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
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.
NASA Astrophysics Data System (ADS)
Fan, Tian-E.; Shao, Gui-Fang; Ji, Qing-Shuang; Zheng, Ji-Wen; Liu, Tun-dong; Wen, Yu-Hua
2016-11-01
Theoretically, the determination of the structure of a cluster is to search the global minimum on its potential energy surface. The global minimization problem is often nondeterministic-polynomial-time (NP) hard and the number of local minima grows exponentially with the cluster size. In this article, a multi-populations multi-strategies differential evolution algorithm has been proposed to search the globally stable structure of Fe and Cr nanoclusters. The algorithm combines a multi-populations differential evolution with an elite pool scheme to keep the diversity of the solutions and avoid prematurely trapping into local optima. Moreover, multi-strategies such as growing method in initialization and three differential strategies in mutation are introduced to improve the convergence speed and lower the computational cost. The accuracy and effectiveness of our algorithm have been verified by comparing the results of Fe clusters with Cambridge Cluster Database. Meanwhile, the performance of our algorithm has been analyzed by comparing the convergence rate and energy evaluations with the classical DE algorithm. The multi-populations, multi-strategies mutation and growing method in initialization in our algorithm have been considered respectively. Furthermore, the structural growth pattern of Cr clusters has been predicted by this algorithm. The results show that the lowest-energy structure of Cr clusters contains many icosahedra, and the number of the icosahedral rings rises with increasing size.
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.
Xia, Xuewen
2016-01-01
In recent years, some researchers considered image color quantization as a single-objective problem and applied heuristic algorithms to solve it. This paper establishes a multiobjective image color quantization model with intracluster distance and intercluster separation as its objectives. Inspired by a multipopulation idea, a multiobjective image color quantization algorithm based on self-adaptive hybrid differential evolution (MoDE-CIQ) is then proposed to solve this model. Two numerical experiments on four common test images are conducted to analyze the effectiveness and competitiveness of the multiobjective model and the proposed algorithm. PMID:27738423
Yang, Zhiyong; Zhang, Taohong; Zhang, Dezheng
2016-02-01
Extreme learning machine (ELM) is a novel and fast learning method to train single layer feed-forward networks. However due to the demand for larger number of hidden neurons, the prediction speed of ELM is not fast enough. An evolutionary based ELM with differential evolution (DE) has been proposed to reduce the prediction time of original ELM. But it may still get stuck at local optima. In this paper, a novel algorithm hybridizing DE and metaheuristic coral reef optimization (CRO), which is called differential evolution coral reef optimization (DECRO), is proposed to balance the explorative power and exploitive power to reach better performance. The thought and the implement of DECRO algorithm are discussed in this article with detail. DE, CRO and DECRO are applied to ELM training respectively. Experimental results show that DECRO-ELM can reduce the prediction time of original ELM, and obtain better performance for training ELM than both DE and CRO.
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, 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
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.
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.
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
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.
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)
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)
Fan, Li; Faryad, Muhammad; Barber, Greg D.; Mallouk, Thomas E.; Monk, Peter B.; Lakhtakia, Akhlesh
2015-01-01
A spectrum splitter can be used to spatially multiplex different solar cells that have high efficiency in mutually exclusive parts of the solar spectrum. We investigated the use of a grating, comprising an array of dielectric cylinders embedded in a dielectric slab, for specularly transmitting one part of the solar spectrum while the other part is transmitted nonspecularly and the total reflectance is very low. A combination of (1) the rigorous coupled-wave approach for computing the reflection and transmission coefficients of the grating and (2) the differential evolution algorithm for optimizing the grating geometry and the refractive indices of dielectric materials was devised as a design tool. We used this tool to optimize two candidate gratings and obtained definite improvements to the initial guesses for the structural and constitutive parameters. Significant spectrum splitting can be achieved if the angle of incidence does not exceed 15 deg.
NASA Astrophysics Data System (ADS)
Binol, Hamidullah; Bal, Abdullah; Cukur, Huseyin
2015-10-01
The performance of the kernel based techniques depends on the selection of kernel parameters. That's why; suitable parameter selection is an important problem for many kernel based techniques. This article presents a novel technique to learn the kernel parameters in kernel Fukunaga-Koontz Transform based (KFKT) classifier. The proposed approach determines the appropriate values of kernel parameters through optimizing an objective function constructed based on discrimination ability of KFKT. For this purpose we have utilized differential evolution algorithm (DEA). The new technique overcomes some disadvantages such as high time consumption existing in the traditional cross-validation method, and it can be utilized in any type of data. The experiments for target detection applications on the hyperspectral images verify the effectiveness of the proposed method.
Liu, Chang; Wang, Guofeng; Xie, Qinglu; Zhang, Yanchao
2014-06-16
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.
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)
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.
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)
Dawood Al-Dabbagh, Mohanad; Dawoud Al-Dabbagh, Rawaa; Raja Abdullah, R. S. A.; Hashim, F.
2015-06-01
The main intention of this study was to investigate the development of a new optimization technique based on the differential evolution (DE) algorithm, for the purpose of linear frequency modulation radar signal de-noising. As the standard DE algorithm is a fixed length optimizer, it is not suitable for solving signal de-noising problems that call for variability. A modified crossover scheme called rand-length crossover was designed to fit the proposed variable-length DE, and the new DE algorithm is referred to as the random variable-length crossover differential evolution (rvlx-DE) algorithm. The measurement results demonstrate a highly efficient capability for target detection in terms of frequency response and peak forming that was isolated from noise distortion. The modified method showed significant improvements in performance over traditional de-noising techniques.
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.
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
DNA strand generation for DNA computing by using a multi-objective differential evolution algorithm.
Chaves-González, José M; Vega-Rodríguez, Miguel A
2014-02-01
In this paper, we use an adapted multi-objective version of the differential evolution (DE) metaheuristics for the design and generation of reliable DNA libraries that can be used for computation. DNA sequence design is a very relevant task in many recent research fields, e.g. nanotechnology or DNA computing. Specifically, DNA computing is a new computational model which uses DNA molecules as information storage and their possible biological interactions as processing operators. Therefore, the possible reactions and interactions among molecules must be strictly controlled to prevent incorrect computations. The design of reliable DNA libraries for bio-molecular computing is an NP-hard combinatorial problem which involves many heterogeneous and conflicting design criteria. For this reason, we modelled DNA sequence design as a multiobjective optimization problem and we solved it by using an adapted multi-objective version of DE metaheuristics. Seven different bio-chemical design criteria have been simultaneously considered to obtain high quality DNA sequences which are suitable for molecular computing. Furthermore, we have developed the multiobjective standard fast non-dominated sorting genetic algorithm (NSGA-II) in order to perform a formal comparative study by using multi-objective indicators. Additionally, we have also compared our results with other relevant results published in the literature. We conclude that our proposal is a promising approach which is able to generate reliable real-world DNA sequences that significantly improve other DNA libraries previously published in the literature.
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
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
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.
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.
Zheng, Weijia; Pi, Youguo
2016-07-01
A tuning method of the fractional order proportional integral speed controller for a permanent magnet synchronous motor is proposed in this paper. Taking the combination of the integral of time and absolute error and the phase margin as the optimization index, the robustness specification as the constraint condition, the differential evolution algorithm is applied to search the optimal controller parameters. The dynamic response performance and robustness of the obtained optimal controller are verified by motor speed-tracking experiments on the motor speed control platform. Experimental results show that the proposed tuning method can enable the obtained control system to achieve both the optimal dynamic response performance and the robustness to gain variations.
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.
Zhan, Choujun; Situ, Wuchao; Yeung, Lam Fat; Tsang, Peter Wai-Ming; Yang, Genke
2014-01-01
The inverse problem of identifying unknown parameters of known structure dynamical biological systems, which are modelled by ordinary differential equations or delay differential equations, from experimental data is treated in this paper. A two stage approach is adopted: first, combine spline theory and Nonlinear Programming (NLP), the parameter estimation problem is formulated as an optimization problem with only algebraic constraints; then, a new differential evolution (DE) algorithm is proposed to find a feasible solution. The approach is designed to handle problem of realistic size with noisy observation data. Three cases are studied to evaluate the performance of the proposed algorithm: two are based on benchmark models with priori-determined structure and parameters; the other one is a particular biological system with unknown model structure. In the last case, only a set of observation data available and in this case a nominal model is adopted for the identification. All the test systems were successfully identified by using a reasonable amount of experimental data within an acceptable computation time. Experimental evaluation reveals that the proposed method is capable of fast estimation on the unknown parameters with good precision.
Islam, Sk Minhazul; Das, Swagatam; Ghosh, Saurav; Roy, Subhrajit; Suganthan, Ponnuthurai Nagaratnam
2012-04-01
Differential evolution (DE) is one of the most powerful stochastic real parameter optimizers of current interest. In this paper, we propose a new mutation strategy, a fitness-induced parent selection scheme for the binomial crossover of DE, and a simple but effective scheme of adapting two of its most important control parameters with an objective of achieving improved performance. The new mutation operator, which we call DE/current-to-gr_best/1, is a variant of the classical DE/current-to-best/1 scheme. It uses the best of a group (whose size is q% of the population size) of randomly selected solutions from current generation to perturb the parent (target) vector, unlike DE/current-to-best/1 that always picks the best vector of the entire population to perturb the target vector. In our modified framework of recombination, a biased parent selection scheme has been incorporated by letting each mutant undergo the usual binomial crossover with one of the p top-ranked individuals from the current population and not with the target vector with the same index as used in all variants of DE. A DE variant obtained by integrating the proposed mutation, crossover, and parameter adaptation strategies with the classical DE framework (developed in 1995) is compared with two classical and four state-of-the-art adaptive DE variants over 25 standard numerical benchmarks taken from the IEEE Congress on Evolutionary Computation 2005 competition and special session on real parameter optimization. Our comparative study indicates that the proposed schemes improve the performance of DE by a large magnitude such that it becomes capable of enjoying statistical superiority over the state-of-the-art DE variants for a wide variety of test problems. Finally, we experimentally demonstrate that, if one or more of our proposed strategies are integrated with existing powerful DE variants such as jDE and JADE, their performances can also be enhanced.
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.
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
Zheng, Weijia; Pi, Youguo
2016-07-01
A tuning method of the fractional order proportional integral speed controller for a permanent magnet synchronous motor is proposed in this paper. Taking the combination of the integral of time and absolute error and the phase margin as the optimization index, the robustness specification as the constraint condition, the differential evolution algorithm is applied to search the optimal controller parameters. The dynamic response performance and robustness of the obtained optimal controller are verified by motor speed-tracking experiments on the motor speed control platform. Experimental results show that the proposed tuning method can enable the obtained control system to achieve both the optimal dynamic response performance and the robustness to gain variations. PMID:27129766
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
Algorithms, games, and evolution.
Chastain, Erick; Livnat, Adi; Papadimitriou, Christos; Vazirani, Umesh
2014-07-22
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.
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
Wang, Lin; Qu, Hui; 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
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
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.
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.
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.
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
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.
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.
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.
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.
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
[Evolution of differential chromosome banding].
Rodionov, A V
1999-03-01
Specific chromosome banding patterns in different eukaryotic taxons are reviewed. In all eukaryotes, chromosomes are composed of alternating bands, each differing from the adjacent material by the molecular composition and structural characteristics. In minute chromosomes of fungi and Protozoa, these bands are represented by kinetochores (Kt- (Cd-)bands), nucleolus organizers (N-bands), and telomeres as well as the euchromatin. In genomes of most fungi and protists, long clusters of tandem repeats and, consequently, C-bands were not revealed but they are likely to be found out in species with chromosomes visible under a light microscope, which are several tens of million bp in size. Chromosomes of Metazoa are usually larger. Even in Cnidaria, they contain C-bands, which are replicated late in the S phase. In Deuterostomia, chromosome euchromatin regions differ by replication time: bands replicating at the first half of the S phase alternate with bands replicating at the second half of the S phase. Longitudinal differentiation in the replication pattern of euchromatic regions is observed in all classes of Vertebrata beginning with the bony fish although the time when it developed in Deuterostomia is unknown. Apparently, the evolution of early and late replicating subdomains in Vertebrata euchromatin promoted fast accumulation of differences in the molecular composition of nucleoproteid complexes characteristic of early and late replicating bands. As a result, the more contrasting G/R and Q-banding patterns of chromosomes developed especially in Eutheria. The evolution of Protostomia and Plantae followed another path. An increase in chromosome size was not accompanied by the appearance of wide RBE and RBL euchromatin bands. The G/R-like banding within the interstitial chromosome regions observed in some representatives of Invertebrates and higher plants arose independently in different phylogenetic lineages. This banding pattern seems to be closer to that of C
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.
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.
Algorithm refinement for stochastic partial differential equations.
Alexander, F. J.; Garcia, Alejandro L.,; Tartakovsky, D. M.
2001-01-01
A hybrid particle/continuum algorithm is formulated for Fickian diffusion in the fluctuating hydrodynamic limit. The particles are taken as independent random walkers; the fluctuating diffusion equation is solved by finite differences with deterministic and white-noise fluxes. At the interface between the particle and continuum computations the coupling is by flux matching, giving exact mass conservation. This methodology is an extension of Adaptive Mesh and Algorithm Refinement to stochastic partial differential equations. A variety of numerical experiments were performed for both steady and time-dependent scenarios. In all cases the mean and variance of density are captured correctly by the stochastic hybrid algorithm. For a non-stochastic version (i.e., using only deterministic continuum fluxes) the mean density is correct, but the variance is reduced except within the particle region, far from the interface. Extensions of the methodology to fluid mechanics applications are discussed.
Ozone differential absorption lidar algorithm intercomparison.
Godin, S; Carswell, A I; Donovan, D P; Claude, H; Steinbrecht, W; McDermid, I S; McGee, T J; Gross, M R; Nakane, H; Swart, D P; Bergwerff, H B; Uchino, O; von der Gathen, P; Neuber, R
1999-10-20
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.
Remote sensing image subpixel mapping based on adaptive differential evolution.
Zhong, Yanfei; Zhang, Liangpei
2012-10-01
In this paper, a novel subpixel mapping algorithm based on an adaptive differential evolution (DE) algorithm, namely, adaptive-DE subpixel mapping (ADESM), is developed to perform the subpixel mapping task for remote sensing images. Subpixel mapping may provide a fine-resolution map of class labels from coarser spectral unmixing fraction images, with the assumption of spatial dependence. In ADESM, to utilize DE, the subpixel mapping problem is transformed into an optimization problem by maximizing the spatial dependence index. The traditional DE algorithm is an efficient and powerful population-based stochastic global optimizer in continuous optimization problems, but it cannot be applied to the subpixel mapping problem in a discrete search space. In addition, it is not an easy task to properly set control parameters in DE. To avoid these problems, this paper utilizes an adaptive strategy without user-defined parameters, and a reversible-conversion strategy between continuous space and discrete space, to improve the classical DE algorithm. During the process of evolution, they are further improved by enhanced evolution operators, e.g., mutation, crossover, repair, exchange, insertion, and an effective local search to generate new candidate solutions. Experimental results using different types of remote images show that the ADESM algorithm consistently outperforms the previous subpixel mapping algorithms in all the experiments. Based on sensitivity analysis, ADESM, with its self-adaptive control parameter setting, is better than, or at least comparable to, the standard DE algorithm, when considering the accuracy of subpixel mapping, and hence provides an effective new approach to subpixel mapping for remote sensing imagery.
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
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
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.
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
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.
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
NASA Technical Reports Server (NTRS)
Madavan, Nateri K.
2004-01-01
Differential Evolution (DE) is a simple, fast, and robust evolutionary algorithm that has proven effective in determining the global optimum for several difficult single-objective optimization problems. The DE algorithm has been recently extended to multiobjective optimization problem by using a Pareto-based approach. In this paper, a Pareto DE algorithm is applied to multiobjective aerodynamic shape optimization problems that are characterized by computationally expensive objective function evaluations. To improve computational expensive the algorithm is coupled with generalized response surface meta-models based on artificial neural networks. Results are presented for some test optimization problems from the literature to demonstrate the capabilities of the method.
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
Algorithms to detect multiprotein modularity conserved during evolution.
Hodgkinson, Luqman; Karp, Richard M
2012-01-01
Detecting essential multiprotein modules that change infrequently during evolution is a challenging algorithmic task that is important for understanding the structure, function, and evolution of the biological cell. In this paper, we define a measure of modularity for interactomes and present a linear-time algorithm, Produles, for detecting multiprotein modularity conserved during evolution that improves on the running time of previous algorithms for related problems and offers desirable theoretical guarantees. We present a biologically motivated graph theoretic set of evaluation measures complementary to previous evaluation measures, demonstrate that Produles exhibits good performance by all measures, and describe certain recurrent anomalies in the performance of previous algorithms that are not detected by previous measures. Consideration of the newly defined measures and algorithm performance on these measures leads to useful insights on the nature of interactomics data and the goals of previous and current algorithms. Through randomization experiments, we demonstrate that conserved modularity is a defining characteristic of interactomes. Computational experiments on current experimentally derived interactomes for Homo sapiens and Drosophila melanogaster, combining results across algorithms, show that nearly 10 percent of current interactome proteins participate in multiprotein modules with good evidence in the protein interaction data of being conserved between human and Drosophila.
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 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.
Computation of multiple Lie derivatives by algorithmic differentiation
NASA Astrophysics Data System (ADS)
Robenack, Klaus
2008-04-01
Lie derivatives are often used in nonlinear control and system theory. In general, these Lie derivatives are computed symbolically using computer algebra software. Although this approach is well-suited for small and medium-size problems, it is difficult to apply this technique to very complicated systems. We suggest an alternative method to compute the values of iterated and mixed Lie derivatives by algorithmic differentiation.
Automated algorithm for breast tissue differentiation in optical coherence tomography
Mujat, Mircea; Ferguson, R. Daniel; Hammer, Daniel X.; Gittins, Christopher; Iftimia, Nicusor
2010-01-01
An automated algorithm for differentiating breast tissue types based on optical coherence tomography (OCT) data is presented. Eight parameters are derived from the OCT reflectivity profiles and their means and covariance matrices are calculated for each tissue type from a training set (48 samples) selected based on histological examination. A quadratic discrimination score is then used to assess the samples from a validation set. The algorithm results for a set of 89 breast tissue samples were correlated with the histological findings, yielding specificity and sensitivity of 0.88. If further perfected to work in real time and yield even higher sensitivity and specificity, this algorithm would be a valuable tool for biopsy guidance and could significantly increase procedure reliability by reducing both the number of nondiagnostic aspirates and the number of false negatives. PMID:19566332
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.
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.
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.
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.
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.
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.
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.
Wrinkling pattern evolution of cylindrical biological tissues with differential growth
NASA Astrophysics Data System (ADS)
Jia, Fei; Li, Bo; Cao, Yan-Ping; Xie, Wei-Hua; Feng, Xi-Qiao
2015-01-01
Three-dimensional surface wrinkling of soft cylindrical tissues induced by differential growth is explored. Differential volumetric growth can cause their morphological stability, leading to the formation of hexagonal and labyrinth wrinkles. During postbuckling, multiple bifurcations and morphological transitions may occur as a consequence of continuous growth in the surface layer. The physical mechanisms underpinning the morphological evolution are examined from the viewpoint of energy. Surface curvature is found to play a regulatory role in the pattern evolution. This study may not only help understand the morphogenesis of soft biological tissues, but also inspire novel routes for creating desired surface patterns of soft materials.
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
Sensitivity of Probabilistic Seismic Hazard Obtained by Algorithmic Differentiation
NASA Astrophysics Data System (ADS)
Molkenthin, C.; Scherbaum, F.; Griewank, A.; Kuehn, N. M.; Stafford, P.; Leövey, H.
2014-12-01
Probabilistic seismic hazard analysis (PSHA) is the current tool of the trade to assess seismogenic threat at a site of interest. A modern PSHA represents a complex framework which combines different models with several inputs. It is important to understand and assess the impact of these inputs on the model output in a quantitative way. 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; however, obtaining the derivatives of complex models can be challenging. In this study we show how differential sensitivity analysis of a complex framework such as PSHA can be carried out using Algorithmic Differentiation (AD). AD has been successfully applied for sensitivity analyses in various domains such as meteorology or aerodynamics. 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.
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.
Hui, Sheldon; Suganthan, Ponnuthurai N
2016-01-01
Multimodal optimization problems consists of multiple equal or comparable spatially distributed solutions. Niching and clustering differential evolution (DE) techniques have been demonstrated to be highly effective for solving such problems. The key challenge in the speciation niching technique is to balance between local solution exploitation and global exploration. Our proposal enhances exploration by applying arithmetic recombination with speciation and improves exploitation of individual peaks by applying neighborhood mutation with ensemble strategies. Our novel algorithm, called ensemble and arithmetic recombination-based speciation DE, is shown to either outperform or perform comparably to the state-of-the-art algorithms on 29 common multimodal benchmark problems. Comparable performance is observed only when some problems are solved perfectly by the algorithms in the literature. PMID:25781971
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.
Robust algorithms for solving stochastic partial differential equations
Werner, M.J.; Drummond, P.D.
1997-04-01
A robust semi-implicit central partial difference algorithm for the numerical solution of coupled stochastic parabolic partial differential equations (PDEs) is described. This can be used for calculating correlation functions of systems of interacting stochastic fields. Such field equations can arise in the description of Hamiltonian and open systems in the physics of nonlinear processes, and may include multiplicative noise sources. The algorithm can be used for studying the properties of nonlinear quantum or classical field theories. The general approach is outlined and applied to a specific example, namely the quantum statistical fluctuations of ultra-short optical pulses in X{sup 2} parametric waveguides. This example uses non-diagonal coherent state representation, and correctly predicts the sub-shot noise level spectral fluctuations observed in homodyne detection measurements. It is expected that the methods used will be applicable for higher-order correlation functions and other physical problems as well. A stochastic differencing technique for reducing sampling errors is also introduced. This involves solving nonlinear stochastic parabolic PDEs in combination with a reference process, which uses the Wigner representation in the example presented here. A computer implementation on MIMD parallel architectures is discussed. 27 refs., 4 figs.
Differential Evolution with Gaussian Mutation for Economic Dispatch
NASA Astrophysics Data System (ADS)
Basu, Mousumi; Jena, Chitralekha; Panigrahi, Chinmoy Kumar
2015-05-01
This paper presents differential evolution with Gaussian mutation (DEGM) to solve economic dispatch problem of thermal generating units with non-smooth/non-convex cost functions due to valve-point loading, taking into account transmission losses and nonlinear generator constraints such as prohibited operating zones. Differential evolution (DE) is a simple yet powerful global optimization technique. It exploits the differences of randomly sampled pairs of objective vectors for its mutation process. This mutation process is not suitable for complex multimodal optimization. This paper proposes Gaussian mutation in DE which improves search efficiency and guarantees a high probability of obtaining the global optimum without significantly impairing the simplicity of the structure of DE. The effectiveness of the proposed method has been verified on three different test systems. From the comparison with other evolutionary methods, it is found that DEGM based approach is able to provide better solution.
Senkerik, Roman; Zelinka, Ivan; 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.
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.
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.
A SLAM based on auxiliary marginalised particle filter and differential evolution
NASA Astrophysics Data System (ADS)
Havangi, R.; Nekoui, M. A.; Teshnehlab, M.; Taghirad, H. D.
2014-09-01
FastSLAM is a framework for simultaneous localisation and mapping (SLAM) using a Rao-Blackwellised particle filter. In FastSLAM, particle filter is used for the robot pose (position and orientation) estimation, and parametric filter (i.e. EKF and UKF) is used for the feature location's estimation. However, in the long term, FastSLAM is an inconsistent algorithm. In this paper, a new approach to SLAM based on hybrid auxiliary marginalised particle filter and differential evolution (DE) is proposed. In the proposed algorithm, the robot pose is estimated based on auxiliary marginal particle filter that operates directly on the marginal distribution, and hence avoids performing importance sampling on a space of growing dimension. In addition, static map is considered as a set of parameters that are learned using DE. Compared to other algorithms, the proposed algorithm can improve consistency for longer time periods and also, improve the estimation accuracy. Simulations and experimental results indicate that the proposed algorithm is effective.
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 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)
Quaranta, Giuseppe; Monti, Giorgio; Marano, Giuseppe Carlo
2010-10-01
Many of the proposed approaches for non-linear systems control are developed under the assumption that all involved parameters are known in advance. Unfortunately, their estimation is not so simple because the nature of the non-linear behaviors is very complex in the most part of the cases. In view of this complication, parameters identification of non-linear oscillators has attracted increasing interests in various research fields: from a pure mathematical point-of-view, parameters identification can be formalized as a multi-dimensional optimization problem, typically over real bounded domains. In doing this, the use of the so-called non-classical methods based on soft computing theories seems to be promising because they do not require a priori information and the robustness of the identification against the noise contamination is satisfactory. However, further studies are required to evaluate the general effectiveness of these methodologies. In this sense, the paper addresses the consistency of two classes of soft computing based methods for the identification of Van der Pol-Duffing oscillators. A large numerical investigation has been conducted to evaluate the performances of six differential evolution algorithms (including a modified differential evolution algorithm proposed by the authors) and four swarm intelligence based algorithms (including a chaotic particle swarm optimization algorithm). Single well, double well and double-hump oscillators are identified and noisy system responses are considered in order to evaluate the robustness of the identification processes. The investigated soft computing techniques behave very well and thus they are suitable for practical applications.
Multi-path planning algorithm based on fitness sharing and species evolution
NASA Astrophysics Data System (ADS)
Zhang, Jing-Juan; Li, Xue-Lian; Hao, Yan-Ling
2003-06-01
A new algorithm is proposed for underwater vehicles multi-path planning. This algorithm is based on fitness sharing genetic algorithm, clustering and evolution of multiple populations, which can keep the diversity of the solution path, and decrease the operating time because of the independent evolution of each subpopulation. The multi-path planning algorithm is demonstrated by a number of two-dimensional path planning problems. The results show that the multi-path planning algorithm has the following characteristics: high searching capability, rapid convergence and high reliability.
On exploring the genetic algorithm for modeling the evolution of cooperation in a population
NASA Astrophysics Data System (ADS)
Schimit, P. H. T.
2014-08-01
In this paper, we propose a genetic algorithm approximation for modeling a population which individuals compete with each other based on prisoner's dilemma game. Players act according to their genome, which gives them a strategy (phenotype); in our case, a probability for cooperation. The most successful players will produce more offspring and that depends directly of the strategy adopted. As individuals die, the newborns parents will be those fittest individuals in a certain spatial region. Four different fitness functions are tested to investigate the influence in the evolution of cooperation. Individuals live in a lattice modeled by probabilistic cellular automata and play the game with some of their neighborhoods. In spite of players homogeneously distributed over the space, a mean-field approximation is presented in terms of ordinary differential equations.
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
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.
Learning to control the program evolution process with cultural algorithms
Zannoni; Reynolds
1997-01-01
Traditional software engineering dictates the use of modular and structured programming and top-down stepwise refinement techniques that reduce the amount of variability arising in the development process by establishing standard procedures to be followed while writing software. This focusing leads to reduced variability in the resulting products, due to the use of standardized constructs. Genetic programming (GP) performs heuristic search in the space of programs. Programs produced through the GP paradigm emerge as the result of simulated evolution and are built through a bottom-up process, incrementally augmenting their functionality until a satisfactory level of performance is reached. Can we automatically extract knowledge from the GP programming process that can be useful to focus the search and reduce product variability, thus leading to a more effective use of the available resources? An answer to this question is investigated with the aid of cultural algorithms. A new system, cultural algorithms with genetic programming (CAGP), is presented. The system has two levels. The first is the pool of genetic programs (population level), and the second is a knowledge repository (belief set) that is built during the GP run and is used to guide the search process. The microevolution within the population brings about potentially meaningful characteristics of the programs for the achievement of the given task, such as properties exhibited by the best performers in the population. CAGP extracts these features and represents them as the set of the current beliefs. Beliefs correspond to constraints that all the genetic operators and programs must follow. Interaction between the two levels occurs in one direction through the extraction process and, in the other, through the modulation of an individual's program parameters according to which, and how many, of the constraints it follows. CAGP is applied to solve an instance of the symbolic regression problem, in which a
Suresh, Kaushik; Kundu, Debarati; Ghosh, Sayan; Das, Swagatam; Abraham, Ajith; Han, Sang Yong
2009-01-01
This paper applies the Differential Evolution (DE) algorithm to the task of automatic fuzzy clustering in a Multi-objective Optimization (MO) framework. It compares the performances of two multi-objective variants of DE over the fuzzy clustering problem, where two conflicting fuzzy validity indices are simultaneously optimized. The resultant Pareto optimal set of solutions from each algorithm consists of a number of non-dominated solutions, from which the user can choose the most promising ones according to the problem specifications. A real-coded representation of the search variables, accommodating variable number of cluster centers, is used for DE. The performances of the multi-objective DE-variants have also been contrasted to that of two most well-known schemes of MO clustering, namely the Non Dominated Sorting Genetic Algorithm (NSGA II) and Multi-Objective Clustering with an unknown number of Clusters K (MOCK). Experimental results using six artificial and four real life datasets of varying range of complexities indicate that DE holds immense promise as a candidate algorithm for devising MO clustering schemes. PMID:22412346
Placozoa and the evolution of Metazoa and intrasomatic cell differentiation.
Schierwater, Bernd; de Jong, Danielle; Desalle, Rob
2009-02-01
The multicellular Metazoa evolved from single-celled organisms (Protozoa) and usually - but not necessarily - consist of more cells than Protozoa. In all cases, and thus by definition, Metazoa possess more than one somatic cell type, i.e. they show-in sharp contrast to protists-intrasomatic differentiation. Placozoa have the lowest degree of intrasomatic variation; the number of somatic cell types according to text books is four (but see also Jakob W, Sagasser S, Dellaporta S, Holland P, Kuhn K, and Schierwater B. The Trox-2 Hox/ParaHox gene of Trichoplax (Placozoa) marks an epithelial boundary. Dev Genes Evol 2004;214:170-5). For this and several other reasons Placozoa have been regarded by many as the most basal metazoan phylum. Thus, the morphologically most simply organized metazoan animal, the placozoan Trichoplax adhaerens, resembles a unique model system for cell differentiation studies and also an intriguing model for a prominent "urmetazoon" hypotheses-the placula hypothesis. A basal position of Placozoa would provide answers to several key issues of metazoan-specific inventions (including for example different lines of somatic cell differentiation leading to organ development and axis formation) and would determine a root for unraveling their evolution. However, the phylogenetic relationships at the base of Metazoa are controversial and a basal position of Placozoa is not generally accepted (e.g. Schierwater B, DeSalle R. Can we ever identify the Urmetazoan? Integr Comp Biol 2007;47:670-76; DeSalle R, Schierwater B. An even "newer" animal phylogeny. Bioessays 2008;30:1043-47). Here we review and discuss (i) long-standing morphological evidence for the simple placozoan bauplan resembling an ancestral metazoan stage, (ii) some rapidly changing alternative hypotheses derived from molecular analyses, (iii) the surprising idea that triploblasts (Bilateria) and diploblasts may be sister groups, and (iv) the presence of genes involved in cell differentiation and
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.
Optimization of Neutrino Oscillation Parameters Using Differential Evolution
NASA Astrophysics Data System (ADS)
Ghulam, Mustafa; Faisal, Akram; Bilal, Masud
2013-03-01
We show how the traditional grid based method for finding neutrino oscillation parameters Δm2 and tan2 θ can be combined with an optimization technique, Differential Evolution (DE), to get a significant decrease in computer processing time required to obtain minimal chi-square (χ2) in four different regions of the parameter space. We demonstrate efficiency for the two-neutrinos case. For this, the χ2 function for neutrino oscillations is evaluated for grids with different density of points in standard allowed regions of the parameter space of Δm2 and tan2 θ using experimental and theoretical total event rates of chlorine (Homestake), Gallex+GNO, SAGE, Superkamiokande, and SNO detectors. We find that using DE in combination with the grid based method with small density of points can produce the results comparable with the one obtained using high density grid, in much lesser computation time.
Rearrangements of immunoglobulin genes during differentiation and evolution.
Honjo, T; Nakai, S; Nishida, Y; Kataoka, T; Yamawaki-Kataoka, Y; Takahashi, N; Obata, M; Shimizu, A; Yaoita, Y; Nikaido, T; Ishida, N
1981-01-01
Immunoglobulin genes are shown to undergo dynamic rearrangements during differentiation as well as evolution. We have demonstrated that a complete immunoglobulin heavy chain gene is formed by at least two types of DNA rearrangement during B cell differentiation. The first type of rearrangement is V-D-J recombination to complete a variable region sequence and the second type is S-S recombination to switch a constant region sequence. Both types of recombination are accompanied by deletion of the intervening DNA segment. Structure and organization of CH genes are elucidated by molecular cloning and nucleotide sequence determination. Organization of H chain genes is summarized as VH-(unknown distance)-JH-(6.5 kb)-C mu-(4.5 kb)-C delta-(unknown distance)-C gamma 3-(34 kb)-C gamma 1-(21 kb)-C gamma 2b-(15 kb)-C gamma 2a-(14.5 kb)-C epsilon-(12.5 kb)-C alpha. The S-S recombination takes place at the S region which is located at the 5' side of each CH gene. Nucleotide sequence of the S region comprises tandem repetition of closely related sequences. The S-S recombination seems to be mediated by short common sequences shared among S regions. A sister chromatid exchange model was proposed as a mechanism for S-S recombination. Comparison of nucleotide sequences of CH genes indicates that immunoglobulin genes have scrambled by intervening sequence-mediated domain transfer during their evolution.
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.
Biswas, Subhodip; Kundu, Souvik; Das, Swagatam
2014-10-01
In real life, we often need to find multiple optimally sustainable solutions of an optimization problem. Evolutionary multimodal optimization algorithms can be very helpful in such cases. They detect and maintain multiple optimal solutions during the run by incorporating specialized niching operations in their actual framework. Differential evolution (DE) is a powerful evolutionary algorithm (EA) well-known for its ability and efficiency as a single peak global optimizer for continuous spaces. This article suggests a niching scheme integrated with DE for achieving a stable and efficient niching behavior by combining the newly proposed parent-centric mutation operator with synchronous crowding replacement rule. The proposed approach is designed by considering the difficulties associated with the problem dependent niching parameters (like niche radius) and does not make use of such control parameter. The mutation operator helps to maintain the population diversity at an optimum level by using well-defined local neighborhoods. Based on a comparative study involving 13 well-known state-of-the-art niching EAs tested on an extensive collection of benchmarks, we observe a consistent statistical superiority enjoyed by our proposed niching algorithm.
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.
Yu, Hui; Mitra, Ramkrishna; Yang, Jing; Li, YuanYuan; Zhao, ZhongMing
2014-11-01
Identification of differential regulators is critical to understand the dynamics of cellular systems and molecular mechanisms of diseases. Several computational algorithms have recently been developed for this purpose by using transcriptome and network data. However, it remains largely unclear which algorithm performs better under a specific condition. Such knowledge is important for both appropriate application and future enhancement of these algorithms. Here, we systematically evaluated seven main algorithms (TED, TDD, TFactS, RIF1, RIF2, dCSA_t2t, and dCSA_r2t), using both simulated and real datasets. In our simulation evaluation, we artificially inactivated either a single regulator or multiple regulators and examined how well each algorithm detected known gold standard regulators. We found that all these algorithms could effectively discern signals arising from regulatory network differences, indicating the validity of our simulation schema. Among the seven tested algorithms, TED and TFactS were placed first and second when both discrimination accuracy and robustness against data variation were considered. When applied to two independent lung cancer datasets, both TED and TFactS replicated a substantial fraction of their respective differential regulators. Since TED and TFactS rely on two distinct features of transcriptome data, namely differential co-expression and differential expression, both may be applied as mutual references during practical application.
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
Algorithmic differentiation and the calculation of forces by quantum Monte Carlo.
Sorella, Sandro; Capriotti, Luca
2010-12-21
We describe an efficient algorithm to compute forces in quantum Monte Carlo using adjoint algorithmic differentiation. This allows us to apply the space warp coordinate transformation in differential form, and compute all the 3M force components of a system with M atoms with a computational effort comparable with the one to obtain the total energy. Few examples illustrating the method for an electronic system containing several water molecules are presented. With the present technique, the calculation of finite-temperature thermodynamic properties of materials with quantum Monte Carlo will be feasible in the near future.
Genetic algorithms and their application to in silico evolution of genetic regulatory networks.
Knabe, Johannes F; Wegner, Katja; Nehaniv, Chrystopher L; Schilstra, Maria J
2010-01-01
A genetic algorithm (GA) is a procedure that mimics processes occurring in Darwinian evolution to solve computational problems. A GA introduces variation through "mutation" and "recombination" in a "population" of possible solutions to a problem, encoded as strings of characters in "genomes," and allows this population to evolve, using selection procedures that favor the gradual enrichment of the gene pool with the genomes of the "fitter" individuals. GAs are particularly suitable for optimization problems in which an effective system design or set of parameter values is sought.In nature, genetic regulatory networks (GRNs) form the basic control layer in the regulation of gene expression levels. GRNs are composed of regulatory interactions between genes and their gene products, and are, inter alia, at the basis of the development of single fertilized cells into fully grown organisms. This paper describes how GAs may be applied to find functional regulatory schemes and parameter values for models that capture the fundamental GRN characteristics. The central ideas behind evolutionary computation and GRN modeling, and the considerations in GA design and use are discussed, and illustrated with an extended example. In this example, a GRN-like controller is sought for a developmental system based on Lewis Wolpert's French flag model for positional specification, in which cells in a growing embryo secrete and detect morphogens to attain a specific spatial pattern of cellular differentiation. PMID:20835807
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
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.
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
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 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.
Isaksson, Hanna; van Donkelaar, Corrinus C; Huiskes, Rik; Ito, Keita
2006-05-01
Several mechanoregulation algorithms proposed to control tissue differentiation during bone healing have been shown to accurately predict temporal and spatial tissue distributions during normal fracture healing. As these algorithms are different in nature and biophysical parameters, it raises the question of which reflects the actual mechanobiological processes the best. The aim of this study was to resolve this issue by corroborating the mechanoregulatory algorithms with more extensive in vivo bone healing data from animal experiments. A poroelastic three-dimensional finite element model of an ovine tibia with a 2.4 mm gap and external callus was used to simulate the course of tissue differentiation during fracture healing in an adaptive model. The mechanical conditions applied were similar to those used experimentally, with axial compression or torsional rotation as two distinct cases. Histological data at 4 and 8 weeks, and weekly radiographs, were used for comparison. By applying new mechanical conditions, torsional rotation, the predictions of the algorithms were distinguished successfully. In torsion, the algorithms regulated by strain and hydrostatic pressure failed to predict healing and bone formation as seen in experimental data. The algorithm regulated by deviatoric strain and fluid velocity predicted bridging and healing in torsion, as observed in vivo. The predictions of the algorithm regulated by deviatoric strain alone did not agree with in vivo data. None of the algorithms predicted patterns of healing entirely similar to those observed experimentally for both loading modes. However, patterns predicted by the algorithm based on deviatoric strain and fluid velocity was closest to experimental results. It was the only algorithm able to predict healing with torsional loading as seen in vivo.
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
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.
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
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…
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
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.
Spline based iterative phase retrieval algorithm for X-ray differential phase contrast radiography.
Nilchian, Masih; Wang, Zhentian; Thuering, Thomas; Unser, Michael; Stampanoni, Marco
2015-04-20
Differential phase contrast imaging using grating interferometer is a promising alternative to conventional X-ray radiographic methods. It provides the absorption, differential phase and scattering information of the underlying sample simultaneously. Phase retrieval from the differential phase signal is an essential problem for quantitative analysis in medical imaging. In this paper, we formalize the phase retrieval as a regularized inverse problem, and propose a novel discretization scheme for the derivative operator based on B-spline calculus. The inverse problem is then solved by a constrained regularized weighted-norm algorithm (CRWN) which adopts the properties of B-spline and ensures a fast implementation. The method is evaluated with a tomographic dataset and differential phase contrast mammography data. We demonstrate that the proposed method is able to produce phase image with enhanced and higher soft tissue contrast compared to conventional absorption-based approach, which can potentially provide useful information to mammographic investigations.
Differential sampling for fast frequency acquisition via adaptive extended least squares algorithm
NASA Technical Reports Server (NTRS)
Kumar, Rajendra
1987-01-01
This paper presents a differential signal model along with appropriate sampling techinques for least squares estimation of the frequency and frequency derivatives and possibly the phase and amplitude of a sinusoid received in the presence of noise. The proposed algorithm is recursive in mesurements and thus the computational requirement increases only linearly with the number of measurements. The dimension of the state vector in the proposed algorithm does not depend upon the number of measurements and is quite small, typically around four. This is an advantage when compared to previous algorithms wherein the dimension of the state vector increases monotonically with the product of the frequency uncertainty and the observation period. Such a computational simplification may possibly result in some loss of optimality. However, by applying the sampling techniques of the paper such a possible loss in optimality can made small.
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.
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.
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
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)
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
Discussion of "the evolution of boosting algorithms" and "extending statistical boosting".
Bühlmann, P; Gertheiss, J; Hieke, S; Kneib, T; Ma, S; Schumacher, M; Tutz, G; Wang, C-Y; Wang, Z; Ziegler, A
2014-01-01
This article is part of a For-Discussion-Section of Methods of Information in Medicine about the papers "The Evolution of Boosting Algorithms - From Machine Learning to Statistical Modelling" and "Extending Statistical Boosting - An Overview of Recent Methodological Developments", written by Andreas Mayr and co-authors. It is introduced by an editorial. This article contains the combined commentaries invited to independently comment on the Mayr et al. papers. In subsequent issues the discussion can continue through letters to the editor.
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.
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.
Computational processes of evolution and the gene expression messy genetic algorithm
Kargupta, H.
1996-05-01
This paper makes an effort to project the theoretical lessons of the SEARCH (Search Envisioned As Relation and Class Hierarchizing) framework introduced elsewhere (Kargupta, 1995b) in the context of natural evolution and introduce the gene expression messy genetic algorithm (GEMGA) -- a new generation of messy GAs that directly search for relations among the members of the search space. The GEMGA is an O({vert_bar}{Lambda}{vert_bar}{sup k}({ell} + k)) sample complexity algorithm for the class of order-k delineable problems (Kargupta, 1995a) (problems that can be solved by considering no higher than order-k relations) in sequence representation of length {ell} and alphabet set {Lambda}. Unlike the traditional evolutionary search algorithms, the GEMGA emphasizes the computational role of gene expression and uses a transcription operator to detect appropriate relations. Theoretical conclusions are also substantiated by experimental results for large multimodal problems with bounded inappropriateness of representation.
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.
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.
NASA Astrophysics Data System (ADS)
Zhang, X.; Kusari, A.; Glennie, C. L.; Oskin, M. E.; Hinojosa-Corona, A.; Borsa, A. A.; Arrowsmith, R.
2013-12-01
Differential LiDAR (Light Detection and Ranging) from repeated surveys has recently emerged as an effective tool to measure three-dimensional (3D) change for applications such as quantifying slip and spatially distributed warping associated with earthquake ruptures, and examining the spatial distribution of beach erosion after hurricane impact. Currently, the primary method for determining 3D change is through the use of the iterative closest point (ICP) algorithm and its variants. However, all current studies using ICP have assumed that all LiDAR points in the compared point clouds have uniform accuracy. This assumption is simplistic given that the error for each LiDAR point is variable, and dependent upon highly variable factors such as target range, angle of incidence, and aircraft trajectory accuracy. Therefore, to rigorously determine spatial change, it would be ideal to model the random error for every LiDAR observation in the differential point cloud, and use these error estimates as apriori weights in the ICP algorithm. To test this approach, we implemented a rigorous LiDAR observation error propagation method to generate estimated random error for each point in a LiDAR point cloud, and then determine 3D displacements between two point clouds using an anistropic weighted ICP algorithm. The algorithm was evaluated by qualitatively and quantitatively comparing post earthquake slip estimates from the 2010 El Mayor-Cucapah Earthquake between a uniform weight and anistropically weighted ICP algorithm, using pre-event LiDAR collected in 2006 by Instituto Nacional de Estadística y Geografía (INEGI), and post-event LiDAR collected by The National Center for Airborne Laser Mapping (NCALM).
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.
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.
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
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.
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
Patterson, Larissa B; Bain, Emily J; Parichy, David M
2014-11-06
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.
Kiupel, M; Smedley, R C; Pfent, C; Xie, Y; Xue, Y; Wise, A G; DeVaul, J M; Maes, R K
2011-01-01
Differentiating between inflammatory bowel disease (IBD) and small intestinal lymphoma in cats is often difficult, especially when only endoscopic biopsy specimens are available for evaluation. However, a correct diagnosis is imperative for proper treatment and prognosis. A retrospective study was performed using surgical and endoscopic intestinal biopsy specimens from 63 cats with a history of chronic diarrhea or vomiting or weight loss. A diagnosis of lymphoma or inflammation was based on microscopic examination of hematoxylin and eosin (HE)-stained sections alone, HE-stained sections plus results of immunohistochemical labeling (IHC) for CD3e and CD79a, and HE staining, immunophenotyping, and polymerase chain reaction (PCR) results for B and/or T cell clonality. In addition, various histomorphologic parameters were evaluated for significant differences between lymphoma and IBD using Fisher's exact test. The sensitivity and specificity of each parameter in the diagnosis of lymphoma were also determined. Results of Bayesian statistical analysis demonstrated that combining histologic evaluation of small intestinal biopsy specimens with immunophenotyping and analysis of clonality of lymphoid infiltrates results in more accurate differentiation of neoplastic versus inflammatory lymphocytes. Important histologic features that differentiated intestinal lymphoma from IBD included lymphoid infiltration of the intestinal wall beyond the mucosa, epitheliotropism (especially intraepithelial nests and plaques), heterogeneity, and nuclear size of lymphocytes. Based on the results of this study, a stepwise diagnostic algorithm that first uses histologic assessment, followed by immunophenotyping and then PCR to determine clonality of the lymphocytes, was developed to more accurately differentiate between intestinal lymphoma and IBD.
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
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.
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.
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
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.
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
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
Tosa, Yukio; Osue, Jun; Eto, Yukiko; Oh, Hong-Sik; Nakayashiki, Hitoshi; Mayama, Shigeyuki; Leong, Sally A
2005-11-01
The significance of AVR1-CO39, an avirulence gene of the blast fungus corresponding to Pi-CO39(t) in rice cultivars, during the evolution and differentiation of the blast fungus was evaluated by studying its function and distribution in Pyricularia spp. When the presence or absence of AVR1-CO39 was plotted on a dendrogram constructed from ribosomal DNA sequences, a perfect parallelism was observed between its distribution and the phylogeny of Pyricularia isolates. AVR1-CO39 homologs were exclusively present in one species, Pyricularia oryzae, suggesting that AVR1-CO39 appeared during the early stage of evolution of P. oryzae. Transformation assays showed that all the cloned homologs tested are functional as an avirulence gene, indicating that selection has maintained their function. Nevertheless, Oryza isolates (isolates virulent on Oryza spp.) in P. oryzae were exceptionally noncarriers of AVR1-CO39. All Oryza isolates suffered from one of the two types of known rearrangements at the Avr1-CO39 locus (i.e., G type and J type). These types were congruous to the two major lineages of Oryza isolates from Japan determined by MGR586 and MAGGY. These results indicate that AVR1-CO39 was lost during the early stage of evolution of the Oryza-specific subgroup of P. oryzae. Interestingly, its corresponding resistance gene, Pi-CO39(t), is not widely distributed in Oryza spp. PMID:16353550
[Method of Entirely Parallel Differential Evolution for Model Adaptation in Systems Biology].
Kozlov, K N; Samsonov, A M; Samsonova, M G
2015-01-01
We developed a method of entirely parallel differential evolution for identification of unknown parameters of mathematical models by minimization of the objective function that describes the discrepancy of the model solution and the experimental data. The method is implemented in the free and open source software available on the Internet. The method demonstrated a good performance comparable to the top three methods from CEC-2014 and was successfully applied to several biological problems.
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.
A branch-and-bound algorithm for makespan minimization in differentiation flow shops
NASA Astrophysics Data System (ADS)
Liu, Yen-Cheng; Fang, Kuei-Tang; Lin, Bertrand
2013-12-01
This article considers a differentiation flow-shop model, where the jobs are divided into various categories, each of which consists of two stages of operations. All products should be processed first on the single common machine at stage 1. At the second stage, each individual product proceeds to a dedicated machine according to its type. The problem of makespan minimization under the setting with two product types is known to be strongly NP hard. This article considers an arbitrary number of job types by developing a lower bound and two dominance rules, based upon which branch-and-bound algorithms are designed. Computational experiments are carried out to examine the performance of the proposed properties. The statistics show that the proposed properties can substantially reduce the computing efforts required for finding optimal solutions.
Huang, Yanping; Zhang, Qinqin; Thorell, Mariana Rossi; An, Lin; Durbin, Mary; Laron, Michal; Sharma, Utkarsh; Gregori, Giovanni; Rosenfeld, Philip J.; Wang, Ruikang K
2014-01-01
Background and Objective To demonstrate the feasibility of using a 1050 nm swept-source OCT (SS-OCT) system to achieve noninvasive retinal vasculature imaging in human eyes. Materials and Methods Volumetric datasets were acquired using a ZEISS 1 µm SS-OCT prototype that operated at an A-line rate of 100 kHz. A scanning protocol designed to allow for motion contrast processing, referred to as OCT angiography or optical microangiography (OMAG), was used to scan ~3 mm × 3 mm area in the central macular region of the retina within ~4.5 seconds. Intensity differentiation based OMAG algorithm was used to extract 3-D retinal functional microvasculature information. Results Intensity signal differentiation generated capillary-level resolution en face OMAG images of the retina. The parafoveal capillaries were clearly visible, thereby allowing visualization of the foveal avascular zone (FAZ) in normal subjects. Conclusion The capability of OMAG to produce retinal vascular images was demonstrated using the ZEISS 1 µm SS-OCT prototype. This technique can potentially have clinical value for studying retinal vasculature abnormalities. PMID:25230403
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
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.
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
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.
A hybrid algorithm for coupling partial differential equation and compartment-based dynamics
Yates, Christian A.
2016-01-01
Stochastic simulation methods can be applied successfully to model exact spatio-temporally resolved reaction–diffusion systems. However, in many cases, these methods can quickly become extremely computationally intensive with increasing particle numbers. An alternative description of many of these systems can be derived in the diffusive limit as a deterministic, continuum system of partial differential equations (PDEs). Although the numerical solution of such PDEs is, in general, much more efficient than the full stochastic simulation, the deterministic continuum description is generally not valid when copy numbers are low and stochastic effects dominate. Therefore, to take advantage of the benefits of both of these types of models, each of which may be appropriate in different parts of a spatial domain, we have developed an algorithm that can be used to couple these two types of model together. This hybrid coupling algorithm uses an overlap region between the two modelling regimes. By coupling fluxes at one end of the interface and using a concentration-matching condition at the other end, we ensure that mass is appropriately transferred between PDE- and compartment-based regimes. Our methodology gives notable reductions in simulation time in comparison with using a fully stochastic model, while maintaining the important stochastic features of the system and providing detail in appropriate areas of the domain. We test our hybrid methodology robustly by applying it to several biologically motivated problems including diffusion and morphogen gradient formation. Our analysis shows that the resulting error is small, unbiased and does not grow over time. PMID:27628171
A hybrid algorithm for coupling partial differential equation and compartment-based dynamics.
Harrison, Jonathan U; Yates, Christian A
2016-09-01
Stochastic simulation methods can be applied successfully to model exact spatio-temporally resolved reaction-diffusion systems. However, in many cases, these methods can quickly become extremely computationally intensive with increasing particle numbers. An alternative description of many of these systems can be derived in the diffusive limit as a deterministic, continuum system of partial differential equations (PDEs). Although the numerical solution of such PDEs is, in general, much more efficient than the full stochastic simulation, the deterministic continuum description is generally not valid when copy numbers are low and stochastic effects dominate. Therefore, to take advantage of the benefits of both of these types of models, each of which may be appropriate in different parts of a spatial domain, we have developed an algorithm that can be used to couple these two types of model together. This hybrid coupling algorithm uses an overlap region between the two modelling regimes. By coupling fluxes at one end of the interface and using a concentration-matching condition at the other end, we ensure that mass is appropriately transferred between PDE- and compartment-based regimes. Our methodology gives notable reductions in simulation time in comparison with using a fully stochastic model, while maintaining the important stochastic features of the system and providing detail in appropriate areas of the domain. We test our hybrid methodology robustly by applying it to several biologically motivated problems including diffusion and morphogen gradient formation. Our analysis shows that the resulting error is small, unbiased and does not grow over time. PMID:27628171
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
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.
NASA Astrophysics Data System (ADS)
Demiralp, Metin
2012-11-01
The boundary value problems of ordinary differential equations have many important features in comparison with the initial value problems of ODEs. However, one of the basic tendencies is to evaluate forward and backward evolutions and then to combine them to satisfy the boundary conditions. One other issue is also the possibility of more than one type of problems: eigenvalue problems and inversion problems. In this or that way, the basic issue is the solution of ODEs. We use a recently developed method we call "Probabilistic Evolution Approach" whose mainlines are recalled in this paper. Then the boundary condition are imposed for the resulting infinite number of ODEs. This is perhaps the most elementary but promising approach to the boundary value problems of ODEs and works not only for linear but also for nonlinear ODEs.
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
Wavelet-based noise-model driven denoising algorithm for differential phase contrast mammography.
Arboleda, Carolina; Wang, Zhentian; Stampanoni, Marco
2013-05-01
Traditional mammography can be positively complemented by phase contrast and scattering x-ray imaging, because they can detect subtle differences in the electron density of a material and measure the local small-angle scattering power generated by the microscopic density fluctuations in the specimen, respectively. The grating-based x-ray interferometry technique can produce absorption, differential phase contrast (DPC) and scattering signals of the sample, in parallel, and works well with conventional X-ray sources; thus, it constitutes a promising method for more reliable breast cancer screening and diagnosis. Recently, our team proved that this novel technology can provide images superior to conventional mammography. This new technology was used to image whole native breast samples directly after mastectomy. The images acquired show high potential, but the noise level associated to the DPC and scattering signals is significant, so it is necessary to remove it in order to improve image quality and visualization. The noise models of the three signals have been investigated and the noise variance can be computed. In this work, a wavelet-based denoising algorithm using these noise models is proposed. It was evaluated with both simulated and experimental mammography data. The outcomes demonstrated that our method offers a good denoising quality, while simultaneously preserving the edges and important structural features. Therefore, it can help improve diagnosis and implement further post-processing techniques such as fusion of the three signals acquired.
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.
NASA Astrophysics Data System (ADS)
Miranda, M.; Dorrío, B. V.; Blanco, J.; Diz-Bugarín, J.; Ribas, F.
2011-01-01
Several metrological applications base their measurement principle in the phase sum or difference between two patterns, one original s(r,phi) and another modified t(r,phi+Δphi). Additive or differential phase shifting algorithms directly recover the sum 2phi+Δphi or the difference Δphi of phases without requiring prior calculation of the individual phases. These algorithms can be constructed, for example, from a suitable combination of known phase shifting algorithms. Little has been written on the design, analysis and error compensation of these new two-stage algorithms. Previously we have used computer simulation to study, in a linear approach or with a filter process in reciprocal space, the response of several families of them to the main error sources. In this work we present an error analysis that uses Monte Carlo simulation to achieve results in good agreement with those obtained with spatial and temporal methods.
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.
Baldwin, C; Eliassi-Rad, T; Abdulla, G; Critchlow, T
2003-04-16
As scientific data sets grow exponentially in size, the need for scalable algorithms that heuristically partition the data increases. In this paper, we describe the three-step evolution of a hierarchical partitioning algorithm for large-scale spatio-temporal scientific data sets generated by massive simulations. The first version of our algorithm uses a simple top-down partitioning technique, which divides the data by using a four-way bisection of the spatio-temporal space. The shortcomings of this algorithm lead to the second version of our partitioning algorithm, which uses a bottom-up approach. In this version, a partition hierarchy is constructed by systematically agglomerating the underlying Cartesian grid that is placed on the data. Finally, the third version of our algorithm utilizes the intrinsic topology of the data given in the original scientific problem to build the partition hierarchy in a bottom-up fashion. Specifically, the topology is used to heuristically agglomerate the data at each level of the partition hierarchy. Despite the growing complexity in our algorithms, the third version of our algorithm builds partition hierarchies in less time and is able to build trees for larger size data sets as compared to the previous two versions.
Lobo, Daniel; Vico, Francisco J
2010-01-01
The emergence of novelties, as a generator of diversity, in the form and function of the organisms have long puzzled biologists. The study of the developmental process and the anatomical properties of an organism provides scarce information into the means by which its morphology evolved. Some have argued that the very nature of novelty is believed to be linked to the evolution of gene regulation, rather than to the emergence of new structural genes. In order to gain further insight into the evolution of novelty and diversity, we describe a simple computational model of gene regulation that controls the development of locomotive multicellular organisms through a fixed set of simple structural genes. Organisms, modeled as two-dimensional spring networks, are simulated in a virtual environment to evaluate their steering skills for path-following. Proposed as a behavior-finding problem, this fitness function guides an evolutionary algorithm that produces structures whose function is well-adapted to the environment (i.e., good path-followers). We show that, despite the fixed simple set of structural genes, the evolution of gene regulation yields a rich variety of body plans, including symmetries, body segments, and modularity, resulting in a diversity of original behaviors to follow a simple path. These results suggest that the sole variation in the regulation of gene expression is a sufficient condition for the emergence of novelty and diversity.
Pena-Cristóbal, Maite; Otero-Rey, Eva-María; Tomás, Inmaculada; Blanco-Carrión, Andrés
2016-01-01
Objectives To determine the diagnostic value of diascopy and other non-invasive clinical aids on recent differential diagnosis algorithms of oral mucosal pigmentations affecting subjects of any age. Material and Methods Data Sources: this systematic review was conducted by searching PubMed, Scopus, Dentistry & Oral Sciences Source and the Cochrane Library (2000-2015); Study Selection: two reviewers independently selected all types of English articles describing differential diagnosis algorithms of oral pigmentations and checked the references of finally included papers; Data Extraction: one reviewer performed the data extraction and quality assessment based on previously defined fields while the other reviewer checked their validity. Results Data Synthesis: eight narrative reviews and one single case report met the inclusion criteria. Diascopy was used on six algorithms (66.67%) and X-ray was included once (11.11%; 44.44% with text mentions); these were considered helpful tools in the diagnosis of intravascular and exogenous pigmentations, respectively. Surface rubbing was described once in the text (11.11%). Conclusions Diascopy was the most applied method followed by X-ray and surface rubbing. The limited scope of these procedures only makes them useful when a positive result is obtained, turning biopsy into the most recommended technique when diagnosis cannot be established on clinical grounds alone. Key words:Algorithm, differential diagnosis, flow chart, oral mucosa, oral pigmentation, systematic review. PMID:27703615
Peng, Duo; Gu, Xi; Xue, Liang-Jiao; Leebens-Mack, James H.; Tsai, Chung-Jui
2014-01-01
Sucrose transporters (SUTs) are essential for the export and efficient movement of sucrose from source leaves to sink organs in plants. The angiosperm SUT family was previously classified into three or four distinct groups, Types I, II (subgroup IIB), and III, with dicot-specific Type I and monocot-specific Type IIB functioning in phloem loading. To shed light on the underlying drivers of SUT evolution, Bayesian phylogenetic inference was undertaken using 41 sequenced plant genomes, including seven basal lineages at key evolutionary junctures. Our analysis supports four phylogenetically and structurally distinct SUT subfamilies, originating from two ancient groups (AG1 and AG2) that diverged early during terrestrial colonization. In both AG1 and AG2, multiple intron acquisition events in the progenitor vascular plant established the gene structures of modern SUTs. Tonoplastic Type III and plasmalemmal Type II represent evolutionarily conserved descendants of AG1 and AG2, respectively. Type I and Type IIB were previously thought to evolve after the dicot-monocot split. We show, however, that divergence of Type I from Type III SUT predated basal angiosperms, likely associated with evolution of vascular cambium and phloem transport. Type I SUT was subsequently lost in monocots along with vascular cambium, and independent evolution of Type IIB coincided with modified monocot vasculature. Both Type I and Type IIB underwent lineage-specific expansion. In multiple unrelated taxa, the newly-derived SUTs exhibit biased expression in reproductive tissues, suggesting a functional link between phloem loading and reproductive fitness. Convergent evolution of Type I and Type IIB for SUT function in phloem loading and reproductive organs supports the idea that differential vascular development in dicots and monocots is a strong driver for SUT family evolution in angiosperms. PMID:25429293
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.
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.
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.
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.
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
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.
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
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.
Golan, Guy; Oksenberg, Adi; Peleg, Zvi
2015-09-01
Wheat is one of the Neolithic founder crops domesticated ~10 500 years ago. Following the domestication episode, its evolution under domestication has resulted in various genetic modifications. Grain weight, embryo weight, and the interaction between those factors were examined among domesticated durum wheat and its direct progenitor, wild emmer wheat. Experimental data show that grain weight has increased over the course of wheat evolution without any parallel change in embryo weight, resulting in a significantly reduced (30%) embryo weight/grain weight ratio in domesticated wheat. The genetic factors associated with these modifications were further investigated using a population of recombinant inbred substitution lines that segregated for chromosome 2A. A cluster of loci affecting grain weight and shape was identified on the long arm of chromosome 2AL. Interestingly, a novel locus controlling embryo weight was mapped on chromosome 2AS, on which the wild emmer allele promotes heavier embryos and greater seedling vigour. To the best of our knowledge, this is the first report of a QTL for embryo weight in wheat. The results suggest a differential selection of grain and embryo weight during the evolution of domesticated wheat. It is argued that conscious selection by early farmers favouring larger grains and smaller embryos appears to have resulted in a significant change in endosperm weight/embryo weight ratio in the domesticated wheat. Exposing the genetic factors associated with endosperm and embryo size improves our understanding of the evolutionary dynamics of wheat under domestication and is likely to be useful for future wheat-breeding efforts.
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).
Zhang, Xue-Dian; Huang, Xian; Xu, Ke-Xin
2007-11-01
Differential optical absorption spectroscopy, or DOAS, is a widely used method to determine concentrations of atmospheric species. The principle of DOAS for measuring the concentration of air pollutants is presented in briefly. Using the linear relationship between the area of the measured differential absorbance curve and that of the differential absorption cross-section curve as taken from the literature, an alternative method for calculating the gas concentration on the basis of the proportionality between differential absorbance and differential absorption cross section of the gas under study was developed. The method can be used on its own for single-component analysis or as a complement to the standard technique in multi-component cases. The procedure can be used with differential absorption cross sections measured in the laboratory or taken from the literature. In addition, the method provides a criterion to discriminate between different species having absorption features in the same wavelength range.
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.
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
Vasiliu, Daniel; Clamons, Samuel; McDonough, Molly; Rabe, Brian; Saha, Margaret
2015-01-01
Global gene expression analysis using microarrays and, more recently, RNA-seq, has allowed investigators to understand biological processes at a system level. However, the identification of differentially expressed genes in experiments with small sample size, high dimensionality, and high variance remains challenging, limiting the usability of these tens of thousands of publicly available, and possibly many more unpublished, gene expression datasets. We propose a novel variable selection algorithm for ultra-low-n microarray studies using generalized linear model-based variable selection with a penalized binomial regression algorithm called penalized Euclidean distance (PED). Our method uses PED to build a classifier on the experimental data to rank genes by importance. In place of cross-validation, which is required by most similar methods but not reliable for experiments with small sample size, we use a simulation-based approach to additively build a list of differentially expressed genes from the rank-ordered list. Our simulation-based approach maintains a low false discovery rate while maximizing the number of differentially expressed genes identified, a feature critical for downstream pathway analysis. We apply our method to microarray data from an experiment perturbing the Notch signaling pathway in Xenopus laevis embryos. This dataset was chosen because it showed very little differential expression according to limma, a powerful and widely-used method for microarray analysis. Our method was able to detect a significant number of differentially expressed genes in this dataset and suggest future directions for investigation. Our method is easily adaptable for analysis of data from RNA-seq and other global expression experiments with low sample size and high dimensionality.
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.
Chitalia, Rhea; Mueller, Jenna; Fu, Henry L.; Whitley, Melodi Javid; Kirsch, David G.; Brown, J. Quincy; Willett, Rebecca; Ramanujam, Nimmi
2016-01-01
Fluorescence microscopy can be used to acquire real-time images of tissue morphology and with appropriate algorithms can rapidly quantify features associated with disease. The objective of this study was to assess the ability of various segmentation algorithms to isolate fluorescent positive features (FPFs) in heterogeneous images and identify an approach that can be used across multiple fluorescence microscopes with minimal tuning between systems. Specifically, we show a variety of image segmentation algorithms applied to images of stained tumor and muscle tissue acquired with 3 different fluorescence microscopes. Results indicate that a technique called maximally stable extremal regions followed by thresholding (MSER + Binary) yielded the greatest contrast in FPF density between tumor and muscle images across multiple microscopy systems. PMID:27699108
Chitalia, Rhea; Mueller, Jenna; Fu, Henry L.; Whitley, Melodi Javid; Kirsch, David G.; Brown, J. Quincy; Willett, Rebecca; Ramanujam, Nimmi
2016-01-01
Fluorescence microscopy can be used to acquire real-time images of tissue morphology and with appropriate algorithms can rapidly quantify features associated with disease. The objective of this study was to assess the ability of various segmentation algorithms to isolate fluorescent positive features (FPFs) in heterogeneous images and identify an approach that can be used across multiple fluorescence microscopes with minimal tuning between systems. Specifically, we show a variety of image segmentation algorithms applied to images of stained tumor and muscle tissue acquired with 3 different fluorescence microscopes. Results indicate that a technique called maximally stable extremal regions followed by thresholding (MSER + Binary) yielded the greatest contrast in FPF density between tumor and muscle images across multiple microscopy systems.
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
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
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
2012-11-01
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 α(A)-globin gene) and HbD (the minor adult Hb isoform, with α-chain subunits encoded by the α(D)-globin gene). The α(D)-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 O(2) 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-O(2) affinity stems primarily from changes in the O(2) 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 α(D)-globin gene that is shared with the embryonic α-like globin gene.
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
Raj, Dibyendu; Ghosh, Esha; Mukherjee, Avik K; Nozaki, Tomoyoshi; Ganguly, Sandipan
2014-02-10
Giardia lamblia is a unicellular, early branching eukaryote causing giardiasis, one of the most common human enteric diseases. Giardia, a microaerophilic protozoan parasite has to build up mechanisms to protect themselves against oxidative stress within the human gut (oxygen concentration 60 μM) to establish its pathogenesis. G. lamblia is devoid of the conventional mechanisms of the oxidative stress management system, including superoxide dismutase, catalase, peroxidase, and glutathione cycling, which are present in most eukaryotes. NADH oxidase is a major component of the electron transport chain of G. lamblia, which in concurrence with disulfide reductase, protects oxygen-labile proteins such as pyruvate: ferredoxin oxidoreductase against oxidative stress by sustaining a reduced intracellular environment. It also contains the arginine dihydrolase pathway, which occurs in a number of anaerobic prokaryotes, includes substrate level phosphorylation and adequately active to make a major contribution to ATP production. To study differential gene expression under three types of oxidative stress, a Giardia genomic DNA array was constructed and hybridized with labeled cDNA of cells with or without stress. The transcriptomic data has been analyzed and further validated using real time PCR. We identified that out of 9216 genes represented on the array, more than 200 genes encoded proteins with functions in metabolism, oxidative stress management, signaling, reproduction and cell division, programmed cell death and cytoskeleton. We recognized genes modulated by at least ≥ 2 fold at a significant time point in response to oxidative stress. The study has highlighted the genes that are differentially expressed during the three experimental conditions which regulate the stress management pathway differently to achieve redox homeostasis. Identification of some unique genes in oxidative stress regulation may help in new drug designing for this common enteric parasite prone to
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.
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.
Integrated Model of Multiple Kernel Learning and Differential Evolution for EUR/USD Trading
Deng, Shangkun; Sakurai, Akito
2014-01-01
Currency trading is an important area for individual investors, government policy decisions, and organization investments. In this study, we propose a hybrid approach referred to as MKL-DE, which combines multiple kernel learning (MKL) with differential evolution (DE) for trading a currency pair. MKL is used to learn a model that predicts changes in the target currency pair, whereas DE is used to generate the buy and sell signals for the target currency pair based on the relative strength index (RSI), while it is also combined with MKL as a trading signal. The new hybrid implementation is applied to EUR/USD trading, which is the most traded foreign exchange (FX) currency pair. MKL is essential for utilizing information from multiple information sources and DE is essential for formulating a trading rule based on a mixture of discrete structures and continuous parameters. Initially, the prediction model optimized by MKL predicts the returns based on a technical indicator called the moving average convergence and divergence. Next, a combined trading signal is optimized by DE using the inputs from the prediction model and technical indicator RSI obtained from multiple timeframes. The experimental results showed that trading using the prediction learned by MKL yielded consistent profits. PMID:25097891
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.
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
Extended Kalman smoother with differential evolution technique for denoising of ECG signal.
Panigrahy, D; Sahu, P K
2016-09-01
Electrocardiogram (ECG) signal gives a lot of information on the physiology of heart. In reality, noise from various sources interfere with the ECG signal. To get the correct information on physiology of the heart, noise cancellation of the ECG signal is required. In this paper, the effectiveness of extended Kalman smoother (EKS) with the differential evolution (DE) technique for noise cancellation of the ECG signal is investigated. DE is used as an automatic parameter selection method for the selection of ten optimized components of the ECG signal, and those are used to create the ECG signal according to the real ECG signal. These parameters are used by the EKS for the development of the state equation and also for initialization of the parameters of EKS. EKS framework is used for denoising the ECG signal from the single channel. The effectiveness of proposed noise cancellation technique has been evaluated by adding white, colored Gaussian noise and real muscle artifact noise at different SNR to some visually clean ECG signals from the MIT-BIH arrhythmia database. The proposed noise cancellation technique of ECG signal shows better signal to noise ratio (SNR) improvement, lesser mean square error (MSE) and percent of distortion (PRD) compared to other well-known methods. PMID:27542170
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
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.
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
Extended Kalman smoother with differential evolution technique for denoising of ECG signal.
Panigrahy, D; Sahu, P K
2016-09-01
Electrocardiogram (ECG) signal gives a lot of information on the physiology of heart. In reality, noise from various sources interfere with the ECG signal. To get the correct information on physiology of the heart, noise cancellation of the ECG signal is required. In this paper, the effectiveness of extended Kalman smoother (EKS) with the differential evolution (DE) technique for noise cancellation of the ECG signal is investigated. DE is used as an automatic parameter selection method for the selection of ten optimized components of the ECG signal, and those are used to create the ECG signal according to the real ECG signal. These parameters are used by the EKS for the development of the state equation and also for initialization of the parameters of EKS. EKS framework is used for denoising the ECG signal from the single channel. The effectiveness of proposed noise cancellation technique has been evaluated by adding white, colored Gaussian noise and real muscle artifact noise at different SNR to some visually clean ECG signals from the MIT-BIH arrhythmia database. The proposed noise cancellation technique of ECG signal shows better signal to noise ratio (SNR) improvement, lesser mean square error (MSE) and percent of distortion (PRD) compared to other well-known methods.
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
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)
Pareto evolution of gene networks: an algorithm to optimize multiple fitness objectives.
Warmflash, Aryeh; Francois, Paul; Siggia, Eric D
2012-10-01
The computational evolution of gene networks functions like a forward genetic screen to generate, without preconceptions, all networks that can be assembled from a defined list of parts to implement a given function. Frequently networks are subject to multiple design criteria that cannot all be optimized simultaneously. To explore how these tradeoffs interact with evolution, we implement Pareto optimization in the context of gene network evolution. In response to a temporal pulse of a signal, we evolve networks whose output turns on slowly after the pulse begins, and shuts down rapidly when the pulse terminates. The best performing networks under our conditions do not fall into categories such as feed forward and negative feedback that also encode the input-output relation we used for selection. Pareto evolution can more efficiently search the space of networks than optimization based on a single ad hoc combination of the design criteria.
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.
Algorithms and design for a second-order automatic differentiation module
Abate, J.; Bischof, C.; Roh, L.; Carle, A.
1997-07-01
This article describes approaches to computing second-order derivatives with automatic differentiation (AD) based on the forward mode and the propagation of univariate Taylor series. Performance results are given that show the speedup possible with these techniques relative to existing approaches. The authors also describe a new source transformation AD module for computing second-order derivatives of C and Fortran codes and the underlying infrastructure used to create a language-independent translation tool.
S.R. Hudson
2010-10-13
A method for approximately solving magnetic differential equations is described. The approach is to include a small diffusion term to the equation, which regularizes the linear operator to be inverted. The extra term allows a "source-correction" term to be defned, which is generally required in order to satisfy the solvability conditions. The approach is described in the context of computing the pressure and parallel currents in the iterative approach for computing magnetohydrodynamic equilibria. __________________________________________________
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.
McClain, M T; Henao, R; Williams, J; Nicholson, B; Veldman, T; Hudson, L; Tsalik, E L; Lambkin-Williams, R; Gilbert, A; Mann, A; Ginsburg, G S; Woods, C W
2016-03-01
Exposure to influenza virus triggers a complex cascade of events in the human host. In order to understand more clearly the evolution of this intricate response over time, human volunteers were inoculated with influenza A/Wisconsin/67/2005 (H3N2), and then had serial peripheral blood samples drawn and tested for the presence of 25 major human cytokines. Nine of 17 (53%) inoculated subjects developed symptomatic influenza infection. Individuals who will go on to become symptomatic demonstrate increased circulating levels of interleukin (IL)-6, IL-8, IL-15, monocyte chemotactic protein (MCP)-1 and interferon (IFN) gamma-induced protein (IP)-10 as early as 12-29 h post-inoculation (during the presymptomatic phase), whereas challenged patients who remain asymptomatic do not. Overall, the immunological pathways of leucocyte recruitment, Toll-like receptor (TLR)-signalling, innate anti-viral immunity and fever production are all over-represented in symptomatic individuals very early in disease, but are also dynamic and evolve continuously over time. Comparison with simultaneous peripheral blood genomics demonstrates that some inflammatory mediators (MCP-1, IP-10, IL-15) are being expressed actively in circulating cells, while others (IL-6, IL-8, IFN-α and IFN-γ) are probable effectors produced locally at the site of infection. Interestingly, asymptomatic exposed subjects are not quiescent either immunologically or genomically, but instead exhibit early and persistent down-regulation of important inflammatory mediators in the periphery. The host inflammatory response to influenza infection is variable but robust, and evolves over time. These results offer critical insight into pathways driving influenza-related symptomatology and offer the potential to contribute to early detection and differentiation of infected hosts.
NASA Astrophysics Data System (ADS)
Cao, Qixin; Leng, Chuntao; Huang, Yanwen
2007-12-01
The traditional artificial potential field (APF) method is widely used for motion planning of traditional mobile robot, but there is little research about the application to the omnidirectional mobile robot (OMR). To propose a more suitable motion planning for OMR, an evolutional APF is presented in this paper, by introducing the revolving factor into the APF. The revolving factor synthesizes the anisotropy of OMR and the affect of dynamic environment. Finally simulation is carried out to demonstrate that, the evolutional APF is a high-speed and high-efficiency motion planning by comparing with the traditional APF, and the advantages of OMR is exerted.
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
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.
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.
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.
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
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)
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
Sai, Linwei; Zhao, Jijun; Huang, Xiaoming; Wang, Jun
2012-01-01
Using genetic algorithm incorporated with density functional theory, we have explored the size evolution of structural and electronic properties of neutral gallium clusters of 20-40 atoms in terms of their ground state structures, binding energies, second differences of energy, HOMO-LUMO gaps, distributions of bond length and bond angle, and electron density of states. In the size range studied, the Ga(n) clusters exhibit several growth patterns, and the core-shell structures become dominant from Ga31. With high point group symmetries, Ga23 and Ga36 show particularly high stability and Ga36 owns a large HOMO-LUMO gap. The atomic structures and electronic states of Ga(n) clusters significantly differ from the a solid but resemble beta solid and liquid to certain extent.
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.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
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.
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
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.
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
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.
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…
Depolli, Matjaž; Trobec, Roman; Filipič, Bogdan
2013-01-01
In this paper, we present AMS-DEMO, an asynchronous master-slave implementation of DEMO, an evolutionary algorithm for multi-objective optimization. AMS-DEMO was designed for solving time-intensive problems efficiently on both homogeneous and heterogeneous parallel computer architectures. The algorithm is used as a test case for the asynchronous master-slave parallelization of multi-objective optimization that has not yet been thoroughly investigated. Selection lag is identified as the key property of the parallelization method, which explains how its behavior depends on the type of computer architecture and the number of processors. It is arrived at analytically and from the empirical results. AMS-DEMO is tested on a benchmark problem and a time-intensive industrial optimization problem, on homogeneous and heterogeneous parallel setups, providing performance results for the algorithm and an insight into the parallelization method. A comparison is also performed between AMS-DEMO and generational master-slave DEMO to demonstrate how the asynchronous parallelization method enhances the algorithm and what benefits it brings compared to the synchronous method.
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
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.
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.
Habel, Jan Christian; Borghesio, Luca; Newmark, William D; Day, Julia J; Lens, Luc; Husemann, Martin; Ulrich, Werner
2015-11-01
The moist and cool cloud forests of East Africa represent a network of isolated habitats that are separated by dry and warm lowland savannah, offering an opportunity to investigate how strikingly different selective regimes affect species diversification. Here, we used the passerine genus Zosterops (white-eyes) from this region as our model system. Species of the genus occur in contrasting distribution settings, with geographical mountain isolation driving diversification, and savannah interconnectivity preventing differentiation. We analyze (1) patterns of phenotypic and genetic differentiation in high- and lowland species (different distribution settings), (2) investigate the potential effects of natural selection and temporal and spatial isolation (evolutionary drivers), and (3) critically review the taxonomy of this species complex. We found strong phenotypic and genetic differentiation among and within the three focal species, both in the highland species complex and in the lowland taxa. Altitude was a stronger predictor of phenotypic patterns than the current taxonomic classification. We found longitudinal and latitudinal phenotypic gradients for all three species. Furthermore, wing length and body weight were significantly correlated with altitude and habitat type in the highland species Z. poliogaster. Genetic and phenotypic divergence showed contrasting inter- and intraspecific structures. We suggest that the evolution of phenotypic characters is mainly driven by natural selection due to differences in the two macro-habitats, cloud forest and savannah. In contrast, patterns of neutral genetic variation appear to be rather driven by geographical isolation of the respective mountain massifs. Populations of the Z. poliogaster complex, as well as Z. senegalensis and Z. abyssinicus, are not monophyletic based on microsatellite data and have higher levels of intraspecific differentiation compared to the currently accepted species. PMID:26640665
Habel, Jan Christian; Borghesio, Luca; Newmark, William D; Day, Julia J; Lens, Luc; Husemann, Martin; Ulrich, Werner
2015-11-01
The moist and cool cloud forests of East Africa represent a network of isolated habitats that are separated by dry and warm lowland savannah, offering an opportunity to investigate how strikingly different selective regimes affect species diversification. Here, we used the passerine genus Zosterops (white-eyes) from this region as our model system. Species of the genus occur in contrasting distribution settings, with geographical mountain isolation driving diversification, and savannah interconnectivity preventing differentiation. We analyze (1) patterns of phenotypic and genetic differentiation in high- and lowland species (different distribution settings), (2) investigate the potential effects of natural selection and temporal and spatial isolation (evolutionary drivers), and (3) critically review the taxonomy of this species complex. We found strong phenotypic and genetic differentiation among and within the three focal species, both in the highland species complex and in the lowland taxa. Altitude was a stronger predictor of phenotypic patterns than the current taxonomic classification. We found longitudinal and latitudinal phenotypic gradients for all three species. Furthermore, wing length and body weight were significantly correlated with altitude and habitat type in the highland species Z. poliogaster. Genetic and phenotypic divergence showed contrasting inter- and intraspecific structures. We suggest that the evolution of phenotypic characters is mainly driven by natural selection due to differences in the two macro-habitats, cloud forest and savannah. In contrast, patterns of neutral genetic variation appear to be rather driven by geographical isolation of the respective mountain massifs. Populations of the Z. poliogaster complex, as well as Z. senegalensis and Z. abyssinicus, are not monophyletic based on microsatellite data and have higher levels of intraspecific differentiation compared to the currently accepted species.
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.
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.
From lanosterol to cholesterol: structural evolution and differential effects on lipid bilayers.
Miao, Ling; Nielsen, Morten; Thewalt, Jenifer; Ipsen, John H; Bloom, Myer; Zuckermann, Martin J; Mouritsen, Ole G
2002-01-01
Cholesterol is an important molecular component of the plasma membranes of mammalian cells. Its precursor in the sterol biosynthetic pathway, lanosterol, has been argued by Konrad Bloch (Bloch, K. 1965. Science. 150:19-28; 1983. CRC Crit. Rev. Biochem. 14:47-92; 1994. Blonds in Venetian Paintings, the Nine-Banded Armadillo, and Other Essays in Biochemistry. Yale University Press, New Haven, CT.) to also be a precursor in the molecular evolution of cholesterol. We present a comparative study of the effects of cholesterol and lanosterol on molecular conformational order and phase equilibria of lipid-bilayer membranes. By using deuterium NMR spectroscopy on multilamellar lipid-sterol systems in combination with Monte Carlo simulations of microscopic models of lipid-sterol interactions, we demonstrate that the evolution in the molecular chemistry from lanosterol to cholesterol is manifested in the model lipid-sterol membranes by an increase in the ability of the sterols to promote and stabilize a particular membrane phase, the liquid-ordered phase, and to induce collective order in the acyl-chain conformations of lipid molecules. We also discuss the biological relevance of our results, in particular in the context of membrane domains and rafts. PMID:11867458
Cooper, L A; Scott, T W
2001-01-01
Arthropod-borne viruses (arboviruses) cycle between hosts in two widely separated taxonomic groups, vertebrate amplifying hosts and invertebrate vectors, both of which may separately or in concert shape the course of arbovirus evolution. To elucidate the selective pressures associated with virus replication within each portion of this two-host life cycle, the effects of host type on the growth characteristics of the New World alphavirus, eastern equine encephalitis (EEE) virus, were investigated. Multiple lineages of an ancestral EEE virus stock were repeatedly transferred through either mosquito or avian cells or in alternating passages between these two cell types. When assayed in both cell types, derived single host lineages exhibited significant differences in infectivity, growth pattern, plaque morphology, and total virus yield, demonstrating that this virus is capable of host-specific evolution. Virus lineages grown in alternation between the two cell types expressed intermediate phenotypes consistent with dual adaptation to both cellular environments. Both insect-adapted and alternated lineages greatly increased in their ability to infect insect cells. These results indicate that different selective pressures exist for virus replication within each portion of the two-host life cycle, and that alternation of hosts selects for virus populations well adapted for replication in both host systems. PMID:11290699
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)
Agarwal, Naman; Yoon, Jiho; Garcia-Caurel, Enric; Novikova, Tatiana; Vanel, Jean-Charles; Pierangelo, Angelo; Bykov, Alexander; Popov, Alexey; Meglinski, Igor; Ossikovski, Razvigor
2015-12-01
We show, through visible-range Mueller polarimetry, as well as numerical simulations, that the depolarization in a homogeneous turbid medium consisting of submicron spherical particles follows a parabolic law with the path-length traveled by light through the medium. This result is in full agreement with the phenomenological theory of the fluctuating medium within the framework of the differential Mueller matrix formalism. We further found that the standard deviations of the fluctuating elementary polarization properties of the medium depend linearly on the concentration of particles. These findings are believed to be useful for the phenomenological interpretation of polarimetric experiments, with special emphasis on biomedical applications.
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.
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
Uchihara, Toshiki; Hara, Makoto; Nakamura, Ayako; Hirokawa, Katsuiku
2012-02-01
Double immunofluorolabeling for 3-repeat (3R) and 4-repeat (4R) tau was performed with two monoclonal antibodies, RD3 and RD4, after an additional pretreatment with potassium permanganate and oxalic acid to eliminate nonspecific 3R tau cytoplasmic staining. This method involves hyperdilution of one of the primary monoclonal antibodies (≥100-fold), making it undetectable by usual secondary antibodies. The hyperdiluted primary antibody can then only be detected after tyramide amplification. Subsequent application of the other monoclonal antibody at its usual concentration allows double immunofluorolabeling without cross-reaction. This novel method revealed that tau immunoreactivity (IR) in the hippocampal pyramidal neurons of Alzheimer's disease (AD) brains is heterogeneous in that pretangle neurons exhibit 4R-selective (3R-/4R+) IR, ghost tangles exhibit 3R-selective (3R+/4R-) IR, and neurofibrillary tangles exhibit both 3R and 4R (3R+/4R+) IR. Some nigral neurons exhibited RD3 IR in both AD and corticobasal degeneration/progressive supranuclear palsy (CBD/PSP) brains. However, in CBD/PSP cases, 3R IR was always superimposed on 4R IR, while 3R-selective neurons were present in AD cases. These differential isoform profiles may provide a pivotal molecular reference, closely related to the morphological evolution of tau-positive neurons, which may be variable according to disease (CBD/PSP vs. AD), lesion site (cerebral cortex and substantia nigra), or the stage of evolution (from pretangles to ghost tangles). These findings should provide a more comprehensive understanding of the histological differentiation of various tau deposits in human neurodegenerative disease.
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
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
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
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.
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
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
Selection on Network Dynamics Drives Differential Rates of Protein Domain Evolution.
Mannakee, Brian K; Gutenkunst, Ryan N
2016-07-01
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.
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.
Garcia, C; Oliveira, C; Almeida-Toledo, L F
2010-01-01
Among catfish species of the genus Rhamdia reported for the Brazilian territory, R. quelen is the most widespread, being found in nearly all hydrographic basins of Brazil. Nowadays, R. quelen is a synonym for at least 47 other species in this genus, its taxonomic status still being controversial. The available cytogenetic reports show a wide variation in the karyotypic macrostructure, with the frequent presence of supernumerary chromosomes. The remarkable cytogenetic variability associated with taxonomic issues in this species indicates that R. quelen is actually a species complex. In order to carry out a wide comparative cytogenetic study in R. quelen from southern and southeastern Brazil and examine a species complex, we analyzed the chromosomes of 14 populations from the main hydrographic basins of these two regions. Using classic and molecular cytogenetic techniques, we found seven distinct karyotypic formulae, all bearing 2n = 58 chromosomes. Supernumerary chromosomes were present in most of the populations; their number, size and C-banding pattern allowed us to differentiate populations with similar karyotypic compositions. We examined patterns of chromosomal evolution as well as the probable mechanisms involved in the origin and morphological differentiation of their supernumerary chromosomes. PMID:20309823
NASA Astrophysics Data System (ADS)
Ioannidis, P.; Schmitt, J. H. M. M.
2016-10-01
We use high accuracy photometric data obtained with the Kepler satellite to monitor the activity modulations of the Kepler-210 planet host star over a time span of more than four years. Following the phenomenology of the star's light curve in combination with a five spot model, we identify six different so-called spot seasons. A characteristic, which is common in the majority of the seasons, is the persistent appearance of spots in a specific range of longitudes on the stellar surface. The most prominent period of the observed activity modulations is different for each season and appears to evolve following a specific pattern, resembling the changes in the sunspot periods during the solar magnetic cycle. Under the hypothesis that the star exhibits solar-like differential rotation, we suggest differential rotation values of Kepler-210 that are similar to or smaller than that of the Sun. Finally, we estimate spot life times between ~60 days and ~90 days, taking into consideration the evolution of the total covered stellar surface computed from our model.
NASA Astrophysics Data System (ADS)
Wang, L.; Wang, T. G.; Wu, J. H.; Cheng, G. P.
2016-09-01
A novel multi-objective optimization algorithm incorporating evolution strategies and vector mechanisms, referred as VD-MOEA, is proposed and applied in aerodynamic- structural integrated design of wind turbine blade. In the algorithm, a set of uniformly distributed vectors is constructed to guide population in moving forward to the Pareto front rapidly and maintain population diversity with high efficiency. For example, two- and three- objective designs of 1.5MW wind turbine blade are subsequently carried out for the optimization objectives of maximum annual energy production, minimum blade mass, and minimum extreme root thrust. The results show that the Pareto optimal solutions can be obtained in one single simulation run and uniformly distributed in the objective space, maximally maintaining the population diversity. In comparison to conventional evolution algorithms, VD-MOEA displays dramatic improvement of algorithm performance in both convergence and diversity preservation for handling complex problems of multi-variables, multi-objectives and multi-constraints. This provides a reliable high-performance optimization approach for the aerodynamic-structural integrated design of wind turbine blade.
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.
Zhou, Qi; Bachtrog, Doris
2015-01-01
Sex chromosomes evolve distinctive types of chromatin from a pair of ancestral autosomes that are usually euchromatic. In Drosophila, the dosage-compensated X becomes enriched for hyperactive chromatin in males (mediated by H4K16ac), while the Y chromosome acquires silencing heterochromatin (enriched for H3K9me2/3). Drosophila autosomes are typically mostly euchromatic but the small dot chromosome has evolved a heterochromatin-like milieu (enriched for H3K9me2/3) that permits the normal expression of dot-linked genes, but which is different from typical pericentric heterochromatin. In Drosophila busckii, the dot chromosomes have fused to the ancestral sex chromosomes, creating a pair of ‘neo-sex’ chromosomes. Here we collect genomic, transcriptomic and epigenomic data from D. busckii, to investigate the evolutionary trajectory of sex chromosomes from a largely heterochromatic ancestor. We show that the neo-sex chromosomes formed <1 million years ago, but nearly 60% of neo-Y linked genes have already become non-functional. Expression levels are generally lower for the neo-Y alleles relative to their neo-X homologs, and the silencing heterochromatin mark H3K9me2, but not H3K9me3, is significantly enriched on silenced neo-Y genes. Despite rampant neo-Y degeneration, we find that the neo-X is deficient for the canonical histone modification mark of dosage compensation (H4K16ac), relative to autosomes or the compensated ancestral X chromosome, possibly reflecting constraints imposed on evolving hyperactive chromatin in an originally heterochromatic environment. Yet, neo-X genes are transcriptionally more active in males, relative to females, suggesting the evolution of incipient dosage compensation on the neo-X. Our data show that Y degeneration proceeds quickly after sex chromosomes become established through genomic and epigenetic changes, and are consistent with the idea that the evolution of sex-linked chromatin is influenced by its ancestral configuration. PMID
Zhou, Qi; Bachtrog, Doris
2015-06-01
Sex chromosomes evolve distinctive types of chromatin from a pair of ancestral autosomes that are usually euchromatic. In Drosophila, the dosage-compensated X becomes enriched for hyperactive chromatin in males (mediated by H4K16ac), while the Y chromosome acquires silencing heterochromatin (enriched for H3K9me2/3). Drosophila autosomes are typically mostly euchromatic but the small dot chromosome has evolved a heterochromatin-like milieu (enriched for H3K9me2/3) that permits the normal expression of dot-linked genes, but which is different from typical pericentric heterochromatin. In Drosophila busckii, the dot chromosomes have fused to the ancestral sex chromosomes, creating a pair of 'neo-sex' chromosomes. Here we collect genomic, transcriptomic and epigenomic data from D. busckii, to investigate the evolutionary trajectory of sex chromosomes from a largely heterochromatic ancestor. We show that the neo-sex chromosomes formed <1 million years ago, but nearly 60% of neo-Y linked genes have already become non-functional. Expression levels are generally lower for the neo-Y alleles relative to their neo-X homologs, and the silencing heterochromatin mark H3K9me2, but not H3K9me3, is significantly enriched on silenced neo-Y genes. Despite rampant neo-Y degeneration, we find that the neo-X is deficient for the canonical histone modification mark of dosage compensation (H4K16ac), relative to autosomes or the compensated ancestral X chromosome, possibly reflecting constraints imposed on evolving hyperactive chromatin in an originally heterochromatic environment. Yet, neo-X genes are transcriptionally more active in males, relative to females, suggesting the evolution of incipient dosage compensation on the neo-X. Our data show that Y degeneration proceeds quickly after sex chromosomes become established through genomic and epigenetic changes, and are consistent with the idea that the evolution of sex-linked chromatin is influenced by its ancestral configuration.
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.
D'Oliveira Albanus, Ricardo; Siqueira Dalmolin, Rodrigo Juliani; Rybarczyk-Filho, José Luiz; Alves Castro, Mauro Antônio; Fonseca Moreira, José Cláudio
2014-01-01
Chemoreception is among the most important sensory modalities in animals. Organisms use the ability to perceive chemical compounds in all major ecological activities. Recent studies have allowed the characterization of chemoreceptor gene families. These genes present strikingly high variability in copy numbers and pseudogenization degrees among different species, but the mechanisms underlying their evolution are not fully understood. We have analyzed the functional networks of these genes, their orthologs distribution, and performed phylogenetic analyses in order to investigate their evolutionary dynamics. We have modeled the chemosensory networks and compared the evolutionary constraints of their genes in Mus musculus, Homo sapiens, and Rattus norvegicus. We have observed significant differences regarding the constraints on the orthologous groups and network topologies of chemoreceptors and signal transduction machinery. Our findings suggest that chemosensory receptor genes are less constrained than their signal transducing machinery, resulting in greater receptor diversity and conservation of information processing pathways. More importantly, we have observed significant differences among the receptors themselves, suggesting that olfactory and bitter taste receptors are more conserved than vomeronasal receptors.
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.
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
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
Structural evolution of differential amino acid effector regulation in plant chorismate mutases.
Westfall, Corey S; Xu, Ang; Jez, Joseph M
2014-10-10
Chorismate mutase converts chorismate into prephenate for aromatic amino acid biosynthesis. To understand the molecular basis of allosteric regulation in the plant chorismate mutases, we analyzed the three Arabidopsis thaliana chorismate mutase isoforms (AtCM1-3) and determined the x-ray crystal structures of AtCM1 in complex with phenylalanine and tyrosine. Functional analyses show a wider range of effector control in the Arabidopsis chorismate mutases than previously reported. AtCM1 is activated by tryptophan with phenylalanine and tyrosine acting as negative effectors; however, tryptophan, cysteine, and histidine activate AtCM3. AtCM2 is a nonallosteric form. The crystal structure of AtCM1 in complex with tyrosine and phenylalanine identifies differences in the effector sites of the allosterically regulated yeast enzyme and the other two Arabidopsis isoforms. Site-directed mutagenesis of residues in the effector site reveals key features leading to differential effector regulation in these enzymes. In AtCM1, mutations of Gly-213 abolish allosteric regulation, as observed in AtCM2. A second effector site position, Gly-149 in AtCM1 and Asp-132 in AtCM3, controls amino acid effector specificity in AtCM1 and AtCM3. Comparisons of chorismate mutases from multiple plants suggest that subtle differences in the effector site are conserved in different lineages and may lead to specialized regulation of this branch point enzyme.
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
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.
Papaefthimiou, Dimitra; Hrouzek, Pavel; Mugnai, Maria Angela; Lukesova, Alena; Turicchia, Silvia; Rasmussen, Ulla; Ventura, Stefano
2008-03-01
Many cyanobacteria commonly identified as belonging to the genus Nostoc are well-known cyanobionts (symbionts) of a wide variety of plants and fungi. They form symbioses with bryophytes, pteridophytes, gymnosperms and angiosperms that are considerably different in the type of reciprocal interaction between the host and the cyanobiont. The phylogenetic and taxonomic relationships among cyanobionts isolated from different hosts and Nostoc strains isolated from free-living conditions are still not well understood. We compared phylogeny and morphology of symbiotic cyanobacteria originating from different host plants (genera Gunnera, Azolla, Cycas, Dioon, Encephalartos, Macrozamia and Anthoceros) with free-living Nostoc isolates originating from different habitats. After preliminary clustering with ARDRA (amplified rDNA restriction analysis), phylogeny was reconstructed on the basis of 16S rRNA gene sequences and compared with morphological characterization, obtaining several supported clusters. Two main Nostoc clusters harboured almost all cyanobionts of Gunnera, Anthoceros and of several cycads, together with free-living strains of the species Nostoc muscorum, Nostoc calcicola, Nostoc edaphicum, Nostoc ellipsosporum and strains related to Nostoc commune. We suggest that the frequent occurrence of symbiotic strains within these clusters is explained by the intensive hormogonia production that was observed in many of the strains studied. However, no evidence for discrimination between symbiotic and free-living strains, either by molecular or morphological approaches, could be found. Sequences of Azolla cyanobiont filaments, taken directly from leaf cavities, clustered tightly with sequences from the planktic cyanobacterium Cylindrospermopsis raciborskii, from the benthic Anabaena cylindrica 133 and from Anabaena oscillarioides HINDAK 1984/43, with high bootstrap values. The phylogenetic analysis showed that two distinct patterns of evolution of symbiotic behaviour might
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.
Yeh, Jung-Yong; Lee, Ji-Hye; Park, Jee-Yong; Seo, Hyun-Ji; Moon, Jin-San; Cho, In-Soo; Kim, Hee-Pah; Yang, Young-Jin; Ahn, Kei-Myung; Kyung, Soon-Goo; Choi, In-Soo; Lee, Joong-Bok
2012-05-01
The detection of West Nile virus (WNV) in areas endemic for Japanese encephalitis virus (JEV) is complicated by the extensive serological cross-reactivity between the two viruses. A testing algorithm was developed and employed for the detection of anti-WNV antibody in areas endemic for JEV. Using this differentiation algorithm, a serological survey of poultry (2004 through 2009) and horses (2007 through 2009) was performed. Among 2681 poultry sera, 125 samples were interpreted as being positive for antibodies against JEV, and 14 were suspected to be positive for antibodies against undetermined flaviviruses other than WNV and JEV. Of the 2601 horse sera tested, a total of 1914 (73.6%) were positive to the initial screening test. Of these positive sera, 132 sera (5.1%) had been collected from horses that had been imported from the United States, where WNV is endemic. These horses had WNV vaccination records, and no significant pattern of increasing titer was observed in paired sera tests. Of the remaining 1782 positive sera 1468 sera (56.4%) were also found to contain anti-JEV antibodies, and were interpreted to be JEV-specific antibodies by the differentiation algorithm developed in this study. The remaining 314 horses (12.1%) for which a fourfold difference in neutralizing antibody titer could not be demonstrated, were determined to contain an antibody against an unknown (unidentified or undetermined) flavivirus. No evidence of WNV infections were found during the period of this study.
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.
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
Charles, Mathieu; Belcram, Harry; Just, Jérémy; Huneau, Cécile; Viollet, Agnès; Couloux, Arnaud; Segurens, Béatrice; Carter, Meredith; Huteau, Virginie; Coriton, Olivier; Appels, Rudi; Samain, Sylvie; Chalhoub, Boulos
2008-01-01
Transposable elements (TEs) constitute >80% of the wheat genome but their dynamics and contribution to size variation and evolution of wheat genomes (Triticum and Aegilops species) remain unexplored. In this study, 10 genomic regions have been sequenced from wheat chromosome 3B and used to constitute, along with all publicly available genomic sequences of wheat, 1.98 Mb of sequence (from 13 BAC clones) of the wheat B genome and 3.63 Mb of sequence (from 19 BAC clones) of the wheat A genome. Analysis of TE sequence proportions (as percentages), ratios of complete to truncated copies, and estimation of insertion dates of class I retrotransposons showed that specific types of TEs have undergone waves of differential proliferation in the B and A genomes of wheat. While both genomes show similar rates and relatively ancient proliferation periods for the Athila retrotransposons, the Copia retrotransposons proliferated more recently in the A genome whereas Gypsy retrotransposon proliferation is more recent in the B genome. It was possible to estimate for the first time the proliferation periods of the abundant CACTA class II DNA transposons, relative to that of the three main retrotransposon superfamilies. Proliferation of these TEs started prior to and overlapped with that of the Athila retrotransposons in both genomes. However, they also proliferated during the same periods as Gypsy and Copia retrotransposons in the A genome, but not in the B genome. As estimated from their insertion dates and confirmed by PCR-based tracing analysis, the majority of differential proliferation of TEs in B and A genomes of wheat (87 and 83%, respectively), leading to rapid sequence divergence, occurred prior to the allotetraploidization event that brought them together in Triticum turgidum and Triticum aestivum, <0.5 million years ago. More importantly, the allotetraploidization event appears to have neither enhanced nor repressed retrotranspositions. We discuss the apparent proliferation
Charles, Mathieu; Belcram, Harry; Just, Jérémy; Huneau, Cécile; Viollet, Agnès; Couloux, Arnaud; Segurens, Béatrice; Carter, Meredith; Huteau, Virginie; Coriton, Olivier; Appels, Rudi; Samain, Sylvie; Chalhoub, Boulos
2008-10-01
Transposable elements (TEs) constitute >80% of the wheat genome but their dynamics and contribution to size variation and evolution of wheat genomes (Triticum and Aegilops species) remain unexplored. In this study, 10 genomic regions have been sequenced from wheat chromosome 3B and used to constitute, along with all publicly available genomic sequences of wheat, 1.98 Mb of sequence (from 13 BAC clones) of the wheat B genome and 3.63 Mb of sequence (from 19 BAC clones) of the wheat A genome. Analysis of TE sequence proportions (as percentages), ratios of complete to truncated copies, and estimation of insertion dates of class I retrotransposons showed that specific types of TEs have undergone waves of differential proliferation in the B and A genomes of wheat. While both genomes show similar rates and relatively ancient proliferation periods for the Athila retrotransposons, the Copia retrotransposons proliferated more recently in the A genome whereas Gypsy retrotransposon proliferation is more recent in the B genome. It was possible to estimate for the first time the proliferation periods of the abundant CACTA class II DNA transposons, relative to that of the three main retrotransposon superfamilies. Proliferation of these TEs started prior to and overlapped with that of the Athila retrotransposons in both genomes. However, they also proliferated during the same periods as Gypsy and Copia retrotransposons in the A genome, but not in the B genome. As estimated from their insertion dates and confirmed by PCR-based tracing analysis, the majority of differential proliferation of TEs in B and A genomes of wheat (87 and 83%, respectively), leading to rapid sequence divergence, occurred prior to the allotetraploidization event that brought them together in Triticum turgidum and Triticum aestivum, <0.5 million years ago. More importantly, the allotetraploidization event appears to have neither enhanced nor repressed retrotranspositions. We discuss the apparent proliferation
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
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
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
Quigley, Ian K; Turner, Jessica M; Nuckels, Richard J; Manuel, Joan L; Budi, Erine H; MacDonald, Erin L; Parichy, David M
2004-12-01
Latent precursors or stem cells of neural crest origin are present in a variety of post-embryonic tissues. Although these cells are of biomedical interest for roles in human health and disease, their potential evolutionary significance has been underappreciated. As a first step towards elucidating the contributions of such cells to the evolution of vertebrate form, we investigated the relative roles of neural crest cells and post-embryonic latent precursors during the evolutionary diversification of adult pigment patterns in Danio fishes. These pigment patterns result from the numbers and arrangements of embryonic melanophores that are derived from embryonic neural crest cells, as well as from post-embryonic metamorphic melanophores that are derived from latent precursors of presumptive neural crest origin. In the zebrafish D. rerio, a pattern of melanophore stripes arises during the larval-to-adult transformation by the recruitment of metamorphic melanophores from latent precursors. Using a comparative approach in the context of new phylogenetic data, we show that adult pigment patterns in five additional species also arise from metamorphic melanophores, identifying this as an ancestral mode of adult pigment pattern development. By contrast, superficially similar adult stripes of D. nigrofasciatus (a sister species to D. rerio) arise by the reorganization of melanophores that differentiated at embryonic stages, with a diminished contribution from metamorphic melanophores. Genetic mosaic and molecular marker analyses reveal evolutionary changes that are extrinsic to D. nigrofasciatus melanophore lineages, including a dramatic reduction of metamorphic melanophore precursors. Finally, interspecific complementation tests identify a candidate genetic pathway for contributing to the evolutionary reduction in metamorphic melanophores and the increased contribution of early larval melanophores to D. nigrofasciatus adult pigment pattern development. These results
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.
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.
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
NASA Astrophysics Data System (ADS)
Lee, Cin-Ty A.; Lee, Tien Chang; Wu, Chi-Tang
2014-10-01
Equations are presented to describe the compositional evolution of magma chambers undergoing simultaneous recharge (R), evacuation (E), and fractional crystallization (FC). Constant mass magma chambers undergoing REFC will eventually approach a steady state composition due to the “buffering” effect of recharging magma. Steady state composition is attained after ∼3/(Dαx + αe) overturns of the magma chamber, where D is the bulk solid/melt partition coefficient for the element of interest and αx and αe are the proportions of crystallization and eruption/evacuation relative to the recharge rate. Steady state composition is given by Cre/(Dαx + αe). For low evacuation rates, steady state concentration and the time to reach steady state scale inversely with D. Compatible (D > 1) elements reach steady state faster than incompatible (D < 1) elements. Thus, magma chambers undergoing REFC will eventually evolve towards high incompatible element enrichments for a given depletion in a compatible element compared to magma chambers undergoing pure fractional crystallization. For example, REFC magma chambers will evolve to high incompatible element concentrations for a given MgO content compared to fractional crystallization. Not accounting for REFC will lead to over-estimation of the incompatible element content of primary magmas. Furthermore, unlike fractional crystallization alone, REFC can efficiently fractionate highly incompatible element ratios because the fractionation effect scales with the ratio of bulk D's. By contrast, in pure fractional crystallization, ratios fractionate according to the arithmetic difference between the bulk D's. The compositional impact of REFC should be most pronounced for magma chambers that are long-lived, have low rates of eruption/evacuation, and/or are characterized by high recharge rates relative to the mass of the magma chamber. The first two conditions are likely favored in deep crustal magma chambers where confining pressures
A Breeder Algorithm for Stellarator Optimization
NASA Astrophysics Data System (ADS)
Wang, S.; Ware, A. S.; Hirshman, S. P.; Spong, D. A.
2003-10-01
An optimization algorithm that combines the global parameter space search properties of a genetic algorithm (GA) with the local parameter search properties of a Levenberg-Marquardt (LM) algorithm is described. Optimization algorithms used in the design of stellarator configurations are often classified as either global (such as GA and differential evolution algorithm) or local (such as LM). While nonlinear least-squares methods such as LM are effective at minimizing a cost-function based on desirable plasma properties such as quasi-symmetry and ballooning stability, whether or not this is a local or global minimum is unknown. The advantage of evolutionary algorithms such as GA is that they search a wider range of parameter space and are not susceptible to getting stuck in a local minimum of the cost function. Their disadvantage is that in some cases the evolutionary algorithms are ineffective at finding a minimum state. Here, we describe the initial development of the Breeder Algorithm (BA). BA consists of a genetic algorithm outer loop with an inner loop in which each generation is refined using a LM step. Initial results for a quasi-poloidal stellarator optimization will be presented, along with a comparison to existing optimization algorithms.
Pei, Yan
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.
NASA Astrophysics Data System (ADS)
Drăgoi, Elena-Niculina; Curteanu, Silvia; Lisa, Cătălin
2012-10-01
A simple self-adaptive version of the differential evolution algorithm was applied for simultaneous architectural and parametric optimization of feed-forward neural networks, used to classify the crystalline liquid property of a series of organic compounds. The developed optimization methodology was called self-adaptive differential evolution neural network (SADE-NN) and has the following characteristics: the base vector used is chosen as the best individual in the current population, two differential terms participate in the mutation process, the crossover type is binomial, a simple self-adaptive mechanism is employed to determine the near-optimal control parameters of the algorithm, and the integration of the neural network into the differential evolution algorithm is performed using a direct encoding scheme. It was found that a network with one hidden layer is able to make accurate predictions, indicating that the proposed methodology is efficient and, owing to its flexibility, it can be applied to a large range of problems.
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)
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)
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. 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
Alicea, Bradly; Gordon, Richard
2016-08-18
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.
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.
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
Shiang, Keh-Dong
2009-05-01
Conversion of complex phenomena in medicine, pharmaceutical and systems biology fields to a system of ordinary differential equations (ODEs) and identification of parameters from experimental data and theoretical model equations can be treated as a computational engine to arrive at the best solution for chemical reactions, biochemical metabolic and intracellular pathways. Particularly, to gain insight into the pathophysiology of diabetes's metabolism in our current clinical studies, glucose kinetics and insulin secretion can be assessed by the ODE model. Parameter estimation is usually performed by minimizing a cost function which quantifies the difference between theoretical model predictions and experimental measurements. This paper explores how the numerical method and iteration program are developed to search ODE's parameters using the perturbation method, instead of the Gauss-Newton or Levenberg-Marquardt method. Several interesting applications, including Lotka-Volterra chemical reaction system, Lorenz chaos, dynamics of tetracycline hydrochloride concentration, and Bergman's Minimal Model for glucose kinetics are illustrated.
A Novel Hybrid Firefly Algorithm for Global Optimization
Zhang, Lina; Liu, Liqiang; Yang, Xin-She; Dai, Yuntao
2016-01-01
Global optimization is challenging to solve due to its nonlinearity and multimodality. Traditional algorithms such as the gradient-based methods often struggle to deal with such problems and one of the current trends is to use metaheuristic algorithms. In this paper, a novel hybrid population-based global optimization algorithm, called hybrid firefly algorithm (HFA), is proposed by combining the advantages of both the firefly algorithm (FA) and differential evolution (DE). FA and DE are executed in parallel to promote information sharing among the population and thus enhance searching efficiency. In order to evaluate the performance and efficiency of the proposed algorithm, a diverse set of selected benchmark functions are employed and these functions fall into two groups: unimodal and multimodal. The experimental results show better performance of the proposed algorithm compared to the original version of the firefly algorithm (FA), differential evolution (DE) and particle swarm optimization (PSO) in the sense of avoiding local minima and increasing the convergence rate. PMID:27685869
NASA Astrophysics Data System (ADS)
Novelli, Antonio
2016-08-01
Leaf Area Index (LAI) is essential in ecosystem and agronomic studies, since it measures energy and gas exchanges between vegetation and atmosphere. In the last decades, LAI values have widely been estimated from passive remotely sensed data. Common approaches are based on semi-empirical/statistic techniques or on radiative transfer model inversion. Although the scientific community has been providing several LAI retrieval methods, the estimated results are often affected by noise and measurement uncertainties. The sequential data assimilation theory provides a theoretical framework to combine an imperfect model with incomplete observation data. In this document a data fusion Kalman filter algorithm is proposed in order to estimate the time evolution of LAI by combining MODIS LAI data and PROBA-V surface reflectance data. The reflectance data were linked to LAI by using the Reduced Simple Ratio index. The main working hypotheses were lacking input data necessary for climatic models and canopy reflectance models.
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.
NASA Astrophysics Data System (ADS)
Povoleri, A.; Lavagna, M.; Finzi, A. E.
The paper presents a new approach to deal with the preliminary space mission analysis design of particularly complex trajectories focused on interplanetary targets. According to an optimisation approach, a multi-objective strategy is selected on a mixed continuous and discrete state variables domain in order to deal with possible multi-gravity assist manoeuvres (GAM) as further degrees of freedom of the problem, in terms of both number and planets sequence selection to minimize both the ?v expense and the time trip time span. A further added value to the proposed algorithm stays in that, according to planets having an atmosphere, aero-gravity assist manoeuvres (AGAM) are considered too within the overall mission design optimisation, and the consequent optimal control problem related to the aerodynamic angles history, is solved. According to the target planet different capture strategies are managed by the algorithm, the aerocapture manoeuvre too, whenever possible (e.g. Venus, Mars target planets). In order not to be trapped in local solution the Evolutionary Algorithms (EAs) have been selected to solve such a complex problem. Simulations and comparison with already designed space missions showed the ability of the proposed architecture in correctly selecting both the sequences and the planets type of either GAMs or AGAMs to optimise the selected criteria vector, in a multidisciplinary environment, switching on the optimal control problem whenever the atmospheric interaction is involved in the optimisation by the search process. Symbols δ = semi-angular deviation for GAM between the v∞ -, v∞ + inoutcoming vectors [rad] φ = Angular deviation for AGAM between the v∞ -, v∞ + inoutcoming vectors [rad] ρ = Atmospheric density [kgm-3 ] γ = Flight path angle [rad] µ = Bank angle [rad] δ?ttransf j = j-th heliocentric transfer time variation with respect to the linked conics solution ?|v∞| = Relative velocity losses because of drag [ms-1 ] ωI = i
Liu, Boning; Reckhow, David A; Li, Yun
2014-04-15
A general framework for modeling the bulk chlorine decay that accommodates effects of pH, temperature in water distribution system and in-home heating profiles is developed. With a single set of readily interpreted parameters, and various fictive concentrations of reactive constituents in the water, chlorine decay for the different water systems could be simultaneously modeled. Differential Evolution is employed to estimate the parameters stochastically. By using Bayesian Information Criterion, it is shown that a model consisting of two reactive species is preferred over models that consist of one or three reactive species. The flexibility and power of the framework is demonstrated with a case study of both types of effects.
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)
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.
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
Iwata, Takayuki; Otsuka, Satoshi; Tsubokura, Kazuki; Kurbangalieva, Almira; Arai, Daisuke; Fukase, Koichi; Nakao, Yoichi; Tanaka, Katsunori
2016-10-01
A bio-inspired cascade reaction has been developed for the construction of the marine natural product ageladine A and a de novo array of its N1-substituted derivatives. This cascade features a 2-aminoimidazole formation that is modeled after an arginine post-translational modification and an aza-electrocyclization. It can be effectively carried out in a one-pot procedure from simple anilines or guanidines, leading to structural analogues of ageladine A that had been otherwise synthetically inaccessible. We found that some compounds out of this structurally novel library show a significant activity in modulating the neural differentiation. Namely, these compounds selectively activate or inhibit the differentiation of neural stem cells to neurons, while being negligible in the differentiation to astrocytes. This study represents a successful case in which the native biofunction of a natural product could be altered by structural modifications.
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.
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
Fatigue Life Prediction of Ductile Iron Based on DE-SVM Algorithm
NASA Astrophysics Data System (ADS)
Yiqun, Ma; Xiaoping, Wang; lun, An
the model, predicting fatigue life of ductile iron, based on SVM (Support Vector Machine, SVM) has been established. For it is easy to fall into local optimum during parameter optimization of SVM, DE (Differential Evolution algorithm, DE) algorithm was adopted to optimize to improve prediction precision. Fatigue life of ductile iron is predicted combining with concrete examples, and simulation experiment to optimize SVM is conducted adopting GA (Genetic Algorithm), ACO (Ant Colony Optimization) and POS (Partial Swarm Optimization). Results reveal that DE-SVM algorithm is of a better prediction performance.
NASA Astrophysics Data System (ADS)
Yan, Zuomao; Lu, Fangxia
2016-08-01
In this paper, we introduce the optimal control problems governed by a new class of impulsive stochastic partial neutral evolution equations with infinite delay in Hilbert spaces. First, by using stochastic analysis, the analytic semigroup theory, fractional powers of closed operators, and suitable fixed point theorems, we prove an existence result of mild solutions for the control systems in the α-norm without the assumptions of compactness. Next, we derive the existence conditions of optimal pairs of these systems. Finally, application to a nonlinear impulsive stochastic parabolic optimal control system is considered.
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
NASA Astrophysics Data System (ADS)
Kuritani, Takeshi; Yokoyama, Tetsuya; Kitagawa, Hiroshi; Kobayashi, Katsura; Nakamura, Eizo
2011-01-01
The mechanisms and the timescales of magmatic evolution were investigated for historical lavas from the Askja central volcano in the Dyngjufjöll volcanic massif, Iceland, using major and trace element and Sr, Nd, and Pb isotopic data, as well as 238U- 230Th- 226Ra systematics. Lavas from the volcano show marked compositional variation from magnesian basalt through ferrobasalt to rhyolite. In the magnesian basalt-ferrobasalt suite (5-10 wt% MgO), consisting of lavas older than 1875 A.D., 87Sr/ 86Sr increases systematically with increasing SiO 2 content; this suite is suggested to have evolved in a magma chamber located at ˜600 MPa through assimilation and fractional crystallization. On the other hand, in the ferrobasalt-rhyolite suite (1-5 wt% MgO), including 1875 A.D. basalt and rhyolite and 20th century lavas, 87Sr/ 86Sr tends to decrease slightly with increasing SiO 2 content. It is suggested that a relatively large magma chamber occupied by ferrobasalt magma was present at ˜100 MPa beneath the Öskjuvatn caldera, and that icelandite and rhyolite magmas were produced by extraction of the less and more evolved interstitial melt, respectively, from the mushy boundary layer along the margin of the ferrobasalt magma chamber, followed by accumulation of the melt to form separate magma bodies. Ferrobasalt and icelandite lavas in the ferrobasalt-rhyolite suite have a significant radioactive disequilibrium in terms of ( 226Ra/ 230Th), and its systematic decrease with magmatic evolution is considered to reflect aging, along with assimilation and fractional crystallization processes. Using a mass-balance model in which simultaneous fractional crystallization, crustal assimilation, and radioactive decay are taken into account, the timescale for the generation of icelandite magma from ferrobasalt was constrained to be <˜3 kyr which is largely dependent on Ra crystal-melt partition coefficients we used.
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.
Dorken, Marcel E; Mitchard, Edward T A
2008-04-01
Separate sexes can evolve under nuclear inheritance when unisexuals have more than twice the reproductive fitness of hermaphrodites through one sex function (e.g., when females have more than twice the seed fertility of hermaphrodites). Because separate sexes are thought to evolve most commonly via a gynodioecious intermediate (i.e., populations in which females and hermaphrodites cooccur), the conditions under which females can become established in populations of hermaphrodites are of considerable interest. It has been proposed that resource-poor conditions could promote the establishment of females if hermaphrodites are plastic in their sex allocation and allocate fewer resources to seed production under these conditions. If this occurs, the seed fertility of females could exceed the doubling required for the evolution of unisexuality under low-, but not high-resource conditions (the sex-differential plasticity hypothesis). We tested this hypothesis using replicate experimental arrays of the aquatic herb Sagittaria latifolia grown under two fertilizer treatments. The results supported the sex-differential plasticity hypothesis, with females having more than twice the seed fertility of hermaphrodites under low-, but not high-fertilizer conditions. Our findings are consistent with the idea that separate sexes are more likely to evolve under unfavorable conditions.
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
2012-01-01
Brüne's proposal that erstwhile 'vulnerability' genes need to be reconsidered as 'plasticity' genes, given the potential for certain environments to yield increased positive function in the same domain as potential dysfunction, has implications for psychiatric nosology as well as a more dynamic understanding of the relationship between genes and culture. In addition to validating neuropsychiatric spectrum disorder nosologies by calling for similar methodological shifts in gene-environment-interaction studies, Brüne's position elevates the importance of environmental contexts - inclusive of socio-cultural variables - as mechanisms that contribute to clinical presentation. We assert that when models of susceptibility to plasticity and neuropsychiatric spectrum disorders are concomitantly considered, a new line of inquiry emerges into the co-evolution and co-determination of socio-cultural contexts and endophenotypes. This presents potentially unique opportunities, benefits, challenges, and responsibilities for research and practice in psychiatry. Please see related manuscript: http://www.biomedcentral.com/1741-7015/10/38 PMID:22510307
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
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.
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.
Applebaum, Scott L; Finn, Roderick Nigel; Faulk, Cynthia K; Joan Holt, G; Scott Nunez, B
2012-03-01
Interactions between the thyroid hormone (TH) and corticosteroid (CS) hormone axes are suggested to regulate developmental processes in vertebrates with a larval phase. To investigate this hypothesis, we isolated three nuclear receptors from a larval acanthomorph teleost, the red drum (Sciaenops ocellatus), and established their orthologies as thraa, thrb-L and gra-L using phylogenomic and functional analyses. Functional characterization of the TH receptors in COS-1 cells revealed that Thraa and Thrb-L exhibit dose-dependent transactivation of a luciferase reporter in response to T3, while SoThraa is constitutively active at a low level in the absence of ligand. To test whether interactions between the TH and CS systems occur during development, we initially quantified the in vivo receptor transcript expression levels, and then examined their response to treatment with triiodothyronine (T3) or cortisol. We find that sothraa and sothrb-L are autoregulated in response to exogenous T3 only during early larval development. T3 did not affect sogra-L expression levels, nor did cortisol alter levels of sothraa or sothrb-L at any stage. While differential expression of the receptors in response to non-canonical ligand hormone was not observed under the conditions in this study, the correlation between sothraa and sogra-L transcript abundance during development suggests a coordinated function of the TH and CS systems. By comparing the findings in the present study to earlier investigations, we suggest that the up-regulation of thraa may be a specific feature of metamorphosis in acanthomorph teleosts. PMID:22226731
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
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.
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.
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.
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.
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.
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.
A smart repair embedded memetic algorithm for 2D shape matching problems
NASA Astrophysics Data System (ADS)
Sharif Khan, Mohammad; Mohamad Ayob, Ahmad F.; Isaacs, Amitay; Ray, Tapabrata
2012-10-01
Shape representation plays a major role in any shape optimization exercise. The ability to identify a shape with good performance is dependent on both the flexibility of the shape representation scheme and the efficiency of the optimization algorithm. In this article, a memetic algorithm is presented for 2D shape matching problems. The shape is represented using B-splines, in which the control points representing the shape are repaired and subsequently evolved within the optimization framework. The underlying memetic algorithm is a multi-feature hybrid that combines the strength of a real coded genetic algorithm, differential evolution and a local search. The efficiency of the proposed algorithm is illustrated using three test problems, wherein the shapes were identified using a mere 5000 function evaluations. Extension of the approach to deal with problems of unknown shape complexity is also presented in the article.
Comparison of evolutionary algorithms for LPDA antenna optimization
NASA Astrophysics Data System (ADS)
Lazaridis, Pavlos I.; Tziris, Emmanouil N.; Zaharis, Zaharias D.; Xenos, Thomas D.; Cosmas, John P.; Gallion, Philippe B.; Holmes, Violeta; Glover, Ian A.
2016-08-01
A novel approach to broadband log-periodic antenna design is presented, where some of the most powerful evolutionary algorithms are applied and compared for the optimal design of wire log-periodic dipole arrays (LPDA) using Numerical Electromagnetics Code. The target is to achieve an optimal antenna design with respect to maximum gain, gain flatness, front-to-rear ratio (F/R) and standing wave ratio. The parameters of the LPDA optimized are the dipole lengths, the spacing between the dipoles, and the dipole wire diameters. The evolutionary algorithms compared are the Differential Evolution (DE), Particle Swarm (PSO), Taguchi, Invasive Weed (IWO), and Adaptive Invasive Weed Optimization (ADIWO). Superior performance is achieved by the IWO (best results) and PSO (fast convergence) algorithms.
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.
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.
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.
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.
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.
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
Interpolation algorithms for machine tools
Burleson, R.R.
1981-08-01
There are three types of interpolation algorithms presently used in most numerical control systems: digital differential analyzer, pulse-rate multiplier, and binary-rate multiplier. A method for higher order interpolation is in the experimental stages. The trends point toward the use of high-speed micrprocessors to perform these interpolation algorithms.
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.
Vergés, Alvaro; Steinley, Douglas; Trull, Timothy J.; Sher, Kenneth J.
2010-01-01
The validity of the abuse/dependence distinction within alcohol use disorders (AUDs) has been increasingly questioned on psychometric and conceptual grounds. Two types of findings are often cited as support for the validity of this distinction: (1) dependence is more persistent than abuse, and (2) dependence is more highly comorbid with other Axis I and Axis II disorders than is abuse. Using data from the National Epidemiologic Survey of Alcohol and Related Conditions (NESARC), we examined the extent to which the current diagnostic algorithm (three of seven dependence criteria for a diagnosis of dependence; one of four abuse criteria for a diagnosis of abuse if dependence criteria are not met) produces this pattern of findings independent of item set. Analyses where all 330 permutations of the 11 AUD criteria were partitioned into a four-item “abuse” set and a seven-item “dependence” set were conducted to examine the relevance of the criteria sets to estimates of persistence and chronicity independent of criteria. Regardless of the criteria employed, the “dependence set” (i.e., 3/7 criteria) always and substantially outperformed the “abuse set” (1/4) with respect to both persistence and comorbidity. These data indicate that chronicity and comorbidity are flawed indicators for the abuse/dependence distinction (and likely other conditions where hierarchical decision rules are employed). In addition, our analyses show that the current set of criteria defining alcohol dependence and abuse are not optimal. PMID:20853915
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
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.
ERIC Educational Resources Information Center
Csikszentmihalyi, Mihaly
1998-01-01
Suggests the time has come for humans to direct their own individual evolution and the evolution of the entire species. Argues that ways must be found to encourage individuals, families, and cultures to discover and develop their differentiating characteristics and help these groups integrate with other cultures, customs, and belief systems.…
NASA Astrophysics Data System (ADS)
Smith, Matthew R.; Kuo, Fang-An; Hsieh, Chih-Wei; Yu, Jen-Perng; Wu, Jong-Shinn; Ferguson, Alex
2010-06-01
Presented is a rapid calculation tool for the optimization of blast wave related mitigation strategies. The motion of gas resulting from a blast wave (specified by the user) is solved by the Quiet Direct Simulation (QDS) method - a rapid kinetic theory-based finite volume method. The optimization routine employed is a newly developed Genetic Algorithm (GA) which is demonstrated to be similar to a Differential Evolution (DE) scheme with several modifications. In any Genetic Algorithm, individuals contain genetic information which is passed on to newly created individuals in successive generations. The results from unsteady QDS simulations are used to determine the individual's "genetic fitness" which is employed by the proposed Genetic Algorithm during the reproduction process. The combined QDS/GA algorithm is applied to various test cases and finally the optimization of a non-trivial blast wave mitigation strategy. Both QDS and the proposed GA are demonstrated to perform with minimal computational expense while accurately solving the optimization problems presented.
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.
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.
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.
Depolarizing differential Mueller matrices.
Ortega-Quijano, Noé; Arce-Diego, José Luis
2011-07-01
The evolution of a polarized beam can be described by the differential formulation of Mueller calculus. The nondepolarizing differential Mueller matrices are well known. However, they only account for 7 out of the 16 independent parameters that are necessary to model a general anisotropic depolarizing medium. In this work we present the nine differential Mueller matrices for general depolarizing media, highlighting the physical implications of each of them. Group theory is applied to establish the relationship between the differential matrix and the set of transformation generators in the Minkowski space, of which Lorentz generators constitute a particular subgroup. PMID:21725434
NASA Astrophysics Data System (ADS)
Chaudhuri, Sutapa; Khan, Fatema; Pal, Jayanti; Goswami, Sayantika; Middey, Anirban
2015-02-01
Thunderstorms are well-known severe weather phenomena of the Gangetic West Bengal (GWB) region of India. The objective of the present study is to identify the ranges of Max_ Z parameters of Doppler Weather Radar (DWR) associated with precipitating clouds that eventually grow into thunderstorms and to obtain a model to assess the predictability of thunderstorm and non-thunderstorm events with maximum possible accuracy during the pre-monsoon season (April-May) over the metropolis Kolkata (22.6°N; 88.4°E) enclosed within GWB (20-26°N, 85-91°E), India. The DWR imageries are analyzed to identify the stages of thunderstorm development. The survival of the fittest principle of genetic algorithm (GA) is implemented to find a suitable combination of the DWR Max_ Z parameters; the reflectivity, distance of the first detected echo from Kolkata where the DWR is installed and the echo top height for the genesis of thunderstorms. The problem is posed as an optimization problem and the values of the parameters are converted into binary strings. The result reveals that the echoes with reflectivity between 44 and 48 dBZ at a distance of 250-300 km from Kolkata with echo top height between 13 and 15 km have the maximum possibility to grow into a thunderstorm. The artificial neural network (ANN) model is developed with the values of the Max_ Z parameters optimized by GA as the inputs. The target of the ANN model is to forecast the type of the echo cells leading either to thunderstorm or non-thunderstorm events. The result further reveals that the ANN model with three hidden layers and one node in each layer is the most suitable model for estimating the likelihood of thunderstorm/non-thunderstorm events with mean absolute error (MAE) of 0.71/2.81. The result of the study is validated with the observation of India Meteorological Department.
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.
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.
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
NASA Astrophysics Data System (ADS)
Lu, Yanfei; Lekszycki, Tomasz
2016-10-01
During fracture healing, a series of complex coupled biological and mechanical phenomena occurs. They include: (i) growth and remodelling of bone, whose Young's modulus varies in space and time; (ii) nutrients' diffusion and consumption by living cells. In this paper, we newly propose to model these evolution phenomena. The considered features include: (i) a new constitutive equation for growth simulation involving the number of sensor cells; (ii) an improved equation for nutrient concentration accounting for the switch between Michaelis-Menten kinetics and linear consumption regime; (iii) a new constitutive equation for Young's modulus evolution accounting for its dependence on nutrient concentration and variable number of active cells. The effectiveness of the model and its predictive capability are qualitatively verified by numerical simulations (using COMSOL) describing the healing of bone in the presence of damaged tissue between fractured parts.
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.
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
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.
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
Offline Parameter Estimation of Induction Motor Using a Meta Heuristic Algorithm
NASA Astrophysics Data System (ADS)
Giri, Ritwik; Chowdhury, Aritra; Ghosh, Arnob; Panigrahi, B. K.; Das, Swagatam
An offline parameter estimation problem of an induction motor using a well known, efficient yet simple meta heuristic algorithm DEGL (Differential Evolution with a neighborhood based mutation scheme) has been presented in this article. Two different induction motor models such as approximate and exact models are considered. The parameter estimation methodology describes a method for estimating the steady-state equivalent circuit parameters from the motor performance characteristics, which is normally available from the manufacturer data or from tests. Differential Evolution is not completely free from the problems of slow or premature convergence, that's why the idea of a much more efficient variant of DE comes. The variant of DE used for solving this problem utilize the concept of the neighborhood of each population member. The feasibility of the proposed method is demonstrated for two different motors and it is compared with the genetic algorithm and the Particle Swarm Optimization algorithm. From the simulation results it is evident that DEGL outperforms both the algorithms (GA and PSO) in the estimation of the parameters of the induction motor.
Optical rate sensor algorithms
NASA Astrophysics Data System (ADS)
Uhde-Lacovara, Jo A.
1989-12-01
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.
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.
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.
[Metatarsalgia. Differential diagnosis and therapeutic algorithm].
Fuhrmann, R A; Roth, A; Venbrocks, R A
2005-08-01
Metatarsalgia is explained as localized or more diffuse tenderness beneath the metatarsal heads. The pain may be attributed to various etiologies. Pathological changes affecting the positional relationship of the metatarsals in the sagittal plane can cause increased pressure and friction forces during weight bearing. Since the length of the metatarsals displays a wide range of disparity only a few pathological settings, i.e., brachymetatarsia, require surgical correction. Beside those disorders of positional relationship, metatarsalgia may be due to lesser toe deformities, osteonecrosis of a lesser metatarsal head (Koehler's disease), and neurological disorders (Morton's neuroma). Apart from the etiology increased load, which is transferred to the central metatarsals, can be treated successfully with orthotic devices. If conservative measures fail, surgical treatment can be indicated. Prior to any operative therapy it is mandatory to perform a detailed analysis of the underlying pathology to avoid persistent pain or recurrence of the deformity. PMID:15995873
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
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
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.
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
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.
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.
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.
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
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.
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
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.
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.
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.
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.
Thermal evolution of the earth
NASA Technical Reports Server (NTRS)
Spohn, T.
1984-01-01
The earth's heat budget and models of the earth's thermal evolution are discussed. Sources of the planetary heat are considered and modes of heat transport are addressed, including conduction, convection, and chemical convection. Thermal and convectional models of the earth are covered, and models of thermal evolution are discussed in detail, including changes in the core, the influence of layered mantle convection on the thermal evolution, and the effect of chemical differentiation on the continents.
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
Noise-enhanced clustering and competitive learning algorithms.
Osoba, Osonde; Kosko, Bart
2013-01-01
Noise can provably speed up convergence in many centroid-based clustering algorithms. This includes the popular k-means clustering algorithm. The clustering noise benefit follows from the general noise benefit for the expectation-maximization algorithm because many clustering algorithms are special cases of the expectation-maximization algorithm. Simulations show that noise also speeds up convergence in stochastic unsupervised competitive learning, supervised competitive learning, and differential competitive learning.
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
NASA Astrophysics Data System (ADS)
Parwani, Ajit K.; Talukdar, Prabal; Subbarao, P. M. V.
2013-09-01
An inverse heat transfer problem is discussed to estimate simultaneously the unknown position and timewise varying strength of a heat source by utilizing differential evolution approach. A two dimensional enclosure with isothermal and black boundaries containing non-scattering, absorbing and emitting gray medium is considered. Both radiation and conduction heat transfer are included. No prior information is used for the functional form of timewise varying strength of heat source. The finite volume method is used to solve the radiative transfer equation and the energy equation. In this work, instead of measured data, some temperature data required in the solution of the inverse problem are taken from the solution of the direct problem. The effect of measurement errors on the accuracy of estimation is examined by introducing errors in the temperature data of the direct problem. The prediction of source strength and its position by the differential evolution (DE) algorithm is found to be quite reasonable.
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
Duality quantum algorithm efficiently simulates open quantum systems.
Wei, Shi-Jie; Ruan, Dong; Long, Gui-Lu
2016-07-28
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.
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
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.
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.
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.
Seeker optimization algorithm for parameter estimation of time-delay chaotic systems
NASA Astrophysics Data System (ADS)
Dai, Chaohua; Chen, Weirong; Li, Lixiang; Zhu, Yunfang; Yang, Yixian
2011-03-01
Time-delay chaotic systems have some very interesting properties, and their parameter estimation has received increasing interest in the recent years. It is well known that parameter estimation of a chaotic system is a nonlinear, multivariable, and multimodal optimization problem for which global optimization techniques are required in order to avoid local minima. In this work, a seeker-optimization-algorithm (SOA)-based method is proposed to address this issue. In the SOA, search direction is based on the empirical gradients by evaluating the response to the position changes, and step length is based on uncertainty reasoning by using a simple fuzzy rule. The performance of the algorithm is evaluated on two typical test systems. Moreover, two state-of-the-art algorithms (i.e., particle swarm optimization and differential evolution) are also considered for comparison. The simulation results show that the proposed algorithm is better than or at least as good as the other two algorithms and can effectively solve the parameter estimation problem of time-delay chaotic systems.
Fourth Order Algorithms for Solving Diverse Many-Body Problems
NASA Astrophysics Data System (ADS)
Chin, Siu A.; Forbert, Harald A.; Chen, Chia-Rong; Kidwell, Donald W.; Ciftja, Orion
2001-03-01
We show that the method of factorizing an evolution operator of the form e^ɛ(A+B) to fourth order with purely positive coefficient yields new classes of symplectic algorithms for solving classical dynamical problems, unitary algorithms for solving the time-dependent Schrödinger equation, norm preserving algorithms for solving the Langevin equation and large time step convergent Diffusion Monte Carlo algorithms. Results for each class of problems will be presented and disucss
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.
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.
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.
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
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
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.
ERIC Educational Resources Information Center
Geisinger, Robert W.; And Others
This report describes school operation changes in scheduling, curriculum, decisionmaking powers, and individualization of instruction that are concurrent with the adoption of differentiated staffing. The author defines differentiated staffing, explains where and at what levels it has been utilized, provides descriptions of results achieved, gives…
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.
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.
Panniculitides, an algorithmic approach.
Zelger, B
2013-08-01
The issue of inflammatory diseases of subcutis and its mimicries is generally considered a difficult field of dermatopathology. Yet, in my experience, with appropriate biopsies and good clinicopathological correlation, a specific diagnosis of panniculitides can usually be made. Thereby, knowledge about some basic anatomic and pathological issues is essential. Anatomy differentiates within the panniculus between the fatty lobules separated by fibrous septa. Pathologically, inflammation of panniculus is defined and recognized by an inflammatory process which leads to tissue damage and necrosis. Several types of fat necrosis are observed: xanthomatized macrophages in lipophagic necrosis; granular fat necrosis and fat micropseudocysts in liquefactive fat necrosis; mummified adipocytes in "hyalinizing" fat necrosis with/without saponification and/or calcification; and lipomembranous membranes in membranous fat necrosis. In an algorithmic approach the recognition of an inflammatory process recognized by features as elaborated above is best followed in three steps: recognition of pattern, second of subpattern, and finally of presence and composition of inflammatory cells. Pattern differentiates a mostly septal or mostly lobular distribution at scanning magnification. In the subpattern category one looks for the presence or absence of vasculitis, and, if this is the case, the size and the nature of the involved blood vessel: arterioles and small arteries or veins; capillaries or postcapillary venules. The third step will be to identify the nature of the cells present in the inflammatory infiltrate and, finally, to look for additional histopathologic features that allow for a specific final diagnosis in the language of clinical dermatology of disease involving the subcutaneous fat.
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!
Boolean differentiation and integration using Karnaugh maps
NASA Technical Reports Server (NTRS)
Tucker, J. H.; Tapia, M. A.; Bennett, A. W.
1977-01-01
Algorithms are presented for differentiation and integration of Boolean functions by means of Karnaugh maps. The algorithms are considered simple when the number of variables is six or less; in this case Boolean differentiation and integration is said to be as easy as the Karnaugh map method of simplifying switching functions. It is suggested that the algorithms would be useful in the analysis of faults in combinational systems and in the synthesis of asynchronous sequential systems which utilize edge-sensitive flip-flops.
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.
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.
Howe, C J; Barbrook, A C; Spencer, M; Robinson, P; Bordalejo, B; Mooney, L R
2001-03-01
Frequently, letters, words and sentences are used in undergraduate textbooks and the popular press as an analogy for the coding, transfer and corruption of information in DNA. We discuss here how the converse can be exploited, by using programs designed for biological analysis of sequence evolution to uncover the relationships between different manuscript versions of a text. We point out similarities between the evolution of DNA and the evolution of texts.
Howe, C J; Barbrook, A C; Spencer, M; Robinson, P; Bordalejo, B; Mooney, L R
2001-09-01
Frequently, letters, words and sentences are used in undergraduate textbooks and the popular press as an analogy for the coding, transfer and corruption of information in DNA. We discuss here how the converse can be exploited, by using programs designed for biological analysis of sequence evolution to uncover the relationships between different manuscript versions of a text. We point out similarities between the evolution of DNA and the evolution of texts.
Stochastic Evolution of Halo Spin
NASA Astrophysics Data System (ADS)
Kim, Juhan
2015-08-01
We will introduce an excursion set model for the evolution of halo spin from cosmological N-body simulations. A stochastic differential equation is derived from the definition of halo spin and the distribution of angular momentum changes are measured from simulations. The log-normal distribution of halo spin is found to be a natural consequence of the stochastic differential equation and the resulting spin distribution is found be a function of local environments, halo mass, and redshift.
Optimal robust motion controller design using multiobjective genetic algorithm.
Sarjaš, Andrej; Svečko, Rajko; Chowdhury, Amor
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
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.
Sinisi, A A; Pasquali, D; Notaro, A; Bellastella, A
2003-01-01
In humans, like as in other mammals, the gonads, the internal genital ducts, and the external genital structures all develop from bipotential embryologic tissues. Male or female phenotype develops through a cascade of processes which initiate with sex determination and follow with sex differentiation. The karyotype (46, XY or 46, XX) of the embryo (genetic sex) determines whether primordial gonad differentiates into a testis or an ovary, respectively (gonadal differentiation). A Y-related gene, SRY, acts as a switch signal for testis differentiation. Testis development process involves several steps controlled by other non-OY-linked genes, such as Wilms tumor gene 1 (WT1), EMX2, LIM1, steroidogenic factor 1(SF-1), SRY box-related gene 9 (SOX9). Since other genes, such as Wnt-4 and DAX-1, are necessary for the initiation of female pathway in sex determination, female development cannot be considered a default process. Hormonal production of differentiated gonads is relevant for differentiation of the internal and external genitalia during fetal life, and for the development of secondary sex characteristics at puberty. Antimullerian hormone (AMH) secreted by Sertoli cells inhibits the development of female internal genitalia (tube, uterus, upper part of vagina); testosterone secreted by Leydig cells induces stabilization of wolffian ducts and development of internal male genitalia. Differentiation of external male genitalia requires the transformation of testosterone to dihydrotestosterone by 5alpha reductase type 2 expressed in genital skin and urogenital sinus. The effects of androgens occur in presence of functional androgen receptor (AR) protein. Mutations of genes coding for steroidogenic enzymes, AMH, AMH receptor, AR and 5alpha reductase are all associated with impairment of sex differentiation and result in genital ambiguity. PMID:12834017
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.
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
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.
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
Approximate controllability of nonlinear impulsive differential systems
NASA Astrophysics Data System (ADS)
Sakthivel, R.; Mahmudov, N. I.; Kim, J. H.
2007-08-01
Many practical systems in physical and biological sciences have impulsive dynamical be- haviours during the evolution process which can be modeled by impulsive differential equations. This paper studies the approximate controllability issue for nonlinear impulsive differential and neutral functional differential equations in Hilbert spaces. Based on the semigroup theory and fixed point approach, sufficient conditions for approximate controllability of impulsive differential and neutral functional differential equations are established. Finally, two examples are presented to illustrate the utility of the proposed result. The results improve some recent results.
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.
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.
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. PMID:27616876
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…
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.
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
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.
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.
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.
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
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.
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.
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.
ERIC Educational Resources Information Center
Stebbins, Robert C.; Allen, Brockenbrough
1975-01-01
Described are simulations that can be used to illustrate evolution by natural selection. Suggestions for simulating phenomena such as adaptive radiation, color match to background and vision of predators are offered. (BR)
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
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.
Integral and integrable algorithms for a nonlinear shallow-water wave equation
NASA Astrophysics Data System (ADS)
Camassa, Roberto; Huang, Jingfang; Lee, Long
2006-08-01
An asymptotic higher-order model of wave dynamics in shallow water is examined in a combined analytical and numerical study, with the aim of establishing robust and efficient numerical solution methods. Based on the Hamiltonian structure of the nonlinear equation, an algorithm corresponding to a completely integrable particle lattice is implemented first. Each "particle" in the particle method travels along a characteristic curve. The resulting system of nonlinear ordinary differential equations can have solutions that blow-up in finite time. We isolate the conditions for global existence and prove l1-norm convergence of the method in the limit of zero spatial step size and infinite particles. The numerical results show that this method captures the essence of the solution without using an overly large number of particles. A fast summation algorithm is introduced to evaluate the integrals of the particle method so that the computational cost is reduced from O( N2) to O( N), where N is the number of particles. The method possesses some analogies with point vortex methods for 2D Euler equations. In particular, near singular solutions exist and singularities are prevented from occurring in finite time by mechanisms akin to those in the evolution of vortex patches. The second method is based on integro-differential formulations of the equation. Two different algorithms are proposed, based on different ways of extracting the time derivative of the dependent variable by an appropriately defined inverse operator. The integro-differential formulations reduce the order of spatial derivatives, thereby relaxing the stability constraint and allowing large time steps in an explicit numerical scheme. In addition to the Cauchy problem on the infinite line, we include results on the study of the nonlinear equation posed in the quarter (space-time) plane. We discuss the minimum number of boundary conditions required for solution uniqueness and illustrate this with numerical
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.
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.
Is Titan Partially Differentiated?
NASA Astrophysics Data System (ADS)
Mitri, G.; Pappalardo, R. T.; Stevenson, D. J.
2009-12-01
The recent measurement of the gravity coefficients from the Radio Doppler data of the Cassini spacecraft has improved our knowledge of the interior structure of Titan (Rappaport et al. 2008 AGU, P21A-1343). The measured gravity field of Titan is dominated by near hydrostatic quadrupole components. We have used the measured gravitational coefficients, thermal models and the hydrostatic equilibrium theory to derive Titan's interior structure. The axial moment of inertia gives us an indication of the degree of the interior differentiation. The inferred axial moment of inertia, calculated using the quadrupole gravitational coefficients and the Radau-Darwin approximation, indicates that Titan is partially differentiated. If Titan is partially differentiated then the interior must avoid melting of the ice during its evolution. This suggests a relatively late formation of Titan to avoid the presence of short-lived radioisotopes (Al-26). This also suggests the onset of convection after accretion to efficiently remove the heat from the interior. The outer layer is likely composed mainly of water in solid phase. Thermal modeling indicates that water could be present also in liquid phase forming a subsurface ocean between an outer ice I shell and a high pressure ice layer. Acknowledgments: This work was conducted at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.
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.
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.
Volcanic Particle Aggregation: A Fast Algorithm for the Smoluchowski Coagulation Equation
NASA Astrophysics Data System (ADS)
Rossi, E.; Bagheri, G.; Bonadonna, C.
2014-12-01
Particle aggregation is a key process that significantly affects dispersal and sedimentation of volcanic ash, with obvious implications for the associated hazards. Most theoretical studies of particle aggregation have been based on the Smoluchowski Coagulation Equation (SCE), which describes the expected time evolution of the total grain-size distribution under the hypothesis that particles can collide and stick together following specific mathematical relations (kernels). Nonetheless, the practical application of the SCE to real erupting scenarios is made extremely difficult - if not even impossible - by the large number of Ordinary Differential Equations (ODE) which have to be solved to study the typical sizes of volcanic ash (1 micron to 1 mm). We propose an algorithm to approximate the discrete solutions of the SCE, which can describe the time evolution of the total grain-size distribution of the erupted material with an increased computational efficiency. This algorithm has been applied to observed volcanic eruptions (i.e., Eyjafjallajokull 2010, Sakurajima 2013 and Mt. Saint Helens 1980) to see if the commonly used kernels can explain field data and to study how aggregation processes can modify the tephra dispersal on the ground. Different scenarios of sticking efficiencies and aggregate porosity have been used to test the sensitiveness of the SCE to these parameters. Constraints on these parameters come from field observations and laboratory experiments.
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
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
Prediction of CBS tidal evolution
NASA Astrophysics Data System (ADS)
Dryomova, G. N.
The time series of basic processes, accompanying the tidal evolution of star components of Close Binary Systems (CBS) are predicted in the framework of evolutionary stellar models by Claret (2004). The series includes the apsidal motion period, timescale of synchronization of axial rotation of a star with the orbital revolution, the orbit circularization timescale, and the age. Data from the catalogues by Svechnikov & Perevozkina (1999) and by Torres, Andersen, Gimenez (2010) are used for testing the sensitivity of the numerical prediction algorithm.
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.
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.