensional problems. Based on this global design optimization method, a design optimization of a lifting surface in high speed flow is carried out and this method saves about 10 h compared with genetic algorithms. This method possesses favorable performance on efficiency, robustness
A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems.
Cao, Leilei; Xu, Lihong; Goodman, Erik D
2016-01-01
A Guiding Evolutionary Algorithm (GEA) with greedy strategy for global optimization problems is proposed. Inspired by Particle Swarm Optimization, the Genetic Algorithm, and the Bat Algorithm, the GEA was designed to retain some advantages of each method while avoiding some disadvantages. In contrast to the usual Genetic Algorithm, each individual in GEA is crossed with the current global best one instead of a randomly selected individual. The current best individual served as a guide to attract offspring to its region of genotype space. Mutation was added to offspring according to a dynamic mutation probability. To increase the capability of exploitation, a local search mechanism was applied to new individuals according to a dynamic probability of local search. Experimental results show that GEA outperformed the other three typical global optimization algorithms with which it was compared. PMID:27293421
A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems
Cao, Leilei; Xu, Lihong; Goodman, Erik D.
2016-01-01
A Guiding Evolutionary Algorithm (GEA) with greedy strategy for global optimization problems is proposed. Inspired by Particle Swarm Optimization, the Genetic Algorithm, and the Bat Algorithm, the GEA was designed to retain some advantages of each method while avoiding some disadvantages. In contrast to the usual Genetic Algorithm, each individual in GEA is crossed with the current global best one instead of a randomly selected individual. The current best individual served as a guide to attract offspring to its region of genotype space. Mutation was added to offspring according to a dynamic mutation probability. To increase the capability of exploitation, a local search mechanism was applied to new individuals according to a dynamic probability of local search. Experimental results show that GEA outperformed the other three typical global optimization algorithms with which it was compared. PMID:27293421
A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems.
Cao, Leilei; Xu, Lihong; Goodman, Erik D
2016-01-01
A Guiding Evolutionary Algorithm (GEA) with greedy strategy for global optimization problems is proposed. Inspired by Particle Swarm Optimization, the Genetic Algorithm, and the Bat Algorithm, the GEA was designed to retain some advantages of each method while avoiding some disadvantages. In contrast to the usual Genetic Algorithm, each individual in GEA is crossed with the current global best one instead of a randomly selected individual. The current best individual served as a guide to attract offspring to its region of genotype space. Mutation was added to offspring according to a dynamic mutation probability. To increase the capability of exploitation, a local search mechanism was applied to new individuals according to a dynamic probability of local search. Experimental results show that GEA outperformed the other three typical global optimization algorithms with which it was compared.
Method for using global optimization to the estimation of surface-consistent residual statics
Reister, David B.; Barhen, Jacob; Oblow, Edward M.
2001-01-01
An efficient method for generating residual statics corrections to compensate for surface-consistent static time shifts in stacked seismic traces. The method includes a step of framing the residual static corrections as a global optimization problem in a parameter space. The method also includes decoupling the global optimization problem involving all seismic traces into several one-dimensional problems. The method further utilizes a Stochastic Pijavskij Tunneling search to eliminate regions in the parameter space where a global minimum is unlikely to exist so that the global minimum may be quickly discovered. The method finds the residual statics corrections by maximizing the total stack power. The stack power is a measure of seismic energy transferred from energy sources to receivers.
Optimization of global model composed of radial basis functions using the term-ranking approach
Cai, Peng; Tao, Chao Liu, Xiao-Jun
2014-03-15
A term-ranking method is put forward to optimize the global model composed of radial basis functions to improve the predictability of the model. The effectiveness of the proposed method is examined by numerical simulation and experimental data. Numerical simulations indicate that this method can significantly lengthen the prediction time and decrease the Bayesian information criterion of the model. The application to real voice signal shows that the optimized global model can capture more predictable component in chaos-like voice data and simultaneously reduce the predictable component (periodic pitch) in the residual signal.
More on conditions of local and global minima coincidence in discrete optimization problems
Lebedeva, T.T.; Sergienko, I.V.; Soltan, V.P.
1994-05-01
In some areas of discrete optimization, it is necessary to isolate classes of problems whose target functions do not have local or strictly local minima that differ from the global minima. Examples include optimizations on discrete metric spaces and graphs, lattices and partially ordered sets, and linear combinatorial problems. A unified schema that to a certain extent generalizes the convexity models on which the above-cited works are based has been presented in articles. This article is a continuation of that research.
An adaptive metamodel-based global optimization algorithm for black-box type problems
NASA Astrophysics Data System (ADS)
Jie, Haoxiang; Wu, Yizhong; Ding, Jianwan
2015-11-01
In this article, an adaptive metamodel-based global optimization (AMGO) algorithm is presented to solve unconstrained black-box problems. In the AMGO algorithm, a type of hybrid model composed of kriging and augmented radial basis function (RBF) is used as the surrogate model. The weight factors of hybrid model are adaptively selected in the optimization process. To balance the local and global search, a sub-optimization problem is constructed during each iteration to determine the new iterative points. As numerical experiments, six standard two-dimensional test functions are selected to show the distributions of iterative points. The AMGO algorithm is also tested on seven well-known benchmark optimization problems and contrasted with three representative metamodel-based optimization methods: efficient global optimization (EGO), GutmannRBF and hybrid and adaptive metamodel (HAM). The test results demonstrate the efficiency and robustness of the proposed method. The AMGO algorithm is finally applied to the structural design of the import and export chamber of a cycloid gear pump, achieving satisfactory results.
NASA Astrophysics Data System (ADS)
Raimunda da Silva, E.; Camacho, R. G. R.; Filho, N. M.
2010-08-01
It presents a methodology for global optimization with constraints of expensive functions using response surfaces models for aerodynamic cascade representing the turbomachine axial with profiles of family NACA65. For the calculation of flow, is used Fluent CFD software, which is on a local and global variations in the flow field. It has been verified that small geometric on the stagger angle, format airfoil and the spacing between the blades, can lead to changes in the efficiency of the blade. Accordingly, we intend to integrate the solution flow through CFD optimization programs based on the construction of metamodels, aiming to obtain considerable gains in computational time. Integration with the optimization programs is necessary to build "script" command to automatically generate the mesh, where the design variables that define the geometry of the blade cascade as stagger angle, pitch to chord and the camber be modified among pre-established limits based on optimization algorithms, in order to achieve an objective function pre-defined, how to obtain the maximum ratio of Cl/ Cd (lift/drag). This methodology for global optimization based on the construction of metamodels together with the random search algorithm controlled (CRSA) is based on iterative construction of response surfaces with radial basis functions (multiquadric) and the application of heuristic criteria to update the database during the optimization process. Cyclical patterns of search are iteratively used to determine the candidate points to be included in the database.
Autonomous Modelling of X-ray Spectra Using Robust Global Optimization Methods
NASA Astrophysics Data System (ADS)
Rogers, Adam; Safi-Harb, Samar; Fiege, Jason
2015-08-01
The standard approach to model fitting in X-ray astronomy is by means of local optimization methods. However, these local optimizers suffer from a number of problems, such as a tendency for the fit parameters to become trapped in local minima, and can require an involved process of detailed user intervention to guide them through the optimization process. In this work we introduce a general GUI-driven global optimization method for fitting models to X-ray data, written in MATLAB, which searches for optimal models with minimal user interaction. We directly interface with the commonly used XSPEC libraries to access the full complement of pre-existing spectral models that describe a wide range of physics appropriate for modelling astrophysical sources, including supernova remnants and compact objects. Our algorithm is powered by the Ferret genetic algorithm and Locust particle swarm optimizer from the Qubist Global Optimization Toolbox, which are robust at finding families of solutions and identifying degeneracies. This technique will be particularly instrumental for multi-parameter models and high-fidelity data. In this presentation, we provide details of the code and use our techniques to analyze X-ray data obtained from a variety of astrophysical sources.
Zhong, Shangping; Chen, Tianshun; He, Fengying; Niu, Yuzhen
2014-09-01
For a practical pattern classification task solved by kernel methods, the computing time is mainly spent on kernel learning (or training). However, the current kernel learning approaches are based on local optimization techniques, and hard to have good time performances, especially for large datasets. Thus the existing algorithms cannot be easily extended to large-scale tasks. In this paper, we present a fast Gaussian kernel learning method by solving a specially structured global optimization (SSGO) problem. We optimize the Gaussian kernel function by using the formulated kernel target alignment criterion, which is a difference of increasing (d.i.) functions. Through using a power-transformation based convexification method, the objective criterion can be represented as a difference of convex (d.c.) functions with a fixed power-transformation parameter. And the objective programming problem can then be converted to a SSGO problem: globally minimizing a concave function over a convex set. The SSGO problem is classical and has good solvability. Thus, to find the global optimal solution efficiently, we can adopt the improved Hoffman's outer approximation method, which need not repeat the searching procedure with different starting points to locate the best local minimum. Also, the proposed method can be proven to converge to the global solution for any classification task. We evaluate the proposed method on twenty benchmark datasets, and compare it with four other Gaussian kernel learning methods. Experimental results show that the proposed method stably achieves both good time-efficiency performance and good classification performance. PMID:24929345
Zhong, Shangping; Chen, Tianshun; He, Fengying; Niu, Yuzhen
2014-09-01
For a practical pattern classification task solved by kernel methods, the computing time is mainly spent on kernel learning (or training). However, the current kernel learning approaches are based on local optimization techniques, and hard to have good time performances, especially for large datasets. Thus the existing algorithms cannot be easily extended to large-scale tasks. In this paper, we present a fast Gaussian kernel learning method by solving a specially structured global optimization (SSGO) problem. We optimize the Gaussian kernel function by using the formulated kernel target alignment criterion, which is a difference of increasing (d.i.) functions. Through using a power-transformation based convexification method, the objective criterion can be represented as a difference of convex (d.c.) functions with a fixed power-transformation parameter. And the objective programming problem can then be converted to a SSGO problem: globally minimizing a concave function over a convex set. The SSGO problem is classical and has good solvability. Thus, to find the global optimal solution efficiently, we can adopt the improved Hoffman's outer approximation method, which need not repeat the searching procedure with different starting points to locate the best local minimum. Also, the proposed method can be proven to converge to the global solution for any classification task. We evaluate the proposed method on twenty benchmark datasets, and compare it with four other Gaussian kernel learning methods. Experimental results show that the proposed method stably achieves both good time-efficiency performance and good classification performance.
NASA Astrophysics Data System (ADS)
Shabbir, Faisal; Omenzetter, Piotr
2014-04-01
Much effort is devoted nowadays to derive accurate finite element (FE) models to be used for structural health monitoring, damage detection and assessment. However, formation of a FE model representative of the original structure is a difficult task. Model updating is a branch of optimization which calibrates the FE model by comparing the modal properties of the actual structure with these of the FE predictions. As the number of experimental measurements is usually much smaller than the number of uncertain parameters, and, consequently, not all uncertain parameters are selected for model updating, different local minima may exist in the solution space. Experimental noise further exacerbates the problem. The attainment of a global solution in a multi-dimensional search space is a challenging problem. Global optimization algorithms (GOAs) have received interest in the previous decade to solve this problem, but no GOA can ensure the detection of the global minimum either. To counter this problem, a combination of GOA with sequential niche technique (SNT) has been proposed in this research which systematically searches the whole solution space. A dynamically tested full scale pedestrian bridge is taken as a case study. Two different GOAs, namely particle swarm optimization (PSO) and genetic algorithm (GA), are investigated in combination with SNT. The results of these GOA are compared in terms of their efficiency in detecting global minima. The systematic search enables to find different solutions in the search space, thus increasing the confidence of finding the global minimum.
Optimal Design of Grid-Stiffened Composite Panels Using Global and Local Buckling Analysis
Ambur, D.R.; Jaunky, N.; Knight, N.F. Jr.
1996-04-01
A design strategy for optimal design of composite grid-stiffened panels subjected to global and local buckling constraints is developed using a discrete optimizer. An improved smeared stiffener theory is used for the global buckling analysis. Local buckling of skin segments is assessed using a Rayleigh-Ritz method that accounts for material anisotropy and transverse shear flexibility. The local buckling of stiffener segments is also assessed. Design variables are the axial and transverse stiffener spacing, stiffener height and thickness, skin laminate, and stiffening configuration. The design optimization process is adapted to identify the lightest-weight stiffening configuration and pattern for grid stiffened composite panels given the overall panel dimensions, design in-plane loads, material properties, and boundary conditions of the grid-stiffened panel.
Optimal Design of Grid-Stiffened Composite Panels Using Global and Local Buckling Analysis
NASA Technical Reports Server (NTRS)
Ambur, Damodar R.; Jaunky, Navin; Knight, Norman F., Jr.
1996-01-01
A design strategy for optimal design of composite grid-stiffened panels subjected to global and local buckling constraints is developed using a discrete optimizer. An improved smeared stiffener theory is used for the global buckling analysis. Local buckling of skin segments is assessed using a Rayleigh-Ritz method that accounts for material anisotropy and transverse shear flexibility. The local buckling of stiffener segments is also assessed. Design variables are the axial and transverse stiffener spacing, stiffener height and thickness, skin laminate, and stiffening configuration. The design optimization process is adapted to identify the lightest-weight stiffening configuration and pattern for grid stiffened composite panels given the overall panel dimensions, design in-plane loads, material properties, and boundary conditions of the grid-stiffened panel.
Chaotic Teaching-Learning-Based Optimization with Lévy Flight for Global Numerical Optimization.
He, Xiangzhu; Huang, Jida; Rao, Yunqing; Gao, Liang
2016-01-01
Recently, teaching-learning-based optimization (TLBO), as one of the emerging nature-inspired heuristic algorithms, has attracted increasing attention. In order to enhance its convergence rate and prevent it from getting stuck in local optima, a novel metaheuristic has been developed in this paper, where particular characteristics of the chaos mechanism and Lévy flight are introduced to the basic framework of TLBO. The new algorithm is tested on several large-scale nonlinear benchmark functions with different characteristics and compared with other methods. Experimental results show that the proposed algorithm outperforms other algorithms and achieves a satisfactory improvement over TLBO. PMID:26941785
Chaotic Teaching-Learning-Based Optimization with Lévy Flight for Global Numerical Optimization.
He, Xiangzhu; Huang, Jida; Rao, Yunqing; Gao, Liang
2016-01-01
Recently, teaching-learning-based optimization (TLBO), as one of the emerging nature-inspired heuristic algorithms, has attracted increasing attention. In order to enhance its convergence rate and prevent it from getting stuck in local optima, a novel metaheuristic has been developed in this paper, where particular characteristics of the chaos mechanism and Lévy flight are introduced to the basic framework of TLBO. The new algorithm is tested on several large-scale nonlinear benchmark functions with different characteristics and compared with other methods. Experimental results show that the proposed algorithm outperforms other algorithms and achieves a satisfactory improvement over TLBO.
Chaotic Teaching-Learning-Based Optimization with Lévy Flight for Global Numerical Optimization
He, Xiangzhu; Huang, Jida; Rao, Yunqing; Gao, Liang
2016-01-01
Recently, teaching-learning-based optimization (TLBO), as one of the emerging nature-inspired heuristic algorithms, has attracted increasing attention. In order to enhance its convergence rate and prevent it from getting stuck in local optima, a novel metaheuristic has been developed in this paper, where particular characteristics of the chaos mechanism and Lévy flight are introduced to the basic framework of TLBO. The new algorithm is tested on several large-scale nonlinear benchmark functions with different characteristics and compared with other methods. Experimental results show that the proposed algorithm outperforms other algorithms and achieves a satisfactory improvement over TLBO. PMID:26941785
A comparative study of expected improvement-assisted global optimization with different surrogates
NASA Astrophysics Data System (ADS)
Wang, Hu; Ye, Fan; Li, Enying; Li, Guangyao
2016-08-01
Efficient global optimization (EGO) uses the surrogate uncertainty estimator called expected improvement (EI) to guide the selection of the next sampling candidates. Theoretically, any modelling methods can be integrated with the EI criterion. To improve the convergence ratio, a multi-surrogate efficient global optimization (MSEGO) was suggested. In practice, the EI-based optimization methods with different surrogates show widely divergent characteristics. Therefore, it is important to choose the most suitable algorithm for a certain problem. For this purpose, four single-surrogate efficient global optimizations (SSEGOs) and an MSEGO involving four surrogates are investigated. According to numerical tests, both the SSEGOs and the MSEGO are feasible for weak nonlinear problems. However, they are not robust for strong nonlinear problems, especially for multimodal and high-dimensional problems. Moreover, to investigate the feasibility of EGO in practice, a material identification benchmark is designed to demonstrate the performance of EGO methods. According to the tests in this study, the kriging EGO is generally the most robust method.
Global optimization for optical thin-film design using Latin Squares
NASA Astrophysics Data System (ADS)
Li, Dong-guang; Watson, Anthony
1997-10-01
There are many advanced local and global optimization techniques, such as Gradient, Simplex, Flip-flop, Needle, Genetic and Simulated Annealing, which have been successfully applied to optical thin-film design. However, all these optimization techniques either require a selection of a reasonable starting design, which is a big obstacle to an inexperienced designer, or they have some kind of inbuilt random feature, which may give rise to different answers each time. To find the true global optimized solution for a thin film design problem, we need to solve an array of interlinked multi-dimensional simultaneous equations. Until recently, for more than just a few layers, this has been a very difficult task, requiring the use of a supercomputer and highly skilled programming. By using orthogonal Latin Square theory and an experimental design methodology in a search space reduction process, a Windows based program has been written that can operate on even a 20 MHz 386 computer. It can find the global optimum design for up to 23 layers using as many dispersive and lossy materials as one wishes, within a period of hours. Additionally this methodology (called DGL-Optimization) allows the use of multiple target spectra with such as both s & p polarization, for reflection and transmission simultaneously.
NASA Astrophysics Data System (ADS)
Wang, Xuewu; Shi, Yingpan; Ding, Dongyan; Gu, Xingsheng
2016-02-01
Spot-welding robots have a wide range of applications in manufacturing industries. There are usually many weld joints in a welding task, and a reasonable welding path to traverse these weld joints has a significant impact on welding efficiency. Traditional manual path planning techniques can handle a few weld joints effectively, but when the number of weld joints is large, it is difficult to obtain the optimal path. The traditional manual path planning method is also time consuming and inefficient, and cannot guarantee optimality. Double global optimum genetic algorithm-particle swarm optimization (GA-PSO) based on the GA and PSO algorithms is proposed to solve the welding robot path planning problem, where the shortest collision-free paths are used as the criteria to optimize the welding path. Besides algorithm effectiveness analysis and verification, the simulation results indicate that the algorithm has strong searching ability and practicality, and is suitable for welding robot path planning.
Global Optimization of Interplanetary Trajectories in the Presence of Realistic Mission Contraints
NASA Technical Reports Server (NTRS)
Hinckley, David, Jr.; Englander, Jacob; Hitt, Darren
2015-01-01
Interplanetary missions are often subject to difficult constraints, like solar phase angle upon arrival at the destination, velocity at arrival, and altitudes for flybys. Preliminary design of such missions is often conducted by solving the unconstrained problem and then filtering away solutions which do not naturally satisfy the constraints. However this can bias the search into non-advantageous regions of the solution space, so it can be better to conduct preliminary design with the full set of constraints imposed. In this work two stochastic global search methods are developed which are well suited to the constrained global interplanetary trajectory optimization problem.
NASA Astrophysics Data System (ADS)
Kanazaki, Masahiro; Matsuno, Takashi; Maeda, Kengo; Kawazoe, Hiromitsu
2015-09-01
A kriging-based genetic algorithm called efficient global optimization (EGO) was employed to optimize the parameters for the operating conditions of plasma actuators. The aerodynamic performance was evaluated by wind tunnel testing to overcome the disadvantages of time-consuming numerical simulations. The proposed system was used on two design problems to design the power supply for a plasma actuator. The first case was the drag minimization problem around a semicircular cylinder. In this case, the inhibitory effect of flow separation was also observed. The second case was the lift maximization problem around a circular cylinder. This case was similar to the aerofoil design, because the circular cylinder has potential to work as an aerofoil owing to the control of the flow circulation by the plasma actuators with four design parameters. In this case, applicability to the multi-variant design problem was also investigated. Based on these results, optimum designs and global design information were obtained while drastically reducing the number of experiments required compared to a full factorial experiment.
The fully actuated traffic control problem solved by global optimization and complementarity
NASA Astrophysics Data System (ADS)
Ribeiro, Isabel M.; de Lurdes de Oliveira Simões, Maria
2016-02-01
Global optimization and complementarity are used to determine the signal timing for fully actuated traffic control, regarding effective green and red times on each cycle. The average values of these parameters can be used to estimate the control delay of vehicles. In this article, a two-phase queuing system for a signalized intersection is outlined, based on the principle of minimization of the total waiting time for the vehicles. The underlying model results in a linear program with linear complementarity constraints, solved by a sequential complementarity algorithm. Departure rates of vehicles during green and yellow periods were treated as deterministic, while arrival rates of vehicles were assumed to follow a Poisson distribution. Several traffic scenarios were created and solved. The numerical results reveal that it is possible to use global optimization and complementarity over a reasonable number of cycles and determine with efficiency effective green and red times for a signalized intersection.
Guo, Y C; Wang, H; Wu, H P; Zhang, M Q
2015-12-21
Aimed to address the defects of the large mean square error (MSE), and the slow convergence speed in equalizing the multi-modulus signals of the constant modulus algorithm (CMA), a multi-modulus algorithm (MMA) based on global artificial fish swarm (GAFS) intelligent optimization of DNA encoding sequences (GAFS-DNA-MMA) was proposed. To improve the convergence rate and reduce the MSE, this proposed algorithm adopted an encoding method based on DNA nucleotide chains to provide a possible solution to the problem. Furthermore, the GAFS algorithm, with its fast convergence and global search ability, was used to find the best sequence. The real and imaginary parts of the initial optimal weight vector of MMA were obtained through DNA coding of the best sequence. The simulation results show that the proposed algorithm has a faster convergence speed and smaller MSE in comparison with the CMA, the MMA, and the AFS-DNA-MMA.
Ringed Seal Search for Global Optimization via a Sensitive Search Model.
Saadi, Younes; Yanto, Iwan Tri Riyadi; Herawan, Tutut; Balakrishnan, Vimala; Chiroma, Haruna; Risnumawan, Anhar
2016-01-01
The efficiency of a metaheuristic algorithm for global optimization is based on its ability to search and find the global optimum. However, a good search often requires to be balanced between exploration and exploitation of the search space. In this paper, a new metaheuristic algorithm called Ringed Seal Search (RSS) is introduced. It is inspired by the natural behavior of the seal pup. This algorithm mimics the seal pup movement behavior and its ability to search and choose the best lair to escape predators. The scenario starts once the seal mother gives birth to a new pup in a birthing lair that is constructed for this purpose. The seal pup strategy consists of searching and selecting the best lair by performing a random walk to find a new lair. Affected by the sensitive nature of seals against external noise emitted by predators, the random walk of the seal pup takes two different search states, normal state and urgent state. In the normal state, the pup performs an intensive search between closely adjacent lairs; this movement is modeled via a Brownian walk. In an urgent state, the pup leaves the proximity area and performs an extensive search to find a new lair from sparse targets; this movement is modeled via a Levy walk. The switch between these two states is realized by the random noise emitted by predators. The algorithm keeps switching between normal and urgent states until the global optimum is reached. Tests and validations were performed using fifteen benchmark test functions to compare the performance of RSS with other baseline algorithms. The results show that RSS is more efficient than Genetic Algorithm, Particles Swarm Optimization and Cuckoo Search in terms of convergence rate to the global optimum. The RSS shows an improvement in terms of balance between exploration (extensive) and exploitation (intensive) of the search space. The RSS can efficiently mimic seal pups behavior to find best lair and provide a new algorithm to be used in global
Ringed Seal Search for Global Optimization via a Sensitive Search Model
Saadi, Younes; Yanto, Iwan Tri Riyadi; Herawan, Tutut; Balakrishnan, Vimala; Chiroma, Haruna; Risnumawan, Anhar
2016-01-01
The efficiency of a metaheuristic algorithm for global optimization is based on its ability to search and find the global optimum. However, a good search often requires to be balanced between exploration and exploitation of the search space. In this paper, a new metaheuristic algorithm called Ringed Seal Search (RSS) is introduced. It is inspired by the natural behavior of the seal pup. This algorithm mimics the seal pup movement behavior and its ability to search and choose the best lair to escape predators. The scenario starts once the seal mother gives birth to a new pup in a birthing lair that is constructed for this purpose. The seal pup strategy consists of searching and selecting the best lair by performing a random walk to find a new lair. Affected by the sensitive nature of seals against external noise emitted by predators, the random walk of the seal pup takes two different search states, normal state and urgent state. In the normal state, the pup performs an intensive search between closely adjacent lairs; this movement is modeled via a Brownian walk. In an urgent state, the pup leaves the proximity area and performs an extensive search to find a new lair from sparse targets; this movement is modeled via a Levy walk. The switch between these two states is realized by the random noise emitted by predators. The algorithm keeps switching between normal and urgent states until the global optimum is reached. Tests and validations were performed using fifteen benchmark test functions to compare the performance of RSS with other baseline algorithms. The results show that RSS is more efficient than Genetic Algorithm, Particles Swarm Optimization and Cuckoo Search in terms of convergence rate to the global optimum. The RSS shows an improvement in terms of balance between exploration (extensive) and exploitation (intensive) of the search space. The RSS can efficiently mimic seal pups behavior to find best lair and provide a new algorithm to be used in global
Ringed Seal Search for Global Optimization via a Sensitive Search Model.
Saadi, Younes; Yanto, Iwan Tri Riyadi; Herawan, Tutut; Balakrishnan, Vimala; Chiroma, Haruna; Risnumawan, Anhar
2016-01-01
The efficiency of a metaheuristic algorithm for global optimization is based on its ability to search and find the global optimum. However, a good search often requires to be balanced between exploration and exploitation of the search space. In this paper, a new metaheuristic algorithm called Ringed Seal Search (RSS) is introduced. It is inspired by the natural behavior of the seal pup. This algorithm mimics the seal pup movement behavior and its ability to search and choose the best lair to escape predators. The scenario starts once the seal mother gives birth to a new pup in a birthing lair that is constructed for this purpose. The seal pup strategy consists of searching and selecting the best lair by performing a random walk to find a new lair. Affected by the sensitive nature of seals against external noise emitted by predators, the random walk of the seal pup takes two different search states, normal state and urgent state. In the normal state, the pup performs an intensive search between closely adjacent lairs; this movement is modeled via a Brownian walk. In an urgent state, the pup leaves the proximity area and performs an extensive search to find a new lair from sparse targets; this movement is modeled via a Levy walk. The switch between these two states is realized by the random noise emitted by predators. The algorithm keeps switching between normal and urgent states until the global optimum is reached. Tests and validations were performed using fifteen benchmark test functions to compare the performance of RSS with other baseline algorithms. The results show that RSS is more efficient than Genetic Algorithm, Particles Swarm Optimization and Cuckoo Search in terms of convergence rate to the global optimum. The RSS shows an improvement in terms of balance between exploration (extensive) and exploitation (intensive) of the search space. The RSS can efficiently mimic seal pups behavior to find best lair and provide a new algorithm to be used in global
Economic optimization of a global strategy to address the pandemic threat.
Pike, Jamison; Bogich, Tiffany; Elwood, Sarah; Finnoff, David C; Daszak, Peter
2014-12-30
Emerging pandemics threaten global health and economies and are increasing in frequency. Globally coordinated strategies to combat pandemics, similar to current strategies that address climate change, are largely adaptive, in that they attempt to reduce the impact of a pathogen after it has emerged. However, like climate change, mitigation strategies have been developed that include programs to reduce the underlying drivers of pandemics, particularly animal-to-human disease transmission. Here, we use real options economic modeling of current globally coordinated adaptation strategies for pandemic prevention. We show that they would be optimally implemented within 27 y to reduce the annual rise of emerging infectious disease events by 50% at an estimated one-time cost of approximately $343.7 billion. We then analyze World Bank data on multilateral "One Health" pandemic mitigation programs. We find that, because most pandemics have animal origins, mitigation is a more cost-effective policy than business-as-usual adaptation programs, saving between $344.0.7 billion and $360.3 billion over the next 100 y if implemented today. We conclude that globally coordinated pandemic prevention policies need to be enacted urgently to be optimally effective and that strategies to mitigate pandemics by reducing the impact of their underlying drivers are likely to be more effective than business as usual. PMID:25512538
Economic optimization of a global strategy to address the pandemic threat.
Pike, Jamison; Bogich, Tiffany; Elwood, Sarah; Finnoff, David C; Daszak, Peter
2014-12-30
Emerging pandemics threaten global health and economies and are increasing in frequency. Globally coordinated strategies to combat pandemics, similar to current strategies that address climate change, are largely adaptive, in that they attempt to reduce the impact of a pathogen after it has emerged. However, like climate change, mitigation strategies have been developed that include programs to reduce the underlying drivers of pandemics, particularly animal-to-human disease transmission. Here, we use real options economic modeling of current globally coordinated adaptation strategies for pandemic prevention. We show that they would be optimally implemented within 27 y to reduce the annual rise of emerging infectious disease events by 50% at an estimated one-time cost of approximately $343.7 billion. We then analyze World Bank data on multilateral "One Health" pandemic mitigation programs. We find that, because most pandemics have animal origins, mitigation is a more cost-effective policy than business-as-usual adaptation programs, saving between $344.0.7 billion and $360.3 billion over the next 100 y if implemented today. We conclude that globally coordinated pandemic prevention policies need to be enacted urgently to be optimally effective and that strategies to mitigate pandemics by reducing the impact of their underlying drivers are likely to be more effective than business as usual.
SU-E-J-130: Automating Liver Segmentation Via Combined Global and Local Optimization
Li, Dengwang; Wang, Jie; Kapp, Daniel S.; Xing, Lei
2015-06-15
Purpose: The aim of this work is to develop a robust algorithm for accurate segmentation of liver with special attention paid to the problems with fuzzy edges and tumor. Methods: 200 CT images were collected from radiotherapy treatment planning system. 150 datasets are selected as the panel data for shape dictionary and parameters estimation. The remaining 50 datasets were used as test images. In our study liver segmentation was formulated as optimization process of implicit function. The liver region was optimized via local and global optimization during iterations. Our method consists five steps: 1)The livers from the panel data were segmented manually by physicians, and then We estimated the parameters of GMM (Gaussian mixture model) and MRF (Markov random field). Shape dictionary was built by utilizing the 3D liver shapes. 2)The outlines of chest and abdomen were located according to rib structure in the input images, and the liver region was initialized based on GMM. 3)The liver shape for each 2D slice was adjusted using MRF within the neighborhood of liver edge for local optimization. 4)The 3D liver shape was corrected by employing SSR (sparse shape representation) based on liver shape dictionary for global optimization. Furthermore, H-PSO(Hybrid Particle Swarm Optimization) was employed to solve the SSR equation. 5)The corrected 3D liver was divided into 2D slices as input data of the third step. The iteration was repeated within the local optimization and global optimization until it satisfied the suspension conditions (maximum iterations and changing rate). Results: The experiments indicated that our method performed well even for the CT images with fuzzy edge and tumors. Comparing with physician delineated results, the segmentation accuracy with the 50 test datasets (VOE, volume overlap percentage) was on average 91%–95%. Conclusion: The proposed automatic segmentation method provides a sensible technique for segmentation of CT images. This work is
NASA Astrophysics Data System (ADS)
Göktürkler, G.; Balkaya, Ç.
2012-10-01
Three naturally inspired meta-heuristic algorithms—the genetic algorithm (GA), simulated annealing (SA) and particle swarm optimization (PSO)—were used to invert some of the self-potential (SP) anomalies originated by some polarized bodies with simple geometries. Both synthetic and field data sets were considered. The tests with the synthetic data comprised of the solutions with both noise-free and noisy data; in the tests with the field data some SP anomalies observed over a copper belt (India), graphite deposits (Germany) and metallic sulfide (Turkey) were inverted. The model parameters included the electric dipole moment, polarization angle, depth, shape factor and origin of the anomaly. The estimated parameters were compared with those from previous studies using various optimization algorithms, mainly least-squares approaches, on the same data sets. During the test studies the solutions by GA, PSO and SA were characterized as being consistent with each other; a good starting model was not a requirement to reach the global minimum. It can be concluded that the global optimization algorithms considered in this study were able to yield compatible solutions with those from widely used local optimization algorithms.
NASA Technical Reports Server (NTRS)
Jaunky, N.; Ambur, D. R.; Knight, N. F., Jr.
1998-01-01
A design strategy for optimal design of composite grid-stiffened cylinders subjected to global and local buckling constraints and strength constraints was developed using a discrete optimizer based on a genetic algorithm. An improved smeared stiffener theory was used for the global analysis. Local buckling of skin segments were assessed using a Rayleigh-Ritz method that accounts for material anisotropy. The local buckling of stiffener segments were also assessed. Constraints on the axial membrane strain in the skin and stiffener segments were imposed to include strength criteria in the grid-stiffened cylinder design. Design variables used in this study were the axial and transverse stiffener spacings, stiffener height and thickness, skin laminate stacking sequence and stiffening configuration, where stiffening configuration is a design variable that indicates the combination of axial, transverse and diagonal stiffener in the grid-stiffened cylinder. The design optimization process was adapted to identify the best suited stiffening configurations and stiffener spacings for grid-stiffened composite cylinder with the length and radius of the cylinder, the design in-plane loads and material properties as inputs. The effect of having axial membrane strain constraints in the skin and stiffener segments in the optimization process is also studied for selected stiffening configurations.
NASA Technical Reports Server (NTRS)
Malone, Brett; Mason, W. H.
1992-01-01
An extension of our parametric multidisciplinary optimization method to include design results connecting multiple objective functions is presented. New insight into the effect of the figure of merit (objective function) on aircraft configuration size and shape is demonstrated using this technique. An aircraft concept, subject to performance and aerodynamic constraints, is optimized using the global sensitivity equation method for a wide range of objective functions. These figures of merit are described parametrically such that a series of multiobjective optimal solutions can be obtained. Computational speed is facilitated by use of algebraic representations of the system technologies. Using this method, the evolution of an optimum design from one objective function to another is demonstrated. Specifically, combinations of minimum takeoff gross weight, fuel weight, and maximum cruise performance and productivity parameters are used as objective functions.
Peña, J M; Lozano, J A; Larrañaga, P
2005-01-01
Many optimization problems are what can be called globally multimodal, i.e., they present several global optima. Unfortunately, this is a major source of difficulties for most estimation of distribution algorithms, making their effectiveness and efficiency degrade, due to genetic drift. With the aim of overcoming these drawbacks for discrete globally multimodal problem optimization, this paper introduces and evaluates a new estimation of distribution algorithm based on unsupervised learning of Bayesian networks. We report the satisfactory results of our experiments with symmetrical binary optimization problems.
Tsai, Ko-Fan; Chu, Shu-Chun
2016-09-19
The one-time ray-tracing optimization method is a fast way to design LED illumination systems [Opt. Express22, 5357 (2014)10.1364/OE.22.005357]. The method optimizes the performance of LED illumination systems by modifying the LEDs' luminous intensity distribution curve (LIDC) with a freeform lens, instead of modifying the illumination system structure. In finding the LEDs' LIDC for optimizing the illumination system's performance, the LEDs' LIDC found by means of a general gradient descent method can be trapped in a local solution. This study develops a matrix operation method to directly find the global solution of the LEDs' LIDC for the optimization of the illumination system's performance for any initial design of an illumination system structure. As compared with the gradient descent method, using the proposed characteristic matrix operation method to find the best LEDs' LIDC reduces the cost in time by several orders of magnitude. The proposed characteristic matrix operation method ensures that the one-time ray-tracing optimization method is an efficient and reliable method for designing LED illumination systems. PMID:27661876
Tsai, Ko-Fan; Chu, Shu-Chun
2016-09-19
The one-time ray-tracing optimization method is a fast way to design LED illumination systems [Opt. Express22, 5357 (2014)10.1364/OE.22.005357]. The method optimizes the performance of LED illumination systems by modifying the LEDs' luminous intensity distribution curve (LIDC) with a freeform lens, instead of modifying the illumination system structure. In finding the LEDs' LIDC for optimizing the illumination system's performance, the LEDs' LIDC found by means of a general gradient descent method can be trapped in a local solution. This study develops a matrix operation method to directly find the global solution of the LEDs' LIDC for the optimization of the illumination system's performance for any initial design of an illumination system structure. As compared with the gradient descent method, using the proposed characteristic matrix operation method to find the best LEDs' LIDC reduces the cost in time by several orders of magnitude. The proposed characteristic matrix operation method ensures that the one-time ray-tracing optimization method is an efficient and reliable method for designing LED illumination systems.
Global-Local Analysis and Optimization of a Composite Civil Tilt-Rotor Wing
NASA Technical Reports Server (NTRS)
Rais-Rohani, Masound
1999-01-01
This report gives highlights of an investigation on the design and optimization of a thin composite wing box structure for a civil tilt-rotor aircraft. Two different concepts are considered for the cantilever wing: (a) a thin monolithic skin design, and (b) a thick sandwich skin design. Each concept is examined with three different skin ply patterns based on various combinations of 0, +/-45, and 90 degree plies. The global-local technique is used in the analysis and optimization of the six design models. The global analysis is based on a finite element model of the wing-pylon configuration while the local analysis uses a uniformly supported plate representing a wing panel. Design allowables include those on vibration frequencies, panel buckling, and material strength. The design optimization problem is formulated as one of minimizing the structural weight subject to strength, stiffness, and d,vnamic constraints. Six different loading conditions based on three different flight modes are considered in the design optimization. The results of this investigation reveal that of all the loading conditions the one corresponding to the rolling pull-out in the airplane mode is the most stringent. Also the frequency constraints are found to drive the skin thickness limits, rendering the buckling constraints inactive. The optimum skin ply pattern for the monolithic skin concept is found to be (((0/+/-45/90/(0/90)(sub 2))(sub s))(sub s), while for the sandwich skin concept the optimal ply pattern is found to be ((0/+/-45/90)(sub 2s))(sub s).
Optimizing rice yields while minimizing yield-scaled global warming potential.
Pittelkow, Cameron M; Adviento-Borbe, Maria A; van Kessel, Chris; Hill, James E; Linquist, Bruce A
2014-05-01
To meet growing global food demand with limited land and reduced environmental impact, agricultural greenhouse gas (GHG) emissions are increasingly evaluated with respect to crop productivity, i.e., on a yield-scaled as opposed to area basis. Here, we compiled available field data on CH4 and N2 O emissions from rice production systems to test the hypothesis that in response to fertilizer nitrogen (N) addition, yield-scaled global warming potential (GWP) will be minimized at N rates that maximize yields. Within each study, yield N surplus was calculated to estimate deficit or excess N application rates with respect to the optimal N rate (defined as the N rate at which maximum yield was achieved). Relationships between yield N surplus and GHG emissions were assessed using linear and nonlinear mixed-effects models. Results indicate that yields increased in response to increasing N surplus when moving from deficit to optimal N rates. At N rates contributing to a yield N surplus, N2 O and yield-scaled N2 O emissions increased exponentially. In contrast, CH4 emissions were not impacted by N inputs. Accordingly, yield-scaled CH4 emissions decreased with N addition. Overall, yield-scaled GWP was minimized at optimal N rates, decreasing by 21% compared to treatments without N addition. These results are unique compared to aerobic cropping systems in which N2 O emissions are the primary contributor to GWP, meaning yield-scaled GWP may not necessarily decrease for aerobic crops when yields are optimized by N fertilizer addition. Balancing gains in agricultural productivity with climate change concerns, this work supports the concept that high rice yields can be achieved with minimal yield-scaled GWP through optimal N application rates. Moreover, additional improvements in N use efficiency may further reduce yield-scaled GWP, thereby strengthening the economic and environmental sustainability of rice systems. PMID:24115565
Optimizing rice yields while minimizing yield-scaled global warming potential.
Pittelkow, Cameron M; Adviento-Borbe, Maria A; van Kessel, Chris; Hill, James E; Linquist, Bruce A
2014-05-01
To meet growing global food demand with limited land and reduced environmental impact, agricultural greenhouse gas (GHG) emissions are increasingly evaluated with respect to crop productivity, i.e., on a yield-scaled as opposed to area basis. Here, we compiled available field data on CH4 and N2 O emissions from rice production systems to test the hypothesis that in response to fertilizer nitrogen (N) addition, yield-scaled global warming potential (GWP) will be minimized at N rates that maximize yields. Within each study, yield N surplus was calculated to estimate deficit or excess N application rates with respect to the optimal N rate (defined as the N rate at which maximum yield was achieved). Relationships between yield N surplus and GHG emissions were assessed using linear and nonlinear mixed-effects models. Results indicate that yields increased in response to increasing N surplus when moving from deficit to optimal N rates. At N rates contributing to a yield N surplus, N2 O and yield-scaled N2 O emissions increased exponentially. In contrast, CH4 emissions were not impacted by N inputs. Accordingly, yield-scaled CH4 emissions decreased with N addition. Overall, yield-scaled GWP was minimized at optimal N rates, decreasing by 21% compared to treatments without N addition. These results are unique compared to aerobic cropping systems in which N2 O emissions are the primary contributor to GWP, meaning yield-scaled GWP may not necessarily decrease for aerobic crops when yields are optimized by N fertilizer addition. Balancing gains in agricultural productivity with climate change concerns, this work supports the concept that high rice yields can be achieved with minimal yield-scaled GWP through optimal N application rates. Moreover, additional improvements in N use efficiency may further reduce yield-scaled GWP, thereby strengthening the economic and environmental sustainability of rice systems.
Research on global path planning based on ant colony optimization for AUV
NASA Astrophysics Data System (ADS)
Wang, Hong-Jian; Xiong, Wei
2009-03-01
Path planning is an important issue for autonomous underwater vehicles (AUVs) traversing an unknown environment such as a sea floor, a jungle, or the outer celestial planets. For this paper, global path planning using large-scale chart data was studied, and the principles of ant colony optimization (ACO) were applied. This paper introduced the idea of a visibility graph based on the grid workspace model. It also brought a series of pheromone updating rules for the ACO planning algorithm. The operational steps of the ACO algorithm are proposed as a model for a global path planning method for AUV. To mimic the process of smoothing a planned path, a cutting operator and an insertion-point operator were designed. Simulation results demonstrated that the ACO algorithm is suitable for global path planning. The system has many advantages, including that the operating path of the AUV can be quickly optimized, and it is shorter, safer, and smoother. The prototype system successfully demonstrated the feasibility of the concept, proving it can be applied to surveys of unstructured unmanned environments.
Schafroth, Heather D; Floudas, Christodoulos A
2004-02-15
Development of a computational prediction method based on molecular modeling, global optimization, and implicit solvation has produced accurate structure and relative binding affinity predictions for peptide amino acids binding to five pockets of the MHC molecule HLA-DRB1*0101. Because peptide binding to MHC molecules is essential to many immune responses, development of such a method for understanding and predicting the forces that drive binding is crucial for pharmaceutical design and disease treatment. Underlying the development of this prediction method are two hypotheses. The first is that pockets formed by the peptide binding groove of MHC molecules are independent, separating the prediction of peptide amino acids that bind within individual pockets from those that bind between pockets. The second hypothesis is that the native state of a system composed of an amino acid bound to a protein pocket corresponds to the system's lowest free energy. The prediction method developed from these hypotheses uses atomistic-level modeling, deterministic global optimization, and three methods of implicit solvation: solvent-accessible area, solvent-accessible volume, and Poisson-Boltzmann electrostatics. The method predicts relative binding affinities of peptide amino acids for pockets of HLA-DRB1*0101 by determining computationally an amino acid's global minimum energy conformation. Prediction results from the method are in agreement with X-ray crystallography data and experimental binding assays.
CH4 parameter estimation in CLM4.5bgc using surrogate global optimization
NASA Astrophysics Data System (ADS)
Müller, J.; Paudel, R.; Shoemaker, C. A.; Woodbury, J.; Wang, Y.; Mahowald, N.
2015-10-01
Over the anthropocene methane has increased dramatically. Wetlands are one of the major sources of methane to the atmosphere, but the role of changes in wetland emissions is not well understood. The Community Land Model (CLM) of the Community Earth System Models contains a module to estimate methane emissions from natural wetlands and rice paddies. Our comparison of CH4 emission observations at 16 sites around the planet reveals, however, that there are large discrepancies between the CLM predictions and the observations. The goal of our study is to adjust the model parameters in order to minimize the root mean squared error (RMSE) between model predictions and observations. These parameters have been selected based on a sensitivity analysis. Because of the cost associated with running the CLM simulation (15 to 30 min on the Yellowstone Supercomputing Facility), only relatively few simulations can be allowed in order to find a near-optimal solution within an acceptable time. Our results indicate that the parameter estimation problem has multiple local minima. Hence, we use a computationally efficient global optimization algorithm that uses a radial basis function (RBF) surrogate model to approximate the objective function. We use the information from the RBF to select parameter values that are most promising with respect to improving the objective function value. We show with pseudo data that our optimization algorithm is able to make excellent progress with respect to decreasing the RMSE. Using the true CH4 emission observations for optimizing the parameters, we are able to significantly reduce the overall RMSE between observations and model predictions by about 50 %. The methane emission predictions of the CLM using the optimized parameters agree better with the observed methane emission data in northern and tropical latitudes. With the optimized parameters, the methane emission predictions are higher in northern latitudes than when the default parameters are
A Globally Optimal Particle Tracking Technique for Stereo Imaging Velocimetry Experiments
NASA Technical Reports Server (NTRS)
McDowell, Mark
2008-01-01
An important phase of any Stereo Imaging Velocimetry experiment is particle tracking. Particle tracking seeks to identify and characterize the motion of individual particles entrained in a fluid or air experiment. We analyze a cylindrical chamber filled with water and seeded with density-matched particles. In every four-frame sequence, we identify a particle track by assigning a unique track label for each camera image. The conventional approach to particle tracking is to use an exhaustive tree-search method utilizing greedy algorithms to reduce search times. However, these types of algorithms are not optimal due to a cascade effect of incorrect decisions upon adjacent tracks. We examine the use of a guided evolutionary neural net with simulated annealing to arrive at a globally optimal assignment of tracks. The net is guided both by the minimization of the search space through the use of prior limiting assumptions about valid tracks and by a strategy which seeks to avoid high-energy intermediate states which can trap the net in a local minimum. A stochastic search algorithm is used in place of back-propagation of error to further reduce the chance of being trapped in an energy well. Global optimization is achieved by minimizing an objective function, which includes both track smoothness and particle-image utilization parameters. In this paper we describe our model and present our experimental results. We compare our results with a nonoptimizing, predictive tracker and obtain an average increase in valid track yield of 27 percent
The L_infinity constrained global optimal histogram equalization technique for real time imaging
NASA Astrophysics Data System (ADS)
Ren, Qiongwei; Niu, Yi; Liu, Lin; Jiao, Yang; Shi, Guangming
2015-08-01
Although the current imaging sensors can achieve 12 or higher precision, the current display devices and the commonly used digital image formats are still only 8 bits. This mismatch causes significant waste of the sensor precision and loss of information when storing and displaying the images. For better usage of the precision-budget, tone mapping operators have to be used to map the high-precision data into low-precision digital images adaptively. In this paper, the classic histogram equalization tone mapping operator is reexamined in the sense of optimization. We point out that the traditional histogram equalization technique and its variants are fundamentally improper by suffering from local optimum problems. To overcome this drawback, we remodel the histogram equalization tone mapping task based on graphic theory which achieves the global optimal solutions. Another advantage of the graphic-based modeling is that the tone-continuity is also modeled as a vital constraint in our approach which suppress the annoying boundary artifacts of the traditional approaches. In addition, we propose a novel dynamic programming technique to solve the histogram equalization problem in real time. Experimental results shows that the proposed tone-preserved global optimal histogram equalization technique outperforms the traditional approaches by exhibiting more subtle details in the foreground while preserving the smoothness of the background.
Liang, Faming; Cheng, Yichen; Lin, Guang
2014-06-13
Simulated annealing has been widely used in the solution of optimization problems. As known by many researchers, the global optima cannot be guaranteed to be located by simulated annealing unless a logarithmic cooling schedule is used. However, the logarithmic cooling schedule is so slow that no one can afford to have such a long CPU time. This paper proposes a new stochastic optimization algorithm, the so-called simulated stochastic approximation annealing algorithm, which is a combination of simulated annealing and the stochastic approximation Monte Carlo algorithm. Under the framework of stochastic approximation Markov chain Monte Carlo, it is shown that the new algorithm can work with a cooling schedule in which the temperature can decrease much faster than in the logarithmic cooling schedule, e.g., a square-root cooling schedule, while guaranteeing the global optima to be reached when the temperature tends to zero. The new algorithm has been tested on a few benchmark optimization problems, including feed-forward neural network training and protein-folding. The numerical results indicate that the new algorithm can significantly outperform simulated annealing and other competitors.
Comparison of global optimization approaches for robust calibration of hydrologic model parameters
NASA Astrophysics Data System (ADS)
Jung, I. W.
2015-12-01
Robustness of the calibrated parameters of hydrologic models is necessary to provide a reliable prediction of future performance of watershed behavior under varying climate conditions. This study investigated calibration performances according to the length of calibration period, objective functions, hydrologic model structures and optimization methods. To do this, the combination of three global optimization methods (i.e. SCE-UA, Micro-GA, and DREAM) and four hydrologic models (i.e. SAC-SMA, GR4J, HBV, and PRMS) was tested with different calibration periods and objective functions. Our results showed that three global optimization methods provided close calibration performances under different calibration periods, objective functions, and hydrologic models. However, using the agreement of index, normalized root mean square error, Nash-Sutcliffe efficiency as the objective function showed better performance than using correlation coefficient and percent bias. Calibration performances according to different calibration periods from one year to seven years were hard to generalize because four hydrologic models have different levels of complexity and different years have different information content of hydrological observation. Acknowledgements This research was supported by a grant (14AWMP-B082564-01) from Advanced Water Management Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government.
Energy landscape paving with local search for global optimization of the BLN off-lattice model
NASA Astrophysics Data System (ADS)
Liu, Jingfa; Huang, Weibo; Liu, Wenjie; Song, Beibei; Sun, Yuanyuan; Chen, Mao
2014-02-01
The optimization problem for finding the global minimum energy structure is one of the main problems of protein structure prediction and is known to be an NP-hard problem in computational molecular biology. The low-energy conformational search problem in the hydrophobic-hydrophilic-neutral (BLN) off-lattice model is studied. We convert the problem into an unconstrained optimization problem by introducing the penalty function. By putting forward a new updating mechanism of the histogram function in the energy landscape paving (ELP) method and incorporating heuristic conformation update strategies into the ELP method, we obtain an improved ELP (IELP) method. Subsequently, by combining the IELP method with the local search (LS) based on the gradient descent method, we propose a hybrid algorithm, denoted by IELP-LS, for the conformational search of the off-lattice BLN model. Simulation results indicate that IELP-LS can find lower-energy states than other methods in the literature, showing that the proposed method is an effective tool for global optimization in the BLN off-lattice protein model.
Global Optimization of N-Maneuver, High-Thrust Trajectories Using Direct Multiple Shooting
NASA Technical Reports Server (NTRS)
Vavrina, Matthew A.; Englander, Jacob A.; Ellison, Donald H.
2016-01-01
The performance of impulsive, gravity-assist trajectories often improves with the inclusion of one or more maneuvers between flybys. However, grid-based scans over the entire design space can become computationally intractable for even one deep-space maneuver, and few global search routines are capable of an arbitrary number of maneuvers. To address this difficulty a trajectory transcription allowing for any number of maneuvers is developed within a multi-objective, global optimization framework for constrained, multiple gravity-assist trajectories. The formulation exploits a robust shooting scheme and analytic derivatives for computational efficiency. The approach is applied to several complex, interplanetary problems, achieving notable performance without a user-supplied initial guess.
Global Optimization of N-Maneuver, High-Thrust Trajectories Using Direct Multiple Shooting
NASA Technical Reports Server (NTRS)
Vavrina, Matthew A.; Englander, Jacob A.; Ellison, Donald H.
2015-01-01
The performance of impulsive, gravity-assist trajectories often improves with the inclusion of one or more maneuvers between flybys. However, grid-based scans over the entire design space can become computationally intractable for even one deep-space maneuver, and few global search routines are capable of an arbitrary number of maneuvers. To address this difficulty a trajectory transcription allow-ing for any number of maneuvers is developed within a multi-objective, global optimization framework for constrained, multiple gravity-assist trajectories. The formulation exploits a robust shooting scheme and analytic derivatives for com-putational efficiency. The approach is applied to several complex, interplanetary problems, achieving notable performance without a user-supplied initial guess.
Lee, JongHyup; Pak, Dohyun
2016-01-01
For practical deployment of wireless sensor networks (WSN), WSNs construct clusters, where a sensor node communicates with other nodes in its cluster, and a cluster head support connectivity between the sensor nodes and a sink node. In hybrid WSNs, cluster heads have cellular network interfaces for global connectivity. However, when WSNs are active and the load of cellular networks is high, the optimal assignment of cluster heads to base stations becomes critical. Therefore, in this paper, we propose a game theoretic model to find the optimal assignment of base stations for hybrid WSNs. Since the communication and energy cost is different according to cellular systems, we devise two game models for TDMA/FDMA and CDMA systems employing power prices to adapt to the varying efficiency of recent wireless technologies. The proposed model is defined on the assumptions of the ideal sensing field, but our evaluation shows that the proposed model is more adaptive and energy efficient than local selections. PMID:27589743
Lee, JongHyup; Pak, Dohyun
2016-01-01
For practical deployment of wireless sensor networks (WSN), WSNs construct clusters, where a sensor node communicates with other nodes in its cluster, and a cluster head support connectivity between the sensor nodes and a sink node. In hybrid WSNs, cluster heads have cellular network interfaces for global connectivity. However, when WSNs are active and the load of cellular networks is high, the optimal assignment of cluster heads to base stations becomes critical. Therefore, in this paper, we propose a game theoretic model to find the optimal assignment of base stations for hybrid WSNs. Since the communication and energy cost is different according to cellular systems, we devise two game models for TDMA/FDMA and CDMA systems employing power prices to adapt to the varying efficiency of recent wireless technologies. The proposed model is defined on the assumptions of the ideal sensing field, but our evaluation shows that the proposed model is more adaptive and energy efficient than local selections. PMID:27589743
NASA Astrophysics Data System (ADS)
Do, Khac Duc
2015-03-01
This paper presents a design of optimal controllers with respect to a meaningful cost function to force an underactuated omni-directional intelligent navigator (ODIN) under unknown constant environmental loads to track a reference trajectory in two-dimensional space. Motivated by the vehicle's steering practice, the yaw angle regarded as a virtual control plus the surge thrust force are used to force the position of the vehicle to globally track its reference trajectory. The control design is based on several recent results developed for inverse optimal control and stability analysis of nonlinear systems, a new design of bounded disturbance observers, and backstepping and Lyapunov's direct methods. Both state- and output-feedback control designs are addressed. Simulations are included to illustrate the effectiveness of the proposed results.
Lee, JongHyup; Pak, Dohyun
2016-08-29
For practical deployment of wireless sensor networks (WSN), WSNs construct clusters, where a sensor node communicates with other nodes in its cluster, and a cluster head support connectivity between the sensor nodes and a sink node. In hybrid WSNs, cluster heads have cellular network interfaces for global connectivity. However, when WSNs are active and the load of cellular networks is high, the optimal assignment of cluster heads to base stations becomes critical. Therefore, in this paper, we propose a game theoretic model to find the optimal assignment of base stations for hybrid WSNs. Since the communication and energy cost is different according to cellular systems, we devise two game models for TDMA/FDMA and CDMA systems employing power prices to adapt to the varying efficiency of recent wireless technologies. The proposed model is defined on the assumptions of the ideal sensing field, but our evaluation shows that the proposed model is more adaptive and energy efficient than local selections.
Using support vector machine and dynamic parameter encoding to enhance global optimization
NASA Astrophysics Data System (ADS)
Zheng, Z.; Chen, X.; Liu, C.; Huang, K.
2016-05-01
This study presents an approach which combines support vector machine (SVM) and dynamic parameter encoding (DPE) to enhance the run-time performance of global optimization with time-consuming fitness function evaluations. SVMs are used as surrogate models to partly substitute for fitness evaluations. To reduce the computation time and guarantee correct convergence, this work proposes a novel strategy to adaptively adjust the number of fitness evaluations needed according to the approximate error of the surrogate model. Meanwhile, DPE is employed to compress the solution space, so that it not only accelerates the convergence but also decreases the approximate error. Numerical results of optimizing a few benchmark functions and an antenna in a practical application are presented, which verify the feasibility, efficiency and robustness of the proposed approach.
A genetic algorithm for first principles global structure optimization of supported nano structures
Vilhelmsen, Lasse B.; Hammer, Bjørk
2014-07-28
We present a newly developed publicly available genetic algorithm (GA) for global structure optimisation within atomic scale modeling. The GA is focused on optimizations using first principles calculations, but it works equally well with empirical potentials. The implementation is described and benchmarked through a detailed statistical analysis employing averages across many independent runs of the GA. This analysis focuses on the practical use of GA’s with a description of optimal parameters to use. New results for the adsorption of M{sub 8} clusters (M = Ru, Rh, Pd, Ag, Pt, Au) on the stoichiometric rutile TiO{sub 2}(110) surface are presented showing the power of automated structure prediction and highlighting the diversity of metal cluster geometries at the atomic scale.
Electronic neural network for solving traveling salesman and similar global optimization problems
NASA Technical Reports Server (NTRS)
Thakoor, Anilkumar P. (Inventor); Moopenn, Alexander W. (Inventor); Duong, Tuan A. (Inventor); Eberhardt, Silvio P. (Inventor)
1993-01-01
This invention is a novel high-speed neural network based processor for solving the 'traveling salesman' and other global optimization problems. It comprises a novel hybrid architecture employing a binary synaptic array whose embodiment incorporates the fixed rules of the problem, such as the number of cities to be visited. The array is prompted by analog voltages representing variables such as distances. The processor incorporates two interconnected feedback networks, each of which solves part of the problem independently and simultaneously, yet which exchange information dynamically.
Model-data fusion across ecosystems: from multi-site optimizations to global simulations
NASA Astrophysics Data System (ADS)
Kuppel, S.; Peylin, P.; Maignan, F.; Chevallier, F.; Kiely, G.; Montagnani, L.; Cescatti, A.
2014-05-01
This study uses a variational data assimilation framework to simultaneously constrain a global ecosystem model with eddy covariance measurements of daily net carbon (NEE) and latent heat (LE) fluxes from a large number of sites grouped in seven plant functional types (PFTs). It is an attempt to bridge the gap between the numerous site-specific parameter optimization works found in the literature and the generic parameterization used by most land surface models within each PFT. The present multi-site approach allows deriving PFT-generic sets of optimized parameters enhancing the agreement between measured and simulated fluxes at most of the sites considered, with performances often comparable to those of the corresponding site-specific optimizations. Besides reducing the PFT-averaged model-data root-mean-square difference (RMSD) and the associated daily output uncertainty, the optimization improves the simulated CO2 balance at tropical and temperate forests sites. The major site-level NEE adjustments at the seasonal scale are: reduced amplitude in C3 grasslands and boreal forests, increased seasonality in temperate evergreen forests, and better model-data phasing in temperate deciduous broadleaf forests. Conversely, the poorer performances in tropical evergreen broadleaf forests points to deficiencies regarding the modeling of phenology and soil water stress for this PFT. An evaluation with data-oriented estimates of photosynthesis (GPP) and ecosystem respiration (Reco) rates indicates distinctively improved simulations of both gross fluxes. The multi-site parameter sets are then tested against CO2 concentrations measured at 53 locations around the globe, showing significant adjustments of the modeled seasonality of atmospheric CO2 concentration, whose relevance seems PFT-dependent, along with an improved interannual variability. Lastly, a global scale evaluation with remote sensing NDVI measurements indicates an improvement of the simulated seasonal variations of
Model-data fusion across ecosystems: from multisite optimizations to global simulations
NASA Astrophysics Data System (ADS)
Kuppel, S.; Peylin, P.; Maignan, F.; Chevallier, F.; Kiely, G.; Montagnani, L.; Cescatti, A.
2014-11-01
This study uses a variational data assimilation framework to simultaneously constrain a global ecosystem model with eddy covariance measurements of daily net ecosystem exchange (NEE) and latent heat (LE) fluxes from a large number of sites grouped in seven plant functional types (PFTs). It is an attempt to bridge the gap between the numerous site-specific parameter optimization works found in the literature and the generic parameterization used by most land surface models within each PFT. The present multisite approach allows deriving PFT-generic sets of optimized parameters enhancing the agreement between measured and simulated fluxes at most of the sites considered, with performances often comparable to those of the corresponding site-specific optimizations. Besides reducing the PFT-averaged model-data root-mean-square difference (RMSD) and the associated daily output uncertainty, the optimization improves the simulated CO2 balance at tropical and temperate forests sites. The major site-level NEE adjustments at the seasonal scale are reduced amplitude in C3 grasslands and boreal forests, increased seasonality in temperate evergreen forests, and better model-data phasing in temperate deciduous broadleaf forests. Conversely, the poorer performances in tropical evergreen broadleaf forests points to deficiencies regarding the modelling of phenology and soil water stress for this PFT. An evaluation with data-oriented estimates of photosynthesis (GPP - gross primary productivity) and ecosystem respiration (Reco) rates indicates distinctively improved simulations of both gross fluxes. The multisite parameter sets are then tested against CO2 concentrations measured at 53 locations around the globe, showing significant adjustments of the modelled seasonality of atmospheric CO2 concentration, whose relevance seems PFT-dependent, along with an improved interannual variability. Lastly, a global-scale evaluation with remote sensing NDVI (normalized difference vegetation index
Global design optimization for an axial-flow tandem pump based on surrogate method
NASA Astrophysics Data System (ADS)
Li, D. H.; Zhao, Y.; Y Wang, G.
2013-12-01
Tandem pump, compared with multistage pump, goes without guide vanes between impellers. Better cavitation performance and significant reduction of the axial geometry scale is important for high-speed propulsion. This study presents a global design optimization method based on surrogated method for an axial-flow tandem pump to enhance trade-off performances: energy and cavitation performances. At the same time, interactions between impellers and impacts on the performances are analyzed. Fixed angle of blades in impellers and phase angle are performed as design variables. Efficiency and minimum average pressure coefficient (MAPC) on axial sectional surface in front impeller are the objective function, which can represent energy and cavitation performances well. Different surrogate models are constructed, and Global Sensitivity Analysis and Pareto Front method are used. The results show that, 1) Influence from phase angle on performances can be neglected compared with other two design variables, 2) Impact ratio of fixed angle of blades in two impellers on efficiency are the same as their designed loading distributions, which is 4:6, 3) The optimization results can enhance the trade-off performances well: efficiency is improved by 0.6%, and the MAPC is improved by 4.5%.
An optimal adaptive design to address local regulations in global clinical trials.
Luo, Xiaolong; Shih, Weichung Joe; Ouyang, S Peter; Delap, Robert J
2010-01-01
After multi-regional clinical trials (MRCTs) have demonstrated overall significant effects, evaluation for a region-specific effect is often important. Recent guidance from regulatory authorities regarding evaluation for possible country-specific effects has led to research on statistical designs that incorporate such evaluations in MRCTs. These statistical designs are intended to use the MRCTs to address the requirements for global registration of a medicinal product. Adding a regional requirement could change the probability for declaring positive effect for the region when there is indeed no treatment difference as well as when there is in fact a true difference within the region. In this paper, we first quantify those probability structures based on the guidance issued by the Ministry of Health, Labour and Welfare (MHLW) of Japan. An adaptive design is proposed to consider those probabilities and to optimize the efficiency for regional objectives. This two-stage approach incorporates comprehensive global objectives into an integrated study design and may mitigate the need for a separate local bridging study. A procedure is used to optimize region-specific enrollment based on an objective function. The overall sample size requirement is assessed. We will use simulation analyses to illustrate the performance of the proposed study design. PMID:20872620
Hierarchical Grid-based Multi-People Tracking-by-Detection With Global Optimization.
Chen, Lili; Wang, Wei; Panin, Giorgio; Knoll, Alois
2015-11-01
We present a hierarchical grid-based, globally optimal tracking-by-detection approach to track an unknown number of targets in complex and dense scenarios, particularly addressing the challenges of complex interaction and mutual occlusion. Frame-by-frame detection is performed by hierarchical likelihood grids, matching shape templates through a fast oriented distance transform. To allow recovery from misdetections, common heuristics such as nonmaxima suppression within observations is eschewed. Within a discretized state-space, the data association problem is formulated as a grid-based network flow model, resulting in a convex problem casted into an integer linear programming form, giving a global optimal solution. In addition, we show how a behavior cue (body orientation) can be integrated into our association affinity model, providing valuable hints for resolving ambiguities between crossing trajectories. Unlike traditional motion-based approaches, we estimate body orientation by a hybrid methodology, which combines the merits of motion-based and 3D appearance-based orientation estimation, thus being capable of dealing also with still-standing or slowly moving targets. The performance of our method is demonstrated through experiments on a large variety of benchmark video sequences, including both indoor and outdoor scenarios.
Export dynamics as an optimal growth problem in the network of global economy
Caraglio, Michele; Baldovin, Fulvio; Stella, Attilio L.
2016-01-01
We analyze export data aggregated at world global level of 219 classes of products over a period of 39 years. Our main goal is to set up a dynamical model to identify and quantify plausible mechanisms by which the evolutions of the various exports affect each other. This is pursued through a stochastic differential description, partly inspired by approaches used in population dynamics or directed polymers in random media. We outline a complex network of transfer rates which describes how resources are shifted between different product classes, and determines how casual favorable conditions for one export can spread to the other ones. A calibration procedure allows to fit four free model-parameters such that the dynamical evolution becomes consistent with the average growth, the fluctuations, and the ranking of the export values observed in real data. Growth crucially depends on the balance between maintaining and shifting resources to different exports, like in an explore-exploit problem. Remarkably, the calibrated parameters warrant a close-to-maximum growth rate under the transient conditions realized in the period covered by data, implying an optimal self organization of the global export. According to the model, major structural changes in the global economy take tens of years. PMID:27530505
Export dynamics as an optimal growth problem in the network of global economy.
Caraglio, Michele; Baldovin, Fulvio; Stella, Attilio L
2016-01-01
We analyze export data aggregated at world global level of 219 classes of products over a period of 39 years. Our main goal is to set up a dynamical model to identify and quantify plausible mechanisms by which the evolutions of the various exports affect each other. This is pursued through a stochastic differential description, partly inspired by approaches used in population dynamics or directed polymers in random media. We outline a complex network of transfer rates which describes how resources are shifted between different product classes, and determines how casual favorable conditions for one export can spread to the other ones. A calibration procedure allows to fit four free model-parameters such that the dynamical evolution becomes consistent with the average growth, the fluctuations, and the ranking of the export values observed in real data. Growth crucially depends on the balance between maintaining and shifting resources to different exports, like in an explore-exploit problem. Remarkably, the calibrated parameters warrant a close-to-maximum growth rate under the transient conditions realized in the period covered by data, implying an optimal self organization of the global export. According to the model, major structural changes in the global economy take tens of years.
Export dynamics as an optimal growth problem in the network of global economy.
Caraglio, Michele; Baldovin, Fulvio; Stella, Attilio L
2016-01-01
We analyze export data aggregated at world global level of 219 classes of products over a period of 39 years. Our main goal is to set up a dynamical model to identify and quantify plausible mechanisms by which the evolutions of the various exports affect each other. This is pursued through a stochastic differential description, partly inspired by approaches used in population dynamics or directed polymers in random media. We outline a complex network of transfer rates which describes how resources are shifted between different product classes, and determines how casual favorable conditions for one export can spread to the other ones. A calibration procedure allows to fit four free model-parameters such that the dynamical evolution becomes consistent with the average growth, the fluctuations, and the ranking of the export values observed in real data. Growth crucially depends on the balance between maintaining and shifting resources to different exports, like in an explore-exploit problem. Remarkably, the calibrated parameters warrant a close-to-maximum growth rate under the transient conditions realized in the period covered by data, implying an optimal self organization of the global export. According to the model, major structural changes in the global economy take tens of years. PMID:27530505
Export dynamics as an optimal growth problem in the network of global economy
NASA Astrophysics Data System (ADS)
Caraglio, Michele; Baldovin, Fulvio; Stella, Attilio L.
2016-08-01
We analyze export data aggregated at world global level of 219 classes of products over a period of 39 years. Our main goal is to set up a dynamical model to identify and quantify plausible mechanisms by which the evolutions of the various exports affect each other. This is pursued through a stochastic differential description, partly inspired by approaches used in population dynamics or directed polymers in random media. We outline a complex network of transfer rates which describes how resources are shifted between different product classes, and determines how casual favorable conditions for one export can spread to the other ones. A calibration procedure allows to fit four free model-parameters such that the dynamical evolution becomes consistent with the average growth, the fluctuations, and the ranking of the export values observed in real data. Growth crucially depends on the balance between maintaining and shifting resources to different exports, like in an explore-exploit problem. Remarkably, the calibrated parameters warrant a close-to-maximum growth rate under the transient conditions realized in the period covered by data, implying an optimal self organization of the global export. According to the model, major structural changes in the global economy take tens of years.
NASA Astrophysics Data System (ADS)
Mikhalev, A. S.; Rouban, A. I.
2016-04-01
The algorithms of global non-differentiable minimization of functions on set of the mixed variables: continuous and discrete with unordered specific possible values are constructed. The method of optimization is based on selective averaging of required variables, on adaptive reorganization of the sizes of admissible domain of trial movements and on use of relative values for minimised functions. Existence of discrete variables leads to solution of a sequence of global minimization problems of the functions in space of only continuous variables at the presence: 1) of their inequality restrictions for each problem; 2) of the general inequality restrictions for all problems (i.e. at the absence of dependence of functions fore inequality restrictions from discrete variables). In the first case, presence of discrete variables with unordered non-numeric possible values leads to solution of sequence of problems of global minimization of multiextreme functions on set only of continuous variables at the presence of their inequality restrictions. As a result, among the received optimum solutions the best is selected. In the second variant all minimized functions is convoluted in each sampling point in one multiextreme function and this function is minimised on continuous variables.
Song, Zhaoliang; Parr, Jeffrey F.; Guo, Fengshan
2013-01-01
The occlusion of carbon (C) by phytoliths, the recalcitrant silicified structures deposited within plant tissues, is an important persistent C sink mechanism for croplands and other grass-dominated ecosystems. By constructing a silica content-phytolith content transfer function and calculating the magnitude of phytolith C sink in global croplands with relevant crop production data, this study investigated the present and potential of phytolith C sinks in global croplands and its contribution to the cropland C balance to understand the cropland C cycle and enhance long-term C sequestration in croplands. Our results indicate that the phytolith sink annually sequesters 26.35±10.22 Tg of carbon dioxide (CO2) and may contribute 40±18% of the global net cropland soil C sink for 1961–2100. Rice (25%), wheat (19%) and maize (23%) are the dominant contributing crop species to this phytolith C sink. Continentally, the main contributors are Asia (49%), North America (17%) and Europe (16%). The sink has tripled since 1961, mainly due to fertilizer application and irrigation. Cropland phytolith C sinks may be further enhanced by adopting cropland management practices such as optimization of cropping system and fertilization. PMID:24066067
NASA Astrophysics Data System (ADS)
Hew, Y. M.; Linscott, I.; Close, S.
2015-12-01
Meteoroids and orbital debris, collectively referred to as hypervelocity impactors, travel between 7 and 72 km/s in free space. Upon their impact onto the spacecraft, the energy conversion from kinetic to ionization/vaporization occurs within a very brief timescale and results in a small and dense expanding plasma with a very strong optical flash. The radio frequency (RF) emission produced by this plasma can potentially lead to electrical anomalies within the spacecraft. In addition, space weather, such as solar activity and background plasma, can establish spacecraft conditions which can exaggerate the damages done by these impacts. During the impact, a very strong impact flash will be generated. Through the studying of this emission spectrum of the impact, we hope to study the impact generated gas cloud/plasma properties. The impact flash emitted from a ground-based hypervelocity impact test is long expected by many scientists to contain the characteristics of the impact generated plasma, such as plasma temperature and density. This paper presents a method for the time-resolved plasma temperature estimation using three-color visible band photometry data with a global pattern search optimization method. The equilibrium temperature of the plasma can be estimated using an optical model which accounts for both the line emission and continuum emission from the plasma. Using a global pattern search based optimizer, the model can isolate the contribution of the continuum emission versus the line emission from the plasma. The plasma temperature can thus be estimated. Prior to the optimization step, a Gaussian process is also applied to extract the optical emission signal out of the noisy background. The resultant temperature and line-to-continuum emission weighting factor are consistent with the spectrum of the impactor material and current literature.
NASA Astrophysics Data System (ADS)
Huang, Zhipeng; Gao, Lihong; Wang, Yangwei; Wang, Fuchi
2016-06-01
The Johnson-Cook (J-C) constitutive model is widely used in the finite element simulation, as this model shows the relationship between stress and strain in a simple way. In this paper, a cluster global optimization algorithm is proposed to determine the J-C constitutive model parameters of materials. A set of assumed parameters is used for the accuracy verification of the procedure. The parameters of two materials (401 steel and 823 steel) are determined. Results show that the procedure is reliable and effective. The relative error between the optimized and assumed parameters is no more than 4.02%, and the relative error between the optimized and assumed stress is 0.2% × 10-5. The J-C constitutive parameters can be determined more precisely and quickly than the traditional manual procedure. Furthermore, all the parameters can be simultaneously determined using several curves under different experimental conditions. A strategy is also proposed to accurately determine the constitutive parameters.
Stellar structure modeling using a parallel genetic algorithm for objective global optimization
NASA Astrophysics Data System (ADS)
Metcalfe, Travis S.; Charbonneau, Paul
2003-02-01
Genetic algorithms are a class of heuristic search techniques that apply basic evolutionary operators in a computational setting. We have designed a fully parallel and distributed hardware/software implementation of the generalized optimization subroutine PIKAIA, which utilizes a genetic algorithm to provide an objective determination of the globally optimal parameters for a given model against an observational data set. We have used this modeling tool in the context of white dwarf asteroseismology, i.e., the art and science of extracting physical and structural information about these stars from observations of their oscillation frequencies. The efficient, parallel exploration of parameter-space made possible by genetic-algorithm-based numerical optimization led us to a number of interesting physical results: (1) resolution of a hitherto puzzling discrepancy between stellar evolution models and prior asteroseismic inferences of the surface helium layer mass for a DBV white dwarf; (2) precise determination of the central oxygen mass fraction in a white dwarf star; and (3) a preliminary estimate of the astrophysically important but experimentally uncertain rate for the 12C(α,γ)16O nuclear reaction. These successes suggest that a broad class of computationally intensive modeling applications could also benefit from this approach.
NASA Astrophysics Data System (ADS)
Huang, Zhipeng; Gao, Lihong; Wang, Yangwei; Wang, Fuchi
2016-09-01
The Johnson-Cook (J-C) constitutive model is widely used in the finite element simulation, as this model shows the relationship between stress and strain in a simple way. In this paper, a cluster global optimization algorithm is proposed to determine the J-C constitutive model parameters of materials. A set of assumed parameters is used for the accuracy verification of the procedure. The parameters of two materials (401 steel and 823 steel) are determined. Results show that the procedure is reliable and effective. The relative error between the optimized and assumed parameters is no more than 4.02%, and the relative error between the optimized and assumed stress is 0.2% × 10-5. The J-C constitutive parameters can be determined more precisely and quickly than the traditional manual procedure. Furthermore, all the parameters can be simultaneously determined using several curves under different experimental conditions. A strategy is also proposed to accurately determine the constitutive parameters.
Searchlight Correlation Detectors: Optimal Seismic Monitoring Using Regional and Global Networks
NASA Astrophysics Data System (ADS)
Gibbons, Steven J.; Kværna, Tormod; Näsholm, Sven Peter
2015-04-01
The sensitivity of correlation detectors increases greatly when the outputs from multiple seismic traces are considered. For single-array monitoring, a zero-offset stack of individual correlation traces will provide significant noise suppression and enhanced sensitivity for a source region surrounding the hypocenter of the master event. The extent of this region is limited only by the decrease in waveform similarity with increasing hypocenter separation. When a regional or global network of arrays and/or 3-component stations is employed, the zero-offset approach is only optimal when the master and detected events are co-located exactly. In many monitoring situations, including nuclear test sites and geothermal fields, events may be separated by up to many hundreds of meters while still retaining sufficient waveform similarity for correlation detection on single channels. However, the traveltime differences resulting from the hypocenter separation may result in significant beam loss on the zero-offset stack and a deployment of many beams for different hypothetical source locations in geographical space is required. The beam deployment necessary for optimal performance of the correlation detectors is determined by an empirical network response function which is most easily evaluated using the auto-correlation functions of the waveform templates from the master event. The correlation detector beam deployments for providing optimal network sensitivity for the North Korea nuclear test site are demonstrated for both regional and teleseismic monitoring configurations.
Selection of Thermal Worst-Case Orbits via Modified Efficient Global Optimization
NASA Technical Reports Server (NTRS)
Moeller, Timothy M.; Wilhite, Alan W.; Liles, Kaitlin A.
2014-01-01
Efficient Global Optimization (EGO) was used to select orbits with worst-case hot and cold thermal environments for the Stratospheric Aerosol and Gas Experiment (SAGE) III. The SAGE III system thermal model changed substantially since the previous selection of worst-case orbits (which did not use the EGO method), so the selections were revised to ensure the worst cases are being captured. The EGO method consists of first conducting an initial set of parametric runs, generated with a space-filling Design of Experiments (DoE) method, then fitting a surrogate model to the data and searching for points of maximum Expected Improvement (EI) to conduct additional runs. The general EGO method was modified by using a multi-start optimizer to identify multiple new test points at each iteration. This modification facilitates parallel computing and decreases the burden of user interaction when the optimizer code is not integrated with the model. Thermal worst-case orbits for SAGE III were successfully identified and shown by direct comparison to be more severe than those identified in the previous selection. The EGO method is a useful tool for this application and can result in computational savings if the initial Design of Experiments (DoE) is selected appropriately.
Piro, M. H. A.; Simunovic, S.
2016-03-17
Several global optimization methods are reviewed that attempt to ensure that the integral Gibbs energy of a closed isothermal isobaric system is a global minimum to satisfy the necessary and sufficient conditions for thermodynamic equilibrium. In particular, the integral Gibbs energy function of a multicomponent system containing non-ideal phases may be highly non-linear and non-convex, which makes finding a global minimum a challenge. Consequently, a poor numerical approach may lead one to the false belief of equilibrium. Furthermore, confirming that one reaches a global minimum and that this is achieved with satisfactory computational performance becomes increasingly more challenging in systemsmore » containing many chemical elements and a correspondingly large number of species and phases. Several numerical methods that have been used for this specific purpose are reviewed with a benchmark study of three of the more promising methods using five case studies of varying complexity. A modification of the conventional Branch and Bound method is presented that is well suited to a wide array of thermodynamic applications, including complex phases with many constituents and sublattices, and ionic phases that must adhere to charge neutrality constraints. Also, a novel method is presented that efficiently solves the system of linear equations that exploits the unique structure of the Hessian matrix, which reduces the calculation from a O(N3) operation to a O(N) operation. As a result, this combined approach demonstrates efficiency, reliability and capabilities that are favorable for integration of thermodynamic computations into multi-physics codes with inherent performance considerations.« less
How best to optimize a global process-based carbon land surface model ?
NASA Astrophysics Data System (ADS)
Peylin, Philippe; Bacour, Cedric; MacBean, Natasha; Leonard, Sebastien; Maignan, Fabienne; Thum, Tea; Chevallier, Frederic; Ciais, Philippe; Cadule, Patricia; Santaren, Diego
2014-05-01
Global process-based land surface models are used to predict the response of the Earth's ecosystems to environmental changes. However, the estimated water and carbon fluxes remain subject to large uncertainties, partly because of unknown or poorly calibrated parameters. Assimilation of in situ data, remote sensing products, and/or atmospheric trace gas concentrations, into these models is a promising approach to optimize key parameters, providing that all major processes are well represented. So far, most of the studies have focused on using one single data stream, either remotely sensed estimates of the vegetation activity (fAPAR or NDVI) to constrain the modeled plant phenology, in situ measurements of net CO2 and latent heat fluxes (NEE, LE at FluxNet sites) or atmospheric CO2 concentrations (through the use of a transport model) to provide constraint on the net carbon fluxes at hourly to inter-annual time-scales. However, the combination of these data streams is expected to provide a much larger constraint on ecosystem carbon, water and energy dynamics. At LSCE we have constructed a global Carbon Cycle Multi-Data Assimilation System (CCDAS) to assimilate i) MODIS-NDVI observations at around 15 points for each plant functional type (PFT) in the model, ii) in situ NEE and LE fluxes at around 70 FluxNet sites and iii) atmospheric CO2 measurements at more than 80 sites. We used different methods of data assimilation (including a 4D-Var approach), depending on the number and type of data streams that are considered in order to optimize the main parameters of the global vegetation model ORCHIDEE (around 15 parameters per PFT). Using such a CCDAS, we investigated several methodological to scientific questions: How does a variational scheme perform compared to a "Monte Carlo" approach (the genetic algorithm) to minimize an objective function (using FluxNet data)? What is the additional information brought by the measurements of above ground biomass data on the top of
Efficient Parallel Global Optimization for High Resolution Hydrologic and Climate Impact Models
NASA Astrophysics Data System (ADS)
Shoemaker, C. A.; Mueller, J.; Pang, M.
2013-12-01
High Resolution hydrologic models are typically computationally expensive, requiring many minutes or perhaps hours for one simulation. Optimization can be used with these models for parameter estimation or for analyzing management alternatives. However Optimization of these computationally expensive simulations requires algorithms that can obtain accurate answers with relatively few simulations to avoid infeasibly long computation times. We have developed a number of efficient parallel algorithms and software codes for optimization of expensive problems with multiple local minimum. This is open source software we are distributing. It runs in Matlab and Python, and has been run on Yellowstone supercomputer. The talk will quickly discuss the characteristics of the problem (e.g. the presence of integer as well as continuous variables, the number of dimensions, the availability of parallel/grid computing, the number of simulations that can be allowed to find a solution, etc. ) that determine which algorithms are most appropriate for each type of problem. A major application of this optimization software is for parameter estimation for nonlinear hydrologic models, including contaminant transport in subsurface (e.g. for groundwater remediation or multi-phase flow for carbon sequestration), nutrient transport in watersheds, and climate models. We will present results for carbon sequestration plume monitoring (multi-phase, multi-constiuent), for groundwater remediation, and for the CLM climate model. The carbon sequestration example is based on the Frio CO2 field site and the groundwater example is for a 50,000 acre remediation site (with model requiring about 1 hour per simulation). Parallel speed-ups are excellent in most cases, and our serial and parallel algorithms tend to outperform alternative methods on complex computationally expensive simulations that have multiple global minima.
NASA Astrophysics Data System (ADS)
Libraro, Paola
The general electric propulsion orbit-raising maneuver of a spacecraft must contend with four main limiting factors: the longer time of flight, multiple eclipses prohibiting continuous thrusting, long exposure to radiation from the Van Allen belt and high power requirement of the electric engines. In order to optimize a low-thrust transfer with respect to these challenges, the choice of coordinates and corresponding equations of motion used to describe the kinematical and dynamical behavior of the satellite is of critical importance. This choice can potentially affect the numerical optimization process as well as limit the set of mission scenarios that can be investigated. To increase the ability to determine the feasible set of mission scenarios able to address the challenges of an all-electric orbit-raising, a set of equations free of any singularities is required to consider a completely arbitrary injection orbit. For this purpose a new quaternion-based formulation of a spacecraft translational dynamics that is globally nonsingular has been developed. The minimum-time low-thrust problem has been solved using the new set of equations of motion inside a direct optimization scheme in order to investigate optimal low-thrust trajectories over the full range of injection orbit inclinations between 0 and 90 degrees with particular focus on high-inclinations. The numerical results consider a specific mission scenario in order to analyze three key aspects of the problem: the effect of the initial guess on the shape and duration of the transfer, the effect of Earth oblateness on transfer time and the role played by, radiation damage and power degradation in all-electric minimum-time transfers. Finally trade-offs between mass and cost savings are introduced through a test case.
NASA Astrophysics Data System (ADS)
Tsoulos, Ioannis G.; Lagaris, Isaac E.
2006-01-01
A new stochastic method for locating the global minimum of a multidimensional function inside a rectangular hyperbox is presented. A sampling technique is employed that makes use of the procedure known as grammatical evolution. The method can be considered as a "genetic" modification of the Controlled Random Search procedure due to Price. The user may code the objective function either in C++ or in Fortran 77. We offer a comparison of the new method with others of similar structure, by presenting results of computational experiments on a set of test functions. Program summaryTitle of program: GenPrice Catalogue identifier:ADWP Program summary URL:http://cpc.cs.qub.ac.uk/summaries/ADWP Program available from: CPC Program Library, Queen's University of Belfast, N. Ireland Computer for which the program is designed and others on which it has been tested: the tool is designed to be portable in all systems running the GNU C++ compiler Installation: University of Ioannina, Greece Programming language used: GNU-C++, GNU-C, GNU Fortran-77 Memory required to execute with typical data: 200 KB No. of bits in a word: 32 No. of processors used: 1 Has the code been vectorized or parallelized?: no No. of lines in distributed program, including test data, etc.:13 135 No. of bytes in distributed program, including test data, etc.: 78 512 Distribution format: tar. gz Nature of physical problem: A multitude of problems in science and engineering are often reduced to minimizing a function of many variables. There are instances that a local optimum does not correspond to the desired physical solution and hence the search for a better solution is required. Local optimization techniques are frequently trapped in local minima. Global optimization is hence the appropriate tool. For example, solving a nonlinear system of equations via optimization, employing a "least squares" type of objective, one may encounter many local minima that do not correspond to solutions, i.e. minima with values
NASA Astrophysics Data System (ADS)
Debiane, L.; Ivorra, B.; Mohammadi, B.; Nicoud, F.; Poinsot, T.; Ern, A.; Pitsch, H.
2006-02-01
Controlling flame shapes and emissions is a major objective for all combustion engineers. Considering the complexity of reacting flows, novel optimization methods are required: this paper explores the application of control theory for partial differential equations to combustion. Both flame temperature and pollutant levels are optimized in a laminar Bunsen burner computed with complex chemistry using a recursive semi-deterministic global optimization algorithm. In order to keep the computational time low, the optimization procedure is coupled with mesh adaptation and incomplete gradient techniques.
Optimizing Orbit-Instrument Configuration for Global Precipitation Mission (GPM) Satellite Fleet
NASA Technical Reports Server (NTRS)
Smith, Eric A.; Adams, James; Baptista, Pedro; Haddad, Ziad; Iguchi, Toshio; Im, Eastwood; Kummerow, Christian; Einaudi, Franco (Technical Monitor)
2001-01-01
Following the scientific success of the Tropical Rainfall Measuring Mission (TRMM) spearheaded by a group of NASA and NASDA scientists, their external scientific collaborators, and additional investigators within the European Union's TRMM Research Program (EUROTRMM), there has been substantial progress towards the development of a new internationally organized, global scale, and satellite-based precipitation measuring mission. The highlights of this newly developing mission are a greatly expanded scope of measuring capability and a more diversified set of science objectives. The mission is called the Global Precipitation Mission (GPM). Notionally, GPM will be a constellation-type mission involving a fleet of nine satellites. In this fleet, one member is referred to as the "core" spacecraft flown in an approximately 70 degree inclined non-sun-synchronous orbit, somewhat similar to TRMM in that it carries both a multi-channel polarized passive microwave radiometer (PMW) and a radar system, but in this case it will be a dual frequency Ku-Ka band radar system enabling explicit measurements of microphysical DSD properties. The remainder of fleet members are eight orbit-synchronized, sun-synchronous "constellation" spacecraft each carrying some type of multi-channel PMW radiometer, enabling no worse than 3-hour diurnal sampling over the entire globe. In this configuration the "core" spacecraft serves as a high quality reference platform for training and calibrating the PMW rain retrieval algorithms used with the "constellation" radiometers. Within NASA, GPM has advanced to the pre-formulation phase which has enabled the initiation of a set of science and technology studies which will help lead to the final mission design some time in the 2003 period. This presentation first provides an overview of the notional GPM program and mission design, including its organizational and programmatic concepts, scientific agenda, expected instrument package, and basic flight
Automated reconstruction of dendritic and axonal trees by global optimization with geometric priors.
Türetken, Engin; González, Germán; Blum, Christian; Fua, Pascal
2011-09-01
We present a novel probabilistic approach to fully automated delineation of tree structures in noisy 2D images and 3D image stacks. Unlike earlier methods that rely mostly on local evidence, ours builds a set of candidate trees over many different subsets of points likely to belong to the optimal tree and then chooses the best one according to a global objective function that combines image evidence with geometric priors. Since the best tree does not necessarily span all the points, the algorithm is able to eliminate false detections while retaining the correct tree topology. Manually annotated brightfield micrographs, retinal scans and the DIADEM challenge datasets are used to evaluate the performance of our method. We used the DIADEM metric to quantitatively evaluate the topological accuracy of the reconstructions and showed that the use of the geometric regularization yields a substantial improvement. PMID:21573886
NASA Technical Reports Server (NTRS)
Crassidis, John L.; Lightsey, E. Glenn; Markley, F. Landis
1998-01-01
In this paper, a new and efficient algorithm is developed for attitude determination from Global Positioning System signals. The new algorithm is derived from a generalized nonlinear predictive filter for nonlinear systems. This uses a one time-step ahead approach to propagate a simple kinematics model for attitude determination. The advantages of the new algorithm over previously developed methods include: it provides optimal attitudes even for coplanar baseline configurations; it guarantees convergence even for poor initial conditions; it is a non-iterative algorithm; and it is computationally efficient. These advantages clearly make the new algorithm well suited to on-board applications. The performance of the new algorithm is tested on a dynamic hardware simulator. Results indicate that the new algorithm accurately estimates the attitude of a moving vehicle, and provides robust attitude estimates even when other methods, such as a linearized least-squares approach, fail due to poor initial starting conditions.
Globally optimal rotation alignment of spherical surfaces with associated scalar values
NASA Astrophysics Data System (ADS)
Pan, Rongjiang; Skala, Vaclav; Müller, Rolf
2013-09-01
We propose a new approach to global optimization algorithm based on controlled random search techniques for rotational alignment of spherical surfaces with associated scalar values. To reduce the distortion in correspondence and increase efficiency, the spherical surface is first re-sampled using a geodesic sphere. The rotation in space is represented using the modified Rodrigues parameters. Correspondence between two spherical surfaces is implemented in the parametric domain. We applied the methods to the alignment of beam patterns computed from the outer ear shapes of bats. The proposed method is compared with other approaches such as principal component analysis (PCA), exhaustive search in the discrete space of rotations defined by Euler angles and direct search using uniform samples over the special orthogonal group of rotations in 3D space. Experimental results demonstrate that the rotation alignment obtained using the proposed algorithm has a high degree of precision and gives the best results among the four approaches. [Figure not available: see fulltext.
Pozo, Carlos; Guillén-Gosálbez, Gonzalo; Sorribas, Albert; Jiménez, Laureano
2012-01-01
Optimization models in metabolic engineering and systems biology focus typically on optimizing a unique criterion, usually the synthesis rate of a metabolite of interest or the rate of growth. Connectivity and non-linear regulatory effects, however, make it necessary to consider multiple objectives in order to identify useful strategies that balance out different metabolic issues. This is a fundamental aspect, as optimization of maximum yield in a given condition may involve unrealistic values in other key processes. Due to the difficulties associated with detailed non-linear models, analysis using stoichiometric descriptions and linear optimization methods have become rather popular in systems biology. However, despite being useful, these approaches fail in capturing the intrinsic nonlinear nature of the underlying metabolic systems and the regulatory signals involved. Targeting more complex biological systems requires the application of global optimization methods to non-linear representations. In this work we address the multi-objective global optimization of metabolic networks that are described by a special class of models based on the power-law formalism: the generalized mass action (GMA) representation. Our goal is to develop global optimization methods capable of efficiently dealing with several biological criteria simultaneously. In order to overcome the numerical difficulties of dealing with multiple criteria in the optimization, we propose a heuristic approach based on the epsilon constraint method that reduces the computational burden of generating a set of Pareto optimal alternatives, each achieving a unique combination of objectives values. To facilitate the post-optimal analysis of these solutions and narrow down their number prior to being tested in the laboratory, we explore the use of Pareto filters that identify the preferred subset of enzymatic profiles. We demonstrate the usefulness of our approach by means of a case study that optimizes the
Optimizing global CO concentrations and emissions based on DART/CAM-CHEM
NASA Astrophysics Data System (ADS)
Gaubert, B.; Arellano, A. F.; Barre, J.; Worden, H. M.; Emmons, L. K.; Wiedinmyer, C.; Anderson, J. L.; Deeter, M. N.; Mizzi, A. P.; Edwards, D. P.
2014-12-01
Atmospheric Carbon Monoxide (CO) is an important trace gas in tropospheric chemistry through its impact on the oxidizing capacity of the troposphere, as precursor of ozone, and as a good tracer of combustion from both anthropogenic sources and wildfires. We will investigate the potential of the assimilation of TERRA/MOPITT observations to constrain the regional to global CO budget using DART (Data assimilation Research Testbed) together with the global Community Atmospheric Model (CAM-Chem). DART/CAM-Chem is based on an ensemble adjustment Kalman filter (EAKF) framework which facilitates statistical estimation of error correlations between chemical states (CO and related species) and parameters (including sources) in the model using the ensemble statistics derived from dynamical and chemical perturbations in the model. Here, we estimate CO emissions within DART/CAM-Chem using a state augmentation approach where CO emissions are added to the CO state vector being analyzed. We compare these optimized emissions to estimates derived from a traditional Bayesian synthesis inversion using the CO analyses (assimilated CO states) as observational constraints. The spatio-temporal distribution of CO and other chemical species will be compared to profile measurements from aircraft and other satellite instruments (e.g., INTEX-B, ARCTAS).
Improving global CD uniformity by optimizing post-exposure bake and develop sequences
NASA Astrophysics Data System (ADS)
Osborne, Stephen P.; Mueller, Mark; Lem, Homer; Reyland, David; Baik, KiHo
2003-12-01
Improvements in the final uniformity of masks can be shrouded by error contributions from many sources. The final Global CD Uniformity (GCDU) of a mask is degraded by individual contributions of the writing tool, the Post Applied Bake (PAB), the Post Exposure Bake (PEB), the Develop sequence and the Etch step. Final global uniformity will improve by isolating and minimizing the variability of the PEB and Develop. We achieved this de-coupling of the PEB and Develop process from the whole process stream by using "dark loss" which is the loss of unexposed resist during the develop process. We confirmed a correspondence between Angstroms of dark loss and nanometer sized deviations in the chrome CD. A plate with a distinctive dark loss pattern was related to a nearly identical pattern in the chrome CD. This pattern was verified to have originated during the PEB process and displayed a [Δ(Final CD)/Δ(Dark Loss)] ratio of 6 for TOK REAP200 resist. Previous papers have reported a sensitive linkage between Angstroms of dark loss and nanometers in the final uniformity of the written plate. These initial studies reported using this method to improve the PAB of resists for greater uniformity of sensitivity and contrast. Similarly, this paper demonstrates an outstanding optimization of PEB and Develop processes.
Sartelli, Massimo; Weber, Dieter G; Ruppé, Etienne; Bassetti, Matteo; Wright, Brian J; Ansaloni, Luca; Catena, Fausto; Coccolini, Federico; Abu-Zidan, Fikri M; Coimbra, Raul; Moore, Ernest E; Moore, Frederick A; Maier, Ronald V; De Waele, Jan J; Kirkpatrick, Andrew W; Griffiths, Ewen A; Eckmann, Christian; Brink, Adrian J; Mazuski, John E; May, Addison K; Sawyer, Rob G; Mertz, Dominik; Montravers, Philippe; Kumar, Anand; Roberts, Jason A; Vincent, Jean-Louis; Watkins, Richard R; Lowman, Warren; Spellberg, Brad; Abbott, Iain J; Adesunkanmi, Abdulrashid Kayode; Al-Dahir, Sara; Al-Hasan, Majdi N; Agresta, Ferdinando; Althani, Asma A; Ansari, Shamshul; Ansumana, Rashid; Augustin, Goran; Bala, Miklosh; Balogh, Zsolt J; Baraket, Oussama; Bhangu, Aneel; Beltrán, Marcelo A; Bernhard, Michael; Biffl, Walter L; Boermeester, Marja A; Brecher, Stephen M; Cherry-Bukowiec, Jill R; Buyne, Otmar R; Cainzos, Miguel A; Cairns, Kelly A; Camacho-Ortiz, Adrian; Chandy, Sujith J; Che Jusoh, Asri; Chichom-Mefire, Alain; Colijn, Caroline; Corcione, Francesco; Cui, Yunfeng; Curcio, Daniel; Delibegovic, Samir; Demetrashvili, Zaza; De Simone, Belinda; Dhingra, Sameer; Diaz, José J; Di Carlo, Isidoro; Dillip, Angel; Di Saverio, Salomone; Doyle, Michael P; Dorj, Gereltuya; Dogjani, Agron; Dupont, Hervé; Eachempati, Soumitra R; Enani, Mushira Abdulaziz; Egiev, Valery N; Elmangory, Mutasim M; Ferrada, Paula; Fitchett, Joseph R; Fraga, Gustavo P; Guessennd, Nathalie; Giamarellou, Helen; Ghnnam, Wagih; Gkiokas, George; Goldberg, Staphanie R; Gomes, Carlos Augusto; Gomi, Harumi; Guzmán-Blanco, Manuel; Haque, Mainul; Hansen, Sonja; Hecker, Andreas; Heizmann, Wolfgang R; Herzog, Torsten; Hodonou, Adrien Montcho; Hong, Suk-Kyung; Kafka-Ritsch, Reinhold; Kaplan, Lewis J; Kapoor, Garima; Karamarkovic, Aleksandar; Kees, Martin G; Kenig, Jakub; Kiguba, Ronald; Kim, Peter K; Kluger, Yoram; Khokha, Vladimir; Koike, Kaoru; Kok, Kenneth Y Y; Kong, Victory; Knox, Matthew C; Inaba, Kenji; Isik, Arda; Iskandar, Katia; Ivatury, Rao R; Labbate, Maurizio; Labricciosa, Francesco M; Laterre, Pierre-François; Latifi, Rifat; Lee, Jae Gil; Lee, Young Ran; Leone, Marc; Leppaniemi, Ari; Li, Yousheng; Liang, Stephen Y; Loho, Tonny; Maegele, Marc; Malama, Sydney; Marei, Hany E; Martin-Loeches, Ignacio; Marwah, Sanjay; Massele, Amos; McFarlane, Michael; Melo, Renato Bessa; Negoi, Ionut; Nicolau, David P; Nord, Carl Erik; Ofori-Asenso, Richard; Omari, AbdelKarim H; Ordonez, Carlos A; Ouadii, Mouaqit; Pereira Júnior, Gerson Alves; Piazza, Diego; Pupelis, Guntars; Rawson, Timothy Miles; Rems, Miran; Rizoli, Sandro; Rocha, Claudio; Sakakhushev, Boris; Sanchez-Garcia, Miguel; Sato, Norio; Segovia Lohse, Helmut A; Sganga, Gabriele; Siribumrungwong, Boonying; Shelat, Vishal G; Soreide, Kjetil; Soto, Rodolfo; Talving, Peep; Tilsed, Jonathan V; Timsit, Jean-Francois; Trueba, Gabriel; Trung, Ngo Tat; Ulrych, Jan; van Goor, Harry; Vereczkei, Andras; Vohra, Ravinder S; Wani, Imtiaz; Uhl, Waldemar; Xiao, Yonghong; Yuan, Kuo-Ching; Zachariah, Sanoop K; Zahar, Jean-Ralph; Zakrison, Tanya L; Corcione, Antonio; Melotti, Rita M; Viscoli, Claudio; Viale, Perluigi
2016-01-01
Intra-abdominal infections (IAI) are an important cause of morbidity and are frequently associated with poor prognosis, particularly in high-risk patients. The cornerstones in the management of complicated IAIs are timely effective source control with appropriate antimicrobial therapy. Empiric antimicrobial therapy is important in the management of intra-abdominal infections and must be broad enough to cover all likely organisms because inappropriate initial antimicrobial therapy is associated with poor patient outcomes and the development of bacterial resistance. The overuse of antimicrobials is widely accepted as a major driver of some emerging infections (such as C. difficile), the selection of resistant pathogens in individual patients, and for the continued development of antimicrobial resistance globally. The growing emergence of multi-drug resistant organisms and the limited development of new agents available to counteract them have caused an impending crisis with alarming implications, especially with regards to Gram-negative bacteria. An international task force from 79 different countries has joined this project by sharing a document on the rational use of antimicrobials for patients with IAIs. The project has been termed AGORA (Antimicrobials: A Global Alliance for Optimizing their Rational Use in Intra-Abdominal Infections). The authors hope that AGORA, involving many of the world's leading experts, can actively raise awareness in health workers and can improve prescribing behavior in treating IAIs. PMID:27429642
Corzo, Gerald; Solomatine, Dimitri
2007-05-01
Natural phenomena are multistationary and are composed of a number of interacting processes, so one single model handling all processes often suffers from inaccuracies. A solution is to partition data in relation to such processes using the available domain knowledge or expert judgment, to train separate models for each of the processes, and to merge them in a modular model (committee). In this paper a problem of water flow forecast in watershed hydrology is considered where the flow process can be presented as consisting of two subprocesses -- base flow and excess flow, so that these two processes can be separated. Several approaches to data separation techniques are studied. Two case studies with different forecast horizons are considered. Parameters of the algorithms responsible for data partitioning are optimized using genetic algorithms and global pattern search. It was found that modularization of ANN models using domain knowledge makes models more accurate, if compared with a global model trained on the whole data set, especially when forecast horizon (and hence the complexity of the modelled processes) is increased.
Sartelli, Massimo; Weber, Dieter G; Ruppé, Etienne; Bassetti, Matteo; Wright, Brian J; Ansaloni, Luca; Catena, Fausto; Coccolini, Federico; Abu-Zidan, Fikri M; Coimbra, Raul; Moore, Ernest E; Moore, Frederick A; Maier, Ronald V; De Waele, Jan J; Kirkpatrick, Andrew W; Griffiths, Ewen A; Eckmann, Christian; Brink, Adrian J; Mazuski, John E; May, Addison K; Sawyer, Rob G; Mertz, Dominik; Montravers, Philippe; Kumar, Anand; Roberts, Jason A; Vincent, Jean-Louis; Watkins, Richard R; Lowman, Warren; Spellberg, Brad; Abbott, Iain J; Adesunkanmi, Abdulrashid Kayode; Al-Dahir, Sara; Al-Hasan, Majdi N; Agresta, Ferdinando; Althani, Asma A; Ansari, Shamshul; Ansumana, Rashid; Augustin, Goran; Bala, Miklosh; Balogh, Zsolt J; Baraket, Oussama; Bhangu, Aneel; Beltrán, Marcelo A; Bernhard, Michael; Biffl, Walter L; Boermeester, Marja A; Brecher, Stephen M; Cherry-Bukowiec, Jill R; Buyne, Otmar R; Cainzos, Miguel A; Cairns, Kelly A; Camacho-Ortiz, Adrian; Chandy, Sujith J; Che Jusoh, Asri; Chichom-Mefire, Alain; Colijn, Caroline; Corcione, Francesco; Cui, Yunfeng; Curcio, Daniel; Delibegovic, Samir; Demetrashvili, Zaza; De Simone, Belinda; Dhingra, Sameer; Diaz, José J; Di Carlo, Isidoro; Dillip, Angel; Di Saverio, Salomone; Doyle, Michael P; Dorj, Gereltuya; Dogjani, Agron; Dupont, Hervé; Eachempati, Soumitra R; Enani, Mushira Abdulaziz; Egiev, Valery N; Elmangory, Mutasim M; Ferrada, Paula; Fitchett, Joseph R; Fraga, Gustavo P; Guessennd, Nathalie; Giamarellou, Helen; Ghnnam, Wagih; Gkiokas, George; Goldberg, Staphanie R; Gomes, Carlos Augusto; Gomi, Harumi; Guzmán-Blanco, Manuel; Haque, Mainul; Hansen, Sonja; Hecker, Andreas; Heizmann, Wolfgang R; Herzog, Torsten; Hodonou, Adrien Montcho; Hong, Suk-Kyung; Kafka-Ritsch, Reinhold; Kaplan, Lewis J; Kapoor, Garima; Karamarkovic, Aleksandar; Kees, Martin G; Kenig, Jakub; Kiguba, Ronald; Kim, Peter K; Kluger, Yoram; Khokha, Vladimir; Koike, Kaoru; Kok, Kenneth Y Y; Kong, Victory; Knox, Matthew C; Inaba, Kenji; Isik, Arda; Iskandar, Katia; Ivatury, Rao R; Labbate, Maurizio; Labricciosa, Francesco M; Laterre, Pierre-François; Latifi, Rifat; Lee, Jae Gil; Lee, Young Ran; Leone, Marc; Leppaniemi, Ari; Li, Yousheng; Liang, Stephen Y; Loho, Tonny; Maegele, Marc; Malama, Sydney; Marei, Hany E; Martin-Loeches, Ignacio; Marwah, Sanjay; Massele, Amos; McFarlane, Michael; Melo, Renato Bessa; Negoi, Ionut; Nicolau, David P; Nord, Carl Erik; Ofori-Asenso, Richard; Omari, AbdelKarim H; Ordonez, Carlos A; Ouadii, Mouaqit; Pereira Júnior, Gerson Alves; Piazza, Diego; Pupelis, Guntars; Rawson, Timothy Miles; Rems, Miran; Rizoli, Sandro; Rocha, Claudio; Sakakhushev, Boris; Sanchez-Garcia, Miguel; Sato, Norio; Segovia Lohse, Helmut A; Sganga, Gabriele; Siribumrungwong, Boonying; Shelat, Vishal G; Soreide, Kjetil; Soto, Rodolfo; Talving, Peep; Tilsed, Jonathan V; Timsit, Jean-Francois; Trueba, Gabriel; Trung, Ngo Tat; Ulrych, Jan; van Goor, Harry; Vereczkei, Andras; Vohra, Ravinder S; Wani, Imtiaz; Uhl, Waldemar; Xiao, Yonghong; Yuan, Kuo-Ching; Zachariah, Sanoop K; Zahar, Jean-Ralph; Zakrison, Tanya L; Corcione, Antonio; Melotti, Rita M; Viscoli, Claudio; Viale, Perluigi
2016-01-01
Intra-abdominal infections (IAI) are an important cause of morbidity and are frequently associated with poor prognosis, particularly in high-risk patients. The cornerstones in the management of complicated IAIs are timely effective source control with appropriate antimicrobial therapy. Empiric antimicrobial therapy is important in the management of intra-abdominal infections and must be broad enough to cover all likely organisms because inappropriate initial antimicrobial therapy is associated with poor patient outcomes and the development of bacterial resistance. The overuse of antimicrobials is widely accepted as a major driver of some emerging infections (such as C. difficile), the selection of resistant pathogens in individual patients, and for the continued development of antimicrobial resistance globally. The growing emergence of multi-drug resistant organisms and the limited development of new agents available to counteract them have caused an impending crisis with alarming implications, especially with regards to Gram-negative bacteria. An international task force from 79 different countries has joined this project by sharing a document on the rational use of antimicrobials for patients with IAIs. The project has been termed AGORA (Antimicrobials: A Global Alliance for Optimizing their Rational Use in Intra-Abdominal Infections). The authors hope that AGORA, involving many of the world's leading experts, can actively raise awareness in health workers and can improve prescribing behavior in treating IAIs.
Local search for optimal global map generation using mid-decadal landsat images
Khatib, L.; Gasch, J.; Morris, R.; Covington, S.
2007-01-01
NASA and the US Geological Survey (USGS) are seeking to generate a map of the entire globe using Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensor data from the "mid-decadal" period of 2004 through 2006. The global map is comprised of thousands of scene locations and, for each location, tens of different images of varying quality to chose from. Furthermore, it is desirable for images of adjacent scenes be close together in time of acquisition, to avoid obvious discontinuities due to seasonal changes. These characteristics make it desirable to formulate an automated solution to the problem of generating the complete map. This paper formulates a Global Map Generator problem as a Constraint Optimization Problem (GMG-COP) and describes an approach to solving it using local search. Preliminary results of running the algorithm on image data sets are summarized. The results suggest a significant improvement in map quality using constraint-based solutions. Copyright ?? 2007, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Nacelle Chine Installation Based on Wind-Tunnel Test Using Efficient Global Optimization
NASA Astrophysics Data System (ADS)
Kanazaki, Masahiro; Yokokawa, Yuzuru; Murayama, Mitsuhiro; Ito, Takeshi; Jeong, Shinkyu; Yamamoto, Kazuomi
Design exploration of a nacelle chine installation was carried out. The nacelle chine improves stall performance when deploying multi-element high-lift devices. This study proposes an efficient design process using a Kriging surrogate model to determine the nacelle chine installation point in wind-tunnel tests. The design exploration was conducted in a wind-tunnel using the JAXA high-lift aircraft model at the JAXA Large-scale Low-speed Wind Tunnel. The objective was to maximize the maximum lift. The chine installation points were designed on the engine nacelle in the axial and chord-wise direction, while the geometry of the chine was fixed. In the design process, efficient global optimization (EGO) which includes Kriging model and genetic algorithm (GA) was employed. This method makes it possible both to improve the accuracy of the response surface and to explore the global optimum efficiently. Detailed observations of flowfields using the Particle Image Velocimetry method confirmed the chine effect and design results.
How can we Optimize Global Satellite Observations of Glacier Velocity and Elevation Changes?
NASA Astrophysics Data System (ADS)
Willis, M. J.; Pritchard, M. E.; Zheng, W.
2015-12-01
We have started a global compilation of glacier surface elevation change rates measured by altimeters and differencing of Digital Elevation Models and glacier velocities measured by Synthetic Aperture Radar (SAR) and optical feature tracking as well as from Interferometric SAR (InSAR). Our goal is to compile statistics on recent ice flow velocities and surface elevation change rates near the fronts of all available glaciers using literature and our own data sets of the Russian Arctic, Patagonia, Alaska, Greenland and Antarctica, the Himalayas, and other locations. We quantify the percentage of the glaciers on the planet that can be regarded as fast flowing glaciers, with surface velocities of more than 50 meters per year, while also recording glaciers that have elevation change rates of more than 2 meters per year. We examine whether glaciers have significant interannual variations in velocities, or have accelerated or stagnated where time series of ice motions are available. We use glacier boundaries and identifiers from the Randolph Glacier Inventory. Our survey highlights glaciers that are likely to react quickly to changes in their mass accumulation rates. The study also identifies geographical areas where our knowledge of glacier dynamics remains poor. Our survey helps guide how frequently observations must be made in order to provide quality satellite-derived velocity and ice elevation observations at a variety of glacier thermal regimes, speeds and widths. Our objectives are to determine to what extent the joint NASA and Indian Space Research Organization Synthetic Aperture Radar mission (NISAR) will be able to provide global precision coverage of ice speed changes and to determine how to optimize observations from the global constellation of satellite missions to record important changes to glacier elevations and velocities worldwide.
Optimal estimation for global ground-level fine particulate matter concentrations
NASA Astrophysics Data System (ADS)
Donkelaar, Aaron; Martin, Randall V.; Spurr, Robert J. D.; Drury, Easan; Remer, Lorraine A.; Levy, Robert C.; Wang, Jun
2013-06-01
We develop an optimal estimation (OE) algorithm based on top-of-atmosphere reflectances observed by the MODIS satellite instrument to retrieve near-surface fine particulate matter (PM2.5). The GEOS-Chem chemical transport model is used to provide prior information for the Aerosol Optical Depth (AOD) retrieval and to relate total column AOD to PM2.5. We adjust the shape of the GEOS-Chem relative vertical extinction profiles by comparison with lidar retrievals from the CALIOP satellite instrument. Surface reflectance relationships used in the OE algorithm are indexed by land type. Error quantities needed for this OE algorithm are inferred by comparison with AOD observations taken by a worldwide network of sun photometers (AERONET) and extended globally based upon aerosol speciation and cross correlation for simulated values, and upon land type for observational values. Significant agreement in PM2.5 is found over North America for 2005 (slope = 0.89; r = 0.82; 1-σ error = 1 µg/m3 + 27%), with improved coverage and correlation relative to previous work for the same region and time period, although certain subregions, such as the San Joaquin Valley of California are better represented by previous estimates. Independently derived error estimates of the OE PM2.5 values at in situ locations over North America (of ±(2.5 µg/m3 + 31%) and Europe of ±(3.5 µg/m3 + 30%) are corroborated by comparison with in situ observations, although globally (error estimates of ±(3.0 µg/m3 + 35%), may be underestimated. Global population-weighted PM2.5 at 50% relative humidity is estimated as 27.8 µg/m3 at 0.1° × 0.1° resolution.
A hybrid cuckoo search algorithm with Nelder Mead method for solving global optimization problems.
Ali, Ahmed F; Tawhid, Mohamed A
2016-01-01
Cuckoo search algorithm is a promising metaheuristic population based method. It has been applied to solve many real life problems. In this paper, we propose a new cuckoo search algorithm by combining the cuckoo search algorithm with the Nelder-Mead method in order to solve the integer and minimax optimization problems. We call the proposed algorithm by hybrid cuckoo search and Nelder-Mead method (HCSNM). HCSNM starts the search by applying the standard cuckoo search for number of iterations then the best obtained solution is passing to the Nelder-Mead algorithm as an intensification process in order to accelerate the search and overcome the slow convergence of the standard cuckoo search algorithm. The proposed algorithm is balancing between the global exploration of the Cuckoo search algorithm and the deep exploitation of the Nelder-Mead method. We test HCSNM algorithm on seven integer programming problems and ten minimax problems and compare against eight algorithms for solving integer programming problems and seven algorithms for solving minimax problems. The experiments results show the efficiency of the proposed algorithm and its ability to solve integer and minimax optimization problems in reasonable time. PMID:27217988
A hybrid cuckoo search algorithm with Nelder Mead method for solving global optimization problems.
Ali, Ahmed F; Tawhid, Mohamed A
2016-01-01
Cuckoo search algorithm is a promising metaheuristic population based method. It has been applied to solve many real life problems. In this paper, we propose a new cuckoo search algorithm by combining the cuckoo search algorithm with the Nelder-Mead method in order to solve the integer and minimax optimization problems. We call the proposed algorithm by hybrid cuckoo search and Nelder-Mead method (HCSNM). HCSNM starts the search by applying the standard cuckoo search for number of iterations then the best obtained solution is passing to the Nelder-Mead algorithm as an intensification process in order to accelerate the search and overcome the slow convergence of the standard cuckoo search algorithm. The proposed algorithm is balancing between the global exploration of the Cuckoo search algorithm and the deep exploitation of the Nelder-Mead method. We test HCSNM algorithm on seven integer programming problems and ten minimax problems and compare against eight algorithms for solving integer programming problems and seven algorithms for solving minimax problems. The experiments results show the efficiency of the proposed algorithm and its ability to solve integer and minimax optimization problems in reasonable time.
Need for coordinated programs to improve global health by optimizing salt and iodine intake.
Campbell, Norm R C; Dary, Omar; Cappuccio, Francesco P; Neufeld, Lynnette M; Harding, Kim B; Zimmermann, Michael B
2012-10-01
High dietary salt is a major cause of increased blood pressure, the leading risk for death worldwide. The World Health Organization (WHO) has recommended that salt intake be less than 5 g/day, a goal that only a small proportion of people achieve. Iodine deficiency can cause cognitive and motor impairment and, if severe, hypothyroidism with serious mental and growth retardation. More than 2 billion people worldwide are at risk of iodine deficiency. Preventing iodine deficiency by using salt fortified with iodine is a major global public health success. Programs to reduce dietary salt are technically compatible with programs to prevent iodine deficiency through salt fortification. However, for populations to fully benefit from optimum intake of salt and iodine, the programs must be integrated. This review summarizes the scientific basis for salt reduction and iodine fortification programs, the compatibility of the programs, and the steps that need to be taken by the WHO, national governments, and nongovernmental organizations to ensure that populations fully benefit from optimal intake of salt and iodine. Specifically, expert groups must be convened to help countries implement integrated programs and context-specific case studies of successfully integrated programs; lessons learned need to be compiled and disseminated. Integrated surveillance programs will be more efficient and will enhance current efforts to optimize intake of iodine and salt. For populations to fully benefit, governments need to place a high priority on integrating these two important public health programs. PMID:23299289
NASA Astrophysics Data System (ADS)
Arteaga, Lionel; Pahlow, Markus; Oschlies, Andreas
2014-07-01
The widely used concept of constant "Redfield" phytoplankton stoichiometry is often applied for estimating which nutrient limits phytoplankton growth in the surface ocean. Culture experiments, in contrast, show strong relations between growth conditions and cellular stoichiometry with often substantial deviations from Redfield stoichiometry. Here we investigate to what extent both views agree by analyzing remote sensing and in situ data with an optimality-based model of nondiazotrophic phytoplankton growth in order to infer seasonally varying patterns of colimitation by light, nitrogen (N), and phosphorus (P) in the global ocean. Our combined model-data analysis suggests strong N and N-P colimitation in the tropical ocean, seasonal light, and N-P colimitation in the Northern Hemisphere, and strong light limitation only during winter in the Southern Ocean. The eastern equatorial Pacific appears as the only ocean area that is essentially not limited by N, P, or light. Even though our optimality-based approach specifically accounts for flexible stoichiometry, inferred patterns of N and P limitation are to some extent consistent with those obtained from an analysis of surface inorganic nutrients with respect to the Redfield N:P ratio. Iron is not part of our analysis, implying that we cannot accurately predict N cell quotas in high-nutrient, low-chlorophyll regions. Elsewhere, we do not expect a major effect of iron on the relative distribution of N, P, and light colimitation areas. The relative importance of N, P, and light in limiting phytoplankton growth diagnosed here by combining observations and an optimal growth model provides a useful constraint for models used to predict future marine biological production under changing environmental conditions. 2014. American Geophysical Union. All Rights Reserved.
NASA Astrophysics Data System (ADS)
Vaziri Yazdi Pin, Mohammad
practices. Single criterion optimization algorithms using mathematical programming for globally optimal solutions have been developed for three objectives of cost, reliability, and the social/environmental impacts. Additional algorithms for inclusions of upgrade and optimal load assignment possibilities have been developed. Algorithms have been developed to handle the expansion as a multiobjective decision process. Typical data from both major investor owned and major municipal utilities operating in California USA, have been utilized to implement and test the algorithms on practical test cases. Results of the case studies and associated analyses indicate that the developed algorithms also perform efficiently in solving the multistage and multiobjective expansion problem.
Recursive Ant Colony Global Optimization: a new technique for the inversion of geophysical data
NASA Astrophysics Data System (ADS)
Gupta, D. K.; Gupta, J. P.; Arora, Y.; Singh, U. K.
2011-12-01
We present a new method called Recursive Ant Colony Global Optimization (RACO) technique, a modified form of general ACO, which can be used to find the best solutions to inversion problems in geophysics. RACO simulates the social behaviour of ants to find the best path between the nest and the food source. A new term depth has been introduced, which controls the extent of recursion. A selective number of cities get qualified for the successive depth. The results of one depth are used to construct the models for the next depth and the range of values for each of the parameters is reduced without any change to the number of models. The three additional steps performed after each depth, are the pheromone tracking, pheromone updating and city selection. One of the advantages of RACO over ACO is that if a problem has multiple solutions, then pheromone accumulation will take place at more than one city thereby leading to formation of multiple nested ACO loops within the ACO loop of the previous depth. Also, while the convergence of ACO is almost linear, RACO shows exponential convergence and hence is faster than the ACO. RACO proves better over some other global optimization techniques, as it does not require any initial values to be assigned to the parameters function. The method has been tested on some mathematical functions, synthetic self-potential (SP) and synthetic gravity data. The obtained results reveal the efficiency and practicability of the method. The method is found to be efficient enough to solve the problems of SP and gravity anomalies due to a horizontal cylinder, a sphere, an inclined sheet and multiple idealized bodies buried inside the earth. These anomalies with and without noise were inverted using the RACO algorithm. The obtained results were compared with those obtained from the conventional methods and it was found that RACO results are more accurate. Finally this optimization technique was applied to real field data collected over the Surda
Using R for Global Optimization of a Fully-distributed Hydrologic Model at Continental Scale
NASA Astrophysics Data System (ADS)
Zambrano-Bigiarini, M.; Zajac, Z.; Salamon, P.
2013-12-01
Nowadays hydrologic model simulations are widely used to better understand hydrologic processes and to predict extreme events such as floods and droughts. In particular, the spatially distributed LISFLOOD model is currently used for flood forecasting at Pan-European scale, within the European Flood Awareness System (EFAS). Several model parameters can not be directly measured, and they need to be estimated through calibration, in order to constrain simulated discharges to their observed counterparts. In this work we describe how the free software 'R' has been used as a single environment to pre-process hydro-meteorological data, to carry out global optimization, and to post-process calibration results in Europe. Historical daily discharge records were pre-processed for 4062 stream gauges, with different amount and distribution of data in each one of them. The hydroTSM, raster and sp R packages were used to select ca. 700 stations with an adequate spatio-temporal coverage. Selected stations span a wide range of hydro-climatic characteristics, from arid and ET-dominated watersheds in the Iberian Peninsula to snow-dominated watersheds in Scandinavia. Nine parameters were selected to be calibrated based on previous expert knowledge. Customized R scripts were used to extract observed time series for each catchment and to prepare the input files required to fully set up the calibration thereof. The hydroPSO package was then used to carry out a single-objective global optimization on each selected catchment, by using the Standard Particle Swarm 2011 (SPSO-2011) algorithm. Among the many goodness-of-fit measures available in the hydroGOF package, the Nash-Sutcliffe efficiency was used to drive the optimization. User-defined functions were developed for reading model outputs and passing them to the calibration engine. The long computational time required to finish the calibration at continental scale was partially alleviated by using 4 multi-core machines (with both GNU
NASA Astrophysics Data System (ADS)
Portnoy, David; Feuerbach, Robert; Heimberg, Jennifer
2011-10-01
Today there is a tremendous amount of interest in systems that can detect radiological or nuclear threats. Many of these systems operate in extremely high throughput situations where delays caused by false alarms can have a significant negative impact. Thus, calculating the tradeoff between detection rates and false alarm rates is critical for their successful operation. Receiver operating characteristic (ROC) curves have long been used to depict this tradeoff. The methodology was first developed in the field of signal detection. In recent years it has been used increasingly in machine learning and data mining applications. It follows that this methodology could be applied to radiological/nuclear threat detection systems. However many of these systems do not fit into the classic principles of statistical detection theory because they tend to lack tractable likelihood functions and have many parameters, which, in general, do not have a one-to-one correspondence with the detection classes. This work proposes a strategy to overcome these problems by empirically finding parameter values that maximize the probability of detection for a selected number of probabilities of false alarm. To find these parameter values a statistical global optimization technique that seeks to estimate portions of a ROC curve is proposed. The optimization combines elements of simulated annealing with elements of genetic algorithms. Genetic algorithms were chosen because they can reduce the risk of getting stuck in local minima. However classic genetic algorithms operate on arrays of Booleans values or bit strings, so simulated annealing is employed to perform mutation in the genetic algorithm. The presented initial results were generated using an isotope identification algorithm developed at Johns Hopkins University Applied Physics Laboratory. The algorithm has 12 parameters: 4 real-valued and 8 Boolean. A simulated dataset was used for the optimization study; the "threat" set of spectra
Climate, Agriculture, Energy and the Optimal Allocation of Global Land Use
NASA Astrophysics Data System (ADS)
Steinbuks, J.; Hertel, T. W.
2011-12-01
The allocation of the world's land resources over the course of the next century has become a pressing research question. Continuing population increases, improving, land-intensive diets amongst the poorest populations in the world, increasing production of biofuels and rapid urbanization in developing countries are all competing for land even as the world looks to land resources to supply more environmental services. The latter include biodiversity and natural lands, as well as forests and grasslands devoted to carbon sequestration. And all of this is taking place in the context of faster than expected climate change which is altering the biophysical environment for land-related activities. The goal of the paper is to determine the optimal profile for global land use in the context of growing commercial demands for food and forest products, increasing non-market demands for ecosystem services, and more stringent GHG mitigation targets. We then seek to assess how the uncertainty associated with the underlying biophysical and economic processes influences this optimal profile of land use, in light of potential irreversibility in these decisions. We develop a dynamic long-run, forward-looking partial equilibrium framework in which the societal objective function being maximized places value on food production, liquid fuels (including biofuels), timber production, forest carbon and biodiversity. Given the importance of land-based emissions to any GHG mitigation strategy, as well as the potential impacts of climate change itself on the productivity of land in agriculture, forestry and ecosystem services, we aim to identify the optimal allocation of the world's land resources, over the course of the next century, in the face of alternative GHG constraints. The forestry sector is characterized by multiple forest vintages which add considerable computational complexity in the context of this dynamic analysis. In order to solve this model efficiently, we have employed the
NASA Astrophysics Data System (ADS)
Zhang, X.; Cai, X.; Zhu, T.
2013-12-01
Biofuels is booming in recent years due to its potential contributions to energy sustainability, environmental improvement and economic opportunities. Production of biofuels not only competes for land and water with food production, but also directly pushes up food prices when crops such as maize and sugarcane are used as biofuels feedstock. Meanwhile, international trade of agricultural commodities exports and imports water and land resources in a virtual form among different regions, balances overall water and land demands and resource endowment, and provides a promising solution to the increasingly severe food-energy competition. This study investigates how to optimize water and land resources uses for overall welfare at global scale in the framework of 'virtual resources'. In contrast to partial equilibrium models that usually simulate trades year-by-year, this optimization model explores the ideal world where malnourishment is minimized with optimal resources uses and trade flows. Comparing the optimal production and trade patterns with historical data can provide meaningful implications regarding how to utilize water and land resources more efficiently and how the trade flows would be changed for overall welfare at global scale. Valuable insights are obtained in terms of the interactions among food, water and bioenergy systems. A global hydro-economic optimization model is developed, integrating agricultural production, market demands (food, feed, fuel and other), and resource and environmental constraints. Preliminary results show that with the 'free market' mechanism and land as well as water resources use optimization, the malnourished population can be reduced by as much as 65%, compared to the 2000 historical value. Expected results include: 1) optimal trade paths to achieve global malnourishment minimization, 2) how water and land resources constrain local supply, 3) how policy affects the trade pattern as well as resource uses. Furthermore, impacts of
Donner, René; Menze, Bjoern H; Bischof, Horst; Langs, Georg
2013-12-01
The accurate localization of anatomical landmarks is a challenging task, often solved by domain specific approaches. We propose a method for the automatic localization of landmarks in complex, repetitive anatomical structures. The key idea is to combine three steps: (1) a classifier for pre-filtering anatomical landmark positions that (2) are refined through a Hough regression model, together with (3) a parts-based model of the global landmark topology to select the final landmark positions. During training landmarks are annotated in a set of example volumes. A classifier learns local landmark appearance, and Hough regressors are trained to aggregate neighborhood information to a precise landmark coordinate position. A non-parametric geometric model encodes the spatial relationships between the landmarks and derives a topology which connects mutually predictive landmarks. During the global search we classify all voxels in the query volume, and perform regression-based agglomeration of landmark probabilities to highly accurate and specific candidate points at potential landmark locations. We encode the candidates' weights together with the conformity of the connecting edges to the learnt geometric model in a Markov Random Field (MRF). By solving the corresponding discrete optimization problem, the most probable location for each model landmark is found in the query volume. We show that this approach is able to consistently localize the model landmarks despite the complex and repetitive character of the anatomical structures on three challenging data sets (hand radiographs, hand CTs, and whole body CTs), with a median localization error of 0.80 mm, 1.19 mm and 2.71 mm, respectively. PMID:23664450
Waldman, Yedael Y; Tuller, Tamir; Shlomi, Tomer; Sharan, Roded; Ruppin, Eytan
2010-05-01
Various studies in unicellular and multicellular organisms have shown that codon bias plays a significant role in translation efficiency (TE) by co-adaptation to the tRNA pool. Yet, in humans and other mammals the role of codon bias is still an open question, with contradictory results from different studies. Here we address this question, performing a large-scale tissue-specific analysis of TE in humans, using the tRNA Adaptation Index (tAI) as a direct measure for TE. We find tAI to significantly correlate with expression levels both in tissue-specific and in global expression measures, testifying to the TE of human tissues. Interestingly, we find significantly higher correlations in adult tissues as opposed to fetal tissues, suggesting that the tRNA pool is more adjusted to the adult period. Optimization based analysis suggests that the tRNA pool-codon bias co-adaptation is globally (and not tissue-specific) driven. Additionally, we find that tAI correlates with several measures related to the protein functionally importance, including gene essentiality. Using inferred tissue-specific tRNA pools lead to similar results and shows that tissue-specific genes are more adapted to their tRNA pool than other genes and that related sets of functional gene groups are translated efficiently in each tissue. Similar results are obtained for other mammals. Taken together, these results demonstrate the role of codon bias in TE in humans, and pave the way for future studies of tissue-specific TE in multicellular organisms. PMID:20097653
Donner, René; Menze, Bjoern H.; Bischof, Horst; Langs, Georg
2013-01-01
The accurate localization of anatomical landmarks is a challenging task, often solved by domain specific approaches. We propose a method for the automatic localization of landmarks in complex, repetitive anatomical structures. The key idea is to combine three steps: (1) a classifier for pre-filtering anatomical landmark positions that (2) are refined through a Hough regression model, together with (3) a parts-based model of the global landmark topology to select the final landmark positions. During training landmarks are annotated in a set of example volumes. A classifier learns local landmark appearance, and Hough regressors are trained to aggregate neighborhood information to a precise landmark coordinate position. A non-parametric geometric model encodes the spatial relationships between the landmarks and derives a topology which connects mutually predictive landmarks. During the global search we classify all voxels in the query volume, and perform regression-based agglomeration of landmark probabilities to highly accurate and specific candidate points at potential landmark locations. We encode the candidates’ weights together with the conformity of the connecting edges to the learnt geometric model in a Markov Random Field (MRF). By solving the corresponding discrete optimization problem, the most probable location for each model landmark is found in the query volume. We show that this approach is able to consistently localize the model landmarks despite the complex and repetitive character of the anatomical structures on three challenging data sets (hand radiographs, hand CTs, and whole body CTs), with a median localization error of 0.80 mm, 1.19 mm and 2.71 mm, respectively. PMID:23664450
GOSIM: A multi-scale iterative multiple-point statistics algorithm with global optimization
NASA Astrophysics Data System (ADS)
Yang, Liang; Hou, Weisheng; Cui, Chanjie; Cui, Jie
2016-04-01
Most current multiple-point statistics (MPS) algorithms are based on a sequential simulation procedure, during which grid values are updated according to the local data events. Because the realization is updated only once during the sequential process, errors that occur while updating data events cannot be corrected. Error accumulation during simulations decreases the realization quality. Aimed at improving simulation quality, this study presents an MPS algorithm based on global optimization, called GOSIM. An objective function is defined for representing the dissimilarity between a realization and the TI in GOSIM, which is minimized by a multi-scale EM-like iterative method that contains an E-step and M-step in each iteration. The E-step searches for TI patterns that are most similar to the realization and match the conditioning data. A modified PatchMatch algorithm is used to accelerate the search process in E-step. M-step updates the realization based on the most similar patterns found in E-step and matches the global statistics of TI. During categorical data simulation, k-means clustering is used for transforming the obtained continuous realization into a categorical realization. The qualitative and quantitative comparison results of GOSIM, MS-CCSIM and SNESIM suggest that GOSIM has a better pattern reproduction ability for both unconditional and conditional simulations. A sensitivity analysis illustrates that pattern size significantly impacts the time costs and simulation quality. In conditional simulations, the weights of conditioning data should be as small as possible to maintain a good simulation quality. The study shows that big iteration numbers at coarser scales increase simulation quality and small iteration numbers at finer scales significantly save simulation time.
Covariance and crossover matrix guided differential evolution for global numerical optimization.
Li, YongLi; Feng, JinFu; Hu, JunHua
2016-01-01
Differential evolution (DE) is an efficient and robust evolutionary algorithm and has wide application in various science and engineering fields. DE is sensitive to the selection of mutation and crossover strategies and their associated control parameters. However, the structure and implementation of DEs are becoming more complex because of the diverse mutation and crossover strategies that use distinct parameter settings during the different stages of the evolution. A novel strategy is used in this study to improve the crossover and mutation operations. The crossover matrix, instead of a crossover operator and its control parameter CR, is proposed to implement the function of the crossover operation. Meanwhile, Gaussian distribution centers the best individuals found in each generation based on the proposed covariance matrix, which is generated between the best individual and several better individuals. Improved mutation operator based on the crossover matrix is randomly selected to generate the trial population. This operator is used to generate high-quality solutions to improve the capability of exploitation and enhance the preference of exploration. In addition, the memory population is randomly chosen from previous generation and used to control the search direction in the novel mutation strategy. Accordingly, the diversity of the population is improved. Thus, CCDE, which is a novel efficient and simple DE variant, is presented in this paper. CCDE has been tested on 30 benchmarks and 5 real-world optimization problems from the IEEE Congress on Evolutionary Computation (CEC) 2014 and CEC 2011, respectively. Experimental and statistical results demonstrate the effectiveness of CCDE for global numerical and engineering optimization. CCDE can solve the test benchmark functions and engineering problems more successfully than the other DE variants and algorithms from CEC 2014.
Template based protein structure modeling by global optimization in CASP11.
Joo, Keehyoung; Joung, InSuk; Lee, Sun Young; Kim, Jong Yun; Cheng, Qianyi; Manavalan, Balachandran; Joung, Jong Young; Heo, Seungryong; Lee, Juyong; Nam, Mikyung; Lee, In-Ho; Lee, Sung Jong; Lee, Jooyoung
2016-09-01
For the template-based modeling (TBM) of CASP11 targets, we have developed three new protein modeling protocols (nns for server prediction and LEE and LEER for human prediction) by improving upon our previous CASP protocols (CASP7 through CASP10). We applied the powerful global optimization method of conformational space annealing to three stages of optimization, including multiple sequence-structure alignment, three-dimensional (3D) chain building, and side-chain remodeling. For more successful fold recognition, a new alignment method called CRFalign was developed. It can incorporate sensitive positional and environmental dependence in alignment scores as well as strong nonlinear correlations among various features. Modifications and adjustments were made to the form of the energy function and weight parameters pertaining to the chain building procedure. For the side-chain remodeling step, residue-type dependence was introduced to the cutoff value that determines the entry of a rotamer to the side-chain modeling library. The improved performance of the nns server method is attributed to successful fold recognition achieved by combining several methods including CRFalign and to the current modeling formulation that can incorporate native-like structural aspects present in multiple templates. The LEE protocol is identical to the nns one except that CASP11-released server models are used as templates. The success of LEE in utilizing CASP11 server models indicates that proper template screening and template clustering assisted by appropriate cluster ranking promises a new direction to enhance protein 3D modeling. Proteins 2016; 84(Suppl 1):221-232. © 2015 Wiley Periodicals, Inc.
Covariance and crossover matrix guided differential evolution for global numerical optimization.
Li, YongLi; Feng, JinFu; Hu, JunHua
2016-01-01
Differential evolution (DE) is an efficient and robust evolutionary algorithm and has wide application in various science and engineering fields. DE is sensitive to the selection of mutation and crossover strategies and their associated control parameters. However, the structure and implementation of DEs are becoming more complex because of the diverse mutation and crossover strategies that use distinct parameter settings during the different stages of the evolution. A novel strategy is used in this study to improve the crossover and mutation operations. The crossover matrix, instead of a crossover operator and its control parameter CR, is proposed to implement the function of the crossover operation. Meanwhile, Gaussian distribution centers the best individuals found in each generation based on the proposed covariance matrix, which is generated between the best individual and several better individuals. Improved mutation operator based on the crossover matrix is randomly selected to generate the trial population. This operator is used to generate high-quality solutions to improve the capability of exploitation and enhance the preference of exploration. In addition, the memory population is randomly chosen from previous generation and used to control the search direction in the novel mutation strategy. Accordingly, the diversity of the population is improved. Thus, CCDE, which is a novel efficient and simple DE variant, is presented in this paper. CCDE has been tested on 30 benchmarks and 5 real-world optimization problems from the IEEE Congress on Evolutionary Computation (CEC) 2014 and CEC 2011, respectively. Experimental and statistical results demonstrate the effectiveness of CCDE for global numerical and engineering optimization. CCDE can solve the test benchmark functions and engineering problems more successfully than the other DE variants and algorithms from CEC 2014. PMID:27512635
Efficiency of Pareto joint inversion of 2D geophysical data using global optimization methods
NASA Astrophysics Data System (ADS)
Miernik, Katarzyna; Bogacz, Adrian; Kozubal, Adam; Danek, Tomasz; Wojdyła, Marek
2016-04-01
Pareto joint inversion of two or more sets of data is a promising new tool of modern geophysical exploration. In the first stage of our investigation we created software enabling execution of forward solvers of two geophysical methods (2D magnetotelluric and gravity) as well as inversion with possibility of constraining solution with seismic data. In the algorithm solving MT forward solver Helmholtz's equations, finite element method and Dirichlet's boundary conditions were applied. Gravity forward solver was based on Talwani's algorithm. To limit dimensionality of solution space we decided to describe model as sets of polygons, using Sharp Boundary Interface (SBI) approach. The main inversion engine was created using Particle Swarm Optimization (PSO) algorithm adapted to handle two or more target functions and to prevent acceptance of solutions which are non - realistic or incompatible with Pareto scheme. Each inversion run generates single Pareto solution, which can be added to Pareto Front. The PSO inversion engine was parallelized using OpenMP standard, what enabled execution code for practically unlimited amount of threads at once. Thereby computing time of inversion process was significantly decreased. Furthermore, computing efficiency increases with number of PSO iterations. In this contribution we analyze the efficiency of created software solution taking under consideration details of chosen global optimization engine used as a main joint minimization engine. Additionally we study the scale of possible decrease of computational time caused by different methods of parallelization applied for both forward solvers and inversion algorithm. All tests were done for 2D magnetotelluric and gravity data based on real geological media. Obtained results show that even for relatively simple mid end computational infrastructure proposed solution of inversion problem can be applied in practice and used for real life problems of geophysical inversion and interpretation.
NASA Astrophysics Data System (ADS)
Lera, Daniela; Sergeyev, Yaroslav D.
2015-06-01
In this paper, the global optimization problem miny∈S F (y) with S being a hyperinterval in RN and F (y) satisfying the Lipschitz condition with an unknown Lipschitz constant is considered. It is supposed that the function F (y) can be multiextremal, non-differentiable, and given as a 'black-box'. To attack the problem, a new global optimization algorithm based on the following two ideas is proposed and studied both theoretically and numerically. First, the new algorithm uses numerical approximations to space-filling curves to reduce the original Lipschitz multi-dimensional problem to a univariate one satisfying the Hölder condition. Second, the algorithm at each iteration applies a new geometric technique working with a number of possible Hölder constants chosen from a set of values varying from zero to infinity showing so that ideas introduced in a popular DIRECT method can be used in the Hölder global optimization. Convergence conditions of the resulting deterministic global optimization method are established. Numerical experiments carried out on several hundreds of test functions show quite a promising performance of the new algorithm in comparison with its direct competitors.
Optimal estimation of regional N2O emissions using a three-dimensional global model
NASA Astrophysics Data System (ADS)
Huang, J.; Golombek, A.; Prinn, R.
2004-12-01
In this study, we use the MATCH (Model of Atmospheric Transport and Chemistry) model and Kalman filtering techniques to optimally estimate N2O emissions from seven source regions around the globe. The MATCH model was used with NCEP assimilated winds at T62 resolution (192 longitude by 94 latitude surface grid, and 28 vertical levels) from July 1st 1996 to December 31st 2000. The average concentrations of N2O in the lowest four layers of the model were then compared with the monthly mean observations from six national/global networks (AGAGE, CMDL (HATS), CMDL (CCGG), CSIRO, CSIR and NIES), at 48 surface sites. A 12-month-running-mean smoother was applied to both the model results and the observations, due to the fact that the model was not able to reproduce the very small observed seasonal variations. The Kalman filter was then used to solve for the time-averaged regional emissions of N2O for January 1st 1997 to June 30th 2000. The inversions assume that the model stratospheric destruction rates, which lead to a global N2O lifetime of 130 years, are correct. It also assumes normalized emission spatial distributions from each region based on previous studies. We conclude that the global N2O emission flux is about 16.2 TgN/yr, with {34.9±1.7%} from South America and Africa, {34.6±1.5%} from South Asia, {13.9±1.5%} from China/Japan/South East Asia, {8.0±1.9%} from all oceans, {6.4±1.1%} from North America and North and West Asia, {2.6±0.4%} from Europe, and {0.9±0.7%} from New Zealand and Australia. The errors here include the measurement standard deviation, calibration differences among the six groups, grid volume/measurement site mis-match errors estimated from the model, and a procedure to account approximately for the modeling errors.
Lihoreau, Mathieu; Ings, Thomas C; Chittka, Lars; Reynolds, Andy M
2016-01-01
Simulated annealing is a powerful stochastic search algorithm for locating a global maximum that is hidden among many poorer local maxima in a search space. It is frequently implemented in computers working on complex optimization problems but until now has not been directly observed in nature as a searching strategy adopted by foraging animals. We analysed high-speed video recordings of the three-dimensional searching flights of bumblebees (Bombus terrestris) made in the presence of large or small artificial flowers within a 0.5 m(3) enclosed arena. Analyses of the three-dimensional flight patterns in both conditions reveal signatures of simulated annealing searches. After leaving a flower, bees tend to scan back-and forth past that flower before making prospecting flights (loops), whose length increases over time. The search pattern becomes gradually more expansive and culminates when another rewarding flower is found. Bees then scan back and forth in the vicinity of the newly discovered flower and the process repeats. This looping search pattern, in which flight step lengths are typically power-law distributed, provides a relatively simple yet highly efficient strategy for pollinators such as bees to find best quality resources in complex environments made of multiple ephemeral feeding sites with nutritionally variable rewards.
Lihoreau, Mathieu; Ings, Thomas C; Chittka, Lars; Reynolds, Andy M
2016-01-01
Simulated annealing is a powerful stochastic search algorithm for locating a global maximum that is hidden among many poorer local maxima in a search space. It is frequently implemented in computers working on complex optimization problems but until now has not been directly observed in nature as a searching strategy adopted by foraging animals. We analysed high-speed video recordings of the three-dimensional searching flights of bumblebees (Bombus terrestris) made in the presence of large or small artificial flowers within a 0.5 m(3) enclosed arena. Analyses of the three-dimensional flight patterns in both conditions reveal signatures of simulated annealing searches. After leaving a flower, bees tend to scan back-and forth past that flower before making prospecting flights (loops), whose length increases over time. The search pattern becomes gradually more expansive and culminates when another rewarding flower is found. Bees then scan back and forth in the vicinity of the newly discovered flower and the process repeats. This looping search pattern, in which flight step lengths are typically power-law distributed, provides a relatively simple yet highly efficient strategy for pollinators such as bees to find best quality resources in complex environments made of multiple ephemeral feeding sites with nutritionally variable rewards. PMID:27459948
NASA Astrophysics Data System (ADS)
Lihoreau, Mathieu; Ings, Thomas C.; Chittka, Lars; Reynolds, Andy M.
2016-07-01
Simulated annealing is a powerful stochastic search algorithm for locating a global maximum that is hidden among many poorer local maxima in a search space. It is frequently implemented in computers working on complex optimization problems but until now has not been directly observed in nature as a searching strategy adopted by foraging animals. We analysed high-speed video recordings of the three-dimensional searching flights of bumblebees (Bombus terrestris) made in the presence of large or small artificial flowers within a 0.5 m3 enclosed arena. Analyses of the three-dimensional flight patterns in both conditions reveal signatures of simulated annealing searches. After leaving a flower, bees tend to scan back-and forth past that flower before making prospecting flights (loops), whose length increases over time. The search pattern becomes gradually more expansive and culminates when another rewarding flower is found. Bees then scan back and forth in the vicinity of the newly discovered flower and the process repeats. This looping search pattern, in which flight step lengths are typically power-law distributed, provides a relatively simple yet highly efficient strategy for pollinators such as bees to find best quality resources in complex environments made of multiple ephemeral feeding sites with nutritionally variable rewards.
NASA Astrophysics Data System (ADS)
Yang, Jian; Cong, Weijian; Chen, Yang; Fan, Jingfan; Liu, Yue; Wang, Yongtian
2014-02-01
The clinical value of the 3D reconstruction of a coronary artery is important for the diagnosis and intervention of cardiovascular diseases. This work proposes a method based on a deformable model for reconstructing coronary arteries from two monoplane angiographic images acquired from different angles. First, an external force back-projective composition model is developed to determine the external force, for which the force distributions in different views are back-projected to the 3D space and composited in the same coordinate system based on the perspective projection principle of x-ray imaging. The elasticity and bending forces are composited as an internal force to maintain the smoothness of the deformable curve. Second, the deformable curve evolves rapidly toward the true vascular centerlines in 3D space and angiographic images under the combination of internal and external forces. Third, densely matched correspondence among vessel centerlines is constructed using a curve alignment method. The bundle adjustment method is then utilized for the global optimization of the projection parameters and the 3D structures. The proposed method is validated on phantom data and routine angiographic images with consideration for space and re-projection image errors. Experimental results demonstrate the effectiveness and robustness of the proposed method for the reconstruction of coronary arteries from two monoplane angiographic images. The proposed method can achieve a mean space error of 0.564 mm and a mean re-projection error of 0.349 mm.
Lihoreau, Mathieu; Ings, Thomas C.; Chittka, Lars; Reynolds, Andy M.
2016-01-01
Simulated annealing is a powerful stochastic search algorithm for locating a global maximum that is hidden among many poorer local maxima in a search space. It is frequently implemented in computers working on complex optimization problems but until now has not been directly observed in nature as a searching strategy adopted by foraging animals. We analysed high-speed video recordings of the three-dimensional searching flights of bumblebees (Bombus terrestris) made in the presence of large or small artificial flowers within a 0.5 m3 enclosed arena. Analyses of the three-dimensional flight patterns in both conditions reveal signatures of simulated annealing searches. After leaving a flower, bees tend to scan back-and forth past that flower before making prospecting flights (loops), whose length increases over time. The search pattern becomes gradually more expansive and culminates when another rewarding flower is found. Bees then scan back and forth in the vicinity of the newly discovered flower and the process repeats. This looping search pattern, in which flight step lengths are typically power-law distributed, provides a relatively simple yet highly efficient strategy for pollinators such as bees to find best quality resources in complex environments made of multiple ephemeral feeding sites with nutritionally variable rewards. PMID:27459948
Duprey, Sonia; Cheze, Laurence; Dumas, Raphaël
2010-10-19
In order to obtain the lower limb kinematics from skin-based markers, the soft tissue artefact (STA) has to be compensated. Global optimization (GO) methods rely on a predefined kinematic model and attempt to limit STA by minimizing the differences between model predicted and skin-based marker positions. Thus, the reliability of GO methods depends directly on the chosen model, whose influence is not well known yet. This study develops a GO method that allows to easily implement different sets of joint constraints in order to assess their influence on the lower limb kinematics during gait. The segment definition was based on generalized coordinates giving only linear or quadratic joint constraints. Seven sets of joint constraints were assessed, corresponding to different kinematic models at the ankle, knee and hip: SSS, USS, PSS, SHS, SPS, UHS and PPS (where S, U and H stand for spherical, universal and hinge joints and P for parallel mechanism). GO was applied to gait data from five healthy males. Results showed that the lower limb kinematics, except hip kinematics, knee and ankle flexion-extension, significantly depend on the chosen ankle and knee constraints. The knee parallel mechanism generated some typical knee rotation patterns previously observed in lower limb kinematic studies. Furthermore, only the parallel mechanisms produced joint displacements. Thus, GO using parallel mechanism seems promising. It also offers some perspectives of subject-specific joint constraints.
A GPS-Based Pitot-Static Calibration Method Using Global Output-Error Optimization
NASA Technical Reports Server (NTRS)
Foster, John V.; Cunningham, Kevin
2010-01-01
Pressure-based airspeed and altitude measurements for aircraft typically require calibration of the installed system to account for pressure sensing errors such as those due to local flow field effects. In some cases, calibration is used to meet requirements such as those specified in Federal Aviation Regulation Part 25. Several methods are used for in-flight pitot-static calibration including tower fly-by, pacer aircraft, and trailing cone methods. In the 1990 s, the introduction of satellite-based positioning systems to the civilian market enabled new inflight calibration methods based on accurate ground speed measurements provided by Global Positioning Systems (GPS). Use of GPS for airspeed calibration has many advantages such as accuracy, ease of portability (e.g. hand-held) and the flexibility of operating in airspace without the limitations of test range boundaries or ground telemetry support. The current research was motivated by the need for a rapid and statistically accurate method for in-flight calibration of pitot-static systems for remotely piloted, dynamically-scaled research aircraft. Current calibration methods were deemed not practical for this application because of confined test range size and limited flight time available for each sortie. A method was developed that uses high data rate measurements of static and total pressure, and GPSbased ground speed measurements to compute the pressure errors over a range of airspeed. The novel application of this approach is the use of system identification methods that rapidly compute optimal pressure error models with defined confidence intervals in nearreal time. This method has been demonstrated in flight tests and has shown 2- bounds of approximately 0.2 kts with an order of magnitude reduction in test time over other methods. As part of this experiment, a unique database of wind measurements was acquired concurrently with the flight experiments, for the purpose of experimental validation of the
Eckermann, Simon; Willan, Andrew R
2013-05-01
Risk sharing arrangements relate to adjusting payments for new health technologies given evidence of their performance over time. Such arrangements rely on prospective information regarding the incremental net benefit of the new technology, and its use in practice. However, once the new technology has been adopted in a particular jurisdiction, randomized clinical trials within that jurisdiction are likely to be infeasible and unethical in the cases where they would be most helpful, i.e. with current evidence of positive while uncertain incremental health and net monetary benefit. Informed patients in these cases would likely be reluctant to participate in a trial, preferring instead to receive the new technology with certainty. Consequently, informing risk sharing arrangements within a jurisdiction is problematic given the infeasibility of collecting prospective trial data. To overcome such problems, we demonstrate that global trials facilitate trialling post adoption, leading to more complete and robust risk sharing arrangements that mitigate the impact of costs of reversal on expected value of information in jurisdictions who adopt while a global trial is undertaken. More generally, optimally designed global trials offer distinct advantages over locally optimal solutions for decision makers and manufacturers alike: avoiding opportunity costs of delay in jurisdictions that adopt; overcoming barriers to evidence collection; and improving levels of expected implementation. Further, the greater strength and translatability of evidence across jurisdictions inherent in optimal global trial design reduces barriers to translation across jurisdictions characteristic of local trials. Consequently, efficiently designed global trials better align the interests of decision makers and manufacturers, increasing the feasibility of risk sharing and the expected strength of evidence over local trials, up until the point that current evidence is globally sufficient.
Eckermann, Simon; Willan, Andrew R
2013-05-01
Risk sharing arrangements relate to adjusting payments for new health technologies given evidence of their performance over time. Such arrangements rely on prospective information regarding the incremental net benefit of the new technology, and its use in practice. However, once the new technology has been adopted in a particular jurisdiction, randomized clinical trials within that jurisdiction are likely to be infeasible and unethical in the cases where they would be most helpful, i.e. with current evidence of positive while uncertain incremental health and net monetary benefit. Informed patients in these cases would likely be reluctant to participate in a trial, preferring instead to receive the new technology with certainty. Consequently, informing risk sharing arrangements within a jurisdiction is problematic given the infeasibility of collecting prospective trial data. To overcome such problems, we demonstrate that global trials facilitate trialling post adoption, leading to more complete and robust risk sharing arrangements that mitigate the impact of costs of reversal on expected value of information in jurisdictions who adopt while a global trial is undertaken. More generally, optimally designed global trials offer distinct advantages over locally optimal solutions for decision makers and manufacturers alike: avoiding opportunity costs of delay in jurisdictions that adopt; overcoming barriers to evidence collection; and improving levels of expected implementation. Further, the greater strength and translatability of evidence across jurisdictions inherent in optimal global trial design reduces barriers to translation across jurisdictions characteristic of local trials. Consequently, efficiently designed global trials better align the interests of decision makers and manufacturers, increasing the feasibility of risk sharing and the expected strength of evidence over local trials, up until the point that current evidence is globally sufficient. PMID:23529209
Developments of global greenhouse gas retrieval algorithm based on Optimal Estimation Method
NASA Astrophysics Data System (ADS)
Kim, W. V.; Kim, J.; Lee, H.; Jung, Y.; Boesch, H.
2013-12-01
After the industrial revolution, atmospheric carbon dioxide concentration increased drastically over the last 250 years. It is still increasing and over than 400ppm of carbon dioxide was measured at Mauna Loa observatory for the first time which value was considered as important milestone. Therefore, understanding the source, emission, transport and sink of global carbon dioxide is unprecedentedly important. Currently, Total Carbon Column Observing Network (TCCON) is operated to observe CO2 concentration by ground base instruments. However, the number of site is very few and concentrated to Europe and North America. Remote sensing of CO2 could supplement those limitations. Greenhouse Gases Observing SATellite (GOSAT) which was launched 2009 is measuring column density of CO2 and other satellites are planned to launch in a few years. GOSAT provide valuable measurement data but its low spatial resolution and poor success rate of retrieval due to aerosol and cloud, forced the results to cover less than half of the whole globe. To improve data availability, accurate aerosol information is necessary, especially for East Asia region where the aerosol concentration is higher than other region. For the first step, we are developing CO2 retrieval algorithm based on optimal estimation method with VLIDORT the vector discrete ordinate radiative transfer model. Proto type algorithm, developed from various combinations of state vectors to find best combination of state vectors, shows appropriate result and good agreement with TCCON measurements. To reduce calculation cost low-stream interpolation is applied for model simulation and the simulation time is drastically reduced. For the further study, GOSAT CO2 retrieval algorithm will be combined with accurate GOSAT-CAI aerosol retrieval algorithm to obtain more accurate result especially for East Asia.
NASA Astrophysics Data System (ADS)
Lera, Daniela; Sergeyev, Yaroslav D.
2016-06-01
In this paper the global optimization problem where the objective function is multiextremal and satisfying the Lipschitz condition over a hyperinterval is considered. An algorithm that uses Peano-type space-filling curves to reduce the original Lipschitz multi-dimensional problem to a univariate one satisfying the Hölder condition is proposed. The algorithm at each iteration applies a new geometric technique working with a number of possible Hölder constants chosen from a set of values varying from zero to infinity showing so that ideas introduced in a popular DIRECT method can be used in the Hölder global optimization, as well. Convergence condition are given. Numerical experiments show quite a promising performance of the new technique.
Korenromp, Eline L.; Glaziou, Philippe; Fitzpatrick, Christopher; Floyd, Katherine; Hosseini, Mehran; Raviglione, Mario; Atun, Rifat; Williams, Brian
2012-01-01
Background The Global Plan to Stop TB estimates funding required in low- and middle-income countries to achieve TB control targets set by the Stop TB Partnership within the context of the Millennium Development Goals. We estimate the contribution and impact of Global Fund investments under various scenarios of allocations across interventions and regions. Methodology/Principal Findings Using Global Plan assumptions on expected cases and mortality, we estimate treatment costs and mortality impact for diagnosis and treatment for drug-sensitive and multidrug-resistant TB (MDR-TB), including antiretroviral treatment (ART) during DOTS for HIV-co-infected patients, for four country groups, overall and for the Global Fund investments. In 2015, China and India account for 24% of funding need, Eastern Europe and Central Asia (EECA) for 33%, sub-Saharan Africa (SSA) for 20%, and other low- and middle-income countries for 24%. Scale-up of MDR-TB treatment, especially in EECA, drives an increasing global TB funding need – an essential investment to contain the mortality burden associated with MDR-TB and future disease costs. Funding needs rise fastest in SSA, reflecting increasing coverage need of improved TB/HIV management, which saves most lives per dollar spent in the short term. The Global Fund is expected to finance 8–12% of Global Plan implementation costs annually. Lives saved through Global Fund TB support within the available funding envelope could increase 37% if allocations shifted from current regional demand patterns to a prioritized scale-up of improved TB/HIV treatment and secondly DOTS, both mainly in Africa − with EECA region, which has disproportionately high per-patient costs, funded from alternative resources. Conclusions/Significance These findings, alongside country funding gaps, domestic funding and implementation capacity and equity considerations, should inform strategies and policies for international donors, national governments and disease
Lithological and Surface Geometry Joint Inversions Using Multi-Objective Global Optimization Methods
NASA Astrophysics Data System (ADS)
Lelièvre, Peter; Bijani, Rodrigo; Farquharson, Colin
2016-04-01
surfaces are set to a priori values. The inversion is tasked with calculating the geometry of the contact surfaces instead of some piecewise distribution of properties in a mesh. Again, no coupling measure is required and joint inversion is simplified. Both of these inverse problems involve high nonlinearity and discontinuous or non-obtainable derivatives. They can also involve the existence of multiple minima. Hence, one can not apply the standard descent-based local minimization methods used to solve typical minimum-structure inversions. Instead, we are applying Pareto multi-objective global optimization (PMOGO) methods, which generate a suite of solutions that minimize multiple objectives (e.g. data misfits and regularization terms) in a Pareto-optimal sense. Providing a suite of models, as opposed to a single model that minimizes a weighted sum of objectives, allows a more complete assessment of the possibilities and avoids the often difficult choice of how to weight each objective. While there are definite advantages to PMOGO joint inversion approaches, the methods come with significantly increased computational requirements. We are researching various strategies to ameliorate these computational issues including parallelization and problem dimension reduction.
Near-surface ocean velocity from infrared images: Global Optimal Solution to an inverse model
NASA Astrophysics Data System (ADS)
Chen, Wei; Mied, Richard P.; Shen, Colin Y.
2008-10-01
We address the problem of obtaining ocean surface velocities from sequences of thermal (AVHRR) space-borne images by inverting the heat conservation equation (including sources of surface heat fluxes and vertical entrainment). We demonstrate the utility of the technique by deriving surface velocities from (1) The motion of a synthetic surface tracer in a numerical model and (2) a sequence of five actual AVHRR images from 1 day. Typical formulations of this tracer inversion problem yield too few equations at each pixel, which is often remedied by imposing additional constraints (e.g., horizontal divergence, vorticity, and energy). In contrast, we propose an alternate strategy to convert the underdetermined equation set to an overdetermined one. We divide the image scene into many subarrays and define velocities and sources within each subarray using bilinear expressions in terms of the corner points (called knots). In turn, all velocities and sources on the knots can be determined by seeking an optimum solution to these linear equations over the large scale, which we call the Global Optimal Solution (GOS). We test the accuracy of the GOS by contaminating the model output with up to 10% white noise but find that filtering the data with a Gaussian convolution filter yields velocities nearly indistinguishable from those without the added noise. We compare the GOS velocity fields with those from the numerical model and from the Maximum Cross Correlation (MCC) technique. A histogram of the difference between GOS and numerical model velocities is narrower and more peaked than the similar comparison with MCC, irrespective of the time interval (Δt = 2 or 4 h) between images. The calculation of the root mean square error difference between the GOS (and MCC) results and the model velocities indicates that the GOS/model error is only half that of the MCC/model error irrespective of the time interval (Δt = 2 or 4 h) between images. Finally, the application of the technique to
Order-Constrained Solutions in K-Means Clustering: Even Better than Being Globally Optimal
ERIC Educational Resources Information Center
Steinley, Douglas; Hubert, Lawrence
2008-01-01
This paper proposes an order-constrained K-means cluster analysis strategy, and implements that strategy through an auxiliary quadratic assignment optimization heuristic that identifies an initial object order. A subsequent dynamic programming recursion is applied to optimally subdivide the object set subject to the order constraint. We show that…
NASA Astrophysics Data System (ADS)
Fan, Shu-Kai S.; Chang, Ju-Ming
2010-05-01
This article presents a novel parallel multi-swarm optimization (PMSO) algorithm with the aim of enhancing the search ability of standard single-swarm PSOs for global optimization of very large-scale multimodal functions. Different from the existing multi-swarm structures, the multiple swarms work in parallel, and the search space is partitioned evenly and dynamically assigned in a weighted manner via the roulette wheel selection (RWS) mechanism. This parallel, distributed framework of the PMSO algorithm is developed based on a master-slave paradigm, which is implemented on a cluster of PCs using message passing interface (MPI) for information interchange among swarms. The PMSO algorithm handles multiple swarms simultaneously and each swarm performs PSO operations of its own independently. In particular, one swarm is designated for global search and the others are for local search. The first part of the experimental comparison is made among the PMSO, standard PSO, and two state-of-the-art algorithms (CTSS and CLPSO) in terms of various un-rotated and rotated benchmark functions taken from the literature. In the second part, the proposed multi-swarm algorithm is tested on large-scale multimodal benchmark functions up to 300 dimensions. The results of the PMSO algorithm show great promise in solving high-dimensional problems.
NASA Astrophysics Data System (ADS)
Theos, F. V.; Lagaris, I. E.; Papageorgiou, D. G.
2004-05-01
We present two sequential and one parallel global optimization codes, that belong to the stochastic class, and an interface routine that enables the use of the Merlin/MCL environment as a non-interactive local optimizer. This interface proved extremely important, since it provides flexibility, effectiveness and robustness to the local search task that is in turn employed by the global procedures. We demonstrate the use of the parallel code to a molecular conformation problem. Program summaryTitle of program: PANMIN Catalogue identifier: ADSU Program summary URL:http://cpc.cs.qub.ac.uk/summaries/ADSU Program obtainable from: CPC Program Library, Queen's University of Belfast, N. Ireland Computer for which the program is designed and others on which it has been tested: PANMIN is designed for UNIX machines. The parallel code runs on either shared memory architectures or on a distributed system. The code has been tested on a SUN Microsystems ENTERPRISE 450 with four CPUs, and on a 48-node cluster under Linux, with both the GNU g77 and the Portland group compilers. The parallel implementation is based on MPI and has been tested with LAM MPI and MPICH Installation: University of Ioannina, Greece Programming language used: Fortran-77 Memory required to execute with typical data: Approximately O( n2) words, where n is the number of variables No. of bits in a word: 64 No. of processors used: 1 or many Has the code been vectorised or parallelized?: Parallelized using MPI No. of bytes in distributed program, including test data, etc.: 147163 No. of lines in distributed program, including the test data, etc.: 14366 Distribution format: gzipped tar file Nature of physical problem: A multitude of problems in science and engineering are often reduced to minimizing a function of many variables. There are instances that a local optimum does not correspond to the desired physical solution and hence the search for a better solution is required. Local optimization techniques can be
NASA Astrophysics Data System (ADS)
Alberti, Luca; Oberto, Gabriele; Pianosi, Francesca; Castelletti, Andrea
2013-04-01
Infiltration galleries and scavenger wells are often constructed to prevent saltwater intrusion in coastal aquifers. The optimal design of these infrastructures can be framed as a multi-objective optimization problem balancing availability of fresh water supply and installation/operation costs. High fidelity simulation models of the flow and transport processes can be used to link design parameters (e.g. wells location, size and pumping rates) to objective functions. However, the incorporation of these simulation models within an optimization-based planning framework is not straightforward because of the computational requirements of the model itself and the computational limitations of the optimization algorithms. In this study we investigate the potential for the Global Interactive Response Surface (GIRS) methodology to overcome these technical limitations. The GIRS methodology is used to recursively build a non-dynamic emulator of the process-based simulation model that maps design options into objectives values and can be used in place of the original model to more quickly explore the design space. The approach is used to plan infrastructural interventions for controlling saltwater intrusion and ensuring sustainable groundwater supply for Nauru, a Pacific island republic in Micronesia. GIRS is used to emulate a SEAWAT density driven groundwater flow-and-transport simulation model. Results show the potential applicability of the proposed approach for optimal planning of coastal aquifers.
Jarrar, Mu’taman; Rahman, Hamzah Abdul; Don, Mohammad Sobri
2016-01-01
Background and Objective: Demand for health care service has significantly increased, while the quality of healthcare and patient safety has become national and international priorities. This paper aims to identify the gaps and the current initiatives for optimizing the quality of care and patient safety in Malaysia. Design: Review of the current literature. Highly cited articles were used as the basis to retrieve and review the current initiatives for optimizing the quality of care and patient safety. The country health plan of Ministry of Health (MOH) Malaysia and the MOH Malaysia Annual Reports were reviewed. Results: The MOH has set four strategies for optimizing quality and sustaining quality of life. The 10th Malaysia Health Plan promotes the theme “1 Care for 1 Malaysia” in order to sustain the quality of care. Despite of these efforts, the total number of complaints received by the medico-legal section of the MOH Malaysia is increasing. The current global initiatives indicted that quality performance generally belong to three main categories: patient; staffing; and working environment related factors. Conclusions: There is no single intervention for optimizing quality of care to maintain patient safety. Multidimensional efforts and interventions are recommended in order to optimize the quality of care and patient safety in Malaysia. PMID:26755459
Mutation-Based Artificial Fish Swarm Algorithm for Bound Constrained Global Optimization
NASA Astrophysics Data System (ADS)
Rocha, Ana Maria A. C.; Fernandes, Edite M. G. P.
2011-09-01
The herein presented mutation-based artificial fish swarm (AFS) algorithm includes mutation operators to prevent the algorithm to falling into local solutions, diversifying the search, and to accelerate convergence to the global optima. Three mutation strategies are introduced into the AFS algorithm to define the trial points that emerge from random, leaping and searching behaviors. Computational results show that the new algorithm outperforms other well-known global stochastic solution methods.
Gálvez, Akemi; Iglesias, Andrés; Cabellos, Luis
2014-01-01
The problem of data fitting is very important in many theoretical and applied fields. In this paper, we consider the problem of optimizing a weighted Bayesian energy functional for data fitting by using global-support approximating curves. By global-support curves we mean curves expressed as a linear combination of basis functions whose support is the whole domain of the problem, as opposed to other common approaches in CAD/CAM and computer graphics driven by piecewise functions (such as B-splines and NURBS) that provide local control of the shape of the curve. Our method applies a powerful nature-inspired metaheuristic algorithm called cuckoo search, introduced recently to solve optimization problems. A major advantage of this method is its simplicity: cuckoo search requires only two parameters, many fewer than other metaheuristic approaches, so the parameter tuning becomes a very simple task. The paper shows that this new approach can be successfully used to solve our optimization problem. To check the performance of our approach, it has been applied to five illustrative examples of different types, including open and closed 2D and 3D curves that exhibit challenging features, such as cusps and self-intersections. Our results show that the method performs pretty well, being able to solve our minimization problem in an astonishingly straightforward way. PMID:24977175
Gálvez, Akemi; Iglesias, Andrés; Cabellos, Luis
2014-01-01
The problem of data fitting is very important in many theoretical and applied fields. In this paper, we consider the problem of optimizing a weighted Bayesian energy functional for data fitting by using global-support approximating curves. By global-support curves we mean curves expressed as a linear combination of basis functions whose support is the whole domain of the problem, as opposed to other common approaches in CAD/CAM and computer graphics driven by piecewise functions (such as B-splines and NURBS) that provide local control of the shape of the curve. Our method applies a powerful nature-inspired metaheuristic algorithm called cuckoo search, introduced recently to solve optimization problems. A major advantage of this method is its simplicity: cuckoo search requires only two parameters, many fewer than other metaheuristic approaches, so the parameter tuning becomes a very simple task. The paper shows that this new approach can be successfully used to solve our optimization problem. To check the performance of our approach, it has been applied to five illustrative examples of different types, including open and closed 2D and 3D curves that exhibit challenging features, such as cusps and self-intersections. Our results show that the method performs pretty well, being able to solve our minimization problem in an astonishingly straightforward way.
Gálvez, Akemi; Iglesias, Andrés; Cabellos, Luis
2014-01-01
The problem of data fitting is very important in many theoretical and applied fields. In this paper, we consider the problem of optimizing a weighted Bayesian energy functional for data fitting by using global-support approximating curves. By global-support curves we mean curves expressed as a linear combination of basis functions whose support is the whole domain of the problem, as opposed to other common approaches in CAD/CAM and computer graphics driven by piecewise functions (such as B-splines and NURBS) that provide local control of the shape of the curve. Our method applies a powerful nature-inspired metaheuristic algorithm called cuckoo search, introduced recently to solve optimization problems. A major advantage of this method is its simplicity: cuckoo search requires only two parameters, many fewer than other metaheuristic approaches, so the parameter tuning becomes a very simple task. The paper shows that this new approach can be successfully used to solve our optimization problem. To check the performance of our approach, it has been applied to five illustrative examples of different types, including open and closed 2D and 3D curves that exhibit challenging features, such as cusps and self-intersections. Our results show that the method performs pretty well, being able to solve our minimization problem in an astonishingly straightforward way. PMID:24977175
NASA Astrophysics Data System (ADS)
de Pascale, P.; Vasile, M.; Casotto, S.
The design of interplanetary trajectories requires the solution of an optimization problem, which has been traditionally solved by resorting to various local optimization techniques. All such approaches, apart from the specific method employed (direct or indirect), require an initial guess, which deeply influences the convergence to the optimal solution. The recent developments in low-thrust propulsion have widened the perspectives of exploration of the Solar System, while they have at the same time increased the difficulty related to the trajectory design process. Continuous thrust transfers, typically characterized by multiple spiraling arcs, have a broad number of design parameters and thanks to the flexibility offered by such engines, they typically turn out to be characterized by a multi-modal domain, with a consequent larger number of optimal solutions. Thus the definition of the first guesses is even more challenging, particularly for a broad search over the design parameters, and it requires an extensive investigation of the domain in order to locate the largest number of optimal candidate solutions and possibly the global optimal one. In this paper a tool for the preliminary definition of interplanetary transfers with coast-thrust arcs and multiple swing-bys is presented. Such goal is achieved combining a novel methodology for the description of low-thrust arcs, with a global optimization algorithm based on a hybridization of an evolutionary step and a deterministic step. Low thrust arcs are described in a 3D model in order to account the beneficial effects of low-thrust propulsion for a change of inclination, resorting to a new methodology based on an inverse method. The two-point boundary values problem (TPBVP) associated with a thrust arc is solved by imposing a proper parameterized evolution of the orbital parameters, by which, the acceleration required to follow the given trajectory with respect to the constraints set is obtained simply through
NASA Astrophysics Data System (ADS)
Ait moussa, Abdellah; Jassemnejad, Bahaeddin
2014-05-01
Nanocomposites with high-aspect ratio fillers attract enormous attention because of the superior physical properties of the composite over the parent matrix. Nanocomposites with functionalized graphene as fillers did not produce the high thermal conductivity expected due to the high interfacial thermal resistance between the functional groups and graphene flakes. We report here a robust and efficient technique that identifies the configuration of the functionalities for improved thermal conductivity. The method combines linearization of the interatomic interactions, calculation, and optimization of the thermal conductivity using the globalized and bounded Nelder-Mead algorithm.
Multi-objective global optimization of a butterfly valve using genetic algorithms.
Corbera, Sergio; Olazagoitia, José Luis; Lozano, José Antonio
2016-07-01
A butterfly valve is a type of valve typically used for isolating or regulating flow where the closing mechanism takes the form of a disc. For a long time, the attention of many researchers has focused on carrying out structural (FEM) and computational fluid dynamics (CFD) analysis in order to increase the performance of this type of flow-control device. This paper proposes a novel multi-objective approach for the design optimization of a butterfly valve using advanced genetic algorithms based on Pareto dominance. Firstly, after defining the need for this study and analyzing previous papers on the subject, the initial butterfly valve is presented and the initial fluid and structural analysis are carried out. Secondly, the optimization problem is defined and the optimization strategy is presented. The design variables are identified and a parameterization model of the valve is made. Thirdly, initial design candidates are generated by DOE and design optimization using genetic algorithms is performed. In this part of the process structural and CFD analysis are calculated for each candidate simultaneously. The optimization process involves various types of software and Python scripts are needed for their interaction and the connection of all steps. Finally, a set of optimal solutions is obtained and the optimum design that provides a 65.4% stress reduction, a 5% mass reduction and a 11.3% flow increase is selected in accordance with manufacturer preferences. Validation of the results is provided by comparing experimental test results with the values obtained for the initial design. The results demonstrate the capability and potential of the proposed methodology.
Global Optimization of Interplanetary Trajectories in the Presence of Realistic Mission Constraints
NASA Technical Reports Server (NTRS)
Hinckley, David; Englander, Jacob; Hitt, Darren
2015-01-01
Single trial evaluations Trial creation by Phase-wise GA-style or DE-inspired recombination Bin repository structure requires an initialization period Non-exclusionary Kill Distance Population collapse mechanic Main loop Creation Probabilistic switch between GA and DE creation types Locally optimize Submit to repository Repeat.
Daily Time Step Refinement of Optimized Flood Control Rule Curves for a Global Warming Scenario
NASA Astrophysics Data System (ADS)
Lee, S.; Fitzgerald, C.; Hamlet, A. F.; Burges, S. J.
2009-12-01
Pacific Northwest temperatures have warmed by 0.8 °C since 1920 and are predicted to further increase in the 21st century. Simulated streamflow timing shifts associated with climate change have been found in past research to degrade water resources system performance in the Columbia River Basin when using existing system operating policies. To adapt to these hydrologic changes, optimized flood control operating rule curves were developed in a previous study using a hybrid optimization-simulation approach which rebalanced flood control and reservoir refill at a monthly time step. For the climate change scenario, use of the optimized flood control curves restored reservoir refill capability without increasing flood risk. Here we extend the earlier studies using a detailed daily time step simulation model applied over a somewhat smaller portion of the domain (encompassing Libby, Duncan, and Corra Linn dams, and Kootenai Lake) to evaluate and refine the optimized flood control curves derived from monthly time step analysis. Moving from a monthly to daily analysis, we found that the timing of flood control evacuation needed adjustment to avoid unintended outcomes affecting Kootenai Lake. We refined the flood rule curves derived from monthly analysis by creating a more gradual evacuation schedule, but kept the timing and magnitude of maximum evacuation the same as in the monthly analysis. After these refinements, the performance at monthly time scales reported in our previous study proved robust at daily time scales. Due to a decrease in July storage deficits, additional benefits such as more revenue from hydropower generation and more July and August outflow for fish augmentation were observed when the optimized flood control curves were used for the climate change scenario.
Kamph, Jerome Henri; Robinson, Darren; Wetter, Michael
2009-09-01
There is an increasing interest in the use of computer algorithms to identify combinations of parameters which optimise the energy performance of buildings. For such problems, the objective function can be multi-modal and needs to be approximated numerically using building energy simulation programs. As these programs contain iterative solution algorithms, they introduce discontinuities in the numerical approximation to the objective function. Metaheuristics often work well for such problems, but their convergence to a global optimum cannot be established formally. Moreover, different algorithms tend to be suited to particular classes of optimization problems. To shed light on this issue we compared the performance of two metaheuristics, the hybrid CMA-ES/HDE and the hybrid PSO/HJ, in minimizing standard benchmark functions and real-world building energy optimization problems of varying complexity. From this we find that the CMA-ES/HDE performs well on more complex objective functions, but that the PSO/HJ more consistently identifies the global minimum for simpler objective functions. Both identified similar values in the objective functions arising from energy simulations, but with different combinations of model parameters. This may suggest that the objective function is multi-modal. The algorithms also correctly identified some non-intuitive parameter combinations that were caused by a simplified control sequence of the building energy system that does not represent actual practice, further reinforcing their utility.
2014-01-01
Background Optimization is the key to solving many problems in computational biology. Global optimization methods, which provide a robust methodology, and metaheuristics in particular have proven to be the most efficient methods for many applications. Despite their utility, there is a limited availability of metaheuristic tools. Results We present MEIGO, an R and Matlab optimization toolbox (also available in Python via a wrapper of the R version), that implements metaheuristics capable of solving diverse problems arising in systems biology and bioinformatics. The toolbox includes the enhanced scatter search method (eSS) for continuous nonlinear programming (cNLP) and mixed-integer programming (MINLP) problems, and variable neighborhood search (VNS) for Integer Programming (IP) problems. Additionally, the R version includes BayesFit for parameter estimation by Bayesian inference. The eSS and VNS methods can be run on a single-thread or in parallel using a cooperative strategy. The code is supplied under GPLv3 and is available at http://www.iim.csic.es/~gingproc/meigo.html. Documentation and examples are included. The R package has been submitted to BioConductor. We evaluate MEIGO against optimization benchmarks, and illustrate its applicability to a series of case studies in bioinformatics and systems biology where it outperforms other state-of-the-art methods. Conclusions MEIGO provides a free, open-source platform for optimization that can be applied to multiple domains of systems biology and bioinformatics. It includes efficient state of the art metaheuristics, and its open and modular structure allows the addition of further methods. PMID:24885957
A global earthquake discrimination scheme to optimize ground-motion prediction equation selection
Garcia, Daniel; Wald, David J.; Hearne, Michael
2012-01-01
We present a new automatic earthquake discrimination procedure to determine in near-real time the tectonic regime and seismotectonic domain of an earthquake, its most likely source type, and the corresponding ground-motion prediction equation (GMPE) class to be used in the U.S. Geological Survey (USGS) Global ShakeMap system. This method makes use of the Flinn–Engdahl regionalization scheme, seismotectonic information (plate boundaries, global geology, seismicity catalogs, and regional and local studies), and the source parameters available from the USGS National Earthquake Information Center in the minutes following an earthquake to give the best estimation of the setting and mechanism of the event. Depending on the tectonic setting, additional criteria based on hypocentral depth, style of faulting, and regional seismicity may be applied. For subduction zones, these criteria include the use of focal mechanism information and detailed interface models to discriminate among outer-rise, upper-plate, interface, and intraslab seismicity. The scheme is validated against a large database of recent historical earthquakes. Though developed to assess GMPE selection in Global ShakeMap operations, we anticipate a variety of uses for this strategy, from real-time processing systems to any analysis involving tectonic classification of sources from seismic catalogs.
NASA Astrophysics Data System (ADS)
Miyazaki, Takahiko; Akisawa, Atsushi; Kashiwagi, Takao; Akahira, Akira
The study used the particle swarm optimization to maximize the specific cooling capacity (SCC) of a single-stage adsorption chiller, as well as to maximize the coefficient of performance (COP) at part load conditions of the chiller. The cycle time, which consists of adsorption/desorption time and pre-heating/ pre-cooling time, was chosen as a design parameter. The simulation results of a mathematical model showed a good agreement with experimental results on SCC and COP. It was shown that the SCC could be improved by the optimum cycle time as much as by 30% compared with that by the fixed cycle time. It was also presented that the part load COP would be significantly increased by the cycle time optimization at part load conditions.
Use of a generalized fisher equation for global optimization in chemical kinetics.
Villaverde, Alejandro F; Ross, John; Morán, Federico; Balsa-Canto, Eva; Banga, Julio R
2011-08-01
A new approach for parameter estimation in chemical kinetics has been recently proposed (Ross et al. Proc. Natl. Acad. Sci. U.S.A. 2010, 107, 12777). It makes use of an optimization criterion based on a Generalized Fisher Equation (GFE). Its utility has been demonstrated with two reaction mechanisms, the chlorite-iodide and Oregonator, which are computationally stiff systems. In this Article, the performance of the GFE-based algorithm is compared to that obtained from minimization of the squared distances between the observed and predicted concentrations obtained by solving the corresponding initial value problem (we call this latter approach "traditional" for simplicity). Comparison of the proposed GFE-based optimization method with the "traditional" one has revealed their differences in performance. This difference can be seen as a trade-off between speed (which favors GFE) and accuracy (which favors the traditional method). The chlorite-iodide and Oregonator systems are again chosen as case studies. An identifiability analysis is performed for both of them, followed by an optimal experimental design based on the Fisher Information Matrix (FIM). This allows to identify and overcome most of the previously encountered identifiability issues, improving the estimation accuracy. With the new data, obtained from optimally designed experiments, it is now possible to estimate effectively more parameters than with the previous data. This result, which holds for both GFE-based and traditional methods, stresses the importance of an appropriate experimental design. Finally, a new hybrid method that combines advantages from the GFE and traditional approaches is presented.
Carver, Charles S.; Scheier, Michael F.; Segerstrom, Suzanne C.
2010-01-01
Optimism is an individual difference variable that reflects the extent to which people hold generalized favorable expectancies for their future. Higher levels of optimism have been related prospectively to better subjective well-being in times of adversity or difficulty (i.e., controlling for previous well-being). Consistent with such findings, optimism has been linked to higher levels of engagement coping and lower levels of avoidance, or disengagement, coping. There is evidence that optimism is associated with taking proactive steps to protect one's health, whereas pessimism is associated with health-damaging behaviors. Consistent with such findings, optimism is also related to indicators of better physical health. The energetic, task-focused approach that optimists take to goals also relates to benefits in the socioeconomic world. Some evidence suggests that optimism relates to more persistence in educational efforts and to higher later income. Optimists also appear to fare better than pessimists in relationships. Although there are instances in which optimism fails to convey an advantage, and instances in which it may convey a disadvantage, those instances are relatively rare. In sum, the behavioral patterns of optimists appear to provide models of living for others to learn from. PMID:20170998
Laguzet, Laetitia; Turinici, Gabriel
2015-05-01
This work focuses on optimal vaccination policies for an Susceptible-Infected-Recovered (SIR) model; the impact of the disease is minimized with respect to the vaccination strategy. The problem is formulated as an optimal control problem and we show that the value function is the unique viscosity solution of an Hamilton-Jacobi-Bellman (HJB) equation. This allows to find the best vaccination policy. At odds with existing literature, it is seen that the value function is not always smooth (sometimes only Lipschitz) and the optimal vaccination policies are not unique. Moreover we rigorously analyze the situation when vaccination can be modeled as instantaneous (with respect to the time evolution of the epidemic) and identify the global optimum solutions. Numerical applications illustrate the theoretical results. In addition the pertussis vaccination in adults is considered from two perspectives: first the maximization of DALY averted in presence of vaccine side-effects; then the impact of the herd immunity on the cost-effectiveness analysis is discussed on a concrete example. PMID:25771436
Loizzo, Joseph
2009-08-01
This overview surveys the new optimism about the aging mind/brain, focusing on the potential for self-regulation practices to advance research in stress-protection and optimal health. It reviews recent findings and offers a research framework. The review links the age-related biology of stress and regeneration to the variability of mind/brain function found under a range of conditions from trauma to enrichment. The framework maps this variation along a biphasic continuum from atrophic dysfunction to peak performance. It adopts the concept of allostatic load as a measure of the wear-and-tear caused by stress, and environmental enrichment as a measure of the use-dependent enhancement caused by positive reinforcement. It frames the dissociation, aversive affect and stereotyped reactions linked with stress as cognitive, affective and behavioral forms of allostatic drag; and the association, positive affect, and creative responses in enrichment as forms of allostatic lift. It views the human mind/brain as a heterarchy of higher intelligence systems that shift between a conservative, egocentric mode heightening self-preservation and memory and a generative, altruistic mode heightening self-correction and learning. Cultural practices like meditation and psychotherapy work by teaching the self-regulation of shifts from the conservative to the generative mode. This involves a systems shift from allostatic drag to allostatic lift, minimizing wear-and-tear and optimizing plasticity and learning. For cultural practices to speed research and application, a universal typology is needed. This framework includes a typology aligning current brain models of stress and learning with traditional Indo-Tibetan models of meditative stress-cessation and learning enrichment.
Li, Xingyuan; He, Zhili; Zhou, Jizhong
2005-10-30
The oligonucleotide specificity for microarray hybridizationcan be predicted by its sequence identity to non-targets, continuousstretch to non-targets, and/or binding free energy to non-targets. Mostcurrently available programs only use one or two of these criteria, whichmay choose 'false' specific oligonucleotides or miss 'true' optimalprobes in a considerable proportion. We have developed a software tool,called CommOligo using new algorithms and all three criteria forselection of optimal oligonucleotide probes. A series of filters,including sequence identity, free energy, continuous stretch, GC content,self-annealing, distance to the 3'-untranslated region (3'-UTR) andmelting temperature (Tm), are used to check each possibleoligonucleotide. A sequence identity is calculated based on gapped globalalignments. A traversal algorithm is used to generate alignments for freeenergy calculation. The optimal Tm interval is determined based on probecandidates that have passed all other filters. Final probes are pickedusing a combination of user-configurable piece-wise linear functions andan iterative process. The thresholds for identity, stretch and freeenergy filters are automatically determined from experimental data by anaccessory software tool, CommOligo_PE (CommOligo Parameter Estimator).The program was used to design probes for both whole-genome and highlyhomologous sequence data. CommOligo and CommOligo_PE are freely availableto academic users upon request.
Optimal integer resolution for attitude determination using global positioning system signals
NASA Technical Reports Server (NTRS)
Crassidis, John L.; Markley, F. Landis; Lightsey, E. Glenn
1998-01-01
In this paper, a new motion-based algorithm for GPS integer ambiguity resolution is derived. The first step of this algorithm converts the reference sightline vectors into body frame vectors. This is accomplished by an optimal vectorized transformation of the phase difference measurements. The result of this transformation leads to the conversion of the integer ambiguities to vectorized biases. This essentially converts the problem to the familiar magnetometer-bias determination problem, for which an optimal and efficient solution exists. Also, the formulation in this paper is re-derived to provide a sequential estimate, so that a suitable stopping condition can be found during the vehicle motion. The advantages of the new algorithm include: it does not require an a-priori estimate of the vehicle's attitude; it provides an inherent integrity check using a covariance-type expression; and it can sequentially estimate the ambiguities during the vehicle motion. The only disadvantage of the new algorithm is that it requires at least three non-coplanar baselines. The performance of the new algorithm is tested on a dynamic hardware simulator.
NASA Astrophysics Data System (ADS)
Ma, Hongliang; Xu, Shijie
2016-11-01
By defining two open-time impulse points, the optimization of a two-impulse, open-time terminal rendezvous and docking with target spacecraft on large-eccentricity elliptical orbit is proposed in this paper. The purpose of optimization is to minimize the velocity increment for a terminal elliptic-reference-orbit rendezvous and docking. Current methods for solving this type of optimization problem include for example genetic algorithms and gradient based optimization. Unlike these methods, interval methods can guarantee that the globally best solution is found for a given parameterization of the input. The non-linear Tschauner- Hempel(TH) equations of the state transitions for a terminal elliptic target orbit are transformed form time domain to target orbital true anomaly domain. Their homogenous solutions and approximate state transition matrix for the control with a short true anomaly interval can be used to avoid interval integration. The interval branch and bound optimization algorithm is introduced for solving the presented rendezvous and docking optimization problem and optimizing two open-time impulse points and thruster pulse amplitudes, which systematically eliminates parts of the control and open-time input spaces that do not satisfy the path and final time state constraints. Several numerical examples are undertaken to validate the interval optimization algorithm. The results indicate that the sufficiently narrow spaces containing the global optimization solution for the open-time two-impulse terminal rendezvous and docking with target spacecraft on large-eccentricity elliptical orbit can be obtained by the interval algorithm (IA). Combining the gradient-based method, the global optimization solution for the discontinuous nonconvex optimization problem in the specifically remained search space can be found. Interval analysis is shown to be a useful tool and preponderant in the discontinuous nonconvex optimization problem of the terminal rendezvous and
Towards continuous global measurements and optimal emission estimates of NF3
NASA Astrophysics Data System (ADS)
Arnold, T.; Muhle, J.; Salameh, P.; Harth, C.; Ivy, D. J.; Weiss, R. F.
2011-12-01
We present an analytical method for the continuous in situ measurement of nitrogen trifluoride (NF3) - an anthropogenic gas with a global warming potential of ~16800 over a 100 year time horizon. NF3 is not included in national reporting emissions inventories under the United Nations Framework Convention on Climate Change (UNFCCC). However, it is a rapidly emerging greenhouse gas due to emission from a growing number of manufacturing facilities with increasing output and modern end-use applications, namely in microcircuit etching, and in production of flat panel displays and thin-film photovoltaic cells. Despite success in measuring the most volatile long lived halogenated species such as CF4, the Medusa preconcentration GC/MS system of Miller et al. (2008) is unable to detect NF3 under remote operation. Using altered techniques of gas separation and chromatography after initial preconcentration, we are now able to make continuous atmospheric measurements of NF3 with average precisions < 1.5% (1 s.d.) for modern background air samples. Most notably, the suite of gases previously measured by Medusa (the significant halogenated species listed under both the Montreal and Kyoto Protocols), can also be quantified from the same sample. Our technique was used to extend the most recent atmospheric measurements into 2011 and complete the background Southern Hemispheric trend over the past three decades using samples from the Cape Grim Air Archive. Using these latest results and those from Weiss et al. (2008) we present optimised annual emission estimates using a 2D atmospheric transport model (AGAGE 12-box model) and an inverse method (Rigby et al., 2011). We calculate emissions during 2010 of 7.6 +/- 1.3 kt (equivalent to 13 million metric tons of CO2), which is estimated to be around 6% of the total NF3 produced. Emission factors are shown to have reduced over the last decade; however, rising production and end-use have caused the average global atmospheric concentration
Contact-assisted protein structure modeling by global optimization in CASP11.
Joo, Keehyoung; Joung, InSuk; Cheng, Qianyi; Lee, Sung Jong; Lee, Jooyoung
2016-09-01
We have applied the conformational space annealing method to the contact-assisted protein structure modeling in CASP11. For Tp targets, where predicted residue-residue contact information was provided, the contact energy term in the form of the Lorentzian function was implemented together with the physical energy terms used in our template-free modeling of proteins. Although we observed some structural improvement of Tp models over the models predicted without the Tp information, the improvement was not substantial on average. This is partly due to the inaccuracy of the provided contact information, where only about 18% of it was correct. For Ts targets, where the information of ambiguous NOE (Nuclear Overhauser Effect) restraints was provided, we formulated the modeling in terms of the two-tier optimization problem, which covers: (1) the assignment of NOE peaks and (2) the three-dimensional (3D) model generation based on the assigned NOEs. Although solving the problem in a direct manner appears to be intractable at first glance, we demonstrate through CASP11 that remarkably accurate protein 3D modeling is possible by brute force optimization of a relevant energy function. For 19 Ts targets of the average size of 224 residues, generated protein models were of about 3.6 Å Cα atom accuracy. Even greater structural improvement was observed when additional Tc contact information was provided. For 20 out of the total 24 Tc targets, we were able to generate protein structures which were better than the best model from the rest of the CASP11 groups in terms of GDT-TS. Proteins 2016; 84(Suppl 1):189-199. © 2015 Wiley Periodicals, Inc.
Islam, Sk Minhazul; Das, Swagatam; Ghosh, Saurav; Roy, Subhrajit; Suganthan, Ponnuthurai Nagaratnam
2012-04-01
Differential evolution (DE) is one of the most powerful stochastic real parameter optimizers of current interest. In this paper, we propose a new mutation strategy, a fitness-induced parent selection scheme for the binomial crossover of DE, and a simple but effective scheme of adapting two of its most important control parameters with an objective of achieving improved performance. The new mutation operator, which we call DE/current-to-gr_best/1, is a variant of the classical DE/current-to-best/1 scheme. It uses the best of a group (whose size is q% of the population size) of randomly selected solutions from current generation to perturb the parent (target) vector, unlike DE/current-to-best/1 that always picks the best vector of the entire population to perturb the target vector. In our modified framework of recombination, a biased parent selection scheme has been incorporated by letting each mutant undergo the usual binomial crossover with one of the p top-ranked individuals from the current population and not with the target vector with the same index as used in all variants of DE. A DE variant obtained by integrating the proposed mutation, crossover, and parameter adaptation strategies with the classical DE framework (developed in 1995) is compared with two classical and four state-of-the-art adaptive DE variants over 25 standard numerical benchmarks taken from the IEEE Congress on Evolutionary Computation 2005 competition and special session on real parameter optimization. Our comparative study indicates that the proposed schemes improve the performance of DE by a large magnitude such that it becomes capable of enjoying statistical superiority over the state-of-the-art DE variants for a wide variety of test problems. Finally, we experimentally demonstrate that, if one or more of our proposed strategies are integrated with existing powerful DE variants such as jDE and JADE, their performances can also be enhanced.
Sabesan, Shivkumar; Chakravarthy, Niranjan; Tsakalis, Kostas; Pardalos, Panos; Iasemidis, Leon
2009-01-01
Epileptic seizures are manifestations of intermittent spatiotemporal transitions of the human brain from chaos to order. Measures of chaos, namely maximum Lyapunov exponents (STL(max)), from dynamical analysis of the electroencephalograms (EEGs) at critical sites of the epileptic brain, progressively converge (diverge) before (after) epileptic seizures, a phenomenon that has been called dynamical synchronization (desynchronization). This dynamical synchronization/desynchronization has already constituted the basis for the design and development of systems for long-term (tens of minutes), on-line, prospective prediction of epileptic seizures. Also, the criterion for the changes in the time constants of the observed synchronization/desynchronization at seizure points has been used to show resetting of the epileptic brain in patients with temporal lobe epilepsy (TLE), a phenomenon that implicates a possible homeostatic role for the seizures themselves to restore normal brain activity. In this paper, we introduce a new criterion to measure this resetting that utilizes changes in the level of observed synchronization/desynchronization. We compare this criterion's sensitivity of resetting with the old one based on the time constants of the observed synchronization/desynchronization. Next, we test the robustness of the resetting phenomena in terms of the utilized measures of EEG dynamics by a comparative study involving STL(max), a measure of phase (ϕ(max)) and a measure of energy (E) using both criteria (i.e. the level and time constants of the observed synchronization/desynchronization). The measures are estimated from intracranial electroencephalographic (iEEG) recordings with subdural and depth electrodes from two patients with focal temporal lobe epilepsy and a total of 43 seizures. Techniques from optimization theory, in particular quadratic bivalent programming, are applied to optimize the performance of the three measures in detecting preictal entrainment. It is
Optimal ?-Control for the Global Cauchy Problem of The Relativistic Vlasov-Poisson System
NASA Astrophysics Data System (ADS)
Young, Brent
2011-12-01
Recently, M.K.-H. Kiessling and A.S. Tahvildar-Zadeh proved that a unique global classical solution to the relativistic Vlasov-Poisson system exists whenever the positive, integrable initial datum is spherically symmetric, compactly supported in momentum space, vanishes on characteristics with vanishing angular momentum, and for β⩾3/2 has ?-norm strictly below a positive, critical value ?. Everything else being equal, data leading to finite time blow-up can be found with ?-norm surpassing ? for any β>1, with ? if and only if β⩾3/2. In their paper, the critical value for β=3/2 is calculated explicitly while the value for all other β is merely characterized as the infimum of a functional over an appropriate function space. In this work, the existence of minimizers is established, and the exact expression of ? is calculated in terms of the famous Lane-Emden functions. Numerical computations of the ? are presented along with some elementary asymptotics near the critical exponent 3/2.
NASA Astrophysics Data System (ADS)
Selva, D.
2014-10-01
Requirements from the different disciplines of the Earth sciences on satellite missions have become considerably more stringent in the past decade, while budgets in space organizations have not increased to support the implementation of new systems meeting these requirements. At the same time, new technologies such as optical communications, electrical propulsion, nanosatellite technology, and new commercial agents and models such as hosted payloads are now available. The technical and programmatic environment is thus ideal to conduct architectural studies that look with renewed breadth and adequate depth to the myriad of new possible architectures for Earth Observing Systems. Such studies are challenging tasks, since they require formidable amounts of data and expert knowledge in order to be conducted. Indeed, trade-offs between hundreds or thousands of requirements from different disciplines need to be considered, and millions of combinations of instrument technologies and orbits are possible. This paper presents a framework and tool to support the exploration of such large architectural tradespaces. The framework can be seen as a model-based, executable science traceability matrix that can be used to compare the relative value of millions of different possible architectures. It is demonstrated with an operational climate-centric case study. Ultimately, this framework can be used to assess opportunities for international collaboration and look at architectures for a global Earth observing system, including space, air, and ground assets.
Guiding automated NMR structure determination using a global optimization metric, the NMR DP score.
Huang, Yuanpeng Janet; Mao, Binchen; Xu, Fei; Montelione, Gaetano T
2015-08-01
ASDP is an automated NMR NOE assignment program. It uses a distinct bottom-up topology-constrained network anchoring approach for NOE interpretation, with 2D, 3D and/or 4D NOESY peak lists and resonance assignments as input, and generates unambiguous NOE constraints for iterative structure calculations. ASDP is designed to function interactively with various structure determination programs that use distance restraints to generate molecular models. In the CASD-NMR project, ASDP was tested and further developed using blinded NMR data, including resonance assignments, either raw or manually-curated (refined) NOESY peak list data, and in some cases (15)N-(1)H residual dipolar coupling data. In these blinded tests, in which the reference structure was not available until after structures were generated, the fully-automated ASDP program performed very well on all targets using both the raw and refined NOESY peak list data. Improvements of ASDP relative to its predecessor program for automated NOESY peak assignments, AutoStructure, were driven by challenges provided by these CASD-NMR data. These algorithmic improvements include (1) using a global metric of structural accuracy, the discriminating power score, for guiding model selection during the iterative NOE interpretation process, and (2) identifying incorrect NOESY cross peak assignments caused by errors in the NMR resonance assignment list. These improvements provide a more robust automated NOESY analysis program, ASDP, with the unique capability of being utilized with alternative structure generation and refinement programs including CYANA, CNS, and/or Rosetta. PMID:26081575
Ona, Ofelia; Facelli, Julio C.; Bazterra, Victor E.; Caputo, Maria C.; Ferraro, Marta B.
2005-11-15
The results of ab initio global optimizations of the structures of Si{sub n}H, n=4-10, atomic clusters using a parallel genetic algorithm are presented. Driving the global search with the parallel implementation of the genetic algorithm are presented and using the density functional theory as implemented in the Carr-Parinello molecular dynamics code to calculate atomic cluster energies and perform the local optimization of their structures, we have been able to demonstrate that it is possible to perform global optimizations of the structure of atomic clusters using ab initio methods. The results show that this approach is able to find many structures that were not previously reported in the literature. Moreover, in most cases the new structures have considerable lower energies than those previously known. The results clearly demonstrate that these calculations are now possible and in spite of their larger computational demands provide more reliable results.
Lin, Jingjing; Jing, Honglei
2016-01-01
Artificial immune system is one of the most recently introduced intelligence methods which was inspired by biological immune system. Most immune system inspired algorithms are based on the clonal selection principle, known as clonal selection algorithms (CSAs). When coping with complex optimization problems with the characteristics of multimodality, high dimension, rotation, and composition, the traditional CSAs often suffer from the premature convergence and unsatisfied accuracy. To address these concerning issues, a recombination operator inspired by the biological combinatorial recombination is proposed at first. The recombination operator could generate the promising candidate solution to enhance search ability of the CSA by fusing the information from random chosen parents. Furthermore, a modified hypermutation operator is introduced to construct more promising and efficient candidate solutions. A set of 16 common used benchmark functions are adopted to test the effectiveness and efficiency of the recombination and hypermutation operators. The comparisons with classic CSA, CSA with recombination operator (RCSA), and CSA with recombination and modified hypermutation operator (RHCSA) demonstrate that the proposed algorithm significantly improves the performance of classic CSA. Moreover, comparison with the state-of-the-art algorithms shows that the proposed algorithm is quite competitive. PMID:27698662
Lin, Jingjing; Jing, Honglei
2016-01-01
Artificial immune system is one of the most recently introduced intelligence methods which was inspired by biological immune system. Most immune system inspired algorithms are based on the clonal selection principle, known as clonal selection algorithms (CSAs). When coping with complex optimization problems with the characteristics of multimodality, high dimension, rotation, and composition, the traditional CSAs often suffer from the premature convergence and unsatisfied accuracy. To address these concerning issues, a recombination operator inspired by the biological combinatorial recombination is proposed at first. The recombination operator could generate the promising candidate solution to enhance search ability of the CSA by fusing the information from random chosen parents. Furthermore, a modified hypermutation operator is introduced to construct more promising and efficient candidate solutions. A set of 16 common used benchmark functions are adopted to test the effectiveness and efficiency of the recombination and hypermutation operators. The comparisons with classic CSA, CSA with recombination operator (RCSA), and CSA with recombination and modified hypermutation operator (RHCSA) demonstrate that the proposed algorithm significantly improves the performance of classic CSA. Moreover, comparison with the state-of-the-art algorithms shows that the proposed algorithm is quite competitive.
NASA Astrophysics Data System (ADS)
Cai, X.; Zhang, X.; Zhu, T.
2014-12-01
Global food security is constrained by local and regional land and water availability, as well as other agricultural input limitations and inappropriate national and global regulations. In a theoretical context, this study assumes that optimal water and land uses in local food production to maximize food security and social welfare at the global level can be driven by global trade. It follows the context of "virtual resources trade", i.e., utilizing international trade of agricultural commodities to reduce dependency on local resources, and achieves land and water savings in the world. An optimization model based on the partial equilibrium of agriculture is developed for the analysis, including local commodity production and land and water resources constraints, demand by country, and global food market. Through the model, the marginal values (MVs) of social welfare for water and land at the level of so-called food production units (i.e., sub-basins with similar agricultural production conditions) are derived and mapped in the world. In this personation, we will introduce the model structure, explain the meaning of MVs at the local level and their distribution around the world, and discuss the policy implications for global communities to enhance global food security. In particular, we will examine the economic values of water and land under different world targets of food security (e.g., number of malnourished population or children in a future year). In addition, we will also discuss the opportunities on data to improve such global modeling exercises.
Chao, Ming; Wei, Jie; Li, Tianfang; Yuan, Yading; Rosenzweig, Kenneth E; Lo, Yeh-Chi
2016-04-21
We present a study of extracting respiratory signals from cone beam computed tomography (CBCT) projections within the framework of the Amsterdam Shroud (AS) technique. Acquired prior to the radiotherapy treatment, CBCT projections were preprocessed for contrast enhancement by converting the original intensity images to attenuation images with which the AS image was created. An adaptive robust z-normalization filtering was applied to further augment the weak oscillating structures locally. From the enhanced AS image, the respiratory signal was extracted using a two-step optimization approach to effectively reveal the large-scale regularity of the breathing signals. CBCT projection images from five patients acquired with the Varian Onboard Imager on the Clinac iX System Linear Accelerator (Varian Medical Systems, Palo Alto, CA) were employed to assess the proposed technique. Stable breathing signals can be reliably extracted using the proposed algorithm. Reference waveforms obtained using an air bellows belt (Philips Medical Systems, Cleveland, OH) were exported and compared to those with the AS based signals. The average errors for the enrolled patients between the estimated breath per minute (bpm) and the reference waveform bpm can be as low as -0.07 with the standard deviation 1.58. The new algorithm outperformed the original AS technique for all patients by 8.5% to 30%. The impact of gantry rotation on the breathing signal was assessed with data acquired with a Quasar phantom (Modus Medical Devices Inc., London, Canada) and found to be minimal on the signal frequency. The new technique developed in this work will provide a practical solution to rendering markerless breathing signal using the CBCT projections for thoracic and abdominal patients.
NASA Astrophysics Data System (ADS)
Chao, Ming; Wei, Jie; Li, Tianfang; Yuan, Yading; Rosenzweig, Kenneth E.; Lo, Yeh-Chi
2016-04-01
We present a study of extracting respiratory signals from cone beam computed tomography (CBCT) projections within the framework of the Amsterdam Shroud (AS) technique. Acquired prior to the radiotherapy treatment, CBCT projections were preprocessed for contrast enhancement by converting the original intensity images to attenuation images with which the AS image was created. An adaptive robust z-normalization filtering was applied to further augment the weak oscillating structures locally. From the enhanced AS image, the respiratory signal was extracted using a two-step optimization approach to effectively reveal the large-scale regularity of the breathing signals. CBCT projection images from five patients acquired with the Varian Onboard Imager on the Clinac iX System Linear Accelerator (Varian Medical Systems, Palo Alto, CA) were employed to assess the proposed technique. Stable breathing signals can be reliably extracted using the proposed algorithm. Reference waveforms obtained using an air bellows belt (Philips Medical Systems, Cleveland, OH) were exported and compared to those with the AS based signals. The average errors for the enrolled patients between the estimated breath per minute (bpm) and the reference waveform bpm can be as low as -0.07 with the standard deviation 1.58. The new algorithm outperformed the original AS technique for all patients by 8.5% to 30%. The impact of gantry rotation on the breathing signal was assessed with data acquired with a Quasar phantom (Modus Medical Devices Inc., London, Canada) and found to be minimal on the signal frequency. The new technique developed in this work will provide a practical solution to rendering markerless breathing signal using the CBCT projections for thoracic and abdominal patients.
Chao, Ming; Wei, Jie; Li, Tianfang; Yuan, Yading; Rosenzweig, Kenneth E; Lo, Yeh-Chi
2016-04-21
We present a study of extracting respiratory signals from cone beam computed tomography (CBCT) projections within the framework of the Amsterdam Shroud (AS) technique. Acquired prior to the radiotherapy treatment, CBCT projections were preprocessed for contrast enhancement by converting the original intensity images to attenuation images with which the AS image was created. An adaptive robust z-normalization filtering was applied to further augment the weak oscillating structures locally. From the enhanced AS image, the respiratory signal was extracted using a two-step optimization approach to effectively reveal the large-scale regularity of the breathing signals. CBCT projection images from five patients acquired with the Varian Onboard Imager on the Clinac iX System Linear Accelerator (Varian Medical Systems, Palo Alto, CA) were employed to assess the proposed technique. Stable breathing signals can be reliably extracted using the proposed algorithm. Reference waveforms obtained using an air bellows belt (Philips Medical Systems, Cleveland, OH) were exported and compared to those with the AS based signals. The average errors for the enrolled patients between the estimated breath per minute (bpm) and the reference waveform bpm can be as low as -0.07 with the standard deviation 1.58. The new algorithm outperformed the original AS technique for all patients by 8.5% to 30%. The impact of gantry rotation on the breathing signal was assessed with data acquired with a Quasar phantom (Modus Medical Devices Inc., London, Canada) and found to be minimal on the signal frequency. The new technique developed in this work will provide a practical solution to rendering markerless breathing signal using the CBCT projections for thoracic and abdominal patients. PMID:27008349
Pleban, Dariusz
2014-01-01
This paper describes the results of a study aimed at developing a tool for optimizing the location of machinery and workstations. A global index of acoustic assessment of machines was developed for this purpose. This index and a genetic algorithm were used in a computer tool for predicting noise emission of machines as well as optimizing the location of machines and workstations in industrial rooms. The results of laboratory and simulation tests demonstrate that the developed global index and the genetic algorithm support measures aimed at noise reduction at workstations.
Caproni, A.; Toffoli, R. T.; Monteiro, H.; Abraham, Z.; Teixeira, D. M.
2011-07-20
We present a new technique for obtaining model fittings to very long baseline interferometric images of astrophysical jets. The method minimizes a performance function proportional to the sum of the squared difference between the model and observed images. The model image is constructed by summing N{sub s} elliptical Gaussian sources characterized by six parameters: two-dimensional peak position, peak intensity, eccentricity, amplitude, and orientation angle of the major axis. We present results for the fitting of two main benchmark jets: the first constructed from three individual Gaussian sources, the second formed by five Gaussian sources. Both jets were analyzed by our cross-entropy technique in finite and infinite signal-to-noise regimes, the background noise chosen to mimic that found in interferometric radio maps. Those images were constructed to simulate most of the conditions encountered in interferometric images of active galactic nuclei. We show that the cross-entropy technique is capable of recovering the parameters of the sources with a similar accuracy to that obtained from the very traditional Astronomical Image Processing System Package task IMFIT when the image is relatively simple (e.g., few components). For more complex interferometric maps, our method displays superior performance in recovering the parameters of the jet components. Our methodology is also able to show quantitatively the number of individual components present in an image. An additional application of the cross-entropy technique to a real image of a BL Lac object is shown and discussed. Our results indicate that our cross-entropy model-fitting technique must be used in situations involving the analysis of complex emission regions having more than three sources, even though it is substantially slower than current model-fitting tasks (at least 10,000 times slower for a single processor, depending on the number of sources to be optimized). As in the case of any model fitting
Uauy, Ricardo; Corvalan, Camila; Dangour, Alan D
2009-02-01
Optimal health and well-being are now considered the true measures of human development. Integrated strategies for infant, child and adult nutrition are required that take a life-course perspective to achieve life-long health. The major nutrition challenges faced today include: (a) addressing the pending burden of undernutrition (low birth weight, severe wasting, stunting and Zn, retinol, Fe, iodine and folic acid deficits) affecting those individuals living in conditions of poverty and deprivation; (b) preventing nutrition-related chronic diseases (obesity, diabetes, CVD, some forms of cancer and osteoporosis) that, except in sub-Saharan Africa, are the main causes of death and disability globally. This challenge requires a life-course perspective as effective prevention starts before conception and continues at each stage of life. While death is unavoidable, premature death and disability can be postponed by providing the right amount and quality of food and by maintaining an active life; (c) delaying or avoiding, via appropriate nutrition and physical activity interventions, the functional declines associated with advancing age. To help tackle these challenges, it is proposed that the term 'malnutrition in all its forms', which encompasses the full spectrum of nutritional disorders, should be used to engender a broader understanding of global nutrition problems. This term may prove particularly helpful when interacting with policy makers and the public. Finally, a greater effort by the UN agencies and private and public development partners is called for to strengthen local, regional and international capacity to support the much needed change in policy and programme activities focusing on all forms of malnutrition with a unified agenda.
Uauy, Ricardo; Corvalan, Camila; Dangour, Alan D
2009-02-01
Optimal health and well-being are now considered the true measures of human development. Integrated strategies for infant, child and adult nutrition are required that take a life-course perspective to achieve life-long health. The major nutrition challenges faced today include: (a) addressing the pending burden of undernutrition (low birth weight, severe wasting, stunting and Zn, retinol, Fe, iodine and folic acid deficits) affecting those individuals living in conditions of poverty and deprivation; (b) preventing nutrition-related chronic diseases (obesity, diabetes, CVD, some forms of cancer and osteoporosis) that, except in sub-Saharan Africa, are the main causes of death and disability globally. This challenge requires a life-course perspective as effective prevention starts before conception and continues at each stage of life. While death is unavoidable, premature death and disability can be postponed by providing the right amount and quality of food and by maintaining an active life; (c) delaying or avoiding, via appropriate nutrition and physical activity interventions, the functional declines associated with advancing age. To help tackle these challenges, it is proposed that the term 'malnutrition in all its forms', which encompasses the full spectrum of nutritional disorders, should be used to engender a broader understanding of global nutrition problems. This term may prove particularly helpful when interacting with policy makers and the public. Finally, a greater effort by the UN agencies and private and public development partners is called for to strengthen local, regional and international capacity to support the much needed change in policy and programme activities focusing on all forms of malnutrition with a unified agenda. PMID:19012808
Ong, M L; Ng, E Y K
2005-12-01
In the lower brain, body temperature is continually being regulated almost flawlessly despite huge fluctuations in ambient and physiological conditions that constantly threaten the well-being of the body. The underlying control problem defining thermal homeostasis is one of great enormity: Many systems and sub-systems are involved in temperature regulation and physiological processes are intrinsically complex and intertwined. Thus the defining control system has to take into account the complications of nonlinearities, system uncertainties, delayed feedback loops as well as internal and external disturbances. In this paper, we propose a self-tuning adaptive thermal controller based upon Hebbian feedback covariance learning where the system is to be regulated continually to best suit its environment. This hypothesis is supported in part by postulations of the presence of adaptive optimization behavior in biological systems of certain organisms which face limited resources vital for survival. We demonstrate the use of Hebbian feedback covariance learning as a possible self-adaptive controller in body temperature regulation. The model postulates an important role of Hebbian covariance adaptation as a means of reinforcement learning in the thermal controller. The passive system is based on a simplified 2-node core and shell representation of the body, where global responses are captured. Model predictions are consistent with observed thermoregulatory responses to conditions of exercise and rest, and heat and cold stress. An important implication of the model is that optimal physiological behaviors arising from self-tuning adaptive regulation in the thermal controller may be responsible for the departure from homeostasis in abnormal states, e.g., fever. This was previously unexplained using the conventional "set-point" control theory.
NASA Astrophysics Data System (ADS)
Carroll, Rosemary W. H.; Pohll, Greg M.; Earman, Sam; Hershey, Ronald L.
2007-10-01
SummaryAs part of a larger study to estimate groundwater recharge volumes in the area of the eastern Nevada Test Site (NTS), [Campana, M.E., 1975. Finite-state models of transport phenomena in hydrologic systems, PhD Dissertation: University of Arizona, Tucson] Discrete-state compartment model (DSCM) was re-coded to simulate steady-state groundwater concentrations of a conservative tracer. It was then dynamically linked with the shuffled complex evolution (SCE) optimization algorithm [Duan, Q., Soroosh, S., Gupta, V., 1992. Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resources Research 28(4), 1015-1031] in which both flow direction and magnitude were adjusted to minimize errors in predicted tracer concentrations. Code validation on a simple four-celled model showed the algorithm consistent in model predictions and capable of reproducing expected cell outflows with relatively little error. The DSCM-SCE code was then applied to a 15-basin (cell) eastern NTS model developed for the DSCM. Auto-calibration of the NTS model was run given two modeling scenarios, (a) assuming known groundwater flow directions and solving only for magnitudes and, (b) solving for groundwater flow directions and magnitudes. The SCE is a fairly robust algorithm, unlike simulated annealing or modified Gauss-Newton approaches. The DSCM-SCE improves upon its original counterpart by being more user-friendly and by auto-calibrating complex models in minutes to hours. While the DSCM-SCE can provide numerical support to a working hypothesis, it can not definitively define a flow system based solely on δD values given few hydrogeologic constraints on boundary conditions and cell-to-cell interactions.
NASA Astrophysics Data System (ADS)
Bos, Brent J.; Howard, Joseph M.; Young, Philip J.; Gracey, Renee; Seals, Lenward T.; Ohl, Raymond G.
2012-09-01
During cryogenic vacuum testing of the James Webb Space Telescope (JWST) Integrated Science Instrument Module (ISIM), the global alignment of the ISIM with respect to the designed interface of the JWST optical telescope element (OTE) will be measured through a series of optical characterization tests. These tests will determine the locations and orientations of the JWST science instrument projected focal surfaces and entrance pupils with respect to their corresponding OTE optical interfaces. Thermal, finite element and optical modeling will then be used to predict the on-orbit optical performance of the observatory. If any optical performance non-compliances are identified, the ISIM will be adjusted to improve its performance. If this becomes necessary, ISIM has a variety of adjustments that can be made. The lengths of the six kinematic mount struts that attach the ISIM to the OTE can be modified and five science instrument focus positions and two pupil positions can be individually adjusted as well. In order to understand how to manipulate the ISIM’s degrees of freedom properly and to prepare for the ISIM flight model testing, we have completed a series of optical-mechanical analyses to develop and identify the best approaches for bringing a non-compliant ISIM Element back into compliance. During this work several unknown misalignment scenarios were produced and the simulated optical performance metrics were input into various mathematical modeling and optimization tools to determine how the ISIM degrees of freedom should be adjusted to provide the best overall optical performance.
Zhang, N.; Chen, F. Y.; Wu, X.Q.
2015-01-01
The structure of 38 atoms Ag-Cu cluster is studied by using a combination of a genetic algorithm global optimization technique and density functional theory (DFT) calculations. It is demonstrated that the truncated octahedral (TO) Ag32Cu6 core-shell cluster is less stable than the polyicosahedral (pIh) Ag32Cu6 core-shell cluster from the atomistic models and the DFT calculation shows an agreeable result, so the newfound pIh Ag32Cu6 core-shell cluster is further investigated for potential application for O2 dissociation in oxygen reduction reaction (ORR). The activation energy barrier for the O2 dissociation on pIh Ag32Cu6 core-shell cluster is 0.715 eV, where the d-band center is −3.395 eV and the density of states at the Fermi energy level is maximal for the favorable absorption site, indicating that the catalytic activity is attributed to a maximal charge transfer between an oxygen molecule and the pIh Ag32Cu6 core-shell cluster. This work revises the earlier idea that Ag32Cu6 core-shell nanoparticles are not suitable as ORR catalysts and confirms that Ag-Cu nanoalloy is a potential candidate to substitute noble Pt-based catalyst in alkaline fuel cells. PMID:26148904
Deshpande, Nandini; Hewston, Patricia; Yoshikawa, Mika
2015-04-01
The ability to safely perform cognitive-motor dual-tasks is critical for independence of older adults. We compared age-associated differences in global and segmental control during dual-task walking in sub-optimal sensory conditions. Thirteen young (YA) and 13 healthy older (OA) adults walked a straight pathway with cognitive dual-task of walking-while-talking (WT) or no-WT under four sensory conditions. On randomly selected trials, visual and vestibular inputs were manipulated using blurring goggles (BV) and Galvanic Vestibular Stimulation (GVS), respectively. Gait speed decreased more in YA than OA during WT. Gait speed increased with GVS with normal vision but not BV. Step length considerably decreased with WT. Trunk roll significantly decreased only in OA with GVS in WT. Head roll significantly decreased with GVS regardless of age. Results indicate GVS-induced adaptations were dependent on available visual information. YA reduced their gait speed more than OA to achieve a similar pace to safely perform WT. GVS resulted in both age-groups to reduce head movement. However, with the addition of WT during GVS, OA also stiffened their trunk. Therefore, with increased attentional demands healthy OA employed different compensatory strategies than YA to maintain postural control.
Nakajima, T Y; Nakajima, T; Nakajima, M; Fukushima, H; Kuji, M; Uchiyama, A; Kishino, M
1998-05-20
The channel specifications of the Global Imager onboard the Advanced Earth Observing Satellite II have been determined by extensive numerical experiments. The results show that there is an optimum feasible position for each ocean color channel. The bandwidth of the 0.763-microm channel should be less than 10 nm for good sensitivity to the cloud top height and geometric thickness of the cloud layer; a 40-nm bandwidth is suitable for the 1.38-microm channel to have the strongest contrast between cloudy and clear radiance with a sufficient radiant energy; and a 3.7-microm channel is better than a 3.95-microm channel for estimation of the sea surface temperature (SST) and determination of the cloud particle size when the bandwidth of the channel is 0.33 microm. A three-wavelength combination of 6.7, 7.3, and 7.5 microm is an optimized choice for water vapor profiling. The combination of 8.6, 10.8, and 12.0 microm is suitable for cloud microphysics and SST retrievals with the split-window technique.
Thompson, Kimberly M; Duintjer Tebbens, Radboud J
2016-07-01
Managing the dynamics of vaccine supply and demand represents a significant challenge with very high stakes. Insufficient vaccine supplies can necessitate rationing, lead to preventable adverse health outcomes, delay the achievements of elimination or eradication goals, and/or pose reputation risks for public health authorities and/or manufacturers. This article explores the dynamics of global vaccine supply and demand to consider the opportunities to develop and maintain optimal global vaccine stockpiles for universal vaccines, characterized by large global demand (for which we use measles vaccines as an example), and nonuniversal (including new and niche) vaccines (for which we use oral cholera vaccine as an example). We contrast our approach with other vaccine stockpile optimization frameworks previously developed for the United States pediatric vaccine stockpile to address disruptions in supply and global emergency response vaccine stockpiles to provide on-demand vaccines for use in outbreaks. For measles vaccine, we explore the complexity that arises due to different formulations and presentations of vaccines, consideration of rubella, and the context of regional elimination goals. We conclude that global health policy leaders and stakeholders should procure and maintain appropriate global vaccine rotating stocks for measles and rubella vaccine now to support current regional elimination goals, and should probably also do so for other vaccines to help prevent and control endemic or epidemic diseases. This work suggests the need to better model global vaccine supplies to improve efficiency in the vaccine supply chain, ensure adequate supplies to support elimination and eradication initiatives, and support progress toward the goals of the Global Vaccine Action Plan. PMID:25109229
Thompson, Kimberly M; Duintjer Tebbens, Radboud J
2016-07-01
Managing the dynamics of vaccine supply and demand represents a significant challenge with very high stakes. Insufficient vaccine supplies can necessitate rationing, lead to preventable adverse health outcomes, delay the achievements of elimination or eradication goals, and/or pose reputation risks for public health authorities and/or manufacturers. This article explores the dynamics of global vaccine supply and demand to consider the opportunities to develop and maintain optimal global vaccine stockpiles for universal vaccines, characterized by large global demand (for which we use measles vaccines as an example), and nonuniversal (including new and niche) vaccines (for which we use oral cholera vaccine as an example). We contrast our approach with other vaccine stockpile optimization frameworks previously developed for the United States pediatric vaccine stockpile to address disruptions in supply and global emergency response vaccine stockpiles to provide on-demand vaccines for use in outbreaks. For measles vaccine, we explore the complexity that arises due to different formulations and presentations of vaccines, consideration of rubella, and the context of regional elimination goals. We conclude that global health policy leaders and stakeholders should procure and maintain appropriate global vaccine rotating stocks for measles and rubella vaccine now to support current regional elimination goals, and should probably also do so for other vaccines to help prevent and control endemic or epidemic diseases. This work suggests the need to better model global vaccine supplies to improve efficiency in the vaccine supply chain, ensure adequate supplies to support elimination and eradication initiatives, and support progress toward the goals of the Global Vaccine Action Plan.
NASA Astrophysics Data System (ADS)
Dusek, Jaromir; Dohnal, Michal; Snehota, Michal; Sobotkova, Martina; Ray, Chittaranjan; Vogel, Tomas
2015-04-01
The fate of pesticides in tropical soils is still not understood as well as it is for soils in temperate regions. In this study, water flow and transport of bromide tracer and five pesticides (atrazine, imazaquin, sulfometuron methyl, S-metolachlor, and imidacloprid) through an undisturbed soil column of tropical Oxisol were analyzed using a one-dimensional numerical model. The numerical model is based on Richards' equation for solving water flow, and the advection-dispersion equation for solving solute transport. Data from a laboratory column leaching experiment were used in the uncertainty analysis using a global optimization methodology to evaluate the model's sensitivity to transport parameters. All pesticides were found to be relatively mobile (sorption distribution coefficients lower than 2 cm3 g- 1). Experimental data indicated significant non-conservative behavior of bromide tracer. All pesticides, with the exception of imidacloprid, were found less persistent (degradation half-lives smaller than 45 days). Three of the five pesticides (atrazine, sulfometuron methyl, and S-metolachlor) were better described by the linear kinetic sorption model, while the breakthrough curves of imazaquin and imidacloprid were more appropriately approximated using nonlinear instantaneous sorption. Sensitivity analysis suggested that the model is most sensitive to sorption distribution coefficient. The prediction limits contained most of the measured points of the experimental breakthrough curves, indicating adequate model concept and model structure for the description of transport processes in the soil column under study. Uncertainty analysis using a physically-based Monte Carlo modeling of pesticide fate and transport provides useful information for the evaluation of chemical leaching in Hawaii soils.
Dusek, Jaromir; Dohnal, Michal; Snehota, Michal; Sobotkova, Martina; Ray, Chittaranjan; Vogel, Tomas
2015-01-01
The fate of pesticides in tropical soils is still not understood as well as it is for soils in temperate regions. In this study, water flow and transport of bromide tracer and five pesticides (atrazine, imazaquin, sulfometuron methyl, S-metolachlor, and imidacloprid) through an undisturbed soil column of tropical Oxisol were analyzed using a one-dimensional numerical model. The numerical model is based on Richards' equation for solving water flow, and the advection-dispersion equation for solving solute transport. Data from a laboratory column leaching experiment were used in the uncertainty analysis using a global optimization methodology to evaluate the model's sensitivity to transport parameters. All pesticides were found to be relatively mobile (sorption distribution coefficients lower than 2 cm(3) g(-1)). Experimental data indicated significant non-conservative behavior of bromide tracer. All pesticides, with the exception of imidacloprid, were found less persistent (degradation half-lives smaller than 45 days). Three of the five pesticides (atrazine, sulfometuron methyl, and S-metolachlor) were better described by the linear kinetic sorption model, while the breakthrough curves of imazaquin and imidacloprid were more appropriately approximated using nonlinear instantaneous sorption. Sensitivity analysis suggested that the model is most sensitive to sorption distribution coefficient. The prediction limits contained most of the measured points of the experimental breakthrough curves, indicating adequate model concept and model structure for the description of transport processes in the soil column under study. Uncertainty analysis using a physically-based Monte Carlo modeling of pesticide fate and transport provides useful information for the evaluation of chemical leaching in Hawaii soils. PMID:25703186
Rossi, Giulia; Ferrando, Riccardo; Rapallo, Arnaldo; Fortunelli, Alessandro; Curley, Benjamin C; Lloyd, Lesley D; Johnston, Roy L
2005-05-15
Genetic algorithm global optimization of Ag-Pd, Ag-Au, and Pd-Pt clusters is performed. The 34- and 38-atom clusters are optimized for all compositions. The atom-atom interactions are modeled by a semiempirical potential. All three systems are characterized by a small size mismatch and a weak tendency of the larger atoms to segregate at the surface of the smaller ones. As a result, the global minimum structures exhibit a larger mixing than in Ag-Cu and Ag-Ni clusters. Polyicosahedral structures present generally favorable energetic configurations, even though they are less favorable than in the case of the size-mismatched systems. A comparison between all the systems studied here and in the previous paper (on size-mismatched systems) is presented.
Aranow, Cynthia
2015-12-01
The Physician Global Assessment (PGA) is an important and useful outcome measurement of lupus disease activity, but consensus on whether the PGA should be performed prior to or after the receipt of laboratory values is lacking. The objective of this study was to collect preliminary data on the optimal time to perform a PGA. In this pilot study, a PGA was performed by a single clinician upon completion of an outpatient clinical encounter and again after receipt of pertinent laboratory values. Laboratory values obtained at each clinical visit included a CBC, comprehensive chemistries, C3, C4, anti-dsDNA antibody levels, urinalysis and, if pertinent, a spot urinary protein/creatinine ratio. Disease activity was additionally determined by the SELENA-SLEDAI. Fifty-four patients, 3 males and 51 females with an average SLE disease duration of 12.3 (SD 10.5) years contributed 74 assessments to this study. The average SELENA-SLEDAI was 2.2. The average pre-laboratory PGA was 0.46, and the average post-laboratory PGA was 0.55 (p < 0.02 paired Student's t test). Among the 48 encounters with active disease and a mean SELENA-SLEDAI of 3.37, concordance of the pre-laboratory and post-laboratory PGAs occurred in only third of the patient encounters. Both pre- and post-PGA correlated with the SELENA-SLEDAI. However, the correlation of the post-PGA with the SELENA-SLEDAI was significantly greater than the correlation of the pre-PGA and SELENA-SLEDAI [r = 0.69 vs 0.79, respectively (p < 0.0179)]. In some lupus patients, the PGA determined prior to receipt of laboratory values may be the same as the PGA determined after laboratory values are received. However, in these preliminary data, there was a significant difference between pre-laboratory and post-laboratory PGA with a significantly greater correlation of the post-laboratory PGA with the SELENA-SLEDAI. Further studies in a larger patient population with a greater range of disease activity are needed to confirm and extend these
NASA Astrophysics Data System (ADS)
Auluck, S. K. H.
2014-12-01
Dense plasma focus (DPF) is known to produce highly energetic ions, electrons and plasma environment which can be used for breeding short-lived isotopes, plasma nanotechnology and other material processing applications. Commercial utilization of DPF in such areas would need a design tool that can be deployed in an automatic search for the best possible device configuration for a given application. The recently revisited (Auluck 2013 Phys. Plasmas 20 112501) Gratton-Vargas (GV) two-dimensional analytical snowplow model of plasma focus provides a numerical formula for dynamic inductance of a Mather-type plasma focus fitted to thousands of automated computations, which enables the construction of such a design tool. This inductance formula is utilized in the present work to explore global optimization, based on first-principles optimality criteria, in a four-dimensional parameter-subspace of the zero-resistance GV model. The optimization process is shown to reproduce the empirically observed constancy of the drive parameter over eight decades in capacitor bank energy. The optimized geometry of plasma focus normalized to the anode radius is shown to be independent of voltage, while the optimized anode radius is shown to be related to capacitor bank inductance.
NASA Astrophysics Data System (ADS)
Biswas, A.; Sharma, S. P.
2012-12-01
Self-Potential anomaly is an important geophysical technique that measures the electrical potential due natural source of current in the Earth's subsurface. An inclined sheet type model is a very familiar structure associated with mineralization, fault plane, groundwater flow and many other geological features which exhibits self potential anomaly. A number of linearized and global inversion approaches have been developed for the interpretation of SP anomaly over different structures for various purposes. Mathematical expression to compute the forward response over a two-dimensional dipping sheet type structures can be described in three different ways using five variables in each case. Complexities in the inversion using three different forward approaches are different. Interpretation of self-potential anomaly using very fast simulated annealing global optimization has been developed in the present study which yielded a new insight about the uncertainty and equivalence in model parameters. Interpretation of the measured data yields the location of the causative body, depth to the top, extension, dip and quality of the causative body. In the present study, a comparative performance of three different forward approaches in the interpretation of self-potential anomaly is performed to assess the efficacy of the each approach in resolving the possible ambiguity. Even though each forward formulation yields the same forward response but optimization of different sets of variable using different forward problems poses different kinds of ambiguity in the interpretation. Performance of the three approaches in optimization has been compared and it is observed that out of three methods, one approach is best and suitable for this kind of study. Our VFSA approach has been tested on synthetic, noisy and field data for three different methods to show the efficacy and suitability of the best method. It is important to use the forward problem in the optimization that yields the
Reck, R.
1993-12-31
This paper will discuss possible United States policy responses to global warming. The components of a voluntary program for emissions control will be presented as well as regulatory options, including a carbon tax and tradeable permits. The advantages and disadvantages of both options will be discussed as well as the need for a consistent overall policy response to climate change.
Assured Optimism in a Scottish Girls' School: Habitus and the (Re)production of Global Privilege
ERIC Educational Resources Information Center
Forbes, Joan; Lingard, Bob
2015-01-01
This paper examines how high levels of social-cultural connectedness and academic excellence, inflected by gender and social class, constitute a particular school habitus of "assured optimism" at an elite Scottish girls' school. In Bourdieuian terms, Dalrymple is a "forcing ground" for the "intense cultivation"…
Castillo, Edward; Castillo, Richard; Fuentes, David; Guerrero, Thomas
2014-01-01
Purpose: Block matching is a well-known strategy for estimating corresponding voxel locations between a pair of images according to an image similarity metric. Though robust to issues such as image noise and large magnitude voxel displacements, the estimated point matches are not guaranteed to be spatially accurate. However, the underlying optimization problem solved by the block matching procedure is similar in structure to the class of optimization problem associated with B-spline based registration methods. By exploiting this relationship, the authors derive a numerical method for computing a global minimizer to a constrained B-spline registration problem that incorporates the robustness of block matching with the global smoothness properties inherent to B-spline parameterization. Methods: The method reformulates the traditional B-spline registration problem as a basis pursuit problem describing the minimal l1-perturbation to block match pairs required to produce a B-spline fitting error within a given tolerance. The sparsity pattern of the optimal perturbation then defines a voxel point cloud subset on which the B-spline fit is a global minimizer to a constrained variant of the B-spline registration problem. As opposed to traditional B-spline algorithms, the optimization step involving the actual image data is addressed by block matching. Results: The performance of the method is measured in terms of spatial accuracy using ten inhale/exhale thoracic CT image pairs (available for download at www.dir-lab.com) obtained from the COPDgene dataset and corresponding sets of expert-determined landmark point pairs. The results of the validation procedure demonstrate that the method can achieve a high spatial accuracy on a significantly complex image set. Conclusions: The proposed methodology is demonstrated to achieve a high spatial accuracy and is generalizable in that in can employ any displacement field parameterization described as a least squares fit to block match
Chen, Tinggui; Xiao, Renbin
2014-01-01
Artificial bee colony (ABC) algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA), artificial colony optimization (ACO), and particle swarm optimization (PSO). However, the ABC still has some limitations. For example, ABC can easily get trapped in the local optimum when handing in functions that have a narrow curving valley, a high eccentric ellipse, or complex multimodal functions. As a result, we proposed an enhanced ABC algorithm called EABC by introducing self-adaptive searching strategy and artificial immune network operators to improve the exploitation and exploration. The simulation results tested on a suite of unimodal or multimodal benchmark functions illustrate that the EABC algorithm outperforms ACO, PSO, and the basic ABC in most of the experiments. PMID:24772023
Chen, Tinggui; Xiao, Renbin
2014-01-01
Artificial bee colony (ABC) algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA), artificial colony optimization (ACO), and particle swarm optimization (PSO). However, the ABC still has some limitations. For example, ABC can easily get trapped in the local optimum when handing in functions that have a narrow curving valley, a high eccentric ellipse, or complex multimodal functions. As a result, we proposed an enhanced ABC algorithm called EABC by introducing self-adaptive searching strategy and artificial immune network operators to improve the exploitation and exploration. The simulation results tested on a suite of unimodal or multimodal benchmark functions illustrate that the EABC algorithm outperforms ACO, PSO, and the basic ABC in most of the experiments. PMID:24772023
NASA Astrophysics Data System (ADS)
Kvaerna, Tormod; Gibbons, Steven; Fyen, Jan; Roth, Michael
2014-05-01
The IMS infrasound array I37NO near Bardufoss in northern Norway became operational in October 2013 and was certified on December 19, 2013. The 10-element array has an aperture of approximately 1.5 km and is deployed in low-lying woodland about 2.5 degrees north of the Arctic Circle. Its location in the European Arctic means that the array fills an important gap in the global IMS infrasound monitoring network. In addition, I37NO extends significantly the network of infrasound stations in northern Norway, Sweden, Finland, and Russia: operated by NORSAR, the Swedish Institute for Space Physics, and the Kola Regional Seismological Center in Apatity. The geometry is based on the highly successful classical design for regional seismic arrays with sensors arranged in two approximately concentric rings surrounding a central site. A 4-site subarray with an aperture of approximately 450 meters, comprising the central element and the inner ring of 3 sites, provides an excellent array response function and detection capability for relatively high frequency (2-4 Hz) signals. Such signals are usually generated by events at distances within 1000 km and often lack energy in the lower frequency bands for which the larger aperture arrays provide signal coherence. These so-called regional signals are of increasing importance in civil applications and the need to characterize the infrasonic wavefield over these distances is increasingly important in the remote monitoring of natural hazards. I37NO will provide good characterization of Ground Truth industrial and military explosions in the region which are well-constrained by seismic data. The full array aperture provides excellent backazimuth and slowness resolution for lower frequency signals and it is anticipated that I37NO will contribute significantly to the detection and association of signals on a global scale. Already within the first few months of operation, we have examples of high-quality recordings from meteors, accidental
NASA Astrophysics Data System (ADS)
Denning, A.; Lokupitiya, R. S.; Zupanski, D.; Kawa, S. R.; Baker, D. F.; Doney, S. C.; Gurney, K. R.
2009-12-01
We present a system to analyze GOSAT/Tanso data using a combination of existing models of CO2 exchanges due to hourly photosynthesis and respiration, daily air-sea gas exchange, biomass burning, Fossil Fuel Emissions, and atmospheric transport. This comprehensive system allows direct comparison to the observed record of both in-situ and remotely sensed atmospheric CO2 at hourly timescales. We have previously demonstrated that a lower-resolution version of the system has good skill at replicating diurnal, synoptic, and seasonal variations over vegetated land surfaces. The system is driven by meteorological output from the NASA Goddard EOS Data Assimilation System, version 5. Surface weather from the system drives calculations of terrestrial ecosystem metabolism (radiation, precipitation, humidity, temperature) and air-sea gas exchange (wind), with other input data coming from satellite data products (e.g., fPAR and LAI from MODIS, and ocean color from SeaWiFS and MODIS). The analysis system is evaluated using synthetic data on a 2 x 2.5 degree (lat x lon) global grid. Synthetic data are sampled in cloud-free columns along the GOSAT orbital ephemeris and used to estimate multiplicative biases to component fluxes by Ensemble Data Assimilation. The system is quite successful at retrieving mechanistic estimates of spatial patterns of surface carbon fluxes on monthly and annual timescales over land, but is less skillful over the oceans.
Luthra, Suman A; Hodge, Ian M; Pikal, Michael J
2008-09-01
The purpose of this research was to investigate the effect of annealing on the molecular mobility in lyophilized glasses using differential scanning calorimetry (DSC) and isothermal microcalorimetry (IMC) techniques. A second objective that emerged was a systematic study of the unusual pre-T(g) thermal events that were observed during DSC warming scans after annealing. Aspartame lyophilized with three different excipients; sucrose, trehalose and poly vinyl pyrrolidone (PVP) was studied. The aim of this work was to quantify the decrease in mobility in amorphous lyophilized aspartame formulations upon systematic postlyophilization annealing. DSC scans of aspartame:sucrose formulation (T(g) = 73 degrees C) showed the presence of a pre-T(g) endotherm which disappeared upon annealing. Aspartame:trehalose (T(g) = 112 degrees C) and aspartame:PVP (T(g) = 100 degrees C) showed a broad exotherm before T(g) and annealing caused appearance of endothermic peaks before T(g). This work also employed IMC to measure the global molecular mobility represented by structural relaxation time (tau(beta)) in both un-annealed and annealed formulations. The effect of annealing on the enthalpy relaxation of lyophilized glasses, as measured by DSC and IMC, was consistent with the behavior predicted using the Tool-Narayanaswamy-Moynihan (TNM) phenomenology (Luthra et al., 2007, in press). The results show that the systems annealed at T(g) -15 degrees C to T(g) -20 degrees C have the lowest molecular mobility.
Guo, Fumin; Yuan, Jing; Rajchl, Martin; Svenningsen, Sarah; Capaldi, Dante P I; Sheikh, Khadija; Fenster, Aaron; Parraga, Grace
2015-07-01
Pulmonary imaging using hyperpolarized (3)He/(129)Xe gas is emerging as a new way to understand the regional nature of pulmonary ventilation abnormalities in obstructive lung diseases. However, the quantitative information derived is completely dependent on robust methods to segment both functional and structural/anatomical data. Here, we propose an approach to jointly segment the lung cavity from (1)H and (3)He pulmonary magnetic resonance images (MRI) by constraining the spatial consistency of the two segmentation regions, which simultaneously employs the image features from both modalities. We formulated the proposed co-segmentation problem as a coupled continuous min-cut model and showed that this combinatorial optimization problem can be solved globally and exactly by means of convex relaxation. In particular, we introduced a dual coupled continuous max-flow model to study the convex relaxed coupled continuous min-cut model under a primal and dual perspective. This gave rise to an efficient duality-based convex optimization algorithm. We implemented the proposed algorithm in parallel using general-purpose programming on graphics processing unit (GPGPU), which substantially increased its computational efficiency. Our experiments explored a clinical dataset of 25 subjects with chronic obstructive pulmonary disease (COPD) across a wide range of disease severity. The results showed that the proposed co-segmentation approach yielded superior performance compared to single-channel image segmentation in terms of precision, accuracy and robustness.
NASA Astrophysics Data System (ADS)
Figueiro, Thiago; Choi, Kang-Hoon; Gutsch, Manuela; Freitag, Martin; Hohle, Christoph; Tortai, Jean-Hervé; Saib, Mohamed; Schiavone, Patrick
2012-11-01
In electron proximity effect correction (PEC), the quality of a correction is highly dependent on the quality of the model. Therefore it is of primary importance to have a reliable methodology to extract the parameters and assess the quality of a model. Among others the model describes how the energy of the electrons spreads out in the target material (via the Point Spread Function, PSF) as well as the influence of the resist process. There are different models available in previous studies, as well as several different approaches to obtain the appropriate value for their parameters. However, those are restricted in terms of complexity, or require a prohibitive number of measurements, which is limited for a certain PSF model. In this work, we propose a straightforward approach to obtain the value of parameters of a PSF. The methodology is general enough to apply for more sophisticated models as well. It focused on improving the three steps of model calibration procedure: First, it is using a good set of calibration patterns. Secondly, it secures the optimization step and avoids falling into a local optimum. And finally the developed method provides an improved analysis of the calibration step, which allows quantifying the quality of the model as well as enabling a comparison of different models. The methodology described in the paper is implemented as specific module in a commercial tool.
Barry, Jeremy A; Muddiman, David C
2011-12-15
Design of experiments (DOE) is a systematic and cost-effective approach to system optimization by which the effects of multiple parameters and parameter interactions on a given response can be measured in few experiments. Herein, we describe the use of statistical DOE to improve a few of the analytical figures of merit of the infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) source for mass spectrometry. In a typical experiment, bovine cytochrome c was ionized via electrospray, and equine cytochrome c was desorbed and ionized by IR-MALDESI such that the ratio of equine:bovine was used as a measure of the ionization efficiency of IR-MALDESI. This response was used to rank the importance of seven source parameters including flow rate, laser fluence, laser repetition rate, ESI emitter to mass spectrometer inlet distance, sample stage height, sample plate voltage, and the sample to mass spectrometer inlet distance. A screening fractional factorial DOE was conducted to designate which of the seven parameters induced the greatest amount of change in the response. These important parameters (flow rate, stage height, sample to mass spectrometer inlet distance, and laser fluence) were then studied at higher resolution using a full factorial DOE to obtain the globally optimized combination of parameter settings. The optimum combination of settings was then compared with our previously determined settings to quantify the degree of improvement in detection limit. The limit of detection for the optimized conditions was approximately 10 attomoles compared with 100 femtomoles for the previous settings, which corresponds to a four orders of magnitude improvement in the detection limit of equine cytochrome c.
Optimization of the GOSAT global observation from space with region-by-region target-mode operations
NASA Astrophysics Data System (ADS)
kuze, A.; Suto, H.; Shiomi, K.; Kawakami, S.; Nakajima, M.
2013-12-01
observations, by uploading the pointing angles from the ground every day, TANSO-FTS can target a maximum of about 1,000 points per day. Dithering over fractional clouds area and targeting coast and islands avoiding bay and channels can increase yield rate. GOSAT has a UV band (380nm) in TANSO-CAI to observe dark land and FTS-SWIR bands has been acquiring two linear polarizations simultaneously. Multi-angle observations with forward, nadir and backward viewing with two axis pointing mirror will distinguish aerosol scattering from surface reflection and reduce aerosol related errors. The optimized target mode allocation are now considered. We will add the classification information of the target such as validation site, mega cities, volcano in the future Level 1B product to identify high bias possibility in XCO2. In addition, the geo-location information after the best estimate pointing-offset correction will be added. Lastly, after optimizing the observation locations, consistency between different gains, target brightness and aerosol optical thickness has to be confirmed. Validation other than TCCON site is also discussed.
NASA Astrophysics Data System (ADS)
Rodiet, Christophe; Remy, Benjamin; Degiovanni, Alain
2016-05-01
In this paper, it is shown how to select the optimal wavelengths minimizing the relative error and the standard deviation of the temperature. Furthermore, it is shown that the optimal wavelengths in mono-spectral and bi-spectral methods (for a Planck's law) can be determined by laws analogous to the displacement Wien's law. The simplicity of these laws can thus allow real-time selection of optimal wavelengths for a control/optimization of industrial processes, for example. A more general methodology to obtain the optimal wavelengths selection in a multi-spectral method (taking into account the spectral variations of the global transfer function including the emissivity variations) for temperature measurement of surfaces exhibiting non-uniform emissivity, is also presented. This latter can then find an interest in glass furnaces temperature measurement with spatiotemporal non-uniformities of emissivity, the control of biomass pyrolysis, the surface temperature measurement of buildings or heating devices, for example. The goal consists of minimizing the standard deviation of the estimated temperature (optimal design experiment). For the multi-spectral method, two cases will be treated: optimal global and optimal constrained wavelengths selection (to the spectral range of the detector, for example). The estimated temperature results obtained by different models and for different number of parameters and wavelengths are compared. These different points are treated from theoretical, numerical and experimental points of view.
NASA Astrophysics Data System (ADS)
Horton, Pascal; Weingartner, Rolf; Obled, Charles; Jaboyedoff, Michel
2016-04-01
The Analogue Method (AM) aims at forecasting a local meteorological variable of interest (the predictand), often the daily precipitation total, on the basis of a statistical relationship with synoptic predictor variables. A certain number of similar situations are sampled in order to establish the empirical conditional distribution which is considered as the prediction for a given date. The method is used in operational medium-range forecasting in several hydropower companies or flood forecasting services, as well as in climate impact studies. The statistical relationship is usually established by means of a semi-automatic sequential procedure that has strong limitations: it is made of successive steps and thus cannot handle parameters dependencies, and it cannot automatically optimize certain parameters, such as the selection of the pressure levels and the temporal windows on which the predictors are compared. A global optimization technique based on Genetic Algorithms was introduced in order to surpass these limitations and to provide a fully automatic and objective determination of the AM parameters. The parameters that were previously assessed manually, such as the selection of the pressure levels and the temporal windows, on which the predictors are compared, are now automatically determined. The next question is: Are Genetic Algorithms able to select the meteorological variable, in a reanalysis dataset, that is the best predictor for the considered predictand, along with the analogy criteria itself? Even though we may not find better predictors for precipitation prediction that the ones often used in Europe, due to numerous other studies which consisted in systematic assessments, the ability of an automatic selection offers new perspectives in order to adapt the AM for new predictands or new regions under different meteorological influences.
Carter, Patrick M.; Desmond, Jeffery S.; Akanbobnaab, Christopher; Oteng, Rockefeller A.; Rominski, Sarah; Barsan, William G.; Cunningham, Rebecca
2012-01-01
Background Although many global health programs focus on providing clinical care or medical education, improving clinical operations can have a significant effect on patient care delivery, especially in developing health systems without high-level operations management. Lean manufacturing techniques have been effective in decreasing emergency department (ED) length of stay, patient waiting times, numbers of patients leaving without being seen, and door-to-balloon times for ST-elevation myocardial infarction in developed health systems; but use of Lean in low to middle income countries with developing emergency medicine systems has not been well characterized. Objectives To describe the application of Lean manufacturing techniques to improve clinical operations at Komfo Anokye Teaching Hospital in Ghana and to identify key lessons learned to aid future global EM initiatives. Methods A three-week Lean improvement program focused on the hospital admissions process at Komfo Anokye Teaching Hospital was completed by a 14-person team in six stages: problem definition, scope of project planning, value stream mapping, root cause analysis, future state planning, and implementation planning. Results The authors identified eight lessons learned during our use of Lean to optimize the operations of an ED in a global health setting: 1) the Lean process aided in building a partnership with Ghanaian colleagues; 2) obtaining and maintaining senior institutional support is necessary and challenging; 3) addressing power differences among the team to obtain feedback from all team members is critical to successful Lean analysis; 4) choosing a manageable initial project is critical to influence long-term Lean use in a new environment; 5) data intensive Lean tools can be adapted and are effective in a less resourced health system; 6) several Lean tools focused on team problem solving techniques worked well in a low resource system without modification; 7) using Lean highlighted that
Jin Chen
2009-12-07
Efficient and robust Variable Relaxation Solver, based on pseudo-transient continuation, is developed to solve nonlinear anisotropic thermal conduction arising from fusion plasma simulations. By adding first and/or second order artificial time derivatives to the system, this type of method advances the resulting time-dependent nonlinear PDEs to steady state, which is the solution to be sought. In this process, only the stiffness matrix itself is involved so that the numerical complexity and errors can be greatly reduced. In fact, this work is an extension of integrating efficient linear elliptic solvers for fusion simulation on Cray XIE. Two schemes are derived in this work, first and second order Variable Relaxations. Four factors are observed to be critical for efficiency and preservation of solution's symmetric structure arising from periodic boundary condition: refining meshes in different coordinate directions, initializing nonlinear process, varying time steps in both temporal and spatial directions, and accurately generating nonlinear stiffness matrix. First finer mesh scale should be taken in strong transport direction; Next the system is carefully initialized by the solution with linear conductivity; Third, time step and relaxation factor are vertex-based varied and optimized at each time step; Finally, the nonlinear stiffness matrix is updated by just scaling corresponding linear one with the vector generated from nonlinear thermal conductivity.
ERIC Educational Resources Information Center
Schwartz, Robert
2012-01-01
This issue of ETS Policy Notes (Vol. 20, No. 3) provides highlights from the Salzburg Global Seminar in December 2011. The seminar focused on bettering the educational and life prospects of students up to age 18 worldwide. [This article was written with the assistance of Beth Brody.
NASA Technical Reports Server (NTRS)
Robertson, Franklin; Goodman, Steven J.; Christy, John R.; Fitzjarrald, Daniel E.; Chou, Shi-Hung; Crosson, William; Wang, Shouping; Ramirez, Jorge
1993-01-01
This research is the MSFC component of a joint MSFC/Pennsylvania State University Eos Interdisciplinary Investigation on the global water cycle extension across the earth sciences. The primary long-term objective of this investigation is to determine the scope and interactions of the global water cycle with all components of the Earth system and to understand how it stimulates and regulates change on both global and regional scales. Significant accomplishments in the past year are presented and include the following: (1) water vapor variability; (2) multi-phase water analysis; (3) global modeling; and (4) optimal precipitation and stream flow analysis and hydrologic processes.
Yang, Zili
2009-03-19
In the duration of this project, we finished the main tasks set up in the initial proposal. These tasks include: collecting needed data of regional aerosol emissions (mainly SO2); building the RICES model; conducting preliminary simulation runs on some policy scenarios. We established a unified and transparent IA modeling platform that connecting climate change and local air pollution. The RICES model is the pioneering IA model that treats climate change and local air pollution as correlated global and local stock externalities.
NASA Astrophysics Data System (ADS)
Sperna Weiland, F. C.; Tisseuil, C.; Dürr, H. H.; Vrac, M.; van Beek, L. P. H.
2012-03-01
Potential evaporation (PET) is one of the main inputs of hydrological models. Yet, there is limited consensus on which PET equation is most applicable in hydrological climate impact assessments. In this study six different methods to derive global scale reference PET daily time series from Climate Forecast System Reanalysis (CFSR) data are compared: Penman-Monteith, Priestley-Taylor and original and re-calibrated versions of the Hargreaves and Blaney-Criddle method. The calculated PET time series are (1) evaluated against global monthly Penman-Monteith PET time series calculated from CRU data and (2) tested on their usability for modeling of global discharge cycles. A major finding is that for part of the investigated basins the selection of a PET method may have only a minor influence on the resulting river flow. Within the hydrological model used in this study the bias related to the PET method tends to decrease while going from PET, AET and runoff to discharge calculations. However, the performance of individual PET methods appears to be spatially variable, which stresses the necessity to select the most accurate and spatially stable PET method. The lowest root mean squared differences and the least significant deviations (95% significance level) between monthly CFSR derived PET time series and CRU derived PET were obtained for a cell-specific re-calibrated Blaney-Criddle equation. However, results show that this re-calibrated form is likely to be unstable under changing climate conditions and less reliable for the calculation of daily time series. Although often recommended, the Penman-Monteith equation applied to the CFSR data did not outperform the other methods in a evaluation against PET derived with the Penman-Monteith equation from CRU data. In arid regions (e.g. Sahara, central Australia, US deserts), the equation resulted in relatively low PET values and, consequently, led to relatively high discharge values for dry basins (e.g. Orange, Murray and
Ibinson, James W; Vogt, Keith M; Taylor, Kevin B; Dua, Shiv B; Becker, Christopher J; Loggia, Marco; Wasan, Ajay D
2015-12-01
The insula is uniquely located between the temporal and parietal cortices, making it anatomically well-positioned to act as an integrating center between the sensory and affective domains for the processing of painful stimulation. This can be studied through resting-state functional connectivity (fcMRI) imaging; however, the lack of a clear methodology for the analysis of fcMRI complicates the interpretation of these data during acute pain. Detected connectivity changes may reflect actual alterations in low-frequency synchronous neuronal activity related to pain, may be due to changes in global cerebral blood flow or the superimposed task-induced neuronal activity. The primary goal of this study was to investigate the effects of global signal regression (GSR) and task paradigm regression (TPR) on the changes in functional connectivity of the left (contralateral) insula in healthy subjects at rest and during acute painful electric nerve stimulation of the right hand. The use of GSR reduced the size and statistical significance of connectivity clusters and created negative correlation coefficients for some connectivity clusters. TPR with cyclic stimulation gave task versus rest connectivity differences similar to those with a constant task, suggesting that analysis which includes TPR is more accurately reflective of low-frequency neuronal activity. Both GSR and TPR have been inconsistently applied to fcMRI analysis. Based on these results, investigators need to consider the impact GSR and TPR have on connectivity during task performance when attempting to synthesize the literature.
ERIC Educational Resources Information Center
Tosti, Donald T.
1999-01-01
Defines global fluency as a facility with cultural behaviors that help an organization thrive in an ever-changing global business environment; and discusses business culture, global culture, an example of a change effort at a global company, leadership values, company values, and defining global values and practices. (Author/LRW)
NASA Astrophysics Data System (ADS)
Myers, S. C.; Johannesson, G.; Simmons, N. A.
2011-04-01
We extend the Bayesloc seismic multiple-event location algorithm for application to global arrival time data sets. Bayesloc is a formulation of the joint probability distribution spanning multiple-event location parameters, including hypocenters, travel time corrections, pick precision, and phase labels. Stochastic priors may be used to constrain any of the Bayesloc parameters. Markov Chain Monte Carlo sampling is used to draw samples from the joint probability distribution, and the posteriori samples are summarized to infer conventional location parameters such as the hypocenter. The first application of the broad area Bayesloc algorithm is to a data set consisting of all well-recorded events in the Middle East and the most well-recorded events with 5° spatial sampling globally. This sampling strategy is designed to provide the ray coverage needed to determine lithospheric-scale P wave velocity structure in the Middle East using the complementary ray geometry provided by regional (subhorizontal) and teleseismic (subvertical) raypaths and to determine a consistent, albeit lower-resolution, image of global mantle structure. The data set consists of 5401 events and 878,535 P, Pn, pP, sP, and PcP arrivals recorded at 4606 stations. Relocated epicenters are an average of 16 km from bulletin locations. The data set included events that are known to an accuracy of 1 km (a.k.a. GT1) based on nonseismic information. The average distance between GT1 epicenters and our relocated epicenters is 5.6 km. For arrivals labeled P, Pn, and PcP, ˜92%, ˜90%, and 96% are properly labeled with probability >0.9, respectively. Incorrect phase labels are found to be erroneous at rates of 0.6%, 0.2%, 1.6%, and 2.5% for P, Pn, PcP, and depth phases (pP and sP), respectively. Labels found to be incorrect, but not erroneous, were reassigned to another phase label. P and Pn residual standard deviation with respect to ak135 travel times are dramatically reduced from 3.45 s to 1.01 s. The
Globalization and global health.
Berlinguer, G
1999-01-01
Along with the positive or negative consequences of the globalization of health, we can consider global health as a goal, responding to human rights and to common interests. History tells us that after the "microbial unification" of the world, which began in 1492, over three centuries elapsed before the recognition of common risks and attempts to cope with them in a cross-boundary effort. In the 19th and 20th centuries, the struggle against epidemics united countries, world health became a common goal, and considerable results were achieved. However, in recent decades the notion of health as a cornerstone of economic development has been replaced by the idea that public health and health services are an obstacle to the wealth of nations. Meanwhile, new common threats are growing: among them, the exacerbation of old infections and emergence of new ones, the impact of environmental changes, drug traffic on a world scale, and destructive and self-destructive violence. New and stronger empirical motives relate the interests of peoples to universal rights and to global health. The author concludes with some proposals for policies.
NASA Astrophysics Data System (ADS)
Hemmings, J. C. P.; Challenor, P. G.; Yool, A.
2015-03-01
Biogeochemical ocean circulation models used to investigate the role of plankton ecosystems in global change rely on adjustable parameters to capture the dominant biogeochemical dynamics of a complex biological system. In principle, optimal parameter values can be estimated by fitting models to observational data, including satellite ocean colour products such as chlorophyll that achieve good spatial and temporal coverage of the surface ocean. However, comprehensive parametric analyses require large ensemble experiments that are computationally infeasible with global 3-D simulations. Site-based simulations provide an efficient alternative but can only be used to make reliable inferences about global model performance if robust quantitative descriptions of their relationships with the corresponding 3-D simulations can be established. The feasibility of establishing such a relationship is investigated for an intermediate complexity biogeochemistry model (MEDUSA) coupled with a widely used global ocean model (NEMO). A site-based mechanistic emulator is constructed for surface chlorophyll output from this target model as a function of model parameters. The emulator comprises an array of 1-D simulators and a statistical quantification of the uncertainty in their predictions. The unknown parameter-dependent biogeochemical environment, in terms of initial tracer concentrations and lateral flux information required by the simulators, is a significant source of uncertainty. It is approximated by a mean environment derived from a small ensemble of 3-D simulations representing variability of the target model behaviour over the parameter space of interest. The performance of two alternative uncertainty quantification schemes is examined: a direct method based on comparisons between simulator output and a sample of known target model "truths" and an indirect method that is only partially reliant on knowledge of the target model output. In general, chlorophyll records at a
NASA Technical Reports Server (NTRS)
Taylor, Brian R.
2012-01-01
A novel, efficient air data calibration method is proposed for aircraft with limited envelopes. This method uses output-error optimization on three-dimensional inertial velocities to estimate calibration and wind parameters. Calibration parameters are based on assumed calibration models for static pressure, angle of attack, and flank angle. Estimated wind parameters are the north, east, and down components. The only assumptions needed for this method are that the inertial velocities and Euler angles are accurate, the calibration models are correct, and that the steady-state component of wind is constant throughout the maneuver. A two-minute maneuver was designed to excite the aircraft over the range of air data calibration parameters and de-correlate the angle-of-attack bias from the vertical component of wind. Simulation of the X-48B (The Boeing Company, Chicago, Illinois) aircraft was used to validate the method, ultimately using data derived from wind-tunnel testing to simulate the un-calibrated air data measurements. Results from the simulation were accurate and robust to turbulence levels comparable to those observed in flight. Future experiments are planned to evaluate the proposed air data calibration in a flight environment.
Sassen, D. S.; Peterson, J. E.
2010-03-15
.g. Bautu et al., 2006). In the technique of algebraic reconstruction tomography (ART), which is used herein for the travel time inversion (Peterson et al., 1985), a small relaxation parameter will smooth imaging artifacts caused by data errors at the expense of resolution and contrast (Figure 2). However, large data errors such as unaccounted well deviations cannot be adequately suppressed through inversion weighting schemes. Previously, problems with tomograms were treated manually. However, in large data sets and/or networks of data sets, trial and error changes to well geometries become increasingly difficult and ineffective. Mislocation of the transmitter and receiver stations of GPR cross-well tomography data sets can lead to serious imaging artifacts if not accounted for prior to inversion. Previously, problems with tomograms have been treated manually prior to inversion. In large data sets and/or networks of tomographic data sets, trial and error changes to well geometries become increasingly difficult and ineffective. Our approach is to use cross-well data quality checks and a simplified model of borehole deviation with particle swarm optimization (PSO) to automatically correct for source and receiver locations prior to tomographic inversion. We present a simple model of well deviation, which is designed to minimize potential corruption of actual data trends. We also provide quantitative quality control measures based on minimizing correlations between take-off angle and apparent velocity, and a quality check on the continuity of velocity between adjacent wells. This methodology is shown to be accurate and robust for simple 2-D synthetic test cases. Plus, we demonstrate the method on actual field data where it is compared to deviation logs. This study shows the promise for automatic correction of well deviations in GPR tomographic data. Analysis of synthetic data shows that very precise estimates of well deviation can be made for small deviations, even in the
ERIC Educational Resources Information Center
1997
This document contains four papers from a symposium on global human resource development (HRD). "Globalization of Human Resource Management (HRM) in Government: A Cross-Cultural Perspective" (Pan Suk Kim) relates HRM to national cultures and addresses its specific functional aspects with a unique dimension in a global organization. "An…
ERIC Educational Resources Information Center
Berkley, June, Ed.
1982-01-01
The articles in this collection deal with various methods of global education--education to prepare students to function as understanding and informed citizens of the world. Topics discussed in the 26 articles include: (1) the necessity of global education; (2) global education in the elementary school language arts curriculum; (3) science fiction…
ERIC Educational Resources Information Center
Longstreet, Wilma S., Ed.
1988-01-01
This issue contains an introduction ("The Promise and Perplexity of Globalism," by W. Longstreet) and seven articles dedicated to exploring the meaning of global education for today's schools. "Global Education: An Overview" (J. Becker) develops possible definitions, identifies objectives and skills, and addresses questions and issues in this…
Global interrupt and barrier networks
Blumrich, Matthias A.; Chen, Dong; Coteus, Paul W.; Gara, Alan G.; Giampapa, Mark E; Heidelberger, Philip; Kopcsay, Gerard V.; Steinmacher-Burow, Burkhard D.; Takken, Todd E.
2008-10-28
A system and method for generating global asynchronous signals in a computing structure. Particularly, a global interrupt and barrier network is implemented that implements logic for generating global interrupt and barrier signals for controlling global asynchronous operations performed by processing elements at selected processing nodes of a computing structure in accordance with a processing algorithm; and includes the physical interconnecting of the processing nodes for communicating the global interrupt and barrier signals to the elements via low-latency paths. The global asynchronous signals respectively initiate interrupt and barrier operations at the processing nodes at times selected for optimizing performance of the processing algorithms. In one embodiment, the global interrupt and barrier network is implemented in a scalable, massively parallel supercomputing device structure comprising a plurality of processing nodes interconnected by multiple independent networks, with each node including one or more processing elements for performing computation or communication activity as required when performing parallel algorithm operations. One multiple independent network includes a global tree network for enabling high-speed global tree communications among global tree network nodes or sub-trees thereof. The global interrupt and barrier network may operate in parallel with the global tree network for providing global asynchronous sideband signals.
NASA Technical Reports Server (NTRS)
Robertson, Franklin R.; Christy, John R.; Goodman, Steven J.; Miller, Tim L.; Fitzjarrald, Dan; Lapenta, Bill; Wang, Shouping
1991-01-01
The primary objective is to determine the scope and interactions of the global water cycle with all components of the Earth system and to understand how it stimulates and regulates changes on both global and regional scales. The following subject areas are covered: (1) water vapor variability; (2) multi-phase water analysis; (3) diabatic heating; (4) MSU (Microwave Sounding Unit) temperature analysis; (5) Optimal precipitation and streamflow analysis; (6) CCM (Community Climate Model) hydrological cycle; (7) CCM1 climate sensitivity to lower boundary forcing; and (8) mesoscale modeling of atmosphere/surface interaction.
Algorithms for bilevel optimization
NASA Technical Reports Server (NTRS)
Alexandrov, Natalia; Dennis, J. E., Jr.
1994-01-01
General multilevel nonlinear optimization problems arise in design of complex systems and can be used as a means of regularization for multi-criteria optimization problems. Here, for clarity in displaying our ideas, we restrict ourselves to general bi-level optimization problems, and we present two solution approaches. Both approaches use a trust-region globalization strategy, and they can be easily extended to handle the general multilevel problem. We make no convexity assumptions, but we do assume that the problem has a nondegenerate feasible set. We consider necessary optimality conditions for the bi-level problem formulations and discuss results that can be extended to obtain multilevel optimization formulations with constraints at each level.
Improving Global Development Using Agile
NASA Astrophysics Data System (ADS)
Avritzer, Alberto; Bronsard, Francois; Matos, Gilberto
Global development promises important productivity and capability advantages over centralized work by optimally allocating tasks according to locality, expertise or cost. All too often, global development also introduces a different set of communication and coordination challenges that can negate all the expected benefits and even cause project failures. Most common problems have to do with building trust or quick feedback loops between distributed teams, or with the integration of globally developed components. Agile processes tend to emphasize the intensity of communication, and would seem to be negatively impacted by team distribution. In our experience, these challenges can be overcome, and agile processes can address some of the pitfalls of global development more effectively than plan-driven development. This chapter discusses how to address the difficulties faced when adapting agile processes to global development and the improvements to global development that adopting agile can produce.
Atmospheric Science Data Center
2013-04-19
article title: MISR Global Images See the Light of Day View Larger Image ... than its nadir counterpart due to enhanced reflection of light by atmospheric particulates. MISR data are processed at the ...
Atmospheric Science Data Center
2013-04-19
... estimation of crop yields and disease outbreaks) and land management. Global MISR DHR maps are also available for all other parts of the ... of Directional Hemispherical Reflectance. project: MISR category: gallery date: ...
Terascale Optimal PDE Simulations
David Keyes
2009-07-28
The Terascale Optimal PDE Solvers (TOPS) Integrated Software Infrastructure Center (ISIC) was created to develop and implement algorithms and support scientific investigations performed by DOE-sponsored researchers. These simulations often involve the solution of partial differential equations (PDEs) on terascale computers. The TOPS Center researched, developed and deployed an integrated toolkit of open-source, optimal complexity solvers for the nonlinear partial differential equations that arise in many DOE application areas, including fusion, accelerator design, global climate change and reactive chemistry. The algorithms created as part of this project were also designed to reduce current computational bottlenecks by orders of magnitude on terascale computers, enabling scientific simulation on a scale heretofore impossible.
NASA Technical Reports Server (NTRS)
Laird, Philip
1992-01-01
We distinguish static and dynamic optimization of programs: whereas static optimization modifies a program before runtime and is based only on its syntactical structure, dynamic optimization is based on the statistical properties of the input source and examples of program execution. Explanation-based generalization is a commonly used dynamic optimization method, but its effectiveness as a speedup-learning method is limited, in part because it fails to separate the learning process from the program transformation process. This paper describes a dynamic optimization technique called a learn-optimize cycle that first uses a learning element to uncover predictable patterns in the program execution and then uses an optimization algorithm to map these patterns into beneficial transformations. The technique has been used successfully for dynamic optimization of pure Prolog.
Global Simulation of Aviation Operations
NASA Technical Reports Server (NTRS)
Sridhar, Banavar; Sheth, Kapil; Ng, Hok Kwan; Morando, Alex; Li, Jinhua
2016-01-01
The simulation and analysis of global air traffic is limited due to a lack of simulation tools and the difficulty in accessing data sources. This paper provides a global simulation of aviation operations combining flight plans and real air traffic data with historical commercial city-pair aircraft type and schedule data and global atmospheric data. The resulting capability extends the simulation and optimization functions of NASA's Future Air Traffic Management Concept Evaluation Tool (FACET) to global scale. This new capability is used to present results on the evolution of global air traffic patterns from a concentration of traffic inside US, Europe and across the Atlantic Ocean to a more diverse traffic pattern across the globe with accelerated growth in Asia, Australia, Africa and South America. The simulation analyzes seasonal variation in the long-haul wind-optimal traffic patterns in six major regions of the world and provides potential time-savings of wind-optimal routes compared with either great circle routes or current flight-plans if available.
ERIC Educational Resources Information Center
McCoubrey, Sharon
1994-01-01
This theme issue focuses on topics related to global issues. (1) "Recycling for Art Projects" (Wendy Stephenson) gives an argument for recycling in the art classroom; (2) "Winds of Change: Tradition and Innovation in Circumpolar Art" (Bill Zuk and Robert Dalton) includes profiles of Alaskan Yupik artist, Larry Beck, who creates art from recycled…
ERIC Educational Resources Information Center
Hileman, Bette
1989-01-01
States the foundations of the theory of global warming. Describes methodologies used to measure the changes in the atmosphere. Discusses steps currently being taken in the United States and the world to slow the warming trend. Recognizes many sources for the warming and the possible effects on the earth. (MVL)
ERIC Educational Resources Information Center
Eichman, Julia Christensen; Brown, Jeff A.
1994-01-01
Presents information and data on an experiment designed to test whether different atmosphere compositions are affected by light and temperature during both cooling and heating. Although flawed, the experiment should help students appreciate the difficulties that researchers face when trying to find evidence of global warming. (PR)
,
1993-01-01
Global change is a relatively new area of scientific study using research from many disciplines to determine how Earth systems change, and to assess the influence of human activity on these changes. This teaching packet consists of a poster and three activity sheets. In teaching these activities four themes are important: time, change, cycles, and Earth as home.
Panwapa: Global Kids, Global Connections
ERIC Educational Resources Information Center
Berson, Ilene R.; Berson, Michael J.
2009-01-01
Panwapa, created by the Sesame Street Workshop of PBS, is an example of an initiative on the Internet designed to enhance students' learning by exposing them to global communities. Panwapa means "Here on Earth" in Tshiluba, a Bantu language spoken in the Democratic Republic of Congo. At the Panwapa website, www.panwapa.org, children aged four to…
ERIC Educational Resources Information Center
Boulard, Garry
2010-01-01
In a move to increase its out-of-state and international student enrollment, officials at the University of Iowa are stepping up their global recruitment efforts--even in the face of criticism that the school may be losing sight of its mission. The goal is to increase enrollment across the board, with both in-state as well as out-of-state and…
2006-02-23
The Global Arrays (GA) toolkit provides an efficient and portable shared-memory programming interface for distributed-memory computers. Each process in a MIMD parallel program can asynchronously access logical blocks of physically distributed dense multi-dimensional arrays, without need for explicit cooperation by other processes. Unlike other shared-memory environments, the GA model exposes to the programmer the non-uniform memory access (NUMA) characteristics of the high performance computers and acknowledges that access to a remote portion of the sharedmore » data is slower than to the local portion. The locality information for the shared data is available, and a direct access to the local portions of shared data is provided. Global Arrays have been designed to complement rather than substitute for the message-passing programming model. The programmer is free to use both the shared-memory and message-passing paradigms in the same program, and to take advantage of existing message-passing software libraries. Global Arrays are compatible with the Message Passing Interface (MPI).« less
Carver, Charles S; Scheier, Michael F
2014-06-01
Optimism is a cognitive construct (expectancies regarding future outcomes) that also relates to motivation: optimistic people exert effort, whereas pessimistic people disengage from effort. Study of optimism began largely in health contexts, finding positive associations between optimism and markers of better psychological and physical health. Physical health effects likely occur through differences in both health-promoting behaviors and physiological concomitants of coping. Recently, the scientific study of optimism has extended to the realm of social relations: new evidence indicates that optimists have better social connections, partly because they work harder at them. In this review, we examine the myriad ways this trait can benefit an individual, and our current understanding of the biological basis of optimism.
A survey of compiler optimization techniques
NASA Technical Reports Server (NTRS)
Schneck, P. B.
1972-01-01
Major optimization techniques of compilers are described and grouped into three categories: machine dependent, architecture dependent, and architecture independent. Machine-dependent optimizations tend to be local and are performed upon short spans of generated code by using particular properties of an instruction set to reduce the time or space required by a program. Architecture-dependent optimizations are global and are performed while generating code. These optimizations consider the structure of a computer, but not its detailed instruction set. Architecture independent optimizations are also global but are based on analysis of the program flow graph and the dependencies among statements of source program. A conceptual review of a universal optimizer that performs architecture-independent optimizations at source-code level is also presented.
Krishnamoorthy, Sriram; Daily, Jeffrey A.; Vishnu, Abhinav; Palmer, Bruce J.
2015-11-01
Global Arrays (GA) is a distributed-memory programming model that allows for shared-memory-style programming combined with one-sided communication, to create a set of tools that combine high performance with ease-of-use. GA exposes a relatively straightforward programming abstraction, while supporting fully-distributed data structures, locality of reference, and high-performance communication. GA was originally formulated in the early 1990’s to provide a communication layer for the Northwest Chemistry (NWChem) suite of chemistry modeling codes that was being developed concurrently.
Environmental economics: Optimal carbon tax doubled
NASA Astrophysics Data System (ADS)
Warren, Rachel
2014-07-01
Cost-benefit analysis and risk assessment approaches inform global climate change mitigation policy-making processes. Now, a development in the former shows that optimal carbon tax levels have previously been underestimated by a factor of two.
Adaptive approximation models in optimization
Voronin, A.N.
1995-05-01
The paper proposes a method for optimization of functions of several variables that substantially reduces the number of objective function evaluations compared to traditional methods. The method is based on the property of iterative refinement of approximation models of the optimand function in approximation domains that contract to the extremum point. It does not require subjective specification of the starting point, step length, or other parameters of the search procedure. The method is designed for efficient optimization of unimodal functions of several (not more than 10-15) variables and can be applied to find the global extremum of polymodal functions and also for optimization of scalarized forms of vector objective functions.
Global teaching of global seismology
NASA Astrophysics Data System (ADS)
Stein, S.; Wysession, M.
2005-12-01
Our recent textbook, Introduction to Seismology, Earthquakes, & Earth Structure (Blackwell, 2003) is used in many countries. Part of the reason for this may be our deliberate attempt to write the book for an international audience. This effort appears in several ways. We stress seismology's long tradition of global data interchange. Our brief discussions of the science's history illustrate the contributions of scientists around the world. Perhaps most importantly, our discussions of earthquakes, tectonics, and seismic hazards take a global view. Many examples are from North America, whereas others are from other areas. Our view is that non-North American students should be exposed to North American examples that are type examples, and that North American students should be similarly exposed to examples elsewhere. For example, we illustrate how the Euler vector geometry changes a plate boundary from spreading, to strike-slip, to convergence using both the Pacific-North America boundary from the Gulf of California to Alaska and the Eurasia-Africa boundary from the Azores to the Mediterranean. We illustrate diffuse plate boundary zones using western North America, the Andes, the Himalayas, the Mediterranean, and the East Africa Rift. The subduction zone discussions examine Japan, Tonga, and Chile. We discuss significant earthquakes both in the U.S. and elsewhere, and explore hazard mitigation issues in different contexts. Both comments from foreign colleagues and our experience lecturing overseas indicate that this approach works well. Beyond the specifics of our text, we believe that such a global approach is facilitated by the international traditions of the earth sciences and the world youth culture that gives students worldwide common culture. For example, a video of the scene in New Madrid, Missouri that arose from a nonsensical earthquake prediction in 1990 elicits similar responses from American and European students.
NASA Technical Reports Server (NTRS)
Macready, William; Wolpert, David
2005-01-01
We demonstrate a new framework for analyzing and controlling distributed systems, by solving constrained optimization problems with an algorithm based on that framework. The framework is ar. information-theoretic extension of conventional full-rationality game theory to allow bounded rational agents. The associated optimization algorithm is a game in which agents control the variables of the optimization problem. They do this by jointly minimizing a Lagrangian of (the probability distribution of) their joint state. The updating of the Lagrange parameters in that Lagrangian is a form of automated annealing, one that focuses the multi-agent system on the optimal pure strategy. We present computer experiments for the k-sat constraint satisfaction problem and for unconstrained minimization of NK functions.
Optimal Foraging in Semantic Memory
ERIC Educational Resources Information Center
Hills, Thomas T.; Jones, Michael N.; Todd, Peter M.
2012-01-01
Do humans search in memory using dynamic local-to-global search strategies similar to those that animals use to forage between patches in space? If so, do their dynamic memory search policies correspond to optimal foraging strategies seen for spatial foraging? Results from a number of fields suggest these possibilities, including the shared…
Optimal Preprocessing Of GPS Data
NASA Technical Reports Server (NTRS)
Wu, Sien-Chong; Melbourne, William G.
1994-01-01
Improved technique for preprocessing data from Global Positioning System (GPS) receiver reduces processing time and number of data to be stored. Technique optimal in sense it maintains strength of data. Also sometimes increases ability to resolve ambiguities in numbers of cycles of received GPS carrier signals.
Optimal Preprocessing Of GPS Data
NASA Technical Reports Server (NTRS)
Wu, Sien-Chong; Melbourne, William G.
1994-01-01
Improved technique for preprocessing data from Global Positioning System receiver reduces processing time and number of data to be stored. Optimal in sense that it maintains strength of data. Also increases ability to resolve ambiguities in numbers of cycles of received GPS carrier signals.
NASA Technical Reports Server (NTRS)
Douglas, I.
1985-01-01
Any global view of landforms must include an evaluation of the link between plate tectonics and geomorphology. To explain the broad features of the continents and ocean floors, a basic distinction between the tectogene and cratogene part of the Earth's surface must be made. The tectogene areas are those that are dominated by crustal movements, earthquakes and volcanicity at the present time and are essentially those of the great mountain belts and mid ocean ridges. Cratogene areas comprise the plate interiors, especially the old lands of Gondwanaland and Laurasia. Fundamental as this division between plate margin areas and plate interiors is, it cannot be said to be a simple case of a distinction between tectonically active and stable areas. Indeed, in terms of megageomorphology, former plate margins and tectonic activity up to 600 million years ago have to be considered.
NASA Astrophysics Data System (ADS)
Houghton, John
2005-06-01
'Global warming' is a phrase that refers to the effect on the climate of human activities, in particular the burning of fossil fuels (coal, oil and gas) and large-scale deforestation, which cause emissions to the atmosphere of large amounts of 'greenhouse gases', of which the most important is carbon dioxide. Such gases absorb infrared radiation emitted by the Earth's surface and act as blankets over the surface keeping it warmer than it would otherwise be. Associated with this warming are changes of climate. The basic science of the 'greenhouse effect' that leads to the warming is well understood. More detailed understanding relies on numerical models of the climate that integrate the basic dynamical and physical equations describing the complete climate system. Many of the likely characteristics of the resulting changes in climate (such as more frequent heat waves, increases in rainfall, increase in frequency and intensity of many extreme climate events) can be identified. Substantial uncertainties remain in knowledge of some of the feedbacks within the climate system (that affect the overall magnitude of change) and in much of the detail of likely regional change. Because of its negative impacts on human communities (including for instance substantial sea-level rise) and on ecosystems, global warming is the most important environmental problem the world faces. Adaptation to the inevitable impacts and mitigation to reduce their magnitude are both necessary. International action is being taken by the world's scientific and political communities. Because of the need for urgent action, the greatest challenge is to move rapidly to much increased energy efficiency and to non-fossil-fuel energy sources.
MacMillan, Ian C; van Putten, Alexander B; McGrath, Rita Gunther
2003-05-01
Competition among multinationals these days is likely to be a three-dimensional game of global chess: The moves an organization makes in one market are designed to achieve goals in another in ways that aren't immediately apparent to its rivals. The authors--all management professors-call this approach "competing under strategic interdependence," or CSI. And where this interdependence exists, the complexity of the situation can quickly overwhelm ordinary analysis. Indeed, most business strategists are terrible at anticipating the consequences of interdependent choices, and they're even worse at using interdependency to their advantage. In this article, the authors offer a process for mapping the competitive landscape and anticipating how your company's moves in one market can influence its competitive interactions in others. They outline the six types of CSI campaigns--onslaughts, contests, guerrilla campaigns, feints, gambits, and harvesting--available to any multiproduct or multimarket corporation that wants to compete skillfully. They cite real-world examples such as the U.S. pricing battle Philip Morris waged with R.J. Reynolds--not to gain market share in the domestic cigarette market but to divert R.J. Reynolds's resources and attention from the opportunities Philip Morris was pursuing in Eastern Europe. And, using data they collected from their studies of consumer-products companies Procter & Gamble and Unilever, the authors describe how to create CSI tables and bubble charts that present a graphical look at the competitive landscape and that may uncover previously hidden opportunities. The CSI mapping process isn't just for global corporations, the authors explain. Smaller organizations that compete with a portfolio of products in just one national or regional market may find it just as useful for planning their next business moves.
Sejnowski, Terrence J.; Poizner, Howard; Lynch, Gary; Gepshtein, Sergei; Greenspan, Ralph J.
2014-01-01
Human performance approaches that of an ideal observer and optimal actor in some perceptual and motor tasks. These optimal abilities depend on the capacity of the cerebral cortex to store an immense amount of information and to flexibly make rapid decisions. However, behavior only approaches these limits after a long period of learning while the cerebral cortex interacts with the basal ganglia, an ancient part of the vertebrate brain that is responsible for learning sequences of actions directed toward achieving goals. Progress has been made in understanding the algorithms used by the brain during reinforcement learning, which is an online approximation of dynamic programming. Humans also make plans that depend on past experience by simulating different scenarios, which is called prospective optimization. The same brain structures in the cortex and basal ganglia that are active online during optimal behavior are also active offline during prospective optimization. The emergence of general principles and algorithms for goal-directed behavior has consequences for the development of autonomous devices in engineering applications. PMID:25328167
Sghaier, W; Hergon, E; Desroches, A
2015-08-01
Risk management is a fundamental component of any successful company, whether it is in economic, societal or environmental aspect. Risk management is an especially important activity for companies that optimal security challenge of products and services is great. This is the case especially for the health sector institutions. Risk management is therefore a decision support tool and a means to ensure the sustainability of an organization. In this context, what methods and approaches implemented to manage the risks? Through this state of the art, we are interested in the concept of risk and risk management processes. Then we focus on the different methods of risk management and the criteria for choosing among these methods. Finally we highlight the need to supplement these methods by a systemic and global approach including through risk assessment by the audits.
NASA Technical Reports Server (NTRS)
Megie, G.; Chanin, M.-L.; Ehhalt, D.; Fraser, P.; Frederick, J. F.; Gille, J. C.; Mccormick, M. P.; Schoebert, M.; Bishop, L.; Bojkov, R. D.
1990-01-01
Measuring trends in ozone, and most other geophysical variables, requires that a small systematic change with time be determined from signals that have large periodic and aperiodic variations. Their time scales range from the day-to-day changes due to atmospheric motions through seasonal and annual variations to 11 year cycles resulting from changes in the sun UV output. Because of the magnitude of all of these variations is not well known and highly variable, it is necessary to measure over more than one period of the variations to remove their effects. This means that at least 2 or more times the 11 year sunspot cycle. Thus, the first requirement is for a long term data record. The second related requirement is that the record be consistent. A third requirement is for reasonable global sampling, to ensure that the effects are representative of the entire Earth. The various observational methods relevant to trend detection are reviewed to characterize their quality and time and space coverage. Available data are then examined for long term trends or recent changes in ozone total content and vertical distribution, as well as related parameters such as stratospheric temperature, source gases and aerosols.
Damon, P E; Kunen, S M
1976-08-01
The world's inhabitants, including Scientists, live primarily in the Northern Hemisphere. It is quite natural to be concerned about events that occur close to home and neglect faraway events. Hence, it is not surprising that so little attention has been given to the Southern Hemisphere. Evidence for global cooling has been based, in large part, on a severe cooling trend at high northern latitudes. This article points out that the Northern Hemisphere cooling trend appears to be out of phase with a warming trend at high latitudes in the Southern Hemisphere. The data are scanty. We cannot be sure that these temperature fluctuations are be not the result of natural causes. How it seems most likely that human activity has already significantly perturbed the atmospheric weather system. The effect of particulate matter pollution should be most severe in the highly populated and industrialized Northern Hemisphere. Because of the rapid diffusion of CO(2) molecules within the atmosphere, both hemispheres will be subject to warming due to the atmospheric (greenhouse) effect as the CO(2) content of the atmosphere builds up from the combustion of fossil fuels. Because of the differential effects of the two major sources of atmospheric pollution, the CO(2) greenhouse effect warming trend should first become evident in the Southern Hemisphere. The socioeconomic and political consequences of climate change are profound. We need an early warning system such as would be provided by a more intensive international world weather watch, particularly at high northern and southern latitudes.
Lee, John R.
1975-01-01
Optimal fluoridation has been defined as that fluoride exposure which confers maximal cariostasis with minimal toxicity and its values have been previously determined to be 0.5 to 1 mg per day for infants and 1 to 1.5 mg per day for an average child. Total fluoride ingestion and urine excretion were studied in Marin County, California, children in 1973 before municipal water fluoridation. Results showed fluoride exposure to be higher than anticipated and fulfilled previously accepted criteria for optimal fluoridation. Present and future water fluoridation plans need to be reevaluated in light of total environmental fluoride exposure. PMID:1130041
Coupled Low-thrust Trajectory and System Optimization via Multi-Objective Hybrid Optimal Control
NASA Technical Reports Server (NTRS)
Vavrina, Matthew A.; Englander, Jacob Aldo; Ghosh, Alexander R.
2015-01-01
The optimization of low-thrust trajectories is tightly coupled with the spacecraft hardware. Trading trajectory characteristics with system parameters ton identify viable solutions and determine mission sensitivities across discrete hardware configurations is labor intensive. Local independent optimization runs can sample the design space, but a global exploration that resolves the relationships between the system variables across multiple objectives enables a full mapping of the optimal solution space. A multi-objective, hybrid optimal control algorithm is formulated using a multi-objective genetic algorithm as an outer loop systems optimizer around a global trajectory optimizer. The coupled problem is solved simultaneously to generate Pareto-optimal solutions in a single execution. The automated approach is demonstrated on two boulder return missions.
Multilevel algorithms for nonlinear optimization
NASA Technical Reports Server (NTRS)
Alexandrov, Natalia; Dennis, J. E., Jr.
1994-01-01
Multidisciplinary design optimization (MDO) gives rise to nonlinear optimization problems characterized by a large number of constraints that naturally occur in blocks. We propose a class of multilevel optimization methods motivated by the structure and number of constraints and by the expense of the derivative computations for MDO. The algorithms are an extension to the nonlinear programming problem of the successful class of local Brown-Brent algorithms for nonlinear equations. Our extensions allow the user to partition constraints into arbitrary blocks to fit the application, and they separately process each block and the objective function, restricted to certain subspaces. The methods use trust regions as a globalization strategy, and they have been shown to be globally convergent under reasonable assumptions. The multilevel algorithms can be applied to all classes of MDO formulations. Multilevel algorithms for solving nonlinear systems of equations are a special case of the multilevel optimization methods. In this case, they can be viewed as a trust-region globalization of the Brown-Brent class.
Sui, Yue; Huang, Wan-Hua; Yang, Xiao-Guang; Li, Mao-Song
2013-11-01
Southern China is an important agricultural planting region of China, but the seasonal drought severely impacted the regional agricultural production. Based on the 1981-2007 meteorological data from 13 typical meteorological stations in the seasonal drought areas in southern China and the data of related crops growth period and yield, three precipitation year types (drought year, normal year, and wet year) were classified based on the yearly precipitation, and by using five indices (coupling degree of water requirement and precipitation during crop water critical stages, meteorological crop yield, output value per unit area, and water use efficiency and precipitation during whole growth period), the comprehensive benefit of all possible cropping patterns in each typical region was evaluated, and the optimal cropping patterns in the different regions of southern China in different precipitation years were obtained. In the semi-arid region, the optimal cropping patterns in dry year included potato-maize-sweet potato and winter wheat-rice-sweet potato. In the semi-humid region and during dry year, winter wheat-rice-sweet potato was the best choice, and rape seed-rice-sweet potato was the second one. In the warm and humid region (the typical region where seasonal drought happened), the best cropping pattern in Jiangnan area in different precipitation years was potato-double cropping rice, and the suitable patterns in southwest area were the triple cropping systems with drought-resistant crops, such as winter wheat-rice-sweet potato, winter wheat-maize-sweet potato, and potato-double cropping rice. From the aspect of maximally utilizing water and heat resources, triple cropping would be the best choice, with the rice and upland crop rotation as the main and with the rice arranged in pairs in wet year.
Design optimization of transonic airfoils
NASA Technical Reports Server (NTRS)
Joh, C.-Y.; Grossman, B.; Haftka, R. T.
1991-01-01
Numerical optimization procedures were considered for the design of airfoils in transonic flow based on the transonic small disturbance (TSD) and Euler equations. A sequential approximation optimization technique was implemented with an accurate approximation of the wave drag based on the Nixon's coordinate straining approach. A modification of the Euler surface boundary conditions was implemented in order to efficiently compute design sensitivities without remeshing the grid. Two effective design procedures producing converged designs in approximately 10 global iterations were developed: interchanging the role of the objective function and constraint and the direct lift maximization with move limits which were fixed absolute values of the design variables.
THE IMPACTS OF GLOBAL INFRASTRUCTURE UPON GLOBAL PRODUCTION-TRADE STRUCTURE
NASA Astrophysics Data System (ADS)
Xu, Daming; Kobayashi, Kiyoshi; Matsushima, Kakuya
In this paper, a two countries model is formulated to investigate the global production-trade patters emerged by the location behaviors of multi-national enterprises. Two countries are characterized by heterogeneous labor productivities. The multi-national enterprises, heterogeneous in productivities, are supposed to provide the differentiated commodities in the monopolistically competitive global market. The enterprises purchase differentiated knowledge at the fixed cost from the international research organization. They determine production locations in the global economy. The comparative static analysis is carried out to investigate the impacts of global infrastructure arrangement, associated with the decrease of transportation costs, upon the optimal production-trade patterns in the economy.
Second-order neural nets for constrained optimization.
Zhang, S; Zhu, X; Zou, L H
1992-01-01
Analog neural nets for constrained optimization are proposed as an analogue of Newton's algorithm in numerical analysis. The neural model is globally stable and can converge to the constrained stationary points. Nonlinear neurons are introduced into the net, making it possible to solve optimization problems where the variables take discrete values, i.e., combinatorial optimization.
Multiobjective Optimization Using a Pareto Differential Evolution Approach
NASA Technical Reports Server (NTRS)
Madavan, Nateri K.; Biegel, Bryan A. (Technical Monitor)
2002-01-01
Differential Evolution is a simple, fast, and robust evolutionary algorithm that has proven effective in determining the global optimum for several difficult single-objective optimization problems. In this paper, the Differential Evolution algorithm is extended to multiobjective optimization problems by using a Pareto-based approach. The algorithm performs well when applied to several test optimization problems from the literature.
Wind Farm Turbine Type and Placement Optimization
NASA Astrophysics Data System (ADS)
Graf, Peter; Dykes, Katherine; Scott, George; Fields, Jason; Lunacek, Monte; Quick, Julian; Rethore, Pierre-Elouan
2016-09-01
The layout of turbines in a wind farm is already a challenging nonlinear, nonconvex, nonlinearly constrained continuous global optimization problem. Here we begin to address the next generation of wind farm optimization problems by adding the complexity that there is more than one turbine type to choose from. The optimization becomes a nonlinear constrained mixed integer problem, which is a very difficult class of problems to solve. This document briefly summarizes the algorithm and code we have developed, the code validation steps we have performed, and the initial results for multi-turbine type and placement optimization (TTP_OPT) we have run.
NASA Technical Reports Server (NTRS)
Patterson, Michael J.; Mohajeri, Kayhan
1991-01-01
The preliminary results of a test program to optimize a neutralizer design for 30 cm xenon ion thrusters are discussed. The impact of neutralizer geometry, neutralizer axial location, and local magnetic fields on neutralizer performance is discussed. The effect of neutralizer performance on overall thruster performance is quantified, for thruster operation in the 0.5-3.2 kW power range. Additionally, these data are compared to data published for other north-south stationkeeping (NSSK) and primary propulsion xenon ion thruster neutralizers.
Feasible optimality implies Hack's Law
NASA Astrophysics Data System (ADS)
Rigon, Riccardo; Rodriguez-Iturbe, Ignacio; Rinaldo, Andrea
1998-11-01
We analyze the elongation (the scaling properties of drainage area with mainstream length) in optimal channel networks (OCNs) obtained through different algorithms searching for the minimum of a functional computing the total energy dissipation of the drainage system. The algorithms have different capabilities to overcome the imprinting of initial and boundary conditions, and thus they have different chances of attaining the global optimum. We find that suboptimal shapes, i.e., dynamically accessible states characterized by locally stationary total potential energy, show the robust type of elongation that is consistently observed in nature. This suggestive and directly measurable property is not found in the so-called ground state, i.e., the global minimum, whose features, including elongation, are known exactly. The global minimum is shown to be too regular and symmetric to be dynamically accessible in nature, owing to features and constraints of erosional processes. Thus Hack's law is seen as a signature of feasible optimality thus yielding further support to the suggestion that optimality of the system as a whole explains the dynamic origin of fractal forms in nature.
An Attainable Global Perspective.
ERIC Educational Resources Information Center
de Castaneda, Viann Pedersen
Concordia College (Minnesota) has established a global studies curriculum that encourages the development of a global perspective in future business leaders. Global perspective is seen as having five dimensions: (1) perspective consciousness; (2) "state of the planet" awareness; (3) cross-cultural awareness; (4) knowledge of global dynamics; and…
Macitentan: first global approval.
Patel, Trina; McKeage, Kate
2014-01-01
Macitentan (Opsumit®) is a novel dual endothelin receptor antagonist (ERA) with sustained receptor binding properties developed by Actelion Pharmaceuticals Ltd. In October 2013, oral macitentan 10 mg once daily received its first global approval in the US, followed closely by Canada, for the treatment of pulmonary arterial hypertension (PAH). The drug has also received a positive opinion in the EU from the Committee for Medicinal Products for Human Use for the treatment of PAH, and is under regulatory review in several other countries for the same indication. Endothelin (ET)-1 influences pathological changes via two ET receptor subtypes (ETA and ETB), to which it binds with high affinity. ET-1 is implicated in several forms of vascular disease making it a valid target for the treatment of pulmonary vascular diseases such as PAH. Clinical development is underway for other indications, including Eisenmenger syndrome, ischaemic digital ulcers secondary to systemic sclerosis, and glioblastoma. Macitentan was also evaluated in idiopathic pulmonary fibrosis; however, a phase 2 trial did not meet its primary endpoint and further investigation in this indication was discontinued. Macitentan was developed by modifying the structure of bosentan in the search for an optimal dual ERA with improved efficacy and tolerability compared with other ERAs. This article summarizes the milestones in the development of macitentan leading to this first approval for PAH.
One-dimensional global search: Nature-inspired vs. Lipschitz methods
NASA Astrophysics Data System (ADS)
Kvasov, Dmitri E.; Mukhametzhanov, Marat S.
2016-06-01
Lipschitz global optimization appears in many practical problems: decision making, optimal control, stability problems, finding the minimal root problems, etc. In many engineering applications the objective function is a "black-box", multiextremal, non-differentiable and hard to evaluate. Another common property of the function to be optimized very often is the Lipschitz condition. In this talk, the Lipschitz global optimization problem is considered and several nature-inspired and Lipschitz global optimization algorithms are briefly described and compared with respect to the number of evaluations of the objective function.
[SIAM conference on optimization
Not Available
1992-05-10
Abstracts are presented of 63 papers on the following topics: large-scale optimization, interior-point methods, algorithms for optimization, problems in control, network optimization methods, and parallel algorithms for optimization problems.
Globalization and human cooperation.
Buchan, Nancy R; Grimalda, Gianluca; Wilson, Rick; Brewer, Marilynn; Fatas, Enrique; Foddy, Margaret
2009-03-17
Globalization magnifies the problems that affect all people and that require large-scale human cooperation, for example, the overharvesting of natural resources and human-induced global warming. However, what does globalization imply for the cooperation needed to address such global social dilemmas? Two competing hypotheses are offered. One hypothesis is that globalization prompts reactionary movements that reinforce parochial distinctions among people. Large-scale cooperation then focuses on favoring one's own ethnic, racial, or language group. The alternative hypothesis suggests that globalization strengthens cosmopolitan attitudes by weakening the relevance of ethnicity, locality, or nationhood as sources of identification. In essence, globalization, the increasing interconnectedness of people worldwide, broadens the group boundaries within which individuals perceive they belong. We test these hypotheses by measuring globalization at both the country and individual levels and analyzing the relationship between globalization and individual cooperation with distal others in multilevel sequential cooperation experiments in which players can contribute to individual, local, and/or global accounts. Our samples were drawn from the general populations of the United States, Italy, Russia, Argentina, South Africa, and Iran. We find that as country and individual levels of globalization increase, so too does individual cooperation at the global level vis-à-vis the local level. In essence, "globalized" individuals draw broader group boundaries than others, eschewing parochial motivations in favor of cosmopolitan ones. Globalization may thus be fundamental in shaping contemporary large-scale cooperation and may be a positive force toward the provision of global public goods. PMID:19255433
Globalization and human cooperation.
Buchan, Nancy R; Grimalda, Gianluca; Wilson, Rick; Brewer, Marilynn; Fatas, Enrique; Foddy, Margaret
2009-03-17
Globalization magnifies the problems that affect all people and that require large-scale human cooperation, for example, the overharvesting of natural resources and human-induced global warming. However, what does globalization imply for the cooperation needed to address such global social dilemmas? Two competing hypotheses are offered. One hypothesis is that globalization prompts reactionary movements that reinforce parochial distinctions among people. Large-scale cooperation then focuses on favoring one's own ethnic, racial, or language group. The alternative hypothesis suggests that globalization strengthens cosmopolitan attitudes by weakening the relevance of ethnicity, locality, or nationhood as sources of identification. In essence, globalization, the increasing interconnectedness of people worldwide, broadens the group boundaries within which individuals perceive they belong. We test these hypotheses by measuring globalization at both the country and individual levels and analyzing the relationship between globalization and individual cooperation with distal others in multilevel sequential cooperation experiments in which players can contribute to individual, local, and/or global accounts. Our samples were drawn from the general populations of the United States, Italy, Russia, Argentina, South Africa, and Iran. We find that as country and individual levels of globalization increase, so too does individual cooperation at the global level vis-à-vis the local level. In essence, "globalized" individuals draw broader group boundaries than others, eschewing parochial motivations in favor of cosmopolitan ones. Globalization may thus be fundamental in shaping contemporary large-scale cooperation and may be a positive force toward the provision of global public goods.
Assessing the impact of global price interdependencies.
Richter, Anke
2008-01-01
Documented launch delays and the ensuing debate over their underlying causes have focused on assessment from the individual country's perspective. Seen in a larger game theoretical framework this may cause problems, because although the countries see an individual game, the pharmaceutical firm sees a repeated linked game. The links are due to external reference pricing and parallel trade. Behaviours that are optimal in the single, individual game (for either the country or the pharmaceutical firm) may no longer be optimal when considering the global repeated game. A theoretical mixed integer linear model of the firm's launch and pricing decisions is presented along with examples wherein international price dependencies most likely played a role. This model can help countries understand the implication of their external reference pricing policies on the global repeated pricing game. Understanding the behaviour of the pharmaceutical firm in this global context aids countries in designing policies to maximize the welfare of their citizens.
An overview of the optimization modelling applications
NASA Astrophysics Data System (ADS)
Singh, Ajay
2012-10-01
SummaryThe optimal use of available resources is of paramount importance in the backdrop of the increasing food, fiber, and other demands of the burgeoning global population and the shrinking resources. The optimal use of these resources can be determined by employing an optimization technique. The comprehensive reviews on the use of various programming techniques for the solution of different optimization problems have been provided in this paper. The past reviews are grouped into nine sections based on the solutions of the theme-based real world problems. The sections include: use of optimization modelling for conjunctive use planning, groundwater management, seawater intrusion management, irrigation management, achieving optimal cropping pattern, management of reservoir systems operation, management of resources in arid and semi-arid regions, solid waste management, and miscellaneous uses which comprise, managing problems of hydropower generation and sugar industry. Conclusions are drawn where gaps exist and more research needs to be focused.
Tractable Pareto Optimization of Temporal Preferences
NASA Technical Reports Server (NTRS)
Morris, Robert; Morris, Paul; Khatib, Lina; Venable, Brent
2003-01-01
This paper focuses on temporal constraint problems where the objective is to optimize a set of local preferences for when events occur. In previous work, a subclass of these problems has been formalized as a generalization of Temporal CSPs, and a tractable strategy for optimization has been proposed, where global optimality is defined as maximizing the minimum of the component preference values. This criterion for optimality, which we call 'Weakest Link Optimization' (WLO), is known to have limited practical usefulness because solutions are compared only on the basis of their worst value; thus, there is no requirement to improve the other values. To address this limitation, we introduce a new algorithm that re-applies WLO iteratively in a way that leads to improvement of all the values. We show the value of this strategy by proving that, with suitable preference functions, the resulting solutions are Pareto Optimal.
Adaptation and optimization of biological transport networks.
Hu, Dan; Cai, David
2013-09-27
It has been hypothesized that topological structures of biological transport networks are consequences of energy optimization. Motivated by experimental observation, we propose that adaptation dynamics may underlie this optimization. In contrast to the global nature of optimization, our adaptation dynamics responds only to local information and can naturally incorporate fluctuations in flow distributions. The adaptation dynamics minimizes the global energy consumption to produce optimal networks, which may possess hierarchical loop structures in the presence of strong fluctuations in flow distribution. We further show that there may exist a new phase transition as there is a critical open probability of sinks, above which there are only trees for network structures whereas below which loops begin to emerge.
Optimal Network Modularity for Information Diffusion
NASA Astrophysics Data System (ADS)
Nematzadeh, Azadeh; Ferrara, Emilio; Flammini, Alessandro; Ahn, Yong-Yeol
2014-08-01
We investigate the impact of community structure on information diffusion with the linear threshold model. Our results demonstrate that modular structure may have counterintuitive effects on information diffusion when social reinforcement is present. We show that strong communities can facilitate global diffusion by enhancing local, intracommunity spreading. Using both analytic approaches and numerical simulations, we demonstrate the existence of an optimal network modularity, where global diffusion requires the minimal number of early adopters.
Transforming Academic Globalization into Globalization for All
ERIC Educational Resources Information Center
Ramalhoto, M. F.
2006-01-01
Driving innovation and continuous improvement with regard to ecological, environmental and human sustainability is essential for win-win globalization. That calls for research on strategic and monitoring planning to manage globalization and technological and scientific change. This paper describes a new basic function of the university institution…
Globalization, global health, and access to healthcare.
Collins, Téa
2003-01-01
It is now commonly realized that the globalization of the world economy is shaping the patterns of global health, and that associated morbidity and mortality is affecting countries' ability to achieve economic growth. The globalization of public health has important implications for access to essential healthcare. The rise of inequalities among and within countries negatively affects access to healthcare. Poor people use healthcare services less frequently when sick than do the rich. The negative impact of globalization on access to healthcare is particularly well demonstrated in countries of transitional economies. No longer protected by a centralized health sector that provided free universal access to services for everyone, large segments of the populations in the transition period found themselves denied even the most basic medical services. Only countries where regulatory institutions are strong, domestic markets are competitive and social safety nets are in place, have a good chance to enjoy the health benefits of globalization.
Adaptive cuckoo search algorithm for unconstrained optimization.
Ong, Pauline
2014-01-01
Modification of the intensification and diversification approaches in the recently developed cuckoo search algorithm (CSA) is performed. The alteration involves the implementation of adaptive step size adjustment strategy, and thus enabling faster convergence to the global optimal solutions. The feasibility of the proposed algorithm is validated against benchmark optimization functions, where the obtained results demonstrate a marked improvement over the standard CSA, in all the cases. PMID:25298971
Adaptive cuckoo search algorithm for unconstrained optimization.
Ong, Pauline
2014-01-01
Modification of the intensification and diversification approaches in the recently developed cuckoo search algorithm (CSA) is performed. The alteration involves the implementation of adaptive step size adjustment strategy, and thus enabling faster convergence to the global optimal solutions. The feasibility of the proposed algorithm is validated against benchmark optimization functions, where the obtained results demonstrate a marked improvement over the standard CSA, in all the cases.
Semiclassical guided optimal control of molecular dynamics
Kondorskiy, A.; Mil'nikov, G.; Nakamura, H.
2005-10-15
An efficient semiclassical optimal control theory applicable to multidimensional systems is formulated for controlling wave packet dynamics on a single adiabatic potential energy surface. The approach combines advantages of different formulations of optimal control theory: quantum and classical on one hand and global and local on the other. Numerical applications to the control of HCN-CNH isomerization demonstrate that this theory can provide an efficient tool to manipulate molecular dynamics of many degrees of freedom by laser pulses.
NASA Astrophysics Data System (ADS)
Allahverdyan, Armen E.; Hovhannisyan, Karen; Mahler, Guenter
2010-05-01
We study a refrigerator model which consists of two n -level systems interacting via a pulsed external field. Each system couples to its own thermal bath at temperatures Th and Tc , respectively (θ≡Tc/Th<1) . The refrigerator functions in two steps: thermally isolated interaction between the systems driven by the external field and isothermal relaxation back to equilibrium. There is a complementarity between the power of heat transfer from the cold bath and the efficiency: the latter nullifies when the former is maximized and vice versa. A reasonable compromise is achieved by optimizing the product of the heat-power and efficiency over the Hamiltonian of the two systems. The efficiency is then found to be bounded from below by ζCA=(1)/(1-θ)-1 (an analog of the Curzon-Ahlborn efficiency), besides being bound from above by the Carnot efficiency ζC=(1)/(1-θ)-1 . The lower bound is reached in the equilibrium limit θ→1 . The Carnot bound is reached (for a finite power and a finite amount of heat transferred per cycle) for lnn≫1 . If the above maximization is constrained by assuming homogeneous energy spectra for both systems, the efficiency is bounded from above by ζCA and converges to it for n≫1 .
Essential Web Sites to Research the Globalization Process
ERIC Educational Resources Information Center
Scott, Thomas J.; O'Sullivan, Michael
2002-01-01
The terrorist attacks of September 11, 2001, brought a stark reality to social studies classrooms throughout the United States. Globalism and the expansion of world trade relations created optimism about enhanced cultural understanding, peace, and economic prosperity. However, it is clear that globalization also has a dark side. Suddenly…
NASA Astrophysics Data System (ADS)
Qin, Sitian; Fan, Dejun; Su, Peng; Liu, Qinghe
2014-04-01
In this paper, the optimization techniques for solving pseudoconvex optimization problems are investigated. A simplified recurrent neural network is proposed according to the optimization problem. We prove that the optimal solution of the optimization problem is just the equilibrium point of the neural network, and vice versa if the equilibrium point satisfies the linear constraints. The proposed neural network is proven to be globally stable in the sense of Lyapunov and convergent to an exact optimal solution of the optimization problem. A numerical simulation is given to illustrate the global convergence of the neural network. Applications in business and chemistry are given to demonstrate the effectiveness of the neural network.
Adams, Vincanne; Novotny, Thomas E; Leslie, Hannah
2008-01-01
A variety of shifts emergent with globalization, which are reflected in part by nascent programs in "Global Public Health," "Global Health Sciences," and "Global Health," are redefining international public health. We explore three of these shifts as a critical discourse and intervention in global health diplomacy: the expansion in non-governmental organization participation in international health programs, the globalization of science and pharmaceutical research, and the use of militarized languages of biosecurity to recast public health programs. Using contemporary anthropological and international health literature, we offer a critical yet hopeful exploration of the implications of these shifts for critical inquiry, health, and the health professions.
Adaptive particle swarm optimization for optimal orbital elements of binary stars
NASA Astrophysics Data System (ADS)
Attia, Abdel-Fattah
2016-10-01
The paper presents an adaptive particle swarm optimization (APSO) as an alternative method to determine the optimal orbital elements of the star η Bootis of MK type G0 IV. The proposed algorithm transforms the problem of finding periodic orbits into the problem of detecting global minimizers as a function, to get a best fit of Keplerian and Phase curves. The experimental results demonstrate that the proposed approach of APSO generally more accurate than the standard particle swarm optimization (PSO) and other published optimization algorithms, in terms of solution accuracy, convergence speed and algorithm reliability.
Global solidarity, migration and global health inequity.
Eckenwiler, Lisa; Straehle, Christine; Chung, Ryoa
2012-09-01
The grounds for global solidarity have been theorized and conceptualized in recent years, and many have argued that we need a global concept of solidarity. But the question remains: what can motivate efforts of the international community and nation-states? Our focus is the grounding of solidarity with respect to global inequities in health. We explore what considerations could motivate acts of global solidarity in the specific context of health migration, and sketch briefly what form this kind of solidarity could take. First, we argue that the only plausible conceptualization of persons highlights their interdependence. We draw upon a conception of persons as 'ecological subjects' and from there illustrate what such a conception implies with the example of nurses migrating from low and middle-income countries to more affluent ones. Next, we address potential critics who might counter any such understanding of current international politics with a reference to real-politik and the insights of realist international political theory. We argue that national governments--while not always or even often motivated by moral reasons alone--may nevertheless be motivated to acts of global solidarity by prudential arguments. Solidarity then need not be, as many argue, a function of charitable inclination, or emergent from an acknowledgment of injustice suffered, but may in fact serve national and transnational interests. We conclude on a positive note: global solidarity may be conceptualized to helpfully address global health inequity, to the extent that personal and transnational interdependence are enough to motivate national governments into action.
IMERG Global Precipitation Rates
NASA's Global Precipitation Measurement mission has produced its first global map of rainfall and snowfall. The GPM Core Observatory launched one year ago on Feb. 27, 2014 as a collaboration betwee...
Global Health Observatory (GHO)
... repository Reports Country statistics Map gallery Standards Global Health Observatory (GHO) data Monitoring health for the SDGs ... relevant web pages on the theme. Monitoring the health goal: indicators of overall progress Mortality and global ...
Global Tuberculosis Report 2015
... Feed Youtube Twitter Facebook Google + iTunes Play Store Tuberculosis (TB) Menu Tuberculosis The End TB Strategy Areas ... data News, events and features About us Global tuberculosis report 2015 This is the twentieth global report ...
NASA Technical Reports Server (NTRS)
Levine, Joel S.
1991-01-01
Present processes of global climate change are reviewed. The processes determining global temperature are briefly described and the concept of effective temperature is elucidated. The greenhouse effect is examined, including the sources and sinks of greenhouse gases.
Trajectory optimization for the National Aerospace Plane
NASA Technical Reports Server (NTRS)
Lu, Ping
1993-01-01
The objective of this second phase research is to investigate the optimal ascent trajectory for the National Aerospace Plane (NASP) from runway take-off to orbital insertion and address the unique problems associated with the hypersonic flight trajectory optimization. The trajectory optimization problem for an aerospace plane is a highly challenging problem because of the complexity involved. Previous work has been successful in obtaining sub-optimal trajectories by using energy-state approximation and time-scale decomposition techniques. But it is known that the energy-state approximation is not valid in certain portions of the trajectory. This research aims at employing full dynamics of the aerospace plane and emphasizing direct trajectory optimization methods. The major accomplishments of this research include the first-time development of an inverse dynamics approach in trajectory optimization which enables us to generate optimal trajectories for the aerospace plane efficiently and reliably, and general analytical solutions to constrained hypersonic trajectories that has wide application in trajectory optimization as well as in guidance and flight dynamics. Optimal trajectories in abort landing and ascent augmented with rocket propulsion and thrust vectoring control were also investigated. Motivated by this study, a new global trajectory optimization tool using continuous simulated annealing and a nonlinear predictive feedback guidance law have been under investigation and some promising results have been obtained, which may well lead to more significant development and application in the near future.
Global Atmospheric Aerosol Modeling
NASA Technical Reports Server (NTRS)
Hendricks, Johannes; Aquila, Valentina; Righi, Mattia
2012-01-01
Global aerosol models are used to study the distribution and properties of atmospheric aerosol particles as well as their effects on clouds, atmospheric chemistry, radiation, and climate. The present article provides an overview of the basic concepts of global atmospheric aerosol modeling and shows some examples from a global aerosol simulation. Particular emphasis is placed on the simulation of aerosol particles and their effects within global climate models.
Optimal foraging in semantic memory.
Hills, Thomas T; Jones, Michael N; Todd, Peter M
2012-04-01
Do humans search in memory using dynamic local-to-global search strategies similar to those that animals use to forage between patches in space? If so, do their dynamic memory search policies correspond to optimal foraging strategies seen for spatial foraging? Results from a number of fields suggest these possibilities, including the shared structure of the search problems-searching in patchy environments-and recent evidence supporting a domain-general cognitive search process. To investigate these questions directly, we asked participants to recover from memory as many animal names as they could in 3 min. Memory search was modeled over a representation of the semantic search space generated from the BEAGLE memory model of Jones and Mewhort (2007), via a search process similar to models of associative memory search (e.g., Raaijmakers & Shiffrin, 1981). We found evidence for local structure (i.e., patches) in memory search and patch depletion preceding dynamic local-to-global transitions between patches. Dynamic models also significantly outperformed nondynamic models. The timing of dynamic local-to-global transitions was consistent with optimal search policies in space, specifically the marginal value theorem (Charnov, 1976), and participants who were more consistent with this policy recalled more items.
ERIC Educational Resources Information Center
Stein, Sharon
2015-01-01
The demand to cultivate global citizenship is frequently invoked as central to colleges' and universities' internationalization efforts. However, the term "global citizenship" remains undertheorized in the context of U.S. higher education. This article maps and engages three common global citizenship positions--entrepreneurial, liberal…
Globalization: Myths and Realities.
ERIC Educational Resources Information Center
McMichael, Philip
1996-01-01
Nationally oriented institutions of the developmentalist era are being replaced by globally oriented institutions under the legitimizing cloak of efficiency and financial credibility. Meanwhile, producing communities either seek niches in the global economy or resist global pressures, thereby newly emphasizing the local. Explores the conjunction…
Globalization and American Education
ERIC Educational Resources Information Center
Merriman, William; Nicoletti, Augustine
2008-01-01
Globalization is a potent force in today's world. The welfare of the United States is tied to the welfare of other countries by economics, the environment, politics, culture, information, and technology. This paper identifies the implications of globalization for education, presents applications of important aspects of globalization that teachers…
Developing Successful Global Leaders
ERIC Educational Resources Information Center
Training, 2011
2011-01-01
Everyone seems to agree the world desperately needs strong leaders who can manage a global workforce and all the inherent challenges that go with it. That's a big part of the raison d'etre for global leadership development programs. But are today's organizations fully utilizing these programs to develop global leaders, and, if so, are they…
Optimal noncoplanar interorbital single-impulse flight
NASA Astrophysics Data System (ADS)
Kirpichnikov, S. N.
1990-04-01
The paper is concerned with the problem of finding minimum-fuel orbits for single-impulse transfers between specified noncoplanar boundary Keplerian orbits. In transfers between noncoplanar circular orbits, the Hohmann single-impulse transfer in the initial orbit plane is shown to be globally optimal from the energy standpoint. Approximate optimal solutions are obtained in the practically important case of weakly elliptical boundary orbits with a small angle between the planes. The optimal solutions obtained here can be used as initial approximations when determining real orbits for interorbital and interplanetary flights.
The Microsoft Global Ortho Program
NASA Astrophysics Data System (ADS)
Walcher, W.; Leberl, F.; Gruber, M.
2012-07-01
Wide area and thus continental mapping extending beyond national borders is a novel concept in civilian photogrammetry. The Microsoft Global Ortho Program was launched in the Spring of 2009 as a result of Microsoft's need for global geo-data at a high geometric resolution and radiometric excellence. By fall of 2012 more than 10 million km2 of the USA and 14 European countries will have been covered by seamless 30 cm GSD color-, 60 cm GSD false-color infrared ortho-mosaics and a 1 meter GSD digital surface model. The ortho-maps are being published to Microsoft's Bing Maps Internet mapping portal. The Global Ortho Program was designed for highly and unprecedented automated mapping of essentially entire continents. In 2011, exclusive of flight operations, the product output per person has been measured in excess of 275,000 square km per year. We describe research efforts that made this achievement possible. Those include a specially designed aerial sensor (Ultracam G), logistics simulation for fight planning and optimization, in-flight blur detection and subsequent automatic blur removal, modeling and removal of atmospheric and environmental conditions, automated shear detection and DTM refinement, an IT architecture to process >200,000 aerial images/day, and for creating over 1,000,000 km2 ortho-imagery and DSM data in 24 hours. While addressing these issues, we provide ideas how this might affect the future of spatial infrastructure initiatives.
Global social identity and global cooperation.
Buchan, Nancy R; Brewer, Marilynn B; Grimalda, Gianluca; Wilson, Rick K; Fatas, Enrique; Foddy, Margaret
2011-06-01
This research examined the question of whether the psychology of social identity can motivate cooperation in the context of a global collective. Our data came from a multinational study of choice behavior in a multilevel public-goods dilemma conducted among samples drawn from the general populations of the United States, Italy, Russia, Argentina, South Africa, and Iran. Results demonstrate that an inclusive social identification with the world community is a meaningful psychological construct that plays a role in motivating cooperation that transcends parochial interests. Self-reported identification with the world as a whole predicts behavioral contributions to a global public good beyond what is predicted from expectations about what other people are likely to contribute. Furthermore, global social identification is conceptually distinct from general attitudes about global issues, and has unique effects on cooperative behavior.
Global social identity and global cooperation.
Buchan, Nancy R; Brewer, Marilynn B; Grimalda, Gianluca; Wilson, Rick K; Fatas, Enrique; Foddy, Margaret
2011-06-01
This research examined the question of whether the psychology of social identity can motivate cooperation in the context of a global collective. Our data came from a multinational study of choice behavior in a multilevel public-goods dilemma conducted among samples drawn from the general populations of the United States, Italy, Russia, Argentina, South Africa, and Iran. Results demonstrate that an inclusive social identification with the world community is a meaningful psychological construct that plays a role in motivating cooperation that transcends parochial interests. Self-reported identification with the world as a whole predicts behavioral contributions to a global public good beyond what is predicted from expectations about what other people are likely to contribute. Furthermore, global social identification is conceptually distinct from general attitudes about global issues, and has unique effects on cooperative behavior. PMID:21586763
Global Health and the Global Economic Crisis
Gill, Stephen; Bakker, Isabella
2011-01-01
Although the resources and knowledge for achieving improved global health exist, a new, critical paradigm on health as an aspect of human development, human security, and human rights is needed. Such a shift is required to sufficiently modify and credibly reduce the present dominance of perverse market forces on global health. New scientific discoveries can make wide-ranging contributions to improved health; however, improved global health depends on achieving greater social justice, economic redistribution, and enhanced democratization of production, caring social institutions for essential health care, education, and other public goods. As with the quest for an HIV vaccine, the challenge of improved global health requires an ambitious multidisciplinary research program. PMID:21330597
Global temperatures and the global warming ``debate''
NASA Astrophysics Data System (ADS)
Aubrecht, Gordon
2009-04-01
Many ordinary citizens listen to pronouncements on talk radio casting doubt on anthropogenic global warming. Some op-ed columnists likewise cast doubts, and are read by credulous citizens. For example, on 8 March 2009, the Boston Globe published a column by Jeff Jacoby, ``Where's global warming?'' According to Jacoby, ``But it isn't such hints of a planetary warming trend that have been piling up in profusion lately. Just the opposite.'' He goes on to write, ``the science of climate change is not nearly as important as the religion of climate change,'' and blamed Al Gore for getting his mistaken views accepted. George Will at the Washington Post also expressed denial. As a result, 44% of U.S. voters, according to the January 19 2009 Rasmussen Report, blame long-term planetary trends for global warming, not human beings. Is there global cooling, as skeptics claim? We examine the temperature record.
Beyond global warming: Ecology and global change
Vitousek, P.M. )
1994-10-01
While ecologists involved in management or policy often are advised to learn to deal with uncertainty, some components of global environmental change are certainly occurring and are certainly human-caused. All have important ecological consequences. Well-documented global changes include: Increasing concentrations of carbon dioxide in the atmosphere; alterations in the biogeochemistry of the global nitrogen cycle; and ongoing land use/land cover change. Human activity - now primarily fossil fuel combustion - has increased carbon dioxide concentrations from [approximately] 280 to 355 [mu]L/L since 1800 and is likely to have climatic consequences and direct effects on biota in all terrestrial ecosystems. The global nitrogen cycle has been altered so that more nitrogen is fixed annually by humanity than by all natural pathways combined. Altering atmospheric chemistry and aquatic ecosystems, contributes to eutrophication of the biosphere, and has substantial regional effects on biological diversity. Finally, human land use/land cover change has transformed one-third to one-half of Earth's ice-free surface, representing the most important component of global change now. Any clear dichotomy between pristine ecosystems and human-altered areas that may have existed in the past has vanished, and ecological research should account for this reality. Certain components of global environmental change are the primary causes of anticipated changes in climate, and of ongoing losses of biological diversity. They are caused by the extraordinary growth in size and resource use of the human population. On a broad scale, there is little uncertainty about any of these components of change or their causes. However, much of the public believes the causes of global change to be uncertain and contentious. By speaking out effectively,the focus of public discussion towards what can and should be done about global environmental change can be shifted. 135 refs., 13 figs., 1 tab.
Global perspectives: A new global ethic, a new global partnership
Brundtland, G.H.
1990-06-01
In her keynote address at the opening plenary session of the Globe '90 Conference held in Vancouver in March, Mrs. Brundtland called for a new global partnership of government, industry, producers and consumers to meet present and future environmental challenges. This partnership would require help to developing countries to help free them from their handicaps of debt, overpopulation and poverty; that improvements made to the environment would not be offset by ecological damage in other areas. She was encouraged that the policy of sustainable development has been widely adapted as the only viable strategy for global change.
Synergy optimization and operation management on syndicate complementary knowledge cooperation
NASA Astrophysics Data System (ADS)
Tu, Kai-Jan
2014-10-01
The number of multi enterprises knowledge cooperation has grown steadily, as a result of global innovation competitions. I have conducted research based on optimization and operation studies in this article, and gained the conclusion that synergy management is effective means to break through various management barriers and solve cooperation's chaotic systems. Enterprises must communicate system vision and access complementary knowledge. These are crucial considerations for enterprises to exert their optimization and operation knowledge cooperation synergy to meet global marketing challenges.
Reducing global health inequalities. Part 1.
Stuart, Kenneth; Soulsby, E J L
2011-08-01
This paper summarizes four UK reviews of socially stratified health inequalities that were undertaken during the past five decades. It describes the background of misplaced optimism and false hopes which characterized the UK's own record of health inequalities; the broken promises on debt cancellations which was the experience of developing countries. It describes why the UK's past leadership record in international health provides grounds for optimism for the future and for benefits for both developed and developing countries through the adoption of more collaborative approaches to global health than have characterized international relationships in the past. It recalls the enthusiasm generated in the UK, and internationally, by the establishment of the Global Commission on the Social Determinants of Health. It promotes the perception of health both as a global public good and as a developmental issue and why a focus on poverty is essential to the address of global health issues. It sees the designing of appropriate strategies and partnerships towards the achievement of the Millennium Development Goals as an important first step for achieving successful address to global public health issues. PMID:21816930
Lighting design for globally illuminated volume rendering.
Zhang, Yubo; Ma, Kwan-Liu
2013-12-01
With the evolution of graphics hardware, high quality global illumination becomes available for real-time volume rendering. Compared to local illumination, global illumination can produce realistic shading effects which are closer to real world scenes, and has proven useful for enhancing volume data visualization to enable better depth and shape perception. However, setting up optimal lighting could be a nontrivial task for average users. There were lighting design works for volume visualization but they did not consider global light transportation. In this paper, we present a lighting design method for volume visualization employing global illumination. The resulting system takes into account view and transfer-function dependent content of the volume data to automatically generate an optimized three-point lighting environment. Our method fully exploits the back light which is not used by previous volume visualization systems. By also including global shadow and multiple scattering, our lighting system can effectively enhance the depth and shape perception of volumetric features of interest. In addition, we propose an automatic tone mapping operator which recovers visual details from overexposed areas while maintaining sufficient contrast in the dark areas. We show that our method is effective for visualizing volume datasets with complex structures. The structural information is more clearly and correctly presented under the automatically generated light sources.
Reducing global health inequalities. Part 1
Stuart, Kenneth; Soulsby, EJL
2011-01-01
This paper summarizes four UK reviews of socially stratified health inequalities that were undertaken during the past five decades. It describes the background of misplaced optimism and false hopes which characterized the UK's own record of health inequalities; the broken promises on debt cancellations which was the experience of developing countries. It describes why the UK's past leadership record in international health provides grounds for optimism for the future and for benefits for both developed and developing countries through the adoption of more collaborative approaches to global health than have characterized international relationships in the past. It recalls the enthusiasm generated in the UK, and internationally, by the establishment of the Global Commission on the Social Determinants of Health. It promotes the perception of health both as a global public good and as a developmental issue and why a focus on poverty is essential to the address of global health issues. It sees the designing of appropriate strategies and partnerships towards the achievement of the Millennium Development Goals as an important first step for achieving successful address to global public health issues. PMID:21816930
Chen, Sheng; Wang, Xunxian; Harris, Chris J
2005-08-01
Many signal processing applications pose optimization problems with multimodal and nonsmooth cost functions. Gradient methods are ineffective in these situations, and optimization methods that require no gradient and can achieve a global optimal solution are highly desired to tackle these difficult problems. The paper proposes a guided global search optimization technique, referred to as the repeated weighted boosting search. The proposed optimization algorithm is extremely simple and easy to implement, involving a minimum programming effort. Heuristic explanation is given for the global search capability of this technique. Comparison is made with the two better known and widely used guided global search techniques, known as the genetic algorithm and adaptive simulated annealing, in terms of the requirements for algorithmic parameter tuning. The effectiveness of the proposed algorithm as a global optimizer are investigated through several application examples.
Global Collaborative STEM Education
NASA Astrophysics Data System (ADS)
Meabh Kelly, Susan; Smith, Walter
2016-04-01
Global Collaborative STEM Education, as the name suggests, simultaneously supports two sets of knowledge and skills. The first set is STEM -- science, technology, engineering and math. The other set of content knowledge and skills is that of global collaboration. Successful global partnerships require awareness of one's own culture, the biases embedded within that culture, as well as developing awareness of the collaborators' culture. Workforce skills fostered include open-mindedness, perseverance when faced with obstacles, and resourceful use of technological "bridges" to facilitate and sustain communication. In respect for the 2016 GIFT Workshop focus, Global Collaborative STEM Education projects dedicated to astronomy research will be presented. The projects represent different benchmarks within the Global Collaborative STEM Education continuum, culminating in an astronomy research experience that fully reflects how the global STEM workforce collaborates. To facilitate wider engagement in Global Collaborative STEM Education, project summaries, classroom resources and contact information for established international collaborative astronomy research projects will be disseminated.
MOGO: Model-Oriented Global Optimization of Petascale Applications
Malony, Allen D.; Shende, Sameer S.
2012-09-14
The MOGO project was initiated under in 2008 under the DOE Program Announcement for Software Development Tools for Improved Ease-of-Use on Petascale systems (LAB 08-19). The MOGO team consisted of Oak Ridge National Lab, Argonne National Lab, and the University of Oregon. The overall goal of MOGO was to attack petascale performance analysis by developing a general framework where empirical performance data could be efficiently and accurately compared with performance expectations at various levels of abstraction. This information could then be used to automatically identify and remediate performance problems. MOGO was be based on performance models derived from application knowledge, performance experiments, and symbolic analysis. MOGO was able to make reasonable impact on existing DOE applications and systems. New tools and techniques were developed, which, in turn, were used on important DOE applications on DOE LCF systems to show significant performance improvements.
A Globally Optimal Minimax Solution for Spectral Overbounding and Factorization
NASA Technical Reports Server (NTRS)
Scheid, Robert E.; Bayard, David S.
1995-01-01
In this paper, an algorithm is introduced to find a minimum phase transfer function of specified order whose magnitude "tightly" overbounds a specified real-valued nonparametric function of frequency. This method has direct application to transforming nonparametric uncertainty bounds (available from system identification experiments and/or plant modeling) into parametric representations required for modern robust control design software (i.e., a minimum-phase transfer function multiplied by a norm-bounded perturbation).
Global solidarity, migration and global health inequity.
Eckenwiler, Lisa; Straehle, Christine; Chung, Ryoa
2012-09-01
The grounds for global solidarity have been theorized and conceptualized in recent years, and many have argued that we need a global concept of solidarity. But the question remains: what can motivate efforts of the international community and nation-states? Our focus is the grounding of solidarity with respect to global inequities in health. We explore what considerations could motivate acts of global solidarity in the specific context of health migration, and sketch briefly what form this kind of solidarity could take. First, we argue that the only plausible conceptualization of persons highlights their interdependence. We draw upon a conception of persons as 'ecological subjects' and from there illustrate what such a conception implies with the example of nurses migrating from low and middle-income countries to more affluent ones. Next, we address potential critics who might counter any such understanding of current international politics with a reference to real-politik and the insights of realist international political theory. We argue that national governments--while not always or even often motivated by moral reasons alone--may nevertheless be motivated to acts of global solidarity by prudential arguments. Solidarity then need not be, as many argue, a function of charitable inclination, or emergent from an acknowledgment of injustice suffered, but may in fact serve national and transnational interests. We conclude on a positive note: global solidarity may be conceptualized to helpfully address global health inequity, to the extent that personal and transnational interdependence are enough to motivate national governments into action. PMID:22827320
Global toxification: A manageable problem?
Meent, D. van de; Verbruggen, E.M.J.
1995-12-31
Using the multimedia fate model GLOBETOX, concentration-time series of chemicals in air, water, sediment, and soil, on continental and global spatial scales, are predicted from former, present and future emissions, on the basis of transport and transformation rate constants. GLOBETOX was designed as a sub-model for RIVM`s decision support system TARGETS, an integrated assessment tool for analyzing the environmental consequences of socioeconomic developments. GLOBETOX is a nested version of the Mackay-type multimedia model SIMPLEBOX. GLOBETOX models the global environment as consisting of continental islands (air, water, sediment, soil), embedded in a global oceanic background (air and water only). The ``islands`` considered are generic representations of the main continents: North America, Europe, Asia, and the combined continents on the southern hemisphere (South America, Africa, Australia). The fully mechanistic GLCBETOX model was applied, without further calibration of the parameters to the actual situation, to four test chemicals: DDT, PCBs, DEHP and lead. The predicted concentrations were interpreted as spatially averaged values and compared with available monitoring data. The results indicate that the model simulations do reflect reality in the sense that (1) predicted concentrations have the right order of magnitude and (2) predicted time-responses seem reasonable. This preliminary analysis gives reason for moderate optimism about the potential for controlling global toxification. While the use of organic chemicals will increase, rapid response to emission reduction programs and to shift towards use of more degradable alternatives can be expected. Metals remain a source of concern, mainly because of their absolute non-degradability. The effects of local exposure in many hot spots may be of more concern than the effects of continental-scale background exposure.
Optimal packings of superballs
NASA Astrophysics Data System (ADS)
Jiao, Y.; Stillinger, F. H.; Torquato, S.
2009-04-01
Dense hard-particle packings are intimately related to the structure of low-temperature phases of matter and are useful models of heterogeneous materials and granular media. Most studies of the densest packings in three dimensions have considered spherical shapes, and it is only more recently that nonspherical shapes (e.g., ellipsoids) have been investigated. Superballs (whose shapes are defined by |x1|2p+|x2|2p+|x3|2p≤1 ) provide a versatile family of convex particles (p≥0.5) with both cubic-like and octahedral-like shapes as well as concave particles (0
global symmetries that are consistent with certain symmetries of a superball. We also provide strong evidence that our packings for convex superballs (p≥0.5) are most likely the optimal ones. The maximal packing density as a function of p is nonanalytic at the sphere point (p=1) and increases dramatically as p moves away from unity. Two more nontrivial nonanalytic behaviors occur at pc∗=1.1509… and po∗=ln3/ln4=0.7924… for “cubic” and “octahedral” superballs, respectively, where different Bravais lattice packings possess the same densities. The packing characteristics determined by the broken rotational symmetry of superballs are similar to but richer than their two-dimensional “superdisk” counterparts [Y. Jiao , Phys. Rev. Lett. 100, 245504 (2008)] and are distinctly different from that of ellipsoid packings. Our candidate optimal superball packings provide a starting point to quantify the equilibrium phase behavior of superball systems, which should deepen our understanding of the statistical thermodynamics of nonspherical-particle systems.
Optimal piecewise locally linear modeling
NASA Astrophysics Data System (ADS)
Harris, Chris J.; Hong, Xia; Feng, M.
1999-03-01
Associative memory networks such as Radial Basis Functions, Neurofuzzy and Fuzzy Logic used for modelling nonlinear processes suffer from the curse of dimensionality (COD), in that as the input dimension increases the parameterization, computation cost, training data requirements, etc. increase exponentially. Here a new algorithm is introduced for the construction of a Delaunay input space partitioned optimal piecewise locally linear models to overcome the COD as well as generate locally linear models directly amenable to linear control and estimation algorithms. The training of the model is configured as a new mixture of experts network with a new fast decision rule derived using convex set theory. A very fast simulated reannealing (VFSR) algorithm is utilized to search a global optimal solution of the Delaunay input space partition. A benchmark non-linear time series is used to demonstrate the new approach.
Particle swarm optimization applied to impulsive orbital transfers
NASA Astrophysics Data System (ADS)
Pontani, Mauro; Conway, Bruce A.
2012-05-01
The particle swarm optimization (PSO) technique is a population-based stochastic method developed in recent years and successfully applied in several fields of research. It mimics the unpredictable motion of bird flocks while searching for food, with the intent of determining the optimal values of the unknown parameters of the problem under consideration. At the end of the process, the best particle (i.e. the best solution with reference to the objective function) is expected to contain the globally optimal values of the unknown parameters. The central idea underlying the method is contained in the formula for velocity updating. This formula includes three terms with stochastic weights. This research applies the particle swarm optimization algorithm to the problem of optimizing impulsive orbital transfers. More specifically, the following problems are considered and solved with the PSO algorithm: (i) determination of the globally optimal two- and three-impulse transfer trajectories between two coplanar circular orbits; (ii) determination of the optimal transfer between two coplanar, elliptic orbits with arbitrary orientation; (iii) determination of the optimal two-impulse transfer between two circular, non-coplanar orbits; (iv) determination of the globally optimal two-impulse transfer between two non-coplanar elliptic orbits. Despite its intuitiveness and simplicity, the particle swarm optimization method proves to be capable of effectively solving the orbital transfer problems of interest with great numerical accuracy.
Global warming without global mean precipitation increase?
Salzmann, Marc
2016-06-01
Global climate models simulate a robust increase of global mean precipitation of about 1.5 to 2% per kelvin surface warming in response to greenhouse gas (GHG) forcing. Here, it is shown that the sensitivity to aerosol cooling is robust as well, albeit roughly twice as large. This larger sensitivity is consistent with energy budget arguments. At the same time, it is still considerably lower than the 6.5 to 7% K(-1) decrease of the water vapor concentration with cooling from anthropogenic aerosol because the water vapor radiative feedback lowers the hydrological sensitivity to anthropogenic forcings. When GHG and aerosol forcings are combined, the climate models with a realistic 20th century warming indicate that the global mean precipitation increase due to GHG warming has, until recently, been completely masked by aerosol drying. This explains the apparent lack of sensitivity of the global mean precipitation to the net global warming recently found in observations. As the importance of GHG warming increases in the future, a clear signal will emerge. PMID:27386558
Global warming without global mean precipitation increase?
Salzmann, Marc
2016-01-01
Global climate models simulate a robust increase of global mean precipitation of about 1.5 to 2% per kelvin surface warming in response to greenhouse gas (GHG) forcing. Here, it is shown that the sensitivity to aerosol cooling is robust as well, albeit roughly twice as large. This larger sensitivity is consistent with energy budget arguments. At the same time, it is still considerably lower than the 6.5 to 7% K−1 decrease of the water vapor concentration with cooling from anthropogenic aerosol because the water vapor radiative feedback lowers the hydrological sensitivity to anthropogenic forcings. When GHG and aerosol forcings are combined, the climate models with a realistic 20th century warming indicate that the global mean precipitation increase due to GHG warming has, until recently, been completely masked by aerosol drying. This explains the apparent lack of sensitivity of the global mean precipitation to the net global warming recently found in observations. As the importance of GHG warming increases in the future, a clear signal will emerge. PMID:27386558
Global warming without global mean precipitation increase?
Salzmann, Marc
2016-06-01
Global climate models simulate a robust increase of global mean precipitation of about 1.5 to 2% per kelvin surface warming in response to greenhouse gas (GHG) forcing. Here, it is shown that the sensitivity to aerosol cooling is robust as well, albeit roughly twice as large. This larger sensitivity is consistent with energy budget arguments. At the same time, it is still considerably lower than the 6.5 to 7% K(-1) decrease of the water vapor concentration with cooling from anthropogenic aerosol because the water vapor radiative feedback lowers the hydrological sensitivity to anthropogenic forcings. When GHG and aerosol forcings are combined, the climate models with a realistic 20th century warming indicate that the global mean precipitation increase due to GHG warming has, until recently, been completely masked by aerosol drying. This explains the apparent lack of sensitivity of the global mean precipitation to the net global warming recently found in observations. As the importance of GHG warming increases in the future, a clear signal will emerge.
Maneuver Optimization through Simulated Annealing
NASA Astrophysics Data System (ADS)
de Vries, W.
2011-09-01
We developed an efficient method for satellite maneuver optimization. It is based on a Monte Carlo (MC) approach in combination with Simulated Annealing. The former component enables us to consider all imaginable trajectories possible given the current satellite position and its available thrust, while the latter approach ensures that we reliably find the best global optimization solution. Furthermore, this optimization setup is eminently scalable. It runs efficiently on the current multi-core generation of desktop computers, but is equally at home on massively parallel high performance computers (HPC). The baseline method for desktops uses a modified two-body propagator that includes the lunar gravitational force, and corrects for nodal and apsidal precession. For the HPC environment, on the other hand, we can include all the necessary components for a full force-model propagation: higher gravitational moments, atmospheric drag, solar radiation pressure, etc. A typical optimization scenario involves an initial orbit and a destination orbit / trajectory, a time period under consideration, and an available amount of thrust. After selecting a particular optimization (e.g., least amount of fuel, shortest maneuver), the program will determine when and in what direction to burn by what amount. Since we are considering all possible trajectories, we are not constrained to any particular transfer method (e.g., Hohmann transfers). Indeed, in some cases gravitational slingshots around the Earth turn out to be the best result. The paper will describe our approach in detail, its complement of optimizations for single- and multi-burn sequences, and some in-depth examples. In particular, we highlight an example where it is used to analyze a sequence of maneuvers after the fact, as well as showcase its utility as a planning and analysis tool for future maneuvers.
Hu, Y.; Liu, Z.; Shi, X.; Wang, B.
2006-07-01
A brief introduction of characteristic statistic algorithm (CSA) is given in the paper, which is a new global optimization algorithm to solve the problem of PWR in-core fuel management optimization. CSA is modified by the adoption of back propagation neural network and fast local adjustment. Then the modified CSA is applied to PWR Equilibrium Cycle Reloading Optimization, and the corresponding optimization code of CSA-DYW is developed. CSA-DYW is used to optimize the equilibrium cycle of 18 month reloading of Daya bay nuclear plant Unit 1 reactor. The results show that CSA-DYW has high efficiency and good global performance on PWR Equilibrium Cycle Reloading Optimization. (authors)
A collective neurodynamic optimization approach to bound-constrained nonconvex optimization.
Yan, Zheng; Wang, Jun; Li, Guocheng
2014-07-01
This paper presents a novel collective neurodynamic optimization method for solving nonconvex optimization problems with bound constraints. First, it is proved that a one-layer projection neural network has a property that its equilibria are in one-to-one correspondence with the Karush-Kuhn-Tucker points of the constrained optimization problem. Next, a collective neurodynamic optimization approach is developed by utilizing a group of recurrent neural networks in framework of particle swarm optimization by emulating the paradigm of brainstorming. Each recurrent neural network carries out precise constrained local search according to its own neurodynamic equations. By iteratively improving the solution quality of each recurrent neural network using the information of locally best known solution and globally best known solution, the group can obtain the global optimal solution to a nonconvex optimization problem. The advantages of the proposed collective neurodynamic optimization approach over evolutionary approaches lie in its constraint handling ability and real-time computational efficiency. The effectiveness and characteristics of the proposed approach are illustrated by using many multimodal benchmark functions. PMID:24705545
Optimal stomatal behaviour around the world
Lin, Yan-Shih; Medlyn, Belinda E.; Duursma, Remko A.; Prentice, I. Colin; Wang, Han; Baig, Sofia; Eamus, Derek; de Dios, Victor Resco; Mitchell, Patrick; Ellsworth, David S.; et al
2015-03-02
Stomatal conductance (gs) is a key land-surface attribute as it links transpiration, the dominant component of global land evapotranspiration, and photosynthesis, the driving force of the global carbon cycle. Despite the pivotal role of gs in predictions of global water and carbon cycle changes, a global-scale database and an associated globally applicable model of gs that allow predictions of stomatal behaviour are lacking. Here, we present a database of globally distributed gs obtained in the field for a wide range of plant functional types (PFTs) and biomes. We find that stomatal behaviour differs among PFTs according to their marginal carbonmore » cost of water use, as predicted by the theory underpinning the optimal stomatal model1 and the leaf and wood economics spectrum2,3. We also demonstrate a global relationship with climate. In conclusion, these findings provide a robust theoretical framework for understanding and predicting the behaviour of gs across biomes and across PFTs that can be applied to regional, continental and global-scale modelling of ecosystem productivity, energy balance and ecohydrological processes in a future changing climate.« less
Optimal stomatal behaviour around the world
Lin, Yan-Shih; Medlyn, Belinda E.; Duursma, Remko A.; Prentice, I. Colin; Wang, Han; Baig, Sofia; Eamus, Derek; de Dios, Victor Resco; Mitchell, Patrick; Ellsworth, David S.; de Beeck, Maarten Op; Wallin, Göran; Uddling, Johan; Tarvainen, Lasse; Linderson, Maj-Lena; Cernusak, Lucas A.; Nippert, Jesse B.; Ocheltree, Troy W.; Tissue, David T.; Martin-StPaul, Nicolas K.; Rogers, Alistair; Warren, Jeff M.; De Angelis, Paolo; Hikosaka, Kouki; Han, Qingmin; Onoda, Yusuke; Gimeno, Teresa E.; Barton, Craig V. M.; Bennie, Jonathan; Bonal, Damien; Bosc, Alexandre; Löw, Markus; Macinins-Ng, Cate; Rey, Ana; Rowland, Lucy; Setterfield, Samantha A.; Tausz-Posch, Sabine; Zaragoza-Castells, Joana; Broadmeadow, Mark S. J.; Drake, John E.; Freeman, Michael; Ghannoum, Oula; Hutley, Lindsay B.; Kelly, Jeff W.; Kikuzawa, Kihachiro; Kolari, Pasi; Koyama, Kohei; Limousin, Jean-Marc; Meir, Patrick; Lola da Costa, Antonio C.; Mikkelsen, Teis N.; Salinas, Norma; Sun, Wei; Wingate, Lisa
2015-03-02
Stomatal conductance (g_{s}) is a key land-surface attribute as it links transpiration, the dominant component of global land evapotranspiration, and photosynthesis, the driving force of the global carbon cycle. Despite the pivotal role of g_{s} in predictions of global water and carbon cycle changes, a global-scale database and an associated globally applicable model of g_{s} that allow predictions of stomatal behaviour are lacking. Here, we present a database of globally distributed g_{s} obtained in the field for a wide range of plant functional types (PFTs) and biomes. We find that stomatal behaviour differs among PFTs according to their marginal carbon cost of water use, as predicted by the theory underpinning the optimal stomatal model^{1} and the leaf and wood economics spectrum^{2,3}. We also demonstrate a global relationship with climate. In conclusion, these findings provide a robust theoretical framework for understanding and predicting the behaviour of g_{s} across biomes and across PFTs that can be applied to regional, continental and global-scale modelling of ecosystem productivity, energy balance and ecohydrological processes in a future changing climate.
Globalization, culture and psychology.
Melluish, Steve
2014-10-01
This article outlines the cultural and psychological effects of globalization. It looks at the impact of globalization on identity; ideas of privacy and intimacy; the way we understand and perceive psychological distress; and the development of the profession of psychology around the world. The article takes a critical perspective on globalization, seeing it as aligned with the spread of neoliberal capitalism, a tendency towards cultural homogenization, the imposition of dominant 'global north' ideas and the resultant growing inequalities in health and well-being. However, it also argues that the increased interconnectedness created by globalization allows for greater acknowledgement of our common humanity and for collective efforts to be developed to tackle what are increasingly global problems. This requires the development of more nuanced understandings of cultural differences and of indigenous psychologies. PMID:25343628
Simone, Patricia M.; Davison, Veronica; Slutsker, Laurence
2013-01-01
Global health reflects the realities of globalization, including worldwide dissemination of infectious and noninfectious public health risks. Global health architecture is complex and better coordination is needed between multiple organizations. Three overlapping themes determine global health action and prioritization: development, security, and public health. These themes play out against a background of demographic change, socioeconomic development, and urbanization. Infectious diseases remain critical factors, but are no longer the major cause of global illness and death. Traditional indicators of public health, such as maternal and infant mortality rates no longer describe the health status of whole societies; this change highlights the need for investment in vital registration and disease-specific reporting. Noncommunicable diseases, injuries, and mental health will require greater attention from the world in the future. The new global health requires broader engagement by health organizations and all countries for the objectives of health equity, access, and coverage as priorities beyond the Millennium Development Goals are set. PMID:23876365
Globalization, culture and psychology.
Melluish, Steve
2014-10-01
This article outlines the cultural and psychological effects of globalization. It looks at the impact of globalization on identity; ideas of privacy and intimacy; the way we understand and perceive psychological distress; and the development of the profession of psychology around the world. The article takes a critical perspective on globalization, seeing it as aligned with the spread of neoliberal capitalism, a tendency towards cultural homogenization, the imposition of dominant 'global north' ideas and the resultant growing inequalities in health and well-being. However, it also argues that the increased interconnectedness created by globalization allows for greater acknowledgement of our common humanity and for collective efforts to be developed to tackle what are increasingly global problems. This requires the development of more nuanced understandings of cultural differences and of indigenous psychologies.
De Cock, Kevin M; Simone, Patricia M; Davison, Veronica; Slutsker, Laurence
2013-08-01
Global health reflects the realities of globalization, including worldwide dissemination of infectious and noninfectious public health risks. Global health architecture is complex and better coordination is needed between multiple organizations. Three overlapping themes determine global health action and prioritization: development, security, and public health. These themes play out against a background of demographic change, socioeconomic development, and urbanization. Infectious diseases remain critical factors, but are no longer the major cause of global illness and death. Traditional indicators of public health, such as maternal and infant mortality rates no longer describe the health status of whole societies; this change highlights the need for investment in vital registration and disease-specific reporting. Noncommunicable diseases, injuries, and mental health will require greater attention from the world in the future. The new global health requires broader engagement by health organizations and all countries for the objectives of health equity, access, and coverage as priorities beyond the Millennium Development Goals are set.
Optimal Phase Oscillatory Network
NASA Astrophysics Data System (ADS)
Follmann, Rosangela
2013-03-01
Important topics as preventive detection of epidemics, collective self-organization, information flow and systemic robustness in clusters are typical examples of processes that can be studied in the context of the theory of complex networks. It is an emerging theory in a field, which has recently attracted much interest, involving the synchronization of dynamical systems associated to nodes, or vertices, of the network. Studies have shown that synchronization in oscillatory networks depends not only on the individual dynamics of each element, but also on the combination of the topology of the connections as well as on the properties of the interactions of these elements. Moreover, the response of the network to small damages, caused at strategic points, can enhance the global performance of the whole network. In this presentation we explore an optimal phase oscillatory network altered by an additional term in the coupling function. The application to associative-memory network shows improvement on the correct information retrieval as well as increase of the storage capacity. The inclusion of some small deviations on the nodes, when solutions are attracted to a false state, results in additional enhancement of the performance of the associative-memory network. Supported by FAPESP - Sao Paulo Research Foundation, grant number 2012/12555-4
Optimizing management of glycaemia.
Chatterjee, Sudesna; Khunti, Kamlesh; Davies, Melanie J
2016-06-01
The global epidemic of type 2 diabetes (T2DM) continues largely unabated due to an increasingly sedentary lifestyle and obesogenic environment. A cost-effective patient-centred approach, incorporating glucose-lowering therapy and modification of cardiovascular risk factors, could help prevent the inevitable development and progression of macrovascular and microvascular complications. Glycaemic optimization requires patient structured education, self-management and empowerment, and psychological support along with early and proactive use of glucose lowering therapies, which should be delivered in a system of care as shown by the Chronic Care Model. From diagnosis, intensive glycaemic control and individualised care is aimed at reducing complications. In older people, the goal is maintaining quality of life and minimizing morbidity, especially as overtreatment increases hypoglycaemia risk. Maintaining durable glycaemic control is challenging and complex to achieve without hypoglycaemia, weight gain and other significant adverse effects. Overcoming patient and physician barriers can help ensure adequate treatment initiation and intensification. Cardiovascular safety studies with newer glucose-lowering agents are now mandatory, with a sodium glucose co-transporter-2 inhibitor (empagliflozin), and two glucagon like peptide-1 receptor agonists (liraglutide and semaglutide) being the first to demonstrate superior CV outcomes compared with placebo. PMID:27432074
NASA Technical Reports Server (NTRS)
Naftel, Chris
2014-01-01
The NASA Global Hawk Project is supporting Earth Science research customers. These customers include: US Government agencies, civilian organizations, and universities. The combination of the Global Hawks range, endurance, altitude, payload power, payload volume and payload weight capabilities separates the Global Hawk platform from all other platforms available to the science community. This presentation includes an overview of the concept of operations and an overview of the completed science campaigns. In addition, the future science plans, using the NASA Global Hawk System, will be presented.
NASA Astrophysics Data System (ADS)
Marec, J. P.
The optimization of rendezvous and transfer orbits is introduced. Optimal transfer is defined and propulsion system modeling is outlined. Parameter optimization, including the Hohmann transfer, is discussed. Optimal transfer in general, uniform, and central gravitational fields is covered. Interplanetary rendezvous is treated.
NASA Astrophysics Data System (ADS)
Tang, Zhili
2016-06-01
This paper solved aerodynamic drag reduction of transport wing fuselage configuration in transonic regime by using a parallel Nash evolutionary/deterministic hybrid optimization algorithm. Two sets of parameters are used, namely globally and locally. It is shown that optimizing separately local and global parameters by using Nash algorithms is far more efficient than considering these variables as a whole.
"Global Competency" Is Imperative for Global Success
ERIC Educational Resources Information Center
Reimers, Fernando
2009-01-01
According to a recent report of scenarios prepared by the National Intelligence Council, the next 15 years will bring significant global changes, including the transformation of the international political system built after World War II, a transfer of wealth from the West to the East, pressure on natural resources resulting from continuing…
From Global Knowledge to Global Civic Engagement
ERIC Educational Resources Information Center
Lorenzini, Michelle
2013-01-01
In this article, I argue that student learning is enhanced when civic engagement is a component of international education initiatives. When only presented with knowledge about global challenges, students can become frustrated and overwhelmed unless they also understand how they might contribute to solutions. Political science programs are…
Optimism's Explicative Role for Chronic Diseases.
Avvenuti, Giulia; Baiardini, Ilaria; Giardini, Anna
2016-01-01
The increasing interest about dispositional optimism's role in health status and its positive modulating effect on health outcomes has led to a remarkable scientific production in the last decade. To date lot is known for which diseases optimism is relevant, instead much less is known about how optimism interacts with other factors, both biological and psychological, in determining health status. The aim of this mini review is to explore the literature derived from clinical and experimental research assessing the associations between dispositional optimism and health status. Dispositional optimism can be considered as facet of personality that is cognitive in nature which holds the global expectation that the future will be plenty of good events. Optimists view desired goals as obtainable, so they often confront adversities in active manners resulting in perseverance and increased goal attainment. Only studies that explicitly included optimism and health outcomes, as measurable variables, and that reported a clear association between them have been reviewed. Cancer, cardiovascular disease, respiratory failure, and aging with multimorbidity were considered. Among the possible explicative hypotheses, two seem to best describe results: optimism may have a direct effect on the neuroendocrine system and on immune responses, and it may have an indirect effect on health outcomes by promoting protective health behaviors, adaptive coping strategies and enhancing positive mood. The research on optimism and health status has already shed light on important mechanisms regarding chronic diseases' management, however, further studies are needed to deepen the knowledge. PMID:26973582
Optimal vaccination and treatment of an epidemic network model
NASA Astrophysics Data System (ADS)
Chen, Lijuan; Sun, Jitao
2014-08-01
In this Letter, we firstly propose an epidemic network model incorporating two controls which are vaccination and treatment. For the constant controls, by using Lyapunov function, global stability of the disease-free equilibrium and the endemic equilibrium of the model is investigated. For the non-constant controls, by using the optimal control strategy, we discuss an optimal strategy to minimize the total number of the infected and the cost associated with vaccination and treatment. Table 1 and Figs. 1-5 are presented to show the global stability and the efficiency of this optimal control.
Optimization of composite structures
NASA Technical Reports Server (NTRS)
Stroud, W. J.
1982-01-01
Structural optimization is introduced and examples which illustrate potential problems associated with optimized structures are presented. Optimized structures may have very low load carrying ability for an off design condition. They tend to have multiple modes of failure occurring simultaneously and can, therefore, be sensitive to imperfections. Because composite materials provide more design variables than do metals, they allow for more refined tailoring and more extensive optimization. As a result, optimized composite structures can be especially susceptible to these problems.
Learning to Plunder: Global Education, Global Inequality and the Global City
ERIC Educational Resources Information Center
Tannock, Stuart
2010-01-01
Most research and policy discussions of education in the global city have focused on the ways in which globalization and the emergence of global or globalizing cities can create social, economic and educational inequality locally, within the global city itself. Global cities, however, are, by definition, powerful places, where the core…
ERIC Educational Resources Information Center
Roberts, Dennis C.; Welch, Lucas; Al-Khanji, Khalid
2013-01-01
Global citizens are those who are aware of, demonstrate respect for, and are comfortable engaging across cultural boundaries. This article explores why preparing global citizens is important and how positive psychology can inform our understanding of those who engage comfortably in today's complicated world. Soliya's Connect program is described…
Globalization and Peace Education
ERIC Educational Resources Information Center
Basiga, Brenda
2004-01-01
Today, it would be difficult to find a community that has been unaffected by globalization, yet its effects are still unknown to many people. This paper is an attempt to bring the subject to the awareness of educators while particularly focusing on those in the Philippines. It is ironic that globalization on one hand has incited people all over…
ERIC Educational Resources Information Center
2002
This document contains three papers from a symposium on globalization and human resource development (HRD). "Challenges and Strategies of Developing Human Resources in the Surge of Globalization: A Case of the People's Republic of China" (De Zhang, Baiyin Yang, Yichi Zhang) analyzes the challenges and strategies of HRD in China and discusses the…
Critically Theorizing the Global
ERIC Educational Resources Information Center
Gaudelli, William
2013-01-01
Globalization has unleashed profound changes in education. These include positivistic international school comparisons, a singular focus on schools as drivers of economic development, and the adoption of neoliberal market principles in school. These changes, however, generally go unexamined within the field and literature of global education.…
ERIC Educational Resources Information Center
Peet, Richard
2002-01-01
Describes the course, "Global Society," for first-year International Studies students at a Massachusetts liberal arts college. The course, which takes a historical approach, informs students about the nature, history, and present characteristics of the global system, taking theoretical, historical, and critical approaches that stress the…
Multiculturalism vs. Globalism.
ERIC Educational Resources Information Center
Ukpokodu, Nelly
1999-01-01
Addresses the error of treating multiculturalism and globalism as the same concept. Considers the boundaries and shared purposes of multiculturalism and globalism. Defines the former as using multiple perspectives that reflect the diversity within a society and the latter as developing students' understanding of peoples and cultures of other…
Assessing Individuals' Global Perspective
ERIC Educational Resources Information Center
Merrill, Kelly Carter; Braskamp, David C.; Braskamp, Larry A.
2012-01-01
This article introduces the Global Perspective Inventory (GPI), a survey instrument that measures participants' global perspective in terms of cognitive, intrapersonal, and interpersonal domains--each in terms of both development and acquisition. A summary of the recent research on the GPI is provided along with a discussion of potential uses.
Globally Collaborative Experiential Learning
ERIC Educational Resources Information Center
Utsumi, Takeshi
2005-01-01
The Global University System (GUS) [Utsumi, et al, 2003] is a worldwide initiative to create advanced telecommunications infrastructure for access to educational resources across national and cultural boundaries for global peace. GUS aims to create a worldwide consortium of universities to provide the underdeveloped world with access to 21st…
Building Global Learning Communities
ERIC Educational Resources Information Center
Cochrane, Thomas; Buchem, Ilona; Camacho, Mar; Cronin, Catherine; Gordon, Averill; Keegan, Helen
2013-01-01
Within the background where education is increasingly driven by the economies of scale and research funding, we propose an alternative online open and connected framework (OOC) for building global learning communities using mobile social media. We critique a three year action research case study involving building collaborative global learning…
Globalization, Interdependence and Education
ERIC Educational Resources Information Center
Neubauer, Deane
2007-01-01
Contemporary globalization is marked by rapidly and dramatically increasing interdependence, which operates both within and among countries. Increasing global interdependence has profound influence on education at all levels, such as how to deal with a world with more permeable boundaries in which people are on the move more frequently (migration)…
Global Managers' Career Competencies
ERIC Educational Resources Information Center
Cappellen, Tineke; Janssens, Maddy
2008-01-01
Purpose: This study aims to empirically examine the career competencies of global managers having world-wide coordination responsibility: knowing-why, knowing-how and knowing-whom career competencies. Design/methodology/approach: Based on in-depth interviews with 45 global managers, the paper analyzes career stories from a content analysis…
Global Diversity and Leadership.
ERIC Educational Resources Information Center
Ruiz, Art
2003-01-01
Argues that global diversity has become a business imperative in today's business climate. Global diversity is of core importance even for companies that are considered domestic. Suggests community colleges need help in understanding their customer base and their shifting values in order to meet their needs and win customer loyalty. (NB)
Globalization of Management Education
ERIC Educational Resources Information Center
Bruner, Robert F.; Iannarelli, Juliane
2011-01-01
A new study, sponsored by the Association to Advance Collegiate Schools of Business, presented a comprehensive new perspective on the globalization of management education, (AACSB International, 2011). Its findings are sobering: with regard to emerging global trends in higher education and cross-border business, the report reveals a sizable gap…
ERIC Educational Resources Information Center
Journal of College Science Teaching, 2005
2005-01-01
This brief article describes a new global wind-power map that has quantified global wind power and may help planners place turbines in locations that can maximize power from the winds and provide widely available low-cost energy. The researchers report that their study can assist in locating wind farms in regions known for strong and consistent…
Simulating Global Climate Summits
ERIC Educational Resources Information Center
Vesperman, Dean P.; Haste, Turtle; Alrivy, Stéphane
2014-01-01
One of the most persistent and controversial issues facing the global community is climate change. With the creation of the UN Framework Convention on Climate Change (UNFCCC) in 1992 and the Kyoto Protocol (1997), the global community established some common ground on how to address this issue. However, the last several climate summits have failed…
ERIC Educational Resources Information Center
Bennett, Audrey Grace
2010-01-01
Based on a virtual conference, Glide'08 (Global Interaction in Design Education), that brought international design scholars together online, this special issue expands on the topics of cross-cultural communication and design and the technological affordances that support such interaction. The author discusses the need for global interaction in…
ERIC Educational Resources Information Center
Petrie, James
The paper discusses the Global Awareness Test administered to 6,396 grade 9 students in New Brunswick, Canada at the beginning and end of the 1987 school year. The test was designed to better assess the impact of the grade 9 social studies program and the activities of New Brunswick's Global Education Centre. By comparing the results of the pre-…
ERIC Educational Resources Information Center
Molina, Sarina; Lattimer, Heather
2013-01-01
As the world is becoming increasingly flat, it has become important for educators to prepare students to understand global perspectives and engage with people from countries and cultures around the world. Although there is no question as to the importance of global education to meet with the demands of a flat world, what internationalization and…
Global Education: An Overview.
ERIC Educational Resources Information Center
Becker, James M.
1988-01-01
Discusses the different definitions and conceptualizations of global education, stating that much of the traditional curriculum of international studies can be reinterpreted to prepare students to participate in an interdependent society. Gives nine objectives for global education, and delineates the issues surrounding current conceptions of…
ERIC Educational Resources Information Center
Suarez-Orozco, Marcelo M.; Sattin, Carolyn
2007-01-01
Young people need more innovative thinking skills, cultural awareness, higher-order cognitive skills, and sophisticated communication and collaboration skills than ever before. To prepare students for their global futures, schools must be in tune with the new global reality. Schools need to restructure curriculum and pedagogy to place student…
ERIC Educational Resources Information Center
Zero Population Growth, Inc., Washington, DC.
Fourteen units for high school global education classes are based on "The Global 2000 Report to the President," which examines the relationships between worldwide population growth and resource and environmental consequences. Topics of the units are population; income; food; fisheries; forests; water; nonfuel minerals; energy; impacts on…
Translation as (Global) Writing
ERIC Educational Resources Information Center
Horner, Bruce; Tetreault, Laura
2016-01-01
This article explores translation as a useful point of departure and framework for taking a translingual approach to writing engaging globalization. Globalization and the knowledge economy are putting renewed emphasis on translation as a key site of contest between a dominant language ideology of monolingualism aligned with fast capitalist…
Zhu, Zhen; Puliga, Michelangelo; Cerina, Federica; Chessa, Alessandro; Riccaboni, Massimo
2015-01-01
The fragmentation of production across countries has become an important feature of the globalization in recent decades and is often conceptualized by the term “global value chains” (GVCs). When empirically investigating the GVCs, previous studies are mainly interested in knowing how global the GVCs are rather than how the GVCs look like. From a complex networks perspective, we use the World Input-Output Database (WIOD) to study the evolution of the global production system. We find that the industry-level GVCs are indeed not chain-like but are better characterized by the tree topology. Hence, we compute the global value trees (GVTs) for all the industries available in the WIOD. Moreover, we compute an industry importance measure based on the GVTs and compare it with other network centrality measures. Finally, we discuss some future applications of the GVTs. PMID:25978067
Optimal Control via Self-Generated Stochasticity
NASA Technical Reports Server (NTRS)
Zak, Michail
2011-01-01
The problem of global maxima of functionals has been examined. Mathematical roots of local maxima are the same as those for a much simpler problem of finding global maximum of a multi-dimensional function. The second problem is instability even if an optimal trajectory is found, there is no guarantee that it is stable. As a result, a fundamentally new approach is introduced to optimal control based upon two new ideas. The first idea is to represent the functional to be maximized as a limit of a probability density governed by the appropriately selected Liouville equation. Then, the corresponding ordinary differential equations (ODEs) become stochastic, and that sample of the solution that has the largest value will have the highest probability to appear in ODE simulation. The main advantages of the stochastic approach are that it is not sensitive to local maxima, the function to be maximized must be only integrable but not necessarily differentiable, and global equality and inequality constraints do not cause any significant obstacles. The second idea is to remove possible instability of the optimal solution by equipping the control system with a self-stabilizing device. The applications of the proposed methodology will optimize the performance of NASA spacecraft, as well as robot performance.
A Breeder Algorithm for Stellarator Optimization
NASA Astrophysics Data System (ADS)
Wang, S.; Ware, A. S.; Hirshman, S. P.; Spong, D. A.
2003-10-01
An optimization algorithm that combines the global parameter space search properties of a genetic algorithm (GA) with the local parameter search properties of a Levenberg-Marquardt (LM) algorithm is described. Optimization algorithms used in the design of stellarator configurations are often classified as either global (such as GA and differential evolution algorithm) or local (such as LM). While nonlinear least-squares methods such as LM are effective at minimizing a cost-function based on desirable plasma properties such as quasi-symmetry and ballooning stability, whether or not this is a local or global minimum is unknown. The advantage of evolutionary algorithms such as GA is that they search a wider range of parameter space and are not susceptible to getting stuck in a local minimum of the cost function. Their disadvantage is that in some cases the evolutionary algorithms are ineffective at finding a minimum state. Here, we describe the initial development of the Breeder Algorithm (BA). BA consists of a genetic algorithm outer loop with an inner loop in which each generation is refined using a LM step. Initial results for a quasi-poloidal stellarator optimization will be presented, along with a comparison to existing optimization algorithms.
Global Health Education in Pulmonary and Critical Care Medicine Fellowships.
Siddharthan, Trishul; North, Crystal M; Attia, Engi F; Christiani, David C; Checkley, William; West, T Eoin
2016-06-01
A growing number of pulmonary and critical care medicine fellowship programs in the United States offer global health training opportunities. Formal, integrated global health programs within pulmonary and critical care fellowships are relatively new but are built on principles and ideals of global health that focus on the mutually beneficial exchange of knowledge and social justice. Although core competencies consistent with these overarching themes in global health education have not been formalized for pulmonary and critical care trainees, relevant competency areas include clinical knowledge, international research training, cultural competency, and clinical and research capacity building. Existing global health education in U.S. pulmonary and critical care medicine training programs can generally be classified as one of three different models: integrated global health tracks, global health electives, and additional research years. Successful global health education programs foster partnerships and collaborations with international sites that emphasize bidirectional exchange. This bidirectional exchange includes ongoing, equitable commitments to mutual opportunities for training and professional development, including a focus on the particular knowledge and skill sets critical for addressing the unique priorities of individual countries. However, barriers related to the availability of mentorship, funding, and dedicated time exist to expanding global health education in pulmonary and critical care medicine. The implementation of global health training within pulmonary and critical care medicine programs requires continued optimization, but this training is essential to prepare the next generation of physicians to address the global aspects of respiratory disease and critical illness. PMID:26974557
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.
Turbomachinery Airfoil Design Optimization Using Differential Evolution
NASA Technical Reports Server (NTRS)
Madavan, Nateri K.; Biegel, Bryan (Technical Monitor)
2002-01-01
An aerodynamic design optimization procedure that is based on a evolutionary algorithm known at Differential Evolution is described. Differential Evolution is a simple, fast, and robust evolutionary strategy that has been proven effective in determining the global optimum for several difficult optimization problems, including highly nonlinear systems with discontinuities and multiple local optima. The method is combined with a Navier-Stokes solver that evaluates the various intermediate designs and provides inputs to the optimization procedure. An efficient constraint handling mechanism is also incorporated. Results are presented for the inverse design of a turbine airfoil from a modern jet engine and compared to earlier methods. The capability of the method to search large design spaces and obtain the optimal airfoils in an automatic fashion is demonstrated. Substantial reductions in the overall computing time requirements are achieved by using the algorithm in conjunction with neural networks.
Turbomachinery Airfoil Design Optimization Using Differential Evolution
NASA Technical Reports Server (NTRS)
Madavan, Nateri K.; Biegel, Bryan A. (Technical Monitor)
2002-01-01
An aerodynamic design optimization procedure that is based on a evolutionary algorithm known at Differential Evolution is described. Differential Evolution is a simple, fast, and robust evolutionary strategy that has been proven effective in determining the global optimum for several difficult optimization problems, including highly nonlinear systems with discontinuities and multiple local optima. The method is combined with a Navier-Stokes solver that evaluates the various intermediate designs and provides inputs to the optimization procedure. An efficient constraint handling mechanism is also incorporated. Results are presented for the inverse design of a turbine airfoil from a modern jet engine. The capability of the method to search large design spaces and obtain the optimal airfoils in an automatic fashion is demonstrated. Substantial reductions in the overall computing time requirements are achieved by using the algorithm in conjunction with neural networks.
Aerodynamic Shape Optimization Using Hybridized Differential Evolution
NASA Technical Reports Server (NTRS)
Madavan, Nateri K.
2003-01-01
An aerodynamic shape optimization method that uses an evolutionary algorithm known at Differential Evolution (DE) in conjunction with various hybridization strategies is described. DE is a simple and robust evolutionary strategy that has been proven effective in determining the global optimum for several difficult optimization problems. Various hybridization strategies for DE are explored, including the use of neural networks as well as traditional local search methods. A Navier-Stokes solver is used to evaluate the various intermediate designs and provide inputs to the hybrid DE optimizer. The method is implemented on distributed parallel computers so that new designs can be obtained within reasonable turnaround times. Results are presented for the inverse design of a turbine airfoil from a modern jet engine. (The final paper will include at least one other aerodynamic design application). The capability of the method to search large design spaces and obtain the optimal airfoils in an automatic fashion is demonstrated.
Two-dimensional optimal sensor placement
Zhang, H.
1995-05-01
A method for determining the optimal two-dimensional spatial placement of multiple sensors participating in a robot perception task is introduced in this paper. This work is motivated by the fact that sensor data fusion is an effective means of reducing uncertainties in sensor observations, and that the combined uncertainty varies with the relative placement of the sensors with respect to each other. The problem of optimal sensor placement is formulated and a solution is presented in the two dimensional space. The algebraic structure of the combined sensor uncertainty with respect to the placement of sensor is studied. A necessary condition for optimal placement is derived and this necessary condition is used to obtain an efficient closed-form solution for the global optimal placement. Numerical examples are provided to illustrate the effectiveness and efficiency of the solution. 11 refs.
Martin, Greg
2005-04-22
This debut editorial of Globalization and Health introduces the journal, briefly delineating its goals and objectives and outlines its scope of subject matter. 'Open Access' publishing is expected to become an increasingly important format for peer reviewed academic journals and that Globalization and Health is 'Open Access' is appropriate. The rationale behind starting a journal dedicated to globalization and health is three fold:Firstly: Globalization is reshaping the social geography within which we might strive to create health or prevent disease. The determinants of health - be they a SARS virus or a predilection for fatty foods - have joined us in our global mobility. Driven by economic liberalization and changing technologies, the phenomenon of 'access' is likely to dominate to an increasing extent the unfolding experience of human disease and wellbeing.Secondly: Understanding globalization as a subject matter itself needs certain benchmarks and barometers of its successes and failings. Health is one such barometer. It is a marker of social infrastructure and social welfare and as such can be used to either sound an alarm or give a victory cheer as our interconnectedness hurts and heals the populations we serve.And lastly: In as much as globalization can have an effect on health, it is also true that health and disease has an effect on globalization as exemplified by the existence of quarantine laws and the devastating economic effects of the AIDS pandemic.A balanced view would propose that the effects of globalization on health (and health systems) are neither universally good nor bad, but rather context specific. If the dialogue pertaining to globalization is to be directed or biased in any direction, then it must be this: that we consider the poor first.
Martin, Greg
2005-04-22
This debut editorial of Globalization and Health introduces the journal, briefly delineating its goals and objectives and outlines its scope of subject matter. 'Open Access' publishing is expected to become an increasingly important format for peer reviewed academic journals and that Globalization and Health is 'Open Access' is appropriate. The rationale behind starting a journal dedicated to globalization and health is three fold:Firstly: Globalization is reshaping the social geography within which we might strive to create health or prevent disease. The determinants of health - be they a SARS virus or a predilection for fatty foods - have joined us in our global mobility. Driven by economic liberalization and changing technologies, the phenomenon of 'access' is likely to dominate to an increasing extent the unfolding experience of human disease and wellbeing.Secondly: Understanding globalization as a subject matter itself needs certain benchmarks and barometers of its successes and failings. Health is one such barometer. It is a marker of social infrastructure and social welfare and as such can be used to either sound an alarm or give a victory cheer as our interconnectedness hurts and heals the populations we serve.And lastly: In as much as globalization can have an effect on health, it is also true that health and disease has an effect on globalization as exemplified by the existence of quarantine laws and the devastating economic effects of the AIDS pandemic.A balanced view would propose that the effects of globalization on health (and health systems) are neither universally good nor bad, but rather context specific. If the dialogue pertaining to globalization is to be directed or biased in any direction, then it must be this: that we consider the poor first. PMID:15847699
Particle Swarm Optimization Method Based on Chaotic Local Search and Roulette Wheel Mechanism
NASA Astrophysics Data System (ADS)
Xia, Xiaohua
Combining the particle swarm optimization (PSO) technique with the chaotic local search (CLS) and roulette wheel mechanism (RWM), an efficient optimization method solving the constrained nonlinear optimization problems is presented in this paper. PSO can be viewed as the global optimizer while the CLS and RWM are employed for the local search. Thus, the possibility of exploring a global minimum in problems with many local optima is increased. The search will continue until a termination criterion is satisfied. Benefit from the fast globally converging characteristics of PSO and the effective local search ability of CLS and RWM, the proposed method can obtain the global optimal results quickly which was tested for six benchmark optimization problems. And the improved performance comparing with the standard PSO and genetic algorithm (GA) testified its validity.
Involute composite design evaluation using global design sensitivity derivatives
NASA Technical Reports Server (NTRS)
Hart, J. K.; Stanton, E. L.
1989-01-01
An optimization capability for involute structures has been developed. Its key feature is the use of global material geometry variables which are so chosen that all combinations of design variables within a set of lower and upper bounds correspond to manufacturable designs. A further advantage of global variables is that their number does not increase with increasing mesh density. The accuracy of the sensitivity derivatives has been verified both through finite difference tests and through the successful use of the derivatives by an optimizer. The state of the art in composite design today is still marked by point design algorithms linked together using ad hoc methods not directly related to a manufacturing procedure. The global design sensitivity approach presented here for involutes can be applied to filament wound shells and other composite constructions using material form features peculiar to each construction. The present involute optimization technology is being applied to the Space Shuttle SRM nozzle boot ring redesigns by PDA Engineering.
Global challenges and globalization of bioethics.
Nezhmetdinova, Farida
2013-02-01
This article analyzes problems and implications for man and nature connected with the formation of a new architecture of science, based on the convergence of nanotechnology, biotechnology, information technology, and cognitive science (NBIC). It also describes evolution and genesis of bioethics, a scientific discipline and social practice with a special role of ethical management of potential risks of scientific research. The aim was to demonstrate the necessity of bioethical social control in the development of a global bioeconomy driven by NBIC technologies.
Particle Swarm Optimization Toolbox
NASA Technical Reports Server (NTRS)
Grant, Michael J.
2010-01-01
The Particle Swarm Optimization Toolbox is a library of evolutionary optimization tools developed in the MATLAB environment. The algorithms contained in the library include a genetic algorithm (GA), a single-objective particle swarm optimizer (SOPSO), and a multi-objective particle swarm optimizer (MOPSO). Development focused on both the SOPSO and MOPSO. A GA was included mainly for comparison purposes, and the particle swarm optimizers appeared to perform better for a wide variety of optimization problems. All algorithms are capable of performing unconstrained and constrained optimization. The particle swarm optimizers are capable of performing single and multi-objective optimization. The SOPSO and MOPSO algorithms are based on swarming theory and bird-flocking patterns to search the trade space for the optimal solution or optimal trade in competing objectives. The MOPSO generates Pareto fronts for objectives that are in competition. A GA, based on Darwin evolutionary theory, is also included in the library. The GA consists of individuals that form a population in the design space. The population mates to form offspring at new locations in the design space. These offspring contain traits from both of the parents. The algorithm is based on this combination of traits from parents to hopefully provide an improved solution than either of the original parents. As the algorithm progresses, individuals that hold these optimal traits will emerge as the optimal solutions. Due to the generic design of all optimization algorithms, each algorithm interfaces with a user-supplied objective function. This function serves as a "black-box" to the optimizers in which the only purpose of this function is to evaluate solutions provided by the optimizers. Hence, the user-supplied function can be numerical simulations, analytical functions, etc., since the specific detail of this function is of no concern to the optimizer. These algorithms were originally developed to support entry
Responsibility for global health.
Buchanan, Allen; DeCamp, Matthew
2006-01-01
There are several reasons for the current prominence of global health issues. Among the most important is the growing awareness that some risks to health are global in scope and can only be countered by global cooperation. In addition, human rights discourse and, more generally, the articulation of a coherent cosmopolitan ethical perspective that acknowledges the importance of all persons, regardless of where they live, provide a normative basis for taking global health seriously as a moral issue. In this paper we begin the task of translating the vague commitment to doing something to improve global health into a coherent set of more determinate obligations. One chief conclusion of our inquiry is that the responsibilities of states regarding global health are both more determinate and more extensive than is usually assumed. We also argue, however, that institutional innovation will be needed to achieve a more comprehensive, fair distribution of concrete responsibilities regarding global health and to provide effective mechanisms for holding various state and nonstate actors accountable for fulfilling them.
Ridzal, Danis
2007-03-01
Aristos is a Trilinos package for nonlinear continuous optimization, based on full-space sequential quadratic programming (SQP) methods. Aristos is specifically designed for the solution of large-scale constrained optimization problems in which the linearized constraint equations require iterative (i.e. inexact) linear solver techniques. Aristos' unique feature is an efficient handling of inexactness in linear system solves. Aristos currently supports the solution of equality-constrained convex and nonconvex optimization problems. It has been used successfully in the area of PDE-constrained optimization, for the solution of nonlinear optimal control, optimal design, and inverse problems.
2007-03-01
Aristos is a Trilinos package for nonlinear continuous optimization, based on full-space sequential quadratic programming (SQP) methods. Aristos is specifically designed for the solution of large-scale constrained optimization problems in which the linearized constraint equations require iterative (i.e. inexact) linear solver techniques. Aristos' unique feature is an efficient handling of inexactness in linear system solves. Aristos currently supports the solution of equality-constrained convex and nonconvex optimization problems. It has been used successfully in the areamore » of PDE-constrained optimization, for the solution of nonlinear optimal control, optimal design, and inverse problems.« less
Holt, Douglas B; Quelch, John A; Taylor, Earl L
2004-09-01
It's time to rethink global branding. More than two decades ago, Harvard Business School professor Theodore Levitt argued that corporations should grow by selling standardized products all over the world. But consumers in most countries had trouble relating to generic products, so executives instead strove for global scale on backstage activities such as production while customizing product features and selling techniques to local tastes. Such "glocal" strategies now rule marketing. Global branding has lost more luster recently because transnational companies have been under siege, with brands like Coca-Cola and Nike becoming lightning rods for antiglobalization protests. The instinctive reaction of most transnational companies has been to try to fly below the radar. But global brands can't escape notice. In fact, most transnational corporations don't realize that because of their power and pervasiveness, people view them differently than they do other firms. In a research project involving 3,300 consumers in 41 countries, the authors found that most people choose one global brand over another because of differences in the brands'global qualities. Ratherthan ignore the global characteristics of their brands, firms must learn to manage those characteristics. That's critical, because future growth for most companies will likely come from foreign markets. Consumers base preferences on three dimensions of global brands--quality (signaled by a company's global stature); the cultural myths that brands author; and firms' efforts to address social problems. The authors also found that it didn't matter to consumers whether the brands they bought were American--a remarkable finding considering that the study was conducted when anti-American sentiment in many nations was on the rise.
Holt, Douglas B; Quelch, John A; Taylor, Earl L
2004-09-01
It's time to rethink global branding. More than two decades ago, Harvard Business School professor Theodore Levitt argued that corporations should grow by selling standardized products all over the world. But consumers in most countries had trouble relating to generic products, so executives instead strove for global scale on backstage activities such as production while customizing product features and selling techniques to local tastes. Such "glocal" strategies now rule marketing. Global branding has lost more luster recently because transnational companies have been under siege, with brands like Coca-Cola and Nike becoming lightning rods for antiglobalization protests. The instinctive reaction of most transnational companies has been to try to fly below the radar. But global brands can't escape notice. In fact, most transnational corporations don't realize that because of their power and pervasiveness, people view them differently than they do other firms. In a research project involving 3,300 consumers in 41 countries, the authors found that most people choose one global brand over another because of differences in the brands'global qualities. Ratherthan ignore the global characteristics of their brands, firms must learn to manage those characteristics. That's critical, because future growth for most companies will likely come from foreign markets. Consumers base preferences on three dimensions of global brands--quality (signaled by a company's global stature); the cultural myths that brands author; and firms' efforts to address social problems. The authors also found that it didn't matter to consumers whether the brands they bought were American--a remarkable finding considering that the study was conducted when anti-American sentiment in many nations was on the rise. PMID:15449856
Architecture of optimal transport networks
NASA Astrophysics Data System (ADS)
Durand, Marc
2006-01-01
We analyze the structure of networks minimizing the global resistance to flow (or dissipative energy) with respect to two different constraints: fixed total channel volume and fixed total channel surface area. First, we show that channels must be straight and have uniform cross-sectional areas in such optimal networks. We then establish a relation between the cross-sectional areas of adjoining channels at each junction. Indeed, this relation is a generalization of Murray’s law, originally established in the context of local optimization. We establish a relation too between angles and cross-sectional areas of adjoining channels at each junction, which can be represented as a vectorial force balance equation, where the force weight depends on the channel cross-sectional area. A scaling law between the minimal resistance value and the total volume or surface area value is also derived from the analysis. Furthermore, we show that no more than three or four channels meet at each junction of optimal bidimensional networks, depending on the flow profile (e.g., Poiseuille-like or pluglike) and the considered constraint (fixed volume or surface area). In particular, we show that sources are directly connected to wells, without intermediate junctions, for minimal resistance networks preserving the total channel volume in case of plug flow regime. Finally, all these results are compared with the structure of natural networks.
Combined control-structure optimization
NASA Technical Reports Server (NTRS)
Salama, M.; Milman, M.; Bruno, R.; Scheid, R.; Gibson, S.
1989-01-01
An approach for combined control-structure optimization keyed to enhancing early design trade-offs is outlined and illustrated by numerical examples. The approach employs a homotopic strategy and appears to be effective for generating families of designs that can be used in these early trade studies. Analytical results were obtained for classes of structure/control objectives with linear quadratic Gaussian (LQG) and linear quadratic regulator (LQR) costs. For these, researchers demonstrated that global optima can be computed for small values of the homotopy parameter. Conditions for local optima along the homotopy path were also given. Details of two numerical examples employing the LQR control cost were given showing variations of the optimal design variables along the homotopy path. The results of the second example suggest that introducing a second homotopy parameter relating the two parts of the control index in the LQG/LQR formulation might serve to enlarge the family of Pareto optima, but its effect on modifying the optimal structural shapes may be analogous to the original parameter lambda.
Wehmeyer, Christoph; Falk von Rudorff, Guido; Wolf, Sebastian; Kabbe, Gabriel; Schärf, Daniel; Kühne, Thomas D; Sebastiani, Daniel
2012-11-21
We present a stochastic, swarm intelligence-based optimization algorithm for the prediction of global minima on potential energy surfaces of molecular cluster structures. Our optimization approach is a modification of the artificial bee colony (ABC) algorithm which is inspired by the foraging behavior of honey bees. We apply our modified ABC algorithm to the problem of global geometry optimization of molecular cluster structures and show its performance for clusters with 2-57 particles and different interatomic interaction potentials. PMID:23181297
NASA Astrophysics Data System (ADS)
Wehmeyer, Christoph; Falk von Rudorff, Guido; Wolf, Sebastian; Kabbe, Gabriel; Schärf, Daniel; Kühne, Thomas D.; Sebastiani, Daniel
2012-11-01
We present a stochastic, swarm intelligence-based optimization algorithm for the prediction of global minima on potential energy surfaces of molecular cluster structures. Our optimization approach is a modification of the artificial bee colony (ABC) algorithm which is inspired by the foraging behavior of honey bees. We apply our modified ABC algorithm to the problem of global geometry optimization of molecular cluster structures and show its performance for clusters with 2-57 particles and different interatomic interaction potentials.
Piver, W T
1991-01-01
Increasing concentrations of CO2 and other greenhouse gases in the atmosphere can be directly related to global warming. In terms of human health, because a major cause of increasing atmospheric concentrations of CO2 is the increased combustion of fossil fuels, global warming also may result in increases in air pollutants, acid deposition, and exposure to ultraviolet (UV) radiation. To understand better the impacts of global warming phenomena on human health, this review emphasizes the processes that are responsible for the greenhouse effect, air pollution, acid deposition, and increased exposure to UV radiation. PMID:1820255
NASA Astrophysics Data System (ADS)
Dando, Owen; Gregory, Ruth
1998-07-01
We examine the field equations of a self-gravitating global string in low energy superstring gravity, allowing for an arbitrary coupling of the global string to the dilaton. Massive and massless dilatons are considered. For the massive dilaton the spacetime is similar to the recently discovered non-singular time-dependent Einstein self-gravitating global string, but the massless dilaton generically gives a singular spacetime, even allowing for time dependence. We also demonstrate a time-dependent non-singular string-antistring configuration, in which the string pair causes a compactification of two of the spatial dimensions, albeit on a very large scale.
Piver, W T
1991-12-01
Increasing concentrations of CO2 and other greenhouse gases in the atmosphere can be directly related to global warming. In terms of human health, because a major cause of increasing atmospheric concentrations of CO2 is the increased combustion of fossil fuels, global warming also may result in increases in air pollutants, acid deposition, and exposure to ultraviolet (UV) radiation. To understand better the impacts of global warming phenomena on human health, this review emphasizes the processes that are responsible for the greenhouse effect, air pollution, acid deposition, and increased exposure to UV radiation.
Walt, G
2001-01-01
Globalization means different things to different people; a general definition is the increasing movement of information, material and people across borders. It can be considered in terms of five conflicting but inter-relating themes, economic transformation; new patterns of trade; an increasing poverty gap associated with widening health inequalities; the revolution in electronic communication; and the growing role of non-state actors, such as non-governmental organizations and transnational corporations, in global governance. Globalization is both an opportunity and a threat, but it is not inexorable. Successful action against its undesirable aspects is possible.
NASA Astrophysics Data System (ADS)
Chandra, Rishabh
Partial differential equation-constrained combinatorial optimization (PDECCO) problems are a mixture of continuous and discrete optimization problems. PDECCO problems have discrete controls, but since the partial differential equations (PDE) are continuous, the optimization space is continuous as well. Such problems have several applications, such as gas/water network optimization, traffic optimization, micro-chip cooling optimization, etc. Currently, no efficient classical algorithm which guarantees a global minimum for PDECCO problems exists. A new mapping has been developed that transforms PDECCO problem, which only have linear PDEs as constraints, into quadratic unconstrained binary optimization (QUBO) problems that can be solved using an adiabatic quantum optimizer (AQO). The mapping is efficient, it scales polynomially with the size of the PDECCO problem, requires only one PDE solve to form the QUBO problem, and if the QUBO problem is solved correctly and efficiently on an AQO, guarantees a global optimal solution for the original PDECCO problem.
Design criteria for optimal photosynthetic energy conversion
NASA Astrophysics Data System (ADS)
Fingerhut, Benjamin P.; Zinth, Wolfgang; de Vivie-Riedle, Regina
2008-12-01
Photochemical solar energy conversion is considered as an alternative of clean energy. For future light converting nano-machines photosynthetic reaction centers are used as prototypes optimized during evolution. We introduce a reaction scheme for global optimization and simulate the ultrafast charge separation in photochemical energy conversion. Multiple molecular charge carriers are involved in this process and are linked by Marcus-type electron transfer. In combination with evolutionary algorithms, we unravel the biological strategies for high quantum efficiency in photosynthetic reaction centers and extend these concepts to the design of artificial photochemical devices for energy conversion.
Multidisciplinary Optimization for Aerospace Using Genetic Optimization
NASA Technical Reports Server (NTRS)
Pak, Chan-gi; Hahn, Edward E.; Herrera, Claudia Y.
2007-01-01
In support of the ARMD guidelines NASA's Dryden Flight Research Center is developing a multidisciplinary design and optimization tool This tool will leverage existing tools and practices, and allow the easy integration and adoption of new state-of-the-art software. Optimization has made its way into many mainstream applications. For example NASTRAN(TradeMark) has its solution sequence 200 for Design Optimization, and MATLAB(TradeMark) has an Optimization Tool box. Other packages, such as ZAERO(TradeMark) aeroelastic panel code and the CFL3D(TradeMark) Navier-Stokes solver have no built in optimizer. The goal of the tool development is to generate a central executive capable of using disparate software packages ina cross platform network environment so as to quickly perform optimization and design tasks in a cohesive streamlined manner. A provided figure (Figure 1) shows a typical set of tools and their relation to the central executive. Optimization can take place within each individual too, or in a loop between the executive and the tool, or both.
Multidisciplinary design optimization of optomechanical devices
NASA Astrophysics Data System (ADS)
Williams, Antonio St. Clair Lloyd
2000-11-01
The current process for designing optomechanical devices typically involves independent design optimization within each discipline. For instance, an optics engineer would optimize the optics of the device for image quality using ray-tracing software. The structural engineer would optimize the design to minimize deformation using the finite element method. Independently optimizing the optics and structures of optomechanical systems negates the possibility of exploiting the interdisciplinary interactions. This can lead to increased product development time and cost. Multidisciplinary Design Optimization (MDO) techniques have been in development over the last decade and have been applied primarily to aerospace problems. The goal of MDO is to take advantage of the interactions between disciplines as well as to improve the product development time. The application of MDO formulations to the design of Optomechanical systems has not been achieved thus far. The aim of this study is to evaluate and develop MDO formulations for optomechanical devices that may be used to reduce the product development time and cost. In addition, the feasibility of obtaining a more global optimum design using these multidisciplinary optimization techniques is investigated. Several MDO formulations were evaluated during this study and compared to the current design optimization process. The formulations evaluated were the Multidisciplinary Design Feasible (MDF), the Sequenced Individual Discipline Feasible (SDO-IDF), and the Sequenced Multidisciplinary Design Feasible (SDO-MDF). The current optimization process is called Independent Design Optimization (IDO). For the examples examined, the results showed that the IDO formulation optimizes each discipline but does not guarantee a multidisciplinary optimum for coupled problems. The SDO-MDF formulation was found to be the least efficient of the formulations examined, while the SDO-IDF showed the most promise in terms of efficiency.
Hodges, Sarah
2015-01-01
Summary The history of medicine has gone ‘global.’ Why? Can the proliferation of the ‘global’ in our writing be explained away as a product of staying true to our historical subjects’ categories? Or has this historiography in fact delivered a new ‘global’ problematic or performed serious ‘global’ analytic work? The situation is far from clear, and it is the tension between the global as descriptor and an analytics of the global that concerns me here. I have three main concerns: (1) that there is an epistemic collusion between the discourses of universality that inform medical science and global-talk; (2) that the embrace of the ‘global’ authorises a turning away from analyses of power in history-writing in that (3) this turning away from analyses of power in history-writing leads to scholarship that reproduces rather than critiques globalisation as a set of institutions, discourses and practices. PMID:26345469
NASA Astrophysics Data System (ADS)
Grübler, Arnulf
2003-10-01
Technology and Global Change describes how technology has shaped society and the environment over the last 200 years. Technology has led us from the farm to the factory to the internet, and its impacts are now global. Technology has eliminated many problems, but has added many others (ranging from urban smog to the ozone hole to global warming). This book is the first to give a comprehensive description of the causes and impacts of technological change and how they relate to global environmental change. Written for specialists and nonspecialists alike, it will be useful for researchers and professors, as a textbook for graduate students, for people engaged in long-term policy planning in industry (strategic planning departments) and government (R & D and technology ministries, environment ministries), for environmental activists (NGOs), and for the wider public interested in history, technology, or environmental issues.