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.
Differential evolution algorithm for global optimizations in nuclear physics
NASA Astrophysics Data System (ADS)
Qi, Chong
2017-04-01
We explore the applicability of the differential evolution algorithm in finding the global minima of three typical nuclear structure physics problems: the global deformation minimum in the nuclear potential energy surface, the optimization of mass model parameters and the lowest eigenvalue of a nuclear Hamiltonian. The algorithm works very effectively and efficiently in identifying the minima in all problems we have tested. We also show that the algorithm can be parallelized in a straightforward way.
Fan, Qinqin; Yan, Xuefeng
2016-01-01
The performance of the differential evolution (DE) algorithm is significantly affected by the choice of mutation strategies and control parameters. Maintaining the search capability of various control parameter combinations throughout the entire evolution process is also a key issue. A self-adaptive DE algorithm with zoning evolution of control parameters and adaptive mutation strategies is proposed in this paper. In the proposed algorithm, the mutation strategies are automatically adjusted with population evolution, and the control parameters evolve in their own zoning to self-adapt and discover near optimal values autonomously. The proposed algorithm is compared with five state-of-the-art DE algorithm variants according to a set of benchmark test functions. Furthermore, seven nonparametric statistical tests are implemented to analyze the experimental results. The results indicate that the overall performance of the proposed algorithm is better than those of the five existing improved algorithms.
An Enhanced Differential Evolution Algorithm Based on Multiple Mutation Strategies
Xiang, Wan-li; Meng, Xue-lei; An, Mei-qing; Li, Yin-zhen; Gao, Ming-xia
2015-01-01
Differential evolution algorithm is a simple yet efficient metaheuristic for global optimization over continuous spaces. However, there is a shortcoming of premature convergence in standard DE, especially in DE/best/1/bin. In order to take advantage of direction guidance information of the best individual of DE/best/1/bin and avoid getting into local trap, based on multiple mutation strategies, an enhanced differential evolution algorithm, named EDE, is proposed in this paper. In the EDE algorithm, an initialization technique, opposition-based learning initialization for improving the initial solution quality, and a new combined mutation strategy composed of DE/current/1/bin together with DE/pbest/bin/1 for the sake of accelerating standard DE and preventing DE from clustering around the global best individual, as well as a perturbation scheme for further avoiding premature convergence, are integrated. In addition, we also introduce two linear time-varying functions, which are used to decide which solution search equation is chosen at the phases of mutation and perturbation, respectively. Experimental results tested on twenty-five benchmark functions show that EDE is far better than the standard DE. In further comparisons, EDE is compared with other five state-of-the-art approaches and related results show that EDE is still superior to or at least equal to these methods on most of benchmark functions. PMID:26609304
Application of Differential Evolution Algorithm on Self-Potential Data
Li, Xiangtao; Yin, Minghao
2012-01-01
Differential evolution (DE) is a population based evolutionary algorithm widely used for solving multidimensional global optimization problems over continuous spaces, and has been successfully used to solve several kinds of problems. In this paper, differential evolution is used for quantitative interpretation of self-potential data in geophysics. Six parameters are estimated including the electrical dipole moment, the depth of the source, the distance from the origin, the polarization angle and the regional coefficients. This study considers three kinds of data from Turkey: noise-free data, contaminated synthetic data, and Field example. The differential evolution and the corresponding model parameters are constructed as regards the number of the generations. Then, we show the vibration of the parameters at the vicinity of the low misfit area. Moreover, we show how the frequency distribution of each parameter is related to the number of the DE iteration. Experimental results show the DE can be used for solving the quantitative interpretation of self-potential data efficiently compared with previous methods. PMID:23240004
An Adaptive Cauchy Differential Evolution Algorithm for Global Numerical Optimization
Choi, Tae Jong; Ahn, Chang Wook; An, Jinung
2013-01-01
Adaptation of control parameters, such as scaling factor (F), crossover rate (CR), and population size (NP), appropriately is one of the major problems of Differential Evolution (DE) literature. Well-designed adaptive or self-adaptive parameter control method can highly improve the performance of DE. Although there are many suggestions for adapting the control parameters, it is still a challenging task to properly adapt the control parameters for problem. In this paper, we present an adaptive parameter control DE algorithm. In the proposed algorithm, each individual has its own control parameters. The control parameters of each individual are adapted based on the average parameter value of successfully evolved individuals' parameter values by using the Cauchy distribution. Through this, the control parameters of each individual are assigned either near the average parameter value or far from that of the average parameter value which might be better parameter value for next generation. The experimental results show that the proposed algorithm is more robust than the standard DE algorithm and several state-of-the-art adaptive DE algorithms in solving various unimodal and multimodal problems. PMID:23935445
An adaptive Cauchy differential evolution algorithm for global numerical optimization.
Choi, Tae Jong; Ahn, Chang Wook; An, Jinung
2013-01-01
Adaptation of control parameters, such as scaling factor (F), crossover rate (CR), and population size (NP), appropriately is one of the major problems of Differential Evolution (DE) literature. Well-designed adaptive or self-adaptive parameter control method can highly improve the performance of DE. Although there are many suggestions for adapting the control parameters, it is still a challenging task to properly adapt the control parameters for problem. In this paper, we present an adaptive parameter control DE algorithm. In the proposed algorithm, each individual has its own control parameters. The control parameters of each individual are adapted based on the average parameter value of successfully evolved individuals' parameter values by using the Cauchy distribution. Through this, the control parameters of each individual are assigned either near the average parameter value or far from that of the average parameter value which might be better parameter value for next generation. The experimental results show that the proposed algorithm is more robust than the standard DE algorithm and several state-of-the-art adaptive DE algorithms in solving various unimodal and multimodal problems.
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.
NASA Astrophysics Data System (ADS)
Iwan Solihin, Mahmud; Fauzi Zanil, Mohd
2016-11-01
Cuckoo Search (CS) and Differential Evolution (DE) algorithms are considerably robust meta-heuristic algorithms to solve constrained optimization problems. In this study, the performance of CS and DE are compared in solving the constrained optimization problem from selected benchmark functions. Selection of the benchmark functions are based on active or inactive constraints and dimensionality of variables (i.e. number of solution variable). In addition, a specific constraint handling and stopping criterion technique are adopted in the optimization algorithm. The results show, CS approach outperforms DE in term of repeatability and the quality of the optimum solutions.
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.
Self-adaptive differential evolution algorithm incorporating local search for protein-ligand docking
NASA Astrophysics Data System (ADS)
Chung, Hwan Won; Cho, Seung Joo; Lee, Kwang-Ryeol; Lee, Kyu-Hwan
2013-02-01
Differential Evolution (DE) algorithm is powerful in optimization problems over several real parameters. DE depends on strategies to generate new trial solutions and the associated parameter values for searching performance. In self-adaptive DE, the automatic learning about previous evolution was used to determine the best mutation strategy and its parameter settings. By combining the self-adaptive DE and Hooke Jeeves local search, we developed a new docking method named SADock (Strategy Adaptation Dock) with the help of AutoDock4 scoring function. As the accuracy and performance of SADock was evaluated in self-docking using the Astex diverse set, the introduced SADock showed better success ratio (89%) than the success ratio (60%) of the Lamarckian genetic algorithm (LGA) of AutoDock4. The self-adapting scheme enabled our new docking method to converge fast and to be robust through the various docking problems.
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
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.
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
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.
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)
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.
NASA Astrophysics Data System (ADS)
Wang, Congzhe; Fang, Yuefa; Guo, Sheng
2015-07-01
Dimensional synthesis is one of the most difficult issues in the field of parallel robots with actuation redundancy. To deal with the optimal design of a redundantly actuated parallel robot used for ankle rehabilitation, a methodology of dimensional synthesis based on multi-objective optimization is presented. First, the dimensional synthesis of the redundant parallel robot is formulated as a nonlinear constrained multi-objective optimization problem. Then four objective functions, separately reflecting occupied space, input/output transmission and torque performances, and multi-criteria constraints, such as dimension, interference and kinematics, are defined. In consideration of the passive exercise of plantar/dorsiflexion requiring large output moment, a torque index is proposed. To cope with the actuation redundancy of the parallel robot, a new output transmission index is defined as well. The multi-objective optimization problem is solved by using a modified Differential Evolution(DE) algorithm, which is characterized by new selection and mutation strategies. Meanwhile, a special penalty method is presented to tackle the multi-criteria constraints. Finally, numerical experiments for different optimization algorithms are implemented. The computation results show that the proposed indices of output transmission and torque, and constraint handling are effective for the redundant parallel robot; the modified DE algorithm is superior to the other tested algorithms, in terms of the ability of global search and the number of non-dominated solutions. The proposed methodology of multi-objective optimization can be also applied to the dimensional synthesis of other redundantly actuated parallel robots only with rotational movements.
Liu, Chang; Wang, Guofeng; Xie, Qinglu; Zhang, Yanchao
2014-06-16
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.
An improved generalized differential evolution algorithm for multi-objective reactive power dispatch
NASA Astrophysics Data System (ADS)
Ramesh, S.; Kannan, S.; Baskar, S.
2012-04-01
An improved multi-objective generalized differential evolution (I-GDE3) approach to solve optimal reactive power dispatch (ORPD) with multiple and competing objectives is proposed in this article. The objective functions are minimization of real power loss and bus voltage profile improvement. For maintaining good diversity, the concepts of simulated binary crossover (SBX) based recombination and dynamic crowding distance (DCD), are implemented in the GDE3 algorithm. I-GDE3 obtains the Pareto-solution set for ORPD that is impervious to load drifts and perturbations. The performance of the proposed approach is tested in standard IEEE 118-bus and IEEE 300-bus test systems and the result demonstrates the capability of the I-GDE3 algorithm in generating diverse and well distributed Pareto-optimal solutions that are less sensitive to various loading conditions along with load perturbations. The performance of I-GDE3 is compared with respect to multi-objective performance measures namely span, hyper-volume and C-measure. The results show the effectiveness of I-GDE3 and confirm its potential to solve the multi-objective RPD problem.
NASA Astrophysics Data System (ADS)
Le-Duc, Thang; Ho-Huu, Vinh; Nguyen-Thoi, Trung; Nguyen-Quoc, Hung
2016-12-01
In recent years, various types of magnetorheological brakes (MRBs) have been proposed and optimized by different optimization algorithms that are integrated in commercial software such as ANSYS and Comsol Multiphysics. However, many of these optimization algorithms often possess some noteworthy shortcomings such as the trap of solutions at local extremes, or the limited number of design variables or the difficulty of dealing with discrete design variables. Thus, to overcome these limitations and develop an efficient computation tool for optimal design of the MRBs, an optimization procedure that combines differential evolution (DE), a gradient-free global optimization method with finite element analysis (FEA) is proposed in this paper. The proposed approach is then applied to the optimal design of MRBs with different configurations including conventional MRBs and MRBs with coils placed on the side housings. Moreover, to approach a real-life design, some necessary design variables of MRBs are considered as discrete variables in the optimization process. The obtained optimal design results are compared with those of available optimal designs in the literature. The results reveal that the proposed method outperforms some traditional approaches.
NASA Astrophysics Data System (ADS)
Addawe, Rizavel C.; Addawe, Joel M.; Magadia, Joselito C.
2016-11-01
The Least Squares (LS), Least Median Squares (LMdS), Reweighted Least Squares (RLS) and Trimmed Least Squares (TLS) estimators are used to obtain parameter estimates of AR models using DE algorithm. The empirical study indicated that, the RLS estimator seems to be very reasonable because of having smaller root mean square error (RMSE), particularly for the Gaussian AR(1) process with unknown drift and additive outliers. Moreover, while LS performs well on shorter processes with less percentage and smaller magnitude of additive outliers (AOS); RLS and TLS compare favorably with respect to LS for longer AR processes. Thus, this study recommends the Reweighted Least Squares estimator as an alternative to the LS estimator in the case of autoregressive processes with additive outliers. The experiment also demonstrates that Differential Evolution (DE) algorithm obtains optimal solutions for fitting first-order autoregressive processes with outliers using the estimators. At the request of all authors of the paper, and with the agreement of the Proceedings Editor, an updated version of this article was published on 15 December 2016. The original version supplied to AIP Publishing contained errors in some of the mathematical equations and in Table 2. The errors have been corrected in the updated and re-published article.
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
A Novel Discrete Differential Evolution Algorithm for the Vehicle Routing Problem in B2C E-Commerce
NASA Astrophysics Data System (ADS)
Xia, Chao; Sheng, Ying; Jiang, Zhong-Zhong; Tan, Chunqiao; Huang, Min; He, Yuanjian
2015-12-01
In this paper, a novel discrete differential evolution (DDE) algorithm is proposed to solve the vehicle routing problems (VRP) in B2C e-commerce, in which VRP is modeled by the incomplete graph based on the actual urban road system. First, a variant of classical VRP is described and a mathematical programming model for the variant is given. Second, the DDE is presented, where individuals are represented as the sequential encoding scheme, and a novel reparation operator is employed to repair the infeasible solutions. Furthermore, a FLOYD operator for dealing with the shortest route is embedded in the proposed DDE. Finally, an extensive computational study is carried out in comparison with the predatory search algorithm and genetic algorithm, and the results show that the proposed DDE is an effective algorithm for VRP in B2C e-commerce.
He, Feng; Zhang, Wei; Zhang, Guoqiang
2016-01-01
A differential evolution algorithm for solving Nash equilibrium in nonlinear continuous games is presented in this paper, called NIDE (Nikaido-Isoda differential evolution). At each generation, parent and child strategy profiles are compared one by one pairwisely, adapting Nikaido-Isoda function as fitness function. In practice, the NE of nonlinear game model with cubic cost function and quadratic demand function is solved, and this method could also be applied to non-concave payoff functions. Moreover, the NIDE is compared with the existing Nash Domination Evolutionary Multiplayer Optimization (NDEMO), the result showed that NIDE was significantly better than NDEMO with less iterations and shorter running time. These numerical examples suggested that the NIDE method is potentially useful. PMID:27589229
Khan, S. U.; Qureshi, I. M.; Zaman, F.; Shoaib, B.; Naveed, A.; Basit, A.
2014-01-01
Three issues regarding sensor failure at any position in the antenna array are discussed. We assume that sensor position is known. The issues include raise in sidelobe levels, displacement of nulls from their original positions, and diminishing of null depth. The required null depth is achieved by making the weight of symmetrical complement sensor passive. A hybrid method based on memetic computing algorithm is proposed. The hybrid method combines the cultural algorithm with differential evolution (CADE) which is used for the reduction of sidelobe levels and placement of nulls at their original positions. Fitness function is used to minimize the error between the desired and estimated beam patterns along with null constraints. Simulation results for various scenarios have been given to exhibit the validity and performance of the proposed algorithm. PMID:24688440
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.
Optimization of a mirror-based neutron source using differential evolution algorithm
NASA Astrophysics Data System (ADS)
Yurov, D. V.; Prikhodko, V. V.
2016-12-01
This study is dedicated to the assessment of capabilities of gas-dynamic trap (GDT) and gas-dynamic multiple-mirror trap (GDMT) as potential neutron sources for subcritical hybrids. In mathematical terms the problem of the study has been formulated as determining the global maximum of fusion gain (Q pl), the latter represented as a function of trap parameters. A differential evolution method has been applied to perform the search. Considered in all calculations has been a configuration of the neutron source with 20 m long distance between the mirrors and 100 MW heating power. It is important to mention that the numerical study has also taken into account a number of constraints on plasma characteristics so as to provide physical credibility of searched-for trap configurations. According to the results obtained the traps considered have demonstrated fusion gain up to 0.2, depending on the constraints applied. This enables them to be used either as neutron sources within subcritical reactors for minor actinides incineration or as material-testing facilities.
Bor, E; Turduev, M; Kurt, H
2016-08-01
Photonic structure designs based on optimization algorithms provide superior properties compared to those using intuition-based approaches. In the present study, we numerically and experimentally demonstrate subwavelength focusing of light using wavelength scale absorption-free dielectric scattering objects embedded in an air background. An optimization algorithm based on differential evolution integrated into the finite-difference time-domain method was applied to determine the locations of each circular dielectric object with a constant radius and refractive index. The multiobjective cost function defined inside the algorithm ensures strong focusing of light with low intensity side lobes. The temporal and spectral responses of the designed compact photonic structure provided a beam spot size in air with a full width at half maximum value of 0.19λ, where λ is the wavelength of light. The experiments were carried out in the microwave region to verify numerical findings, and very good agreement between the two approaches was found. The subwavelength light focusing is associated with a strong interference effect due to nonuniformly arranged scatterers and an irregular index gradient. Improving the focusing capability of optical elements by surpassing the diffraction limit of light is of paramount importance in optical imaging, lithography, data storage, and strong light-matter interaction.
Bor, E.; Turduev, M.; Kurt, H.
2016-01-01
Photonic structure designs based on optimization algorithms provide superior properties compared to those using intuition-based approaches. In the present study, we numerically and experimentally demonstrate subwavelength focusing of light using wavelength scale absorption-free dielectric scattering objects embedded in an air background. An optimization algorithm based on differential evolution integrated into the finite-difference time-domain method was applied to determine the locations of each circular dielectric object with a constant radius and refractive index. The multiobjective cost function defined inside the algorithm ensures strong focusing of light with low intensity side lobes. The temporal and spectral responses of the designed compact photonic structure provided a beam spot size in air with a full width at half maximum value of 0.19λ, where λ is the wavelength of light. The experiments were carried out in the microwave region to verify numerical findings, and very good agreement between the two approaches was found. The subwavelength light focusing is associated with a strong interference effect due to nonuniformly arranged scatterers and an irregular index gradient. Improving the focusing capability of optical elements by surpassing the diffraction limit of light is of paramount importance in optical imaging, lithography, data storage, and strong light-matter interaction. PMID:27477060
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.
NASA Astrophysics Data System (ADS)
Addawe, Rizavel C.; Addawe, Joel M.; Magadia, Joselito C.
2016-10-01
Accurate forecasting of dengue cases would significantly improve epidemic prevention and control capabilities. This paper attempts to provide useful models in forecasting dengue epidemic specific to the young and adult population of Baguio City. To capture the seasonal variations in dengue incidence, this paper develops a robust modeling approach to identify and estimate seasonal autoregressive integrated moving average (SARIMA) models in the presence of additive outliers. Since the least squares estimators are not robust in the presence of outliers, we suggest a robust estimation based on winsorized and reweighted least squares estimators. A hybrid algorithm, Differential Evolution - Simulated Annealing (DESA), is used to identify and estimate the parameters of the optimal SARIMA model. The method is applied to the monthly reported dengue cases in Baguio City, Philippines.
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
NASA Astrophysics Data System (ADS)
Balkaya, Çağlayan; Ekinci, Yunus Levent; Göktürkler, Gökhan; Turan, Seçil
2017-01-01
3D non-linear inversion of total field magnetic anomalies caused by vertical-sided prismatic bodies has been achieved by differential evolution (DE), which is one of the population-based evolutionary algorithms. We have demonstrated the efficiency of the algorithm on both synthetic and field magnetic anomalies by estimating horizontal distances from the origin in both north and east directions, depths to the top and bottom of the bodies, inclination and declination angles of the magnetization, and intensity of magnetization of the causative bodies. In the synthetic anomaly case, we have considered both noise-free and noisy data sets due to two vertical-sided prismatic bodies in a non-magnetic medium. For the field case, airborne magnetic anomalies originated from intrusive granitoids at the eastern part of the Biga Peninsula (NW Turkey) which is composed of various kinds of sedimentary, metamorphic and igneous rocks, have been inverted and interpreted. Since the granitoids are the outcropped rocks in the field, the estimations for the top depths of two prisms representing the magnetic bodies were excluded during inversion studies. Estimated bottom depths are in good agreement with the ones obtained by a different approach based on 3D modelling of pseudogravity anomalies. Accuracy of the estimated parameters from both cases has been also investigated via probability density functions. Based on the tests in the present study, it can be concluded that DE is a useful tool for the parameter estimation of source bodies using magnetic anomalies.
Moonchai, Sompop; Madlhoo, Weeranuch; Jariyachavalit, Kanidtha; Shimizu, Hiroshi; Shioya, Suteaki; Chauvatcharin, Somchai
2005-11-01
The effect of pH and temperature on cell growth and bacteriocin production in Lactococcus lactis C7 was investigated in order to optimize the production of bacteriocin. The study showed that the bacteriocin production was growth-associated, but declined after reaching the maximum titer. The decrease of bacteriocin was caused by a cell-bound protease. Maximum bacteriocin titer was obtained at pH 5.5 and at 22 degrees C. In order to obtain a global optimized solution for production of bacteriocin, the optimal temperature for bacteriocin production was further studied. Mathematical models were developed for cell growth, substrate consumption, lactic acid production and bacteriocin production. A Differential Evolution algorithm was used both to estimate the model parameters from the experimental data and to compute a temperature profile for maximizing the final bacteriocin titer and bacteriocin productivity. This simulation showed that maximum bacteriocin production was obtained at the optimal temperature profile, starting at 30 degrees C and terminating at 22 degrees C, which was validated by experiment. This temperature profile yielded 20% higher maximum bacteriocin productivity than that obtained at a constant temperature of 22 degrees C, although the total amount of bacteriocin obtained was slightly decreased.
Algorithms, games, and evolution
Chastain, Erick; Livnat, Adi; Papadimitriou, Christos; Vazirani, Umesh
2014-01-01
Even the most seasoned students of evolution, starting with Darwin himself, have occasionally expressed amazement that the mechanism of natural selection has produced the whole of Life as we see it around us. There is a computational way to articulate the same amazement: “What algorithm could possibly achieve all this in a mere three and a half billion years?” In this paper we propose an answer: We demonstrate that in the regime of weak selection, the standard equations of population genetics describing natural selection in the presence of sex become identical to those of a repeated game between genes played according to multiplicative weight updates (MWUA), an algorithm known in computer science to be surprisingly powerful and versatile. MWUA maximizes a tradeoff between cumulative performance and entropy, which suggests a new view on the maintenance of diversity in evolution. PMID:24979793
Multiobjective Optimization Using a Pareto Differential Evolution Approach
NASA Technical Reports Server (NTRS)
Madavan, Nateri K.; Biegel, Bryan A. (Technical Monitor)
2002-01-01
Differential Evolution is a simple, fast, and robust evolutionary algorithm that has proven effective in determining the global optimum for several difficult single-objective optimization problems. In this paper, the Differential Evolution algorithm is extended to multiobjective optimization problems by using a Pareto-based approach. The algorithm performs well when applied to several test optimization problems from the literature.
Huang, Lei; Liao, Li; Wu, Cathy H.
2016-01-01
Revealing the underlying evolutionary mechanism plays an important role in understanding protein interaction networks in the cell. While many evolutionary models have been proposed, the problem about applying these models to real network data, especially for differentiating which model can better describe evolutionary process for the observed network urgently remains as a challenge. The traditional way is to use a model with presumed parameters to generate a network, and then evaluate the fitness by summary statistics, which however cannot capture the complete network structures information and estimate parameter distribution. In this work we developed a novel method based on Approximate Bayesian Computation and modified Differential Evolution (ABC-DEP) that is capable of conducting model selection and parameter estimation simultaneously and detecting the underlying evolutionary mechanisms more accurately. We tested our method for its power in differentiating models and estimating parameters on the simulated data and found significant improvement in performance benchmark, as compared with a previous method. We further applied our method to real data of protein interaction networks in human and yeast. Our results show Duplication Attachment model as the predominant evolutionary mechanism for human PPI networks and Scale-Free model as the predominant mechanism for yeast PPI networks. PMID:26357273
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.
El-Qulity, Said Ali; Mohamed, Ali Wagdy
2016-01-01
This paper proposes a nonlinear integer goal programming model (NIGPM) for solving the general problem of admission capacity planning in a country as a whole. The work aims to satisfy most of the required key objectives of a country related to the enrollment problem for higher education. The system general outlines are developed along with the solution methodology for application to the time horizon in a given plan. The up-to-date data for Saudi Arabia is used as a case study and a novel evolutionary algorithm based on modified differential evolution (DE) algorithm is used to solve the complexity of the NIGPM generated for different goal priorities. The experimental results presented in this paper show their effectiveness in solving the admission capacity for higher education in terms of final solution quality and robustness. PMID:26819583
El-Qulity, Said Ali; Mohamed, Ali Wagdy
2016-01-01
This paper proposes a nonlinear integer goal programming model (NIGPM) for solving the general problem of admission capacity planning in a country as a whole. The work aims to satisfy most of the required key objectives of a country related to the enrollment problem for higher education. The system general outlines are developed along with the solution methodology for application to the time horizon in a given plan. The up-to-date data for Saudi Arabia is used as a case study and a novel evolutionary algorithm based on modified differential evolution (DE) algorithm is used to solve the complexity of the NIGPM generated for different goal priorities. The experimental results presented in this paper show their effectiveness in solving the admission capacity for higher education in terms of final solution quality and robustness.
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.
A Hybrid Differential Invasive Weed Algorithm for Congestion Management
NASA Astrophysics Data System (ADS)
Basak, Aniruddha; Pal, Siddharth; Pandi, V. Ravikumar; Panigrahi, B. K.; Das, Swagatam
This work is dedicated to solve the problem of congestion management in restructured power systems. Nowadays we have open access market which pushes the power system operation to their limits for maximum economic benefits but at the same time making the system more susceptible to congestion. In this regard congestion management is absolutely vital. In this paper we try to remove congestion by generation rescheduling where the cost involved in the rescheduling process is minimized. The proposed algorithm is a hybrid of Invasive Weed Optimization (IWO) and Differential Evolution (DE). The resultant hybrid algorithm was applied on standard IEEE 30 bus system and observed to beat existing algorithms like Simple Bacterial foraging (SBF), Genetic Algorithm (GA), Invasive Weed Optimization (IWO), Differential Evolution (DE) and hybrid algorithms like Hybrid Bacterial Foraging and Differential Evolution (HBFDE) and Adaptive Bacterial Foraging with Nelder Mead (ABFNM).
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)
Primorac, E.; Kuhlenbeck, H.; Freund, H.-J.
2016-07-01
The structure of a thin MoO3 layer on Au(111) with a c(4 × 2) superstructure was studied with LEED I/V analysis. As proposed previously (Quek et al., Surf. Sci. 577 (2005) L71), the atomic structure of the layer is similar to that of a MoO3 single layer as found in regular α-MoO3. The layer on Au(111) has a glide plane parallel to the short unit vector of the c(4 × 2) unit cell and the molybdenum atoms are bridge-bonded to two surface gold atoms with the structure of the gold surface being slightly distorted. The structural refinement of the structure was performed with the CMA-ES evolutionary strategy algorithm which could reach a Pendry R-factor of ∼ 0.044. In the second part the performance of CMA-ES is compared with that of the differential evolution method, a genetic algorithm and the Powell optimization algorithm employing I/V curves calculated with tensor LEED.
NASA Astrophysics Data System (ADS)
Cihan, A.; Birkholzer, J. T.; Bianchi, M.
2014-12-01
Injection of large volume of CO2 into deep geological reservoirs for geologic carbon sequestration (GCS) is expected to cause significant pressure perturbations in subsurface. Large-scale pressure increases in injection reservoirs during GCS operations, if not controlled properly, may limit dynamic storage capacity and increase risk of environmental impacts. The high pressure may impact caprock integrity, induce fault slippage, and cause leakage of brine and/or CO2 into shallow fresh groundwater resources. Thus, monitoring and controlling pressure buildup are critically important for environmentally safe implementation of GCS projects. Extraction of native brine during GCS operations is a pressure management approach to reduce significant pressure buildup. Extracted brine can be transferred to the surface for utilization or re-injected into overlying/underlying saline aquifers. However, pumping, transportation, treatment and disposal of extracted brine can be challenging and costly. Therefore, minimizing volume of extracted brine, while maximizing CO2 storage, is an essential objective of the pressure management with brine extraction schemes. Selection of optimal well locations and extraction rates are critical for maximizing storage and minimizing brine extraction during GCS. However, placing of injection and extraction wells is not intuitive because of heterogeneity in reservoir properties and complex reservoir geometry. Efficient computerized algorithms combining reservoir models and optimization methods are needed to make proper decisions on well locations and control parameters. This study presents a global optimization methodology for pressure management during geologic CO2 sequestration. A constrained differential evolution (CDE) algorithm is introduced for solving optimization problems involving well placement and injection/extraction control. The CDE methodology is tested and applied for realistic CO2 storage scenarios with the presence of uncertainty in
NASA Astrophysics Data System (ADS)
Elçi, Alper; Ayvaz, M. Tamer
2014-04-01
The objective of this study is to present an optimization approach to determine locations of new groundwater production wells, where groundwater is relatively less susceptible to groundwater contamination (i.e. more likely to obtain clean groundwater), the pumping rate is maximum or the cost of well installation and operation is minimum for a prescribed set of constraints. The approach also finds locations that are in suitable areas for new groundwater exploration with respect to land use. A regional-scale groundwater flow model is coupled with a hybrid optimization model that uses the Differential Evolution (DE) algorithm and the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method as the global and local optimizers, respectively. Several constraints such as the depth to the water table, total well length and the restriction of seawater intrusion are considered in the optimization process. The optimization problem can be formulated either as the maximization of the pumping rate or as the minimization of total costs of well installation and pumping operation from existing and new wells. Pumping rates of existing wells that are prone to seawater intrusion are optimized to prevent groundwater flux from the shoreline towards these wells. The proposed simulation-optimization model is demonstrated on an existing groundwater flow model for the Tahtalı watershed in Izmir-Turkey. The model identifies for the demonstration study locations and pumping rates for up to four new wells and one new well in the cost minimization and maximization problem, respectively. All new well locations in the optimized solution coincide with areas of relatively low groundwater vulnerability. Considering all solutions of the demonstration study, groundwater vulnerability indices for new well locations range from 29.64 to 40.48 (on a scale of 0-100, where 100 indicates high vulnerability). All identified wells are located relatively close to each other. This implies that the method pinpoints the
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.
Efficient receiver tuning using differential evolution strategies
NASA Astrophysics Data System (ADS)
Wheeler, Caleb H.; Toland, Trevor G.
2016-08-01
Differential evolution (DE) is a powerful and computationally inexpensive optimization strategy that can be used to search an entire parameter space or to converge quickly on a solution. The Kilopixel Array Pathfinder Project (KAPPa) is a heterodyne receiver system delivering 5 GHz of instantaneous bandwidth in the tuning range of 645-695 GHz. The fully automated KAPPa receiver test system finds optimal receiver tuning using performance feedback and DE. We present an adaptation of DE for use in rapid receiver characterization. The KAPPa DE algorithm is written in Python 2.7 and is fully integrated with the KAPPa instrument control, data processing, and visualization code. KAPPa develops the technologies needed to realize heterodyne focal plane arrays containing 1000 pixels. Finding optimal receiver tuning by investigating large parameter spaces is one of many challenges facing the characterization phase of KAPPa. This is a difficult task via by-hand techniques. Characterizing or tuning in an automated fashion without need for human intervention is desirable for future large scale arrays. While many optimization strategies exist, DE is ideal for time and performance constraints because it can be set to converge to a solution rapidly with minimal computational overhead. We discuss how DE is utilized in the KAPPa system and discuss its performance and look toward the future of 1000 pixel array receivers and consider how the KAPPa DE system might be applied.
Differential evolution with ranking-based mutation operators.
Gong, Wenyin; Cai, Zhihua
2013-12-01
Differential evolution (DE) has been proven to be one of the most powerful global numerical optimization algorithms in the evolutionary algorithm family. The core operator of DE is the differential mutation operator. Generally, the parents in the mutation operator are randomly chosen from the current population. In nature, good species always contain good information, and hence, they have more chance to be utilized to guide other species. Inspired by this phenomenon, in this paper, we propose the ranking-based mutation operators for the DE algorithm, where some of the parents in the mutation operators are proportionally selected according to their rankings in the current population. The higher ranking a parent obtains, the more opportunity it will be selected. In order to evaluate the influence of our proposed ranking-based mutation operators on DE, our approach is compared with the jDE algorithm, which is a highly competitive DE variant with self-adaptive parameters, with different mutation operators. In addition, the proposed ranking-based mutation operators are also integrated into other advanced DE variants to verify the effect on them. Experimental results indicate that our proposed ranking-based mutation operators are able to enhance the performance of the original DE algorithm and the advanced DE algorithms.
New sampling distributions for evolution algorithms
NASA Astrophysics Data System (ADS)
Sweeney, Francis Dermot
Evolution algorithms are stochastic optimization methods based on evolutionary principles. They have long been used in optimization, and are gaining in popularity. They are particularly useful in high dimensional problems, or in problems where gradient methods fails. Evolution strategies, a class of evolutionary algorithms, are stochastic searches which evolve by mutation. This work proposes a new mutation distribution for use in single objective optimization. Up to now, cost function information obtained by mutations that do not improve fitness has been discarded. In many problems, particularly when cost function calls are expensive, it is desirable to use all available information to guide the search. The new method in this work patches Gaussians of different variances together to create a sampling distribution which delivers mutations designed to direct the search away from regions where low values of fitness have been observed. Analytic results for this new method are derived on idealized problems. The method is compared with existing methods on a range of test problems, and its overall performance attributes are assessed. A new method for multiobjective optimization is also developed. Genetic Algorithms introduce innovation into their populations by a process of bit mutation. This small scale mutation is often insufficient to successfully direct the search, unless the initial population is of sufficient quality. The new method proposed here, termed Rank Biased Sampling, uses the population to create new members, which are resampled across the entire search space from a distribution designed to favor regions which are inadequately represented by the current population. Again, this method is compared to existing methods on some standard test problems. These new optimization methods are then applied to some real-world problems of engineering interest. The optimization routines developed in this work performed well on these applications, and provide good
NASA Astrophysics Data System (ADS)
Sarkar, Soham; Das, Swagatam
In recent years particle swarm optimization emerges as one of the most efficient global optimization tools. In this paper, a hybrid particle swarm with differential evolution operator, termed DEPSO, is applied for the synthesis of linear array geometry. Here, the minimum side lobe level and null control, both are obtained by optimizing the spacing between the array elements by this technique. Moreover, a statistical comparison is also provided to establish its performance against the results obtained by Genetic Algorithm (GA), classical Particle Swarm Optimization (PSO), Tabu Search Algorithm (TSA), Differential Evolution (DE) and Memetic Algorithm (MA).
Differential evolution for many-particle adaptive quantum metrology.
Lovett, Neil B; Crosnier, Cécile; Perarnau-Llobet, Martí; Sanders, Barry C
2013-05-31
We devise powerful algorithms based on differential evolution for adaptive many-particle quantum metrology. Our new approach delivers adaptive quantum metrology policies for feedback control that are orders-of-magnitude more efficient and surpass the few-dozen-particle limitation arising in methods based on particle-swarm optimization. We apply our method to the binary-decision-tree model for quantum-enhanced phase estimation as well as to a new problem: a decision tree for adaptive estimation of the unknown bias of a quantum coin in a quantum walk and show how this latter case can be realized experimentally.
GPU-Accelerated Adjoint Algorithmic Differentiation.
Gremse, Felix; Höfter, Andreas; Razik, Lukas; Kiessling, Fabian; Naumann, Uwe
2016-03-01
Many scientific problems such as classifier training or medical image reconstruction can be expressed as minimization of differentiable real-valued cost functions and solved with iterative gradient-based methods. Adjoint algorithmic differentiation (AAD) enables automated computation of gradients of such cost functions implemented as computer programs. To backpropagate adjoint derivatives, excessive memory is potentially required to store the intermediate partial derivatives on a dedicated data structure, referred to as the "tape". Parallelization is difficult because threads need to synchronize their accesses during taping and backpropagation. This situation is aggravated for many-core architectures, such as Graphics Processing Units (GPUs), because of the large number of light-weight threads and the limited memory size in general as well as per thread. We show how these limitations can be mediated if the cost function is expressed using GPU-accelerated vector and matrix operations which are recognized as intrinsic functions by our AAD software. We compare this approach with naive and vectorized implementations for CPUs. We use four increasingly complex cost functions to evaluate the performance with respect to memory consumption and gradient computation times. Using vectorization, CPU and GPU memory consumption could be substantially reduced compared to the naive reference implementation, in some cases even by an order of complexity. The vectorization allowed usage of optimized parallel libraries during forward and reverse passes which resulted in high speedups for the vectorized CPU version compared to the naive reference implementation. The GPU version achieved an additional speedup of 7.5 ± 4.4, showing that the processing power of GPUs can be utilized for AAD using this concept. Furthermore, we show how this software can be systematically extended for more complex problems such as nonlinear absorption reconstruction for fluorescence-mediated tomography.
GPU-accelerated adjoint algorithmic differentiation
NASA Astrophysics Data System (ADS)
Gremse, Felix; Höfter, Andreas; Razik, Lukas; Kiessling, Fabian; Naumann, Uwe
2016-03-01
Many scientific problems such as classifier training or medical image reconstruction can be expressed as minimization of differentiable real-valued cost functions and solved with iterative gradient-based methods. Adjoint algorithmic differentiation (AAD) enables automated computation of gradients of such cost functions implemented as computer programs. To backpropagate adjoint derivatives, excessive memory is potentially required to store the intermediate partial derivatives on a dedicated data structure, referred to as the "tape". Parallelization is difficult because threads need to synchronize their accesses during taping and backpropagation. This situation is aggravated for many-core architectures, such as Graphics Processing Units (GPUs), because of the large number of light-weight threads and the limited memory size in general as well as per thread. We show how these limitations can be mediated if the cost function is expressed using GPU-accelerated vector and matrix operations which are recognized as intrinsic functions by our AAD software. We compare this approach with naive and vectorized implementations for CPUs. We use four increasingly complex cost functions to evaluate the performance with respect to memory consumption and gradient computation times. Using vectorization, CPU and GPU memory consumption could be substantially reduced compared to the naive reference implementation, in some cases even by an order of complexity. The vectorization allowed usage of optimized parallel libraries during forward and reverse passes which resulted in high speedups for the vectorized CPU version compared to the naive reference implementation. The GPU version achieved an additional speedup of 7.5 ± 4.4, showing that the processing power of GPUs can be utilized for AAD using this concept. Furthermore, we show how this software can be systematically extended for more complex problems such as nonlinear absorption reconstruction for fluorescence-mediated tomography.
GPU-Accelerated Adjoint Algorithmic Differentiation
Gremse, Felix; Höfter, Andreas; Razik, Lukas; Kiessling, Fabian; Naumann, Uwe
2015-01-01
Many scientific problems such as classifier training or medical image reconstruction can be expressed as minimization of differentiable real-valued cost functions and solved with iterative gradient-based methods. Adjoint algorithmic differentiation (AAD) enables automated computation of gradients of such cost functions implemented as computer programs. To backpropagate adjoint derivatives, excessive memory is potentially required to store the intermediate partial derivatives on a dedicated data structure, referred to as the “tape”. Parallelization is difficult because threads need to synchronize their accesses during taping and backpropagation. This situation is aggravated for many-core architectures, such as Graphics Processing Units (GPUs), because of the large number of light-weight threads and the limited memory size in general as well as per thread. We show how these limitations can be mediated if the cost function is expressed using GPU-accelerated vector and matrix operations which are recognized as intrinsic functions by our AAD software. We compare this approach with naive and vectorized implementations for CPUs. We use four increasingly complex cost functions to evaluate the performance with respect to memory consumption and gradient computation times. Using vectorization, CPU and GPU memory consumption could be substantially reduced compared to the naive reference implementation, in some cases even by an order of complexity. The vectorization allowed usage of optimized parallel libraries during forward and reverse passes which resulted in high speedups for the vectorized CPU version compared to the naive reference implementation. The GPU version achieved an additional speedup of 7.5 ± 4.4, showing that the processing power of GPUs can be utilized for AAD using this concept. Furthermore, we show how this software can be systematically extended for more complex problems such as nonlinear absorption reconstruction for fluorescence-mediated tomography
An Adaptive Multipopulation Differential Evolution With Dynamic Population Reduction.
Ali, Mostafa Z; Awad, Noor H; Suganthan, Ponnuthurai Nagaratnam; Reynolds, Robert G
2016-10-25
Developing efficient evolutionary algorithms attracts many researchers due to the existence of optimization problems in numerous real-world applications. A new differential evolution algorithm, sTDE-dR, is proposed to improve the search quality, avoid premature convergence, and stagnation. The population is clustered in multiple tribes and utilizes an ensemble of different mutation and crossover strategies. In this algorithm, a competitive success-based scheme is introduced to determine the life cycle of each tribe and its participation ratio for the next generation. In each tribe, a different adaptive scheme is used to control the scaling factor and crossover rate. The mean success of each subgroup is used to calculate the ratio of its participation for the next generation. This guarantees that successful tribes with the best adaptive schemes are only the ones that guide the search toward the optimal solution. The population size is dynamically reduced using a dynamic reduction method. Comprehensive comparison of the proposed heuristic over a challenging set of benchmarks from the CEC2014 real parameter single objective competition against several state-of-the-art algorithms is performed. The results affirm robustness of the proposed approach compared to other state-of-the-art algorithms.
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 Astrophysics Data System (ADS)
Gujarathi, Ashish M.; Purohit, S.; Srikanth, B.
2015-06-01
Detailed working principle of jumping gene adaptation of differential evolution (DE-JGa) is presented. The performance of the DE-JGa algorithm is compared with the performance of differential evolution (DE) and modified DE (MDE) by applying these algorithms on industrial problems. In this study Reactor network design (RND) problem is solved using DE, MDE, and DE-JGa algorithms: These industrial processes are highly nonlinear and complex with reference to optimal operating conditions with many equality and inequality constraints. Extensive computational comparisons have been made for all the chemical engineering problems considered. The results obtained in the present study show that DE-JGa algorithm outperforms the other algorithms (DE and MDE). Several comparisons are made among the algorithms with regard to the number of function evaluations (NFE)/CPU- time required to find the global optimum. The standard deviation and the variance values obtained using DE-JGa, DE and MDE algorithms also show that the DE-JGa algorithm gives consistent set of results for the majority of the test problems and the industrial real world problems.
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 ordinary differential equation based solution path algorithm.
Wu, Yichao
2011-01-01
Efron, Hastie, Johnstone and Tibshirani (2004) proposed Least Angle Regression (LAR), a solution path algorithm for the least squares regression. They pointed out that a slight modification of the LAR gives the LASSO (Tibshirani, 1996) solution path. However it is largely unknown how to extend this solution path algorithm to models beyond the least squares regression. In this work, we propose an extension of the LAR for generalized linear models and the quasi-likelihood model by showing that the corresponding solution path is piecewise given by solutions of ordinary differential equation systems. Our contribution is twofold. First, we provide a theoretical understanding on how the corresponding solution path propagates. Second, we propose an ordinary differential equation based algorithm to obtain the whole solution path.
Differential evolution Markov chain with snooker updater and fewer chains
Vrugt, Jasper A; Ter Braak, Cajo J F
2008-01-01
Differential Evolution Markov Chain (DE-MC) is an adaptive MCMC algorithm, in which multiple chains are run in parallel. Standard DE-MC requires at least N=2d chains to be run in parallel, where d is the dimensionality of the posterior. This paper extends DE-MC with a snooker updater and shows by simulation and real examples that DE-MC can work for d up to 50--100 with fewer parallel chains (e.g. N=3) by exploiting information from their past by generating jumps from differences of pairs of past states. This approach extends the practical applicability of DE-MC and is shown to be about 5--26 times more efficient than the optimal Normal random walk Metropolis sampler for the 97.5% point of a variable from a 25--50 dimensional Student T{sub 3} distribution. In a nonlinear mixed effects model example the approach outperformed a block-updater geared to the specific features of the model.
Fast wavelet based algorithms for linear evolution equations
NASA Technical Reports Server (NTRS)
Engquist, Bjorn; Osher, Stanley; Zhong, Sifen
1992-01-01
A class was devised of fast wavelet based algorithms for linear evolution equations whose coefficients are time independent. The method draws on the work of Beylkin, Coifman, and Rokhlin which they applied to general Calderon-Zygmund type integral operators. A modification of their idea is applied to linear hyperbolic and parabolic equations, with spatially varying coefficients. A significant speedup over standard methods is obtained when applied to hyperbolic equations in one space dimension and parabolic equations in multidimensions.
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.
A Global Optimization Algorithm Using Stochastic Differential Equations.
1985-02-01
Bari (Italy).2Istituto di Fisica , 2 UniversitA di Roma "Tor Vergata", Via Orazio Raimondo, 00173 (La Romanina) Roma (Italy). 3Istituto di Matematica ...accompanying Algorithm. lDipartininto di Matematica , Universita di Bari, 70125 Bar (Italy). Istituto di Fisica , 2a UniversitA di Roim ’"Tor Vergata", Via...Optimization, Stochastic Differential Equations Work Unit Number 5 (Optimization and Large Scale Systems) 6Dipartimento di Matematica , Universita di Bari, 70125
Algorithm Refinement for Stochastic Partial Differential Equations. I. Linear Diffusion
NASA Astrophysics Data System (ADS)
Alexander, Francis J.; Garcia, Alejandro L.; Tartakovsky, Daniel M.
2002-10-01
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. Results from a variety of numerical experiments are presented 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 nonstochastic version (i.e., using only deterministic continuum fluxes) the mean density is correct, but the variance is reduced except in particle regions away from the interface. Extensions of the methodology to fluid mechanics applications are discussed.
Li, Jing; Sun, Yi; Zhu, Peiping
2013-08-21
Differential phase-contrast computed tomography (DPC-CT) reconstruction problems are usually solved by using parallel-, fan- or cone-beam algorithms. For rod-shaped objects, the x-ray beams cannot recover all the slices of the sample at the same time. Thus, if a rod-shaped sample is required to be reconstructed by the above algorithms, one should alternately perform translation and rotation on this sample, which leads to lower efficiency. The helical cone-beam CT may significantly improve scanning efficiency for rod-shaped objects over other algorithms. In this paper, we propose a theoretically exact filter-backprojection algorithm for helical cone-beam DPC-CT, which can be applied to reconstruct the refractive index decrement distribution of the samples directly from two-dimensional differential phase-contrast images. Numerical simulations are conducted to verify the proposed algorithm. Our work provides a potential solution for inspecting the rod-shaped samples using DPC-CT, which may be applicable with the evolution of DPC-CT equipments.
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.
Differential Evolution approach to detect recent admixture.
Kozlov, Konstantin; Chebotarev, Dmitri; Hassan, Mehedi; Triska, Martin; Triska, Petr; Flegontov, Pavel; Tatarinova, Tatiana V
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/.
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
PATHOME: an algorithm for accurately detecting differentially expressed subpathways
Nam, S; Chang, H R; Kim, K-T; Kook, M-C; Hong, D; Kwon, C H; Jung, H R; Park, H S; Powis, G; Liang, H; Park, T; Kim, Y H
2014-01-01
The translation of high-throughput gene expression data into biologically meaningful information remains a bottleneck. We developed a novel computational algorithm, PATHOME, for detecting differentially expressed biological pathways. This algorithm employs straightforward statistical tests to evaluate the significance of differential expression patterns along subpathways. Applying it to gene expression data sets of gastric cancer (GC), we compared its performance with those of other leading programs. Based on a literature-driven reference set, PATHOME showed greater consistency in identifying known cancer-related pathways. For the WNT pathway uniquely identified by PATHOME, we validated its involvement in gastric carcinogenesis through experimental perturbation of both cell lines and animal models. We identified HNF4α-WNT5A regulation in the cross-talk between the AMPK metabolic pathway and the WNT signaling pathway, and further identified WNT5A as a potential therapeutic target for GC. We have demonstrated PATHOME to be a powerful tool, with improved sensitivity for identifying disease-related dysregulated pathways. PMID:24681952
Eigenvalue estimation of differential operators with a quantum algorithm
NASA Astrophysics Data System (ADS)
Szkopek, Thomas; Roychowdhury, Vwani; Yablonovitch, Eli; Abrams, Daniel S.
2005-12-01
We demonstrate how linear differential operators could be emulated by a quantum processor, should one ever be built, using the Abrams-Lloyd algorithm. Given a linear differential operator of order 2S , acting on functions ψ(x1,x2,…,xD) with D arguments, the computational cost required to estimate a low order eigenvalue to accuracy Θ(1/N2) is Θ((2(S+1)(1+1/ν)+D)lnN) qubits and O(N2(S+1)(1+1/ν)lncND) gate operations, where N is the number of points to which each argument is discretized, ν and c are implementation dependent constants of O(1) . Optimal classical methods require Θ(ND) bits and Ω(ND) gate operations to perform the same eigenvalue estimation. The Abrams-Lloyd algorithm thereby leads to exponential reduction in memory and polynomial reduction in gate operations, provided the domain has sufficiently large dimension D>2(S+1)(1+1/ν) . In the case of Schrödinger’s equation, ground state energy estimation of two or more particles can in principle be performed with fewer quantum mechanical gates than classical gates.
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.
Differential Evolution with Gaussian Mutation for Economic Dispatch
NASA Astrophysics Data System (ADS)
Basu, Mousumi; Jena, Chitralekha; Panigrahi, Chinmoy Kumar
2016-12-01
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.
Utilization of the Discrete Differential Evolution for Optimization in Multidimensional Point Clouds
Radecký, Michal; Snášel, Václav
2016-01-01
The Differential Evolution (DE) is a widely used bioinspired optimization algorithm developed by Storn and Price. It is popular for its simplicity and robustness. This algorithm was primarily designed for real-valued problems and continuous functions, but several modified versions optimizing both integer and discrete-valued problems have been developed. The discrete-coded DE has been mostly used for combinatorial problems in a set of enumerative variants. However, the DE has a great potential in the spatial data analysis and pattern recognition. This paper formulates the problem as a search of a combination of distinct vertices which meet the specified conditions. It proposes a novel approach called the Multidimensional Discrete Differential Evolution (MDDE) applying the principle of the discrete-coded DE in discrete point clouds (PCs). The paper examines the local searching abilities of the MDDE and its convergence to the global optimum in the PCs. The multidimensional discrete vertices cannot be simply ordered to get a convenient course of the discrete data, which is crucial for good convergence of a population. A novel mutation operator utilizing linear ordering of spatial data based on the space filling curves is introduced. The algorithm is tested on several spatial datasets and optimization problems. The experiments show that the MDDE is an efficient and fast method for discrete optimizations in the multidimensional point clouds. PMID:27974884
Uher, Vojtěch; Gajdoš, Petr; Radecký, Michal; Snášel, Václav
2016-01-01
The Differential Evolution (DE) is a widely used bioinspired optimization algorithm developed by Storn and Price. It is popular for its simplicity and robustness. This algorithm was primarily designed for real-valued problems and continuous functions, but several modified versions optimizing both integer and discrete-valued problems have been developed. The discrete-coded DE has been mostly used for combinatorial problems in a set of enumerative variants. However, the DE has a great potential in the spatial data analysis and pattern recognition. This paper formulates the problem as a search of a combination of distinct vertices which meet the specified conditions. It proposes a novel approach called the Multidimensional Discrete Differential Evolution (MDDE) applying the principle of the discrete-coded DE in discrete point clouds (PCs). The paper examines the local searching abilities of the MDDE and its convergence to the global optimum in the PCs. The multidimensional discrete vertices cannot be simply ordered to get a convenient course of the discrete data, which is crucial for good convergence of a population. A novel mutation operator utilizing linear ordering of spatial data based on the space filling curves is introduced. The algorithm is tested on several spatial datasets and optimization problems. The experiments show that the MDDE is an efficient and fast method for discrete optimizations in the multidimensional point clouds.
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.
What Drives the Differential Evolution of Lyman Break Galaxies?
NASA Astrophysics Data System (ADS)
Sawicki, Marcin; Thompson, David
2005-06-01
Analysis of the luminosity function of Lyman Break Galaxies in our deep, wide Keck Deep Fields survey robustly shows that the evolution of the LBG population is differential with luminosity from z~4 to z~3. Two of the possible mechanisms driving this evolution relate to (1) changes in the properties of dust and (2) changes in the duration of starbursting episodes in sub-L* LBGs. We will use archival Spitzer IRAC, HST, and ground-based imaging of GOODS and the HDFs to compare the spectral energy distributions of LBGs as a function of redshift and luminosity and search for related differences in reddening and starburst age. Finding such differences will not only identify the processes responsible for the evolution of the luminosity function but will thereby also point us towards the underlying physical mechanisms that control how galaxies form and evolve at high redshift. The non-detection of such differences will mean that the responsible mechanism lies elsewhere and will give impetus to other lines of attacking the problem. We have experience in analyzing the spectral energy distributions of Lyman Break Galaxies and request salary and other support to allow us to extend our techniques to higher redshifts, larger samples, and fainter objects with no spectroscopic redshifts that we need to help understand the observed evolution of the luminosity function. Significantly, our analysis will compare LBG subsamples in a differential and hence very robust way and thus will help us understand how galaxies are assembled at high redshift.
An Evolution Based Biosensor Receptor DNA Sequence Generation Algorithm
Kim, Eungyeong; Lee, Malrey; Gatton, Thomas M.; Lee, Jaewan; Zang, Yupeng
2010-01-01
A biosensor is composed of a bioreceptor, an associated recognition molecule, and a signal transducer that can selectively detect target substances for analysis. DNA based biosensors utilize receptor molecules that allow hybridization with the target analyte. However, most DNA biosensor research uses oligonucleotides as the target analytes and does not address the potential problems of real samples. The identification of recognition molecules suitable for real target analyte samples is an important step towards further development of DNA biosensors. This study examines the characteristics of DNA used as bioreceptors and proposes a hybrid evolution-based DNA sequence generating algorithm, based on DNA computing, to identify suitable DNA bioreceptor recognition molecules for stable hybridization with real target substances. The Traveling Salesman Problem (TSP) approach is applied in the proposed algorithm to evaluate the safety and fitness of the generated DNA sequences. This approach improves efficiency and stability for enhanced and variable-length DNA sequence generation and allows extension to generation of variable-length DNA sequences with diverse receptor recognition requirements. PMID:22315543
A high-speed algorithm for computation of fractional differentiation and fractional integration.
Fukunaga, Masataka; Shimizu, Nobuyuki
2013-05-13
A high-speed algorithm for computing fractional differentiations and fractional integrations in fractional differential equations is proposed. In this algorithm, the stored data are not the function to be differentiated or integrated but the weighted integrals of the function. The intervals of integration for the memory can be increased without loss of accuracy as the computing time-step n increases. The computing cost varies as n log n, as opposed to n(2) of standard algorithms.
Aerodynamic optimization of supersonic compressor cascade using differential evolution on GPU
NASA Astrophysics Data System (ADS)
Aissa, Mohamed Hasanine; Verstraete, Tom; Vuik, Cornelis
2016-06-01
Differential Evolution (DE) is a powerful stochastic optimization method. Compared to gradient-based algorithms, DE is able to avoid local minima but requires at the same time more function evaluations. In turbomachinery applications, function evaluations are performed with time-consuming CFD simulation, which results in a long, non affordable, design cycle. Modern High Performance Computing systems, especially Graphic Processing Units (GPUs), are able to alleviate this inconvenience by accelerating the design evaluation itself. In this work we present a validated CFD Solver running on GPUs, able to accelerate the design evaluation and thus the entire design process. An achieved speedup of 20x to 30x enabled the DE algorithm to run on a high-end computer instead of a costly large cluster. The GPU-enhanced DE was used to optimize the aerodynamics of a supersonic compressor cascade, achieving an aerodynamic loss minimization of 20%.
NASA Astrophysics Data System (ADS)
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.
NASA Astrophysics Data System (ADS)
Kannan, S. M.; Renuga, P.; Kalyani, S.; Muthukumaran, E.
2015-12-01
This paper proposes new methods to select the optimal values of fixed and switched shunt capacitors in Radial distribution feeders for varying load conditions so as to maximize the annual savings and minimizes the energy loss by taking the capacitor cost into account. The identification of the weak buses, where the capacitors should be placed is decided by a set of rules given by the fuzzy expert system. Then the sizing of the fixed and switched capacitors is modeled using differential evolution (DE) and particle swarm optimization (PSO). A case study with an existing 15 bus rural distribution feeder is presented to illustrate the applicability of the algorithm. Simulation results show the better saving in cost over previous capacitor placement algorithm.
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.
Estimation of drying parameters in rotary dryers using differential evolution
NASA Astrophysics Data System (ADS)
Lobato, F. S.; Steffen, V., Jr.; Arruda, E. B.; Barrozo, M. A. S.
2008-11-01
Inverse problems arise from the necessity of obtaining parameters of theoretical models to simulate the behavior of the system for different operating conditions. Several heuristics that mimic different phenomena found in nature have been proposed for the solution of this kind of problem. In this work, the Differential Evolution Technique is used for the estimation of drying parameters in realistic rotary dryers, which is formulated as an optimization problem by using experimental data. Test case results demonstrate both the feasibility and the effectiveness of the proposed methodology.
Thermal and Structural Evolution of a Partially Differentiated Titan
NASA Astrophysics Data System (ADS)
Bland, Michael T.; McKinnon, W. B.
2012-10-01
Titan’s moment of inertia (C/MR2) has been measured by Cassini to be 0.34, indicating either partial differentiation, or full differentiation with a low-density (hydrated) silicate core. Fully differentiated models have been constructed [Castillo-Rogez and Lunine, 2010], but require specific geochemical assumptions (e.g., rapid accretion, minimal core dehydration). In contrast, the alternative, partially differentiated models have not yet been fully vetted. Here we investigate the thermal stability of such partially differentiated internal structures by evaluating whether complete differentiation can be avoided. Our model assumes an initial three-layer internal structure consisting of a pure ice layer, mixed ice-rock layer, and silicate core, and calculates the temperature of each layer following the numerical approach in Bland et al. (2008, 2009). The model allows melting in the pure ice and mixed layer, and dehydration of the initially hydrated silicate core (leading to densification and absorption of latent heat). Melting of the mixed layer liberates silicate material, which is assumed to sink to the top of the silicate layer over time scales short relative to simulation time scales (in reality some may mx back into the convecting mixed ice-rock layer). Simulations so far indicate that melting of Titan’s pure ice shell is common early in Solar System history, and that melting frequently extends into Titan’s nominal mixed ice-rock layer. Such melting leads to irreversible unmixing of some of the mixed ice-rock layer. Nearly complete dehydration of the silicate core occurs when condritic K is retained in the rock component. The structural evolution decreases Titan’s initial moment of inertia; however, long-lived radiogenic species are generally incapable of completely melting and separating Titan’s mixed layer. To date, thermally stable structural models with C/MR2 as large as 0.33 have been achieved. We continue to investigate how realistic ocean and
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.
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.
Differential paralog divergence modulates genome evolution across yeast species
Lynch, Bryony; Huang, Mei; Alcantara, Erica; DeSevo, Christopher G.; Pai, Dave A.; Hoang, Margaret L.
2017-01-01
Evolutionary outcomes depend not only on the selective forces acting upon a species, but also on the genetic background. However, large timescales and uncertain historical selection pressures can make it difficult to discern such important background differences between species. Experimental evolution is one tool to compare evolutionary potential of known genotypes in a controlled environment. Here we utilized a highly reproducible evolutionary adaptation in Saccharomyces cerevisiae to investigate whether experimental evolution of other yeast species would select for similar adaptive mutations. We evolved populations of S. cerevisiae, S. paradoxus, S. mikatae, S. uvarum, and interspecific hybrids between S. uvarum and S. cerevisiae for ~200–500 generations in sulfate-limited continuous culture. Wild-type S. cerevisiae cultures invariably amplify the high affinity sulfate transporter gene, SUL1. However, while amplification of the SUL1 locus was detected in S. paradoxus and S. mikatae populations, S. uvarum cultures instead selected for amplification of the paralog, SUL2. We measured the relative fitness of strains bearing deletions and amplifications of both SUL genes from different species, confirming that, converse to S. cerevisiae, S. uvarum SUL2 contributes more to fitness in sulfate limitation than S. uvarum SUL1. By measuring the fitness and gene expression of chimeric promoter-ORF constructs, we were able to delineate the cause of this differential fitness effect primarily to the promoter of S. uvarum SUL1. Our data show evidence of differential sub-functionalization among the sulfate transporters across Saccharomyces species through recent changes in noncoding sequence. Furthermore, these results show a clear example of how such background differences due to paralog divergence can drive changes in genome evolution. PMID:28196070
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
A hybrid algorithm for Caputo fractional differential equations
NASA Astrophysics Data System (ADS)
Salgado, G. H. O.; Aguirre, L. A.
2016-04-01
This paper is concerned with the numerical solution of fractional initial value problems (FIVP) in sense of Caputo's definition for dynamical systems. Unlike for integer-order derivatives that have a single definition, there is more than one definition of non integer-order derivatives and the solution of an FIVP is definition-dependent. In this paper, the chief differences of the main definitions of fractional derivatives are revisited and a numerical algorithm to solve an FIVP for Caputo derivative is proposed. The main advantages of the algorithm are twofold: it can be initialized with integer-order derivatives, and it is faster than the corresponding standard algorithm. The performance of the proposed algorithm is illustrated with examples which suggest that it requires about half the computation time to achieve the same accuracy than the standard algorithm.
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.
Photons and evolution: quantum mechanical processes modulate sexual differentiation.
Davis, George E; Lowell, Walter E
2009-09-01
This paper will show that the fractional difference in the human gender ratio (GR) between the GR(at death) for those born in solar cycle peak years (maxima) and the GR(at death) in those born in solar cycle non-peak years (minima), e.g., 0.023, divided by Pi, yields a reasonable approximation of the quantum mechanical constant, alpha, or the fine structure constant (FSC) approximately 0.007297... or approximately 1/137. This finding is based on a sample of approximately 50 million cases using common, readily available demographic data, e.g., state of birth, birth date, death date, and gender. Physicists Nair, Geim et al. had found precisely the same fractional difference, 0.023, in the absorption of white light (sunlight) by a single-atom thick layer of graphene, a carbon skeleton resembling chicken wire fencing. This absorption fraction, when divided by Pi, yielded the FSC and was the first time this constant could "so directly be assessed practically by the naked eye". As the GR is a reflection of sexual differentiation, this paper reveals that a quantum mechanical process, as manifested by the FSC, is playing a role in the primordial process of replication, a necessary requirement of life. Successful replication is the primary engine driving evolution, which at a biochemical level, is a quantum mechanical process dependent upon photonic energy from the Sun. We propose that a quantum-mechanical, photon-driven chemical evolution preceded natural selection in biology and the mechanisms of mitosis and meiosis are manifestations of this chemical evolution in ancient seas over 3 billion years ago. Evolutionary processes became extant first in self-replicating molecules forced to adapt to high energy photons, mostly likely in the ultraviolet spectrum. These events led to evolution by natural selection as complex mixing of genetic material within species creating the variety needed to match changing environments reflecting the same process initiated at the dawn of life
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.
NASA Astrophysics Data System (ADS)
Kela, K. B.; Arya, L. D.
2014-09-01
This paper describes a methodology for determination of optimum failure rate and repair time for each section of a radial distribution system. An objective function in terms of reliability indices and their target values is selected. These indices depend mainly on failure rate and repair time of a section present in a distribution network. A cost is associated with the modification of failure rate and repair time. Hence the objective function is optimized subject to failure rate and repair time of each section of the distribution network considering the total budget allocated to achieve the task. The problem has been solved using differential evolution and bare bones particle swarm optimization. The algorithm has been implemented on a sample radial distribution system.
Modelling of the thermal evolution and differentiation of early Ceres
NASA Astrophysics Data System (ADS)
Neumann, Wladimir; Breuer, Doris; Spohn, Tilman
2015-04-01
The asteroid 1 Ceres is one of the remaining examples of the intermediate stages of planetary accretion. Studies of such protoplanetary objects provide insight into the history of the formation of Earth and other planets. One of Ceres' remarkable properties is the relatively low average bulk density of 2077±36 kg m-3[1]. Assuming a nearly chondritic composition, this low value can be explained either by the presence of a low density phase[2,3] (possibly water ice or hydrated silicates) that could have differentiated forming an icy mantle over a rocky core[2,3], or by a relatively high average porosity[4]. The shape and the moment of inertia of Ceres are consistent with both a homogeneous and a differentiated structure. In the first case Ceres would be just a large version of a common asteroid. In the second case this body could exhibit properties characteristic for a planet rather than an asteroid: presence of a core, mantle and crust, as well as a liquid ocean in the past and maybe still a thin basal ocean today. We study the evolution of a Ceres-like body via numerical modelling in order to draw conclusions about the thermal metamorphism of the interior and its present-day structure. A numerical model of an ice-silicate planetesimal, considering both water-rock and metal-silicate differentiation of Ceres is being developed. In particular, accretion from a km-sized porous seed to a Ceres-sized asteroid is considered. Further relevant processes, such as transition from amorphous to crystalline ice, melting of ice, hydrothermal convection, as well as melting and percolation of metal and silicates are included in the model. The model is suited to prioritise between the two possible structures mentioned above and to constrain the present-day state of Ceres' interior. The necessary conditions for the differentiation as well as the influence of the vital parameters, such as the accretion duration, will be discussed. [1] Thomas, C. et al. (2005) Nature, 437, 224-226. [2
The Evolution of the Algorithms for Collective Behavior.
Gordon, Deborah M
2016-12-21
Collective behavior is the outcome of a network of local interactions. Here, I consider collective behavior as the result of algorithms that have evolved to operate in response to a particular environment and physiological context. I discuss how algorithms are shaped by the costs of operating under the constraints that the environment imposes, the extent to which the environment is stable, and the distribution, in space and time, of resources. I suggest that a focus on the dynamics of the environment may provide new hypotheses for elucidating the algorithms that produce the collective behavior of cellular systems.
Differences in Cell Division Rates Drive the Evolution of Terminal Differentiation in Microbes
Matias Rodrigues, João F.; Rankin, Daniel J.; Rossetti, Valentina; Wagner, Andreas; Bagheri, Homayoun C.
2012-01-01
Multicellular differentiated organisms are composed of cells that begin by developing from a single pluripotent germ cell. In many organisms, a proportion of cells differentiate into specialized somatic cells. Whether these cells lose their pluripotency or are able to reverse their differentiated state has important consequences. Reversibly differentiated cells can potentially regenerate parts of an organism and allow reproduction through fragmentation. In many organisms, however, somatic differentiation is terminal, thereby restricting the developmental paths to reproduction. The reason why terminal differentiation is a common developmental strategy remains unexplored. To understand the conditions that affect the evolution of terminal versus reversible differentiation, we developed a computational model inspired by differentiating cyanobacteria. We simulated the evolution of a population of two cell types –nitrogen fixing or photosynthetic– that exchange resources. The traits that control differentiation rates between cell types are allowed to evolve in the model. Although the topology of cell interactions and differentiation costs play a role in the evolution of terminal and reversible differentiation, the most important factor is the difference in division rates between cell types. Faster dividing cells always evolve to become the germ line. Our results explain why most multicellular differentiated cyanobacteria have terminally differentiated cells, while some have reversibly differentiated cells. We further observed that symbioses involving two cooperating lineages can evolve under conditions where aggregate size, connectivity, and differentiation costs are high. This may explain why plants engage in symbiotic interactions with diazotrophic bacteria. PMID:22511858
Hui, YU; Ramkrishna, MITRA; Jing, YANG; YuanYuan, LI; ZhongMing, ZHAO
2016-01-01
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. PMID:25326829
Landscape evolution by soil differentiation on African catenas
NASA Astrophysics Data System (ADS)
Khomo, Lesego; Hartshorn, Tony; Heimsath, Arjun; Rogers, Kevin; Chadwick, Oliver
2010-05-01
Landscapes in most of the Southern Hemisphere have escaped geomorphic agents shaping some of Earth's most charismatic stretches of land in the North. Amongst these are mountainous terrains presided over by tumultuous tectonics and landforms created by remnants of past glaciers. In Southern Africa, tectonic activities and glaciations have had a much more limited impact on the present landforms. This quiescent geomorphic regime has led to relatively stable landscapes enabling prolonged soil differentiation to culminate in catenas. Over these extended time scales the soil surprisingly emerges as a significant geomorphic agent in its own right. Catenas in the Kruger National Park, South Africa, form on gentle <8% slopes over 100 to 2000m between hillcrests and footslopes. Erosion rates inferred from in-situ produced cosmogenic Be-10 are relatively low in the range 2 and 4m per Ma and chemical weathering is responsible for up to 80% of these losses. The long soil residence times necessitated by the slow tempo of erosion give rise to intense weathering-driven differentiation between highly leached crests and depositional areas at the foot of the catenas. The crests are as a result volumetrically collapsed relative to parent material as indicated by elevated Zr concentrations while lower down on the catenas the soil fabric becomes dilated and depleted in Zr. The collapses and dilations are measurable, and can be as much as 3m at the crest and a meter's expansion at the foot. Hence pedogenic collapse and dilation on this scale produces real changes in hillslope geomorphology. Is it thus possible for soil development outside physical erosion to act as a geomorphic agent. Soil dilation at the footslope has a further consequence on hillslope geomorphology. Due to material build-up in footslope soils, mostly clay, soil pores are clogged resulting in diversion of water flowpaths to the surface. Over time, this process can result in a fluvial incision that migrates up the slope
NASA Astrophysics Data System (ADS)
Calogero, Francesco
2016-10-01
Recently a simple differential algorithm to compute all the zeros of a generic polynomial was introduced. In this paper an analogous, but finite-difference, algorithm is introduced and discussed. At the end of the paper a minor generalization of the differential algorithm is also mentioned.
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.
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.
Differentiated service in OBS networks using a dynamic FDL bank partitioning algorithm.
Yonggyu, Lee; Kim, Namuk; Kim, Jaegwan; Ahn, Junseop; Kang, Minho
2007-05-14
In order to solve the differentiated service problem in optical burst switching (OBS) networks, we propose a dynamic fiber delay line (FDL) bank partitioning algorithm, which divides a FDL bank into several groups, using a feed-forward output buffering architecture. In the analysis, three classes and groups are considered for traffic and FDL, respectively, and each group is assigned to each class. This paper shows that the loss differentiation in OBS networks is easily accomplished in Poisson traffic environments when our dynamic algorithm is adopted.
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.
Seaton, Colin C; Tremayne, Maryjane
2002-04-21
The crystal structure of a previously unknown triclinic polymorph of adipamide has been solved from laboratory X-ray powder diffraction data using a new direct space global optimisation method based on differential evolution.
Physicians' diagnoses compared with algorithmic differentiation of causes of jaundice.
Boom, R; Chavez-Oest, J; Gonzalez, C; Cantu, M A; Rivero, F; Reyes, A; Aguilar, E; Santamaria, J
1988-01-01
Clinical data were collected in 194 cases of jaundiced patients treated at the "Adolfo Lopez Mateos" ISSSTE Hospital in Mexico City from July 1985 to July 1986. A copy of the clinical history of each patient was given to each of four physicians--one recently graduated from medical school, another in his first year of gastroenterology, and two others who were experienced gastroenterologists. The same clinical data were processed by a computer set up to use a modified Danish COMIC algorithm. All physicians and the computer technician were blinded to the "gold standard" pathologic diagnoses, with which their diagnoses were compared. Accuracy rates of the physicians in distinguishing intrahepatic (medical) from extrahepatic (surgical) jaundice were 78%, 86%, 86%, and 91%, and the accuracy of computer-assisted diagnoses was 96%. Chi-squared analysis of the diagnoses of three of the physicians and those of the computer showed significant differences (p between 0.1 and 0.01). For the diagnoses of the remaining physician, however, no significant difference was found after chi-squared continuity correction.
NASA Astrophysics Data System (ADS)
Salcedo-Sanz, S.; Camacho-Gómez, C.; Magdaleno, A.; Pereira, E.; Lorenzana, A.
2017-04-01
In this paper we tackle a problem of optimal design and location of Tuned Mass Dampers (TMDs) for structures subjected to earthquake ground motions, using a novel meta-heuristic algorithm. Specifically, the Coral Reefs Optimization (CRO) with Substrate Layer (CRO-SL) is proposed as a competitive co-evolution algorithm with different exploration procedures within a single population of solutions. The proposed approach is able to solve the TMD design and location problem, by exploiting the combination of different types of searching mechanisms. This promotes a powerful evolutionary-like algorithm for optimization problems, which is shown to be very effective in this particular problem of TMDs tuning. The proposed algorithm's performance has been evaluated and compared with several reference algorithms in two building models with two and four floors, respectively.
EDGA: A Population Evolution Direction-Guided Genetic Algorithm for Protein-Ligand Docking.
Guan, Boxin; Zhang, Changsheng; Ning, Jiaxu
2016-07-01
Protein-ligand docking can be formulated as a search algorithm associated with an accurate scoring function. However, most current search algorithms cannot show good performance in docking problems, especially for highly flexible docking. To overcome this drawback, this article presents a novel and robust optimization algorithm (EDGA) based on the Lamarckian genetic algorithm (LGA) for solving flexible protein-ligand docking problems. This method applies a population evolution direction-guided model of genetics, in which search direction evolves to the optimum solution. The method is more efficient to find the lowest energy of protein-ligand docking. We consider four search methods-a tradition genetic algorithm, LGA, SODOCK, and EDGA-and compare their performance in docking of six protein-ligand docking problems. The results show that EDGA is the most stable, reliable, and successful.
Efficient algorithms for ordinary differential equation model identification of biological systems.
Gennemark, P; Wedelin, D
2007-03-01
Algorithms for parameter estimation and model selection that identify both the structure and the parameters of an ordinary differential equation model from experimental data are presented. The work presented here focuses on the case of an unknown structure and some time course information available for every variable to be analysed, and this is exploited to make the algorithms as efficient as possible. The algorithms are designed to handle problems of realistic size, where reactions can be nonlinear in the parameters and where data can be sparse and noisy. To achieve computational efficiency, parameters are mostly estimated for one equation at a time, giving a fast and accurate parameter estimation algorithm compared with other algorithms in the literature. The model selection is done with an efficient heuristic search algorithm, where the structure is built incrementally. Two test systems are used that have previously been used to evaluate identification algorithms, a metabolic pathway and a genetic network. Both test systems were successfully identified by using a reasonable amount of simulated data. Besides, measurement noise of realistic levels can be handled. In comparison to other methods that were used for these test systems, the main strengths of the presented algorithms are that a fully specified model, and not only a structure, is identified, and that they are considerably faster compared with other identification algorithms.
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…
Sazonova, V Iu; Fedorova, V E; Danilova, N V
2013-01-01
Pretumoral changes in the epithelium of the cervix uteri include cervical intraepithelial neoplasia (CIN). CIN III should be differentiated with regenerative changes during epidermization of endocervicoses. Epidermization is proliferation of undifferentiated reserve cells that differentiate towards the squamous epithelium, by superseding the ectopic endocervical glandular epithelium. This process was called immature squamous metaplasia (ISM). The objective of the investigation was to define the significance of different morphological signs in the differential diagnosis of CIN III and ISM. One hundred and twelve cervical, CIN III, and immature squamous metaplasia biopsies were selected for examination. The selected cervical specimens were divided into 2 groups according to the presence or absence of p16 and CK17 expression. The p16+, CK17- cases were taken as true CIN III and the pl 6-, CK17+ as a regenerative process. The basis for this investigation is the signs included by O.K. Khmelnitsky into an algorithm for the differential diagnosis of epidermizing pseudoerosion and intraepithelial cancer of the cervix uteri. The algorithm was reconsidered to objectify. The investigation established great differences in the number of significant mitoses in the study groups. A clear trend was found for differences in the number of acanthotic strands. A new differential diagnostic algorithm for CIN III and ISM, which included the number of significant mitoses and acanthotic strands and p16 and CK17 expression, was proposed.
Farzinfar, Mahshid; Teoh, Eam Khwang; Xue, Zhong
2011-11-01
This study proposes an expectation-maximization (EM)-based curve evolution algorithm for segmentation of magnetic resonance brain images. In the proposed algorithm, the evolution curve is constrained not only by a shape-based statistical model but also by a hidden variable model from image observation. The hidden variable model herein is defined by the local voxel labeling, which is unknown and estimated by the expected likelihood function derived from the image data and prior anatomical knowledge. In the M-step, the shapes of the structures are estimated jointly by encoding the hidden variable model and the statistical prior model obtained from the training stage. In the E-step, the expected observation likelihood and the prior distribution of the hidden variables are estimated. In experiments, the proposed automatic segmentation algorithm is applied to multiple gray nuclei structures such as caudate, putamens and thalamus of three-dimensional magnetic resonance imaging in volunteers and patients. As for the robustness and accuracy of the segmentation algorithm, the results of the proposed EM-joint shape-based algorithm outperformed those obtained using the statistical shape model-based techniques in the same framework and a current state-of-the-art region competition level set method.
Analysis of planetary evolution with emphasis on differentiation and dynamics
NASA Technical Reports Server (NTRS)
Kaula, William M.; Newman, William I.
1987-01-01
In order to address the early stages of nebula evolution, a three-dimensional collapse code which includes not only hydrodynamics and radiative transfer, but also the effects of ionization and, possibly, magnetic fields is being addressed. As part of the examination of solar system evolution, an N-body code was developed which describes the latter stages of planet formation from the accretion of planetesimals. To test the code for accuracy and run-time efficiency, and to develop a stronger theoretical foundation, problems were studied in orbital dynamics. A regional analysis of the correlation in the gravity and topography fields of Venus was performed in order to determine the small and intermediate scale subsurface structure.
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
Dong, Li-Yang; Zhou, Wei-Zhong; Ni, Jun-Wei; Xiang, Wei; Hu, Wen-Hao; Yu, Chang; Li, Hai-Yan
2017-02-01
The objective of this study was to identify the optimal gene and gene set for hepatocellular carcinoma (HCC) utilizing differential expression and differential co-expression (DEDC) algorithm. The DEDC algorithm consisted of four parts: calculating differential expression (DE) by absolute t-value in t-statistics; computing differential co-expression (DC) based on Z-test; determining optimal thresholds on the basis of Chi-squared (χ2) maximization and the corresponding gene was the optimal gene; and evaluating functional relevance of genes categorized into different partitions to determine the optimal gene set with highest mean minimum functional information (FI) gain (Δ*G). The optimal thresholds divided genes into four partitions, high DE and high DC (HDE-HDC), high DE and low DC (HDE-LDC), low DE and high DC (LDE‑HDC), and low DE and low DC (LDE-LDC). In addition, the optimal gene was validated by conducting reverse transcription-polymerase chain reaction (RT-PCR) assay. The optimal threshold for DC and DE were 1.032 and 1.911, respectively. Using the optimal gene, the genes were divided into four partitions including: HDE-HDC (2,053 genes), HED-LDC (2,822 genes), LDE-HDC (2,622 genes), and LDE-LDC (6,169 genes). The optimal gene was microtubule‑associated protein RP/EB family member 1 (MAPRE1), and RT-PCR assay validated the significant difference between the HCC and normal state. The optimal gene set was nucleoside metabolic process (GO\\GO:0009116) with Δ*G = 18.681 and 24 HDE-HDC partitions in total. In conclusion, we successfully investigated the optimal gene, MAPRE1, and gene set, nucleoside metabolic process, which may be potential biomarkers for targeted therapy and provide significant insight for revealing the pathological mechanism underlying HCC.
Chang, P
2004-09-15
A differential algebraic integration algorithm is developed for symplectic mapping through a three-dimensional (3-D) magnetic field. The self-consistent reference orbit in phase space is obtained by making a canonical transformation to eliminate the linear part of the Hamiltonian. Transfer maps from the entrance to the exit of any 3-D magnetic field are then obtained through slice-by-slice symplectic integration. The particle phase-space coordinates are advanced by using the integrable polynomial procedure. This algorithm is a powerful tool to attain nonlinear maps for insertion devices in synchrotron light source or complicated magnetic field in the interaction region in high energy colliders.
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.
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
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.
A Knowledge-based Evolution Algorithm approach to political districting problem
NASA Astrophysics Data System (ADS)
Chou, Chung-I.
2011-01-01
The political districting problem is to study how to partition a comparatively large zone into many minor electoral districts. In our previously works, we have mapped this political problem onto a q-state Potts model system by using statistical physics methods. The political constraints (such as contiguity, population equality, etc.) are transformed to an energy function with interactions between sites or external fields acting on the system. Several optimization algorithms such as simulated annealing method and genetic algorithm have been applied to this problem. In this report, we will show how to apply the Knowledge-based Evolution Algorithm (KEA) to the problem. Our test objects include two real cities (Taipei and Kaohsiung) and the simulated cities. The results showed the KEA can reach the same minimum which has been found by using other methods in each test case.
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 adaptive left-right eigenvector evolution algorithm for vibration isolation control
NASA Astrophysics Data System (ADS)
Wu, T. Y.
2009-11-01
The purpose of this research is to investigate the feasibility of utilizing an adaptive left and right eigenvector evolution (ALREE) algorithm for active vibration isolation. As depicted in the previous paper presented by Wu and Wang (2008 Smart Mater. Struct. 17 015048), the structural vibration behavior depends on both the disturbance rejection capability and mode shape distributions, which correspond to the left and right eigenvector distributions of the system, respectively. In this paper, a novel adaptive evolution algorithm is developed for finding the optimal combination of left-right eigenvectors of the vibration isolator, which is an improvement over the simultaneous left-right eigenvector assignment (SLREA) method proposed by Wu and Wang (2008 Smart Mater. Struct. 17 015048). The isolation performance index used in the proposed algorithm is defined by combining the orthogonality index of left eigenvectors and the modal energy ratio index of right eigenvectors. Through the proposed ALREE algorithm, both the left and right eigenvectors evolve such that the isolation performance index decreases, and therefore one can find the optimal combination of left-right eigenvectors of the closed-loop system for vibration isolation purposes. The optimal combination of left-right eigenvectors is then synthesized to determine the feedback gain matrix of the closed-loop system. The result of the active isolation control shows that the proposed method can be utilized to improve the vibration isolation performance compared with the previous approaches.
Towards developing robust algorithms for solving partial differential equations on MIMD machines
NASA Technical Reports Server (NTRS)
Saltz, Joel H.; Naik, Vijay K.
1988-01-01
Methods for efficient computation of numerical algorithms on a wide variety of MIMD machines are proposed. These techniques reorganize the data dependency patterns to improve the processor utilization. The model problem finds the time-accurate solution to a parabolic partial differential equation discretized in space and implicitly marched forward in time. The algorithms are extensions of Jacobi and SOR. The extensions consist of iterating over a window of several timesteps, allowing efficient overlap of computation with communication. The methods increase the degree to which work can be performed while data are communicated between processors. The effect of the window size and of domain partitioning on the system performance is examined both by implementing the algorithm on a simulated multiprocessor system.
Towards developing robust algorithms for solving partial differential equations on MIMD machines
NASA Technical Reports Server (NTRS)
Saltz, J. H.; Naik, V. K.
1985-01-01
Methods for efficient computation of numerical algorithms on a wide variety of MIMD machines are proposed. These techniques reorganize the data dependency patterns to improve the processor utilization. The model problem finds the time-accurate solution to a parabolic partial differential equation discretized in space and implicitly marched forward in time. The algorithms are extensions of Jacobi and SOR. The extensions consist of iterating over a window of several timesteps, allowing efficient overlap of computation with communication. The methods increase the degree to which work can be performed while data are communicated between processors. The effect of the window size and of domain partitioning on the system performance is examined both by implementing the algorithm on a simulated multiprocessor system.
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.
Çoban, Gürsan; Çelebi, M Serdar
2016-09-10
In this work, we constructed a novel collagen fibre remodelling algorithm that incorporates the complex nature of random evolution acting on single fibres causing macroscopic fibre dispersion. The proposed framework is different from the existing remodelling algorithms, in that the microscopic random force on cellular scales causing a rotational-type Brownian motion alone is considered as an aspect of vascular tissue remodelling. A continuum mechanical framework for the evolution of local dispersion and how it could be used for modeling the evolution of internal radius of biaxially strained artery structures under constant internal blood pressure are presented. A linear evolution form for the statistical fibre dispersion is employed in the model. The random force component of the evolution, which depends on the mechanical stress stimuli, is described by a single parameter. Although the mathematical form of the proposed model is simple, there is a strong link between the microscopic evolution of collagen dispersion on the cellular level and its effects on the macroscopic visible world through mechanical variables. We believe that the proposed algorithm utilizes a better understanding of the relationship between the evolution rates of mean fibre direction and fibre dispersion. The predictive capability of the algorithm is presented using experimental data. The model has been simulated by solving a single-layered axisymmetric artery (adventitia) deformation problem. The algorithm performed well for estimating the quantitative features of experimental anisotropy, the mean fibre direction vector and the dispersion ([Formula: see text]) measurements under strain-dependent evolution assumptions.
Finitely approximable random sets and their evolution via differential equations
NASA Astrophysics Data System (ADS)
Ananyev, B. I.
2016-12-01
In this paper, random closed sets (RCS) in Euclidean space are considered along with their distributions and approximation. Distributions of RCS may be used for the calculation of expectation and other characteristics. Reachable sets on initial data and some ways of their approximate evolutionary description are investigated for stochastic differential equations (SDE) with initial state in some RCS. Markov property of random reachable sets is proved in the space of closed sets. For approximate calculus, the initial RCS is replaced by a finite set on the integer multidimensional grid and the multistage Markov chain is substituted for SDE. The Markov chain is constructed by methods of SDE numerical integration. Some examples are also given.
Grainsize evolution and differential comminution in an experimental regolith
NASA Technical Reports Server (NTRS)
Horz, F.; Cintala, M.; See, T.
1984-01-01
The comminution of planetary surfaces by exposure to continuous meteorite bombardment was simulated by impacting the same fragmental gabbro target 200 times. The role of comminution and in situ gardening of planetary regoliths was addressed. Mean grain size continuously decreased with increasing shot number. Initially it decreased linearly with accumulated energy, but at some stage comminution efficiency started to decrease gradually. Point counting techniques, aided by the electron microprobe for mineral identification, were performed on a number of comminution products. Bulk chemical analyses of specific grain size fractions were also carried out. The finest sizes ( 10 microns) display generally the strongest enrichment/depletion factors. Similar, if not exactly identical, trends are reported from lunar soils. It is, therefore, not necessarily correct to explain the chemical characteristics of various grain sizes via different admixtures of materials from distant source terrains. Differential comminution of local source rocks may be the dominating factor.
NASA Astrophysics Data System (ADS)
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 linear-time algorithm for Gaussian and non-Gaussian trait evolution models.
Ho, Lam si Tung; Ané, Cécile
2014-05-01
We developed a linear-time algorithm applicable to a large class of trait evolution models, for efficient likelihood calculations and parameter inference on very large trees. Our algorithm solves the traditional computational burden associated with two key terms, namely the determinant of the phylogenetic covariance matrix V and quadratic products involving the inverse of V. Applications include Gaussian models such as Brownian motion-derived models like Pagel's lambda, kappa, delta, and the early-burst model; Ornstein-Uhlenbeck models to account for natural selection with possibly varying selection parameters along the tree; as well as non-Gaussian models such as phylogenetic logistic regression, phylogenetic Poisson regression, and phylogenetic generalized linear mixed models. Outside of phylogenetic regression, our algorithm also applies to phylogenetic principal component analysis, phylogenetic discriminant analysis or phylogenetic prediction. The computational gain opens up new avenues for complex models or extensive resampling procedures on very large trees. We identify the class of models that our algorithm can handle as all models whose covariance matrix has a 3-point structure. We further show that this structure uniquely identifies a rooted tree whose branch lengths parametrize the trait covariance matrix, which acts as a similarity matrix. The new algorithm is implemented in the R package phylolm, including functions for phylogenetic linear regression and phylogenetic logistic regression.
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).
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.
odNEAT: An Algorithm for Decentralised Online Evolution of Robotic Controllers.
Silva, Fernando; Urbano, Paulo; Correia, Luís; Christensen, Anders Lyhne
2015-01-01
Online evolution gives robots the capacity to learn new tasks and to adapt to changing environmental conditions during task execution. Previous approaches to online evolution of neural controllers are typically limited to the optimisation of weights in networks with a prespecified, fixed topology. In this article, we propose a novel approach to online learning in groups of autonomous robots called odNEAT. odNEAT is a distributed and decentralised neuroevolution algorithm that evolves both weights and network topology. We demonstrate odNEAT in three multirobot tasks: aggregation, integrated navigation and obstacle avoidance, and phototaxis. Results show that odNEAT approximates the performance of rtNEAT, an efficient centralised method, and outperforms IM-(μ + 1), a decentralised neuroevolution algorithm. Compared with rtNEAT and IM-(μ + 1), odNEAT's evolutionary dynamics lead to the synthesis of less complex neural controllers with superior generalisation capabilities. We show that robots executing odNEAT can display a high degree of fault tolerance as they are able to adapt and learn new behaviours in the presence of faults. We conclude with a series of ablation studies to analyse the impact of each algorithmic component on performance.
Evolution of Social Insect Polyphenism Facilitated by the Sex Differentiation Cascade
Klein, Antonia; Schultner, Eva; Lowak, Helena; Schrader, Lukas; Heinze, Jürgen; Holman, Luke; Oettler, Jan
2016-01-01
The major transition to eusociality required the evolution of a switch to canalize development into either a reproductive or a helper, the nature of which is currently unknown. Following predictions from the ‘theory of facilitated variation’, we identify sex differentiation pathways as promising candidates because of their pre-adaptation to regulating development of complex phenotypes. We show that conserved core genes, including the juvenile hormone-sensitive master sex differentiation gene doublesex (dsx) and a krüppel homolog 2 (kr-h2) with putative regulatory function, exhibit both sex and morph-specific expression across life stages in the ant Cardiocondyla obscurior. We hypothesize that genes in the sex differentiation cascade evolved perception of alternative input signals for caste differentiation (i.e. environmental or genetic cues), and that their inherent switch-like and epistatic behavior facilitated signal transfer to downstream targets, thus allowing them to control differential development into morphological castes. PMID:27031240
Patterson, Larissa B; Bain, Emily J; Parichy, David M
2014-11-06
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.
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
Macro-micro interlocked simulation algorithm: an exemplification for aurora arc evolution
NASA Astrophysics Data System (ADS)
Sato, Tetsuya; Hasegawa, Hiroki; Ohno, Nobuaki
2009-01-01
Using an innovative holistic simulation algorithm that can self-consistently treat a system that evolves as cooperation between macroscopic and microscopic processes, the evolution of a colorful aurora arc is beautifully reproduced as the result of cooperation between the global field-aligned feedback instability of the coupled magnetosphere-ionosphere system and the ensuing microscopic ion-acoustic instability that generates electric double layers and accelerates aurora electrons. These results are in agreement with rocket and satellite observations. This shows that the proposed holistic algorithm could be a reliable tool to reveal complex real dramatic events and become, in the near future, a viable scientifically secure prediction tool for natural disasters such as earthquakes, landslides and floods caused by typhoons.
Cognitive algorithms: dynamic logic, working of the mind, evolution of consciousness and cultures
NASA Astrophysics Data System (ADS)
Perlovsky, Leonid I.
2007-04-01
The paper discusses evolution of consciousness driven by the knowledge instinct, a fundamental mechanism of the mind which determines its higher cognitive functions. Dynamic logic mathematically describes the knowledge instinct. It overcomes past mathematical difficulties encountered in modeling intelligence and relates it to mechanisms of concepts, emotions, instincts, consciousness and unconscious. The two main aspects of the knowledge instinct are differentiation and synthesis. Differentiation is driven by dynamic logic and proceeds from vague and unconscious states to more crisp and conscious states, from less knowledge to more knowledge at each hierarchical level of the mind. Synthesis is driven by dynamic logic operating in a hierarchical organization of the mind; it strives to achieve unity and meaning of knowledge: every concept finds its deeper and more general meaning at a higher level. These mechanisms are in complex relationship of symbiosis and opposition, which leads to complex dynamics of evolution of consciousness and cultures. Modeling this dynamics in a population leads to predictions for the evolution of consciousness, and cultures. Cultural predictive models can be compared to experimental data and used for improvement of human conditions. We discuss existing evidence and future research directions.
2009-01-01
Whether phenotypic evolution proceeds predominantly through changes in regulatory sequences is a controversial issue in evolutionary genetics. Ample evidence indicates that the evolution of gene regulatory networks via changes in cis-regulatory sequences is an important determinant of phenotypic diversity. However, recent experimental work suggests that the role of transcription factor (TF) divergence in developmental evolution may be underestimated. In order to help understand what levels of constraints are acting on the coding sequence of developmental regulatory genes, evolutionary rates were investigated among 48 TFs required for neuronal development in Caenorhabditis elegans. Allelic variation was then sampled for 28 of these genes within a population of the related species Caenorhabditis remanei. Neuronal TFs are more divergent, both within and between species, than structural genes. TFs affecting different neuronal classes are under different levels of selective constraints. The regulatory genes controlling the differentiation of chemosensory neurons evolve particularly fast and exhibit higher levels of within- and between-species nucleotide variation than TFs required for the development of several neuronal classes and TFs required for motorneuron differentiation. The TFs affecting chemosensory neuron development are also more divergent than chemosensory genes expressed in the neurons they differentiate. These results illustrate that TFs are not as highly constrained as commonly thought and suggest that the role of divergence in developmental regulatory genes during the evolution of gene regulatory networks requires further attention. PMID:19589887
Vrettas, Michail D; Opper, Manfred; Cornford, Dan
2015-01-01
This work introduces a Gaussian variational mean-field approximation for inference in dynamical systems which can be modeled by ordinary stochastic differential equations. This new approach allows one to express the variational free energy as a functional of the marginal moments of the approximating Gaussian process. A restriction of the moment equations to piecewise polynomial functions, over time, dramatically reduces the complexity of approximate inference for stochastic differential equation models and makes it comparable to that of discrete time hidden Markov models. The algorithm is demonstrated on state and parameter estimation for nonlinear problems with up to 1000 dimensional state vectors and compares the results empirically with various well-known inference methodologies.
NASA Technical Reports Server (NTRS)
Green, Lawrence L.; Newman, Perry A.; Haigler, Kara J.
1993-01-01
The computational technique of automatic differentiation (AD) is applied to a three-dimensional thin-layer Navier-Stokes multigrid flow solver to assess the feasibility and computational impact of obtaining exact sensitivity derivatives typical of those needed for sensitivity analyses. Calculations are performed for an ONERA M6 wing in transonic flow with both the Baldwin-Lomax and Johnson-King turbulence models. The wing lift, drag, and pitching moment coefficients are differentiated with respect to two different groups of input parameters. The first group consists of the second- and fourth-order damping coefficients of the computational algorithm, whereas the second group consists of two parameters in the viscous turbulent flow physics modelling. Results obtained via AD are compared, for both accuracy and computational efficiency with the results obtained with divided differences (DD). The AD results are accurate, extremely simple to obtain, and show significant computational advantage over those obtained by DD for some cases.
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.
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.
NASA Astrophysics Data System (ADS)
Moryakov, A. V.
2016-12-01
An algorithm for solving the linear Cauchy problem for large systems of ordinary differential equations is presented. The algorithm for systems of first-order differential equations is implemented in the EDELWEISS code with the possibility of parallel computations on supercomputers employing the MPI (Message Passing Interface) standard for the data exchange between parallel processes. The solution is represented by a series of orthogonal polynomials on the interval [0, 1]. The algorithm is characterized by simplicity and the possibility to solve nonlinear problems with a correction of the operator in accordance with the solution obtained in the previous iterative process.
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.
Multiple Active Contours Guided by Differential Evolution for Medical Image Segmentation
Cruz-Aceves, I.; Avina-Cervantes, J. G.; Lopez-Hernandez, J. M.; Rostro-Gonzalez, H.; Garcia-Capulin, C. H.; Torres-Cisneros, M.; Guzman-Cabrera, R.
2013-01-01
This paper presents a new image segmentation method based on multiple active contours guided by differential evolution, called MACDE. The segmentation method uses differential evolution over a polar coordinate system to increase the exploration and exploitation capabilities regarding the classical active contour model. To evaluate the performance of the proposed method, a set of synthetic images with complex objects, Gaussian noise, and deep concavities is introduced. Subsequently, MACDE is applied on datasets of sequential computed tomography and magnetic resonance images which contain the human heart and the human left ventricle, respectively. Finally, to obtain a quantitative and qualitative evaluation of the medical image segmentations compared to regions outlined by experts, a set of distance and similarity metrics has been adopted. According to the experimental results, MACDE outperforms the classical active contour model and the interactive Tseng method in terms of efficiency and robustness for obtaining the optimal control points and attains a high accuracy segmentation. PMID:23983809
Martín, Fernando; Moreno, Luis; Garrido, Santiago; Blanco, Dolores
2015-09-16
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.
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
Ohtani, Misato; Akiyoshi, Nobuhiro; Takenaka, Yuto; Sano, Ryosuke; Demura, Taku
2017-01-01
One crucial problem that plants faced during their evolution, particularly during the transition to growth on land, was how to transport water, nutrients, metabolites, and small signaling molecules within a large, multicellular body. As a solution to this problem, land plants developed specific tissues for conducting molecules, called water-conducting cells (WCCs) and food-conducting cells (FCCs). The well-developed WCCs and FCCs in extant plants are the tracheary elements and sieve elements, respectively, which are found in vascular plants. Recent molecular genetic studies revealed that transcriptional networks regulate the differentiation of tracheary and sieve elements, and that the networks governing WCC differentiation are largely conserved among land plant species. In this review, we discuss the molecular evolution of plant conducting cells. By focusing on the evolution of the key transcription factors that regulate vascular cell differentiation, the NAC transcription factor VASCULAR-RELATED NAC-DOMAIN for WCCs and the MYB-coiled-coil (CC)-type transcription factor ALTERED PHLOEM DEVELOPMENT for sieve elements, we describe how land plants evolved molecular systems to produce the specialized cells that function as WCCs and FCCs.
Model based on a quantum algorithm to study the evolution of an epidemics.
León, A; Pozo, J
2007-03-01
A model based on a quantum algorithm is used to study the spread of HIV virus and to predict infection rates on individuals who are not aware of their particular condition. The model makes an analogy between quantum systems and individuals who are infected by the disease. Individuals are represented by two-level quantum systems (quantum "bit"), and the interactions among individuals who cause the infection are represented by unitary transformations. The population is divided into categories according to their behaviour, and the interactions among those individuals in the same category and those in different categories are simulated. The objective is to obtain statistical data on the number of infected individuals depending on the time for every category and for the entire population. Besides, we analyse the impact of the evolution of the disease on individuals who have not knowledge of their specific sanitary condition.
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.
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.
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
Deakin, Janine E
2017-04-01
Studies of chromosomes from monotremes and marsupials endemic to Australasia have provided important insight into the evolution of their genomes as well as uncovering fundamental differences in their sex determination/differentiation pathways. Great advances have been made this century into solving the mystery of the complicated sex chromosome system in monotremes. Monotremes possess multiple different X and Y chromosomes and a candidate sex determining gene has been identified. Even greater advancements have been made for marsupials, with reconstruction of the ancestral karyotype enabling the evolutionary history of marsupial chromosomes to be determined. Furthermore, the study of sex chromosomes in intersex marsupials has afforded insight into differences in the sexual differentiation pathway between marsupials and eutherians, together with experiments showing the insensitivity of the mammary glands, pouch and scrotum to exogenous hormones, led to the hypothesis that there is a gene (or genes) on the X chromosome responsible for the development of either pouch or scrotum. This review highlights the major advancements made towards understanding chromosome evolution and how this has impacted on our understanding of sex determination and differentiation in these interesting mammals.
Gong, Li-gang; Yang, Wen-lun
2014-01-01
Unmanned combat aerial vehicles (UCAVs) have been of great interest to military organizations throughout the world due to their outstanding capabilities to operate in dangerous or hazardous environments. UCAV path planning aims to obtain an optimal flight route with the threats and constraints in the combat field well considered. In this work, a novel artificial bee colony (ABC) algorithm improved by a balance-evolution strategy (BES) is applied in this optimization scheme. In this new algorithm, convergence information during the iteration is fully utilized to manipulate the exploration/exploitation accuracy and to pursue a balance between local exploitation and global exploration capabilities. Simulation results confirm that BE-ABC algorithm is more competent for the UCAV path planning scheme than the conventional ABC algorithm and two other state-of-the-art modified ABC algorithms. PMID:24790555
Li, Bai; Gong, Li-gang; Yang, Wen-lun
2014-01-01
Unmanned combat aerial vehicles (UCAVs) have been of great interest to military organizations throughout the world due to their outstanding capabilities to operate in dangerous or hazardous environments. UCAV path planning aims to obtain an optimal flight route with the threats and constraints in the combat field well considered. In this work, a novel artificial bee colony (ABC) algorithm improved by a balance-evolution strategy (BES) is applied in this optimization scheme. In this new algorithm, convergence information during the iteration is fully utilized to manipulate the exploration/exploitation accuracy and to pursue a balance between local exploitation and global exploration capabilities. Simulation results confirm that BE-ABC algorithm is more competent for the UCAV path planning scheme than the conventional ABC algorithm and two other state-of-the-art modified ABC algorithms.
Integrable nonlinear evolution partial differential equations in 4 + 2 and 3 + 1 dimensions.
Fokas, A S
2006-05-19
The derivation and solution of integrable nonlinear evolution partial differential equations in three spatial dimensions has been the holy grail in the field of integrability since the late 1970s. The celebrated Korteweg-de Vries and nonlinear Schrödinger equations, as well as the Kadomtsev-Petviashvili (KP) and Davey-Stewartson (DS) equations, are prototypical examples of integrable evolution equations in one and two spatial dimensions, respectively. Do there exist integrable analogs of these equations in three spatial dimensions? In what follows, I present a positive answer to this question. In particular, I first present integrable generalizations of the KP and DS equations, which are formulated in four spatial dimensions and which have the novelty that they involve complex time. I then impose the requirement of real time, which implies a reduction to three spatial dimensions. I also present a method of solution.
NASA Astrophysics Data System (ADS)
Wang, Guanghui; Chen, Jie; Cai, Tao; Xin, Bin
2013-09-01
This article proposes a decomposition-based multi-objective differential evolution particle swarm optimization (DMDEPSO) algorithm for the design of a tubular permanent magnet linear synchronous motor (TPMLSM) which takes into account multiple conflicting objectives. In the optimization process, the objectives are evaluated by an artificial neural network response surface (ANNRS), which is trained by the samples of the TPMSLM whose performances are calculated by finite element analysis (FEA). DMDEPSO which hybridizes differential evolution (DE) and particle swarm optimization (PSO) together, first decomposes the multi-objective optimization problem into a number of single-objective optimization subproblems, each of which is associated with a Pareto optimal solution, and then optimizes these subproblems simultaneously. PSO updates the position of each particle (solution) according to the best information about itself and its neighbourhood. If any particle stagnates continuously, DE relocates its position by using two different particles randomly selected from the whole swarm. Finally, based on the DMDEPSO, optimization is gradually carried out to maximize the thrust of TPMLSM and minimize the ripple, permanent magnet volume, and winding volume simultaneously. The result shows that the optimized TPMLSM meets or exceeds the performance requirements. In addition, comparisons with chosen algorithms illustrate the effectiveness of DMDEPSO to find the Pareto optimal solutions for the TPMLSM optimization problem.
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.
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
NASA Astrophysics Data System (ADS)
Yan, Zhenya
2003-04-01
In this paper based on a system of Riccati equations with variable coefficients, we present a new Riccati equation with variable coefficients expansion method and its algorithm, which are direct and more powerful than the tanh-function method, sine-cosine method, the generalized hyperbolic-function method and the generalized Riccati equation with constant coefficient expansion method to construct more new exact solutions of nonlinear differential equations in mathematical physics. A pair of generalized Hamiltonian equations is chosen to illustrate our algorithm such that more families of new exact solutions are obtained which contain soliton-like solution and periodic solutions. This algorithm can also be applied to other nonlinear differential equations.
Lobato, Fran Sérgio; Machado, Vinicius Silvério; Steffen, Valder
2016-07-01
The mathematical modeling of physical and biologic systems represents an interesting alternative to study the behavior of these phenomena. In this context, the development of mathematical models to simulate the dynamic behavior of tumors is configured as an important theme in the current days. Among the advantages resulting from using these models is their application to optimization and inverse problem approaches. Traditionally, the formulated Optimal Control Problem (OCP) has the objective of minimizing the size of tumor cells by the end of the treatment. In this case an important aspect is not considered, namely, the optimal concentrations of drugs may affect the patients' health significantly. In this sense, the present work has the objective of obtaining an optimal protocol for drug administration to patients with cancer, through the minimization of both the cancerous cells concentration and the prescribed drug concentration. The resolution of this multi-objective problem is obtained through the Multi-objective Optimization Differential Evolution (MODE) algorithm. The Pareto's Curve obtained supplies a set of optimal protocols from which an optimal strategy for drug administration can be chosen, according to a given criterion.
NASA Astrophysics Data System (ADS)
Peckham, S. D.
2010-12-01
The Community Surface Dynamics Modeling System (CSDMS) has an ever-growing collection of reusable, plug-and-play components for earth surface process modeling and this includes numerous components for spatial hydrologic and landscape evolution modeling. While components may represent any level of granularity from a simple function to a complete hydrologic model, the optimum level appears to be that of a particular physical process, such as infiltration, evaporation or snowmelt. It is at this level of complexity that researchers are most often interested in "swapping out" one method of modeling a process for another that differs in terms of required input, complexity, accuracy, or computational efficiency. CSDMS model components are designed for maximum reusability and strict adherence to this simple-sounding goal has proven to be a powerful decider when it comes to chosing between a number of different design choices. For example, it determines key aspects of a component's interface, and the need for each component to have or manage its own state variables, input files, output files and help files. As a result, each component can be used either as a stand-alone "submodel" or as a component in some larger model. Components do not, however, need to be written in the same language because the CSDMS project employs a powerful language-interoperability tool called Babel. The purpose of this talk is to share a few lessons learned from the CSDMS project, to provide an overview of the many components that are currently available, and to briefly present performance results from a new fluvial landscape evolution algorithm.
Differential adhesion between moving particles as a mechanism for the evolution of social groups.
Garcia, Thomas; Brunnet, Leonardo Gregory; De Monte, Silvia
2014-02-01
The evolutionary stability of cooperative traits, that are beneficial to other individuals but costly to their carrier, is considered possible only through the establishment of a sufficient degree of assortment between cooperators. Chimeric microbial populations, characterized by simple interactions between unrelated individuals, restrain the applicability of standard mechanisms generating such assortment, in particular when cells disperse between successive reproductive events such as happens in Dicyostelids and Myxobacteria. In this paper, we address the evolutionary dynamics of a costly trait that enhances attachment to others as well as group cohesion. By modeling cells as self-propelled particles moving on a plane according to local interaction forces and undergoing cycles of aggregation, reproduction and dispersal, we show that blind differential adhesion provides a basis for assortment in the process of group formation. When reproductive performance depends on the social context of players, evolution by natural selection can lead to the success of the social trait, and to the concomitant emergence of sizeable groups. We point out the conditions on the microscopic properties of motion and interaction that make such evolutionary outcome possible, stressing that the advent of sociality by differential adhesion is restricted to specific ecological contexts. Moreover, we show that the aggregation process naturally implies the existence of non-aggregated particles, and highlight their crucial evolutionary role despite being largely neglected in theoretical models for the evolution of sociality.
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.
Sex-differential selection and the evolution of X inactivation strategies.
Connallon, Tim; Clark, Andrew G
2013-04-01
X inactivation--the transcriptional silencing of one X chromosome copy per female somatic cell--is universal among therian mammals, yet the choice of which X to silence exhibits considerable variation among species. X inactivation strategies can range from strict paternally inherited X inactivation (PXI), which renders females haploid for all maternally inherited alleles, to unbiased random X inactivation (RXI), which equalizes expression of maternally and paternally inherited alleles in each female tissue. However, the underlying evolutionary processes that might account for this observed diversity of X inactivation strategies remain unclear. We present a theoretical population genetic analysis of X inactivation evolution and specifically consider how conditions of dominance, linkage, recombination, and sex-differential selection each influence evolutionary trajectories of X inactivation. The results indicate that a single, critical interaction between allelic dominance and sex-differential selection can select for a broad and continuous range of X inactivation strategies, including unequal rates of inactivation between maternally and paternally inherited X chromosomes. RXI is favored over complete PXI as long as alleles deleterious to female fitness are sufficiently recessive, and the criteria for RXI evolution is considerably more restrictive when fitness variation is sexually antagonistic (i.e., alleles deleterious to females are beneficial to males) relative to variation that is deleterious to both sexes. Evolutionary transitions from PXI to RXI also generally increase mean relative female fitness at the expense of decreased male fitness. These results provide a theoretical framework for predicting and interpreting the evolution of chromosome-wide expression of X-linked genes and lead to several useful predictions that could motivate future studies of allele-specific gene expression variation.
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.
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
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.
NASA Astrophysics Data System (ADS)
Wang, Xiaodong; Ye, Meiying; Jiang, Minlan
2013-10-01
A signal processing method for reflective fiber optic displacement sensor is presented by means of a differential evolution optimized extreme learning machine (DE-ELM). The sensing head of the sensor is a combination of an illuminating fiber bundle transmitting incoming light beam and two receiving fiber bundles employed to collect the reflected beam from the reflector. Three fiber bundles with same type are put together and arranged side by side, but the two receiving fiber bundles enfaces have different distances from the reflector surface. The DE-ELM is used for extending the measuring the range of reflective fiber optic displacement sensor. A simulation experiment has been illustrated. The experimental results show that the measuring range can be extended to the whole response characteristics of the fiber optic displacement sensor and a high measuring accuracy can be obtained by the proposed method.
NASA Astrophysics Data System (ADS)
Amjad, M.; Salam, Z.; Ishaque, K.
2014-04-01
In order to design an efficient resonant power supply for ozone gas generator, it is necessary to accurately determine the parameters of the ozone chamber. In the conventional method, the information from Lissajous plot is used to estimate the values of these parameters. However, the experimental setup for this purpose can only predict the parameters at one operating frequency and there is no guarantee that it results in the highest ozone gas yield. This paper proposes a new approach to determine the parameters using a search and optimization technique known as Differential Evolution (DE). The desired objective function of DE is set at the resonance condition and the chamber parameter values can be searched regardless of experimental constraints. The chamber parameters obtained from the DE technique are validated by experiment.
On Improving Efficiency of Differential Evolution for Aerodynamic Shape Optimization Applications
NASA Technical Reports Server (NTRS)
Madavan, Nateri K.
2004-01-01
Differential Evolution (DE) is a simple and robust evolutionary strategy that has been proven effective in determining the global optimum for several difficult optimization problems. Although DE offers several advantages over traditional optimization approaches, its use in applications such as aerodynamic shape optimization where the objective function evaluations are computationally expensive is limited by the large number of function evaluations often required. In this paper various approaches for improving the efficiency of DE are reviewed and discussed. These approaches are implemented in a DE-based aerodynamic shape optimization method that uses a Navier-Stokes solver for the objective function evaluations. Parallelization techniques on distributed computers are used to reduce turnaround times. Results are presented for the inverse design of a turbine airfoil. The efficiency improvements achieved by the different approaches are evaluated and compared.
Thermal Evolution of Charon and the Major Satellites of Uranus: Constraints on Early Differentiation
NASA Astrophysics Data System (ADS)
Spohn, T.; Multhaup, K.
2007-12-01
A thermal history model developed for medium-sized icy satellites containing silicate rock at low volume fractions is applied to Charon and the satellites of Uranus Ariel, Umbriel, Titania, Oberon and Miranda. The model assumes homogeneously accreted satellites. To calculate the initial temperature profile we assume that infalling planetesimals deposit a fraction h of their kinetic energy as heat at the instantaneous surface of the growing satellites. The parameter h is varied between models. The model continuously checks for convectively unstable shells in the interior by updating the temperature profile and calculating the Rayleigh number and the temperature-dependent viscosity. The viscosity parameter values are taken as those of ice I although the satellites under consideration likely contain admixtures of lighter constituents. Their effects and those of rock on the viscosity are discussed. Convective heat transport is calculated assuming the stagnant lid model for strongly temperature dependent viscosity. In convectively stable regions heat transfer is by conduction with a temperature dependent thermal conductivity. Thermal evolution calculations considering radiogenic heating by the long-lived radiogenic isotopes of U, Th, and K suggest that Ariel, Umbriel, Titania, Oberon and Charon may have started to differentiate after a few hundred million years of evolution. With short-lived isotopes -- if present in sizeable concentrations -- this time will move earlier. Results for Miranda -- the smallest satellite of Uranus -- indicate that it never convected or differentiated if heated by the said long-lived isotopes only. Miranda's interior temperature was found to be not even close to the melting temperatures of reasonable mixtures of water and ammonia. This finding is in contrast to its heavily modified surface and supports theories that propose alternative heating mechanisms such as the decay of short-lived isotopes or early tidal heating.
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
2015-01-01
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. PMID:26019253
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.
Shi, Mingren; Renton, Michael
2011-10-01
Computational simulation models can provide a way of understanding and predicting insect population dynamics and evolution of resistance, but the usefulness of such models depends on generating or estimating the values of key parameters. In this paper, we describe four numerical algorithms generating or estimating key parameters for simulating four different processes within such models. First, we describe a novel method to generate an offspring genotype table for one- or two-locus genetic models for simulating evolution of resistance, and how this method can be extended to create offspring genotype tables for models with more than two loci. Second, we describe how we use a generalized inverse matrix to find a least-squares solution to an over-determined linear system for estimation of parameters in probit models of kill rates. This algorithm can also be used for the estimation of parameters of Freundlich adsorption isotherms. Third, we describe a simple algorithm to randomly select initial frequencies of genotypes either without any special constraints or with some pre-selected frequencies. Also we give a simple method to calculate the "stable" Hardy-Weinberg equilibrium proportions that would result from these initial frequencies. Fourth we describe how the problem of estimating the intrinsic rate of natural increase of a population can be converted to a root-finding problem and how the bisection algorithm can then be used to find the rate. We implemented all these algorithms using MATLAB and Python code; the key statements in both codes consist of only a few commands and are given in the appendices. The results of numerical experiments are also provided to demonstrate that our algorithms are valid and efficient.
Fixation times in differentiation and evolution in the presence of bottlenecks, deserts, and oases.
Chou, Tom; Wang, Yu
2015-05-07
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
2015-01-01
Cellular differentiation and evolution are stochastic processes that can involve multiple types (or states) of particles moving on a complex, high-dimensional state-space or “fitness” landscape. Cells of each specific type can thus be quantified by their population at a corresponding node within a network of states. Their dynamics across the state-space network involve genotypic or phenotypic transitions that can occur upon cell division, such as during symmetric or asymmetric cell differentiation, or upon spontaneous mutation. Here, we use a general multi-type branching processes to study first passage time statistics for a single cell to appear in a specific state. Our approach readily allows for nonexponentially distributed waiting times between transitions, reflecting, e.g., the cell cycle. For simplicity, we restrict most of our detailed analysis to exponentially distributed waiting times (Poisson processes). We present results for a sequential evolutionary process in which L successive transitions propel a population from a “wild-type” state to a given “terminally differentiated,” “resistant,” or “cancerous” state. Analytic and numeric results are also found for first passage times across an evolutionary chain containing a node with increased death or proliferation rate, representing a desert/bottleneck or an oasis. Processes involving cell proliferation are shown to be “nonlinear” (even though mean-field equations for the expected particle numbers are linear) resulting in first passage time statistics that depend on the position of the bottleneck or oasis. Our results highlight the sensitivity of stochastic measures to cell division fate and quantify the limitations of using certain approximations (such as the fixed-population and mean-field assumptions) in evaluating fixation times. PMID:25744205
NASA Astrophysics Data System (ADS)
Chang, L.; Chen, Y.; Pan, C.
2009-12-01
Surface water resources are strongly influenced by hydrological conditions, and using only surface water resources as water supplies may have higher shortage risk than before because of the climate change caused by the global warming. Conjunctive use of surface and subsurface water is one of the most effective water resource practices to increase water supply reliability with minimal cost and environmental impact. Therefore, this paper presents a novel stepwise optimization model for optimizing the conjunctive use of surface and subsurface water resources management. At each time step, a two level decomposition approach was proposed to divide the nonlinear optimal conjunctive use problem into a linear surface water subproblem and a nonlinear groundwater subproblem. Because of the two level decomposition approach, a hybrid framework is used for the implementation of the conjunctive use model. In the hybrid framework, evolution algorithms, Genetic Algorithm (GA) and Artificial Neural Network (ANN), and Linear Programming (LP) are used for model solving. GA and LP are respectively used for determining the optimal pumping quantities and reservoir allocation, and ANN is used for the groundwater simulation. In the groundwater simulation, this study uses an ANN to simulate groundwater response and greatly reduce computational loading for unconfined aquifers, unlike conventional “response matrix method” or “embedding method”. Because of the very high performance of LP, the usage of LP for the linear surface water subproblem can significantly decrease the computational burden of entire model. In this study, four cases have been demonstrated. Case #1 is a pure surface water case and others are conjunctive use cases. In Case #2, “surface water supply firstly” is the supply principle between surface water. In Case #3 and #4, the “Index Balance” theory is the supply principle and different operation curves used in different cases respectively. The case result
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).
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)
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
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
2012-11-02
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
2012-01-01
Background This paper lies in the context of modeling the evolution of gene expression away from stationary states, for example in systems subject to external perturbations or during the development of an organism. We base our analysis on experimental data and proceed in a top-down approach, where we start from data on a system's transcriptome, and deduce rules and models from it without a priori knowledge. We focus here on a publicly available DNA microarray time series, representing the transcriptome of Drosophila across evolution from the embryonic to the adult stage. Results In the first step, genes were clustered on the basis of similarity of their expression profiles, measured by a translation-invariant and scale-invariant distance that proved appropriate for detecting transitions between development stages. Average profiles representing each cluster were computed and their time evolution was analyzed using coupled differential equations. A linear and several non-linear model structures involving a transcription and a degradation term were tested. The parameters were identified in three steps: determination of the strongest connections between genes, optimization of the parameters defining these connections, and elimination of the unnecessary parameters using various reduction schemes. Different solutions were compared on the basis of their abilities to reproduce the data, to keep realistic gene expression levels when extrapolated in time, to show the biologically expected robustness with respect to parameter variations, and to contain as few parameters as possible. Conclusions We showed that the linear model did very well in reproducing the data with few parameters, but was not sufficiently robust and yielded unrealistic values upon extrapolation in time. In contrast, the non-linear models all reached the latter two objectives, but some were unable to reproduce the data. A family of non-linear models, constructed from the exponential of linear combinations
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. PMID:25738861
Bhattacharya, Sukanta Shekhar; Garlapati, Vijay Kumar; Banerjee, Rintu
2011-01-31
In the present study, laccase production from a locally isolated hyperactive strain of Pleurotus sp. under solid state fermentation (SSF) was carried out and the interactions between different parameters of fermentation were studied using response surface methodology. The saddle shaped response surface plots depicting dual conditions for the enhanced production indicated the presence of isozymes with production optima at different conditions which was verified experimentally. Isoelectric focusing of the enzyme extract revealed that two isoforms were found with a widely varying pI of 3.8 and 9.3 emphasizing the capacity of the enzyme to be deployed at both acidic and alkaline conditions. Optimization of production conditions by coupling the regression equation with differential evolution technique yielded over 54,600IU/gds (3,412,500U/L) with a surfactant concentration of 0.016%, pH 7.99, particle size of 0.25cm, liquid to solid ratio of 4.99 and an incubation period of 8 days. In this study, the optimization process yielded highest titer value of laccase reported to date.
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.
Optimal layout design of obstacles for panic evacuation using differential evolution
NASA Astrophysics Data System (ADS)
Zhao, Yongxiang; Li, Meifang; Lu, Xin; Tian, Lijun; Yu, Zhiyong; Huang, Kai; Wang, Yana; Li, Ting
2017-01-01
To improve the pedestrian outflow in panic situations by suitably placing an obstacle in front of the exit, it is vital to understand the physical mechanism behind the evacuation efficiency enhancement. In this paper, a robust differential evolution is firstly employed to optimize the geometrical parameters of different shaped obstacles in order to achieve an optimal evacuation efficiency. Moreover, it is found that all the geometrical parameters of obstacles could markedly influence the evacuation efficiency of pedestrians, and the best way for achieving an optimal pedestrian outflow is to slightly shift the obstacle from the center of the exit which is consistent with findings of extant literature. Most importantly, by analyzing the profiles of density, velocity and specific flow, as well as the spatial distribution of crowd pressure, we have proven that placing an obstacle in panic situations does not reduce or absorb the pressure in the region of exit, on the contrary, promotes the pressure to a much higher level, hence the physical mechanism behind the evacuation efficiency enhancement is not a pressure decrease in the region of exit, but a significant reduction of high density region by effective separation in space which finally causes the increasing of escape speed and evacuation outflow. Finally, it is clearly demonstrated that the panel-like obstacle is considerably more robust and stable than the pillar-like obstacle to guarantee the enhancement of evacuation efficiency under different initial pedestrian distributions, different initial crowd densities as well as different desired velocities.
Long-term density evolution through semi-analytical and differential algebra techniques
NASA Astrophysics Data System (ADS)
Wittig, Alexander; Colombo, Camilla; Armellin, Roberto
2017-02-01
This paper introduces and combines for the first time two techniques to allow long-term density propagation in astrodynamics. First, we introduce an efficient method for the propagation of phase space densities based on differential algebra (DA) techniques. Second, this DA density propagator is used in combination with a DA implementation of the averaged orbital dynamics through semi-analytical methods. This approach combines the power of orbit averaging with the efficiency of DA techniques. While the DA-based method for the propagation of densities introduced in this paper is independent of the dynamical system under consideration, the particular combination of DA techniques with averaged equations of motion yields a fast and accurate technique to propagate large clouds of initial conditions and their associated probability density functions very efficiently for long time. This enables the study of the long-term behavior of particles subjected to the given dynamics. To demonstrate the effectiveness of the proposed approach, the evolution of a cloud of high area-to-mass objects in Medium Earth Orbit is reproduced considering the effects of solar radiation pressure, the Earth's oblateness and luni-solar perturbations. The method can propagate 10,000 random fragments and their density for 1 year within a few seconds on a common desktop PC.
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.
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.
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.
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.
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.
Chitalia, Rhea; Mueller, Jenna; Fu, Henry L; Whitley, Melodi Javid; Kirsch, David G; Brown, J Quincy; Willett, Rebecca; Ramanujam, Nimmi
2016-09-01
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.
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
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.
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
Columns of differential reflectivity: a precursor for storm evolution and convective rain
NASA Astrophysics Data System (ADS)
Troemel, S.; Diederich, M.; Kumjian, M. R.; Picca, J. C.; Simmer, C.
2012-12-01
Nowcasting aims at providing accurate information about weather hazards related to convection at a very high refresh rate well suited for fast evolving convective systems. Polarimetric weather radars arise as a key tool to provide "seamless" analysis and nowcast of convective risk to aviation, because of their ability to observe 3dimensional storm structure, evolution, microphysical processes, and generated precipitation. Columns of differential reflectivity ZDR measured by polarimetric weather radars are prominent signatures associated with thunderstorm updrafts. Since greater vertical velocities can loft larger drops and water-coated ice particles to higher altitudes above the environmental freezing level, the integrated ZDR column above the freezing level increases with increasing updraft intensity. Frequently, they can extend several kilometers above the environmental freezing level. These positive ZDR values above the environmental freezing level point to the presence of large, oblate raindrops and perhaps water-coated hailstones and graupel. Analyses on the informative content of ZDR columns as precursor for storm evolution will be presented based on both the X-band polarimetric data collected by the twin radars (XPol Bonn and XPol Jülich) in the Bonn area, Germany, and volume radar data collected with the S-band KOUN radar, in Norman, Oklahoma. In order to derive the ZDR column product, radar volume data is interpolated onto a three-dimensional Cartesian (x,y,z) grid and then, for each (x,y) coordinate, the number of vertical grid boxes above the freezing level containing ZDR values in excess of a predetermined threshold (=1dB) are counted. The ZDR column product is simply a count of the number of grid boxes, which can be converted into "ZDR column volume" by simply multiplying the count by the dimension ΔxΔyΔz of the grid box. Interdependencies between the volumes of ZDR columns above the environmental freezing level, precipitation near the surface, the
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.
Zhu, Xinjun; Chen, Zhanqing; Tang, Chen; Mi, Qinghua; Yan, Xiusheng
2013-03-20
In this paper, we are concerned with denoising in experimentally obtained electronic speckle pattern interferometry (ESPI) speckle fringe patterns with poor quality. We extend the application of two existing oriented partial differential equation (PDE) filters, including the second-order single oriented PDE filter and the double oriented PDE filter, to two experimentally obtained ESPI speckle fringe patterns with very poor quality, and compare them with other efficient filtering methods, including the adaptive weighted filter, the improved nonlinear complex diffusion PDE, and the windowed Fourier transform method. All of the five filters have been illustrated to be efficient denoising methods through previous comparative analyses in published papers. The experimental results have demonstrated that the two oriented PDE models are applicable to low-quality ESPI speckle fringe patterns. Then for solving the main shortcoming of the two oriented PDE models, we develop the numerically fast algorithms based on Gauss-Seidel strategy for the two oriented PDE models. The proposed numerical algorithms are capable of accelerating the convergence greatly, and perform significantly better in terms of computational efficiency. Our numerically fast algorithms are extended automatically to some other PDE filtering models.
NASA Astrophysics Data System (ADS)
Kiesewetter, Simon; Drummond, Peter D.
2017-03-01
A variance reduction method for stochastic integration of Fokker-Planck equations is derived. This unifies the cumulant hierarchy and stochastic equation approaches to obtaining moments, giving a performance superior to either. We show that the brute force method of reducing sampling error by just using more trajectories in a sampled stochastic equation is not the best approach. The alternative of using a hierarchy of moment equations is also not optimal, as it may converge to erroneous answers. Instead, through Bayesian conditioning of the stochastic noise on the requirement that moment equations are satisfied, we obtain improved results with reduced sampling errors for a given number of stochastic trajectories. The method used here converges faster in time-step than Ito-Euler algorithms. This parallel optimized sampling (POS) algorithm is illustrated by several examples, including a bistable nonlinear oscillator case where moment hierarchies fail to converge.
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.
NASA Astrophysics Data System (ADS)
Han, Dong; Chen, Liangfu; Tao, Jinhua; Su, Lin; Li, Shenshen; Yu, Chao; Yan, Huanhuan
Inelastic Vibrational Raman Scattering (VRS) by liquid water is one significant limitation to the accuracy of the retrieval of trace gas constituents in atmosphere over waters, particularly over clear ocean waters, while using satellite data with Differential Optical Absorption Spec-troscopy technique (DOAS).The effect which is similar to the Ring effect in atmosphere results in the filling in of Fraunhofer lines, which is known as solar absorption lines. The inelastic component of the liquid water scattering causes a net increase of radiance in the line because more radiation is shifted to the wavelength of an absorption line than shifted from this wave-length to other wavelengths. The spectrum at the top of the atmosphere over land measured by OMI (Ozone Monitoring Instrument)/AURA is convolved with Vibrational Raman Scat-tering coefficients of liquid water, divided by the original measured spectrum, with a cubic polynomial subtracted off, to create differential water Ring spectrum. The OMI spectrum over land is chosen to avoid the effect of VRS by liquid water. This method has been suggested in order to obtain an effective differential water Ring coeffients for the DOAS fitting process.The differential water Ring spectrum could be used to improve the accuracy of the retrieval of the trace gases concentration. The method is not relying on RTM, which would be time-consuming and depending on lot of parameters. Therefore, it is very fast and convenient.
Performance Comparison Of Evolutionary Algorithms For Image Clustering
NASA Astrophysics Data System (ADS)
Civicioglu, P.; Atasever, U. H.; Ozkan, C.; Besdok, E.; Karkinli, A. E.; Kesikoglu, A.
2014-09-01
Evolutionary computation tools are able to process real valued numerical sets in order to extract suboptimal solution of designed problem. Data clustering algorithms have been intensively used for image segmentation in remote sensing applications. Despite of wide usage of evolutionary algorithms on data clustering, their clustering performances have been scarcely studied by using clustering validation indexes. In this paper, the recently proposed evolutionary algorithms (i.e., Artificial Bee Colony Algorithm (ABC), Gravitational Search Algorithm (GSA), Cuckoo Search Algorithm (CS), Adaptive Differential Evolution Algorithm (JADE), Differential Search Algorithm (DSA) and Backtracking Search Optimization Algorithm (BSA)) and some classical image clustering techniques (i.e., k-means, fcm, som networks) have been used to cluster images and their performances have been compared by using four clustering validation indexes. Experimental test results exposed that evolutionary algorithms give more reliable cluster-centers than classical clustering techniques, but their convergence time is quite long.
NASA Astrophysics Data System (ADS)
Chae, Kyu-Hyun
2010-03-01
We study galaxy evolution from z = 1 to 0 as a function of velocity dispersion σ for galaxies with σ >~ 95kms-1 based on the measured and Monte Carlo realized local velocity dispersion functions (VDFs) of galaxies and the revised statistical properties of 30 strongly lensed sources from the Cosmic Lens All-Sky Survey, the PMN-NVSS Extragalactic Lens Survey and the Hubble Space Telescope Snapshot survey. We assume that the total (luminous plus dark) mass profile of a galaxy is isothermal in the optical region for 0 <= z <= 1 as suggested by mass modelling of lensing galaxies. This study is the first to investigate the evolution of the VDF shape as well as the overall number density. It is also the first to study the evolution of the total and the late-type VDFs in addition to the early-type VDF. For the evolutionary behaviours of the VDFs, we find that: (1) the number density of massive (mostly early-type) galaxies with σ >~ 200kms-1 evolves differentially in the way that the number density evolution is greater at a higher velocity dispersion; (2) the number density of intermediate- and low-mass early-type galaxies (95kms-1 <~ σ <~ 200kms-1) is nearly constant and (3) the late-type VDF transformed from the Monte Carlo realized circular velocity function is consistent with no evolution in its shape or integrated number density consistent with galaxy survey results. These evolutionary behaviours of the VDFs are strikingly similar to those of the dark halo mass function (DMF) from N-body simulations and the stellar mass function (SMF) predicted by recent semi-analytic models of galaxy formation under the current Λ cold dark matter hierarchical structure formation paradigm. Interestingly, the VDF evolutions appear to be qualitatively different from `stellar-mass-downsizing' evolutions obtained by many galaxy surveys. The co-evolution of the DMF, the VDF and the SMF is investigated in quantitative detail based on up-to-date theoretical and observational results in a
Algorithmic Approximation of Optimal Value Differential Stability Bounds in Nonlinear Programming,
1981-08-01
NCLASSIFIED RANO/PA6659 N IN *~4 112.0.0 ~11111,.. I32 111 IIIII 111111.25 MICROCOPY RESOLUTION TESI CHART NATIOt AL BJRLAU Of SIANDARD 1964 A * LEVEL 00 o pm...Sensitivity Analysis in Parametric Nonlinear Programming, Doctoral Dissertation, School of Engineering and Applied Science, The George Washington University...Differential Stability of the Optimal Value Function in Constrained Nonlinear Programing, Doctoral Disser- tation, School of Engineering and Applied
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
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.
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.
NASA Astrophysics Data System (ADS)
Develi, Ibrahim
2007-06-01
The principal objective of a rain attenuation prediction method is to achieve acceptable estimates of the attenuation incurred on the signal due to rain. In this paper, a differential evolution (DE) based model for predicting rain attenuation in a terrestrial point-to-point line of sight (LOS) link at 97 GHz is proposed using previously available experimental data obtained in the southern United Kingdom. Rainfall rate and percentage of time are used as input data in the proposed prediction model. Excellent agreement between the experimental data and the model output indicates that the presented DE based method may efficiently be used for accurate prediction of the rain attenuation levels.
NASA Astrophysics Data System (ADS)
Argenti, Luca; Colle, Renato
2008-12-01
We propose an effective procedure to fit triple differential cross sections of atomic double photoionization processes, which is based on a general expression of the transition amplitude between arbitrary states of the target atom and the parent ion, with the transition operator expressed at any order of its multipolar expansion. The major advantage of our expression, which in the dipole approximation is equivalent to those of Manakov (1996 J. Phys. B: At. Mol. Opt. Phys. 29 2711) and Malegat (1997 J. Phys. B: At. Mol. Opt. Phys. 30 251), is that it is expressed only in terms of elementary angular functions (Clebsch-Gordan coefficients, spherical harmonics and 6 - j factors). Therefore our expression can be easily implemented in a general code for any kinematic condition and any order of the multipolar expansion of the transition operator. Our fitting procedure takes into account also the finite instrumental resolution in measuring energies and angles. Test calculations on helium and argon show that this further capability is often essential to remove important discrepancies between simulated and measured angular distributions.
Morphological evolution of protective works by Genetic Algorithms: An application to Mt Etna
NASA Astrophysics Data System (ADS)
Marocco, Davide; Spataro, William; D'Ambrosio, Donato; Filippone, Giuseppe; Rongo, Rocco; Iovine, Giulio; Neri, Marco
2013-04-01
The hazard induced by dangerous flow-type phenomena - e.g. lava flows, earth flows, debris flows, and debris avalanches - has increased in recent years due to continuous urbanization. In many cases, the numerical simulation of hypothetical events can help to forecast the flow path in advance and therefore give indications about the areas that can be considered for the construction of protective works - e.g. earth barriers or channels. In this way, urbanized areas, as well as cultural heritage sites or even important infrastructures, can be protected by diverting the flow towards lower interest regions. Here, we have considered the numerical Cellular Automata model Sciara-fv2 for simulating lava flows at Mt Etna and Genetic Algorithms for optimizing the position, orientation and extension of an earth barrier built to protect the Rifugio Sapienza, a well-known touristic facility located near the summit of the volcano. The Rifugio Sapienza area was in fact interested by a lava flow in 2003, which destroyed a Service Center, a parking area and a Cafeteria. In this study, a perimeter was devised around the Rifugio (i.e., security perimeter), which delimitates the area that has to be protected by the flow. Furthermore, another perimeter was devised (i.e., work perimeter), specifying the area in which the earth barrier can be located. The barrier is specified by three parameters, namely the two geographic coordinates of the vertex and the height. In fact, in this preliminary analysis the barrier was modeled as a segment (in plant) having a constant height. Though preliminary, the study has produced extremely positive results. Among different alternatives generated by the genetic algorithm, an interesting scenario consists of a 35 meters barrier high solution, which completely deviates the flow avoiding that the lava reaches the inhabited area. The relative elevated height of the barrier is high due to the fact that the crater is located close to the area to be protected
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
PCA algorithm for detection, localisation and evolution of damages in gearbox bearings
NASA Astrophysics Data System (ADS)
Pirra, M.; Gandino, E.; Torri, A.; Garibaldi, L.; Machorro-López, J. M.
2011-07-01
A fundamental aspect when dealing with rolling element bearings, which often represent a key component in rotating machineries, consists in correctly identifying a degraded behaviour of a bearing with a reasonable level of confidence. This is one of the main requirements a health and usage monitoring system (HUMS) should have. This paper introduces a monitoring technique for the diagnosis of bearing faults based on Principal Component Analysis (PCA). This method overcomes the problem of acquiring data under different environmental conditions (hardly biasing the data) and allows accurate damage recognition, also assuring a rather low number of False Alarms (FA). In addition, a novel criterion is proposed in order to isolate the area in which the faulty bearing stands. Another useful feature of this PCA-based method concerns the capability to observe an increasing trend in the evolution of bearing degradation. The described technique is tested on an industrial rig (designed by Avio S.p.A.), consisting of a full size aeroengine gearbox. Healthy and variously damaged bearings, such as with an inner or rolling element fault, are set up and vibration signals are collected and processed in order to properly detect a fault. Finally, data collected from a test rig assembled by the Dynamics & Identification Research Group (DIRG) are used to demonstrate that the proposed method is able to correctly detect and to classify different levels of the same type of fault and also to localise it.
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.
None, None
2015-09-28
Coulomb interaction between charged particles inside a bunch is one of the most importance collective effects in beam dynamics, becoming even more significant as the energy of the particle beam is lowered to accommodate analytical and low-Z material imaging purposes such as in the time resolved Ultrafast Electron Microscope (UEM) development currently underway at Michigan State University. In addition, space charge effects are the key limiting factor in the development of ultrafast atomic resolution electron imaging and diffraction technologies and are also correlated with an irreversible growth in rms beam emittance due to fluctuating components of the nonlinear electron dynamics. In the short pulse regime used in the UEM, space charge effects also lead to virtual cathode formation in which the negative charge of the electrons emitted at earlier times, combined with the attractive surface field, hinders further emission of particles and causes a degradation of the pulse properties. Space charge and virtual cathode effects and their remediation are core issues for the development of the next generation of high-brightness UEMs. Since the analytical models are only applicable for special cases, numerical simulations, in addition to experiments, are usually necessary to accurately understand the space charge effect. In this paper we will introduce a grid-free differential algebra based multiple level fast multipole algorithm, which calculates the 3D space charge field for n charged particles in arbitrary distribution with an efficiency of O(n), and the implementation of the algorithm to a simulation code for space charge dominated photoemission processes.
None, None
2015-09-28
Coulomb interaction between charged particles inside a bunch is one of the most importance collective effects in beam dynamics, becoming even more significant as the energy of the particle beam is lowered to accommodate analytical and low-Z material imaging purposes such as in the time resolved Ultrafast Electron Microscope (UEM) development currently underway at Michigan State University. In addition, space charge effects are the key limiting factor in the development of ultrafast atomic resolution electron imaging and diffraction technologies and are also correlated with an irreversible growth in rms beam emittance due to fluctuating components of the nonlinear electron dynamics.more » In the short pulse regime used in the UEM, space charge effects also lead to virtual cathode formation in which the negative charge of the electrons emitted at earlier times, combined with the attractive surface field, hinders further emission of particles and causes a degradation of the pulse properties. Space charge and virtual cathode effects and their remediation are core issues for the development of the next generation of high-brightness UEMs. Since the analytical models are only applicable for special cases, numerical simulations, in addition to experiments, are usually necessary to accurately understand the space charge effect. In this paper we will introduce a grid-free differential algebra based multiple level fast multipole algorithm, which calculates the 3D space charge field for n charged particles in arbitrary distribution with an efficiency of O(n), and the implementation of the algorithm to a simulation code for space charge dominated photoemission processes.« less
Vijay, Nagarjun; Bossu, Christen M; Poelstra, Jelmer W; Weissensteiner, Matthias H; Suh, Alexander; Kryukov, Alexey P; Wolf, Jochen B W
2016-10-31
Uncovering the genetic basis of species diversification is a central goal in evolutionary biology. Yet, the link between the accumulation of genomic changes during population divergence and the evolutionary forces promoting reproductive isolation is poorly understood. Here, we analysed 124 genomes of crow populations with various degrees of genome-wide differentiation, with parallelism of a sexually selected plumage phenotype, and ongoing hybridization. Overall, heterogeneity in genetic differentiation along the genome was best explained by linked selection exposed on a shared genome architecture. Superimposed on this common background, we identified genomic regions with signatures of selection specific to independent phenotypic contact zones. Candidate pigmentation genes with evidence for divergent selection were only partly shared, suggesting context-dependent selection on a multigenic trait architecture and parallelism by pathway rather than by repeated single-gene effects. This study provides insight into how various forms of selection shape genome-wide patterns of genomic differentiation as populations diverge.
Vijay, Nagarjun; Bossu, Christen M.; Poelstra, Jelmer W.; Weissensteiner, Matthias H.; Suh, Alexander; Kryukov, Alexey P.; Wolf, Jochen B. W.
2016-01-01
Uncovering the genetic basis of species diversification is a central goal in evolutionary biology. Yet, the link between the accumulation of genomic changes during population divergence and the evolutionary forces promoting reproductive isolation is poorly understood. Here, we analysed 124 genomes of crow populations with various degrees of genome-wide differentiation, with parallelism of a sexually selected plumage phenotype, and ongoing hybridization. Overall, heterogeneity in genetic differentiation along the genome was best explained by linked selection exposed on a shared genome architecture. Superimposed on this common background, we identified genomic regions with signatures of selection specific to independent phenotypic contact zones. Candidate pigmentation genes with evidence for divergent selection were only partly shared, suggesting context-dependent selection on a multigenic trait architecture and parallelism by pathway rather than by repeated single-gene effects. This study provides insight into how various forms of selection shape genome-wide patterns of genomic differentiation as populations diverge. PMID:27796282
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.
Genetic differentiation and the evolution of cooperation in chimpanzees and humans
Langergraber, Kevin; Schubert, Grit; Rowney, Carolyn; Wrangham, Richard; Zommers, Zinta; Vigilant, Linda
2011-01-01
It has been proposed that human cooperation is unique among animals for its scale and complexity, its altruistic nature and its occurrence among large groups of individuals that are not closely related or are even strangers. One potential solution to this puzzle is that the unique aspects of human cooperation evolved as a result of high levels of lethal competition (i.e. warfare) between genetically differentiated groups. Although between-group migration would seem to make this scenario unlikely, the plausibility of the between-group competition model has recently been supported by analyses using estimates of genetic differentiation derived from contemporary human groups hypothesized to be representative of those that existed during the time period when human cooperation evolved. Here, we examine levels of between-group genetic differentiation in a large sample of contemporary human groups selected to overcome some of the problems with earlier estimates, and compare them with those of chimpanzees. We find that our estimates of between-group genetic differentiation in contemporary humans are lower than those used in previous tests, and not higher than those of chimpanzees. Because levels of between-group competition in contemporary humans and chimpanzees are also similar, these findings suggest that the identification of other factors that differ between chimpanzees and humans may be needed to provide a compelling explanation of why humans, but not chimpanzees, display the unique features of human cooperation. PMID:21247955
Differential Evolution of MAGE Genes Based on Expression Pattern and Selection Pressure
Zhao, Qi; Caballero, Otavia L.; Simpson, Andrew J. G.; Strausberg, Robert L.
2012-01-01
Starting from publicly-accessible datasets, we have utilized comparative and phylogenetic genome analyses to characterize the evolution of the human MAGE gene family. Our characterization of genomic structures in representative genomes of primates, rodents, carnivora, and macroscelidea indicates that both Type I and Type II MAGE genes have undergone lineage-specific evolution. The restricted expression pattern in germ cells of Type I MAGE orthologs is observed throughout evolutionary history. Unlike Type II MAGEs that have conserved promoter sequences, Type I MAGEs lack promoter conservation, suggesting that epigenetic regulation is a central mechanism for controlling their expression. Codon analysis shows that Type I but not Type II MAGE genes have been under positive selection. The combination of genomic and expression analysis suggests that Type 1 MAGE promoters and genes continue to evolve in the hominin lineage, perhaps towards functional diversification or acquiring additional specific functions, and that selection pressure at codon level is associated with expression spectrum. PMID:23133577
Differential evolution of MAGE genes based on expression pattern and selection pressure.
Zhao, Qi; Caballero, Otavia L; Simpson, Andrew J G; Strausberg, Robert L
2012-01-01
Starting from publicly-accessible datasets, we have utilized comparative and phylogenetic genome analyses to characterize the evolution of the human MAGE gene family. Our characterization of genomic structures in representative genomes of primates, rodents, carnivora, and macroscelidea indicates that both Type I and Type II MAGE genes have undergone lineage-specific evolution. The restricted expression pattern in germ cells of Type I MAGE orthologs is observed throughout evolutionary history. Unlike Type II MAGEs that have conserved promoter sequences, Type I MAGEs lack promoter conservation, suggesting that epigenetic regulation is a central mechanism for controlling their expression. Codon analysis shows that Type I but not Type II MAGE genes have been under positive selection. The combination of genomic and expression analysis suggests that Type 1 MAGE promoters and genes continue to evolve in the hominin lineage, perhaps towards functional diversification or acquiring additional specific functions, and that selection pressure at codon level is associated with expression spectrum.
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.
Bresadola, Vittorio; Feo, Carlo V
2012-04-01
Achalasia is a rare disease of the esophagus, characterized by the absence of peristalsis in the esophageal body and incomplete relaxation of the lower esophageal sphincter, which may be hypertensive. The cause of this disease is unknown; therefore, the aim of the therapy is to improve esophageal emptying by eliminating the outflow resistance caused by the lower esophageal sphincter. This goal can be accomplished either by pneumatic dilatation or surgical myotomy, which are the only long-term effective therapies for achalasia. Historically, pneumatic dilatation was preferred over surgical myotomy because of the morbidity associated with a thoracotomy or a laparotomy. However, with the development of minimally invasive techniques, the surgical approach has gained widespread acceptance among patients and gastroenterologists and, consequently, the role of surgery has changed. The aim of this study was to review the changes occurred in the surgical treatment of achalasia over the last 2 decades; specifically, the development of minimally invasive techniques with the evolution from a thoracoscopic approach without an antireflux procedure to a laparoscopic myotomy with a partial fundoplication, the changes in the length of the myotomy, and the modification of the therapeutic algorithm.
Jaeger, Markus; Butz, Tilman; Iwig, Kornelius
2011-06-15
A user-friendly fully digital time differential perturbed angular correlation (TDPAC)-spectrometer with six detectors and fast digitizers using field programmable gate arrays (FPGA) is described and performance data are given. The new spectrometer has an online data analysis feature, a compact size, and a time resolution such as conventional analog spectrometers. Its calculation intensive part was implemented inside the digitizer. This gives the possibility to change parameters (energy windows, constant fraction trigger delay) and see their influence immediately in the {gamma}-{gamma} correlation diagrams. Tests were performed which showed that the time resolution using a {sup 60}Co source with energy window set at 1.17 MeV and 1.33 MeV is 265 ps with LaBr{sub 3}(Ce) scintillators and 254 ps with BaF{sub 2} scintillators. A true constant fraction algorithm turned out to be slightly better than the constant fraction of amplitude method. The spectrometer performance was tested with a TDPAC measurement using a {sup 44}Ti in rutile source and a positron lifetime measurement using {sup 22}Na. The maximum possible data rate of the spectrometer is 1.1 x 10{sup 6} {gamma} quanta per detector and second.
Schreiber-Agus, N; Horner, J; Torres, R; Chiu, F C; DePinho, R A
1993-01-01
To gain insight into the role of Myc family oncoproteins and their associated protein Max in vertebrate growth and development, we sought to identify homologs in the zebra fish (Brachydanio rerio). A combination of a polymerase chain reaction-based cloning strategy and low-stringency hybridization screening allowed for the isolation of zebra fish c-, N-, and L-myc and max genes; subsequent structural characterization showed a high degree of conservation in regions that encode motifs of known functional significance. On the functional level, zebra fish Max, like its mammalian counterpart, served to suppress the transformation activity of mouse c-Myc in rat embryo fibroblasts. In addition, the zebra fish c-myc gene proved capable of cooperating with an activated H-ras to effect the malignant transformation of mammalian cells, albeit with diminished potency compared with mouse c-myc. With respect to their roles in normal developing tissues, the differential temporal and spatial patterns of steady-state mRNA expression observed for each zebra fish myc family member suggest unique functions for L-myc in early embryogenesis, for N-myc in establishment and growth of early organ systems, and for c-myc in increasingly differentiated tissues. Furthermore, significant alterations in the steady-state expression of zebra fish myc family genes concomitant with relatively constant max expression support the emerging model of regulation of Myc function in cellular growth and differentiation. Images PMID:8474440
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.
Harju, Inka; Lange, Christoph; Kostrzewa, Markus; Maier, Thomas; Rantakokko-Jalava, Kaisu; Haanperä, Marjo
2017-03-01
Reliable distinction of Streptococcus pneumoniae and viridans group streptococci is important because of the different pathogenic properties of these organisms. Differentiation between S. pneumoniae and closely related Sreptococcusmitis species group streptococci has always been challenging, even when using such modern methods as 16S rRNA gene sequencing or matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry. In this study, a novel algorithm combined with an enhanced database was evaluated for differentiation between S. pneumoniae and S. mitis species group streptococci. One hundred one clinical S. mitis species group streptococcal strains and 188 clinical S. pneumoniae strains were identified by both the standard MALDI Biotyper database alone and that combined with a novel algorithm. The database update from 4,613 strains to 5,627 strains drastically improved the differentiation of S. pneumoniae and S. mitis species group streptococci: when the new database version containing 5,627 strains was used, only one of the 101 S. mitis species group isolates was misidentified as S. pneumoniae, whereas 66 of them were misidentified as S. pneumoniae when the earlier 4,613-strain MALDI Biotyper database version was used. The updated MALDI Biotyper database combined with the novel algorithm showed even better performance, producing no misidentifications of the S. mitis species group strains as S. pneumoniae All S. pneumoniae strains were correctly identified as S. pneumoniae with both the standard MALDI Biotyper database and the standard MALDI Biotyper database combined with the novel algorithm. This new algorithm thus enables reliable differentiation between pneumococci and other S. mitis species group streptococci with the MALDI Biotyper.
Selection by differential molecular survival: a possible mechanism of early chemical evolution.
de Duve, C
1987-01-01
A model is proposed to account for selective chemical evolution, progressing from a relatively simple initial set of abiotic synthetic phenomena up to the elaborately sophisticated processes that are almost certainly required to produce the complex molecules, such as replicatable RNA-like oligonucleotides, needed for a Darwinian form of selection to start operating. The model makes the following assumptions: (i) that a small number of micromolecular substances were present at high concentration; (ii) that a random assembly mechanism combined these molecules into a variety of multimeric compounds comprising a wide repertoire of rudimentary catalytic activities; and (iii) that a lytic system capable of breaking down the assembled products existed. The model assumes further that catalysts supplied with substrates were significantly protected against breakdown. It is shown that, by granting these assumptions, an increasingly complex network of metabolic pathways would progressively be established. At the same time, the catalysts concerned would accumulate selectively to become choice substrates for elongation and other modifications that could enhance their efficiency, as well as their survival. Chemical evolution would thus proceed by a dual process of metabolic extension and catalytic innovation. Such a process should be largely deterministic and predictable from initial conditions. PMID:3479788
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
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)
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
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.
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.
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.
Pilot, Małgorzata; Jędrzejewski, Włodzimierz; Sidorovich, Vadim E.; Meier-Augenstein, Wolfram; Hoelzel, A. Rus
2012-01-01
Recent studies on highly mobile carnivores revealed cryptic population genetic structures correlated to transitions in habitat types and prey species composition. This led to the hypothesis that natal-habitat-biased dispersal may be responsible for generating population genetic structure. However, direct evidence for the concordant ecological and genetic differentiation between populations of highly mobile mammals is rare. To address this we analyzed stable isotope profiles (δ13C and δ15N values) for Eastern European wolves (Canis lupus) as a quantifiable proxy measure of diet for individuals that had been genotyped in an earlier study (showing cryptic genetic structure), to provide a quantitative assessment of the relationship between individual foraging behavior and genotype. We found a significant correlation between genetic distances and dietary differentiation (explaining 46% of the variation) in both the marginal test and crucially, when geographic distance was accounted for as a co-variable. These results, interpreted in the context of other possible mechanisms such as allopatry and isolation by distance, reinforce earlier studies suggesting that diet and associated habitat choice are influencing the structuring of populations in highly mobile carnivores. PMID:22768075
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.
Wu, J; Krutovskii, K V; Strauss, S H
1998-01-01
We examined mitochondrial DNA polymorphisms via the analysis of restriction fragment length polymorphisms in three closely related species of pines from western North America: knobcone (Pinus attenuata Lemm.), Monterey (P. radiata D. Don), and bishop (P. muricata D. Don). A total of 343 trees derived from 13 populations were analyzed using 13 homologous mitochondrial gene probes amplified from three species by polymerase chain reaction. Twenty-eight distinct mitochondrial DNA haplotypes were detected and no common haplotypes were found among the species. All three species showed limited variability within populations, but strong differentiation among populations. Based on haplotype frequencies, genetic diversity within populations (HS) averaged 0.22, and population differentiation (GST and theta) exceeded 0.78. Analysis of molecular variance also revealed that >90% of the variation resided among populations. For the purposes of genetic conservation and breeding programs, species and populations could be readily distinguished by unique haplotypes, often using the combination of only a few probes. Neighbor-joining phenograms, however, strongly disagreed with those based on allozymes, chloroplast DNA, and morphological traits. Thus, despite its diagnostic haplotypes, the genome appears to evolve via the rearrangement of multiple, convergent subgenomic domains. PMID:9832536
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
Brüne, Martin
2012-04-17
The diathesis-stress model of psychiatric conditions has recently been challenged by the view that it might be more accurate to speak of 'differential susceptibility' or 'plasticity' genes, rather than one-sidedly focusing on individual vulnerability. That is, the same allelic variation that predisposes to a psychiatric disorder if associated with (developmentally early) environmental adversity may lead to a better-than-average functional outcome in the same domain under thriving (or favourable) environmental conditions. Studies of polymorphic variations of the serotonin transporter gene, the monoamino-oxidase-inhibitor A coding gene or the dopamine D4 receptor gene indicate that the early environment plays a crucial role in the development of favourable versus unfavourable outcomes. Current evidence is limited, however, to establishing a link between genetic variation and behavioural phenotypes. In contrast, little is known about how plasticity may be expressed at the neuroanatomical level as a 'hard-wired' correlate of observable behaviour. The present review article seeks to further strengthen the argument in favour of the differential susceptibility theory by incorporating findings from behavioural and neuroanatomical studies in relation to genetic variation of the oxytocin receptor gene. It is suggested that polymorphic variation at the oxytocin receptor gene (rs2254298) is associated with sociability, amygdala volume and differential risk for psychiatric conditions including autism, depression and anxiety disorder, depending on the quality of early environmental experiences. Seeing genetic variation at the core of developmental plasticity can explain, in contrast to the diathesis-stress perspective, why evolution by natural selection has maintained such 'risk' alleles in the gene pool of a population.
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.
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.
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.
Differential scaling patterns of vertebrae and the evolution of neck length in mammals.
Arnold, Patrick; Amson, Eli; Fischer, Martin S
2017-03-21
Almost all mammals have seven vertebrae in their cervical spines. This consistency represents one of the most prominent examples of morphological stasis in vertebrae evolution. Hence, the requirements associated with evolutionary modifications of neck length have to be met with a fixed number of vertebrae. It has not been clear whether body size influences the overall length of the cervical spine and its inner organization (i.e., if the mammalian neck is subject to allometry). Here, we provide the first large scale analysis of the scaling patterns of the cervical spine and its constituting cervical vertebrae. Our findings reveal that the opposite allometric scaling of C1 and C2-C7 accommodate the increase of neck bending moment with body size. The internal organization of the neck skeleton exhibits surprisingly uniformity in the vast majority of mammals. Deviations from this general pattern only occur under extreme loading regimes associated with particular functional and allometric demands. Our results indicate that the main source of variation in the mammalian neck stems from the disparity of overall cervical spine length. The mammalian neck reveals how evolutionary disparity manifests itself in a structure that is otherwise highly restricted by meristic constraints. This article is protected by copyright. All rights reserved.
NASA Astrophysics Data System (ADS)
Kumari, S.; Nirala, A. K.
2016-11-01
In the present paper intensity-based algorithms have been applied to differentiate the bruised and fresh regions of an Indian apple through biospeckle technique during its 9 day shelf life. Existing algorithms such as the co-occurrence matrix, inertia moment, absolute value difference, generalized difference, parameterized Fujii, biospeckle activity (BA) value, granulometric size distribution (GSD) and grey-level co-occurrence matrix (GLCM), as well as three new proposed algorithms namely parameterized generalized difference, alternative generalized difference (AGD) and parameterized global average Fujii, have been used for qualitative and quantitative analysis. Co-occurrence matrix and activity level spectral maps have been used for qualitative analysis, whereas mean activity plots, curve of the BA index, GSD plots and texture features have been used for quantitative analysis. The experimental results suggest that overall difference in biospeckle activity between the bruised and fresh regions is maximum for the inertia moment method (521.99). Of the three proposed algorithms AGD gives the maximum overall difference in biospeckle activity (42.35). In addition, the BA value and parameters of the GLCM have also been applied for the first time to distinguish between the bruised and fresh regions of an Indian apple, and it is concluded that both the methods may be used for good differentiation between the bruised and fresh regions of apples.
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
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.
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
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
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
Manousaki, Tereza; Hull, Pincelli M; Kusche, Henrik; Machado-Schiaffino, Gonzalo; Franchini, Paolo; Harrod, Chris; Elmer, Kathryn R; Meyer, Axel
2013-02-01
The study of parallel evolution facilitates the discovery of common rules of diversification. Here, we examine the repeated evolution of thick lips in Midas cichlid fishes (the Amphilophus citrinellus species complex)-from two Great Lakes and two crater lakes in Nicaragua-to assess whether similar changes in ecology, phenotypic trophic traits and gene expression accompany parallel trait evolution. Using next-generation sequencing technology, we characterize transcriptome-wide differential gene expression in the lips of wild-caught sympatric thick- and thin-lipped cichlids from all four instances of repeated thick-lip evolution. Six genes (apolipoprotein D, myelin-associated glycoprotein precursor, four-and-a-half LIM domain protein 2, calpain-9, GTPase IMAP family member 8-like and one hypothetical protein) are significantly underexpressed in the thick-lipped morph across all four lakes. However, other aspects of lips' gene expression in sympatric morphs differ in a lake-specific pattern, including the magnitude of differentially expressed genes (97-510). Generally, fewer genes are differentially expressed among morphs in the younger crater lakes than in those from the older Great Lakes. Body shape, lower pharyngeal jaw size and shape, and stable isotopes (δ(13)C and δ(15)N) differ between all sympatric morphs, with the greatest differentiation in the Great Lake Nicaragua. Some ecological traits evolve in parallel (those related to foraging ecology; e.g. lip size, body and head shape) but others, somewhat surprisingly, do not (those related to diet and food processing; e.g. jaw size and shape, stable isotopes). Taken together, this case of parallelism among thick- and thin-lipped cichlids shows a mosaic pattern of parallel and nonparallel evolution.
Watson, Richard A; Mills, Rob; Buckley, C L; Kouvaris, Kostas; Jackson, Adam; Powers, Simon T; Cox, Chris; Tudge, Simon; Davies, Adam; Kounios, Loizos; Power, Daniel
2016-01-01
The mechanisms of variation, selection and inheritance, on which evolution by natural selection depends, are not fixed over evolutionary time. Current evolutionary biology is increasingly focussed on understanding how the evolution of developmental organisations modifies the distribution of phenotypic variation, the evolution of ecological relationships modifies the selective environment, and the evolution of reproductive relationships modifies the heritability of the evolutionary unit. The major transitions in evolution, in particular, involve radical changes in developmental, ecological and reproductive organisations that instantiate variation, selection and inheritance at a higher level of biological organisation. However, current evolutionary theory is poorly equipped to describe how these organisations change over evolutionary time and especially how that results in adaptive complexes at successive scales of organisation (the key problem is that evolution is self-referential, i.e. the products of evolution change the parameters of the evolutionary process). Here we first reinterpret the central open questions in these domains from a perspective that emphasises the common underlying themes. We then synthesise the findings from a developing body of work that is building a new theoretical approach to these questions by converting well-understood theory and results from models of cognitive learning. Specifically, connectionist models of memory and learning demonstrate how simple incremental mechanisms, adjusting the relationships between individually-simple components, can produce organisations that exhibit complex system-level behaviours and improve the adaptive capabilities of the system. We use the term "evolutionary connectionism" to recognise that, by functionally equivalent processes, natural selection acting on the relationships within and between evolutionary entities can result in organisations that produce complex system-level behaviours in evolutionary
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.
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
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…
Lidz, B.H.; Shinn, E.A.; Hine, A.C.; Locker, S.D.
1997-01-01
Closely spaced, high-resolution, seismic-reflection profiles acquired off the upper Florida Keys (i.e., north) reveal a platform-margin reef-and-trough system grossly similar to, yet quite different from, that previously described off the lower Keys (i.e., south). Profiles and maps generated for both areas show that development was controlled by antecedent Pleistocene topography (presence or absence of an upper-slope bedrock terrace), sediment availability, fluctuating sea level, and coral growth rate and distribution. The north terrace is sediment-covered and exhibits linear, buried, low-relief, seismic features of unknown character and origin. The south terrace is essentially sediment-free and supports multiple, massive, high-relief outlier reefs. Uranium disequilibrium series dates on outlier-reef corals indicate a Pleistocene age (~83-84 ka). A massive Pleistocene reef with both aggradational (north) and progradational (south) aspects forms the modern margin escarpment landward of the terrace. Depending upon interpretation (the north margin-escarpment reef may or may not be an outlier reef), the north margin is either more advanced or less advanced than the south margin. During Holocene sea-level rise, Pleistocene bedrock was inundated earlier and faster first to the north (deeper offbank terrace), then to the south (deeper platform surface). Holocene overgrowth is thick (8 m) on the north outer-bank reefs but thin (0.3 m) on the south outlier reefs. Differential evolution resulted from interplay between fluctuating sea level and energy regime established by prevailing east-southeasterly winds and waves along an arcuate (ENE-WSW) platform margin.
Sannino, Giovanna; De Falco, Ivanoe; De Pietro, Giuseppe
2014-06-01
Real-time Obstructive Sleep Apnea (OSA) episode detection and monitoring are important for society in terms of an improvement in the health of the general population and of a reduction in mortality and healthcare costs. Currently, to diagnose OSA patients undergo PolySomnoGraphy (PSG), a complicated and invasive test to be performed in a specialized center involving many sensors and wires. Accordingly, each patient is required to stay in the same position throughout the duration of one night, thus restricting their movements. This paper proposes an easy, cheap, and portable approach for the monitoring of patients with OSA, which collects single-channel ElectroCardioGram (ECG) data only. It is easy to perform from the patient's point of view because only one wearable sensor is required, so the patient is not restricted to keeping the same position all night long, and the detection and monitoring can be carried out in any place through the use of a mobile device. Our approach is based on the automatic extraction, from a database containing information about the monitored patient, of explicit knowledge in the form of a set of IF…THEN rules containing typical parameters derived from Heart Rate Variability (HRV) analysis. The extraction is carried out off-line by means of a Differential Evolution algorithm. This set of rules can then be exploited in the real-time mobile monitoring system developed at our Laboratory: the ECG data is gathered by a wearable sensor and sent to a mobile device, where it is processed in real time. Subsequently, HRV-related parameters are computed from this data, and, if their values activate some of the rules describing the occurrence of OSA, an alarm is automatically produced. This approach has been tested on a well-known literature database of OSA patients. The numerical results show its effectiveness in terms of accuracy, sensitivity, and specificity, and the achieved sets of rules evidence the user-friendliness of the approach
Monte Carlo algorithms for lattice gauge theory
Creutz, M.
1987-05-01
Various techniques are reviewed which have been used in numerical simulations of lattice gauge theories. After formulating the problem, the Metropolis et al. algorithm and some interesting variations are discussed. The numerous proposed schemes for including fermionic fields in the simulations are summarized. Langevin, microcanonical, and hybrid approaches to simulating field theories via differential evolution in a fictitious time coordinate are treated. Some speculations are made on new approaches to fermionic simulations.
NASA Astrophysics Data System (ADS)
Ma, S.; Yan, W.; Xu, L.
2013-12-01
The quantitative retrieval of the 3-D spatial distribution of the parent energetic ions of ENA from a 2-D ENA image is a quite challenge task. The Two Wide-angle Imaging Neutral-atom Spectrometers (TWINS) mission of NASA is the first constellation to perform stereoscopic magnetospheric imaging of energetic neutral atoms (ENA) from a pair of spacecraft flying on two widely-separated Molniya orbits. TWINS provides a unique opportunity to retrieve the 3-D distribution of ions in the ring current (RC) by using a volumetric pixel (voxel) CT inversion method. In this study the voxel CT method is implemented for a series of differential ENA fluxes averaged over about 6 to 7 sweeps (corresponding to a time period of about 9 min.) at different energy levels ranging from 5 to 100 keV, obtained simultaneously by the two satellites during the main phase of a great magnetic storm with minimum Sym-H of -156 nT on 24-25 October 2011. The data were selected to span a period about 50 minutes during which a large substorm was undergoing its expansion phase first and then recovery. The ENA species of O and H are distinguished for some time-segments by analyzing the signals of pulse heights of second electrons emitted from the carbon foil and impacted on the MCP detector in the TWINS sensors. In order to eliminate the possible influence on retrieval induced by instrument bias error, a differential voxel CT technique is applied. The flux intensity of the ENAs' parent ions in the RC has been obtained as a function of energy, L value, MLT sector and latitude, along with their time evolution during the storm-time substorm expansion phase. Forward calculations proved the reliability of the retrieved results. It shows that the RC is highly asymmetric, with a major concentration in the midnight to dawn sector for equatorial latitudes. Halfway through the substorm expansion there occurred a large enhancement of equatorial ion flux at lower energy (5 keV) in the dusk sector, with narrow extent
NASA Astrophysics Data System (ADS)
Ma, S. Y.; Xu, Liang; Yan, Wei-Nan
The quantitative retrieval of the 3-D spatial distribution of the parent energetic ions of ENA from a 2-D ENA image is a challenge task. The Two Wide-angle Imaging Neutral-atom Spectrometers (TWINS) mission of NASA provides an unique opportunity to retrieve the 3-D distribution of ions in the ring current (RC) by using a volumetric pixel (voxel) CT inversion method. In this study the voxel CT method is implemented for a series of differential ENA fluxes at different energy levels ranging from 5 to 80 keV obtained simultaneously by the two satellites of TWINS flying on two widely-separated Molniya orbits during the main phase of the magnetic storm of 24-25 October 2011 with minimum Sym-H index of -160 nT. The data were selected to span a period of about 50 minutes during which a large substorm was undergoing its expansion phase first and then recovery. The ENA species of O and H are distinguished for lower energy-mass ratio in some time- segments by analyzing the signals of pulse heights of second electrons emitted from the carbon foil and impacted on the MCP detector in the TWINS sensors. In order to eliminate the possible influence on retrieval caused by instrument bias error, a differential voxel CT technique is applied. To weaken the influence of low altitude emission (LAE) produced by ion precipitation, a correction is made for the ENA intensity along the line-of-sight that run deep into the high latitude atmosphere, invoking the so called thick-target approximation. The flux intensity of the ENAs’ parent ions in the RC has been obtained as a function of energy, L value, MLT sector and latitude, along with their time evolution during the storm-time substorm expansion phase. Forward calculations proved the reliability of the retrieved results. It shows that the RC is highly asymmetric with a major concentration in the midnight to dawn sector for equatorial latitudes. The ion flux spectra undergo dramatic changes from pre-storm to the main phase. Besides
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
Johnson, Richard Wayne
2003-05-01
The application of collocation methods using spline basis functions to solve differential model equations has been in use for a few decades. However, the application of spline collocation to the solution of the nonlinear, coupled, partial differential equations (in primitive variables) that define the motion of fluids has only recently received much attention. The issues that affect the effectiveness and accuracy of B-spline collocation for solving differential equations include which points to use for collocation, what degree B-spline to use and what level of continuity to maintain. Success using higher degree B-spline curves having higher continuity at the knots, as opposed to more traditional approaches using orthogonal collocation, have recently been investigated along with collocation at the Greville points for linear (1D) and rectangular (2D) geometries. The development of automatic knot insertion techniques to provide sufficient accuracy for B-spline collocation has been underway. The present article reviews recent progress for the application of B-spline collocation to fluid motion equations as well as new work in developing a novel adaptive knot insertion algorithm for a 1D convection-diffusion model equation.
2010-01-01
Background Growing interest and burgeoning technology for discovering genetic mechanisms that influence disease processes have ushered in a flood of genetic association studies over the last decade, yet little heritability in highly studied complex traits has been explained by genetic variation. Non-additive gene-gene interactions, which are not often explored, are thought to be one source of this "missing" heritability. Methods Stochastic methods employing evolutionary algorithms have demonstrated promise in being able to detect and model gene-gene and gene-environment interactions that influence human traits. Here we demonstrate modifications to a neural network algorithm in ATHENA (the Analysis Tool for Heritable and Environmental Network Associations) resulting in clear performance improvements for discovering gene-gene interactions that influence human traits. We employed an alternative tree-based crossover, backpropagation for locally fitting neural network weights, and incorporation of domain knowledge obtainable from publicly accessible biological databases for initializing the search for gene-gene interactions. We tested these modifications in silico using simulated datasets. Results We show that the alternative tree-based crossover modification resulted in a modest increase in the sensitivity of the ATHENA algorithm for discovering gene-gene interactions. The performance increase was highly statistically significant when backpropagation was used to locally fit NN weights. We also demonstrate that using domain knowledge to initialize the search for gene-gene interactions results in a large performance increase, especially when the search space is larger than the search coverage. Conclusions We show that a hybrid optimization procedure, alternative crossover strategies, and incorporation of domain knowledge from publicly available biological databases can result in marked increases in sensitivity and performance of the ATHENA algorithm for detecting and
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.
2016-05-01
subproblems. Our approach is expected to have wide applications in continuous dynamic games, control theory problems, and elsewhere. Mathematics...differential dynamic games, control theory problems, and dynamical systems coming from the physical world, e.g. [11]. An important application is to...rapid numerical solutions for a non-convex Hamiltonian in high dimensions, and this method is expected to have wide applications in optimal control
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
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.
Chow, Sy- Miin; Lu, Zhaohua; Zhu, Hongtu; Sherwood, Andrew
2014-01-01
The past decade has evidenced the increased prevalence of irregularly spaced longitudinal data in social sciences. Clearly lacking, however, are modeling tools that allow researchers to fit dynamic models to irregularly spaced data, particularly data that show nonlinearity and heterogeneity in dynamical structures. We consider the issue of fitting multivariate nonlinear differential equation models with random effects and unknown initial conditions to irregularly spaced data. A stochastic approximation expectation–maximization algorithm is proposed and its performance is evaluated using a benchmark nonlinear dynamical systems model, namely, the Van der Pol oscillator equations. The empirical utility of the proposed technique is illustrated using a set of 24-h ambulatory cardiovascular data from 168 men and women. Pertinent methodological challenges and unresolved issues are discussed. PMID:25416456
Chow, Sy-Miin; Lu, Zhaohua; Sherwood, Andrew; Zhu, Hongtu
2016-03-01
The past decade has evidenced the increased prevalence of irregularly spaced longitudinal data in social sciences. Clearly lacking, however, are modeling tools that allow researchers to fit dynamic models to irregularly spaced data, particularly data that show nonlinearity and heterogeneity in dynamical structures. We consider the issue of fitting multivariate nonlinear differential equation models with random effects and unknown initial conditions to irregularly spaced data. A stochastic approximation expectation-maximization algorithm is proposed and its performance is evaluated using a benchmark nonlinear dynamical systems model, namely, the Van der Pol oscillator equations. The empirical utility of the proposed technique is illustrated using a set of 24-h ambulatory cardiovascular data from 168 men and women. Pertinent methodological challenges and unresolved issues are discussed.
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)
Kassab, R.; Treuillet, S.; Marzani, F.; Pieralli, C.; Lapayre, J. C.
2013-03-01
We propose a new system that makes possible to monitor the evolution of scars after the excision of a tumorous dermatosis. The hardware part of this system is composed of a new optical innovative probe with which two types of images can be acquired simultaneously: an anatomic image acquired under a white light and a functional one based on autofluorescence from the protoporphyrin within the cancer cells. For technical reasons related to the maximum size of the area covered by the probe, acquired images are too small to cover the whole scar. That is why a sequence of overlapping images is taken in order to cover the required area. The main goal of this paper is to describe the creation of two panoramic images (anatomic and functional). Fluorescence images do not have enough salient information for matching the images; stitching algorithms are applied over each couple of successive white light images to produce an anatomic panorama of the entire scar. The same transformations obtained from this step are used to register and stitch the functional images. Several experiments have been implemented using different stitching algorithms (SIFT, ASIFT and SURF), with various transformation parameters (angles of rotation, projection, scaling, etc…) and different types of skin images. We present the results of these experiments that propose the best solution. Thus, clinician has two panoramic images superimposed and usable for diagnostic support. A collaborative layer is added to the system to allow sharing panoramas among several practitioners over different places.
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 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.
AI-BL1.0: a program for automatic on-line beamline optimization using the evolutionary algorithm.
Xi, Shibo; Borgna, Lucas Santiago; Zheng, Lirong; Du, Yonghua; Hu, Tiandou
2017-01-01
In this report, AI-BL1.0, an open-source Labview-based program for automatic on-line beamline optimization, is presented. The optimization algorithms used in the program are Genetic Algorithm and Differential Evolution. Efficiency was improved by use of a strategy known as Observer Mode for Evolutionary Algorithm. The program was constructed and validated at the XAFCA beamline of the Singapore Synchrotron Light Source and 1W1B beamline of the Beijing Synchrotron Radiation Facility.
NASA Astrophysics Data System (ADS)
G R, R. K.; C, S.
2015-12-01
The fundamental challenge in understanding the origin and evolution of the continental crust is to recognize how primary mantle source, and oceanic crust, which are essentially mafic to ultramafic in composition, could differentiate into a more or less felsic compositions. It is possible to understand growth and differentiation of the continental crust by constraining the interplay of magmatism, deformation, and high-grade metamorphism in the lower crust. Here, we apply this knowledge on the lower crustal granitoids of southern India and speculate on the variations in geochemistry as a consequence of differentiation and secular evolution of the continental crust.The major groups of granitoids of southern India are classified as metatonalites, comparable to typical Archaean TTGs with pronounced calc-alkaline affinity, and metagranites which are magmatic fractionation produced by reworking of early crust. Metatonalites are sodic-trondhjemites with slightly magnesian, moderate LREE (average LaN = 103) and low HREE (average YbN = 2) characerestics, where as metagranites are calc-alkaline ferroan types with enriched LREE (average LaN = 427) and HREE (average YbN = 23). Petrogenetic characteristics of granitoids illustrate continuous evolution of a primary crust into diverse magmatic units by multiple stages of intracrustal differentiation processes attributed to following tectonic scenarios: (1) formation of tonalitic magma by low- to moderate-degree partial melting of hydrated basaltic crust at pressures high enough to stabilize garnet-amphibole residue and (2) genesis of granite in a continental arc-accretion setting by an episode of crustal remelting of the tonalitic crust, within plagioclase stability field. The first-stage formed in a flat-subduction setting of an volcanic-arc, leading to the formation of tonalites. The heat budget required is ascribed to the upwelling of the mantle and/or basaltic underplating. Progressive decline in mantle potential temperature
Fukuda, Ikuo; Nakamura, Haruki
2006-02-01
For an arbitrary ordinary differential equation (ODE), a scheme for constructing an extended ODE endowed with a time-invariant function is here proposed. This scheme enables us to examine the accuracy of the numerical integration of an ODE that may itself have had no invariant. These quantities are constructed by referring to the Nosé-Hoover molecular dynamics equation and its related conserved quantity. By applying this procedure to several molecular dynamics equations, the conventional conserved quantity individually defined in each dynamics can be reproduced in a uniform, generalized way; our concept allows a transparent outlook underlying these quantities and ideas. Developing the technique, for a certain class of ODEs we construct a numerical integrator that is not only explicit and symmetric, but preserves a unit Jacobian for a suitably defined extended ODE, which also provides an invariant. Our concept is thus to simply build a divergence-free extended ODE whose solution is just a lift-up of the original ODE, and to constitute an efficient integrator that preserves the phase-space volume on the extended system. We present precise discussions about the general mathematical properties of the integrator and provide specific conditions that should be incorporated for practical applications.
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
2016-10-04
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.
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…
Um, Sang-Won; Joung, Je-Gun; Lee, Hyun; Kim, Hojoong; Kim, Kyu-Tae; Park, Jinha; Hayes, D Neil; Park, Woong-Yang
2016-11-15
Tumor heterogeneity influences the clinical outcome of patients with cancer, and the diagnostic method to measure the tumor heterogeneity needs to be developed. We analyzed genomic features on pairs of primary and multiple metastatic lymph nodes from six patients with lung cancer using whole-exome sequencing and RNA sequencing. Although somatic single-nucleotide variants were shared in primary lung cancer and metastases, tumor evolution predicted by the pattern of genomic alterations was matched to anatomic location of the tumors. Four of six cases exhibited a branched clonal evolution pattern. Lymph nodes with acquired somatic variants demonstrated resistance to the cancer treatment. In this study, we demonstrated that multiple biopsies and sequencing strategies for different tumor regions are required for a comprehensive understanding of the landscape of genetic alteration and for guiding targeted therapy in advanced primary lung cancer. Cancer Res; 76(22); 6568-76. ©2016 AACR.
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.
Kreutz, Jason E; Shukhaev, Anton; Du, Wenbin; Druskin, Sasha; Daugulis, Olafs; Ismagilov, Rustem F
2010-03-10
This paper uses microfluidics to implement genetic algorithms (GA) to discover new homogeneous catalysts using the oxidation of methane by molecular oxygen as a model system. The parameters of the GA were the catalyst, a cocatalyst capable of using molecular oxygen as the terminal oxidant, and ligands that could tune the catalytic system. The GA required running hundreds of reactions to discover and optimize catalyst systems of high fitness, and microfluidics enabled these numerous reactions to be run in parallel. The small scale and volumes of microfluidics offer significant safety benefits. The microfluidic system included methods to form diverse arrays of plugs containing catalysts, introduce gaseous reagents at high pressure, run reactions in parallel, and detect catalyst activity using an in situ indicator system. Platinum(II) was identified as an active catalyst, and iron(II) and the polyoxometalate H(5)PMo(10)V(2)O(40) (POM-V2) were identified as active cocatalysts. The Pt/Fe system was further optimized and characterized using NMR experiments. After optimization, turnover numbers of approximately 50 were achieved with approximately equal production of methanol and formic acid. The Pt/Fe system demonstrated the compatibility of iron with the entire catalytic cycle. This approach of GA-guided evolution has the potential to accelerate discovery in catalysis and other areas where exploration of chemical space is essential, including optimization of materials for hydrogen storage and CO(2) capture and modifications.
Hasbún, Rodrigo; Iturra, Carolina; Bravo, Soraya; Rebolledo-Jaramillo, Boris; Valledor, Luis
2016-01-01
Epigenetic regulation plays important biological roles in plants, including timing of flowering and endosperm development. Little is known about the mechanisms controlling heterochrony (the change in the timing or rate of developmental events during ontogeny) in Eucalyptus globulus. DNA methylation has been proposed as a potential heterochrony regulatory mechanism in model species, but its role during the vegetative phase in E. globulus has not been explored. In order to investigate the molecular mechanisms governing heterochrony in E. globulus, we have developed a workflow aimed at generating high-resolution hypermethylome and hypomethylome maps that have been tested in two stages of vegetative growth phase: juvenile (6-month leaves) and adult (30-month leaves). We used the M&M algorithm, a computational approach that integrates MeDIP-seq and MRE-seq data, to identify differentially methylated regions (DMRs). Thousands of DMRs between juvenile and adult leaves of E. globulus were found. Although further investigations are required to define the loci associated with heterochrony/heteroblasty that are regulated by DNA methylation, these results suggest that locus-specific methylation could be major regulators of vegetative phase change. This information can support future conservation programs, for example, selecting the best methylomes for a determinate environment in a restoration project. PMID:27123440
NASA Astrophysics Data System (ADS)
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.
Advancing x-ray scattering metrology using inverse genetic algorithms
NASA Astrophysics Data System (ADS)
Hannon, Adam F.; Sunday, Daniel F.; Windover, Donald; Joseph Kline, R.
2016-07-01
We compare the speed and effectiveness of two genetic optimization algorithms to the results of statistical sampling via a Markov chain Monte Carlo algorithm to find which is the most robust method for determining real-space structure in periodic gratings measured using critical dimension small-angle x-ray scattering. Both a covariance matrix adaptation evolutionary strategy and differential evolution algorithm are implemented and compared using various objective functions. The algorithms and objective functions are used to minimize differences between diffraction simulations and measured diffraction data. These simulations are parameterized with an electron density model known to roughly correspond to the real-space structure of our nanogratings. The study shows that for x-ray scattering data, the covariance matrix adaptation coupled with a mean-absolute error log objective function is the most efficient combination of algorithm and goodness of fit criterion for finding structures with little foreknowledge about the underlying fine scale structure features of the nanograting.
Advancing X-ray scattering metrology using inverse genetic algorithms.
Hannon, Adam F; Sunday, Daniel F; Windover, Donald; Kline, R Joseph
2016-01-01
We compare the speed and effectiveness of two genetic optimization algorithms to the results of statistical sampling via a Markov chain Monte Carlo algorithm to find which is the most robust method for determining real space structure in periodic gratings measured using critical dimension small angle X-ray scattering. Both a covariance matrix adaptation evolutionary strategy and differential evolution algorithm are implemented and compared using various objective functions. The algorithms and objective functions are used to minimize differences between diffraction simulations and measured diffraction data. These simulations are parameterized with an electron density model known to roughly correspond to the real space structure of our nanogratings. The study shows that for X-ray scattering data, the covariance matrix adaptation coupled with a mean-absolute error log objective function is the most efficient combination of algorithm and goodness of fit criterion for finding structures with little foreknowledge about the underlying fine scale structure features of the nanograting.
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
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.
Florio, Ermanno; Keller, Simona; Coretti, Lorena; Affinito, Ornella; Scala, Giovanni; Errico, Francesco; Fico, Annalisa; Boscia, Francesca; Sisalli, Maria Josè; Reccia, Mafalda Giovanna; Miele, Gennaro; Monticelli, Antonella; Scorziello, Antonella; Lembo, Francesca; Colucci-D'Amato, Luca; Minchiotti, Gabriella; Avvedimento, Vittorio Enrico; Usiello, Alessandro; Cocozza, Sergio; Chiariotti, Lorenzo
2017-01-01
ABSTRACT We performed ultra-deep methylation analysis at single molecule level of the promoter region of developmentally regulated D-Aspartate oxidase (Ddo), as a model gene, during brain development and embryonic stem cell neural differentiation. Single molecule methylation analysis enabled us to establish the effective epiallele composition within mixed or pure brain cell populations. In this framework, an epiallele is defined as a specific combination of methylated CpG within Ddo locus and can represent the epigenetic haplotype revealing a cell-to-cell methylation heterogeneity. Using this approach, we found a high degree of polymorphism of methylated alleles (epipolymorphism) evolving in a remarkably conserved fashion during brain development. The different sets of epialleles mark stage, brain areas, and cell type and unravel the possible role of specific CpGs in favoring or inhibiting local methylation. Undifferentiated embryonic stem cells showed non-organized distribution of epialleles that apparently originated by stochastic methylation events on individual CpGs. Upon neural differentiation, despite detecting no changes in average methylation, we observed that the epiallele distribution was profoundly different, gradually shifting toward organized patterns specific to the glial or neuronal cell types. Our findings provide a deep view of gene methylation heterogeneity in brain cell populations promising to furnish innovative ways to unravel mechanisms underlying methylation patterns generation and alteration in brain diseases. PMID:27858532
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.
NASA Astrophysics Data System (ADS)
Miesch, Mark S.; Elliott, Julian R.; Toomre, Juri; Clune, Tom L.; Glatzmaier, Gary A.; Gilman, Peter A.
2000-03-01
Rotationally constrained convection possesses velocity correlations that transport momentum and drive mean flows such as differential rotation. The nature of this transport can be very complex in turbulent flow regimes, where large-scale, coherent vorticity structures and mean flows can be established by smaller scale turbulence through inverse cascades. The dynamics of the highly turbulent solar convection zone therefore may be quite different than in early global-scale numerical models, which were limited by computational resources to nearly laminar flows. Recent progress in high-performance computing technology and ongoing helioseismic investigations of the dynamics of the solar interior have motivated us to develop more sophisticated numerical models of global-scale solar convection. Here we report three-dimensional simulations of compressible, penetrative convection in rotating spherical shells in both laminar and turbulent parameter regimes. The convective structure in the laminar case is dominated by ``banana cells,'' but the turbulent case is much more complex, with an intricate, rapidly evolving downflow network in the upper convection zone and an intermittent, plume-dominated structure in the lower convection zone and overshoot region. Convective patterns generally propagate prograde at low latitudes and retrograde at high latitudes relative to the local rotation. The differential rotation profiles show some similarity with helioseismic determinations of the solar rotation but still exhibit significantly more cylindrical alignment. Strong, intermittent, vortical downflow lanes and plumes play an important dynamical role in turbulent flow regimes and are responsible for significant differences relative to laminar flows with regard to momentum and energy transport and to the structure of the overshoot region at the base of the convection zone.
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
Niwa, Hitoshi; Sekita, Yoko; Tsend-Ayush, Enkhjargal; Grützner, Frank
2008-01-01
Uterine nourishment of embryos by the placenta is a key feature of mammals. Although a variety of placenta types exist, they are all derived from the trophectoderm (TE) cell layer of the developing embryo. Egg-laying mammals (platypus and echidnas) are distinguished by a very short intrauterine embryo development, in which a simple placenta forms from TE-like cells. The Pou5f1 gene encodes a class V POU family transcription factor Oct3/4. In mice, Oct3/4 together with the highly conserved caudal-related homeobox transcription factor Cdx2, determines TE fate in pre-implantation development. In contrast to Cdx2, Pou5f1 has only been identified in eutherian mammals and marsupials, whereas, in other vertebrates, pou2 is considered to be the Pou5f1 ortholog. Here, we show that platypus and opossum genomes contain a Pou5f1 and pou2 homolog, pou2-related, indicating that these two genes are paralogues and arose by gene duplication in early mammalian evolution. In a complementation assay, we found that platypus or human Pou5f1, but not opossum or zebrafish pou2, restores self-renewal in Pou5f1-null mouse ES cells, showing that platypus possess a fully functional Pou5f1 gene. Interestingly, we discovered that parts of one of the conserved regions (CR4) is missing from the platypus Pou5f1 promoter, suggesting that the autoregulation and reciprocal inhibition between Pou5f1 and Cdx2 evolved after the divergence of monotremes and may be linked to the development of more elaborate placental types in marsupial and eutherian mammals.
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.
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.
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
Mancini, Pamela; Castelli, Michele; Vignali, Robert
2013-01-01
Otx genes are a class of vertebrate homeobox genes, homologous to the orthodenticle gene of Drosophila melanogaster, that play a crucial role in anterior embryo patterning and sensory organ formation. In the frog, Xenopus laevis, at least three members of this class have been isolated: otx1, otx2 and otx5 (crx); they are involved in regulating both shared and differential processes during frog development. In particular, while otx2 and otx5 are both capable to promote cement gland (CG) formation, otx1 is not. We performed a molecular dissection of Otx5 and Otx1 proteins to characterize the functional parts of the proteins that make them differently able to promote CG formation. We show that a CG promoting domain (CGPD) is localized at the Otx5 C-terminus, and is bipartite: CGPD1 (aa210-255) is the most effective domain, while CGPD2 (aa177-209) has a lower activity. A histidine stretch disrupts CGPD1 continuity in Otx1 determining its loss of CG promoting activity; this histidine-rich region acts as an actively CG repressing domain. Another Otx1 specific domain, a serine-rich stretch, may also be involved in repressing Otx1 potential to trigger CG formation, though at a much lower level. This is the first evidence that these domains, specific of the Otx1 orthology group, play a role during development in differentiating Otx1 action compared to other Otx family members. We discuss the potential implications of their appearance in light of the evolution of Otx functional activities.
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.
Lee, Eric M; Yuan, Tian; Ballim, Reyna D; Nguyen, Kristy; Kelsh, Robert N; Medeiros, Daniel M; McCauley, David W
2016-10-01
Vertebrate SoxE genes (Sox8, 9, and 10) are key regulators of neural crest cell (NCC) development. These genes arose by duplication from a single SoxE gene in the vertebrate ancestor. Although SoxE paralogs are coexpressed early in NCC development, later, Sox9 is restricted to skeletogenic lineages in the head, and Sox10 to non-skeletogenic NCC in the trunk and head. When this subfunctionalization evolved and its possible role in the evolution of the neural crest are unknown. Sea lampreys are basal vertebrates that also possess three SoxE genes, while only a single SoxE is present in the cephalochordate amphioxus. In order to address the functional divergence of SoxE genes, and to determine if differences in their biochemical functions may be linked to changes in neural crest developmental potential, we examined the ability of lamprey and amphioxus SoxE genes to regulate differentiation of NCC derivatives in zebrafish colourless (cls) mutants lacking expression of sox10. Our findings suggest that the proto-vertebrate SoxE gene possessed both melanogenic and neurogenic capabilities prior to SoxE gene duplication. Following the agnathan-gnathostome split, lamprey SoxE1 and SoxE3 largely lost their melanogenic and/or enteric neurogenic properties, while gnathostome SoxE paralogs have retained functional conservation. We posit that this difference in protein subfunctionalization is a direct consequence of the independent regulation of SoxE paralog expression between the two lineages. Specifically, we propose that the overlapping expression of gnathostome SoxE paralogs in early neural crest largely constrained the function of gnathostome SoxE proteins. In contrast, the largely non-overlapping expression of lamprey SoxE paralogs allowed them to specialize with regard to their DNA-binding and/or protein interaction properties. Restriction of developmental potential among cranial and trunk neural crest in lampreys may be related to constraints on SoxE activity among
A Cooperative Framework for Fireworks Algorithm.
Zheng, Shaoqiu; Li, Junzhi; Janecek, Andreas; Tan, Ying
2017-01-01
This paper presents a cooperative framework for fireworks algorithm (CoFFWA). A detailed analysis of existing fireworks algorithm (FWA) and its recently developed variants has revealed that ( i) the current selection strategy has the drawback that the contribution of the firework with the best fitness (denoted as core firework) overwhelms the contributions of all other fireworks (non-core fireworks) in the explosion operator, ( ii) the Gaussian mutation operator is not as effective as it is designed to be. To overcome these limitations, the CoFFWA is proposed, which significantly improves the exploitation capability by using an independent selection method and also increases the exploration capability by incorporating a crowdness-avoiding cooperative strategy among the fireworks. Experimental results on the CEC2013 benchmark functions indicate that CoFFWA outperforms the state-of-the-art FWA variants, artificial bee colony, differential evolution, and the standard particle swarm optimization SPSO2007/SPSO2011 in terms of convergence performance.
2011-01-01
Background In the model system Drosophila melanogaster, doublesex (dsx) is the double-switch gene at the bottom of the somatic sex determination cascade that determines the differentiation of sexually dimorphic traits. Homologues of dsx are functionally conserved in various dipteran species, including the malaria vector Anopheles gambiae. They show a striking conservation of sex-specific regulation, based on alternative splicing, and of the encoded sex-specific proteins, which are transcriptional regulators of downstream terminal genes that influence sexual differentiation of cells, tissues and organs. Results In this work, we report on the molecular characterization of the dsx homologue in the dengue and yellow fever vector Aedes aegypti (Aeadsx). Aeadsx produces sex-specific transcripts by alternative splicing, which encode isoforms with a high degree of identity to Anopheles gambiae and Drosophila melanogaster homologues. Interestingly, Aeadsx produces an additional novel female-specific splicing variant. Genomic comparative analyses between the Aedes and Anopheles dsx genes revealed a partial conservation of the exon organization and extensive divergence in the intron lengths. An expression analysis showed that Aeadsx transcripts were present from early stages of development and that sex-specific regulation starts at least from late larval stages. The analysis of the female-specific untranslated region (UTR) led to the identification of putative regulatory cis-elements potentially involved in the sex-specific splicing regulation. The Aedes dsx sex-specific splicing regulation seems to be more complex with the respect of other dipteran species, suggesting slightly novel evolutionary trajectories for its regulation and hence for the recruitment of upstream splicing regulators. Conclusions This study led to uncover the molecular evolution of Aedes aegypti dsx splicing regulation with the respect of the more closely related Culicidae Anopheles gambiae orthologue
Symbolic Solution of Linear Differential Equations
NASA Technical Reports Server (NTRS)
Feinberg, R. B.; Grooms, R. G.
1981-01-01
An algorithm for solving linear constant-coefficient ordinary differential equations is presented. The computational complexity of the algorithm is discussed and its implementation in the FORMAC system is described. A comparison is made between the algorithm and some classical algorithms for solving differential equations.
Jiang, Wenjuan; Shi, Yunbo; Zhao, Wenjie; Wang, Xiangxin
2016-01-01
The main part of the magnetic fluxgate sensor is the magnetic core, the hysteresis characteristic of which affects the performance of the sensor. When the fluxgate sensors are modelled for design purposes, an accurate model of hysteresis characteristic of the cores is necessary to achieve good agreement between modelled and experimental data. The Jiles-Atherton model is simple and can reflect the hysteresis properties of the magnetic material precisely, which makes it widely used in hysteresis modelling and simulation of ferromagnetic materials. However, in practice, it is difficult to determine the parameters accurately owing to the sensitivity of the parameters. In this paper, the Biogeography-Based Optimization (BBO) algorithm is applied to identify the Jiles-Atherton model parameters. To enhance the performances of the BBO algorithm such as global search capability, search accuracy and convergence rate, an improved Biogeography-Based Optimization (IBBO) algorithm is put forward by using Arnold map and mutation strategy of Differential Evolution (DE) algorithm. Simulation results show that IBBO algorithm is superior to Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm, Differential Evolution algorithm and BBO algorithm in identification accuracy and convergence rate. The IBBO algorithm is applied to identify Jiles-Atherton model parameters of selected permalloy. The simulation hysteresis loop is in high agreement with experimental data. Using permalloy as core of fluxgate probe, the simulation output is consistent with experimental output. The IBBO algorithm can identify the parameters of Jiles-Atherton model accurately, which provides a basis for the precise analysis and design of instruments and equipment with magnetic core. PMID:27347974
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.
Comparison of commercially available genetic algorithms: GAs as variable selection tool
NASA Astrophysics Data System (ADS)
Schefzick, Sabine; Bradley, Mary
2004-07-01
Many commercially available software programs claim similar efficiency and accuracy as variable selection tools. Genetic algorithms are commonly used variable selection methods where most relevant variables can be differentiated from `less important' variables using evolutionary computing techniques. However, different vendors offer several algorithms, and the puzzling question is: which one is the appropriate method of choice? In this study, several genetic algorithm tools (e.g. GFA from Cerius2, QuaSAR-Evolution from MOE and Partek's genetic algorithm) were compared. Stepwise multiple linear regression models were generated using the most relevant variables identified by the above genetic algorithms. This procedure led to the successful generation of Quantitative Structure-activity Relationship (QSAR) models for (a) proprietary datasets and (b) the Selwood dataset.
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.
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.
Optimization of Straight Cylindrical Turning Using Artificial Bee Colony (ABC) Algorithm
NASA Astrophysics Data System (ADS)
Prasanth, Rajanampalli Seshasai Srinivasa; Hans Raj, Kandikonda
2016-06-01
Artificial bee colony (ABC) algorithm, that mimics the intelligent foraging behavior of honey bees, is increasingly gaining acceptance in the field of process optimization, as it is capable of handling nonlinearity, complexity and uncertainty. Straight cylindrical turning is a complex and nonlinear machining process which involves the selection of appropriate cutting parameters that affect the quality of the workpiece. This paper presents the estimation of optimal cutting parameters of the straight cylindrical turning process using the ABC algorithm. The ABC algorithm is first tested on four benchmark problems of numerical optimization and its performance is compared with genetic algorithm (GA) and ant colony optimization (ACO) algorithm. Results indicate that, the rate of convergence of ABC algorithm is better than GA and ACO. Then, the ABC algorithm is used to predict optimal cutting parameters such as cutting speed, feed rate, depth of cut and tool nose radius to achieve good surface finish. Results indicate that, the ABC algorithm estimated a comparable surface finish when compared with real coded genetic algorithm and differential evolution algorithm.
Hernández-Ocaña, Betania; Pozos-Parra, Ma. Del Pilar; Mezura-Montes, Efrén; Portilla-Flores, Edgar Alfredo; Vega-Alvarado, Eduardo; Calva-Yáñez, Maria Bárbara
2016-01-01
This paper presents two-swim operators to be added to the chemotaxis process of the modified bacterial foraging optimization algorithm to solve three instances of the synthesis of four-bar planar mechanisms. One swim favors exploration while the second one promotes fine movements in the neighborhood of each bacterium. The combined effect of the new operators looks to increase the production of better solutions during the search. As a consequence, the ability of the algorithm to escape from local optimum solutions is enhanced. The algorithm is tested through four experiments and its results are compared against two BFOA-based algorithms and also against a differential evolution algorithm designed for mechanical design problems. The overall results indicate that the proposed algorithm outperforms other BFOA-based approaches and finds highly competitive mechanisms, with a single set of parameter values and with less evaluations in the first synthesis problem, with respect to those mechanisms obtained by the differential evolution algorithm, which needed a parameter fine-tuning process for each optimization problem. PMID:27057156
Hernández-Ocaña, Betania; Pozos-Parra, Ma Del Pilar; Mezura-Montes, Efrén; Portilla-Flores, Edgar Alfredo; Vega-Alvarado, Eduardo; Calva-Yáñez, Maria Bárbara
2016-01-01
This paper presents two-swim operators to be added to the chemotaxis process of the modified bacterial foraging optimization algorithm to solve three instances of the synthesis of four-bar planar mechanisms. One swim favors exploration while the second one promotes fine movements in the neighborhood of each bacterium. The combined effect of the new operators looks to increase the production of better solutions during the search. As a consequence, the ability of the algorithm to escape from local optimum solutions is enhanced. The algorithm is tested through four experiments and its results are compared against two BFOA-based algorithms and also against a differential evolution algorithm designed for mechanical design problems. The overall results indicate that the proposed algorithm outperforms other BFOA-based approaches and finds highly competitive mechanisms, with a single set of parameter values and with less evaluations in the first synthesis problem, with respect to those mechanisms obtained by the differential evolution algorithm, which needed a parameter fine-tuning process for each optimization problem.
Perspective: reverse evolution.
Teotónio, H; Rose, M R
2001-04-01
For some time, the reversibility of evolution was primarily discussed in terms of comparative patterns. Only recently has this problem been studied using experimental evolution over shorter evolutionary time frames. This has raised questions of definition, experimental procedure, and the hypotheses being tested. Experimental evolution has provided evidence for multiple population genetic mechanisms in reverse evolution, including pleiotropy and mutation accumulation. It has also pointed to genetic factors that might prevent reverse evolution, such as a lack of genetic variability, epistasis, and differential genotype-by-environment interactions. The main focus of this perspective is on laboratory studies and their relevance to the genetics of reverse evolution. We discuss reverse evolution experiments with Drosophila, bacterial, and viral populations. Field studies of the reverse evolution of melanism in the peppered moth are also reviewed.
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)
Levandowski, Will; Boyd, Oliver S.; Briggs, Rich W.; Gold, Ryan D.
2015-12-01
This paper develops a Monte Carlo algorithm for extracting three-dimensional lithospheric density models from geophysical data. Empirical scaling relationships between velocity and density create a 3-D starting density model, which is then iteratively refined until it reproduces observed gravity and topography. This approach permits deviations from uniform crustal velocity-density scaling, which provide insight into crustal lithology and prevent spurious mapping of crustal anomalies into the mantle. We test this algorithm on the Proterozoic Midcontinent Rift (MCR), north-central United States. The MCR provides a challenge because it hosts a gravity high overlying low shear-wave velocity crust in a generally flat region. Our initial density estimates are derived from a seismic velocity/crustal thickness model based on joint inversion of surface-wave dispersion and receiver functions. By adjusting these estimates to reproduce gravity and topography, we generate a lithospheric-scale model that reveals dense middle crust and eclogitized lowermost crust within the rift. Mantle lithospheric density beneath the MCR is not anomalous, consistent with geochemical evidence that lithospheric mantle was not the primary source of rift-related magmas and suggesting that extension occurred in response to far-field stress rather than a hot mantle plume. Similarly, the subsequent inversion of normal faults resulted from changing far-field stress that exploited not only warm, recently faulted crust but also a gravitational potential energy low in the MCR. The success of this density modeling algorithm in the face of such apparently contradictory geophysical properties suggests that it may be applicable to a variety of tectonic and geodynamic problems.
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.
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.
Selfish Gene Algorithm Vs Genetic Algorithm: A Review
NASA Astrophysics Data System (ADS)
Ariff, Norharyati Md; Khalid, Noor Elaiza Abdul; Hashim, Rathiah; Noor, Noorhayati Mohamed
2016-11-01
Evolutionary algorithm is one of the algorithms inspired by the nature. Within little more than a decade hundreds of papers have reported successful applications of EAs. In this paper, the Selfish Gene Algorithms (SFGA), as one of the latest evolutionary algorithms (EAs) inspired from the Selfish Gene Theory which is an interpretation of Darwinian Theory ideas from the biologist Richards Dawkins on 1989. In this paper, following a brief introduction to the Selfish Gene Algorithm (SFGA), the chronology of its evolution is presented. It is the purpose of this paper is to present an overview of the concepts of Selfish Gene Algorithm (SFGA) as well as its opportunities and challenges. Accordingly, the history, step involves in the algorithm are discussed and its different applications together with an analysis of these applications are evaluated.
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
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.
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)
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.
Ding, Pan; Gong, Xue-Qing
2016-05-01
Titanium dioxide (TiO2) is an important metal oxide that has been used in many different applications. TiO2 has also been widely employed as a model system to study basic processes and reactions in surface chemistry and heterogeneous catalysis. In this work, we investigated the (011) surface of rutile TiO2 by focusing on its reconstruction. Density functional theory calculations aided by a genetic algorithm based optimization scheme were performed to extensively sample the potential energy surfaces of reconstructed rutile TiO2 structures that obey (2 × 1) periodicity. A lot of stable surface configurations were located, including the global-minimum configuration that was proposed previously. The wide variety of surface structures determined through the calculations performed in this work provide insight into the relationship between the atomic configuration of a surface and its stability. More importantly, several analytical schemes were proposed and tested to gauge the differences and similarities among various surface structures, aiding the construction of the complete pathway for the reconstruction process.
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.
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.
Czernic, Pierre; Gully, Djamel; Cartieaux, Fabienne; Moulin, Lionel; Guefrachi, Ibtissem; Patrel, Delphine; Pierre, Olivier; Fardoux, Joël; Chaintreuil, Clémence; Nguyen, Phuong; Gressent, Frédéric; Da Silva, Corinne; Poulain, Julie; Wincker, Patrick; Rofidal, Valérie; Hem, Sonia; Barrière, Quentin; Arrighi, Jean-François; Mergaert, Peter; Giraud, Eric
2015-01-01
Nutritional symbiotic interactions require the housing of large numbers of microbial symbionts, which produce essential compounds for the growth of the host. In the legume-rhizobium nitrogen-fixing symbiosis, thousands of rhizobium microsymbionts, called bacteroids, are confined intracellularly within highly specialized symbiotic host cells. In Inverted Repeat-Lacking Clade (IRLC) legumes such as Medicago spp., the bacteroids are kept under control by an arsenal of nodule-specific cysteine-rich (NCR) peptides, which induce the bacteria in an irreversible, strongly elongated, and polyploid state. Here, we show that in Aeschynomene spp. legumes belonging to the more ancient Dalbergioid lineage, bacteroids are elongated or spherical depending on the Aeschynomene spp. and that these bacteroids are terminally differentiated and polyploid, similar to bacteroids in IRLC legumes. Transcriptome, in situ hybridization, and proteome analyses demonstrated that the symbiotic cells in the Aeschynomene spp. nodules produce a large diversity of NCR-like peptides, which are transported to the bacteroids. Blocking NCR transport by RNA interference-mediated inactivation of the secretory pathway inhibits bacteroid differentiation. Together, our results support the view that bacteroid differentiation in the Dalbergioid clade, which likely evolved independently from the bacteroid differentiation in the IRLC clade, is based on very similar mechanisms used by IRLC legumes. PMID:26286718
Czernic, Pierre; Gully, Djamel; Cartieaux, Fabienne; Moulin, Lionel; Guefrachi, Ibtissem; Patrel, Delphine; Pierre, Olivier; Fardoux, Joël; Chaintreuil, Clémence; Nguyen, Phuong; Gressent, Frédéric; Da Silva, Corinne; Poulain, Julie; Wincker, Patrick; Rofidal, Valérie; Hem, Sonia; Barrière, Quentin; Arrighi, Jean-François; Mergaert, Peter; Giraud, Eric
2015-10-01
Nutritional symbiotic interactions require the housing of large numbers of microbial symbionts, which produce essential compounds for the growth of the host. In the legume-rhizobium nitrogen-fixing symbiosis, thousands of rhizobium microsymbionts, called bacteroids, are confined intracellularly within highly specialized symbiotic host cells. In Inverted Repeat-Lacking Clade (IRLC) legumes such as Medicago spp., the bacteroids are kept under control by an arsenal of nodule-specific cysteine-rich (NCR) peptides, which induce the bacteria in an irreversible, strongly elongated, and polyploid state. Here, we show that in Aeschynomene spp. legumes belonging to the more ancient Dalbergioid lineage, bacteroids are elongated or spherical depending on the Aeschynomene spp. and that these bacteroids are terminally differentiated and polyploid, similar to bacteroids in IRLC legumes. Transcriptome, in situ hybridization, and proteome analyses demonstrated that the symbiotic cells in the Aeschynomene spp. nodules produce a large diversity of NCR-like peptides, which are transported to the bacteroids. Blocking NCR transport by RNA interference-mediated inactivation of the secretory pathway inhibits bacteroid differentiation. Together, our results support the view that bacteroid differentiation in the Dalbergioid clade, which likely evolved independently from the bacteroid differentiation in the IRLC clade, is based on very similar mechanisms used by IRLC legumes.
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.
NASA Technical Reports Server (NTRS)
Abrams, D.; Williams, C.
1999-01-01
This thesis describes several new quantum algorithms. These include a polynomial time algorithm that uses a quantum fast Fourier transform to find eigenvalues and eigenvectors of a Hamiltonian operator, and that can be applied in cases for which all know classical algorithms require exponential time.
Optimization Algorithms in Optimal Predictions of Atomistic Properties by Kriging.
Di Pasquale, Nicodemo; Davie, Stuart J; Popelier, Paul L A
2016-04-12
The machine learning method kriging is an attractive tool to construct next-generation force fields. Kriging can accurately predict atomistic properties, which involves optimization of the so-called concentrated log-likelihood function (i.e., fitness function). The difficulty of this optimization problem quickly escalates in response to an increase in either the number of dimensions of the system considered or the size of the training set. In this article, we demonstrate and compare the use of two search algorithms, namely, particle swarm optimization (PSO) and differential evolution (DE), to rapidly obtain the maximum of this fitness function. The ability of these two algorithms to find a stationary point is assessed by using the first derivative of the fitness function. Finally, the converged position obtained by PSO and DE is refined through the limited-memory Broyden-Fletcher-Goldfarb-Shanno bounded (L-BFGS-B) algorithm, which belongs to the class of quasi-Newton algorithms. We show that both PSO and DE are able to come close to the stationary point, even in high-dimensional problems. They do so in a reasonable amount of time, compared to that with the Newton and quasi-Newton algorithms, regardless of the starting position in the search space of kriging hyperparameters. The refinement through L-BFGS-B is able to give the position of the maximum with whichever precision is desired.
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.
Hamada, Mayuko; Goricki, Spela; Byerly, Mardi S.; Satoh, Noriyuki; Jeffery, William R.
2015-01-01
The regeneration of the oral siphon (OS) and other distal structures in the ascidian Ciona intestinalis occurs by epimorphosis involving the formation of a blastema of proliferating cells. Despite the longstanding use of Ciona as a model in molecular developmental biology, regeneration in this system has not been previously explored by molecular analysis. Here we have employed microarray analysis and quantitative real time RT-PCR to identify genes with differential expression profiles during OS regeneration. The majority of differentially expressed genes were downregulated during OS regeneration, suggesting roles in normal growth and homeostasis. However, a subset of differentially expressed genes was upregulated in the regenerating OS, suggesting functional roles during regeneration. Among the upregulated genes were key members of the Notch signaling pathway, including those encoding the delta and jagged ligands, two fringe modulators, and to a lesser extent the notch receptor. In situ hybridization showed a complementary pattern of delta1 and notch gene expression in the blastema of the regenerating OS. Chemical inhibition of the Notch signaling pathway reduced the levels of cell proliferation in the branchial sac, a stem cell niche that contributes progenitor cells to the regenerating OS, and in the OS regeneration blastema, where siphon muscle fibers eventually re-differentiate. Chemical inhibition also prevented the replacement of oral siphon pigment organs, sensory receptors rimming the entrance of the OS, and siphon muscle fibers, but had no effects on the formation of the wound epidermis. Since Notch signaling is involved in the maintenance of proliferative activity in both the Ciona and vertebrate regeneration blastema, the results suggest a conserved evolutionary role of this signaling pathway in chordate regeneration. The genes identified in this investigation provide the foundation for future molecular analysis of OS regeneration. PMID:26206613
NASA Astrophysics Data System (ADS)
Brassinnes, S.; Balaganskaya, E.; Demaiffe, D.
2005-11-01
The nature of the petrogenetic links between carbonatites and associated silicate rocks is still under discussion (i.e., [ Gittins J., Harmer R.E., 2003. Myth and reality of the carbonatite-silicate rock "association". Period di Mineral. 72, 19-26.]). In the Paleozoic Kola alkaline province (NW Russia), the carbonatites are spatially and temporally associated to ultramafic cumulates (clinopyroxenite, wehrlite and dunite) and alkaline silicate rocks of the ijolite-melteigite series [ Kogarko, L.N., 1987. Alkaline rocks of the eastern part of the Baltic Shield (Kola Peninsula). In: Fitton, J.G., and Upton, B.G.J. (eds). Alkaline igneous rocks. Geol. Soc. Special Publication 30, 531-544; Kogarko, L.N., Kononova, V.A., Orlova, M.P., Woolley, A.R., 1995. Alkaline rocks and carbonatites of the world. Part 2. Former USSR. Chapman and Hall, London, 225 pp; Verhulst, A., Balaganskya, E., Kirnarsky, Y., Demaiffe, D., 2000. Petrological and geochemical (trace elements and Sr-Nd isotopes) characteristics of the Paleozoic Kovdor ultramafic, alkaline and carbonatite intrusion (Kola Peninsula, NW Russia). Lithos 51, 1-25; Dunworth, E.A., Bell, K., 2001. The Turiy Massif, Kola Peninsula, Russia; isotopic and geochemical evidence for a multi-source evolution. J. Petrol. 42, 377-405; Woolley, A.R., 2003. Igneous silicate rocks associated with carbonatites: their diversity, relative abundances and implications for carbonatite genesis. Period. di Mineral. 72, 9-17)]. In the small (≈ 20 km 2) Vuoriyarvi massif, apatite is typically a liquidus phase during the magmatic evolution and so it can be used to test genetic relationships. Trace elements contents have been obtained for both whole rocks and apatite (by LA-ICP-MS). The apatites define a single continuous chemical evolution marked by an increase in REE and Na (belovite-type of substitution, i.e., 2Ca 2+ = Na + + REE 3+). This evolution possibly reflects a fractional crystallisation process of a single batch of isotopically
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.
Rauscher, Emily; Showman, Adam P.
2014-04-01
As a planet ages, it cools and its radius shrinks at a rate set by the efficiency with which heat is transported from the interior out to space. The bottleneck for this transport is at the boundary between the convective interior and the radiative atmosphere; the opacity there sets the global cooling rate. Models of planetary evolution are often one dimensional (1D), such that the radiative-convective boundary (RCB) is defined by a single temperature, pressure, and opacity. In reality the spatially inhomogeneous stellar heating pattern and circulation in the atmosphere could deform the RCB, allowing heat from the interior to escape more efficiently through regions with lower opacity. We present an analysis of the degree to which the RCB could be deformed and the resultant change in the evolutionary cooling rate. In this initial work we calculate the upper limit for this effect by comparing an atmospheric structure in local radiative equilibrium to its 1D equivalent. We find that the cooling through an uneven RCB could be enhanced over cooling through a uniform RCB by as much as 10%-50%. We also show that the deformation of the RCB (and the enhancement of the cooling rate) increases with a greater incident stellar flux or a lower inner entropy. Our results indicate that this mechanism could significantly change a planet's thermal evolution, causing it to cool and shrink more quickly than would otherwise be expected. This may exacerbate the well-known difficulty in explaining the very large radii observed for some hot Jupiters.
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.
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
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
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.
Learning partial differential equations via data discovery and sparse optimization
NASA Astrophysics Data System (ADS)
Schaeffer, Hayden
2017-01-01
We investigate the problem of learning an evolution equation directly from some given data. This work develops a learning algorithm to identify the terms in the underlying partial differential equations and to approximate the coefficients of the terms only using data. The algorithm uses sparse optimization in order to perform feature selection and parameter estimation. The features are data driven in the sense that they are constructed using nonlinear algebraic equations on the spatial derivatives of the data. Several numerical experiments show the proposed method's robustness to data noise and size, its ability to capture the true features of the data, and its capability of performing additional analytics. Examples include shock equations, pattern formation, fluid flow and turbulence, and oscillatory convection.
Learning partial differential equations via data discovery and sparse optimization.
Schaeffer, Hayden
2017-01-01
We investigate the problem of learning an evolution equation directly from some given data. This work develops a learning algorithm to identify the terms in the underlying partial differential equations and to approximate the coefficients of the terms only using data. The algorithm uses sparse optimization in order to perform feature selection and parameter estimation. The features are data driven in the sense that they are constructed using nonlinear algebraic equations on the spatial derivatives of the data. Several numerical experiments show the proposed method's robustness to data noise and size, its ability to capture the true features of the data, and its capability of performing additional analytics. Examples include shock equations, pattern formation, fluid flow and turbulence, and oscillatory convection.
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.
Pokorná, Martina; Rábová, Marie; Ráb, Petr; Ferguson-Smith, Malcolm A; Rens, Willem; Kratochvíl, Lukáš
2010-11-01
The eyelid geckos (family Eublepharidae) include both species with temperature-dependent sex determination and species where genotypic sex determination (GSD) was suggested based on the observation of equal sex ratios at several incubation temperatures. In this study, we present data on karyotypes and chromosomal characteristics in 12 species (Aeluroscalabotes felinus, Coleonyx brevis, Coleonyx elegans, Coleonyx variegatus, Eublepharis angramainyu, Eublepharis macularius, Goniurosaurus araneus, Goniurosaurus lichtenfelderi, Goniurosaurus luii, Goniurosaurus splendens, Hemitheconyx caudicinctus, and Holodactylus africanus) covering all genera of the family, and search for the presence of heteromorphic sex chromosomes. Phylogenetic mapping of chromosomal changes showed a long evolutionary stasis of karyotypes with all acrocentric chromosomes followed by numerous chromosomal rearrangements in the ancestors of two lineages. We have found heteromorphic sex chromosomes in only one species, which suggests that sex chromosomes in most GSD species of the eyelid geckos are not morphologically differentiated. The sexual difference in karyotype was detected only in C. elegans which has a multiple sex chromosome system (X(1)X(2)Y). The metacentric Y chromosome evolved most likely via centric fusion of two acrocentric chromosomes involving loss of interstitial telomeric sequences. We conclude that the eyelid geckos exhibit diversity in sex determination ranging from the absence of any sexual differences to heteromorphic sex chromosomes, which makes them an interesting system for exploring the evolutionary origin of sexually dimorphic genomes.
NASA Astrophysics Data System (ADS)
Wolfe, William J.; Wood, David; Sorensen, Stephen E.
1996-12-01
This paper discusses automated scheduling as it applies to complex domains such as factories, transportation, and communications systems. The window-constrained-packing problem is introduced as an ideal model of the scheduling trade offs. Specific algorithms are compared in terms of simplicity, speed, and accuracy. In particular, dispatch, look-ahead, and genetic algorithms are statistically compared on randomly generated job sets. The conclusion is that dispatch methods are fast and fairly accurate; while modern algorithms, such as genetic and simulate annealing, have excessive run times, and are too complex to be practical.
Sobel, E.; Lange, K.; O`Connell, J.R.
1996-12-31
Haplotyping is the logical process of inferring gene flow in a pedigree based on phenotyping results at a small number of genetic loci. This paper formalizes the haplotyping problem and suggests four algorithms for haplotype reconstruction. These algorithms range from exhaustive enumeration of all haplotype vectors to combinatorial optimization by simulated annealing. Application of the algorithms to published genetic analyses shows that manual haplotyping is often erroneous. Haplotyping is employed in screening pedigrees for phenotyping errors and in positional cloning of disease genes from conserved haplotypes in population isolates. 26 refs., 6 figs., 3 tabs.
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.
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.
Erba, H.P.; Eddy, R.; Shows, T.; Kedes, L.; Gunning, P.
1988-04-01
The accumulation of the cytoskeletal ..beta..-and ..gamma..-actin mRNAs was determined in a variety of mouse tissues and organs. The ..beta..-iosform is always expressed in excess of the ..gamma..-isoform. However, the molar ratio of ..beta..- to ..gamma..-actin mRNA varies from 1.7 in kidney and testis to 12 in sarcomeric muscle to 114 in liver. The authors conclude that, whereas the cytoskeletal ..beta..- and ..gamma..-actins are truly coexpressed, their mRNA levels are subject to differential regulation between different cell types. The human ..gamma..-actin gene has been cloned and sequenced, and its chromosome location has been determined. The gene is located on human chromosome 17, unlike ..beta..-actin which is on chromosome 7. Thus, if these genes are also unlinked in the mouse, the coexpression of the ..beta..- and ..gamma..-actin genes in rodent tissues cannot be determined by gene linkage. Comparison of the human ..beta..- and ..gamma..-actin genes reveals that noncoding sequences in the 5'-flanking region and in intron III have been conserved since the duplication that gave rise to these two genes. In contrast, there are sequences in intron III and the 3'-untranslated region which are not present in the ..beta..-actin gene but are conserved between the human ..gamma..-actin and the Xenopus borealis type 1 actin genes. Such conserved noncoding sequences may contribute to the coexpression of ..beta..- and ..gamma..-actin or to the unique regulation and function of the ..gamma..-actin gene. Finally, the authors demonstrate that the human ..gamma..-actin gene is expressed after introduction into mouse L cells and C2 myoblasts and that, upon fusion of C2 cells to form myotubes, the human ..gamma..-actin gene is appropriately regulated.
de Vladar, Harold P; Santos, Mauro; Szathmáry, Eörs
2017-02-25
Despite major advances in evolutionary theories, some aspects of evolution remain neglected: whether evolution: would come to a halt without abiotic change; is unbounded and open-ended; or is progressive and something beyond fitness is maximized. Here, we discuss some models of ecology and evolution and argue that ecological change, resulting in Red Queen dynamics, facilitates (but does not ensure) innovation. We distinguish three forms of open-endedness. In weak open-endedness, novel phenotypes can occur indefinitely. Strong open-endedness requires the continual appearance of evolutionary novelties and/or innovations. Ultimate open-endedness entails an indefinite increase in complexity, which requires unlimited heredity. Open-ended innovation needs exaptations that generate novel niches. This can result in new traits and new rules as the dynamics unfolds, suggesting that evolution is not fully algorithmic.
Stubbs, Allston Julius; Atilla, Halis Atil
2016-01-01
Summary Background Despite the rapid advancement of imaging and arthroscopic techniques about the hip joint, missed diagnoses are still common. As a deep joint and compared to the shoulder and knee joints, localization of hip symptoms is difficult. Hip pathology is not easily isolated and is often related to intra and extra-articular abnormalities. In light of these diagnostic challenges, we recommend an algorithmic approach to effectively diagnoses and treat hip pain. Methods In this review, hip pain is evaluated from diagnosis to treatment in a clear decision model. First we discuss emergency hip situations followed by the differentiation of intra and extra-articular causes of the hip pain. We differentiate the intra-articular hip as arthritic and non-arthritic and extra-articular pain as surrounding or remote tissue generated. Further, extra-articular hip pain is evaluated according to pain location. Finally we summarize the surgical treatment approach with an algorithmic diagram. Conclusion Diagnosis of hip pathology is difficult because the etiologies of pain may be various. An algorithmic approach to hip restoration from diagnosis to rehabilitation is crucial to successfully identify and manage hip pathologies. Level of evidence: V. PMID:28066734
Sunspots and Coronal Bright Points Tracking using a Hybrid Algorithm of PSO and Active Contour Model
NASA Astrophysics Data System (ADS)
Dorotovic, I.; Shahamatnia, E.; Lorenc, M.; Rybansky, M.; Ribeiro, R. A.; Fonseca, J. M.
2014-02-01
In the last decades there has been a steady increase of high-resolution data, from ground-based and space-borne solar instruments, and also of solar data volume. These huge image archives require efficient automatic image processing software tools capable of detecting and tracking various features in the solar atmosphere. Results of application of such tools are essential for studies of solar activity evolution, climate change understanding and space weather prediction. The follow up of interplanetary and near-Earth phenomena requires, among others, automatic tracking algorithms that can determine where a feature is located, on successive images taken along the period of observation. Full-disc solar images, obtained both with the ground-based solar telescopes and the instruments onboard the satellites, provide essential observational material for solar physicists and space weather researchers for better understanding the Sun, studying the evolution of various features in the solar atmosphere, and also investigating solar differential rotation by tracking such features along time. Here we demonstrate and discuss the suitability of applying a hybrid Particle Swarm Optimization (PSO) algorithm and Active Contour model for tracking and determining the differential rotation of sunspots and coronal bright points (CBPs) on a set of selected solar images. The results obtained confirm that the proposed approach constitutes a promising tool for investigating the evolution of solar activity and also for automating tracking features on massive solar image archives.
Computing Algorithms for Nuffield Advanced Physics.
ERIC Educational Resources Information Center
Summers, M. K.
1978-01-01
Defines all recurrence relations used in the Nuffield course, to solve first- and second-order differential equations, and describes a typical algorithm for computer generation of solutions. (Author/GA)
NASA 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.
Contour detection based on wavelet differentiation
NASA Astrophysics Data System (ADS)
Bezuglov, D.; Kuzin, A.; Voronin, V.
2016-05-01
This work proposes a novel algorithm for contour detection based on high-performance algorithm of wavelet analysis for multimedia applications. To solve the noise effect on the result of peaking in this paper we consider the direct and inverse wavelet differentiation. Extensive experimental evaluation on noisy images demonstrates that our contour detection method significantly outperform competing algorithms. The proposed algorithm provides a means of coupling our system to recognition application such as detection and identification of vehicle number plate.
Physical Principles of Evolution
NASA Astrophysics Data System (ADS)
Schuster, Peter
Theoretical biology is incomplete without a comprehensive theory of evolution, since evolution is at the core of biological thought. Evolution is visualized as a migration process in genotype or sequence space that is either an adaptive walk driven by some fitness gradient or a random walk in the absence of (sufficiently large) fitness differences. The Darwinian concept of natural selection consisting in the interplay of variation and selection is based on a dichotomy: All variations occur on genotypes whereas selection operates on phenotypes, and relations between genotypes and phenotypes, as encapsulated in a mapping from genotype space into phenotype space, are central to an understanding of evolution. Fitness is conceived as a function of the phenotype, represented by a second mapping from phenotype space into nonnegative real numbers. In the biology of organisms, genotype-phenotype maps are enormously complex and relevant information on them is exceedingly scarce. The situation is better in the case of viruses but so far only one example of a genotype-phenotype map, the mapping of RNA sequences into RNA secondary structures, has been investigated in sufficient detail. It provides direct information on RNA selection in vitro and test-tube evolution, and it is a basis for testing in silico evolution on a realistic fitness landscape. Most of the modeling efforts in theoretical and mathematical biology today are done by means of differential equations but stochastic effects are of undeniably great importance for evolution. Population sizes are much smaller than the numbers of genotypes constituting sequence space. Every mutant, after all, has to begin with a single copy. Evolution can be modeled by a chemical master equation, which (in principle) can be approximated by a stochastic differential equation. In addition, simulation tools are available that compute trajectories for master equations. The accessible population sizes in the range of 10^7le Nle 10
Numerical Differentiation of Noisy, Nonsmooth Data
Chartrand, Rick
2011-01-01
We consider the problem of differentiating a function specified by noisy data. Regularizing the differentiation process avoids the noise amplification of finite-difference methods. We use total-variation regularization, which allows for discontinuous solutions. The resulting simple algorithm accurately differentiates noisy functions, including those which have a discontinuous derivative.
Algorithms, complexity, and the sciences
Papadimitriou, Christos
2014-01-01
Algorithms, perhaps together with Moore’s law, compose the engine of the information technology revolution, whereas complexity—the antithesis of algorithms—is one of the deepest realms of mathematical investigation. After introducing the basic concepts of algorithms and complexity, and the fundamental complexity classes P (polynomial time) and NP (nondeterministic polynomial time, or search problems), we discuss briefly the P vs. NP problem. We then focus on certain classes between P and NP which capture important phenomena in the social and life sciences, namely the Nash equlibrium and other equilibria in economics and game theory, and certain processes in population genetics and evolution. Finally, an algorithm known as multiplicative weights update (MWU) provides an algorithmic interpretation of the evolution of allele frequencies in a population under sex and weak selection. All three of these equivalences are rife with domain-specific implications: The concept of Nash equilibrium may be less universal—and therefore less compelling—than has been presumed; selection on gene interactions may entail the maintenance of genetic variation for longer periods than selection on single alleles predicts; whereas MWU can be shown to maximize, for each gene, a convex combination of the gene’s cumulative fitness in the population and the entropy of the allele distribution, an insight that may be pertinent to the maintenance of variation in evolution. PMID:25349382
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.
Schulz, Andreas S.; Shmoys, David B.; Williamson, David P.
1997-01-01
Increasing global competition, rapidly changing markets, and greater consumer awareness have altered the way in which corporations do business. To become more efficient, many industries have sought to model some operational aspects by gigantic optimization problems. It is not atypical to encounter models that capture 106 separate “yes” or “no” decisions to be made. Although one could, in principle, try all 2106 possible solutions to find the optimal one, such a method would be impractically slow. Unfortunately, for most of these models, no algorithms are known that find optimal solutions with reasonable computation times. Typically, industry must rely on solutions of unguaranteed quality that are constructed in an ad hoc manner. Fortunately, for some of these models there are good approximation algorithms: algorithms that produce solutions quickly that are provably close to optimal. Over the past 6 years, there has been a sequence of major breakthroughs in our understanding of the design of approximation algorithms and of limits to obtaining such performance guarantees; this area has been one of the most flourishing areas of discrete mathematics and theoretical computer science. PMID:9370525
Iterative algorithms for large sparse linear systems on parallel computers
NASA Technical Reports Server (NTRS)
Adams, L. M.
1982-01-01
Algorithms for assembling in parallel the sparse system of linear equations that result from finite difference or finite element discretizations of elliptic partial differential equations, such as those that arise in structural engineering are developed. Parallel linear stationary iterative algorithms and parallel preconditioned conjugate gradient algorithms are developed for solving these systems. In addition, a model for comparing parallel algorithms on array architectures is developed and results of this model for the algorithms are given.
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.
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
2016-07-28
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.
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.
Inverse problem of HIV cell dynamics using Genetic Algorithms
NASA Astrophysics Data System (ADS)
González, J. A.; Guzmán, F. S.
2017-01-01
In order to describe the cell dynamics of T-cells in a patient infected with HIV, we use a flavour of Perelson's model. This is a non-linear system of Ordinary Differential Equations that describes the evolution of healthy, latently infected, infected T-cell concentrations and the free viral cells. Different parameters in the equations give different dynamics. Considering the concentration of these types of cells is known for a particular patient, the inverse problem consists in estimating the parameters in the model. We solve this inverse problem using a Genetic Algorithm (GA) that minimizes the error between the solutions of the model and the data from the patient. These errors depend on the parameters of the GA, like mutation rate and population, although a detailed analysis of this dependence will be described elsewhere.
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.
Shibuya, K.; Hamada, T.; Masuda, K.; Shimada, K.
1989-05-02
A differential gear for permitting a difference in rotational speed between two output shafts is described, the differential gear including an input shaft and two output shafts. The improvement consists of means for limiting the difference in rotational speed between the two output shafts in response to the rotational speed of the input shaft, the rotational speed limiting means comprising a differential casing coupled to the input shaft and adapted to be rotated by the input shaft, a differential pinion shaft radially extending within the differential casing and rotatably mounted at its opposite ends in the differential casing. A plurality of differential pinion gears rotatably mounted on the differential pinion shaft is also included, and also a pair of side gears having a rotational axis common to that of the differential casing, wherein the side gears mesh with the differential pinion gears and the two output shafts are fixed to the side gears, the means for limiting the difference in rotational speed between the two output shafts comprising a weight means radially movable in the differential casing, the weight means limiting the difference in rotational speed between the two output shafts in response to the centrifugal force applied to the weight means, the weight means being slidably mounted on the differential pinion shaft and being biased radially inwardly.
Fast differential elimination in C: The CDiffElim environment*
NASA Astrophysics Data System (ADS)
Wittkopf, Allan D.; Reid, Gregory J.
2001-09-01
We introduce the CDiffElim environment, written in C, and an algorithm developed in this environment for simplifying systems of overdetermined partial differential equations by using differentiation and elimination. This environment has strategies for addressing difficulties encountered in differential elimination algorithms, such as exhaustion of computer memory due to intermediate expression swell, and failure to complete due to the massive number of calculations involved. These strategies include low-level memory management strategies and data representations that are tailored for efficient differential elimination algorithms. These strategies, which are coded in a low-level C implementation, seem much more difficult to implement in high-level general purpose computer algebra systems. A differential elimination algorithm written in this environment is applied to the determination of symmetry properties of classes of (n+1)-dimensional coupled nonlinear partial differential equations of form iut+∇ 2u+(a(t)| x| 2+ b(t)· x+c(t)+d| u| 4/n) u= 0, where u is an m-component vector-valued function. The resulting systems of differential equations for the symmetries have been made available on the web, to be used as benchmark systems for other researchers. The new differential elimination algorithm in C, runs on the test suite an average of 400 times faster than our RifSimp algorithm in Maple. New algorithms, including an enhanced GCD algorithm, and a hybrid symbolic-numeric differential elimination algorithm, are also described.
The fast debris evolution model
NASA Astrophysics Data System (ADS)
Lewis, H. G.; Swinerd, G. G.; Newland, R. J.; Saunders, A.
2009-09-01
The 'particles-in-a-box' (PIB) model introduced by Talent [Talent, D.L. Analytic model for orbital debris environmental management. J. Spacecraft Rocket, 29 (4), 508-513, 1992.] removed the need for computer-intensive Monte Carlo simulation to predict the gross characteristics of an evolving debris environment. The PIB model was described using a differential equation that allows the stability of the low Earth orbit (LEO) environment to be tested by a straightforward analysis of the equation's coefficients. As part of an ongoing research effort to investigate more efficient approaches to evolutionary modelling and to develop a suite of educational tools, a new PIB model has been developed. The model, entitled Fast Debris Evolution (FADE), employs a first-order differential equation to describe the rate at which new objects ⩾10 cm are added and removed from the environment. Whilst Talent [Talent, D.L. Analytic model for orbital debris environmental management. J. Spacecraft Rocket, 29 (4), 508-513, 1992.] based the collision theory for the PIB approach on collisions between gas particles and adopted specific values for the parameters of the model from a number of references, the form and coefficients of the FADE model equations can be inferred from the outputs of future projections produced by high-fidelity models, such as the DAMAGE model. The FADE model has been implemented as a client-side, web-based service using JavaScript embedded within a HTML document. Due to the simple nature of the algorithm, FADE can deliver the results of future projections immediately in a graphical format, with complete user-control over key simulation parameters. Historical and future projections for the ⩾10 cm LEO debris environment under a variety of different scenarios are possible, including business as usual, no future launches, post-mission disposal and remediation. A selection of results is presented with comparisons with predictions made using the DAMAGE environment model
The Fast Debris Evolution Model
NASA Astrophysics Data System (ADS)
Lewis, Hugh G.; Swinerd, Graham; Newland, Rebecca; Saunders, Arrun
The ‘Particles-in-a-box' (PIB) model introduced by Talent (1992) removed the need for computerintensive Monte Carlo simulation to predict the gross characteristics of an evolving debris environment. The PIB model was described using a differential equation that allows the stability of the low Earth orbit (LEO) environment to be tested by a straightforward analysis of the equation's coefficients. As part of an ongoing research effort to investigate more efficient approaches to evolutionary modelling and to develop a suite of educational tools, a new PIB model has been developed. The model, entitled Fast Debris Evolution (FaDE), employs a first-order differential equation to describe the rate at which new objects (˜ 10 cm) are added and removed from the environment. Whilst Talent (1992) based the collision theory for the PIB approach on collisions between gas particles and adopted specific values for the parameters of the model from a number of references, the form and coefficients of the FaDE model equations can be inferred from the outputs of future projections produced by high-fidelity models, such as the DAMAGE model. The FaDE model has been implemented as a client-side, web-based service using Javascript embedded within a HTML document. Due to the simple nature of the algorithm, FaDE can deliver the results of future projections immediately in a graphical format, with complete user-control over key simulation parameters. Historical and future projections for the ˜ 10 cm low Earth orbit (LEO) debris environment under a variety of different scenarios are possible, including business as usual, no future launches, post-mission disposal and remediation. A selection of results is presented with comparisons with predictions made using the DAMAGE environment model. The results demonstrate that the FaDE model is able to capture comparable time-series of collisions and number of objects as predicted by DAMAGE in several scenarios. Further, and perhaps more importantly
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.
[Differential diagnosis of aortitis].
Rousselin, C; Pontana, F; Puech, P; Lambert, M
2016-04-01
Aortitis are mainly described in inflammatory disorders such as Takayasu arteritis, giant cell arteritis or Behçet's disease. Aortitis is sometimes qualified as idiopathic. However, differential diagnoses must be searched since they need specific interventions. Infectious aortitis should be ruled out first as its rapid evolution and short-term poor prognosis makes it a therapeutic emergency. Furthermore, rarer differential diagnoses should be known as they require specific care that might sometimes differ from the treatment of inflammatory aortitis, such as retroperitoneal fibrosis mostly idiopathic but also secondary to neoplasia or malignant hemopathies. IgG4 related disease, Erdheim-Chester disease and inflammatory abdominal aortic aneurysm due to atherosclerosis are other differential diagnoses to mention in the presence of aortitis in order to adapt patients' care consequently.
Piecewise Integration of Differential Variational Inequality
NASA Astrophysics Data System (ADS)
Wang, Zhengyu; Wu, Xinyuan
2009-09-01
Differential variational inequality (DVI) is a new mathematical paradigm consisting of a system of ordinary differential equations and a parametric variational inequality problem as the constraints. The solution of DVI was shown at best piecewise differentiable, for which the existing integrators possess only convergence of order one. In this paper we present an algorithm of finding the pieces where the solution is differentiable, and propose applying the methods in the pieces for a moderate stepsize, while for smaller stepsize around the boundary of the pieces for achieving high accuracy. Numerical example of bridge collapse is given to illustrate the efficiency of our algorithms.
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.
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
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…
Approximated Lax pairs for the reduced order integration of nonlinear evolution equations
NASA Astrophysics Data System (ADS)
Gerbeau, Jean-Frédéric; Lombardi, Damiano
2014-05-01
A reduced-order model algorithm, called ALP, is proposed to solve nonlinear evolution partial differential equations. It is based on approximations of generalized Lax pairs. Contrary to other reduced-order methods, like Proper Orthogonal Decomposition, the basis on which the solution is searched for evolves in time according to a dynamics specific to the problem. It is therefore well-suited to solving problems with progressive front or wave propagation. Another difference with other reduced-order methods is that it is not based on an off-line/on-line strategy. Numerical examples are shown for the linear advection, KdV and FKPP equations, in one and two dimensions.
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!
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.
Efficient GPS Position Determination Algorithms
2007-06-01
Dilution of Precision ( GDOP ) conditions. The novel differential GPS algorithm for a network of users that has been developed in this research uses a...performance is achieved, even under high Geometric Dilution of Precision ( GDOP ) conditions. The second part of this research investigates a...respect to the receiver produces high Geometric Dilution of Precision ( GDOP ), which can adversely affect GPS position solutions [1]. Four
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.
NASA Technical Reports Server (NTRS)
Provost, David E.
1990-01-01
Viewgraphs on flight telerobotic servicer evolution are presented. Topics covered include: paths for FTS evolution; frequently performed actions; primary task states; EPS radiator panel installation; generic task definitions; path planning; non-contact alignment; contact planning and control; and human operator interface.
ERIC Educational Resources Information Center
Bryner, Jeanna
2005-01-01
Eighty years after the famous 1925 Scopes "monkey trial," which tested a teacher's right to discuss the theory of evolution in the classroom, evolution--and its most recent counterview, called "intelligent design"--are in the headlines again, and just about everyone seems to have an opinion. This past July, President Bush weighed in, telling…
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.
ERIC Educational Resources Information Center
Allen, Dwight W.; Kline, Lloyd W.
The traditional educational structure requires the teacher to be part bookkeeper, part clerical assistant, and part psychologist, among other roles, while his salary scale is based on length of service. Differentiated staffing offers ways of changing this pattern. The details of differentiated duties are largely a matter of local option and…
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.
Dual format algorithm for monostatic SAR
NASA Astrophysics Data System (ADS)
Gorham, LeRoy A.; Rigling, Brian D.
2010-04-01
The polar format algorithm for monostatic synthetic aperture radar imaging is based on a linear approximation of the differential range to a scatterer, which leads to spatially-variant distortion and defocus in the resultant image. While approximate corrections may be applied to compensate for these effects, these corrections are ad-hoc in nature. Here, we introduce an alternative imaging algorithm called the Dual Format Algorithm (DFA) that provides better isolation of the defocus effects and reduces distortion. Quadratic phase errors are isolated along a single dimension by allowing image formation to an arbitrary grid instead of a Cartesian grid. This provides an opportunity for more efficient phase error corrections. We provide a description of the arbitrary image grid and we show the quadratic phase error correction derived from a second-order Taylor series approximation of the differential range. The algorithm is demonstrated with a point target simulation.
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.
Differentiating Knowledge, Differentiating (Occupational) Practice
ERIC Educational Resources Information Center
Hordern, Jim
2016-01-01
This paper extends arguments for differentiating knowledge into conceptualisations of occupational practice. It is argued that specialised forms of knowledge and practice require recognition and differentiation in ways that many contemporary approaches to practice theory deny. Drawing on Hager's interpretation of MacIntyre, it is suggested that…
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.
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.
Modified Discrete Grey Wolf Optimizer Algorithm for Multilevel Image Thresholding
Sun, Lijuan; Guo, Jian; Xu, Bin; Li, Shujing
2017-01-01
The computation of image segmentation has become more complicated with the increasing number of thresholds, and the option and application of the thresholds in image thresholding fields have become an NP problem at the same time. The paper puts forward the modified discrete grey wolf optimizer algorithm (MDGWO), which improves on the optimal solution updating mechanism of the search agent by the weights. Taking Kapur's entropy as the optimized function and based on the discreteness of threshold in image segmentation, the paper firstly discretizes the grey wolf optimizer (GWO) and then proposes a new attack strategy by using the weight coefficient to replace the search formula for optimal solution used in the original algorithm. The experimental results show that MDGWO can search out the optimal thresholds efficiently and precisely, which are very close to the result examined by exhaustive searches. In comparison with the electromagnetism optimization (EMO), the differential evolution (DE), the Artifical Bee Colony (ABC), and the classical GWO, it is concluded that MDGWO has advantages over the latter four in terms of image segmentation quality and objective function values and their stability. PMID:28127305
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.
Modified Discrete Grey Wolf Optimizer Algorithm for Multilevel Image Thresholding.
Li, Linguo; Sun, Lijuan; Guo, Jian; Qi, Jin; Xu, Bin; Li, Shujing
2017-01-01
The computation of image segmentation has become more complicated with the increasing number of thresholds, and the option and application of the thresholds in image thresholding fields have become an NP problem at the same time. The paper puts forward the modified discrete grey wolf optimizer algorithm (MDGWO), which improves on the optimal solution updating mechanism of the search agent by the weights. Taking Kapur's entropy as the optimized function and based on the discreteness of threshold in image segmentation, the paper firstly discretizes the grey wolf optimizer (GWO) and then proposes a new attack strategy by using the weight coefficient to replace the search formula for optimal solution used in the original algorithm. The experimental results show that MDGWO can search out the optimal thresholds efficiently and precisely, which are very close to the result examined by exhaustive searches. In comparison with the electromagnetism optimization (EMO), the differential evolution (DE), the Artifical Bee Colony (ABC), and the classical GWO, it is concluded that MDGWO has advantages over the latter four in terms of image segmentation quality and objective function values and their stability.
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.
A novel algorithm for generating libration point orbits about the collinear points
NASA Astrophysics Data System (ADS)
Ren, Yuan; Shan, Jinjun
2014-09-01
This paper presents a numerical algorithm that can generate long-term libration points orbits (LPOs) and the transfer orbits from the parking orbits to the LPOs in the circular-restricted three-body problem (CR3BP) and the full solar system model without initial guesses. The families of the quasi-periodic LPOs in the CR3BP can also be constructed with this algorithm. By using the dynamical behavior of LPO, the transfer orbit from the parking orbit to the LPO is generated using a bisection method. At the same time, a short segment of the target LPO connected with the transfer orbit is obtained, then the short segment of LPO is extended by correcting the state towards its adjacent point on the stable manifold of the target LPO with differential evolution algorithm. By implementing the correction strategy repeatedly, the LPOs can be extended to any length as needed. Moreover, combining with the continuation procedure, this algorithm can be used to generate the families of the quasi-periodic LPOs in the CR3BP.
Kourmpetis, Yiannis Ai; van Dijk, Aalt Dj; Ter Braak, Cajo Jf
2013-03-27
: Gene Ontology (GO) is a hierarchical vocabulary for the description of biological functions and locations, often employed by computational methods for protein function prediction. Due to the structure of GO, function predictions can be self- contradictory. For example, a protein may be predicted to belong to a detailed functional class, but not in a broader class that, due to the vocabulary structure, includes the predicted one.We present a novel discrete optimization algorithm called Functional Annotation with Labeling CONsistency (FALCON) that resolves such contradictions. The GO is modeled as a discrete Bayesian Network. For any given input of GO term membership probabilities, the algorithm returns the most probable GO term assignments that are in accordance with the Gene Ontology structure. The optimization is done using the Differential Evolution algorithm. Performance is evaluated on simulated and also real data from Arabidopsis thaliana showing improvement compared to related approaches. We finally applied the FALCON algorithm to obtain genome-wide function predictions for six eukaryotic species based on data provided by the CAFA (Critical Assessment of Function Annotation) project.
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.
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.
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.
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.
Algorithm Animation with Galant.
Stallmann, Matthias F
2017-01-01
Although surveys suggest positive student attitudes toward the use of algorithm animations, it is not clear that they improve learning outcomes. The Graph Algorithm Animation Tool, or Galant, challenges and motivates students to engage more deeply with algorithm concepts, without distracting them with programming language details or GUIs. Even though Galant is specifically designed for graph algorithms, it has also been used to animate other algorithms, most notably sorting algorithms.
The Evolution and Space Weather Effects of Solar Coronal Holes
NASA Astrophysics Data System (ADS)
Krista, Larisza; Gallagher, P.
2011-05-01
As solar activity is the foremost important aspect of space weather, the forecasting of flare and CME related transient geomagnetic storms has become a primary initiative. Minor magnetic storms caused by coronal holes (CHs) have also proven to be important due to their long-lasting and recurrent geomagnetic effects. In order to forecast CH related geomagnetic storms, the author developed the Coronal Hole Automated Recognition and Monitoring (CHARM) algorithm to replace the user-dependent CH detection methods commonly used. CHARM uses an intensity thresholding method to identify low intensity regions in EUV or X-ray images. Since CHs are regions of "open” magnetic field and predominant polarity, magnetograms were used to differentiate CHs from other low intensity regions. The Coronal Hole Evolution (CHEVOL) algorithm was developed and used in conjunction with CHARM to study the boundary evolution of CHs. It is widely accepted that the short-term changes in CH boundaries are due to the interchange reconnection between the CH open field lines and small loops. We determined the magnetic reconnection rate and the diffusion coefficient at CH boundaries in order to test the interchange reconnection model. The author also developed the Minor Storm (MIST) package to link CHs to high-speed solar wind (HSSW) periods detected at Earth. Using the algorithm the relationship between CHs, the corresponding HSSW properties, and geomagnetic indices were studied between 2000-2009. The results showed a strong correlation between the velocity and HSSW proton plasma temperature, which indicates that the heating and acceleration of the solar wind plasma in CHs are closely related, and perhaps caused by the same mechanism. The research presented here includes analysis of CHs on small and large spatial/temporal scales, allowing us to further our understanding of CHs as a whole.
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
A Modified Adiabatic Quantum Algorithm for Evaluation of Boolean Functions
NASA Astrophysics Data System (ADS)
Sun, Jie; Lu, Songfeng; Liu, Fang
2015-09-01
In this paper, we propose a modified construction of the quantum adiabatic algorithm for Boolean functions studied by M. Andrecut et al. [13, 14]. Our algorithm has the time complexity O(1) for the evaluation of Boolean functions, without additional computational cost of implementing the driving Hamiltonian, which is required by the adiabatic evolution described in [13, 14].
Differentially Private Empirical Risk Minimization.
Chaudhuri, Kamalika; Monteleoni, Claire; Sarwate, Anand D
2011-03-01
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.
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.
Quantum Algorithms, Symmetry, and Fourier Analysis
NASA Astrophysics Data System (ADS)
Denney, Aaron
I describe the role of symmetry in two quantum algorithms, with a focus on how that symmetry is made manifest by the Fourier transform. The Fourier transform can be considered in a wider context than the familiar one of functions on
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
Fast Numerical Methods for Stochastic Partial Differential Equations
2016-04-15
uncertainty quantification. In the last decade much progress has been made in the construction of numerical algorithms to efficiently solve SPDES with...applicable SPDES with efficient numerical methods. This project is intended to address the numerical analysis as well as algorithm aspects of SPDES. Three...differential equations. Our work contains algorithm constructions, rigorous error analysis, and extensive numerical experiments to demonstrate our algorithm
Sensitivity Analysis of Differential-Algebraic Equations and Partial Differential Equations
Petzold, L; Cao, Y; Li, S; Serban, R
2005-08-09
Sensitivity analysis generates essential information for model development, design optimization, parameter estimation, optimal control, model reduction and experimental design. In this paper we describe the forward and adjoint methods for sensitivity analysis, and outline some of our recent work on theory, algorithms and software for sensitivity analysis of differential-algebraic equation (DAE) and time-dependent partial differential equation (PDE) systems.
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.
Geist, G.A.; Howell, G.W.; Watkins, D.S.
1997-11-01
The BR algorithm, a new method for calculating the eigenvalues of an upper Hessenberg matrix, is introduced. It is a bulge-chasing algorithm like the QR algorithm, but, unlike the QR algorithm, it is well adapted to computing the eigenvalues of the narrowband, nearly tridiagonal matrices generated by the look-ahead Lanczos process. This paper describes the BR algorithm and gives numerical evidence that it works well in conjunction with the Lanczos process. On the biggest problems run so far, the BR algorithm beats the QR algorithm by a factor of 30--60 in computing time and a factor of over 100 in matrix storage space.
Rouet, François; Ekouevi, Didier K.; Inwoley, André; Chaix, Marie-Laure; Burgard, Marianne; Bequet, Laurence; Viho, Ida; Leroy, Valériane; Simon, François; Dabis, François; Rouzioux, Christine
2004-01-01
We evaluated a two-rapid-test serial algorithm using the Determine and Genie II rapid assays, performed on-site in four peripheral laboratories during the French Agence Nationale de Recherches sur le SIDA (ANRS) 1201/1202 Ditrame Plus cohort developed for prevention of mother-to-child transmission of human immunodeficiency virus (HIV) infection in Côte d'Ivoire. A total of 1,039 specimens were retested by two commercial enzyme-linked immunosorbent assays (ELISAs). The following specimens were tested: 315 specimens found on-site to be infected with HIV type 1 (HIV-1), 8 specimens found on-site to be infected with HIV-2, 71 specimens found on-site to be infected with both HIV-1 and HIV-2, 40 specimens found on-site to have indeterminate results for HIV infection, and 605 specimens taken during a quality assurance program. For HIV discrimination, 99 positive serum samples (20 with HIV-1, 8 with HIV-2, and 71 with HIV-1 and HIV-2 on the basis of our rapid test algorithm) were retested by the Peptilav test, Western blot (WB) assays, and homemade monospecific ELISAs. Real-time DNA PCRs for the detection of HIV-1 and HIV-2 were performed with peripheral blood mononuclear cells from 35 women diagnosed on-site with HIV-1 and HIV-2 infections. Compared to the results of the ELISAs, the sensitivities of the Determine and Genie II assays were 100% (95% lower limit [95% LL], 99.1%) and 99.5% (95% confidence interval [95% CI], 98.2 to 99.9%), respectively. The specificities were 98.4% (95% CI, 96.9 to 99.3%) and 100% (95% LL, 99.3%), respectively. All serological assays gave concordant results for infections with single types. By contrast, for samples found to be infected with dual HIV types by the Genie II assay, dual reactivity was detected for only 37 samples (52.1%) by WB assays, 34 samples (47.9%) by the Peptilav assay, and 23 samples (32.4%) by the monospecific ELISAs. For specimens with dual reactivity by the Genie II assay, the rates of concordance between the real
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.
NASA Astrophysics Data System (ADS)
Melekhova, E.; Blundy, J.
2012-12-01
Few erupted arc magmas are sufficiently primitive to be in equilibrium with mantle wedge peridotite, meaning a significant volume of arc crust must comprise plutonic cumulates formed during differentiation of primitive basalts. This cumulate material is typically not available for petrological study. A notable exception is the Lesser Antilles arc, which is renowned for the exceptional abundance and variety of cumulate xenoliths. Additionally, several Lesser Antilles islands erupt primitive basaltic magmas that are close to being in mantle equilibrium. The abundance of plutonic cumulate xenolith and presence of primitive basalts make the Lesser Antilles an ideal natural laboratory for understanding crust-building processes. Here we evaluate the chemical consequences of basalt differentiation in the mid to lower crust and uppermost mantle (10 to 30 km) by means of experiments on a primitive basalt from St. Vincent. The results were combined with compositional and textural observation of plutonic cumulate xenoliths from the island. Our goal was to constrain the conditions under which basalt differentiation can generate the observed chemical diversity of erupted magmas at St. Vincent and the compositions of minerals in cumulate xenoliths. Our experimental results show that it is possible to produce a wide compositional range of melts by differentiation at different depths and water contents from the same primitive source. The melts provide a close match to the full range of erupted lavas on the island. The cumulate assemblages, however, have a consistently lower pressure origin (6-10 km). They are formed by crystallisation of ascending melts generated in the deep crust. Phencocrysts in the lavas are distinct from those in cumulates, notably in the absence of amphibole. The phenocrysts demonstrably grew in response to crystallisation at very shallow depth, probably in sub-volcanic magma chambers. Thus St. Vincent shows clear evidence for polybaric crustal
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 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.
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.
Mapping the Geometric Evolution of Protein Folding Motor
Hazam, Prakash Kishore; Shekhar, Shashi
2016-01-01
Polypeptide chain has an invariant main-chain and a variant side-chain sequence. How the side-chain sequence determines fold in terms of its chemical constitution has been scrutinized extensively and verified periodically. However, a focussed investigation on the directive effect of side-chain geometry may provide important insights supplementing existing algorithms in mapping the geometrical evolution of protein chains and its structural preferences. Geometrically, folding of protein structure may be envisaged as the evolution of its geometric variables: ϕ, and ψ dihedral angles of polypeptide main-chain directed by χ1, and χ2 of side chain. In this work, protein molecule is metaphorically modelled as a machine with 4 rotors ϕ, ψ, χ1 and χ2, with its evolution to the functional fold is directed by combinations of its rotor directions. We observe that differential rotor motions lead to different secondary structure formations and the combinatorial pattern is unique and consistent for particular secondary structure type. Further, we found that combination of rotor geometries of each amino acid is unique which partly explains how different amino acid sequence combinations have unique structural evolution and functional adaptation. Quantification of these amino acid rotor preferences, resulted in the generation of 3 substitution matrices, which later on plugged in the BLAST tool, for evaluating their efficiency in aligning sequences. We have employed BLOSUM62 and PAM30 as standard for primary evaluation. Generation of substitution matrices is a logical extension of the conceptual framework we attempted to build during the development of this work. Optimization of matrices following the conventional routines and possible application with biologically relevant data sets are beyond the scope of this manuscript, though it is a part of the larger project design. PMID:27716851
OSAWA, Syozo; SU, Zhi-Hui; NISHIKAWA, Masaaki; TOMINAGA, Osamu
2016-01-01
Phylogenetic analyses using mitochondrial DNA sequences of several kinds of beetles have shown that their evolution included a silent stage in which no morphological changes took place. We thus propose a new category of evolutionary process called “silent evolution”. PMID:27840392
ERIC Educational Resources Information Center
De Patta, Joe
2003-01-01
Examines how to evaluate school security, begin making schools safe, secure schools without turning them into fortresses, and secure schools easily and affordably; the evolution of security systems into information technology systems; using schools' high-speed network lines; how one specific security system was developed; pros and cons of the…
ERIC Educational Resources Information Center
Terry, Mark
2005-01-01
In this article, the author presents a two-week evolution unit for his biology class. He uses Maria Sybilla Merian (1647-1717) as an example of an Enlightenment mind at work--in this case a woman recognized as one of the great artists and natural scientists of her time. Her representations of butterflies, caterpillars and their pupae, and the…
Trees, bialgebras and intrinsic numerical algorithms
NASA Technical Reports Server (NTRS)
Crouch, Peter; Grossman, Robert; Larson, Richard
1990-01-01
Preliminary work about intrinsic numerical integrators evolving on groups is described. Fix a finite dimensional Lie group G; let g denote its Lie algebra, and let Y(sub 1),...,Y(sub N) denote a basis of g. A class of numerical algorithms is presented that approximate solutions to differential equations evolving on G of the form: dot-x(t) = F(x(t)), x(0) = p is an element of G. The algorithms depend upon constants c(sub i) and c(sub ij), for i = 1,...,k and j is less than i. The algorithms have the property that if the algorithm starts on the group, then it remains on the group. In addition, they also have the property that if G is the abelian group R(N), then the algorithm becomes the classical Runge-Kutta algorithm. The Cayley algebra generated by labeled, ordered trees is used to generate the equations that the coefficients c(sub i) and c(sub ij) must satisfy in order for the algorithm to yield an rth order numerical integrator and to analyze the resulting algorithms.
Controlling Tensegrity Robots Through Evolution
NASA Technical Reports Server (NTRS)
Iscen, Atil; Agogino, Adrian; SunSpiral, Vytas; Tumer, Kagan
2013-01-01
Tensegrity structures (built from interconnected rods and cables) have the potential to offer a revolutionary new robotic design that is light-weight, energy-efficient, robust to failures, capable of unique modes of locomotion, impact tolerant, and compliant (reducing damage between the robot and its environment). Unfortunately robots built from tensegrity structures are difficult to control with traditional methods due to their oscillatory nature, nonlinear coupling between components and overall complexity. Fortunately this formidable control challenge can be overcome through the use of evolutionary algorithms. In this paper we show that evolutionary algorithms can be used to efficiently control a ball-shaped tensegrity robot. Experimental results performed with a variety of evolutionary algorithms in a detailed soft-body physics simulator show that a centralized evolutionary algorithm performs 400 percent better than a hand-coded solution, while the multi-agent evolution performs 800 percent better. In addition, evolution is able to discover diverse control solutions (both crawling and rolling) that are robust against structural failures and can be adapted to a wide range of energy and actuation constraints. These successful controls will form the basis for building high-performance tensegrity robots in the near future.
Kumar, Manjeet; Rawat, Tarun Kumar; Aggarwal, Apoorva
2017-03-01
In this paper, a new meta-heuristic optimization technique, called interior search algorithm (ISA) with Lèvy flight is proposed and applied to determine the optimal parameters of an unknown infinite impulse response (IIR) system for the system identification problem. ISA is based on aesthetics, which is commonly used in interior design and decoration processes. In ISA, composition phase and mirror phase are applied for addressing the nonlinear and multimodal system identification problems. System identification using modified-ISA (M-ISA) based method involves faster convergence, single parameter tuning and does not require derivative information because it uses a stochastic random search using the concepts of Lèvy flight. A proper tuning of control parameter has been performed in order to achieve a balance between intensification and diversification phases. In order to evaluate the performance of the proposed method, mean square error (MSE), computation time and percentage improvement are considered as the performance measure. To validate the performance of M-ISA based method, simulations has been carried out for three benchmarked IIR systems using same order and reduced order system. Genetic algorithm (GA), particle swarm optimization (PSO), cat swarm optimization (CSO), cuckoo search algorithm (CSA), differential evolution using wavelet mutation (DEWM), firefly algorithm (FFA), craziness based particle swarm optimization (CRPSO), harmony search (HS) algorithm, opposition based harmony search (OHS) algorithm, hybrid particle swarm optimization-gravitational search algorithm (HPSO-GSA) and ISA are also used to model the same examples and simulation results are compared. Obtained results confirm the efficiency of the proposed method.
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.
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.
Margolis, C Z
1983-02-04
The clinical algorithm (flow chart) is a text format that is specially suited for representing a sequence of clinical decisions, for teaching clinical decision making, and for guiding patient care. A representative clinical algorithm is described in detail; five steps for writing an algorithm and seven steps for writing a set of algorithms are outlined. Five clinical education and patient care uses of algorithms are then discussed, including a map for teaching clinical decision making and protocol charts for guiding step-by-step care of specific problems. Clinical algorithms are compared as to their clinical usefulness with decision analysis. Three objections to clinical algorithms are answered, including the one that they restrict thinking. It is concluded that methods should be sought for writing clinical algorithms that represent expert consensus. A clinical algorithm could then be written for any area of medical decision making that can be standardized. Medical practice could then be taught more effectively, monitored accurately, and understood better.
Olsavsky Goyak, Katy M; Laurenzana, Elizabeth M; Omiecinski, Curtis J
2010-01-01
Increasingly, research suggests that for certain systems, animal models are insufficient for human toxicology testing. The development of robust, in vitro models of human toxicity is required to decrease our dependence on potentially misleading in vivo animal studies. A critical development in human toxicology testing is the use of human primary hepatocytes to model processes that occur in the intact liver. However, in order to serve as an appropriate model, primary hepatocytes must be maintained in such a way that they persist in their differentiated state. While many hepatocyte culture methods exist, the two-dimensional collagen "sandwich" system combined with a serum-free medium, supplemented with physiological glucocorticoid concentrations, appears to robustly maintain hepatocyte character. Studies in rat and human hepatocytes have shown that when cultured under these conditions, hepatocytes maintain many markers of differentiation including morphology, expression of plasma proteins, hepatic nuclear factors, phase I and II metabolic enzymes. Functionally, these culture conditions also preserve hepatic stress response pathways, such as the SAPK and MAPK pathways, as well as prototypical xenobiotic induction responses. This chapter will briefly review culture methodologies but will primarily focus on hallmark hepatocyte structural, expression and functional markers that characterize the differentiation status of the hepatocyte.
Constructing backbone network by using tinker algorithm
NASA Astrophysics Data System (ADS)
He, Zhiwei; Zhan, Meng; Wang, Jianxiong; Yao, Chenggui
2017-01-01
Revealing how a biological network is organized to realize its function is one of the main topics in systems biology. The functional backbone network, defined as the primary structure of the biological network, is of great importance in maintaining the main function of the biological network. We propose a new algorithm, the tinker algorithm, to determine this core structure and apply it in the cell-cycle system. With this algorithm, the backbone network of the cell-cycle network can be determined accurately and efficiently in various models such as the Boolean model, stochastic model, and ordinary differential equation model. Results show that our algorithm is more efficient than that used in the previous research. We hope this method can be put into practical use in relevant future studies.
A Two-Phase Coverage-Enhancing Algorithm for Hybrid Wireless Sensor Networks
Zhang, Qingguo; Fok, Mable P.
2017-01-01
Providing field coverage is a key task in many sensor network applications. In certain scenarios, the sensor field may have coverage holes due to random initial deployment of sensors; thus, the desired level of coverage cannot be achieved. A hybrid wireless sensor network is a cost-effective solution to this problem, which is achieved by repositioning a portion of the mobile sensors in the network to meet the network coverage requirement. This paper investigates how to redeploy mobile sensor nodes to improve network coverage in hybrid wireless sensor networks. We propose a two-phase coverage-enhancing algorithm for hybrid wireless sensor networks. In phase one, we use a differential evolution algorithm to compute the candidate’s target positions in the mobile sensor nodes that could potentially improve coverage. In the second phase, we use an optimization scheme on the candidate’s target positions calculated from phase one to reduce the accumulated potential moving distance of mobile sensors, such that the exact mobile sensor nodes that need to be moved as well as their final target positions can be determined. Experimental results show that the proposed algorithm provided significant improvement in terms of area coverage rate, average moving distance, area coverage–distance rate and the number of moved mobile sensors, when compare with other approaches. PMID:28075365
A Two-Phase Coverage-Enhancing Algorithm for Hybrid Wireless Sensor Networks.
Zhang, Qingguo; Fok, Mable P
2017-01-09
Providing field coverage is a key task in many sensor network applications. In certain scenarios, the sensor field may have coverage holes due to random initial deployment of sensors; thus, the desired level of coverage cannot be achieved. A hybrid wireless sensor network is a cost-effective solution to this problem, which is achieved by repositioning a portion of the mobile sensors in the network to meet the network coverage requirement. This paper investigates how to redeploy mobile sensor nodes to improve network coverage in hybrid wireless sensor networks. We propose a two-phase coverage-enhancing algorithm for hybrid wireless sensor networks. In phase one, we use a differential evolution algorithm to compute the candidate's target positions in the mobile sensor nodes that could potentially improve coverage. In the second phase, we use an optimization scheme on the candidate's target positions calculated from phase one to reduce the accumulated potential moving distance of mobile sensors, such that the exact mobile sensor nodes that need to be moved as well as their final target positions can be determined. Experimental results show that the proposed algorithm provided significant improvement in terms of area coverage rate, average moving distance, area coverage-distance rate and the number of moved mobile sensors, when compare with other approaches.
NASA Astrophysics Data System (ADS)
Sander, Tobias; Kresse, Georg
2017-02-01
Linear optical properties can be calculated by solving the time-dependent density functional theory equations. Linearization of the equation of motion around the ground state orbitals results in the so-called Casida equation, which is formally very similar to the Bethe-Salpeter equation. Alternatively one can determine the spectral functions by applying an infinitely short electric field in time and then following the evolution of the electron orbitals and the evolution of the dipole moments. The long wavelength response function is then given by the Fourier transformation of the evolution of the dipole moments in time. In this work, we compare the results and performance of these two approaches for the projector augmented wave method. To allow for large time steps and still rely on a simple difference scheme to solve the differential equation, we correct for the errors in the frequency domain, using a simple analytic equation. In general, we find that both approaches yield virtually indistinguishable results. For standard density functionals, the time evolution approach is, with respect to the computational performance, clearly superior compared to the solution of the Casida equation. However, for functionals including nonlocal exchange, the direct solution of the Casida equation is usually much more efficient, even though it scales less beneficial with the system size. We relate this to the large computational prefactors in evaluating the nonlocal exchange, which renders the time evolution algorithm fairly inefficient.
Software For Genetic Algorithms
NASA Technical Reports Server (NTRS)
Wang, Lui; Bayer, Steve E.
1992-01-01
SPLICER computer program is genetic-algorithm software tool used to solve search and optimization problems. Provides underlying framework and structure for building genetic-algorithm application program. Written in Think C.
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.
Accurate Finite Difference Algorithms
NASA Technical Reports Server (NTRS)
Goodrich, John W.
1996-01-01
Two families of finite difference algorithms for computational aeroacoustics are presented and compared. All of the algorithms are single step explicit methods, they have the same order of accuracy in both space and time, with examples up to eleventh order, and they have multidimensional extensions. One of the algorithm families has spectral like high resolution. Propagation with high order and high resolution algorithms can produce accurate results after O(10(exp 6)) periods of propagation with eight grid points per wavelength.
Quantum algorithms: an overview
NASA Astrophysics Data System (ADS)
Montanaro, Ashley
2016-01-01
Quantum computers are designed to outperform standard computers by running quantum algorithms. Areas in which quantum algorithms can be applied include cryptography, search and optimisation, simulation of quantum systems and solving large systems of linear equations. Here we briefly survey some known quantum algorithms, with an emphasis on a broad overview of their applications rather than their technical details. We include a discussion of recent developments and near-term applications of quantum algorithms.
INSENS classification algorithm report
Hernandez, J.E.; Frerking, C.J.; Myers, D.W.
1993-07-28
This report describes a new algorithm developed for the Imigration and Naturalization Service (INS) in support of the INSENS project for classifying vehicles and pedestrians using seismic data. This algorithm is less sensitive to nuisance alarms due to environmental events than the previous algorithm. Furthermore, the algorithm is simple enough that it can be implemented in the 8-bit microprocessor used in the INSENS system.
[Acute muscle weakness: differential diagnoses].
Antoniuk, Sérgio A
2013-09-06
Acute muscle weakness, a common disorder in pediatrics, can occur from impairment of any part of the motor unit, including the upper motor neuron, lower motor neuron, peripheral nerve, neuromuscular junction or muscle. It usually manifests itself as an acute or hyperacute motor disorder of progressive or rapidly progressive course. Acute muscle weakness is a neuromuscular emergency, especially if it affects the respiratory or oropharyngeal musculature. The location of the motor weakness and associated neurological signs and symptoms usually indicate the location of the lesion. The onset, speed and clinical evolution, as well as other data from the patient's history, suggest the pathophysiological differential diagnosis. Successful treatment depends on the immediate and correct differential diagnosis. This paper presents the main differential diagnosis of main neuromuscular diseases that cause acute muscle weakness in children.
NASA Astrophysics Data System (ADS)
Graf, Norman A.
2001-07-01
An object-oriented framework for undertaking clustering algorithm studies has been developed. We present here the definitions for the abstract Cells and Clusters as well as the interface for the algorithm. We intend to use this framework to investigate the interplay between various clustering algorithms and the resulting jet reconstruction efficiency and energy resolutions to assist in the design of the calorimeter detector.
2007-12-06
problems studied in this project involve numerically solving partial differential equations with either discontinuous or rapidly changing solutions ...REPORT Algorithm Development and Application of High Order Numerical Methods for Shocked and Rapid Changing Solutions 14. ABSTRACT 16. SECURITY...discontinuous Galerkin finite element methods, for solving partial differential equations with discontinuous or rapidly changing solutions . Algorithm
An Image Encryption Algorithm Based on Information Hiding
NASA Astrophysics Data System (ADS)
Ge, Xin; Lu, Bin; Liu, Fenlin; Gong, Daofu
Aiming at resolving the conflict between security and efficiency in the design of chaotic image encryption algorithms, an image encryption algorithm based on information hiding is proposed based on the “one-time pad” idea. A random parameter is introduced to ensure a different keystream for each encryption, which has the characteristics of “one-time pad”, improving the security of the algorithm rapidly without significant increase in algorithm complexity. The random parameter is embedded into the ciphered image with information hiding technology, which avoids negotiation for its transport and makes the application of the algorithm easier. Algorithm analysis and experiments show that the algorithm is secure against chosen plaintext attack, differential attack and divide-and-conquer attack, and has good statistical properties in ciphered images.
Geometry Genetics and Evolution
NASA Astrophysics Data System (ADS)
Siggia, Eric
2011-03-01
Darwin argued that highly perfected organs such as the vertebrate eye could evolve by a series of small changes, each of which conferred a selective advantage. In the context of gene networks, this idea can be recast into a predictive algorithm, namely find networks that can be built by incremental adaptation (gradient search) to perform some task. It embodies a ``kinetic'' view of evolution where a solution that is quick to evolve is preferred over a global optimum. Examples of biochemical kinetic networks were evolved for temporal adaptation, temperature compensated entrainable clocks, explore-exploit trade off in signal discrimination, will be presented as well as networks that model the spatially periodic somites (vertebrae) and HOX gene expression in the vertebrate embryo. These models appear complex by the criterion of 19th century applied mathematics since there is no separation of time or spatial scales, yet they are all derivable by gradient optimization of simple functions (several in the Pareto evolution) often based on the Shannon entropy of the time or spatial response. Joint work with P. Francois, Physics Dept. McGill University. With P. Francois, Physics Dept. McGill University
Tamiya, S.
1986-07-29
A differential for motor vehicles is described and the like comprising, an input drive shaft, a pair of coaxially spaced drive gears simultaneously driven by the input shaft in a same direction at a same speed of rotation about a common axis of rotation, a driven gear driven peripherally by the pair of drive gears for transmission of power from the input drive shaft, two coaxial opposed bevel sun gears having an axis of rotation concentric with an axis of rotation of the driven gear, two planetary gears disposed between the sun gears for differential driving thereof during turns of the vehicle to the right and to the left of each meshing with the sun gears for driving the suns gears. Each planetary gear has a separate axis of rotation carried by the driven gear disposed therein radially and symmetrically relative to the axis of rotation of the sun gears, and each sun gear having a respective power output shaft connected thereto for rotation therewith.
Program for solution of ordinary differential equations
NASA Technical Reports Server (NTRS)
Sloate, H.
1973-01-01
A program for the solution of linear and nonlinear first order ordinary differential equations is described and user instructions are included. The program contains a new integration algorithm for the solution of initial value problems which is particularly efficient for the solution of differential equations with a wide range of eigenvalues. The program in its present form handles up to ten state variables, but expansion to handle up to fifty state variables is being investigated.
Selecting materialized views using random algorithm
NASA Astrophysics Data System (ADS)
Zhou, Lijuan; Hao, Zhongxiao; Liu, Chi
2007-04-01
The data warehouse is a repository of information collected from multiple possibly heterogeneous autonomous distributed databases. The information stored at the data warehouse is in form of views referred to as materialized views. The selection of the materialized views is one of the most important decisions in designing a data warehouse. Materialized views are stored in the data warehouse for the purpose of efficiently implementing on-line analytical processing queries. The first issue for the user to consider is query response time. So in this paper, we develop algorithms to select a set of views to materialize in data warehouse in order to minimize the total view maintenance cost under the constraint of a given query response time. We call it query_cost view_ selection problem. First, cost graph and cost model of query_cost view_ selection problem are presented. Second, the methods for selecting materialized views by using random algorithms are presented. The genetic algorithm is applied to the materialized views selection problem. But with the development of genetic process, the legal solution produced become more and more difficult, so a lot of solutions are eliminated and producing time of the solutions is lengthened in genetic algorithm. Therefore, improved algorithm has been presented in this paper, which is the combination of simulated annealing algorithm and genetic algorithm for the purpose of solving the query cost view selection problem. Finally, in order to test the function and efficiency of our algorithms experiment simulation is adopted. The experiments show that the given methods can provide near-optimal solutions in limited time and works better in practical cases. Randomized algorithms will become invaluable tools for data warehouse evolution.
Sequential Quadratic Programming Algorithms for Optimization
1989-08-01
brief history of the evolution of SQP algorithms. Surveys for this area can be found in [GMWSl]. (Po831 or fGNISW ,] for example. The origins Ihe...0) S (TnI(P(O) K __jnfl’flj)j 2 < 0. lhe adjust uncut of thleslack variables. s in step (Ii) oft he algorith (-ii a ii only lvad to a fu rt her red
Algorithmic quantum simulation of memory effects
NASA Astrophysics Data System (ADS)
Alvarez-Rodriguez, U.; Di Candia, R.; Casanova, J.; Sanz, M.; Solano, E.
2017-02-01
We propose a method for the algorithmic quantum simulation of memory effects described by integrodifferential evolution equations. It consists in the systematic use of perturbation theory techniques and a Markovian quantum simulator. Our method aims to efficiently simulate both completely positive and nonpositive dynamics without the requirement of engineering non-Markovian environments. Finally, we find that small error bounds can be reached with polynomially scaling resources, evaluated as the time required for the simulation.
Continuous time boolean modeling for biological signaling: application of Gillespie algorithm
2012-01-01
Mathematical modeling is used as a Systems Biology tool to answer biological questions, and more precisely, to validate a network that describes biological observations and predict the effect of perturbations. This article presents an algorithm for modeling biological networks in a discrete framework with continuous time. Background There exist two major types of mathematical modeling approaches: (1) quantitative modeling, representing various chemical species concentrations by real numbers, mainly based on differential equations and chemical kinetics formalism; (2) and qualitative modeling, representing chemical species concentrations or activities by a finite set of discrete values. Both approaches answer particular (and often different) biological questions. Qualitative modeling approach permits a simple and less detailed description of the biological systems, efficiently describes stable state identification but remains inconvenient in describing the transient kinetics leading to these states. In this context, time is represented by discrete steps. Quantitative modeling, on the other hand, can describe more accurately the dynamical behavior of biological processes as it follows the evolution of concentration or activities of chemical species as a function of time, but requires an important amount of information on the parameters difficult to find in the literature. Results Here, we propose a modeling framework based on a qualitative approach that is intrinsically continuous in time. The algorithm presented in this article fills the gap between qualitative and quantitative modeling. It is based on continuous time Markov process applied on a Boolean state space. In order to describe the temporal evolution of the biological process we wish to model, we explicitly specify the transition rates for each node. For that purpose, we built a language that can be seen as a generalization of Boolean equations. Mathematically, this approach can be translated in a set of
Computer Corner. A Rich Differential Equation for Computer Demonstrations.
ERIC Educational Resources Information Center
Banks, Bernard W.
1990-01-01
Presents an example using a computer to illustrate concepts graphically in an introductory course on differential equations. Discusses the algorithms of the computer program displaying the solutions to an equation and the inclination field of the equation. (YP)
New knowledge-based genetic algorithm for excavator boom structural optimization
NASA Astrophysics Data System (ADS)
Hua, Haiyan; Lin, Shuwen
2014-03-01
Due to the insufficiency of utilizing knowledge to guide the complex optimal searching, existing genetic algorithms fail to effectively solve excavator boom structural optimization problem. To improve the optimization efficiency and quality, a new knowledge-based real-coded genetic algorithm is proposed. A dual evolution mechanism combining knowledge evolution with genetic algorithm is established to extract, handle and utilize the shallow and deep implicit constraint knowledge to guide the optimal searching of genetic algorithm circularly. Based on this dual evolution mechanism, knowledge evolution and population evolution can be connected by knowledge influence operators to improve the configurability of knowledge and genetic operators. Then, the new knowledge-based selection operator, crossover operator and mutation operator are proposed to integrate the optimal process knowledge and domain culture to guide the excavator boom structural optimization. Eight kinds of testing algorithms, which include different genetic operators, are taken as examples to solve the structural optimization of a medium-sized excavator boom. By comparing the results of optimization, it is shown that the algorithm including all the new knowledge-based genetic operators can more remarkably improve the evolutionary rate and searching ability than other testing algorithms, which demonstrates the effectiveness of knowledge for guiding optimal searching. The proposed knowledge-based genetic algorithm by combining multi-level knowledge evolution with numerical optimization provides a new effective method for solving the complex engineering optimization problem.
Numerical Algorithms Based on Biorthogonal Wavelets
NASA Technical Reports Server (NTRS)
Ponenti, Pj.; Liandrat, J.
1996-01-01
Wavelet bases are used to generate spaces of approximation for the resolution of bidimensional elliptic and parabolic problems. Under some specific hypotheses relating the properties of the wavelets to the order of the involved operators, it is shown that an approximate solution can be built. This approximation is then stable and converges towards the exact solution. It is designed such that fast algorithms involving biorthogonal multi resolution analyses can be used to resolve the corresponding numerical problems. Detailed algorithms are provided as well as the results of numerical tests on partial differential equations defined on the bidimensional torus.
Duboule, D; Wilkins, A S
1998-02-01
The past ten years of developmental genetics have revealed that most of our genes are shared by other species throughout the animal kingdom. Consequently, animal diversity might largely rely on the differential use of the same components, either at the individual level through divergent functional recruitment, or at a more integrated level, through their participation in various genetic networks. Here, we argue that this inevitably leads to an increase in the interdependency between functions that, in turn, influences the degree to which novel variations can be tolerated. In this 'transitionist' scheme, evolution is neither inherently gradualist nor punctuated but, instead, progresses from one extreme to the other, together with the increased complexity of organisms.
Chen, Tingjian; Romesberg, Floyd E.
2014-01-01
Polymerases evolved in nature to synthesize DNA and RNA, and they underlie the storage and flow of genetic information in all cells. The availability of these enzymes for use at the bench has driven a revolution in biotechnology and medicinal research; however, polymerases did not evolve to function efficiently under the conditions required for some applications and their high substrate fidelity precludes their use for most applications that involve modified substrates. To circumvent these limitations, researchers have turned to directed evolution to tailor the properties and/or substrate repertoire of polymerases for different applications, and several systems have been developed for this purpose. These systems draw on different methods of creating a pool of randomly mutated polymerases and are differentiated by the process used to isolate the most fit members. A variety of polymerases have been evolved, providing new or improved functionality, as well as interesting new insight into the factors governing activity. PMID:24211837
Speijer, Dave
2011-02-01
Oxygen radical formation in mitochondria is a highly important, but incompletely understood, attribute of eukaryotic cells. I propose a kinetic model in which the ratio between electrons entering the respiratory chain via FADH₂ or NADH is a major determinant in radical formation. During the breakdown of glucose, this ratio is low; during fatty acid breakdown, this ratio is much higher. The longer the fatty acid, the higher the ratio and the higher the level of radical formation. This means that very long chain fatty acids should be broken down without generation of FADH₂ for mitochondria. This is accomplished in peroxisomes, thus explaining their role and evolution. The model explains many recent observations regarding radical formation by the respiratory chain. It also sheds light on the reasons for the lack of neuronal fatty acid (beta-) oxidation and for beneficial aspects of unsaturated fatty acids. Last but not least, it has very important implications for all models describing eukaryotic origins.
An enhanced algorithm for multiple sequence alignment of protein sequences using genetic algorithm
Kumar, Manish
2015-01-01
One of the most fundamental operations in biological sequence analysis is multiple sequence alignment (MSA). The basic of multiple sequence alignment problems is to determine the most biologically plausible alignments of protein or DNA sequences. In this paper, an alignment method using genetic algorithm for multiple sequence alignment has been proposed. Two different genetic operators mainly crossover and mutation were defined and implemented with the proposed method in order to know the population evolution and quality of the sequence aligned. The proposed method is assessed with protein benchmark dataset, e.g., BALIBASE, by comparing the obtained results to those obtained with other alignment algorithms, e.g., SAGA, RBT-GA, PRRP, HMMT, SB-PIMA, CLUSTALX, CLUSTAL W, DIALIGN and PILEUP8 etc. Experiments on a wide range of data have shown that the proposed algorithm is much better (it terms of score) than previously proposed algorithms in its ability to achieve high alignment quality. PMID:27065770
Fast stochastic algorithm for simulating evolutionary population dynamics
NASA Astrophysics Data System (ADS)
Tsimring, Lev; Hasty, Jeff; Mather, William
2012-02-01
Evolution and co-evolution of ecological communities are stochastic processes often characterized by vastly different rates of reproduction and mutation and a coexistence of very large and very small sub-populations of co-evolving species. This creates serious difficulties for accurate statistical modeling of evolutionary dynamics. In this talk, we introduce a new exact algorithm for fast fully stochastic simulations of birth/death/mutation processes. It produces a significant speedup compared to the direct stochastic simulation algorithm in a typical case when the total population size is large and the mutation rates are much smaller than birth/death rates. We illustrate the performance of the algorithm on several representative examples: evolution on a smooth fitness landscape, NK model, and stochastic predator-prey system.
Turbopump Performance Improved by Evolutionary Algorithms
NASA Technical Reports Server (NTRS)
Oyama, Akira; Liou, Meng-Sing
2002-01-01
The development of design optimization technology for turbomachinery has been initiated using the multiobjective evolutionary algorithm under NASA's Intelligent Synthesis Environment and Revolutionary Aeropropulsion Concepts programs. As an alternative to the traditional gradient-based methods, evolutionary algorithms (EA's) are emergent design-optimization algorithms modeled after the mechanisms found in natural evolution. EA's search from multiple points, instead of moving from a single point. In addition, they require no derivatives or gradients of the objective function, leading to robustness and simplicity in coupling any evaluation codes. Parallel efficiency also becomes very high by using a simple master-slave concept for function evaluations, since such evaluations often consume the most CPU time, such as computational fluid dynamics. Application of EA's to multiobjective design problems is also straightforward because EA's maintain a population of design candidates in parallel. Because of these advantages, EA's are a unique and attractive approach to real-world design optimization problems.
Optimizing quantum gas production by an evolutionary algorithm
NASA Astrophysics Data System (ADS)
Lausch, T.; Hohmann, M.; Kindermann, F.; Mayer, D.; Schmidt, F.; Widera, A.
2016-05-01
We report on the application of an evolutionary algorithm (EA) to enhance performance of an ultra-cold quantum gas experiment. The production of a ^{87}rubidium Bose-Einstein condensate (BEC) can be divided into fundamental cooling steps, specifically magneto-optical trapping of cold atoms, loading of atoms to a far-detuned crossed dipole trap, and finally the process of evaporative cooling. The EA is applied separately for each of these steps with a particular definition for the feedback, the so-called fitness. We discuss the principles of an EA and implement an enhancement called differential evolution. Analyzing the reasons for the EA to improve, e.g., the atomic loading rates and increase the BEC phase-space density, yields an optimal parameter set for the BEC production and enables us to reduce the BEC production time significantly. Furthermore, we focus on how additional information about the experiment and optimization possibilities can be extracted and how the correlations revealed allow for further improvement. Our results illustrate that EAs are powerful optimization tools for complex experiments and exemplify that the application yields useful information on the dependence of these experiments on the optimized parameters.
Optimal Robust Motion Controller Design Using Multiobjective Genetic Algorithm
Svečko, Rajko
2014-01-01
This paper describes the use of a multiobjective genetic algorithm for robust motion controller design. Motion controller structure is based on a disturbance obser