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.
Comparison of the Asynchronous Differential Evolution and JADE Minimization Algorithms
NASA Astrophysics Data System (ADS)
Zhabitsky, Mikhail
2016-02-01
Differential Evolution (DE) is an efficient evolutionary algorithm to solve global optimization problems. In this work we compare performance of the recently proposed Asynchronous Differential Evolution with Adaptive Correlation Matrix (ADEACM) to the widely used JADE algorithm, a DE variant with adaptive control parameters.
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.
Solving SAT Problem Based on Hybrid Differential Evolution Algorithm
NASA Astrophysics Data System (ADS)
Liu, Kunqi; Zhang, Jingmin; Liu, Gang; Kang, Lishan
Satisfiability (SAT) problem is an NP-complete problem. Based on the analysis about it, SAT problem is translated equally into an optimization problem on the minimum of objective function. A hybrid differential evolution algorithm is proposed to solve the Satisfiability problem. It makes full use of strong local search capacity of hill-climbing algorithm and strong global search capability of differential evolution algorithm, which makes up their disadvantages, improves the efficiency of algorithm and avoids the stagnation phenomenon. The experiment results show that the hybrid algorithm is efficient in solving SAT problem.
Shape Optimization of Rubber Bushing Using Differential Evolution Algorithm
2014-01-01
The objective of this study is to design rubber bushing at desired level of stiffness characteristics in order to achieve the ride quality of the vehicle. A differential evolution algorithm based approach is developed to optimize the rubber bushing through integrating a finite element code running in batch mode to compute the objective function values for each generation. Two case studies were given to illustrate the application of proposed approach. Optimum shape parameters of 2D bushing model were determined by shape optimization using differential evolution algorithm. PMID:25276848
Differential Evolution algorithm applied to FSW model calibration
NASA Astrophysics Data System (ADS)
Idagawa, H. S.; Santos, T. F. A.; Ramirez, A. J.
2014-03-01
Friction Stir Welding (FSW) is a solid state welding process that can be modelled using a Computational Fluid Dynamics (CFD) approach. These models use adjustable parameters to control the heat transfer and the heat input to the weld. These parameters are used to calibrate the model and they are generally determined using the conventional trial and error approach. Since this method is not very efficient, we used the Differential Evolution (DE) algorithm to successfully determine these parameters. In order to improve the success rate and to reduce the computational cost of the method, this work studied different characteristics of the DE algorithm, such as the evolution strategy, the objective function, the mutation scaling factor and the crossover rate. The DE algorithm was tested using a friction stir weld performed on a UNS S32205 Duplex Stainless Steel.
An Enhanced Differential Evolution Algorithm Based on Multiple Mutation Strategies
Xiang, Wan-li; Meng, Xue-lei; An, Mei-qing; Li, Yin-zhen; Gao, Ming-xia
2015-01-01
Differential evolution algorithm is a simple yet efficient metaheuristic for global optimization over continuous spaces. However, there is a shortcoming of premature convergence in standard DE, especially in DE/best/1/bin. In order to take advantage of direction guidance information of the best individual of DE/best/1/bin and avoid getting into local trap, based on multiple mutation strategies, an enhanced differential evolution algorithm, named EDE, is proposed in this paper. In the EDE algorithm, an initialization technique, opposition-based learning initialization for improving the initial solution quality, and a new combined mutation strategy composed of DE/current/1/bin together with DE/pbest/bin/1 for the sake of accelerating standard DE and preventing DE from clustering around the global best individual, as well as a perturbation scheme for further avoiding premature convergence, are integrated. In addition, we also introduce two linear time-varying functions, which are used to decide which solution search equation is chosen at the phases of mutation and perturbation, respectively. Experimental results tested on twenty-five benchmark functions show that EDE is far better than the standard DE. In further comparisons, EDE is compared with other five state-of-the-art approaches and related results show that EDE is still superior to or at least equal to these methods on most of benchmark functions. PMID:26609304
Application of Differential Evolution Algorithm on Self-Potential Data
Li, Xiangtao; Yin, Minghao
2012-01-01
Differential evolution (DE) is a population based evolutionary algorithm widely used for solving multidimensional global optimization problems over continuous spaces, and has been successfully used to solve several kinds of problems. In this paper, differential evolution is used for quantitative interpretation of self-potential data in geophysics. Six parameters are estimated including the electrical dipole moment, the depth of the source, the distance from the origin, the polarization angle and the regional coefficients. This study considers three kinds of data from Turkey: noise-free data, contaminated synthetic data, and Field example. The differential evolution and the corresponding model parameters are constructed as regards the number of the generations. Then, we show the vibration of the parameters at the vicinity of the low misfit area. Moreover, we show how the frequency distribution of each parameter is related to the number of the DE iteration. Experimental results show the DE can be used for solving the quantitative interpretation of self-potential data efficiently compared with previous methods. PMID:23240004
NASA Astrophysics Data System (ADS)
Salami, M. J. E.; Tijani, I. B.; Abdullateef, A. I.; Aibinu, M. A.
2013-12-01
A hybrid optimization algorithm using Differential Evolution (DE) and Genetic Algorithm (GA) is proposed in this study to address the problem of network parameters determination associated with the Nonlinear Autoregressive with eXogenous inputs Network (NARX-network). The proposed algorithm involves a two level optimization scheme to search for both optimal network architecture and weights. The DE at the upper level is formulated as combinatorial optimization to search for the network architecture while the associated network weights that minimize the prediction error is provided by the GA at the lower level. The performance of the algorithm is evaluated on identification of a laboratory rotary motion system. The system identification results show the effectiveness of the proposed algorithm for nonparametric model development.
Optimization of Electrical Energy Production by using Modified Differential Evolution Algorithm
NASA Astrophysics Data System (ADS)
Glotic, Arnel
The dissertation addressed the optimization of electrical energy production from hydro power plants and thermal power plants. It refers to short-term optimization and presents a complex optimization problem. The complexity of the problem arises from an extensive number of co-dependent variables and power plant constraints. According to the complexity of the problem, the differential evolution algorithm known as the successful and robust optimization algorithm was selected as an appropriate algorithm for optimization. The performance of this differential evolution algorithm is closely connected with a control parameters' set and its capabilities being inter alia improved by the algorithm's parallelization. The capabilities of achieving a global optimal solution within the optimization of electrical energy production are improved by the proposed modified differential evolution algorithm with new parallelization mode. This algorithm's performance is also improved by its proposed dynamic population size throughout the optimization process. In addition to achieving better optimization results in comparison with the classic differential evolution algorithm, the proposed dynamic population size reduces convergence time. The improvements of this algorithm presented in the dissertation, besides power plant models mostly used in scientific publications, were also tested on the power plant models represented by real parameters'. The optimization of electrical energy from hydro and thermal power plants is followed by certain criteria; satisfying system demand, minimizing usage of water quantity per produced electrical energy unit, minimizing or eliminating water spillage, satisfying the final reservoir states of hydro power plants and minimizing fuel costs and emissions of thermal power plants.
A Modified Differential Evolution Algorithm with Cauchy Mutation for Global Optimization
NASA Astrophysics Data System (ADS)
Ali, Musrrat; Pant, Millie; Singh, Ved Pal
Differential Evolution (DE) is a powerful yet simple evolutionary algorithm for optimization of real valued, multi modal functions. DE is generally considered as a reliable, accurate and robust optimization technique. However, the algorithm suffers from premature convergence, slow convergence rate and large computational time for optimizing the computationally expensive objective functions. Therefore, an attempt to speed up DE is considered necessary. This research introduces a modified differential evolution (MDE), a modification to DE that enhances the convergence rate without compromising with the solution quality. In Modified differential evolution (MDE) algorithm, if an individual fails in continuation to improve its performance to a specified number of times then new point is generated using Cauchy mutation. MDE on a test bed of functions is compared with original DE. It is found that MDE requires less computational effort to locate global optimal solution.
NASA Astrophysics Data System (ADS)
Mingolo, Nusharin; Sarakorn, Weerachai
2016-04-01
In this research, the Modified Differential Evolution (DE) algorithm is proposed and applied to the Magnetotelluric (MT) and Vertical Electrical sounding (VES) data to reveal the reasonable resistivity structure. The common processes of DE algorithm, including initialization, mutation and crossover, are modified by introducing both new control parameters and some constraints to obtain the fitting-reasonable resistivity model. The validity and efficiency of our developed modified DE algorithm is tested on both synthetic and real observed data. Our developed DE algorithm is also compared to the well-known OCCAM's algorithm for real case of MT data. For the synthetic case, our modified DE algorithm with appropriate control parameters can reveal the reasonable-fitting models when compared to the original synthetic models. For the real data case, the resistivity structures revealed by our algorithm are closed to those obtained by OCCAM's inversion, but our obtained structures reveal layers more apparently.
A New Differential Evolution Algorithm and Its Application to Real Life Problems
NASA Astrophysics Data System (ADS)
Pant, Millie; Ali, Musrrat; Singh, V. P.
2009-07-01
Most of the real life problems occurring in various disciplines of science and engineering can be modeled as optimization problems. Also, most of these problems are nonlinear in nature which requires a suitable and efficient optimization algorithm to reach to an optimum value. In the past few years various algorithms has been proposed to deal with nonlinear optimization problems. Differential Evolution (DE) is a stochastic, population based search technique, which can be classified as an Evolutionary Algorithm (EA) using the concepts of selection crossover and reproduction to guide the search. It has emerged as a powerful tool for solving optimization problems in the past few years. However, the convergence rate of DE still does not meet all the requirements, and attempts to speed up differential evolution are considered necessary. In order to improve the performance of DE, we propose a modified DE algorithm called DEPCX which uses parent centric approach to manipulate the solution vectors. The performance of DEPCX is validated on a test bed of five benchmark functions and five real life engineering design problems. Numerical results are compared with original differential evolution (DE) and with TDE, another recently modified version of DE. Empirical analysis of the results clearly indicates the competence and efficiency of the proposed DEPCX algorithm for solving benchmark as well as real life problems with a good convergence rate.
Li, Xiaofang; Xu, Lizhong; Wang, Huibin; Song, Jie; Yang, Simon X
2010-01-01
The traditional Low Energy Adaptive Cluster Hierarchy (LEACH) routing protocol is a clustering-based protocol. The uneven selection of cluster heads results in premature death of cluster heads and premature blind nodes inside the clusters, thus reducing the overall lifetime of the network. With a full consideration of information on energy and distance distribution of neighboring nodes inside the clusters, this paper proposes a new routing algorithm based on differential evolution (DE) to improve the LEACH routing protocol. To meet the requirements of monitoring applications in outdoor environments such as the meteorological, hydrological and wetland ecological environments, the proposed algorithm uses the simple and fast search features of DE to optimize the multi-objective selection of cluster heads and prevent blind nodes for improved energy efficiency and system stability. Simulation results show that the proposed new LEACH routing algorithm has better performance, effectively extends the working lifetime of the system, and improves the quality of the wireless sensor networks. PMID:22219670
Wang, Xiaolong; Jiang, Aipeng; Jiangzhou, Shu; Li, Ping
2014-01-01
A large-scale parallel-unit seawater reverse osmosis desalination plant contains many reverse osmosis (RO) units. If the operating conditions change, these RO units will not work at the optimal design points which are computed before the plant is built. The operational optimization problem (OOP) of the plant is to find out a scheduling of operation to minimize the total running cost when the change happens. In this paper, the OOP is modelled as a mixed-integer nonlinear programming problem. A two-stage differential evolution algorithm is proposed to solve this OOP. Experimental results show that the proposed method is satisfactory in solution quality. PMID:24701180
Wang, Jian; Wang, Xiaolong; Jiang, Aipeng; Jiangzhou, Shu; Li, Ping
2014-01-01
A large-scale parallel-unit seawater reverse osmosis desalination plant contains many reverse osmosis (RO) units. If the operating conditions change, these RO units will not work at the optimal design points which are computed before the plant is built. The operational optimization problem (OOP) of the plant is to find out a scheduling of operation to minimize the total running cost when the change happens. In this paper, the OOP is modelled as a mixed-integer nonlinear programming problem. A two-stage differential evolution algorithm is proposed to solve this OOP. Experimental results show that the proposed method is satisfactory in solution quality. PMID:24701180
Best band selection of hyperspectral remote sensing image based on differential evolution algorithm
NASA Astrophysics Data System (ADS)
Cai, Z.; Li, Z.; Jiang, A.; Chen, X.
2010-12-01
The hyperspectral remote sensing makes use of spectrum resolution with the nano-scale collecting image data simultaneously in dozens or hundreds of narrow and adjacent spectral bands above the earth's surface. These hyperspectral remote sensors make it possible to derive a continuous spectrum line for each image pixel (or a special sort of material). It can acquire space information, radiated information and spectrum information of images synchronously, so that it has remarkable application value and extensive development prospect in many related fields. However, the hyperspectral remote sensing images' characteristics, such as hundreds of bands, high spectral resolution and large volumes of data, have induced many problems such as high ratio of redundant information, large-scale storage space query, long processing delay, the Hughes phenomenon and so on. The main approach to solve these problems is making dimensional reduction before the classification or visual interpretation with the hyperspectral image data. There are two main methods for dimensional reduction: feature abstraction and bands selection. Although the feature abstraction that can achieve the purpose of dimensional reduction, in the process of feature abstraction or non-linear changes in both linear transformation, it will cause the loss of the physical implication of the original image data and also make it hard to apply hyperspectral images to visual interpretation. In contrast, band selection method outperforms in terms of being more universal for application. The selected bands can not only be used as attributes (features) for classification but also synthesize RGB false color image for visual interpretation. Therefore, band selection of hyperspectral remote sensing images is an important dimensional reduction method. Here, we design a hyperspectral remote sensing image band selection algorithm based on differential evolution algorithm. Differential evolution is an evolutionary method based on
An implementation of differential evolution algorithm for inversion of geoelectrical data
NASA Astrophysics Data System (ADS)
Balkaya, Çağlayan
2013-11-01
Differential evolution (DE), a population-based evolutionary algorithm (EA) has been implemented to invert self-potential (SP) and vertical electrical sounding (VES) data sets. The algorithm uses three operators including mutation, crossover and selection similar to genetic algorithm (GA). Mutation is the most important operator for the success of DE. Three commonly used mutation strategies including DE/best/1 (strategy 1), DE/rand/1 (strategy 2) and DE/rand-to-best/1 (strategy 3) were applied together with a binomial type crossover. Evolution cycle of DE was realized without boundary constraints. For the test studies performed with SP data, in addition to both noise-free and noisy synthetic data sets two field data sets observed over the sulfide ore body in the Malachite mine (Colorado) and over the ore bodies in the Neem-Ka Thana cooper belt (India) were considered. VES test studies were carried out using synthetically produced resistivity data representing a three-layered earth model and a field data set example from Gökçeada (Turkey), which displays a seawater infiltration problem. Mutation strategies mentioned above were also extensively tested on both synthetic and field data sets in consideration. Of these, strategy 1 was found to be the most effective strategy for the parameter estimation by providing less computational cost together with a good accuracy. The solutions obtained by DE for the synthetic cases of SP were quite consistent with particle swarm optimization (PSO) which is a more widely used population-based optimization algorithm than DE in geophysics. Estimated parameters of SP and VES data were also compared with those obtained from Metropolis-Hastings (M-H) sampling algorithm based on simulated annealing (SA) without cooling to clarify uncertainties in the solutions. Comparison to the M-H algorithm shows that DE performs a fast approximate posterior sampling for the case of low-dimensional inverse geophysical problems.
A modified differential evolution algorithm for damage identification in submerged shell structures
NASA Astrophysics Data System (ADS)
Reed, H. M.; Nichols, J. M.; Earls, C. J.
2013-08-01
Obtaining good estimates of structural parameters from observed data is a particularly challenging task owing to the complex (often multi-modal) likelihood functions that often accompany such problems. As a result, sophisticated optimization routines are typically required to produce maximum likelihood estimates of the desired parameters. Evolutionary algorithms comprise one such approach, whereby nature-inspired mutation and crossover operations allow the sensible exploration of even multi-modal functions, in search of a global maximum. The challenge, of course, is to balance broad coverage in parameter space with the speed required to obtain such estimates. This work focuses directly on this problem by proposing a modified version of the Differential Evolution algorithm. The idea is to adjust both mutation and cross-over rates, during the optimization, in a manner that increases the convergence rate to the desired solution. Performance is demonstrated on the challenging problem of identifying imperfections in submerged shell structures.
NASA Astrophysics Data System (ADS)
Pitakaso, Rapeepan; Sethanan, Kanchana
2016-02-01
This article proposes the differential evolution algorithm (DE) and the modified differential evolution algorithm (DE-C) to solve a simple assembly line balancing problem type 1 (SALBP-1) and SALBP-1 when the maximum number of machine types in a workstation is considered (SALBP-1M). The proposed algorithms are tested and compared with existing effective heuristics using various sets of test instances found in the literature. The computational results show that the proposed heuristics is one of the best methods, compared with the other approaches.
NASA Astrophysics Data System (ADS)
Song, Xianhai; Li, Lei; Zhang, Xueqiang; Huang, Jianquan; Shi, Xinchun; Jin, Si; Bai, Yiming
2014-10-01
In recent years, Rayleigh waves are gaining popularity to obtain near-surface shear (S)-wave velocity profiles. However, inversion of Rayleigh wave dispersion curves is challenging for most local-search methods due to its high nonlinearity and to its multimodality. In this study, we proposed and tested a new Rayleigh wave dispersion curve inversion scheme based on differential evolution (DE) algorithm. DE is a novel stochastic search approach that possesses several attractive advantages: (1) Capable of handling non-differentiable, non-linear and multimodal objective functions because of its stochastic search strategy; (2) Parallelizability to cope with computation intensive objective functions without being time consuming by using a vector population where the stochastic perturbation of the population vectors can be done independently; (3) Ease of use, i.e. few control variables to steer the minimization/maximization by DE's self-organizing scheme; and (4) Good convergence properties. The proposed inverse procedure was applied to nonlinear inversion of fundamental-mode Rayleigh wave dispersion curves for near-surface S-wave velocity profiles. To evaluate calculation efficiency and stability of DE, we firstly inverted four noise-free and four noisy synthetic data sets. Secondly, we investigated effects of the number of layers on DE algorithm and made an uncertainty appraisal analysis by DE algorithm. Thirdly, we made a comparative analysis with genetic algorithms (GA) by a synthetic data set to further investigate the performance of the proposed inverse procedure. Finally, we inverted a real-world example from a waste disposal site in NE Italy to examine the applicability of DE on Rayleigh wave dispersion curves. Furthermore, we compared the performance of the proposed approach to that of GA to further evaluate scores of the inverse procedure described here. Results from both synthetic and actual field data demonstrate that differential evolution algorithm applied
Zhu, Wu; Fang, Jian-an; Tang, Yang; Zhang, Wenbing; Du, Wei
2012-01-01
Design of a digital infinite-impulse-response (IIR) filter is the process of synthesizing and implementing a recursive filter network so that a set of prescribed excitations results a set of desired responses. However, the error surface of IIR filters is usually non-linear and multi-modal. In order to find the global minimum indeed, an improved differential evolution (DE) is proposed for digital IIR filter design in this paper. The suggested algorithm is a kind of DE variants with a controllable probabilistic (CPDE) population size. It considers the convergence speed and the computational cost simultaneously by nonperiodic partial increasing or declining individuals according to fitness diversities. In addition, we discuss as well some important aspects for IIR filter design, such as the cost function value, the influence of (noise) perturbations, the convergence rate and successful percentage, the parameter measurement, etc. As to the simulation result, it shows that the presented algorithm is viable and comparable. Compared with six existing State-of-the-Art algorithms-based digital IIR filter design methods obtained by numerical experiments, CPDE is relatively more promising and competitive. PMID:22808191
NASA Astrophysics Data System (ADS)
Zheng, Feifei; Simpson, Angus R.; Zecchin, Aaron C.
2011-08-01
This paper proposes a novel optimization approach for the least cost design of looped water distribution systems (WDSs). Three distinct steps are involved in the proposed optimization approach. In the first step, the shortest-distance tree within the looped network is identified using the Dijkstra graph theory algorithm, for which an extension is proposed to find the shortest-distance tree for multisource WDSs. In the second step, a nonlinear programming (NLP) solver is employed to optimize the pipe diameters for the shortest-distance tree (chords of the shortest-distance tree are allocated the minimum allowable pipe sizes). Finally, in the third step, the original looped water network is optimized using a differential evolution (DE) algorithm seeded with diameters in the proximity of the continuous pipe sizes obtained in step two. As such, the proposed optimization approach combines the traditional deterministic optimization technique of NLP with the emerging evolutionary algorithm DE via the proposed network decomposition. The proposed methodology has been tested on four looped WDSs with the number of decision variables ranging from 21 to 454. Results obtained show the proposed approach is able to find optimal solutions with significantly less computational effort than other optimization techniques.
NASA Astrophysics Data System (ADS)
Gurarslan, Gurhan; Karahan, Halil
2015-09-01
In this study, an accurate model was developed for solving problems of groundwater-pollution-source identification. In the developed model, the numerical simulations of flow and pollutant transport in groundwater were carried out using MODFLOW and MT3DMS software. The optimization processes were carried out using a differential evolution algorithm. The performance of the developed model was tested on two hypothetical aquifer models using real and noisy observation data. In the first model, the release histories of the pollution sources were determined assuming that the numbers, locations and active stress periods of the sources are known. In the second model, the release histories of the pollution sources were determined assuming that there is no information on the sources. The results obtained by the developed model were found to be better than those reported in literature.
NASA Astrophysics Data System (ADS)
Wang, Congzhe; Fang, Yuefa; Guo, Sheng
2015-07-01
Dimensional synthesis is one of the most difficult issues in the field of parallel robots with actuation redundancy. To deal with the optimal design of a redundantly actuated parallel robot used for ankle rehabilitation, a methodology of dimensional synthesis based on multi-objective optimization is presented. First, the dimensional synthesis of the redundant parallel robot is formulated as a nonlinear constrained multi-objective optimization problem. Then four objective functions, separately reflecting occupied space, input/output transmission and torque performances, and multi-criteria constraints, such as dimension, interference and kinematics, are defined. In consideration of the passive exercise of plantar/dorsiflexion requiring large output moment, a torque index is proposed. To cope with the actuation redundancy of the parallel robot, a new output transmission index is defined as well. The multi-objective optimization problem is solved by using a modified Differential Evolution(DE) algorithm, which is characterized by new selection and mutation strategies. Meanwhile, a special penalty method is presented to tackle the multi-criteria constraints. Finally, numerical experiments for different optimization algorithms are implemented. The computation results show that the proposed indices of output transmission and torque, and constraint handling are effective for the redundant parallel robot; the modified DE algorithm is superior to the other tested algorithms, in terms of the ability of global search and the number of non-dominated solutions. The proposed methodology of multi-objective optimization can be also applied to the dimensional synthesis of other redundantly actuated parallel robots only with rotational movements.
Liu, Chang; Wang, Guofeng; Xie, Qinglu; Zhang, Yanchao
2014-01-01
Effective fault classification of rolling element bearings provides an important basis for ensuring safe operation of rotating machinery. In this paper, a novel vibration sensor-based fault diagnosis method using an Ellipsoid-ARTMAP network (EAM) and a differential evolution (DE) algorithm is proposed. The original features are firstly extracted from vibration signals based on wavelet packet decomposition. Then, a minimum-redundancy maximum-relevancy algorithm is introduced to select the most prominent features so as to decrease feature dimensions. Finally, a DE-based EAM (DE-EAM) classifier is constructed to realize the fault diagnosis. The major characteristic of EAM is that the sample distribution of each category is realized by using a hyper-ellipsoid node and smoothing operation algorithm. Therefore, it can depict the decision boundary of disperse samples accurately and effectively avoid over-fitting phenomena. To optimize EAM network parameters, the DE algorithm is presented and two objectives, including both classification accuracy and nodes number, are simultaneously introduced as the fitness functions. Meanwhile, an exponential criterion is proposed to realize final selection of the optimal parameters. To prove the effectiveness of the proposed method, the vibration signals of four types of rolling element bearings under different loads were collected. Moreover, to improve the robustness of the classifier evaluation, a two-fold cross validation scheme is adopted and the order of feature samples is randomly arranged ten times within each fold. The results show that DE-EAM classifier can recognize the fault categories of the rolling element bearings reliably and accurately. PMID:24936949
NASA Astrophysics Data System (ADS)
Mozdgir, A.; Mahdavi, Iraj; Seyyedi, I.; Shiraqei, M. E.
2011-06-01
An assembly line is a flow-oriented production system where the productive units performing the operations, referred to as stations, are aligned in a serial manner. The assembly line balancing problem arises and has to be solved when an assembly line has to be configured or redesigned. The so-called simple assembly line balancing problem (SALBP), a basic version of the general problem, has attracted attention of researchers and practitioners of operations research for almost half a century. There are four types of objective functions which are considered to this kind of problem. The versions of SALBP may be complemented by a secondary objective which consists of smoothing station loads. Many heuristics have been proposed for the assembly line balancing problem due to its computational complexity and difficulty in identifying an optimal solution and so many heuristic solutions are supposed to solve this problem. In this paper a differential evolution algorithm is developed to minimize workload smoothness index in SALBP-2 and the algorithm parameters are optimized using Taguchi method.
NASA Astrophysics Data System (ADS)
Zhang, Yanjun; Yu, Chunjuan; Fu, Xinghu; Liu, Wenzhe; Bi, Weihong
2015-12-01
In the distributed optical fiber sensing system based on Brillouin scattering, strain and temperature are the main measuring parameters which can be obtained by analyzing the Brillouin center frequency shift. The novel algorithm which combines the cuckoo search algorithm (CS) with the improved differential evolution (IDE) algorithm is proposed for the Brillouin scattering parameter estimation. The CS-IDE algorithm is compared with CS algorithm and analyzed in different situation. The results show that both the CS and CS-IDE algorithm have very good convergence. The analysis reveals that the CS-IDE algorithm can extract the scattering spectrum features with different linear weight ratio, linewidth combination and SNR. Moreover, the BOTDR temperature measuring system based on electron optical frequency shift is set up to verify the effectiveness of the CS-IDE algorithm. Experimental results show that there is a good linear relationship between the Brillouin center frequency shift and temperature changes.
Design of wideband multilayer planar absorber using a new differential evolution algorithm
NASA Astrophysics Data System (ADS)
Lin, Chuan; Qing, Anyong; Zang, Jiefeng
2015-03-01
Design of wideband planar absorber using real electromagnetic composite materials is considered in this paper. A new differential evolution, dynamic differential evolution with best of random differential mutation, is applied to solve this problem. To simplify the problem, a suitable objective function and regulation scheme have been designed to transform the constrained multi-objective problem into unconstrained single-objective one. The effects of total thickness and investigated frequency band have been studied. Four-layer planar absorbers for different cases have been successfully designed.
NASA Technical Reports Server (NTRS)
Koshak, William; Solakiewicz, Richard
2012-01-01
The ability to estimate the fraction of ground flashes in a set of flashes observed by a satellite lightning imager, such as the future GOES-R Geostationary Lightning Mapper (GLM), would likely improve operational and scientific applications (e.g., severe weather warnings, lightning nitrogen oxides studies, and global electric circuit analyses). A Bayesian inversion method, called the Ground Flash Fraction Retrieval Algorithm (GoFFRA), was recently developed for estimating the ground flash fraction. The method uses a constrained mixed exponential distribution model to describe a particular lightning optical measurement called the Maximum Group Area (MGA). To obtain the optimum model parameters (one of which is the desired ground flash fraction), a scalar function must be minimized. This minimization is difficult because of two problems: (1) Label Switching (LS), and (2) Parameter Identity Theft (PIT). The LS problem is well known in the literature on mixed exponential distributions, and the PIT problem was discovered in this study. Each problem occurs when one allows the numerical minimizer to freely roam through the parameter search space; this allows certain solution parameters to interchange roles which leads to fundamental ambiguities, and solution error. A major accomplishment of this study is that we have employed a state-of-the-art genetic-based global optimization algorithm called Differential Evolution (DE) that constrains the parameter search in such a way as to remove both the LS and PIT problems. To test the performance of the GoFFRA when DE is employed, we applied it to analyze simulated MGA datasets that we generated from known mixed exponential distributions. Moreover, we evaluated the GoFFRA/DE method by applying it to analyze actual MGAs derived from low-Earth orbiting lightning imaging sensor data; the actual MGA data were classified as either ground or cloud flash MGAs using National Lightning Detection Network[TM] (NLDN) data. Solution error
A Novel Discrete Differential Evolution Algorithm for the Vehicle Routing Problem in B2C E-Commerce
NASA Astrophysics Data System (ADS)
Xia, Chao; Sheng, Ying; Jiang, Zhong-Zhong; Tan, Chunqiao; Huang, Min; He, Yuanjian
2015-12-01
In this paper, a novel discrete differential evolution (DDE) algorithm is proposed to solve the vehicle routing problems (VRP) in B2C e-commerce, in which VRP is modeled by the incomplete graph based on the actual urban road system. First, a variant of classical VRP is described and a mathematical programming model for the variant is given. Second, the DDE is presented, where individuals are represented as the sequential encoding scheme, and a novel reparation operator is employed to repair the infeasible solutions. Furthermore, a FLOYD operator for dealing with the shortest route is embedded in the proposed DDE. Finally, an extensive computational study is carried out in comparison with the predatory search algorithm and genetic algorithm, and the results show that the proposed DDE is an effective algorithm for VRP in B2C e-commerce.
He, Feng; Zhang, Wei; Zhang, Guoqiang
2016-01-01
A differential evolution algorithm for solving Nash equilibrium in nonlinear continuous games is presented in this paper, called NIDE (Nikaido-Isoda differential evolution). At each generation, parent and child strategy profiles are compared one by one pairwisely, adapting Nikaido-Isoda function as fitness function. In practice, the NE of nonlinear game model with cubic cost function and quadratic demand function is solved, and this method could also be applied to non-concave payoff functions. Moreover, the NIDE is compared with the existing Nash Domination Evolutionary Multiplayer Optimization (NDEMO), the result showed that NIDE was significantly better than NDEMO with less iterations and shorter running time. These numerical examples suggested that the NIDE method is potentially useful. PMID:27589229
Khan, S. U.; Qureshi, I. M.; Zaman, F.; Shoaib, B.; Naveed, A.; Basit, A.
2014-01-01
Three issues regarding sensor failure at any position in the antenna array are discussed. We assume that sensor position is known. The issues include raise in sidelobe levels, displacement of nulls from their original positions, and diminishing of null depth. The required null depth is achieved by making the weight of symmetrical complement sensor passive. A hybrid method based on memetic computing algorithm is proposed. The hybrid method combines the cultural algorithm with differential evolution (CADE) which is used for the reduction of sidelobe levels and placement of nulls at their original positions. Fitness function is used to minimize the error between the desired and estimated beam patterns along with null constraints. Simulation results for various scenarios have been given to exhibit the validity and performance of the proposed algorithm. PMID:24688440
NASA Astrophysics Data System (ADS)
Han, Yu-Yan; Gong, Dunwei; Sun, Xiaoyan
2015-07-01
A flow-shop scheduling problem with blocking has important applications in a variety of industrial systems but is underrepresented in the research literature. In this study, a novel discrete artificial bee colony (ABC) algorithm is presented to solve the above scheduling problem with a makespan criterion by incorporating the ABC with differential evolution (DE). The proposed algorithm (DE-ABC) contains three key operators. One is related to the employed bee operator (i.e. adopting mutation and crossover operators of discrete DE to generate solutions with good quality); the second is concerned with the onlooker bee operator, which modifies the selected solutions using insert or swap operators based on the self-adaptive strategy; and the last is for the local search, that is, the insert-neighbourhood-based local search with a small probability is adopted to improve the algorithm's capability in exploitation. The performance of the proposed DE-ABC algorithm is empirically evaluated by applying it to well-known benchmark problems. The experimental results show that the proposed algorithm is superior to the compared algorithms in minimizing the makespan criterion.
NASA Astrophysics Data System (ADS)
Li, Hong; Zhang, Li; Jiao, Yong-Chang
2016-07-01
This paper presents an interactive approach based on a discrete differential evolution algorithm to solve a class of integer bilevel programming problems, in which integer decision variables are controlled by an upper-level decision maker and real-value or continuous decision variables are controlled by a lower-level decision maker. Using the Karush--Kuhn-Tucker optimality conditions in the lower-level programming, the original discrete bilevel formulation can be converted into a discrete single-level nonlinear programming problem with the complementarity constraints, and then the smoothing technique is applied to deal with the complementarity constraints. Finally, a discrete single-level nonlinear programming problem is obtained, and solved by an interactive approach. In each iteration, for each given upper-level discrete variable, a system of nonlinear equations including the lower-level variables and Lagrange multipliers is solved first, and then a discrete nonlinear programming problem only with inequality constraints is handled by using a discrete differential evolution algorithm. Simulation results show the effectiveness of the proposed approach.
NASA Astrophysics Data System (ADS)
Bor, E.; Turduev, M.; Kurt, H.
2016-08-01
Photonic structure designs based on optimization algorithms provide superior properties compared to those using intuition-based approaches. In the present study, we numerically and experimentally demonstrate subwavelength focusing of light using wavelength scale absorption-free dielectric scattering objects embedded in an air background. An optimization algorithm based on differential evolution integrated into the finite-difference time-domain method was applied to determine the locations of each circular dielectric object with a constant radius and refractive index. The multiobjective cost function defined inside the algorithm ensures strong focusing of light with low intensity side lobes. The temporal and spectral responses of the designed compact photonic structure provided a beam spot size in air with a full width at half maximum value of 0.19λ, where λ is the wavelength of light. The experiments were carried out in the microwave region to verify numerical findings, and very good agreement between the two approaches was found. The subwavelength light focusing is associated with a strong interference effect due to nonuniformly arranged scatterers and an irregular index gradient. Improving the focusing capability of optical elements by surpassing the diffraction limit of light is of paramount importance in optical imaging, lithography, data storage, and strong light-matter interaction.
Bor, E; Turduev, M; Kurt, H
2016-01-01
Photonic structure designs based on optimization algorithms provide superior properties compared to those using intuition-based approaches. In the present study, we numerically and experimentally demonstrate subwavelength focusing of light using wavelength scale absorption-free dielectric scattering objects embedded in an air background. An optimization algorithm based on differential evolution integrated into the finite-difference time-domain method was applied to determine the locations of each circular dielectric object with a constant radius and refractive index. The multiobjective cost function defined inside the algorithm ensures strong focusing of light with low intensity side lobes. The temporal and spectral responses of the designed compact photonic structure provided a beam spot size in air with a full width at half maximum value of 0.19λ, where λ is the wavelength of light. The experiments were carried out in the microwave region to verify numerical findings, and very good agreement between the two approaches was found. The subwavelength light focusing is associated with a strong interference effect due to nonuniformly arranged scatterers and an irregular index gradient. Improving the focusing capability of optical elements by surpassing the diffraction limit of light is of paramount importance in optical imaging, lithography, data storage, and strong light-matter interaction. PMID:27477060
Bor, E.; Turduev, M.; Kurt, H.
2016-01-01
Photonic structure designs based on optimization algorithms provide superior properties compared to those using intuition-based approaches. In the present study, we numerically and experimentally demonstrate subwavelength focusing of light using wavelength scale absorption-free dielectric scattering objects embedded in an air background. An optimization algorithm based on differential evolution integrated into the finite-difference time-domain method was applied to determine the locations of each circular dielectric object with a constant radius and refractive index. The multiobjective cost function defined inside the algorithm ensures strong focusing of light with low intensity side lobes. The temporal and spectral responses of the designed compact photonic structure provided a beam spot size in air with a full width at half maximum value of 0.19λ, where λ is the wavelength of light. The experiments were carried out in the microwave region to verify numerical findings, and very good agreement between the two approaches was found. The subwavelength light focusing is associated with a strong interference effect due to nonuniformly arranged scatterers and an irregular index gradient. Improving the focusing capability of optical elements by surpassing the diffraction limit of light is of paramount importance in optical imaging, lithography, data storage, and strong light-matter interaction. PMID:27477060
NASA Astrophysics Data System (ADS)
Ekinci, Yunus Levent; Balkaya, Çağlayan; Göktürkler, Gökhan; Turan, Seçil
2016-06-01
An efficient approach to estimate model parameters from residual gravity data based on differential evolution (DE), a stochastic vector-based metaheuristic algorithm, has been presented. We have showed the applicability and effectiveness of this algorithm on both synthetic and field anomalies. According to our knowledge, this is a first attempt of applying DE for the parameter estimations of residual gravity anomalies due to isolated causative sources embedded in the subsurface. The model parameters dealt with here are the amplitude coefficient (A), the depth and exact origin of causative source (zo and xo, respectively) and the shape factors (q and ƞ). The error energy maps generated for some parameter pairs have successfully revealed the nature of the parameter estimation problem under consideration. Noise-free and noisy synthetic single gravity anomalies have been evaluated with success via DE/best/1/bin, which is a widely used strategy in DE. Additionally some complicated gravity anomalies caused by multiple source bodies have been considered, and the results obtained have showed the efficiency of the algorithm. Then using the strategy applied in synthetic examples some field anomalies observed for various mineral explorations such as a chromite deposit (Camaguey district, Cuba), a manganese deposit (Nagpur, India) and a base metal sulphide deposit (Quebec, Canada) have been considered to estimate the model parameters of the ore bodies. Applications have exhibited that the obtained results such as the depths and shapes of the ore bodies are quite consistent with those published in the literature. Uncertainty in the solutions obtained from DE algorithm has been also investigated by Metropolis-Hastings (M-H) sampling algorithm based on simulated annealing without cooling schedule. Based on the resulting histogram reconstructions of both synthetic and field data examples the algorithm has provided reliable parameter estimations being within the sampling limits of
Sumithra, Subramaniam; Victoire, T. Aruldoss Albert
2015-01-01
Due to large dimension of clusters and increasing size of sensor nodes, finding the optimal route and cluster for large wireless sensor networks (WSN) seems to be highly complex and cumbersome. This paper proposes a new method to determine a reasonably better solution of the clustering and routing problem with the highest concern of efficient energy consumption of the sensor nodes for extending network life time. The proposed method is based on the Differential Evolution (DE) algorithm with an improvised search operator called Diversified Vicinity Procedure (DVP), which models a trade-off between energy consumption of the cluster heads and delay in forwarding the data packets. The obtained route using the proposed method from all the gateways to the base station is comparatively lesser in overall distance with less number of data forwards. Extensive numerical experiments demonstrate the superiority of the proposed method in managing energy consumption of the WSN and the results are compared with the other algorithms reported in the literature. PMID:26516635
Sumithra, Subramaniam; Victoire, T Aruldoss Albert
2015-01-01
Due to large dimension of clusters and increasing size of sensor nodes, finding the optimal route and cluster for large wireless sensor networks (WSN) seems to be highly complex and cumbersome. This paper proposes a new method to determine a reasonably better solution of the clustering and routing problem with the highest concern of efficient energy consumption of the sensor nodes for extending network life time. The proposed method is based on the Differential Evolution (DE) algorithm with an improvised search operator called Diversified Vicinity Procedure (DVP), which models a trade-off between energy consumption of the cluster heads and delay in forwarding the data packets. The obtained route using the proposed method from all the gateways to the base station is comparatively lesser in overall distance with less number of data forwards. Extensive numerical experiments demonstrate the superiority of the proposed method in managing energy consumption of the WSN and the results are compared with the other algorithms reported in the literature. PMID:26516635
NASA Astrophysics Data System (ADS)
Rashed, Ghamgeen I.; Sun, Yuanzhang; Shaheen, H. I.
Flexible Alternating Current Transmission Systems (FACTS) devices have been proposed as an effective solution for controlling power flow and regulating bus voltage in electrical power systems, resulting in an increased transfer capability, low system losses, and improve stability. However to what extent the performance of FACTS devices can be brought out highly depends upon the location and the parameters of these devices. In this paper, we propose two Evolutionary Optimization Techniques, namely Differential Evolution (DE) and Genetic Algorithm (GA) to select the optimal location and the optimal parameter setting of TCSC which minimize the active power losses in the power network, and compare their performances. To show the validity of the proposed techniques and for comparison purposes, simulations are carried out on several power systems, a three-bus power system, a five-bus power system, and an IEEE-14 bus power system. The results, we have obtained, indicate that DE is an easy to use, fast, robust and powerful optimization technique compared with genetic algorithm (GA). The results are presented in the paper together with appropriate discussion.
Algorithms, games, and evolution
Chastain, Erick; Livnat, Adi; Papadimitriou, Christos; Vazirani, Umesh
2014-01-01
Even the most seasoned students of evolution, starting with Darwin himself, have occasionally expressed amazement that the mechanism of natural selection has produced the whole of Life as we see it around us. There is a computational way to articulate the same amazement: “What algorithm could possibly achieve all this in a mere three and a half billion years?” In this paper we propose an answer: We demonstrate that in the regime of weak selection, the standard equations of population genetics describing natural selection in the presence of sex become identical to those of a repeated game between genes played according to multiplicative weight updates (MWUA), an algorithm known in computer science to be surprisingly powerful and versatile. MWUA maximizes a tradeoff between cumulative performance and entropy, which suggests a new view on the maintenance of diversity in evolution. PMID:24979793
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.
Huang, Lei; Liao, Li; Wu, Cathy H.
2016-01-01
Revealing the underlying evolutionary mechanism plays an important role in understanding protein interaction networks in the cell. While many evolutionary models have been proposed, the problem about applying these models to real network data, especially for differentiating which model can better describe evolutionary process for the observed network urgently remains as a challenge. The traditional way is to use a model with presumed parameters to generate a network, and then evaluate the fitness by summary statistics, which however cannot capture the complete network structures information and estimate parameter distribution. In this work we developed a novel method based on Approximate Bayesian Computation and modified Differential Evolution (ABC-DEP) that is capable of conducting model selection and parameter estimation simultaneously and detecting the underlying evolutionary mechanisms more accurately. We tested our method for its power in differentiating models and estimating parameters on the simulated data and found significant improvement in performance benchmark, as compared with a previous method. We further applied our method to real data of protein interaction networks in human and yeast. Our results show Duplication Attachment model as the predominant evolutionary mechanism for human PPI networks and Scale-Free model as the predominant mechanism for yeast PPI networks. PMID:26357273
Chen, Tao; Yan, Fang-Ping
2013-01-01
The integration with different decisions in the supply chain is a trend, since it can avoid the suboptimal decisions. In this paper, we provide an effective intelligent algorithm for a modified joint replenishment and location-inventory problem (JR-LIP). The problem of the JR-LIP is to determine the reasonable number and location of distribution centers (DCs), the assignment policy of customers, and the replenishment policy of DCs such that the overall cost is minimized. However, due to the JR-LIP's difficult mathematical properties, simple and effective solutions for this NP-hard problem have eluded researchers. To find an effective approach for the JR-LIP, a hybrid self-adapting differential evolution algorithm (HSDE) is designed. To verify the effectiveness of the HSDE, two intelligent algorithms that have been proven to be effective algorithms for the similar problems named genetic algorithm (GA) and hybrid DE (HDE) are chosen to compare with it. Comparative results of benchmark functions and randomly generated JR-LIPs show that HSDE outperforms GA and HDE. Moreover, a sensitive analysis of cost parameters reveals the useful managerial insight. All comparative results show that HSDE is more stable and robust in handling this complex problem especially for the large-scale problem. PMID:24453822
El-Qulity, Said Ali; Mohamed, Ali Wagdy
2016-01-01
This paper proposes a nonlinear integer goal programming model (NIGPM) for solving the general problem of admission capacity planning in a country as a whole. The work aims to satisfy most of the required key objectives of a country related to the enrollment problem for higher education. The system general outlines are developed along with the solution methodology for application to the time horizon in a given plan. The up-to-date data for Saudi Arabia is used as a case study and a novel evolutionary algorithm based on modified differential evolution (DE) algorithm is used to solve the complexity of the NIGPM generated for different goal priorities. The experimental results presented in this paper show their effectiveness in solving the admission capacity for higher education in terms of final solution quality and robustness. PMID:26819583
El-Qulity, Said Ali; Mohamed, Ali Wagdy
2016-01-01
This paper proposes a nonlinear integer goal programming model (NIGPM) for solving the general problem of admission capacity planning in a country as a whole. The work aims to satisfy most of the required key objectives of a country related to the enrollment problem for higher education. The system general outlines are developed along with the solution methodology for application to the time horizon in a given plan. The up-to-date data for Saudi Arabia is used as a case study and a novel evolutionary algorithm based on modified differential evolution (DE) algorithm is used to solve the complexity of the NIGPM generated for different goal priorities. The experimental results presented in this paper show their effectiveness in solving the admission capacity for higher education in terms of final solution quality and robustness. PMID:26819583
A Hybrid Differential Invasive Weed Algorithm for Congestion Management
NASA Astrophysics Data System (ADS)
Basak, Aniruddha; Pal, Siddharth; Pandi, V. Ravikumar; Panigrahi, B. K.; Das, Swagatam
This work is dedicated to solve the problem of congestion management in restructured power systems. Nowadays we have open access market which pushes the power system operation to their limits for maximum economic benefits but at the same time making the system more susceptible to congestion. In this regard congestion management is absolutely vital. In this paper we try to remove congestion by generation rescheduling where the cost involved in the rescheduling process is minimized. The proposed algorithm is a hybrid of Invasive Weed Optimization (IWO) and Differential Evolution (DE). The resultant hybrid algorithm was applied on standard IEEE 30 bus system and observed to beat existing algorithms like Simple Bacterial foraging (SBF), Genetic Algorithm (GA), Invasive Weed Optimization (IWO), Differential Evolution (DE) and hybrid algorithms like Hybrid Bacterial Foraging and Differential Evolution (HBFDE) and Adaptive Bacterial Foraging with Nelder Mead (ABFNM).
Ahmed, Ashik; Ullah, Md Shahid
2016-01-01
This paper proposes the application of differential evolution (DE) algorithm for the optimal tuning of proportional-integral (PI) controller designed to improve the small signal dynamic response of a stand-alone solid oxide fuel cell (SOFC) system. The small signal model of the study system is derived and considered for the controller design as the target here is to track small variations in SOFC load current. Two PI controllers are incorporated in the feedback loops of hydrogen and oxygen partial pressures with an aim to improve the small signal dynamic responses. The controller design problem is formulated as the minimization of an eigenvalue based objective function where the target is to find out the optimal gains of the PI controllers in such a way that the discrepancy of the obtained and desired eigenvalues are minimized. Eigenvalue and time domain simulations are presented for both open-loop and closed loop systems. To test the efficacy of DE over other optimization tools, the results obtained with DE are compared with those obtained by particle swarm optimization (PSO) algorithm and invasive weed optimization (IWO) algorithm. Three different types of load disturbances are considered for the time domain based results to investigate the performances of different optimizers under different sorts of load variations. Moreover, non-parametric statistical analyses, namely, one sample Kolmogorov-Smirnov (KS) test and paired sample t test are used to identify the statistical advantage of one optimizer over the other for the problem under study. The presented results suggest the supremacy of DE over PSO and IWO in finding the optimal solution. PMID:27066389
NASA Astrophysics Data System (ADS)
Primorac, E.; Kuhlenbeck, H.; Freund, H.-J.
2016-07-01
The structure of a thin MoO3 layer on Au(111) with a c(4 × 2) superstructure was studied with LEED I/V analysis. As proposed previously (Quek et al., Surf. Sci. 577 (2005) L71), the atomic structure of the layer is similar to that of a MoO3 single layer as found in regular α-MoO3. The layer on Au(111) has a glide plane parallel to the short unit vector of the c(4 × 2) unit cell and the molybdenum atoms are bridge-bonded to two surface gold atoms with the structure of the gold surface being slightly distorted. The structural refinement of the structure was performed with the CMA-ES evolutionary strategy algorithm which could reach a Pendry R-factor of ∼ 0.044. In the second part the performance of CMA-ES is compared with that of the differential evolution method, a genetic algorithm and the Powell optimization algorithm employing I/V curves calculated with tensor LEED.
NASA Astrophysics Data System (ADS)
Cihan, A.; Birkholzer, J. T.; Bianchi, M.
2014-12-01
Injection of large volume of CO2 into deep geological reservoirs for geologic carbon sequestration (GCS) is expected to cause significant pressure perturbations in subsurface. Large-scale pressure increases in injection reservoirs during GCS operations, if not controlled properly, may limit dynamic storage capacity and increase risk of environmental impacts. The high pressure may impact caprock integrity, induce fault slippage, and cause leakage of brine and/or CO2 into shallow fresh groundwater resources. Thus, monitoring and controlling pressure buildup are critically important for environmentally safe implementation of GCS projects. Extraction of native brine during GCS operations is a pressure management approach to reduce significant pressure buildup. Extracted brine can be transferred to the surface for utilization or re-injected into overlying/underlying saline aquifers. However, pumping, transportation, treatment and disposal of extracted brine can be challenging and costly. Therefore, minimizing volume of extracted brine, while maximizing CO2 storage, is an essential objective of the pressure management with brine extraction schemes. Selection of optimal well locations and extraction rates are critical for maximizing storage and minimizing brine extraction during GCS. However, placing of injection and extraction wells is not intuitive because of heterogeneity in reservoir properties and complex reservoir geometry. Efficient computerized algorithms combining reservoir models and optimization methods are needed to make proper decisions on well locations and control parameters. This study presents a global optimization methodology for pressure management during geologic CO2 sequestration. A constrained differential evolution (CDE) algorithm is introduced for solving optimization problems involving well placement and injection/extraction control. The CDE methodology is tested and applied for realistic CO2 storage scenarios with the presence of uncertainty in
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.
Geometric differential evolution for combinatorial and programs spaces.
Moraglio, A; Togelius, J; Silva, S
2013-01-01
Geometric differential evolution (GDE) is a recently introduced formal generalization of traditional differential evolution (DE) that can be used to derive specific differential evolution algorithms for both continuous and combinatorial spaces retaining the same geometric interpretation of the dynamics of the DE search across representations. In this article, we first review the theory behind the GDE algorithm, then, we use this framework to formally derive specific GDE for search spaces associated with binary strings, permutations, vectors of permutations and genetic programs. The resulting algorithms are representation-specific differential evolution algorithms searching the target spaces by acting directly on their underlying representations. We present experimental results for each of the new algorithms on a number of well-known problems comprising NK-landscapes, TSP, and Sudoku, for binary strings, permutations, and vectors of permutations. We also present results for the regression, artificial ant, parity, and multiplexer problems within the genetic programming domain. Experiments show that overall the new DE algorithms are competitive with well-tuned standard search algorithms. PMID:23270388
Parallel Algorithm Solves Coupled Differential Equations
NASA Technical Reports Server (NTRS)
Hayashi, A.
1987-01-01
Numerical methods adapted to concurrent processing. Algorithm solves set of coupled partial differential equations by numerical integration. Adapted to run on hypercube computer, algorithm separates problem into smaller problems solved concurrently. Increase in computing speed with concurrent processing over that achievable with conventional sequential processing appreciable, especially for large problems.
Algorithms For Integrating Nonlinear Differential Equations
NASA Technical Reports Server (NTRS)
Freed, A. D.; Walker, K. P.
1994-01-01
Improved algorithms developed for use in numerical integration of systems of nonhomogenous, nonlinear, first-order, ordinary differential equations. In comparison with integration algorithms, these algorithms offer greater stability and accuracy. Several asymptotically correct, thereby enabling retention of stability and accuracy when large increments of independent variable used. Accuracies attainable demonstrated by applying them to systems of nonlinear, first-order, differential equations that arise in study of viscoplastic behavior, spread of acquired immune-deficiency syndrome (AIDS) virus and predator/prey populations.
Image segmentation using an improved differential algorithm
NASA Astrophysics Data System (ADS)
Gao, Hao; Shi, Yujiao; Wu, Dongmei
2014-10-01
Among all the existing segmentation techniques, the thresholding technique is one of the most popular due to its simplicity, robustness, and accuracy (e.g. the maximum entropy method, Otsu's method, and K-means clustering). However, the computation time of these algorithms grows exponentially with the number of thresholds due to their exhaustive searching strategy. As a population-based optimization algorithm, differential algorithm (DE) uses a population of potential solutions and decision-making processes. It has shown considerable success in solving complex optimization problems within a reasonable time limit. Thus, applying this method into segmentation algorithm should be a good choice during to its fast computational ability. In this paper, we first propose a new differential algorithm with a balance strategy, which seeks a balance between the exploration of new regions and the exploitation of the already sampled regions. Then, we apply the new DE into the traditional Otsu's method to shorten the computation time. Experimental results of the new algorithm on a variety of images show that, compared with the EA-based thresholding methods, the proposed DE algorithm gets more effective and efficient results. It also shortens the computation time of the traditional Otsu method.
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.
Heterogeneous differential evolution for numerical optimization.
Wang, Hui; Wang, Wenjun; Cui, Zhihua; Sun, Hui; Rahnamayan, Shahryar
2014-01-01
Differential evolution (DE) is a population-based stochastic search algorithm which has shown a good performance in solving many benchmarks and real-world optimization problems. Individuals in the standard DE, and most of its modifications, exhibit the same search characteristics because of the use of the same DE scheme. This paper proposes a simple and effective heterogeneous DE (HDE) to balance exploration and exploitation. In HDE, individuals are allowed to follow different search behaviors randomly selected from a DE scheme pool. Experiments are conducted on a comprehensive set of benchmark functions, including classical problems and shifted large-scale problems. The results show that heterogeneous DE achieves promising performance on a majority of the test problems. PMID:24683329
Differential-Evolution Control Parameter Optimization for Unmanned Aerial Vehicle Path Planning.
Kok, Kai Yit; Rajendran, Parvathy
2016-01-01
The differential evolution algorithm has been widely applied on unmanned aerial vehicle (UAV) path planning. At present, four random tuning parameters exist for differential evolution algorithm, namely, population size, differential weight, crossover, and generation number. These tuning parameters are required, together with user setting on path and computational cost weightage. However, the optimum settings of these tuning parameters vary according to application. Instead of trial and error, this paper presents an optimization method of differential evolution algorithm for tuning the parameters of UAV path planning. The parameters that this research focuses on are population size, differential weight, crossover, and generation number. The developed algorithm enables the user to simply define the weightage desired between the path and computational cost to converge with the minimum generation required based on user requirement. In conclusion, the proposed optimization of tuning parameters in differential evolution algorithm for UAV path planning expedites and improves the final output path and computational cost. PMID:26943630
Differential-Evolution Control Parameter Optimization for Unmanned Aerial Vehicle Path Planning
Kok, Kai Yit; Rajendran, Parvathy
2016-01-01
The differential evolution algorithm has been widely applied on unmanned aerial vehicle (UAV) path planning. At present, four random tuning parameters exist for differential evolution algorithm, namely, population size, differential weight, crossover, and generation number. These tuning parameters are required, together with user setting on path and computational cost weightage. However, the optimum settings of these tuning parameters vary according to application. Instead of trial and error, this paper presents an optimization method of differential evolution algorithm for tuning the parameters of UAV path planning. The parameters that this research focuses on are population size, differential weight, crossover, and generation number. The developed algorithm enables the user to simply define the weightage desired between the path and computational cost to converge with the minimum generation required based on user requirement. In conclusion, the proposed optimization of tuning parameters in differential evolution algorithm for UAV path planning expedites and improves the final output path and computational cost. PMID:26943630
Algorithm evolution for signal understanding
Teller, A.
1996-12-31
Automated program evolution has existed in some form for over thirty years. Signal understanding (e.g., signal classification) has been a scientific concern for even longer than that. Interest in generating, through machine learning techniques, a general signal understanding system is a newer topic, but has recently attracted considerable attention. First, I have proposed to define and create a machine learning mechanism for generating signal understanding systems independent of the signal`s type and size. Second, I have proposed to do this through an evolutionary strategy that is an extension of genetic programming. Third, I have proposed to introduce a suite of sub-mechanisms that not only contribute to the power of the thesis mechanism, but are also contributions to the understanding of the learning technique developed.
Algorithmic Differentiation for Calculus-based Optimization
NASA Astrophysics Data System (ADS)
Walther, Andrea
2010-10-01
For numerous applications, the computation and provision of exact derivative information plays an important role for optimizing the considered system but quite often also for its simulation. This presentation introduces the technique of Algorithmic Differentiation (AD), a method to compute derivatives of arbitrary order within working precision. Quite often an additional structure exploitation is indispensable for a successful coupling of these derivatives with state-of-the-art optimization algorithms. The talk will discuss two important situations where the problem-inherent structure allows a calculus-based optimization. Examples from aerodynamics and nano optics illustrate these advanced optimization approaches.
Ozone Differential Absorption Lidar Algorithm Intercomparison
NASA Astrophysics Data System (ADS)
Godin, Sophie; Carswell, Allen I.; Donovan, David P.; Claude, Hans; Steinbrecht, Wolfgang; McDermid, I. Stuart; McGee, Thomas J.; Gross, Michael R.; Nakane, Hideaki; Swart, Daan P. J.; Bergwerff, Hans B.; Uchino, Osamu; von der Gathen, Peter; Neuber, Roland
1999-10-01
An intercomparison of ozone differential absorption lidar algorithms was performed in 1996 within the framework of the Network for the Detection of Stratospheric Changes (NDSC) lidar working group. The objective of this research was mainly to test the differentiating techniques used by the various lidar teams involved in the NDSC for the calculation of the ozone number density from the lidar signals. The exercise consisted of processing synthetic lidar signals computed from simple Rayleigh scattering and three initial ozone profiles. Two of these profiles contained perturbations in the low and the high stratosphere to test the vertical resolution of the various algorithms. For the unperturbed profiles the results of the simulations show the correct behavior of the lidar processing methods in the low and the middle stratosphere with biases of less than 1% with respect to the initial profile to as high as 30 km in most cases. In the upper stratosphere, significant biases reaching 10% at 45 km for most of the algorithms are obtained. This bias is due to the decrease in the signal-to-noise ratio with altitude, which makes it necessary to increase the number of points of the derivative low-pass filter used for data processing. As a consequence the response of the various retrieval algorithms to perturbations in the ozone profile is much better in the lower stratosphere than in the higher range. These results show the necessity of limiting the vertical smoothing in the ozone lidar retrieval algorithm and questions the ability of current lidar systems to detect long-term ozone trends above 40 km. Otherwise the simulations show in general a correct estimation of the ozone profile random error and, as shown by the tests involving the perturbed ozone profiles, some inconsistency in the estimation of the vertical resolution among the lidar teams involved in this experiment.
Differential Search Algorithm Based Edge Detection
NASA Astrophysics Data System (ADS)
Gunen, M. A.; Civicioglu, P.; Beşdok, E.
2016-06-01
In this paper, a new method has been presented for the extraction of edge information by using Differential Search Optimization Algorithm. The proposed method is based on using a new heuristic image thresholding method for edge detection. The success of the proposed method has been examined on fusion of two remote sensed images. The applicability of the proposed method on edge detection and image fusion problems have been analysed in detail and the empirical results exposed that the proposed method is useful for solving the mentioned problems.
An Enhanced Differential Evolution with Elite Chaotic Local Search
Guo, Zhaolu; Huang, Haixia; Deng, Changshou; Yue, Xuezhi; Wu, Zhijian
2015-01-01
Differential evolution (DE) is a simple yet efficient evolutionary algorithm for real-world engineering problems. However, its search ability should be further enhanced to obtain better solutions when DE is applied to solve complex optimization problems. This paper presents an enhanced differential evolution with elite chaotic local search (DEECL). In DEECL, it utilizes a chaotic search strategy based on the heuristic information from the elite individuals to promote the exploitation power. Moreover, DEECL employs a simple and effective parameter adaptation mechanism to enhance the robustness. Experiments are conducted on a set of classical test functions. The experimental results show that DEECL is very competitive on the majority of the test functions. PMID:26379703
An Enhanced Differential Evolution with Elite Chaotic Local Search.
Guo, Zhaolu; Huang, Haixia; Deng, Changshou; Yue, Xuezhi; Wu, Zhijian
2015-01-01
Differential evolution (DE) is a simple yet efficient evolutionary algorithm for real-world engineering problems. However, its search ability should be further enhanced to obtain better solutions when DE is applied to solve complex optimization problems. This paper presents an enhanced differential evolution with elite chaotic local search (DEECL). In DEECL, it utilizes a chaotic search strategy based on the heuristic information from the elite individuals to promote the exploitation power. Moreover, DEECL employs a simple and effective parameter adaptation mechanism to enhance the robustness. Experiments are conducted on a set of classical test functions. The experimental results show that DEECL is very competitive on the majority of the test functions. PMID:26379703
Evolution of music score watermarking algorithm
NASA Astrophysics Data System (ADS)
Busch, Christoph; Nesi, Paolo; Schmucker, Martin; Spinu, Marius B.
2002-04-01
Content protection for multimedia data is widely recognized especially for data types that are frequently distributed, sold or shared using the Internet. Particularly music industry dealing with audio files realized the necessity for content protection. Distribution of music sheets will face the same problems. Digital watermarking techniques provide a certain level of protection for these music sheets. But classical raster-oriented watermarking algorithms for images suffer several drawbacks when directly applied to image representations of music sheets. Therefore new solutions have been developed which are designed regarding the content of the music sheets. In Comparison to other media types the development for watermarking of music scores is a rather young art. The paper reviews the evolution of the early approaches and describes the current state of the art in the field.
GPU-accelerated adjoint algorithmic differentiation
NASA Astrophysics Data System (ADS)
Gremse, Felix; Höfter, Andreas; Razik, Lukas; Kiessling, Fabian; Naumann, Uwe
2016-03-01
Many scientific problems such as classifier training or medical image reconstruction can be expressed as minimization of differentiable real-valued cost functions and solved with iterative gradient-based methods. Adjoint algorithmic differentiation (AAD) enables automated computation of gradients of such cost functions implemented as computer programs. To backpropagate adjoint derivatives, excessive memory is potentially required to store the intermediate partial derivatives on a dedicated data structure, referred to as the "tape". Parallelization is difficult because threads need to synchronize their accesses during taping and backpropagation. This situation is aggravated for many-core architectures, such as Graphics Processing Units (GPUs), because of the large number of light-weight threads and the limited memory size in general as well as per thread. We show how these limitations can be mediated if the cost function is expressed using GPU-accelerated vector and matrix operations which are recognized as intrinsic functions by our AAD software. We compare this approach with naive and vectorized implementations for CPUs. We use four increasingly complex cost functions to evaluate the performance with respect to memory consumption and gradient computation times. Using vectorization, CPU and GPU memory consumption could be substantially reduced compared to the naive reference implementation, in some cases even by an order of complexity. The vectorization allowed usage of optimized parallel libraries during forward and reverse passes which resulted in high speedups for the vectorized CPU version compared to the naive reference implementation. The GPU version achieved an additional speedup of 7.5 ± 4.4, showing that the processing power of GPUs can be utilized for AAD using this concept. Furthermore, we show how this software can be systematically extended for more complex problems such as nonlinear absorption reconstruction for fluorescence-mediated tomography.
GPU-Accelerated Adjoint Algorithmic Differentiation
Gremse, Felix; Höfter, Andreas; Razik, Lukas; Kiessling, Fabian; Naumann, Uwe
2015-01-01
Many scientific problems such as classifier training or medical image reconstruction can be expressed as minimization of differentiable real-valued cost functions and solved with iterative gradient-based methods. Adjoint algorithmic differentiation (AAD) enables automated computation of gradients of such cost functions implemented as computer programs. To backpropagate adjoint derivatives, excessive memory is potentially required to store the intermediate partial derivatives on a dedicated data structure, referred to as the “tape”. Parallelization is difficult because threads need to synchronize their accesses during taping and backpropagation. This situation is aggravated for many-core architectures, such as Graphics Processing Units (GPUs), because of the large number of light-weight threads and the limited memory size in general as well as per thread. We show how these limitations can be mediated if the cost function is expressed using GPU-accelerated vector and matrix operations which are recognized as intrinsic functions by our AAD software. We compare this approach with naive and vectorized implementations for CPUs. We use four increasingly complex cost functions to evaluate the performance with respect to memory consumption and gradient computation times. Using vectorization, CPU and GPU memory consumption could be substantially reduced compared to the naive reference implementation, in some cases even by an order of complexity. The vectorization allowed usage of optimized parallel libraries during forward and reverse passes which resulted in high speedups for the vectorized CPU version compared to the naive reference implementation. The GPU version achieved an additional speedup of 7.5 ± 4.4, showing that the processing power of GPUs can be utilized for AAD using this concept. Furthermore, we show how this software can be systematically extended for more complex problems such as nonlinear absorption reconstruction for fluorescence-mediated tomography
NASA Astrophysics Data System (ADS)
Sarkar, Soham; Das, Swagatam
In recent years particle swarm optimization emerges as one of the most efficient global optimization tools. In this paper, a hybrid particle swarm with differential evolution operator, termed DEPSO, is applied for the synthesis of linear array geometry. Here, the minimum side lobe level and null control, both are obtained by optimizing the spacing between the array elements by this technique. Moreover, a statistical comparison is also provided to establish its performance against the results obtained by Genetic Algorithm (GA), classical Particle Swarm Optimization (PSO), Tabu Search Algorithm (TSA), Differential Evolution (DE) and Memetic Algorithm (MA).
Color separation in forensic image processing using interactive differential evolution.
Mushtaq, Harris; Rahnamayan, Shahryar; Siddiqi, Areeb
2015-01-01
Color separation is an image processing technique that has often been used in forensic applications to differentiate among variant colors and to remove unwanted image interference. This process can reveal important information such as covered text or fingerprints in forensic investigation procedures. However, several limitations prevent users from selecting the appropriate parameters pertaining to the desired and undesired colors. This study proposes the hybridization of an interactive differential evolution (IDE) and a color separation technique that no longer requires users to guess required control parameters. The IDE algorithm optimizes these parameters in an interactive manner by utilizing human visual judgment to uncover desired objects. A comprehensive experimental verification has been conducted on various sample test images, including heavily obscured texts, texts with subtle color variations, and fingerprint smudges. The advantage of IDE is apparent as it effectively optimizes the color separation parameters at a level indiscernible to the naked eyes. PMID:25400037
A Real-Time Algorithm for the Approximation of Level-Set-Based Curve Evolution
Shi, Yonggang; Karl, William Clem
2010-01-01
In this paper, we present a complete and practical algorithm for the approximation of level-set-based curve evolution suitable for real-time implementation. In particular, we propose a two-cycle algorithm to approximate level-set-based curve evolution without the need of solving partial differential equations (PDEs). Our algorithm is applicable to a broad class of evolution speeds that can be viewed as composed of a data-dependent term and a curve smoothness regularization term. We achieve curve evolution corresponding to such evolution speeds by separating the evolution process into two different cycles: one cycle for the data-dependent term and a second cycle for the smoothness regularization. The smoothing term is derived from a Gaussian filtering process. In both cycles, the evolution is realized through a simple element switching mechanism between two linked lists, that implicitly represents the curve using an integer valued level-set function. By careful construction, all the key evolution steps require only integer operations. A consequence is that we obtain significant computation speedups compared to exact PDE-based approaches while obtaining excellent agreement with these methods for problems of practical engineering interest. In particular, the resulting algorithm is fast enough for use in real-time video processing applications, which we demonstrate through several image segmentation and video tracking experiments. PMID:18390371
Fast Micro-Differential Evolution for Topological Active Net Optimization.
Li, Yuan-Long; Zhan, Zhi-Hui; Gong, Yue-Jiao; Zhang, Jun; Li, Yun; Li, Qing
2016-06-01
This paper studies the optimization problem of topological active net (TAN), which is often seen in image segmentation and shape modeling. A TAN is a topological structure containing many nodes, whose positions must be optimized while a predefined topology needs to be maintained. TAN optimization is often time-consuming and even constructing a single solution is hard to do. Such a problem is usually approached by a "best improvement local search" (BILS) algorithm based on deterministic search (DS), which is inefficient because it spends too much efforts in nonpromising probing. In this paper, we propose the use of micro-differential evolution (DE) to replace DS in BILS for improved directional guidance. The resultant algorithm is termed deBILS. Its micro-population efficiently utilizes historical information for potentially promising search directions and hence improves efficiency in probing. Results show that deBILS can probe promising neighborhoods for each node of a TAN. Experimental tests verify that deBILS offers substantially higher search speed and solution quality not only than ordinary BILS, but also the genetic algorithm and scatter search algorithm. PMID:26068933
Differential evolution enhanced with multiobjective sorting-based mutation operators.
Wang, Jiahai; Liao, Jianjun; Zhou, Ying; Cai, Yiqiao
2014-12-01
Differential evolution (DE) is a simple and powerful population-based evolutionary algorithm. The salient feature of DE lies in its mutation mechanism. Generally, the parents in the mutation operator of DE are randomly selected from the population. Hence, all vectors are equally likely to be selected as parents without selective pressure at all. Additionally, the diversity information is always ignored. In order to fully exploit the fitness and diversity information of the population, this paper presents a DE framework with multiobjective sorting-based mutation operator. In the proposed mutation operator, individuals in the current population are firstly sorted according to their fitness and diversity contribution by nondominated sorting. Then parents in the mutation operators are proportionally selected according to their rankings based on fitness and diversity, thus, the promising individuals with better fitness and diversity have more opportunity to be selected as parents. Since fitness and diversity information is simultaneously considered for parent selection, a good balance between exploration and exploitation can be achieved. The proposed operator is applied to original DE algorithms, as well as several advanced DE variants. Experimental results on 48 benchmark functions and 12 real-world application problems show that the proposed operator is an effective approach to enhance the performance of most DE algorithms studied. PMID:24802378
Dynamical evolution of differentiated asteroid families
NASA Astrophysics Data System (ADS)
Martins-Filho, W. S.; Carvano, J.; Mothe-Diniz, T.; Roig, F.
2014-10-01
The project aims to study the dynamical evolution of a family of asteroids formed from a fully differentiated parent body, considering family members with different physical properties consistent with what is expected from the break up of a body formed by a metallic nucleus surrounded by a rocky mantle. Initially, we study the effects of variations in density, bond albedo, and thermal inertia in the semi-major axis drift caused by the Yarkovsky effect. The Yarkovsky effect is a non-conservative force caused by the thermal re-radiation of the solar radiation by an irregular body. In Solar System bodies, it is known to cause changes in the orbital motions (Peterson, 1976), eventually bringing asteroids into transport routes to near-Earth space, such as some mean motion resonances. We expressed the equations of variation of the semi-major axis directly in terms of physical properties (such as the mean motion, frequency of rotation, conductivity, thermal parameter, specific heat, obliquity and bond albedo). This development was based on the original formalism for the Yarkovsky effect (i.e., Bottke et al., 2006 and references therein). The derivation of above equations allowed us to closely study the variation of the semi-major axis individually for each physical parameter, clearly showing that the changes in semi-major axis for silicate bodies is twice or three times greater than for metal bodies. The next step was to calculate the orbital elements of a synthetic family after the break-up. That was accomplished assuming that the catastrophic disruption energy is given by the formalism described by Stewart and Leinhardt (2009) and assuming an isotropic distribution of velocities for the fragments of the nucleus and the mantle. Finally, the orbital evolution of the fragments is implemented using a simpletic integrator, and the result compared with the distribution of real asteroid families.
Differential Evolution between Monotocous and Polytocous Species
Ahn, Hyeonju; Kim, Kyu-Won; Kim, Hyeon Jeong; Cho, Seoae; Kim, Heebal
2014-01-01
One of the most important traits for both animal science and livestock production is the number of offspring for a species. This study was performed to identify differentially evolved genes and their distinct functions that influence the number of offspring at birth by comparative analysis of eight monotocous mammals and seven polytocous mammals in a number of scopes: specific amino acid substitution with site-wise adaptive evolution, gene expansion and specific orthologous group. The mutually exclusive amino acid substitution among the 16 mammalian species identified five candidate genes. These genes were both directly and indirectly related to ovulation. Furthermore, in monotocous mammals, the EPH gene family was found to have undergone expansion. Previously, the EPHA4 gene was found to positively affect litter size in pigs and supports the possibility of the EPH gene playing a role in determining the number of offspring per birth. The identified genes in this study offer a basis from which the differences between monotocous and polytocous species can be studied. Furthermore, these genes may harbor some clues to the underlying mechanism, which determines litter size and may prove useful for livestock breeding strategies. PMID:25049975
PSEMA: An Algorithm for Pattern Stimulated Evolution of Music
NASA Astrophysics Data System (ADS)
Mavrogianni, A. N.; Vlachos, D. S.; Harvalias, G.
2008-11-01
An algorithm for pattern stimulating evolution of music is presented in this work (PSEMA). The system combines a pattern with a genetic algorithm for automatic music composition in order to create a musical phrase uniquely characterizing the pattern. As an example a musical portrait is presented. The initialization of the musical phrases is done with a Markov Chain process. The evolution is dominated by an arbitrary correspondence between the pattern (feature extraction of the pattern may be used in this step) and the esthetic result of the musical phrase.
An ordinary differential equation based solution path algorithm.
Wu, Yichao
2011-01-01
Efron, Hastie, Johnstone and Tibshirani (2004) proposed Least Angle Regression (LAR), a solution path algorithm for the least squares regression. They pointed out that a slight modification of the LAR gives the LASSO (Tibshirani, 1996) solution path. However it is largely unknown how to extend this solution path algorithm to models beyond the least squares regression. In this work, we propose an extension of the LAR for generalized linear models and the quasi-likelihood model by showing that the corresponding solution path is piecewise given by solutions of ordinary differential equation systems. Our contribution is twofold. First, we provide a theoretical understanding on how the corresponding solution path propagates. Second, we propose an ordinary differential equation based algorithm to obtain the whole solution path. PMID:21532936
NASA Astrophysics Data System (ADS)
Gujarathi, Ashish M.; Purohit, S.; Srikanth, B.
2015-06-01
Detailed working principle of jumping gene adaptation of differential evolution (DE-JGa) is presented. The performance of the DE-JGa algorithm is compared with the performance of differential evolution (DE) and modified DE (MDE) by applying these algorithms on industrial problems. In this study Reactor network design (RND) problem is solved using DE, MDE, and DE-JGa algorithms: These industrial processes are highly nonlinear and complex with reference to optimal operating conditions with many equality and inequality constraints. Extensive computational comparisons have been made for all the chemical engineering problems considered. The results obtained in the present study show that DE-JGa algorithm outperforms the other algorithms (DE and MDE). Several comparisons are made among the algorithms with regard to the number of function evaluations (NFE)/CPU- time required to find the global optimum. The standard deviation and the variance values obtained using DE-JGa, DE and MDE algorithms also show that the DE-JGa algorithm gives consistent set of results for the majority of the test problems and the industrial real world problems.
Tirronen, Ville; Neri, Ferrante; Kärkkäinen, Tommi; Majava, Kirsi; Rossi, Tuomo
2008-01-01
This article proposes an Enhanced Memetic Differential Evolution (EMDE) for designing digital filters which aim at detecting defects of the paper produced during an industrial process. Defect detection is handled by means of two Gabor filters and their design is performed by the EMDE. The EMDE is a novel adaptive evolutionary algorithm which combines the powerful explorative features of Differential Evolution with the exploitative features of three local search algorithms employing different pivot rules and neighborhood generating functions. These local search algorithms are the Hooke Jeeves Algorithm, a Stochastic Local Search, and Simulated Annealing. The local search algorithms are adaptively coordinated by means of a control parameter that measures fitness distribution among individuals of the population and a novel probabilistic scheme. Numerical results confirm that Differential Evolution is an efficient evolutionary framework for the image processing problem under investigation and show that the EMDE performs well. As a matter of fact, the application of the EMDE leads to a design of an efficiently tailored filter. A comparison with various popular metaheuristics proves the effectiveness of the EMDE in terms of convergence speed, stagnation prevention, and capability in detecting solutions having high performance. PMID:19053498
An algorithm for the numerical solution of linear differential games
Polovinkin, E S; Ivanov, G E; Balashov, M V; Konstantinov, R V; Khorev, A V
2001-10-31
A numerical algorithm for the construction of stable Krasovskii bridges, Pontryagin alternating sets, and also of piecewise program strategies solving two-person linear differential (pursuit or evasion) games on a fixed time interval is developed on the basis of a general theory. The aim of the first player (the pursuer) is to hit a prescribed target (terminal) set by the phase vector of the control system at the prescribed time. The aim of the second player (the evader) is the opposite. A description of numerical algorithms used in the solution of differential games of the type under consideration is presented and estimates of the errors resulting from the approximation of the game sets by polyhedra are presented.
Differential evolution: Global search problem in LEED-IV surface structural analysis
Nascimento, V.B.; Plummer, E.W.
2015-02-15
The search process associated with the quantitative theory–experiment comparison in Low Energy Electron Diffraction surface structural analysis can be very time consuming, especially in the case of complex materials with many atoms in the unit cell. Global search algorithms need to be employed to locate the global minimum of the reliability factor in the multi-dimensional structural parameter space. In this study we investigate the use of the Differential Evolution algorithm in Low Energy Electron Diffraction structural analysis. Despite the simplicity of its mechanism the Differential Evolution algorithm presents an impressive performance when applied to ultra-thin films of BaTiO{sub 3}(001) in a theory–theory comparison. A scaling relation of N{sup (1.47} {sup ±} {sup 0.08)} was obtained, where N is the total number of parameters to be optimized. - Highlights: • We investigated the use of the Differential Evolution algorithm (DE) for the LEED search problem. • The DE method was applied to the optimization of the surface structure of the BaTiO{sub 3}(001) ultra-thin films. • A very favorable scaling relation of N{sup 1.47} was obtained, where N is the total number of parameters to be optimized.
An enhanced algorithm to estimate BDS satellite's differential code biases
NASA Astrophysics Data System (ADS)
Shi, Chuang; Fan, Lei; Li, Min; Liu, Zhizhao; Gu, Shengfeng; Zhong, Shiming; Song, Weiwei
2016-02-01
This paper proposes an enhanced algorithm to estimate the differential code biases (DCB) on three frequencies of the BeiDou Navigation Satellite System (BDS) satellites. By forming ionospheric observables derived from uncombined precise point positioning and geometry-free linear combination of phase-smoothed range, satellite DCBs are determined together with ionospheric delay that is modeled at each individual station. Specifically, the DCB and ionospheric delay are estimated in a weighted least-squares estimator by considering the precision of ionospheric observables, and a misclosure constraint for different types of satellite DCBs is introduced. This algorithm was tested by GNSS data collected in November and December 2013 from 29 stations of Multi-GNSS Experiment (MGEX) and BeiDou Experimental Tracking Stations. Results show that the proposed algorithm is able to precisely estimate BDS satellite DCBs, where the mean value of day-to-day scattering is about 0.19 ns and the RMS of the difference with respect to MGEX DCB products is about 0.24 ns. In order to make comparison, an existing algorithm based on IGG: Institute of Geodesy and Geophysics, China (IGGDCB), is also used to process the same dataset. Results show that, the DCB difference between results from the enhanced algorithm and the DCB products from Center for Orbit Determination in Europe (CODE) and MGEX is reduced in average by 46 % for GPS satellites and 14 % for BDS satellites, when compared with DCB difference between the results of IGGDCB algorithm and the DCB products from CODE and MGEX. In addition, we find the day-to-day scattering of BDS IGSO satellites is obviously lower than that of GEO and MEO satellites, and a significant bias exists in daily DCB values of GEO satellites comparing with MGEX DCB product. This proposed algorithm also provides a new approach to estimate the satellite DCBs of multiple GNSS systems.
Differential Evolution Based Ascent Phase Trajectory Optimization for a Hypersonic Vehicle
NASA Astrophysics Data System (ADS)
Giri, Ritwik; Ghose, D.
In this paper, a new method for the numerical computation of optimal, or nearly optimal, solutions to aerospace trajectory problems is presented. Differential Evolution (DE), a powerful stochastic real-parameter optimization algorithm is used to optimize the ascent phase of a hypersonic vehicle. The vehicle has to undergo large changes in altitude and associated aerodynamic conditions. As a result, its aerodynamic characteristics, as well as its propulsion parameters, undergo drastic changes. Such trajectory optimization problems can be solved by converting it to a non-linear programming (NLP) problem. One of the issues in the NLP method is that it requires a fairly large number of grid points to arrive at an optimal solution. Differential Evolution based algorithm, proposed in this paper, is shown to perform equally well with lesser number of grid points. This is supported by extensive simulation results.
On the adaptivity and complexity embedded into differential evolution
NASA Astrophysics Data System (ADS)
Senkerik, Roman; Pluhacek, Michal; Zelinka, Ivan; Jasek, Roman
2016-06-01
This research deals with the comparison of the two modern approaches for evolutionary algorithms, which are the adaptivity and complex chaotic dynamics. This paper aims on the investigations on the chaos-driven Differential Evolution (DE) concept. This paper is aimed at the embedding of discrete dissipative chaotic systems in the form of chaotic pseudo random number generators for the DE and comparing the influence to the performance with the state of the art adaptive representative jDE. This research is focused mainly on the possible disadvantages and advantages of both compared approaches. Repeated simulations for Lozi map driving chaotic systems were performed on the simple benchmark functions set, which are more close to the real optimization problems. Obtained results are compared with the canonical not-chaotic and not adaptive DE. Results show that with used simple test functions, the performance of ChaosDE is better in the most cases than jDE and Canonical DE, furthermore due to the unique sequencing in CPRNG given by the hidden chaotic dynamics, thus better and faster selection of unique individuals from population, ChaosDE is faster.
Differential evolution Markov chain with snooker updater and fewer chains
Vrugt, Jasper A; Ter Braak, Cajo J F
2008-01-01
Differential Evolution Markov Chain (DE-MC) is an adaptive MCMC algorithm, in which multiple chains are run in parallel. Standard DE-MC requires at least N=2d chains to be run in parallel, where d is the dimensionality of the posterior. This paper extends DE-MC with a snooker updater and shows by simulation and real examples that DE-MC can work for d up to 50--100 with fewer parallel chains (e.g. N=3) by exploiting information from their past by generating jumps from differences of pairs of past states. This approach extends the practical applicability of DE-MC and is shown to be about 5--26 times more efficient than the optimal Normal random walk Metropolis sampler for the 97.5% point of a variable from a 25--50 dimensional Student T{sub 3} distribution. In a nonlinear mixed effects model example the approach outperformed a block-updater geared to the specific features of the model.
Fast wavelet based algorithms for linear evolution equations
NASA Technical Reports Server (NTRS)
Engquist, Bjorn; Osher, Stanley; Zhong, Sifen
1992-01-01
A class was devised of fast wavelet based algorithms for linear evolution equations whose coefficients are time independent. The method draws on the work of Beylkin, Coifman, and Rokhlin which they applied to general Calderon-Zygmund type integral operators. A modification of their idea is applied to linear hyperbolic and parabolic equations, with spatially varying coefficients. A significant speedup over standard methods is obtained when applied to hyperbolic equations in one space dimension and parabolic equations in multidimensions.
Battery parameterisation based on differential evolution via a boundary evolution strategy
NASA Astrophysics Data System (ADS)
Yang, Guangya
2014-01-01
Attention has been given to the battery modelling in the electric engineering field following the current development of renewable energy and electrification of transportation. The establishment of the equivalent circuit model of the battery requires data preparation and parameterisation. Besides, as the equivalent circuit model is an abstract map of the battery electric characteristics, the determination of the possible ranges of parameters can be a challenging task. In this paper, an efficient yet easy to implement method is proposed to parameterise the equivalent circuit model of batteries utilising the advances of evolutionary algorithms (EAs). Differential evolution (DE) is selected and modified to parameterise an equivalent circuit model of lithium-ion batteries. A boundary evolution strategy (BES) is developed and incorporated into the DE to update the parameter boundaries during the parameterisation. The method can parameterise the model without extensive data preparation. In addition, the approach can also estimate the initial SOC and the available capacity. The efficiency of the approach is verified through two battery packs, one is an 8-cell battery module and one from an electrical vehicle.
Dynamics of Quantum Adiabatic Evolution Algorithm for Number Partitioning
NASA Technical Reports Server (NTRS)
Smelyanskiy, V. N.; Toussaint, U. V.; Timucin, D. A.
2002-01-01
We have developed a general technique to study the dynamics of the quantum adiabatic evolution algorithm applied to random combinatorial optimization problems in the asymptotic limit of large problem size n. We use as an example the NP-complete Number Partitioning problem and map the algorithm dynamics to that of an auxiliary quantum spin glass system with the slowly varying Hamiltonian. We use a Green function method to obtain the adiabatic eigenstates and the minimum excitation gap. g min, = O(n 2(exp -n/2), corresponding to the exponential complexity of the algorithm for Number Partitioning. The key element of the analysis is the conditional energy distribution computed for the set of all spin configurations generated from a given (ancestor) configuration by simultaneous flipping of a fixed number of spins. For the problem in question this distribution is shown to depend on the ancestor spin configuration only via a certain parameter related to 'the energy of the configuration. As the result, the algorithm dynamics can be described in terms of one-dimensional quantum diffusion in the energy space. This effect provides a general limitation of a quantum adiabatic computation in random optimization problems. Analytical results are in agreement with the numerical simulation of the algorithm.
Dynamics of Quantum Adiabatic Evolution Algorithm for Number Partitioning
NASA Technical Reports Server (NTRS)
Smelyanskiy, Vadius; vonToussaint, Udo V.; Timucin, Dogan A.; Clancy, Daniel (Technical Monitor)
2002-01-01
We have developed a general technique to study the dynamics of the quantum adiabatic evolution algorithm applied to random combinatorial optimization problems in the asymptotic limit of large problem size n. We use as an example the NP-complete Number Partitioning problem and map the algorithm dynamics to that of an auxiliary quantum spin glass system with the slowly varying Hamiltonian. We use a Green function method to obtain the adiabatic eigenstates and the minimum exitation gap, gmin = O(n2(sup -n/2)), corresponding to the exponential complexity of the algorithm for Number Partitioning. The key element of the analysis is the conditional energy distribution computed for the set of all spin configurations generated from a given (ancestor) configuration by simultaneous flipping of a fixed number of spins. For the problem in question this distribution is shown to depend on the ancestor spin configuration only via a certain parameter related to the energy of the configuration. As the result, the algorithm dynamics can be described in terms of one-dimensional quantum diffusion in the energy space. This effect provides a general limitation of a quantum adiabatic computation in random optimization problems. Analytical results are in agreement with the numerical simulation of the algorithm.
Differential Evolution approach to detect recent admixture
2015-01-01
The genetic structure of human populations is extraordinarily complex and of fundamental importance to studies of anthropology, evolution, and medicine. As increasingly many individuals are of mixed origin, there is an unmet need for tools that can infer multiple origins. Misclassification of such individuals can lead to incorrect and costly misinterpretations of genomic data, primarily in disease studies and drug trials. We present an advanced tool to infer ancestry that can identify the biogeographic origins of highly mixed individuals. reAdmix can incorporate individual's knowledge of ancestors (e.g. having some ancestors from Turkey or a Scottish grandmother). reAdmix is an online tool available at http://chcb.saban-chla.usc.edu/reAdmix/. PMID:26111206
Chromosome differentiation patterns during cichlid fish evolution
2010-01-01
Background Cichlid fishes have been the subject of increasing scientific interest because of their rapid adaptive radiation which has led to an extensive ecological diversity and their enormous importance to tropical and subtropical aquaculture. To increase our understanding of chromosome evolution among cichlid species, karyotypes of one Asian, 22 African, and 30 South American cichlid species were investigated, and chromosomal data of the family was reviewed. Results Although there is extensive variation in the karyotypes of cichlid fishes (from 2n = 32 to 2n = 60 chromosomes), the modal chromosome number for South American species was 2n = 48 and the modal number for the African ones was 2n = 44. The only Asian species analyzed, Etroplus maculatus, was observed to have 46 chromosomes. The presence of one or two macro B chromosomes was detected in two African species. The cytogenetic mapping of 18S ribosomal RNA (18S rRNA) gene revealed a variable number of clusters among species varying from two to six. Conclusions The karyotype diversification of cichlids seems to have occurred through several chromosomal rearrangements involving fissions, fusions and inversions. It was possible to identify karyotype markers for the subfamilies Pseudocrenilabrinae (African) and Cichlinae (American). The karyotype analyses did not clarify the phylogenetic relationship among the Cichlinae tribes. On the other hand, the two major groups of Pseudocrenilabrinae (tilapiine and haplochromine) were clearly discriminated based on the characteristics of their karyotypes. The cytogenetic mapping of 18S ribosomal RNA (18S rRNA) gene did not follow the chromosome diversification in the family. The dynamic evolution of the repeated units of rRNA genes generates patterns of chromosomal distribution that do not help follows the phylogenetic relationships among taxa. The presence of B chromosomes in cichlids is of particular interest because they may not be represented in the reference genome
Image fusion algorithm for differential phase contrast imaging
NASA Astrophysics Data System (ADS)
Roessl, Ewald; Koehler, Thomas; van Stevendaal, Udo; Martens, Gerhard; Hauser, Nik; Wang, Zhentian; Stampanoni, Marco
2012-03-01
Differential phase-contrast imaging in the x-ray domain provides three physically complementary signals:1, 2 the attenuation, the differential phase-contrast, related to the refractive index, and the dark-field signal, strongly influenced by the total amount of radiation scattered into very small angles. In medical applications, it is of the utmost importance to present to the radiologist all clinically relevant information in as compact a way as possible. Hence, the need arises for a method to combine two or more of the above mentioned signals into one image containing all information relevant for diagnosis. We present an image composition algorithm that fuses the attenuation image and the differential phase contrast image into a composite, final image based on the assumption that the real and imaginary part of the complex refractive index of the sample can be related by a constant scaling factor. The merging is performed in such a way that the composite image is characterized by minimal noise-power at each frequency component.
DEEP—differential evolution entirely parallel method for gene regulatory networks
Samsonov, Alexander
2011-01-01
The Differential Evolution Entirely Parallel (DEEP) method is applied to the biological data fitting problem. We introduce a new migration scheme, in which the best member of the branch substitutes the oldest member of the next branch that provides a high speed of the algorithm convergence. We analyze the performance and efficiency of the developed algorithm on a test problem of finding the regulatory interactions within the network of gap genes that control the development of early Drosophila embryo. The parameters of a set of nonlinear differential equations are determined by minimizing the total error between the model behavior and experimental observations. The age of the individuum is defined by the number of iterations this individuum survived without changes. We used a ring topology for the network of computational nodes. The computer codes are available upon request. PMID:22223930
Random Matrix Approach to Quantum Adiabatic Evolution Algorithms
NASA Technical Reports Server (NTRS)
Boulatov, Alexei; Smelyanskiy, Vadier N.
2004-01-01
We analyze the power of quantum adiabatic evolution algorithms (Q-QA) for solving random NP-hard optimization problems within a theoretical framework based on the random matrix theory (RMT). We present two types of the driven RMT models. In the first model, the driving Hamiltonian is represented by Brownian motion in the matrix space. We use the Brownian motion model to obtain a description of multiple avoided crossing phenomena. We show that the failure mechanism of the QAA is due to the interaction of the ground state with the "cloud" formed by all the excited states, confirming that in the driven RMT models. the Landau-Zener mechanism of dissipation is not important. We show that the QAEA has a finite probability of success in a certain range of parameters. implying the polynomial complexity of the algorithm. The second model corresponds to the standard QAEA with the problem Hamiltonian taken from the Gaussian Unitary RMT ensemble (GUE). We show that the level dynamics in this model can be mapped onto the dynamics in the Brownian motion model. However, the driven RMT model always leads to the exponential complexity of the algorithm due to the presence of the long-range intertemporal correlations of the eigenvalues. Our results indicate that the weakness of effective transitions is the leading effect that can make the Markovian type QAEA successful.
Pluhacek, Michal; Davendra, Donald; Oplatková Kominkova, Zuzana
2014-01-01
Evolutionary technique differential evolution (DE) is used for the evolutionary tuning of controller parameters for the stabilization of set of different chaotic systems. The novelty of the approach is that the selected controlled discrete dissipative chaotic system is used also as the chaotic pseudorandom number generator to drive the mutation and crossover process in the DE. The idea was to utilize the hidden chaotic dynamics in pseudorandom sequences given by chaotic map to help differential evolution algorithm search for the best controller settings for the very same chaotic system. The optimizations were performed for three different chaotic systems, two types of case studies and developed cost functions. PMID:25243230
Improved Differential Evolution for Combined Heat and Power Economic Dispatch
NASA Astrophysics Data System (ADS)
Jena, C.; Basu, M.; Panigrahi, C. K.
2016-04-01
This paper presents an improved differential evolution to solve non-smooth non-convex combined heat and power economic dispatch (CHPED) problem. Valve-point loading and prohibited operating zones of conventional thermal generators are taken into account. Differential evolution (DE) exploits the differences of randomly sampled pairs of objective vectors for its mutation process. Consequently the variation between vectors will outfit the objective function toward the optimization process and therefore provides efficient global optimization capability. However, although DE is shown to be precise, fast as well as robust, its search efficiency will be impaired during solution process with fast descending diversity of population. This paper proposes Gaussian random variable instead of scaling factor which improves search efficiency. The effectiveness of the proposed method has been verified on four test systems. The results of the proposed approach are compared with those obtained by other evolutionary methods. It is found that the proposed improved differential evolution based approach is able to provide better solution.
An Evolution Based Biosensor Receptor DNA Sequence Generation Algorithm
Kim, Eungyeong; Lee, Malrey; Gatton, Thomas M.; Lee, Jaewan; Zang, Yupeng
2010-01-01
A biosensor is composed of a bioreceptor, an associated recognition molecule, and a signal transducer that can selectively detect target substances for analysis. DNA based biosensors utilize receptor molecules that allow hybridization with the target analyte. However, most DNA biosensor research uses oligonucleotides as the target analytes and does not address the potential problems of real samples. The identification of recognition molecules suitable for real target analyte samples is an important step towards further development of DNA biosensors. This study examines the characteristics of DNA used as bioreceptors and proposes a hybrid evolution-based DNA sequence generating algorithm, based on DNA computing, to identify suitable DNA bioreceptor recognition molecules for stable hybridization with real target substances. The Traveling Salesman Problem (TSP) approach is applied in the proposed algorithm to evaluate the safety and fitness of the generated DNA sequences. This approach improves efficiency and stability for enhanced and variable-length DNA sequence generation and allows extension to generation of variable-length DNA sequences with diverse receptor recognition requirements. PMID:22315543
First-order uncertainty analysis using Algorithmic Differentiation of morphodynamic models
NASA Astrophysics Data System (ADS)
Villaret, Catherine; Kopmann, Rebekka; Wyncoll, David; Riehme, Jan; Merkel, Uwe; Naumann, Uwe
2016-05-01
We present here an efficient first-order second moment method using Algorithmic Differentiation (FOSM/AD) which can be applied to quantify uncertainty/sensitivities in morphodynamic models. Changes with respect to variable flow and sediment input parameters are estimated with machine accuracy using the technique of Algorithmic Differentiation (AD). This method is particularly attractive for process-based morphodynamic models like the Telemac-2D/Sisyphe model considering the large number of input parameters and CPU time associated to each simulation. The FOSM/AD method is applied to identify the relevant processes in a trench migration experiment (van Rijn, 1987). A Tangent Linear Model (TLM) of the Telemac-2D/Sisyphe morphodynamic model (release 6.2) was generated using the AD-enabled NAG Fortran compiler. One single run of the TLM is required per variable input parameter and results are then combined to calculate the total uncertainty. The limits of the FOSM/AD method have been assessed by comparison with Monte Carlo (MC) simulations. Similar results were obtained assuming small standard deviation of the variable input parameters. Both settling velocity and grain size have been identified as the most sensitive input parameters and the uncertainty as measured by the standard deviation of the calculated bed evolution increases with time.
THE DIFFERENTIAL ALGORITHM BETWEEN RHEUMATOLOGIC AND MALIGN DISEASES
Këpuska, Arbnore Batalli; Spahiju, Lidvana; Bejiq, Ramush; Manqestena, Rufadije; Stavileci, Valbona; Ibraimi, Zana
2016-01-01
Objective: The aim of this study is to determine the differential algorithm between rheumatism and malignant diseases. For every pediatrician, to be warned when attending joint pain and child arthralgia and prevent and treat within time malignant diseases. Methods: Our case presented in Pediatric Clinic, was referred by Regional Hospital of Ferizaj with suspected diagnose of Febris Rheumatica and Arthralgia. The main complaint was joint pain. Initially the patient was admitted at Cardiology and Rheumatology department. Then after examinations was referred to Hemato-Oncology department. Hospitalized during the period from 12.12.2014 to 18.01.2015. Results: Bone marrow biopsy as terminal diagnostic tool reviled severe malignant hematologic disease, which was masked by clinical and lab findings as Febris Rheumatica. Conclusion: Arthralgia as one of child’s often complain, should have a special attention paid to, as it might be a warning sign for a lot of diseases. Steroid treatment should not be used before final diagnose of the disease and before rolling out hematologic etiology with peripheral blood smear. PMID:27147926
NASA Technical Reports Server (NTRS)
Pflaum, Christoph
1996-01-01
A multilevel algorithm is presented that solves general second order elliptic partial differential equations on adaptive sparse grids. The multilevel algorithm consists of several V-cycles. Suitable discretizations provide that the discrete equation system can be solved in an efficient way. Numerical experiments show a convergence rate of order Omicron(1) for the multilevel algorithm.
Random matrix approach to quantum adiabatic evolution algorithms
Boulatov, A.; Smelyanskiy, V.N.
2005-05-15
We analyze the power of the quantum adiabatic evolution algorithm (QAA) for solving random computationally hard optimization problems within a theoretical framework based on random matrix theory (RMT). We present two types of driven RMT models. In the first model, the driving Hamiltonian is represented by Brownian motion in the matrix space. We use the Brownian motion model to obtain a description of multiple avoided crossing phenomena. We show that nonadiabatic corrections in the QAA are due to the interaction of the ground state with the 'cloud' formed by most of the excited states, confirming that in driven RMT models, the Landau-Zener scenario of pairwise level repulsions is not relevant for the description of nonadiabatic corrections. We show that the QAA has a finite probability of success in a certain range of parameters, implying a polynomial complexity of the algorithm. The second model corresponds to the standard QAA with the problem Hamiltonian taken from the RMT Gaussian unitary ensemble (GUE). We show that the level dynamics in this model can be mapped onto the dynamics in the Brownian motion model. For this reason, the driven GUE model can also lead to polynomial complexity of the QAA. The main contribution to the failure probability of the QAA comes from the nonadiabatic corrections to the eigenstates, which only depend on the absolute values of the transition amplitudes. Due to the mapping between the two models, these absolute values are the same in both cases. Our results indicate that this 'phase irrelevance' is the leading effect that can make both the Markovian- and GUE-type QAAs successful.
NASA Astrophysics Data System (ADS)
Bakkiyaraj, Ashok; Kumarappan, N.
2015-09-01
This paper presents a new approach for evaluating the reliability indices of a composite power system that adopts binary differential evolution (BDE) algorithm in the search mechanism to select the system states. These states also called dominant states, have large state probability and higher loss of load curtailment necessary to maintain real power balance. A chromosome of a BDE algorithm represents the system state. BDE is not applied for its traditional application of optimizing a non-linear objective function, but used as tool for exploring more number of dominant states by producing new chromosomes, mutant vectors and trail vectors based on the fitness function. The searched system states are used to evaluate annualized system and load point reliability indices. The proposed search methodology is applied to RBTS and IEEE-RTS test systems and results are compared with other approaches. This approach evaluates the indices similar to existing methods while analyzing less number of system states.
Aerodynamic optimization of supersonic compressor cascade using differential evolution on GPU
NASA Astrophysics Data System (ADS)
Aissa, Mohamed Hasanine; Verstraete, Tom; Vuik, Cornelis
2016-06-01
Differential Evolution (DE) is a powerful stochastic optimization method. Compared to gradient-based algorithms, DE is able to avoid local minima but requires at the same time more function evaluations. In turbomachinery applications, function evaluations are performed with time-consuming CFD simulation, which results in a long, non affordable, design cycle. Modern High Performance Computing systems, especially Graphic Processing Units (GPUs), are able to alleviate this inconvenience by accelerating the design evaluation itself. In this work we present a validated CFD Solver running on GPUs, able to accelerate the design evaluation and thus the entire design process. An achieved speedup of 20x to 30x enabled the DE algorithm to run on a high-end computer instead of a costly large cluster. The GPU-enhanced DE was used to optimize the aerodynamics of a supersonic compressor cascade, achieving an aerodynamic loss minimization of 20%.
NASA Astrophysics Data System (ADS)
Kannan, S. M.; Renuga, P.; Kalyani, S.; Muthukumaran, E.
2015-12-01
This paper proposes new methods to select the optimal values of fixed and switched shunt capacitors in Radial distribution feeders for varying load conditions so as to maximize the annual savings and minimizes the energy loss by taking the capacitor cost into account. The identification of the weak buses, where the capacitors should be placed is decided by a set of rules given by the fuzzy expert system. Then the sizing of the fixed and switched capacitors is modeled using differential evolution (DE) and particle swarm optimization (PSO). A case study with an existing 15 bus rural distribution feeder is presented to illustrate the applicability of the algorithm. Simulation results show the better saving in cost over previous capacitor placement algorithm.
Reentry trajectories and their optimization by an evolution algorithm
NASA Astrophysics Data System (ADS)
Bade, A.; Voegt, S.; Axmann, J. K.; Rex, D.
The descent of a winged space vehicle from an orbit and the following re-entry into denser atmospheric layers is a rather complex physical process. To get insight into some of the fundamental phenomena, as a first step, the re-entry of simple shaped bodies has to be simulated. In order to improve the understanding of the physics, in this paper, a stepwise approach is presented: re-entry of balls, of capsules and of winged vehicles. For the re-entry of winged space vehicles, the equations of motion are not formulated in Keplerian elements but in earth-oriented coordinate systems, with the help of spherical polar coordinates in the most general form. They include thrust and aerodynamic forces (in three components). The atmosphere is regarded as fixed with the rotating oblate earth. This set of differential equations is solved by numerical integration. The influence of the angle of attack and of the bank angle upon the re-entry trajectory is discussed, also with respect to constraints (e.g., surface temperature). In all practical applications, various optimization problems arise, e.g. if for a required lateral range the heat load for the vehicle has to be minimized. The Institute of Space Technology and Reactor Technology has developed a method based on the evolution strategy, which will shortly be described here. Optimal re-entry trajectories for a preliminary configuration similar to HORUS are presented.
Thermal and Structural Evolution of a Partially Differentiated Titan
NASA Astrophysics Data System (ADS)
Bland, Michael T.; McKinnon, W. B.
2012-10-01
Titan’s moment of inertia (C/MR2) has been measured by Cassini to be 0.34, indicating either partial differentiation, or full differentiation with a low-density (hydrated) silicate core. Fully differentiated models have been constructed [Castillo-Rogez and Lunine, 2010], but require specific geochemical assumptions (e.g., rapid accretion, minimal core dehydration). In contrast, the alternative, partially differentiated models have not yet been fully vetted. Here we investigate the thermal stability of such partially differentiated internal structures by evaluating whether complete differentiation can be avoided. Our model assumes an initial three-layer internal structure consisting of a pure ice layer, mixed ice-rock layer, and silicate core, and calculates the temperature of each layer following the numerical approach in Bland et al. (2008, 2009). The model allows melting in the pure ice and mixed layer, and dehydration of the initially hydrated silicate core (leading to densification and absorption of latent heat). Melting of the mixed layer liberates silicate material, which is assumed to sink to the top of the silicate layer over time scales short relative to simulation time scales (in reality some may mx back into the convecting mixed ice-rock layer). Simulations so far indicate that melting of Titan’s pure ice shell is common early in Solar System history, and that melting frequently extends into Titan’s nominal mixed ice-rock layer. Such melting leads to irreversible unmixing of some of the mixed ice-rock layer. Nearly complete dehydration of the silicate core occurs when condritic K is retained in the rock component. The structural evolution decreases Titan’s initial moment of inertia; however, long-lived radiogenic species are generally incapable of completely melting and separating Titan’s mixed layer. To date, thermally stable structural models with C/MR2 as large as 0.33 have been achieved. We continue to investigate how realistic ocean and
An Improved Differential Evolution Solution for Software Project Scheduling Problem
Biju, A. C.; Victoire, T. Aruldoss Albert; Mohanasundaram, Kumaresan
2015-01-01
This paper proposes a differential evolution (DE) method for the software project scheduling problem (SPSP). The interest on finding a more efficient solution technique for SPSP is always a topic of interest due to the fact of ever growing challenges faced by the software industry. The curse of dimensionality is introduced in the scheduling problem by ever increasing software assignments and the number of staff who handles it. Thus the SPSP is a class of NP-hard problem, which requires a rigorous solution procedure which guarantees a reasonably better solution. Differential evolution is a direct search stochastic optimization technique that is fairly fast and reasonably robust. It is also capable of handling nondifferentiable, nonlinear, and multimodal objective functions like SPSP. This paper proposes a refined DE where a new mutation mechanism is introduced. The superiority of the proposed method is experimented and demonstrated by solving the SPSP on 50 random instances and the results are compared with some of the techniques in the literature. PMID:26495419
A hybrid algorithm for Caputo fractional differential equations
NASA Astrophysics Data System (ADS)
Salgado, G. H. O.; Aguirre, L. A.
2016-04-01
This paper is concerned with the numerical solution of fractional initial value problems (FIVP) in sense of Caputo's definition for dynamical systems. Unlike for integer-order derivatives that have a single definition, there is more than one definition of non integer-order derivatives and the solution of an FIVP is definition-dependent. In this paper, the chief differences of the main definitions of fractional derivatives are revisited and a numerical algorithm to solve an FIVP for Caputo derivative is proposed. The main advantages of the algorithm are twofold: it can be initialized with integer-order derivatives, and it is faster than the corresponding standard algorithm. The performance of the proposed algorithm is illustrated with examples which suggest that it requires about half the computation time to achieve the same accuracy than the standard algorithm.
NASA Astrophysics Data System (ADS)
Krys, Sebastian; Jankowski, Stanislaw; Piatkowska-Janko, Ewa
2009-06-01
This paper presents the application of differential evolution, an evolutionary algorithm of solving a single objective optimization problem - tuning the hiperparameters of least-square support vector machine classifier. The goal was to improve the classification of patients with sustained ventricular tachycardia after myocardial infarction based on a signal-averaged electrocardiography dataset received from the Medical University of Warsaw. The applied method attained a classification rate of 96% of the SVT+ group.
Modification of species-based differential evolution for multimodal optimization
NASA Astrophysics Data System (ADS)
Idrus, Said Iskandar Al; Syahputra, Hermawan; Firdaus, Muliawan
2015-12-01
At this time optimization has an important role in various fields as well as between other operational research, industry, finance and management. Optimization problem is the problem of maximizing or minimizing a function of one variable or many variables, which include unimodal and multimodal functions. Differential Evolution (DE), is a random search technique using vectors as an alternative solution in the search for the optimum. To localize all local maximum and minimum on multimodal function, this function can be divided into several domain of fitness using niching method. Species-based niching method is one of method that build sub-populations or species in the domain functions. This paper describes the modification of species-based previously to reduce the computational complexity and run more efficiently. The results of the test functions show species-based modifications able to locate all the local optima in once run the program.
Luo, Xiongbiao; Jayarathne, Uditha L; McLeod, A Jonathan; Mori, Kensaku
2014-01-01
Endoscopic navigation generally integrates different modalities of sensory information in order to continuously locate an endoscope relative to suspicious tissues in the body during interventions. Current electromagnetic tracking techniques for endoscopic navigation have limited accuracy due to tissue deformation and magnetic field distortion. To avoid these limitations and improve the endoscopic localization accuracy, this paper proposes a new endoscopic navigation framework that uses an optical mouse sensor to measure the endoscope movements along its viewing direction. We then enhance the differential evolution algorithm by modifying its mutation operation. Based on the enhanced differential evolution method, these movement measurements and image structural patches in endoscopic videos are fused to accurately determine the endoscope position. An evaluation on a dynamic phantom demonstrated that our method provides a more accurate navigation framework. Compared to state-of-the-art methods, it improved the navigation accuracy from 2.4 to 1.6 mm and reduced the processing time from 2.8 to 0.9 seconds. PMID:25485397
Vrugt, Jasper A; Hyman, James M; Robinson, Bruce A; Higdon, Dave; Ter Braak, Cajo J F; Diks, Cees G H
2008-01-01
Markov chain Monte Carlo (MCMC) methods have found widespread use in many fields of study to estimate the average properties of complex systems, and for posterior inference in a Bayesian framework. Existing theory and experiments prove convergence of well constructed MCMC schemes to the appropriate limiting distribution under a variety of different conditions. In practice, however this convergence is often observed to be disturbingly slow. This is frequently caused by an inappropriate selection of the proposal distribution used to generate trial moves in the Markov Chain. Here we show that significant improvements to the efficiency of MCMC simulation can be made by using a self-adaptive Differential Evolution learning strategy within a population-based evolutionary framework. This scheme, entitled DiffeRential Evolution Adaptive Metropolis or DREAM, runs multiple different chains simultaneously for global exploration, and automatically tunes the scale and orientation of the proposal distribution in randomized subspaces during the search. Ergodicity of the algorithm is proved, and various examples involving nonlinearity, high-dimensionality, and multimodality show that DREAM is generally superior to other adaptive MCMC sampling approaches. The DREAM scheme significantly enhances the applicability of MCMC simulation to complex, multi-modal search problems.
On Improving Efficiency of Differential Evolution for Aerodynamic Shape Optimization Applications
NASA Technical Reports Server (NTRS)
Madavan, Nateri K.
2004-01-01
Differential Evolution (DE) is a simple and robust evolutionary strategy that has been provEn effective in determining the global optimum for several difficult optimization problems. Although DE offers several advantages over traditional optimization approaches, its use in applications such as aerodynamic shape optimization where the objective function evaluations are computationally expensive is limited by the large number of function evaluations often required. In this paper various approaches for improving the efficiency of DE are reviewed and discussed. Several approaches that have proven effective for other evolutionary algorithms are modified and implemented in a DE-based aerodynamic shape optimization method that uses a Navier-Stokes solver for the objective function evaluations. Parallelization techniques on distributed computers are used to reduce turnaround times. Results are presented for standard test optimization problems and for the inverse design of a turbine airfoil. The efficiency improvements achieved by the different approaches are evaluated and compared.
NASA Astrophysics Data System (ADS)
Kela, K. B.; Arya, L. D.
2014-09-01
This paper describes a methodology for determination of optimum failure rate and repair time for each section of a radial distribution system. An objective function in terms of reliability indices and their target values is selected. These indices depend mainly on failure rate and repair time of a section present in a distribution network. A cost is associated with the modification of failure rate and repair time. Hence the objective function is optimized subject to failure rate and repair time of each section of the distribution network considering the total budget allocated to achieve the task. The problem has been solved using differential evolution and bare bones particle swarm optimization. The algorithm has been implemented on a sample radial distribution system.
Modelling of the thermal evolution and differentiation of early Ceres
NASA Astrophysics Data System (ADS)
Neumann, Wladimir; Breuer, Doris; Spohn, Tilman
2015-04-01
The asteroid 1 Ceres is one of the remaining examples of the intermediate stages of planetary accretion. Studies of such protoplanetary objects provide insight into the history of the formation of Earth and other planets. One of Ceres' remarkable properties is the relatively low average bulk density of 2077±36 kg m-3[1]. Assuming a nearly chondritic composition, this low value can be explained either by the presence of a low density phase[2,3] (possibly water ice or hydrated silicates) that could have differentiated forming an icy mantle over a rocky core[2,3], or by a relatively high average porosity[4]. The shape and the moment of inertia of Ceres are consistent with both a homogeneous and a differentiated structure. In the first case Ceres would be just a large version of a common asteroid. In the second case this body could exhibit properties characteristic for a planet rather than an asteroid: presence of a core, mantle and crust, as well as a liquid ocean in the past and maybe still a thin basal ocean today. We study the evolution of a Ceres-like body via numerical modelling in order to draw conclusions about the thermal metamorphism of the interior and its present-day structure. A numerical model of an ice-silicate planetesimal, considering both water-rock and metal-silicate differentiation of Ceres is being developed. In particular, accretion from a km-sized porous seed to a Ceres-sized asteroid is considered. Further relevant processes, such as transition from amorphous to crystalline ice, melting of ice, hydrothermal convection, as well as melting and percolation of metal and silicates are included in the model. The model is suited to prioritise between the two possible structures mentioned above and to constrain the present-day state of Ceres' interior. The necessary conditions for the differentiation as well as the influence of the vital parameters, such as the accretion duration, will be discussed. [1] Thomas, C. et al. (2005) Nature, 437, 224-226. [2
The Structure of Evolution LOCBURST: The BATSE Burst Location Algorithm
NASA Technical Reports Server (NTRS)
Pendleton, Geoffrey N.; Briggs, Michael S.; Kippen, R. March; Paciesas, William S.; Stollberg, Mark; Woods, Pete; Meegan, C. A.; Fishman, G. J.; McCollough, M. L.; Connaughton, V.
1998-01-01
The gamma-ray bursts (GRB) location algorithm used to produce the BATSE GRB locations is described. The general flow of control of the current location algorithm is presented and the significant properties of the various physical inputs required are identified. The development of the burst location algorithm during the releases of the BATSE 1B, 2B, and 3B gamma-ray burst catalogs is presented so that the reasons for the differences in the positions and error estimates between the catalogs can be understood. In particular, differences between the 2B and 3B locations are discussed for events that have moved significantly and the reasons for the changes explained. The locations of bursts located independently by the interplanetary network are used to illustrate the effect on burst location accuracy of various components of the algorithm. IPN data as well as locations from other gamma-ray instruments are used to calculate estimates of the systematic errors on BATSE burst locations.
Landscape evolution by soil differentiation on African catenas
NASA Astrophysics Data System (ADS)
Khomo, Lesego; Hartshorn, Tony; Heimsath, Arjun; Rogers, Kevin; Chadwick, Oliver
2010-05-01
Landscapes in most of the Southern Hemisphere have escaped geomorphic agents shaping some of Earth's most charismatic stretches of land in the North. Amongst these are mountainous terrains presided over by tumultuous tectonics and landforms created by remnants of past glaciers. In Southern Africa, tectonic activities and glaciations have had a much more limited impact on the present landforms. This quiescent geomorphic regime has led to relatively stable landscapes enabling prolonged soil differentiation to culminate in catenas. Over these extended time scales the soil surprisingly emerges as a significant geomorphic agent in its own right. Catenas in the Kruger National Park, South Africa, form on gentle <8% slopes over 100 to 2000m between hillcrests and footslopes. Erosion rates inferred from in-situ produced cosmogenic Be-10 are relatively low in the range 2 and 4m per Ma and chemical weathering is responsible for up to 80% of these losses. The long soil residence times necessitated by the slow tempo of erosion give rise to intense weathering-driven differentiation between highly leached crests and depositional areas at the foot of the catenas. The crests are as a result volumetrically collapsed relative to parent material as indicated by elevated Zr concentrations while lower down on the catenas the soil fabric becomes dilated and depleted in Zr. The collapses and dilations are measurable, and can be as much as 3m at the crest and a meter's expansion at the foot. Hence pedogenic collapse and dilation on this scale produces real changes in hillslope geomorphology. Is it thus possible for soil development outside physical erosion to act as a geomorphic agent. Soil dilation at the footslope has a further consequence on hillslope geomorphology. Due to material build-up in footslope soils, mostly clay, soil pores are clogged resulting in diversion of water flowpaths to the surface. Over time, this process can result in a fluvial incision that migrates up the slope
NASA Astrophysics Data System (ADS)
Contreras-Astorga, Alonso; Schulze-Halberg, Axel
2015-08-01
We construct a relationship between integral and differential representation of second-order Jordan chains. Conditions to obtain regular potentials through the confluent supersymmetry algorithm when working with the differential representation are obtained using this relationship. Furthermore, it is used to find normalization constants of wave functions of quantum systems that feature energy-dependent potentials. Additionally, this relationship is used to express certain integrals involving functions that are solution of Schrödinger equations through derivatives.
3D face reconstruction from limited images based on differential evolution
NASA Astrophysics Data System (ADS)
Wang, Qun; Li, Jiang; Asari, Vijayan K.; Karim, Mohammad A.
2011-09-01
3D face modeling has been one of the greatest challenges for researchers in computer graphics for many years. Various methods have been used to model the shape and texture of faces under varying illumination and pose conditions from a single given image. In this paper, we propose a novel method for the 3D face synthesis and reconstruction by using a simple and efficient global optimizer. A 3D-2D matching algorithm which employs the integration of the 3D morphable model (3DMM) and the differential evolution (DE) algorithm is addressed. In 3DMM, the estimation process of fitting shape and texture information into 2D images is considered as the problem of searching for the global minimum in a high dimensional feature space, in which optimization is apt to have local convergence. Unlike the traditional scheme used in 3DMM, DE appears to be robust against stagnation in local minima and sensitiveness to initial values in face reconstruction. Benefitting from DE's successful performance, 3D face models can be created based on a single 2D image with respect to various illuminating and pose contexts. Preliminary results demonstrate that we are able to automatically create a virtual 3D face from a single 2D image with high performance. The validation process shows that there is only an insignificant difference between the input image and the 2D face image projected by the 3D model.
Algorithms for computing Minkowski operators and their application in differential games
NASA Astrophysics Data System (ADS)
Dvurechensky, P. E.; Ivanov, G. E.
2014-02-01
The Minkowski operators are considered, which extend the concepts of the Minkowski sum and difference to the case where one of the summands depends on an element of the other term. The properties of these operators are examined. Convolution methods of computer geometry and algorithms for computing the values of the Minkowski operators are developed. These algorithms are used to construct epsilon-optimal control strategies in a nonlinear differential game with a nonconvex target set. The errors of the proposed algorithms are estimated in detail. Numerical results for the conflicting control of a nonlinear pendulum are presented.
NASA Astrophysics Data System (ADS)
Neumann, Wladimir; Breuer, Doris; Spohn, Tilman; Henke, Stephan; Gail, Hans-Peter; Schwarz, Winfried; Trieloff, Mario; Hopp, Jens
2015-04-01
The acapulcoites and lodranites are rare groups of achondritic meteorites. Several characteristics such as unique oxygen isotope composition and similar cosmic ray exposure ages indicate that these meteorites originate from a common parent body (Weigel et al. 1999). By contrast to both undifferentiated and differentiated meteorites, acapulcoites and lodranites are especially interesting because they experienced melting that was, however, not complete (McCoy et al. 2006). Thus, unravelling their origin contributes directly to the understanding of the initial differentiation stage of planetary objects in the Solar system. The information preserved in the structure and composition of meteorites can be recovered by modelling the evolution of their parent bodies and comparing the results with the laboratory investigations. Model calculations for the thermal evolution of the parent body of the Acapulco and Lodran-like meteorite clan were performed using two numerical models. Both models (from [3] and [4], termed (a) and (b), respectively) solve a 1D heat conduction equation in spherical symmetry considering heating by short- and long-lived radioactive isotopes, temperature- and porosity-dependent parameters, compaction of initially porous material, and melting. The calculations with (a) were compared to the maximum metamorphic temperatures and thermo-chronological data available for acapulcoites and lodranites. Applying a genetic algorithm, an optimised set of parameters of a common parent body was determined, which fits to the data for the cooling histories of these meteorites. The optimum fit corresponds to a body with the radius of 270 km and a formation time of 1.66 Ma after the CAIs. Using the model by (b) that considers differentiation by porous flow and magmatic heat transport, the differentiation of the optimum fit body was calculated. The resulting structure consists of a metallic core, a silicate mantle, a partially differentiated layer, an undifferentiated
Experiences and evolutions of the ALICE DAQ Detector Algorithms framework
NASA Astrophysics Data System (ADS)
Chapeland, Sylvain; Carena, Franco; Carena, Wisla; Chibante Barroso, Vasco; Costa, Filippo; Denes, Ervin; Divia, Roberto; Fuchs, Ulrich; Grigore, Alexandru; Simonetti, Giuseppe; Soos, Csaba; Telesca, Adriana; Vande Vyvre, Pierre; von Haller, Barthelemy
2012-12-01
ALICE (A Large Ion Collider Experiment) is the heavy-ion detector studying the physics of strongly interacting matter and the quark-gluon plasma at the CERN LHC (Large Hadron Collider). The 18 ALICE sub-detectors are regularly calibrated in order to achieve most accurate physics measurements. Some of these procedures are done online in the DAQ (Data Acquisition System) so that calibration results can be directly used for detector electronics configuration before physics data taking, at run time for online event monitoring, and offline for data analysis. A framework was designed to collect statistics and compute calibration parameters, and has been used in production since 2008. This paper focuses on the recent features developed to benefit from the multi-cores architecture of CPUs, and to optimize the processing power available for the calibration tasks. It involves some C++ base classes to effectively implement detector specific code, with independent processing of events in parallel threads and aggregation of partial results. The Detector Algorithm (DA) framework provides utility interfaces for handling of input and output (configuration, monitored physics data, results, logging), and self-documentation of the produced executable. New algorithms are created quickly by inheritance of base functionality and implementation of few ad-hoc virtual members, while the framework features are kept expandable thanks to the isolation of the detector calibration code. The DA control system also handles unexpected processes behaviour, logs execution status, and collects performance statistics.
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
NASA Technical Reports Server (NTRS)
Kracher, A.
1985-01-01
Some of the properties of IAB and IIICD iron meteorites thought to be derived from partially differentiated planetesimals are summarized, and the physical aspects that may have controlled parent body differentiation and affected the composition of the sulfide melt are outlined. The chemical evolution of the parent body is then discussed, and observations supporting the partial differentiation model are examined. Finally, an attempt is made to reinterpret barometric and chronometric data in light of the partial differentiation model, and tentative conclusions are presented.
The stochastic evolution of a protocell: the Gillespie algorithm in a dynamically varying volume.
Carletti, T; Filisetti, A
2012-01-01
We propose an improvement of the Gillespie algorithm allowing us to study the time evolution of an ensemble of chemical reactions occurring in a varying volume, whose growth is directly related to the amount of some specific molecules, belonging to the reactions set. This allows us to study the stochastic evolution of a protocell, whose volume increases because of the production of container molecules. Several protocell models are considered and compared with the deterministic models. PMID:22536297
Luo, Xiongbiao; Wan, Ying; He, Xiangjian; Mori, Kensaku
2015-08-01
This paper proposes an observation-driven adaptive differential evolution algorithm that fuses bronchoscopic video sequences, electromagnetic sensor measurements, and computed tomography images for accurate and smooth bronchoscope three-dimensional motion tracking. Currently an electromagnetic tracker with a position sensor fixed at the bronchoscope tip is commonly used to estimate bronchoscope movements. The large tracking error from directly using sensor measurements, which may be deteriorated heavily by patient respiratory motion and the magnetic field distortion of the tracker, limits clinical applications. How to effectively use sensor measurements for precise and stable bronchoscope electromagnetic tracking remains challenging. We here exploit an observation-driven adaptive differential evolution framework to address such a challenge and boost the tracking accuracy and smoothness. In our framework, two advantageous points are distinguished from other adaptive differential evolution methods: (1) the current observation including sensor measurements and bronchoscopic video images is used in the mutation equation and the fitness computation, respectively and (2) the mutation factor and the crossover rate are determined adaptively on the basis of the current image observation. The experimental results demonstrate that our framework provides much more accurate and smooth bronchoscope tracking than the state-of-the-art methods. Our approach reduces the tracking error from 3.96 to 2.89 mm, improves the tracking smoothness from 4.08 to 1.62 mm, and increases the visual quality from 0.707 to 0.741. PMID:25660001
EDGA: A Population Evolution Direction-Guided Genetic Algorithm for Protein-Ligand Docking.
Guan, Boxin; Zhang, Changsheng; Ning, Jiaxu
2016-07-01
Protein-ligand docking can be formulated as a search algorithm associated with an accurate scoring function. However, most current search algorithms cannot show good performance in docking problems, especially for highly flexible docking. To overcome this drawback, this article presents a novel and robust optimization algorithm (EDGA) based on the Lamarckian genetic algorithm (LGA) for solving flexible protein-ligand docking problems. This method applies a population evolution direction-guided model of genetics, in which search direction evolves to the optimum solution. The method is more efficient to find the lowest energy of protein-ligand docking. We consider four search methods-a tradition genetic algorithm, LGA, SODOCK, and EDGA-and compare their performance in docking of six protein-ligand docking problems. The results show that EDGA is the most stable, reliable, and successful. PMID:26895461
Chain Copolymerization Reactions: An Algorithm to Predict the Reaction Evolution with Conversion
ERIC Educational Resources Information Center
Gallardo, Alberto; Aguilar, Maria Rosa; Abraham, Gustavo A.; Roman, Julio San
2004-01-01
An algorithm is developed to study and understand the behavior of chain copolymerization reactions. When a binary copolymerization reaction follows the terminal model, Conversion is able to predict the evolution of different parameters, such as instantaneous and cumulative copolymer molar fractions, or molar fractions of any sequence with the…
Optimization of the mean radius flow path of a multi-stage steam turbine with evolution algorithms
NASA Astrophysics Data System (ADS)
Huttunen, Jukka; Larjola, Jaakko; Turunen-Saaresti, Teemu; Backman, Jari
2011-08-01
A prototype model of the mean radius flow path of a four-stage, high speed 1 MWe axial steam turbine was optimized by using evolution algorithms, DE (differential evolution) algorithm in this case. Also the cost-benefits of the optimization were inspected. The optimization was successfully performed but the accuracy of the optimization was slightly less than hoped when compared to the control modeling executed with the CFD (computational fluid dynamics). The mentioned inaccuracy could have been hardly avoided because of problems with an initial presumption involving semi-empiric calculations and of the uncertainty concerning the absolute areas of qualification of the functions. This kind of algebraic modeling was essential for the success of the optimization because e.g. CFD-calculation could not have been done on each step of the optimization. During the optimization some problems occurred with the adequacy of the computer capacity and with finding a suitable solution that would keep the algorithms within mathematically allowable boundaries but would not restrict the progress of the optimization too much. The rest of the problems were due to the novelty of the application and problems with preciseness when handling the areas of qualification of the functions. Although the accuracy of the optimization results was not exactly in accordance with the objective, they did have a favorable effect on the designing of the turbine. The optimization executed with the help of the DE-algorithm got at least about 3.5 % more power out of the turbine which means about 150 000 € cost-benefit per turbine in the form of additional electricity capacity.
Thermal evolution and chemical differentiation of the terrestrial magma ocean
NASA Technical Reports Server (NTRS)
Abe, Y.
1992-01-01
The release of gravitational energy resulted in global melting and formation of a magma ocean during accretion of the Earth. Although it is believed that the formation of the magma ocean resulted in gravitational differentiation of melt and solid, the differentiation might be disturbed by the following processes: (1) convective mixing; (2) cooling and solidification; and (3) growth of the earth, which results in secular increase of pressure, and stirring by planetesimal impacts. The purpose of this study is to investigate the differentiation processes of the terrestrial magma ocean by taking into account various disturbing processes.
Panda, Sidhartha; Yegireddy, Narendra Kumar
2015-09-01
In this paper, a hybrid Improved Differential Evolution and Pattern Search (hIDEPS) approach is proposed for the design of a PI-Type Multi-Input Single Output (MISO) Static Synchronous Series Compensator (SSSC) based damping controller. The improvement in Differential Evolution (DE) algorithm is introduced by a simple but effective scheme of changing two of its most important control parameters i.e. step size and crossover probability with an objective of achieving improved performance. Pattern Search (PS) is subsequently employed to fine tune the best solution provided by modified DE algorithm. The superiority of a proposed hIDEPS technique over DE and improved DE has also been demonstrated. At the outset, this concept is applied to a SSSC connected in a Single Machine Infinite Bus (SMIB) power system and then extended to a multi-machine power system. To show the effectiveness and robustness of the proposed design approach, simulation results are presented and compared with DE and Particle Swarm Optimization (PSO) optimized Single Input Single Output (SISO) SSSC based damping controllers. It is observed that the proposed approach yield superior damping performance compared to some approaches available in the literature. PMID:25864132
Zhang, Huanping; Song, Xiaofeng; Wang, Huinan; Zhang, Xiaobai
2009-01-01
Computational analysis of microarray data has provided an effective way to identify disease-related genes. Traditional disease gene selection methods from microarray data such as statistical test always focus on differentially expressed genes in different samples by individual gene prioritization. These traditional methods might miss differentially coexpressed (DCE) gene subsets because they ignore the interaction between genes. In this paper, MIClique algorithm is proposed to identify DEC gene subsets based on mutual information and clique analysis. Mutual information is used to measure the coexpression relationship between each pair of genes in two different kinds of samples. Clique analysis is a commonly used method in biological network, which generally represents biological module of similar function. By applying the MIClique algorithm to real gene expression data, some DEC gene subsets which correlated under one experimental condition but uncorrelated under another condition are detected from the graph of colon dataset and leukemia dataset. PMID:20169000
Towards developing robust algorithms for solving partial differential equations on MIMD machines
NASA Technical Reports Server (NTRS)
Saltz, Joel H.; Naik, Vijay K.
1988-01-01
Methods for efficient computation of numerical algorithms on a wide variety of MIMD machines are proposed. These techniques reorganize the data dependency patterns to improve the processor utilization. The model problem finds the time-accurate solution to a parabolic partial differential equation discretized in space and implicitly marched forward in time. The algorithms are extensions of Jacobi and SOR. The extensions consist of iterating over a window of several timesteps, allowing efficient overlap of computation with communication. The methods increase the degree to which work can be performed while data are communicated between processors. The effect of the window size and of domain partitioning on the system performance is examined both by implementing the algorithm on a simulated multiprocessor system.
Towards developing robust algorithms for solving partial differential equations on MIMD machines
NASA Technical Reports Server (NTRS)
Saltz, J. H.; Naik, V. K.
1985-01-01
Methods for efficient computation of numerical algorithms on a wide variety of MIMD machines are proposed. These techniques reorganize the data dependency patterns to improve the processor utilization. The model problem finds the time-accurate solution to a parabolic partial differential equation discretized in space and implicitly marched forward in time. The algorithms are extensions of Jacobi and SOR. The extensions consist of iterating over a window of several timesteps, allowing efficient overlap of computation with communication. The methods increase the degree to which work can be performed while data are communicated between processors. The effect of the window size and of domain partitioning on the system performance is examined both by implementing the algorithm on a simulated multiprocessor system.
Culbreath, Karissa; Ager, Edward; Nemeyer, Ronald J.; Kerr, Alan
2012-01-01
We present the evolution of testing algorithms at our institution in which the C. Diff Quik Chek Complete immunochromatographic cartridge assay determines the presence of both glutamate dehydrogenase and Clostridium difficile toxins A and B as a primary screen for C. difficile infection and indeterminate results (glutamate dehydrogenase positive, toxin A and B negative) are confirmed by the GeneXpert C. difficile PCR assay. This two-step algorithm is a cost-effective method for highly sensitive detection of toxigenic C. difficile. PMID:22718938
Evolution of domain wall networks: The Press-Ryden-Spergel algorithm
Sousa, L.; Avelino, P. P.
2010-04-15
The Press-Ryden-Spergel (PRS) algorithm is a modification to the field theory equations of motion, parametrized by two parameters ({alpha} and {beta}), implemented in numerical simulations of cosmological domain wall networks, in order to ensure a fixed comoving resolution. In this paper we explicitly demonstrate that the PRS algorithm provides the correct domain wall dynamics in (N+1)-dimensional Friedmann-Robertson-Walker universes if {alpha}+{beta}/2=N, fully validating its use in numerical studies of cosmic domain evolution. We further show that this result is valid for generic thin featureless domain walls, independently of the Lagrangian of the model.
NASA Technical Reports Server (NTRS)
Walker, K. P.; Freed, A. D.
1991-01-01
New methods for integrating systems of stiff, nonlinear, first order, ordinary differential equations are developed by casting the differential equations into integral form. Nonlinear recursive relations are obtained that allow the solution to a system of equations at time t plus delta t to be obtained in terms of the solution at time t in explicit and implicit forms. Examples of accuracy obtained with the new technique are given by considering systems of nonlinear, first order equations which arise in the study of unified models of viscoplastic behaviors, the spread of the AIDS virus, and predator-prey populations. In general, the new implicit algorithm is unconditionally stable, and has a Jacobian of smaller dimension than that which is acquired by current implicit methods, such as the Euler backward difference algorithm; yet, it gives superior accuracy. The asymptotic explicit and implicit algorithms are suitable for solutions that are of the growing and decaying exponential kinds, respectively, whilst the implicit Euler-Maclaurin algorithm is superior when the solution oscillates, i.e., when there are regions in which both growing and decaying exponential solutions exist.
Jiao, Z.Y.; Li, Y.B.; Mao, J.; Liu, X.Y.; Yang, X.C.; Tan, C.; Chu, J.M.; Liu, X.P.
2016-01-01
Our objective is to evaluate the accuracy of three algorithms in differentiating the origins of outflow tract ventricular arrhythmias (OTVAs). This study involved 110 consecutive patients with OTVAs for whom a standard 12-lead surface electrocardiogram (ECG) showed typical left bundle branch block morphology with an inferior axis. All the ECG tracings were retrospectively analyzed using the following three recently published ECG algorithms: 1) the transitional zone (TZ) index, 2) the V2 transition ratio, and 3) V2 R wave duration and R/S wave amplitude indices. Considering all patients, the V2 transition ratio had the highest sensitivity (92.3%), while the R wave duration and R/S wave amplitude indices in V2 had the highest specificity (93.9%). The latter finding had a maximal area under the ROC curve of 0.925. In patients with left ventricular (LV) rotation, the V2 transition ratio had the highest sensitivity (94.1%), while the R wave duration and R/S wave amplitude indices in V2 had the highest specificity (87.5%). The former finding had a maximal area under the ROC curve of 0.892. All three published ECG algorithms are effective in differentiating the origin of OTVAs, while the V2 transition ratio, and the V2 R wave duration and R/S wave amplitude indices are the most sensitive and specific algorithms, respectively. Amongst all of the patients, the V2 R wave duration and R/S wave amplitude algorithm had the maximal area under the ROC curve, but in patients with LV rotation the V2 transition ratio algorithm had the maximum area under the ROC curve. PMID:27143173
BIMMER: a novel algorithm for detecting differential DNA methylation regions from MBDCap-seq data.
Mao, Zijing; Ma, Chifeng; Huang, Tim H-M; Chen, Yidong; Huang, Yufei
2014-01-01
DNA methylation is a common epigenetic marker that regulates gene expression. A robust and cost-effective way for measuring whole genome methylation is Methyl-CpG binding domain-based capture followed by sequencing (MBDCap-seq). In this study, we proposed BIMMER, a Hidden Markov Model (HMM) for differential Methylation Regions (DMRs) identification, where HMMs were proposed to model the methylation status in normal and cancer samples in the first layer and another HMM was introduced to model the relationship between differential methylation and methylation statuses in normal and cancer samples. To carry out the prediction for BIMMER, an Expectation-Maximization algorithm was derived. BIMMER was validated on the simulated data and applied to real MBDCap-seq data of normal and cancer samples. BIMMER revealed that 8.83% of the breast cancer genome are differentially methylated and the majority are hypo-methylated in breast cancer. PMID:25474268
BIMMER: a novel algorithm for detecting differential DNA methylation regions from MBDCap-seq data
2014-01-01
DNA methylation is a common epigenetic marker that regulates gene expression. A robust and cost-effective way for measuring whole genome methylation is Methyl-CpG binding domain-based capture followed by sequencing (MBDCap-seq). In this study, we proposed BIMMER, a Hidden Markov Model (HMM) for differential Methylation Regions (DMRs) identification, where HMMs were proposed to model the methylation status in normal and cancer samples in the first layer and another HMM was introduced to model the relationship between differential methylation and methylation statuses in normal and cancer samples. To carry out the prediction for BIMMER, an Expectation-Maximization algorithm was derived. BIMMER was validated on the simulated data and applied to real MBDCap-seq data of normal and cancer samples. BIMMER revealed that 8.83% of the breast cancer genome are differentially methylated and the majority are hypo-methylated in breast cancer. PMID:25474268
Collective Dynamics Differentiates Functional Divergence in Protein Evolution
Glembo, Tyler J.; Farrell, Daniel W.; Gerek, Z. Nevin; Thorpe, M. F.; Ozkan, S. Banu
2012-01-01
Protein evolution is most commonly studied by analyzing related protein sequences and generating ancestral sequences through Bayesian and Maximum Likelihood methods, and/or by resurrecting ancestral proteins in the lab and performing ligand binding studies to determine function. Structural and dynamic evolution have largely been left out of molecular evolution studies. Here we incorporate both structure and dynamics to elucidate the molecular principles behind the divergence in the evolutionary path of the steroid receptor proteins. We determine the likely structure of three evolutionarily diverged ancestral steroid receptor proteins using the Zipping and Assembly Method with FRODA (ZAMF). Our predictions are within ∼2.7 Å all-atom RMSD of the respective crystal structures of the ancestral steroid receptors. Beyond static structure prediction, a particular feature of ZAMF is that it generates protein dynamics information. We investigate the differences in conformational dynamics of diverged proteins by obtaining the most collective motion through essential dynamics. Strikingly, our analysis shows that evolutionarily diverged proteins of the same family do not share the same dynamic subspace, while those sharing the same function are simultaneously clustered together and distant from those, that have functionally diverged. Dynamic analysis also enables those mutations that most affect dynamics to be identified. It correctly predicts all mutations (functional and permissive) necessary to evolve new function and ∼60% of permissive mutations necessary to recover ancestral function. PMID:22479170
NASA Astrophysics Data System (ADS)
Civicioglu, Pinar
2012-09-01
In order to solve numerous practical navigational, geodetic and astro-geodetic problems, it is necessary to transform geocentric cartesian coordinates into geodetic coordinates or vice versa. It is very easy to solve the problem of transforming geodetic coordinates into geocentric cartesian coordinates. On the other hand, it is rather difficult to solve the problem of transforming geocentric cartesian coordinates into geodetic coordinates as it is very hard to define a mathematical relationship between the geodetic latitude (φ) and the geocentric cartesian coordinates (X, Y, Z). In this paper, a new algorithm, the Differential Search Algorithm (DS), is presented to solve the problem of transforming the geocentric cartesian coordinates into geodetic coordinates and its performance is compared with the performances of the classical methods (i.e., Borkowski, 1989; Bowring, 1976; Fukushima, 2006; Heikkinen, 1982; Jones, 2002; Zhang, 2005; Borkowski, 1987; Shu, 2010 and Lin, 1995) and Computational-Intelligence algorithms (i.e., ABC, JDE, JADE, SADE, EPSDE, GSA, PSO2011, and CMA-ES). The statistical tests realized for the comparison of performances indicate that the problem-solving success of DS algorithm in transforming the geocentric cartesian coordinates into geodetic coordinates is higher than those of all classical methods and Computational-Intelligence algorithms used in this paper.
NASA Astrophysics Data System (ADS)
Roselyn, J. Preetha; Devaraj, D.; Dash, Subhransu Sekhar
2013-11-01
Voltage stability is an important issue in the planning and operation of deregulated power systems. The voltage stability problems is a most challenging one for the system operators in deregulated power systems because of the intense use of transmission line capabilities and poor regulation in market environment. This article addresses the congestion management problem avoiding offline transmission capacity limits related to voltage stability by considering Voltage Security Constrained Optimal Power Flow (VSCOPF) problem in deregulated environment. This article presents the application of Multi Objective Differential Evolution (MODE) algorithm to solve the VSCOPF problem in new competitive power systems. The maximum of L-index of the load buses is taken as the indicator of voltage stability and is incorporated in the Optimal Power Flow (OPF) problem. The proposed method in hybrid power market which also gives solutions to voltage stability problems by considering the generation rescheduling cost and load shedding cost which relieves the congestion problem in deregulated environment. The buses for load shedding are selected based on the minimum eigen value of Jacobian with respect to the load shed. In the proposed approach, real power settings of generators in base case and contingency cases, generator bus voltage magnitudes, real and reactive power demands of selected load buses using sensitivity analysis are taken as the control variables and are represented as the combination of floating point numbers and integers. DE/randSF/1/bin strategy scheme of differential evolution with self-tuned parameter which employs binomial crossover and difference vector based mutation is used for the VSCOPF problem. A fuzzy based mechanism is employed to get the best compromise solution from the pareto front to aid the decision maker. The proposed VSCOPF planning model is implemented on IEEE 30-bus system, IEEE 57 bus practical system and IEEE 118 bus system. The pareto optimal
An implementation of differential search algorithm (DSA) for inversion of surface wave data
NASA Astrophysics Data System (ADS)
Song, Xianhai; Li, Lei; Zhang, Xueqiang; Shi, Xinchun; Huang, Jianquan; Cai, Jianchao; Jin, Si; Ding, Jianping
2014-12-01
Surface wave dispersion analysis is widely used in geophysics to infer near-surface shear (S)-wave velocity profiles for a wide variety of applications. However, inversion of surface wave data is challenging for most local-search methods due to its high nonlinearity and to its multimodality. In this work, we proposed and implemented a new Rayleigh wave dispersion curve inversion scheme based on differential search algorithm (DSA), one of recently developed swarm intelligence-based algorithms. DSA is inspired from seasonal migration behavior of species of the living beings throughout the year for solving highly nonlinear, multivariable, and multimodal optimization problems. The proposed inverse procedure is applied to nonlinear inversion of fundamental-mode Rayleigh wave dispersion curves for near-surface S-wave velocity profiles. To evaluate calculation efficiency and stability of DSA, four noise-free and four noisy synthetic data sets are firstly inverted. Then, the performance of DSA is compared with that of genetic algorithms (GA) by two noise-free synthetic data sets. Finally, a real-world example from a waste disposal site in NE Italy is inverted to examine the applicability and robustness of the proposed approach on surface wave data. Furthermore, the performance of DSA is compared against that of GA by real data to further evaluate scores of the inverse procedure described here. Simulation results from both synthetic and actual field data demonstrate that differential search algorithm (DSA) applied to nonlinear inversion of surface wave data should be considered good not only in terms of the accuracy but also in terms of the convergence speed. The great advantages of DSA are that the algorithm is simple, robust and easy to implement. Also there are fewer control parameters to tune.
The knowledge instinct, cognitive algorithms, modeling of language and cultural evolution
NASA Astrophysics Data System (ADS)
Perlovsky, Leonid I.
2008-04-01
The talk discusses mechanisms of the mind and their engineering applications. The past attempts at designing "intelligent systems" encountered mathematical difficulties related to algorithmic complexity. The culprit turned out to be logic, which in one way or another was used not only in logic rule systems, but also in statistical, neural, and fuzzy systems. Algorithmic complexity is related to Godel's theory, a most fundamental mathematical result. These difficulties were overcome by replacing logic with a dynamic process "from vague to crisp," dynamic logic. It leads to algorithms overcoming combinatorial complexity, and resulting in orders of magnitude improvement in classical problems of detection, tracking, fusion, and prediction in noise. I present engineering applications to pattern recognition, detection, tracking, fusion, financial predictions, and Internet search engines. Mathematical and engineering efficiency of dynamic logic can also be understood as cognitive algorithm, which describes fundamental property of the mind, the knowledge instinct responsible for all our higher cognitive functions: concepts, perception, cognition, instincts, imaginations, intuitions, emotions, including emotions of the beautiful. I present our latest results in modeling evolution of languages and cultures, their interactions in these processes, and role of music in cultural evolution. Experimental data is presented that support the theory. Future directions are outlined.
A hybrid differential evolution/Levenberg-Marquardt method for solving inverse transport problems
Bledsoe, Keith C; Favorite, Jeffrey A
2010-01-01
Recently, the Differential Evolution (DE) optimization method was applied to solve inverse transport problems in finite cylindrical geometries and was shown to be far superior to the Levenberg-Marquardt optimization method at finding a global optimum for problems with several unknowns. However, while extremely adept at finding a global optimum solution, the DE method often requires a large number (hundreds or thousands) of transport calculations, making it much slower than the Levenberg-Marquardt method. In this paper, a hybridization of the Differential Evolution and Levenberg-Marquardt approaches is presented. This hybrid method takes advantage of the robust search capability of the Differential Evolution method and the speed of the Levenberg-Marquardt technique.
NASA Astrophysics Data System (ADS)
Tanaka, Hiroshi; Nakajima, Asumi; Nishiyama, Akinobu; Tokihiro, Tetsuji
2009-03-01
A differential equation exhibiting replicative time-evolution patterns is derived by inverse ultradiscretizatrion of Fredkin’s game, which is one of the simplest replicative cellular automaton (CA) in two dimensions. This is achieved by employing a certain filter and a clock function in the equation. These techniques are applicable to the inverse ultra-discretization (IUD) of other CA and stabilize the time-evolution of the obtained differential equation. Application to the game of life, another CA in two dimensions, is also presented.
Grainsize evolution and differential comminution in an experimental regolith
NASA Technical Reports Server (NTRS)
Horz, F.; Cintala, M.; See, T.
1984-01-01
The comminution of planetary surfaces by exposure to continuous meteorite bombardment was simulated by impacting the same fragmental gabbro target 200 times. The role of comminution and in situ gardening of planetary regoliths was addressed. Mean grain size continuously decreased with increasing shot number. Initially it decreased linearly with accumulated energy, but at some stage comminution efficiency started to decrease gradually. Point counting techniques, aided by the electron microprobe for mineral identification, were performed on a number of comminution products. Bulk chemical analyses of specific grain size fractions were also carried out. The finest sizes ( 10 microns) display generally the strongest enrichment/depletion factors. Similar, if not exactly identical, trends are reported from lunar soils. It is, therefore, not necessarily correct to explain the chemical characteristics of various grain sizes via different admixtures of materials from distant source terrains. Differential comminution of local source rocks may be the dominating factor.
NASA Astrophysics Data System (ADS)
Vrettas, Michail D.; Opper, Manfred; Cornford, Dan
2015-01-01
This work introduces a Gaussian variational mean-field approximation for inference in dynamical systems which can be modeled by ordinary stochastic differential equations. This new approach allows one to express the variational free energy as a functional of the marginal moments of the approximating Gaussian process. A restriction of the moment equations to piecewise polynomial functions, over time, dramatically reduces the complexity of approximate inference for stochastic differential equation models and makes it comparable to that of discrete time hidden Markov models. The algorithm is demonstrated on state and parameter estimation for nonlinear problems with up to 1000 dimensional state vectors and compares the results empirically with various well-known inference methodologies.
NASA Technical Reports Server (NTRS)
Green, Lawrence L.; Newman, Perry A.; Haigler, Kara J.
1993-01-01
The computational technique of automatic differentiation (AD) is applied to a three-dimensional thin-layer Navier-Stokes multigrid flow solver to assess the feasibility and computational impact of obtaining exact sensitivity derivatives typical of those needed for sensitivity analyses. Calculations are performed for an ONERA M6 wing in transonic flow with both the Baldwin-Lomax and Johnson-King turbulence models. The wing lift, drag, and pitching moment coefficients are differentiated with respect to two different groups of input parameters. The first group consists of the second- and fourth-order damping coefficients of the computational algorithm, whereas the second group consists of two parameters in the viscous turbulent flow physics modelling. Results obtained via AD are compared, for both accuracy and computational efficiency with the results obtained with divided differences (DD). The AD results are accurate, extremely simple to obtain, and show significant computational advantage over those obtained by DD for some cases.
odNEAT: An Algorithm for Decentralised Online Evolution of Robotic Controllers.
Silva, Fernando; Urbano, Paulo; Correia, Luís; Christensen, Anders Lyhne
2015-01-01
Online evolution gives robots the capacity to learn new tasks and to adapt to changing environmental conditions during task execution. Previous approaches to online evolution of neural controllers are typically limited to the optimisation of weights in networks with a prespecified, fixed topology. In this article, we propose a novel approach to online learning in groups of autonomous robots called odNEAT. odNEAT is a distributed and decentralised neuroevolution algorithm that evolves both weights and network topology. We demonstrate odNEAT in three multirobot tasks: aggregation, integrated navigation and obstacle avoidance, and phototaxis. Results show that odNEAT approximates the performance of rtNEAT, an efficient centralised method, and outperforms IM-(μ + 1), a decentralised neuroevolution algorithm. Compared with rtNEAT and IM-(μ + 1), odNEAT's evolutionary dynamics lead to the synthesis of less complex neural controllers with superior generalisation capabilities. We show that robots executing odNEAT can display a high degree of fault tolerance as they are able to adapt and learn new behaviours in the presence of faults. We conclude with a series of ablation studies to analyse the impact of each algorithmic component on performance. PMID:25478664
Evolution of the environmental justice movement: activism, formalization and differentiation
NASA Astrophysics Data System (ADS)
Colsa Perez, Alejandro; Grafton, Bernadette; Mohai, Paul; Hardin, Rebecca; Hintzen, Katy; Orvis, Sara
2015-10-01
To complement a recent flush of research on transnational environmental justice movements, we sought a deeper organizational history of what we understand as the contemporary environmental justice movement in the United States. We thus conducted in-depth interviews with 31 prominent environmental justice activists, scholars, and community leaders across the US. Today’s environmental justice groups have transitioned from specific local efforts to broader national and global mandates, and more sophisticated political, technological, and activist strategies. One of the most significant transformations has been the number of groups adopting formal legal status, and emerging as registered environmental justice organizations (REJOs) within complex partnerships. This article focuses on the emergence of REJOs, and describes the respondents’ views about the implications of this for more local grassroots groups. It reveals a central irony animating work across groups in today’s movement: legal formalization of many environmental justice organizations has made the movement increasingly internally differentiated, dynamic, and networked, even as the passage of actual national laws on environmental justice has proven elusive.
Differential Evolution and Neofunctionalization of Snake Venom Metalloprotease Domains*
Brust, Andreas; Sunagar, Kartik; Undheim, Eivind A.B.; Vetter, Irina; Yang, Daryl C.; Casewell, Nicholas R.; Jackson, Timothy N. W.; Koludarov, Ivan; Alewood, Paul F.; Hodgson, Wayne C.; Lewis, Richard J.; King, Glenn F.; Antunes, Agostinho; Hendrikx, Iwan; Fry, Bryan G.
2013-01-01
Snake venom metalloproteases (SVMP) are composed of five domains: signal peptide, propeptide, metalloprotease, disintegrin, and cysteine-rich. Secreted toxins are typically combinatorial variations of the latter three domains. The SVMP-encoding genes of Psammophis mossambicus venom are unique in containing only the signal and propeptide domains. We show that the Psammophis SVMP propeptide evolves rapidly and is subject to a high degree of positive selection. Unlike Psammophis, some species of Echis express both the typical multidomain and the unusual monodomain (propeptide only) SVMP, with the result that a lower level of variation is exerted upon the latter. We showed that most mutations in the multidomain Echis SVMP occurred in the protease domain responsible for proteolytic and hemorrhagic activities. The cysteine-rich and disintegrin-like domains, which are putatively responsible for making the P-III SVMPs more potent than the P-I and P-II forms, accumulate the remaining variation. Thus, the binding sites on the molecule's surface are evolving rapidly whereas the core remains relatively conserved. Bioassays conducted on two post-translationally cleaved novel proline-rich peptides from the P. mossambicus propeptide domain showed them to have been neofunctionalized for specific inhibition of mammalian a7 neuronal nicotinic acetylcholine receptors. We show that the proline rich postsynaptic specific neurotoxic peptides from Azemiops feae are the result of convergent evolution within the precursor region of the C-type natriuretic peptide instead of the SVMP. The results of this study reinforce the value of studying obscure venoms for biodiscovery of novel investigational ligands. PMID:23242553
Evolution of Social Insect Polyphenism Facilitated by the Sex Differentiation Cascade.
Klein, Antonia; Schultner, Eva; Lowak, Helena; Schrader, Lukas; Heinze, Jürgen; Holman, Luke; Oettler, Jan
2016-03-01
The major transition to eusociality required the evolution of a switch to canalize development into either a reproductive or a helper, the nature of which is currently unknown. Following predictions from the 'theory of facilitated variation', we identify sex differentiation pathways as promising candidates because of their pre-adaptation to regulating development of complex phenotypes. We show that conserved core genes, including the juvenile hormone-sensitive master sex differentiation gene doublesex (dsx) and a krüppel homolog 2 (kr-h2) with putative regulatory function, exhibit both sex and morph-specific expression across life stages in the ant Cardiocondyla obscurior. We hypothesize that genes in the sex differentiation cascade evolved perception of alternative input signals for caste differentiation (i.e. environmental or genetic cues), and that their inherent switch-like and epistatic behavior facilitated signal transfer to downstream targets, thus allowing them to control differential development into morphological castes. PMID:27031240
Evolution of Social Insect Polyphenism Facilitated by the Sex Differentiation Cascade
Klein, Antonia; Schultner, Eva; Lowak, Helena; Schrader, Lukas; Heinze, Jürgen; Holman, Luke; Oettler, Jan
2016-01-01
The major transition to eusociality required the evolution of a switch to canalize development into either a reproductive or a helper, the nature of which is currently unknown. Following predictions from the ‘theory of facilitated variation’, we identify sex differentiation pathways as promising candidates because of their pre-adaptation to regulating development of complex phenotypes. We show that conserved core genes, including the juvenile hormone-sensitive master sex differentiation gene doublesex (dsx) and a krüppel homolog 2 (kr-h2) with putative regulatory function, exhibit both sex and morph-specific expression across life stages in the ant Cardiocondyla obscurior. We hypothesize that genes in the sex differentiation cascade evolved perception of alternative input signals for caste differentiation (i.e. environmental or genetic cues), and that their inherent switch-like and epistatic behavior facilitated signal transfer to downstream targets, thus allowing them to control differential development into morphological castes. PMID:27031240
Patterson, Larissa B.; Bain, Emily J.; Parichy, David M.
2014-01-01
Fishes have diverse pigment patterns, yet mechanisms of pattern evolution remain poorly understood. In zebrafish, Danio rerio, pigment-cell autonomous interactions generate dark stripes of melanophores that alternate with light interstripes of xanthophores and iridophores. Here, we identify mechanisms underlying the evolution of a uniform pattern in D. albolineatus in which all three pigment cell classes are intermingled. We show that in this species xanthophores differentiate precociously over a wider area, and that cis regulatory evolution has increased expression of xanthogenic Colony Stimulating Factor-1 (Csf1). Expressing Csf1 similarly in D. rerio has cascading effects, driving the intermingling of all three pigment cell classes and resulting in the loss of stripes, as in D. albolineatus. Our results identify novel mechanisms of pattern development and illustrate how pattern diversity can be generated when a core network of pigment-cell autonomous interactions is coupled with changes in pigment cell differentiation. PMID:25374113
Single- and Multiple-Objective Optimization with Differential Evolution and Neural Networks
NASA Technical Reports Server (NTRS)
Rai, Man Mohan
2006-01-01
Genetic and evolutionary algorithms have been applied to solve numerous problems in engineering design where they have been used primarily as optimization procedures. These methods have an advantage over conventional gradient-based search procedures became they are capable of finding global optima of multi-modal functions and searching design spaces with disjoint feasible regions. They are also robust in the presence of noisy data. Another desirable feature of these methods is that they can efficiently use distributed and parallel computing resources since multiple function evaluations (flow simulations in aerodynamics design) can be performed simultaneously and independently on ultiple processors. For these reasons genetic and evolutionary algorithms are being used more frequently in design optimization. Examples include airfoil and wing design and compressor and turbine airfoil design. They are also finding increasing use in multiple-objective and multidisciplinary optimization. This lecture will focus on an evolutionary method that is a relatively new member to the general class of evolutionary methods called differential evolution (DE). This method is easy to use and program and it requires relatively few user-specified constants. These constants are easily determined for a wide class of problems. Fine-tuning the constants will off course yield the solution to the optimization problem at hand more rapidly. DE can be efficiently implemented on parallel computers and can be used for continuous, discrete and mixed discrete/continuous optimization problems. It does not require the objective function to be continuous and is noise tolerant. DE and applications to single and multiple-objective optimization will be included in the presentation and lecture notes. A method for aerodynamic design optimization that is based on neural networks will also be included as a part of this lecture. The method offers advantages over traditional optimization methods. It is more
NASA Astrophysics Data System (ADS)
Goldberg, Daniel N.; Krishna Narayanan, Sri Hari; Hascoet, Laurent; Utke, Jean
2016-05-01
We apply an optimized method to the adjoint generation of a time-evolving land ice model through algorithmic differentiation (AD). The optimization involves a special treatment of the fixed-point iteration required to solve the nonlinear stress balance, which differs from a straightforward application of AD software, and leads to smaller memory requirements and in some cases shorter computation times of the adjoint. The optimization is done via implementation of the algorithm of Christianson (1994) for reverse accumulation of fixed-point problems, with the AD tool OpenAD. For test problems, the optimized adjoint is shown to have far lower memory requirements, potentially enabling larger problem sizes on memory-limited machines. In the case of the land ice model, implementation of the algorithm allows further optimization by having the adjoint model solve a sequence of linear systems with identical (as opposed to varying) matrices, greatly improving performance. The methods introduced here will be of value to other efforts applying AD tools to ice models, particularly ones which solve a hybrid shallow ice/shallow shelf approximation to the Stokes equations.
Lafreniere, D; Marois, C; Doyon, R; Artigau, E; Nadeau, D
2006-09-19
Direct imaging of exoplanets is limited by bright quasi-static speckles in the point spread function (PSF) of the central star. This limitation can be reduced by subtraction of reference PSF images. We have developed an algorithm to construct an optimal reference PSF image from an arbitrary set of reference images. This image is built as a linear combination of all available images and is optimized independently inside multiple subsections of the image to ensure that the absolute minimum residual noise is achieved within each subsection. The algorithm developed is completely general and can be used with many high contrast imaging observing strategies, such as angular differential imaging (ADI), roll subtraction, spectral differential imaging, reference star observations, etc. The performance of the algorithm is demonstrated for ADI data. It is shown that for this type of data the new algorithm provides a gain in sensitivity by up 22 to a factor 3 at small separation over the algorithm previously used.
Colautti, Robert I; Lau, Jennifer A
2015-05-01
Biological invasions are 'natural' experiments that can improve our understanding of contemporary evolution. We evaluate evidence for population differentiation, natural selection and adaptive evolution of invading plants and animals at two nested spatial scales: (i) among introduced populations (ii) between native and introduced genotypes. Evolution during invasion is frequently inferred, but rarely confirmed as adaptive. In common garden studies, quantitative trait differentiation is only marginally lower (~3.5%) among introduced relative to native populations, despite genetic bottlenecks and shorter timescales (i.e. millennia vs. decades). However, differentiation between genotypes from the native vs. introduced range is less clear and confounded by nonrandom geographic sampling; simulations suggest this causes a high false-positive discovery rate (>50%) in geographically structured populations. Selection differentials (¦s¦) are stronger in introduced than in native species, although selection gradients (¦β¦) are not, consistent with introduced species experiencing weaker genetic constraints. This could facilitate rapid adaptation, but evidence is limited. For example, rapid phenotypic evolution often manifests as geographical clines, but simulations demonstrate that nonadaptive trait clines can evolve frequently during colonization (~two-thirds of simulations). Additionally, QST-FST studies may often misrepresent the strength and form of natural selection acting during invasion. Instead, classic approaches in evolutionary ecology (e.g. selection analysis, reciprocal transplant, artificial selection) are necessary to determine the frequency of adaptive evolution during invasion and its influence on establishment, spread and impact of invasive species. These studies are rare but crucial for managing biological invasions in the context of global change. PMID:25891044
Improving Efficiency in SMD Simulations Through a Hybrid Differential Relaxation Algorithm.
Ramírez, Claudia L; Zeida, Ari; Jara, Gabriel E; Roitberg, Adrián E; Martí, Marcelo A
2014-10-14
The fundamental object for studying a (bio)chemical reaction obtained from simulations is the free energy profile, which can be directly related to experimentally determined properties. Although quite accurate hybrid quantum (DFT based)-classical methods are available, achieving statistically accurate and well converged results at a moderate computational cost is still an open challenge. Here, we present and thoroughly test a hybrid differential relaxation algorithm (HyDRA), which allows faster equilibration of the classical environment during the nonequilibrium steering of a (bio)chemical reaction. We show and discuss why (in the context of Jarzynski's Relationship) this method allows obtaining accurate free energy profiles with smaller number of independent trajectories and/or faster pulling speeds, thus reducing the overall computational cost. Moreover, due to the availability and straightforward implementation of the method, we expect that it will foster theoretical studies of key enzymatic processes. PMID:26588154
NASA Astrophysics Data System (ADS)
Setyawan, Iwan; Lagendijk, Reginald L.
2001-08-01
Digital video data distribution through the internet is becoming more common. Film trailers, video clips and even video footage from computer and video games are now seen as very powerful means to boost sales of the aforementioned products. These materials need to be protected to avoid copyright infringement issues. However, these materials are encoded at a low bit-rate to facilitate internet distribution and this poses a challenge to the watermarking operation. In this paper we present an extension to the Differential Energy Watermarking algorithm, to use it in low bit-rate environment. We present the extension scheme and its evaluate its performance in terms of watermark capacity, robustness and visual impact.
Multiple Active Contours Guided by Differential Evolution for Medical Image Segmentation
Cruz-Aceves, I.; Avina-Cervantes, J. G.; Lopez-Hernandez, J. M.; Rostro-Gonzalez, H.; Garcia-Capulin, C. H.; Torres-Cisneros, M.; Guzman-Cabrera, R.
2013-01-01
This paper presents a new image segmentation method based on multiple active contours guided by differential evolution, called MACDE. The segmentation method uses differential evolution over a polar coordinate system to increase the exploration and exploitation capabilities regarding the classical active contour model. To evaluate the performance of the proposed method, a set of synthetic images with complex objects, Gaussian noise, and deep concavities is introduced. Subsequently, MACDE is applied on datasets of sequential computed tomography and magnetic resonance images which contain the human heart and the human left ventricle, respectively. Finally, to obtain a quantitative and qualitative evaluation of the medical image segmentations compared to regions outlined by experts, a set of distance and similarity metrics has been adopted. According to the experimental results, MACDE outperforms the classical active contour model and the interactive Tseng method in terms of efficiency and robustness for obtaining the optimal control points and attains a high accuracy segmentation. PMID:23983809
Martín, Fernando; Moreno, Luis; Garrido, Santiago; Blanco, Dolores
2015-01-01
One of the most important skills desired for a mobile robot is the ability to obtain its own location even in challenging environments. The information provided by the sensing system is used here to solve the global localization problem. In our previous work, we designed different algorithms founded on evolutionary strategies in order to solve the aforementioned task. The latest developments are presented in this paper. The engine of the localization module is a combination of the Markov chain Monte Carlo sampling technique and the Differential Evolution method, which results in a particle filter based on the minimization of a fitness function. The robot’s pose is estimated from a set of possible locations weighted by a cost value. The measurements of the perceptive sensors are used together with the predicted ones in a known map to define a cost function to optimize. Although most localization methods rely on quadratic fitness functions, the sensed information is processed asymmetrically in this filter. The Kullback-Leibler divergence is the basis of a cost function that makes it possible to deal with different types of occlusions. The algorithm performance has been checked in a real map. The results are excellent in environments with dynamic and unmodeled obstacles, a fact that causes occlusions in the sensing area. PMID:26389914
Martín, Fernando; Moreno, Luis; Garrido, Santiago; Blanco, Dolores
2015-01-01
One of the most important skills desired for a mobile robot is the ability to obtain its own location even in challenging environments. The information provided by the sensing system is used here to solve the global localization problem. In our previous work, we designed different algorithms founded on evolutionary strategies in order to solve the aforementioned task. The latest developments are presented in this paper. The engine of the localization module is a combination of the Markov chain Monte Carlo sampling technique and the Differential Evolution method, which results in a particle filter based on the minimization of a fitness function. The robot's pose is estimated from a set of possible locations weighted by a cost value. The measurements of the perceptive sensors are used together with the predicted ones in a known map to define a cost function to optimize. Although most localization methods rely on quadratic fitness functions, the sensed information is processed asymmetrically in this filter. The Kullback-Leibler divergence is the basis of a cost function that makes it possible to deal with different types of occlusions. The algorithm performance has been checked in a real map. The results are excellent in environments with dynamic and unmodeled obstacles, a fact that causes occlusions in the sensing area. PMID:26389914
Sarkar, Soham; Das, Swagatam
2013-12-01
Multilevel thresholding amounts to segmenting a gray-level image into several distinct regions. This paper presents a 2D histogram based multilevel thresholding approach to improve the separation between objects. Recent studies indicate that the results obtained with 2D histogram oriented approaches are superior to those obtained with 1D histogram based techniques in the context of bi-level thresholding. Here, a method to incorporate 2D histogram related information for generalized multilevel thresholding is proposed using the maximum Tsallis entropy. Differential evolution (DE), a simple yet efficient evolutionary algorithm of current interest, is employed to improve the computational efficiency of the proposed method. The performance of DE is investigated extensively through comparison with other well-known nature inspired global optimization techniques such as genetic algorithm, particle swarm optimization, artificial bee colony, and simulated annealing. In addition, the outcome of the proposed method is evaluated using a well known benchmark--the Berkley segmentation data set (BSDS300) with 300 distinct images. PMID:23955760
Okunade, Akintunde Akangbe
2005-06-01
Qualitative and quantitative equivalence of spectra transmitted by two different elemental filters require a good match in terms of shape and size over the entire energy range of 0-150 keV used in medical diagnostic radiology. However, the photoelectric absorptions and Compton scattering involved in the interaction of x rays with matter at these relatively low photon energies differ in a nonuniform manner with energy and atomic number. By careful choice of thicknesses for filter materials with an atomic number between 12 and 39, when compared with aluminum, it is possible to obtain transmitted beams of the same shape (quality) but not of the same size (quantity). In this paper, calculations have been carried out for the matching of the shapes and sizes of beams transmitted through specified thicknesses of aluminium filter and spectrally equivalent thicknesses of other filter materials (different from aluminium) using FORTRAN source codes traceable to the American Association of Physics in Medicine (AAPM), College Park, MD, USA. Parametrized algorithms for the evaluation of quantitative differentials (deficit or surplus) in radiation output (namely, photon fluence, exposure, kerma, energy imparted, absorbed dose, and effective dose) from these transmitted spectrally equivalent beams were developed. These differentials range between 1%, and 4% at 1 mm Al filtration and between 8%, and 25% for filtration of 6 mm Al for different filter materials in comparison with aluminum. Also developed were models for factors for converting measures of photon fluence, exposure-area product, (EAP), and kerma-area product (KAP) to risk related quantities such as energy imparted, absorbed dose, and effective dose from the spectrally equivalent beams. The thicknesses of other filter materials that are spectrally equivalent to given thicknesses of aluminum filter were characterized using polynomial functions. The fact that the use of equivalent spectra in radiological practice can
Okunade, Akintunde Akangbe
2005-06-15
Qualitative and quantitative equivalence of spectra transmitted by two different elemental filters require a good match in terms of shape and size over the entire energy range of 0-150 keV used in medical diagnostic radiology. However, the photoelectric absorptions and Compton scattering involved in the interaction of x rays with matter at these relatively low photon energies differ in a nonuniform manner with energy and atomic number. By careful choice of thicknesses for filter materials with an atomic number between 12 and 39, when compared with aluminum, it is possible to obtain transmitted beams of the same shape (quality) but not of the same size (quantity). In this paper, calculations have been carried out for the matching of the shapes and sizes of beams transmitted through specified thicknesses of aluminium filter and spectrally equivalent thicknesses of other filter materials (different from aluminium) using FORTRAN source codes traceable to the American Association of Physics in Medicine (AAPM), College Park, MD, USA. Parametrized algorithms for the evaluation of quantitative differentials (deficit or surplus) in radiation output (namely, photon fluence, exposure, kerma, energy imparted, absorbed dose, and effective dose) from these transmitted spectrally equivalent beams were developed. These differentials range between 1%, and 4% at 1 mm Al filtration and between 8%, and 25% for filtration of 6 mm Al for different filter materials in comparison with aluminum. Also developed were models for factors for converting measures of photon fluence, exposure-area product, (EAP), and kerma-area product (KAP) to risk related quantities such as energy imparted, absorbed dose, and effective dose from the spectrally equivalent beams. The thicknesses of other filter materials that are spectrally equivalent to given thicknesses of aluminum filter were characterized using polynomial functions. The fact that the use of equivalent spectra in radiological practice can
Using Neighborhood-Algorithm Inversion to Test and Calibrate Landscape Evolution Models
NASA Astrophysics Data System (ADS)
Perignon, M. C.; Tucker, G. E.; Van Der Beek, P.; Hilley, G. E.; Arrowsmith, R.
2011-12-01
Landscape evolution models use mass transport rules to simulate the development of topography over timescales too long for humans to observe. The ability of models to reproduce various attributes of real landscapes must be tested against natural systems in which driving forces, boundary conditions, and timescales of landscape evolution can be well constrained. We test and calibrate a landscape evolution model by comparing it with a well-constrained natural experiment using a formal inversion method to obtain best-fitting parameter values. Our case study is the Dragon's Back Pressure Ridge, a region of elevated terrain parallel to the south central San Andreas Fault that serves as a natural laboratory for studying how the timing and spatial distribution of uplift affects topography. We apply an optimization procedure to identify the parameter ranges and combinations that best account for the observed topography. Through the use of repeat forward modeling, direct-search inversion models can be used to convert observations from such natural systems into inferences of the processes that governed their formation. Simple inversion techniques have been used before in landscape evolution modeling, but these are imprecise and computationally expensive. We present the application of a more efficient inversion technique, the Neighborhood Algorithm (NA), to optimize the search for the model parameters values that are most consistent with the formation of the Dragon's Back Pressure Ridge through repeat forward modeling using CHILD. Inversion techniques require the comparison of model results with direct observations to evaluate misfit. For our target landscape, this is done through a series of topographic metrics that include hypsometry, slope-area curves, and channel concavity. NA uses an initial Monte Carlo simulation for which misfits have been calculated to guide a new iteration of forward models. At each iteration, NA uses n-dimensional Voronoi cells to explore the
NASA Astrophysics Data System (ADS)
Nath, Pranav; Ramanan, R. V.
2016-01-01
The mission design to a halo orbit around the libration points from Earth involves two important steps. In the first step, we design a halo orbit for a specified size and in the second step, we obtain an optimal transfer trajectory design to the halo orbit from an Earth parking orbit. Conventionally, the preliminary design for these steps is obtained using higher order analytical solution and the dynamical systems theory respectively. Refinements of the design are carried out using gradient based methods such as differential correction and pseudo arc length continuation method under the of circular restricted three body model. In this paper, alternative single level schemes are developed for both of these steps based on differential evolution, an evolutionary optimization technique. The differential evolution based scheme for halo orbit design produces precise halo orbit design avoiding the refinement steps. Further, in this approach, prior knowledge of higher order analytical solutions for the halo orbit design is not needed. The differential evolution based scheme for the transfer trajectory, identifies the precise location on the halo orbit that needs minimum energy for insertion and avoids exploration of multiple points. The need of a close guess is removed because the present scheme operates on a set of bounds for the unknowns. The constraint on the closest approach altitude from Earth is handled through objective function. The use of these schemes as the design and analysis tools within the of circular restricted three body model is demonstrated through case studies for missions to the first libration point of Sun-Earth system.
NASA Astrophysics Data System (ADS)
Huang, Ding-jiang; Ivanova, Nataliya M.
2016-02-01
In this paper, we explain in more details the modern treatment of the problem of group classification of (systems of) partial differential equations (PDEs) from the algorithmic point of view. More precisely, we revise the classical Lie algorithm of construction of symmetries of differential equations, describe the group classification algorithm and discuss the process of reduction of (systems of) PDEs to (systems of) equations with smaller number of independent variables in order to construct invariant solutions. The group classification algorithm and reduction process are illustrated by the example of the generalized Zakharov-Kuznetsov (GZK) equations of form ut +(F (u)) xxx +(G (u)) xyy +(H (u)) x = 0. As a result, a complete group classification of the GZK equations is performed and a number of new interesting nonlinear invariant models which have non-trivial invariance algebras are obtained. Lie symmetry reductions and exact solutions for two important invariant models, i.e., the classical and modified Zakharov-Kuznetsov equations, are constructed. The algorithmic framework for group analysis of differential equations presented in this paper can also be applied to other nonlinear PDEs.
NASA Astrophysics Data System (ADS)
Boujibar, A.; Andrault, D.; Bolfan-Casanova, N.; Bouhifd, M. A.
2013-10-01
Our experimental data show that silicate melts produced by low degree of melting of Enstatite Chondrite become less viscous but denser with increasing pressure. We thus discuss implications for chemical and thermal evolution of differentiated bodies.
Romero, Juan Manuel; Martin, Mariano; Ramirez, Claudia Lilián; Dumas, Victoria Gisel; Marti, Marcelo Adrián
2015-01-01
Determination of the free energy profile for an enzyme reaction mechanism is of primordial relevance, paving the way for our understanding of the enzyme's catalytic power at the molecular level. Although hybrid, mostly DFT-based, QM/MM methods have been extensively applied to this type of studies, achieving accurate and statistically converged results at a moderate computational cost is still an open challenge. Recently, we have shown that accurate results can be achieved in less computational time, combining Jarzynski's relationship with a hybrid differential relaxation algorithm (HyDRA), which allows partial relaxation of the solvent during the nonequilibrium steering of the reaction. In this work, we have applied this strategy to study two mycobacterial zinc hydrolases. Mycobacterium tuberculosis infections are still a worldwide problem and thus characterization and validation of new drug targets is an intense field of research. Among possible drug targets, recently two essential zinc hydrolases, MshB (Rv1170) and MA-amidase (Rv3717), have been proposed and structurally characterized. Although possible mechanisms have been proposed by analogy to the widely studied human Zn hydrolases, several key issues, particularly those related to Zn coordination sphere and its role in catalysis, remained unanswered. Our results show that mycobacterial Zn hydrolases share a basic two-step mechanism. First, the attacking water becomes deprotonated by the conserved base and establishes the new C-O bond leading to a tetrahedral intermediate. The intermediate requires moderate reorganization to allow for proton transfer to the amide N and C-N bond breaking to occur in the second step. Zn ion plays a key role in stabilizing the tetrahedral intermediate and balancing the negative charge of the substrate during hydroxide ion attack. Finally, comparative analysis of other Zn hydrolases points to a convergent mechanistic evolution. PMID:26415840
Gong, Li-gang; Yang, Wen-lun
2014-01-01
Unmanned combat aerial vehicles (UCAVs) have been of great interest to military organizations throughout the world due to their outstanding capabilities to operate in dangerous or hazardous environments. UCAV path planning aims to obtain an optimal flight route with the threats and constraints in the combat field well considered. In this work, a novel artificial bee colony (ABC) algorithm improved by a balance-evolution strategy (BES) is applied in this optimization scheme. In this new algorithm, convergence information during the iteration is fully utilized to manipulate the exploration/exploitation accuracy and to pursue a balance between local exploitation and global exploration capabilities. Simulation results confirm that BE-ABC algorithm is more competent for the UCAV path planning scheme than the conventional ABC algorithm and two other state-of-the-art modified ABC algorithms. PMID:24790555
Li, Bai; Gong, Li-gang; Yang, Wen-lun
2014-01-01
Unmanned combat aerial vehicles (UCAVs) have been of great interest to military organizations throughout the world due to their outstanding capabilities to operate in dangerous or hazardous environments. UCAV path planning aims to obtain an optimal flight route with the threats and constraints in the combat field well considered. In this work, a novel artificial bee colony (ABC) algorithm improved by a balance-evolution strategy (BES) is applied in this optimization scheme. In this new algorithm, convergence information during the iteration is fully utilized to manipulate the exploration/exploitation accuracy and to pursue a balance between local exploitation and global exploration capabilities. Simulation results confirm that BE-ABC algorithm is more competent for the UCAV path planning scheme than the conventional ABC algorithm and two other state-of-the-art modified ABC algorithms. PMID:24790555
Fast computation of derivative based sensitivities of PSHA models via algorithmic differentiation
NASA Astrophysics Data System (ADS)
Leövey, Hernan; Molkenthin, Christian; Scherbaum, Frank; Griewank, Andreas; Kuehn, Nicolas; Stafford, Peter
2015-04-01
Probabilistic seismic hazard analysis (PSHA) is the preferred tool for estimation of potential ground-shaking hazard due to future earthquakes at a site of interest. A modern PSHA represents a complex framework which combines different models with possible many inputs. Sensitivity analysis is a valuable tool for quantifying changes of a model output as inputs are perturbed, identifying critical input parameters and obtaining insight in the model behavior. Differential sensitivity analysis relies on calculating first-order partial derivatives of the model output with respect to its inputs. Moreover, derivative based global sensitivity measures (Sobol' & Kucherenko '09) can be practically used to detect non-essential inputs of the models, thus restricting the focus of attention to a possible much smaller set of inputs. Nevertheless, obtaining first-order partial derivatives of complex models with traditional approaches can be very challenging, and usually increases the computation complexity linearly with the number of inputs appearing in the models. In this study we show how Algorithmic Differentiation (AD) tools can be used in a complex framework such as PSHA to successfully estimate derivative based sensitivities, as is the case in various other domains such as meteorology or aerodynamics, without no significant increase in the computation complexity required for the original computations. First we demonstrate the feasibility of the AD methodology by comparing AD derived sensitivities to analytically derived sensitivities for a basic case of PSHA using a simple ground-motion prediction equation. In a second step, we derive sensitivities via AD for a more complex PSHA study using a ground motion attenuation relation based on a stochastic method to simulate strong motion. The presented approach is general enough to accommodate more advanced PSHA studies of higher complexity.
NASA Technical Reports Server (NTRS)
Crouch, P. E.; Grossman, Robert
1992-01-01
This note is concerned with the explicit symbolic computation of expressions involving differential operators and their actions on functions. The derivation of specialized numerical algorithms, the explicit symbolic computation of integrals of motion, and the explicit computation of normal forms for nonlinear systems all require such computations. More precisely, if R = k(x(sub 1),...,x(sub N)), where k = R or C, F denotes a differential operator with coefficients from R, and g member of R, we describe data structures and algorithms for efficiently computing g. The basic idea is to impose a multiplicative structure on the vector space with basis the set of finite rooted trees and whose nodes are labeled with the coefficients of the differential operators. Cancellations of two trees with r + 1 nodes translates into cancellation of O(N(exp r)) expressions involving the coefficient functions and their derivatives.
Santra, Tapesh; Delatola, Eleni Ioanna
2016-01-01
Presence of considerable noise and missing data points make analysis of mass-spectrometry (MS) based proteomic data a challenging task. The missing values in MS data are caused by the inability of MS machines to reliably detect proteins whose abundances fall below the detection limit. We developed a Bayesian algorithm that exploits this knowledge and uses missing data points as a complementary source of information to the observed protein intensities in order to find differentially expressed proteins by analysing MS based proteomic data. We compared its accuracy with many other methods using several simulated datasets. It consistently outperformed other methods. We then used it to analyse proteomic screens of a breast cancer (BC) patient cohort. It revealed large differences between the proteomic landscapes of triple negative and Luminal A, which are the most and least aggressive types of BC. Unexpectedly, majority of these differences could be attributed to the direct transcriptional activity of only seven transcription factors some of which are known to be inactive in triple negative BC. We also identified two new proteins which significantly correlated with the survival of BC patients, and therefore may have potential diagnostic/prognostic values. PMID:27444576
NASA Astrophysics Data System (ADS)
Santra, Tapesh; Delatola, Eleni Ioanna
2016-07-01
Presence of considerable noise and missing data points make analysis of mass-spectrometry (MS) based proteomic data a challenging task. The missing values in MS data are caused by the inability of MS machines to reliably detect proteins whose abundances fall below the detection limit. We developed a Bayesian algorithm that exploits this knowledge and uses missing data points as a complementary source of information to the observed protein intensities in order to find differentially expressed proteins by analysing MS based proteomic data. We compared its accuracy with many other methods using several simulated datasets. It consistently outperformed other methods. We then used it to analyse proteomic screens of a breast cancer (BC) patient cohort. It revealed large differences between the proteomic landscapes of triple negative and Luminal A, which are the most and least aggressive types of BC. Unexpectedly, majority of these differences could be attributed to the direct transcriptional activity of only seven transcription factors some of which are known to be inactive in triple negative BC. We also identified two new proteins which significantly correlated with the survival of BC patients, and therefore may have potential diagnostic/prognostic values.
Santra, Tapesh; Delatola, Eleni Ioanna
2016-01-01
Presence of considerable noise and missing data points make analysis of mass-spectrometry (MS) based proteomic data a challenging task. The missing values in MS data are caused by the inability of MS machines to reliably detect proteins whose abundances fall below the detection limit. We developed a Bayesian algorithm that exploits this knowledge and uses missing data points as a complementary source of information to the observed protein intensities in order to find differentially expressed proteins by analysing MS based proteomic data. We compared its accuracy with many other methods using several simulated datasets. It consistently outperformed other methods. We then used it to analyse proteomic screens of a breast cancer (BC) patient cohort. It revealed large differences between the proteomic landscapes of triple negative and Luminal A, which are the most and least aggressive types of BC. Unexpectedly, majority of these differences could be attributed to the direct transcriptional activity of only seven transcription factors some of which are known to be inactive in triple negative BC. We also identified two new proteins which significantly correlated with the survival of BC patients, and therefore may have potential diagnostic/prognostic values. PMID:27444576
THE ONSET OF DIFFERENTIATION AND INTERNAL EVOLUTION: THE CASE OF 21 LUTETIA
Formisano, M.; Turrini, D.; Federico, C.; Capaccioni, F.; De Sanctis, M. C.
2013-06-10
Asteroid 21 Lutetia, seen by the Rosetta spacecraft, plays a crucial role in the reconstruction of primordial phases of planetary objects. Its high bulk density and its primitive chondritic crust suggest that Lutetia could be partially differentiated. We developed a numerical code, also used for studying the geophysical history of Vesta, to explore several scenarios of internal evolution of Lutetia. These scenarios differ in the strength of their radiogenic sources and in their global post-sintering porosity. The only significant heat source for partial differentiation is {sup 26}Al; the other possible sources ({sup 60}Fe, accretion, and differentiation) are negligible. In scenarios in which Lutetia completed its accretion in less than 0.7 Myr from the injection of {sup 26}Al in the solar nebula and for post-sintering values of macroporosity not exceeding 30% by volume, the asteroid experienced only partial differentiation. The formation of the proto-core, a structure enriched in metals and also containing pristine silicates, requires 1-4 Myr and the size of the proto-core varies from 6-30 km.
Lobato, Fran Sérgio; Machado, Vinicius Silvério; Steffen, Valder
2016-07-01
The mathematical modeling of physical and biologic systems represents an interesting alternative to study the behavior of these phenomena. In this context, the development of mathematical models to simulate the dynamic behavior of tumors is configured as an important theme in the current days. Among the advantages resulting from using these models is their application to optimization and inverse problem approaches. Traditionally, the formulated Optimal Control Problem (OCP) has the objective of minimizing the size of tumor cells by the end of the treatment. In this case an important aspect is not considered, namely, the optimal concentrations of drugs may affect the patients' health significantly. In this sense, the present work has the objective of obtaining an optimal protocol for drug administration to patients with cancer, through the minimization of both the cancerous cells concentration and the prescribed drug concentration. The resolution of this multi-objective problem is obtained through the Multi-objective Optimization Differential Evolution (MODE) algorithm. The Pareto's Curve obtained supplies a set of optimal protocols from which an optimal strategy for drug administration can be chosen, according to a given criterion. PMID:27265048
Hydrodynamic evolution of sperm swimming: Optimal flagella by a genetic algorithm.
Ishimoto, Kenta
2016-06-21
Swimming performance of spermatozoa is an important index for the success of fertilization. For many years, numerous studies have reported the optimal swimming of flagellar organisms. Nevertheless, there is still a question as to which is optimal among planar, circular helical and ellipsoidal helical beating. In this paper, we use a genetic algorithm to investigate the beat pattern with the best swimming efficiency based on hydrodynamic dissipation and internal torque exertion. For the parameters considered, our results show that the planar beat is optimal for small heads and the helical flagellum is optimum for a larger heads, while the ellipsoidal beat is never optimal. Also, the genetic optimization reveals that the wavenumber and shape of wave envelope are relevant parameters, whereas the wave shape and head geometry have relatively minor effects on efficiency. The optimal beat with respect to the efficiency based on the internal torque exertion of an active elastic flagellum is characterized by a small-wavenumber and large-amplitude wave in a lower-viscosity medium. The obtained results on the optimal waveform are consistent with observations for planar waveforms, but in many respects, the results suggest the necessity of a detailed flagellar structure-fluid interaction to address whether real spermatozoa exhibit hydrodynamically efficient swimming. The evolutional optimization approach used in this study has distinguished biologically important parameters, and the methodology can potentially be applicable to various swimmers. PMID:27063642
NASA Astrophysics Data System (ADS)
Shahamatnia, Ehsan; Dorotovič, Ivan; Fonseca, Jose M.; Ribeiro, Rita A.
2016-03-01
Developing specialized software tools is essential to support studies of solar activity evolution. With new space missions such as Solar Dynamics Observatory (SDO), solar images are being produced in unprecedented volumes. To capitalize on that huge data availability, the scientific community needs a new generation of software tools for automatic and efficient data processing. In this paper a prototype of a modular framework for solar feature detection, characterization, and tracking is presented. To develop an efficient system capable of automatic solar feature tracking and measuring, a hybrid approach combining specialized image processing, evolutionary optimization, and soft computing algorithms is being followed. The specialized hybrid algorithm for tracking solar features allows automatic feature tracking while gathering characterization details about the tracked features. The hybrid algorithm takes advantages of the snake model, a specialized image processing algorithm widely used in applications such as boundary delineation, image segmentation, and object tracking. Further, it exploits the flexibility and efficiency of Particle Swarm Optimization (PSO), a stochastic population based optimization algorithm. PSO has been used successfully in a wide range of applications including combinatorial optimization, control, clustering, robotics, scheduling, and image processing and video analysis applications. The proposed tool, denoted PSO-Snake model, was already successfully tested in other works for tracking sunspots and coronal bright points. In this work, we discuss the application of the PSO-Snake algorithm for calculating the sidereal rotational angular velocity of the solar corona. To validate the results we compare them with published manual results performed by an expert.
Griffiths, Roland R; Johnson, Matthew W
2005-01-01
Hypnotic drugs, including benzodiazepine receptor ligands, barbiturates, antihistamines, and melatonin receptor ligands, are useful in treating insomnia, but clinicians should consider the relative abuse liability of these drugs when prescribing them. Two types of problematic hypnotic self-administration are distinguished. First, recreational abuse occurs when medications are used purposefully for the subjective "high." This type of abuse usually occurs in polydrug abusers, who are most often young and male. Second, chronic quasi-therapeutic abuse is a problematic use of hypnotic drugs in which patients continue long-term use despite medical recommendations to the contrary. Relative abuse liability is defined as an interaction between the relative reinforcing effects (i.e., the capacity to maintain drug self-administration behavior, thereby increasing the likelihood of nonmedical problematic use) and the relative toxicity (i.e., adverse effects having the capacity to harm the individual and/or society). An algorithm is provided that differentiates relative likelihood of abuse and relative toxicity of 19 hypnotic compounds: pentobarbital, methaqualone, diazepam, flunitrazepam, lorazepam, GHB (gamma-hydroxybutyrate, also known as sodium oxybate), temazepam, zaleplon, eszopiclone, triazolam, zopiclone, flurazepam, zolpidem, oxazepam, estazolam, diphenhydramine, quazepam, tra-zodone, and ramelteon. Factors in the analysis include preclinical and clinical assessment of reinforcing effects, preclinical and clinical assessment of withdrawal, actual abuse, acute sedation/memory impairment, and overdose lethality. The analysis shows that both the likelihood of abuse and the toxicity vary from high to none across these compounds. The primary clinical implication of the range of differences in abuse liability is that concern about recreational abuse, inappropriate long-term use, or adverse effects should not deter physicians from prescribing hypnotics when clinically
NASA Astrophysics Data System (ADS)
Paul, Bryan
Waveform design that allows for a wide variety of frequency-modulation (FM) has proven benefits. However, dictionary based optimization is limited and gradient search methods are often intractable. A new method is proposed using differential evolution to design waveforms with instantaneous frequencies (IFs) with cubic FM functions whose coefficients are constrained to the surface of the three dimensional unit sphere. Cubic IF functions subsume well-known IF functions such as linear, quadratic monomial, and cubic monomial IF functions. In addition, all nonlinear IF functions sufficiently approximated by a third order Taylor series over the unit time sequence can be represented in this space. Analog methods for generating polynomial IF waveforms are well established allowing for practical implementation in real world systems. By sufficiently constraining the search space to these waveforms of interest, alternative optimization methods such as differential evolution can be used to optimize tracking performance in a variety of radar environments. While simplified tracking models and finite waveform dictionaries have information theoretic results, continuous waveform design in high SNR, narrowband, cluttered environments is explored.
Nonlinear evolution of r-modes: The role of differential rotation
Sa, Paulo M.; Tome, Brigitte
2005-02-15
Recent work has shown that differential rotation, producing large scale drifts of fluid elements along stellar latitudes, is an unavoidable feature of r-modes in the nonlinear theory. We investigate the role of this differential rotation in the evolution of the l=2 r-mode instability of a newly born, hot, rapidly-rotating neutron star. It is shown that the amplitude of the r-mode saturates a few hundred seconds after the mode instability sets in. The saturation amplitude depends on the amount of differential rotation at the time the instability becomes active and can take values much smaller than unity. It is also shown that, independently of the saturation amplitude of the mode, the star spins down to rotation rates that are comparable to the inferred initial rotation rates of the fastest pulsars associated with supernova remnants. Finally, it is shown that, when the drift of fluid elements at the time the instability sets in is significant, most of the initial angular momentum of the star is transferred to the r-mode and, consequently, almost none is carried away by gravitational-radiation.
Lougheed, Stephen C; Austin, James D; Bogart, James P; Boag, Peter T; Chek, Andrew A
2006-01-01
Background Multi-character empirical studies are important contributions to our understanding of the process of speciation. The relatively conserved morphology of, and importance of the mate recognition system in anurans, combined with phylogenetic tools, provide an opportunity to address predictions about the relative role of each in the process of speciation. We examine the relationship among patterns of variation in morphology, call characters, and 16S gene sequences across seven populations of a neotropical hylid frog, Hyla leucophyllata, to infer their relative importance in predicting the early stages of population differentiation. Results Multivariate analyses demonstrate that both morphological and call characteristics were significantly variable among populations, characterized by significantly lower intra-population dispersion in call space than morphological space, and significantly greater among-population variation in call structure. We found lack of concordance between a 16S DNA phylogeny of Hyla leucophyllata and the significant population-level differentiation evident in both external morphology and male advertisement call. Comparisons of the reconstructed gene trees to simulated lineages support the notion that variation in call cannot be simply explained by population history. Conclusion Discordance among traits may reflect sampling biases (e.g. single genetic marker effects), or imply a decoupling of evolution of different suites of characters. Diagnostic differences among populations in call structure possibly reflect local selection pressures presented by different heterospecific calling assemblages and may serve as a precursor of species-wide differentiation. Differentiation among populations in morphology may be due to ecophenotypic variation or to diversifying selection on body size directly, or on frequency attributes of calls (mediated by female choice) that show a strong relationship to body size. PMID:16539709
NASA Astrophysics Data System (ADS)
Amjad, M.; Salam, Z.; Ishaque, K.
2014-04-01
In order to design an efficient resonant power supply for ozone gas generator, it is necessary to accurately determine the parameters of the ozone chamber. In the conventional method, the information from Lissajous plot is used to estimate the values of these parameters. However, the experimental setup for this purpose can only predict the parameters at one operating frequency and there is no guarantee that it results in the highest ozone gas yield. This paper proposes a new approach to determine the parameters using a search and optimization technique known as Differential Evolution (DE). The desired objective function of DE is set at the resonance condition and the chamber parameter values can be searched regardless of experimental constraints. The chamber parameters obtained from the DE technique are validated by experiment.
On Improving Efficiency of Differential Evolution for Aerodynamic Shape Optimization Applications
NASA Technical Reports Server (NTRS)
Madavan, Nateri K.
2004-01-01
Differential Evolution (DE) is a simple and robust evolutionary strategy that has been proven effective in determining the global optimum for several difficult optimization problems. Although DE offers several advantages over traditional optimization approaches, its use in applications such as aerodynamic shape optimization where the objective function evaluations are computationally expensive is limited by the large number of function evaluations often required. In this paper various approaches for improving the efficiency of DE are reviewed and discussed. These approaches are implemented in a DE-based aerodynamic shape optimization method that uses a Navier-Stokes solver for the objective function evaluations. Parallelization techniques on distributed computers are used to reduce turnaround times. Results are presented for the inverse design of a turbine airfoil. The efficiency improvements achieved by the different approaches are evaluated and compared.
NASA Astrophysics Data System (ADS)
Nakib, Amir; Aiboud, Fazia; Hodel, Jerome; Siarry, Patrick; Decq, Philippe
2010-03-01
In this paper, we present an original method to evaluate the deformations in the third cerebral ventricle on a brain cine- MR imaging. First, a segmentation process, based on a fractional differentiation method, is directly applied on a 2D+t dataset to detect the contours of the region of interest (i.e. lamina terminalis). Then, the successive segmented contours are matched using a procedure of global alignment, followed by a morphing process, based on the Covariance Matrix Adaptation Evolution Strategy (CMAES). Finally, local measurements of deformations are derived from the previously determined matched contours. The validation step is realized by comparing our results with the measurements achieved on the same patients by an expert.
Size evolution of ultrafine particles: Differential signatures of normal and episodic events.
Joshi, Manish; Khan, Arshad; Anand, S; Sapra, B K
2016-01-01
The effect of fireworks on the aerosol number characteristics of atmosphere was studied for an urban mega city. Measurements were made at 50 m height to assess the local changes around the festival days. Apart from the increase in total number concentration and characteristic accumulation mode, short-term increase of ultrafine particle concentration was noted. Total number concentration varies an order of magnitude during the measurement period in which peak occurs at a frequency of approximately one per day. On integral scale, it seems not possible to distinguish an episodic (e.g. firework bursting induced aerosol emission) and a normal (ambient atmospheric changes) event. However these events could be differentiated on the basis of size evolution analysis around number concentration peaks. The results are discussed relative to past studies and inferences are drawn towards aerosol signatures of firework bursting. The short-term burst in ultrafine particle concentration can pose an inhalation hazard. PMID:26552523
NASA Technical Reports Server (NTRS)
Hoang, TY
1994-01-01
A real-time, high-rate precision navigation Kalman filter algorithm is developed and analyzed. This Navigation algorithm blends various navigation data collected during terminal area approach of an instrumented helicopter. Navigation data collected include helicopter position and velocity from a global position system in differential mode (DGPS) as well as helicopter velocity and attitude from an inertial navigation system (INS). The goal of the Navigation algorithm is to increase the DGPS accuracy while producing navigational data at the 64 Hertz INS update rate. It is important to note that while the data was post flight processed, the Navigation algorithm was designed for real-time analysis. The design of the Navigation algorithm resulted in a nine-state Kalman filter. The Kalman filter's state matrix contains position, velocity, and velocity bias components. The filter updates positional readings with DGPS position, INS velocity, and velocity bias information. In addition, the filter incorporates a sporadic data rejection scheme. This relatively simple model met and exceeded the ten meter absolute positional requirement. The Navigation algorithm results were compared with truth data derived from a laser tracker. The helicopter flight profile included terminal glideslope angles of 3, 6, and 9 degrees. Two flight segments extracted during each terminal approach were used to evaluate the Navigation algorithm. The first segment recorded small dynamic maneuver in the lateral plane while motion in the vertical plane was recorded by the second segment. The longitudinal, lateral, and vertical averaged positional accuracies for all three glideslope approaches are as follows (mean plus or minus two standard deviations in meters): longitudinal (-0.03 plus or minus 1.41), lateral (-1.29 plus or minus 2.36), and vertical (-0.76 plus or minus 2.05).
Pearse, Devon E; Hayes, Sean A; Bond, Morgan H; Hanson, Chad V; Anderson, Eric C; Macfarlane, R Bruce; Garza, John Carlos
2009-01-01
Adaptation to novel habitats and phenotypic plasticity can be counteracting forces in evolution, but both are key characteristics of the life history of steelhead/rainbow trout (Oncorhynchus mykiss). Anadromous steelhead reproduce in freshwater river systems and small coastal streams but grow and mature in the ocean. Resident rainbow trout, either sympatric with steelhead or isolated above barrier dams or waterfalls, represent an alternative life-history form that lives entirely in freshwater. We analyzed population genetic data from 1486 anadromous and resident O. mykiss from a small stream in coastal California with multiple barrier waterfalls. Based on data from 18 highly variable microsatellite loci (He = 0.68), we conclude that the resident population above one barrier, Big Creek Falls, is the result of a recent anthropogenic introduction from the anadromous population of O. mykiss below the falls. Furthermore, fish from this above-barrier population occasionally descend over the falls and have established a genetically differentiated below-barrier subpopulation at the base of the falls, which appears to remain reproductively isolated from their now-sympatric anadromous ancestors. These results support a hypothesis of rapid evolution of a purely resident life history in the above-barrier population in response to strong selection against downstream movement. PMID:19561050
Differential evolution of the UV luminosity function of Lyman break galaxies from z ~ 5 to 3
NASA Astrophysics Data System (ADS)
Iwata, I.; Ohta, K.; Tamura, N.; Akiyama, M.; Aoki, K.; Ando, M.; Kiuchi, G.; Sawicki, M.
2007-04-01
We report the ultraviolet luminosity function (UVLF) of Lyman break galaxies at z ~ 5 derived from a deep and wide survey using the prime focus camera of the 8.2 m Subaru telescope (Suprime-Cam). Target fields consist of two blank regions of the sky, namely, the region including the Hubble Deep Field-North and the J0053+1234 region, and the total effective surveyed area is 1290 arcmin2. Applications of carefully determined colour selection criteria in V - Ic and Ic - z' yield a detection of 853 z ~ 5 candidates with z'AB < 26.5 mag. The UVLF at z ~ 5 based on this sample shows no significant change in the number density of bright (L >~ L*z=3) LBGs from that at z ~ 3, while there is a significant decline in the LF's faint end with increasing look-back time. This result means that the evolution of the number densities is differential with UV luminosity: the number density of UV luminous objects remains almost constant from z ~ 5 to 3 (the cosmic age is about 1.2 to 2.1 Gyr) while the number density of fainter objects gradually increases with cosmic time. This trend becomes apparent thanks to the small uncertainties in number densities both in the bright and faint parts of LFs at different epochs that are made possible by the deep and wide surveys we use. We discuss the origins of this differential evolution of the UVLF along the cosmic time and suggest that our observational findings are consistent with the biased galaxy evolution scenario: a galaxy population hosted by massive dark haloes starts active star formation preferentially at early cosmic time, while less massive galaxies increase their number density later. We also calculated the UV luminosity density by integrating the UVLF and at z ~ 5 found it to be 38.8+6.7-4.1 per cent of that at z ~ 3 for the luminosity range L > 0.1L*z=3. By combining our results with those from the literature, we find that the cosmic UV luminosity density marks its peak at and then slowly declines towards higher redshift. Based on
Fixation times in differentiation and evolution in the presence of bottlenecks, deserts, and oases.
Chou, Tom; Wang, Yu
2015-05-01
Cellular differentiation and evolution are stochastic processes that can involve multiple types (or states) of particles moving on a complex, high-dimensional state-space or "fitness" landscape. Cells of each specific type can thus be quantified by their population at a corresponding node within a network of states. Their dynamics across the state-space network involve genotypic or phenotypic transitions that can occur upon cell division, such as during symmetric or asymmetric cell differentiation, or upon spontaneous mutation. Here, we use a general multi-type branching processes to study first passage time statistics for a single cell to appear in a specific state. Our approach readily allows for nonexponentially distributed waiting times between transitions, reflecting, e.g., the cell cycle. For simplicity, we restrict most of our detailed analysis to exponentially distributed waiting times (Poisson processes). We present results for a sequential evolutionary process in which L successive transitions propel a population from a "wild-type" state to a given "terminally differentiated," "resistant," or "cancerous" state. Analytic and numeric results are also found for first passage times across an evolutionary chain containing a node with increased death or proliferation rate, representing a desert/bottleneck or an oasis. Processes involving cell proliferation are shown to be "nonlinear" (even though mean-field equations for the expected particle numbers are linear) resulting in first passage time statistics that depend on the position of the bottleneck or oasis. Our results highlight the sensitivity of stochastic measures to cell division fate and quantify the limitations of using certain approximations (such as the fixed-population and mean-field assumptions) in evaluating fixation times. PMID:25744205
Fixation times in differentiation and evolution in the presence of bottlenecks, deserts, and oases
Chou, Tom; Wang, Yu
2015-01-01
Cellular differentiation and evolution are stochastic processes that can involve multiple types (or states) of particles moving on a complex, high-dimensional state-space or “fitness” landscape. Cells of each specific type can thus be quantified by their population at a corresponding node within a network of states. Their dynamics across the state-space network involve genotypic or phenotypic transitions that can occur upon cell division, such as during symmetric or asymmetric cell differentiation, or upon spontaneous mutation. Here, we use a general multi-type branching processes to study first passage time statistics for a single cell to appear in a specific state. Our approach readily allows for nonexponentially distributed waiting times between transitions, reflecting, e.g., the cell cycle. For simplicity, we restrict most of our detailed analysis to exponentially distributed waiting times (Poisson processes). We present results for a sequential evolutionary process in which L successive transitions propel a population from a “wild-type” state to a given “terminally differentiated,” “resistant,” or “cancerous” state. Analytic and numeric results are also found for first passage times across an evolutionary chain containing a node with increased death or proliferation rate, representing a desert/bottleneck or an oasis. Processes involving cell proliferation are shown to be “nonlinear” (even though mean-field equations for the expected particle numbers are linear) resulting in first passage time statistics that depend on the position of the bottleneck or oasis. Our results highlight the sensitivity of stochastic measures to cell division fate and quantify the limitations of using certain approximations (such as the fixed-population and mean-field assumptions) in evaluating fixation times. PMID:25744205
Sunaguchi, Naoki; Yuasa, Tetsuya; Gupta, Rajiv; Ando, Masami
2015-12-21
The main focus of this paper is reconstruction of tomographic phase-contrast image from a set of projections. We propose an efficient reconstruction algorithm for differential phase-contrast computed tomography that can considerably reduce the number of projections required for reconstruction. The key result underlying this research is a projection theorem that states that the second derivative of the projection set is linearly related to the Laplacian of the tomographic image. The proposed algorithm first reconstructs the Laplacian image of the phase-shift distribution from the second-derivative of the projections using total variation regularization. The second step is to obtain the phase-shift distribution by solving a Poisson equation whose source is the Laplacian image previously reconstructed under the Dirichlet condition. We demonstrate the efficacy of this algorithm using both synthetically generated simulation data and projection data acquired experimentally at a synchrotron. The experimental phase data were acquired from a human coronary artery specimen using dark-field-imaging optics pioneered by our group. Our results demonstrate that the proposed algorithm can reduce the number of projections to approximately 33% as compared with the conventional filtered backprojection method, without any detrimental effect on the image quality.
Yu, Qiang; Vegh, Viktor; Liu, Fawang; Turner, Ian
2015-01-01
Texture enhancement is one of the most important techniques in digital image processing and plays an essential role in medical imaging since textures discriminate information. Most image texture enhancement techniques use classical integral order differential mask operators or fractional differential mask operators using fixed fractional order. These masks can produce excessive enhancement of low spatial frequency content, insufficient enhancement of large spatial frequency content, and retention of high spatial frequency noise. To improve upon existing approaches of texture enhancement, we derive an improved Variable Order Fractional Centered Difference (VOFCD) scheme which dynamically adjusts the fractional differential order instead of fixing it. The new VOFCD technique is based on the second order Riesz fractional differential operator using a Lagrange 3-point interpolation formula, for both grey scale and colour image enhancement. We then use this method to enhance photographs and a set of medical images related to patients with stroke and Parkinson's disease. The experiments show that our improved fractional differential mask has a higher signal to noise ratio value than the other fractional differential mask operators. Based on the corresponding quantitative analysis we conclude that the new method offers a superior texture enhancement over existing methods. PMID:26186221
Yu, Qiang; Vegh, Viktor
2015-01-01
Texture enhancement is one of the most important techniques in digital image processing and plays an essential role in medical imaging since textures discriminate information. Most image texture enhancement techniques use classical integral order differential mask operators or fractional differential mask operators using fixed fractional order. These masks can produce excessive enhancement of low spatial frequency content, insufficient enhancement of large spatial frequency content, and retention of high spatial frequency noise. To improve upon existing approaches of texture enhancement, we derive an improved Variable Order Fractional Centered Difference (VOFCD) scheme which dynamically adjusts the fractional differential order instead of fixing it. The new VOFCD technique is based on the second order Riesz fractional differential operator using a Lagrange 3-point interpolation formula, for both grey scale and colour image enhancement. We then use this method to enhance photographs and a set of medical images related to patients with stroke and Parkinson’s disease. The experiments show that our improved fractional differential mask has a higher signal to noise ratio value than the other fractional differential mask operators. Based on the corresponding quantitative analysis we conclude that the new method offers a superior texture enhancement over existing methods. PMID:26186221
DiffeRential Evolution Adaptive Metropolis with Sampling From Past States
NASA Astrophysics Data System (ADS)
Vrugt, J. A.; Laloy, E.; Ter Braak, C.
2010-12-01
Markov chain Monte Carlo (MCMC) methods have found widespread use in many fields of study to estimate the average properties of complex systems, and for posterior inference in a Bayesian framework. Existing theory and experiments prove convergence of well constructed MCMC schemes to the appropriate limiting distribution under a variety of different conditions. In practice, however this convergence is often observed to be disturbingly slow. This is frequently caused by an inappropriate selection of the proposal distribution used to generate trial moves in the Markov Chain. In a previous paper te{vrugt_1} we have presented the {D}iffe{R}ential {E}volution {A}daptive {M}etropolis (DREAM) MCMC scheme that automatically tunes the scale and orientation of the proposal distribution during evolution to the posterior target distribution. In the same paper, detailed balance and ergodicity of DREAM have been proved, and various examples involving nonlinearity, high-dimensionality, and multimodality have shown that DREAM is generally superior to other adaptive MCMC sampling approaches. Standard DREAM requires at least N = d chains to be run in parallel, where d is the dimensionality of the posterior. Unfortunately, running many parallel chains is a potential source of inefficiency, as each individual chain must travel to high density region of the posterior. The lower the number of parallel chains required, the greater the practical applicability of DREAM for computationally demanding problems. This paper extends DREAM with a snooker updater and shows by simulation and real examples that DREAM can work for d up to 50-100 with far fewer parallel chains (e.g. N = 3) by generating jumps using differences of pairs of past states
Kepler Observations of Starspot Evolution, Differential Rotation, and Flares on Late-Type Stars
NASA Astrophysics Data System (ADS)
Brown, Alexander; Korhonen, H.; Berdyugina, S.; Walkowicz, L.; Kowalski, A.; Hawley, S.; Neff, J.; Ramsey, L.; Redman, S.; Saar, S.; Furesz, G.; Piskunov, N.; Harper, G.; Ayres, T.; Tofany, B.
2011-05-01
The Kepler satellite is providing spectacular optical photometric light-curves of unprecedented precision and duration that routinely allow detailed studies of stellar magnetic activity on late-type stars that were difficult, if not impossible, to attempt previously. Rotational modulation due to starspots is commonly seen in the Kepler light-curves of late-type stars, allowing detailed study of the surface distribution of their photospheric magnetic activity. Kepler is providing multi-year duration light-curves that allow us to investigate how activity phenomena -- such as the growth, migration, and decay of starspots, differential rotation, activity cycles, and flaring -- operate on single and binary stars with a wide range of mass and convection zone depth. We present the first results from detailed starspot modeling using newly-developed light-curve inversion codes for a range of GALEX-selected stars with typical rotation periods of a few days, that we have observed as part of our 200 target Kepler Cycle 1/2 Guest Observer programs. The physical properties of the stars have been measured using high resolution optical spectroscopy, which allows the Kepler results to be placed within the existing framework of knowledge regarding stellar magnetic activity. These results demonstrate the powerful diagnostic capability provided by tracking starspot evolution essentially continuously for more than 16 months. The starspots are clearly sampling the stellar rotation rate at different latitudes, enabling us to measure the differential rotation and starspot lifetimes. As would be expected, stars with few day rotation show frequent flaring that is easily seen as "white-light" flares in Kepler light-curves. We compare the observed flare rates and occurrence with the starspot properties. This work contains results obtained using the NASA Kepler satellite and from the Apache Point Observatory, the MMT (using NOAO community access time), and the Hobby-Eberly Telescope. Funding
Evolution of a magnetic field in a differentially rotating radiative zone
NASA Astrophysics Data System (ADS)
Gaurat, M.; Jouve, L.; Lignières, F.; Gastine, T.
2015-08-01
Context. Recent spectropolarimetric surveys of main-sequence intermediate-mass stars have exhibited a dichotomy in the distribution of the observed magnetic field between the kG dipoles of Ap/Bp stars and the sub-Gauss magnetism of Vega and Sirius. Aims: We would like to test whether this dichotomy is linked to the stability versus instability of large-scale magnetic configurations in differentially rotating radiative zones. Methods: We computed the axisymmetric magnetic field obtained from the evolution of a dipolar field threading a differentially rotating shell. A full parameter study including various density profiles and initial and boundary conditions was performed with a 2D numerical code. We then focused on the ratio between the toroidal and poloidal components of the magnetic field and discuss the stability of the configurations dominated by the toroidal component using local stability criteria and insights from recent 3D numerical simulations. Results: The numerical results and a simple model show that the ratio between the toroidal and the poloidal magnetic fields is highest after an Alfvén crossing time of the initial poloidal field. For high density contrasts, this ratio converges towards an asymptotic value that can thus be extrapolated to realistic stellar cases. We then consider the stability of the magnetic configurations to non-axisymmetric perturbations and find that configurations dominated by the toroidal component are likely to be unstable if the shear strength is significantly higher than the poloidal Alfvén frequency. An expression for the critical poloidal field below which magnetic fields are likely to be unstable is found and is compared to the lower bound of Ap/Bp magnetic fields.
Gene Duplication and the Evolution of Hemoglobin Isoform Differentiation in Birds*
Grispo, Michael T.; Natarajan, Chandrasekhar; Projecto-Garcia, Joana; Moriyama, Hideaki; Weber, Roy E.; Storz, Jay F.
2012-01-01
The majority of bird species co-express two functionally distinct hemoglobin (Hb) isoforms in definitive erythrocytes as follows: HbA (the major adult Hb isoform, with α-chain subunits encoded by the αA-globin gene) and HbD (the minor adult Hb isoform, with α-chain subunits encoded by the αD-globin gene). The αD-globin gene originated via tandem duplication of an embryonic α-like globin gene in the stem lineage of tetrapod vertebrates, which suggests the possibility that functional differentiation between the HbA and HbD isoforms may be attributable to a retained ancestral character state in HbD that harkens back to a primordial, embryonic function. To investigate this possibility, we conducted a combined analysis of protein biochemistry and sequence evolution to characterize the structural and functional basis of Hb isoform differentiation in birds. Functional experiments involving purified HbA and HbD isoforms from 11 different bird species revealed that HbD is characterized by a consistently higher O2 affinity in the presence of allosteric effectors such as organic phosphates and Cl− ions. In the case of both HbA and HbD, analyses of oxygenation properties under the two-state Monod-Wyman-Changeux allosteric model revealed that the pH dependence of Hb-O2 affinity stems primarily from changes in the O2 association constant of deoxy (T-state)-Hb. Ancestral sequence reconstructions revealed that the amino acid substitutions that distinguish the adult-expressed Hb isoforms are not attributable to the retention of an ancestral (pre-duplication) character state in the αD-globin gene that is shared with the embryonic α-like globin gene. PMID:22962007
The Structure and Evolution of LOCBURST: The BATSE Burst Location Algorithm
NASA Technical Reports Server (NTRS)
Pendleton, Geoffrey N.; Briggs, Michael S.; Kippen, R. Marc; Paciesas, William S.; Stollberg, Mark; Woods, Pete; Meegan, Charles A.; Fishman, Gerald J.; McCollough, Mike L.; Connaughton, Valerie
1999-01-01
The gamma-ray burst (GRB) location algorithm used to produce the BATSE GRB locations is described. The general flow of control of the current location algorithm is presented, and the significant properties of the various physical inputs required are identified. The development of the burst location algorithm during the releases of the BATSE IB, 2B, and 3B GRB catalogs is presented so that the reasons for the differences in the positions and error estimates between the catalogs can be understood. In particular, differences between the 2B and 3B locations are discussed for events that have moved significantly and the reasons for the changes explained. The locations of bursts located independently by the interplanetary network (IPN) are used to illustrate the effect on burst location accuracy of various components of the algorithm. IPN data and locations from other gamma-ray instruments are used to calculate estimates of the systematic errors on BATSE burst locations.
EFFICIENT ALGORITHMS FOR SOLVING SYSTEMS OF ORDINARY DIFFERENTIAL EQUATIONS FOR ECOSYSTEM MODELING
This report presents three packages of subroutines for the numerical solution of systems of first order ordinary differential equations. The three integration methods were chosen to provide an efficient means of solving the full range of initial value problems encountered in basi...
LO Peg: surface differential rotation, flares, and spot-topographic evolution
NASA Astrophysics Data System (ADS)
Karmakar, Subhajeet; Pandey, J. C.; Savanov, I. S.; Taş, G.; Pandey, S. B.; Misra, K.; Joshi, S.; Dmitrienko, E. S.; Sakamoto, T.; Gehrels, N.; Okajima, T.
2016-07-01
Using the wealth of ˜24 yr multiband data, we present an in-depth study of the star-spot cycles, surface differential rotations (SDR), optical flares, evolution of star-spot distributions, and coronal activities on the surface of young, single, main-sequence, ultrafast rotator LO Peg. From the long-term V-band photometry, we derive rotational period of LO Peg to be 0.4231 ± 0.0001 d. Using the seasonal variations on the rotational period, the SDR pattern is investigated, and shows a solar-like pattern of SDR. A cyclic pattern with period of ˜2.7 yr appears to be present in rotational period variation. During the observations, 20 optical flares are detected with a flare frequency of ˜1 flare per two days and with flare energy of ˜1031-34 erg. The surface coverage of cool spots is found to be in the range of ˜9-26 per cent. It appears that the high- and low-latitude spots are interchanging their positions. Quasi-simultaneous observations in X-ray, UV, and optical photometric bands show a signature of an excess of X-ray and UV activities in spotted regions.
Kumar, Narender; Mariappan, Vanitha; Baddam, Ramani; Lankapalli, Aditya K.; Shaik, Sabiha; Goh, Khean-Lee; Loke, Mun Fai; Perkins, Tim; Benghezal, Mohammed; Hasnain, Seyed E.; Vadivelu, Jamuna; Marshall, Barry J.; Ahmed, Niyaz
2015-01-01
The discordant prevalence of Helicobacter pylori and its related diseases, for a long time, fostered certain enigmatic situations observed in the countries of the southern world. Variation in H. pylori infection rates and disease outcomes among different populations in multi-ethnic Malaysia provides a unique opportunity to understand dynamics of host–pathogen interaction and genome evolution. In this study, we extensively analyzed and compared genomes of 27 Malaysian H. pylori isolates and identified three major phylogeographic lineages: hspEastAsia, hpEurope and hpSouthIndia. The analysis of the virulence genes within the core genome, however, revealed a comparable pathogenic potential of the strains. In addition, we identified four genes limited to strains of East-Asian lineage. Our analyses identified a few strain-specific genes encoding restriction modification systems and outlined 311 core genes possibly under differential evolutionary constraints, among the strains representing different ethnic groups. The cagA and vacA genes also showed variations in accordance with the host genetic background of the strains. Moreover, restriction modification genes were found to be significantly enriched in East-Asian strains. An understanding of these variations in the genome content would provide significant insights into various adaptive and host modulation strategies harnessed by H. pylori to effectively persist in a host-specific manner. PMID:25452339
Rosenzweig, R. F.; Sharp, R. R.; Treves, D. S.; Adams, J.
1994-01-01
Populations of Escherichia coli initiated with a single clone and maintained for long periods in glucose-limited continuous culture, become polymorphic. In one population, three clones were isolated and by means of reconstruction experiments were shown to be maintained in stable polymorphism, although they exhibited substantial differences in maximum specific growth rates and in glucose uptake kinetics. Analysis of these three clones revealed that their stable coexistence could be explained by differential patterns of the secretion and uptake of two alternative metabolites acetate and glycerol. Regulatory (constitutive and null) mutations in acetyl-coenzyme A synthetase accounted for different patterns of acetate secretion and uptake seen. Altered patterns in glycerol uptake are most likely explained by mutations which result in quantitative differences in the induction of the glycerol regulon and/or structural changes in glycerol kinase that reduce allosteric inhibition by effector molecules associated with glycolysis. The evolution of resource partitioning, and consequent polymorphisms which arise may illustrate incipient processes of speciation in asexual organisms. PMID:7982572
Using Differential Evolution to Optimize Learning from Signals and Enhance Network Security
Harmer, Paul K; Temple, Michael A; Buckner, Mark A; Farquhar, Ethan
2011-01-01
Computer and communication network attacks are commonly orchestrated through Wireless Access Points (WAPs). This paper summarizes proof-of-concept research activity aimed at developing a physical layer Radio Frequency (RF) air monitoring capability to limit unauthorizedWAP access and mprove network security. This is done using Differential Evolution (DE) to optimize the performance of a Learning from Signals (LFS) classifier implemented with RF Distinct Native Attribute (RF-DNA) fingerprints. Performance of the resultant DE-optimized LFS classifier is demonstrated using 802.11a WiFi devices under the most challenging conditions of intra-manufacturer classification, i.e., using emissions of like-model devices that only differ in serial number. Using identical classifier input features, performance of the DE-optimized LFS classifier is assessed relative to a Multiple Discriminant Analysis / Maximum Likelihood (MDA/ML) classifier that has been used for previous demonstrations. The comparative assessment is made using both Time Domain (TD) and Spectral Domain (SD) fingerprint features. For all combinations of classifier type, feature type, and signal-to-noise ratio considered, results show that the DEoptimized LFS classifier with TD features is uperior and provides up to 20% improvement in classification accuracy with proper selection of DE parameters.
A differential evolution based approach for estimating minimal model parameters from IVGTT data.
Ghosh, Subhojit
2014-03-01
Estimation of insulin sensitivity plays a crucial role in the diagnosis and clinical investigation of glucose related diseases. The Bergman minimal model provides a non-invasive approach for estimating insulin sensitivity from the glucose insulin time series data of intravenous glucose tolerance test (IVGTT). However, quite often in the traditional gradient based techniques for deriving insulin sensitivity from the minimal model, improper initialization leads to convergence problems and results in final solution, which are either incorrect or physiologically not feasible. This paper deals with a differential evolution-based approach for the determination of insulin sensitivity from the minimal model using clinical test data. Being a direct search based technique, the proposed approach enables the determination of the global solution irrespective of the initial parameter values. The fitting performance of the model with parameters estimated from the proposed approach is found to be higher than the corresponding model estimated from the widely used gradient based approach. A high correlation coefficient of 0.964 (95% confidence interval of [0.897,0.988]) is acheived between the estimated insulin sensitivity and the one obtained from the population based approach for 16 subjects. The high correlation signifies the relative similarity between the two estimated indices in representing the same physiological phenomena. The simulation results and statistical analysis reveal that the proposed technique provides a reliable estimate of insulin sensitivity with minimum prior knowledge. PMID:24529205
Using meta-differential evolution to enhance a calculation of a continuous blood glucose level.
Koutny, Tomas
2016-09-01
We developed a new model of glucose dynamics. The model calculates blood glucose level as a function of transcapillary glucose transport. In previous studies, we validated the model with animal experiments. We used analytical method to determine model parameters. In this study, we validate the model with subjects with type 1 diabetes. In addition, we combine the analytic method with meta-differential evolution. To validate the model with human patients, we obtained a data set of type 1 diabetes study that was coordinated by Jaeb Center for Health Research. We calculated a continuous blood glucose level from continuously measured interstitial fluid glucose level. We used 6 different scenarios to ensure robust validation of the calculation. Over 96% of calculated blood glucose levels fit A+B zones of the Clarke Error Grid. No data set required any correction of model parameters during the time course of measuring. We successfully verified the possibility of calculating a continuous blood glucose level of subjects with type 1 diabetes. This study signals a successful transition of our research from an animal experiment to a human patient. Researchers can test our model with their data on-line at https://diabetes.zcu.cz. PMID:27393799
Olugbara, Oludayo
2014-01-01
This paper presents an annual multiobjective crop-mix planning as a problem of concurrent maximization of net profit and maximization of crop production to determine an optimal cropping pattern. The optimal crop production in a particular planting season is a crucial decision making task from the perspectives of economic management and sustainable agriculture. A multiobjective optimal crop-mix problem is formulated and solved using the generalized differential evolution 3 (GDE3) metaheuristic to generate a globally optimal solution. The performance of the GDE3 metaheuristic is investigated by comparing its results with the results obtained using epsilon constrained and nondominated sorting genetic algorithms—being two representatives of state-of-the-art in evolutionary optimization. The performance metrics of additive epsilon, generational distance, inverted generational distance, and spacing are considered to establish the comparability. In addition, a graphical comparison with respect to the true Pareto front for the multiobjective optimal crop-mix planning problem is presented. Empirical results generally show GDE3 to be a viable alternative tool for solving a multiobjective optimal crop-mix planning problem. PMID:24883369
Bhattacharya, Sukanta Shekhar; Garlapati, Vijay Kumar; Banerjee, Rintu
2011-01-31
In the present study, laccase production from a locally isolated hyperactive strain of Pleurotus sp. under solid state fermentation (SSF) was carried out and the interactions between different parameters of fermentation were studied using response surface methodology. The saddle shaped response surface plots depicting dual conditions for the enhanced production indicated the presence of isozymes with production optima at different conditions which was verified experimentally. Isoelectric focusing of the enzyme extract revealed that two isoforms were found with a widely varying pI of 3.8 and 9.3 emphasizing the capacity of the enzyme to be deployed at both acidic and alkaline conditions. Optimization of production conditions by coupling the regression equation with differential evolution technique yielded over 54,600IU/gds (3,412,500U/L) with a surfactant concentration of 0.016%, pH 7.99, particle size of 0.25cm, liquid to solid ratio of 4.99 and an incubation period of 8 days. In this study, the optimization process yielded highest titer value of laccase reported to date. PMID:20541634
Zhu, Xinjun; Chen, Zhanqing; Tang, Chen; Mi, Qinghua; Yan, Xiusheng
2013-03-20
In this paper, we are concerned with denoising in experimentally obtained electronic speckle pattern interferometry (ESPI) speckle fringe patterns with poor quality. We extend the application of two existing oriented partial differential equation (PDE) filters, including the second-order single oriented PDE filter and the double oriented PDE filter, to two experimentally obtained ESPI speckle fringe patterns with very poor quality, and compare them with other efficient filtering methods, including the adaptive weighted filter, the improved nonlinear complex diffusion PDE, and the windowed Fourier transform method. All of the five filters have been illustrated to be efficient denoising methods through previous comparative analyses in published papers. The experimental results have demonstrated that the two oriented PDE models are applicable to low-quality ESPI speckle fringe patterns. Then for solving the main shortcoming of the two oriented PDE models, we develop the numerically fast algorithms based on Gauss-Seidel strategy for the two oriented PDE models. The proposed numerical algorithms are capable of accelerating the convergence greatly, and perform significantly better in terms of computational efficiency. Our numerically fast algorithms are extended automatically to some other PDE filtering models. PMID:23518722
NASA Astrophysics Data System (ADS)
Lee, Junyung; Yi, Kyongsu; Yoo, Hyunjae; Chong, Hyokjin; Ko, Bongchul
2015-06-01
This paper describes a risk management algorithm for rear-side collision avoidance. The proposed risk management algorithm consists of a supervisor and a coordinator. The supervisor is designed to monitor collision risks between the subject vehicle and approaching vehicle in the adjacent lane. An appropriate criterion of intervention, which satisfies high acceptance to drivers through the consideration of a realistic traffic, has been determined based on the analysis of the kinematics of the vehicles in longitudinal and lateral directions. In order to assist the driver actively and increase driver's safety, a coordinator is designed to combine lateral control using a steering torque overlay by motor-driven power steering and differential braking by vehicle stability control. In order to prevent the collision while limiting actuator's control inputs and vehicle dynamics to safe values for the assurance of the driver's comfort, the Lyapunov theory and linear matrix inequalities based optimisation methods have been used. The proposed risk management algorithm has been evaluated via simulation using CarSim and MATLAB/Simulink.
ERIC Educational Resources Information Center
Mayr, Ernst
1978-01-01
Traces the history of evolution theory from Lamarck and Darwin to the present. Discusses natural selection in detail. Suggests that, besides biological evolution, there is also a cultural evolution which is more rapid than the former. (MA)
NASA Astrophysics Data System (ADS)
Gallagher, Kerry; Sambridge, Malcolm; Drijkoningen, Guy
In providing a method for solving non-linear optimization problems Monte Carlo techniques avoid the need for linearization but, in practice, are often prohibitive because of the large number of models that must be considered. A new class of methods known as Genetic Algorithms have recently been devised in the field of Artificial Intelligence. We outline the basic concept of genetic algorithms and discuss three examples. We show that, in locating an optimal model, the new technique is far superior in performance to Monte Carlo techniques in all cases considered. However, Monte Carlo integration is still regarded as an effective method for the subsequent model appraisal.
NASA Technical Reports Server (NTRS)
Gunzburger, M. D.; Nicolaides, R. A.
1986-01-01
Substructuring methods are in common use in mechanics problems where typically the associated linear systems of algebraic equations are positive definite. Here these methods are extended to problems which lead to nonpositive definite, nonsymmetric matrices. The extension is based on an algorithm which carries out the block Gauss elimination procedure without the need for interchanges even when a pivot matrix is singular. Examples are provided wherein the method is used in connection with finite element solutions of the stationary Stokes equations and the Helmholtz equation, and dual methods for second-order elliptic equations.
Ferrauto, Tomassino; Parisi, Domenico; Di Stefano, Gabriele; Baldassarre, Gianluca
2013-01-01
Organisms that live in groups, from microbial symbionts to social insects and schooling fish, exhibit a number of highly efficient cooperative behaviors, often based on role taking and specialization. These behaviors are relevant not only for the biologist but also for the engineer interested in decentralized collective robotics. We address these phenomena by carrying out experiments with groups of two simulated robots controlled by neural networks whose connection weights are evolved by using genetic algorithms. These algorithms and controllers are well suited to autonomously find solutions for decentralized collective robotic tasks based on principles of self-organization. The article first presents a taxonomy of role-taking and specialization mechanisms related to evolved neural network controllers. Then it introduces two cooperation tasks, which can be accomplished by either role taking or specialization, and uses these tasks to compare four different genetic algorithms to evaluate their capacity to evolve a suitable behavioral strategy, which depends on the task demands. Interestingly, only one of the four algorithms, which appears to have more biological plausibility, is capable of evolving role taking or specialization when they are needed. The results are relevant for both collective robotics and biology, as they can provide useful hints on the different processes that can lead to the emergence of specialization in robots and organisms. PMID:23514239
Performance Comparison Of Evolutionary Algorithms For Image Clustering
NASA Astrophysics Data System (ADS)
Civicioglu, P.; Atasever, U. H.; Ozkan, C.; Besdok, E.; Karkinli, A. E.; Kesikoglu, A.
2014-09-01
Evolutionary computation tools are able to process real valued numerical sets in order to extract suboptimal solution of designed problem. Data clustering algorithms have been intensively used for image segmentation in remote sensing applications. Despite of wide usage of evolutionary algorithms on data clustering, their clustering performances have been scarcely studied by using clustering validation indexes. In this paper, the recently proposed evolutionary algorithms (i.e., Artificial Bee Colony Algorithm (ABC), Gravitational Search Algorithm (GSA), Cuckoo Search Algorithm (CS), Adaptive Differential Evolution Algorithm (JADE), Differential Search Algorithm (DSA) and Backtracking Search Optimization Algorithm (BSA)) and some classical image clustering techniques (i.e., k-means, fcm, som networks) have been used to cluster images and their performances have been compared by using four clustering validation indexes. Experimental test results exposed that evolutionary algorithms give more reliable cluster-centers than classical clustering techniques, but their convergence time is quite long.
NASA Astrophysics Data System (ADS)
Reiss, Martin; Hofmeister, Stefan; DeVisscher, Ruben; Temmer, Manuela; Veronig, Astrid; Delouille, Veronique; Rotter, Thomas
2015-04-01
In combination with the Sun's rotation, coronal holes and their associated high speed solar wind streams (HSSs) shape the solar wind distribution in the interplanetary space. The structuring of interplanetary space is especially important for deriving changes in the kinematics of coronal mass ejections. In order to forecast HSSs we empirically relate the fractional coronal hole area to the solar wind speed at 1AU. We apply an automated method for the identification and extraction of coronal hole regions in SDO/AIA 193Å images. Due to the almost equal low intensity of coronal holes and filament channels the intensity-based detection method cannot differentiation filament channels from coronal holes. Hence, to improve the HSS forecasting method we need to distinguish filament channels from coronal holes. Compared to coronal holes, filament channels are regions of closed magnetic field lines along a polarity inversion line and are therefore different in their magnetic field configuration. Acting on this physical background we investigate the benefits of using Haralick's textural features to analyze the intrinsic texture information contained with coronal holes and filament channels in AIA and HMI images. In combination with first order statistics and shape measures, we tested several classifiers to find the most suitable decision rule for a differentiation. In order to evaluate the performance of each classifier the Hanssen-Kuiper skill score, also called True Skill Statistic, was calculated. The results reveal that all classifiers, including Support Vector Machine (SVM), Linear SVM, Decision Tree and Random Forest classifier provide good results in general.
NASA Astrophysics Data System (ADS)
Fakir, Md. Moslemuddin; Khatun, Sabira; Jusoh, Abdul Wahab; Ramli, Mohammad Fadzli; Muhamad, Wan Zuki Azman Wan
2015-05-01
Finite Element Method (FEM) and Differential Quadrature Method (DQM) are two very important numerical solution techniques to solve engineering and physical science problems. Usually elements are sub-divided uniformly in FEM (conventional FEM, CFEM) to obtain temperature distribution behavior in a fin with extra computational complexity to obtain a fair solution with required accuracy. In this paper an algorithm to enhance the FEM (named EFEM) is presented by considering non-uniform sub-elements and applied successfully to investigate one dimensional heat distribution phenomenon in an insulated-tip thin rectangular fin. The obtained results are compared with CFEM, efficient DQM (EDQM, with non-uniform mesh generation) and exact solution. EFEM results exhibits more accuracy than CFEM and EDQM and agree very well with exact solution showing its potentiality.
Two Level Parallel Grammatical Evolution
NASA Astrophysics Data System (ADS)
Ošmera, Pavel
This paper describes a Two Level Parallel Grammatical Evolution (TLPGE) that can evolve complete programs using a variable length linear genome to govern the mapping of a Backus Naur Form grammar definition. To increase the efficiency of Grammatical Evolution (GE) the influence of backward processing was tested and a second level with differential evolution was added. The significance of backward coding (BC) and the comparison with standard coding of GEs is presented. The new method is based on parallel grammatical evolution (PGE) with a backward processing algorithm, which is further extended with a differential evolution algorithm. Thus a two-level optimization method was formed in attempt to take advantage of the benefits of both original methods and avoid their difficulties. Both methods used are discussed and the architecture of their combination is described. Also application is discussed and results on a real-word application are described.
Learning Qualitative Differential Equation models: a survey of algorithms and applications
PANG, WEI; COGHILL, GEORGE M.
2013-01-01
Over the last two decades, qualitative reasoning (QR) has become an important domain in Artificial Intelligence. QDE (Qualitative Differential Equation) model learning (QML), as a branch of QR, has also received an increasing amount of attention; many systems have been proposed to solve various significant problems in this field. QML has been applied to a wide range of fields, including physics, biology and medical science. In this paper, we first identify the scope of this review by distinguishing QML from other QML systems, and then review all the noteworthy QML systems within this scope. The applications of QML in several application domains are also introduced briefly. Finally, the future directions of QML are explored from different perspectives. PMID:23704803
NASA Astrophysics Data System (ADS)
Hesameddini, Esmail; Rahimi, Azam; Asadollahifard, Elham
2016-05-01
In this paper, we will introduce the reconstruction of variational iteration method (RVIM) to solve multi-order fractional differential equations (M-FDEs), which include linear and nonlinear ones. We will easily obtain approximate analytical solutions of M-FDEs by means of the RVIM based on the properties of fractional calculus. Moreover, the convergence of proposed method will be shown. Our scheme has been constructed for the fully general set of M-FDEs without any special assumptions, and is easy to implement numerically. Therefore, our method is more practical and helpful for solving a broad class of M-FDEs. Numerical results are carried out to confirm the accuracy and efficiency of proposed method. Several numerical examples are presented in the format of table and graphs to make comparison with the results that previously obtained by some other well known methods.
Muhammad, Durreshahwar; Foret, Jessica; Brady, Siobhan M.; Ducoste, Joel J.; Tuck, James; Long, Terri A.; Williams, Cranos
2015-01-01
Time course transcriptome datasets are commonly used to predict key gene regulators associated with stress responses and to explore gene functionality. Techniques developed to extract causal relationships between genes from high throughput time course expression data are limited by low signal levels coupled with noise and sparseness in time points. We deal with these limitations by proposing the Cluster and Differential Alignment Algorithm (CDAA). This algorithm was designed to process transcriptome data by first grouping genes based on stages of activity and then using similarities in gene expression to predict influential connections between individual genes. Regulatory relationships are assigned based on pairwise alignment scores generated using the expression patterns of two genes and some inferred delay between the regulator and the observed activity of the target. We applied the CDAA to an iron deficiency time course microarray dataset to identify regulators that influence 7 target transcription factors known to participate in the Arabidopsis thaliana iron deficiency response. The algorithm predicted that 7 regulators previously unlinked to iron homeostasis influence the expression of these known transcription factors. We validated over half of predicted influential relationships using qRT-PCR expression analysis in mutant backgrounds. One predicted regulator-target relationship was shown to be a direct binding interaction according to yeast one-hybrid (Y1H) analysis. These results serve as a proof of concept emphasizing the utility of the CDAA for identifying unknown or missing nodes in regulatory cascades, providing the fundamental knowledge needed for constructing predictive gene regulatory networks. We propose that this tool can be used successfully for similar time course datasets to extract additional information and infer reliable regulatory connections for individual genes. PMID:26317202
Koryachko, Alexandr; Matthiadis, Anna; Muhammad, Durreshahwar; Foret, Jessica; Brady, Siobhan M; Ducoste, Joel J; Tuck, James; Long, Terri A; Williams, Cranos
2015-01-01
Time course transcriptome datasets are commonly used to predict key gene regulators associated with stress responses and to explore gene functionality. Techniques developed to extract causal relationships between genes from high throughput time course expression data are limited by low signal levels coupled with noise and sparseness in time points. We deal with these limitations by proposing the Cluster and Differential Alignment Algorithm (CDAA). This algorithm was designed to process transcriptome data by first grouping genes based on stages of activity and then using similarities in gene expression to predict influential connections between individual genes. Regulatory relationships are assigned based on pairwise alignment scores generated using the expression patterns of two genes and some inferred delay between the regulator and the observed activity of the target. We applied the CDAA to an iron deficiency time course microarray dataset to identify regulators that influence 7 target transcription factors known to participate in the Arabidopsis thaliana iron deficiency response. The algorithm predicted that 7 regulators previously unlinked to iron homeostasis influence the expression of these known transcription factors. We validated over half of predicted influential relationships using qRT-PCR expression analysis in mutant backgrounds. One predicted regulator-target relationship was shown to be a direct binding interaction according to yeast one-hybrid (Y1H) analysis. These results serve as a proof of concept emphasizing the utility of the CDAA for identifying unknown or missing nodes in regulatory cascades, providing the fundamental knowledge needed for constructing predictive gene regulatory networks. We propose that this tool can be used successfully for similar time course datasets to extract additional information and infer reliable regulatory connections for individual genes. PMID:26317202
Drallos, P.J.; Wadehra, J.M.
1988-06-01
We are presenting a novel numerical technique for obtaining the time evolution of the electron velocity and electron energy distribution functions in the presence of a uniform electric field. Using this technique, the various swarm parameters can be evolved for sufficiently long times so that equilibrium can be reached without incurring any numerical instabilities. Results are presented for electron swarms in gaseous neon for various values of E/N.
García-Pedrajas, Nicolás; Ortiz-Boyer, Domingo; Hervás-Martínez, César
2006-05-01
In this work we present a new approach to crossover operator in the genetic evolution of neural networks. The most widely used evolutionary computation paradigm for neural network evolution is evolutionary programming. This paradigm is usually preferred due to the problems caused by the application of crossover to neural network evolution. However, crossover is the most innovative operator within the field of evolutionary computation. One of the most notorious problems with the application of crossover to neural networks is known as the permutation problem. This problem occurs due to the fact that the same network can be represented in a genetic coding by many different codifications. Our approach modifies the standard crossover operator taking into account the special features of the individuals to be mated. We present a new model for mating individuals that considers the structure of the hidden layer and redefines the crossover operator. As each hidden node represents a non-linear projection of the input variables, we approach the crossover as a problem on combinatorial optimization. We can formulate the problem as the extraction of a subset of near-optimal projections to create the hidden layer of the new network. This new approach is compared to a classical crossover in 25 real-world problems with an excellent performance. Moreover, the networks obtained are much smaller than those obtained with classical crossover operator. PMID:16343847
NASA Astrophysics Data System (ADS)
Kummerow, Christian; Hong, Y.; Olson, W. S.; Yang, S.; Adler, R. F.; McCollum, J.; Ferraro, R.; Petty, G.; Shin, D.-B.; Wilheit, T. T.
2001-11-01
This paper describes the latest improvements applied to the Goddard profiling algorithm (GPROF), particularly as they apply to the Tropical Rainfall Measuring Mission (TRMM). Most of these improvements, however, are conceptual in nature and apply equally to other passive microwave sensors. The improvements were motivated by a notable overestimation of precipitation in the intertropical convergence zone. This problem was traced back to the algorithm's poor separation between convective and stratiform precipitation coupled with a poor separation between stratiform and transition regions in the a priori cloud model database. In addition to now using an improved convective-stratiform classification scheme, the new algorithm also makes use of emission and scattering indices instead of individual brightness temperatures. Brightness temperature indices have the advantage of being monotonic functions of rainfall. This, in turn, has allowed the algorithm to better define the uncertainties needed by the scheme's Bayesian inversion approach. Last, the algorithm over land has been modified primarily to better account for ambiguous classification where the scattering signature of precipitation could be confused with surface signals. All these changes have been implemented for both the TRMM Microwave Imager (TMI) and the Special Sensor Microwave Imager (SSM/I). Results from both sensors are very similar at the storm scale and for global averages. Surface rainfall products from the algorithm's operational version have been compared with conventional rainfall data over both land and oceans. Over oceans, GPROF results compare well with atoll gauge data. GPROF is biased negatively by 9% with a correlation of 0.86 for monthly 2.5° averages over the atolls. If only grid boxes with two or more atolls are used, the correlation increases to 0.91 but GPROF becomes positively biased by 6%. Comparisons with TRMM ground validation products from Kwajalein reveal that GPROF is negatively
A genetic algorithm approach to probing the evolution of self-organized nanostructured systems.
Siepmann, Peter; Martin, Christopher P; Vancea, Ioan; Moriarty, Philip J; Krasnogor, Natalio
2007-07-01
We present a new methodology, based on a combination of genetic algorithms and image morphometry, for matching the outcome of a Monte Carlo simulation to experimental observations of a far-from-equilibrium nanosystem. The Monte Carlo model used simulates a colloidal solution of nanoparticles drying on a solid substrate and has previously been shown to produce patterns very similar to those observed experimentally. Our approach enables the broad parameter space associated with simulated nanoparticle self-organization to be searched effectively for a given experimental target morphology. PMID:17552572
Kellermann, Vanessa; Hoffmann, Ary A; Kristensen, Torsten Nygaard; Moghadam, Neda Nasiri; Loeschcke, Volker
2015-11-01
Experimental evolution can be a useful tool for testing the impact of environmental factors on adaptive changes in populations, and this approach is being increasingly used to understand the potential for evolutionary responses in populations under changing climates. However, selective factors will often be more complex in natural populations than in laboratory environments and produce different patterns of adaptive differentiation. Here we test the ability of laboratory experimental evolution under different temperature cycles to reproduce well-known patterns of clinal variation in Drosophila melanogaster. Six fluctuating thermal regimes mimicking the natural temperature conditions along the east coast of Australia were initiated. Contrary to expectations, on the basis of field patterns there was no evidence for adaptation to thermal regimes as reflected by changes in cold and heat resistance after 1-3 years of laboratory natural selection. While laboratory evolution led to changes in starvation resistance, development time, and body size, patterns were not consistent with those seen in natural populations. These findings highlight the complexity of factors affecting trait evolution in natural populations and indicate that caution is required when inferring likely evolutionary responses from the outcome of experimental evolution studies. PMID:26655772
Wright, Thomas; Ward, Jamie
2013-08-01
Sensory substitution is a promising technique for mitigating the loss of a sensory modality. Sensory substitution devices (SSDs) work by converting information from the impaired sense (e.g., vision) into another, intact sense (e.g., audition). However, there are a potentially infinite number of ways of converting images into sounds, and it is important that the conversion takes into account the limits of human perception and other user-related factors (e.g., whether the sounds are pleasant to listen to). The device explored here is termed "polyglot" because it generates a very large set of solutions. Specifically, we adapt a procedure that has been in widespread use in the design of technology but has rarely been used as a tool to explore perception-namely, interactive genetic algorithms. In this procedure, a very large range of potential sensory substitution devices can be explored by creating a set of "genes" with different allelic variants (e.g., different ways of translating luminance into loudness). The most successful devices are then "bred" together, and we statistically explore the characteristics of the selected-for traits after multiple generations. The aim of the present study is to produce design guidelines for a better SSD. In three experiments, we vary the way that the fitness of the device is computed: by asking the user to rate the auditory aesthetics of different devices (Experiment 1), and by measuring the ability of participants to match sounds to images (Experiment 2) and the ability to perceptually discriminate between two sounds derived from similar images (Experiment 3). In each case, the traits selected for by the genetic algorithm represent the ideal SSD for that task. Taken together, these traits can guide the design of a better SSD. PMID:23298393
None, None
2015-09-28
Coulomb interaction between charged particles inside a bunch is one of the most importance collective effects in beam dynamics, becoming even more significant as the energy of the particle beam is lowered to accommodate analytical and low-Z material imaging purposes such as in the time resolved Ultrafast Electron Microscope (UEM) development currently underway at Michigan State University. In addition, space charge effects are the key limiting factor in the development of ultrafast atomic resolution electron imaging and diffraction technologies and are also correlated with an irreversible growth in rms beam emittance due to fluctuating components of the nonlinear electron dynamics. In the short pulse regime used in the UEM, space charge effects also lead to virtual cathode formation in which the negative charge of the electrons emitted at earlier times, combined with the attractive surface field, hinders further emission of particles and causes a degradation of the pulse properties. Space charge and virtual cathode effects and their remediation are core issues for the development of the next generation of high-brightness UEMs. Since the analytical models are only applicable for special cases, numerical simulations, in addition to experiments, are usually necessary to accurately understand the space charge effect. In this paper we will introduce a grid-free differential algebra based multiple level fast multipole algorithm, which calculates the 3D space charge field for n charged particles in arbitrary distribution with an efficiency of O(n), and the implementation of the algorithm to a simulation code for space charge dominated photoemission processes.
None, None
2015-09-28
Coulomb interaction between charged particles inside a bunch is one of the most importance collective effects in beam dynamics, becoming even more significant as the energy of the particle beam is lowered to accommodate analytical and low-Z material imaging purposes such as in the time resolved Ultrafast Electron Microscope (UEM) development currently underway at Michigan State University. In addition, space charge effects are the key limiting factor in the development of ultrafast atomic resolution electron imaging and diffraction technologies and are also correlated with an irreversible growth in rms beam emittance due to fluctuating components of the nonlinear electron dynamics.more » In the short pulse regime used in the UEM, space charge effects also lead to virtual cathode formation in which the negative charge of the electrons emitted at earlier times, combined with the attractive surface field, hinders further emission of particles and causes a degradation of the pulse properties. Space charge and virtual cathode effects and their remediation are core issues for the development of the next generation of high-brightness UEMs. Since the analytical models are only applicable for special cases, numerical simulations, in addition to experiments, are usually necessary to accurately understand the space charge effect. In this paper we will introduce a grid-free differential algebra based multiple level fast multipole algorithm, which calculates the 3D space charge field for n charged particles in arbitrary distribution with an efficiency of O(n), and the implementation of the algorithm to a simulation code for space charge dominated photoemission processes.« less
NASA Astrophysics Data System (ADS)
Borisova, E.; Troyanova, P.; Avramov, L.
2006-09-01
The goals of this work were investigation of base-cell skin lesions by the method of laser-induced autofluorescence spectroscopy. Fluorescence spectra were obtained from benign base-cell papilloma and malignant base-cell carcinoma, as well as from healthy skin areas near to the lesions that were used posteriori to reveal changes between healthy and lesion skin spectra. Preliminarily lesions were classified by dermatoscopic method (MoleMax II, DERMA Instruments). All suspicious lesions were excised and were investigated histologically. The experimental set-up consists of a nitrogen laser (337 nm, 14 μJ, 10 Hz), lenses, filters, optical fibers, and a microspectrometer (PC2000, "Ocean Optics"). A computer controls this system. Spectrum of healthy skin consists of one main maximum at 470-500 nm spectral region and secondary maxima at in the regions around 400 and 440 nm. In cases of papilloma and base-cell carcinoma an intensity decrease was observed, related to accumulation of pigments in these cutaneous lesions. An relative increase of the fluorescence peak at 440 nm were registered in the case of base-cell carcinoma, related to metabolism activity increase, and appearance of green fluorescence, related to increase of keratin content in benign papilloma lesions were detected. The results, obtained were used to develop multispectral diagnostic algorithm of these base-cell lesions. An sensitivity of 89,4% and 91,0% and specificity of 99,6% and 97,4% for differentiation between normal skin and papilloma and carcinoma respectively were obtained. The capability of the human skin fluorescence spectroscopy for early diagnosis and differentiation of cutaneous lesions is shown.
Search for the algorithm of genes distribution during the process of microbial evolution
NASA Astrophysics Data System (ADS)
Pikuta, Elena V.
2015-09-01
Previous two and three dimensional graph analysis of eco-physiological data of Archaea demonstrated specific geometry for distribution of major Prokaryotic groups in a hyperboloid function. The function of a two-sheet hyperboloid covered all known biological groups, and therefore, could be applied for the entire evolution of life on Earth. The vector of evolution was indicated from the point of hyper temperature, extreme acidity and low salinity to the point of low temperature and increased alkalinity and salinity. According to this vector, the following groups were chosen for the gene screening analysis. In the vector "High-Temperature → Low-Temperature" within extreme acidic pH (0-3), it is: 1) the hyperthermophilic Crenarchaeota - order Sulfolobales, 2) moderately thermophilic Euryarchaeota - Class Thermoplasmata, and 3) mesophilic acidophiles- genus Thiobacillus and others. In the vector "Low pH → High pH" the following groups were selected in three temperature ranges: a) Hyperthermophilic Archaea and Eubacteria, b) moderately thermophilic - representatives of the genera Anaerobacter and Anoxybacillus, and c) mesophilic haloalkaliphiles (Eubacteria and Archaea). The genes associated with acidophily (H+ pump), chemolitho-autotrophy (proteins of biochemichal cycles), polymerases, and histones were proposed for the first vector, and for the second vector the genes associated with halo-alkaliphily (Na+ pumps), enzymes of organotrophic metabolisms (sugar- and proteolytics), and others were indicated for the screening. Here, an introduction to the phylogenetic constant (ρη) is presented and discussed. This universal characteristic is calculated for two principally different life forms -Prokaryotes and Eukaryotes; Existence of the second type of living forms is impossible without the first one. The number of chromosomes in Prokaryotic organisms is limited to one (with very rare exceptions, to two), while in Eukaryotic organisms this number is larger. Currently
Burri, Reto; Nater, Alexander; Kawakami, Takeshi; Mugal, Carina F.; Olason, Pall I.; Smeds, Linnea; Suh, Alexander; Dutoit, Ludovic; Bureš, Stanislav; Garamszegi, Laszlo Z.; Hogner, Silje; Moreno, Juan; Qvarnström, Anna; Ružić, Milan; Sæther, Stein-Are; Sætre, Glenn-Peter; Török, Janos; Ellegren, Hans
2015-01-01
Speciation is a continuous process during which genetic changes gradually accumulate in the genomes of diverging species. Recent studies have documented highly heterogeneous differentiation landscapes, with distinct regions of elevated differentiation (“differentiation islands”) widespread across genomes. However, it remains unclear which processes drive the evolution of differentiation islands; how the differentiation landscape evolves as speciation advances; and ultimately, how differentiation islands are related to speciation. Here, we addressed these questions based on population genetic analyses of 200 resequenced genomes from 10 populations of four Ficedula flycatcher sister species. We show that a heterogeneous differentiation landscape starts emerging among populations within species, and differentiation islands evolve recurrently in the very same genomic regions among independent lineages. Contrary to expectations from models that interpret differentiation islands as genomic regions involved in reproductive isolation that are shielded from gene flow, patterns of sequence divergence (dxy and relative node depth) do not support a major role of gene flow in the evolution of the differentiation landscape in these species. Instead, as predicted by models of linked selection, genome-wide variation in diversity and differentiation can be explained by variation in recombination rate and the density of targets for selection. We thus conclude that the heterogeneous landscape of differentiation in Ficedula flycatchers evolves mainly as the result of background selection and selective sweeps in genomic regions of low recombination. Our results emphasize the necessity of incorporating linked selection as a null model to identify genome regions involved in adaptation and speciation. PMID:26355005
Li, Jing; Hong, Wenxue
2014-12-01
The feature extraction and feature selection are the important issues in pattern recognition. Based on the geometric algebra representation of vector, a new feature extraction method using blade coefficient of geometric algebra was proposed in this study. At the same time, an improved differential evolution (DE) feature selection method was proposed to solve the elevated high dimension issue. The simple linear discriminant analysis was used as the classifier. The result of the 10-fold cross-validation (10 CV) classification of public breast cancer biomedical dataset was more than 96% and proved superior to that of the original features and traditional feature extraction method. PMID:25868233
Jäger, Markus; Iwig, Kornelius; Butz, Tilman
2011-06-01
A user-friendly fully digital time differential perturbed angular correlation (TDPAC)-spectrometer with six detectors and fast digitizers using field programmable gate arrays (FPGA) is described and performance data are given. The new spectrometer has an online data analysis feature, a compact size, and a time resolution such as conventional analog spectrometers. Its calculation intensive part was implemented inside the digitizer. This gives the possibility to change parameters (energy windows, constant fraction trigger delay) and see their influence immediately in the γ-γ correlation diagrams. Tests were performed which showed that the time resolution using a (60)Co source with energy window set at 1.17 MeV and 1.33 MeV is 265 ps with LaBr(3)(Ce) scintillators and 254 ps with BaF(2) scintillators. A true constant fraction algorithm turned out to be slightly better than the constant fraction of amplitude method. The spectrometer performance was tested with a TDPAC measurement using a (44)Ti in rutile source and a positron lifetime measurement using (22)Na. The maximum possible data rate of the spectrometer is 1.1 × 10(6) γ quanta per detector and second. PMID:21721728
Jaeger, Markus; Butz, Tilman; Iwig, Kornelius
2011-06-15
A user-friendly fully digital time differential perturbed angular correlation (TDPAC)-spectrometer with six detectors and fast digitizers using field programmable gate arrays (FPGA) is described and performance data are given. The new spectrometer has an online data analysis feature, a compact size, and a time resolution such as conventional analog spectrometers. Its calculation intensive part was implemented inside the digitizer. This gives the possibility to change parameters (energy windows, constant fraction trigger delay) and see their influence immediately in the {gamma}-{gamma} correlation diagrams. Tests were performed which showed that the time resolution using a {sup 60}Co source with energy window set at 1.17 MeV and 1.33 MeV is 265 ps with LaBr{sub 3}(Ce) scintillators and 254 ps with BaF{sub 2} scintillators. A true constant fraction algorithm turned out to be slightly better than the constant fraction of amplitude method. The spectrometer performance was tested with a TDPAC measurement using a {sup 44}Ti in rutile source and a positron lifetime measurement using {sup 22}Na. The maximum possible data rate of the spectrometer is 1.1 x 10{sup 6} {gamma} quanta per detector and second.
NASA Astrophysics Data System (ADS)
Jäger, Markus; Iwig, Kornelius; Butz, Tilman
2011-06-01
A user-friendly fully digital time differential perturbed angular correlation (TDPAC)-spectrometer with six detectors and fast digitizers using field programmable gate arrays (FPGA) is described and performance data are given. The new spectrometer has an online data analysis feature, a compact size, and a time resolution such as conventional analog spectrometers. Its calculation intensive part was implemented inside the digitizer. This gives the possibility to change parameters (energy windows, constant fraction trigger delay) and see their influence immediately in the γ-γ correlation diagrams. Tests were performed which showed that the time resolution using a 60Co source with energy window set at 1.17 MeV and 1.33 MeV is 265 ps with LaBr3(Ce) scintillators and 254 ps with BaF2 scintillators. A true constant fraction algorithm turned out to be slightly better than the constant fraction of amplitude method. The spectrometer performance was tested with a TDPAC measurement using a 44Ti in rutile source and a positron lifetime measurement using 22Na. The maximum possible data rate of the spectrometer is 1.1 × 106 γ quanta per detector and second.
Modeling the evolution of Sm and Eu abundances during lunar igneous differentiation
NASA Technical Reports Server (NTRS)
Weill, D. F.; Mckay, G. A.; Kridelbaugh, S. J.; Grutzeck, M.
1974-01-01
The current work presents models for the evolution of europium and samarium abundances during lunar igneous processes. The effect of probable variations in lunar temperature and oxygen fugacity, mineral-liquid distribution coefficients, and the crystallization or melting progression are considered in the model calculations. Changes in the proportions of crystallizing phases strongly influence the evolution of trace element abundances during fractional crystallization, and models must include realistic estimates of the major phase equilibria during crystallization. The results are applied to evaluating the possibility of generating KREEP-rich materials by lunar igneous processes.
ERIC Educational Resources Information Center
Kinnebrew, John S.; Biswas, Gautam
2012-01-01
Our learning-by-teaching environment, Betty's Brain, captures a wealth of data on students' learning interactions as they teach a virtual agent. This paper extends an exploratory data mining methodology for assessing and comparing students' learning behaviors from these interaction traces. The core algorithm employs sequence mining techniques to…
NASA Technical Reports Server (NTRS)
Frederickson, P. O.; Wessel, W. R.
1979-01-01
Certain physical processes are modeled by partial differential equations which are parabolic over part of the domain and elliptic over the remainder. A family of semi-implicit algorithms which are well suited to initial-boundary value problems of this mixed type is discussed. One important feature of these algorithms is the use of an approximate inverse for the solution of the implicit linear system. A strong error analysis results in an estimate of the total error as a function of approximate inverse error e and time step h.
Hamad, Kotiba; Chung, Bong Kwon; Ko, Young Gun
2014-08-15
This paper reports the effect of the deformation path on the microstructure, microhardness, and texture evolution of interstitial free (IF) steel processed by differential speed rolling (DSR) method. For this purpose, total height reductions of 50% and 75% were imposed on the samples by a series of differential speed rolling operations with various height reductions per pass (deformation levels) ranging from 10 to 50% under a fixed roll speed ratio of 1:4 for the upper and lower rolls, respectively. Microstructural observations using transmission electron microscopy and electron backscattered diffraction measurements showed that the samples rolled at deformation level of 50% had the finest mean grain size (∼ 0.5 μm) compared to the other counterparts; also the samples rolled at deformation level of 50% showed a more uniform microstructure. Based on the microhardness measurements along the thickness direction of the deformed samples, gradual evolution of the microhardness value and its homogeneity was observed with the increase of the deformation level per pass. Texture analysis showed that, as the deformation level per pass increased, the fraction of alpha fiber and gamma fiber in the deformed samples increased. The textures obtained by the differential speed rolling process under the lubricated condition would be equivalent to those obtained by the conventional rolling. - Highlights: • Effect of DSR deformation path on microstructure of IF steel is significant. • IF steel rolled at deformation level of 50% has the ultrafine grains of ∼ 0.5 μm. • Rolling texture components are pronounced with increasing deformation level.
Albre, Jérôme; Liénard, Marjorie A.; Sirey, Tamara M.; Schmidt, Silvia; Tooman, Leah K.; Carraher, Colm; Greenwood, David R.; Löfstedt, Christer; Newcomb, Richard D.
2012-01-01
Chemical signals are prevalent in sexual communication systems. Mate recognition has been extensively studied within the Lepidoptera, where the production and recognition of species-specific sex pheromone signals are typically the defining character. While the specific blend of compounds that makes up the sex pheromones of many species has been characterized, the molecular mechanisms underpinning the evolution of pheromone-based mate recognition systems remain largely unknown. We have focused on two sets of sibling species within the leafroller moth genera Ctenopseustis and Planotortrix that have rapidly evolved the use of distinct sex pheromone blends. The compounds within these blends differ almost exclusively in the relative position of double bonds that are introduced by desaturase enzymes. Of the six desaturase orthologs isolated from all four species, functional analyses in yeast and gene expression in pheromone glands implicate three in pheromone biosynthesis, two Δ9-desaturases, and a Δ10-desaturase, while the remaining three desaturases include a Δ6-desaturase, a terminal desaturase, and a non-functional desaturase. Comparative quantitative real-time PCR reveals that the Δ10-desaturase is differentially expressed in the pheromone glands of the two sets of sibling species, consistent with differences in the pheromone blend in both species pairs. In the pheromone glands of species that utilize (Z)-8-tetradecenyl acetate as sex pheromone component (Ctenopseustis obliquana and Planotortrix octo), the expression levels of the Δ10-desaturase are significantly higher than in the pheromone glands of their respective sibling species (C. herana and P. excessana). Our results demonstrate that interspecific sex pheromone differences are associated with differential regulation of the same desaturase gene in two genera of moths. We suggest that differential gene regulation among members of a multigene family may be an important mechanism of molecular innovation in
Genetic differentiation and the evolution of cooperation in chimpanzees and humans
Langergraber, Kevin; Schubert, Grit; Rowney, Carolyn; Wrangham, Richard; Zommers, Zinta; Vigilant, Linda
2011-01-01
It has been proposed that human cooperation is unique among animals for its scale and complexity, its altruistic nature and its occurrence among large groups of individuals that are not closely related or are even strangers. One potential solution to this puzzle is that the unique aspects of human cooperation evolved as a result of high levels of lethal competition (i.e. warfare) between genetically differentiated groups. Although between-group migration would seem to make this scenario unlikely, the plausibility of the between-group competition model has recently been supported by analyses using estimates of genetic differentiation derived from contemporary human groups hypothesized to be representative of those that existed during the time period when human cooperation evolved. Here, we examine levels of between-group genetic differentiation in a large sample of contemporary human groups selected to overcome some of the problems with earlier estimates, and compare them with those of chimpanzees. We find that our estimates of between-group genetic differentiation in contemporary humans are lower than those used in previous tests, and not higher than those of chimpanzees. Because levels of between-group competition in contemporary humans and chimpanzees are also similar, these findings suggest that the identification of other factors that differ between chimpanzees and humans may be needed to provide a compelling explanation of why humans, but not chimpanzees, display the unique features of human cooperation. PMID:21247955
Thermal evolution of a differentiated Ganymede and implications for surface features
Kirk, R.L.; Stevenson, D.J.
1987-01-01
Thermodynamic models are developed for the processes which controlled the evolution of the surface Ganymede, an icy Jovian satellite assumed to have a rock-rich core surrounded by a water-ice mantle. Account is taken of a heat pulse which would have arisen from a Rayleigh-Taylor instability at a deep-seated liquid-solid water interface, rapid fracturing from global stresses imposed by warm ice diapiric upwelling, impacts by large meteorites, and resurfacing by ice flows (rather than core formation). Comparisons are made with existing models for the evolution of Callisto, and the difficulties in defining a mechanism which produced the groove terrain of Ganymede are discussed. 100 references.
NASA Technical Reports Server (NTRS)
Solomatov, V. S.; Stevenson, D. J.
1992-01-01
The evolution of an initially totally molten magma ocean is constrained on the basis of analysis of various physical problems in the magma ocean. First of all an equilibrium thermodynamics of the magma ocean is developed in the melting temperature range. The equilibrium thermodynamical parameters are found as functions only of temperature and pressure and are used in the subsequent models of kinetics and convection. Kinematic processes determine the crystal size and also determine a non-equilibrium thermodynamics of the system. Rheology controls all dynamical regimes of the magma ocean. The thermal convection models for different rheological laws are developed for both the laminar convection and for turbulent convection in the case of equilibrium thermodynamics of the multiphase system. The evolution is estimated on the basis of all the above analysis.
NASA Astrophysics Data System (ADS)
Bandara, Kaushala; Crampton, D.; Peng, C. Y.; Simard, L.
2012-01-01
We take advantage of the magnification in size and flux of a galaxy, provided by gravitational lensing, to analyze the properties of 62 strongly lensed galaxies of the Sloan Lens ACS (SLACS) Survey. The sample of lensed galaxies span a redshift range of 0.20 <= z <= 1.20 with a median redshift of z = 0.61. We use the lens modeling code LENSFIT to derive the luminosities, sizes and Sersic indices of the lensed galaxies. The measured properties of the lensed galaxies show a primarily compact, "disk"-like population with the peaks of the size and Sersic index distributions corresponding to ˜1.50 kpc and n˜1 respectively. Comparison of the SLACS lensed galaxies to a non-lensing, broad-band imaging based survey shows that a lensing survey allows us to probe a galaxy population that is typically ˜ 2 magnitudes fainter. Our analysis allows us to compare the
Bandara, Kaushala; Crampton, David; Peng, Chien; Simard, Luc
2013-11-01
We take advantage of the magnification in size and flux of a galaxy provided by gravitational lensing to analyze the properties of 62 strongly lensed galaxies from the Sloan Lens ACS (SLACS) Survey. The sample of lensed galaxies spans a redshift range of 0.20 ≤ z ≤ 1.20 with a median redshift of z = 0.61. We use the lens modeling code LENSFIT to derive the luminosities, sizes, and Sérsic indices of the lensed galaxies. The measured properties of the lensed galaxies show a primarily compact, {sup d}isk{sup -}like population with the peaks of the size and Sérsic index distributions corresponding to ∼1.50 kpc and n ∼ 1, respectively. Comparison of the SLACS galaxies to a non-lensing, broadband imaging survey shows that a lensing survey allows us to probe a galaxy population that reaches ∼2 mag fainter. Our analysis allows us to compare the (z) = 0.61 disk galaxy sample (n ≤ 2.5) to an unprecedented local galaxy sample of ∼670, 000 SDSS galaxies at z ∼ 0.1; this analysis indicates that the evolution of the luminosity-size relation since z ∼ 1 may not be fully explained by a pure-size or pure-luminosity evolution but may instead require a combination of both. Our observations are also in agreement with recent numerical simulations of disk galaxies that show evidence of a mass-dependent evolution since z ∼ 1, where high-mass disk galaxies (M{sub *} > 10{sup 9} M{sub ☉}) evolve more in size and low-mass disk galaxies (M{sub *} ≤ 10{sup 9} M{sub ☉}) evolve more in luminosity.
BUREŠ, PETR; PAVLÍČEK, TOMÁŠ; HOROVÁ, LUCIE; NEVO, EVIATAR
2004-01-01
• Background and Aims We tested whether the local differences in genome size recorded earlier in the wild barley, Hordeum spontaneum, at ‘Evolution Canyon’, Mount Carmel, Israel, can also be found in other organisms. As a model species for our test we chose the evergreen carob tree, Ceratonia siliqua. • Methods Genome size was measured by means of DAPI flow cytometry. • Key Results In adults, significantly more DNA was recorded in trees growing on the more illuminated, warmer, drier, microclimatically more fluctuating ‘African’ south‐facing slope than in trees on the opposite, less illuminated, cooler and more humid, ‘European’ north‐facing slope in spite of an interslope distance of only 100 m at the canyon bottom and 400 m at the top. The amount of DNA was significantly negatively correlated with leaf length and tree circumference. In seedlings, interslope differences in the amount of genome DNA were not found. In addition, the first cases of triploidy and tetraploidy were found in C. siliqua. • Conclusions The data on C. siliqua at ‘Evolution Canyon’ showed that local variability in the C‐value exists in this species and that ecological stress might be a strong evolutionary driving force in shaping the amount of DNA. PMID:15026300
Selection by differential molecular survival: a possible mechanism of early chemical evolution.
de Duve, C
1987-01-01
A model is proposed to account for selective chemical evolution, progressing from a relatively simple initial set of abiotic synthetic phenomena up to the elaborately sophisticated processes that are almost certainly required to produce the complex molecules, such as replicatable RNA-like oligonucleotides, needed for a Darwinian form of selection to start operating. The model makes the following assumptions: (i) that a small number of micromolecular substances were present at high concentration; (ii) that a random assembly mechanism combined these molecules into a variety of multimeric compounds comprising a wide repertoire of rudimentary catalytic activities; and (iii) that a lytic system capable of breaking down the assembled products existed. The model assumes further that catalysts supplied with substrates were significantly protected against breakdown. It is shown that, by granting these assumptions, an increasingly complex network of metabolic pathways would progressively be established. At the same time, the catalysts concerned would accumulate selectively to become choice substrates for elongation and other modifications that could enhance their efficiency, as well as their survival. Chemical evolution would thus proceed by a dual process of metabolic extension and catalytic innovation. Such a process should be largely deterministic and predictable from initial conditions. PMID:3479788
NASA Technical Reports Server (NTRS)
Head, James W.; Parmentier, E. M.; Hess, P. C.
1993-01-01
Observations from Magellan show that: (1) the surface of Venus is generally geologically young, (2) there is no evidence for widespread recent crustal spreading or subduction, (3) the crater population permits the hypothesis that the surface is in production, and (4) relatively few impact craters appear to be embayed by volcanic deposits suggesting that the volcanic flux has drastically decreased as a function of time. These observations have led to consideration of hypotheses suggesting that the geological history of Venus may have changed dramatically as a function of time due to general thermal evolution, and/or thermal and chemical evolution of a depleted mantle layer, perhaps punctuated by catastrophic overturn of upper layers or episodic plate tectonics. We have previously examined the geological implications of some of these models, and here we review the predictions associated with two periods of Venus history. Stationary thick lithosphere and depleted mantle layer, and development of regional to global development of regional to global instabilities, and compare these predictions to the geological characteristics of Venus revealed by Magellan.
NASA Astrophysics Data System (ADS)
Mandal, S. K.; Singh, Harshavardhan; Mahanti, G. K.; Ghatak, Rowdra
2014-10-01
This paper presents a new technique based on optimization tools to design phase only, digitally controlled, reconfigurable antenna arrays through time modulation. In the proposed approach, the on-time durations of the time-modulated elements and the static amplitudes of the array elements are perturbed in such a way that the same on-time sequence and discrete values of static amplitudes for four bit digital attenuators produces either a pencil or a flat-top beam pattern, depending on the suitable discrete phase distributions of five bit digital phase shifters. In order to illustrate the technique, three optimization tools: differential evolution (DE), artificial bee colony (ABC), and particle swarm optimization (PSO) are employed and their performances are compared. The numerical results for a 20-element linear array are presented.
NASA Astrophysics Data System (ADS)
Zhabitskaya, Evgeniya; Zemlyanaya, Elena; Kiselev, Mikhail; Gruzinov, Andrey
2016-02-01
In this work we use an Asynchronous Differential Evolution (ADE) method to estimate parameters of the Separated Form Factor (SFF) model which is used to investigate a structure of drug delivery Phospholipid Transport Nano System (PTNS) unilamellar vesicles by experimental small angle synchrotron X-ray scattering spectra (SAXS). We compare the efficiency of different optimizing procedures (OP) for the search for the SFF-model parameters. It is shown that the probability to find the global solution of this problem by ADE-methods is significantly higher than that by either Nelder-Mead method or a Quasi-Newton method with Davidon-Fletcher-Powell formula. The parallel realization of ADE accelerates the calculations significantly. The speed-up obtained by the parallel realization of ADE and results of the model are presented. The work has been performed under the grant of Russian Scientific Foundation (project No 14-12-00516)
NASA Astrophysics Data System (ADS)
Peter, Ulmschneider
When we are looking for intelligent life outside the Earth, there is a fundamental question: Assuming that life has formed on an extraterrestrial planet, will it also develop toward intelligence? As this is hotly debated, we will now describe the development of life on Earth in more detail in order to show that there are good reasons why evolution should culminate in intelligent beings.
Dlouhy, Brian J; Dahdaleh, Nader S; Menezes, Arnold H
2015-04-01
The craniovertebral junction (CVJ), or the craniocervical junction (CCJ) as it is otherwise known, houses the crossroads of the CNS and is composed of the occipital bone that surrounds the foramen magnum, the atlas vertebrae, the axis vertebrae, and their associated ligaments and musculature. The musculoskeletal organization of the CVJ is unique and complex, resulting in a wide range of congenital, developmental, and acquired pathology. The refinements of the transoral approach to the CVJ by the senior author (A.H.M.) in the late 1970s revolutionized the treatment of CVJ pathology. At the same time, a physiological approach to CVJ management was adopted at the University of Iowa Hospitals and Clinics in 1977 based on the stability and motion dynamics of the CVJ and the site of encroachment, incorporating the transoral approach for irreducible ventral CVJ pathology. Since then, approaches and techniques to treat ventral CVJ lesions have evolved. In the last 40 years at University of Iowa Hospitals and Clinics, multiple approaches to the CVJ have evolved and a better understanding of CVJ pathology has been established. In addition, new reduction strategies that have diminished the need to perform ventral decompressive approaches have been developed and implemented. In this era of surgical subspecialization, to properly treat complex CVJ pathology, the CVJ specialist must be trained in skull base transoral and endoscopic endonasal approaches, pediatric and adult CVJ spine surgery, and must understand and be able to treat the complex CSF dynamics present in CVJ pathology to provide the appropriate, optimal, and tailored treatment strategy for each individual patient, both child and adult. This is a comprehensive review of the history and evolution of the transoral approaches, extended transoral approaches, endoscopie assisted transoral approaches, endoscopie endonasal approaches, and CVJ reduction strategies. Incorporating these advancements, the authors update the
Manonmani, N.; Subbiah, V.; Sivakumar, L.
2015-01-01
The key objective of wind turbine development is to ensure that output power is continuously increased. It is authenticated that wind turbines (WTs) supply the necessary reactive power to the grid at the time of fault and after fault to aid the flowing grid voltage. At this juncture, this paper introduces a novel heuristic based controller module employing differential evolution and neural network architecture to improve the low-voltage ride-through rate of grid-connected wind turbines, which are connected along with doubly fed induction generators (DFIGs). The traditional crowbar-based systems were basically applied to secure the rotor-side converter during the occurrence of grid faults. This traditional controller is found not to satisfy the desired requirement, since DFIG during the connection of crowbar acts like a squirrel cage module and absorbs the reactive power from the grid. This limitation is taken care of in this paper by introducing heuristic controllers that remove the usage of crowbar and ensure that wind turbines supply necessary reactive power to the grid during faults. The controller is designed in this paper to enhance the DFIG converter during the grid fault and this controller takes care of the ride-through fault without employing any other hardware modules. The paper introduces a double wavelet neural network controller which is appropriately tuned employing differential evolution. To validate the proposed controller module, a case study of wind farm with 1.5 MW wind turbines connected to a 25 kV distribution system exporting power to a 120 kV grid through a 30 km 25 kV feeder is carried out by simulation. PMID:26516636
Manonmani, N; Subbiah, V; Sivakumar, L
2015-01-01
The key objective of wind turbine development is to ensure that output power is continuously increased. It is authenticated that wind turbines (WTs) supply the necessary reactive power to the grid at the time of fault and after fault to aid the flowing grid voltage. At this juncture, this paper introduces a novel heuristic based controller module employing differential evolution and neural network architecture to improve the low-voltage ride-through rate of grid-connected wind turbines, which are connected along with doubly fed induction generators (DFIGs). The traditional crowbar-based systems were basically applied to secure the rotor-side converter during the occurrence of grid faults. This traditional controller is found not to satisfy the desired requirement, since DFIG during the connection of crowbar acts like a squirrel cage module and absorbs the reactive power from the grid. This limitation is taken care of in this paper by introducing heuristic controllers that remove the usage of crowbar and ensure that wind turbines supply necessary reactive power to the grid during faults. The controller is designed in this paper to enhance the DFIG converter during the grid fault and this controller takes care of the ride-through fault without employing any other hardware modules. The paper introduces a double wavelet neural network controller which is appropriately tuned employing differential evolution. To validate the proposed controller module, a case study of wind farm with 1.5 MW wind turbines connected to a 25 kV distribution system exporting power to a 120 kV grid through a 30 km 25 kV feeder is carried out by simulation. PMID:26516636
Tümpel, Stefan; Cambronero, Francisco; Wiedemann, Leanne M; Krumlauf, Robb
2006-04-01
Sequence divergence in cis-regulatory elements is an important mechanism contributing to functional diversity of genes during evolution. Gene duplication and divergence provide an opportunity for selectively preserving initial functions and evolving new activities. Many vertebrates have 39 Hox genes organized into four clusters (Hoxa-Hoxd); however, some ray-finned fishes have extra Hox clusters. There is a single Hoxa2 gene in most vertebrates, whereas fugu (Takifugu rubripes) and medaka (Oryzias latipes) have two coparalogous genes [Hoxa2(a) and Hoxa2(b)]. In the hindbrain, both genes are expressed in rhombomere (r) 2, but only Hoxa2(b) is expressed in r3, r4, and r5. Multiple regulatory modules directing segmental expression of chicken and mouse Hoxa2 genes have been identified, and each module is composed of a series of discrete elements. We used these modules to investigate the basis of differential expression of duplicated Hoxa2 genes, as a model for understanding the divergence of cis-regulatory elements. Therefore, we cloned putative regulatory regions of the fugu and medaka Hoxa2(a) and -(b) genes and assayed their activity. We found that these modules direct reporter expression in a chicken assay, in a manner corresponding to their endogenous expression pattern in fugu. Although sequence comparisons reveal many differences between the two coparalogous genes, specific subtle changes in seven cis elements of the Hoxa2(a) gene restore segmental regulatory activity. Therefore, drift in subsets of the elements in the regulatory modules is responsible for the differential expression of the two coparalogous genes, thus providing insight into the evolution of cis elements. PMID:16569696
Pilot, Małgorzata; Jędrzejewski, Włodzimierz; Sidorovich, Vadim E.; Meier-Augenstein, Wolfram; Hoelzel, A. Rus
2012-01-01
Recent studies on highly mobile carnivores revealed cryptic population genetic structures correlated to transitions in habitat types and prey species composition. This led to the hypothesis that natal-habitat-biased dispersal may be responsible for generating population genetic structure. However, direct evidence for the concordant ecological and genetic differentiation between populations of highly mobile mammals is rare. To address this we analyzed stable isotope profiles (δ13C and δ15N values) for Eastern European wolves (Canis lupus) as a quantifiable proxy measure of diet for individuals that had been genotyped in an earlier study (showing cryptic genetic structure), to provide a quantitative assessment of the relationship between individual foraging behavior and genotype. We found a significant correlation between genetic distances and dietary differentiation (explaining 46% of the variation) in both the marginal test and crucially, when geographic distance was accounted for as a co-variable. These results, interpreted in the context of other possible mechanisms such as allopatry and isolation by distance, reinforce earlier studies suggesting that diet and associated habitat choice are influencing the structuring of populations in highly mobile carnivores. PMID:22768075
Wu, J; Krutovskii, K V; Strauss, S H
1998-01-01
We examined mitochondrial DNA polymorphisms via the analysis of restriction fragment length polymorphisms in three closely related species of pines from western North America: knobcone (Pinus attenuata Lemm.), Monterey (P. radiata D. Don), and bishop (P. muricata D. Don). A total of 343 trees derived from 13 populations were analyzed using 13 homologous mitochondrial gene probes amplified from three species by polymerase chain reaction. Twenty-eight distinct mitochondrial DNA haplotypes were detected and no common haplotypes were found among the species. All three species showed limited variability within populations, but strong differentiation among populations. Based on haplotype frequencies, genetic diversity within populations (HS) averaged 0.22, and population differentiation (GST and theta) exceeded 0.78. Analysis of molecular variance also revealed that >90% of the variation resided among populations. For the purposes of genetic conservation and breeding programs, species and populations could be readily distinguished by unique haplotypes, often using the combination of only a few probes. Neighbor-joining phenograms, however, strongly disagreed with those based on allozymes, chloroplast DNA, and morphological traits. Thus, despite its diagnostic haplotypes, the genome appears to evolve via the rearrangement of multiple, convergent subgenomic domains. PMID:9832536
Pilot, Małgorzata; Jędrzejewski, Włodzimierz; Sidorovich, Vadim E; Meier-Augenstein, Wolfram; Hoelzel, A Rus
2012-01-01
Recent studies on highly mobile carnivores revealed cryptic population genetic structures correlated to transitions in habitat types and prey species composition. This led to the hypothesis that natal-habitat-biased dispersal may be responsible for generating population genetic structure. However, direct evidence for the concordant ecological and genetic differentiation between populations of highly mobile mammals is rare. To address this we analyzed stable isotope profiles (δ(13)C and δ(15)N values) for Eastern European wolves (Canis lupus) as a quantifiable proxy measure of diet for individuals that had been genotyped in an earlier study (showing cryptic genetic structure), to provide a quantitative assessment of the relationship between individual foraging behavior and genotype. We found a significant correlation between genetic distances and dietary differentiation (explaining 46% of the variation) in both the marginal test and crucially, when geographic distance was accounted for as a co-variable. These results, interpreted in the context of other possible mechanisms such as allopatry and isolation by distance, reinforce earlier studies suggesting that diet and associated habitat choice are influencing the structuring of populations in highly mobile carnivores. PMID:22768075
Differential evolution based on the node degree of its complex network: Initial study
NASA Astrophysics Data System (ADS)
Skanderova, Lenka; Zelinka, Ivan
2016-06-01
In this paper is reported our progress in the synthesis of two partially different areas of research: complex networks and evolutionary computation. Ideas and results reported and mentioned here are based on our previous results and experiments. The main core of our participation is an evolutionary algorithm performance improvement by means of complex network use. Complex network is related to the evolutionary dynamics and reflect it. We report here our latest results as well as propositions on further research that is in process in our group (http://navy.cs.vsb.cz/). Only the main ideas and results are reported here, for more details it is recommended to read related literature of our previous research and results.
Permeability Evolution of Shale and Coal Under Differential Sorption of He, CH4 And CO2
NASA Astrophysics Data System (ADS)
Kumar, H.; Elsworth, D.; Marone, C. J.; Mathews, J.
2010-12-01
Carbon dioxide injection in coal seams or in shales may be an option for geological sequestration of CO2 each with concurrent methane production. Permeability of the fractured porous medium is a crucial parameter influencing injectivity of CO2. The evolution of permeability is further complicated by dynamic changes in the coal/shale shrinkage/swelling with the reduction/increase in gas content. Complex geomechanical processes (transport of gas, adsorption, desorption, adjusting horizontal stresses and vertical strains) and chemical interaction between CO2, water and mineral matter content are some factors responsible for the various responses in permeability evolution. Adsorption of CO2 in micropores may result in matrix swelling therefore closing the existing natural fractures and lowering the ability of fluid flow. On the other hand presence of water may react with CO2 forming carbonic acid and removing carbonaceous mineral matter - either increasing or decreasing permeability. To address these issues we report experimental measurements of permeability evolution in shales infiltrated by helium, methane and carbon dioxide under varying pore pressure and deviatoric stresses. The role of gas (CO2 and CH4) adsorption and desorption under variable moisture contents and pore pressures have also been examined for sub-bituminous coals. Adsorption of CO2 in Coal and shale reduces the reservoir permeability even when the fractured media are mechanically unconstrained. However we found that permeability loss is temporary. In the specific case of Marcellus shale, adsorption of CO2 in the sample reduces the permeability to half the original value. Permeability values returns to its original value if sample is allowed to interact for sufficient time. Variation of permeability with deviotoric stress suggests the compaction band formation above a threshold value of stress. These deformations are permanent and shale loses its permeability. Several observations on permeability
Engel, A E
1963-04-12
The oldest decipherable rock complexes within continents (more than 2.5 billion years old) are largely basaltic volcanics and graywacke. Recent and modern analogs are the island arcs formed along and adjacent to the unstable interface of continental and oceanic crusts. The major interfacial reactions (orogenies) incorporate pre-existing sial, oceanic crust, and mantle into crust of a more continental type. Incipient stages of continental evolution, more than 3 billion years ago, remain obscure. They may involve either a cataclysmic granite-forming event or a succession of volcanic-sedimentary and granite-forming cycles. Intermediate and recent stages of continental evolution, as indicated by data for North America, involve accretion of numerous crustal interfaces with fragments of adjacent continental crust and their partial melting, reinjection, elevation, unroofing, and stabilization. Areas of relict provinces defined by ages of granites suggest that continental growth is approximately linear. But the advanced differentiation found in many provinces and the known overlaps permit wide deviation from linearity in the direction of a more explosive early or intermediate growth. PMID:17819825
Mikhaylenko, D S; Efremov, G D; Sivkov, A V; Zaletaev, D V
2016-01-01
Progression of malignant tumors is largely due to clonal evolution of the primary tumor, clones acquiring different sets of molecular genetic lesions. Lesions can confer a selective advantage in proliferation rate or metastasis on the tumor cell population, especially if developing resistance to anticancer therapy. Prostate cancer (PCa) provides an illustrative example of clinically significant clonal evolution. The review considers the genetic alterations that occur in primary PCa and the mechanism whereby hormone-refractory PCa develops on hormone therapy, including mutations and alternative splicing of the androgen receptor gene (AR) and intratumoral androgen synthesis. Certain molecular genetic lesions determine resistance to new generation inhibitors (AR mutations that block the antagonist effect or allow other hormones to activate the receptor) or lead to neuroendocrine differentiation (repression of the AR signaling pathway, TP53 mutations, and amplification of the AURKA or MYCN oncogene). Multistep therapy based on the data about somatic mutations associated with progression and metastasis of the primary tumor can be expected to significantly improve the survival of patients with advanced PCa in the nearest future. PMID:27028809
Selection on Network Dynamics Drives Differential Rates of Protein Domain Evolution
Mannakee, Brian K.; Gutenkunst, Ryan N.
2016-01-01
The long-held principle that functionally important proteins evolve slowly has recently been challenged by studies in mice and yeast showing that the severity of a protein knockout only weakly predicts that protein’s rate of evolution. However, the relevance of these studies to evolutionary changes within proteins is unknown, because amino acid substitutions, unlike knockouts, often only slightly perturb protein activity. To quantify the phenotypic effect of small biochemical perturbations, we developed an approach to use computational systems biology models to measure the influence of individual reaction rate constants on network dynamics. We show that this dynamical influence is predictive of protein domain evolutionary rate within networks in vertebrates and yeast, even after controlling for expression level and breadth, network topology, and knockout effect. Thus, our results not only demonstrate the importance of protein domain function in determining evolutionary rate, but also the power of systems biology modeling to uncover unanticipated evolutionary forces. PMID:27380265
Bouc-Wen hysteresis model identification using Modified Firefly Algorithm
NASA Astrophysics Data System (ADS)
Zaman, Mohammad Asif; Sikder, Urmita
2015-12-01
The parameters of Bouc-Wen hysteresis model are identified using a Modified Firefly Algorithm. The proposed algorithm uses dynamic process control parameters to improve its performance. The algorithm is used to find the model parameter values that results in the least amount of error between a set of given data points and points obtained from the Bouc-Wen model. The performance of the algorithm is compared with the performance of conventional Firefly Algorithm, Genetic Algorithm and Differential Evolution algorithm in terms of convergence rate and accuracy. Compared to the other three optimization algorithms, the proposed algorithm is found to have good convergence rate with high degree of accuracy in identifying Bouc-Wen model parameters. Finally, the proposed method is used to find the Bouc-Wen model parameters from experimental data. The obtained model is found to be in good agreement with measured data.
Brüne, Martin
2012-01-01
The diathesis-stress model of psychiatric conditions has recently been challenged by the view that it might be more accurate to speak of 'differential susceptibility' or 'plasticity' genes, rather than one-sidedly focusing on individual vulnerability. That is, the same allelic variation that predisposes to a psychiatric disorder if associated with (developmentally early) environmental adversity may lead to a better-than-average functional outcome in the same domain under thriving (or favourable) environmental conditions. Studies of polymorphic variations of the serotonin transporter gene, the monoamino-oxidase-inhibitor A coding gene or the dopamine D4 receptor gene indicate that the early environment plays a crucial role in the development of favourable versus unfavourable outcomes. Current evidence is limited, however, to establishing a link between genetic variation and behavioural phenotypes. In contrast, little is known about how plasticity may be expressed at the neuroanatomical level as a 'hard-wired' correlate of observable behaviour. The present review article seeks to further strengthen the argument in favour of the differential susceptibility theory by incorporating findings from behavioural and neuroanatomical studies in relation to genetic variation of the oxytocin receptor gene. It is suggested that polymorphic variation at the oxytocin receptor gene (rs2254298) is associated with sociability, amygdala volume and differential risk for psychiatric conditions including autism, depression and anxiety disorder, depending on the quality of early environmental experiences. Seeing genetic variation at the core of developmental plasticity can explain, in contrast to the diathesis-stress perspective, why evolution by natural selection has maintained such 'risk' alleles in the gene pool of a population. PMID:22510359
Quasi-Newton methods for parameter estimation in functional differential equations
NASA Technical Reports Server (NTRS)
Brewer, Dennis W.
1988-01-01
A state-space approach to parameter estimation in linear functional differential equations is developed using the theory of linear evolution equations. A locally convergent quasi-Newton type algorithm is applied to distributed systems with particular emphasis on parameters that induce unbounded perturbations of the state. The algorithm is computationally implemented on several functional differential equations, including coefficient and delay estimation in linear delay-differential equations.
An Algorithmic Framework for Multiobjective Optimization
Ganesan, T.; Elamvazuthi, I.; Shaari, Ku Zilati Ku; Vasant, P.
2013-01-01
Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-boundary intersection (NBI) method to solve MO problems. Nevertheless, many challenges still arise especially when dealing with problems with multiple objectives (especially in cases more than two). In addition, problems with extensive computational overhead emerge when dealing with hybrid algorithms. This paper discusses these issues by proposing an alternative framework that utilizes algorithmic concepts related to the problem structure for generating efficient and effective algorithms. This paper proposes a framework to generate new high-performance algorithms with minimal computational overhead for MO optimization. PMID:24470795
An algorithmic framework for multiobjective optimization.
Ganesan, T; Elamvazuthi, I; Shaari, Ku Zilati Ku; Vasant, P
2013-01-01
Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-boundary intersection (NBI) method to solve MO problems. Nevertheless, many challenges still arise especially when dealing with problems with multiple objectives (especially in cases more than two). In addition, problems with extensive computational overhead emerge when dealing with hybrid algorithms. This paper discusses these issues by proposing an alternative framework that utilizes algorithmic concepts related to the problem structure for generating efficient and effective algorithms. This paper proposes a framework to generate new high-performance algorithms with minimal computational overhead for MO optimization. PMID:24470795
Differentiating tectonic from climatic factors in the evolution of alluvial fans
Wilson, D.S.; West, R.B. . Dept. of Geology)
1993-04-01
Alluvial fans are integral parts of landscapes of arid and semi-arid regions and are most commonly found along the flanks of tectonically active mountain ranges. Alluvial fans are sensitive indicators of tectonic and climatic activity through time. Three dimensional fan modelling has the potential to discriminate between these two forces and provide quantitative estimates of deformation of fan surfaces due to tilting, faulting, or folding. The model has tremendous potential for seismic hazard evaluation at both the reconnaissance and detailed level of investigation. The ability to recognize deformation of alluvial fans alleviates the need for postulation of complex interactions between climate and internal variables in the depositional system leading to present fan morphology. The greatest problems associated with fan modelling come from failure to identify individual segments. Inclusion of more than one segment can lead to poor model performance or, more likely, inaccurate results. The long term tectonic influence on a fan's evolution can be assessed from the differences in deformation of different segments. Reliable correlations of segments from different fans along the same mountain front can provide a means to asses regional deformation. Once tectonic effects are taken into account, then climatic effects can be evaluated. Previous fan models have failed to recognize areal limitations, failed to account for deformation, or assumed deformation geometry.
Bassino, Jean-Pascal; Coclanis, Peter A
2008-07-01
Did economic development result in an improvement in biological welfare in the tropics before the diffusion of modern public health techniques in the 1950s and 1960s? Between the mid-19th and early 20th century, Lower Burma experienced a rapid rise in population and became increasingly commercialized as a major rice exporter. Land reclamation on a massive scale in the Irrawaddy delta required an arduous process of jungle clearance, land drainage and preparation, and canal and bund construction, mostly in malarial swamps. Once paddy lands were created, rice was grown with rudimentary tools in malarial zones. By contrast, in most parts of Upper Burma the economy remained more subsistence-oriented, and less commercialized. In this paper, we investigate changes in physical stature by processing and analyzing data reported in two anthropometric surveys conducted in various regions of Upper and Lower Burma in 1904 and in 1938-1941. An inverted U curve is observed in the evolution of average height in Lower Burma, while stature remained fairly stable in Upper Burma until the 1930s. PMID:18407811
Gatti, Roberto Cazzolla
2011-01-01
A. McFayden and G.E. Hutchinson defined a niche as a multidimensional space or hypervolume within the environment that allows an individual or a species to survive, we consider niches as a fundamental ecological variable that regulate species' composition and relation in ecosystems. Successively the niche concept has been associated to the genetic term "phenotype" by MacArthurstressing the importance on what a species or a genome can show outside, either in the environmental functions or in body characteristics. Several indexes have been developed to evaluate the grade of overlapping and similarities of species' niches, even utilizing the theory of information. However, which are the factors that determine the number of species that can coexist in a determinate environment and why a generalist species do not compete until the exclusion of the remaining species to maximize its fitness, is still quite unknown. Moreover, there are few studies and theories that clearly explain why the number of niches is so variable through ecosystems and how can several species live in the same basal niche, intended in a comprehensive sense as the range of basic conditions (temperature, humidity, food-guild, etc.). Here I show that the number of niches in an ecosystem depends on the number of species present in a particular moment and that the species themselves allow the enhancement of niches in terms of space and number. I found that using a three-dimensional model as hypervolume and testing the theory on a Mediterranean, temperate and tropical forest ecosystem it is possible to demonstrate that each species plays a fundamental role in facilitating the colonization by other species by simply modifying the environment and exponentially increasing the available niches' space and number. I resumed these hypothesis, after some preliminary empiric tests, in the Biodiversity-related Niches Differentiation Theory (BNDT), stressing with these definition that the process of niches
Macagno, Anna L M; Beckers, Oliver M; Moczek, Armin P
2015-11-01
Fecundity is a fundamental determinant of fitness, yet the proximate developmental and physiological mechanisms that enable its often rapid evolution in natural populations are poorly understood. Here, we investigated two populations of the dung beetle Onthophagus taurus that were established in exotic ranges in the early 1970s. These populations are subject to drastically different levels of resource competition in the field, and have diverged dramatically in female fecundity. Specifically, Western Australian O. taurus experience high levels of resource competition, and exhibit greatly elevated reproductive output compared to beetles from the Eastern US, where resource competition is minimal and female fecundity is low. We compared patterns of ovarian maturation, relative investment into and timing of egg production, and potential trade-offs between ovarian investment and the duration of larval development and adult body size between populations representative of both exotic ranges. We found that the rapid divergence in fecundity between exotic populations is associated with striking differences in several aspects of ovarian development: (1) Western Australian females exhibit accelerated ovarian development, (2) produce more eggs, (3) bigger eggs, and (4) start laying eggs earlier compared to their Eastern US counterparts. At the same time, divergence in ovarian maturation patterns occurred alongside changes in (5) larval developmental time, and (6) adult body size, and (7) mass. Western Australian females take longer to complete larval development and, surprisingly, emerge into smaller yet heavier adults than size-matched Eastern US females. We discuss our results in the context of the evolutionary developmental biology of fecundity in exotic populations. PMID:26300520
Differential Genome Evolution Between Companion Symbionts in an Insect-Bacterial Symbiosis
McCutcheon, John P.; MacDonald, Bradon R.; Romanovicz, Dwight; Moran, Nancy A.
2014-01-01
ABSTRACT Obligate symbioses with bacteria allow insects to feed on otherwise unsuitable diets. Some symbionts have extremely reduced genomes and have lost many genes considered to be essential in other bacteria. To understand how symbiont genome degeneration proceeds, we compared the genomes of symbionts in two leafhopper species, Homalodisca vitripennis (glassy-winged sharpshooter [GWSS]) and Graphocephala atropunctata (blue-green sharpshooter [BGSS]) (Hemiptera: Cicadellidae). Each host species is associated with the anciently acquired “Candidatus Sulcia muelleri” (Bacteroidetes) and the more recently acquired “Candidatus Baumannia cicadellinicola” (Gammaproteobacteria). BGSS “Ca. Baumannia” retains 89 genes that are absent from GWSS “Ca. Baumannia”; these underlie central cellular functions, including cell envelope biogenesis, cellular replication, and stress response. In contrast, “Ca. Sulcia” strains differ by only a few genes. Although GWSS “Ca. Baumannia” cells are spherical or pleomorphic (a convergent trait of obligate symbionts), electron microscopy reveals that BGSS “Ca. Baumannia” maintains a rod shape, possibly due to its retention of genes involved in cell envelope biogenesis and integrity. Phylogenomic results suggest that “Ca. Baumannia” is derived from the clade consisting of Sodalis and relatives, a group that has evolved symbiotic associations with numerous insect hosts. Finally, the rates of synonymous and nonsynonymous substitutions are higher in “Ca. Baumannia” than in “Ca. Sulcia,” which may be due to a lower mutation rate in the latter. Taken together, our results suggest that the two “Ca. Baumannia” genomes represent different stages of genome reduction in which many essential functions are being lost and likely compensated by hosts. “Ca. Sulcia” exhibits much greater genome stability and slower sequence evolution, although the mechanisms underlying these differences are poorly understood
NASA Astrophysics Data System (ADS)
Luo, Yusheng; Du, Z. W.; Yang, Y. J.; Chen, P.; Tian, Q.; Shang, X. L.; Liu, Z. C.; Yao, X. Q.; Wang, J. Z.; Wang, X. H.; Cheng, Y.; Peng, J.; Shen, A. G.; Hu, J. M.
2013-04-01
Early and differential diagnosis of Alzheimer’s disease (AD) has puzzled many clinicians. In this work, laser Raman spectroscopy (LRS) was developed to diagnose AD from platelet samples from AD transgenic mice and non-transgenic controls of different ages. An adaptive Gaussian process (GP) classification algorithm was used to re-establish the classification models of early AD, advanced AD and the control group with just two features and the capacity for noise reduction. Compared with the previous multilayer perceptron network method, the GP showed much better classification performance with the same feature set. Besides, spectra of platelets isolated from AD and Parkinson’s disease (PD) mice were also discriminated. Spectral data from 4 month AD (n = 39) and 12 month AD (n = 104) platelets, as well as control data (n = 135), were collected. Prospective application of the algorithm to the data set resulted in a sensitivity of 80%, a specificity of about 100% and a Matthews correlation coefficient of 0.81. Samples from PD (n = 120) platelets were also collected for differentiation from 12 month AD. The results suggest that platelet LRS detection analysis with the GP appears to be an easier and more accurate method than current ones for early and differential diagnosis of AD.
Re-Os Isotopic Constraints on the Chemical Evolution and Differentiation of the Martian Mantle
NASA Technical Reports Server (NTRS)
Brandon, Alan D.; Walker, Richard J.
2002-01-01
The (187)Re-187Os isotopic systematics of SNC meteorites, thought to be from Mars, provide valuable information regarding the chemical processes that affected the Martian mantle, particularly with regard to the relative abundances of highly siderophile elements (HSE). Previously published data (Birck and Allegre 1994, Brandon et al. 2000), and new data obtained since these studies, indicate that the HSE and Os isotopic composition of the Martian mantle was primarily set in its earliest differentiation history. If so, then these meteorites provide key constraints on the processes that lead to variation in HSE observed in not only Mars, but also Earth, the Moon and other rocky bodies in the Solar System. Processes that likely have an effect on the HSE budgets of terrestrial mantles include core formation, magma ocean crystallization, development of juvenile crust, and the addition of a late veneer. Each of these processes will result in different HSE variation and the isotopic composition of mantle materials and mantle derived lavas. Two observations on the SNC data to present provide a framework for which to test the importance of each of these processes. First, the concentrations of Re and Os in SNC meteorites indicate that they are derived from a mantle that has similar concentrations to the Earth's mantle. Such an observation is consistent with a model where a chondritic late veneer replenished the Earth and Martian mantles subsequent to core formation on each planet. Alternative models to explain this observation do exist, but will require additional data to test the limitations of each. Second, Re-Os isotopic results from Brandon et al. (2000) and new data presented here, show that initial yos correlates with variations in the short-lived systems of (182)Hf- (182)W and (142)Sm-142Nd in the SNC meteorites (epsilon(sub W) and epsilon(sub 142Nd)). These systematics require an isolation of mantle reservoirs during the earliest differentiation history of Mars, and
Evolution of late stage differentiates in the Palisades Sill, New York and New Jersey
NASA Astrophysics Data System (ADS)
Block, Karin A.; Steiner, Jeffrey C.; Puffer, John H.; Jones, Kevin M.; Goldstein, Steven L.
2015-08-01
The Palisades Sill at Upper Nyack, NY contains evolved rocks that crystallized as ferrodiabase and ferrogranophyre and occupy 50% to 60% of the local thickness. 143Nd/144Nd isotope values for rocks representing Palisades diversity range between 0.512320 and 0.512331, and indicate a homogeneous source for the Palisades and little or no contamination from shallow crustal sediments. Petrographic analysis of ferrodiabase suggests that strong iron enrichment was the result of prolonged quiescence in cycles of magmatic input. Ferrogranophyres in the updip northern Palisades at Upper Nyack are members of a suite of cogenetic rocks with similar composition to 'sandwich horizon' rocks of the southern Palisades at Fort Lee, NJ, but display distinct mineralogical and textural features. Differences in textural and mineralogical features are attributed to a) updip (lateral) migration of residual liquid as the sill propagated closer to the surface; b) deformation caused by tectonic shifts; and c) crystallization in the presence of deuteric hydrothermal fluids resulting in varying degrees of alteration. A model connecting multiple magmatic pulses, compaction and mobilization of residual liquid by compositional convection, closed-system differentiation, synchronous with tapping of the sill for extrusion of coeval basaltic subaerial flows is presented. The persistence of a low-temperature mushy layer, represented by ferrogranophyres, supports the possibility of a long-lived conduit subject to reopening after periods of quiescence in magmatic input, leading to the extrusion of the multiple flows of the Orange Mountain Basalt and perhaps even subsequent Preakness Basalt flows, depending on solidification conditions. A sub-Newark Basin network of sills subjected to similar protracted input of pulses as hypothesized for the Palisades was likely responsible for 600 ka of magmatic activity required to emplace a third set of Watchung flood basalts, the Hook Mountain Basalt.
Takashima, Eizo; Williams, Marni; Eiglmeier, Karin; Pain, Adrien; Guelbeogo, Wamdaogo M.; Gneme, Awa; Brito-Fravallo, Emma; Holm, Inge; Lavazec, Catherine; Sagnon, N’Fale; Baxter, Richard H.; Riehle, Michelle M.; Vernick, Kenneth D.
2015-01-01
Nucleotide variation patterns across species are shaped by the processes of natural selection, including exposure to environmental pathogens. We examined patterns of genetic variation in two sister species, Anopheles gambiae and Anopheles coluzzii, both efficient natural vectors of human malaria in West Africa. We used the differentiation signature displayed by a known coordinate selective sweep of immune genes APL1 and TEP1 in A. coluzzii to design a population genetic screen trained on the sweep, classified a panel of 26 potential immune genes for concordance with the signature, and functionally tested their immune phenotypes. The screen results were strongly predictive for genes with protective immune phenotypes: genes meeting the screen criteria were significantly more likely to display a functional phenotype against malaria infection than genes not meeting the criteria (p = 0.0005). Thus, an evolution-based screen can efficiently prioritize candidate genes for labor-intensive downstream functional testing, and safely allow the elimination of genes not meeting the screen criteria. The suite of immune genes with characteristics similar to the APL1-TEP1 selective sweep appears to be more widespread in the A. coluzzii genome than previously recognized. The immune gene differentiation may be a consequence of adaptation of A. coluzzii to new pathogens encountered in its niche expansion during the separation from A. gambiae, although the role, if any of natural selection by Plasmodium is unknown. Application of the screen allowed identification of new functional immune factors, and assignment of new functions to known factors. We describe biochemical binding interactions between immune proteins that underlie functional activity for malaria infection, which highlights the interplay between pathogen specificity and the structure of immune complexes. We also find that most malaria-protective immune factors display phenotypes for either human or rodent malaria, with
Mitri, Christian; Bischoff, Emmanuel; Takashima, Eizo; Williams, Marni; Eiglmeier, Karin; Pain, Adrien; Guelbeogo, Wamdaogo M; Gneme, Awa; Brito-Fravallo, Emma; Holm, Inge; Lavazec, Catherine; Sagnon, N'Fale; Baxter, Richard H; Riehle, Michelle M; Vernick, Kenneth D
2015-12-01
Nucleotide variation patterns across species are shaped by the processes of natural selection, including exposure to environmental pathogens. We examined patterns of genetic variation in two sister species, Anopheles gambiae and Anopheles coluzzii, both efficient natural vectors of human malaria in West Africa. We used the differentiation signature displayed by a known coordinate selective sweep of immune genes APL1 and TEP1 in A. coluzzii to design a population genetic screen trained on the sweep, classified a panel of 26 potential immune genes for concordance with the signature, and functionally tested their immune phenotypes. The screen results were strongly predictive for genes with protective immune phenotypes: genes meeting the screen criteria were significantly more likely to display a functional phenotype against malaria infection than genes not meeting the criteria (p = 0.0005). Thus, an evolution-based screen can efficiently prioritize candidate genes for labor-intensive downstream functional testing, and safely allow the elimination of genes not meeting the screen criteria. The suite of immune genes with characteristics similar to the APL1-TEP1 selective sweep appears to be more widespread in the A. coluzzii genome than previously recognized. The immune gene differentiation may be a consequence of adaptation of A. coluzzii to new pathogens encountered in its niche expansion during the separation from A. gambiae, although the role, if any of natural selection by Plasmodium is unknown. Application of the screen allowed identification of new functional immune factors, and assignment of new functions to known factors. We describe biochemical binding interactions between immune proteins that underlie functional activity for malaria infection, which highlights the interplay between pathogen specificity and the structure of immune complexes. We also find that most malaria-protective immune factors display phenotypes for either human or rodent malaria, with
Improved Bat Algorithm Applied to Multilevel Image Thresholding
2014-01-01
Multilevel image thresholding is a very important image processing technique that is used as a basis for image segmentation and further higher level processing. However, the required computational time for exhaustive search grows exponentially with the number of desired thresholds. Swarm intelligence metaheuristics are well known as successful and efficient optimization methods for intractable problems. In this paper, we adjusted one of the latest swarm intelligence algorithms, the bat algorithm, for the multilevel image thresholding problem. The results of testing on standard benchmark images show that the bat algorithm is comparable with other state-of-the-art algorithms. We improved standard bat algorithm, where our modifications add some elements from the differential evolution and from the artificial bee colony algorithm. Our new proposed improved bat algorithm proved to be better than five other state-of-the-art algorithms, improving quality of results in all cases and significantly improving convergence speed. PMID:25165733
Improved bat algorithm applied to multilevel image thresholding.
Alihodzic, Adis; Tuba, Milan
2014-01-01
Multilevel image thresholding is a very important image processing technique that is used as a basis for image segmentation and further higher level processing. However, the required computational time for exhaustive search grows exponentially with the number of desired thresholds. Swarm intelligence metaheuristics are well known as successful and efficient optimization methods for intractable problems. In this paper, we adjusted one of the latest swarm intelligence algorithms, the bat algorithm, for the multilevel image thresholding problem. The results of testing on standard benchmark images show that the bat algorithm is comparable with other state-of-the-art algorithms. We improved standard bat algorithm, where our modifications add some elements from the differential evolution and from the artificial bee colony algorithm. Our new proposed improved bat algorithm proved to be better than five other state-of-the-art algorithms, improving quality of results in all cases and significantly improving convergence speed. PMID:25165733
Wan, Qiang; Xiao, Lihua; Zhang, Xichen; Li, Yijing; Lu, Yixin; Song, Mingxin
2016-01-01
Enterocytozoon bieneusi is a widespread parasite with high genetic diversity among hosts. Its natural reservoir remains elusive and data on population structure are available only in isolates from primates. Here we describe a population genetic study of 101 E. bieneusi isolates from pigs using sequence analysis of the ribosomal internal transcribed spacer (ITS) and four mini- and microsatellite markers. The presence of strong linkage disequilibrium (LD) and limited genetic recombination indicated a clonal structure for the population. Bayesian inference of phylogeny, structural analysis, and principal coordinates analysis separated the overall population into three subpopulations (SP3 to SP5) with genetic segregation of the isolates at some geographic level. Comparative analysis showed the differentiation of SP3 to SP5 from the two known E. bieneusi subpopulations (SP1 and SP2) from primates. The placement of a human E. bieneusi isolate in pig subpopulation SP4 supported the zoonotic potential of some E. bieneusi isolates. Network analysis showed directed evolution of SP5 to SP3/SP4 and SP1 to SP2. The high LD and low number of inferred recombination events are consistent with the possibility of host adaptation in SP2, SP3, and SP4. In contrast, the reduced LD and high genetic diversity in SP1 and SP5 might be results of broad host range and adaptation to new host environment. The data provide evidence of the potential occurrence of host adaptation in some of E. bieneusi isolates that belong to the zoonotic ITS Group 1. PMID:27563718
Wan, Qiang; Xiao, Lihua; Zhang, Xichen; Li, Yijing; Lu, Yixin; Song, Mingxin; Li, Wei
2016-08-01
Enterocytozoon bieneusi is a widespread parasite with high genetic diversity among hosts. Its natural reservoir remains elusive and data on population structure are available only in isolates from primates. Here we describe a population genetic study of 101 E. bieneusi isolates from pigs using sequence analysis of the ribosomal internal transcribed spacer (ITS) and four mini- and microsatellite markers. The presence of strong linkage disequilibrium (LD) and limited genetic recombination indicated a clonal structure for the population. Bayesian inference of phylogeny, structural analysis, and principal coordinates analysis separated the overall population into three subpopulations (SP3 to SP5) with genetic segregation of the isolates at some geographic level. Comparative analysis showed the differentiation of SP3 to SP5 from the two known E. bieneusi subpopulations (SP1 and SP2) from primates. The placement of a human E. bieneusi isolate in pig subpopulation SP4 supported the zoonotic potential of some E. bieneusi isolates. Network analysis showed directed evolution of SP5 to SP3/SP4 and SP1 to SP2. The high LD and low number of inferred recombination events are consistent with the possibility of host adaptation in SP2, SP3, and SP4. In contrast, the reduced LD and high genetic diversity in SP1 and SP5 might be results of broad host range and adaptation to new host environment. The data provide evidence of the potential occurrence of host adaptation in some of E. bieneusi isolates that belong to the zoonotic ITS Group 1. PMID:27563718
Li, Bai; Chiong, Raymond; Lin, Mu
2015-02-01
Protein structure prediction is a fundamental issue in the field of computational molecular biology. In this paper, the AB off-lattice model is adopted to transform the original protein structure prediction scheme into a numerical optimization problem. We present a balance-evolution artificial bee colony (BE-ABC) algorithm to address the problem, with the aim of finding the structure for a given protein sequence with the minimal free-energy value. This is achieved through the use of convergence information during the optimization process to adaptively manipulate the search intensity. Besides that, an overall degradation procedure is introduced as part of the BE-ABC algorithm to prevent premature convergence. Comprehensive simulation experiments based on the well-known artificial Fibonacci sequence set and several real sequences from the database of Protein Data Bank have been carried out to compare the performance of BE-ABC against other algorithms. Our numerical results show that the BE-ABC algorithm is able to outperform many state-of-the-art approaches and can be effectively employed for protein structure optimization. PMID:25463349
Manousaki, Tereza; Hull, Pincelli M; Kusche, Henrik; Machado-Schiaffino, Gonzalo; Franchini, Paolo; Harrod, Chris; Elmer, Kathryn R; Meyer, Axel
2013-02-01
The study of parallel evolution facilitates the discovery of common rules of diversification. Here, we examine the repeated evolution of thick lips in Midas cichlid fishes (the Amphilophus citrinellus species complex)-from two Great Lakes and two crater lakes in Nicaragua-to assess whether similar changes in ecology, phenotypic trophic traits and gene expression accompany parallel trait evolution. Using next-generation sequencing technology, we characterize transcriptome-wide differential gene expression in the lips of wild-caught sympatric thick- and thin-lipped cichlids from all four instances of repeated thick-lip evolution. Six genes (apolipoprotein D, myelin-associated glycoprotein precursor, four-and-a-half LIM domain protein 2, calpain-9, GTPase IMAP family member 8-like and one hypothetical protein) are significantly underexpressed in the thick-lipped morph across all four lakes. However, other aspects of lips' gene expression in sympatric morphs differ in a lake-specific pattern, including the magnitude of differentially expressed genes (97-510). Generally, fewer genes are differentially expressed among morphs in the younger crater lakes than in those from the older Great Lakes. Body shape, lower pharyngeal jaw size and shape, and stable isotopes (δ(13)C and δ(15)N) differ between all sympatric morphs, with the greatest differentiation in the Great Lake Nicaragua. Some ecological traits evolve in parallel (those related to foraging ecology; e.g. lip size, body and head shape) but others, somewhat surprisingly, do not (those related to diet and food processing; e.g. jaw size and shape, stable isotopes). Taken together, this case of parallelism among thick- and thin-lipped cichlids shows a mosaic pattern of parallel and nonparallel evolution. PMID:23057963
ERIC Educational Resources Information Center
Martinez-Torregrosa, Joaquin; Lopez-Gay, Rafael; Gras-Marti, Albert
2006-01-01
Despite its frequent use, there is little understanding of the concept of differential among upper high school and undergraduate students of physics. As a first step to identify the origin of this situation and to revert it, we have done a historic and epistemological study aimed at clarifying the role and the meaning of the differential in…
NASA Astrophysics Data System (ADS)
Buchwaldt, R.; Toulkeridis, T.; Todt, W.
2014-12-01
Structural geological, geochemical and geochronological data were compiled with the purpose to exercise models for the construction of upper crustal batholith. Models for pulsed intrusion of small magma batches over long timescales versus transfer of larger magma bodies on a shorter time scales are able to predict a different thermal, metamorphic, and rheological state of the crust. For this purpose we have applied the chronostratigraphic framework for magma differentiation on three granite complexes namely the St. Francois Mountain granite pluton (Precambrian), the Galway granite (Cambrian), and the Sithonia Plutonic Complex (Eocene). These plutons have similar sizes and range in composition from quartz diorites through granodiorites and granites to alkali granites, indicating multiple intrusive episodes. Thermobarometric calculations imply an upper crustal emplacement. Geochemical, isotopic and petrological data indicate a variety of pulses from each pluton allowing to be related through their liquid line of decent, which is supported by fractional crystallization of predominantly plagioclase, K-feldspar, biotite, hornblende and some minor accessory mineral phases, magma mingling and mixing as well as crustal contamination. To obtain the temporal relationship we carried out high-precision CA-TIMS zircon geochronology on selected samples along the liquid line of decent. The obtained data indicate a wide range of rates: such as different pulses evolved on timescales of about only 10-30ka, although, the construction time of the different complexes ranges from millions of years with prolonged tectonically inactive phases to relatively short lived time ranges of about ~300 ka. For a better understanding how these new data were used and evaluated in order to reconstruct constraints on the dynamics of the magmatic plumbing system, we integrated the short-lived, elevated heat production, due to latent heat of crystallization, into a 2D numerical model of the thermal
Wang, Hongsen; Rus, Eric; Sakuraba, Takahito; Kikuchi, Jun; Kiya, Yasuyuki; Abruña, Héctor D
2014-07-01
A three-electrode differential electrochemical mass spectrometry (DEMS) cell has been developed to study the oxidative decomposition of electrolytes at high voltage cathode materials of Li-ion batteries. In this DEMS cell, the working electrode used was the same as the cathode electrode in real Li-ion batteries, i.e., a lithium metal oxide deposited on a porous aluminum foil current collector. A charged LiCoO2 or LiMn2O4 was used as the reference electrode, because of their insensitivity to air, when compared to lithium. A lithium sheet was used as the counter electrode. This DEMS cell closely approaches real Li-ion battery conditions, and thus the results obtained can be readily correlated with reactions occurring in real Li-ion batteries. Using DEMS, the oxidative stability of three electrolytes (1 M LiPF6 in EC/DEC, EC/DMC, and PC) at three cathode materials including LiCoO2, LiMn2O4, and LiNi(0.5)Mn(1.5)O4 were studied. We found that 1 M LiPF6 + EC/DMC electrolyte is quite stable up to 5.0 V, when LiNi(0.5)Mn(1.5)O4 is used as the cathode material. The EC/DMC solvent mixture was found to be the most stable for the three cathode materials, while EC/DEC was the least stable. The oxidative decomposition of the EC/DEC mixture solvent could be readily observed under operating conditions in our cell even at potentials as low as 4.4 V in 1 M LiPF6 + EC/DEC electrolyte on a LiCoO2 cathode, as indicated by CO2 and O2 evolution. The features of this DEMS cell to unveil solvent and electrolyte decomposition pathways are also described. PMID:24845246
Lidz, B.H.; Shinn, E.A.; Hine, A.C.; Locker, S.D.
1997-01-01
Closely spaced, high-resolution, seismic-reflection profiles acquired off the upper Florida Keys (i.e., north) reveal a platform-margin reef-and-trough system grossly similar to, yet quite different from, that previously described off the lower Keys (i.e., south). Profiles and maps generated for both areas show that development was controlled by antecedent Pleistocene topography (presence or absence of an upper-slope bedrock terrace), sediment availability, fluctuating sea level, and coral growth rate and distribution. The north terrace is sediment-covered and exhibits linear, buried, low-relief, seismic features of unknown character and origin. The south terrace is essentially sediment-free and supports multiple, massive, high-relief outlier reefs. Uranium disequilibrium series dates on outlier-reef corals indicate a Pleistocene age (~83-84 ka). A massive Pleistocene reef with both aggradational (north) and progradational (south) aspects forms the modern margin escarpment landward of the terrace. Depending upon interpretation (the north margin-escarpment reef may or may not be an outlier reef), the north margin is either more advanced or less advanced than the south margin. During Holocene sea-level rise, Pleistocene bedrock was inundated earlier and faster first to the north (deeper offbank terrace), then to the south (deeper platform surface). Holocene overgrowth is thick (8 m) on the north outer-bank reefs but thin (0.3 m) on the south outlier reefs. Differential evolution resulted from interplay between fluctuating sea level and energy regime established by prevailing east-southeasterly winds and waves along an arcuate (ENE-WSW) platform margin.
Sannino, Giovanna; De Falco, Ivanoe; De Pietro, Giuseppe
2014-06-01
Real-time Obstructive Sleep Apnea (OSA) episode detection and monitoring are important for society in terms of an improvement in the health of the general population and of a reduction in mortality and healthcare costs. Currently, to diagnose OSA patients undergo PolySomnoGraphy (PSG), a complicated and invasive test to be performed in a specialized center involving many sensors and wires. Accordingly, each patient is required to stay in the same position throughout the duration of one night, thus restricting their movements. This paper proposes an easy, cheap, and portable approach for the monitoring of patients with OSA, which collects single-channel ElectroCardioGram (ECG) data only. It is easy to perform from the patient's point of view because only one wearable sensor is required, so the patient is not restricted to keeping the same position all night long, and the detection and monitoring can be carried out in any place through the use of a mobile device. Our approach is based on the automatic extraction, from a database containing information about the monitored patient, of explicit knowledge in the form of a set of IF…THEN rules containing typical parameters derived from Heart Rate Variability (HRV) analysis. The extraction is carried out off-line by means of a Differential Evolution algorithm. This set of rules can then be exploited in the real-time mobile monitoring system developed at our Laboratory: the ECG data is gathered by a wearable sensor and sent to a mobile device, where it is processed in real time. Subsequently, HRV-related parameters are computed from this data, and, if their values activate some of the rules describing the occurrence of OSA, an alarm is automatically produced. This approach has been tested on a well-known literature database of OSA patients. The numerical results show its effectiveness in terms of accuracy, sensitivity, and specificity, and the achieved sets of rules evidence the user-friendliness of the approach
2015-01-01
We present and discuss philosophy and methodology of chaotic evolution that is theoretically supported by chaos theory. We introduce four chaotic systems, that is, logistic map, tent map, Gaussian map, and Hénon map, in a well-designed chaotic evolution algorithm framework to implement several chaotic evolution (CE) algorithms. By comparing our previous proposed CE algorithm with logistic map and two canonical differential evolution (DE) algorithms, we analyse and discuss optimization performance of CE algorithm. An investigation on the relationship between optimization capability of CE algorithm and distribution characteristic of chaotic system is conducted and analysed. From evaluation result, we find that distribution of chaotic system is an essential factor to influence optimization performance of CE algorithm. We propose a new interactive EC (IEC) algorithm, interactive chaotic evolution (ICE) that replaces fitness function with a real human in CE algorithm framework. There is a paired comparison-based mechanism behind CE search scheme in nature. A simulation experimental evaluation is conducted with a pseudo-IEC user to evaluate our proposed ICE algorithm. The evaluation result indicates that ICE algorithm can obtain a significant better performance than or the same performance as interactive DE. Some open topics on CE, ICE, fusion of these optimization techniques, algorithmic notation, and others are presented and discussed. PMID:25879067
Bause, Fabian; Walther, Andrea; Rautenberg, Jens; Henning, Bernd
2013-12-01
For the modeling and simulation of wave propagation in geometrically simple waveguides such as plates or rods, one may employ the analytical global matrix method. That is, a certain (global) matrix depending on the two parameters wavenumber and frequency is built. Subsequently, one must calculate all parameter pairs within the domain of interest where the global matrix becomes singular. For this purpose, one could compute all roots of the determinant of the global matrix when the two parameters vary in the given intervals. This requirement to calculate all roots is actually the method's most concerning restriction. Previous approaches are based on so-called mode-tracers, which use the physical phenomenon that solutions, i.e., roots of the determinant of the global matrix, appear in a certain pattern, the waveguide modes, to limit the root-finding algorithm's search space with respect to consecutive solutions. In some cases, these reductions of the search space yield only an incomplete set of solutions, because some roots may be missed as a result of uncertain predictions. Therefore, we propose replacement of the mode-tracer approach with a suitable version of an interval- Newton method. To apply this interval-based method, we extended the interval and derivative computation provided by a numerical computing environment such that corresponding information is also available for Bessel functions used in circular models of acoustic waveguides. We present numerical results for two different scenarios. First, a polymeric cylindrical waveguide is simulated, and second, we show simulation results of a one-sided fluid-loaded plate. For both scenarios, we compare results obtained with the proposed interval-Newton algorithm and commercial software. PMID:24297025
Chow, Sy-Miin; Lu, Zhaohua; Sherwood, Andrew; Zhu, Hongtu
2016-03-01
The past decade has evidenced the increased prevalence of irregularly spaced longitudinal data in social sciences. Clearly lacking, however, are modeling tools that allow researchers to fit dynamic models to irregularly spaced data, particularly data that show nonlinearity and heterogeneity in dynamical structures. We consider the issue of fitting multivariate nonlinear differential equation models with random effects and unknown initial conditions to irregularly spaced data. A stochastic approximation expectation-maximization algorithm is proposed and its performance is evaluated using a benchmark nonlinear dynamical systems model, namely, the Van der Pol oscillator equations. The empirical utility of the proposed technique is illustrated using a set of 24-h ambulatory cardiovascular data from 168 men and women. Pertinent methodological challenges and unresolved issues are discussed. PMID:25416456
Shin, J.; Malusare, P.; Hovland, P. D.; Mathematics and Computer Science
2008-01-01
Automatic differentiation (AD) has been expanding its role in scientific computing. While several AD tools have been actively developed and used, a wide range of problems remain to be solved. Activity analysis allows AD tools to generate derivative code for fewer variables, leading to a faster run time of the output code. This paper describes a new context-sensitive, flow-sensitive (CSFS) activity analysis, which is developed by extending an existing context-sensitive, flow-insensitive (CSFI) activity analysis. Our experiments with eight benchmarks show that the new CSFS activity analysis is more than 27 times slower but reduces 8 overestimations for the MIT General Circulation Model (MITgcm) and 1 for an ODE solver (c2) compared with the existing CSFI activity analysis implementation. Although the number of reduced overestimations looks small, the additionally identified passive variables may significantly reduce tedious human effort in maintaining a large code base such as MITgcm.
NASA Astrophysics Data System (ADS)
Sullivan, John T.
Although characterizing the interactions of ozone throughout the entire troposphere are important for health and climate processes, there is a lack of routine measurements of vertical profiles within the United States. Current atmospheric satellite instruments cannot peer through the optically thick stratospheric ozone layer to remotely sense boundary layer tropospheric ozone. In order to monitor this lower ozone more effectively, the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center TROPospheric OZone DIfferential Absorption Lidar (GSFC TROPOZ DIAL) has been developed and validated within the Tropospheric Ozone Lidar Network (TOLNet). Two scientifically interesting ozone episodes are presented that were observed during the 2014 Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER AQ) campaign at Ft. Collins, Colorado. The GSFC TROPOZ DIAL measurements are analyzed alongside aircraft spirals over the lidar site, co-located ozonesonde launches, aerosol lidar profiles and other TOLNet ozone lidar profiles. In both case studies, back trajectories, meteorological maps, and comparisons to air quality models are presented to better explain the sources and evolution of ozone. The first case study, occurring between 22-23 July 2014, indicates enhanced concentrations of ozone at Ft. Collins during nighttime hours, which was due to the complex recirculation of ozone within the foothills of the Rocky Mountain region. Although quantifying the ozone increase aloft during recirculation episodes has been historically difficult, results indicate that an increase of 20 - 30 ppbv of ozone at the Ft. Collins site has been attributed to this recirculation. The second case, occurring between Aug 4-8th 2014, characterizes a dynamical exchange of ozone between the stratosphere and the troposphere. This case, along with seasonal model parameters from previous years, is used to estimate
NASA Astrophysics Data System (ADS)
Yan, Wei-Nan; Ma, Shu-Ying; Xu, Liang
2014-05-01
The quantitative retrieval of the 3-D spatial distribution of the parent energetic ions of ENA from a 2-D ENA image is a challenge task. The Two Wide-angle Imaging Neutral-atom Spectrometers (TWINS) mission of NASA provides an unique opportunity to retrieve the 3-D distribution of ions in the ring current (RC) by using a volumetric pixel (voxel) CT inversion method. In this study the voxel CT method is implemented for a series of differential ENA fluxes at different energy levels ranging from 5 to 80 keV obtained simultaneously by the two satellites of TWINS flying on two widely-separated Molniya orbits during the main phase of the magnetic storm of 24-25 October 2011 with minimum Sym-H index of -160 nT. The data were selected to span a period of about 50 minutes during which a large substorm was undergoing its expansion phase first and then recovery. The ENA species of O and H are distinguished for lower energy-mass ratio in some time- segments by analyzing the signals of pulse heights of second electrons emitted from the carbon foil and impacted on the MCP detector in the TWINS sensors. In order to eliminate the possible influence on retrieval caused by instrument bias error, a differential voxel CT technique is applied. To weaken the influence of low altitude emission (LAE) produced by ion precipitation, a correction is made for the ENA intensity along the line-of-sight that run deep into the high latitude atmosphere, invoking the so called thick-target approximation. The flux intensity of the ENAs' parent ions in the RC has been obtained as a function of energy, L value, MLT sector and latitude, along with their time evolution during the storm-time substorm expansion phase. Forward calculations proved the reliability of the retrieved results. It shows that the RC is highly asymmetric with a major concentration in the midnight to dawn sector for equatorial latitudes. The ion flux spectra undergo dramatic changes from pre-storm to the main phase. Besides, halfway
NASA Astrophysics Data System (ADS)
Ma, S.; Yan, W.; Xu, L.
2013-12-01
The quantitative retrieval of the 3-D spatial distribution of the parent energetic ions of ENA from a 2-D ENA image is a quite challenge task. The Two Wide-angle Imaging Neutral-atom Spectrometers (TWINS) mission of NASA is the first constellation to perform stereoscopic magnetospheric imaging of energetic neutral atoms (ENA) from a pair of spacecraft flying on two widely-separated Molniya orbits. TWINS provides a unique opportunity to retrieve the 3-D distribution of ions in the ring current (RC) by using a volumetric pixel (voxel) CT inversion method. In this study the voxel CT method is implemented for a series of differential ENA fluxes averaged over about 6 to 7 sweeps (corresponding to a time period of about 9 min.) at different energy levels ranging from 5 to 100 keV, obtained simultaneously by the two satellites during the main phase of a great magnetic storm with minimum Sym-H of -156 nT on 24-25 October 2011. The data were selected to span a period about 50 minutes during which a large substorm was undergoing its expansion phase first and then recovery. The ENA species of O and H are distinguished for some time-segments by analyzing the signals of pulse heights of second electrons emitted from the carbon foil and impacted on the MCP detector in the TWINS sensors. In order to eliminate the possible influence on retrieval induced by instrument bias error, a differential voxel CT technique is applied. The flux intensity of the ENAs' parent ions in the RC has been obtained as a function of energy, L value, MLT sector and latitude, along with their time evolution during the storm-time substorm expansion phase. Forward calculations proved the reliability of the retrieved results. It shows that the RC is highly asymmetric, with a major concentration in the midnight to dawn sector for equatorial latitudes. Halfway through the substorm expansion there occurred a large enhancement of equatorial ion flux at lower energy (5 keV) in the dusk sector, with narrow extent
NASA Astrophysics Data System (ADS)
Ma, S. Y.; Xu, Liang; Yan, Wei-Nan
The quantitative retrieval of the 3-D spatial distribution of the parent energetic ions of ENA from a 2-D ENA image is a challenge task. The Two Wide-angle Imaging Neutral-atom Spectrometers (TWINS) mission of NASA provides an unique opportunity to retrieve the 3-D distribution of ions in the ring current (RC) by using a volumetric pixel (voxel) CT inversion method. In this study the voxel CT method is implemented for a series of differential ENA fluxes at different energy levels ranging from 5 to 80 keV obtained simultaneously by the two satellites of TWINS flying on two widely-separated Molniya orbits during the main phase of the magnetic storm of 24-25 October 2011 with minimum Sym-H index of -160 nT. The data were selected to span a period of about 50 minutes during which a large substorm was undergoing its expansion phase first and then recovery. The ENA species of O and H are distinguished for lower energy-mass ratio in some time- segments by analyzing the signals of pulse heights of second electrons emitted from the carbon foil and impacted on the MCP detector in the TWINS sensors. In order to eliminate the possible influence on retrieval caused by instrument bias error, a differential voxel CT technique is applied. To weaken the influence of low altitude emission (LAE) produced by ion precipitation, a correction is made for the ENA intensity along the line-of-sight that run deep into the high latitude atmosphere, invoking the so called thick-target approximation. The flux intensity of the ENAs’ parent ions in the RC has been obtained as a function of energy, L value, MLT sector and latitude, along with their time evolution during the storm-time substorm expansion phase. Forward calculations proved the reliability of the retrieved results. It shows that the RC is highly asymmetric with a major concentration in the midnight to dawn sector for equatorial latitudes. The ion flux spectra undergo dramatic changes from pre-storm to the main phase. Besides
Alicea, Bradly; Gordon, Richard
2016-01-01
Embryonic development proceeds through a series of differentiation events. The mosaic version of this process (binary cell divisions) can be analyzed by comparing early development of Ciona intestinalis and Caenorhabditis elegans. To do this, we reorganize lineage trees into differentiation trees using the graph theory ordering of relative cell volume. Lineage and differentiation trees provide us with means to classify each cell using binary codes. Extracting data characterizing lineage tree position, cell volume, and nucleus position for each cell during early embryogenesis, we conduct several statistical analyses, both within and between taxa. We compare both cell volume distributions and cell volume across developmental time within and between single species and assess differences between lineage tree and differentiation tree orderings. This enhances our understanding of the differentiation events in a model of pure mosaic embryogenesis and its relationship to evolutionary conservation. We also contribute several new techniques for assessing both differences between lineage trees and differentiation trees, and differences between differentiation trees of different species. The results suggest that at the level of differentiation trees, there are broad similarities between distantly related mosaic embryos that might be essential to understanding evolutionary change and phylogeny reconstruction. Differentiation trees may therefore provide a basis for an Evo-Devo Postmodern Synthesis. PMID:27548240
Odom, Karan J; Omland, Kevin E; Price, J Jordan
2015-03-01
Female bird song and combined vocal duets of mated pairs are both frequently associated with tropical, monogamous, sedentary natural histories. Little is known, however, about what selects for duetting behavior versus female song. Female song likely preceded duet evolution and could drive apparent relationships between duets and these natural histories. We compared the evolution of female song and male-female duets in the New World blackbirds (Icteridae) by investigating patterns of gains and losses of both traits and their relationships with breeding latitude, mating system, nesting pattern, and migratory behavior. We found that duets evolved only in lineages in which female song was likely ancestral. Both female song and duets were correlated with tropical breeding, social monogamy, territorial nesting, and sedentary behavior when all taxa were included; however, correlations between duets and these natural history traits disappeared when comparisons were limited to taxa with female song. Also, likelihood values supported stronger relationships between the natural history traits and female song than between these traits and duets. Our results suggest that the natural histories thought to favor the evolution of duetting may in fact be associated with female song and that additional selection pressures are responsible for the evolution of duets. PMID:25529233
Chen, Li; Tan, Chih-Hung; Kao, Shuh-Ji; Wang, Tai-Sheng
2008-01-01
Parallel GEGA was constructed by incorporating grammatical evolution (GE) into the parallel genetic algorithm (GA) to improve reservoir water quality monitoring based on remote sensing images. A cruise was conducted to ground-truth chlorophyll-a (Chl-a) concentration longitudinally along the Feitsui Reservoir, the primary water supply for Taipei City in Taiwan. Empirical functions with multiple spectral parameters from the Landsat 7 Enhanced Thematic Mapper (ETM+) data were constructed. The GE, an evolutionary automatic programming type system, automatically discovers complex nonlinear mathematical relationships among observed Chl-a concentrations and remote-sensed imageries. A GA was used afterward with GE to optimize the appropriate function type. Various parallel subpopulations were processed to enhance search efficiency during the optimization procedure with GA. Compared with a traditional linear multiple regression (LMR), the performance of parallel GEGA was found to be better than that of the traditional LMR model with lower estimating errors. PMID:17692356
A comprehensive review of swarm optimization algorithms.
Ab Wahab, Mohd Nadhir; Nefti-Meziani, Samia; Atyabi, Adham
2015-01-01
Many swarm optimization algorithms have been introduced since the early 60's, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known optimization algorithms. Selected algorithms are briefly explained and compared with each other comprehensively through experiments conducted using thirty well-known benchmark functions. Their advantages and disadvantages are also discussed. A number of statistical tests are then carried out to determine the significant performances. The results indicate the overall advantage of Differential Evolution (DE) and is closely followed by Particle Swarm Optimization (PSO), compared with other considered approaches. PMID:25992655
A Comprehensive Review of Swarm Optimization Algorithms
2015-01-01
Many swarm optimization algorithms have been introduced since the early 60’s, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known optimization algorithms. Selected algorithms are briefly explained and compared with each other comprehensively through experiments conducted using thirty well-known benchmark functions. Their advantages and disadvantages are also discussed. A number of statistical tests are then carried out to determine the significant performances. The results indicate the overall advantage of Differential Evolution (DE) and is closely followed by Particle Swarm Optimization (PSO), compared with other considered approaches. PMID:25992655
A novel fitness evaluation method for evolutionary algorithms
NASA Astrophysics Data System (ADS)
Wang, Ji-feng; Tang, Ke-zong
2013-03-01
Fitness evaluation is a crucial task in evolutionary algorithms because it can affect the convergence speed and also the quality of the final solution. But these algorithms may require huge computation power for solving nonlinear programming problems. This paper proposes a novel fitness evaluation approach which employs similarity-base learning embedded in a classical differential evolution (SDE) to evaluate all new individuals. Each individual consists of three elements: parameter vector (v), a fitness value (f), and a reliability value(r). The f is calculated using NFEA, and only when the r is below a threshold is the f calculated using true fitness function. Moreover, applying error compensation system to the proposed algorithm further enhances the performance of the algorithm to make r much closer to true fitness value for each new child. Simulation results over a comprehensive set of benchmark functions show that the convergence rate of the proposed algorithm is much faster than much that of the compared algorithms.
An improved harmony search algorithm for emergency inspection scheduling
NASA Astrophysics Data System (ADS)
Kallioras, Nikos A.; Lagaros, Nikos D.; Karlaftis, Matthew G.
2014-11-01
The ability of nature-inspired search algorithms to efficiently handle combinatorial problems, and their successful implementation in many fields of engineering and applied sciences, have led to the development of new, improved algorithms. In this work, an improved harmony search (IHS) algorithm is presented, while a holistic approach for solving the problem of post-disaster infrastructure management is also proposed. The efficiency of IHS is compared with that of the algorithms of particle swarm optimization, differential evolution, basic harmony search and the pure random search procedure, when solving the districting problem that is the first part of post-disaster infrastructure management. The ant colony optimization algorithm is employed for solving the associated routing problem that constitutes the second part. The comparison is based on the quality of the results obtained, the computational demands and the sensitivity on the algorithmic parameters.
Algorithms and Algorithmic Languages.
ERIC Educational Resources Information Center
Veselov, V. M.; Koprov, V. M.
This paper is intended as an introduction to a number of problems connected with the description of algorithms and algorithmic languages, particularly the syntaxes and semantics of algorithmic languages. The terms "letter, word, alphabet" are defined and described. The concept of the algorithm is defined and the relation between the algorithm and…
NASA Astrophysics Data System (ADS)
G R, R. K.; C, S.
2015-12-01
The fundamental challenge in understanding the origin and evolution of the continental crust is to recognize how primary mantle source, and oceanic crust, which are essentially mafic to ultramafic in composition, could differentiate into a more or less felsic compositions. It is possible to understand growth and differentiation of the continental crust by constraining the interplay of magmatism, deformation, and high-grade metamorphism in the lower crust. Here, we apply this knowledge on the lower crustal granitoids of southern India and speculate on the variations in geochemistry as a consequence of differentiation and secular evolution of the continental crust.The major groups of granitoids of southern India are classified as metatonalites, comparable to typical Archaean TTGs with pronounced calc-alkaline affinity, and metagranites which are magmatic fractionation produced by reworking of early crust. Metatonalites are sodic-trondhjemites with slightly magnesian, moderate LREE (average LaN = 103) and low HREE (average YbN = 2) characerestics, where as metagranites are calc-alkaline ferroan types with enriched LREE (average LaN = 427) and HREE (average YbN = 23). Petrogenetic characteristics of granitoids illustrate continuous evolution of a primary crust into diverse magmatic units by multiple stages of intracrustal differentiation processes attributed to following tectonic scenarios: (1) formation of tonalitic magma by low- to moderate-degree partial melting of hydrated basaltic crust at pressures high enough to stabilize garnet-amphibole residue and (2) genesis of granite in a continental arc-accretion setting by an episode of crustal remelting of the tonalitic crust, within plagioclase stability field. The first-stage formed in a flat-subduction setting of an volcanic-arc, leading to the formation of tonalites. The heat budget required is ascribed to the upwelling of the mantle and/or basaltic underplating. Progressive decline in mantle potential temperature
López-Leal, Gamaliel; Cevallos, Miguel A.; Castillo-Ramírez, Santiago
2016-01-01
Sigma factors are an essential part of bacterial gene regulation and have been extensively studied as far as their molecular mechanisms and protein structure are concerned. However, their molecular evolution, especially for the alternative sigma factors, is poorly understood. Here, we analyze the evolutionary forces that have shaped the rpoH sigma factors within the alphaproteobacteria. We found that an ancient duplication gave rise to two major groups of rpoH sigma factors and that after this event horizontal gene transfer (HGT) occurred in rpoH1 group. We also noted that purifying selection has differentially affected distinct parts of the gene; singularly, the gene segment that encodes the region 4.2, which interacts with the −35 motif of the RpoH-dependent genes, has been under relaxed purifying selection. Furthermore, these two major groups are clearly differentiated from one another regarding their promoter selectivity, as rpoH1 is under the transcriptional control of σ70 and σ32, whereas rpoH2 is under the transcriptional control of σ24. Our results suggest a scenario in which HGT, gene loss, variable purifying selection and clear promoter specialization occurred after the ancestral duplication event. More generally, our study offers insights into the molecular evolution of alternative sigma factors and highlights the importance of analyzing not only the coding regions but also the promoter regions. PMID:27199915
NASA Astrophysics Data System (ADS)
Sarychikhina, O.; Glowacka, E.
2015-11-01
Ground deformation in Mexicali Valley, Baja California, Mexico, the southern part of the Mexicali-Imperial valley, is influenced by active tectonics and human activity, mainly that of geothermal fluid extraction in the Cerro Prieto Geothermal Field. Significant ground deformation, mainly subsidence (~ 18 cm yr-1), and related ground fissures cause severe damage to local infrastructure. The technique of Differential Synthetic Aperture Radar Interferometry (DInSAR) has been demonstrated to be a very effective remote sensing tool for accurately measuring the spatial and temporal evolution of ground displacements over broad areas. In present study ERS-1/2 SAR and ENVISAT ASAR images acquired between 1993 and 2010 were used to perform a historical analysis of aseismic ground deformation in Mexicali Valley, in an attempt to evaluate its spatio-temporal evolution and improve the understanding of its dynamic. For this purpose, the conventional 2-pass DInSAR was used to generate interferograms which were used in stacking procedure to produce maps of annual aseismic ground deformation rates for different periods. Differential interferograms that included strong co-seismic deformation signals were not included in the stacking and analysis. The changes in the ground deformation pattern and rate were identified. The main changes occur between 2000 and 2005 and include increasing deformation rate in the recharge zone and decreasing deformation rate in the western part of the CPGF production zone. We suggested that these changes are mainly caused by production development in the Cerro Prieto Geothermal Field.
Hasbún, Rodrigo; Iturra, Carolina; Bravo, Soraya; Rebolledo-Jaramillo, Boris; Valledor, Luis
2016-01-01
Epigenetic regulation plays important biological roles in plants, including timing of flowering and endosperm development. Little is known about the mechanisms controlling heterochrony (the change in the timing or rate of developmental events during ontogeny) in Eucalyptus globulus. DNA methylation has been proposed as a potential heterochrony regulatory mechanism in model species, but its role during the vegetative phase in E. globulus has not been explored. In order to investigate the molecular mechanisms governing heterochrony in E. globulus, we have developed a workflow aimed at generating high-resolution hypermethylome and hypomethylome maps that have been tested in two stages of vegetative growth phase: juvenile (6-month leaves) and adult (30-month leaves). We used the M&M algorithm, a computational approach that integrates MeDIP-seq and MRE-seq data, to identify differentially methylated regions (DMRs). Thousands of DMRs between juvenile and adult leaves of E. globulus were found. Although further investigations are required to define the loci associated with heterochrony/heteroblasty that are regulated by DNA methylation, these results suggest that locus-specific methylation could be major regulators of vegetative phase change. This information can support future conservation programs, for example, selecting the best methylomes for a determinate environment in a restoration project. PMID:27123440
NASA Astrophysics Data System (ADS)
Ygouf, Marie; Pueyo, Laurent; Soummer, Rémi; Perrin, Marshall D.; van der Marel, Roeland; Macintosh, Bruce
2015-09-01
Direct detection and characterization of mature giant or sub-Neptunes exoplanets in the visible require space-based instruments optimized for high-contrast imaging with contrasts of 10-9. In this context, the Wide-Field Infrared Survey Telescope - Astrophysics Focused Telescope Assets (WFIRST-AFTA) will reach raw contrasts of about 8×10-9 to 10-9 using state-of-the-art starlight suppression and wavefront control techniques. A ten-fold contrast improvement is therefore expected using post-processing techniques to reduce the speckle noise level to a factor of at least 10 lower in order to distinguish 10-9 planets from speckles. Point spread function (PSF) subtractions have been successfully applied to ground-based and space-based data with contrasts up to 10-6 but performance has yet to be demonstrated at higher contrast levels. We use both a classical PSF subtraction and the Karunhen-Loéve Image Projection (KLIP) algorithm to reduce noise free WFIRST-AFTA-like simulated images in the context of reference star differential imaging (RDI). The two WFIRST-AFTA baseline coronagraphs are considered for this study: the hybrid lyot coronagraph (HLC) for the imaging channel and the shaped-pupil coronagraph (SPC) for the integral field spectrograph channel (IFS). The two reduction methods are compared with respect to the amount and stability of the aberrations for detection in the imaging channel and preliminary spectra extractions are performed for characterization in the IFS channel.
Homolka, David; Ivanek, Robert; Forejt, Jiri; Jansa, Petr
2011-01-01
Background Tight regulation of testicular gene expression is a prerequisite for male reproductive success, while differentiation of gene activity in spermatogenesis is important during speciation. Thus, comparison of testicular transcriptomes between closely related species can reveal unique regulatory patterns and shed light on evolutionary constraints separating the species. Methodology/Principal Findings Here, we compared testicular transcriptomes of two closely related mouse species, Mus musculus and Mus spretus, which diverged more than one million years ago. We analyzed testicular expression using tiling arrays overlapping Chromosomes 2, X, Y and mitochondrial genome. An excess of differentially regulated non-coding RNAs was found on Chromosome 2 including the intronic antisense RNAs, intergenic RNAs and premature forms of Piwi-interacting RNAs (piRNAs). Moreover, striking difference was found in the expression of X-linked G6pdx gene, the parental gene of the autosomal retrogene G6pd2. Conclusions/Significance The prevalence of non-coding RNAs among differentially expressed transcripts indicates their role in species-specific regulation of spermatogenesis. The postmeiotic expression of G6pdx in Mus spretus points towards the continuous evolution of X-chromosome silencing and provides an example of expression change accompanying the out-of-the X-chromosomal retroposition. PMID:21347268
Quantum differential cryptanalysis
NASA Astrophysics Data System (ADS)
Zhou, Qing; Lu, Songfeng; Zhang, Zhigang; Sun, Jie
2015-06-01
In this paper, we propose a quantum version of the differential cryptanalysis which offers a quadratic speedup over the existing classical one and show the quantum circuit implementing it. The quantum differential cryptanalysis is based on the quantum minimum/maximum-finding algorithm, where the values to be compared and filtered are obtained by calling the quantum counting algorithm. Any cipher which is vulnerable to the classical differential cryptanalysis based on counting procedures can be cracked more quickly under this quantum differential attack.
Fuller, Zachary L.; Haynes, Gwilym D.; Richards, Stephen; Schaeffer, Stephen W.
2016-01-01
Chromosomal rearrangements can shape the structure of genetic variation in the genome directly through alteration of genes at breakpoints or indirectly by holding combinations of genetic variants together due to reduced recombination. The third chromosome of Drosophila pseudoobscura is a model system to test hypotheses about how rearrangements are established in populations because its third chromosome is polymorphic for >30 gene arrangements that were generated by a series of overlapping inversion mutations. Circumstantial evidence has suggested that these gene arrangements are selected. Despite the expected homogenizing effects of extensive gene flow, the frequencies of arrangements form gradients or clines in nature, which have been stable since the system was first described >80 years ago. Furthermore, multiple arrangements exist at appreciable frequencies across several ecological niches providing the opportunity for heterokaryotypes to form. In this study, we tested whether genes are differentially expressed among chromosome arrangements in first instar larvae, adult females and males. In addition, we asked whether transcriptional patterns in heterokaryotypes are dominant, semidominant, overdominant, or underdominant. We find evidence for a significant abundance of differentially expressed genes across the inverted regions of the third chromosome, including an enrichment of genes involved in sensory perception for males. We find the majority of loci show additivity in heterokaryotypes. Our results suggest that multiple genes have expression differences among arrangements that were either captured by the original inversion mutation or accumulated after it reached polymorphic frequencies, providing a potential source of genetic variation for selection to act upon. These data suggest that the inversions are favored because of their indirect effect of recombination suppression that has held different combinations of differentially expressed genes together in the
Schneeweiss, Gerald M; Pachschwöll, Clemens; Tribsch, Andreas; Schönswetter, Peter; Barfuss, Michael H J; Esfeld, Korinna; Weiss-Schneeweiss, Hanna; Thiv, Mike
2013-12-01
Phyteuma is a chromosomally and ecologically diverse vascular plant genus and constitutes an excellent system for studying both the role of chromosomal change for species diversification and the evolution of high-mountain biota. This kind of research is, however, hampered by the lack of a sound phylogenetic framework exacerbated by the notoriously low predictive power of traditional taxonomy with respect to phylogenetic relationships in Campanulaceae. Based on a comprehensive taxon sampling and analyses of nuclear and plastid sequence and AFLP fingerprint data, Phyteuma is confirmed as a monophyletic group sister to the monotypic Physoplexis, which is in line with their peculiar flower morphologies. Within Phyteuma two clades, largely corresponding to previously recognized sections, are consistently found. The traditional circumscription of taxonomic series is largely rejected. Whereas distinctness of the currently recognized species is mostly corroborated, some interspecific relationships remain ambiguous due to incongruences between nuclear and plastid data. Major forces for diversification and evolution of Phyteuma are descending dysploidy (i.e., a decrease in chromosome base number) as well as allopatric and ecological differentiation within the Alps, the genus' center of species diversity. PMID:23891952
Co-evolution for Problem Simplification
NASA Technical Reports Server (NTRS)
Haith, Gary L.; Lohn, Jason D.; Cplombano, Silvano P.; Stassinopoulos, Dimitris
1999-01-01
This paper explores a co-evolutionary approach applicable to difficult problems with limited failure/success performance feedback. Like familiar "predator-prey" frameworks this algorithm evolves two populations of individuals - the solutions (predators) and the problems (prey). The approach extends previous work by rewarding only the problems that match their difficulty to the level of solut,ion competence. In complex problem domains with limited feedback, this "tractability constraint" helps provide an adaptive fitness gradient that, effectively differentiates the candidate solutions. The algorithm generates selective pressure toward the evolution of increasingly competent solutions by rewarding solution generality and uniqueness and problem tractability and difficulty. Relative (inverse-fitness) and absolute (static objective function) approaches to evaluating problem difficulty are explored and discussed. On a simple control task, this co-evolutionary algorithm was found to have significant advantages over a genetic algorithm with either a static fitness function or a fitness function that changes on a hand-tuned schedule.
NASA Astrophysics Data System (ADS)
Perlock, Patricia A.; González, Pablo J.; Tiampo, Kristy F.; Rodríguez-Velasco, Gema; Samsonov, Sergey; Fernández, José
2008-08-01
Differential interferometry is a very powerful tool for detecting changes in the Earth’s crust where coherence conditions are good, but is difficult to employ in some volcanic areas due to dense vegetation. We apply two differential InSAR methods using the time series associated with the interferograms to perform a phase analysis on a data set for La Palma island (Canary Islands) from the ERS-1 and ERS-2 European Space Agency (ESA) satellites for the time period 1992 to 2000. Both methods involve choosing a master image from the database and creating a series of interferograms with respect to this image. The “Coherent Pixel Time Series” (CPTS) technique chooses pixels with good average coherence, aligns the unwrapped interferograms with a stable area and then performs an inversion to calculate the linear velocity to quantify the deformation. The Coherent Target Modeling (CTM) method calculates the temporal coherence of each pixel to identify stable targets and then determines the best velocity for each pixel by using a linear fit that maximizes the temporal coherence. Using these two methods we have been able to detect deformation on La Palma Island that has been previously undetectable by conventional InSAR methods. There is a roughly circular region on the Southern part of the island that is actively deforming at ~ -4 to -8 mm/yr. This region is located near the Teneguia valcano, the host of the last known eruption on La Palma in 1971. A thorough investigation of the possible sources for this deformation revealed that it was most likely created by a subsurface thermal source.
Brill, E; Kang, L; Michalak, K; Michalak, P; Price, D K
2016-08-01
The Hawaiian Drosophila are an iconic example of sequential colonization, adaptive radiation and speciation on islands. Genetic and phenotypic analysis of closely related species pairs that exhibit incomplete reproductive isolation can provide insights into the mechanisms of speciation. Drosophila silvestris from Hawai'i Island and Drosophila planitibia from Maui are two closely related allopatric Hawaiian picture-winged Drosophila that produce sterile F1 males but fertile F1 females, a pattern consistent with Haldane's rule. Backcrossing F1 hybrid females between these two species to parental species gives rise to recombinant males with three distinct sperm phenotypes despite a similar genomic background: motile sperm, no sperm (sterile), and immotile sperm. We found that these three reproductive morphologies of backcross hybrid males produce divergent gene expression profiles in testes, as measured with RNA sequencing. There were a total of 71 genes significantly differentially expressed between backcross males with no sperm compared with those backcross males with motile sperm and immotile sperm, but no significant differential gene expression between backcross males with motile sperm and backcross males with immotile sperm. All of these genes were underexpressed in males with no sperm, including a number of genes with previously known activities in adult testis. An allele-specific expression analysis showed overwhelmingly more cis-divergent than trans-divergent genes, with no significant difference in the ratio of cis- and trans-divergent genes among the sperm phenotypes. Overall, the results indicate that the regulation of gene expression involved in sperm production likely diverged relatively rapidly between these two closely related species. PMID:27220308
Goel, Ridhi; Pandey, Ashutosh; Trivedi, Prabodh K; Asif, Mehar H
2016-01-01
The WRKY gene family plays an important role in the development and stress responses in plants. As information is not available on the WRKY gene family in Musa species, genome-wide analysis has been carried out in this study using available genomic information from two species, Musa acuminata and Musa balbisiana. Analysis identified 147 and 132 members of the WRKY gene family in M. acuminata and M. balbisiana, respectively. Evolutionary analysis suggests that the WRKY gene family expanded much before the speciation in both the species. Most of the orthologs retained in two species were from the γ duplication event which occurred prior to α and β genome-wide duplication (GWD) events. Analysis also suggests that subtle changes in nucleotide sequences during the course of evolution have led to the development of new motifs which might be involved in neo-functionalization of different WRKY members in two species. Expression and cis-regulatory motif analysis suggest possible involvement of Group II and Group III WRKY members during various stresses and growth/development including fruit ripening process respectively. PMID:27014321
Goel, Ridhi; Pandey, Ashutosh; Trivedi, Prabodh K.; Asif, Mehar H.
2016-01-01
The WRKY gene family plays an important role in the development and stress responses in plants. As information is not available on the WRKY gene family in Musa species, genome-wide analysis has been carried out in this study using available genomic information from two species, Musa acuminata and Musa balbisiana. Analysis identified 147 and 132 members of the WRKY gene family in M. acuminata and M. balbisiana, respectively. Evolutionary analysis suggests that the WRKY gene family expanded much before the speciation in both the species. Most of the orthologs retained in two species were from the γ duplication event which occurred prior to α and β genome-wide duplication (GWD) events. Analysis also suggests that subtle changes in nucleotide sequences during the course of evolution have led to the development of new motifs which might be involved in neo-functionalization of different WRKY members in two species. Expression and cis-regulatory motif analysis suggest possible involvement of Group II and Group III WRKY members during various stresses and growth/development including fruit ripening process respectively. PMID:27014321
Sazzini, Marco; Gnecchi Ruscone, Guido Alberto; Giuliani, Cristina; Sarno, Stefania; Quagliariello, Andrea; De Fanti, Sara; Boattini, Alessio; Gentilini, Davide; Fiorito, Giovanni; Catanoso, Mariagrazia; Boiardi, Luigi; Croci, Stefania; Macchioni, Pierluigi; Mantovani, Vilma; Di Blasio, Anna Maria; Matullo, Giuseppe; Salvarani, Carlo; Franceschi, Claudio; Pettener, Davide; Garagnani, Paolo; Luiselli, Donata
2016-01-01
The Italian peninsula has long represented a natural hub for human migrations across the Mediterranean area, being involved in several prehistoric and historical population movements. Coupled with a patchy environmental landscape entailing different ecological/cultural selective pressures, this might have produced peculiar patterns of population structure and local adaptations responsible for heterogeneous genomic background of present-day Italians. To disentangle this complex scenario, genome-wide data from 780 Italian individuals were generated and set into the context of European/Mediterranean genomic diversity by comparison with genotypes from 50 populations. To maximize possibility of pinpointing functional genomic regions that have played adaptive roles during Italian natural history, our survey included also ~250,000 exomic markers and ~20,000 coding/regulatory variants with well-established clinical relevance. This enabled fine-grained dissection of Italian population structure through the identification of clusters of genetically homogeneous provinces and of genomic regions underlying their local adaptations. Description of such patterns disclosed crucial implications for understanding differential susceptibility to some inflammatory/autoimmune disorders, coronary artery disease and type 2 diabetes of diverse Italian subpopulations, suggesting the evolutionary causes that made some of them particularly exposed to the metabolic and immune challenges imposed by dietary and lifestyle shifts that involved western societies in the last centuries. PMID:27582244
Sazzini, Marco; Gnecchi Ruscone, Guido Alberto; Giuliani, Cristina; Sarno, Stefania; Quagliariello, Andrea; De Fanti, Sara; Boattini, Alessio; Gentilini, Davide; Fiorito, Giovanni; Catanoso, Mariagrazia; Boiardi, Luigi; Croci, Stefania; Macchioni, Pierluigi; Mantovani, Vilma; Di Blasio, Anna Maria; Matullo, Giuseppe; Salvarani, Carlo; Franceschi, Claudio; Pettener, Davide; Garagnani, Paolo; Luiselli, Donata
2016-01-01
The Italian peninsula has long represented a natural hub for human migrations across the Mediterranean area, being involved in several prehistoric and historical population movements. Coupled with a patchy environmental landscape entailing different ecological/cultural selective pressures, this might have produced peculiar patterns of population structure and local adaptations responsible for heterogeneous genomic background of present-day Italians. To disentangle this complex scenario, genome-wide data from 780 Italian individuals were generated and set into the context of European/Mediterranean genomic diversity by comparison with genotypes from 50 populations. To maximize possibility of pinpointing functional genomic regions that have played adaptive roles during Italian natural history, our survey included also ~250,000 exomic markers and ~20,000 coding/regulatory variants with well-established clinical relevance. This enabled fine-grained dissection of Italian population structure through the identification of clusters of genetically homogeneous provinces and of genomic regions underlying their local adaptations. Description of such patterns disclosed crucial implications for understanding differential susceptibility to some inflammatory/autoimmune disorders, coronary artery disease and type 2 diabetes of diverse Italian subpopulations, suggesting the evolutionary causes that made some of them particularly exposed to the metabolic and immune challenges imposed by dietary and lifestyle shifts that involved western societies in the last centuries. PMID:27582244
Chacón-Díaz, Carlos; Quesada-Lobo, Lucía; Martirosyan, Anna; Guzmán-Verri, Caterina; Iriarte, Maite; Mancek-Keber, Mateja; Jerala, Roman; Gorvel, Jean Pierre; Moriyón, Ignacio; Moreno, Edgardo; Chaves-Olarte, Esteban
2009-01-01
Background During evolution, innate immunity has been tuned to recognize pathogen-associated molecular patterns. However, some α-Proteobacteria are stealthy intracellular pathogens not readily detected by this system. Brucella members follow this strategy and are highly virulent, but other Brucellaceae like Ochrobactrum are rhizosphere inhabitants and only opportunistic pathogens. To gain insight into the emergence of the stealthy strategy, we compared these two phylogenetically close but biologically divergent bacteria. Methodology/Principal Findings In contrast to Brucella abortus, Ochrobactrum anthropi did not replicate within professional and non-professional phagocytes and, whereas neutrophils had a limited action on B. abortus, they were essential to control O. anthropi infections. O. anthropi triggered proinflammatory responses markedly lower than Salmonella enterica but higher than B. abortus. In macrophages and dendritic cells, the corresponding lipopolysaccharides reproduced these grades of activation, and binding of O. anthropi lipopolysaccharide to the TLR4 co-receptor MD-2 and NF-κB induction laid between those of B. abortus and enteric bacteria lipopolysaccharides. These differences correlate with reported variations in lipopolysaccharide core sugars, sensitivity to bactericidal peptides and outer membrane permeability. Conclusions/Significance The results suggest that Brucellaceae ancestors carried molecules not readily recognized by innate immunity, so that non-drastic variations led to the emergence of stealthy intracellular parasites. They also suggest that some critical envelope properties, like selective permeability, are profoundly altered upon modification of pathogen-associated molecular patterns, and that this represents a further adaptation to the host. It is proposed that this adaptive trend is relevant in other intracellular α-Proteobacteria like Bartonella, Rickettsia, Anaplasma, Ehrlichia and Wolbachia. PMID:19529776
Sharrock, R.A.; Quail, P.H. )
1989-01-01
Phytochrome is a plant regulatory photoreceptor that mediates red light effects on a wide variety of physiological and molecular responses. DNA blot analysis indicates that the Arabidopsis thaliana genome contains four to five phytochrome-related gene sequences. The authors have isolated and sequenced cDNA clones corresponding to three of these genes and have deduced the amino acid sequence of the full-length polypeptide encoded in each case. One of these proteins (phyA) shows 65-80% amino acid sequence identity with the major, etiolated-tissue phytochrome apoproteins described previously in other plant species. The other two polypeptides (phyB and phyC) are unique in that they have low sequence identity with each other, with phyA, and with all previously described phytochromes. The phyA, phyB, and phyC proteins are of similar molecular mass, have related hydropathic profiles, and contain a conserved chromophore attachment region. However, the sequence comparison data indicate that the three phy genes diverged early in plant evolution, well before the divergence of the two major groups of angiosperms, the monocots and dicots. The steady-state level of the phyA transcript is high in dark-grown A. thaliana seedlings and is down-regulated by light. In contrast, the phyB and phyC transcripts are present at lower levels and are not strongly light-regulated. These findings indicate that the red/far red light-responsive phytochrome photoreceptor system in A. thaliana, and perhaps in all higher plants, consists of a family of chromoproteins that are heterogeneous in structure and regulation.
Gauchat, Dominique; Mazet, Françoise; Berney, Cédric; Schummer, Michèl; Kreger, Sylvia; Pawlowski, Jan; Galliot, Brigitte
2000-01-01
The conservation of developmental functions exerted by Antp-class homeoproteins in protostomes and deuterostomes suggested that homologs with related functions are present in diploblastic animals. Our phylogenetic analyses showed that Antp-class homeodomains belong either to non-Hox or to Hox/paraHox families. Among the 13 non-Hox families, 9 have diploblastic homologs, Msx, Emx, Barx, Evx, Tlx, NK-2, and Prh/Hex, Not, and Dlx, reported here. Among the Hox/paraHox, poriferan sequences were not found, and the cnidarian sequences formed at least five distinct cnox families. Two are significantly related to the paraHox Gsx (cnox-2) and the mox (cnox-5) sequences, whereas three display some relatedness to the Hox paralog groups 1 (cnox-1), 9/10 (cnox-3) and the paraHox cdx (cnox-4). Intermediate Hox/paraHox genes (PG 3 to 8 and lox) did not have clear cnidarian counterparts. In Hydra, cnox-1, cnox-2, and cnox-3 were not found chromosomally linked within a 150-kb range and displayed specific expression patterns in the adult head. During regeneration, cnox-1 was expressed as an early gene whatever the polarity, whereas cnox-2 was up-regulated later during head but not foot regeneration. Finally, cnox-3 expression was reestablished in the adult head once it was fully formed. These results suggest that the Hydra genes related to anterior Hox/paraHox genes are involved at different stages of apical differentiation. However, the positional information defining the oral/aboral axis in Hydra cannot be correlated strictly to that characterizing the anterior–posterior axis in vertebrates or arthropods. PMID:10781050
Chandna, Ruby; Augustine, Rehna; Kanchupati, Praveena; Kumar, Roshan; Kumar, Pawan; Arya, Gulab C; Bisht, Naveen C
2016-01-01
14-3-3s are highly conserved, multigene family proteins that have been implicated in modulating various biological processes. The presence of inherent polyploidy and genome complexity has limited the identification and characterization of 14-3-3 proteins from globally important Brassica crops. Through data mining of Brassica rapa, the model Brassica genome, we identified 21 members encoding 14-3-3 proteins namely, BraA.GRF14.a to BraA.GRF14.u. Phylogenetic analysis indicated that B. rapa contains both ε (epsilon) and non-ε 14-3-3 isoforms, having distinct intron-exon structural organization patterns. The non-ε isoforms showed lower divergence rate (Ks < 0.45) compared to ε protein isoforms (Ks > 0.48), suggesting class-specific divergence pattern. Synteny analysis revealed that mesohexaploid B. rapa genome has retained 1-5 orthologs of each Arabidopsis 14-3-3 gene, interspersed across its three fragmented sub-genomes. qRT-PCR analysis showed that 14 of the 21 BraA.GRF14 were expressed, wherein a higher abundance of non-ε transcripts was observed compared to the ε genes, indicating class-specific transcriptional bias. The BraA.GRF14 genes showed distinct expression pattern during plant developmental stages and in response to abiotic stress, phytohormone treatments, and nutrient deprivation conditions. Together, the distinct expression pattern and differential regulation of BraA.GRF14 genes indicated the occurrence of functional divergence of B. rapa 14-3-3 proteins during plant development and stress responses. PMID:26858736
Gibbs, S.; Fijneman, R.; Wiegant, J.; Van De Putte, P.; Backendorf, C. ); Van Kessel, A.D. )
1993-06-01
SPRR genes (formerly SPR) encode a novel class of polypeptides (small proline rich proteins) that are strongly induced during differentiation of human epidermal keratinocytes in vitro and in vivo. Recently the authors found that the N- and C-terminal domains of these proteins show strong sequence homology to loricrin and involucrin, suggesting that SPRR proteins constitute a new class of cornified envelope precursor proteins. Here they show that SPRR proteins are encoded by closely related members of a gene family, consisting of two genes for SPRR1, approximately seven genes for SPRR2, and a single gene for SPRR3. All SPRR genes are closely linked within a 300-kb DNA segment on human chromosome 1 band q21-q22, a region where the related loricrin and involucrin genes have also been mapped. The most characteristic feature of the SPRR gene family resides in the structure of the central segments of the encoded polypeptides that are built up from tandemly repeated units of either eight (SPRR1 and SPRR3) or nine (SPRR2) amino acids with the general consensus *K*PEP**. Sequencing data of the different members, together with their clustered chromosomal organization, strongly suggest that this gene family has evolved from a single progenitor gene by multiple intra- and intergenic duplications. Analysis of the different SPRR subfamilies reveals a gene-specific bias to either intra- or intergenic duplication. The authors propose that a process of homogenization has acted on the different members of one subfamily, whereas the different subfamilies appear to have diverged from each other, at the levels of both protein structure and gene regulation. 25 refs., 7 figs., 2 tab.
Chandna, Ruby; Augustine, Rehna; Kanchupati, Praveena; Kumar, Roshan; Kumar, Pawan; Arya, Gulab C.; Bisht, Naveen C.
2016-01-01
14-3-3s are highly conserved, multigene family proteins that have been implicated in modulating various biological processes. The presence of inherent polyploidy and genome complexity has limited the identification and characterization of 14-3-3 proteins from globally important Brassica crops. Through data mining of Brassica rapa, the model Brassica genome, we identified 21 members encoding 14-3-3 proteins namely, BraA.GRF14.a to BraA.GRF14.u. Phylogenetic analysis indicated that B. rapa contains both ε (epsilon) and non-ε 14-3-3 isoforms, having distinct intron-exon structural organization patterns. The non-ε isoforms showed lower divergence rate (Ks < 0.45) compared to ε protein isoforms (Ks > 0.48), suggesting class-specific divergence pattern. Synteny analysis revealed that mesohexaploid B. rapa genome has retained 1–5 orthologs of each Arabidopsis 14-3-3 gene, interspersed across its three fragmented sub-genomes. qRT-PCR analysis showed that 14 of the 21 BraA.GRF14 were expressed, wherein a higher abundance of non-ε transcripts was observed compared to the ε genes, indicating class-specific transcriptional bias. The BraA.GRF14 genes showed distinct expression pattern during plant developmental stages and in response to abiotic stress, phytohormone treatments, and nutrient deprivation conditions. Together, the distinct expression pattern and differential regulation of BraA.GRF14 genes indicated the occurrence of functional divergence of B. rapa 14-3-3 proteins during plant development and stress responses. PMID:26858736
Chai, You-Rong; Lei, Bo; Huang, Hua-Lei; Li, Jia-Na; Yin, Jia-Ming; Tang, Zhang-Lin; Wang, Rui; Chen, Li
2009-01-01
Molecular dissection of the Brassica yellow seed trait has been the subject of intense investigation. Arabidopsis thaliana TRANSPARENT TESTA 12 (AtTT12) encodes a multidrug and toxic compound extrusion (MATE) transporter involved in seed coat pigmentation. Two, one, and one full-length TT12 genes were isolated from B. napus, B. oleracea, and B. rapa, respectively, and Southern hybridization confirmed these gene numbers, implying loss of some of the triplicated TT12 genes in Brassica. BnTT12-1, BnTT12-2, BoTT12, and BrTT12 are 2,714, 3,062, 4,760, and 2,716 bp, with the longest mRNAs of 1,749, 1,711, 1,739, and 1,752 bp, respectively. All genes contained alternative transcriptional start and polyadenylation sites. BrTT12 and BoTT12 are the progenitors of BnTT12-1 and BnTT12-2, respectively, validating B. napus as an amphidiploid. All Brassica TT12 proteins displayed high levels of identity (>99%) to each other and to AtTT12 (>92%). Brassica TT12 genes resembled AtTT12 in such basic features as MatE/NorM CDs, subcellular localization, transmembrane helices, and phosphorylation sites. Plant TT12 orthologs differ from other MATE proteins by two specific motifs. Like AtTT12, all Brassica TT12 genes are most highly expressed in developing seeds. However, a range of organ specificity was observed with BnTT12 genes being less organ-specific. TT12 expression is absent in B. rapa yellow-seeded line 06K124, but not downregulated in B. oleracea yellow-seeded line 06K165. In B. napus yellow-seeded line L2, BnTT12-2 expression is absent, whereas BnTT12-1 is expressed normally. Among Brassica species, TT12 genes are differentially related to the yellow seed trait. The molecular basis for the yellow seed trait, in Brassica, and the theoretical and practical implications of the highly variable intron 1 of these TT12 genes are discussed. PMID:19018571
Arya, Gulab C.; Kumar, Roshan; Bisht, Naveen C.
2014-01-01
Heterotrimeric G-proteins, comprising of Gα, Gβ, and Gγ subunits, are important signal transducers which regulate many aspects of fundamental growth and developmental processes in all eukaryotes. Initial studies in model plants Arabidopsis and rice suggest that the repertoire of plant G-protein is much simpler than that observed in metazoans. In order to assess the consequence of whole genome triplication events within Brassicaceae family, we investigated the multiplicity of G-protein subunit genes in mesohexaploid Brassica rapa, a globally important vegetable and oilseed crop. We identified one Gα (BraA.Gα1), three Gβ (BraA.Gβ1, BraA.Gβ2, and BraA.Gβ3), and five Gγ (BraA.Gγ1, BraA.Gγ2, BraA.Gγ3, BraA.Gγ4, and BraA.Gγ5) genes from B. rapa, with a possibility of 15 Gαβγ heterotrimer combinations. Our analysis suggested that the process of genome triplication coupled with gene-loss (gene-fractionation) phenomenon have shaped the quantitative and sequence diversity of G-protein subunit genes in the extant B. rapa genome. Detailed expression analysis using qRT-PCR assays revealed that the G-protein genes have retained ubiquitous but distinct expression profiles across plant development. The expression of multiple G-protein genes was differentially regulated during seed-maturation and germination stages, and in response to various phytohormone treatments and stress conditions. Yeast-based interaction analysis showed that G-protein subunits interacted in most of the possible combinations, with some degree of subunit-specific interaction specificity, to control the functional selectivity of G-protein heterotrimer in different cell and tissue-types or in response to different environmental conditions. Taken together, this research identifies a highly diverse G-protein signaling network known to date from B. rapa, and provides a clue about the possible complexity of G-protein signaling networks present across globally important Brassica species. PMID
Arya, Gulab C; Kumar, Roshan; Bisht, Naveen C
2014-01-01
Heterotrimeric G-proteins, comprising of Gα, Gβ, and Gγ subunits, are important signal transducers which regulate many aspects of fundamental growth and developmental processes in all eukaryotes. Initial studies in model plants Arabidopsis and rice suggest that the repertoire of plant G-protein is much simpler than that observed in metazoans. In order to assess the consequence of whole genome triplication events within Brassicaceae family, we investigated the multiplicity of G-protein subunit genes in mesohexaploid Brassica rapa, a globally important vegetable and oilseed crop. We identified one Gα (BraA.Gα1), three Gβ (BraA.Gβ1, BraA.Gβ2, and BraA.Gβ3), and five Gγ (BraA.Gγ1, BraA.Gγ2, BraA.Gγ3, BraA.Gγ4, and BraA.Gγ5) genes from B. rapa, with a possibility of 15 Gαβγ heterotrimer combinations. Our analysis suggested that the process of genome triplication coupled with gene-loss (gene-fractionation) phenomenon have shaped the quantitative and sequence diversity of G-protein subunit genes in the extant B. rapa genome. Detailed expression analysis using qRT-PCR assays revealed that the G-protein genes have retained ubiquitous but distinct expression profiles across plant development. The expression of multiple G-protein genes was differentially regulated during seed-maturation and germination stages, and in response to various phytohormone treatments and stress conditions. Yeast-based interaction analysis showed that G-protein subunits interacted in most of the possible combinations, with some degree of subunit-specific interaction specificity, to control the functional selectivity of G-protein heterotrimer in different cell and tissue-types or in response to different environmental conditions. Taken together, this research identifies a highly diverse G-protein signaling network known to date from B. rapa, and provides a clue about the possible complexity of G-protein signaling networks present across globally important Brassica species. PMID
Parra, Marilyn; Gee, Sherry; Chan, Nadine; Ryaboy, Dmitriy; Dubchak, Inna; Narla, Mohandas; Gascard, Philippe D.; Conboy, John G.
2004-07-15
The EPB41 (protein 4.1) genes epitomize the resourcefulness of the mammalian genome to encode a complex proteome from a small number of genes. By utilizing alternative transcriptional promoters and tissue-specific alternative pre-mRNA splicing, EPB41, EPB41L2, EPB41L3, and EPB41L1 encode a diverse array of structural adapter proteins. Comparative genomic and transcript analysis of these 140kb-240kb genes indicates several unusual features: differential evolution of highly conserved exons encoding known functional domains, interspersed with unique exons whose size and sequence variations contribute substantially to intergenic diversity: alternative first exons, most of which map far upstream of the coding regions; and complex tissue-specific alternative pre-mRNA splicing that facilitates synthesis of functionally different complements of 4.1 proteins in various cells. Understanding the splicing regulatory networks that control protein 4.1 expression will be critical to a full appreciation of the many roles of 4.1 proteins in normal cell biology and their proposed roles in human cancer.
Parra, Marilyn; Gee, Sherry; Chan, Nadine; Ryaboy, Dmitriy; Dubchak, Inna; Mohandas, Narla; Gascard, Philippe D; Conboy, John G
2004-10-01
The EPB41 (protein 4.1) genes epitomize the resourcefulness of the mammalian genome to encode a complex proteome from a small number of genes. By utilizing alternative transcriptional promoters and tissue-specific alternative pre-mRNA splicing, EPB41, EPB41L2, EPB41L3, and EPB41L1 encode a diverse array of structural adapter proteins. Comparative genomic and transcript analysis of these 140- to 240-kb genes indicates several unusual features: differential evolution of highly conserved exons encoding known functional domains interspersed with unique exons whose size and sequence variations contribute substantially to intergenic diversity; alternative first exons, most of which map far upstream of the coding regions; and complex tissue-specific alternative pre-mRNA splicing that facilitates synthesis of functionally different complements of 4.1 proteins in various cells. Understanding the splicing regulatory networks that control protein 4.1 expression will be critical to a full appreciation of the many roles of 4.1 proteins in normal cell biology and their proposed roles in human cancer. PMID:15475241
DE and NLP Based QPLS Algorithm
NASA Astrophysics Data System (ADS)
Yu, Xiaodong; Huang, Dexian; Wang, Xiong; Liu, Bo
As a novel evolutionary computing technique, Differential Evolution (DE) has been considered to be an effective optimization method for complex optimization problems, and achieved many successful applications in engineering. In this paper, a new algorithm of Quadratic Partial Least Squares (QPLS) based on Nonlinear Programming (NLP) is presented. And DE is used to solve the NLP so as to calculate the optimal input weights and the parameters of inner relationship. The simulation results based on the soft measurement of diesel oil solidifying point on a real crude distillation unit demonstrate that the superiority of the proposed algorithm to linear PLS and QPLS which is based on Sequential Quadratic Programming (SQP) in terms of fitting accuracy and computational costs.
ESTER: Evolution STEllaire en Rotation
NASA Astrophysics Data System (ADS)
Rieutord, Michel
2013-05-01
The ESTER code computes the steady state of an isolated star of mass larger than two solar masses. The only convective region computed as such is the core where isentropy is assumed. ESTER provides solutions of the partial differential equations, for the pressure, density, temperature, angular velocity and meridional velocity for the whole volume. The angular velocity (differential rotation) and meridional circulation are computed consistently with the structure and are driven by the baroclinic torque. The code uses spectral methods, both radially and horizontally, with spherical harmonics and Chebyshev polynomials. The iterations follow Newton's algorithm. The code is object-oriented and is written in C++; a python suite allows an easy visualization of the results. While running, PGPLOT graphs are displayed to show evolution of the iterations.
Symbolic Solution of Linear Differential Equations
NASA Technical Reports Server (NTRS)
Feinberg, R. B.; Grooms, R. G.
1981-01-01
An algorithm for solving linear constant-coefficient ordinary differential equations is presented. The computational complexity of the algorithm is discussed and its implementation in the FORMAC system is described. A comparison is made between the algorithm and some classical algorithms for solving differential equations.
Jiang, Wenjuan; Shi, Yunbo; Zhao, Wenjie; Wang, Xiangxin
2016-01-01
The main part of the magnetic fluxgate sensor is the magnetic core, the hysteresis characteristic of which affects the performance of the sensor. When the fluxgate sensors are modelled for design purposes, an accurate model of hysteresis characteristic of the cores is necessary to achieve good agreement between modelled and experimental data. The Jiles-Atherton model is simple and can reflect the hysteresis properties of the magnetic material precisely, which makes it widely used in hysteresis modelling and simulation of ferromagnetic materials. However, in practice, it is difficult to determine the parameters accurately owing to the sensitivity of the parameters. In this paper, the Biogeography-Based Optimization (BBO) algorithm is applied to identify the Jiles-Atherton model parameters. To enhance the performances of the BBO algorithm such as global search capability, search accuracy and convergence rate, an improved Biogeography-Based Optimization (IBBO) algorithm is put forward by using Arnold map and mutation strategy of Differential Evolution (DE) algorithm. Simulation results show that IBBO algorithm is superior to Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm, Differential Evolution algorithm and BBO algorithm in identification accuracy and convergence rate. The IBBO algorithm is applied to identify Jiles-Atherton model parameters of selected permalloy. The simulation hysteresis loop is in high agreement with experimental data. Using permalloy as core of fluxgate probe, the simulation output is consistent with experimental output. The IBBO algorithm can identify the parameters of Jiles-Atherton model accurately, which provides a basis for the precise analysis and design of instruments and equipment with magnetic core. PMID:27347974
Jiang, Wenjuan; Shi, Yunbo; Zhao, Wenjie; Wang, Xiangxin
2016-01-01
The main part of the magnetic fluxgate sensor is the magnetic core, the hysteresis characteristic of which affects the performance of the sensor. When the fluxgate sensors are modelled for design purposes, an accurate model of hysteresis characteristic of the cores is necessary to achieve good agreement between modelled and experimental data. The Jiles-Atherton model is simple and can reflect the hysteresis properties of the magnetic material precisely, which makes it widely used in hysteresis modelling and simulation of ferromagnetic materials. However, in practice, it is difficult to determine the parameters accurately owing to the sensitivity of the parameters. In this paper, the Biogeography-Based Optimization (BBO) algorithm is applied to identify the Jiles-Atherton model parameters. To enhance the performances of the BBO algorithm such as global search capability, search accuracy and convergence rate, an improved Biogeography-Based Optimization (IBBO) algorithm is put forward by using Arnold map and mutation strategy of Differential Evolution (DE) algorithm. Simulation results show that IBBO algorithm is superior to Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm, Differential Evolution algorithm and BBO algorithm in identification accuracy and convergence rate. The IBBO algorithm is applied to identify Jiles-Atherton model parameters of selected permalloy. The simulation hysteresis loop is in high agreement with experimental data. Using permalloy as core of fluxgate probe, the simulation output is consistent with experimental output. The IBBO algorithm can identify the parameters of Jiles-Atherton model accurately, which provides a basis for the precise analysis and design of instruments and equipment with magnetic core. PMID:27347974
Automatic differentiation bibliography
Corliss, G.F.
1992-07-01
This is a bibliography of work related to automatic differentiation. Automatic differentiation is a technique for the fast, accurate propagation of derivative values using the chain rule. It is neither symbolic nor numeric. Automatic differentiation is a fundamental tool for scientific computation, with applications in optimization, nonlinear equations, nonlinear least squares approximation, stiff ordinary differential equation, partial differential equations, continuation methods, and sensitivity analysis. This report is an updated version of the bibliography which originally appeared in Automatic Differentiation of Algorithms: Theory, Implementation, and Application.
Integration of PDEs by differential geometric means
NASA Astrophysics Data System (ADS)
Tehseen, Naghmana; Prince, Geoff
2013-03-01
We use Vessiot theory and exterior calculus to solve partial differential equations (PDEs) of the type uyy = F(x, y, u, ux, uy, uxx, uxy) and associated evolution equations. These equations are represented by the Vessiot distribution of vector fields. We develop and apply an algorithm to find the largest integrable sub-distributions and hence solutions of the PDEs. We then apply the integrating factor technique Sherring and Prince (1992 Trans. Am. Math. Soc. 433 453) to integrate this integrable Vessiot sub-distribution. The method is successfully applied to a large class of linear and nonlinear PDEs.
Schoenberg, Mike R; Duff, Kevin; Dorfman, Karen; Adams, Russell L
2004-05-01
Data from the WAIS-III standardization sample (The Psychological Corporation, 1997) was used to generate VIQ and PIQ estimation formulae using demographic variables and current WAIS-III subtest performances. The sample (n = 2450) was randomly divided into two groups; the first was used to develop formulas and the second to validate the regression equations. Age, education, ethnicity, gender, region of the country as well as Vocabulary, Matrix Reasoning, and Picture Completion subtests raw scores were used as predictor variables. Prediction formulas were generated using a single verbal and two performance subtest algorithms. The VIQ OPIE-3 model combined Vocabulary raw scores with demographic variables. The PIQ estimation algorithm used Matrix Reasoning and Picture Completion raw scores with demographic variables. The formulas for estimating premorbid VIQ and PIQ were highly significant and accurate in estimation. Differences in estimated VIQ and PIQ scores were evaluated and the OPIE-3 algorithms were found to accurately predict VIQ and PIQ differences within the WAIS-III standardization sample. PMID:15587673
Park, Chan Bong; Nakane, Hideaki; Sugimoto, Nobuo; Matsui, Ichiro; Sasano, Yasuhiro; Fujinuma, Yasumi; Ikeuchi, Izumi; Kurokawa, Jun-Ichi; Furuhashi, Noritaka
2006-05-20
Recently, a data processing and retrieval algorithm (version 2) for ozone, aerosol, and temperature lidar measurements was developed for an ozone lidar system at the National Institute for Environmental Studies (NIES) in Tsukuba (36 degrees N,140 degrees E), Japan. A method for obtaining the aerosol boundary altitude and the aerosol extinction-to-backscatter ratio in the version 2 algorithm enables a more accurate determination of the vertical profiles of aerosols and a more accurate correction of the systematic errors caused by aerosols in the vertical profile of ozone. Improvements in signal processing are incorporated for the correction of systematic errors such as the signal-induced noise and the dead-time effect. The mean vertical ozone profiles of the NIES ozone lidar were compared with those of the Stratospheric Aerosol and Gas Experiment II (SAGE II); they agreed well within a 5% relative difference in the 20-40 km altitude range and within 10% up to 45 km. The long-term variations in the NIES ozone lidar also showed good coincidence with the ozonesonde and SAGE II at 20, 25, 30, and 35 km. The temperatures retrieved from the NIES ozone lidar and those given by the National Center for Environmental Prediction agreed within 7 K in the 35-50 km range. PMID:16708104
The emergence of mechanoregulated endochondral ossification in evolution.
Khayyeri, Hanifeh; Prendergast, Patrick J
2013-02-22
The differentiation of skeletal tissue phenotypes is partly regulated by mechanical forces. This mechanoregulatory aspect of tissue differentiation has been the subject of many experimental and computational investigations. However, little is known about what factors promoted the emergence of mechanoregulated tissue differentiation in evolution, even though mechanoregulated tissue differentiation, for example during development or healing of adult bone, is crucial for vertebrate phylogeny. In this paper, we use a computational framework to test the hypothesis that the emergence of mechanosensitive genes that trigger endochondral ossification in evolution will stabilise in the population and create a variable mechanoregulated response, if the endochondral ossification process enhances fitness for survival. The model combines an evolutionary algorithm that considers genetic change with a mechanoregulated fracture healing model in which the fitness of animals in a population is determined by their ability to heal their bones. The simulations show that, with the emergence of mechanosensitive genes through evolution enabling skeletal cells to modulate their synthetic activities, novel differentiation pathways such as endochondral ossification could have emerged, which when favoured by natural selection is maintained in a population. Furthermore, the model predicts that evolutionary forces do not lead to a single optimal mechanoregulated response but that the capacity of endochondral ossification exists with variability in a population. The simulations correspond with many existing findings about the mechanosensitivity of skeletal tissues in current animal populations, therefore indicating that this kind of multi-level models could be used in future population based simulations of tissue differentiation. PMID:23261239
NASA Astrophysics Data System (ADS)
Darling, A.; Whipple, K. X.; Nichols, K. K.; Bierman, P. R.
2012-12-01
Exhumation and Landscape Evolution along the Colorado River: A Proposed Means of Differentiating the Roles of Baselevel Fall and Lithologic Heterogeneity Varied lithology and complex base-level history are important factors controlling the evolution of the Colorado Plateau landscape. Steep-walled, high-relief canyons inset into low relief surfaces attest to relief production, with volcanic deposits and cosmogenic nuclides in sediments suggesting rates are higher within canyons than on plateau surfaces. Neogene acceleration in the rate of baselevel fall in response to integration of the Colorado River across the Grand Wash Cliffs and recent rock uplift may be responsible for the landscape non-equilibrium. Alternatively, a longer history of baselevel fall and long-lived relief is possible. A reconstructed 30 Ma paleosurface implies uniform incision of ~1.5 km on the Colorado River across the plateau since that time (others have suggested much of this is post 10 Ma). Thermochronologic data suggest substantial relief in the vicinity of Grand Canyon into the Miocene. A steady baselevel fall in the presence of pronounced lithologic heterogeneity (weak rocks over stronger rocks) provides a plausible explanation for non-equilibrium erosion and more recent relief production. As such, it is possible the transition from weaker to harder sedimentary rocks is the cause of the nonequilibrium erosion along tributaries. However, the lithologic heterogeneity will be expressed in the landscape whether or not baselevel fall rate accelerated. Topographically, this transition is indistinguishable from continuous baselevel fall through variable rock strength, because increase in baselevel drop is transmitted via discrete upstream-migrating knickpoints that can have the same form as knickpoints caused by the rock-type transition. Fortunately, there is a diagnostic difference expected in the patterns of erosion rates across the landscape. Naturally, in the case of accelerated baselevel
NASA Astrophysics Data System (ADS)
Henry, Gregory W.; Eaton, Joel A.; Hamer, Jamesia; Hall, Douglas S.
1995-04-01
We have analyzed 15-19 yr of photoelectric photometry, obtained manually and with automated telescopes, of the chromospherically active binaries lambda And, sigma Gem, II Peg, and V711 Tau. These observations let us identify individual dark starspots on the stellar surfaces from periodic dimming of the starlight, follow the evolution of these spots, and search for long-term cyclic changes in the properties of these starspots that might reveal magnetic cycles analogous to the Sun's 11 yr sunspot cycle. We developed a computer code to fit a simple two-spot model to our observed light curves that allows us to extract the most easily determinable and most reliable spot parameters from the light curves, i.e., spot longitudes and radii. We then used these measured properties to identify individual spots and to chart their life histories by constructing migration and amplitude curves. We identified and followed 11 spots in lambda And, 16 in sigma Gem, 12 in II Peg, and 15 in V711 Tau. Lifetimes of individual spots ranged from a few months to longer than 6 yr. Differential rotation coefficients, estimated from the observed range of spot rotation periods for each star and defined by equation (2), were 0.04 for lambda And, 0.038 for sigma Gem, 0.005 for II Peg, and 0.006 for V711 Tau, versus 0.19 for the Sun. We searched for cyclic changes in mean brightness, B-V color index, and spot rotation period as evidence for long-term cycles. Of these, long-term variability in mean brightness appears to offer the best evidence for such cycles in these four stars. Cycles of 11.1 yr for lambda And, 8.5 yr for sigma Gem, 11 yr for II Peg, and 16 yr V711 Tau are implied by these mean brightness changes. Cyclic changes in spot rotation period were found in lambda And and possibly II Peg. Errors in B-V were too large for any long-term changes to be detectable.
NASA Technical Reports Server (NTRS)
Henry, Gregory W.; Eaton, Joel A.; Hamer, Jamesia; Hall, Douglas S.
1995-01-01
We have analyzed 15-19 yr of photoelectric photometry, obtained manually and with automated telescopes, of the chromospherically active binaries lambda And, sigma Gem, II Peg, and V711 Tau. These observations let us identify individual dark starspots on the stellar surfaces from periodic dimming of the starlight, follow the evolution of these spots, and search for long-term cyclic changes in the properties of these starspots that might reveal magnetic cycles analogous to the Sun's 11 yr sunspot cycle. We developed a computer code to fit a simple two-spot model to our observed light curves that allows us to extract the most easily determinable and most reliable spot parameters from the light curves, i.e., spot longitudes and radii. We then used these measured properties to identify individual spots and to chart their life histories by constructing migration and amplitude curves. We identified and followed 11 spots in lambda And, 16 in sigma Gem, 12 in II Peg, and 15 in V711 Tau. Lifetimes of individual spots ranged from a few months to longer than 6 yr. Differential rotation coefficients, estimated from the observed range of spot rotation periods for each star and defined by equation (2), were 0.04 for lambda And, 0.038 for sigma Gem, 0.005 for II Peg, and 0.006 for V711 Tau, versus 0.19 for the Sun. We searched for cyclic changes in mean brightness, B-V color index, and spot rotation period as evidence for long-term cycles. Of these, long-term variability in mean brightness appears to offer the best evidence for such cycles in these four stars. Cycles of 11.1 yr for lambda And, 8.5 yr for sigma Gem, 11 yr for II Peg, and 16 yr V711 Tau are implied by these mean brightness changes. Cyclic changes in spot rotation period were found in lambda And and possibly II Peg. Errors in B-V were too large for any long-term changes to be detectable.
NASA Technical Reports Server (NTRS)
Wang, Lui; Bayer, Steven E.
1991-01-01
Genetic algorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solving methods) based loosely on the processes of natural genetics and Darwinian survival of the fittest. Basic genetic algorithms concepts are introduced, genetic algorithm applications are introduced, and results are presented from a project to develop a software tool that will enable the widespread use of genetic algorithm technology.
Optimization of Straight Cylindrical Turning Using Artificial Bee Colony (ABC) Algorithm
NASA Astrophysics Data System (ADS)
Prasanth, Rajanampalli Seshasai Srinivasa; Hans Raj, Kandikonda
2016-06-01
Artificial bee colony (ABC) algorithm, that mimics the intelligent foraging behavior of honey bees, is increasingly gaining acceptance in the field of process optimization, as it is capable of handling nonlinearity, complexity and uncertainty. Straight cylindrical turning is a complex and nonlinear machining process which involves the selection of appropriate cutting parameters that affect the quality of the workpiece. This paper presents the estimation of optimal cutting parameters of the straight cylindrical turning process using the ABC algorithm. The ABC algorithm is first tested on four benchmark problems of numerical optimization and its performance is compared with genetic algorithm (GA) and ant colony optimization (ACO) algorithm. Results indicate that, the rate of convergence of ABC algorithm is better than GA and ACO. Then, the ABC algorithm is used to predict optimal cutting parameters such as cutting speed, feed rate, depth of cut and tool nose radius to achieve good surface finish. Results indicate that, the ABC algorithm estimated a comparable surface finish when compared with real coded genetic algorithm and differential evolution algorithm.
Hernández-Ocaña, Betania; Pozos-Parra, Ma Del Pilar; Mezura-Montes, Efrén; Portilla-Flores, Edgar Alfredo; Vega-Alvarado, Eduardo; Calva-Yáñez, Maria Bárbara
2016-01-01
This paper presents two-swim operators to be added to the chemotaxis process of the modified bacterial foraging optimization algorithm to solve three instances of the synthesis of four-bar planar mechanisms. One swim favors exploration while the second one promotes fine movements in the neighborhood of each bacterium. The combined effect of the new operators looks to increase the production of better solutions during the search. As a consequence, the ability of the algorithm to escape from local optimum solutions is enhanced. The algorithm is tested through four experiments and its results are compared against two BFOA-based algorithms and also against a differential evolution algorithm designed for mechanical design problems. The overall results indicate that the proposed algorithm outperforms other BFOA-based approaches and finds highly competitive mechanisms, with a single set of parameter values and with less evaluations in the first synthesis problem, with respect to those mechanisms obtained by the differential evolution algorithm, which needed a parameter fine-tuning process for each optimization problem. PMID:27057156
Hernández-Ocaña, Betania; Pozos-Parra, Ma. Del Pilar; Mezura-Montes, Efrén; Portilla-Flores, Edgar Alfredo; Vega-Alvarado, Eduardo; Calva-Yáñez, Maria Bárbara
2016-01-01
This paper presents two-swim operators to be added to the chemotaxis process of the modified bacterial foraging optimization algorithm to solve three instances of the synthesis of four-bar planar mechanisms. One swim favors exploration while the second one promotes fine movements in the neighborhood of each bacterium. The combined effect of the new operators looks to increase the production of better solutions during the search. As a consequence, the ability of the algorithm to escape from local optimum solutions is enhanced. The algorithm is tested through four experiments and its results are compared against two BFOA-based algorithms and also against a differential evolution algorithm designed for mechanical design problems. The overall results indicate that the proposed algorithm outperforms other BFOA-based approaches and finds highly competitive mechanisms, with a single set of parameter values and with less evaluations in the first synthesis problem, with respect to those mechanisms obtained by the differential evolution algorithm, which needed a parameter fine-tuning process for each optimization problem. PMID:27057156
NASA Astrophysics Data System (ADS)
Levandowski, Will; Boyd, Oliver S.; Briggs, Rich W.; Gold, Ryan D.
2015-12-01
This paper develops a Monte Carlo algorithm for extracting three-dimensional lithospheric density models from geophysical data. Empirical scaling relationships between velocity and density create a 3-D starting density model, which is then iteratively refined until it reproduces observed gravity and topography. This approach permits deviations from uniform crustal velocity-density scaling, which provide insight into crustal lithology and prevent spurious mapping of crustal anomalies into the mantle. We test this algorithm on the Proterozoic Midcontinent Rift (MCR), north-central United States. The MCR provides a challenge because it hosts a gravity high overlying low shear-wave velocity crust in a generally flat region. Our initial density estimates are derived from a seismic velocity/crustal thickness model based on joint inversion of surface-wave dispersion and receiver functions. By adjusting these estimates to reproduce gravity and topography, we generate a lithospheric-scale model that reveals dense middle crust and eclogitized lowermost crust within the rift. Mantle lithospheric density beneath the MCR is not anomalous, consistent with geochemical evidence that lithospheric mantle was not the primary source of rift-related magmas and suggesting that extension occurred in response to far-field stress rather than a hot mantle plume. Similarly, the subsequent inversion of normal faults resulted from changing far-field stress that exploited not only warm, recently faulted crust but also a gravitational potential energy low in the MCR. The success of this density modeling algorithm in the face of such apparently contradictory geophysical properties suggests that it may be applicable to a variety of tectonic and geodynamic problems.
Elbridge Gerry Puckett
2008-05-13
All of the work conducted under the auspices of DE-FC02-01ER25473 was characterized by exceptionally close collaboration with researchers at the Lawrence Berkeley National Laboratory (LBNL). This included having one of my graduate students - Sarah Williams - spend the summer working with Dr. Ann Almgren a staff scientist in the Center for Computational Sciences and Engineering (CCSE) which is a part of the National Energy Research Supercomputer Center (NERSC) at LBNL. As a result of this visit Sarah decided to work on a problem suggested by Dr. John Bell the head of CCSE for her PhD thesis, which she finished in June 2007. Writing a PhD thesis while working at one of the University of California (UC) managed DOE laboratories is a long established tradition at the University of California and I have always encouraged my students to consider doing this. For example, in 2000 one of my graduate students - Matthew Williams - finished his PhD thesis while working with Dr. Douglas Kothe at the Los Alamos National Laboratory (LANL). Matt is now a staff scientist in the Diagnostic Applications Group in the Applied Physics Division at LANL. Another one of my graduate students - Christopher Algieri - who was partially supported with funds from DE-FC02-01ER25473 wrote am MS Thesis that analyzed and extended work published by Dr. Phil Colella and his colleagues in 1998. Dr. Colella is the head of the Applied Numerical Algorithms Group (ANAG) in the National Energy Research Supercomputer Center at LBNL and is the lead PI for the APDEC ISIC which was comprised of several National Laboratory research groups and at least five University PI's at five different universities. Chris Algieri is now employed as a staff member in Dr. Bill Collins' research group at LBNL developing computational models for climate change research. Bill Collins was recently hired at LBNL to start and be the Head of the Climate Science Department in the Earth Sciences Division at LBNL. Prior to this he had
Levandowski, William Brower; Boyd, Oliver; Briggs, Richard; Gold, Ryan D.
2015-01-01
We test this algorithm on the Proterozoic Midcontinent Rift (MCR), north-central U.S. The MCR provides a challenge because it hosts a gravity high overlying low shear-wave velocity crust in a generally flat region. Our initial density estimates are derived from a seismic velocity/crustal thickness model based on joint inversion of surface-wave dispersion and receiver functions. By adjusting these estimates to reproduce gravity and topography, we generate a lithospheric-scale model that reveals dense middle crust and eclogitized lowermost crust within the rift. Mantle lithospheric density beneath the MCR is not anomalous, consistent with geochemical evidence that lithospheric mantle was not the primary source of rift-related magmas and suggesting that extension occurred in response to far-field stress rather than a hot mantle plume. Similarly, the subsequent inversion of normal faults resulted from changing far-field stress that exploited not only warm, recently faulted crust but also a gravitational potential energy low in the MCR. The success of this density modeling algorithm in the face of such apparently contradictory geophysical properties suggests that it may be applicable to a variety of tectonic and geodynamic problems.
Arterial cannula shape optimization by means of the rotational firefly algorithm
NASA Astrophysics Data System (ADS)
Tesch, K.; Kaczorowska, K.
2016-03-01
This article presents global optimization results of arterial cannula shapes by means of the newly modified firefly algorithm. The search for the optimal arterial cannula shape is necessary in order to minimize losses and prepare the flow that leaves the circulatory support system of a ventricle (i.e. blood pump) before it reaches the heart. A modification of the standard firefly algorithm, the so-called rotational firefly algorithm, is introduced. It is shown that the rotational firefly algorithm allows for better exploration of search spaces which results in faster convergence and better solutions in comparison with its standard version. This is particularly pronounced for smaller population sizes. Furthermore, it maintains greater diversity of populations for a longer time. A small population size and a low number of iterations are necessary to keep to a minimum the computational cost of the objective function of the problem, which comes from numerical solution of the nonlinear partial differential equations. Moreover, both versions of the firefly algorithm are compared to the state of the art, namely the differential evolution and covariance matrix adaptation evolution strategies.
An, Jaehyun; Kim, Kwangsoo; Chae, Heejoon; Kim, Sun
2014-10-01
Gene expression in the whole cell can be routinely measured by microarray technologies or recently by using sequencing technologies. Using these technologies, identifying differentially expressed genes (DEGs) among multiple phenotypes is the very first step to understand difference between phenotypes. Thus many methods for detecting DEGs between two groups have been developed. For example, T-test and relative entropy are used for detecting difference between two probability distributions. When more than two phenotypes are considered, these methods are not applicable and other methods such as ANOVA F-test and Kruskal-Wallis are used for finding DEGs in the multiclass data. However, ANOVA F-test assumes a normal distribution and it is not designed to identify DEGs where genes are expressed distinctively in each of phenotypes. Kruskal-Wallis method, a non-parametric method, is more robust but sensitive to outliers. In this paper, we propose a non-parametric and information theoretical approach for identifying DEGs. Our method identified DEGs effectively and it is shown less sensitive to outliers in two data sets: a three-class drought resistant rice data set and a three-class breast cancer data set. In extensive experiments with simulated and real data, our method was shown to outperform existing tools in terms of accuracy of characterizing phenotypes using DEGs. A web service is implemented at http://biohealth.snu.ac.kr/software/degpack for the analysis of multi-class data and it includes SAMseq and PoissonSeq methods in addition to the method described in this paper. PMID:24981074
Conservation laws, differential identities, and constraints of partial differential equations
NASA Astrophysics Data System (ADS)
Zharinov, V. V.
2015-11-01
We consider specific cohomological properties such as low-dimensional conservation laws and differential identities of systems of partial differential equations (PDEs). We show that such properties are inherent to complex systems such as evolution systems with constraints. The mathematical tools used here are the algebraic analysis of PDEs and cohomologies over differential algebras and modules.
Experiments with a Parallel Multi-Objective Evolutionary Algorithm for Scheduling
NASA Technical Reports Server (NTRS)
Brown, Matthew; Johnston, Mark D.
2013-01-01
Evolutionary multi-objective algorithms have great potential for scheduling in those situations where tradeoffs among competing objectives represent a key requirement. One challenge, however, is runtime performance, as a consequence of evolving not just a single schedule, but an entire population, while attempting to sample the Pareto frontier as accurately and uniformly as possible. The growing availability of multi-core processors in end user workstations, and even laptops, has raised the question of the extent to which such hardware can be used to speed up evolutionary algorithms. In this paper we report on early experiments in parallelizing a Generalized Differential Evolution (GDE) algorithm for scheduling long-range activities on NASA's Deep Space Network. Initial results show that significant speedups can be achieved, but that performance does not necessarily improve as more cores are utilized. We describe our preliminary results and some initial suggestions from parallelizing the GDE algorithm. Directions for future work are outlined.
Ding, Pan; Gong, Xue-Qing
2016-05-01
Titanium dioxide (TiO2) is an important metal oxide that has been used in many different applications. TiO2 has also been widely employed as a model system to study basic processes and reactions in surface chemistry and heterogeneous catalysis. In this work, we investigated the (011) surface of rutile TiO2 by focusing on its reconstruction. Density functional theory calculations aided by a genetic algorithm based optimization scheme were performed to extensively sample the potential energy surfaces of reconstructed rutile TiO2 structures that obey (2 × 1) periodicity. A lot of stable surface configurations were located, including the global-minimum configuration that was proposed previously. The wide variety of surface structures determined through the calculations performed in this work provide insight into the relationship between the atomic configuration of a surface and its stability. More importantly, several analytical schemes were proposed and tested to gauge the differences and similarities among various surface structures, aiding the construction of the complete pathway for the reconstruction process. PMID:27115517
NASA Astrophysics Data System (ADS)
Raev, M. D.; Sharkov, E. A.; Tikhonov, V. V.; Repina, I. A.; Komarova, N. Yu.
2015-12-01
The GLOBAL-RT database (DB) is composed of long-term radio heat multichannel observation data received from DMSP F08-F17 satellites; it is permanently supplemented with new data on the Earth's exploration from the space department of the Space Research Institute, Russian Academy of Sciences. Arctic ice-cover areas for regions higher than 60° N latitude were calculated using the DB polar version and NASA Team 2 algorithm, which is widely used in foreign scientific literature. According to the analysis of variability of Arctic ice cover during 1987-2014, 2 months were selected when the Arctic ice cover was maximal (February) and minimal (September), and the average ice cover area was calculated for these months. Confidence intervals of the average values are in the 95-98% limits. Several approximations are derived for the time dependences of the ice-cover maximum and minimum over the period under study. Regression dependences were calculated for polynomials from the first degree (linear) to sextic. It was ascertained that the minimal root-mean-square error of deviation from the approximated curve sharply decreased for the biquadratic polynomial and then varied insignificantly: from 0.5593 for the polynomial of third degree to 0.4560 for the biquadratic polynomial. Hence, the commonly used strictly linear regression with a negative time gradient for the September Arctic ice cover minimum over 30 years should be considered incorrect.
Computations involving differential operators and their actions on functions
NASA Technical Reports Server (NTRS)
Crouch, Peter E.; Grossman, Robert; Larson, Richard
1991-01-01
The algorithms derived by Grossmann and Larson (1989) are further developed for rewriting expressions involving differential operators. The differential operators involved arise in the local analysis of nonlinear dynamical systems. These algorithms are extended in two different directions: the algorithms are generalized so that they apply to differential operators on groups and the data structures and algorithms are developed to compute symbolically the action of differential operators on functions. Both of these generalizations are needed for applications.
Optimization Algorithms in Optimal Predictions of Atomistic Properties by Kriging.
Di Pasquale, Nicodemo; Davie, Stuart J; Popelier, Paul L A
2016-04-12
The machine learning method kriging is an attractive tool to construct next-generation force fields. Kriging can accurately predict atomistic properties, which involves optimization of the so-called concentrated log-likelihood function (i.e., fitness function). The difficulty of this optimization problem quickly escalates in response to an increase in either the number of dimensions of the system considered or the size of the training set. In this article, we demonstrate and compare the use of two search algorithms, namely, particle swarm optimization (PSO) and differential evolution (DE), to rapidly obtain the maximum of this fitness function. The ability of these two algorithms to find a stationary point is assessed by using the first derivative of the fitness function. Finally, the converged position obtained by PSO and DE is refined through the limited-memory Broyden-Fletcher-Goldfarb-Shanno bounded (L-BFGS-B) algorithm, which belongs to the class of quasi-Newton algorithms. We show that both PSO and DE are able to come close to the stationary point, even in high-dimensional problems. They do so in a reasonable amount of time, compared to that with the Newton and quasi-Newton algorithms, regardless of the starting position in the search space of kriging hyperparameters. The refinement through L-BFGS-B is able to give the position of the maximum with whichever precision is desired. PMID:26930135
NASA Astrophysics Data System (ADS)
Rains, C. L.; Weeraratne, D. S.
2013-10-01
We investigate the role of metal-silicate plumes with trailing conduits of entrained magma ocean material from large impacts in the differentiation of early bodies using a tri-fluid physical model with liquid gallium and glucose/salt solutions.
Lachmuth, Susanne; Durka, Walter; Schurr, Frank M
2011-10-01
Genetic differentiation in the competitive and reproductive ability of invading populations can result from genetic Allee effects or r/K selection at the local or range-wide scale. However, the neutral relatedness of populations may either mask or falsely suggest adaptation and genetic Allee effects. In a common-garden experiment, we investigated the competitive and reproductive ability of invasive Senecio inaequidens populations that vary in neutral genetic diversity, population age and field vegetation cover. To account for population relatedness, we analysed the experimental results with 'animal models' adopted from quantitative genetics. Consistent with adaptive r/K differentiation at local scales, we found that genotypes from low-competition environments invest more in reproduction and are more sensitive to competition. By contrast, apparent effects of large-scale r/K differentiation and apparent genetic Allee effects can largely be explained by neutral population relatedness. Invading populations should not be treated as homogeneous groups, as they may adapt quickly to small-scale environmental variation in the invaded range. Furthermore, neutral population differentiation may strongly influence invasion dynamics and should be accounted for in analyses of common-garden experiments. PMID:21736567
Variable Selection using MM Algorithms
Hunter, David R.; Li, Runze
2009-01-01
Variable selection is fundamental to high-dimensional statistical modeling. Many variable selection techniques may be implemented by maximum penalized likelihood using various penalty functions. Optimizing the penalized likelihood function is often challenging because it may be nondifferentiable and/or nonconcave. This article proposes a new class of algorithms for finding a maximizer of the penalized likelihood for a broad class of penalty functions. These algorithms operate by perturbing the penalty function slightly to render it differentiable, then optimizing this differentiable function using a minorize-maximize (MM) algorithm. MM algorithms are useful extensions of the well-known class of EM algorithms, a fact that allows us to analyze the local and global convergence of the proposed algorithm using some of the techniques employed for EM algorithms. In particular, we prove that when our MM algorithms converge, they must converge to a desirable point; we also discuss conditions under which this convergence may be guaranteed. We exploit the Newton-Raphson-like aspect of these algorithms to propose a sandwich estimator for the standard errors of the estimators. Our method performs well in numerical tests. PMID:19458786
NASA Astrophysics Data System (ADS)
Niu, Chaojun; Han, Xiang'e.
2015-10-01
Adaptive optics (AO) technology is an effective way to alleviate the effect of turbulence on free space optical communication (FSO). A new adaptive compensation method can be used without a wave-front sensor. Artificial bee colony algorithm (ABC) is a population-based heuristic evolutionary algorithm inspired by the intelligent foraging behaviour of the honeybee swarm with the advantage of simple, good convergence rate, robust and less parameter setting. In this paper, we simulate the application of the improved ABC to correct the distorted wavefront and proved its effectiveness. Then we simulate the application of ABC algorithm, differential evolution (DE) algorithm and stochastic parallel gradient descent (SPGD) algorithm to the FSO system and analyze the wavefront correction capabilities by comparison of the coupling efficiency, the error rate and the intensity fluctuation in different turbulence before and after the correction. The results show that the ABC algorithm has much faster correction speed than DE algorithm and better correct ability for strong turbulence than SPGD algorithm. Intensity fluctuation can be effectively reduced in strong turbulence, but not so effective in week turbulence.
Chen, H-Y; Spagopoulou, F; Maklakov, A A
2016-04-01
Classic theories of ageing evolution predict that increased extrinsic mortality due to an environmental hazard selects for increased early reproduction, rapid ageing and short intrinsic lifespan. Conversely, emerging theory maintains that when ageing increases susceptibility to an environmental hazard, increased mortality due to this hazard can select against ageing in physiological condition and prolong intrinsic lifespan. However, evolution of slow ageing under high-condition-dependent mortality is expected to result from reallocation of resources to different traits and such reallocation may be hampered by sex-specific trade-offs. Because same life-history trait values often have different fitness consequences in males and females, sexually antagonistic selection can preserve genetic variance for lifespan and ageing. We previously showed that increased condition-dependent mortality caused by heat shock leads to evolution of long-life, decelerated late-life mortality in both sexes and increased female fecundity in the nematode, Caenorhabditis remanei. Here, we used these cryopreserved lines to show that males evolving under heat shock suffered from reduced early-life and net reproduction, while mortality rate had no effect. Our results suggest that heat-shock resistance and associated long-life trade-off with male, but not female, reproduction and therefore sexually antagonistic selection contributes to maintenance of genetic variation for lifespan and fitness in this population. PMID:26801472
Directed evolution of bacteriorhodopsin for applications in bioelectronics
Wagner, Nicole L.; Greco, Jordan A.; Ranaghan, Matthew J.; Birge, Robert R.
2013-01-01
In nature, biological systems gradually evolve through complex, algorithmic processes involving mutation and differential selection. Evolution has optimized biological macromolecules for a variety of functions to provide a comparative advantage. However, nature does not optimize molecules for use in human-made devices, as it would gain no survival advantage in such cooperation. Recent advancements in genetic engineering, most notably directed evolution, have allowed for the stepwise manipulation of the properties of living organisms, promoting the expansion of protein-based devices in nanotechnology. In this review, we highlight the use of directed evolution to optimize photoactive proteins, with an emphasis on bacteriorhodopsin (BR), for device applications. BR, a highly stable light-activated proton pump, has shown great promise in three-dimensional optical memories, real-time holographic processors and artificial retinas. PMID:23676894
Optical rate sensor algorithms
NASA Technical Reports Server (NTRS)
Uhde-Lacovara, Jo A.
1989-01-01
Optical sensors, in particular Charge Coupled Device (CCD) arrays, will be used on Space Station to track stars in order to provide inertial attitude reference. Algorithms are presented to derive attitude rate from the optical sensors. The first algorithm is a recursive differentiator. A variance reduction factor (VRF) of 0.0228 was achieved with a rise time of 10 samples. A VRF of 0.2522 gives a rise time of 4 samples. The second algorithm is based on the direct manipulation of the pixel intensity outputs of the sensor. In 1-dimensional simulations, the derived rate was with 0.07 percent of the actual rate in the presence of additive Gaussian noise with a signal to noise ratio of 60 dB.
Hamada, Mayuko; Goricki, Spela; Byerly, Mardi S; Satoh, Noriyuki; Jeffery, William R
2015-09-15
The regeneration of the oral siphon (OS) and other distal structures in the ascidian Ciona intestinalis occurs by epimorphosis involving the formation of a blastema of proliferating cells. Despite the longstanding use of Ciona as a model in molecular developmental biology, regeneration in this system has not been previously explored by molecular analysis. Here we have employed microarray analysis and quantitative real time RT-PCR to identify genes with differential expression profiles during OS regeneration. The majority of differentially expressed genes were downregulated during OS regeneration, suggesting roles in normal growth and homeostasis. However, a subset of differentially expressed genes was upregulated in the regenerating OS, suggesting functional roles during regeneration. Among the upregulated genes were key members of the Notch signaling pathway, including those encoding the delta and jagged ligands, two fringe modulators, and to a lesser extent the notch receptor. In situ hybridization showed a complementary pattern of delta1 and notch gene expression in the blastema of the regenerating OS. Chemical inhibition of the Notch signaling pathway reduced the levels of cell proliferation in the branchial sac, a stem cell niche that contributes progenitor cells to the regenerating OS, and in the OS regeneration blastema, where siphon muscle fibers eventually re-differentiate. Chemical inhibition also prevented the replacement of oral siphon pigment organs, sensory receptors rimming the entrance of the OS, and siphon muscle fibers, but had no effects on the formation of the wound epidermis. Since Notch signaling is involved in the maintenance of proliferative activity in both the Ciona and vertebrate regeneration blastema, the results suggest a conserved evolutionary role of this signaling pathway in chordate regeneration. The genes identified in this investigation provide the foundation for future molecular analysis of OS regeneration. PMID:26206613
NASA Astrophysics Data System (ADS)
Abrams, Daniel S.
This thesis describes several new quantum algorithms. These include a polynomial time algorithm that uses a quantum fast Fourier transform to find eigenvalues and eigenvectors of a Hamiltonian operator, and that can be applied in cases (commonly found in ab initio physics and chemistry problems) for which all known classical algorithms require exponential time. Fast algorithms for simulating many body Fermi systems are also provided in both first and second quantized descriptions. An efficient quantum algorithm for anti-symmetrization is given as well as a detailed discussion of a simulation of the Hubbard model. In addition, quantum algorithms that calculate numerical integrals and various characteristics of stochastic processes are described. Two techniques are given, both of which obtain an exponential speed increase in comparison to the fastest known classical deterministic algorithms and a quadratic speed increase in comparison to classical Monte Carlo (probabilistic) methods. I derive a simpler and slightly faster version of Grover's mean algorithm, show how to apply quantum counting to the problem, develop some variations of these algorithms, and show how both (apparently distinct) approaches can be understood from the same unified framework. Finally, the relationship between physics and computation is explored in some more depth, and it is shown that computational complexity theory depends very sensitively on physical laws. In particular, it is shown that nonlinear quantum mechanics allows for the polynomial time solution of NP-complete and #P oracle problems. Using the Weinberg model as a simple example, the explicit construction of the necessary gates is derived from the underlying physics. Nonlinear quantum algorithms are also presented using Polchinski type nonlinearities which do not allow for superluminal communication. (Copies available exclusively from MIT Libraries, Rm. 14- 0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)
Kirk, R.L.
1987-01-01
Thermal evolution of Ganymede from a hot start is modeled. On cooling ice I forms above the liquid H/sub 2/O and dense ices at higher entropy below it. A novel diapiric instability is proposed to occur if the ocean thins enough, mixing these layers and perhaps leading to resurfacing and groove formation. Rising warm-ice diapirs may cause a dramatic heat pulse and fracturing at the surface, and provide material for surface flows. Timing of the pulse depends on ice rheology but could agree with crater-density dates for resurfacing. Origins of the Ganymede-Callisto dichotomy in light of the model are discussed. Based on estimates of the conductivity of H/sub 2/ (Jupiter, Saturn) and H/sub 2/O (Uranus, Neptune), the zonal winds of the giant planets will, if they penetrate below the visible atmosphere, interact with the magnetic field well outside the metallic core. The scaling argument is supported by a model with zonal velocity constant on concentric cylinders, the Lorentz torque on each balanced by viscous stresses. The problem of two-dimensional photoclinometry, i.e. reconstruction of a surface from its image, is formulated in terms of finite elements and a fast algorithm using Newton-SOR iteration accelerated by multigridding is presented.
Brianti, Mitsue T; Ananina, Galina; Klaczko, Louis B
2013-01-01
Detailed chromosome maps with reliable homologies among chromosomes of different species are the first step to study the evolution of the genetic architecture in any set of species. Here, we present detailed photo maps of the polytene chromosomes of three closely related species of the tripunctata group (subgenus Drosophila): Drosophila mediopunctata, D. roehrae, and D. unipunctata. We identified Muller's elements in each species, using FISH, establishing reliable chromosome homologies among species and D. melanogaster. The simultaneous analysis of chromosome inversions revealed a distribution pattern for the inversion polymorphisms among Muller's elements in the three species. Element E is the most polymorphic, with many inversions in each species. Element C follows; while the least polymorphic elements are B and D. While interesting, it remains to be determined how general this pattern is among species of the tripunctata group. Despite previous studies showing that D. mediopunctata and D. unipunctata are phylogenetically closer to each other than to D. roehrae, D. unipunctata shows rare karyotypic changes. It has two chromosome fusions: an additional heterochromatic chromosome pair and a pericentric inversion in the X chromosome. This especial conformation suggests a fast chromosomal evolution that deserves further study. PMID:23379335
Sobel, E.; Lange, K.; O`Connell, J.R.
1996-12-31
Haplotyping is the logical process of inferring gene flow in a pedigree based on phenotyping results at a small number of genetic loci. This paper formalizes the haplotyping problem and suggests four algorithms for haplotype reconstruction. These algorithms range from exhaustive enumeration of all haplotype vectors to combinatorial optimization by simulated annealing. Application of the algorithms to published genetic analyses shows that manual haplotyping is often erroneous. Haplotyping is employed in screening pedigrees for phenotyping errors and in positional cloning of disease genes from conserved haplotypes in population isolates. 26 refs., 6 figs., 3 tabs.
Algorithm for Stabilizing a POD-Based Dynamical System
NASA Technical Reports Server (NTRS)
Kalb, Virginia L.
2010-01-01
This algorithm provides a new way to improve the accuracy and asymptotic behavior of a low-dimensional system based on the proper orthogonal decomposition (POD). Given a data set representing the evolution of a system of partial differential equations (PDEs), such as the Navier-Stokes equations for incompressible flow, one may obtain a low-dimensional model in the form of ordinary differential equations (ODEs) that should model the dynamics of the flow. Temporal sampling of the direct numerical simulation of the PDEs produces a spatial time series. The POD extracts the temporal and spatial eigenfunctions of this data set. Truncated to retain only the most energetic modes followed by Galerkin projection of these modes onto the PDEs obtains a dynamical system of ordinary differential equations for the time-dependent behavior of the flow. In practice, the steps leading to this system of ODEs entail numerically computing first-order derivatives of the mean data field and the eigenfunctions, and the computation of many inner products. This is far from a perfect process, and often results in the lack of long-term stability of the system and incorrect asymptotic behavior of the model. This algorithm describes a new stabilization method that utilizes the temporal eigenfunctions to derive correction terms for the coefficients of the dynamical system to significantly reduce these errors.
Michalak, Barbara; Berkes, Balázs B; Sommer, Heino; Bergfeldt, Thomas; Brezesinski, Torsten; Janek, Jürgen
2016-03-01
The cycling performance and in operando gas analysis of LiNi0.5Mn1.5O4 (LNMO)/graphite cells with reasonably high loading, containing a "standard" carbonate-based electrolyte is reported. The gas evolution over the first couple of cycles was thoroughly investigated via differential electrochemical mass spectrometry (DEMS), neutron imaging and pressure measurements. The main oxidation and reduction products were identified as CO2, H2 and C2H4. In different sets of experiments graphite was substituted with delithiated LiFePO4 (LFP) and LNMO with LFP to distinguish between processes occurring at either anode or cathode and gain mechanistic insights. Both C2H4 and H2 were found to be mainly formed at the anode side, while CO2 is generated at the cathode. The results from DEMS analysis further suggest that the Ni redox couples play a profound role in the evolution of CO2 at the LNMO/electrolyte interface. Lastly, it is shown that the cycling stability and capacity retention of LNMO/graphite cells can be considerably improved by a simple cell formation procedure. PMID:26813026
Rauscher, Emily; Showman, Adam P.
2014-04-01
As a planet ages, it cools and its radius shrinks at a rate set by the efficiency with which heat is transported from the interior out to space. The bottleneck for this transport is at the boundary between the convective interior and the radiative atmosphere; the opacity there sets the global cooling rate. Models of planetary evolution are often one dimensional (1D), such that the radiative-convective boundary (RCB) is defined by a single temperature, pressure, and opacity. In reality the spatially inhomogeneous stellar heating pattern and circulation in the atmosphere could deform the RCB, allowing heat from the interior to escape more efficiently through regions with lower opacity. We present an analysis of the degree to which the RCB could be deformed and the resultant change in the evolutionary cooling rate. In this initial work we calculate the upper limit for this effect by comparing an atmospheric structure in local radiative equilibrium to its 1D equivalent. We find that the cooling through an uneven RCB could be enhanced over cooling through a uniform RCB by as much as 10%-50%. We also show that the deformation of the RCB (and the enhancement of the cooling rate) increases with a greater incident stellar flux or a lower inner entropy. Our results indicate that this mechanism could significantly change a planet's thermal evolution, causing it to cool and shrink more quickly than would otherwise be expected. This may exacerbate the well-known difficulty in explaining the very large radii observed for some hot Jupiters.
Mathematical and experimental analyses of oppositional algorithms.
Ergezer, Mehmet; Simon, Dan
2014-11-01
Evolutionary algorithms (EAs) are widely employed for solving optimization problems with rugged fitness landscapes. Opposition-based learning (OBL) is a recent tool developed to improve the convergence rate of EAs. In this paper, we derive the probabilities that distances between OBL points and the optimization problem solution are less than the distance between a given EA individual and the optimal solution. We find that the quasi-reflected opposition point yields the highest probability and is the most likely candidate to be closer to the optimal solution. We then employ CEC 2013 competition benchmark problems and select a set of trajectory optimization problems from the European Space Agency to study the performance of three OBL algorithms in conjunction with three different EAs. The CEC 2013 test suit simulations indicate that quasi-reflection accelerates the performance of the EA, especially for more difficult composition functions. The space trajectory experiments reveal that differential evolution with opposition generally returns the best objective function value for the chosen minimization problems. PMID:25330478
Differential attack on mini-AES
NASA Astrophysics Data System (ADS)
Ajeng Gemellia, Asadini Dwi; Indarjani, Santi
2012-05-01
This paper presents the results of differential attack on Mini-AES algorithm. The differential trails are constructed using all combinations of propagation ratio without repetition. To give practical results, we implement the key extraction for differential characteristics which have the highest and lowest probability as a comparison. Based on total propagation ratio and complexity resulted, Mini-AES algorithms are vulnerable to differential attack. The best differential characteristic is the differential characteristic using a single active s-box with the propagation ratio of 8 / 16.
Cornejo, Omar E.; Durrego, Ester; Stanley, Craig E.; Castillo, Andreína I.; Herrera, Sócrates; Escalante, Ananias A.
2016-01-01
Transmission-blocking (TB) vaccines are considered an important tool for malaria control and elimination. Among all the antigens characterized as TB vaccines against Plasmodium vivax, the ookinete surface proteins Pvs28 and Pvs25 are leading candidates. These proteins likely originated by a gene duplication event that took place before the radiation of the known Plasmodium species to primates. We report an evolutionary genetic analysis of a worldwide sample of pvs28 and pvs25 alleles. Our results show that both genes display low levels of genetic polymorphism when compared to the merozoite surface antigens AMA-1 and MSP-1; however, both ookinete antigens can be as polymorphic as other merozoite antigens such as MSP-8 and MSP-10. We found that parasite populations in Asia and the Americas are geographically differentiated with comparable levels of genetic diversity and specific amino acid replacements found only in the Americas. Furthermore, the observed variation was mainly accumulated in the EGF2- and EGF3-like domains for P. vivax in both proteins. This pattern was shared by other closely related non-human primate parasites such as Plasmodium cynomolgi, suggesting that it could be functionally important. In addition, examination with a suite of evolutionary genetic analyses indicated that the observed patterns are consistent with positive natural selection acting on Pvs28 and Pvs25 polymorphisms. The geographic pattern of genetic differentiation and the evidence for positive selection strongly suggest that the functional consequences of the observed polymorphism should be evaluated during development of TBVs that include Pvs25 and Pvs28. PMID:27347876
Chaurio, Ricardo A; Pacheco, M Andreína; Cornejo, Omar E; Durrego, Ester; Stanley, Craig E; Castillo, Andreína I; Herrera, Sócrates; Escalante, Ananias A
2016-06-01
Transmission-blocking (TB) vaccines are considered an important tool for malaria control and elimination. Among all the antigens characterized as TB vaccines against Plasmodium vivax, the ookinete surface proteins Pvs28 and Pvs25 are leading candidates. These proteins likely originated by a gene duplication event that took place before the radiation of the known Plasmodium species to primates. We report an evolutionary genetic analysis of a worldwide sample of pvs28 and pvs25 alleles. Our results show that both genes display low levels of genetic polymorphism when compared to the merozoite surface antigens AMA-1 and MSP-1; however, both ookinete antigens can be as polymorphic as other merozoite antigens such as MSP-8 and MSP-10. We found that parasite populations in Asia and the Americas are geographically differentiated with comparable levels of genetic diversity and specific amino acid replacements found only in the Americas. Furthermore, the observed variation was mainly accumulated in the EGF2- and EGF3-like domains for P. vivax in both proteins. This pattern was shared by other closely related non-human primate parasites such as Plasmodium cynomolgi, suggesting that it could be functionally important. In addition, examination with a suite of evolutionary genetic analyses indicated that the observed patterns are consistent with positive natural selection acting on Pvs28 and Pvs25 polymorphisms. The geographic pattern of genetic differentiation and the evidence for positive selection strongly suggest that the functional consequences of the observed polymorphism should be evaluated during development of TBVs that include Pvs25 and Pvs28. PMID:27347876
A novel metaheuristic for continuous optimization problems: Virus optimization algorithm
NASA Astrophysics Data System (ADS)
Liang, Yun-Chia; Rodolfo Cuevas Juarez, Josue
2016-01-01
A novel metaheuristic for continuous optimization problems, named the virus optimization algorithm (VOA), is introduced and investigated. VOA is an iteratively population-based method that imitates the behaviour of viruses attacking a living cell. The number of viruses grows at each replication and is controlled by an immune system (a so-called 'antivirus') to prevent the explosive growth of the virus population. The viruses are divided into two classes (strong and common) to balance the exploitation and exploration effects. The performance of the VOA is validated through a set of eight benchmark functions, which are also subject to rotation and shifting effects to test its robustness. Extensive comparisons were conducted with over 40 well-known metaheuristic algorithms and their variations, such as artificial bee colony, artificial immune system, differential evolution, evolutionary programming, evolutionary strategy, genetic algorithm, harmony search, invasive weed optimization, memetic algorithm, particle swarm optimization and simulated annealing. The results showed that the VOA is a viable solution for continuous optimization.
Algorithms, complexity, and the sciences.
Papadimitriou, Christos
2014-11-11
Algorithms, perhaps together with Moore's law, compose the engine of the information technology revolution, whereas complexity--the antithesis of algorithms--is one of the deepest realms of mathematical investigation. After introducing the basic concepts of algorithms and complexity, and the fundamental complexity classes P (polynomial time) and NP (nondeterministic polynomial time, or search problems), we discuss briefly the P vs. NP problem. We then focus on certain classes between P and NP which capture important phenomena in the social and life sciences, namely the Nash equlibrium and other equilibria in economics and game theory, and certain processes in population genetics and evolution. Finally, an algorithm known as multiplicative weights update (MWU) provides an algorithmic interpretation of the evolution of allele frequencies in a population under sex and weak selection. All three of these equivalences are rife with domain-specific implications: The concept of Nash equilibrium may be less universal--and therefore less compelling--than has been presumed; selection on gene interactions may entail the maintenance of genetic variation for longer periods than selection on single alleles predicts; whereas MWU can be shown to maximize, for each gene, a convex combination of the gene's cumulative fitness in the population and the entropy of the allele distribution, an insight that may be pertinent to the maintenance of variation in evolution. PMID:25349382
SMACK: A NEW ALGORITHM FOR MODELING COLLISIONS AND DYNAMICS OF PLANETESIMALS IN DEBRIS DISKS
Nesvold, Erika R.; Kuchner, Marc J.; Pan, Margaret; Rein, Hanno E-mail: Marc.Kuchner@nasa.gov E-mail: rein@ias.edu
2013-11-10
We present the Superparticle-Method/Algorithm for Collisions in Kuiper belts and debris disks (SMACK), a new method for simultaneously modeling, in three dimensions, the collisional and dynamical evolution of planetesimals in a debris disk with planets. SMACK can simulate azimuthal asymmetries and how these asymmetries evolve over time. We show that SMACK is stable to numerical viscosity and numerical heating over 10{sup 7} yr and that it can reproduce analytic models of disk evolution. We use SMACK to model the evolution of a debris ring containing a planet on an eccentric orbit. Differential precession creates a spiral structure as the ring evolves, but collisions subsequently break up the spiral, leaving a narrower eccentric ring.
SMACK: A New Algorithm for Modeling Collisions and Dynamics of Planetesimals in Debris Disks
NASA Technical Reports Server (NTRS)
Nesvold, Erika Rose; Kuchner, Marc J.; Rein, Hanno; Pan, Margaret
2013-01-01
We present the Superparticle Model/Algorithm for Collisions in Kuiper belts and debris disks (SMACK), a new method for simultaneously modeling, in 3-D, the collisional and dynamical evolution of planetesimals in a debris disk with planets. SMACK can simulate azimuthal asymmetries and how these asymmetries evolve over time. We show that SMACK is stable to numerical viscosity and numerical heating over 10(exp 7) yr, and that it can reproduce analytic models of disk evolution. We use SMACK to model the evolution of a debris ring containing a planet on an eccentric orbit. Differential precession creates a spiral structure as the ring evolves, but collisions subsequently break up the spiral, leaving a narrower eccentric ring.
Wang, Gai-Ge; Feng, Qingjiang; Zhao, Xiang-Jun
2014-01-01
An effective hybrid cuckoo search algorithm (CS) with improved shuffled frog-leaping algorithm (ISFLA) is put forward for solving 0-1 knapsack problem. First of all, with the framework of SFLA, an improved frog-leap operator is designed with the effect of the global optimal information on the frog leaping and information exchange between frog individuals combined with genetic mutation with a small probability. Subsequently, in order to improve the convergence speed and enhance the exploitation ability, a novel CS model is proposed with considering the specific advantages of Lévy flights and frog-leap operator. Furthermore, the greedy transform method is used to repair the infeasible solution and optimize the feasible solution. Finally, numerical simulations are carried out on six different types of 0-1 knapsack instances, and the comparative results have shown the effectiveness of the proposed algorithm and its ability to achieve good quality solutions, which outperforms the binary cuckoo search, the binary differential evolution, and the genetic algorithm. PMID:25404940
Feng, Yanhong; Wang, Gai-Ge; Feng, Qingjiang; Zhao, Xiang-Jun
2014-01-01
An effective hybrid cuckoo search algorithm (CS) with improved shuffled frog-leaping algorithm (ISFLA) is put forward for solving 0-1 knapsack problem. First of all, with the framework of SFLA, an improved frog-leap operator is designed with the effect of the global optimal information on the frog leaping and information exchange between frog individuals combined with genetic mutation with a small probability. Subsequently, in order to improve the convergence speed and enhance the exploitation ability, a novel CS model is proposed with considering the specific advantages of Lévy flights and frog-leap operator. Furthermore, the greedy transform method is used to repair the infeasible solution and optimize the feasible solution. Finally, numerical simulations are carried out on six different types of 0-1 knapsack instances, and the comparative results have shown the effectiveness of the proposed algorithm and its ability to achieve good quality solutions, which outperforms the binary cuckoo search, the binary differential evolution, and the genetic algorithm. PMID:25404940
NASA Astrophysics Data System (ADS)
Vijay Alagappan, A.; Narasimha Rao, K. V.; Krishna Kumar, R.
2015-02-01
Tyre models are a prerequisite for any vehicle dynamics simulation. Tyre models range from the simplest mathematical models that consider only the cornering stiffness to a complex set of formulae. Among all the steady-state tyre models that are in use today, the Magic Formula tyre model is unique and most popular. Though the Magic Formula tyre model is widely used, obtaining the model coefficients from either the experimental or the simulation data is not straightforward due to its nonlinear nature and the presence of a large number of coefficients. A common procedure used for this extraction is the least-squares minimisation that requires considerable experience for initial guesses. Various researchers have tried different algorithms, namely, gradient and Newton-based methods, differential evolution, artificial neural networks, etc. The issues involved in all these algorithms are setting bounds or constraints, sensitivity of the parameters, the features of the input data such as the number of points, noisy data, experimental procedure used such as slip angle sweep or tyre measurement (TIME) procedure, etc. The extracted Magic Formula coefficients are affected by these variants. This paper highlights the issues that are commonly encountered in obtaining these coefficients with different algorithms, namely, least-squares minimisation using trust region algorithms, Nelder-Mead simplex, pattern search, differential evolution, particle swarm optimisation, cuckoo search, etc. A key observation is that not all the algorithms give the same Magic Formula coefficients for a given data. The nature of the input data and the type of the algorithm decide the set of the Magic Formula tyre model coefficients.
Kuester, Adam; Chang, Shu-Mei; Baucom, Regina S
2015-09-01
Strong human-mediated selection via herbicide application in agroecosystems has repeatedly led to the evolution of resistance in weedy plants. Although resistance can occur among separate populations of a species across the landscape, the spatial scale of resistance in many weeds is often left unexamined. We assessed the potential that resistance to the herbicide glyphosate in the agricultural weed Ipomoea purpurea has evolved independently multiple times across its North American range. We examined both adaptive and neutral genetic variations in 44 populations of I. purpurea by pairing a replicated dose-response greenhouse experiment with SSR genotyping of experimental individuals. We uncovered a mosaic pattern of resistance across the landscape, with some populations exhibiting high-survival postherbicide and other populations showing high death. SSR genotyping revealed little evidence of isolation by distance and very little neutral genetic structure associated with geography. An approximate Bayesian computation (ABC) analysis uncovered evidence for migration and admixture among populations before the widespread use of glyphosate rather than the very recent contemporary gene flow. The pattern of adaptive and neutral genetic variations indicates that resistance in this mixed-mating weed species appears to have evolved in independent hotspots rather than through transmission of resistance alleles across the landscape. PMID:26366199
Hegedus, Dwayne D; Erlandson, Martin; Baldwin, Douglas; Hou, Xingwei; Chamankhah, Mahmood
2008-07-15
Serpins are a unique class of serine protease inhibitors that are becoming increasingly recognized as important regulators of insect defense mechanisms and developmental processes. Previously, we identified three Mamestra configurata serpins that were similar in structure to those encoded by the Manduca sexta Serpin-1 gene. To gain insight into the evolution and function of serpins in lepidopterans, we developed a bacterial artificial chromosome library and sequenced the entire M. configurata gene. The Serpin-1 gene was 28 kbp and had the capacity to encode nine serpin isoforms via alternate splicing of exons encoding variant reactive center loops onto a common scaffold. The relative abundance of each isoform was estimated by expressed sequence tag analysis and their expression patterns examined in various developmental stages and larval tissues. The organization of the M. configurata Serpin-1 gene was very similar to that of M. sexta Serpin-1; however, only the Ms Serpin-1Z (1 of 12) and the Mc Serpin-1a isoforms exhibited a high degree of similarity. Orthologs similar to this variant were also found in other lepidopterans, namely Bombyx mori and Plutella xylostella, suggesting that they are involved in a conserved biochemical process and likely represent the ancestral serpin variant. Expansion of the exon family encoding the Serpin-1 reactive centre loop region appears to be a product of recent duplication events that has given rise to different serpin repertoires in related insect taxa. PMID:18495381
Kuester, Adam; Chang, Shu-Mei; Baucom, Regina S
2015-01-01
Strong human-mediated selection via herbicide application in agroecosystems has repeatedly led to the evolution of resistance in weedy plants. Although resistance can occur among separate populations of a species across the landscape, the spatial scale of resistance in many weeds is often left unexamined. We assessed the potential that resistance to the herbicide glyphosate in the agricultural weed Ipomoea purpurea has evolved independently multiple times across its North American range. We examined both adaptive and neutral genetic variations in 44 populations of I. purpurea by pairing a replicated dose–response greenhouse experiment with SSR genotyping of experimental individuals. We uncovered a mosaic pattern of resistance across the landscape, with some populations exhibiting high-survival postherbicide and other populations showing high death. SSR genotyping revealed little evidence of isolation by distance and very little neutral genetic structure associated with geography. An approximate Bayesian computation (ABC) analysis uncovered evidence for migration and admixture among populations before the widespread use of glyphosate rather than the very recent contemporary gene flow. The pattern of adaptive and neutral genetic variations indicates that resistance in this mixed-mating weed species appears to have evolved in independent hotspots rather than through transmission of resistance alleles across the landscape. PMID:26366199
Efficient multicomponent fuel algorithm
NASA Astrophysics Data System (ADS)
Torres, D. J.; O'Rourke, P. J.; Amsden, A. A.
2003-03-01
We derive equations for multicomponent fuel evaporation in airborne fuel droplets and wall films, and implement the model into KIVA-3V. Temporal and spatial variations in liquid droplet composition and temperature are not modelled but solved for by discretizing the interior of the droplet in an implicit and computationally efficient way. We find that an interior discretization is necessary to correctly compute the evolution of the droplet composition. The details of the one-dimensional numerical algorithm are described. Numerical simulations of multicomponent evaporation are performed for single droplets and compared to experimental data.
Computing Algorithms for Nuffield Advanced Physics.
ERIC Educational Resources Information Center
Summers, M. K.
1978-01-01
Defines all recurrence relations used in the Nuffield course, to solve first- and second-order differential equations, and describes a typical algorithm for computer generation of solutions. (Author/GA)
NASA Technical Reports Server (NTRS)
Barth, Timothy J.; Lomax, Harvard
1987-01-01
The past decade has seen considerable activity in algorithm development for the Navier-Stokes equations. This has resulted in a wide variety of useful new techniques. Some examples for the numerical solution of the Navier-Stokes equations are presented, divided into two parts. One is devoted to the incompressible Navier-Stokes equations, and the other to the compressible form.
Sunspots and Coronal Bright Points Tracking using a Hybrid Algorithm of PSO and Active Contour Model
NASA Astrophysics Data System (ADS)
Dorotovic, I.; Shahamatnia, E.; Lorenc, M.; Rybansky, M.; Ribeiro, R. A.; Fonseca, J. M.
2014-02-01
In the last decades there has been a steady increase of high-resolution data, from ground-based and space-borne solar instruments, and also of solar data volume. These huge image archives require efficient automatic image processing software tools capable of detecting and tracking various features in the solar atmosphere. Results of application of such tools are essential for studies of solar activity evolution, climate change understanding and space weather prediction. The follow up of interplanetary and near-Earth phenomena requires, among others, automatic tracking algorithms that can determine where a feature is located, on successive images taken along the period of observation. Full-disc solar images, obtained both with the ground-based solar telescopes and the instruments onboard the satellites, provide essential observational material for solar physicists and space weather researchers for better understanding the Sun, studying the evolution of various features in the solar atmosphere, and also investigating solar differential rotation by tracking such features along time. Here we demonstrate and discuss the suitability of applying a hybrid Particle Swarm Optimization (PSO) algorithm and Active Contour model for tracking and determining the differential rotation of sunspots and coronal bright points (CBPs) on a set of selected solar images. The results obtained confirm that the proposed approach constitutes a promising tool for investigating the evolution of solar activity and also for automating tracking features on massive solar image archives.
NASA Astrophysics Data System (ADS)
Panda, S.; Mishra, D.; Biswal, B. B.; Tripathy, M.
2014-02-01
Robotic manipulators with three-revolute (3R) motions to attain desired positional configurations are very common in industrial robots. The capability of these robots depends largely on the workspace of the manipulator in addition to other parameters. In this study, an evolutionary optimization algorithm based on the foraging behaviour of the Escherichia coli bacteria present in the human intestine is utilized to optimize the workspace volume of a 3R manipulator. The new optimization method is modified from the original algorithm for faster convergence. This method is also useful for optimization problems in a highly constrained environment, such as robot workspace optimization. The new approach for workspace optimization of 3R manipulators is tested using three cases. The test results are compared with standard results available using other optimization algorithms, i.e. the differential evolution algorithm, the genetic algorithm and the particle swarm optimization algorithm. The present method is found to be superior to the other methods in terms of computational efficiency.
A Runge-Kutta Nystrom algorithm.
NASA Technical Reports Server (NTRS)
Bettis, D. G.
1973-01-01
A Runge-Kutta algorithm of order five is presented for the solution of the initial value problem where the system of ordinary differential equations is of second order and does not contain the first derivative. The algorithm includes the Fehlberg step control procedure.
Numerical Differentiation of Noisy, Nonsmooth Data
Chartrand, Rick
2011-01-01
We consider the problem of differentiating a function specified by noisy data. Regularizing the differentiation process avoids the noise amplification of finite-difference methods. We use total-variation regularization, which allows for discontinuous solutions. The resulting simple algorithm accurately differentiates noisy functions, including those which have a discontinuous derivative.
Algorithms, complexity, and the sciences
Papadimitriou, Christos
2014-01-01
Algorithms, perhaps together with Moore’s law, compose the engine of the information technology revolution, whereas complexity—the antithesis of algorithms—is one of the deepest realms of mathematical investigation. After introducing the basic concepts of algorithms and complexity, and the fundamental complexity classes P (polynomial time) and NP (nondeterministic polynomial time, or search problems), we discuss briefly the P vs. NP problem. We then focus on certain classes between P and NP which capture important phenomena in the social and life sciences, namely the Nash equlibrium and other equilibria in economics and game theory, and certain processes in population genetics and evolution. Finally, an algorithm known as multiplicative weights update (MWU) provides an algorithmic interpretation of the evolution of allele frequencies in a population under sex and weak selection. All three of these equivalences are rife with domain-specific implications: The concept of Nash equilibrium may be less universal—and therefore less compelling—than has been presumed; selection on gene interactions may entail the maintenance of genetic variation for longer periods than selection on single alleles predicts; whereas MWU can be shown to maximize, for each gene, a convex combination of the gene’s cumulative fitness in the population and the entropy of the allele distribution, an insight that may be pertinent to the maintenance of variation in evolution. PMID:25349382
Road Detection by Neural and Genetic Algorithm in Urban Environment
NASA Astrophysics Data System (ADS)
Barsi, A.
2012-07-01
In the urban object detection challenge organized by the ISPRS WG III/4 high geometric and radiometric resolution aerial images about Vaihingen/Stuttgart, Germany are distributed. The acquired data set contains optical false color, near infrared images and airborne laserscanning data. The presented research focused exclusively on the optical image, so the elevation information was ignored. The road detection procedure has been built up of two main phases: a segmentation done by neural networks and a compilation made by genetic algorithms. The applied neural networks were support vector machines with radial basis kernel function and self-organizing maps with hexagonal network topology and Euclidean distance function for neighborhood management. The neural techniques have been compared by hyperbox classifier, known from the statistical image classification practice. The compilation of the segmentation is realized by a novel application of the common genetic algorithm and by differential evolution technique. The genes were implemented to detect the road elements by evaluating a special binary fitness function. The results have proven that the evolutional technique can automatically find major road segments.
Iterative algorithms for large sparse linear systems on parallel computers
NASA Technical Reports Server (NTRS)
Adams, L. M.
1982-01-01
Algorithms for assembling in parallel the sparse system of linear equations that result from finite difference or finite element discretizations of elliptic partial differential equations, such as those that arise in structural engineering are developed. Parallel linear stationary iterative algorithms and parallel preconditioned conjugate gradient algorithms are developed for solving these systems. In addition, a model for comparing parallel algorithms on array architectures is developed and results of this model for the algorithms are given.
Optimal Design of Geodetic Network Using Genetic Algorithms
NASA Astrophysics Data System (ADS)
Vajedian, Sanaz; Bagheri, Hosein
2010-05-01
A geodetic network is a network which is measured exactly by techniques of terrestrial surveying based on measurement of angles and distances and can control stability of dams, towers and their around lands and can monitor deformation of surfaces. The main goals of an optimal geodetic network design process include finding proper location of control station (First order Design) as well as proper weight of observations (second order observation) in a way that satisfy all the criteria considered for quality of the network with itself is evaluated by the network's accuracy, reliability (internal and external), sensitivity and cost. The first-order design problem, can be dealt with as a numeric optimization problem. In this designing finding unknown coordinates of network stations is an important issue. For finding these unknown values, network geodetic observations that are angle and distance measurements must be entered in an adjustment method. In this regard, using inverse problem algorithms is needed. Inverse problem algorithms are methods to find optimal solutions for given problems and include classical and evolutionary computations. The classical approaches are analytical methods and are useful in finding the optimum solution of a continuous and differentiable function. Least squares (LS) method is one of the classical techniques that derive estimates for stochastic variables and their distribution parameters from observed samples. The evolutionary algorithms are adaptive procedures of optimization and search that find solutions to problems inspired by the mechanisms of natural evolution. These methods generate new points in the search space by applying operators to current points and statistically moving toward more optimal places in the search space. Genetic algorithm (GA) is an evolutionary algorithm considered in this paper. This algorithm starts with definition of initial population, and then the operators of selection, replication and variation are applied
Improving CMD Areal Density Analysis: Algorithms and Strategies
NASA Astrophysics Data System (ADS)
Wilson, R. E.
2014-06-01
Essential ideas, successes, and difficulties of Areal Density Analysis (ADA) for color-magnitude diagrams (CMDÂ¡Â¯s) of resolved stellar populations are examined, with explanation of various algorithms and strategies for optimal performance. A CMDgeneration program computes theoretical datasets with simulated observational error and a solution program inverts the problem by the method of Differential Corrections (DC) so as to compute parameter values from observed magnitudes and colors, with standard error estimates and correlation coefficients. ADA promises not only impersonal results, but also significant saving of labor, especially where a given dataset is analyzed with several evolution models. Observational errors and multiple star systems, along with various single star characteristics and phenomena, are modeled directly via the Functional Statistics Algorithm (FSA). Unlike Monte Carlo, FSA is not dependent on a random number generator. Discussions include difficulties and overall requirements, such as need for fast evolutionary computation and realization of goals within machine memory limits. Degradation of results due to influence of pixelization on derivatives, Initial Mass Function (IMF) quantization, IMF steepness, low Areal Densities (A ), and large variation in A are reduced or eliminated through a variety of schemes that are explained sufficiently for general application. The Levenberg-Marquardt and MMS algorithms for improvement of solution convergence are contained within the DC program. An example of convergence, which typically is very good, is shown in tabular form. A number of theoretical and practical solution issues are discussed, as are prospects for further development.
Duality quantum algorithm efficiently simulates open quantum systems
Wei, Shi-Jie; Ruan, Dong; Long, Gui-Lu
2016-01-01
Because of inevitable coupling with the environment, nearly all practical quantum systems are open system, where the evolution is not necessarily unitary. In this paper, we propose a duality quantum algorithm for simulating Hamiltonian evolution of an open quantum system. In contrast to unitary evolution in a usual quantum computer, the evolution operator in a duality quantum computer is a linear combination of unitary operators. In this duality quantum algorithm, the time evolution of the open quantum system is realized by using Kraus operators which is naturally implemented in duality quantum computer. This duality quantum algorithm has two distinct advantages compared to existing quantum simulation algorithms with unitary evolution operations. Firstly, the query complexity of the algorithm is O(d3) in contrast to O(d4) in existing unitary simulation algorithm, where d is the dimension of the open quantum system. Secondly, By using a truncated Taylor series of the evolution operators, this duality quantum algorithm provides an exponential improvement in precision compared with previous unitary simulation algorithm. PMID:27464855
Duality quantum algorithm efficiently simulates open quantum systems
NASA Astrophysics Data System (ADS)
Wei, Shi-Jie; Ruan, Dong; Long, Gui-Lu
2016-07-01
Because of inevitable coupling with the environment, nearly all practical quantum systems are open system, where the evolution is not necessarily unitary. In this paper, we propose a duality quantum algorithm for simulating Hamiltonian evolution of an open quantum system. In contrast to unitary evolution in a usual quantum computer, the evolution operator in a duality quantum computer is a linear combination of unitary operators. In this duality quantum algorithm, the time evolution of the open quantum system is realized by using Kraus operators which is naturally implemented in duality quantum computer. This duality quantum algorithm has two distinct advantages compared to existing quantum simulation algorithms with unitary evolution operations. Firstly, the query complexity of the algorithm is O(d3) in contrast to O(d4) in existing unitary simulation algorithm, where d is the dimension of the open quantum system. Secondly, By using a truncated Taylor series of the evolution operators, this duality quantum algorithm provides an exponential improvement in precision compared with previous unitary simulation algorithm.
Duality quantum algorithm efficiently simulates open quantum systems.
Wei, Shi-Jie; Ruan, Dong; Long, Gui-Lu
2016-01-01
Because of inevitable coupling with the environment, nearly all practical quantum systems are open system, where the evolution is not necessarily unitary. In this paper, we propose a duality quantum algorithm for simulating Hamiltonian evolution of an open quantum system. In contrast to unitary evolution in a usual quantum computer, the evolution operator in a duality quantum computer is a linear combination of unitary operators. In this duality quantum algorithm, the time evolution of the open quantum system is realized by using Kraus operators which is naturally implemented in duality quantum computer. This duality quantum algorithm has two distinct advantages compared to existing quantum simulation algorithms with unitary evolution operations. Firstly, the query complexity of the algorithm is O(d(3)) in contrast to O(d(4)) in existing unitary simulation algorithm, where d is the dimension of the open quantum system. Secondly, By using a truncated Taylor series of the evolution operators, this duality quantum algorithm provides an exponential improvement in precision compared with previous unitary simulation algorithm. PMID:27464855
Quantum Adiabatic Algorithms and Large Spin Tunnelling
NASA Technical Reports Server (NTRS)
Boulatov, A.; Smelyanskiy, V. N.
2003-01-01
We provide a theoretical study of the quantum adiabatic evolution algorithm with different evolution paths proposed in this paper. The algorithm is applied to a random binary optimization problem (a version of the 3-Satisfiability problem) where the n-bit cost function is symmetric with respect to the permutation of individual bits. The evolution paths are produced, using the generic control Hamiltonians H (r) that preserve the bit symmetry of the underlying optimization problem. In the case where the ground state of H(0) coincides with the totally-symmetric state of an n-qubit system the algorithm dynamics is completely described in terms of the motion of a spin-n/2. We show that different control Hamiltonians can be parameterized by a set of independent parameters that are expansion coefficients of H (r) in a certain universal set of operators. Only one of these operators can be responsible for avoiding the tunnelling in the spin-n/2 system during the quantum adiabatic algorithm. We show that it is possible to select a coefficient for this operator that guarantees a polynomial complexity of the algorithm for all problem instances. We show that a successful evolution path of the algorithm always corresponds to the trajectory of a classical spin-n/2 and provide a complete characterization of such paths.
Physical Principles of Evolution
NASA Astrophysics Data System (ADS)
Schuster, Peter
Theoretical biology is incomplete without a comprehensive theory of evolution, since evolution is at the core of biological thought. Evolution is visualized as a migration process in genotype or sequence space that is either an adaptive walk driven by some fitness gradient or a random walk in the absence of (sufficiently large) fitness differences. The Darwinian concept of natural selection consisting in the interplay of variation and selection is based on a dichotomy: All variations occur on genotypes whereas selection operates on phenotypes, and relations between genotypes and phenotypes, as encapsulated in a mapping from genotype space into phenotype space, are central to an understanding of evolution. Fitness is conceived as a function of the phenotype, represented by a second mapping from phenotype space into nonnegative real numbers. In the biology of organisms, genotype-phenotype maps are enormously complex and relevant information on them is exceedingly scarce. The situation is better in the case of viruses but so far only one example of a genotype-phenotype map, the mapping of RNA sequences into RNA secondary structures, has been investigated in sufficient detail. It provides direct information on RNA selection in vitro and test-tube evolution, and it is a basis for testing in silico evolution on a realistic fitness landscape. Most of the modeling efforts in theoretical and mathematical biology today are done by means of differential equations but stochastic effects are of undeniably great importance for evolution. Population sizes are much smaller than the numbers of genotypes constituting sequence space. Every mutant, after all, has to begin with a single copy. Evolution can be modeled by a chemical master equation, which (in principle) can be approximated by a stochastic differential equation. In addition, simulation tools are available that compute trajectories for master equations. The accessible population sizes in the range of 10^7le Nle 10
Newton Algorithms for Analytic Rotation: An Implicit Function Approach
ERIC Educational Resources Information Center
Boik, Robert J.
2008-01-01
In this paper implicit function-based parameterizations for orthogonal and oblique rotation matrices are proposed. The parameterizations are used to construct Newton algorithms for minimizing differentiable rotation criteria applied to "m" factors and "p" variables. The speed of the new algorithms is compared to that of existing algorithms and to…
Detecting compact galactic binaries using a hybrid swarm-based algorithm
NASA Astrophysics Data System (ADS)
Bouffanais, Yann; Porter, Edward K.
2016-03-01
Compact binaries in our galaxy are expected to be one of the main sources of gravitational waves for the future eLISA mission. During the mission lifetime, many thousands of galactic binaries should be individually resolved. However, the identification of the sources and the extraction of the signal parameters in a noisy environment are real challenges for data analysis. So far, stochastic searches have proven to be the most successful for this problem. In this work, we present the first application of a swarm-based algorithm combining Particle Swarm Optimization and Differential Evolution. These algorithms have been shown to converge faster to global solutions on complicated likelihood surfaces than other stochastic methods. We first demonstrate the effectiveness of the algorithm for the case of a single binary in a 1-mHz search bandwidth. This interesting problem gave the algorithm plenty of opportunity to fail, as it can be easier to find a strong noise peak rather than the signal itself. After a successful detection of a fictitious low-frequency source, as well as the verification binary RXJ 0806.3 +1527 , we then applied the algorithm to the detection of multiple binaries, over different search bandwidths, in the cases of low and mild source confusion. In all cases, we show that we can successfully identify the sources and recover the true parameters within a 99% credible interval.
NASA Astrophysics Data System (ADS)
Bolognesi, Tommaso
2011-07-01
In the context of quantum gravity theories, several researchers have proposed causal sets as appropriate discrete models of spacetime. We investigate families of causal sets obtained from two simple models of computation - 2D Turing machines and network mobile automata - that operate on 'high-dimensional' supports, namely 2D arrays of cells and planar graphs, respectively. We study a number of quantitative and qualitative emergent properties of these causal sets, including dimension, curvature and localized structures, or 'particles'. We show how the possibility to detect and separate particles from background space depends on the choice between a global or local view at the causal set. Finally, we spot very rare cases of pseudo-randomness, or deterministic chaos; these exhibit a spontaneous phenomenon of 'causal compartmentation' that appears as a prerequisite for the occurrence of anything of physical interest in the evolution of spacetime.
Genetic algorithm and particle swarm optimization combined with Powell method
NASA Astrophysics Data System (ADS)
Bento, David; Pinho, Diana; Pereira, Ana I.; Lima, Rui
2013-10-01
In recent years, the population algorithms are becoming increasingly robust and easy to use, based on Darwin's Theory of Evolution, perform a search for the best solution around a population that will progress according to several generations. This paper present variants of hybrid genetic algorithm - Genetic Algorithm and a bio-inspired hybrid algorithm - Particle Swarm Optimization, both combined with the local method - Powell Method. The developed methods were tested with twelve test functions from unconstrained optimization context.
Cluster algorithms and computational complexity
NASA Astrophysics Data System (ADS)
Li, Xuenan
Cluster algorithms for the 2D Ising model with a staggered field have been studied and a new cluster algorithm for path sampling has been worked out. The complexity properties of Bak-Seppen model and the Growing network model have been studied by using the Computational Complexity Theory. The dynamic critical behavior of the two-replica cluster algorithm is studied. Several versions of the algorithm are applied to the two-dimensional, square lattice Ising model with a staggered field. The dynamic exponent for the full algorithm is found to be less than 0.5. It is found that odd translations of one replica with respect to the other together with global flips are essential for obtaining a small value of the dynamic exponent. The path sampling problem for the 1D Ising model is studied using both a local algorithm and a novel cluster algorithm. The local algorithm is extremely inefficient at low temperature, where the integrated autocorrelation time is found to be proportional to the fourth power of correlation length. The dynamic exponent of the cluster algorithm is found to be zero and therefore proved to be much more efficient than the local algorithm. The parallel computational complexity of the Bak-Sneppen evolution model is studied. It is shown that Bak-Sneppen histories can be generated by a massively parallel computer in a time that is polylog in the length of the history, which means that the logical depth of producing a Bak-Sneppen history is exponentially less than the length of the history. The parallel dynamics for generating Bak-Sneppen histories is contrasted to standard Bak-Sneppen dynamics. The parallel computational complexity of the Growing Network model is studied. The growth of the network with linear kernels is shown to be not complex and an algorithm with polylog parallel running time is found. The growth of the network with gamma ≥ 2 super-linear kernels can be realized by a randomized parallel algorithm with polylog expected running time.
Some nonlinear space decomposition algorithms
Tai, Xue-Cheng; Espedal, M.
1996-12-31
Convergence of a space decomposition method is proved for a general convex programming problem. The space decomposition refers to methods that decompose a space into sums of subspaces, which could be a domain decomposition or a multigrid method for partial differential equations. Two algorithms are proposed. Both can be used for linear as well as nonlinear elliptic problems and they reduce to the standard additive and multiplicative Schwarz methods for linear elliptic problems. Two {open_quotes}hybrid{close_quotes} algorithms are also presented. They converge faster than the additive one and have better parallelism than the multiplicative method. Numerical tests with a two level domain decomposition for linear, nonlinear and interface elliptic problems are presented for the proposed algorithms.
Fontana, W.
1990-12-13
In this paper complex adaptive systems are defined by a self- referential loop in which objects encode functions that act back on these objects. A model for this loop is presented. It uses a simple recursive formal language, derived from the lambda-calculus, to provide a semantics that maps character strings into functions that manipulate symbols on strings. The interaction between two functions, or algorithms, is defined naturally within the language through function composition, and results in the production of a new function. An iterated map acting on sets of functions and a corresponding graph representation are defined. Their properties are useful to discuss the behavior of a fixed size ensemble of randomly interacting functions. This function gas'', or Turning gas'', is studied under various conditions, and evolves cooperative interaction patterns of considerable intricacy. These patterns adapt under the influence of perturbations consisting in the addition of new random functions to the system. Different organizations emerge depending on the availability of self-replicators.
Computational evolution: taking liberties.
Correia, Luís
2010-09-01
Evolution has, for a long time, inspired computer scientists to produce computer models mimicking its behavior. Evolutionary algorithm (EA) is one of the areas where this approach has flourished. EAs have been used to model and study evolution, but they have been especially developed for their aptitude as optimization tools for engineering. Developed models are quite simple in comparison with their natural sources of inspiration. However, since EAs run on computers, we have the freedom, especially in optimization models, to test approaches both realistic and outright speculative, from the biological point of view. In this article, we discuss different common evolutionary algorithm models, and then present some alternatives of interest. These include biologically inspired models, such as co-evolution and, in particular, symbiogenetics and outright artificial operators and representations. In each case, the advantages of the modifications to the standard model are identified. The other area of computational evolution, which has allowed us to study basic principles of evolution and ecology dynamics, is the development of artificial life platforms for open-ended evolution of artificial organisms. With these platforms, biologists can test theories by directly manipulating individuals and operators, observing the resulting effects in a realistic way. An overview of the most prominent of such environments is also presented. If instead of artificial platforms we use the real world for evolving artificial life, then we are dealing with evolutionary robotics (ERs). A brief description of this area is presented, analyzing its relations to biology. Finally, we present the conclusions and identify future research avenues in the frontier of computation and biology. Hopefully, this will help to draw the attention of more biologists and computer scientists to the benefits of such interdisciplinary research. PMID:20532997
Differential diagnosis of hyponatraemia.
Thompson, Chris; Berl, Tomas; Tejedor, Alberto; Johannsson, Gudmundur
2012-03-01
The appropriate management of hyponatraemia is reliant on the accurate identification of the underlying cause of the hyponatraemia. In the light of evidence which has shown that the use of a clinical algorithm appears to improve accuracy in the differential diagnosis of hyponatraemia, the European Hyponatraemia Network considered the use of two algorithms. One was developed from a nephrologist's view of hyponatraemia, while the other reflected the approach of an endocrinologist. Both of these algorithms concurred on the importance of assessing effective blood volume status and the measurement of urine sodium concentration in the diagnostic process. To demonstrate the importance of accurate diagnosis to the correct treatment of hyponatraemia, special consideration was given to hyponatraemia in neurosurgical patients. The differentiation between the syndrome of inappropriate antidiuretic hormone secretion (SIADH), acute adrenocorticotropic hormone (ACTH) deficiency, fluid overload and cerebral salt-wasting syndrome was discussed. In patients with SIADH, fluid restriction has been the mainstay of treatment despite the absence of an evidence base for its use. An approach to using fluid restriction to raise serum tonicity in patients with SIADH and to identify patients who are likely to be recalcitrant to fluid restriction was also suggested. PMID:22469249
NASA Astrophysics Data System (ADS)
Zhao, Jia-qing; Zeng, Pan; Lei, Li-ping; Ma, Yuan
2012-03-01
Digital image correlation (DIC) has received a widespread research and application in experimental mechanics. In DIC, the performance of subpixel registration algorithm (e.g., Newton-Raphson method, quasi-Newton method) relies heavily on the initial guess of deformation. In the case of small inter-frame deformation, the initial guess could be found by simple search scheme, the coarse-fine search for instance. While for large inter-frame deformation, it is difficult for simple search scheme to robustly estimate displacement parameters and deformation parameters simultaneously with low computational cost. In this paper, we proposed three improving strategies, i.e. Q-stage evolutionary strategy (T), parameter control strategy (C) and space expanding strategy (E), and then combined them into three population-based intelligent algorithms (PIAs), i.e. genetic algorithm (GA), differential evolution (DE) and particle swarm optimization (PSO), and finally derived eighteen different algorithms to calculate the initial guess for qN. The eighteen algorithms were compared in three sets of experiments including large rigid body translation, finite uniaxial strain and large rigid body rotation, and the results showed the effectiveness of proposed improving strategies. Among all compared algorithms, DE-TCE is the best which is robust, convenient and efficient for large inter-frame deformation measurement.
The fast debris evolution model
NASA Astrophysics Data System (ADS)
Lewis, H. G.; Swinerd, G. G.; Newland, R. J.; Saunders, A.
2009-09-01
The 'particles-in-a-box' (PIB) model introduced by Talent [Talent, D.L. Analytic model for orbital debris environmental management. J. Spacecraft Rocket, 29 (4), 508-513, 1992.] removed the need for computer-intensive Monte Carlo simulation to predict the gross characteristics of an evolving debris environment. The PIB model was described using a differential equation that allows the stability of the low Earth orbit (LEO) environment to be tested by a straightforward analysis of the equation's coefficients. As part of an ongoing research effort to investigate more efficient approaches to evolutionary modelling and to develop a suite of educational tools, a new PIB model has been developed. The model, entitled Fast Debris Evolution (FADE), employs a first-order differential equation to describe the rate at which new objects ⩾10 cm are added and removed from the environment. Whilst Talent [Talent, D.L. Analytic model for orbital debris environmental management. J. Spacecraft Rocket, 29 (4), 508-513, 1992.] based the collision theory for the PIB approach on collisions between gas particles and adopted specific values for the parameters of the model from a number of references, the form and coefficients of the FADE model equations can be inferred from the outputs of future projections produced by high-fidelity models, such as the DAMAGE model. The FADE model has been implemented as a client-side, web-based service using JavaScript embedded within a HTML document. Due to the simple nature of the algorithm, FADE can deliver the results of future projections immediately in a graphical format, with complete user-control over key simulation parameters. Historical and future projections for the ⩾10 cm LEO debris environment under a variety of different scenarios are possible, including business as usual, no future launches, post-mission disposal and remediation. A selection of results is presented with comparisons with predictions made using the DAMAGE environment model
The Fast Debris Evolution Model
NASA Astrophysics Data System (ADS)
Lewis, Hugh G.; Swinerd, Graham; Newland, Rebecca; Saunders, Arrun
The ‘Particles-in-a-box' (PIB) model introduced by Talent (1992) removed the need for computerintensive Monte Carlo simulation to predict the gross characteristics of an evolving debris environment. The PIB model was described using a differential equation that allows the stability of the low Earth orbit (LEO) environment to be tested by a straightforward analysis of the equation's coefficients. As part of an ongoing research effort to investigate more efficient approaches to evolutionary modelling and to develop a suite of educational tools, a new PIB model has been developed. The model, entitled Fast Debris Evolution (FaDE), employs a first-order differential equation to describe the rate at which new objects (˜ 10 cm) are added and removed from the environment. Whilst Talent (1992) based the collision theory for the PIB approach on collisions between gas particles and adopted specific values for the parameters of the model from a number of references, the form and coefficients of the FaDE model equations can be inferred from the outputs of future projections produced by high-fidelity models, such as the DAMAGE model. The FaDE model has been implemented as a client-side, web-based service using Javascript embedded within a HTML document. Due to the simple nature of the algorithm, FaDE can deliver the results of future projections immediately in a graphical format, with complete user-control over key simulation parameters. Historical and future projections for the ˜ 10 cm low Earth orbit (LEO) debris environment under a variety of different scenarios are possible, including business as usual, no future launches, post-mission disposal and remediation. A selection of results is presented with comparisons with predictions made using the DAMAGE environment model. The results demonstrate that the FaDE model is able to capture comparable time-series of collisions and number of objects as predicted by DAMAGE in several scenarios. Further, and perhaps more importantly
Algorithme intelligent d'optimisation d'un design structurel de grande envergure
NASA Astrophysics Data System (ADS)
Dominique, Stephane
genetic algorithm that prevents new individuals to be born too close to previously evaluated solutions. The restricted area becomes smaller or larger during the optimisation to allow global or local search when necessary. Also, a new search operator named Substitution Operator is incorporated in GATE. This operator allows an ANN surrogate model to guide the algorithm toward the most promising areas of the design space. The suggested CBR approach and GATE were tested on several simple test problems, as well as on the industrial problem of designing a gas turbine engine rotor's disc. These results are compared to other results obtained for the same problems by many other popular optimisation algorithms, such as (depending of the problem) gradient algorithms, binary genetic algorithm, real number genetic algorithm, genetic algorithm using multiple parents crossovers, differential evolution genetic algorithm, Hookes & Jeeves generalized pattern search method and POINTER from the software I-SIGHT 3.5. Results show that GATE is quite competitive, giving the best results for 5 of the 6 constrained optimisation problem. GATE also provided the best results of all on problem produced by a Maximum Set Gaussian landscape generator. Finally, GATE provided a disc 4.3% lighter than the best other tested algorithm (POINTER) for the gas turbine engine rotor's disc problem. One drawback of GATE is a lesser efficiency for highly multimodal unconstrained problems, for which he gave quite poor results with respect to its implementation cost. To conclude, according to the preliminary results obtained during this thesis, the suggested CBR process, combined with GATE, seems to be a very good candidate to automate and accelerate the structural design of mechanical devices, potentially reducing significantly the cost of industrial preliminary design processes.
Hepatic adenoma and focal nodular hyperplasia: differential diagnosis and treatment.
Herman, P; Pugliese, V; Machado, M A; Montagnini, A L; Salem, M Z; Bacchella, T; D'Albuquerque, L A; Saad, W A; Machado, M C; Pinotti, H W
2000-03-01
The diagnosis of benign hepatic tumors as hepatic adenoma (HA) and focal nodular hyperplasia (FNH) remains a challenge for clinicians and surgeons. The importance of differentiating between these lesions is based on the fact that HA must be surgically resected and FNH can be only observed. A series of 23 female patients with benign liver tumors (13 FNH, 10 HA) were evaluated, and a radiologic diagnostic algorithm was employed with the aim of establishing preoperative criteria for the differential diagnosis. All patients were submitted to surgical biopsy or hepatic resection to confirm the diagnosis. Based only on clinical and laboratory data, distinction was not possible. According to the investigative algorithm, the diagnosis was correct in 82.6% of the cases; but even with the development of imaging methods, which were used in combination, the differentiation was not possible in four patients. For FNH cases scintigraphy presented a sensitivity of 38.4% and specificity of 100%, whereas for HA the sensitivity reached 60% and specificity 85.7%. Magnetic resonance imaging, employed when scintigraphic findings were not typical, presented sensitivities of 71.4% and 80% and specificities of 100% and 100% for FNH and HA, respectively. Preoperative diagnosis of FNH was possible in 10 of 13 (76.9%) patients and was confirmed by histology in all of them. In one case, FNH was misdiagnosed as HA. The diagnosis of HA was possible in 9 of 10 (90%) adenoma cases. Surgical biopsy remains the best method for the differential diagnosis between HA and FNH and must be performed in all doubtful cases. Surgical resection is the treatment of choice for all patients with adenoma and can be performed safely. With the evolution of imaging methods it seems that the preoperative diagnosis of FNH may be considered reliable, thereby avoiding unnecessary surgical resection. PMID:10658075
ERIC Educational Resources Information Center
Scobey, Mary-Margaret, Ed.; Fiorino, A. John, Ed.
This book is a collection of six articles that deal with the concept and the practice of differentiated staffing in education. Included in the collection are articles on the concept itself; on problems, prospects, and the practical implementation of the concept; staff differentiation in a multiunit school; and polemical aspects of differentiated…
NASA Technical Reports Server (NTRS)
Varaiya, P. P.
1972-01-01
General discussion of the theory of differential games with two players and zero sum. Games starting at a fixed initial state and ending at a fixed final time are analyzed. Strategies for the games are defined. The existence of saddle values and saddle points is considered. A stochastic version of a differential game is used to examine the synthesis problem.
NASA Astrophysics Data System (ADS)
Roughgarden, J. E.
2006-12-01
My recent book, Evolution and Christian Faith explores how evolutionary biology can be portrayed from the religious perspective of Christianity. The principal metaphors for evolutionary biology---differential success at breeding and random mutation, probably originate with the dawn of agriculture and clearly occur in the Bible. The central narrative of evolutionary biology can be presented using Biblical passages, providing an account of evolution that is inherently friendly to a Christian perspective. Still, evolutionary biology is far from complete, and problematic areas pertain to species in which the concept of an individual is poorly defined, and to species in which the expression of gender and sexuality depart from Darwin's sexual-selection templates. The present- day controversy in the US about teaching evolution in the schools provides an opportunity to engage the public about science education.
Exponential integration algorithms applied to viscoplasticity
NASA Technical Reports Server (NTRS)
Freed, Alan D.; Walker, Kevin P.
1991-01-01
Four, linear, exponential, integration algorithms (two implicit, one explicit, and one predictor/corrector) are applied to a viscoplastic model to assess their capabilities. Viscoplasticity comprises a system of coupled, nonlinear, stiff, first order, ordinary differential equations which are a challenge to integrate by any means. Two of the algorithms (the predictor/corrector and one of the implicits) give outstanding results, even for very large time steps.
Spectral Representations of Uncertainty: Algorithms and Applications
George Em Karniadakis
2005-04-24
The objectives of this project were: (1) Develop a general algorithmic framework for stochastic ordinary and partial differential equations. (2) Set polynomial chaos method and its generalization on firm theoretical ground. (3) Quantify uncertainty in large-scale simulations involving CFD, MHD and microflows. The overall goal of this project was to provide DOE with an algorithmic capability that is more accurate and three to five orders of magnitude more efficient than the Monte Carlo simulation.
Stability of Bareiss algorithm
NASA Astrophysics Data System (ADS)
Bojanczyk, Adam W.; Brent, Richard P.; de Hoog, F. R.
1991-12-01
In this paper, we present a numerical stability analysis of Bareiss algorithm for solving a symmetric positive definite Toeplitz system of linear equations. We also compare Bareiss algorithm with Levinson algorithm and conclude that the former has superior numerical properties.
Genetic Algorithm for Optimization: Preprocessor and Algorithm
NASA Technical Reports Server (NTRS)
Sen, S. K.; Shaykhian, Gholam A.
2006-01-01
Genetic algorithm (GA) inspired by Darwin's theory of evolution and employed to solve optimization problems - unconstrained or constrained - uses an evolutionary process. A GA has several parameters such the population size, search space, crossover and mutation probabilities, and fitness criterion. These parameters are not universally known/determined a priori for all problems. Depending on the problem at hand, these parameters need to be decided such that the resulting GA performs the best. We present here a preprocessor that achieves just that, i.e., it determines, for a specified problem, the foregoing parameters so that the consequent GA is a best for the problem. We stress also the need for such a preprocessor both for quality (error) and for cost (complexity) to produce the solution. The preprocessor includes, as its first step, making use of all the information such as that of nature/character of the function/system, search space, physical/laboratory experimentation (if already done/available), and the physical environment. It also includes the information that can be generated through any means - deterministic/nondeterministic/graphics. Instead of attempting a solution of the problem straightway through a GA without having/using the information/knowledge of the character of the system, we would do consciously a much better job of producing a solution by using the information generated/created in the very first step of the preprocessor. We, therefore, unstintingly advocate the use of a preprocessor to solve a real-world optimization problem including NP-complete ones before using the statistically most appropriate GA. We also include such a GA for unconstrained function optimization problems.
Self-adaptive parameters in genetic algorithms
NASA Astrophysics Data System (ADS)
Pellerin, Eric; Pigeon, Luc; Delisle, Sylvain
2004-04-01
Genetic algorithms are powerful search algorithms that can be applied to a wide range of problems. Generally, parameter setting is accomplished prior to running a Genetic Algorithm (GA) and this setting remains unchanged during execution. The problem of interest to us here is the self-adaptive parameters adjustment of a GA. In this research, we propose an approach in which the control of a genetic algorithm"s parameters can be encoded within the chromosome of each individual. The parameters" values are entirely dependent on the evolution mechanism and on the problem context. Our preliminary results show that a GA is able to learn and evaluate the quality of self-set parameters according to their degree of contribution to the resolution of the problem. These results are indicative of a promising approach to the development of GAs with self-adaptive parameter settings that do not require the user to pre-adjust parameters at the outset.
Darwinian Evolution and Fractals
NASA Astrophysics Data System (ADS)
Carr, Paul H.
2009-05-01
Did nature's beauty emerge by chance or was it intelligently designed? Richard Dawkins asserts that evolution is blind aimless chance. Michael Behe believes, on the contrary, that the first cell was intelligently designed. The scientific evidence is that nature's creativity arises from the interplay between chance AND design (laws). Darwin's ``Origin of the Species,'' published 150 years ago in 1859, characterized evolution as the interplay between variations (symbolized by dice) and the natural selection law (design). This is evident in recent discoveries in DNA, Madelbrot's Fractal Geometry of Nature, and the success of the genetic design algorithm. Algorithms for generating fractals have the same interplay between randomness and law as evolution. Fractal statistics, which are not completely random, characterize such phenomena such as fluctuations in the stock market, the Nile River, rainfall, and tree rings. As chaos theorist Joseph Ford put it: God plays dice, but the dice are loaded. Thus Darwin, in discovering the evolutionary interplay between variations and natural selection, was throwing God's dice!
Sorensen, E.G.; Gordon, C.M.
1959-02-10
Improvements in analog eomputing machines of the class capable of evaluating differential equations, commonly termed differential analyzers, are described. In general form, the analyzer embodies a plurality of basic computer mechanisms for performing integration, multiplication, and addition, and means for directing the result of any one operation to another computer mechanism performing a further operation. In the device, numerical quantities are represented by the rotation of shafts, or the electrical equivalent of shafts.
Quality of Service Routing in Manet Using a Hybrid Intelligent Algorithm Inspired by Cuckoo Search
Rajalakshmi, S.; Maguteeswaran, R.
2015-01-01
A hybrid computational intelligent algorithm is proposed by integrating the salient features of two different heuristic techniques to solve a multiconstrained Quality of Service Routing (QoSR) problem in Mobile Ad Hoc Networks (MANETs) is presented. The QoSR is always a tricky problem to determine an optimum route that satisfies variety of necessary constraints in a MANET. The problem is also declared as NP-hard due to the nature of constant topology variation of the MANETs. Thus a solution technique that embarks upon the challenges of the QoSR problem is needed to be underpinned. This paper proposes a hybrid algorithm by modifying the Cuckoo Search Algorithm (CSA) with the new position updating mechanism. This updating mechanism is derived from the differential evolution (DE) algorithm, where the candidates learn from diversified search regions. Thus the CSA will act as the main search procedure guided by the updating mechanism derived from DE, called tuned CSA (TCSA). Numerical simulations on MANETs are performed to demonstrate the effectiveness of the proposed TCSA method by determining an optimum route that satisfies various Quality of Service (QoS) constraints. The results are compared with some of the existing techniques in the literature; therefore the superiority of the proposed method is established. PMID:26495429
A novel algorithm for generating libration point orbits about the collinear points
NASA Astrophysics Data System (ADS)
Ren, Yuan; Shan, Jinjun
2014-09-01
This paper presents a numerical algorithm that can generate long-term libration points orbits (LPOs) and the transfer orbits from the parking orbits to the LPOs in the circular-restricted three-body problem (CR3BP) and the full solar system model without initial guesses. The families of the quasi-periodic LPOs in the CR3BP can also be constructed with this algorithm. By using the dynamical behavior of LPO, the transfer orbit from the parking orbit to the LPO is generated using a bisection method. At the same time, a short segment of the target LPO connected with the transfer orbit is obtained, then the short segment of LPO is extended by correcting the state towards its adjacent point on the stable manifold of the target LPO with differential evolution algorithm. By implementing the correction strategy repeatedly, the LPOs can be extended to any length as needed. Moreover, combining with the continuation procedure, this algorithm can be used to generate the families of the quasi-periodic LPOs in the CR3BP.
Quality of Service Routing in Manet Using a Hybrid Intelligent Algorithm Inspired by Cuckoo Search.
Rajalakshmi, S; Maguteeswaran, R
2015-01-01
A hybrid computational intelligent algorithm is proposed by integrating the salient features of two different heuristic techniques to solve a multiconstrained Quality of Service Routing (QoSR) problem in Mobile Ad Hoc Networks (MANETs) is presented. The QoSR is always a tricky problem to determine an optimum route that satisfies variety of necessary constraints in a MANET. The problem is also declared as NP-hard due to the nature of constant topology variation of the MANETs. Thus a solution technique that embarks upon the challenges of the QoSR problem is needed to be underpinned. This paper proposes a hybrid algorithm by modifying the Cuckoo Search Algorithm (CSA) with the new position updating mechanism. This updating mechanism is derived from the differential evolution (DE) algorithm, where the candidates learn from diversified search regions. Thus the CSA will act as the main search procedure guided by the updating mechanism derived from DE, called tuned CSA (TCSA). Numerical simulations on MANETs are performed to demonstrate the effectiveness of the proposed TCSA method by determining an optimum route that satisfies various Quality of Service (QoS) constraints. The results are compared with some of the existing techniques in the literature; therefore the superiority of the proposed method is established. PMID:26495429
Algorithms for computing the multivariable stability margin
NASA Technical Reports Server (NTRS)
Tekawy, Jonathan A.; Safonov, Michael G.; Chiang, Richard Y.
1989-01-01
Stability margin for multiloop flight control systems has become a critical issue, especially in highly maneuverable aircraft designs where there are inherent strong cross-couplings between the various feedback control loops. To cope with this issue, we have developed computer algorithms based on non-differentiable optimization theory. These algorithms have been developed for computing the Multivariable Stability Margin (MSM). The MSM of a dynamical system is the size of the smallest structured perturbation in component dynamics that will destabilize the system. These algorithms have been coded and appear to be reliable. As illustrated by examples, they provide the basis for evaluating the robustness and performance of flight control systems.
Overshooting by differential heating
NASA Astrophysics Data System (ADS)
Andrássy, R.; Spruit, H. C.
2015-06-01
On the long nuclear time scale of stellar main-sequence evolution, even weak mixing processes can become relevant for redistributing chemical species in a star. We investigate a process of "differential heating", which occurs when a temperature fluctuation propagates by radiative diffusion from the boundary of a convection zone into the adjacent radiative zone. The resulting perturbation of the hydrostatic equilibrium causes a flow that extends some distance from the convection zone. We study a simplified differential-heating problem with a static temperature fluctuation imposed on a solid boundary. The astrophysically relevant limit of a high Reynolds number and a low Péclet number (high thermal diffusivity) turns out to be interestingly non-intuitive. We derive a set of scaling relations for the stationary differential heating flow. A numerical method adapted to a high dynamic range in flow amplitude needed to detect weak flows is presented. Our two-dimensional simulations show that the flow reaches a stationary state and confirm the analytic scaling relations. These imply that the flow speed drops abruptly to a negligible value at a finite height above the source of heating. We approximate the mixing rate due to the differential heating flow in a star by a height-dependent diffusion coefficient and show that this mixing extends about 4% of the pressure scale height above the convective core of a 10 M⊙ zero-age main sequence star. Appendix A is available in electronic form at http://www.aanda.org
Differentially Private Frequent Subgraph Mining
Xu, Shengzhi; Xiong, Li; Cheng, Xiang; Xiao, Ke
2016-01-01
Mining frequent subgraphs from a collection of input graphs is an important topic in data mining research. However, if the input graphs contain sensitive information, releasing frequent subgraphs may pose considerable threats to individual's privacy. In this paper, we study the problem of frequent subgraph mining (FGM) under the rigorous differential privacy model. We introduce a novel differentially private FGM algorithm, which is referred to as DFG. In this algorithm, we first privately identify frequent subgraphs from input graphs, and then compute the noisy support of each identified frequent subgraph. In particular, to privately identify frequent subgraphs, we present a frequent subgraph identification approach which can improve the utility of frequent subgraph identifications through candidates pruning. Moreover, to compute the noisy support of each identified frequent subgraph, we devise a lattice-based noisy support derivation approach, where a series of methods has been proposed to improve the accuracy of the noisy supports. Through formal privacy analysis, we prove that our DFG algorithm satisfies ε-differential privacy. Extensive experimental results on real datasets show that the DFG algorithm can privately find frequent subgraphs with high data utility.
Coagulation algorithms with size binning
NASA Technical Reports Server (NTRS)
Statton, David M.; Gans, Jason; Williams, Eric
1994-01-01
The Smoluchowski equation describes the time evolution of an aerosol particle size distribution due to aggregation or coagulation. Any algorithm for computerized solution of this equation requires a scheme for describing the continuum of aerosol particle sizes as a discrete set. One standard form of the Smoluchowski equation accomplishes this by restricting the particle sizes to integer multiples of a basic unit particle size (the monomer size). This can be inefficient when particle concentrations over a large range of particle sizes must be calculated. Two algorithms employing a geometric size binning convention are examined: the first assumes that the aerosol particle concentration as a function of size can be considered constant within each size bin; the second approximates the concentration as a linear function of particle size within each size bin. The output of each algorithm is compared to an analytical solution in a special case of the Smoluchowski equation for which an exact solution is known . The range of parameters more appropriate for each algorithm is examined.
Library of Continuation Algorithms
2005-03-01
LOCA (Library of Continuation Algorithms) is scientific software written in C++ that provides advanced analysis tools for nonlinear systems. In particular, it provides parameter continuation algorithms. bifurcation tracking algorithms, and drivers for linear stability analysis. The algorithms are aimed at large-scale applications that use Newtons method for their nonlinear solve.
Differentially Private Empirical Risk Minimization
Chaudhuri, Kamalika; Monteleoni, Claire; Sarwate, Anand D.
2011-01-01
Privacy-preserving machine learning algorithms are crucial for the increasingly common setting in which personal data, such as medical or financial records, are analyzed. We provide general techniques to produce privacy-preserving approximations of classifiers learned via (regularized) empirical risk minimization (ERM). These algorithms are private under the ε-differential privacy definition due to Dwork et al. (2006). First we apply the output perturbation ideas of Dwork et al. (2006), to ERM classification. Then we propose a new method, objective perturbation, for privacy-preserving machine learning algorithm design. This method entails perturbing the objective function before optimizing over classifiers. If the loss and regularizer satisfy certain convexity and differentiability criteria, we prove theoretical results showing that our algorithms preserve privacy, and provide generalization bounds for linear and nonlinear kernels. We further present a privacy-preserving technique for tuning the parameters in general machine learning algorithms, thereby providing end-to-end privacy guarantees for the training process. We apply these results to produce privacy-preserving analogues of regularized logistic regression and support vector machines. We obtain encouraging results from evaluating their performance on real demographic and benchmark data sets. Our results show that both theoretically and empirically, objective perturbation is superior to the previous state-of-the-art, output perturbation, in managing the inherent tradeoff between privacy and learning performance. PMID:21892342
NASA Technical Reports Server (NTRS)
Provost, David E.
1990-01-01
Viewgraphs on flight telerobotic servicer evolution are presented. Topics covered include: paths for FTS evolution; frequently performed actions; primary task states; EPS radiator panel installation; generic task definitions; path planning; non-contact alignment; contact planning and control; and human operator interface.
ERIC Educational Resources Information Center
Bryner, Jeanna
2005-01-01
Eighty years after the famous 1925 Scopes "monkey trial," which tested a teacher's right to discuss the theory of evolution in the classroom, evolution--and its most recent counterview, called "intelligent design"--are in the headlines again, and just about everyone seems to have an opinion. This past July, President Bush weighed in, telling…
Geist, G.A.; Howell, G.W.; Watkins, D.S.
1997-11-01
The BR algorithm, a new method for calculating the eigenvalues of an upper Hessenberg matrix, is introduced. It is a bulge-chasing algorithm like the QR algorithm, but, unlike the QR algorithm, it is well adapted to computing the eigenvalues of the narrowband, nearly tridiagonal matrices generated by the look-ahead Lanczos process. This paper describes the BR algorithm and gives numerical evidence that it works well in conjunction with the Lanczos process. On the biggest problems run so far, the BR algorithm beats the QR algorithm by a factor of 30--60 in computing time and a factor of over 100 in matrix storage space.
Interactive evolution of camouflage.
Reynolds, Craig
2011-01-01
This article presents an abstract computation model of the evolution of camouflage in nature. The 2D model uses evolved textures for prey, a background texture representing the environment, and a visual predator. A human observer, acting as the predator, is shown a cohort of 10 evolved textures overlaid on the background texture. The observer clicks on the five most conspicuous prey to remove ("eat") them. These lower-fitness textures are removed from the population and replaced with newly bred textures. Biological morphogenesis is represented in this model by procedural texture synthesis. Nested expressions of generators and operators form a texture description language. Natural evolution is represented by genetic programming (GP), a variant of the genetic algorithm. GP searches the space of texture description programs for those that appear least conspicuous to the predator. PMID:21370960
NASA Astrophysics Data System (ADS)
Ge, Xinmin; Fan, Yiren; Cao, Yingchang; Wang, Yang; Cong, Yunhai; Liu, Lailei
2015-06-01
To allow peak searching and parameter estimation for geological and geophysical data with multi-peak distributions, we explore a hybrid method based on a combination of the particle swarm optimization (PSO) and generalized reduced gradient (GRG) algorithms. After characterizing peaks using the additive Gaussian function, a nonlinear objective function is established, which transforms our task into a search for optimal solutions. In this process, PSO is used to obtain the initial values, aiming for global convergence, while GRG is subsequently implemented for higher stability. Iterations are stopped when the convergence criteria are satisfied. Finally, grayscale histograms of backscattering electron images of sandstone show that the proposed algorithm performs much better than other methods such as PSO, GRG, simulated annealing and differential evolution, achieving a faster convergence speed and minimal variances.
Algorithmic Mechanism Design of Evolutionary Computation
Pei, Yan
2015-01-01
We consider algorithmic design, enhancement, and improvement of evolutionary computation as a mechanism design problem. All individuals or several groups of individuals can be considered as self-interested agents. The individuals in evolutionary computation can manipulate parameter settings and operations by satisfying their own preferences, which are defined by an evolutionary computation algorithm designer, rather than by following a fixed algorithm rule. Evolutionary computation algorithm designers or self-adaptive methods should construct proper rules and mechanisms for all agents (individuals) to conduct their evolution behaviour correctly in order to definitely achieve the desired and preset objective(s). As a case study, we propose a formal framework on parameter setting, strategy selection, and algorithmic design of evolutionary computation by considering the Nash strategy equilibrium of a mechanism design in the search process. The evaluation results present the efficiency of the framework. This primary principle can be implemented in any evolutionary computation algorithm that needs to consider strategy selection issues in its optimization process. The final objective of our work is to solve evolutionary computation design as an algorithmic mechanism design problem and establish its fundamental aspect by taking this perspective. This paper is the first step towards achieving this objective by implementing a strategy equilibrium solution (such as Nash equilibrium) in evolutionary computation algorithm. PMID:26257777
Algorithmic Mechanism Design of Evolutionary Computation.
Pei, Yan
2015-01-01
We consider algorithmic design, enhancement, and improvement of evolutionary computation as a mechanism design problem. All individuals or several groups of individuals can be considered as self-interested agents. The individuals in evolutionary computation can manipulate parameter settings and operations by satisfying their own preferences, which are defined by an evolutionary computation algorithm designer, rather than by following a fixed algorithm rule. Evolutionary computation algorithm designers or self-adaptive methods should construct proper rules and mechanisms for all agents (individuals) to conduct their evolution behaviour correctly in order to definitely achieve the desired and preset objective(s). As a case study, we propose a formal framework on parameter setting, strategy selection, and algorithmic design of evolutionary computation by considering the Nash strategy equilibrium of a mechanism design in the search process. The evaluation results present the efficiency of the framework. This primary principle can be implemented in any evolutionary computation algorithm that needs to consider strategy selection issues in its optimization process. The final objective of our work is to solve evolutionary computation design as an algorithmic mechanism design problem and establish its fundamental aspect by taking this perspective. This paper is the first step towards achieving this objective by implementing a strategy equilibrium solution (such as Nash equilibrium) in evolutionary computation algorithm. PMID:26257777
Shapiro, L; Agabian-Keshishian, N; Bendis, I
1971-09-01
The foregoing studies are intended to define a differentiation process and to permit genetic access to the mechanisms that control this process. In order to elucidate the basic mechanisms whereby a cell dictates its own defined morphogenic changes, we have found it helpful to study an organism that can be manipulated both biochemically and genetically. We have attempted to develop the studies initiated by Poindexter,Stove and Stanier, and Schmidt and Stanier (16, 17, 20) with the Caulobacter genus so that these bacteria can serve as a model system for prokaryotic differentiation. The Caulobacter life cycle, defined in synchronously growing cultures, includes a sequential series of morphological changes that occur at specific times in the cycle and at specific locations in the cell. Six distinct cellular characteristics, which are peculiar to these bacteria, have been defined and include (i) the synthesis of a polar organelle which may be membranous (21-23), (ii) a satellite DNA in the stalked cell (26), (iii) pili to which RNA bacteriophage specifically adsorb (16, 33), (iv) a single polar flagellum(17), (v) a lipopolysaccharide phage receptor site (27), and (vi) new cell wall material at the flagellated pole of the cell giving rise to a stalk (19, 20). Cell division, essential for the viability of the organism, is dependent on the irreversible differentiation of a flagellated swarmer cell to a mature stalked cell. The specific features of the Caulobacter system which make it a system of choice for studies of the control of sequential events resulting in cellular differentiation can be summarized as follows. 1) Cell populations can be synchronized, and homogeneous populations at each stage in the differentiation cycle can thus be obtained. 2) A specific technique has been developed whereby the progress of the differentiation cycle can be accurately measured by adsorption of labeled RNA phage or penetration of labeled phage DNA into specific cell forms. This
Chromospheric activity and stellar evolution
NASA Technical Reports Server (NTRS)
Kippenhahn, R.
1973-01-01
A study of stellar chromospheres based on the internal structure of particular stars is presented. Used are complex flow diagrams of the linkage paths between mass loss, angular momentum loss, magnetic field from the turbulent dynamo and its relations to differential rotations and the convection zone, and stellar evolution.
Improved differential 3D shape retrieval
NASA Astrophysics Data System (ADS)
Liu, Tongchuan; Zhou, Canlin; Si, Shuchun; Li, Hui; Lei, Zhenkun
2015-10-01
Phase unwrapping is a complex step in three-dimensional (3D) surface measurement. To simplify the computation process, Martino et al. proposed a differential algorithm. However, it will result in large error when the orthogonal fringes are not in horizontal or vertical direction. To solve this problem, the relationship between projector's and camera's coordinate systems is introduced. With the data obtained from coordinate transformation, the improved differential algorithm can be used for orthogonal fringes in any direction. Besides that, taking advantage of Fourier differentiation theorem makes operation and calculation simpler. By contrast, the results of experiments show that the proposed method is applicable to the patterns with orthogonal fringes in every direction. In addition, Fourier differentiation theorem effectively increases the speed of differential process.
Constrained minimization of smooth functions using a genetic algorithm
NASA Technical Reports Server (NTRS)
Moerder, Daniel D.; Pamadi, Bandu N.
1994-01-01
The use of genetic algorithms for minimization of differentiable functions that are subject to differentiable constraints is considered. A technique is demonstrated for converting the solution of the necessary conditions for a constrained minimum into an unconstrained function minimization. This technique is extended as a global constrained optimization algorithm. The theory is applied to calculating minimum-fuel ascent control settings for an energy state model of an aerospace plane.
Sensitivity Analysis of Differential-Algebraic Equations and Partial Differential Equations
Petzold, L; Cao, Y; Li, S; Serban, R
2005-08-09
Sensitivity analysis generates essential information for model development, design optimization, parameter estimation, optimal control, model reduction and experimental design. In this paper we describe the forward and adjoint methods for sensitivity analysis, and outline some of our recent work on theory, algorithms and software for sensitivity analysis of differential-algebraic equation (DAE) and time-dependent partial differential equation (PDE) systems.
Phase tracking with differential dispersion
NASA Astrophysics Data System (ADS)
Haubois, Xavier; Lacour, Sylvestre; Perrin, Guy S.; Dembet, Roderick; Fedou, Pierre; Eisenhauer, Frank; Rousselet-Perraut, Karine; Straubmeier, Christian; Amorim, Antonio; Brandner, Wolfgang
2014-07-01
Differential chromatic dispersion in single-mode optical fibres leads to a loss of contrast of the white light fringe. For the GRAVITY instrument, this aspect is critical since it limits the fringe tracking performance. We present a real-time algorithm that compensates for differential dispersion due to varying fibre lengths using prior calibration of the optical fibres. This correction is limited by the accuracy to which the fibres stretch is known. We show how this affects the SNR on the white light fringe for different scenarios and we estimate how this phenomenon might eventually impact the astrometric accuracy of GRAVITY observations.
NASA Technical Reports Server (NTRS)
Chiu, H.-Y. (Editor); Muriel, A.
1972-01-01
Aspects of normal stellar evolution are discussed together with evolution near the main sequence, stellar evolution from main sequence to white dwarf or carbon ignition, the structure of massive main-sequence stars, and problems of stellar stability and stellar pulsation. Other subjects considered include variable stars, white dwarfs, close binaries, novae, early supernova luminosity, neutron stars, the photometry of field horizontal-branch stars, and stellar opacity. Transport mechanisms in stars are examined together with thermonuclear reactions and nucleosynthesis, the instability problem in nuclear burning shells, stellar coalescence, and intense magnetic fields in astrophysics. Individual items are announced in this issue.
Trees, bialgebras and intrinsic numerical algorithms
NASA Technical Reports Server (NTRS)
Crouch, Peter; Grossman, Robert; Larson, Richard
1990-01-01
Preliminary work about intrinsic numerical integrators evolving on groups is described. Fix a finite dimensional Lie group G; let g denote its Lie algebra, and let Y(sub 1),...,Y(sub N) denote a basis of g. A class of numerical algorithms is presented that approximate solutions to differential equations evolving on G of the form: dot-x(t) = F(x(t)), x(0) = p is an element of G. The algorithms depend upon constants c(sub i) and c(sub ij), for i = 1,...,k and j is less than i. The algorithms have the property that if the algorithm starts on the group, then it remains on the group. In addition, they also have the property that if G is the abelian group R(N), then the algorithm becomes the classical Runge-Kutta algorithm. The Cayley algebra generated by labeled, ordered trees is used to generate the equations that the coefficients c(sub i) and c(sub ij) must satisfy in order for the algorithm to yield an rth order numerical integrator and to analyze the resulting algorithms.
NASA Technical Reports Server (NTRS)
Yee, H. C.; Sweby, P. K.; Griffiths, D. F.
1991-01-01
Spurious stable as well as unstable steady state numerical solutions, spurious asymptotic numerical solutions of higher period, and even stable chaotic behavior can occur when finite difference methods are used to solve nonlinear differential equations (DE) numerically. The occurrence of spurious asymptotes is independent of whether the DE possesses a unique steady state or has additional periodic solutions and/or exhibits chaotic phenomena. The form of the nonlinear DEs and the type of numerical schemes are the determining factor. In addition, the occurrence of spurious steady states is not restricted to the time steps that are beyond the linearized stability limit of the scheme. In many instances, it can occur below the linearized stability limit. Therefore, it is essential for practitioners in computational sciences to be knowledgeable about the dynamical behavior of finite difference methods for nonlinear scalar DEs before the actual application of these methods to practical computations. It is also important to change the traditional way of thinking and practices when dealing with genuinely nonlinear problems. In the past, spurious asymptotes were observed in numerical computations but tended to be ignored because they all were assumed to lie beyond the linearized stability limits of the time step parameter delta t. As can be seen from the study, bifurcations to and from spurious asymptotic solutions and transitions to computational instability not only are highly scheme dependent and problem dependent, but also initial data and boundary condition dependent, and not limited to time steps that are beyond the linearized stability limit.
NASA Technical Reports Server (NTRS)
Yee, H. C.; Sweby, P. K.; Griffiths, D. F.
1990-01-01
Spurious stable as well as unstable steady state numerical solutions, spurious asymptotic numerical solutions of higher period, and even stable chaotic behavior can occur when finite difference methods are used to solve nonlinear differential equations (DE) numerically. The occurrence of spurious asymptotes is independent of whether the DE possesses a unique steady state or has additional periodic solutions and/or exhibits chaotic phenomena. The form of the nonlinear DEs and the type of numerical schemes are the determining factor. In addition, the occurrence of spurious steady states is not restricted to the time steps that are beyond the linearized stability limit of the scheme. In many instances, it can occur below the linearized stability limit. Therefore, it is essential for practitioners in computational sciences to be knowledgeable about the dynamical behavior of finite difference methods for nonlinear scalar DEs before the actual application of these methods to practical computations. It is also important to change the traditional way of thinking and practices when dealing with genuinely nonlinear problems. In the past, spurious asymptotes were observed in numerical computations but tended to be ignored because they all were assumed to lie beyond the linearized stability limits of the time step parameter delta t. As can be seen from the study, bifurcations to and from spurious asymptotic solutions and transitions to computational instability not only are highly scheme dependent and problem dependent, but also initial data and boundary condition dependent, and not limited to time steps that are beyond the linearized stability limit.
Genetic algorithms and supernovae type Ia analysis
Bogdanos, Charalampos; Nesseris, Savvas E-mail: nesseris@nbi.dk
2009-05-15
We introduce genetic algorithms as a means to analyze supernovae type Ia data and extract model-independent constraints on the evolution of the Dark Energy equation of state w(z) {identical_to} P{sub DE}/{rho}{sub DE}. Specifically, we will give a brief introduction to the genetic algorithms along with some simple examples to illustrate their advantages and finally we will apply them to the supernovae type Ia data. We find that genetic algorithms can lead to results in line with already established parametric and non-parametric reconstruction methods and could be used as a complementary way of treating SNIa data. As a non-parametric method, genetic algorithms provide a model-independent way to analyze data and can minimize bias due to premature choice of a dark energy model.
Genetic algorithms as global random search methods
NASA Technical Reports Server (NTRS)
Peck, Charles C.; Dhawan, Atam P.
1995-01-01
Genetic algorithm behavior is described in terms of the construction and evolution of the sampling distributions over the space of candidate solutions. This novel perspective is motivated by analysis indicating that that schema theory is inadequate for completely and properly explaining genetic algorithm behavior. Based on the proposed theory, it is argued that the similarities of candidate solutions should be exploited directly, rather than encoding candidate solution and then exploiting their similarities. Proportional selection is characterized as a global search operator, and recombination is characterized as the search process that exploits similarities. Sequential algorithms and many deletion methods are also analyzed. It is shown that by properly constraining the search breadth of recombination operators, convergence of genetic algorithms to a global optimum can be ensured.
Genetic algorithms as global random search methods
NASA Technical Reports Server (NTRS)
Peck, Charles C.; Dhawan, Atam P.
1995-01-01
Genetic algorithm behavior is described in terms of the construction and evolution of the sampling distributions over the space of candidate solutions. This novel perspective is motivated by analysis indicating that the schema theory is inadequate for completely and properly explaining genetic algorithm behavior. Based on the proposed theory, it is argued that the similarities of candidate solutions should be exploited directly, rather than encoding candidate solutions and then exploiting their similarities. Proportional selection is characterized as a global search operator, and recombination is characterized as the search process that exploits similarities. Sequential algorithms and many deletion methods are also analyzed. It is shown that by properly constraining the search breadth of recombination operators, convergence of genetic algorithms to a global optimum can be ensured.
Sweeping algorithms for five-point stencils and banded matrices
Kwong, Man Kam.
1992-06-01
We record MATLAB experiments implementing the sweeping algorithms we proposed recently to solve five-point stencils arising from the discretization of partial differential equations, notably the Ginzburg-Landau equations from the theory of superconductivity. Algorithms tested include two-direction, multistage, and partial sweeping.
Prolegomenon to patterns in evolution.
Kauffman, Stuart A
2014-09-01
Despite Darwin, we remain children of Newton and dream of a grand theory that is epistemologically complete and would allow prediction of the evolution of the biosphere. The main purpose of this article is to show that this dream is false, and bears on studying patterns of evolution. To do so, I must justify the use of the word "function" in biology, when physics has only happenings. The concept of "function" lifts biology irreducibly above physics, for as we shall see, we cannot prestate the ever new biological functions that arise and constitute the very phase space of evolution. Hence, we cannot mathematize the detailed becoming of the biosphere, nor write differential equations for functional variables we do not know ahead of time, nor integrate those equations, so no laws "entail" evolution. The dream of a grand theory fails. In place of entailing laws, I propose a post-entailing law explanatory framework in which Actuals arise in evolution that constitute new boundary conditions that are enabling constraints that create new, typically unprestatable, adjacent possible opportunities for further evolution, in which new Actuals arise, in a persistent becoming. Evolution flows into a typically unprestatable succession of adjacent possibles. Given the concept of function, the concept of functional closure of an organism making a living in its world becomes central. Implications for patterns in evolution include historical reconstruction, and statistical laws such as the distribution of extinction events, or species per genus, and the use of formal cause, not efficient cause, laws. PMID:24704211
Quantum lattice gas algorithm for the telegraph equation
NASA Astrophysics Data System (ADS)
Coffey, Mark W.; Colburn, Gabriel G.
2009-06-01
The telegraph equation combines features of both the diffusion and wave equations and has many applications to heat propagation, transport in disordered media, and elsewhere. We describe a quantum lattice gas algorithm (QLGA) for this partial differential equation with one spatial dimension. This algorithm generalizes one previously known for the diffusion equation. We present an analysis of the algorithm and accompanying simulation results. The QLGA is suitable for simulation on combined classical-quantum computers.
Research on numerical algorithms for large space structures
NASA Technical Reports Server (NTRS)
Denman, E. D.
1981-01-01
Numerical algorithms for analysis and design of large space structures are investigated. The sign algorithm and its application to decoupling of differential equations are presented. The generalized sign algorithm is given and its application to several problems discussed. The Laplace transforms of matrix functions and the diagonalization procedure for a finite element equation are discussed. The diagonalization of matrix polynomials is considered. The quadrature method and Laplace transforms is discussed and the identification of linear systems by the quadrature method investigated.
Ramos, Eric M.; Harb, Socorro; Dragavon, Joan; Coombs, Robert W.
2014-01-01
Background An accurate and rapid serologic method to differentiate HIV-2 from HIV-1 infection is required since the confirmatory HIV-1 Western Blot (WB) may demonstrate cross-reactivity with HIV-2 antibodies. Objectives To evaluate the performance of the Bio-Rad Multispot HIV-1/HIV-2 rapid assay as a supplemental test to correctly identify HIV-2 infection and identify HIV-1 WB cross-reactivity with HIV-2 in clinical samples tested at an academic medical center. Study design Between August 2008 and July 2012, clinical samples were screened for HIV using either 3rd-or 4th-generation HIV-1/2 antibody or combination antibody and HIV-1 p24 antigen assays, respectively. All repeatedly reactive samples were reflexed for Multispot rapid testing. Multispot HIV-2 and HIV-1 and HIV-2-reactive samples were further tested using an HIV-2 immunoblot assay and HIV-1 or HIV-2 RNA assays when possible. The HIV-1 WB was performed routinely for additional confirmation and to assess for HIV-2 antibody cross-reactivity. Results Of 46,061 samples screened, 890 (89.6%) of 993 repeatedly reactive samples were also Multispot-reactive: 882 for HIV-1; three for only HIV-2; and five for both HIV-1 and HIV-2. All three HIV-2-only Multispot-positives along with a single dually reactive HIV-1/2 Multispot-positive were also HIV-2 immunoblot-positive; the latter was HIV-1 RNA negative and HIV-2 RNA positive. Conclusions The Multispot rapid test performed well as a supplemental test for HIV-1/2 diagnostic testing. Four new HIV-2 infections (0.45%) were identified from among 890 Multispot-reactive tests. The use of HIV-1 WB alone to confirm HIV-1/2 screening assays may underestimate the true prevalence of HIV-2 infection in the United States. PMID:24342468
Development of the algorithm for life for the search for extraterrestrial life
NASA Astrophysics Data System (ADS)
Kolb, Vera M.
2013-09-01
We first introduce a concept of algorithms in a form which is useful to astrobiology. We follow Dennett's description of algorithms, which he has used to introduce the idea that evolution takes place via natural selection in an algorithmic process. We then bring up various examples and principles of evolution, including inventive evolution for the biosynthesis of secondary metabolites, and propose them as candidates for constituting evolutionary algorithms. Finally, we discuss philosophy papers of Rescher about extraterrestrials and their science and attempt to extract from them some generalized principles for the search for extraterrestrial life.
Reasoning about systolic algorithms
Purushothaman, S.
1986-01-01
Systolic algorithms are a class of parallel algorithms, with small grain concurrency, well suited for implementation in VLSI. They are intended to be implemented as high-performance, computation-bound back-end processors and are characterized by a tesselating interconnection of identical processing elements. This dissertation investigates the problem of providing correctness of systolic algorithms. The following are reported in this dissertation: (1) a methodology for verifying correctness of systolic algorithms based on solving the representation of an algorithm as recurrence equations. The methodology is demonstrated by proving the correctness of a systolic architecture for optimal parenthesization. (2) The implementation of mechanical proofs of correctness of two systolic algorithms, a convolution algorithm and an optimal parenthesization algorithm, using the Boyer-Moore theorem prover. (3) An induction principle for proving correctness of systolic arrays which are modular. Two attendant inference rules, weak equivalence and shift transformation, which capture equivalent behavior of systolic arrays, are also presented.
Algorithm-development activities
NASA Technical Reports Server (NTRS)
Carder, Kendall L.
1994-01-01
The task of algorithm-development activities at USF continues. The algorithm for determining chlorophyll alpha concentration, (Chl alpha) and gelbstoff absorption coefficient for SeaWiFS and MODIS-N radiance data is our current priority.
Ceres: Evolution and Present State
NASA Astrophysics Data System (ADS)
Castillo-Rogez, J.; McCord, T.
2007-08-01
Introduction:We consider Ceres as a prototype for planetary evolution [1]. From thermal modeling by McCord and Sotin [2, 3, 4], Ceres was inferred to have differentiated into a rocky core of hydrated silicates, and an icy outer shell. Thomas et al. [5] confirmed such a model from direct observation of Ceres's shape from Hubble Space Telescope observations, and pervious occultation measurements. McCord and Sotin [4] also suggest that Ceres could have preserved a deep ocean, especially if ammonia or some other ice melting point depressant, such as salts, was incorporated during accretion. We continue to develop thermal modeling of Ceres, using increasingly sophisticated models and new observational information in order to match the observed shape. . In particular, we investigate the evolution of the core. Approach: Our models require the following initial input: initial planetesimal temperature (after [6]); composition; time of formation with respect to Calcium-Aluminum Inclusions (CAIs); and an internal heat profile after initial accretion. Modeling begins with a porous Ceres (after [7, 8]). The rock phase has the composition of an ordinary chondrite (after [9]). Short-lived radiogenic isotopes, including 26Al and 60Fe, have initial concentrations as measured by [10, 11]. Conductive thermal evolution is computed for one-dimensional models following the approach of [4] and [12]. The silicate core evolves through hydration, then dehydration and melting stages. Currently, hydrothermal cooling is not included in our algorithm. Model Results: Conditions were present for full differentiation of Ceres if accretion time t0-CAIs was less than 7 My and/or if ammonia was accreted. For times of formation t0-CAIs shorter than 2 My, the boiling point of water was reached within a few My after accretion, and may have led to major water loss.Under these conditions, hydrothermal activity was inevitable, and might still be taking place inside Ceres. Whether a deep ocean is still
Accurate Finite Difference Algorithms
NASA Technical Reports Server (NTRS)
Goodrich, John W.
1996-01-01
Two families of finite difference algorithms for computational aeroacoustics are presented and compared. All of the algorithms are single step explicit methods, they have the same order of accuracy in both space and time, with examples up to eleventh order, and they have multidimensional extensions. One of the algorithm families has spectral like high resolution. Propagation with high order and high resolution algorithms can produce accurate results after O(10(exp 6)) periods of propagation with eight grid points per wavelength.
Modeling discharge-sediment relationship using neural networks with artificial bee colony algorithm
NASA Astrophysics Data System (ADS)
Kisi, Ozgur; Ozkan, Coskun; Akay, Bahriye
2012-03-01
SummaryEstimation of suspended sediment concentration carried by a river is very important for many water resources projects. The accuracy of artificial neural networks (ANN) with artificial bee colony (ABC) algorithm is investigated in this paper for modeling discharge-suspended sediment relationship. The ANN-ABC was compared with those of the neural differential evolution, adaptive neuro-fuzzy, neural networks and rating curve models. The daily stream flow and suspended sediment concentration data from two stations, Rio Valenciano Station and Quebrada Blanca Station, were used as case studies. For evaluating the ability of the models, mean square error and determination coefficient criteria were used. Comparison results showed that the ANN-ABC was able to produce better results than the neural differential evolution, neuro-fuzzy, neural networks and rating curve models. The logarithm transformed data were also used as input to the proposed ANN-ABC models. It was found that the logarithm transform significantly increased accuracy of the models in suspended sediment estimation.
Improved local linearization algorithm for solving the quaternion equations
NASA Technical Reports Server (NTRS)
Yen, K.; Cook, G.
1980-01-01
The objective of this paper is to develop a new and more accurate local linearization algorithm for numerically solving sets of linear time-varying differential equations. Of special interest is the application of this algorithm to the quaternion rate equations. The results are compared, both analytically and experimentally, with previous results using local linearization methods. The new algorithm requires approximately one-third more calculations per step than the previously developed local linearization algorithm; however, this disadvantage could be reduced by using parallel implementation. For some cases the new algorithm yields significant improvement in accuracy, even with an enlarged sampling interval. The reverse is true in other cases. The errors depend on the values of angular velocity, angular acceleration, and integration step size. One important result is that for the worst case the new algorithm can guarantee eigenvalues nearer the region of stability than can the previously developed algorithm.
Quantum algorithms and the finite element method
NASA Astrophysics Data System (ADS)
Montanaro, Ashley; Pallister, Sam
2016-03-01
The finite element method is used to approximately solve boundary value problems for differential equations. The method discretizes the parameter space and finds an approximate solution by solving a large system of linear equations. Here we investigate the extent to which the finite element method can be accelerated using an efficient quantum algorithm for solving linear equations. We consider the representative general question of approximately computing a linear functional of the solution to a boundary value problem and compare the quantum algorithm's theoretical performance with that of a standard classical algorithm—the conjugate gradient method. Prior work claimed that the quantum algorithm could be exponentially faster but did not determine the overall classical and quantum run times required to achieve a predetermined solution accuracy. Taking this into account, we find that the quantum algorithm can achieve a polynomial speedup, the extent of which grows with the dimension of the partial differential equation. In addition, we give evidence that no improvement of the quantum algorithm can lead to a superpolynomial speedup when the dimension is fixed and the solution satisfies certain smoothness properties.
Chen, Changyou; Buntine, Wray; Ding, Nan; Xie, Lexing; Du, Lan
2015-02-01
In applications we may want to compare different document collections: they could have shared content but also different and unique aspects in particular collections. This task has been called comparative text mining or cross-collection modeling. We present a differential topic model for this application that models both topic differences and similarities. For this we use hierarchical Bayesian nonparametric models. Moreover, we found it was important to properly model power-law phenomena in topic-word distributions and thus we used the full Pitman-Yor process rather than just a Dirichlet process. Furthermore, we propose the transformed Pitman-Yor process (TPYP) to incorporate prior knowledge such as vocabulary variations in different collections into the model. To deal with the non-conjugate issue between model prior and likelihood in the TPYP, we thus propose an efficient sampling algorithm using a data augmentation technique based on the multinomial theorem. Experimental results show the model discovers interesting aspects of different collections. We also show the proposed MCMC based algorithm achieves a dramatically reduced test perplexity compared to some existing topic models. Finally, we show our model outperforms the state-of-the-art for document classification/ideology prediction on a number of text collections. PMID:26353238
Tamiya, S.
1986-07-29
A differential for motor vehicles is described and the like comprising, an input drive shaft, a pair of coaxially spaced drive gears simultaneously driven by the input shaft in a same direction at a same speed of rotation about a common axis of rotation, a driven gear driven peripherally by the pair of drive gears for transmission of power from the input drive shaft, two coaxial opposed bevel sun gears having an axis of rotation concentric with an axis of rotation of the driven gear, two planetary gears disposed between the sun gears for differential driving thereof during turns of the vehicle to the right and to the left of each meshing with the sun gears for driving the suns gears. Each planetary gear has a separate axis of rotation carried by the driven gear disposed therein radially and symmetrically relative to the axis of rotation of the sun gears, and each sun gear having a respective power output shaft connected thereto for rotation therewith.
Program for solution of ordinary differential equations
NASA Technical Reports Server (NTRS)
Sloate, H.
1973-01-01
A program for the solution of linear and nonlinear first order ordinary differential equations is described and user instructions are included. The program contains a new integration algorithm for the solution of initial value problems which is particularly efficient for the solution of differential equations with a wide range of eigenvalues. The program in its present form handles up to ten state variables, but expansion to handle up to fifty state variables is being investigated.
Numerical Algorithms Based on Biorthogonal Wavelets
NASA Technical Reports Server (NTRS)
Ponenti, Pj.; Liandrat, J.
1996-01-01
Wavelet bases are used to generate spaces of approximation for the resolution of bidimensional elliptic and parabolic problems. Under some specific hypotheses relating the properties of the wavelets to the order of the involved operators, it is shown that an approximate solution can be built. This approximation is then stable and converges towards the exact solution. It is designed such that fast algorithms involving biorthogonal multi resolution analyses can be used to resolve the corresponding numerical problems. Detailed algorithms are provided as well as the results of numerical tests on partial differential equations defined on the bidimensional torus.
Concepts in solid tumor evolution
Sidow, Arend; Spies, Noah
2015-01-01
Evolutionary mechanisms in cancer progression give tumors their individuality. Cancer evolution is different from organismal evolution, however, and here we discuss where concepts from evolutionary genetics are useful or limited in facilitating an understanding of cancer. Based on these concepts we construct and apply the simplest plausible model of tumor growth and progression. Simulations using this simple model illustrate the importance of stochastic events early in tumorigenesis, highlight the dominance of exponential growth over linear growth and differentiation, and explain the clonal substructure of tumors. PMID:25733351
Controlling Tensegrity Robots Through Evolution
NASA Technical Reports Server (NTRS)
Iscen, Atil; Agogino, Adrian; SunSpiral, Vytas; Tumer, Kagan
2013-01-01
Tensegrity structures (built from interconnected rods and cables) have the potential to offer a revolutionary new robotic design that is light-weight, energy-efficient, robust to failures, capable of unique modes of locomotion, impact tolerant, and compliant (reducing damage between the robot and its environment). Unfortunately robots built from tensegrity structures are difficult to control with traditional methods due to their oscillatory nature, nonlinear coupling between components and overall complexity. Fortunately this formidable control challenge can be overcome through the use of evolutionary algorithms. In this paper we show that evolutionary algorithms can be used to efficiently control a ball-shaped tensegrity robot. Experimental results performed with a variety of evolutionary algorithms in a detailed soft-body physics simulator show that a centralized evolutionary algorithm performs 400 percent better than a hand-coded solution, while the multi-agent evolution performs 800 percent better. In addition, evolution is able to discover diverse control solutions (both crawling and rolling) that are robust against structural failures and can be adapted to a wide range of energy and actuation constraints. These successful controls will form the basis for building high-performance tensegrity robots in the near future.
ERIC Educational Resources Information Center
Terry, Mark
2005-01-01
In this article, the author presents a two-week evolution unit for his biology class. He uses Maria Sybilla Merian (1647-1717) as an example of an Enlightenment mind at work--in this case a woman recognized as one of the great artists and natural scientists of her time. Her representations of butterflies, caterpillars and their pupae, and the…
ERIC Educational Resources Information Center
De Patta, Joe
2003-01-01
Examines how to evaluate school security, begin making schools safe, secure schools without turning them into fortresses, and secure schools easily and affordably; the evolution of security systems into information technology systems; using schools' high-speed network lines; how one specific security system was developed; pros and cons of the…
Semioptimal practicable algorithmic cooling
Elias, Yuval; Mor, Tal; Weinstein, Yossi
2011-04-15
Algorithmic cooling (AC) of spins applies entropy manipulation algorithms in open spin systems in order to cool spins far beyond Shannon's entropy bound. Algorithmic cooling of nuclear spins was demonstrated experimentally and may contribute to nuclear magnetic resonance spectroscopy. Several cooling algorithms were suggested in recent years, including practicable algorithmic cooling (PAC) and exhaustive AC. Practicable algorithms have simple implementations, yet their level of cooling is far from optimal; exhaustive algorithms, on the other hand, cool much better, and some even reach (asymptotically) an optimal level of cooling, but they are not practicable. We introduce here semioptimal practicable AC (SOPAC), wherein a few cycles (typically two to six) are performed at each recursive level. Two classes of SOPAC algorithms are proposed and analyzed. Both attain cooling levels significantly better than PAC and are much more efficient than the exhaustive algorithms. These algorithms are shown to bridge the gap between PAC and exhaustive AC. In addition, we calculated the number of spins required by SOPAC in order to purify qubits for quantum computation. As few as 12 and 7 spins are required (in an ideal scenario) to yield a mildly pure spin (60% polarized) from initial polarizations of 1% and 10%, respectively. In the latter case, about five more spins are sufficient to produce a highly pure spin (99.99% polarized), which could be relevant for fault-tolerant quantum computing.
Numerical integration of ordinary differential equations on manifolds
NASA Astrophysics Data System (ADS)
Crouch, P. E.; Grossman, R.
1993-12-01
This paper is concerned with the problem of developing numerical integration algorithms for differential equations that, when viewed as equations in some Euclidean space, naturally evolve on some embedded submanifold. It is desired to construct algorithms whose iterates also evolve on the same manifold. These algorithms can therefore be viewed as integrating ordinary differential equations on manifolds. The basic method “decouples” the computation of flows on the submanifold from the numerical integration process. It is shown that two classes of single-step and multistep algorithms can be posed and analyzed theoretically, using the concept of “freezing” the coefficients of differential operators obtained from the defining vector field. Explicit third-order algorithms are derived, with additional equations augmenting those of their classical counterparts, obtained from “obstructions” defined by nonvanishing Lie brackets.
An enhanced algorithm for multiple sequence alignment of protein sequences using genetic algorithm
Kumar, Manish
2015-01-01
One of the most fundamental operations in biological sequence analysis is multiple sequence alignment (MSA). The basic of multiple sequence alignment problems is to determine the most biologically plausible alignments of protein or DNA sequences. In this paper, an alignment method using genetic algorithm for multiple sequence alignment has been proposed. Two different genetic operators mainly crossover and mutation were defined and implemented with the proposed method in order to know the population evolution and quality of the sequence aligned. The proposed method is assessed with protein benchmark dataset, e.g., BALIBASE, by comparing the obtained results to those obtained with other alignment algorithms, e.g., SAGA, RBT-GA, PRRP, HMMT, SB-PIMA, CLUSTALX, CLUSTAL W, DIALIGN and PILEUP8 etc. Experiments on a wide range of data have shown that the proposed algorithm is much better (it terms of score) than previously proposed algorithms in its ability to achieve high alignment quality. PMID:27065770
An enhanced algorithm for multiple sequence alignment of protein sequences using genetic algorithm.
Kumar, Manish
2015-01-01
One of the most fundamental operations in biological sequence analysis is multiple sequence alignment (MSA). The basic of multiple sequence alignment problems is to determine the most biologically plausible alignments of protein or DNA sequences. In this paper, an alignment method using genetic algorithm for multiple sequence alignment has been proposed. Two different genetic operators mainly crossover and mutation were defined and implemented with the proposed method in order to know the population evolution and quality of the sequence aligned. The proposed method is assessed with protein benchmark dataset, e.g., BALIBASE, by comparing the obtained results to those obtained with other alignment algorithms, e.g., SAGA, RBT-GA, PRRP, HMMT, SB-PIMA, CLUSTALX, CLUSTAL W, DIALIGN and PILEUP8 etc. Experiments on a wide range of data have shown that the proposed algorithm is much better (it terms of score) than previously proposed algorithms in its ability to achieve high alignment quality. PMID:27065770
Turbopump Performance Improved by Evolutionary Algorithms
NASA Technical Reports Server (NTRS)
Oyama, Akira; Liou, Meng-Sing
2002-01-01
The development of design optimization technology for turbomachinery has been initiated using the multiobjective evolutionary algorithm under NASA's Intelligent Synthesis Environment and Revolutionary Aeropropulsion Concepts programs. As an alternative to the traditional gradient-based methods, evolutionary algorithms (EA's) are emergent design-optimization algorithms modeled after the mechanisms found in natural evolution. EA's search from multiple points, instead of moving from a single point. In addition, they require no derivatives or gradients of the objective function, leading to robustness and simplicity in coupling any evaluation codes. Parallel efficiency also becomes very high by using a simple master-slave concept for function evaluations, since such evaluations often consume the most CPU time, such as computational fluid dynamics. Application of EA's to multiobjective design problems is also straightforward because EA's maintain a population of design candidates in parallel. Because of these advantages, EA's are a unique and attractive approach to real-world design optimization problems.
Reasoning about systolic algorithms
Purushothaman, S.; Subrahmanyam, P.A.
1988-12-01
The authors present a methodology for verifying correctness of systolic algorithms. The methodology is based on solving a set of Uniform Recurrence Equations obtained from a description of systolic algorithms as a set of recursive equations. They present an approach to mechanically verify correctness of systolic algorithms, using the Boyer-Moore theorem proven. A mechanical correctness proof of an example from the literature is also presented.
Optimizing quantum gas production by an evolutionary algorithm
NASA Astrophysics Data System (ADS)
Lausch, T.; Hohmann, M.; Kindermann, F.; Mayer, D.; Schmidt, F.; Widera, A.
2016-05-01
We report on the application of an evolutionary algorithm (EA) to enhance performance of an ultra-cold quantum gas experiment. The production of a ^{87}rubidium Bose-Einstein condensate (BEC) can be divided into fundamental cooling steps, specifically magneto-optical trapping of cold atoms, loading of atoms to a far-detuned crossed dipole trap, and finally the process of evaporative cooling. The EA is applied separately for each of these steps with a particular definition for the feedback, the so-called fitness. We discuss the principles of an EA and implement an enhancement called differential evolution. Analyzing the reasons for the EA to improve, e.g., the atomic loading rates and increase the BEC phase-space density, yields an optimal parameter set for the BEC production and enables us to reduce the BEC production time significantly. Furthermore, we focus on how additional information about the experiment and optimization possibilities can be extracted and how the correlations revealed allow for further improvement. Our results illustrate that EAs are powerful optimization tools for complex experiments and exemplify that the application yields useful information on the dependence of these experiments on the optimized parameters.
Optimal Robust Motion Controller Design Using Multiobjective Genetic Algorithm
Svečko, Rajko
2014-01-01
This paper describes the use of a multiobjective genetic algorithm for robust motion controller design. Motion controller structure is based on a disturbance observer in an RIC framework. The RIC approach is presented in the form with internal and external feedback loops, in which an internal disturbance rejection controller and an external performance controller must be synthesised. This paper involves novel objectives for robustness and performance assessments for such an approach. Objective functions for the robustness property of RIC are based on simple even polynomials with nonnegativity conditions. Regional pole placement method is presented with the aims of controllers' structures simplification and their additional arbitrary selection. Regional pole placement involves arbitrary selection of central polynomials for both loops, with additional admissible region of the optimized pole location. Polynomial deviation between selected and optimized polynomials is measured with derived performance objective functions. A multiobjective function is composed of different unrelated criteria such as robust stability, controllers' stability, and time-performance indexes of closed loops. The design of controllers and multiobjective optimization procedure involve a set of the objectives, which are optimized simultaneously with a genetic algorithm—differential evolution. PMID:24987749
An efficient algorithm for solving the phase field crystal model
Cheng Mowei Warren, James A.
2008-06-01
We present and discuss the development of an unconditionally stable algorithm used to solve the evolution equations of the phase field crystal (PFC) model. This algorithm allows for an arbitrarily large algorithmic time step. As the basis for our analysis of the accuracy of this algorithm, we determine an effective time step in Fourier space. We then compare our calculations with a set of representative numerical results, and demonstrate that this algorithm is an effective approach for the study of the PFC models, yielding a time step effectively 180 times larger than the Euler algorithm for a representative set of material parameters. As the PFC model is just a simple example of a wide class of density functional theories, we expect this method will have wide applicability to modeling systems of considerable interest to the materials modeling communities.