Image Encryption Using Differential Evolution Approach in Frequency Domain
Hassan, Maaly Awad S; 10.5121/sipij.2011.2105
2011-01-01
This paper presents a new effective method for image encryption which employs magnitude and phase manipulation using Differential Evolution (DE) approach. The novelty of this work lies in deploying the concept of keyed discrete Fourier transform (DFT) followed by DE operations for encryption purpose. To this end, a secret key is shared between both encryption and decryption sides. Firstly two dimensional (2-D) keyed discrete Fourier transform is carried out on the original image to be encrypted. Secondly crossover is performed between two components of the encrypted image, which are selected based on Linear Feedback Shift Register (LFSR) index generator. Similarly, keyed mutation is performed on the real parts of a certain components selected based on LFSR index generator. The LFSR index generator initializes it seed with the shared secret key to ensure the security of the resulting indices. The process shuffles the positions of image pixels. A new image encryption scheme based on the DE approach is developed...
Adaptive differential evolution a robust approach to multimodal problem optimization
Zhang, Jingqiao; Zhang, Jingqiao
2009-01-01
The fundamental theme of this book is theoretical study of differential evolution and algorithmic analysis of parameter adaptive schemes. The book offers real-world insights into a variety of large-scale complex industrial applications.
Özgür Başkan
2014-09-01
Full Text Available Differential Evolution algorithm has effectively been used to solve engineering optimization problems recently. The Differential Evolution algorithm, which uses similar principles with Genetic Algorithms, is more robust on obtaining optimal solution than many other heuristic algorithms with its simpler structure. In this study, Differential Evolution algorithm is applied to the transportation network design problems and its effectiveness on the solution is investigated. In this context, Differential Evolution based models are developed using bi-level programming approach for the solution of the transportation network design problem and determination of the on-street parking places in urban road networks. In these models, optimal investment and parking strategies are investigated on the upper level. On the lower level, deterministic traffic assignment problem, which represents drivers' responses, is solved using Frank-Wolfe algorithm and VISUM traffic modeling software. In order to determine the effectiveness of the proposed models, numerical applications are carried out on Sioux-Falls test network. Results showed that the Differential Evolution algorithm may effectively been used for the solution of transportation network design problems.
S. S. Motsa
2014-01-01
Full Text Available This paper presents a new application of the homotopy analysis method (HAM for solving evolution equations described in terms of nonlinear partial differential equations (PDEs. The new approach, termed bivariate spectral homotopy analysis method (BISHAM, is based on the use of bivariate Lagrange interpolation in the so-called rule of solution expression of the HAM algorithm. The applicability of the new approach has been demonstrated by application on several examples of nonlinear evolution PDEs, namely, Fisher’s, Burgers-Fisher’s, Burger-Huxley’s, and Fitzhugh-Nagumo’s equations. Comparison with known exact results from literature has been used to confirm accuracy and effectiveness of the proposed method.
A Novel Resource-Leveling Approach for Construction Project Based on Differential Evolution
Hong-Hai Tran
2014-01-01
Full Text Available In construction engineering, project schedules are commonly established by the critical path method. Nevertheless, these schedules often lead to substantial fluctuations in the resource profile that are not only impractical but also costly for the contractors to execute. Therefore, in order to smooth out the resource profile, construction managers need to perform resource-leveling procedures. This paper proposes a novel approach for resource leveling, named as resource leveling based on differential evolution (RLDE. The performance of the RLDE is compared to that of Microsoft Project software, the genetic algorithm, and the particle swarm optimization algorithm. Experiments have proved that the newly developed method can deliver the most desirable resource-leveling result. Thus, the RLDE is an effective method and it can be a useful tool for assisting managers/planners in the field of project management.
Abhijit Chandra
2012-04-01
Full Text Available Reduction of computational complexity of digital hardware has drawn the special attention of researchers in recent past. Proper emphasis is needed in this regard towards the settlement of computationally efficient as well as functionally competent design of digital systems. In this communication, we have made one novel attempt for designing multiplier-free Finite duration Impulse Response (FIR digital filter using one robust evolutionary optimization technique, called Differential Evolution (DE. The search has been directed through two sequentially opposite paths which include quantization and optimization as fundamental operations. Besides performing a detailed comparative analysis between these two proposed approaches; the performance evaluation of the designed filter with other existing discrete coefficient FIR models has also been carried out. Finally, the optimum search method for realizing the required set of specifications has been suggested.
Zheng, Feifei; Simpson, Angus R.; Zecchin, Aaron C.
2011-08-01
This paper proposes a novel optimization approach for the least cost design of looped water distribution systems (WDSs). Three distinct steps are involved in the proposed optimization approach. In the first step, the shortest-distance tree within the looped network is identified using the Dijkstra graph theory algorithm, for which an extension is proposed to find the shortest-distance tree for multisource WDSs. In the second step, a nonlinear programming (NLP) solver is employed to optimize the pipe diameters for the shortest-distance tree (chords of the shortest-distance tree are allocated the minimum allowable pipe sizes). Finally, in the third step, the original looped water network is optimized using a differential evolution (DE) algorithm seeded with diameters in the proximity of the continuous pipe sizes obtained in step two. As such, the proposed optimization approach combines the traditional deterministic optimization technique of NLP with the emerging evolutionary algorithm DE via the proposed network decomposition. The proposed methodology has been tested on four looped WDSs with the number of decision variables ranging from 21 to 454. Results obtained show the proposed approach is able to find optimal solutions with significantly less computational effort than other optimization techniques.
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.
J.Jithendranath
2013-07-01
Full Text Available This paper presents an evolutionary based algorithm for solving optimal reactive power dispatch problem in power system. The problem was designed as a Multi-Objective case with loss minimization and voltage stability as objectives. Generator terminal voltages, tap setting of transformers and reactive power generation of capacitor banks were taken as optimization variables. Modal analysis method is adopted to assess the voltage stability of system. The above presented problem was solved on basis of efficient and reliable technique among all evolutionary based algorithms, the Differential Evolution Technique. The proposed method has been tested on IEEE 30 bus system where the obtained results were found satisfactorily to a large extent that of reported earlier.
A Differential Evolution Approach for Protein Folding Using a Lattice Model
Heitor Silverio Lopes; Reginaldo Bitello
2007-01-01
Protein folding is a relevant computational problem in Bioinformatics, for which many heuristic algorithms have been proposed. This work presents a methodology for the application of differential evolution (DE) to the problem of protein folding, using the bi-dimensional hydrophobic-polar model. DE is a relatively recent evolutionary algorithm, and has been used successfully in several engineering optimization problems, usually with continuous variables. We introduce the concept of genotype-phenotype mapping in DE in order to provide a mapping between the real-valued vector and an actual folding. The methodology is detailed and several experiments with benchmarks are done. We compared the results with other similar implementations. The proposed DE has shown to be competitive, statistically consistent and very promising.
Panda, Sidhartha; Yegireddy, Narendra Kumar
2015-09-01
In this paper, a hybrid Improved Differential Evolution and Pattern Search (hIDEPS) approach is proposed for the design of a PI-Type Multi-Input Single Output (MISO) Static Synchronous Series Compensator (SSSC) based damping controller. The improvement in Differential Evolution (DE) algorithm is introduced by a simple but effective scheme of changing two of its most important control parameters i.e. step size and crossover probability with an objective of achieving improved performance. Pattern Search (PS) is subsequently employed to fine tune the best solution provided by modified DE algorithm. The superiority of a proposed hIDEPS technique over DE and improved DE has also been demonstrated. At the outset, this concept is applied to a SSSC connected in a Single Machine Infinite Bus (SMIB) power system and then extended to a multi-machine power system. To show the effectiveness and robustness of the proposed design approach, simulation results are presented and compared with DE and Particle Swarm Optimization (PSO) optimized Single Input Single Output (SISO) SSSC based damping controllers. It is observed that the proposed approach yield superior damping performance compared to some approaches available in the literature.
Topology Optimization of Structure Using Differential Evolution
Chun-Yin Wu
2008-02-01
Full Text Available The population-based evolutionary algorithms have emerged as powerful mechanism for finding optimum solutions of complex optimization problems. A promising new evolutionary algorithm, differential evolution, has garnered significant attention in the engineering optimization research. Differential evolution has the advantage of incorporating a relatively simple and efficient form of mutation and crossover. This paper aims at introducing differential evolution as an alternative approach for topology optimization of truss and continuous structure with stress and displacement constraints. In comparison the results with other studies, it shows that differential evolution algorithms are very effective and efficient in solving topology optimization problem of structure.
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.
Armando Céspedes-Mota
2016-01-01
Full Text Available Location information for wireless sensor nodes is needed in most of the routing protocols for distributed sensor networks to determine the distance between two particular nodes in order to estimate the energy consumption. Differential evolution obtains a suboptimal solution based on three features included in the objective function: area, energy, and redundancy. The use of obstacles is considered to check how these barriers affect the behavior of the whole solution. The obstacles are considered like new restrictions aside of the typical restrictions of area boundaries and the overlap minimization. At each generation, the best element is tested to check whether the node distribution is able to create a minimum spanning tree and then to arrange the nodes using the smallest distance from the initial position to the suboptimal end position based on the Hungarian algorithm. This work presents results for different scenarios delimited by walls and testing whether it is possible to obtain a suboptimal solution with inner obstacles. Also, a case with an area delimited by a star shape is presented showing that the algorithm is able to fill the whole area, even if such area is delimited for the peaks of the star.
A new approach to investigation of evolution differential equations in Banach spaces
Alber, Y I
1993-01-01
and that $B$ is dense in $H$. The stabilization of solutions of evolution equations has been proven either in the sense of weak convergence in $B$ or in the norm of $H$ space, and only asymptotic estimates of stabilization rate have been obtained [15]. In the present paper we consider equations of type (0.1) without conditions (0.2) and establish stabilization with both
Ighravwe, D. E.; Oke, S. A.; Adebiyi, K. A.
2017-08-01
This paper draws on the "human reliability" concept as a structure for gaining insight into the maintenance workforce assessment in a process industry. Human reliability hinges on developing the reliability of humans to a threshold that guides the maintenance workforce to execute accurate decisions within the limits of resources and time allocations. This concept offers a worthwhile point of deviation to encompass three elegant adjustments to literature model in terms of maintenance time, workforce performance and return-on-workforce investments. These fully explain the results of our influence. The presented structure breaks new grounds in maintenance workforce theory and practice from a number of perspectives. First, we have successfully implemented fuzzy goal programming (FGP) and differential evolution (DE) techniques for the solution of optimisation problem in maintenance of a process plant for the first time. The results obtained in this work showed better quality of solution from the DE algorithm compared with those of genetic algorithm and particle swarm optimisation algorithm, thus expressing superiority of the proposed procedure over them. Second, the analytical discourse, which was framed on stochastic theory, focusing on specific application to a process plant in Nigeria is a novelty. The work provides more insights into maintenance workforce planning during overhaul rework and overtime maintenance activities in manufacturing systems and demonstrated capacity in generating substantially helpful information for practice.
Solving Partial Differential Equations Using a New Differential Evolution Algorithm
Natee Panagant
2014-01-01
Full Text Available This paper proposes an alternative meshless approach to solve partial differential equations (PDEs. With a global approximate function being defined, a partial differential equation problem is converted into an optimisation problem with equality constraints from PDE boundary conditions. An evolutionary algorithm (EA is employed to search for the optimum solution. For this approach, the most difficult task is the low convergence rate of EA which consequently results in poor PDE solution approximation. However, its attractiveness remains due to the nature of a soft computing technique in EA. The algorithm can be used to tackle almost any kind of optimisation problem with simple evolutionary operation, which means it is mathematically simpler to use. A new efficient differential evolution (DE is presented and used to solve a number of the partial differential equations. The results obtained are illustrated and compared with exact solutions. It is shown that the proposed method has a potential to be a future meshless tool provided that the search performance of EA is greatly enhanced.
Differential Evolution for Many-Particle Adaptive Quantum Metrology
Lovett, N.B.; Crosnier, C.; Perarnau- Llobet, M.; Sanders, B.
2013-01-01
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
Turbomachinery Airfoil Design Optimization Using Differential Evolution
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.
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.
Dichotomous Binary Differential Evolution for Knapsack Problems
Hu Peng
2016-01-01
Full Text Available Differential evolution (DE is one of the most popular and powerful evolutionary algorithms for the real-parameter global continuous optimization problems. However, how to adapt into combinatorial optimization problems without sacrificing the original evolution mechanism of DE is harder work to the researchers to design an efficient binary differential evolution (BDE. To tackle this problem, this paper presents a novel BDE based on dichotomous mechanism for knapsack problems, called DBDE, in which two new proposed methods (i.e., dichotomous mutation and dichotomous crossover are employed. DBDE almost has any difference with original DE and no additional module or computation has been introduced. The experimental studies have been conducted on a suite of 0-1 knapsack problems and multidimensional knapsack problems. Experimental results have verified the quality and effectiveness of DBDE. Comparison with three state-of-the-art BDE variants and other two state-of-the-art binary particle swarm optimization (PSO algorithms has proved that DBDE is a new competitive algorithm.
Improved Differential Evolution for Combined Heat and Power Economic Dispatch
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.
Differential Evolution with Gaussian Mutation for Economic Dispatch
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.
Optimal Reactive Power Dispatch using Improved Differential Evolution Algorithm
Hamid Falaghi
2014-12-01
Full Text Available Reactive power dispatch plays a key role in secure and economic operation of power systems. Optimal reactive power dispatch (ORPD is a non-linear optimization problem which includes both continues and discrete variables. Due to complex characteristics, heuristic and evolutionary based optimization approaches have become effective tools to solve the ORPD problem. In this paper, a new optimization approach based on improved differential evolution (IDE has been proposed to solve the ORPD problem. IDE is an improved version of differential evolution optimization algorithm in which new solutions are produced in respect to global best solution. In the proposed approach, IDE determines the optimal combination of control variables including generator voltages, transformer taps and setting of VAR compensation devices to obtain minimum real power losses. In order to demonstrate the applicability and efficiency of the proposed IDE based approach, it has been tested on the IEEE 14 and 57-bus test systems and obtained results are compared with those obtained using other existing methods. Simulation results show that the proposed approach is superior to the other existing methods.
Fan, Zhun; Liu, Jinchao; Sørensen, Torben
2009-01-01
This paper introduces an improved differential evolution (DE) algorithm for robust layout synthesis of microelectromechanical system components subject to inherent geometric uncertainties. A case study of the layout synthesis of a combdriven microresonator shows that the approach proposed in this...
Differential Evolution algorithm applied to FSW model calibration
Idagawa, H. S.; Santos, T. F. A.; Ramirez, A. J.
2014-03-01
Friction Stir Welding (FSW) is a solid state welding process that can be modelled using a Computational Fluid Dynamics (CFD) approach. These models use adjustable parameters to control the heat transfer and the heat input to the weld. These parameters are used to calibrate the model and they are generally determined using the conventional trial and error approach. Since this method is not very efficient, we used the Differential Evolution (DE) algorithm to successfully determine these parameters. In order to improve the success rate and to reduce the computational cost of the method, this work studied different characteristics of the DE algorithm, such as the evolution strategy, the objective function, the mutation scaling factor and the crossover rate. The DE algorithm was tested using a friction stir weld performed on a UNS S32205 Duplex Stainless Steel.
Real parameter optimization by an effective differential evolution algorithm
Ali Wagdy Mohamed
2013-03-01
Full Text Available This paper introduces an Effective Differential Evolution (EDE algorithm for solving real parameter optimization problems over continuous domain. The proposed algorithm proposes a new mutation rule based on the best and the worst individuals among the entire population of a particular generation. The mutation rule is combined with the basic mutation strategy through a linear decreasing probability rule. The proposed mutation rule is shown to promote local search capability of the basic DE and to make it faster. Furthermore, a random mutation scheme and a modified Breeder Genetic Algorithm (BGA mutation scheme are merged to avoid stagnation and/or premature convergence. Additionally, the scaling factor and crossover of DE are introduced as uniform random numbers to enrich the search behavior and to enhance the diversity of the population. The effectiveness and benefits of the proposed modifications used in EDE has been experimentally investigated. Numerical experiments on a set of bound-constrained problems have shown that the new approach is efficient, effective and robust. The comparison results between the EDE and several classical differential evolution methods and state-of-the-art parameter adaptive differential evolution variants indicate that the proposed EDE algorithm is competitive with , and in some cases superior to, other algorithms in terms of final solution quality, efficiency, convergence rate, and robustness.
Flow Shop Scheduling using Differential Evolution of CRM
Zuzana Čičková
2010-12-01
Full Text Available The article is focused on the application of differential evolution for solving flow shop problem that belongs to the class of scheduling problems. The scheduling problems arise in diverse areas such as manufacturing systems, production planning, computer design, logistics etc.. Only in very special cases there exist exact polynomial algorithms to reach optimal solution. In most of the other cases, its computational complexity is NP-hard and it seems to be desirable to employ some heuristics to solve it. Nowadays, the use of some methods that are based on metaheuristics is a popular way. One of them is a differential evolution, which belongs to the class of evolutionary techniques. The application of evolutionary algorithms to NP-hard problems generally requires a special modification of these algorithms; therefore the main object of the work is to adapt a canonical version of differential evolution for solving flow shop problem. The effectiveness of the proposed approach is compared with other evolutionary techniques known from the already published results. The available instance of flow shop Car and Rec are used for comparison.
Artificial Neural Networks, Symmetries and Differential Evolution
Urfalioglu, Onay
2010-01-01
Neuroevolution is an active and growing research field, especially in times of increasingly parallel computing architectures. Learning methods for Artificial Neural Networks (ANN) can be divided into two groups. Neuroevolution is mainly based on Monte-Carlo techniques and belongs to the group of global search methods, whereas other methods such as backpropagation belong to the group of local search methods. ANN's comprise important symmetry properties, which can influence Monte-Carlo methods. On the other hand, local search methods are generally unaffected by these symmetries. In the literature, dealing with the symmetries is generally reported as being not effective or even yielding inferior results. In this paper, we introduce the so called Minimum Global Optimum Proximity principle derived from theoretical considerations for effective symmetry breaking, applied to offline supervised learning. Using Differential Evolution (DE), which is a popular and robust evolutionary global optimization method, we experi...
Heterogeneous Differential Evolution for Numerical Optimization
Hui Wang
2014-01-01
Full Text Available Differential evolution (DE is a population-based stochastic search algorithm which has shown a good performance in solving many benchmarks and real-world optimization problems. Individuals in the standard DE, and most of its modifications, exhibit the same search characteristics because of the use of the same DE scheme. This paper proposes a simple and effective heterogeneous DE (HDE to balance exploration and exploitation. In HDE, individuals are allowed to follow different search behaviors randomly selected from a DE scheme pool. Experiments are conducted on a comprehensive set of benchmark functions, including classical problems and shifted large-scale problems. The results show that heterogeneous DE achieves promising performance on a majority of the test problems.
Aerodynamic Shape Optimization Using Hybridized Differential Evolution
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.
Paired Comparisons-based Interactive Differential Evolution
Takagi, Hideyuki
2009-01-01
We propose Interactive Differential Evolution (IDE) based on paired comparisons for reducing user fatigue and evaluate its convergence speed in comparison with Interactive Genetic Algorithms (IGA) and tournament IGA. User interface and convergence performance are two big keys for reducing Interactive Evolutionary Computation (IEC) user fatigue. Unlike IGA and conventional IDE, users of the proposed IDE and tournament IGA do not need to compare whole individuals each other but compare pairs of individuals, which largely decreases user fatigue. In this paper, we design a pseudo-IEC user and evaluate another factor, IEC convergence performance, using IEC simulators and show that our proposed IDE converges significantly faster than IGA and tournament IGA, i.e. our proposed one is superior to others from both user interface and convergence performance points of view.
Student Teachers' Approaches to Teaching Biological Evolution
Borgerding, Lisa A.; Klein, Vanessa A.; Ghosh, Rajlakshmi; Eibel, Albert
2015-06-01
Evolution is fundamental to biology and scientific literacy, but teaching high school evolution is often difficult. Evolution teachers face several challenges including limited content knowledge, personal conflicts with evolution, expectations of resistance, concerns about students' conflicts with religion, and curricular constraints. Evolution teaching can be particularly challenging for student teachers who are just beginning to gain pedagogical knowledge and pedagogical content knowledge related to evolution teaching and who seek approval from university supervisors and cooperating teachers. Science teacher educators need to know how to best support student teachers as they broach the sometimes daunting task of teaching evolution within student teaching placements. This multiple case study report documents how three student teachers approached evolution instruction and what influenced their approaches. Data sources included student teacher interviews, field note observations for 4-5 days of evolution instruction, and evolution instructional artifacts. Data were analyzed using grounded theory approaches to develop individual cases and a cross-case analysis. Seven influences (state exams and standards, cooperating teacher, ideas about teaching and learning, concerns about evolution controversy, personal commitment to evolution, knowledge and preparation for teaching evolution, and own evolution learning experiences) were identified and compared across cases. Implications for science teacher preparation and future research are provided.
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.
Differential Evolution and Particle Swarm Optimization for Partitional Clustering
Krink, Thiemo; Paterlini, Sandra
2006-01-01
for numerical optimisation, which are hardly known outside the search heuristics field, are particle swarm optimisation (PSO) and differential evolution (DE). The performance of GAs for a representative point evolution approach to clustering is compared with PSO and DE. The empirical results show that DE...... is clearly and consistently superior compared to GAs and PSO for hard clustering problems, both with respect to precision as well as robustness (reproducibility) of the results. Only for simple data sets, the GA and PSO can obtain the same quality of results. Apart from superior performance, DE is easy...... to implement and requires hardly any parameter tuning compared to substantial tuning for GAs and PSOs. Our study shows that DE rather than GAs should receive primary attention in partitional clustering algorithms....
2008-01-01
Using functional derivative technique in quantum field theory, the algebraic dy-namics approach for solution of ordinary differential evolution equations was gen-eralized to treat partial differential evolution equations. The partial differential evo-lution equations were lifted to the corresponding functional partial differential equations in functional space by introducing the time translation operator. The functional partial differential evolution equations were solved by algebraic dynam-ics. The algebraic dynamics solutions are analytical in Taylor series in terms of both initial functions and time. Based on the exact analytical solutions, a new nu-merical algorithm—algebraic dynamics algorithm was proposed for partial differ-ential evolution equations. The difficulty of and the way out for the algorithm were discussed. The application of the approach to and computer numerical experi-ments on the nonlinear Burgers equation and meteorological advection equation indicate that the algebraic dynamics approach and algebraic dynamics algorithm are effective to the solution of nonlinear partial differential evolution equations both analytically and numerically.
WANG Shundin; ZHANG Hua
2008-01-01
Using functional derivative technique In quantum field theory,the algebraic dy-namics approach for solution of ordinary differential evolution equations was gen-eralized to treat partial differential evolution equations.The partial differential evo-lution equations were lifted to the corresponding functional partial differential equations in functional space by Introducing the time translation operator.The functional partial differential evolution equations were solved by algebraic dynam-ics.The algebraic dynamics solutions are analytical In Taylor series In terms of both initial functions and time.Based on the exact analytical solutions,a new nu-merical algorithm-algebraic dynamics algorithm was proposed for partial differ-ential evolution equations.The difficulty of and the way out for the algorithm were discussed.The application of the approach to and computer numerical experi-ments on the nonlinear Burgers equation and meteorological advection equation indicate that the algebraic dynamics approach and algebraic dynamics algorithm are effective to the solution of nonlinear partial differential evolution equations both analytically and numerically.
Efficient receiver tuning using differential evolution strategies
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.
Many-Objective Distinct Candidates Optimization using Differential Evolution
Justesen, Peter; Ursem, Rasmus Kjær
2010-01-01
fully nondominated. A more feasible approach is to discover a low number of solutions within a region of interest on the true Pareto front. Here, a convergent secondary selection criterion guide the search toward optimal regions of interest that may incorporate decision maker preferences. However......, diversity must also be taken into account to ensure that the population does not converge prematurely. In this paper, candidate distinctiveness is measured and controlled based on the novel relaxed objective distance (ROD) measure, which enables the decision maker to control the desired level of diversity...... for each objective. The Many-Objective Distinct Candidates Optimization using Differential Evolution (MODCODE) algorithm takes a novel approach by focusing search using a user-defined number of subpopulations each returning a distinct optimal solution within the preferred region of interest. In this paper...
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.
An Enhanced Differential Evolution Algorithm Based on Multiple Mutation Strategies
Wan-li Xiang
2015-01-01
Full Text Available 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.
Novel Feature Selection by Differential Evolution Algorithm
Ali Ghareaghaji
2013-11-01
Full Text Available Iris scan biometrics employs the unique characteristic and features of the human iris in order to verify the identity of in individual. In today's world, where terrorist attacks are on the rise employment of infallible security systems is a must. This makes Iris recognition systems unavoidable in emerging security. Authentication the objective function is minimized using Differential Evolutionary (DE Algorithm where the population vector is encoded using Binary Encoded Decimal to avoid the float number optimization problem. An automatic clustering of the possible values of the Lagrangian multiplier provides a detailed insight of the selected features during the proposed DE based optimization process. The classification accuracy of Support Vector Machine (SVM is used to measure the performance of the selected features. The proposed algorithm outperforms the existing DE based approaches when tested on IRIS, Wine, Wisconsin Breast Cancer, Sonar and Ionosphere datasets. The same algorithm when applied on gait based people identification, using skeleton data points obtained from Microsoft Kinect sensor, exceeds the previously reported accuracies.
Karyotype evolution and species differentiation in the genus Rattus ...
Dhananjoy
Karyotype evolution and species differentiation in the genus Rattus of ... as primitive/ancestral types of chromosomes into either subtelocentric or small metacentrics leads to speciation or simply new ..... The features are quite common in the.
The Power Unit Coordinated Control via Uniform Differential Evolution
Zain Abdalla Zahran; Rui Feng Shi; Xiang Jie Liu
2013-01-01
This paper modified the differential evolution (DE) algorithm adaptively to solve the power unit coordinated control (PUCC) problem. It was modified in two aspects: 1) a uniform initialization, which was controlled and regulated by a zone factor (m), 2) a regular mutation process, to develop an effective searching process and improve the convergence of the basic DE algorithm. A numerical case study was employed to verify the performance of our proposed uniform differential evolution (UDE) a...
Identiﬁcation of bilinear systems using differential evolution algorithm
Saban Ozer; Hasan Zorlu
2011-06-01
In this work, a novel identiﬁcation method based on differential evolution algorithm has been applied to bilinear systems and its performance has been compared to that of genetic algorithm. Box–Jenkins system and different type bilinear systems have been identiﬁed using differential evolution and genetic algorithms. The simulation results have shown that bilinear systems can be successfully and efﬁciently identiﬁed using these algorithms.
Nonlinear evolution operators and semigroups applications to partial differential equations
Pavel, Nicolae H
1987-01-01
This research monograph deals with nonlinear evolution operators and semigroups generated by dissipative (accretive), possibly multivalued operators, as well as with the application of this theory to partial differential equations. It shows that a large class of PDE's can be studied via the semigroup approach. This theory is not available otherwise in the self-contained form provided by these Notes and moreover a considerable part of the results, proofs and methods are not to be found in other books. The exponential formula of Crandall and Liggett, some simple estimates due to Kobayashi and others, the characterization of compact semigroups due to Brézis, the proof of a fundamental property due to Ursescu and the author and some applications to PDE are of particular interest. Assuming only basic knowledge of functional analysis, the book will be of interest to researchers and graduate students in nonlinear analysis and PDE, and to mathematical physicists.
Image evolution approach for contrast enhancement
Sapiro, Guillermo; Casalles, Vicent
1995-09-01
An algorithm for histogram modification via image evolution equations is first presented in this paper. We show that the image histogram can be modified to achieve any given distribution as the steady state solution of this partial differential equation. We then prove that this equation corresponds to a gradient descent flow of a variational problem. That is, the proposed PDE is solving an energy minimization problem. This gives a new interpretation to histogram modification and contrast enhancement in general. This interpretation is completely formulated in the image domain, in contrast with classical techniques for histogram modification which are formulated in a probabilistic domain. From this, new algorithms for contrast enhancement, which include for example, image modeling, can be derived. Based on the energy formulation and its corresponding PDE, we show that the proposed histogram modification algorithm can be combined with denoising schemes. This allows to perform simultaneous contrast enhancement and denoising, avoiding common noise sharpening effects in classical algorithms. The approach is extended to local contrast enhancement as well. Theoretical results regarding the existence of solutions of the proposed equations are presented.
Fuzzy differential equations in various approaches
Gomes, Luciana Takata; Bede, Barnabas
2015-01-01
This book may be used as reference for graduate students interested in fuzzy differential equations and researchers working in fuzzy sets and systems, dynamical systems, uncertainty analysis, and applications of uncertain dynamical systems. Beginning with a historical overview and introduction to fundamental notions of fuzzy sets, including different possibilities of fuzzy differentiation and metric spaces, this book moves on to an overview of fuzzy calculus thorough exposition and comparison of different approaches. Innovative theories of fuzzy calculus and fuzzy differential equations using fuzzy bunches of functions are introduced and explored. Launching with a brief review of essential theories, this book investigates both well-known and novel approaches in this field; such as the Hukuhara differentiability and its generalizations as well as differential inclusions and Zadeh’s extension. Through a unique analysis, results of all these theories are examined and compared.
Proteomic approaches to bacterial differentiation
Norbeck, Angela D.; Callister, Stephen J.; Monroe, Matthew E.; Jaitly, Navdeep; Elias, Dwayne A.; Lipton, Mary S.; Smith, Richard D.
2006-12-01
While genomic approaches have been applied for the detection and identification of individual bacteria within microbial communities, analogous proteomics approaches have been effectively precluded due to their inherent complexity. An in silico assessment of peptides that could potentially be present in the proteomes of artificial simple and complex communities was performed to evaluate the effect of proteome complexity on species detection. A mass spectrometry-based proteomics approach was employed to experimentally detect and validate the predicted tryptic peptides initially identified as distinctive within the simple community. An assessment of peptide distinctiveness and the potential for mapping to a particular bacterium within a community was made throughout each step of the study. A second in silico assessment of peptide distinctiveness for a complex community of 25 microorganisms was conducted to investigate the levels of instrumental performance that would be required to experimentally detect these peptides, as well as how performance varied with complexity (e.g., the number of different microorganisms). The experimental data for a simple community showed that it is feasible to predict, observe, and to quantify distinctive peptides from one organism in the presence of at least a 100-fold greater abundance of another, thus yielding putative markers for identifying a bacterium of interest. This work represents a first step towards quantitative proteomic characterization of complex microbial communities and the possible development of community wide markers of perturbations to such communities.
Proteomic approaches to bacterial differentiation
Norbeck, Angela D.; Callister, Stephen J.; Monroe, Matthew E.; Jaitly, Navdeep; Elias, Dwayne A.; Lipton, Mary S.; Smith, Richard D.
2006-01-02
While genomic approaches have been applied to the detection and identification of individual bacteria within microbial communities, analogous proteomics approaches have been effectively precluded due to the inherent complexity. An in silico assessment of peptides derived from artificial simple and complex communities was performed to evaluate the effect of proteome complexity on species detection. Detection and validation of predicted peptides initially identified as distinctive within the simple community was experimentally performed using a mass spectrometry-based proteomics approach. An assessment of peptide distinctiveness and the potential for mapping to a particular bacterium within a community was made throughout each step of the study. A second assessment performed in silico of peptide distinctiveness for a complex community of 25 microorganisms was also conducted. The experimental data for a simple community, and the in silico data for a complex community revealed that it is feasible to predict, observe, and quantify distinctive peptides from one organism in the presence of at least a 100-fold greater abundance of another, thus yielding putative markers for the identification of a bacterium of interest. This work represents a first step towards quantitative proteomic characterization of complex microbial communities.
Flexible Ligand Docking Using Differential Evolution
Thomsen, René
2003-01-01
the most favorable energetic conformation among the large space of possible protein-ligand complexes. Stochastic search methods, such as evolutionary algorithms (EAs), can be used to sample large search spaces effectively and is one of the preferred methods for flexible ligand docking. The differential...
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.
Autobot Evolution: A Futuristic approach
N.Hariharan#
2011-06-01
Full Text Available SCABOR is an approach for the 3-D lane detection and autonomous driving. Its main attribute is to diagnose the metrology of roads. It has a wide significance in determining the basic structure, the presence of humps and dips and gives a clear and cache information of the way .Our view on this paper is to apply SCABOR along with the application of the GLOBAL POSITIONING SYSTEM in autonomous driving system for safe and fast driving. Here the SCABOR technology’s currentapplication is outlined and its use has been extended to traffic system. We have suggested a unique method for flexible driving. We also suggest ways in which SCABOR technology can be enhanced forfuture applications. Among the significant advantages, these techniques perform well even in high risk zones and congested areas with up to 100% accuracy where many other techniques fail.
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.
Flexible Ligand Docking Using Differential Evolution
Thomsen, René
2003-01-01
evolution algorithm (DE) is applied to the docking problem using the AutoDock program. The introduced DockDE algorithm is compared with the Lamarckian GA (LGA) provided with AutoDock, and the DockEA previously found to outperform the LGA. The comparison is performed on a suite of six commonly used docking...... problems. In conclusion, the introduced DockDE outperformed the other algorithms on all problems. Further, the DockDE showed remarkable performance in terms of convergence speed and robustness regarding the found solution....
Differential evolution Markov chain with snooker updater and fewer chains
Vrugt, Jasper A [Los Alamos National Laboratory; Ter Braak, Cajo J F [NON LANL
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.
Optimal Overlay of Ligands with Flexible Bonds Using Differential Evolution
Pedersen, Christian Storm; Kristensen, Thomas Greve
When designing novel drugs, the need arise to screen databases for structures resembling active ligands, e.g. by generating a query meta-structure which summarizes these. We propose a flexible bond method for making a meta-structure and present Monte Carlo, Nelder-Mead and Differential Evolution ...
Optimal Overlay of Ligands with Flexible Bonds Using Differential Evolution
Pedersen, Christian Storm; Kristensen, Thomas Greve
When designing novel drugs, the need arise to screen databases for structures resembling active ligands, e.g. by generating a query meta-structure which summarizes these. We propose a flexible bond method for making a meta-structure and present Monte Carlo, Nelder-Mead and Differential Evolution...
Fast Micro-Differential Evolution for Topological Active Net Optimization.
Li, Yuan-Long; Zhan, Zhi-Hui; Gong, Yue-Jiao; Zhang, Jun; Li, Yun; Li, Qing
2016-06-01
This paper studies the optimization problem of topological active net (TAN), which is often seen in image segmentation and shape modeling. A TAN is a topological structure containing many nodes, whose positions must be optimized while a predefined topology needs to be maintained. TAN optimization is often time-consuming and even constructing a single solution is hard to do. Such a problem is usually approached by a "best improvement local search" (BILS) algorithm based on deterministic search (DS), which is inefficient because it spends too much efforts in nonpromising probing. In this paper, we propose the use of micro-differential evolution (DE) to replace DS in BILS for improved directional guidance. The resultant algorithm is termed deBILS. Its micro-population efficiently utilizes historical information for potentially promising search directions and hence improves efficiency in probing. Results show that deBILS can probe promising neighborhoods for each node of a TAN. Experimental tests verify that deBILS offers substantially higher search speed and solution quality not only than ordinary BILS, but also the genetic algorithm and scatter search algorithm.
Differential evolution enhanced with multiobjective sorting-based mutation operators.
Wang, Jiahai; Liao, Jianjun; Zhou, Ying; Cai, Yiqiao
2014-12-01
Differential evolution (DE) is a simple and powerful population-based evolutionary algorithm. The salient feature of DE lies in its mutation mechanism. Generally, the parents in the mutation operator of DE are randomly selected from the population. Hence, all vectors are equally likely to be selected as parents without selective pressure at all. Additionally, the diversity information is always ignored. In order to fully exploit the fitness and diversity information of the population, this paper presents a DE framework with multiobjective sorting-based mutation operator. In the proposed mutation operator, individuals in the current population are firstly sorted according to their fitness and diversity contribution by nondominated sorting. Then parents in the mutation operators are proportionally selected according to their rankings based on fitness and diversity, thus, the promising individuals with better fitness and diversity have more opportunity to be selected as parents. Since fitness and diversity information is simultaneously considered for parent selection, a good balance between exploration and exploitation can be achieved. The proposed operator is applied to original DE algorithms, as well as several advanced DE variants. Experimental results on 48 benchmark functions and 12 real-world application problems show that the proposed operator is an effective approach to enhance the performance of most DE algorithms studied.
Optimization of Neutrino Oscillation Parameters using Differential Evolution
Mustafa, Ghulam; Masud, Bilal
2011-01-01
We combine Differential Evolution, a new technique, with the traditional grid based method for optimization of solar neutrino oscillation parameters $\\Delta m^2$ and $\\tan^{2}\\theta$ for the case of two neutrinos. The Differential Evolution is a population based stochastic algorithm for optimization of real valued non-linear non-differentiable objective functions that has become very popular during the last decade. We calculate well known chi-square ($\\chi^2$) function for neutrino oscillations for a grid of the parameters using total event rates of chlorine (Homestake), Gallax+GNO, SAGE, Superkamiokande and SNO detectors and theoretically calculated event rates. We find minimum $\\chi^2$ values in different regions of the parameter space. We explore regions around these minima using Differential Evolution for the fine tuning of the parameters allowing even those values of the parameters which do not lie on any grid. We note as much as 4 times decrease in $\\chi^2$ value in the SMA region and even better goodne...
An Enhanced Differential Evolution with Elite Chaotic Local Search
Zhaolu Guo
2015-01-01
Full Text Available Differential evolution (DE is a simple yet efficient evolutionary algorithm for real-world engineering problems. However, its search ability should be further enhanced to obtain better solutions when DE is applied to solve complex optimization problems. This paper presents an enhanced differential evolution with elite chaotic local search (DEECL. In DEECL, it utilizes a chaotic search strategy based on the heuristic information from the elite individuals to promote the exploitation power. Moreover, DEECL employs a simple and effective parameter adaptation mechanism to enhance the robustness. Experiments are conducted on a set of classical test functions. The experimental results show that DEECL is very competitive on the majority of the test functions.
A Hybrid Backtracking Search Optimization Algorithm with Differential Evolution
Lijin Wang
2015-01-01
Full Text Available The backtracking search optimization algorithm (BSA is a new nature-inspired method which possesses a memory to take advantage of experiences gained from previous generation to guide the population to the global optimum. BSA is capable of solving multimodal problems, but it slowly converges and poorly exploits solution. The differential evolution (DE algorithm is a robust evolutionary algorithm and has a fast convergence speed in the case of exploitive mutation strategies that utilize the information of the best solution found so far. In this paper, we propose a hybrid backtracking search optimization algorithm with differential evolution, called HBD. In HBD, DE with exploitive strategy is used to accelerate the convergence by optimizing one worse individual according to its probability at each iteration process. A suit of 28 benchmark functions are employed to verify the performance of HBD, and the results show the improvement in effectiveness and efficiency of hybridization of BSA and DE.
Parameter Estimation of Damped Compound Pendulum Differential Evolution Algorithm
Saad Mohd Sazli
2016-01-01
Full Text Available This paper present the parameter identification of damped compound pendulum using differential evolution algorithm. The procedure used to achieve the parameter identification of the experimental system consisted of input output data collection, ARX model order selection and parameter estimation using conventional method least square (LS and differential evolution (DE algorithm. PRBS signal is used to be input signal to regulate the motor speed. Whereas, the output signal is taken from position sensor. Both, input and output data is used to estimate the parameter of the ARX model. The residual error between the actual and predicted output responses of the models is validated using mean squares error (MSE. Analysis showed that, MSE value for LS is 0.0026 and MSE value for DE is 3.6601×10-5. Based results obtained, it was found that DE have lower MSE than the LS method.
Differential evolution algorithm for global optimizations in nuclear physics
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.
Modified constrained differential evolution for solving nonlinear global optimization problems
2013-01-01
Nonlinear optimization problems introduce the possibility of multiple local optima. The task of global optimization is to find a point where the objective function obtains its most extreme value while satisfying the constraints. Some methods try to make the solution feasible by using penalty function methods, but the performance is not always satisfactory since the selection of the penalty parameters for the problem at hand is not a straightforward issue. Differential evolut...
Parameters Identification of Photovoltaic Cells Based on Differential Evolution Algorithm
Liao Hui
2016-01-01
Full Text Available For the complex nonlinear model of photovoltaic cells, traditional evolution strategy is easy to fall into the local optimal and its identification time is too long when taking parameters identification, then the difference algorithm is proposed in this study, which is to solve the problems of parameter identification in photovoltaic cell model, where it is very difficult to achieve with other identification algorithms. In this method, the random data is selected as the initial generation; the successful evolution to the next generation is done through a certain strategy of difference algorithm, which can achieve the effective identification of control parameters. It is proved that the method has a good global optimization and the fast convergence ability, and the simulation results are shown that the differential evolution has high identification ability and it is an effective method to identify the parameters of photovoltaic cells, where the photovoltaic cells can be widely used in other places with these parameters.
The Cellular Differential Evolution Based on Chaotic Local Search
Qingfeng Ding
2015-01-01
Full Text Available To avoid immature convergence and tune the selection pressure in the differential evolution (DE algorithm, a new differential evolution algorithm based on cellular automata and chaotic local search (CLS or ccDE is proposed. To balance the exploration and exploitation tradeoff of differential evolution, the interaction among individuals is limited in cellular neighbors instead of controlling parameters in the canonical DE. To improve the optimizing performance of DE, the CLS helps by exploring a large region to avoid immature convergence in the early evolutionary stage and exploiting a small region to refine the final solutions in the later evolutionary stage. What is more, to improve the convergence characteristics and maintain the population diversity, the binomial crossover operator in the canonical DE may be instead by the orthogonal crossover operator without crossover rate. The performance of ccDE is widely evaluated on a set of 14 bound constrained numerical optimization problems compared with the canonical DE and several DE variants. The simulation results show that ccDE has better performances in terms of convergence rate and solution accuracy than other optimizers.
Application of differential evolution algorithm on self-potential data.
Xiangtao Li
Full Text Available 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.
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.
An evolutionary developmental approach to cultural evolution.
Andersson, Claes; Törnberg, Anton; Törnberg, Petter
2014-04-01
Evolutionary developmental theories in biology see the processes and organization of organisms as crucial for understanding the dynamic behavior of organic evolution. Darwinian forces are seen as necessary but not sufficient for explaining observed evolutionary patterns. We here propose that the same arguments apply with even greater force to culture vis-à-vis cultural evolution. In order not to argue entirely in the abstract, we demonstrate the proposed approach by combining a set of different models into a provisional synthetic theory and by applying this theory to a number of short case studies. What emerges is a set of concepts and models that allow us to consider entirely new types of explanations for the evolution of cultures. For example, we see how feedback relations--both within societies and between societies and their ecological environment--have the power to shape evolutionary history in profound ways. The ambition here is not to produce a definitive statement on what such a theory should look like but rather to propose a starting point along with an argumentation and demonstration of its potential.
An Improved Differential Evolution Based Dynamic Economic Dispatch with Nonsmooth Fuel Cost Function
R. Balamurugan
2007-09-01
Full Text Available Dynamic economic dispatch (DED is one of the major operational decisions in electric power systems. DED problem is an optimization problem with an objective to determine the optimal combination of power outputs for all generating units over a certain period of time in order to minimize the total fuel cost while satisfying dynamic operational constraints and load demand in each interval. This paper presents an improved differential evolution (IDE method to solve the DED problem of generating units considering valve-point effects. Heuristic crossover technique and gene swap operator are introduced in the proposed approach to improve the convergence characteristic of the differential evolution (DE algorithm. To illustrate the effectiveness of the proposed approach, two test systems consisting of five and ten generating units have been considered. The results obtained through the proposed method are compared with those reported in the literature.
Roman Senkerik
2016-01-01
Full Text Available In this paper, evolutionary technique Differential Evolution (DE is used for the evolutionary tuning of controller parameters for the stabilization of selected discrete chaotic system, which is the two-dimensional Lozi map. The novelty of the approach is that the selected controlled discrete dissipative chaotic system is used within Chaos enhanced heuristic concept as the chaotic pseudo-random number generator to drive the mutation and crossover process in the DE. The idea was to utilize the hidden chaotic dynamics in pseudo-random sequences given by chaotic map to help Differential evolution algorithm in searching for the best controller settings for the same chaotic system. The optimizations were performed for three different required final behavior of the chaotic system, and two types of developed cost function. To confirm the robustness of presented approach, comparisons with canonical DE strategy and PSO algorithm have been performed.
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.
Student Teachers' Approaches to Teaching Biological Evolution
Borgerding, Lisa A.; Klein, Vanessa A.; Ghosh, Rajlakshmi; Eibel, Albert
2015-01-01
Evolution is fundamental to biology and scientific literacy, but teaching high school evolution is often difficult. Evolution teachers face several challenges including limited content knowledge, personal conflicts with evolution, expectations of resistance, concerns about students' conflicts with religion, and curricular constraints. Evolution…
Student Teachers' Approaches to Teaching Biological Evolution
Borgerding, Lisa A.; Klein, Vanessa A.; Ghosh, Rajlakshmi; Eibel, Albert
2015-01-01
Evolution is fundamental to biology and scientific literacy, but teaching high school evolution is often difficult. Evolution teachers face several challenges including limited content knowledge, personal conflicts with evolution, expectations of resistance, concerns about students' conflicts with religion, and curricular constraints. Evolution…
Registration of image feature points using differential evolution
ZHANG Hao; HUANG Zhan-hua; YU Dao-ying
2005-01-01
This paper introduces a robust global nonlinear optimizer-differential evolution(DE),which is a simple evolution algorithm to search for an optimal transformation that makes the best alignment of two sets of feature points.To map the problem of matching into the framework of DE,the objective function is proportional to the registration error which is measured by Hausdorff distance,while the parameters of transformation are encoded in floating-point as the functional variables.Three termination criteria are proposed for DE.A simulation of 2-dimensional point sets and a similarity transformation are presented to compare the robustness and convergence properties of DE with genetic algorithm's (GA).And the registration of an object and its contour model have been demonstrated by using of DE to natural images.
Nguyen Ngoc Son
2016-12-01
Full Text Available This article proposes a novel advanced differential evolution method which combines the differential evolution with the modified back-propagation algorithm. This new proposed approach is applied to train an adaptive enhanced neural model for approximating the inverse model of the industrial robot arm. Experimental results demonstrate that the proposed modeling procedure using the new identification approach obtains better convergence and more precision than the traditional back-propagation method or the lonely differential evolution approach. Furthermore, the inverse model of the industrial robot arm using the adaptive enhanced neural model performs outstanding results.
Estimation of drying parameters in rotary dryers using differential evolution
Lobato, F S; Jr, V Steffen; Barrozo, M A S; Arruda, E B, E-mail: vsteffen@mecanica.ufu.br, E-mail: masbarrozo@ufu.br
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.
MULTI OBJECTIVE ECONOMIC DISPATCH USING PARETO FRONTIER DIFFERENTIAL EVOLUTION
JAGADEESH GUNDA
2011-10-01
Full Text Available Multi Objective Economic dispatch (MOED problem has gained recent attention due to the deregulation of power industry and environmental regulations. So generating utilities should optimize their emission inaddition to the operating cost. In this paper a Pareto frontier Differential Evolution (PDE technique is developed to solve MOED problem, which provides a set of feasible solutions to the problem. To evaluate the performance and applicability of the proposed method, it is implemented on the standard IEEE-30 bus system having six generating units including valve point effects. The results obtained demonstrate the effectiveness of the proposed method for solving the Multi Objective economic dispatch problem considering security constraints.
A DIFFERENTIAL EVOLUTION ALGORITHM DEVELOPED FOR A NURSE SCHEDULING PROBLEM
Shahnazari-Shahrezaei, P.
2012-11-01
Full Text Available Nurse scheduling is a type of manpower allocation problem that tries to satisfy hospital managers objectives and nurses preferences as much as possible by generating fair shift schedules. This paper presents a nurse scheduling problem based on a real case study, and proposes two meta-heuristics a differential evolution algorithm (DE and a greedy randomised adaptive search procedure (GRASP to solve it. To investigate the efficiency of the proposed algorithms, two problems are solved. Furthermore, some comparison metrics are applied to examine the reliability of the proposed algorithms. The computational results in this paper show that the proposed DE outperforms the GRASP.
Design of Test Wrapper Scan Chain Based on Differential Evolution
Aijun Zhu
2013-08-01
Full Text Available Integrated Circuit has entered the era of design of the IP-based SoC (System on Chip, which makes the IP core reuse become a key issue. SoC test wrapper design for scan chain is a NP Hard problem, we propose an algorithm based on Differential Evolution (DE to design wrapper scan chain. Through group’s mutation, crossover and selection operations, the design of test wrapper scan chain is achieved. Experimental verification is carried out according to the international standard benchmark ITC’02. The results show that the algorithm can obtain shorter longest wrapper scan chains, compared with other algorithms.
Differential evolution to enhance localization of mobile robots
Lisowski, Michal; Fan, Zhun; Ravn, Ole
2011-01-01
This paper focuses on the mobile robot localization problems: pose tracking, global localization and robot kidnap. Differential Evolution (DE) applied to extend Monte Carlo Localization (MCL) was investigated to better solve localization problem by increasing localization reliability and speed....... In addition, a novel mechanism for effective robot kidnap detection was proposed. Experiments were performed using computer simulations based on the odometer data and laser range finder measurements collected in advance by a robot in real-life. Experimental results showed that integrating DE enables MCL...
Two-Stage Eagle Strategy with Differential Evolution
Yang, Xin-She
2012-01-01
Efficiency of an optimization process is largely determined by the search algorithm and its fundamental characteristics. In a given optimization, a single type of algorithm is used in most applications. In this paper, we will investigate the Eagle Strategy recently developed for global optimization, which uses a two-stage strategy by combing two different algorithms to improve the overall search efficiency. We will discuss this strategy with differential evolution and then evaluate their performance by solving real-world optimization problems such as pressure vessel and speed reducer design. Results suggest that we can reduce the computing effort by a factor of up to 10 in many applications.
APPROACHED DECISION OF THE DIFFERENTIAL EQUATIONS
Oleksii B. Krasnozhon
2011-02-01
Full Text Available The urgency of the material stated in the article is caused by necessity of development, updating and improvements of methodical operating time on subject matters of issue "Calculus mathematics" which teaching is carried out in conditions of use of information-communication technologies. In the article the program realizations in Mathcad environment of Adams and Runge-Kutt methods of the approached decision of the differential equations are offered; examples on application of the specified methods are brought; the expediency of application of Mathcad environment during mathematical preparation of experts is proved. Perspective directions of the further scientific researches are methodical, mathematical and algorithmic aspects of creation of effective program realizations of numerical methods in Mathcad environment.
Enhanced differential evolution algorithm for solving reactive power problem
K. Lenin
2016-09-01
Full Text Available Differential evolution (DE is one of the efficient evolutionary computing techniques that seem to be effective to handle optimization problems in many practical applications. Conversely, the performance of DE is not always flawless to guarantee fast convergence to the global optimum. It can certainly get inaction resulting in low accuracy of acquired results. An enhanced differential evolution (EDE algorithm by integrating excited arbitrary confined search (EACS to augment the performance of a basic DE algorithm have been proposed in this paper. EACS is a local search method that is excited to swap the present solution by a superior candidate in the neighbourhood. Only a small subset of arbitrarily selected variables is used in each step of the local exploration for randomly deciding the subsequent provisional solution. The proposed EDE has been tested in standard IEEE 30 bus test system. The simulation results show clearly about the better performance of the proposed algorithm in reducing the real power loss with control variables within the limits.
A New Approach to Evolution of Black Hole Accretion Disks
WANG Ding-Xiong; LEI Wei-Hua; XIAO Kan
2000-01-01
Evolution of black hole (BH) accretion disks is investigated by a new approach, in which the evolution of the central BH can be derived in terms of BH spin directly, and the evolution characteristics of the concerning BH parameters are shown more easily and obviously. As an example, the unusual evolution characteristics of angular velocity of BH horizon and that of accreting particles at the inner edge of the disk are derived by considering the Blandford-Znajek process.
Optimization of Neutrino Oscillation Parameters Using Differential Evolution
Ghulam Mustafa; Faisal Akram; Bilal Masud
2013-01-01
We show how the traditional grid based method for finding neutrino oscillation parameters △m2 and tan2θ can be combined with an optimization technique,Differential Evolution (DE),to get a significant decrease in computer processing time required to obtain minimal chi-square (x2) in four different regions of the parameter space.We demonstrate efficiency for the two-neutrinos case.For this,the x2 function for neutrino oscillations is evaluated for grids with different density of points in standard allowed regions of the parameter space of △m2 and tan2 θ using experimental and theoretical total event rates of chlorine (Homestake),Gallex+GNO,SAGE,Superkamiokande,and SNO detectors.We find that using DE in combination with the grid based method with small density of points can produce the results comparable with the one obtained using high density grid,in much lesser computation time.
Rearrangements of immunoglobulin genes during differentiation and evolution.
Honjo, T; Nakai, S; Nishida, Y; Kataoka, T; Yamawaki-Kataoka, Y; Takahashi, N; Obata, M; Shimizu, A; Yaoita, Y; Nikaido, T; Ishida, N
1981-01-01
Immunoglobulin genes are shown to undergo dynamic rearrangements during differentiation as well as evolution. We have demonstrated that a complete immunoglobulin heavy chain gene is formed by at least two types of DNA rearrangement during B cell differentiation. The first type of rearrangement is V-D-J recombination to complete a variable region sequence and the second type is S-S recombination to switch a constant region sequence. Both types of recombination are accompanied by deletion of the intervening DNA segment. Structure and organization of CH genes are elucidated by molecular cloning and nucleotide sequence determination. Organization of H chain genes is summarized as VH-(unknown distance)-JH-(6.5 kb)-C mu-(4.5 kb)-C delta-(unknown distance)-C gamma 3-(34 kb)-C gamma 1-(21 kb)-C gamma 2b-(15 kb)-C gamma 2a-(14.5 kb)-C epsilon-(12.5 kb)-C alpha. The S-S recombination takes place at the S region which is located at the 5' side of each CH gene. Nucleotide sequence of the S region comprises tandem repetition of closely related sequences. The S-S recombination seems to be mediated by short common sequences shared among S regions. A sister chromatid exchange model was proposed as a mechanism for S-S recombination. Comparison of nucleotide sequences of CH genes indicates that immunoglobulin genes have scrambled by intervening sequence-mediated domain transfer during their evolution.
Differential Evolution Algorithm for Route Optimization Problems of Engineering Networks
O. G. Monahov
2015-01-01
Full Text Available The paper considers problems of structure optimization of engineering networks to provide a minimum total cost of engineering networks in construction and operation. The mathematical statement of the problem in terms of the hyper-network theory takes into account the interdependence of indicators of hyper-network elements, a layout area and a projected network. A digital model of terrain presents the placement area of engineering networks (a territory. In our case, it will be a weighted mesh (graph of primary network of dedicated vertices-consumers and a vertex-source for the utilities. The edges weights will be determined by the costs of construction and operation of the route between the given vertices of the network. The initial solution of the problem of minimizing the total cost will be using the minimum spanning tree, obtained on a weighted complete graph the vertices of which are defined by vertices-consumers and the vertexsource for the utilities, and the weights of edges are the distance between the vertices on the given weighted graph of the primary network. The work offers a method of differential evolution to solve the problem in hyper-network formulation that improves the initial solution by the mapping the edges of the secondary network in the primary network using additional Steiner points. As numerical experiments have shown, a differential evolution algorithm allows us to reduce the average total cost for a given engineering network compared to the initial solution by 5% - 15%, depending on the configuration, parameters, and layout area.
A Constructive Approach To Software Evolution
Ciraci, S.; van den Broek, P.M.; Aksit, Mehmet
2007-01-01
In many software design and evaluation techniques, either the software evolution problem is not systematically elaborated, or only the impact of evolution is considered. Thus, most of the time software is changed by editing the components of the software system, i.e. breaking down the software
Loewner Theory in annulus I: evolution families and differential equations
Contreras, Manuel D; Gumenyuk, Pavel
2010-01-01
Loewner Theory, based on dynamical viewpoint, is a powerful tool in Complex Analysis, which plays a crucial role in such important achievements as the proof of famous Bieberbach's conjecture and well-celebrated Schramm's Stochastic Loewner Evolution (SLE). Recently Bracci et al [Bracci et al, to appear in J. Reine Angew. Math. Available on ArXiv 0807.1594; Bracci et al, Math. Ann. 344(2009), 947--962; Contreras et al, Revista Matematica Iberoamericana 26(2010), 975--1012] have proposed a new approach bringing together all the variants of the (deterministic) Loewner Evolution in a simply connected reference domain. We construct an analogue of this theory for the annulus. In this paper, the first of two articles, we introduce a general notion of an evolution family over a system of annuli and prove that there is a 1-to-1 correspondence between such families and semicomplete weak holomorphic vector fields. Moreover, in the non-degenerate case, we establish a constructive characterization of these vector fields a...
Impulsive differential inclusions a fixed point approach
Ouahab, Abdelghani; Henderson, Johnny
2013-01-01
Impulsive differential equations have been developed in modeling impulsive problems in physics, population dynamics, ecology, biotechnology, industrial robotics, pharmacokinetics, optimal control, etc. The questions of existence and stability of solutions for different classes of initial values problems for impulsive differential equations and inclusions with fixed and variable moments are considered in detail. Attention is also given to boundary value problems and relative questions concerning differential equations. This monograph addresses a variety of side issues that arise from its simple
Senkerik, Roman; Zelinka, Ivan; Pluhacek, Michal; Davendra, Donald; Oplatková Kominkova, Zuzana
2014-01-01
Evolutionary technique differential evolution (DE) is used for the evolutionary tuning of controller parameters for the stabilization of set of different chaotic systems. The novelty of the approach is that the selected controlled discrete dissipative chaotic system is used also as the chaotic pseudorandom number generator to drive the mutation and crossover process in the DE. The idea was to utilize the hidden chaotic dynamics in pseudorandom sequences given by chaotic map to help differential evolution algorithm search for the best controller settings for the very same chaotic system. The optimizations were performed for three different chaotic systems, two types of case studies and developed cost functions.
Roman Senkerik
2014-01-01
Full Text Available 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.
Simplicial approach to derived differential manifolds
Borisov, Dennis
2011-01-01
Derived differential manifolds are constructed using the usual homotopy theory of simplicial rings of smooth functions. They are proved to be equivalent to derived differential manifolds of finite type, constructed using homotopy sheaves of homotopy rings (D.Spivak), thus preserving the classical cobordism ring. This reduction to the usual algebraic homotopy can potentially lead to virtual fundamental classes beyond obstruction theory.
Teaching Modeling with Partial Differential Equations: Several Successful Approaches
Myers, Joseph; Trubatch, David; Winkel, Brian
2008-01-01
We discuss the introduction and teaching of partial differential equations (heat and wave equations) via modeling physical phenomena, using a new approach that encompasses constructing difference equations and implementing these in a spreadsheet, numerically solving the partial differential equations using the numerical differential equation…
Teaching Modeling with Partial Differential Equations: Several Successful Approaches
Myers, Joseph; Trubatch, David; Winkel, Brian
2008-01-01
We discuss the introduction and teaching of partial differential equations (heat and wave equations) via modeling physical phenomena, using a new approach that encompasses constructing difference equations and implementing these in a spreadsheet, numerically solving the partial differential equations using the numerical differential equation…
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.
Synthesis of Spherical 4R Mechanism for Path Generation using Differential Evolution
Penunuri, F; Villanueva, C; Cruz-Villar, Carlos A
2011-01-01
The problem of path generation for the spherical 4R mechanism is solved using the Differential Evolution (DE) algorithm. Formulas for the spherical geodesics are employed in order to obtain the parametric equation for the trajectory of the mechanism end-effector. Direct optimization of the objective function gives solution to the path generation task without prescribed timing. Therefore, there is no need to separate this task into two stages and then proceed to the optimization. Moreover, the order defect problem can be solved without difficulty by means of manipulations of the individuals in the DE algorithm. Two examples of optimum synthesis showing the simplicity and effectiveness of the approach are included.
Differential evolution and simulated annealing algorithms for mechanical systems design
H. Saruhan
2014-09-01
Full Text Available In this study, nature inspired algorithms – the Differential Evolution (DE and the Simulated Annealing (SA – are utilized to seek a global optimum solution for ball bearings link system assembly weight with constraints and mixed design variables. The Genetic Algorithm (GA and the Evolution Strategy (ES will be a reference for the examination and validation of the DE and the SA. The main purpose is to minimize the weight of an assembly system composed of a shaft and two ball bearings. Ball bearings link system is used extensively in many machinery applications. Among mechanical systems, designers pay great attention to the ball bearings link system because of its significant industrial importance. The problem is complex and a time consuming process due to mixed design variables and inequality constraints imposed on the objective function. The results showed that the DE and the SA performed and obtained convergence reliability on the global optimum solution. So the contribution of the DE and the SA application to the mechanical system design can be very useful in many real-world mechanical system design problems. Beside, the comparison confirms the effectiveness and the superiority of the DE over the others algorithms – the SA, the GA, and the ES – in terms of solution quality. The ball bearings link system assembly weight of 634,099 gr was obtained using the DE while 671,616 gr, 728213.8 gr, and 729445.5 gr were obtained using the SA, the ES, and the GA respectively.
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.
Operational Solution of Non-Integer Ordinary and Evolution-Type Partial Differential Equations
Konstantin V. Zhukovsky
2016-12-01
Full Text Available A method for the solution of linear differential equations (DE of non-integer order and of partial differential equations (PDE by means of inverse differential operators is proposed. The solutions of non-integer order ordinary differential equations are obtained with recourse to the integral transforms and the exponent operators. The generalized forms of Laguerre and Hermite orthogonal polynomials as members of more general Appèl polynomial family are used to find the solutions. Operational definitions of these polynomials are used in the context of the operational approach. Special functions are employed to write solutions of DE in convolution form. Some linear partial differential equations (PDE are also explored by the operational method. The Schrödinger and the Black–Scholes-like evolution equations and solved with the help of the operational technique. Examples of the solution of DE of non-integer order and of PDE are considered with various initial functions, such as polynomial, exponential, and their combinations.
Differential-Evolution Control Parameter Optimization for Unmanned Aerial Vehicle Path Planning.
Kok, Kai Yit; Rajendran, Parvathy
2016-01-01
The differential evolution algorithm has been widely applied on unmanned aerial vehicle (UAV) path planning. At present, four random tuning parameters exist for differential evolution algorithm, namely, population size, differential weight, crossover, and generation number. These tuning parameters are required, together with user setting on path and computational cost weightage. However, the optimum settings of these tuning parameters vary according to application. Instead of trial and error, this paper presents an optimization method of differential evolution algorithm for tuning the parameters of UAV path planning. The parameters that this research focuses on are population size, differential weight, crossover, and generation number. The developed algorithm enables the user to simply define the weightage desired between the path and computational cost to converge with the minimum generation required based on user requirement. In conclusion, the proposed optimization of tuning parameters in differential evolution algorithm for UAV path planning expedites and improves the final output path and computational cost.
Differential-Evolution Control Parameter Optimization for Unmanned Aerial Vehicle Path Planning.
Kai Yit Kok
Full Text Available 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.
Estimating Traffic Accidents in Turkey Using Differential Evolution Algorithm
Akgüngör, Ali Payıdar; Korkmaz, Ersin
2017-06-01
Estimating traffic accidents play a vital role to apply road safety procedures. This study proposes Differential Evolution Algorithm (DEA) models to estimate the number of accidents in Turkey. In the model development, population (P) and the number of vehicles (N) are selected as model parameters. Three model forms, linear, exponential and semi-quadratic models, are developed using DEA with the data covering from 2000 to 2014. Developed models are statistically compared to select the best fit model. The results of the DE models show that the linear model form is suitable to estimate the number of accidents. The statistics of this form is better than other forms in terms of performance criteria which are the Mean Absolute Percentage Errors (MAPE) and the Root Mean Square Errors (RMSE). To investigate the performance of linear DE model for future estimations, a ten-year period from 2015 to 2024 is considered. The results obtained from future estimations reveal the suitability of DE method for road safety applications.
Peak alignment using wavelet pattern matching and differential evolution.
Zhang, Zhi-Min; Chen, Shan; Liang, Yi-Zeng
2011-01-30
Retention time shifts badly impair qualitative or quantitative results of chemometric analyses when entire chromatographic data are used. Hence, chromatograms should be aligned to perform further analysis. Being inspired and motivated by this purpose, a practical and handy peak alignment method (alignDE) is proposed, implemented in this research for one-way chromatograms, which basically consists of five steps: (1) chromatogram lengths equalization using linear interpolation; (2) accurate peak pattern matching by continuous wavelet transform (CWT) with the Mexican Hat and Haar wavelets as its mother wavelets; (3) flexible baseline fitting utilizing penalized least squares; (4) peak clustering when gap of two peaks is smaller than a certain threshold; (5) peak alignment using differential evolution (DE) to maximize linear correlation coefficient between reference signal and signal to be aligned. This method is demonstrated with both simulated chromatograms and real chromatograms, for example, chromatograms of fungal extracts and Red Peony Root obtained by HPLC-DAD. It is implemented in R language and available as open source software to a broad range of chromatograph users (http://code.google.com/p/alignde).
Minggang Dong
2014-01-01
Full Text Available Motivated by recent advancements in differential evolution and constraints handling methods, this paper presents a novel modified oracle penalty function-based composite differential evolution (MOCoDE for constrained optimization problems (COPs. More specifically, the original oracle penalty function approach is modified so as to satisfy the optimization criterion of COPs; then the modified oracle penalty function is incorporated in composite DE. Furthermore, in order to solve more complex COPs with discrete, integer, or binary variables, a discrete variable handling technique is introduced into MOCoDE to solve complex COPs with mix variables. This method is assessed on eleven constrained optimization benchmark functions and seven well-studied engineering problems in real life. Experimental results demonstrate that MOCoDE achieves competitive performance with respect to some other state-of-the-art approaches in constrained optimization evolutionary algorithms. Moreover, the strengths of the proposed method include few parameters and its ease of implementation, rendering it applicable to real life. Therefore, MOCoDE can be an efficient alternative to solving constrained optimization problems.
Maryjane TREMAYNE; Samantha Y. CHONG; Duncan BELL
2009-01-01
Evolutionary search and optimisation algorithms have been used successfully in many areas of materials science and chemistry. In recent years, these techniques have been applied to, and revolutionised the study of crystal structures from powder diffraction data. In this paper we present the application of a hybrid global optimisation technique,cultural differential evolution (CDE), to crystal structure determination from powder diffraction data. The combination of the principles of social evolution and biological evolution,through the pruning of the parameter search space shows significant improvement in the efficiency of the calculations over traditional dictates of biological evolution alone. Resuits are presented in which a range of algorithm control parameters, i.e., population size, mutation and recombination rates, extent of culture-based pruning are used to assess the performance of this hybrid technique. The effects of these control parameters on the speed and efficiency of the optimisation calculations are discussed, and the potential advantages of the CDE approach demonstrated through an average 40% improvement in terms of speed of convergence of the calculations presented, and a maximum gain of 68% with larger population size.
Evolution of Terrorist Network using Clustered approach: A Case study
2011-01-01
In the paper we present a cluster based approach for terrorist network evolution. We have applied hierarchical agglomerative clustering approach to 9/11 case study. We show that, how individual actors who are initially isolated from each other are converted in small clusters and result in a fully...... evolved network. This method of network evolution can help intelligence security analysts to understand the structure of the network....
Sociohistorical evolution of judo: introductory approaches
Orozimbo Cordeiro Júnior
2008-06-01
Full Text Available The sociohistorical evolution of judo provided by the research project Methodology for teaching judo from the critical–excelling stance is discussed in this article. The aim of the project was to establish a plan for systematizing judo as body culture constituent and scholastic knowledge of physical education. The ancillary pedagogical material is constituted by an introduction, objectives, contents, teaching methodology and evaluation system.
A cultural evolution approach to digital media
Alberto Acerbi
2016-12-01
Full Text Available Digital media have today an enormous diffusion, and their influence on the behaviour of a vast part of the human population can hardly be underestimated. In this review I propose that cultural evolution theory, including both a sophisticated view of human behaviour and a methodological attitude to modelling and quantitative analysis, provides a useful framework to study the effects and the developments of media in the digital age. I will first give a general presentation of the cultural evolution framework, and I will then introduce this more specific research program with two illustrative topics.The first topic concerns how cultural transmission biases, that is, simple heuristics such as copy prestigious individuals or copy the majority, operate in the novel context of digital media. The existence of transmission biases is generally justified with their adaptivity in small-scale societies. How do they operate in an environment where, for example, prestigious individuals possess not-relevant skills, or popularity is explicitly quantified and advertised?The second aspect relates to fidelity of cultural transmission. Digitally-mediated interactions support cheap and immediate high-fidelity transmission, in opposition, for example, to oral traditions. How does this change the content that is more likely to spread? Overall, I suggest the usefulness of a long view to our contemporary digital environment, contextualised in cognitive science and cultural evolution theory, and I discuss how this perspective could help us to understand what is genuinely new and what is not.
A Cultural Evolution Approach to Digital Media
Acerbi, Alberto
2016-01-01
Digital media have today an enormous diffusion, and their influence on the behavior of a vast part of the human population can hardly be underestimated. In this review I propose that cultural evolution theory, including both a sophisticated view of human behavior and a methodological attitude to modeling and quantitative analysis, provides a useful framework to study the effects and the developments of media in the digital age. I will first give a general presentation of the cultural evolution framework, and I will then introduce this more specific research program with two illustrative topics. The first topic concerns how cultural transmission biases, that is, simple heuristics such as “copy prestigious individuals” or “copy the majority,” operate in the novel context of digital media. The existence of transmission biases is generally justified with their adaptivity in small-scale societies. How do they operate in an environment where, for example, prestigious individuals possess not-relevant skills, or popularity is explicitly quantified and advertised? The second aspect relates to fidelity of cultural transmission. Digitally-mediated interactions support cheap and immediate high-fidelity transmission, in opposition, for example, to oral traditions. How does this change the content that is more likely to spread? Overall, I suggest the usefulness of a “long view” to our contemporary digital environment, contextualized in cognitive science and cultural evolution theory, and I discuss how this perspective could help us to understand what is genuinely new and what is not. PMID:28018200
A Cultural Evolution Approach to Digital Media.
Acerbi, Alberto
2016-01-01
Digital media have today an enormous diffusion, and their influence on the behavior of a vast part of the human population can hardly be underestimated. In this review I propose that cultural evolution theory, including both a sophisticated view of human behavior and a methodological attitude to modeling and quantitative analysis, provides a useful framework to study the effects and the developments of media in the digital age. I will first give a general presentation of the cultural evolution framework, and I will then introduce this more specific research program with two illustrative topics. The first topic concerns how cultural transmission biases, that is, simple heuristics such as "copy prestigious individuals" or "copy the majority," operate in the novel context of digital media. The existence of transmission biases is generally justified with their adaptivity in small-scale societies. How do they operate in an environment where, for example, prestigious individuals possess not-relevant skills, or popularity is explicitly quantified and advertised? The second aspect relates to fidelity of cultural transmission. Digitally-mediated interactions support cheap and immediate high-fidelity transmission, in opposition, for example, to oral traditions. How does this change the content that is more likely to spread? Overall, I suggest the usefulness of a "long view" to our contemporary digital environment, contextualized in cognitive science and cultural evolution theory, and I discuss how this perspective could help us to understand what is genuinely new and what is not.
Material inhomogeneities and their evolution a geometric approach
Epstein, Marcelo
2007-01-01
Presents a unified treatment of the inhomogeneity theory using some of the tools of modern differential geometry. This book deals with the geometrical description of uniform bodies and their homogeneity conditions. It also develops a theory of material evolution and discusses its relevance in various applied contexts.
Algebraic Approaches to Partial Differential Equations
Xu, Xiaoping
2012-01-01
Partial differential equations are fundamental tools in mathematics,sciences and engineering. This book is mainly an exposition of the various algebraic techniques of solving partial differential equations for exact solutions developed by the author in recent years, with emphasis on physical equations such as: the Calogero-Sutherland model of quantum many-body system in one-dimension, the Maxwell equations, the free Dirac equations, the generalized acoustic system, the Kortweg and de Vries (KdV) equation, the Kadomtsev and Petviashvili (KP) equation, the equation of transonic gas flows, the short-wave equation, the Khokhlov and Zabolotskaya equation in nonlinear acoustics, the equation of geopotential forecast, the nonlinear Schrodinger equation and coupled nonlinear Schrodinger equations in optics, the Davey and Stewartson equations of three-dimensional packets of surface waves, the equation of the dynamic convection in a sea, the Boussinesq equations in geophysics, the incompressible Navier-Stokes equations...
Differentiated cell behavior: a multiscale approach using measure theory.
Colombi, Annachiara; Scianna, Marco; Tosin, Andrea
2015-11-01
This paper deals with the derivation of a collective model of cell populations out of an individual-based description of the underlying physical particle system. By looking at the spatial distribution of cells in terms of time-evolving measures, rather than at individual cell paths, we obtain an ensemble representation stemming from the phenomenological behavior of the single component cells. In particular, as a key advantage of our approach, the scale of representation of the system, i.e., microscopic/discrete vs. macroscopic/continuous, can be chosen a posteriori according only to the spatial structure given to the aforesaid measures. The paper focuses in particular on the use of different scales based on the specific functions performed by cells. A two-population hybrid system is considered, where cells with a specialized/differentiated phenotype are treated as a discrete population of point masses while unspecialized/undifferentiated cell aggregates are represented by a continuous approximation. Numerical simulations and analytical investigations emphasize the role of some biologically relevant parameters in determining the specific evolution of such a hybrid cell system.
Mialgias: Approaches to differential diagnosis, treatment
Nadezhda Aleksandrovna Shostak
2013-01-01
Full Text Available Differential diagnosis in muscle pains often presents great difficulties so all existing signs of the disease should be carefully considered to make its diagnosis and to prescribe adequate therapy. The paper considers the causes of muscle pains, laboratory and instrumental studies (immunological tests, determination of the level of specific muscular enzymes, primarily creatine phosphokinase – CPK, etc., and the main reasons for enhanced plasma CPK activity. It also describes acute and chronic mialgias associated with enhanced plasma CPK activity, as well as diseases in which mialgias are related to the normal level of CPK, myofascial syndrome (MFS and fibromyalgia (FM in particular. The characteristic features of MFS are given in its diagnostic criteria. It is stated that a differential diagnosis should be made between MFS and major muscle pain-associated abnormalities, such as polymyalgia rheumatica, FM, etc. Diagnosticcriteria for polymyalgia rheumatica are given. A MFS treatment algorithm is presented. Local exposure methods applied to altered musculoligamentous structures in combination with myorelaxants and non-steroidal anti-inflammatory drugs assume paramount importance in MFS.
Multimodal Evolution Approach to Multidimensional Intrusion Detection
Weng Guang'an; Yu Shengsheng; Zhou Jingli
2006-01-01
An artificial immunity based multimodal evolution algorithm is developed to generate detectors with variable coverage for multidimensional intrusion detection. In this algorithm, a proper fitness function is used to drive the detectors to fill in those detection holes close to self set or among self spheres, and genetic algorithm is adopted to reduce the negative effects that different distribution of self imposes on the detector generating process. The validity of the algorithm is tested with spherical and rectangular detectors,respectively, and experiments performed on two real data sets ( machine learning database and DAPRA99) indicate that the proposed algorithm can obtain good results on spherical detectors, and that its performances in detection rate, false alarm rate, stability, time cost, and adaptability to incomplete training set on spherical detectors are all better than on rectangular ones.
Utilization of the Discrete Differential Evolution for Optimization in Multidimensional Point Clouds
Vojtěch Uher
2016-01-01
Full Text Available 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.
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.
Giovanni Iacca; Fabio Caraffini; Ferrante Neri
2012-01-01
Compact algorithms are Estimation of Distribution Algorithms which mimic the behavior of population-based algorithms by means of a probabilistic representation of the population of candidate solutions.These algorithms have a similar behaviour with respect to population-based algorithms but require a much smaller memory.This feature is crucially important in some engineering applications,especially in robotics.A high performance compact algorithm is the compact Differential Evolution (cDE) algorithm.This paper proposes a novel implementation of cDE,namely compact Differential Evolution light (cDElight),to address not only the memory saving necessities but also real-time requirements.cDElight employs two novel algorithmic modifications for employing a smaller computational overhead without a performance loss,with respect to cDE.Numerical results,carried out on a broad set of test problems,show that cDElight,despite its minimal hardware requirements,does not deteriorate the performance of cDE and thus is competitive with other memory saving and population-based algorithms.An application in the field of mobile robotics highlights the usability and advantages of the proposed approach.
Vrugt, Jasper A [Los Alamos National Laboratory; Hyman, James M [Los Alamos National Laboratory; Robinson, Bruce A [Los Alamos National Laboratory; Higdon, Dave [Los Alamos National Laboratory; Ter Braak, Cajo J F [NETHERLANDS; Diks, Cees G H [UNIV OF AMSTERDAM
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.
Implicit quasilinear differential systems: a geometrical approach
Miguel C. Munoz-Lecanda
1999-04-01
Full Text Available This work is devoted to the study of systems of implicit quasilinear differential equations. In general, no set of initial conditions is admissible for the system. It is shown how to obtain a vector field whose integral curves are the solution of the system, thus reducing the system to one that is ordinary. Using geometrical techniques, we give an algorithmic procedure in order to solve these problems for systems of the form $A(xdot x =alpha (x$ with $A(x$ being a singular matrix. As particular cases, we recover some results of Hamiltonian and Lagrangian Mechanics. In addition, a detailed study of the symmetries of these systems is carried out. This algorithm is applied to several examples arising from technical applications related to control theory.
Differential Pacing: An Approach to Compensatory Education.
Cheek, King V.
Discussed is an individualized educational approach based on a student's strengths and weaknesses. On the basis of findings from a battery of diagnostic tests, a college program is worked out which is commensurate with the student's ability and preparation. He advances at his own rate and takes comprehensive examinations when he feels ready.…
Yongquan Zhou
2013-01-01
Full Text Available In view of the traditional numerical method to solve the nonlinear equations exist is sensitive to initial value and the higher accuracy of defects. This paper presents an invasive weed optimization (IWO algorithm which has population diversity with the heuristic global search of differential evolution (DE algorithm. In the iterative process, the global exploration ability of invasive weed optimization algorithm provides effective search area for differential evolution; at the same time, the heuristic search ability of differential evolution algorithm provides a reliable guide for invasive weed optimization. Based on the test of several typical nonlinear equations and a circle packing problem, the results show that the differential evolution invasive weed optimization (DEIWO algorithm has a higher accuracy and speed of convergence, which is an efficient and feasible algorithm for solving nonlinear systems of equations.
New Approach to Interpret the Firm Evolution
Seyed Amir Yazdanparast Abatari
2017-01-01
Full Text Available Abstract This article is a preliminary step to introduce a new approach for interpreting how a firm evolves. The core idea of this approach is to verify the firms as the dynamic organization which can change and gain different trait upon time. The change and adaption mechanism can be explained through evolutionary theory. This approach could be used as a good tool to interpret reaction of firms to future environmental and internal changes. To achieve this goal, the firm has been defined as a set of Resource, Ideas and capabilities (RIC. As evolutionary theory has been adopted, a wining rule needs to be determined for selection and struggle process. This winning rule has been developed using the transaction cost theory to verify the effect of this so called RIC mechanism, over 200 hours’ interview has been set up to identify and trace a changing capability in Iranian Construction industry. Applying this view to gathered information shows the power of this method for analyzing the firm’s capabilities.
Reconstruction of strain distribution in fiber Bragg grat-ings with differential evolution algorithm
WEN Xiao-yan; YU Qoan
2008-01-01
Differential evolution algorithm is used to solve the inverse problem of strain distribution in tibet Bragg grating (FBG).Linear and nonlinear strain profiles are reconstructed based on the reflection spectra. An approximate solution could beobtained within only 50 rounds of evolutions. Numerical examples show good agreements between target strain profilesand reconstructed ones. Online performance analysis illuminates the efficiency and practicality of differential evolutionalgorithm in solving the inverse problem of FBG.
EXISTENCE RESULTS FOR IMPULSIVE NEUTRAL EVOLUTION DIFFERENTIAL EQUATIONS WITH STATE-DEPENDENT DELAY
无
2011-01-01
This paper is mainly concerned with the existence of mild solutions to a first order impulsive neutral evolution differential equations with state-dependent delay. By suitable fixed point theorems combined with theories of evolution systems,we prove some existence theorems. As an application,an example is also given to illustrate the obtained results.
Thermal evolution of plutons: a parameterized approach.
Spera, F
1980-01-18
A conservation-of-energy equation has been derived for the spatially averaged magma temperature in a spherical pluton undergoing simultaneous crystallization and both internal (magma) and external (hydrothermal fluid) thermal convection. The model accounts for the dependence of magma viscosity on crystallinity, temperature, and bulk composition; it includes latent heat effects and the effects of different initial water concentrations in the melt and quantitatively considers the role that large volumes of circulatory hydrothermal fluids play in dissipating heat. The nonlinear ordinary differential equation describing these processes has been solved for a variety of magma compositions, initial termperatures, initial crystallinities, volume ratios of hydrothermal fluid to magma, and pluton sizes. These calculations are graphically summarized in plots of the average magma temperature versus time after emplacement. Solidification times, defined as the time necessary for magma to cool from the initial emplacement temperature to the solidus temperature vary as R(1,3), where R is the pluton radius. The solidification time of a pluton with a radius of 1 kilometer is 5 x 10(4) years; for an otherwise identical pluton with a radius of 10 kilometers, the solidification time is approximately 10(6) years. The water content has a marked effect on the solidification time. A granodiorite pluton with a radius of 5 kilometers and either 0.5 or 4 percent (by weight) water cools in 3.3 x 10(5) or 5 x 10(4) years, respectively. Convection solidification times are usually but not always less than conduction cooling times.
An Improved Differential Evolution Trained Neural Network Scheme for Nonlinear System Identification
Bidyadhar Subudhi; Debashisha Jena
2009-01-01
This paper prescnts an improved nonlinear system identification scheme using differential evolution (DE), neural network (NN) and Levenberg Marquardt algorithm (LM). With a view to achieve better convergence of NN weights optimization during the training, the DE and LM are used in a combined framework to train the NN. We present the convergence analysis of the DE and demonstrate the efficacy of the proposed improved system identification algorithm by exploiting the combined DE and LM training of the NN and suitably implementing it together with other system identification methods, namely NN and DE+NN on a numbcr of examples including a practical case study. The identification rcsults obtained through a series of simulation studies of these methods on different nonlinear systems demonstrate that the proposed DE and LM trained NN approach to nonlinear system identification can yield better identification results in terms of time of convergence and less identification error.
Design of PID controller with incomplete derivation based on differential evolution algorithm
Wu Lianghong; Wang Yaonan; Zhou Shaowu; Tan Wen
2008-01-01
To determine the optimal or near optimal parameters of PID controller with incomplete derivation, a novel design method based on differential evolution (DE) algorithm is presented. The controller is called DE-PID controller. To overcome the disadvantages of the integral performance criteria in the frequency domain such as IAE, ISE, and ITSE, a new performance criterion in the time domain is proposed. The optimization procedures employing the DE algorithm to search the optimal or near optimal PID controller parameters of a control system are demonstrated in detail. Three typical control systems are chosen to test and evaluate the adaptation and robustness of the proposed DE-PID controller. The simulation results show that the proposed approach has superior features of easy implementation, stable convergence characteristic, and good computational efficiency. Compared with the ZN, GA, and ASA, the proposed design method is indeed more efficient and robust in improving the step response of a control system.
Fan, Li; Faryad, Muhammad; Barber, Greg D.; Mallouk, Thomas E.; Monk, Peter B.; Lakhtakia, Akhlesh
2015-01-01
A spectrum splitter can be used to spatially multiplex different solar cells that have high efficiency in mutually exclusive parts of the solar spectrum. We investigated the use of a grating, comprising an array of dielectric cylinders embedded in a dielectric slab, for specularly transmitting one part of the solar spectrum while the other part is transmitted nonspecularly and the total reflectance is very low. A combination of (1) the rigorous coupled-wave approach for computing the reflection and transmission coefficients of the grating and (2) the differential evolution algorithm for optimizing the grating geometry and the refractive indices of dielectric materials was devised as a design tool. We used this tool to optimize two candidate gratings and obtained definite improvements to the initial guesses for the structural and constitutive parameters. Significant spectrum splitting can be achieved if the angle of incidence does not exceed 15 deg.
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.
An experimental approach to submarine canyon evolution
Lai, Steven Y. J.; Gerber, Thomas P.; Amblas, David
2016-03-01
We present results from a sandbox experiment designed to investigate how sediment gravity flows form and shape submarine canyons. In the experiment, unconfined saline gravity flows were released onto an inclined sand bed bounded on the downstream end by a movable floor that was used to increase relief during the experiment. In areas unaffected by the flows, we observed featureless, angle-of-repose submarine slopes formed by retrogressive breaching processes. In contrast, areas influenced by gravity flows cascading across the shelf break were deeply incised by submarine canyons with well-developed channel networks. Normalized canyon long profiles extracted from successive high-resolution digital elevation models collapse to a single profile when referenced to the migrating shelf-slope break, indicating self-similar growth in the relief defined by the canyon and intercanyon profiles. Although our experimental approach is simple, the resulting canyon morphology and behavior appear similar in several important respects to that observed in the field.
Symmetries of stochastic differential equations: A geometric approach
De Vecchi, Francesco C., E-mail: francesco.devecchi@unimi.it; Ugolini, Stefania, E-mail: stefania.ugolini@unimi.it [Dipartimento di Matematica, Università degli Studi di Milano, via Saldini 50, Milano (Italy); Morando, Paola, E-mail: paola.morando@unimi.it [DISAA, Università degli Studi di Milano, via Celoria 2, Milano (Italy)
2016-06-15
A new notion of stochastic transformation is proposed and applied to the study of both weak and strong symmetries of stochastic differential equations (SDEs). The correspondence between an algebra of weak symmetries for a given SDE and an algebra of strong symmetries for a modified SDE is proved under suitable regularity assumptions. This general approach is applied to a stochastic version of a two dimensional symmetric ordinary differential equation and to the case of two dimensional Brownian motion.
A neuro approach to solve fuzzy Riccati differential equations
Shahrir, Mohammad Shazri; Kumaresan, N.; Kamali, M. Z. M.; Ratnavelu, Kurunathan
2015-10-01
There are many applications of optimal control theory especially in the area of control systems in engineering. In this paper, fuzzy quadratic Riccati differential equation is estimated using neural networks (NN). Previous works have shown reliable results using Runge-Kutta 4th order (RK4). The solution can be achieved by solving the 1st Order Non-linear Differential Equation (ODE) that is found commonly in Riccati differential equation. Research has shown improved results relatively to the RK4 method. It can be said that NN approach shows promising results with the advantage of continuous estimation and improved accuracy that can be produced over RK4.
A neuro approach to solve fuzzy Riccati differential equations
Shahrir, Mohammad Shazri, E-mail: mshazri@gmail.com [InstitutSainsMatematik, Universiti Malaya 50603 Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur (Malaysia); Telekom Malaysia, R& D TM Innovation Centre, LingkaranTeknokrat Timur, 63000 Cyberjaya, Selangor (Malaysia); Kumaresan, N., E-mail: drnk2008@gmail.com; Kamali, M. Z. M.; Ratnavelu, Kurunathan [InstitutSainsMatematik, Universiti Malaya 50603 Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur (Malaysia)
2015-10-22
There are many applications of optimal control theory especially in the area of control systems in engineering. In this paper, fuzzy quadratic Riccati differential equation is estimated using neural networks (NN). Previous works have shown reliable results using Runge-Kutta 4th order (RK4). The solution can be achieved by solving the 1st Order Non-linear Differential Equation (ODE) that is found commonly in Riccati differential equation. Research has shown improved results relatively to the RK4 method. It can be said that NN approach shows promising results with the advantage of continuous estimation and improved accuracy that can be produced over RK4.
A variational approach to nonlinear evolution equations in optics
D Anderson; M Lisak; A Berntson
2001-11-01
A tutorial review is presented of the use of direct variational methods based on RayleighRitz optimization for ﬁnding approximate solutions to various nonlinear evolution equations. The practical application of the approach is demonstrated by some illustrative examples in connection with the nonlinear Schrödinger equation.
SEAS: A simulated evolution approach for analog circuit synthesis
Ning, Zhen-Qiu; Mouthaan, Ton; Wallinga, Hans
1991-01-01
The authors present a simulated evolution approach for analog circuit synthesis based on an analogy with the natural selection process in biological environments and on the iterative improvements in solving engineering problems. A prototype framework based on this idea, called SEAS, has been impleme
A complex Noether approach for variational partial differential equations
Naz, R.; Mahomed, F. M.
2015-10-01
Scalar complex partial differential equations which admit variational formulations are studied. Such a complex partial differential equation, via a complex dependent variable, splits into a system of two real partial differential equations. The decomposition of the Lagrangian of the complex partial differential equation in the real domain is shown to yield two real Lagrangians for the split system. The complex Maxwellian distribution, transonic gas flow, Maxwellian tails, dissipative wave and Klein-Gordon equations are considered. The Noether symmetries and gauge terms of the split system that correspond to both the Lagrangians are constructed by the Noether approach. In the case of coupled split systems, the same Noether symmetries are obtained. The Noether symmetries for the uncoupled split systems are different. The conserved vectors of the split system which correspond to both the Lagrangians are compared to the split conserved vectors of the complex partial differential equation for the examples. The split conserved vectors of the complex partial differential equation are the same as the conserved vectors of the split system of real partial differential equations in the case of coupled systems. Moreover a Noether-like theorem for the split system is proved which provides the Noether-like conserved quantities of the split system from knowledge of the Noether-like operators. An interesting result on the split characteristics and the conservation laws is shown as well. The Noether symmetries and gauge terms of the Lagrangian of the split system with the split Noether-like operators and gauge terms of the Lagrangian of the given complex partial differential equation are compared. Folklore suggests that the split Noether-like operators of a Lagrangian of a complex Euler-Lagrange partial differential equation are symmetries of the Lagrangian of the split system of real partial differential equations. This is not the case. They are proved to be the same if the
Partial Differential Equations A unified Hilbert Space Approach
Picard, Rainer
2011-01-01
This book presents a systematic approach to a solution theory for linear partial differential equations developed in a Hilbert space setting based on a Sobolev Lattice structure, a simple extension of the well established notion of a chain (or scale) of Hilbert spaces. Thefocus on a Hilbert space setting is a highly adaptable and suitable approach providing a more transparent framework for presenting the main issues in the development of a solution theory for partial differential equations.This global point of view is takenby focussing on the issues involved in determining the appropriate func
A New Approach for Solving Fractional Partial Differential Equations
Fanwei Meng
2013-01-01
Full Text Available We propose a new approach for solving fractional partial differential equations based on a nonlinear fractional complex transformation and the general Riccati equation and apply it to solve the nonlinear time fractional biological population model and the (4+1-dimensional space-time fractional Fokas equation. As a result, some new exact solutions for them are obtained. This approach can be suitable for solving fractional partial differential equations with more general forms than the method proposed by S. Zhang and H.-Q. Zhang (2011.
Laser therapy applying the differential approaches and biophotometrical elements
Mamedova, F. M.; Akbarova, Ju. A.; Umarova, D. A.; Yudin, G. A.
1995-04-01
The aim of the present paper is the presentation of biophotometrical data obtained from various anatomic-topographical mouth areas to be used for the development of differential approaches to laser therapy in dentistry. Biophotometrical measurements were carried out using a portative biophotometer, as a portion of a multifunctional equipping system of laser therapy, acupuncture and biophotometry referred to as 'Aura-laser'. The results of biophotometrical measurements allow the implementation of differential approaches to laser therapy of parodontitis and mucous mouth tissue taking their clinic form and rate of disease into account.
Wave packet evolution approach to ionization of hydrogen molecular ion by fast electrons
Serov, V V; Joulakian, B B; Vinitsky, S I; Serov, Vladislav V.; Derbov, Vladimir L.; Joulakian, Boghos B.; Vinitsky, Sergue I.
2000-01-01
The multiply differential cross section of the ionization of hydrogen molecular ion by fast electron impact is calculated by a direct approach, which involves the reduction of the initial 6D Schr\\"{o}dinger equation to a 3D evolution problem followed by the modeling of the wave packet dynamics. This approach avoids the use of stationary Coulomb two-centre functions of the continuous spectrum of the ejected electron which demands cumbersome calculations. The results obtained, after verification of the procedure in the case atomic hydrogen, reveal interesting mechanisms in the case of small scattering angles.
Empirical approaches to the study of language evolution.
Fitch, W Tecumseh
2017-02-01
The study of language evolution, and human cognitive evolution more generally, has often been ridiculed as unscientific, but in fact it differs little from many other disciplines that investigate past events, such as geology or cosmology. Well-crafted models of language evolution make numerous testable hypotheses, and if the principles of strong inference (simultaneous testing of multiple plausible hypotheses) are adopted, there is an increasing amount of relevant data allowing empirical evaluation of such models. The articles in this special issue provide a concise overview of current models of language evolution, emphasizing the testable predictions that they make, along with overviews of the many sources of data available to test them (emphasizing comparative, neural, and genetic data). The key challenge facing the study of language evolution is not a lack of data, but rather a weak commitment to hypothesis-testing approaches and strong inference, exacerbated by the broad and highly interdisciplinary nature of the relevant data. This introduction offers an overview of the field, and a summary of what needed to evolve to provide our species with language-ready brains. It then briefly discusses different contemporary models of language evolution, followed by an overview of different sources of data to test these models. I conclude with my own multistage model of how different components of language could have evolved.
Evolution of biomedical ontologies and mappings: Overview of recent approaches.
Groß, Anika; Pruski, Cédric; Rahm, Erhard
2016-01-01
Biomedical ontologies are heavily used to annotate data, and different ontologies are often interlinked by ontology mappings. These ontology-based mappings and annotations are used in many applications and analysis tasks. Since biomedical ontologies are continuously updated dependent artifacts can become outdated and need to undergo evolution as well. Hence there is a need for largely automated approaches to keep ontology-based mappings up-to-date in the presence of evolving ontologies. In this article, we survey current approaches and novel directions in the context of ontology and mapping evolution. We will discuss requirements for mapping adaptation and provide a comprehensive overview on existing approaches. We will further identify open challenges and outline ideas for future developments.
An Improved Self-adaptive Control Parameter of Differential Evolution for Global Optimization
Jia, Liyuan; Gong, Wenyin; Wu, Hongbin
Differential evolution (DE), a fast and robust evolutionary algorithm for global optimization, has been widely used in many areas. However, the success of DE for solving different problems mainly depends on properly choosing the control parameter values. On the other hand, DE is good at exploring the search space and locating the region of global minimum, but it is slow at exploiting the solution. In order to alleviate these drawbacks of DE, this paper proposes an improved self-adaptive control parameter of DE, referred to as ISADE, for global numerical optimization. The proposed approach employs the individual fitness information to adapt the parameter settings. Hence, it can exploit the information of the individual and generate the promising offspring efficiently. To verify the viability of the proposed ISADE, 10 high-dimensional benchmark problems are chosen from literature. Experiment results indicate that this approach is efficient and effective. It is proved that this approach performs better than the original DE in terms of the convergence rate and the quality of the final solutions. Moreover, ISADE obtains faster convergence than the original self-adaptive control parameter of DE (SADE).
Current approaches in evolution: from molecules to cells and organisms.
Thattai, Mukund; Peisajovich, Sergio G
2014-11-01
This is an exciting time to be an evolutionary biologist. Indeed, it is difficult to keep up with all the studies that fall under the broad category of "Evolution" since they span species, traits, and scales of organization. This special issue gives a flavor of exciting new approaches in evolutionary biology, but also emphasizes universal themes. The reviews contained here discuss important aspects of molecular evolution at multiple scales, from individual proteins to complex regulatory networks, as well as from unicellular organisms to macroscopic traits in animals. Though the model systems are diverse, the issues addressed are fundamental: the origin of evolutionary novelties, and the forces that drive them to fixation.
Zgurovsky, Mikhail Z; Kasyanov, Pavlo O
2011-01-01
Here, the authors present modern mathematical methods to solve problems of differential-operator inclusions and evolution variation inequalities which may occur in fields such as geophysics, aerohydrodynamics, or fluid dynamics. For the first time, they describe the detailed generalization of various approaches to the analysis of fundamentally nonlinear models and provide a toolbox of mathematical equations. These new mathematical methods can be applied to a broad spectrum of problems. Examples of these are phase changes, diffusion of electromagnetic, acoustic, vibro-, hydro- and seismoacousti
Analysis of planetary evolution with emphasis on differentiation and dynamics
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.
Detection of Differential Item Functioning Using the Lasso Approach
Magis, David; Tuerlinckx, Francis; De Boeck, Paul
2015-01-01
This article proposes a novel approach to detect differential item functioning (DIF) among dichotomously scored items. Unlike standard DIF methods that perform an item-by-item analysis, we propose the "LR lasso DIF method": logistic regression (LR) model is formulated for all item responses. The model contains item-specific intercepts,…
The evolution of water transport in plants: an integrated approach.
Pittermann, J
2010-03-01
This review examines the evolution of the plant vascular system from its beginnings in the green algae to modern arborescent plants, highlighting the recent advances in developmental, organismal, geochemical and climatological research that have contributed to our understanding of the evolution of xylem. Hydraulic trade-offs in vascular structure-function are discussed in the context of canopy support and drought and freeze-thaw stress resistance. This qualitative and quantitative neontological approach to palaeobotany may be useful for interpreting the water-transport efficiencies and hydraulic limits in fossil plants. Large variations in atmospheric carbon dioxide levels are recorded in leaf stomatal densities, and may have had profound impacts on the water conservation strategies of ancient plants. A hypothesis that links vascular function with stomatal density is presented and examined in the context of the evolution of wood and/or vessels. A discussion of the broader impacts of plant transport on hydrology and climate concludes this review.
Structural Approaches to Sequence Evolution Molecules, Networks, Populations
Bastolla, Ugo; Roman, H. Eduardo; Vendruscolo, Michele
2007-01-01
Structural requirements constrain the evolution of biological entities at all levels, from macromolecules to their networks, right up to populations of biological organisms. Classical models of molecular evolution, however, are focused at the level of the symbols - the biological sequence - rather than that of their resulting structure. Now recent advances in understanding the thermodynamics of macromolecules, the topological properties of gene networks, the organization and mutation capabilities of genomes, and the structure of populations make it possible to incorporate these key elements into a broader and deeply interdisciplinary view of molecular evolution. This book gives an account of such a new approach, through clear tutorial contributions by leading scientists specializing in the different fields involved.
Hybridizing Differential Evolution with a Genetic Algorithm for Color Image Segmentation
R. V. V. Krishna
2016-10-01
Full Text Available This paper proposes a hybrid of differential evolution and genetic algorithms to solve the color image segmentation problem. Clustering based color image segmentation algorithms segment an image by clustering the features of color and texture, thereby obtaining accurate prototype cluster centers. In the proposed algorithm, the color features are obtained using the homogeneity model. A new texture feature named Power Law Descriptor (PLD which is a modification of Weber Local Descriptor (WLD is proposed and further used as a texture feature for clustering. Genetic algorithms are competent in handling binary variables, while differential evolution on the other hand is more efficient in handling real parameters. The obtained texture feature is binary in nature and the color feature is a real value, which suits very well the hybrid cluster center optimization problem in image segmentation. Thus in the proposed algorithm, the optimum texture feature centers are evolved using genetic algorithms, whereas the optimum color feature centers are evolved using differential evolution.
A hybrid differential evolution algorithm for meta-task scheduling in grids
Kang Qinma; Jiang Changjun; He Hong; Huang Qiangsheng
2009-01-01
Task scheduling is one of the core steps to effectively exploit the capabilities of heterogeneous resources in the grid. This paper presents a new hybrid differential evolution (HDE) algorithm for finding an optimal or near-optimal schedule within reasonable time. The encoding scheme and the adaptation of classical differential evolution algorithm for dealing with discrete variables are discussed. A simple but effective local search is incorporated into differential evolution to stress exploitation. The performance of the proposed HDE algorithm is showed by being compared with a genetic algorithm (GA) on a known static benchmark for the problem. Experimental results indicate that the proposed algorithm has better performance than GA in terms of both solution quality and computational time, and thus it can be used to design efficient dynamic schedulers in batch mode for real grid systems.
Triadic Conceptual Structure of the Maximum Entropy Approach to Evolution
Herrmann-Pillath, Carsten
2010-01-01
Many problems in evolutionary theory are cast in dyadic terms, such as the polar oppositions of organism and environment. We argue that a triadic conceptual structure offers an alternative perspective under which the information generating role of evolution as a physical process can be analyzed, and propose a new diagrammatic approach. Peirce's natural philosophy was deeply influenced by his reception of both Darwin's theory and thermodynamics. Thus, we elaborate on a new synthesis which puts together his theory of signs and modern Maximum Entropy approaches to evolution. Following recent contributions to the naturalization of Peircean semiosis, we show that triadic structures involve the conjunction of three different kinds of causality, efficient, formal and final. We apply this on Ulanowicz's analysis of autocatalytic cycles as primordial patterns of life. This paves the way for a semiotic view of thermodynamics which is built on the idea that Peircean interpretants are systems of physical inference device...
Fixation times in differentiation and evolution in the presence of bottlenecks, deserts, and oases.
Chou, Tom; Wang, Yu
2015-05-01
Cellular differentiation and evolution are stochastic processes that can involve multiple types (or states) of particles moving on a complex, high-dimensional state-space or "fitness" landscape. Cells of each specific type can thus be quantified by their population at a corresponding node within a network of states. Their dynamics across the state-space network involve genotypic or phenotypic transitions that can occur upon cell division, such as during symmetric or asymmetric cell differentiation, or upon spontaneous mutation. Here, we use a general multi-type branching processes to study first passage time statistics for a single cell to appear in a specific state. Our approach readily allows for nonexponentially distributed waiting times between transitions, reflecting, e.g., the cell cycle. For simplicity, we restrict most of our detailed analysis to exponentially distributed waiting times (Poisson processes). We present results for a sequential evolutionary process in which L successive transitions propel a population from a "wild-type" state to a given "terminally differentiated," "resistant," or "cancerous" state. Analytic and numeric results are also found for first passage times across an evolutionary chain containing a node with increased death or proliferation rate, representing a desert/bottleneck or an oasis. Processes involving cell proliferation are shown to be "nonlinear" (even though mean-field equations for the expected particle numbers are linear) resulting in first passage time statistics that depend on the position of the bottleneck or oasis. Our results highlight the sensitivity of stochastic measures to cell division fate and quantify the limitations of using certain approximations (such as the fixed-population and mean-field assumptions) in evaluating fixation times.
Reem A. Al-Omair
2009-03-01
Full Text Available In this paper we prove the existence of a mild solution for a semilinear evolution differential inclusion with nonlocal condition and governed by a family of linear operators, not necessarily bounded or closed, in a Banach space. No compactness assumption is assumed on the evolution operator generated by the family operators. Also, we prove that the set of mild solutions is compact.
Evolution of approaches to economic security problems in Europe
Kuznetsov, Alexey; Toganova, Natalia; Gutnik, Anna
2010-01-01
The report, written by the experts of the Center for European Studies of IMEMO RAN – Dr. Alexey Kuznetsov, Natalia Toganova and Anna Gutnik – analyzes the evolution of the approaches to the problems of economic security in Europe. The report is prepared for the Commission of the Euro-Atlantic Security Initiative (EASI). The authors analyze the reasons why some economic problems in Europe cause the attention as the security problems. The report presents a study on transformation of the appr...
Banaja Mohanty
2014-09-01
Full Text Available This paper presents the design and performance analysis of Differential Evolution (DE algorithm based Proportional–Integral (PI and Proportional–Integral–Derivative (PID controllers for Automatic Generation Control (AGC of an interconnected power system. Initially, a two area thermal system with governor dead-band nonlinearity is considered for the design and analysis purpose. In the proposed approach, the design problem is formulated as an optimization problem control and DE is employed to search for optimal controller parameters. Three different objective functions are used for the design purpose. The superiority of the proposed approach has been shown by comparing the results with a recently published Craziness based Particle Swarm Optimization (CPSO technique for the same interconnected power system. It is noticed that, the dynamic performance of DE optimized PI controller is better than CPSO optimized PI controllers. Additionally, controller parameters are tuned at different loading conditions so that an adaptive gain scheduling control strategy can be employed. The study is further extended to a more realistic network of two-area six unit system with different power generating units such as thermal, hydro, wind and diesel generating units considering boiler dynamics for thermal plants, Generation Rate Constraint (GRC and Governor Dead Band (GDB non-linearity.
Long-term density evolution through semi-analytical and differential algebra techniques
Wittig, Alexander; Colombo, Camilla; Armellin, Roberto
2017-08-01
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.
Three dimensional evolution of differentially rotating magnetized neutron stars
Kiuchi, Kenta; Shibata, Masaru
2012-01-01
We construct a new three-dimensional general relativistic magnetohydrodynamics code, in which a fixed mesh refinement technique is implemented. To ensure the divergence-free condition as well as the magnetic flux conservation, we employ the method by Balsara (2001). Using this new code, we evolve differentially rotating magnetized neutron stars, and find that a magnetically driven outflow is launched from the star exhibiting a kink instability. The matter ejection rate and Poynting flux are still consistent with our previous finding (Shibata et al., 2011) obtained in axisymmetric simulations.
Reconsidering harmonic and anharmonic coherent states: Partial differential equations approach
Toutounji, Mohamad, E-mail: Mtoutounji@uaeu.ac.ae
2015-02-15
This article presents a new approach to dealing with time dependent quantities such as autocorrelation function of harmonic and anharmonic systems using coherent states and partial differential equations. The approach that is normally used to evaluate dynamical quantities involves formidable operator algebra. That operator algebra becomes insurmountable when employing Morse oscillator coherent states. This problem becomes even more complicated in case of Morse oscillator as it tends to exhibit divergent dynamics. This approach employs linear partial differential equations, some of which may be solved exactly and analytically, thereby avoiding the cumbersome noncommutative algebra required to manipulate coherent states of Morse oscillator. Additionally, the arising integrals while using the herein presented method feature stability and high numerical efficiency. The correctness, applicability, and utility of the above approach are tested by reproducing the partition and optical autocorrelation function of the harmonic oscillator. A closed-form expression for the equilibrium canonical partition function of the Morse oscillator is derived using its coherent states and partial differential equations. Also, a nonequilibrium autocorrelation function expression for weak electron–phonon coupling in condensed systems is derived for displaced Morse oscillator in electronic state. Finally, the utility of the method is demonstrated through further simplifying the Morse oscillator partition function or autocorrelation function expressions reported by other researchers in unevaluated form of second-order derivative exponential. Comparison with exact dynamics shows identical results.
Reconsidering harmonic and anharmonic coherent states: Partial differential equations approach
Toutounji, Mohamad
2015-02-01
This article presents a new approach to dealing with time dependent quantities such as autocorrelation function of harmonic and anharmonic systems using coherent states and partial differential equations. The approach that is normally used to evaluate dynamical quantities involves formidable operator algebra. That operator algebra becomes insurmountable when employing Morse oscillator coherent states. This problem becomes even more complicated in case of Morse oscillator as it tends to exhibit divergent dynamics. This approach employs linear partial differential equations, some of which may be solved exactly and analytically, thereby avoiding the cumbersome noncommutative algebra required to manipulate coherent states of Morse oscillator. Additionally, the arising integrals while using the herein presented method feature stability and high numerical efficiency. The correctness, applicability, and utility of the above approach are tested by reproducing the partition and optical autocorrelation function of the harmonic oscillator. A closed-form expression for the equilibrium canonical partition function of the Morse oscillator is derived using its coherent states and partial differential equations. Also, a nonequilibrium autocorrelation function expression for weak electron-phonon coupling in condensed systems is derived for displaced Morse oscillator in electronic state. Finally, the utility of the method is demonstrated through further simplifying the Morse oscillator partition function or autocorrelation function expressions reported by other researchers in unevaluated form of second-order derivative exponential. Comparison with exact dynamics shows identical results.
Evolution of the mirror approach to fusion: some conjectures
Post, R.E.
1984-09-18
Some possible directions for the future evolution of the mirror approach to fusion are outlined, in the context of economically-motivated criteria. Speculations are given as to the potential advantages, economic and otherwise, of the use of axially-symmetric systems, operated in semi-collisional regimes of lower Q (fusion power balance ratio) than that projected for present-day tandem mirror designs. These regims include barely tandem modes, and ion-heated modes, in association with higher efficiency direct conversion. Another possible economically advantageous approach mentioned is the use of a tandem mirror plasma to stabilize a FRM (field-reversed mirror) plasma, with potential synergistic advantages.
Pardinas, J R; Combates, N J; Prouty, S M; Stenn, K S; Parimoo, S
1998-03-15
We have developed a novel efficient approach, termed differential subtraction display, for the identification of differentially expressed genes. Several critical parameters for the reproducibility and enhanced sensitivity of display, as well as steps to reduce the number of false positive cDNA species, have been defined. These include- (a) use of standardized oligo(dT)-primed cDNA pools rather than total RNA as the starting material for differential display, (b) critical role of optimal cDNA input for each distinct class of primers, (c) phenomena of primer dominance and interference, and (d) design of a novel set of enhanced specificity anchor primers. Introduction of an efficient subtractive hybridization step prior to cloning of cDNA species enriches the bona fide cDNA species that are either exclusively present in one sample (+/-) or show altered expression (up-/down-regulation) in RNA samples from two different tissues or cell types. This approach, in comparison to differential display, has several advantages in terms of reproducibility and enhanced sensitivity of display coupled to the cloning of enriched bona fide cDNA species corresponding to differentially expressed RNAs.
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
Multi-Model approach to reconstruct the Mediterranean Freshwater Evolution
Simon, Dirk; Marzocchi, Alice; Flecker, Rachel; Lunt, Dan; Hilgen, Frits; Meijer, Paul
2016-04-01
Today the Mediterranean Sea is isolated from the global ocean by the Strait of Gibraltar. This restricted nature causes the Mediterranean basin to react more sensitively to climatic and tectonic related phenomena than the global ocean. Not just eustatic sea-level and regional river run-off, but also gateway tectonics and connectivity between sub-basins are leaving an enhanced fingerprint in its geological record. To understand its evolution, it is crucial to understand how these different effects are coupled. The Miocene-Pliocene sedimentary record of the Mediterranean shows alternations in composition and colour and has been astronomically tuned. Around the Miocene-Pliocene Boundary the most extreme changes occur in the Mediterranean Sea. About 6% of the salt in the global ocean deposited in the Mediterranean Region, forming an approximately 2 km thick salt layer, which is still present today. This extreme event is named the Messinian Salinity Crisis (MSC, 5.97-5.33 Ma). The gateway and climate evolution is not well constrained for this time, which makes it difficult to distinguish which of the above mentioned drivers might have triggered the MSC. We, therefore, decided to tackle this problem via a multi-model approach: (1) We calculate the Mediterranean freshwater evolution via 30 atmosphere-ocean-vegetation simulations (using HadCM3L), to which we fitted to a function, using a regression model. This allows us to directly relate the orbital curves to evaporation, precipitation and run off. The resulting freshwater evolution can be directly correlated to other sedimentary and proxy records in the late Miocene. (2) By feeding the new freshwater evolution curve into a box/budget model we can predict the salinity and strontium evolution of the Mediterranean for a certain Atlantic-Mediterranean gateway. (3) By comparing these results to the known salinity thresholds of gypsum and halite saturation of sea water, but also to the late Miocene Mediterranean strontium
Finitely approximable random sets and their evolution via differential equations
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.
Evolution and differential expression of a vertebrate vitellogenin gene cluster
Kongshaug Heidi
2009-01-01
Full Text Available Abstract Background The multiplicity or loss of the vitellogenin (vtg gene family in vertebrates has been argued to have broad implications for the mode of reproduction (placental or non-placental, cleavage pattern (meroblastic or holoblastic and character of the egg (pelagic or benthic. Earlier proposals for the existence of three forms of vertebrate vtgs present conflicting models for their origin and subsequent duplication. Results By integrating phylogenetics of novel vtg transcripts from old and modern teleosts with syntenic analyses of all available genomic variants of non-metatherian vertebrates we identify the gene orthologies between the Sarcopterygii (tetrapod branch and Actinopterygii (fish branch. We argue that the vertebrate vtg gene cluster originated in proto-chromosome m, but that vtg genes have subsequently duplicated and rearranged following whole genome duplications. Sequencing of a novel fourth vtg transcript in labrid species, and the presence of duplicated paralogs in certain model organisms supports the notion that lineage-specific gene duplications frequently occur in teleosts. The data show that the vtg gene cluster is more conserved between acanthomorph teleosts and tetrapods, than in ostariophysan teleosts such as the zebrafish. The differential expression of the labrid vtg genes are further consistent with the notion that neofunctionalized Aa-type vtgs are important determinants of the pelagic or benthic character of the eggs in acanthomorph teleosts. Conclusion The vertebrate vtg gene cluster existed prior to the separation of Sarcopterygii from Actinopterygii >450 million years ago, a period associated with the second round of whole genome duplication. The presence of higher copy numbers in a more highly expressed subcluster is particularly prevalent in teleosts. The differential expression and latent neofunctionalization of vtg genes in acanthomorph teleosts is an adaptive feature associated with oocyte hydration
Holistic Approach of the Evolution Theory to TNC Genesis
Rybalko Yuliia S.
2014-02-01
Full Text Available Development of the world economy at the modern stage is characterised with formation of the global model of economic development. Economic evolution theory, from the point of view of its supporters, considers economic development as an irreversible process of growth of complexity, variability and productiveness of production by means of periodical repetition of replacement of technologies, types of products, organisations and institutes, quite surprisingly, insufficiently exhaustively reacted in its development on modern challenges in the form of globalisation and processes caused by a new type of reproduction in the post-industrial society. The article, based on main provisions of the economic evolution theory and modern empiricism, specifies general and specific characteristics of development of trans-national corporations (TNC and provides an improved justification of periodisation of the TNC evolution. The holistic approach of the economic evolution theory to periodisation of the TNC development and the author’s analysis of stages of establishment of TNC give a possibility to identify specific regularities of this development and formulate the hypothesis on probability of coincidence of stages of TNC development and stages of development of the world currency system in the context of time periods, and also to mark out the tendency of formation of corporations of a new generation – mondialised global compositely combined structures.
The Pentabox Master Integrals with the Simplified Differential Equations approach
Papadopoulos, Costas G; Wever, Christopher
2015-01-01
We present the calculation of massless two-loop Master Integrals relevant to five-point amplitudes with one off-shell external leg and derive the complete set of planar Master Integrals with five on-mass-shell legs, that contribute to many $2\\to 3$ amplitudes of interest at the LHC, as for instance three jet production, $\\gamma, V, H +2$ jets etc., based on the Simplified Differential Equations approach.
Stochastic Computational Approach for Complex Nonlinear Ordinary Differential Equations
Junaid Ali Khan; Muhammad Asif Zahoor Raja; Ijaz Mansoor Qureshi
2011-01-01
@@ We present an evolutionary computational approach for the solution of nonlinear ordinary differential equations (NLODEs).The mathematical modeling is performed by a feed-forward artificial neural network that defines an unsupervised error.The training of these networks is achieved by a hybrid intelligent algorithm, a combination of global search with genetic algorithm and local search by pattern search technique.The applicability of this approach ranges from single order NLODEs, to systems of coupled differential equations.We illustrate the method by solving a variety of model problems and present comparisons with solutions obtained by exact methods and classical numerical methods.The solution is provided on a continuous finite time interval unlike the other numerical techniques with comparable accuracy.With the advent of neuroprocessors and digital signal processors the method becomes particularly interesting due to the expected essential gains in the execution speed.%We present an evolutionary computational approach for the solution of nonlinear ordinary differential equations (NLODEs). The mathematical modeling is performed by a feed-forward artificial neural network that defines an unsupervised error. The training of these networks is achieved by a hybrid intelligent algorithm, a combination of global search with genetic algorithm and local search by pattern search technique. The applicability of this approach ranges from single order NLODEs, to systems of coupled differential equations. We illustrate the method by solving a variety of model problems and present comparisons with solutions obtained by exact methods and classical numerical methods. The solution is provided on a continuous finite time interval unlike the other numerical techniques with comparable accuracy. With the advent of neuroprocessors and digital signal processors the method becomes particularly interesting due to the expected essential gains in the execution speed.
Sukanta Nama
2016-04-01
Full Text Available Differential evolution (DE is an effective and powerful approach and it has been widely used in different environments. However, the performance of DE is sensitive to the choice of control parameters. Thus, to obtain optimal performance, time-consuming parameter tuning is necessary. Backtracking Search Optimization Algorithm (BSA is a new evolutionary algorithm (EA for solving real-valued numerical optimization problems. An ensemble algorithm called E-BSADE is proposed which incorporates concepts from DE and BSA. The performance of E-BSADE is evaluated on several benchmark functions and is compared with basic DE, BSA and conventional DE mutation strategy.
Design of Short-Circuited Microstrip Antenna Using Differential Evolution Algorithm
Arindam Deb
2012-08-01
Full Text Available Differential evolution (DE algorithm is used to design a microstrip antenna, loaded with a shorting pin. The position of probe and the position of shorting pin are optimized using DE. The fitness function for DE is obtained using multiport network modelling technique. Antenna is fabricated and measured results are compared with the theoretical results.
Braak, ter C.J.F.
2004-01-01
Differential Evolution (DE) is a simple genetic algorithm for numerical optimization in real parameter spaces. In a statistical context one would not just want the optimum but also its uncertainty. The uncertainty distribution can be obtained by a Bayesian analysis (after specifying prior and likeli
Yusuf Pandir
2012-01-01
Full Text Available We obtain the classification of exact solutions, including soliton, rational, and elliptic solutions, to the one-dimensional general improved Camassa Holm KP equation and KdV equation by the complete discrimination system for polynomial method. In discussion, we propose a more general trial equation method for nonlinear partial differential equations with generalized evolution.
Ali Wagdy Mohamed
2014-11-01
Full Text Available In this paper, a novel version of Differential Evolution (DE algorithm based on a couple of local search mutation and a restart mechanism for solving global numerical optimization problems over continuous space is presented. The proposed algorithm is named as Restart Differential Evolution algorithm with Local Search Mutation (RDEL. In RDEL, inspired by Particle Swarm Optimization (PSO, a novel local mutation rule based on the position of the best and the worst individuals among the entire population of a particular generation is introduced. The novel local mutation scheme is joined with the basic mutation rule through a linear decreasing function. The proposed local mutation scheme is proven to enhance local search tendency of the basic DE and speed up the convergence. Furthermore, a restart mechanism based on random mutation scheme and a modified Breeder Genetic Algorithm (BGA mutation scheme is combined to avoid stagnation and/or premature convergence. Additionally, an exponent increased crossover probability rule and a uniform scaling factors of DE are introduced to promote the diversity of the population and to improve the search process, respectively. The performance of RDEL is investigated and compared with basic differential evolution, and state-of-the-art parameter adaptive differential evolution variants. It is discovered that the proposed modifications significantly improve the performance of DE in terms of quality of solution, efficiency and robustness.
Evolution of the environmental justice movement: activism, formalization and differentiation
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.
Reservoir Flood Control Operation Based on Adaptive Immune Differential Evolution Algorithm
Zou, Qiang; Lu, Jun; Yu, Shan
2017-05-01
Reservoir flood control operation (RFCO) is a high dimensional complex problem with multi-stages, multi-variables and multi-constraints, and its optimal solution is not easy to get. Differential evolution algorithm (DE) can be applied in RFCO, but its species diversity may sharply decline at the last evolution and lead into local optimal. Therefore, based on the adaptively controlling for mutation factor and crossover factor in each generation and immune clonal selection for better individuals, then adaptive immune differential evolution algorithm (AIDE) was proposed. And test function simulation verified the feasibility and efficiency of AIDE. Finally, AIDE was employed for RFCO and case study showed that AIDE could get better flood control benefit with fast convergence and high accuracy, moreover the outcomes of this research provided an effective way for RFCO.
Zhu, Jun; Yan, Xuefeng; Zhao, Weixiang
2013-10-01
To solve chemical process dynamic optimization problems, a differential evolution algorithm integrated with adaptive scheduling mutation strategy (ASDE) is proposed. According to the evolution feedback information, ASDE, with adaptive control parameters, adopts the round-robin scheduling algorithm to adaptively schedule different mutation strategies. By employing an adaptive mutation strategy and control parameters, the real-time optimal control parameters and mutation strategy are obtained to improve the optimization performance. The performance of ASDE is evaluated using a suite of 14 benchmark functions. The results demonstrate that ASDE performs better than four conventional differential evolution (DE) algorithm variants with different mutation strategies, and that the whole performance of ASDE is equivalent to a self-adaptive DE algorithm variant and better than five conventional DE algorithm variants. Furthermore, ASDE was applied to solve a typical dynamic optimization problem of a chemical process. The obtained results indicate that ASDE is a feasible and competitive optimizer for this kind of problem.
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.
Evolution of Social Insect Polyphenism Facilitated by the Sex Differentiation Cascade.
Antonia Klein
2016-03-01
Full Text Available 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.
Evolution of Social Insect Polyphenism Facilitated by the Sex Differentiation Cascade.
Klein, Antonia; Schultner, Eva; Lowak, Helena; Schrader, Lukas; Heinze, Jürgen; Holman, Luke; Oettler, Jan
2016-03-01
The major transition to eusociality required the evolution of a switch to canalize development into either a reproductive or a helper, the nature of which is currently unknown. Following predictions from the 'theory of facilitated variation', we identify sex differentiation pathways as promising candidates because of their pre-adaptation to regulating development of complex phenotypes. We show that conserved core genes, including the juvenile hormone-sensitive master sex differentiation gene doublesex (dsx) and a krüppel homolog 2 (kr-h2) with putative regulatory function, exhibit both sex and morph-specific expression across life stages in the ant Cardiocondyla obscurior. We hypothesize that genes in the sex differentiation cascade evolved perception of alternative input signals for caste differentiation (i.e. environmental or genetic cues), and that their inherent switch-like and epistatic behavior facilitated signal transfer to downstream targets, thus allowing them to control differential development into morphological castes.
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
Self-adaptive learning based discrete differential evolution algorithm for solving CJWTA problem
Yu Xue; Yi Zhuang; Tianquan Ni; Siru Ni; Xuezhi Wen
2014-01-01
Cooperative jamming weapon-target assignment (CJWTA) problem is a key issue in electronic countermeasures (ECM). Some symbols which relevant to the CJWTA are defined firstly. Then, a formulation of jamming fitness is presented. Final y, a model of the CJWTA problem is constructed. In order to solve the CJWTA problem efficiently, a self-adaptive learning based discrete differential evolution (SLDDE) algorithm is proposed by introduc-ing a self-adaptive learning mechanism into the traditional discrete differential evolution algorithm. The SLDDE algorithm steers four candidate solution generation strategies simultaneously in the framework of the self-adaptive learning mechanism. Computa-tional simulations are conducted on ten test instances of CJWTA problem. The experimental results demonstrate that the proposed SLDDE algorithm not only can generate better results than only one strategy based discrete differential algorithms, but also outper-forms two algorithms which are proposed recently for the weapon-target assignment problems.
Triadic conceptual structure of the maximum entropy approach to evolution.
Herrmann-Pillath, Carsten; Salthe, Stanley N
2011-03-01
Many problems in evolutionary theory are cast in dyadic terms, such as the polar oppositions of organism and environment. We argue that a triadic conceptual structure offers an alternative perspective under which the information generating role of evolution as a physical process can be analyzed, and propose a new diagrammatic approach. Peirce's natural philosophy was deeply influenced by his reception of both Darwin's theory and thermodynamics. Thus, we elaborate on a new synthesis which puts together his theory of signs and modern Maximum Entropy approaches to evolution in a process discourse. Following recent contributions to the naturalization of Peircean semiosis, pointing towards 'physiosemiosis' or 'pansemiosis', we show that triadic structures involve the conjunction of three different kinds of causality, efficient, formal and final. In this, we accommodate the state-centered thermodynamic framework to a process approach. We apply this on Ulanowicz's analysis of autocatalytic cycles as primordial patterns of life. This paves the way for a semiotic view of thermodynamics which is built on the idea that Peircean interpretants are systems of physical inference devices evolving under natural selection. In this view, the principles of Maximum Entropy, Maximum Power, and Maximum Entropy Production work together to drive the emergence of information carrying structures, which at the same time maximize information capacity as well as the gradients of energy flows, such that ultimately, contrary to Schrödinger's seminal contribution, the evolutionary process is seen to be a physical expression of the Second Law.
SGO: A fast engine for ab initio atomic structure global optimization by differential evolution
Chen, Zhanghui; Jia, Weile; Jiang, Xiangwei; Li, Shu-Shen; Wang, Lin-Wang
2017-10-01
As the high throughout calculations and material genome approaches become more and more popular in material science, the search for optimal ways to predict atomic global minimum structure is a high research priority. This paper presents a fast method for global search of atomic structures at ab initio level. The structures global optimization (SGO) engine consists of a high-efficiency differential evolution algorithm, accelerated local relaxation methods and a plane-wave density functional theory code running on GPU machines. The purpose is to show what can be achieved by combining the superior algorithms at the different levels of the searching scheme. SGO can search the global-minimum configurations of crystals, two-dimensional materials and quantum clusters without prior symmetry restriction in a relatively short time (half or several hours for systems with less than 25 atoms), thus making such a task a routine calculation. Comparisons with other existing methods such as minima hopping and genetic algorithm are provided. One motivation of our study is to investigate the properties of magnetic systems in different phases. The SGO engine is capable of surveying the local minima surrounding the global minimum, which provides the information for the overall energy landscape of a given system. Using this capability we have found several new configurations for testing systems, explored their energy landscape, and demonstrated that the magnetic moment of metal clusters fluctuates strongly in different local minima.
Integrated Model of Multiple Kernel Learning and Differential Evolution for EUR/USD Trading
Shangkun Deng
2014-01-01
Full Text Available 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.
A SLAM based on auxiliary marginalised particle filter and differential evolution
Havangi, R.; Nekoui, M. A.; Teshnehlab, M.; Taghirad, H. D.
2014-09-01
FastSLAM is a framework for simultaneous localisation and mapping (SLAM) using a Rao-Blackwellised particle filter. In FastSLAM, particle filter is used for the robot pose (position and orientation) estimation, and parametric filter (i.e. EKF and UKF) is used for the feature location's estimation. However, in the long term, FastSLAM is an inconsistent algorithm. In this paper, a new approach to SLAM based on hybrid auxiliary marginalised particle filter and differential evolution (DE) is proposed. In the proposed algorithm, the robot pose is estimated based on auxiliary marginal particle filter that operates directly on the marginal distribution, and hence avoids performing importance sampling on a space of growing dimension. In addition, static map is considered as a set of parameters that are learned using DE. Compared to other algorithms, the proposed algorithm can improve consistency for longer time periods and also, improve the estimation accuracy. Simulations and experimental results indicate that the proposed algorithm is effective.
Tarek Bouktir
2012-06-01
Full Text Available This paper presents solution of optimal power flow (OPF problem of a power system via Differential Evolution (DE algorithm. The purpose of an electric power system is to deliver real power to the greatest number of users at the lowest possible cost all the time. So the objective is to minimize the total fuel cost of the generating units and also maintaining an acceptable system performance in terms of limits on generator reactive power outputs, bus voltages, Static VAR Compensator (SVC parameters and overload in transmission lines. CPU times can be reduced by decomposing the problem in two subproblems, the first subproblem minimize the fuel cost of generation and the second subproblem is a reactive power dispatch so optimum bus voltages can be determined and reduce the losses by controlling tap changes of the transformers and the static Var Compensators (SVC. To verify the proposed approach and for comparison purposes, we perform simulations on the Algerian network with 114 buses, 175 branches (lines and transformers and 15 generators. The obtained results indicate that DE is an easy to use, fast, robust and powerful optimization technique compared to the other global optimization methods such as PSO and GA.
DNA strand generation for DNA computing by using a multi-objective differential evolution algorithm.
Chaves-González, José M; Vega-Rodríguez, Miguel A
2014-02-01
In this paper, we use an adapted multi-objective version of the differential evolution (DE) metaheuristics for the design and generation of reliable DNA libraries that can be used for computation. DNA sequence design is a very relevant task in many recent research fields, e.g. nanotechnology or DNA computing. Specifically, DNA computing is a new computational model which uses DNA molecules as information storage and their possible biological interactions as processing operators. Therefore, the possible reactions and interactions among molecules must be strictly controlled to prevent incorrect computations. The design of reliable DNA libraries for bio-molecular computing is an NP-hard combinatorial problem which involves many heterogeneous and conflicting design criteria. For this reason, we modelled DNA sequence design as a multiobjective optimization problem and we solved it by using an adapted multi-objective version of DE metaheuristics. Seven different bio-chemical design criteria have been simultaneously considered to obtain high quality DNA sequences which are suitable for molecular computing. Furthermore, we have developed the multiobjective standard fast non-dominated sorting genetic algorithm (NSGA-II) in order to perform a formal comparative study by using multi-objective indicators. Additionally, we have also compared our results with other relevant results published in the literature. We conclude that our proposal is a promising approach which is able to generate reliable real-world DNA sequences that significantly improve other DNA libraries previously published in the literature.
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.
A predictive approach to identify genes differentially expressed
Saraiva, Erlandson F.; Louzada, Francisco; Milan, Luís A.; Meira, Silvana; Cobre, Juliana
2012-10-01
The main objective of gene expression data analysis is to identify genes that present significant changes in expression levels between a treatment and a control biological condition. In this paper, we propose a Bayesian approach to identify genes differentially expressed calculating credibility intervals from predictive densities which are constructed using sampled mean treatment effect from all genes in study excluding the treatment effect of genes previously identified with statistical evidence for difference. We compare our Bayesian approach with the standard ones based on the use of the t-test and modified t-tests via a simulation study, using small sample sizes which are common in gene expression data analysis. Results obtained indicate that the proposed approach performs better than standard ones, especially for cases with mean differences and increases in treatment variance in relation to control variance. We also apply the methodologies to a publicly available data set on Escherichia coli bacteria.
Automatic differentiation for Fourier series and the radii polynomial approach
Lessard, Jean-Philippe; Mireles James, J. D.; Ransford, Julian
2016-11-01
In this work we develop a computer-assisted technique for proving existence of periodic solutions of nonlinear differential equations with non-polynomial nonlinearities. We exploit ideas from the theory of automatic differentiation in order to formulate an augmented polynomial system. We compute a numerical Fourier expansion of the periodic orbit for the augmented system, and prove the existence of a true solution nearby using an a-posteriori validation scheme (the radii polynomial approach). The problems considered here are given in terms of locally analytic vector fields (i.e. the field is analytic in a neighborhood of the periodic orbit) hence the computer-assisted proofs are formulated in a Banach space of sequences satisfying a geometric decay condition. In order to illustrate the use and utility of these ideas we implement a number of computer-assisted existence proofs for periodic orbits of the Planar Circular Restricted Three-Body Problem (PCRTBP).
Jiang, Siwei; Cai, Zhihua
Differential evolution is a powerful and robust method to solve the Multi-Objective Problems in MOEAs. To enhance the differential evolution for MOPs, we focus on two aspects: the population initialization and acceptance rule. In this paper, we present a new differential evolution called DEMO_{DV}^{UD}, it mainly include: (1) the first population is constructed by statistical method: Uniform Design, which can get more evenly distributed solutions than random design, (2) a new acceptance rule is firstly presented as Minimum Reduce Hypervolume. Acceptance rule is a metric to decide which solution should be cut off when the archive is full to the setting size. Crowding Distance is frequently used to estimate the length of cuboid enclosing the solution, while Minimum Reduce Hypervolume is used to estimate the volume of cuboid. The new algorithm designs a fitness function Distance/Volume that balance the CD and MRV, which maintains the spread and hypervolume along the Pareto-front. Experiment on different multi-Objective problems include ZDTx and DTLZx by jMetal 2.0, the results show that the new algorithm gets higher hypervolume, faster convergence, better distributed solutions and needs less numbers of fitness function evolutions than NSGA-II, SPEA2 and GDE3.
NATURAL SCIENCE AT SCHOOL: MODERN APPROACHES TO THE DIFFERENTIATED STUDY
Dechtyarenko S.G.
2015-08-01
Full Text Available The article analyzes the possibility of differentiated study natural science at school on the basis of ecological educational process. Natural science is the science about nature as a single unity or totality of the natural sciences, which constituting a single unit. The main aim of the course is to develop student’s natural science competence through integrated mastering system knowledge about nature and man, the basics of environmental knowledge, ways of improving teaching and learning activities, development of value orientations in relation to the nature. There is strong need to review approaches to teaching nature science at schools, taking into account the general trend of greening of the educational process. The aim of the work is to analyze the possibility of practical application of modern approaches to differentiated teaching of the nature science at school greening within the educational process. In our view, the environmental component may be a basis to the formation and differentiated teaching in general. The environmental component of the educational sector has been aimed to the student’s environmental consciousness and compliance with rules of environmentally safe behavior in the environment. The learning of the integrated knowledge about nature and man can be submitted through the prism of action of the environmental factors according classic approach to their classification: abiotic, biotic and anthropogenic factors. In parallel, it is reasonable to raise the issues of practical importance as some natural objects and actions of each of these factors. The new degree of the studying of the environment has been provided by the beginning of the systematization of knowledge about natural objects and structure of the universe, by the formation of primary concepts about the relationship between the world of the living and inanimate nature, between organisms and between human activities and changes that has been occurred in the
Analytical Approach to Eigen-Emittance Evolution in Storage Rings
Nash, Boaz; /SLAC
2006-05-16
This dissertation develops the subject of beam evolution in storage rings with nearly uncoupled symplectic linear dynamics. Linear coupling and dissipative/diffusive processes are treated perturbatively. The beam distribution is assumed Gaussian and a function of the invariants. The development requires two pieces: the global invariants and the local stochastic processes which change the emittances, or averages of the invariants. A map based perturbation theory is described, providing explicit expressions for the invariants near each linear resonance, where small perturbations can have a large effect. Emittance evolution is determined by the damping and diffusion coefficients. The discussion is divided into the cases of uniform and non-uniform stochasticity, synchrotron radiation an example of the former and intrabeam scattering the latter. For the uniform case, the beam dynamics is captured by a global diffusion coefficient and damping decrement for each eigen-invariant. Explicit expressions for these quantities near coupling resonances are given. In many cases, they are simply related to the uncoupled values. Near a sum resonance, it is found that one of the damping decrements becomes negative, indicating an anti-damping instability. The formalism is applied to a number of examples, including synchrobetatron coupling caused by a crab cavity, a case of current interest where there is concern about operation near half integer {nu}{sub x}. In the non-uniform case, the moment evolution is computed directly, which is illustrated through the example of intrabeam scattering. Our approach to intrabeam scattering damping and diffusion has the advantage of not requiring a loosely-defined Coulomb Logarithm. It is found that in some situations there is a small difference between our results and the standard approaches such as Bjorken-Mtingwa, which is illustrated by comparison of the two approaches and with a measurement of Au evolution in RHIC. Finally, in combining IBS
Hybridization of Adaptive Differential Evolution with an Expensive Local Search Method
Rashida Adeeb Khanum
2016-01-01
Full Text Available Differential evolution (DE is an effective and efficient heuristic for global optimization problems. However, it faces difficulty in exploiting the local region around the approximate solution. To handle this issue, local search (LS techniques could be hybridized with DE to improve its local search capability. In this work, we hybridize an updated version of DE, adaptive differential evolution with optional external archive (JADE with an expensive LS method, Broydon-Fletcher-Goldfarb-Shano (BFGS for solving continuous unconstrained global optimization problems. The new hybrid algorithm is denoted by DEELS. To validate the performance of DEELS, we carried out extensive experiments on well known test problems suits, CEC2005 and CEC2010. The experimental results, in terms of function error values, success rate, and some other statistics, are compared with some of the state-of-the-art algorithms, self-adaptive control parameters in differential evolution (jDE, sequential DE enhanced by neighborhood search for large-scale global optimization (SDENS, and differential ant-stigmergy algorithm (DASA. These comparisons reveal that DEELS outperforms jDE and SDENS except DASA on the majority of test instances.
A Systems Approach to Physiologic Evolution: From Micelles to Consciousness.
Torday, John S; Miller, William B
2017-01-23
A systems approach to evolutionary biology offers the promise of an improved understanding of the fundamental principles of life through the effective integration of many biologic disciplines. It is presented that any critical integrative approach to evolutionary development involves a paradigmatic shift in perspective, more than just the engagement of a large number of disciplines. Critical to this differing viewpoint is the recognition that all biological processes originate from the unicellular state and remain permanently anchored to that phase throughout evolutionary development despite their macroscopic appearances. Multicellular eukaryotic development can therefore be viewed as a series of connected responses to epiphenomena that proceeds from that base in continuous iterative maintenance of collective cellular homeostatic equipoise juxtaposed against an ever-changing and challenging environment. By following this trajectory of multicellular eukaryotic evolution from within unicellular First Principles of Physiology forward, the mechanistic nature of complex physiology can be identified through a step-wise analysis of a continuous arc of vertebrate evolution based upon serial exaptations. This article is protected by copyright. All rights reserved.
Stochastic differential equation approach for waves in a random medium.
Dimitropoulos, Dimitris; Jalali, Bahram
2009-03-01
We present a mathematical approach that simplifies the theoretical treatment of electromagnetic localization in random media and leads to closed-form analytical solutions. Starting with the assumption that the dielectric permittivity of the medium has delta-correlated spatial fluctuations, and using Ito's lemma, we derive a linear stochastic differential equation for a one-dimensional random medium. The equation leads to localized wave solutions. The localized wave solutions have a localization length that scales as L approximately omega(-2) for low frequencies whereas in the high-frequency regime this length behaves as L approximately omega(-2/3) .
Evolution of Social Insect Polyphenism Facilitated by the Sex Differentiation Cascade
Klein, Antonia; Schultner, Eva; Lowak, Helena;
2016-01-01
-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......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...
Onset of Differentiation and Internal Evolution: the case of 21 Lutetia
Formisano, M; Federico, C; Capaccioni, F; De Sanctis, M C
2013-01-01
Asteroid 21 Lutetia, visited 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 (Weiss et al. 2011) suggest that Lutetia could be partially differentiated. We developed a numerical code, also used for studying the geophysical history of Vesta (Formisano et al., submitted), to explore several scenarios of internal evolution of Lutetia, differing in the strength of radiogenic sources and in the global post-sintering porosity. The only significant heat source for partial differentiation is represented by Al26, the other possible sources (Fe60, accretion and differentiation) being negligible. In scenarios in which Lutetia completed its accretion in less than 0.7 Ma from injection of Al26 in Solar Nebula and for post-sintering values of macroporosity not exceeding 30 vol. %, the asteroid experienced only partial differentiation. The formation of the proto-core, a structure enriched in metals and a...
A Computational Differential Geometry Approach to Grid Generation
Liseikin, Vladimir D
2007-01-01
The process of breaking up a physical domain into smaller sub-domains, known as meshing, facilitates the numerical solution of partial differential equations used to simulate physical systems. This monograph gives a detailed treatment of applications of geometric methods to advanced grid technology. It focuses on and describes a comprehensive approach based on the numerical solution of inverted Beltramian and diffusion equations with respect to monitor metrics for generating both structured and unstructured grids in domains and on surfaces. In this second edition the author takes a more detailed and practice-oriented approach towards explaining how to implement the method by: Employing geometric and numerical analyses of monitor metrics as the basis for developing efficient tools for controlling grid properties. Describing new grid generation codes based on finite differences for generating both structured and unstructured surface and domain grids. Providing examples of applications of the codes to the genera...
Preparing Biology Teachers to Teach Evolution in a Project-Based Approach
Cook, Kristin; Buck, Gayle; Park Rogers, Meredith
2012-01-01
This study investigates a project-based learning (PBL) approach to teaching evolution to inform efforts in teacher preparation. Data analysis of a secondary biology educator teaching evolution through a PBL approach illuminated: (1) active student voice, which allowed students to reflect on their positioning on evolution and consider multiple…
Neural network approach for differential diagnosis of interstitial lung diseases
Asada, Naoki; Doi, Kunio; MacMahon, Heber; Montner, Steven M.; Giger, Maryellen L.; Abe, Chihiro; Wu, Chris Y.
1990-07-01
A neural network approach was applied for the differential diagnosis of interstitial lung diseases. The neural network was designed for distinguishing between 9 types of interstitial lung diseases based on 20 items of clinical and radiographic information. A database for training and testing the neural network was created with 10 hypothetical cases for each of the 9 diseases. The performance of the neural network was evaluated by ROC analysis. The optimal parameters for the current neural network were determined by selecting those yielding the highest ROC curves. In this case the neural network consisted of one hidden layer including 6 units and was trained with 200 learning iterations. When the decision performances of the neural network chest radiologists and senior radiology residents were compared the neural network indicated high performance comparable to that of chest radiologists and superior to that of senior radiology residents. Our preliminary results suggested strongly that the neural network approach had potential utility in the computer-aided differential diagnosis of interstitial lung diseases. 1_
A Thermodynamical Selection-Based Discrete Differential Evolution for the 0-1 Knapsack Problem
Zhaolu Guo
2014-11-01
Full Text Available Many problems in business and engineering can be modeled as 0-1 knapsack problems. However, the 0-1 knapsack problem is one of the classical NP-hard problems. Therefore, it is valuable to develop effective and efficient algorithms for solving 0-1 knapsack problems. Aiming at the drawbacks of the selection operator in the traditional differential evolution (DE, we present a novel discrete differential evolution (TDDE for solving 0-1 knapsack problem. In TDDE, an enhanced selection operator inspired by the principle of the minimal free energy in thermodynamics is employed, trying to balance the conflict between the selective pressure and the diversity of population to some degree. An experimental study is conducted on twenty 0-1 knapsack test instances. The comparison results show that TDDE can gain competitive performance on the majority of the test instances.
Xinli Xu
2013-01-01
Full Text Available A two-level batch chromosome coding scheme is proposed to solve the lot splitting problem with equipment capacity constraints in flexible job shop scheduling, which includes a lot splitting chromosome and a lot scheduling chromosome. To balance global search and local exploration of the differential evolution algorithm, a hybrid discrete differential evolution algorithm (HDDE is presented, in which the local strategy with dynamic random searching based on the critical path and a random mutation operator is developed. The performance of HDDE was experimented with 14 benchmark problems and the practical dye vat scheduling problem. The simulation results showed that the proposed algorithm has the strong global search capability and can effectively solve the practical lot splitting problems with equipment capacity constraints.
An Improved Brain Storm Optimization with Differential Evolution Strategy for Applications of ANNs
Zijian Cao
2015-01-01
Full Text Available Brain Storm Optimization (BSO algorithm is a swarm intelligence algorithm inspired by human being’s behavior of brainstorming. The performance of BSO is maintained by the creating process of ideas, but when it cannot find a better solution for some successive iterations, the result will be so inefficient that the population might be trapped into local optima. In this paper, we propose an improved BSO algorithm with differential evolution strategy and new step size method. Firstly, differential evolution strategy is incorporated into the creating operator of ideas to allow BSO jump out of stagnation, owing to its strong searching ability. Secondly, we introduce a new step size control method that can better balance exploration and exploitation at different searching generations. Finally, the proposed algorithm is first tested on 14 benchmark functions of CEC 2005 and then is applied to train artificial neural networks. Comparative experimental results illustrate that the proposed algorithm performs significantly better than the original BSO.
Chuii Khim Chong
2012-06-01
Full Text Available This paper introduces an improved Differential Evolution algorithm (IDE which aims at improving its performance in estimating the relevant parameters for metabolic pathway data to simulate glycolysis pathway for yeast. Metabolic pathway data are expected to be of significant help in the development of efficient tools in kinetic modeling and parameter estimation platforms. Many computation algorithms face obstacles due to the noisy data and difficulty of the system in estimating myriad of parameters, and require longer computational time to estimate the relevant parameters. The proposed algorithm (IDE in this paper is a hybrid of a Differential Evolution algorithm (DE and a Kalman Filter (KF. The outcome of IDE is proven to be superior than Genetic Algorithm (GA and DE. The results of IDE from experiments show estimated optimal kinetic parameters values, shorter computation time and increased accuracy for simulated results compared with other estimation algorithms
Differential Evolution based SHEPWM for Seven-Level Inverter with Non-Equal DC Sources
Fayçal CHABNI
2016-09-01
Full Text Available This paper presents the application of differential evolution algorithm to obtain optimal switching angles for a single-phase seven-level to improve AC voltage quality. The proposed inverter in this article is composed of two H-bridge cells with non-equal DC voltage sources in order to generate multiple voltage levels. Selective harmonic elimination pulse width modulation (SHPWM strategy is used to improve the AC output voltage waveform generated by the proposed inverter. The differential evolution (DE optimization algorithm is used to solve non-linear transcendental equations necessary for the (SHPWM. Computational results obtained from computer simulations presented a good agreement with the theoretical predictions. A laboratory prototype based on STM32F407 microcontroller was built in order to validate the simulation results. The experimental results show the effectiveness of the proposed modulation method.
Multiple Active Contours Guided by Differential Evolution for Medical Image Segmentation
I. Cruz-Aceves
2013-01-01
Full Text Available 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.
Chong, Chuii Khim; Mohamad, Mohd Saberi; Deris, Safaai; Shamsir, Mohd Shahir; Abdullah, Afnizanfaizal
2014-01-01
This paper presents an Improved Differential Evolution (IDE) algorithm to improve the kinetic parameter estimation in simulating the glycolysis pathway and the threonine biosynthesis pathway. Experimentally derived time series kinetic data are noisy and possess many unknown parameters. These characteristics of kinetic data cause lengthy computational time to compute the optimum value of the kinetic parameters. To solve this problem, this study had been conducted to develop a hybrid method that combined the Differential Evolution algorithm (DE) and the Kalman Filter (KF) to produce IDE. Results have shown that lesser computation time (6% and 18.5% faster) and more robust to noisy data with significant reduced error rates (93% and 79% reduced error rates) compared with the Genetic Algorithm (GA) and DE, respectively, in glycolysis and threonine biosynthesis pathway simulations. IDE is reliable as it demonstrated consistent standard deviation values which were close to mean values. We foresee the applicability of IDE into other metabolic pathway simulations.
SUN Fan; ZHONG Weimin; CHENG Hui; QIAN Feng
2013-01-01
Two general approaches are adopted in solving dynamic optimization problems in chemical processes,namely,the analytical and numerical methods.The numerical method,which is based on heuristic algorithms,has been widely used.An approach that combines differential evolution (DE) algorithm and control vector parameterization (CVP) is proposed in this paper.In the proposed CVP,control variables are approximated with polynomials based on state variables and time in the entire time interval.Region reduction strategy is used in DE to reduce the width of the search region,which improves the computing efficiency.The results of the case studies demonstrate the feasibility and efficiency of the proposed methods.
Shaheen, Husam I.; Rashed, Ghamgeen I.; Cheng, S.J. [Electric Power Security and High Efficiency Lab, Department of Electrical Engineering, Huazhong University of Science and Technology, Wuhan 430074, Hubei (China)
2011-01-15
This paper presents a new approach based on Differential Evolution (DE) technique to find out the optimal placement and parameter setting of Unified Power Flow Controller (UPFC) for enhancing power system security under single line contingencies. Firstly, we perform a contingency analysis and ranking process to determine the most severe line outage contingencies considering line overloads and bus voltage limit violations as a Performance Index. Secondly, we apply DE technique to find out the optimal location and parameter setting of UPFC under the determined contingency scenarios. To verify our proposed approach, we perform simulations on an IEEE 14-bus and an IEEE 30-bus power systems. The results we have obtained indicate that installing UPFC in the location optimized by DE can significantly enhance the security of power system by eliminating or minimizing the overloaded lines and the bus voltage limit violations. (author)
A Closer Look At Differential Evolution For The Optimal Well Placement Problem
Carosio, Grazieli L. C.; Humphries, Thomas D.; Haynes, Ronald D.; Farquharson, Colin G.
2015-01-01
Energy demand has increased considerably with the growth of world population, increasing the interest in the hydrocarbon reservoir management problem. Companies are concerned with maximizing oil recovery while minimizing capital investment and operational costs. A first step in solving this problem is to consider optimal well placement. In this work, we investigate the Differential Evolution (DE) optimization method, using distinct configurations with respect to population size, mutation fact...
A hybrid differential evolution algorithm to vehicle routing problem with fuzzy demands
Erbao, Cao; Mingyong, Lai
2009-09-01
In this paper, the vehicle routing problem with fuzzy demands (VRPFD) is considered, and a fuzzy chance constrained program model is designed, based on fuzzy credibility theory. Then stochastic simulation and differential evolution algorithm are integrated to design a hybrid intelligent algorithm to solve the fuzzy chance constrained program model. Moreover, the influence of the dispatcher preference index on the final objective of the problem is discussed using stochastic simulation, and the best value of the dispatcher preference index is obtained.
XU Zhi-gao; GUAN Zheng-xi; MA Jing
2005-01-01
The differential evolution (DE) algorithm is applied to solving the models' equations of a whole missile power system, and the steady fault characteristics of the whole system are analyzed. The DE algorithm is robust, requires few control variables, is easy to use and lends itself very well to parallel computation. Calculation results indicate that the DE algorithm simulates faults of a missile power system very well.
Vinayaka : A Semi-Supervised Projected Clustering Method Using Differential Evolution
Satish Gajawada; Durga Toshniwal
2012-01-01
Differential Evolution (DE) is an algorithm for evolutionary optimization. Clustering problems have beensolved by using DE based clustering methods but these methods may fail to find clusters hidden insubspaces of high dimensional datasets. Subspace and projected clustering methods have been proposed inliterature to find subspace clusters that are present in subspaces of dataset. In this paper we proposeVINAYAKA, a semi-supervised projected clustering method based on DE. In this method DE opt...
Glacial landscape evolution by subglacial quarrying: A multiscale computational approach
Ugelvig, Sofie V.; Egholm, David L.; Iverson, Neal R.
2016-11-01
Quarrying of bedrock is a primary agent of subglacial erosion. Although the mechanical theory behind the process has been studied for decades, it has proven difficult to formulate the governing principles so that large-scale landscape evolution models can be used to integrate erosion over time. The existing mechanical theory thus stands largely untested in its ability to explain postglacial topography. In this study we relate the physics of quarrying to long-term landscape evolution with a multiscale approach that connects meter-scale cavities to kilometer-scale glacial landscapes. By averaging the quarrying rate across many small-scale bedrock steps, we quantify how regional trends in basal sliding speed, effective pressure, and bed slope affect the rate of erosion. A sensitivity test indicates that a power law formulated in terms of these three variables provides an acceptable basis for quantifying regional-scale rates of quarrying. Our results highlight the strong influence of effective pressure, which intensifies quarrying by increasing the volume of the bed that is stressed by the ice and thereby the probability of rock failure. The resulting pressure dependency points to subglacial hydrology as a primary factor for influencing rates of quarrying and hence for shaping the bedrock topography under warm-based glaciers. When applied in a landscape evolution model, the erosion law for quarrying produces recognizable large-scale glacial landforms: U-shaped valleys, hanging valleys, and overdeepenings. The landforms produced are very similar to those predicted by more standard sliding-based erosion laws, but overall quarrying is more focused in valleys, and less effective at higher elevations.
An Orthogonal Learning Differential Evolution Algorithm for Remote Sensing Image Registration
Wenping Ma
2014-01-01
Full Text Available We introduce an area-based method for remote sensing image registration. We use orthogonal learning differential evolution algorithm to optimize the similarity metric between the reference image and the target image. Many local and global methods have been used to achieve the optimal similarity metric in the last few years. Because remote sensing images are usually influenced by large distortions and high noise, local methods will fail in some cases. For this reason, global methods are often required. The orthogonal learning (OL strategy is efficient when searching in complex problem spaces. In addition, it can discover more useful information via orthogonal experimental design (OED. Differential evolution (DE is a heuristic algorithm. It has shown to be efficient in solving the remote sensing image registration problem. So orthogonal learning differential evolution algorithm (OLDE is efficient for many optimization problems. The OLDE method uses the OL strategy to guide the DE algorithm to discover more useful information. Experiments show that the OLDE method is more robust and efficient for registering remote sensing images.
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. © The Author 2016. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.
Hao, Xiao-Hu; Zhang, Gui-Jun; Zhou, Xiao-Gen
2017-09-05
Protein structure prediction can be considered as a multimodal optimization problem for sampling the protein conformational space associated with an extremely complex energy landscape. To address this problem, a conformational space sampling method using multi-subpopulation differential evolution, MDE, is proposed. MDE first devotes to generate given numbers of concerned modal under the ultrafast shape recognition based modal identification protocol, which regards each individual as one modal at beginning. Then, differential evolution is used for keeping the preserved modal survival in the evolution process. Meanwhile, a local descent direction used to sample along with is constructed based on the abstract convex underestimate technique for modal enhancement, which could enhance the ability of sampling in the region with lower energy. Through the sampling process of evolution, several certain clusters contain a series of conformations in proportion to the energy score will be obtained. Representative conformations in the generated clusters can be directly picked out as decoy conformations for further refinement with no extra clustering operation needs. A total of 20 target proteins are tested. In which 10 target proteins are tested for comparison with Rosetta and 3 evolutionary algorithms. And 10 easy/hard target proteins in CASP 11 are tested for further verifying the effectiveness of MDE. Test results show strong sampling ability that MDE holds, and near-native conformations can be effectively obtained.
Relational Neural Evolution Approach to Bank Failure Prediction
Abudu, Bolanle; Markose, Sheri
2007-12-01
Relational neural networks as a concept offers a unique opportunity for improving classification accuracy by exploiting relational structure in data. The premise is that a relational classification technique, which uses information implicit in relationships, should classify more accurately than techniques that only examine objects in isolation. In this paper, we study the use of relational neural networks for predicting bank failure. Alongside classical financial ratios normally used as predictor variables, we introduced new relational variables for the network. The relational neural network structure, specified as a combination of feed forward and recurrent neural networks, is determined by bank data through neuro-evolution. We discuss empirical results comparing performance of the relational approach to standard propositional methods used for bank failure prediction.
A systemic approach for modeling biological evolution using Parallel DEVS.
Heredia, Daniel; Sanz, Victorino; Urquia, Alfonso; Sandín, Máximo
2015-08-01
A new model for studying the evolution of living organisms is proposed in this manuscript. The proposed model is based on a non-neodarwinian systemic approach. The model is focused on considering several controversies and open discussions about modern evolutionary biology. Additionally, a simplification of the proposed model, named EvoDEVS, has been mathematically described using the Parallel DEVS formalism and implemented as a computer program using the DEVSLib Modelica library. EvoDEVS serves as an experimental platform to study different conditions and scenarios by means of computer simulations. Two preliminary case studies are presented to illustrate the behavior of the model and validate its results. EvoDEVS is freely available at http://www.euclides.dia.uned.es. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
Battery parameterisation based on differential evolution via a boundary evolution strategy
Yang, Guangya
2013-01-01
. The method can parameterise the model without extensive data preparation. In addition, the approach can also estimate the initial SOC and the available capacity. The efficiency of the approach is verified through two battery packs, one is an 8-cell battery module and one from an electrical vehicle.......Attention has been given to the battery modelling in the electric engineering field following the current development of renewable energy and electrification of transportation. The establishment of the equivalent circuit model of the battery requires data preparation and parameterisation. Besides......, as the equivalent circuit model is an abstract map of the battery electric characteristics, the determination of the possible ranges of parameters can be a challenging task. In this paper, an efficient yet easy to implement method is proposed to parameterise the equivalent circuit model of batteries utilising...
Ren Ziwu
2016-04-01
Full Text Available A humanoid manipulator produces significantly reactive forces against a humanoid body when it operates in a rapid and continuous reaction environment (e.g., playing baseball, ping-pong etc.. This not only disturbs the balance and stability of the humanoid robot, but also influences its operation precision. To solve this problem, a novel approach, which is able to generate a minimum-acceleration and continuous acceleration trajectory for the humanoid manipulator, is presented in this paper. By this method, the whole trajectory of humanoid manipulation is divided into two processes, i.e., the operation process and the return process. Moreover, the target operation point is considered as a particular point that should be passed through. As such, the trajectory of each process is described through a quartic polynomial in the joint space, after which the trajectory planning problem for the humanoid manipulator can be formulated as a global constrained optimization problem. In order to alleviate the reactive force, a fitness function that aims to minimize the maximum acceleration of each joint of the manipulator is defined, while differential evolution is employed to determine the joint accelerations of the target operation point. Thus, a trajectory with a minimum-acceleration and continuous acceleration profile is obtained, which can reduce the effect on the body and be favourable for the balance and stability of the humanoid robot to a certain extent. Finally, a humanoid robot with a 7-DOF manipulator for ping-pong playing is employed as an example. Simulation experiment results show the effectiveness of this method for the trajectory planning problem being studied.
Differential reinforcement of an approach response in zebrafish (Danio rerio).
Manabe, Kazuchika; Dooling, R J; Takaku, Shinichi
2013-09-01
Five zebrafish were trained to approach a target using a fully automated training procedure. During a training session, if the distance between the fish and the target was closer than an arbitrarily set distance, the approach response was reinforced by food. The fish continued to respond under this reinforcement contingency and the distance criterion could be shortened up to eighty times within a 1h session. The initial distance limit was then shortened for the next test training session. Once the initial distance criterion was reduced to a final minimum distance, the distance criterion was fixed at this value for the next nine successive sessions. In a second experiment using different fish, we manipulated approach distances in three conditions. The first condition was identical to the changing criterion training as in Experiment 1. In the second condition, only response distances under a distance criterion were reinforced. And in the last condition, only response distances over the distance criterion were reinforced. Results show that zebrafish can control the distance between themselves and a target. In other words, zebrafish are sensitive to the spatial consequences of their behavior. The present results show that a differential reinforcement paradigm can be successfully applied to zebrafish which therefore enhances their value as a vertebrate model for studies of complex behavior including visuomotor learning.
Tahir Nadeem MALIK; Salman ZAFAR; Saaqib HAROON
2015-01-01
Short-term hydrothermal scheduling (STHTS) is a non-linear and complex optimization problem with a set of oper-ational hydraulic and thermal constraints. Earlier, this problem has been addressed by several classical techniques;however, due to limitations such as non-linearity and non-convexity in cost curves, artificial intelligence tools based techniques are being used to solve the STHTS problem. In this paper an improved chaotic hybrid differential evolution (ICHDE) algorithm is proposed to find an optimal solution to this problem taking into account practical constraints. A self-adjusted parameter setting is obtained in differential evolution (DE) with the application of chaos theory, and a chaotic hybridized local search mechanism is embedded in DE to effectively prevent it from premature convergence. Furthermore, heuristic constraint handling techniques without any penalty factor setting are adopted to handle the complex hydraulic and thermal constraints. The superiority and effectiveness of the developed methodology are evaluated by its application in two illustrated hydrothermal test systems taken from the literature. The transmission line losses, prohibited discharge zones of hydel plants, and ramp rate limits of thermal plants are also taken into account. The simulation results reveal that the proposed technique is competent to produce an encouraging solution as com-pared with other recently established evolutionary approaches.
Redox environment in stem and differentiated cells: A quantitative approach
O.G. Lyublinskaya
2017-08-01
Full Text Available Stem cells are believed to maintain a specific intracellular redox status through a combination of enhanced removal capacity and limited production of ROS. In the present study, we challenge this assumption by developing a quantitative approach for the analysis of the pro- and antioxidant ability of human embryonic stem cells in comparison with their differentiated descendants, as well as adult stem and non-stem cells. Our measurements showed that embryonic stem cells are characterized by low ROS level, low rate of extracellular hydrogen peroxide removal and low threshold for peroxide-induced cytotoxicity. However, biochemical normalization of these parameters to cell volume/protein leads to matching of normalized values in stem and differentiated cells and shows that tested in the present study cells (human embryonic stem cells and their fibroblast-like progenies, adult mesenchymal stem cells, lymphocytes, HeLa maintain similar intracellular redox status. Based on these observations, we propose to use ROS concentration averaged over the cell volume instead of ROS level as a measure of intracellular redox balance. We show that attempts to use ROS level for comparative analysis of redox status of morphologically different cells could lead to false conclusions. Methods for the assessment of ROS concentration based on flow cytometry analysis with the use of H2DCFDA dye and HyPer, genetically encoded probe for hydrogen peroxide, are discussed.
Alloimmunization in autoimmune hemolytic anemia patient: The differential adsorption approach
Ravi C Dara
2017-01-01
Full Text Available Patients of β-thalassemia major are dependent on regular blood transfusions for their entire lifetime. Development of antibodies against red blood cell (RBC antigen which may be alloantibody or autoantibody, several times as a result of frequent red cell component transfusions, further complicates the subsequent transfusion therapy. Among the autoantibodies, warm-reactive autoantibodies are commoner and interfere in the pretransfusion testing. These RBC autoantibodies present in patient's serum potentially react with all the cells of antibody identification panel giving “pan-reactive” picture and making alloantibody identification complex. In this report, we present our approach in a thalassemia patient who presented with warm-type autoimmune hemolytic anemia, low hemoglobin of 5.8 g/dl, and three significant alloantibodies (anti-D, anti-S, and anti-Jk b which were masked by pan-reactive warm autoantibody(s. Differential adsorption was used to unmask underlying alloantibodies. We suggest that differential adsorption procedure is an effective and efficient method for autoantibody adsorption, detection, and identification of masked alloantibody(s, especially in patients with low hemoglobin and history of recent blood transfusion.
Alloimmunization in autoimmune hemolytic anemia patient: The differential adsorption approach
Dara, Ravi C.; Tiwari, Aseem Kumar; Arora, Dinesh; Mitra, Subhasis; Acharya, Devi Prasad; Aggarwal, Geet; Sharma, Jyoti
2017-01-01
Patients of β-thalassemia major are dependent on regular blood transfusions for their entire lifetime. Development of antibodies against red blood cell (RBC) antigen which may be alloantibody or autoantibody, several times as a result of frequent red cell component transfusions, further complicates the subsequent transfusion therapy. Among the autoantibodies, warm-reactive autoantibodies are commoner and interfere in the pretransfusion testing. These RBC autoantibodies present in patient's serum potentially react with all the cells of antibody identification panel giving “pan-reactive” picture and making alloantibody identification complex. In this report, we present our approach in a thalassemia patient who presented with warm-type autoimmune hemolytic anemia, low hemoglobin of 5.8 g/dl, and three significant alloantibodies (anti-D, anti-S, and anti-Jkb) which were masked by pan-reactive warm autoantibody(s). Differential adsorption was used to unmask underlying alloantibodies. We suggest that differential adsorption procedure is an effective and efficient method for autoantibody adsorption, detection, and identification of masked alloantibody(s), especially in patients with low hemoglobin and history of recent blood transfusion. PMID:28316442
A quantitative approach to evolution of music and philosophy
Vieira, Vilson; Fabbri, Renato; Travieso, Gonzalo; Oliveira, Osvaldo N., Jr.; da Fontoura Costa, Luciano
2012-08-01
The development of new statistical and computational methods is increasingly making it possible to bridge the gap between hard sciences and humanities. In this study, we propose an approach based on a quantitative evaluation of attributes of objects in fields of humanities, from which concepts such as dialectics and opposition are formally defined mathematically. As case studies, we analyzed the temporal evolution of classical music and philosophy by obtaining data for 8 features characterizing the corresponding fields for 7 well-known composers and philosophers, which were treated with multivariate statistics and pattern recognition methods. A bootstrap method was applied to avoid statistical bias caused by the small sample data set, with which hundreds of artificial composers and philosophers were generated, influenced by the 7 names originally chosen. Upon defining indices for opposition, skewness and counter-dialectics, we confirmed the intuitive analysis of historians in that classical music evolved according to a master-apprentice tradition, while in philosophy changes were driven by opposition. Though these case studies were meant only to show the possibility of treating phenomena in humanities quantitatively, including a quantitative measure of concepts such as dialectics and opposition, the results are encouraging for further application of the approach presented here to many other areas, since it is entirely generic.
2013-01-01
Identification of the unknown parameters and orders of fractional chaotic systems is of vital significance in controlling and synchronization of fractional-order chaotic systems. In this paper, a non-Lyapunov novel approach is proposed to estimate the unknown parameters and orders together for non-commensurate and hyper fractional chaotic systems based on cuckoo search oriented statistically the differential evolution (CSODE). Firstly, a novel Gao's mathematical model is put and analysed in t...
Mora Van Cauwelaert, Emilio; Arias Del Angel, Juan A.; Benítez, Mariana; Azpeitia, Eugenio M.
2015-01-01
Multicellularity has emerged and continues to emerge in a variety of lineages and under diverse environmental conditions. In order to attain individuality and integration, multicellular organisms must exhibit spatial cell differentiation, which in turn allows cell aggregates to robustly generate traits and behaviors at the multicellular level. Nevertheless, the mechanisms that may lead to the development of cellular differentiation and patterning in emerging multicellular organisms remain unclear. We briefly review two conceptual frameworks that have addressed this issue: the cooperation-defection framework and the dynamical patterning modules (DPMs) framework. Then, situating ourselves in the DPM formalism first put forward by S. A. Newman and collaborators, we state a hypothesis for cell differentiation and arrangement in cellular masses of emerging multicellular organisms. Our hypothesis is based on the role of the generic cell-to-cell communication and adhesion patterning mechanisms, which are two fundamental mechanisms for the evolution of multicellularity, and whose molecules seem to be well-conserved in extant multicellular organisms and their unicellular relatives. We review some fundamental ideas underlying this hypothesis and contrast them with empirical and theoretical evidence currently available. Next, we use a mathematical model to illustrate how the mechanisms and assumptions considered in the hypothesis we postulate may render stereotypical arrangements of differentiated cells in an emerging cellular aggregate and may contribute to the variation and recreation of multicellular phenotypes. Finally, we discuss the potential implications of our approach and compare them to those entailed by the cooperation-defection framework in the study of cell differentiation in the transition to multicellularity. PMID:26157427
Fokker-Planck approach to stochastic delay differential equations
Guillouzic, Steve
2001-10-01
Models written in terms of stochastic delay differential equations (SDDE's) have recently appeared in a number of fields, such as physiology, optics, and climatology. Unfortunately, the development of a Fokker-Planck approach for these equations is being hampered by their non-Markovian nature. In this thesis, an exact Fokker- Planck equation (FPE) is formulated for univariate SDDE's involving Gaussian white noise. Although this FPE is not self-sufficient, it is found to be helpful in at least two different contexts: with a short delay approximation and under an appropriate separation of time scales. In the short delay approximation, a Taylor expansion is applied to an SDDE with nondelayed diffusion and yields a nondelayed stochastic differential equation. The aforementioned FPE then allows the derivation of an alternate and complementary approximation of the original SDDE. This method is illustrated with linear and logistic SDDE's. Under the separation of time scales assumption, the FPE of a bistable system is reduced to a form that is uniquely determined by the steady-state probability density when the diffusion term of the SDDE is nondelayed. In the context of an overdamped particle with delayed coupling to a symmetrical and stochastically driven potential, the resulting FPE is used with standard techniques to express the transition rate between wells in terms of the noise amplitude and of the steady-state probability density. The same is also accomplished for the mean first passage time from one point to another. This whole approach is then applied to the case of a quartic potential, for which all realisations eventually stabilise on an oscillatory trajectory with an ever increasing amplitude. Although this latter phenomenon prevents the existence of a steady-state limit, a pseudo- steady-state probability density can be defined and used instead of the non-existent steady-state one when the transition rate to these unbounded oscillatory trajectories is
Norris, Scott A; Brenner, Michael P; Aziz, Michael J [Harvard School of Engineering and Applied Sciences, Cambridge MA 02138 (United States)
2009-06-03
We develop a methodology for deriving continuum partial differential equations for the evolution of large-scale surface morphology directly from molecular dynamics simulations of the craters formed from individual ion impacts. Our formalism relies on the separation between the length scale of ion impact and the characteristic scale of pattern formation, and expresses the surface evolution in terms of the moments of the crater function. We demonstrate that the formalism reproduces the classical Bradley-Harper results, as well as ballistic atomic drift, under the appropriate simplifying assumptions. Given an actual set of converged molecular dynamics moments and their derivatives with respect to the incidence angle, our approach can be applied directly to predict the presence and absence of surface morphological instabilities. This analysis represents the first work systematically connecting molecular dynamics simulations of ion bombardment to partial differential equations that govern topographic pattern-forming instabilities.
Norris, Scott A; Brenner, Michael P; Aziz, Michael J
2009-06-03
We develop a methodology for deriving continuum partial differential equations for the evolution of large-scale surface morphology directly from molecular dynamics simulations of the craters formed from individual ion impacts. Our formalism relies on the separation between the length scale of ion impact and the characteristic scale of pattern formation, and expresses the surface evolution in terms of the moments of the crater function. We demonstrate that the formalism reproduces the classical Bradley-Harper results, as well as ballistic atomic drift, under the appropriate simplifying assumptions. Given an actual set of converged molecular dynamics moments and their derivatives with respect to the incidence angle, our approach can be applied directly to predict the presence and absence of surface morphological instabilities. This analysis represents the first work systematically connecting molecular dynamics simulations of ion bombardment to partial differential equations that govern topographic pattern-forming instabilities.
An evolution of image source camera attribution approaches.
Jahanirad, Mehdi; Wahab, Ainuddin Wahid Abdul; Anuar, Nor Badrul
2016-05-01
researchers, are also critically analysed and further categorised into four different classes, namely, optical aberrations based, sensor camera fingerprints based, processing statistics based and processing regularities based, to present a classification. Furthermore, this paper aims to investigate the challenging problems, and the proposed strategies of such schemes based on the suggested taxonomy to plot an evolution of the source camera attribution approaches with respect to the subjective optimisation criteria over the last decade. The optimisation criteria were determined based on the strategies proposed to increase the detection accuracy, robustness and computational efficiency of source camera brand, model or device attribution.
Interpreting Evidence: An Approach to Teaching Human Evolution in the Classroom
DeSilva, Jeremy
2004-01-01
Paleoanthropology, which is the study of human evolution through fossil records, can be used as a tool for teaching human evolution in the classrooms. An updated approach to teaching human evolution and a model for explaining what is science and how it is done, is presented.
Interpreting Evidence: An Approach to Teaching Human Evolution in the Classroom
DeSilva, Jeremy
2004-01-01
Paleoanthropology, which is the study of human evolution through fossil records, can be used as a tool for teaching human evolution in the classrooms. An updated approach to teaching human evolution and a model for explaining what is science and how it is done, is presented.
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.
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.
Design of Robust Optimal Fixed Structure Controller Using Self Adaptive Differential Evolution
Joe Amali, S. Miruna; Baskar, S.
This paper presents a design of robust optimal fixed structure controller for systems with uncertainties and disturbance using Self Adaptive Differential Evolution (SaDE) algorithm. PID controller and second order polynomial structure are considered for fixed structure controller. The design problem is formulated as minimization of maximum value of real part of the poles subject to the robust stability criteria and load disturbance attenuation criteria. The performance of the proposed method is demonstrated with a test system. SaDE self adapts the trial vector generation strategy and crossover rate (CR) value during evolution. Self adaptive Penalty (SP) method is used for constraint handling. The results are compared with constrained PSO and mixed Deterministic/Randomized algorithms. It is shown experimentally that the SaDE adapts automatically to the best strategy and CR value. Performance of the SaDE-based controller is superior to other methods in terms of success rate, robust stability, and disturbance attenuation.
Beyer, Horst Reinhard
2007-01-01
The present volume is self-contained and introduces to the treatment of linear and nonlinear (quasi-linear) abstract evolution equations by methods from the theory of strongly continuous semigroups. The theoretical part is accessible to graduate students with basic knowledge in functional analysis. Only some examples require more specialized knowledge from the spectral theory of linear, self-adjoint operators in Hilbert spaces. Particular stress is on equations of the hyperbolic type since considerably less often treated in the literature. Also, evolution equations from fundamental physics need to be compatible with the theory of special relativity and therefore are of hyperbolic type. Throughout, detailed applications are given to hyperbolic partial differential equations occurring in problems of current theoretical physics, in particular to Hermitian hyperbolic systems. This volume is thus also of interest to readers from theoretical physics.
Jian Wang
2014-01-01
Full Text Available 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.
Differential Evolution-Based PID Control of Nonlinear Full-Car Electrohydraulic Suspensions
Jimoh O. Pedro
2013-01-01
Full Text Available This paper presents a differential-evolution- (DE- optimized, independent multiloop proportional-integral-derivative (PID controller design for full-car nonlinear, electrohydraulic suspension systems. The multiloop PID control stabilises the actuator via force feedback and also improves the system performance. Controller gains are computed using manual tuning and through DE optimization to minimise a performance index, which addresses suspension travel, road holding, vehicle handling, ride comfort, and power consumption constraints. Simulation results showed superior performance of the DE-optimized PID-controlled active vehicle suspension system (AVSS over the manually tuned PID-controlled AVSS and the passive vehicle suspension system (PVSS.
THE ONSET OF DIFFERENTIATION AND INTERNAL EVOLUTION: THE CASE OF 21 LUTETIA
Formisano, M.; Turrini, D.; Federico, C.; Capaccioni, F.; De Sanctis, M. C., E-mail: michelangelo.formisano@iaps.inaf.it [INAF-IAPS, Via del Fosso del Cavaliere 100, I-00133 Roma (Italy)
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.
Duo ePeng
2014-11-01
Full Text Available Sucrose transporters (SUTs are essential for the export and efficient movement of sucrose from source leaves to sink organs in plants. The angiosperm SUT family was previously classified into three or four distinct groups, Types I, II (subgroup IIB and III, with dicot-specific Type I and monocot-specific Type IIB functioning in phloem loading. To shed light on the underlying drivers of SUT evolution, Bayesian phylogenetic inference was undertaken using 41 sequenced plant genomes, including seven basal lineages at key evolutionary junctures. Our analysis supports four phylogenetically and structurally distinct SUT subfamilies, originating from two ancient groups (AG1 and AG2 that diverged early during terrestrial colonization. In both AG1 and AG2, multiple intron acquisition events in the progenitor vascular plant established the gene structures of modern SUTs. Tonoplastic Type III and plasmalemmal Type II represent evolutionarily conserved descendants of AG1 and AG2, respectively. Type I and Type IIB were previously thought to evolve after the dicot-monocot split. We show, however, that divergence of Type I from Type III SUT predated basal angiosperms, likely associated with evolution of vascular cambium and phloem transport. Type I SUT was subsequently lost in monocots along with vascular cambium, and independent evolution of Type IIB coincided with modified monocot vasculature. Both Type I and Type IIB underwent lineage-specific expansion. In multiple unrelated taxa, the newly-derived SUTs exhibit biased expression in reproductive tissues, suggesting a functional link between phloem loading and reproductive fitness. Convergent evolution of Type I and Type IIB for SUT function in phloem loading and reproductive organs supports the idea that differential vascular development in dicots and monocots is a strong driver for SUT family evolution in angiosperms.
A Taxonomy for a Constructive Approach to Software Evolution
Ciraci, S.; van den Broek, P.M.; Aksit, Mehmet
In many software design and evaluation techniques, either the software evolution problem is not systematically elaborated, or only the impact of evolution is considered. Thus, most of the time software is changed by editing the components of the software system, i.e. breaking down the software
Ahadi Arif Nugraha
2015-03-01
Full Text Available Salah satu aspek penting dalam perencanaan infrastruktur jaringan seluler adalah Base Transceiver Station (BTS yang merupakan sebuah pemancar dan penerima sinyal telephone seluler. Di satu sisi, peningkatan jumlah menara memang akan mendukung tercapainya pemenuhan kebutuhan masyarakat terhadap layanan telekomunikasi. Namun di sisi lain, penempatan menara yang tanpa perencanaan serta koordinasi yang tepat akan menimbulkan jumlah menara yang berlebih sehingga dapat mengganggu estetika lingkungan, tata ruang suatu wilayah, dan radiasi gelombang radio yang tidak terkontrol sehingga sangat mengganggu. Berdasarkan permasalahan diatas, maka dapat diselesaikan dengan cara menyusun suatu master plan yang lengkap dan rinci tentang penataan lokasi menara di Kabupaten Mojokerto untuk lima tahun mendatang. Penataan lokasi menara dilakukan dengan menggunakan algoritma Differential Evolution (DE untuk menemukan solusi penataan menara yang baik berdasarkan luas cakupan area sel yang dihasilkan, kemudian menggunakan software MapInfo sebagai media visualisasi peta lokasi penempatan menara telekomunikasi. Dalam perancangan menara BTS tahun 2019, Kabupaten Mojokerto membutuhkan 106 menara BTS 2G dan 36 menara BTS 3G. Penempatan menara BTS 2G dan 3G menggunakan algoritma differential evolution mampu mengoptimalkan 2,94% dari luas wilayah Kabupaten Mojokerto
Bech, Michael Møller; Nørgård, Christian; Roemer, Daniel Beck
2016-01-01
This paper illustrates how the relatively simple constrained multi-objective optimization algorithm Generalized Differential Evolution 3 (GDE3), can assist with the practical sizing of mechatronic components used in e.g. digital displacement fluid power machinery. The studied bi- and tri-objectiv...... different optimization control parameter settings and it is concluded that GDE3 is a reliable optimization tool that can assist mechatronic engineers in the design and decision making process.......This paper illustrates how the relatively simple constrained multi-objective optimization algorithm Generalized Differential Evolution 3 (GDE3), can assist with the practical sizing of mechatronic components used in e.g. digital displacement fluid power machinery. The studied bi- and tri......-objective problems having 10+ design variables are both highly constrained, nonlinear and non-smooth but nevertheless the algorithm converges to the Pareto-front within a hours of computation (20k function evaluations). Additionally, the robustness and convergence speed of the algorithm are investigated using...
Rashida Adeeb Khanum
2016-02-01
Full Text Available JADE is an adaptive scheme of nature inspired algorithm, Differential Evolution (DE. It performed considerably improved on a set of well-studied benchmark test problems. In this paper, we evaluate the performance of new JADE with two external archives to deal with unconstrained continuous large-scale global optimization problems labeled as Reflected Adaptive Differential Evolution with Two External Archives (RJADE/TA. The only archive of JADE stores failed solutions. In contrast, the proposed second archive stores superior solutions at regular intervals of the optimization process to avoid premature convergence towards local optima. The superior solutions which are sent to the archive are reflected by new potential solutions. At the end of the search process, the best solution is selected from the second archive and the current population. The performance of RJADE/TA algorithm is then extensively evaluated on two test beds. At first on 28 latest benchmark functions constructed for the 2013 Congress on Evolutionary Computation special session. Secondly on ten benchmark problems from CEC2010 Special Session and Competition on Large-Scale Global Optimization. Experimental results demonstrated a very competitive perfor-mance of the algorithm.
Huseyin Ceylan
2013-01-01
Full Text Available This study proposes a traffic congestion minimization model in which the traffic signal setting optimization is performed through a combined simulation-optimization model. In this model, the TRANSYT traffic simulation software is combined with Differential Evolution (DE optimization algorithm, which is based on the natural selection paradigm. In this context, the EQuilibrium Network Design (EQND problem is formulated as a bilevel programming problem in which the upper level is the minimization of the total network performance index. In the lower level, the traffic assignment problem, which represents the route choice behavior of the road users, is solved using the Path Flow Estimator (PFE as a stochastic user equilibrium assessment. The solution of the bilevel EQND problem is carried out by the proposed Differential Evolution and TRANSYT with PFE, the so-called DETRANSPFE model, on a well-known signal controlled test network. Performance of the proposed model is compared to that of two previous works where the EQND problem has been solved by Genetic-Algorithms- (GAs- and Harmony-Search- (HS- based models. Results show that the DETRANSPFE model outperforms the GA- and HS-based models in terms of the network performance index and the computational time required.
Neumann, Wladimir; Breuer, Doris; Spohn, Tilman; Henke, Stephan; Gail, Hans-Peter; Schwarz, Winfried; Trieloff, Mario; Hopp, Jens
2015-04-01
The acapulcoites and lodranites are rare groups of achondritic meteorites. Several characteristics such as unique oxygen isotope composition and similar cosmic ray exposure ages indicate that these meteorites originate from a common parent body (Weigel et al. 1999). By contrast to both undifferentiated and differentiated meteorites, acapulcoites and lodranites are especially interesting because they experienced melting that was, however, not complete (McCoy et al. 2006). Thus, unravelling their origin contributes directly to the understanding of the initial differentiation stage of planetary objects in the Solar system. The information preserved in the structure and composition of meteorites can be recovered by modelling the evolution of their parent bodies and comparing the results with the laboratory investigations. Model calculations for the thermal evolution of the parent body of the Acapulco and Lodran-like meteorite clan were performed using two numerical models. Both models (from [3] and [4], termed (a) and (b), respectively) solve a 1D heat conduction equation in spherical symmetry considering heating by short- and long-lived radioactive isotopes, temperature- and porosity-dependent parameters, compaction of initially porous material, and melting. The calculations with (a) were compared to the maximum metamorphic temperatures and thermo-chronological data available for acapulcoites and lodranites. Applying a genetic algorithm, an optimised set of parameters of a common parent body was determined, which fits to the data for the cooling histories of these meteorites. The optimum fit corresponds to a body with the radius of 270 km and a formation time of 1.66 Ma after the CAIs. Using the model by (b) that considers differentiation by porous flow and magmatic heat transport, the differentiation of the optimum fit body was calculated. The resulting structure consists of a metallic core, a silicate mantle, a partially differentiated layer, an undifferentiated
Yalin Wang
2013-01-01
Full Text Available The grinding-classification is the prerequisite process for full recovery of the nonrenewable minerals with both production quality and quantity objectives concerned. Its natural formulation is a constrained multiobjective optimization problem of complex expression since the process is composed of one grinding machine and two classification machines. In this paper, a hybrid differential evolution (DE algorithm with multi-population is proposed. Some infeasible solutions with better performance are allowed to be saved, and they participate randomly in the evolution. In order to exploit the meaningful infeasible solutions, a functionally partitioned multi-population mechanism is designed to find an optimal solution from all possible directions. Meanwhile, a simplex method for local search is inserted into the evolution process to enhance the searching strategy in the optimization process. Simulation results from the test of some benchmark problems indicate that the proposed algorithm tends to converge quickly and effectively to the Pareto frontier with better distribution. Finally, the proposed algorithm is applied to solve a multiobjective optimization model of a grinding and classification process. Based on the technique for order performance by similarity to ideal solution (TOPSIS, the satisfactory solution is obtained by using a decision-making method for multiple attributes.
Sex-differential selection and the evolution of X inactivation strategies.
Tim Connallon
2013-04-01
Full Text Available 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.
A Novel Approach to Constraining Uncertain Stellar Evolution Models
Rosenfield, Philip; Girardi, Leo; Dalcanton, Julianne; Johnson, L. C.; Williams, Benjamin F.; Weisz, Daniel R.; Bressan, Alessandro; Fouesneau, Morgan
2017-01-01
Stellar evolution models are fundamental to nearly all studies in astrophysics. They are used to interpret spectral energy distributions of distant galaxies, to derive the star formation histories of nearby galaxies, and to understand fundamental parameters of exoplanets. Despite the success in using stellar evolution models, some important aspects of stellar evolution remain poorly constrained and their uncertainties rarely addressed. We present results using archival Hubble Space Telescope observations of 10 stellar clusters in the Magellanic Clouds to simultaneously constrain the values and uncertainties of the strength of core convective overshooting, metallicity, interstellar extinction, cluster distance, binary fraction, and age.
Nonlinear eigenvalue approach to differential Riccati equations for contraction analysis
Kawano, Yu; Ohtsuka, Toshiyuki
2017-01-01
In this paper, we extend the eigenvalue method of the algebraic Riccati equation to the differential Riccati equation (DRE) in contraction analysis. One of the main results is showing that solutions to the DRE can be expressed as functions of nonlinear eigenvectors of the differential Hamiltonian ma
Solving chemical dynamic optimization problems with ranking-based differential evolution algorithms
Xu Chen; Wenli Du; Feng Qian
2016-01-01
Dynamic optimization problems (DOPs) described by differential equations are often encountered in chemical engineering. Deterministic techniques based on mathematic programming become invalid when the models are non-differentiable or explicit mathematical descriptions do not exist. Recently, evolutionary algorithms are gaining popularity for DOPs as they can be used as robust alternatives when the deterministic techniques are in-valid. In this article, a technology named ranking-based mutation operator (RMO) is presented to enhance the previous differential evolution (DE) algorithms to solve DOPs using control vector parameterization. In the RMO, better individuals have higher probabilities to produce offspring, which is helpful for the performance enhancement of DE algorithms. Three DE-RMO algorithms are designed by incorporating the RMO. The three DE-RMO algorithms and their three original DE algorithms are applied to solve four constrained DOPs from the literature. Our simulation results indicate that DE-RMO algorithms exhibit better performance than previous non-ranking DE algorithms and other four evolutionary algorithms.
Altintas, Esra; Özdemir, Ahmet S.
2015-01-01
The aim of the study is to develop a differentiation approach for the mathematics education of gifted middle school students and to determine the effect of the differentiation approach on creative thinking skills of gifted students based on both cognitive and affective factors. In this context, the answer to the following question was searched:…
Yin, Guisheng; Chi, Kuo, E-mail: chik89769@hrbeu.edu.cn; Dong, Yuxin; Dong, Hongbin
2017-04-25
In this paper, an approach of community evolution based on gravitational relationship refactoring between the nodes in a dynamic network is proposed, and it can be used to simulate the process of community evolution. A static community detection algorithm and a dynamic community evolution algorithm are included in the approach. At first, communities are initialized by constructing the core nodes chains, the nodes can be iteratively searched and divided into corresponding communities via the static community detection algorithm. For a dynamic network, an evolutionary process is divided into three phases, and behaviors of community evolution can be judged according to the changing situation of the core nodes chain in each community. Experiments show that the proposed approach can achieve accuracy and availability in the synthetic and real world networks. - Highlights: • The proposed approach considers both the static community detection and dynamic community evolution. • The approach of community evolution can identify the whole 6 common evolution events. • The proposed approach can judge the evolutionary events according to the variations of the core nodes chains.
ZHANG Xing; BAI YongQiang; XIN Bin; CHEN Jie
2013-01-01
This paper presents online motion planning for UAV (unmanned aerial vehicle) in complex threat field,including both static threats and moving threats,which can be formulated as a dynamic constrained optimal control problem.Receding horizon control (RHC) based on differential evolution (DE) algorithm is adopted.A location-predicting model of moving threats is established to assess the value of threat that UAV faces in flight.Then flyable paths can be generated by the control inputs which are optimized by DE under the guidance of the objective function.Simulation results demonstrate that the proposed method not only generates smooth and flyable paths,but also enables UAV to avoid threats efficiently and arrive at destination safely.
Umesh Kumar Rout
2013-09-01
Full Text Available This paper presents the design and performance analysis of Differential Evolution (DE algorithm based Proportional-Integral (PI controller for Automatic Generation Control (AGC of an interconnected power system. A two area non-reheat thermal system equipped with PI controllers which is widely used in literature is considered for the design and analysis purpose. The design problem is formulated as an optimization problem control and DE is employed to search for optimal controller parameters. Three different objective functions using Integral Time multiply Absolute Error (ITAE, damping ratio of dominant eigenvalues and settling time with appropriate weight coefficients are derived in order to increase the performance of the controller. The superiority of the proposed DE optimized PI controller has been shown by comparing the results with some recently published modern heuristic optimization techniques such as Bacteria Foraging Optimization Algorithm (BFOA and Genetic Algorithm (GA based PI controller for the same interconnected power system.
Aerodynamic optimization of supersonic compressor cascade using differential evolution on GPU
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%.
Mingolo, Nusharin; Sarakorn, Weerachai
2016-04-01
In this research, the Modified Differential Evolution (DE) algorithm is proposed and applied to the Magnetotelluric (MT) and Vertical Electrical sounding (VES) data to reveal the reasonable resistivity structure. The common processes of DE algorithm, including initialization, mutation and crossover, are modified by introducing both new control parameters and some constraints to obtain the fitting-reasonable resistivity model. The validity and efficiency of our developed modified DE algorithm is tested on both synthetic and real observed data. Our developed DE algorithm is also compared to the well-known OCCAM's algorithm for real case of MT data. For the synthetic case, our modified DE algorithm with appropriate control parameters can reveal the reasonable-fitting models when compared to the original synthetic models. For the real data case, the resistivity structures revealed by our algorithm are closed to those obtained by OCCAM's inversion, but our obtained structures reveal layers more apparently.
Zhongbo Hu
2014-01-01
Full Text Available Many improved differential Evolution (DE algorithms have emerged as a very competitive class of evolutionary computation more than a decade ago. However, few improved DE algorithms guarantee global convergence in theory. This paper developed a convergent DE algorithm in theory, which employs a self-adaptation scheme for the parameters and two operators, that is, uniform mutation and hidden adaptation selection (haS operators. The parameter self-adaptation and uniform mutation operator enhance the diversity of populations and guarantee ergodicity. The haS can automatically remove some inferior individuals in the process of the enhancing population diversity. The haS controls the proposed algorithm to break the loop of current generation with a small probability. The breaking probability is a hidden adaptation and proportional to the changes of the number of inferior individuals. The proposed algorithm is tested on ten engineering optimization problems taken from IEEE CEC2011.
Optimal Location and Sizing of UPQC in Distribution Networks Using Differential Evolution Algorithm
Seyed Abbas Taher
2012-01-01
Full Text Available Differential evolution (DE algorithm is used to determine optimal location of unified power quality conditioner (UPQC considering its size in the radial distribution systems. The problem is formulated to find the optimum location of UPQC based on an objective function (OF defined for improving of voltage and current profiles, reducing power loss and minimizing the investment costs considering the OF's weighting factors. Hence, a steady-state model of UPQC is derived to set in forward/backward sweep load flow. Studies are performed on two IEEE 33-bus and 69-bus standard distribution networks. Accuracy was evaluated by reapplying the procedures using both genetic (GA and immune algorithms (IA. Comparative results indicate that DE is capable of offering a nearer global optimal in minimizing the OF and reaching all the desired conditions than GA and IA.
Ika Ayu Fajarwati
2012-09-01
Full Text Available Vehicle Routing Problem (VRP merupakan permasalahan optimasi kombinatorial kompleks yang memiliki peranan penting dalam manajemen sistem distribusi dengan tujuan meminimalkan biaya yang diperlukan, dimana penentuan biaya berkaitan dengan jarak dari rute yang ditempuh oleh armada distribusi. Ciri dari VRP yaitu penggunaan armada dengan kapasitas tertentu dan kegiatannya berpusat pada satu titik depot untuk melayani pelanggan pada titik-titik tertentu dengan jumlah permintaan yang diketahui. Kasus distribusi yang menggabungkan aktifitas pengiriman dan pengambilan produk termasuk dalam salah satu jenis VRP yaitu Vehicle Routing Problem Delivery and Pick-Up (VRP-DP. Banyak metode yang dapat digunakan untuk menyelesaikan permasalahan VRP-DP, salah satunya adalah metode optimasi metaheuristik yaitu Algoritma Differential Evolution yang akan diperkenalkan dalam penelitian ini. Hasil yang diharapkan nantinya adalah rute distribusi optimal untuk armada perusahaan sehingga menghasilkan jarak tempuh dan tentunya total biaya yang minimal dalam memenuhi semua permintaan pelanggan
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.
Chai, Runqi; Savvaris, Al; Tsourdos, Antonios; Chai, Senchun
2017-07-01
Highly constrained trajectory optimization for Space Manoeuvre Vehicles (SMV) is a challenging problem. In practice, this problem becomes more difficult when multiple mission requirements are taken into account. Because of the nonlinearity in the dynamic model and even the objectives, it is usually hard for designers to generate a compromised trajectory without violating strict path and box constraints. In this paper, a new multi-objective SMV optimal control model is formulated and parameterized using combined shooting-collocation technique. A modified game theory approach, coupled with an adaptive differential evolution algorithm, is designed in order to generate the pareto front of the multi-objective trajectory optimization problem. In addition, to improve the quality of obtained solutions, a control logic is embedded in the framework of the proposed approach. Several existing multi-objective evolutionary algorithms are studied and compared with the proposed method. Simulation results indicate that without driving the solution out of the feasible region, the proposed method can perform better in terms of convergence ability and convergence speed than its counterparts. Moreover, the quality of the pareto set generated using the proposed method is higher than other multi-objective evolutionary algorithms, which means the newly proposed algorithm is more attractive for solving multi-criteria SMV trajectory planning problem.
Income differential of female labor in Southern Brazil: dual approach
Rita de Cassia Garcia Margonato
2014-06-01
Full Text Available This study analyzes the formation and income differential of female labor in Southern Brazil in 2002 and 2009 based on microdata from the National Sample Survey (PNAD.The methodology is to estimate the selection and wages equations using the Heckman's Sample Selection Model (1979. For the measurement of the female income differential in commerce, industry and domestic service, compared to income of women in the service sector it is applied an adaptation of the Oaxaca-Blinder Decomposition (1973 adapted by Jann (2008. It was confirmed the hypothesis that segmentation occurs in the female labor market in Southern Brazil because the income differential cannot be explained only by personal attributes (productive or not and by formal work. There are specificities in the sectors (sector effect determining the income differential of women´s income in the labor market, moreover the sector effect explained 33% of the wage differential observed in industry, also explained 29% in the commerce and 35% of the female income gaps when compared to the service sector, which is considered as in advantage.
Yan, Shaomin; Li, Zhenchong; Wu, Guang
2010-04-01
The understanding of evolutionary mechanism is important, and equally important is to describe the evolutionary process. If so, we would know where the biological evolution will go. At species level, we would know whether and when a species will extinct or be prosperous. At protein level, we would know when a protein family will mutate more. In our previous study, we explored the possibility of using the differential equation to describe the evolution of protein family from influenza A virus based on the assumption that the mutation process is the exchange of entropy between protein family and its environment. In this study, we use the analytical solution of system of differential equations to fit the evolution of matrix protein 1 family from influenza A virus. Because the evolutionary process goes along the time course, it can be described by differential equation. The results show that the evolution of a protein family can be fitted by the analytical solution. With the obtained fitted parameters, we may predict the evolution of matrix protein 1 family from influenza A virus. Our model would be the first step towards the systematical modeling of biological evolution and paves the way for further modeling.
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.
Fuzzy logic-based diversity-controlled self-adaptive differential evolution
Amali, S. Miruna Joe; Baskar, S.
2013-08-01
This article presents a novel method using a fuzzy system (FS) to control the population diversity during the various phases of evolution. A local search is applied at regular intervals on an individual selected at random to aid the population in convergence. This diversity control methodology is applied to vary the crossover rate of self-adaptive differential evolution (SaDE). Three variants of the SaDE algorithm are proposed: (1) diversity-controlled SaDE (DCSaDE); (2) SaDE with local search (SaDE-LS); and (3) diversity-controlled SaDE with local search (DCSaDE-LS). The performance of the proposed algorithms is analysed using a set of unconstrained benchmark functions with respect to average function evaluations, success rate and the mean of the objectives of 30 independent trials. The DCSaDE-LS algorithm had a better success rate for high-dimensional multimodal problems and conserved the number of function evaluations required for most of the problems. It is compared with other popular algorithms and the outcome of the proposed DCSaDE-LS algorithm is validated using non-parametric statistical tests. MATLAB codes for the proposed algorithms may be obtained on request.
Chun-Liang Lu
2014-12-01
Full Text Available Differential evolution (DE is a simple, powerful optimization algorithm, which has been widely used in many areas. However, the choices of the best mutation and search strategies are difficult for the specific issues. To alleviate these drawbacks and enhance the performance of DE, in this paper, the hybrid framework based on the adaptive mutation and Wrapper Local Search (WLS schemes, is proposed to improve searching ability to efficiently guide the evolution of the population toward the global optimum. Furthermore, the effective particle encoding representation named Particle Segment Operation-Machine Assignment (PSOMA that we previously published is applied to always produce feasible candidate solutions for solving the Flexible Job-shop Scheduling Problem (FJSP. Experiments were conducted on comprehensive set of complex benchmarks including the unimodal, multimodal and hybrid composition function, to validate performance of the proposed method and to compare with other state-of-the art DE variants such as jDE, JADE, MDE_pBX etc. Meanwhile, the hybrid DE model incorporating PSOMA is used to solve different representative instances based on practical data for multi-objective FJSP verifications. Simulation results indicate that the proposed method performs better for the majority of the single-objective scalable benchmark functions in terms of the solution accuracy and convergence rate. In addition, the wide range of Pareto-optimal solutions and more Gantt chart decision-makings can be provided for the multi-objective FJSP combinatorial optimizations.
Early Stages of the Evolution of Life: a Cybernetic Approach
Melkikh, Alexey V.; Seleznev, Vladimir D.
2008-08-01
Early stages of the evolution of life are considered in terms of control theory. A model is proposed for the transport of substances in a protocell possessing the property of robustness with regard to changes in the environmental concentration of a substance.
The Ecology of Language Evolution. Cambridge Approaches to Language Contact.
Mufwene, Salikoko S.
This book explores the development of creoles and other new languages, highlighting conceptual and methodological issues for genetic linguistics and discussing the significance of ecologies that influence language evolution. It presents examples of changes in the structure, function, and vitality of languages, suggesting that similar ecologies…
Differential forms in Carnot groups: a variational approach
Annalisa Baldi
2011-12-01
Full Text Available Carnot groups (connected simply connected nilpotent stratified Lie groups can be endowed with a complex of ``intrinsic'' differential forms. In this paper we want to provide an evidence of the intrinsic character of Rumin's complex, in the spirit of the Riemannian approximation, like in e.g., the notes of Gromov (Textes Mathématiques 1981 and in Rumin (Geom. Funct. Anal.,2000 . More precisely, we want to show that the intrinsic differential is a limit of suitably weighted usual first order de Rham differentials. As an application, we prove that the L^2-energies associated to classical Maxwell's equations in R^n Gamma-converges to the L^2-energies associated to an ''intrinsic'' Maxwell's equation in a free Carnot group.
Differential approach to treatment of primary nocturnal enuresis in children
Nesterenko O.V.
2011-09-01
Full Text Available The aim of the work is to develop an algorithm of differentiated therapy in children with PNE. 234 children aged 5-15 years were studied. Results of treatment of children with primary nocturnal enuresis using the traditional therapeutic scheme and the algorithm of differential therapy based on identification of individual pathology were analyzed. The best clinical effect (recovery— in 73,1%, improvement— in 19,4% of cases was obtained in children undergone the complex of recommended measures: psychological consultation, rational and family psychotherapy, medication correction, physical and physiotherapy, alarm-monitoring; the complex was used differentially, i.e. depending on the identified pathology. In conclusion the article stated that individual treatment program with the obligatory inclusion of alarm-control for child with PNE should be selected after performing the recommended set of diagnostic measures
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.
Surface primary bone tumors: Systematic approach and differential diagnosis
Mendez Diaz, Cristina; Soler Fernandez, Rafaela; Rodriguez Garcia, Esther; Fernandez Armendariz, Pablo; Diaz Angulo, Carolina [Complejo Hospitalario Universitario A Coruna, Department of Radiology, A Coruna (Spain)
2015-09-15
Surface primary bone tumors may appear similar to their intramedullary counterpart, but because they are rare, they may pose diagnostic challenges when showing different characteristics compared to their intramedullary counterpart. It is important for radiologists to recognize the imaging findings for various uncommon surface primary bone tumors, which may help to reduce the differential diagnosis or to lead to a specific diagnosis. Radiography is typically used for first-line imaging. If necessary, it is followed by CT or MRI for evaluation and characterization of surface bone tumors. The aim of this article is to review the imaging findings and differential diagnosis for surface primary bone tumors. (orig.)
Evolution of a magnetic field in a differentially rotating radiative zone
Gaurat, Mathieu; Lignières, François; Gastine, Thomas
2015-01-01
Recent spectropolarimetric surveys of main-sequence intermediate-mass stars have exhibited a dichotomy in the distribution of the observed magnetic field between the kG dipoles of Ap/Bp stars and the sub-Gauss magnetism of Vega and Sirius. We would like to test whether this dichotomy is linked to the stability versus instability of large-scale magnetic configurations in differentially rotating radiative zones. We computed the axisymmetric magnetic field obtained from the evolution of a dipolar field threading a differentially rotating shell. A full parameter study including various density profiles and initial and boundary conditions was performed with a 2D numerical code. We then focused on the ratio between the toroidal and poloidal components of the magnetic field and discuss the stability of the configurations dominated by the toroidal component using local stability criteria and insights from recent 3D numerical simulations. The numerical results and a simple model show that the ratio between the toroida...
Yi-Fei Pu
2013-01-01
Full Text Available The traditional integer-order partial differential equation-based image denoising approaches often blur the edge and complex texture detail; thus, their denoising effects for texture image are not very good. To solve the problem, a fractional partial differential equation-based denoising model for texture image is proposed, which applies a novel mathematical method—fractional calculus to image processing from the view of system evolution. We know from previous studies that fractional-order calculus has some unique properties comparing to integer-order differential calculus that it can nonlinearly enhance complex texture detail during the digital image processing. The goal of the proposed model is to overcome the problems mentioned above by using the properties of fractional differential calculus. It extended traditional integer-order equation to a fractional order and proposed the fractional Green’s formula and the fractional Euler-Lagrange formula for two-dimensional image processing, and then a fractional partial differential equation based denoising model was proposed. The experimental results prove that the abilities of the proposed denoising model to preserve the high-frequency edge and complex texture information are obviously superior to those of traditional integral based algorithms, especially for texture detail rich images.
Numerical Aspects of Solving Differential Equations: Laboratory Approach for Students.
Witt, Ana
1997-01-01
Describes three labs designed to help students in a first course on ordinary differential equations with three of the most common numerical difficulties they might encounter when solving initial value problems with a numerical software package. The goal of these labs is to help students advance to independent work on common numerical anomalies.…
Direct approach for solving nonlinear evolution and two-point boundary value problems
Jonu Lee; Rathinasamy Sakthivel
2013-12-01
Time-delayed nonlinear evolution equations and boundary value problems have a wide range of applications in science and engineering. In this paper, we implement the differential transform method to solve the nonlinear delay differential equation and boundary value problems. Also, we present some numerical examples including time-delayed nonlinear Burgers equation to illustrate the validity and the great potential of the differential transform method. Numerical experiments demonstrate the use and computational efﬁciency of the method. This method can easily be applied to many nonlinear problems and is capable of reducing the size of computational work.
Approach of Complex-Systems Biology to Reproduction and Evolution
Kaneko, Kunihiko
Two basic issues in biology - the origin of life and evolution of phenotypes - are discussed on the basis of statistical physics and dynamical systems. In section "A Bridge Between Catalytic Reaction Networks and Reproducing Cells", we survey recent developments in the origin of reproducing cells from an ensemble of catalytic reactions. After surveying several models of catalytic reaction networks briefly, we provide possible answers to the following three questions: (1) How are nonequilibrium states sustained in catalytic reaction dynamics? (2) How is recursive production of a cell maintaining composition of a variety of chemicals possible? (3) How does a specific molecule species carry information for heredity? In section "Evolution", general relationships between plasticity, robustness, and evolvability are presented in terms of phenotypic fluctuations. First, proportionality between evolution speed, phenotypic plasticity, and isogenic phenotypic fluctuation is proposed by extending the fluctuation-response relationship in physics. We then derive a general proportionality relationship between the phenotypic fluctuations of epigenetic and genetic origin: the former is the variance of phenotype due to noise in the developmental process, and the latter due to genetic mutation. The relationship also suggests a link between robustness to noise and to mutation. These relationships are confirmed in models of gene expression dynamics, as well as in laboratory experiments, and then are explained by a theory based on an evolutionary stability hypothesis For both sections "A Bridge Between Catalytic Reaction Networks and Reproducing Cells" and "Evolution", consistency between two levels of hierarchy (i.e., molecular and cellular, or genetic and phenotypic levels) is stressed as a principle for complex-systems biology.
Biological evolution of replicator systems: towards a quantitative approach.
Martin, Osmel; Horvath, J E
2013-04-01
The aim of this work is to study the features of a simple replicator chemical model of the relation between kinetic stability and entropy production under the action of external perturbations. We quantitatively explore the different paths leading to evolution in a toy model where two independent replicators compete for the same substrate. To do that, the same scenario described originally by Pross (J Phys Org Chem 17:312-316, 2004) is revised and new criteria to define the kinetic stability are proposed. Our results suggest that fast replicator populations are continually favored by the effects of strong stochastic environmental fluctuations capable to determine the global population, the former assumed to be the only acting evolution force. We demonstrate that the process is continually driven by strong perturbations only, and that population crashes may be useful proxies for these catastrophic environmental fluctuations. As expected, such behavior is particularly enhanced under very large scale perturbations, suggesting a likely dynamical footprint in the recovery patterns of new species after mass extinction events in the Earth's geological past. Furthermore, the hypothesis that natural selection always favors the faster processes may give theoretical support to different studies that claim the applicability of maximum principles like the Maximum Metabolic Flux (MMF) or Maximum Entropy Productions Principle (MEPP), seen as the main goal of biological evolution.
Real-space renormalization-group approach to field evolution equations.
Degenhard, Andreas; Rodríguez-Laguna, Javier
2002-03-01
An operator formalism for the reduction of degrees of freedom in the evolution of discrete partial differential equations (PDE) via real-space renormalization group is introduced, in which cell overlapping is the key concept. Applications to (1+1)-dimensional PDEs are presented for linear and quadratic equations that are first order in time.
G. Darmani; S. Setayeshi; H. Ramezanpour
2012-01-01
In this paper an efficient computational method based on extending the sensitivity approach （SA） is proposed to find an analytic exact solution of nonlinear differential difference equations. In this manner we avoid solving the nonlinear problem directly. By extension of sensitivity approach for differential difference equations （DDEs）, the nonlinear original problem is transformed into infinite linear differential difference equations, which should be solved in a recursive manner. Then the exact solution is determined in the form of infinite terms series and by intercepting series an approximate solution is obtained. Numerical examples are employed to show the effectiveness of the proposed approach.
Robust trajectory tracking: differential game/cheap control approach
Turetsky, Vladimir; Glizer, Valery Y.; Shinar, Josef
2014-11-01
A robust trajectory tracking problem is treated in the framework of a zero-sum linear-quadratic differential game of a general type. For the cheap control version of this game, a novel solvability condition is derived. The sufficient condition, guaranteeing that the tracking problem is solved by the optimal strategy of the minimiser in the cheap control game, is established. The boundedness of the time realisations of this strategy is analysed. An illustrative example is presented.
A unified approach to the helioseismic forward and inverse problems of differential rotation
Ritzwoller, M.H.; Lavely, E.M. (Colorado Univ., Boulder (USA) MIT, Cambridge, MA (USA))
1991-03-01
A general, degenerate perturbation theoretic treatment of the helioseismic forward and inverse problem for solar differential rotation is presented. For the forward problem, differential rotation is represented as the axisymmetric component of a general toroidal flow field using velocity spherical harmonics. This approach allows each degree of differential rotation to be estimated independently from all other degrees. In the inverse problem, the splitting caused by differential rotation is expressed as an expansion in a set of orthonormal polynomials that are intimately related to the solution of the forward problem. The combined use of vector spherical harmonics as basis functions for differential ratio and the Clebsch-Gordon coefficients to represent splitting provides a unified approach to the forward and inverse problems of differential rotation which greatly simplify inversion. 43 refs.
Flores-Rentería, Lluvia; Rymer, Paul D; Riegler, Markus
2017-03-01
Reticulate evolution by hybridization is considered a common process shaping the evolution of many plant species, however, reticulation could also be due to incomplete lineage sorting in biodiverse systems. For our study we selected a group of closely related plant taxa with contrasting yet partially overlapping geographic distributions and different population sizes, to distinguish between reticulated patterns due to hybridization and incomplete lineage sorting. We predicted that sympatric or proximal populations of different species are more likely to have gene flow than geographically distant populations of the same widespread species. Furthermore, for species with restricted distributions, and therefore, small effective population sizes, we predicted complete lineage sorting. Eastern grey box eucalypt species (Eucalyptus supraspecies Moluccanae) provide an ideal system to explore patterns of reticulate evolution. They form a diverse, recently evolved and phylogenetically undefined group within Eucalyptus, with overlapping morphological features and hybridization in nature. We used a multi-faceted approach, combining analyses of chloroplast and nuclear DNA, as well as seedling morphology, flowering time and ecological spatial differentiation in order to test for species delimitation and reticulate evolution in this group. The multiple layers of results were consistent and suggested a lack of monophyly at different hierarchical levels due to multidirectional gene flow among several species, challenging species delimitation. Chloroplast and nuclear haplotypes were shared among different species in geographic proximity, consistent with hybridization zones. Furthermore, species with restricted distributions appeared better resolved due to lineage sorting in the absence of hybridization. We conclude that a combination of molecular, morphological and ecological approaches is required to disentangle patterns of reticulate evolution in the box eucalypts. Published by
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.
Group Dynamics and Individual Roles: A Differentiated Approach to Social-Emotional Learning
Dugas, Daryl
2017-01-01
Differentiated instruction is a set of strategies to help teachers meet each child where he or she is in order to improve students' engagement, lead them to do their best work, and maximize their success. This article describes a differentiated classroom management approach based in group dynamics which focuses on the development of group norms…
Group Dynamics and Individual Roles: A Differentiated Approach to Social-Emotional Learning
Dugas, Daryl
2017-01-01
Differentiated instruction is a set of strategies to help teachers meet each child where he or she is in order to improve students' engagement, lead them to do their best work, and maximize their success. This article describes a differentiated classroom management approach based in group dynamics which focuses on the development of group norms…
Differentiated Approach to the Mucolytic Therapy for Respiratory Diseases in Children
Ye.I. Yulish
2013-11-01
Full Text Available This paper presents the mechanisms of disorders of mucociliary clearance in the tracheobronchial tree in respiratory diseases in children. The authors considered differentiated approach to the prescription of mucoactive and mucolytic drugs, in particular ambroxol.
Differentiated approach to development creative capabilities of students is on employments piano
Tatyana Docenko
2014-04-01
Full Text Available In article on the basis of analysis of the current state of music education the possibility of development of creative abilities of students of piano, the differentiated approach.The experience of Maiminsky children arts school.
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.
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.
Matrix Operator Approach to Quantum Evolution Operator and Geometric Phase
Kim, Sang Pyo; Soh, Kwang Sup
2012-01-01
The Moody-Shapere-Wilczek's adiabatic effective Hamiltonian and Lagrangian method is developed further into the matrix effective Hamiltonian (MEH) and Lagrangian (MEL) approach to a parameter-dependent quantum system. The matrix operator approach formulated in the product integral (PI) provides not only a method to find wave function efficiently in the MEH approach but also higher order corrections to the effective action systematically in the MEL approach, a la the Magnus expansion and the Kubo's cumulant expansion. A coupled quantum system of a light particle of harmonic oscillator is worked out, and as a by-product a new kind of gauge potential (Berry's connection) is found even for nondegenerate case (real eigenfunctions). Moreover, in the PI formulation the holonomy of the induced gauge potential is related to the Schlesinger's exact formula for the gauge field tensor. A superadiabatic expansion is also constructed and a generalized Dykhne formula, depending on the contour integrals of homotopy class of ...
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
Starvation Based Differential Chemotherapy: A Novel Approach for Cancer Treatment
Sidra Naveed
2014-11-01
Full Text Available Cancer patients undergoing chemotherapy treatment are advised to increase food intake to overcome the therapy-induced side effects, and weight loss. Dietary restriction is known to slow down the aging process and hence reduce age-related diseases such as cancer. Fasting or short-term starvation is more effective than dietary restriction to prevent cancer growth since starved cells switch off signals for growth and reproduction and enter a protective mode, while cancer cells, being mutated, are not sensitized by any external growth signals and are not protected against any stress. This phenomenon is known as differential stress resistance (DSR. Nutrient signaling pathways involving growth hormone/insulin-like growth factor-1 axis and its downstream effectors, play a key role in DSR in response to starvation controlling the other cell maintenance systems, such as autophagy and apoptosis, that are related to the tumorigenesis. Yeast cells lacking these effectors are better protected against oxidative stress compared to normal cells. In the same way, starvation protects many cell lines and mice against high-dose chemotherapeutic drugs. According to a series of studies, fasting results in overall reduction in chemotherapy side effects in cancer patients. Data shows that starvation-dependent differential chemotherapy is safe, feasible and effective in cancer treatment, but the possible side effects of starvation limit its efficacy. However, further studies and clinical trials may result in its implementation in cancer treatment.
Zabrina L Brumme
2007-07-01
genes investigated indicates differential HLA class I-driven evolution in different viral genes. The relationship between HLA class I-associated polymorphisms and lower CD4(+ cell count suggests that immune escape correlates with disease status, supporting an essential role of maintenance of effective CTL responses in immune control of HIV-1. The design of preventative and therapeutic CTL-based vaccine approaches could incorporate information on predictable escape pathways.
Selection on Network Dynamics Drives Differential Rates of Protein Domain Evolution.
Mannakee, Brian K; Gutenkunst, Ryan N
2016-07-01
The long-held principle that functionally important proteins evolve slowly has recently been challenged by studies in mice and yeast showing that the severity of a protein knockout only weakly predicts that protein's rate of evolution. However, the relevance of these studies to evolutionary changes within proteins is unknown, because amino acid substitutions, unlike knockouts, often only slightly perturb protein activity. To quantify the phenotypic effect of small biochemical perturbations, we developed an approach to use computational systems biology models to measure the influence of individual reaction rate constants on network dynamics. We show that this dynamical influence is predictive of protein domain evolutionary rate within networks in vertebrates and yeast, even after controlling for expression level and breadth, network topology, and knockout effect. Thus, our results not only demonstrate the importance of protein domain function in determining evolutionary rate, but also the power of systems biology modeling to uncover unanticipated evolutionary forces.
Selection on Network Dynamics Drives Differential Rates of Protein Domain Evolution.
Brian K Mannakee
2016-07-01
Full Text Available 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.
Phylogenetic approach to the evolution of color term systems.
Haynie, Hannah J; Bowern, Claire
2016-11-29
The naming of colors has long been a topic of interest in the study of human culture and cognition. Color term research has asked diverse questions about thought and communication, but no previous research has used an evolutionary framework. We show that there is broad support for the most influential theory of color term development (that most strongly represented by Berlin and Kay [Berlin B, Kay P (1969) (Univ of California Press, Berkeley, CA)]); however, we find extensive evidence for the loss (as well as gain) of color terms. We find alternative trajectories of color term evolution beyond those considered in the standard theories. These results not only refine our knowledge of how humans lexicalize the color space and how the systems change over time; they illustrate the promise of phylogenetic methods within the domain of cognitive science, and they show how language change interacts with human perception.
Coronary artery segmentation in X-ray angiogram using Gabor filters and differential evolution
Cervantes S, F.; Hernandez A, A.; Cruz A, I. [Centro de Investigacion en Matematicas, A. C., Jalisco s/n, Col. Valenciana, 36240 Guanajuato, Gto. (Mexico); Solorio M, S. [IMSS, Unidad de Investigacion, UMAE Hospital de Especialidades No. 1 del Centro Medico Nacional del Bajio, 37260 Leon, Guanajuato (Mexico); Cordova F, T. [Universidad de Guanajuato, Departamento de Ingenieria Fisica, 37150 Leon, Guanajuato (Mexico); Avina C, J. G., E-mail: ivan.cruz@cimat.mx [Universidad de Guanajuato, Departamento de Electronica, 36885 Salamanca, Guanajuato (Mexico)
2016-10-15
Segmentation of coronary arteries in X-ray angiograms represents an essential task for computer-aided diagnosis, since it can help cardiologists in diagnosing and monitoring vascular abnormalities. Due to the main disadvantages of the X-ray angiograms are the nonuniform illumination, and the weak contrast between blood vessels and image background, different vessel enhancement methods have been introduced. In this paper, a novel method for blood vessel enhancement based on Gabor filters tuned using the optimization strategy of Differential evolution (De) is proposed. Because the Gabor filters are governed by three different parameters, the optimal selection of those parameters is highly desirable in order to maximize the vessel detection rate while reducing the computational cost of the training stage. To obtain the optimal set of parameters for the Gabor filters, the area (Az) under the receiver operating characteristic curve is used as objective function. In the experimental results, the proposed method obtained the highest detection performance with Az = 0.956 using a test set of 60 angiograms, and Az = 0.934 with a training set of 20 angiograms compared with different state-of-the-art vessel detection methods. In addition, the experimental results in terms of computational time have also shown that the proposed method can be highly suitable for clinical decision support. (Author)
A software for parameter optimization with Differential Evolution Entirely Parallel method
Konstantin Kozlov
2016-08-01
Full Text Available Summary. Differential Evolution Entirely Parallel (DEEP package is a software for finding unknown real and integer parameters in dynamical models of biological processes by minimizing one or even several objective functions that measure the deviation of model solution from data. Numerical solutions provided by the most efficient global optimization methods are often problem-specific and cannot be easily adapted to other tasks. In contrast, DEEP allows a user to describe both mathematical model and objective function in any programming language, such as R, Octave or Python and others. Being implemented in C, DEEP demonstrates as good performance as the top three methods from CEC-2014 (Competition on evolutionary computation benchmark and was successfully applied to several biological problems. Availability. DEEP method is an open source and free software distributed under the terms of GPL licence version 3. The sources are available at http://deepmethod.sourceforge.net/ and binary packages for Fedora GNU/Linux are provided for RPM package manager at https://build.opensuse.org/project/repositories/home:mackoel:compbio.
Planetary eclipse mapping of CoRoT-2a. Evolution, differential rotation, and spot migration
Huber, K F; Wolter, U; Schmitt, J H M M
2010-01-01
The lightcurve of CoRoT-2 shows substantial rotational modulation and deformations of the planet's transit profiles caused by starspots. We consistently model the entire lightcurve, including both rotational modulation and transits, stretching over approximately 30 stellar rotations and 79 transits. The spot distribution and its evolution on the noneclipsed and eclipsed surface sections are presented and analyzed, making use of the high resolution achievable under the transit path. We measure the average surface brightness on the eclipsed section to be (5\\pm1) % lower than on the noneclipsed section. Adopting a solar spot contrast, the spot coverage on the entire surface reaches up to 19 % and a maximum of almost 40 % on the eclipsed section. Features under the transit path, i.e. close to the equator, rotate with a period close to 4.55 days. Significantly higher rotation periods are found for features on the noneclipsed section indicating a differential rotation of $\\Delta \\Omega > 0.1$. Spotted and unspotted...
LO Peg: surface differential rotation, flares, and spot-topographic evolution
Karmakar, Subhajeet; Savanov, I S; Taş, G; Pandey, S B; Misra, K; Joshi, S; Dmitrienko, E S; Sakamoto, T; Gehrels, N; Okajima, T
2016-01-01
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 (UFR) 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 10^{31-34} 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 a...
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.
I THAMARAI; S MURUGAVALLI
2017-01-01
Software effort estimation is the process of calculating the effort required to develop a software product based on the input parameters that are usually partial in nature. It is an important task but the most difficult and complicated step in the software product development. Estimation requires detailed information about project scope, process requirements and resources available. Inaccurate estimation leads to financial lossand delay in the projects. Due to the intangible nature of software, most of the software estimation process unreliable. But there is a strong relationship between effort estimation and project management activities.Various methodologies have been employed to improve the procedure of software estimation. This paper reviews journal articles on software development to get the direction in the future estimation research. Several methods for software effort estimation are discussed in this paper, including the data sets widely used and metrics used for evaluation. The use of evolutionary computational tools in the estimation is dealt with in detail.A new model for estimation using differential evolution algorithm called DEAPS is proposed and its advantagesare discussed.
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.
苏国韶; 张小飞; 陈光强; 符兴义
2008-01-01
To determine structure and parameters of a rheological constitutive model for rocks,a new method based on differential evolution(DE) algorithm combined with FLAC3D(a numerical code for geotechnical engineering) was proposed for identification of the global optimum coupled of model structure and its parameters.At first,stochastic coupled mode was initialized,the difference in displacement between the numerical value and in-situ measurements was regarded as fitness value to evaluate quality of the coupled mode.Then the coupled-mode was updated continually using DE rule until the optimal parameters were found.Thus,coupled-mode was identified adaptively during back analysis process.The results of applications to Jinping tunnels in China show that the method is feasible and efficient for identifying the coupled-mode of constitutive structure and its parameters.The method overcomes the limitation of the traditional method and improves significantly precision and speed of displacement back analysis process.
Minakhi Rout
2014-01-01
Full Text Available To alleviate the limitations of statistical based methods of forecasting of exchange rates, soft and evolutionary computing based techniques have been introduced in the literature. To further the research in this direction this paper proposes a simple but promising hybrid prediction model by suitably combining an adaptive autoregressive moving average (ARMA architecture and differential evolution (DE based training of its feed-forward and feed-back parameters. Simple statistical features are extracted for each exchange rate using a sliding window of past data and are employed as input to the prediction model for training its internal coefficients using DE optimization strategy. The prediction efficiency is validated using past exchange rates not used for training purpose. Simulation results using real life data are presented for three different exchange rates for one–fifteen months’ ahead predictions. The results of the developed model are compared with other four competitive methods such as ARMA-particle swarm optimization (PSO, ARMA-cat swarm optimization (CSO, ARMA-bacterial foraging optimization (BFO and ARMA-forward backward least mean square (FBLMS. The derivative based ARMA-FBLMS forecasting model exhibits worst prediction performance of the exchange rates. Comparisons of different performance measures including the training time of the all three evolutionary computing based models demonstrate that the proposed ARMA-DE exchange rate prediction model possesses superior short and long range prediction potentiality compared to others.
Optimization of a mirror-based neutron source using differential evolution algorithm
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.
Optimal layout design of obstacles for panic evacuation using differential evolution
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.
Ramin Mansouri
2014-06-01
Full Text Available Iran, has caused most of the water used and as much as possible to avoid losses. One of the important parameters in agriculture is water distribution uniformity coefficient (CU in sprinkler irrigation. CU amount of water sprinkler operating depends on different pressure heads (P, riser height (RH, distance between sprinklers on lateral pipes (Sl and the distance between lateral pipes (Sm. The best combination of the above parameters for maximum CU, is still unknown for applicators. In this research, CU quantities of zb model sprinkler (made in Iran were measured at Hashemabad cotton research station of Gorgan under 3 different pressure heads (2.5, 3 and 3.5 atm, 2 riser heads (60 and 100 cm and 7 sprinkler (Sl×Sm including 9×12, 9×15, 12×12, 15×12, 12×18, 15×15, 15×18m arrangements. By using differential evolution algorithm (DE, CU equation was optimized and the best optimized coefficients obtained. In this algorithm, the coefficients F and CR equal to 2 and 0.5, respectively, with a population of 100 members and 1000 number of generations (iterations, provides the best results. Absolute error between the results of this algorithm with the measured results is 2.2%. As well as values Wilmot (d and the root-mean square error (RMSE, equal to 0.919 and 2.126, respectively. This results show that this algorithm has high accuracy to estimate water distribution uniformity.
A Differential Evolution Based MPPT Method for Photovoltaic Modules under Partial Shading Conditions
Kok Soon Tey
2014-01-01
Full Text Available Partially shaded photovoltaic (PV modules have multiple peaks in the power-voltage (P-V characteristic curve and conventional maximum power point tracking (MPPT algorithm, such as perturbation and observation (P&O, which is unable to track the global maximum power point (GMPP accurately due to its localized search space. Therefore, this paper proposes a differential evolution (DE based optimization algorithm to provide the globalized search space to track the GMPP. The direction of mutation in the DE algorithm is modified to ensure that the mutation always converges to the best solution among all the particles in the generation. This helps to provide the rapid convergence of the algorithm. Simulation of the proposed PV system is carried out in PSIM and the results are compared to P&O algorithm. In the hardware implementation, a high step-up DC-DC converter is employed to verify the proposed algorithm experimentally on partial shading conditions, load variation, and solar intensity variation. The experimental results show that the proposed algorithm is able to converge to the GMPP within 1.2 seconds with higher efficiency than P&O.
Nhat-Duc Hoang
2015-01-01
Full Text Available In construction management, the task of planning project schedules with consideration of labor utilization is very crucial. However, the commonly used critical path method (CPM does not inherently take into account this issue. Consequently, the labor utilization of the project schedule derived from the CPM method often has substantial low ebbs and high peaks. This research proposes a model to obtain project schedule with the least fluctuation in labor demand while still satisfying the project deadline and maintain the project cost. The Differential Evolution (DE, a fast and efficient metaheuristic, is employed to search for the most desirable solution of project execution among numerous combinations of activities’ crew sizes and start times. Furthermore, seven DE’s mutation strategies have also been employed for solving the optimization at hand. Experiment results point out that the Target-to-Best 1 and a new hybrid mutation strategy can attain the best solution of project schedule with the least fluctuation in labor demand. Accordingly, the proposed framework can be an effective tool to assist decision-makers in the project planning phase.
Optimal Path Design of Geared 5-bar mechanism using Differential Evolution Algorithm
Ali Aliniay Saghalaksari
2016-06-01
Full Text Available Five-bar linkage mechanisms with two degrees of freedom (DOF are more capable in generating coupler path than four-bar mechanisms with one DOF. The DOF of these mechanisms is reduced to one and they will have constant ratio of binary input when they are equipped by gear. Therefore, besides keeping the simple structure, it is possible to employ them to generate a more accurate path than that generated by four-bar mechanisms using only one input. In this study, using such mechanism for the considered paths, which are used for the comparison purpose, a singleobjective design is performed to optimize the length of mechanism links and revolution ratio of gears by considering the necessary constraints. The error function of square deviation of positions is considered as the objective function and the differential evolution algorithm is utilized in order to solve the considered optimization problems, which are Triangle Curve with 22 Discrete Points and Asteroid Curve with 41 Discrete Points. Compared with the main reference [9], the final results revealed a significant improvement.
Biwei Tang
2016-05-01
Full Text Available Global path planning is a challenging issue in the filed of mobile robotics due to its complexity and the nature of nondeterministic polynomial-time hard (NP-hard. Particle swarm optimization (PSO has gained increasing popularity in global path planning due to its simplicity and high convergence speed. However, since the basic PSO has difficulties balancing exploration and exploitation, and suffers from stagnation, its efficiency in solving global path planning may be restricted. Aiming at overcoming these drawbacks and solving the global path planning problem efficiently, this paper proposes a hybrid PSO algorithm that hybridizes PSO and differential evolution (DE algorithms. To dynamically adjust the exploration and exploitation abilities of the hybrid PSO, a novel PSO, the nonlinear time-varying PSO (NTVPSO, is proposed for updating the velocities and positions of particles in the hybrid PSO. In an attempt to avoid stagnation, a modified DE, the ranking-based self adaptive DE (RBSADE, is developed to evolve the personal best experience of particles in the hybrid PSO. The proposed algorithm is compared with four state-of-the-art evolutionary algorithms. Simulation results show that the proposed algorithm is highly competitive in terms of path optimality and can be considered as a vital alternative for solving global path planning.
Using Differential Evolution to Optimize Learning from Signals and Enhance Network Security
Harmer, Paul K [Air Force Institute of Technology; Temple, Michael A [Air Force Institute of Technology; Buckner, Mark A [ORNL; Farquhar, Ethan [ORNL
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.
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.
Sang Yong Han
2009-05-01
Full Text Available This paper applies the Differential Evolution (DE algorithm to the task of automatic fuzzy clustering in a Multi-objective Optimization (MO framework. It compares the performances of two multi-objective variants of DE over the fuzzy clustering problem, where two conflicting fuzzy validity indices are simultaneously optimized. The resultant Pareto optimal set of solutions from each algorithm consists of a number of non-dominated solutions, from which the user can choose the most promising ones according to the problem specifications. A real-coded representation of the search variables, accommodating variable number of cluster centers, is used for DE. The performances of the multi-objective DE-variants have also been contrasted to that of two most well-known schemes of MO clustering, namely the Non Dominated Sorting Genetic Algorithm (NSGA II and Multi-Objective Clustering with an unknown number of Clusters K (MOCK. Experimental results using six artificial and four real life datasets of varying range of complexities indicate that DE holds immense promise as a candidate algorithm for devising MO clustering schemes.
Mohd Arfian Ismail
2017-09-01
Full Text Available In this paper, an improve method of multi-objective optimization for biochemical system production is presented and discussed in detail. The optimization process of biochemical system production become hard and difficult when involved a large biochemical system that contain with many components. In addition, the multi-objective problem also need to be considered. Due to that, this study proposed and improve method that comprises with Newton method, differential evolution algorithm (DE and competitive co-evolutionary algorithm(ComCA. The aim of the proposed method is to maximize the production and simultaneously minimize the total amount of chemical concentrations involves. The operation of the proposed method starts with Newton method by dealing with biochemical system production as a nonlinear equations system. Then DE and ComCA are used to represent the variables in nonlinear equation system and tune the variables in order to find the best solution. The used of DE is to maximize the production while ComCA is to minimize the total amount of chemical concentrations involves. The effectiveness of the proposed method is evaluated using two benchmark biochemical systems and the experimental results show that the proposed method perform well compared to other works.
Suresh, Kaushik; Kundu, Debarati; Ghosh, Sayan; Das, Swagatam; Abraham, Ajith; Han, Sang Yong
2009-01-01
This paper applies the Differential Evolution (DE) algorithm to the task of automatic fuzzy clustering in a Multi-objective Optimization (MO) framework. It compares the performances of two multi-objective variants of DE over the fuzzy clustering problem, where two conflicting fuzzy validity indices are simultaneously optimized. The resultant Pareto optimal set of solutions from each algorithm consists of a number of non-dominated solutions, from which the user can choose the most promising ones according to the problem specifications. A real-coded representation of the search variables, accommodating variable number of cluster centers, is used for DE. The performances of the multi-objective DE-variants have also been contrasted to that of two most well-known schemes of MO clustering, namely the Non Dominated Sorting Genetic Algorithm (NSGA II) and Multi-Objective Clustering with an unknown number of Clusters K (MOCK). Experimental results using six artificial and four real life datasets of varying range of complexities indicate that DE holds immense promise as a candidate algorithm for devising MO clustering schemes.
VATS Lobectomy: Surgical Evolution from Conventional VATS to Uniportal Approach
Diego Gonzalez-Rivas
2012-01-01
Full Text Available There is no standardized technique for the VATS lobectomy, though most centres use 2 ports and add a utility incision. However, the procedure can be performed by eliminating the two small ports and using only the utility incision with similar outcomes. Since 2010, when the uniportal approach was introduced for major pulmonary resection, the technique has been spreading worldwide. The single-port technique provides a direct view to the target tissue. The conventional triple port triangulation creates a new optical plane with genesis of dihedral or torsional angle that is not favorable with standard two-dimension monitors. The parallel instrumentation achieved during single-port approach mimics inside the maneuvers performed during open surgery. Furthermore, it represents the less invasive approach possible, and avoiding the use of trocar, we minimize the compression of the intercostal nerve. Further development of new technologies like sealing devices for all vessels and fissure, robotic arms that open inside the thorax, and wireless cameras will facilitate the uniportal approach to become the standard surgical procedure for pulmonary resection in most thoracic departments.
Differentiating between descriptive and interpretive phenomenological research approaches.
Matua, Gerald Amandu; Van Der Wal, Dirk Mostert
2015-07-01
To provide insight into how descriptive and interpretive phenomenological research approaches can guide nurse researchers during the generation and application of knowledge. Phenomenology is a discipline that investigates people's experiences to reveal what lies 'hidden' in them. It has become a major philosophy and research method in the humanities, human sciences and arts. Phenomenology has transitioned from descriptive phenomenology, which emphasises the 'pure' description of people's experiences, to the 'interpretation' of such experiences, as in hermeneutic phenomenology. However, nurse researchers are still challenged by the epistemological and methodological tenets of these two methods. The data came from relevant online databases and research books. A review of selected peer-reviewed research and discussion papers published between January 1990 and December 2013 was conducted using CINAHL, Science Direct, PubMed and Google Scholar databases. In addition, selected textbooks that addressed phenomenology as a philosophy and as a research methodology were used. Evidence from the literature indicates that most studies following the 'descriptive approach' to research are used to illuminate poorly understood aspects of experiences. In contrast, the 'interpretive/hermeneutic approach' is used to examine contextual features of an experience in relation to other influences such as culture, gender, employment or wellbeing of people or groups experiencing the phenomenon. This allows investigators to arrive at a deeper understanding of the experience, so that caregivers can derive requisite knowledge needed to address such clients' needs. Novice nurse researchers should endeavour to understand phenomenology both as a philosophy and research method. This is vitally important because in-depth understanding of phenomenology ensures that the most appropriate method is chosen to implement a study and to generate knowledge for nursing practice. This paper adds to the current
Performance Prediction of Differential Fibers with a Bi-Directional Optimization Approach
Yi Wang
2013-12-01
Full Text Available This paper develops a bi-directional prediction approach to predict the production parameters and performance of differential fibers based on neural networks and a multi-objective evolutionary algorithm. The proposed method does not require accurate description and calculation for the multiple processes, different modes and complex conditions of fiber production. The bi-directional prediction approach includes the forward prediction and backward reasoning. Particle swam optimization algorithms with K-means algorithm are used to minimize the prediction error of the forward prediction results. Based on the forward prediction, backward reasoning uses the multi-objective evolutionary algorithm to find the reasoning results. Experiments with polyester filament parameters of differential production conditions indicate that the proposed approach obtains good prediction results. The results can be used to optimize fiber production and to design differential fibers. This study also has important value and widespread application prospects regarding the spinning of differential fiber optimization.
Random Matrix Approach to Quantum Adiabatic Evolution Algorithms
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.
Phenylalanine ammonia-lyase through evolution: A bioinformatic approach
Shiva Hemmati
2015-03-01
Full Text Available Phenylalanine ammonia-lyase (PAL is the first entry enzyme of the phenylpropanoid pathway that converts phenylalanine to cinnamic acid which is the precursor of various secondary metabolites. PAL is recently formulated for phenylketonuric patients in pegylated forms; therefore, screening a PAL with the highest affinity to the substrate is of a great importance. PAL exists in all higher plants and some fungi and few bacteria. Ancestors of land plants have been adopted by evolving metabolic pathways. A multi-gene family encodes PAL by gene duplication events in most plants. In this study, the taxonomic distribution and phylogeny of pal gene found in land plants, fungi and bacteria have been analyzed. It seems that the ancestor of plants acquired a pal gene via horizontal gene transfer in symbioses with bacteria and fungi. Gymnosperms have kept a diverse set of pal genes that arose from gene duplication events. In angiosperms, after the divergence of dicotyledons from monocots, pal genes were duplicated many times. The close paralogues of pal genes in some species indicate expansion of gene families after the divergence in plant pal gene evolution. Interestingly, some of the plant pals clustered by species in a way that pals within one species are more closely related to each other than to homologs in the other species which indicates this duplication event occurred more recently.
EVOLUTION OF THE PLACE ATTACHMENT: AN ECONOMIC APPROACH
Edgar J. Sánchez Carrera
2013-02-01
Full Text Available Despite relatively cheap mobility and intensive globalization processes, the place attachment remains an important part the human existence (Lewicka, 2010:226, . Our aim is to understand the evolution of the place attachment. For this purpose we apply evolutionary game theory with the replicator dynamics and we follow the literature on the identity economics. A novelty which Akerlof i Kranton (2000 introduce is that an individual may choose an activity opposite to her identity in order to maximize her own utility. In other words, the choice of identity and activities is separated. Pavlinović (2012 develops a basic evolutionary game-theory model of spatial identity where agents can only act in line with their own identity. On the contrary, Akerlof i Kranton (2000 introduce the assumption that an individual may choose an activity opposite to her. Thus, we modify the model in Pavlinović (2012 and consider the choice of identity and action separately. We explore if this modification significantly affects the results.
Detecting Differential Rotation and Starspot Evolution on the M dwarf GJ 1243 with Kepler
Davenport, James R A; Hawley, Suzanne L
2015-01-01
We present an analysis of the starspots on the active M4 dwarf GJ 1243, using four years of time series photometry from Kepler. A rapid $P = 0.592596\\pm0.00021$ day rotation period is measured due to the $\\sim$2.2\\% starspot-induced flux modulations in the light curve. We first use a light curve modeling approach, using a Monte Carlo Markov Chain sampler to solve for the longitudes and radii of the two spots within 5-day windows of data. Within each window of time the starspots are assumed to be unchanging. Only a weak constraint on the starspot latitudes can be implied from our modeling. The primary spot is found to be very stable over many years. A secondary spot feature is present in three portions of the light curve, decays on 100-500 day timescales, and moves in longitude over time. We interpret this longitude shearing as the signature of differential rotation. Using our models we measure an average shear between the starspots of 0.0047 rad day$^{-1}$, which corresponds to a differential rotation rate of...
Stochastic partial differential equations a modeling, white noise functional approach
Holden, Helge; Ubøe, Jan; Zhang, Tusheng
1996-01-01
This book is based on research that, to a large extent, started around 1990, when a research project on fluid flow in stochastic reservoirs was initiated by a group including some of us with the support of VISTA, a research coopera tion between the Norwegian Academy of Science and Letters and Den norske stats oljeselskap A.S. (Statoil). The purpose of the project was to use stochastic partial differential equations (SPDEs) to describe the flow of fluid in a medium where some of the parameters, e.g., the permeability, were stochastic or "noisy". We soon realized that the theory of SPDEs at the time was insufficient to handle such equations. Therefore it became our aim to develop a new mathematically rigorous theory that satisfied the following conditions. 1) The theory should be physically meaningful and realistic, and the corre sponding solutions should make sense physically and should be useful in applications. 2) The theory should be general enough to handle many of the interesting SPDEs that occur in r...
Non-Invasive Ocular Rigidity Measurement: A Differential Tonometry Approach
Efstathios T. Detorakis
2015-12-01
Full Text Available Purpose: Taking into account the fact that Goldmann applanation tonometry (GAT geometrically deforms the corneal apex and displaces volume from the anterior segment whereas Dynamic Contour Tonometry (DCT does not, we aimed at developing an algorithm for the calculation of ocular rigidity (OR based on the differences in pressure and volume between deformed and non-deformed status according to the general Friedenwald principle of differential tonometry. Methods: To avoid deviations of GAT IOP from true IOP in eyes with corneas different from the “calibration cornea” we applied the previously described Orssengo-Pye algorithm to calculate an error coefficient “C/B”. To test the feasibility of the proposed model, we calculated the OR coefficient (r in 17 cataract surgery candidates (9 males and 8 females. Results: The calculated r according to our model (mean ± SD, range was 0.0174 ± 0.010 (0.0123–0.022 mmHg/μL. A negative statistically significant correlation between axial length and r was detected whereas correlations between r and other biometric parameters examined were statistically not significant. Conclusions: The proposed method may prove a valid non-invasive tool for the measurement method of OR, which could help in introducing OR in the decision-making of the routine clinical practice.
无
2007-01-01
Control parameters of original difierential evolution(DE)are kept fixed throughout the entire evolutionary process.However,it is not an easy task to properly set control parameters in DE for difierent optimization problems.According to the relative position of two difierent individual vectors selected to generate a difference vector in the searching place,a self-adapting strategy for the scale factor F of the difference vector is proposed.In terms of the convergence status of the target vector in the current population,a self-adapting crossover probability constant CR strategy is proposed.Therefore,good target vectors have a lower CR while worse target vectors have a large CR.At the same time,the mutation operator is modified to improve the convergence speed.The performance of these proposed approaches are studied with the use of some benchmark problems and applied to the trajectory planning of a three-joint redundant manipulator.Finally,the experiment results show that the proposed approaches can greatly improve robustness and convergence speed.
Evolution of the the ART approach: highlights and achievements
Jo E. Frencken
2009-01-01
Full Text Available Atraumatic Restorative Treatment (ART was initiated in the mid-eighties in Tanzania in response to an inappropriately functioning community oral health programme that was based on western health care models and western technology. The approach has evolved to its present standing as an effective minimal intervention approach mainly because the originators anticipated the great potential of ART to alleviate inequality in oral health care, and because they recognised the need to carry out research to investigate its effectiveness and applicability. Twenty-five years later, ART was accepted by the World Health Organisation (1994 and the FDI World Dental Federation (2002. It is included in textbooks on cariology, restorative dentistry and minimal intervention dentistry. It is being systematically introduced into public oral health service systems in a number of low- and middle income countries. Private practitioners use it. Many publications related to aspects of ART have been published and many more will follow. To achieve quality results with ART one has to attend well-conducted and sufficiently long training courses, preferably in combination with other caries preventive strategies. ART should, therefore, not be considered in isolation and must be part of an evidence-based approach to oral health with a strong foundation based on prevention.
Vorob'eva, É I
2010-01-01
Heightened interest in the evolutionary problems of developmental biology in the 1980s was due to the success of molecular genetics and disappointment in the synthetic theory of evolution, where the chapters of embryology and developmental biology seem to have been left out. Modern evo-devo, which turned out to be antipodean to the methodology of the synthetic theory of evolution, propagandized in the development of evolutionary problems only the mechanical and molecular genetic approach to the evolution of ontogenesis, based on cellular and intercellular interactions. The phonotypical approach to the evaluation of evolutionary occurrences in ontogenesis, which aids in the joining of the genetic and epigenetic levels of research, the theory of natural selection, the nomogenetic conception, and the problem of the wholeness of the organism in onto- and phylogenesis may be against this. The phenotypic approach to ontogenesis is methodologically the most perspective for evolutionary developmental biology.
A Robust Outlier Approach to Prevent Type I Error Inflation in Differential Item Functioning
Magis, David; De Boeck, Paul
2012-01-01
The identification of differential item functioning (DIF) is often performed by means of statistical approaches that consider the raw scores as proxies for the ability trait level. One of the most popular approaches, the Mantel-Haenszel (MH) method, belongs to this category. However, replacing the ability level by the simple raw score is a source…
A robust outlier approach to prevent type I error inflation in Differential Item Functioning
Magis, D.; de Boeck, P.
2012-01-01
The identification of differential item functioning (DIF) is often performed by means of statistical approaches that consider the raw scores as proxies for the ability trait level. One of the most popular approaches, the Mantel-Haenszel (MH) method, belongs to this category. However, replacing the a
Ren-Jie He; Zhen-Yu Yang
2012-01-01
Differential evolution (DE) has become a very popular and effective global optimization algorithm in the area of evolutionary computation.In spite of many advantages such as conceptual simplicity,high efficiency and ease of use,DE has two main components,i.e.,mutation scheme and parameter control,which significantly influence its performance.In this paper we intend to improve the performance of DE by using carefully considered strategies for both of the two components.We first design an adaptive mutation scheme,which adaptively makes use of the bias of superior individuals when generating new solutions.Although introducing such a bias is not a new idea,existing methods often use heuristic rules to control the bias.They can hardly maintain the appropriate balance between exploration and exploitation during the search process,because the preferred bias is often problem and evolution-stage dependent.Instead of using any fixed rule,a novel strategy is adopted in the new adaptive mutation scheme to adjust the bias dynamically based on the identified local fitness landscape captured by the current population.As for the other component,i.e.,parameter control,we propose a mechanism by using the Lévy probability distribution to adaptively control the scale factor F of DE.For every mutation in each generation,an Fi is produced from one of four different Lévy distributions according to their historical performance.With the adaptive mutation scheme and parameter control using Lévy distribution as the main components,we present a new DE variant called Lévy DE (LDE).Experimental studies were carried out on a broad range of benchmark functions in global numerical optimization.The results show that LDE is very competitive,and both of the two main components have contributed to its overall performance.The scalability of LDE is also discussed by conducting experiments on some selected benchmark functions with dimensions from 30 to 200.
An ensemble approach to the evolution of complex systems
Göker Arpağ; Ayşe Erzan
2014-04-01
Adaptive systems frequently incorporate complex structures which can arise spontaneously and which may be non-adaptive in the evolutionary sense. We give examples from phase transition and fractal growth to develop the themes of cooperative phenomena and pattern formation. We discuss RNA interference and transcriptional gene regulation networks, where a major part of the topological properties can be accounted for by mere combinatorics. A discussion of ensemble approaches to biological systems and measures of complexity is presented, and a connection is established between complexity and fitness.
An ensemble approach to the evolution of complex systems.
Arpağ, Göker; Erzan, Ayşe
2014-04-01
Adaptive systems frequently incorporate complex structures which can arise spontaneously and which may be nonadaptive in the evolutionary sense. We give examples from phase transition and fractal growth to develop the themes of cooperative phenomena and pattern formation. We discuss RNA interference and transcriptional gene regulation networks, where a major part of the topological properties can be accounted for by mere combinatorics. A discussion of ensemble approaches to biological systems and measures of complexity is presented, and a connection is established between complexity and fitness.
Neuromolecular computing: a new approach to human brain evolution.
Wallace, R; Price, H
1999-09-01
Evolutionary approaches in human cognitive neurobiology traditionally emphasize macroscopic structures. It may soon be possible to supplement these studies with models of human information-processing of the molecular level. Thin-film, simulation, fluorescence microscopy, and high-resolution X-ray crystallographic studies provide evidence for transiently organized neural membrane molecular systems with possible computational properties. This review article examines evidence for hydrophobic-mismatch molecular interactions within phospholipid microdomains of a neural membrane bilayer. It is proposed that these interactions are a massively parallel algorithm which can rapidly compute near-optimal solutions to complex cognitive and physiological problems. Coupling of microdomain activity to permenant ion movements at ligand-gated and voltage-gated channels permits the conversion of molecular computations into neuron frequency codes. Evidence for microdomain transport of proteins to specific locations within the bilayer suggests that neuromolecular computation may be under some genetic control and thus modifiable by natural selection. A possible experimental approach for examining evolutionary changes in neuromolecular computation is briefly discussed.
WANG Congzhe; FANG Yuefa; GUO Sheng
2015-01-01
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.
Islam, Sk Minhazul; Das, Swagatam; Ghosh, Saurav; Roy, Subhrajit; Suganthan, Ponnuthurai Nagaratnam
2012-04-01
Differential evolution (DE) is one of the most powerful stochastic real parameter optimizers of current interest. In this paper, we propose a new mutation strategy, a fitness-induced parent selection scheme for the binomial crossover of DE, and a simple but effective scheme of adapting two of its most important control parameters with an objective of achieving improved performance. The new mutation operator, which we call DE/current-to-gr_best/1, is a variant of the classical DE/current-to-best/1 scheme. It uses the best of a group (whose size is q% of the population size) of randomly selected solutions from current generation to perturb the parent (target) vector, unlike DE/current-to-best/1 that always picks the best vector of the entire population to perturb the target vector. In our modified framework of recombination, a biased parent selection scheme has been incorporated by letting each mutant undergo the usual binomial crossover with one of the p top-ranked individuals from the current population and not with the target vector with the same index as used in all variants of DE. A DE variant obtained by integrating the proposed mutation, crossover, and parameter adaptation strategies with the classical DE framework (developed in 1995) is compared with two classical and four state-of-the-art adaptive DE variants over 25 standard numerical benchmarks taken from the IEEE Congress on Evolutionary Computation 2005 competition and special session on real parameter optimization. Our comparative study indicates that the proposed schemes improve the performance of DE by a large magnitude such that it becomes capable of enjoying statistical superiority over the state-of-the-art DE variants for a wide variety of test problems. Finally, we experimentally demonstrate that, if one or more of our proposed strategies are integrated with existing powerful DE variants such as jDE and JADE, their performances can also be enhanced.
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
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.
Chang Liu
2014-06-01
Full Text Available 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.
Spectral approach to axisymmetric evolution of Einstein's equations
Schell, Christian
2014-01-01
We present a new formulation of Einstein's equations for an axisymmetric spacetime with vanishing twist in vacuum. We propose a fully constrained scheme and use spherical polar coordinates. A general problem for this choice is the occurrence of coordinate singularities on the axis of symmetry and at the origin. Spherical harmonics are manifestly regular on the axis and hence take care of that issue automatically. In addition a spectral approach has computational advantages when the equations are implemented. Therefore we spectrally decompose all the variables in the appropriate harmonics. A central point in the formulation is the gauge choice. One of our results is that the commonly used maximal-isothermal gauge turns out to be incompatible with tensor harmonic expansions, and we introduce a new gauge that is better suited. We also address the regularisation of the coordinate singularity at the origin.
Waller, Bridget M; Liebal, Katja; Burrows, Anne M; Slocombe, Katie E
2013-07-18
Scientists studying the communication of non-human animals are often aiming to better understand the evolution of human communication, including human language. Some scientists take a phylogenetic perspective, where the goal is to trace the evolutionary history of communicative traits, while others take a functional perspective, where the goal is to understand the selection pressures underpinning specific traits. Both perspectives are necessary to fully understand the evolution of communication, but it is important to understand how the two perspectives differ and what they can and cannot tell us. Here, we suggest that integrating phylogenetic and functional questions can be fruitful in better understanding the evolution of communication. We also suggest that adopting a multimodal approach to communication might help to integrate phylogenetic and functional questions, and provide an interesting avenue for research into language evolution.
Rural credit in Brazil: contrat's evolution at an institutional approach
Luciana Florêncio de Almeida
2009-03-01
Full Text Available This article outlines a New Institutional Economics’ approach of rural credit. The mainstream relies in the understanding that the rural credit contracts are hybrid forms in response to the agrichain´s complexity. The object of the research was operational credit contract for soybeans farms. The research consisted on qualitative researches in the extent that they sought to comprehend in a more profound level the rules of game for the rural financing contracting environment based on the economic agent’s perceptions. The results highlighted the agent’s perception that the judicial system is not strong enough to performance an efficient enforcement of the contracts. In response to this institutional challenge, the agents and the government manage adaptations in the contracts, which has been successful in the agent’s point of view. In the other hand, the informational system has showed weaknesses in protecting the creditor’s right. This scenario open breaches to opportunist actions and adverse selection. In order to mitigate these problems the agents govern interdependent transactions as a tool for risk sharing.
Higher-order terms in sensitivity analysis through a differential approach
Dubi, A.; Dudziak, D.J.
1981-06-01
A differential approach to sensitivity analysis has been developed that eliminates some difficulties existing in previous work. The new development leads to simple explicit expressions for the first-order perturbation as well as any higher-order terms. The higher-order terms are dependent only on differentials of the transport operator, the unperturbed flux, the adjoint flux, and the unperturbed Green's function of the system.
Differential-Psychological and Psychophysiological Approaches to Learning in Modern School
Kabardov M. K.,; Aminov N.A.,; Zhambeeva Z.Z.,
2017-01-01
The article shows the background and specifics of application of differential psychological and psychophysiological approach to learning in modern school. The revealed problems of the use of the process of individualization and differentiation of teaching, the necessity of taking into account the individual learning opportunities, individual style of pedagogical activity, as well as features the method used by the teacher during training at the modern stage of education development. Presents ...
Surge-tectonic evolution of southeastern Asia: a geohydrodynamics approach
Meyerhoff, Arthur A.
The repeated need for ad hoc modifications in plate-tectonic models to explain the evolution of southeastern Asia reveals their inability to fully explain the complex features and dynamics of this region. As one example, the hypothesis does not provide a mechanism to explain the 180° turns and twists along the strike of several foldbelts and island arcs in the region (e.g. Banda arc). Convection-cell configuration renders such 180° contortions and Rayleigh-Bénard-type convection impossible. However, during the last 10 years, new data bearing on the convection-cell problem have become available in the form of seismotomographic images of the earth's interior. These images show that (i) mantle diapirs as proposed by traditional plate-tectonic models do not exist; (ii) there is no discernible pattern of upper or lower mantle convection, and thus no longer an adequate mechanism to move plates; and (iii) the lithosphere above a depth of about 80 km is permeated by an interconnected network of low-velocity channels. Seismic-reflection studies of the low-velocity channels discovered on the seismotomographic images reveal that these channels have walls with a 7.1-7.8 km s -1 P-wave velocity. Commonly, the interiors of the channels are acoustically transparent, with much slower P-wave velocities, in places as low as 5.4 km s -1. The author and co-workers have interpreted the low velocities as evidence for the presence of partial melt in the channels, and they postulated that this melt moves preferentially eastward as a result of the earth's rotation. They named these channels "surge channels" and their new hypothesis for earth dynamics "surge tectonics". Surge channels underlie every type of tectonic belt, which includes mid-ocean ridges, aseismic ridges, continental rifts, strike-slip fracture zones, and foldbelts. In southeastern Asia, surge channels—mainly foldbelts—lie between all platform and cratonic massifs. These massifs, platforms, and tectonics belts
Wang, Hongsen; Rus, Eric; Sakuraba, Takahito; Kikuchi, Jun; Kiya, Yasuyuki; Abruña, Héctor D
2014-07-01
A three-electrode differential electrochemical mass spectrometry (DEMS) cell has been developed to study the oxidative decomposition of electrolytes at high voltage cathode materials of Li-ion batteries. In this DEMS cell, the working electrode used was the same as the cathode electrode in real Li-ion batteries, i.e., a lithium metal oxide deposited on a porous aluminum foil current collector. A charged LiCoO2 or LiMn2O4 was used as the reference electrode, because of their insensitivity to air, when compared to lithium. A lithium sheet was used as the counter electrode. This DEMS cell closely approaches real Li-ion battery conditions, and thus the results obtained can be readily correlated with reactions occurring in real Li-ion batteries. Using DEMS, the oxidative stability of three electrolytes (1 M LiPF6 in EC/DEC, EC/DMC, and PC) at three cathode materials including LiCoO2, LiMn2O4, and LiNi(0.5)Mn(1.5)O4 were studied. We found that 1 M LiPF6 + EC/DMC electrolyte is quite stable up to 5.0 V, when LiNi(0.5)Mn(1.5)O4 is used as the cathode material. The EC/DMC solvent mixture was found to be the most stable for the three cathode materials, while EC/DEC was the least stable. The oxidative decomposition of the EC/DEC mixture solvent could be readily observed under operating conditions in our cell even at potentials as low as 4.4 V in 1 M LiPF6 + EC/DEC electrolyte on a LiCoO2 cathode, as indicated by CO2 and O2 evolution. The features of this DEMS cell to unveil solvent and electrolyte decomposition pathways are also described.
Quigley, Ian K; Turner, Jessica M; Nuckels, Richard J; Manuel, Joan L; Budi, Erine H; MacDonald, Erin L; Parichy, David M
2004-12-01
Latent precursors or stem cells of neural crest origin are present in a variety of post-embryonic tissues. Although these cells are of biomedical interest for roles in human health and disease, their potential evolutionary significance has been underappreciated. As a first step towards elucidating the contributions of such cells to the evolution of vertebrate form, we investigated the relative roles of neural crest cells and post-embryonic latent precursors during the evolutionary diversification of adult pigment patterns in Danio fishes. These pigment patterns result from the numbers and arrangements of embryonic melanophores that are derived from embryonic neural crest cells, as well as from post-embryonic metamorphic melanophores that are derived from latent precursors of presumptive neural crest origin. In the zebrafish D. rerio, a pattern of melanophore stripes arises during the larval-to-adult transformation by the recruitment of metamorphic melanophores from latent precursors. Using a comparative approach in the context of new phylogenetic data, we show that adult pigment patterns in five additional species also arise from metamorphic melanophores, identifying this as an ancestral mode of adult pigment pattern development. By contrast, superficially similar adult stripes of D. nigrofasciatus (a sister species to D. rerio) arise by the reorganization of melanophores that differentiated at embryonic stages, with a diminished contribution from metamorphic melanophores. Genetic mosaic and molecular marker analyses reveal evolutionary changes that are extrinsic to D. nigrofasciatus melanophore lineages, including a dramatic reduction of metamorphic melanophore precursors. Finally, interspecific complementation tests identify a candidate genetic pathway for contributing to the evolutionary reduction in metamorphic melanophores and the increased contribution of early larval melanophores to D. nigrofasciatus adult pigment pattern development. These results
Differential GPS/inertial navigation approach/landing flight test results
Snyder, Scott; Schipper, Brian; Vallot, Larry; Parker, Nigel; Spitzer, Cary
1992-01-01
Results of a joint Honeywell/NASA-Langley differential GPS/inertial flight test conducted in November 1990 are discussed focusing on postflight data analysis. The test was aimed at acquiring a system performance database and demonstrating automatic landing based on an integrated differential GPS/INS with barometric and radar altimeters. Particular attention is given to characteristics of DGPS/inertial error and the magnitude of the differential corrections and vertical channel performance with and without altimeter augmentation. It is shown that DGPS/inertial integrated with a radar altimeter is capable of providing a precision approach and autoland guidance of manned return space vehicles within the Space Shuttle accuracy requirements.
An effective analytic approach for solving nonlinear fractional partial differential equations
Ma, Junchi; Zhang, Xiaolong; Liang, Songxin
2016-08-01
Nonlinear fractional differential equations are widely used for modelling problems in applied mathematics. A new analytic approach with two parameters c1 and c2 is first proposed for solving nonlinear fractional partial differential equations. These parameters are used to improve the accuracy of the resulting series approximations. It turns out that much more accurate series approximations are obtained by choosing proper values of c1 and c2. To demonstrate the applicability and effectiveness of the new method, two typical fractional partial differential equations, the nonlinear gas dynamics equation and the nonlinear KdV-Burgers equation, are solved.
Horiuchi, Youko
2015-12-23
Background Since the development of transcriptome analysis systems, many expression evolution studies characterized evolutionary forces acting on gene expression, without explicit discrimination between global expression differences and tissue specific expression differences. However, different types of gene expression alteration should have different effects on an organism, the evolutionary forces that act on them might be different, and different types of genes might show different types of differential expression between species. To confirm this, we studied differentially expressed (DE) genes among closely related groups that have extensive gene expression atlases, and clarified characteristics of different types of DE genes including the identification of regulating loci for differential expression using expression quantitative loci (eQTL) analysis data. Results We detected differentially expressed (DE) genes between rice subspecies in five homologous tissues that were verified using japonica and indica transcriptome atlases in public databases. Using the transcriptome atlases, we classified DE genes into two types, global DE genes and changed-tissues DE genes. Global type DE genes were not expressed in any tissues in the atlas of one subspecies, however changed-tissues type DE genes were expressed in both subspecies with different tissue specificity. For the five tissues in the two japonica-indica combinations, 4.6 ± 0.8 and 5.9 ± 1.5 % of highly expressed genes were global and changed-tissues DE genes, respectively. Changed-tissues DE genes varied in number between tissues, increasing linearly with the abundance of tissue specifically expressed genes in the tissue. Molecular evolution of global DE genes was rapid, unlike that of changed-tissues DE genes. Based on gene ontology, global and changed-tissues DE genes were different, having no common GO terms. Expression differences of most global DE genes were regulated by cis-eQTLs. Expression
Ting Hou; Liping Zhang; Yuchen Chen
2014-01-01
.... In this paper, a kind of fuzzy self-optimizing control based on differential evolution algorithm is proposed, which applied in the power plant boiler system, the boiler combustion efficiency has been...
Comparative genetic approaches to the evolution of human brain and behavior.
Vallender, Eric J
2011-01-01
With advances in genomic technologies, the amount of genetic data available to scientists today is vast. Genomes are now available or planned for 14 different primate species and complete resequencing of numerous human individuals from numerous populations is underway. Moreover, high-throughput deep sequencing is quickly making whole genome efforts within the reach of single laboratories allowing for unprecedented studies. Comparative genetic approaches to the identification of the underlying basis of human brain, behavior, and cognitive ability are moving to the forefront. Two approaches predominate: inter-species divergence comparisons and intra-species polymorphism studies. These methodological differences are useful for different time scales of evolution and necessarily focus on different evolutionary events in the history of primate and hominin evolution. Inter-species divergence is more useful in studying large scale primate, or hominoid, evolution whereas intra-species polymorphism can be more illuminating of recent hominin evolution. These differences in methodological utility also extend to studies of differing genetic substrates; current divergence studies focus primarily on protein evolution whereas polymorphism studies are substrate ambivalent. Some of the issues inherent in these studies can be ameliorated by current sequencing capabilities whereas others remain intractable. New avenues are also being opened that allow for the incorporation of novel substrates and approaches. In the post-genomic era, the study of human evolution, specifically as it relates to the brain, is becoming more complete focusing increasingly on the totality of the system and better conceptualizing the entirety of the genetic changes that have lead to the human phenotype today.
Gomes, J. M.; Papaderos, P.
2017-07-01
The goal of population spectral synthesis (pss; also referred to as inverse, semi-empirical evolutionary- or fossil record approach) is to decipher from the spectrum of a galaxy the mass, age and metallicity of its constituent stellar populations. This technique, which is the reverse of but complementary to evolutionary synthesis, has been established as fundamental tool in extragalactic research. It has been extensively applied to large spectroscopic data sets, notably the SDSS, leading to important insights into the galaxy assembly history. However, despite significant improvements over the past decade, all current pss codes suffer from two major deficiencies that inhibit us from gaining sharp insights into the star-formation history (SFH) of galaxies and potentially introduce substantial biases in studies of their physical properties (e.g., stellar mass, mass-weighted stellar age and specific star formation rate). These are i) the neglect of nebular emission in spectral fits, consequently; ii) the lack of a mechanism that ensures consistency between the best-fitting SFH and the observed nebular emission characteristics of a star-forming (SF) galaxy (e.g., hydrogen Balmer-line luminosities and equivalent widths-EWs, shape of the continuum in the region around the Balmer and Paschen jump). In this article, we present fado (Fitting Analysis using Differential evolution Optimization) - a conceptually novel, publicly available pss tool with the distinctive capability of permitting identification of the SFH that reproduces the observed nebular characteristics of a SF galaxy. This so-far unique self-consistency concept allows us to significantly alleviate degeneracies in current spectral synthesis, thereby opening a new avenue to the exploration of the assembly history of galaxies. The innovative character of fado is further augmented by its mathematical foundation: fado is the first pss code employing genetic differential evolution optimization. This, in conjunction
Nickerson Cheryl A
2006-01-01
Full Text Available Abstract Background Genomic islands are regions of bacterial genomes that have been acquired by horizontal transfer and often contain blocks of genes that function together for specific processes. Recently, it has become clear that the impact of genomic islands on the evolution of different bacterial species is significant and represents a major force in establishing bacterial genomic variation. However, the study of genomic island evolution has been mostly performed at the sequence level using computer software or hybridization analysis to compare different bacterial genomic sequences. We describe here a novel experimental approach to study the evolution of species-specific bacterial genomic islands that identifies island genes that have evolved in such a way that they are differentially-expressed depending on the bacterial host background into which they are transferred. Results We demonstrate this approach by using a "test" genomic island that we have cloned from the Salmonella typhimurium genome (island 4305 and transferred to a range of Gram negative bacterial hosts of differing evolutionary relationships to S. typhimurium. Systematic analysis of the expression of the island genes in the different hosts compared to proper controls allowed identification of genes with genera-specific expression patterns. The data from the analysis can be arranged in a matrix to give an expression "array" of the island genes in the different bacterial backgrounds. A conserved 19-bp DNA site was found upstream of at least two of the differentially-expressed island genes. To our knowledge, this is the first systematic analysis of horizontally-transferred genomic island gene expression in a broad range of Gram negative hosts. We also present evidence in this study that the IS200 element found in island 4305 in S. typhimurium strain LT2 was inserted after the island had already been acquired by the S. typhimurium lineage and that this element is likely not
Earth’s thermal evolution with multiple convection modes: A Monte-Carlo approach
Höink, Tobias; Lenardic, Adrian; Jellinek, A. Mark
2013-08-01
We present a thermal evolution model, based on the results of recent numerical simulations, in which we consider that different sized oceanic plates are associated with different modes of surface motion (mobile-lid and sluggish-lid tectonics). These different modes are, in turn, associated with different heat loss scalings. Varying initial conditions and system parameters systematically we run several thousand thermal models that we compare with constraints on present-day mantle temperature, present-day Urey ratio and overall minimum core heat flow. The dual heat loss mode approach readily satisfies the Urey ratio constraint that is unexplained by classic thermal evolution models.
Debakanta MISHRA; Erol TUTUMLUER; Timothy D.STARK; James P.HYSLIP; Steven M.CHRISMER; Michael TOMAS
2012-01-01
Railway transitions experience differential movements due to differences in track system stiffness,track damping characteristics,foundation type,ballast settlement from fouling and/or degradation,as well as fill and subgrade settlement.This differential movement is especially problematic for high speed rail infrastructure as the 'bump' at the transition is accentuated at high speeds.Identification of different factors contributing towards this differential movement,as well as development of design and maintenance strategies to mitigate the problem is imperative for the safe and economical operation of both freight and passenger rail networks.This paper presents the research framework and initial instrumentation details from an ongoing research effort at the University of Illinois at Urbana-Champaign.Three bridge approaches experiencing recurrent geometry problems were instrumented using multidepth deflectometers (MDDs) and strain gages to identify different factors contributing to the development of differential movements.
CERN Library
2014-01-01
Tuesday 25 March 2014 at 4 p.m. in the Library, bldg. 52-1-052 "Differential manifolds: a basic approach for experimental physicists" by Paul Baillon, World Scientific, 2013, ISBN 978-981-4449-56-4. Differential manifold is the framework of particle physics and astrophysics nowadays. It is important for all research physicists to be accustomed to it, and even experimental physicists should be able to manipulate equations and expressions in this framework. This book gives a comprehensive description of the basics of differential manifold with a full proof of elements. A large part of the book is devoted to the basic mathematical concepts, which are all necessary for the development of the differential manifold. This book is self-consistent; it starts from first principles. The mathematical framework is the set theory with its axioms and its formal logic. No special knowledge is needed. Coffee will be served from 3.30 p.m.
Ahmed, Faraz; Liu, Alex X
2013-01-01
Online social networks are being increasingly used for analyzing various societal phenomena such as epidemiology, information dissemination, marketing and sentiment flow. Popular analysis techniques such as clustering and influential node analysis, require the computation of eigenvectors of the real graph's adjacency matrix. Recent de-anonymization attacks on Netflix and AOL datasets show that an open access to such graphs pose privacy threats. Among the various privacy preserving models, Differential privacy provides the strongest privacy guarantees. In this paper we propose a privacy preserving mechanism for publishing social network graph data, which satisfies differential privacy guarantees by utilizing a combination of theory of random matrix and that of differential privacy. The key idea is to project each row of an adjacency matrix to a low dimensional space using the random projection approach and then perturb the projected matrix with random noise. We show that as compared to existing approaches for ...
Guo, Yan; Zhao, Shilin; Ye, Fei; Sheng, Quanhu; Shyr, Yu
2014-01-01
After a decade of microarray technology dominating the field of high-throughput gene expression profiling, the introduction of RNAseq has revolutionized gene expression research. While RNAseq provides more abundant information than microarray, its analysis has proved considerably more complicated. To date, no consensus has been reached on the best approach for RNAseq-based differential expression analysis. Not surprisingly, different studies have drawn different conclusions as to the best approach to identify differentially expressed genes based upon their own criteria and scenarios considered. Furthermore, the lack of effective quality control may lead to misleading results interpretation and erroneous conclusions. To solve these aforementioned problems, we propose a simple yet safe and practical rank-sum approach for RNAseq-based differential gene expression analysis named MultiRankSeq. MultiRankSeq first performs quality control assessment. For data meeting the quality control criteria, MultiRankSeq compares the study groups using several of the most commonly applied analytical methods and combines their results to generate a new rank-sum interpretation. MultiRankSeq provides a unique analysis approach to RNAseq differential expression analysis. MultiRankSeq is written in R, and it is easily applicable. Detailed graphical and tabular analysis reports can be generated with a single command line.
Chen, Guiling
2013-01-01
This thesis studies asymptotic behavior and stability of determinsitic and stochastic delay differential equations. The approach used in this thesis is based on fixed point theory, which does not resort to any Liapunov function or Liapunov functional. The main contribution of this thesis is to study
Beaulieu, J. P.; Sasselov, D. D.
1996-01-01
Abstract: We present a differential study of 500 Magellanic Cepheids with 3 million measurements obtained as a by-product of the EROS microlensing survey. The data-set is unbiased and provides an excellent basis for a differential analysis between LMC and SMC. We investigate the pulsational properti
CHEN Jie; XIN Bin; PENG ZhiHong; PAN Feng
2009-01-01
This brief paper reports a hybrid algorithm we developed recently to solve the global optimization problems of multimodal functions, by combining the advantages of two powerful population-based metaheuristics-differential evolution (DE) and particle swarm optimization (PSO). In the hybrid denoted by DEPSO, each individual in one generation chooses its evolution method, DE or PSO, in a statistical learning way. The choice depends on the relative success ratio of the two methods in a previous learning period. The proposed DEPSO is compared with its PSO and DE parents, two advanced DE variants one of which is suggested by the originators of DE, two advanced PSO variants one of which is acknowledged as a recent standard by PSO community, and also a previous DEPSO. Benchmark tests demonstrate that the DEPSO is more competent for the global optimization of multimodal functions due to its high optimization quality.
Jin-Yu Zhang
2014-01-01
Full Text Available This paper has proposed a new thermal wave image sequence compression algorithm by combining double exponential decay fitting model and differential evolution algorithm. This study benchmarked fitting compression results and precision of the proposed method was benchmarked to that of the traditional methods via experiment; it investigated the fitting compression performance under the long time series and improved model and validated the algorithm by practical thermal image sequence compression and reconstruction. The results show that the proposed algorithm is a fast and highly precise infrared image data processing method.
Xuemin, Wang; Anqiang, Li; Rui, Zhang
2017-05-01
Due to the wide construction of wind power and the difficulty for it to join the power grid, a short-term hydro-wind economic dispatch (WHED) problem is proposed. WHED system contains several wind power units and hydropower plants, which are renewable and clean. Combined with hydropower plants, the wind power units can join the power grid stably. Then, a WHED system with four cascaded hydropower plants and two wind units is established, and a modified differential evolution (DE) algorithm with chaotic perturbation is proposed for optimizing. Finally, two cases are simulated and analysed, the dispatch results show that the presented model and algorithm are feasible and effective.
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.
Brezzi, Franco; Cangiani, Andrea; Georgoulis, Emmanuil
2016-01-01
This volume contains contributed survey papers from the main speakers at the LMS/EPSRC Symposium “Building bridges: connections and challenges in modern approaches to numerical partial differential equations”. This meeting took place in July 8-16, 2014, and its main purpose was to gather specialists in emerging areas of numerical PDEs, and explore the connections between the different approaches. The type of contributions ranges from the theoretical foundations of these new techniques, to the applications of them, to new general frameworks and unified approaches that can cover one, or more than one, of these emerging techniques.
Mojtaba Ganjali
Full Text Available In this paper, the problem of identifying differentially expressed genes under different conditions using gene expression microarray data, in the presence of outliers, is discussed. For this purpose, the robust modeling of gene expression data using some powerful distributions known as normal/independent distributions is considered. These distributions include the Student's t and normal distributions which have been used previously, but also include extensions such as the slash, the contaminated normal and the Laplace distributions. The purpose of this paper is to identify differentially expressed genes by considering these distributional assumptions instead of the normal distribution. A Bayesian approach using the Markov Chain Monte Carlo method is adopted for parameter estimation. Two publicly available gene expression data sets are analyzed using the proposed approach. The use of the robust models for detecting differentially expressed genes is investigated. This investigation shows that the choice of model for differentiating gene expression data is very important. This is due to the small number of replicates for each gene and the existence of outlying data. Comparison of the performance of these models is made using different statistical criteria and the ROC curve. The method is illustrated using some simulation studies. We demonstrate the flexibility of these robust models in identifying differentially expressed genes.
无
2010-01-01
We give an equivalent construction of the infinitesimal time translation operator for partial differential evolution equation in the algebraic dynamics algorithm proposed by Shun-Jin Wang and his students. Our construction involves only simple partial differentials and avoids the derivative terms of δ function which appear in the course of computation by means of Wang-Zhang operator. We prove Wang’s equivalent theorem which says that our construction and Wang-Zhang’s are equivalent. We use our construction to deal with several typical equations such as nonlinear advection equation, Burgers equation, nonlinear Schrodinger equation, KdV equation and sine-Gordon equation, and obtain at least second order approximate solutions to them. These equations include the cases of real and complex field variables and the cases of the first and the second order time derivatives.
Liu, Chengshi
2010-08-01
We give an equivalent construction of the infinitesimal time translation operator for partial differential evolution equation in the algebraic dynamics algorithm proposed by Shun-Jin Wang and his students. Our construction involves only simple partial differentials and avoids the derivative terms of δ function which appear in the course of computation by means of Wang-Zhang operator. We prove Wang’s equivalent theorem which says that our construction and Wang-Zhang’s are equivalent. We use our construction to deal with several typical equations such as nonlinear advection equation, Burgers equation, nonlinear Schrodinger equation, KdV equation and sine-Gordon equation, and obtain at least second order approximate solutions to them. These equations include the cases of real and complex field variables and the cases of the first and the second order time derivatives.
Qin, Bo; Tian, Bo; Wang, Yu-Feng; Shen, Yu-Jia; Wang, Ming
2017-10-01
Under investigation in this paper are the Belov-Chaltikian (BC), Leznov and Blaszak-Marciniak (BM) lattice equations, which are associated with the conformal field theory, UToda(m_1,m_2) system and r-matrix, respectively. With symbolic computation, the Bell-polynomial approach is developed to directly bilinearize those three sets of differential-difference nonlinear evolution equations (NLEEs). This Bell-polynomial approach does not rely on any dependent variable transformation, which constitutes the key step and main difficulty of the Hirota bilinear method, and thus has the advantage in the bilinearization of the differential-difference NLEEs. Based on the bilinear forms obtained, the N-soliton solutions are constructed in terms of the N × N Wronskian determinant. Graphic illustrations demonstrate that those solutions, more general than the existing results, permit some new properties, such as the solitonic propagation and interactions for the BC lattice equations, and the nonnegative dark solitons for the BM lattice equations.
Melissa A Metzler
Full Text Available The transcription factor networks that drive parotid salivary gland progenitor cells to terminally differentiate, remain largely unknown and are vital to understanding the regeneration process.A systems biology approach was taken to measure mRNA and microRNA expression in vivo across acinar cell terminal differentiation in the rat parotid salivary gland. Laser capture microdissection (LCM was used to specifically isolate acinar cell RNA at times spanning the month-long period of parotid differentiation.Clustering of microarray measurements suggests that expression occurs in four stages. mRNA expression patterns suggest a novel role for Pparg which is transiently increased during mid postnatal differentiation in concert with several target gene mRNAs. 79 microRNAs are significantly differentially expressed across time. Profiles of statistically significant changes of mRNA expression, combined with reciprocal correlations of microRNAs and their target mRNAs, suggest a putative network involving Klf4, a differentiation inhibiting transcription factor, which decreases as several targeting microRNAs increase late in differentiation. The network suggests a molecular switch (involving Prdm1, Sox11, Pax5, miR-200a, and miR-30a progressively decreases repression of Xbp1 gene transcription, in concert with decreased translational repression by miR-214. The transcription factor Xbp1 mRNA is initially low, increases progressively, and may be maintained by a positive feedback loop with Atf6. Transfection studies show that Xbp1 activates the Mist1 promoter [corrected]. In addition, Xbp1 and Mist1 each activate the parotid secretory protein (Psp gene, which encodes an abundant salivary protein, and is a marker of terminal differentiation.This study identifies novel expression patterns of Pparg, Klf4, and Sox11 during parotid acinar cell differentiation, as well as numerous differentially expressed microRNAs. Network analysis identifies a novel stemness arm, a
Zhengyin; HU; Shu; FANG; Ling; WEI; Yi; WEN; Xian; ZHANG; Min; WANG
2015-01-01
Purpose: This paper introduces an approach to technology evolution analysis using patent information based on Subject-Action-Object(SAO) structures.Design/methodology/approach: First, SAO structures were extracted from patent documents and were cleaned. Second, several dimension-reduction techniques were used to generate technology topics. Then, the hierarchical relationships between technology topics were calculated based on patent clustering. Finally, technology evolution maps were drawn by adding a timeline.Findings: This approach can reveal technology evolution processes from multiple perspectives than other approaches.Research limitations: The semantic annotation of an SAO type is not very accurate and the semantic types of technology topics need to be enriched.Practical implications: The approach can be applied to draw technology evolution maps using different types of technology topics such as problem or solution.Originality/value: This approach offers an analytical dimension and more details than existing techniques, and it helps identify fundamental and emerging technologies more accurately and comprehensively.
Biala, T A; Jator, S N
2015-01-01
In this article, the boundary value method is applied to solve three dimensional elliptic and hyperbolic partial differential equations. The partial derivatives with respect to two of the spatial variables (y, z) are discretized using finite difference approximations to obtain a large system of ordinary differential equations (ODEs) in the third spatial variable (x). Using interpolation and collocation techniques, a continuous scheme is developed and used to obtain discrete methods which are applied via the Block unification approach to obtain approximations to the resulting large system of ODEs. Several test problems are investigated to elucidate the solution process.
Szpigel, S. [Centro de Ciencias e Humanidades, Universidade Presbiteriana Mackenzie, Sao Paulo, SP (Brazil); Timoteo, V.S. [Faculdade de Tecnologia, Universidade Estadual de Campinas, Limeira, SP (Brazil); Duraes, F. de O [Centro de Ciencias e Humanidades, Universidade Presbiteriana Mackenzie, Sao Paulo, SP (Brazil)
2010-02-15
In this work we study the Similarity Renormalization Group (SRG) evolution of effective nucleon-nucleon (NN) interactions derived using the Subtracted Kernel Method (SKM) approach. We present the results for the phaseshifts in the {sup 1}S{sub 0} channel calculated using a SRG potential evolved from an initial effective potential obtained by implementing the SKM scheme for the leading-order NN interaction in chiral effective field theory (ChEFT).
Structure and Evolution of Mediterranean Forest Research: A Science Mapping Approach
Pierfrancesco Nardi; Giovanni Di Matteo; Marc Palahi; Giuseppe Scarascia Mugnozza
2016-01-01
This study aims at conducting the first science mapping analysis of the Mediterranean forest research in order to elucidate its research structure and evolution. We applied a science mapping approach based on co-term and citation analyses to a set of scientific publications retrieved from the Elsevier's Scopus database over the period 1980-2014. The Scopus search retrieved 2,698 research papers and reviews published by 159 peer-reviewed journals. The total number of publications was around 1%...
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
M. Setak
2013-01-01
Full Text Available The hub location problem involves a network of origins and destinations over which transportation takes place. There are many studies associated with finding the location of hub nodes and the allocation of demand nodes to these located hub nodes to transfer the only one kind of commodity under one level of service. However, in this study, carrying different commodity types from origin to destination under various levels of services (e.g. price, punctuality, reliability or transit time is studied. Quality of services experienced by users such as speed, convenience, comfort and security of transportation facilities and services is considered as the level of service. In each system, different kinds of commodities with various levels of services can be transmitted. The appropriate level of service that a commodity can be transmitted through is chosen by customer preferences and the specification of the commodity. So, a mixed integer programming formulation for single allocation hub covering location problem, which is based on the idea of transferring multi commodity flows under multi levels of service is presented. These two are applied concepts, multi-commodity and multi-level of service, which make the model's assumptions closer to the real world problems. In addition, a differential evolution algorithm is designed to find near-optimal solutions. The obtained solutions using differential evolution (DE algorithm (upper bound, where its parameters are tuned by response surface methodology, are compared with exact solutions and computed lower bounds by linear relaxation technique to prove the efficiency of proposed DE algorithm.
NIAN Xiaoyu; WANG Zhenlei; QIAN Feng
2013-01-01
To find the optimal operational condition when the properties of feedstock changes in the cracking furnace online,a hybrid algorithm named differential evolution group search optimization (DEGSO) is proposed,which is based on the differential evolution (DE) and the group search optimization (GSO).The DEGSO combines the advantages of the two algorithms:the high computing speed of DE and the good performance of the GSO for preventing the best particle from converging to local optimum.A cooperative method is also proposed for switching between these two algorithms.If the fitness value of one algorithm keeps invariant in several generations and less than the preset threshold,it is considered to fall into the local optimization and the other algorithm is chosen.Experiments on benchmark functions show that.the hybrid algorithm outperforms GSO in accuracy,global searching ability and efficiency.The optimization of ethylene and propylene yields is illustrated as a case by DEGSO.After optimization,the yield of ethylene and propylene is increased remarkably,which provides the proper operational condition of the ethylene cracking furnace.
Aijun Zhu; Chuanpei Xu; Zhi Li; Jun Wu; Zhenbing Liu
2015-01-01
A new meta-heuristic method is proposed to enhance current meta-heuristic methods for global optimization and test scheduling for three-dimensional (3D) stacked system-on-chip (SoC) by hybridizing grey wolf optimization with differential evo-lution (HGWO). Because basic grey wolf optimization (GWO) is easy to fal into stagnation when it carries out the operation of at-tacking prey, and differential evolution (DE) is integrated into GWO to update the previous best position of grey wolf Alpha, Beta and Delta, in order to force GWO to jump out of the stagnation with DE’s strong searching ability. The proposed algorithm can accele-rate the convergence speed of GWO and improve its performance. Twenty-three wel-known benchmark functions and an NP hard problem of test scheduling for 3D SoC are employed to verify the performance of the proposed algorithm. Experimental results show the superior performance of the proposed algorithm for exploiting the optimum and it has advantages in terms of exploration.
Zhang, Huifeng; Lei, Xiaohui; Wang, Chao; Yue, Dong; Xie, Xiangpeng
2017-01-01
Since wind power is integrated into the thermal power operation system, dynamic economic emission dispatch (DEED) has become a new challenge due to its uncertain characteristics. This paper proposes an adaptive grid based multi-objective Cauchy differential evolution (AGB-MOCDE) for solving stochastic DEED with wind power uncertainty. To properly deal with wind power uncertainty, some scenarios are generated to simulate those possible situations by dividing the uncertainty domain into different intervals, the probability of each interval can be calculated using the cumulative distribution function, and a stochastic DEED model can be formulated under different scenarios. For enhancing the optimization efficiency, Cauchy mutation operation is utilized to improve differential evolution by adjusting the population diversity during the population evolution process, and an adaptive grid is constructed for retaining diversity distribution of Pareto front. With consideration of large number of generated scenarios, the reduction mechanism is carried out to decrease the scenarios number with covariance relationships, which can greatly decrease the computational complexity. Moreover, the constraint-handling technique is also utilized to deal with the system load balance while considering transmission loss among thermal units and wind farms, all the constraint limits can be satisfied under the permitted accuracy. After the proposed method is simulated on three test systems, the obtained results reveal that in comparison with other alternatives, the proposed AGB-MOCDE can optimize the DEED problem while handling all constraint limits, and the optimal scheme of stochastic DEED can decrease the conservation of interval optimization, which can provide a more valuable optimal scheme for real-world applications.
Dubuisson, Jean-Yves; Hennequin, Sabine; Bary, Sophie; Ebihara, Atsushi; Boucheron-Dubuisson, Elodie
2011-12-01
To infer the anatomical evolution of the Hymenophyllaceae (filmy ferns) and to test previously suggested scenarios of regressive evolution, we performed an exhaustive investigation of stem anatomy in the most variable lineage of the family, the trichomanoids, using a representative sampling of 50 species. The evolution of qualitative and quantitative anatomical characters and possibly related growth-forms was analyzed using a maximum likelihood approach. Potential correlations between selected characters were then statistically tested using a phylogenetic comparative method. Our investigations support the anatomical homogeneity of this family at the generic and sub-generic levels. Reduced and sub-collateral/collateral steles likely derived from an ancestral massive protostele, and sub-collateral/collateral types appear to be related to stem thickness reduction and root apparatus regression. These results corroborate the hypothesis of regressive evolution in the lineage, in terms of morphology as well as anatomy. In addition, a heterogeneous cortex, which is derived in the lineage, appears to be related to a colonial strategy and likely to a climbing phenotype. The evolutionary hypotheses proposed in this study lay the ground for further evolutionary analyses that take into account trichomanoid habitats and accurate ecological preferences.
Clinical and morphological approaches to the differential diagnosis of diphtheric colitis
V. A. Tsinserling
2015-01-01
Full Text Available Inflammatory bowel diseases is a large group of nosologic forms such as more frequent acute intestinal infection, especially dysentery, chronic inflammatory diseases such as ulcerative colitis and Cron’s disease with lesions of the bowel, antibiotics-associated and ischemic colitis. There are some difficulties in differential diagnostics of inflammatory bowel diseases despite their widespread and tendency to more frequent occurrence. On the one hand, this is largely due to similar clinical picture which is most often presented by diarrhea and abdominal pain of different degree of intensity, and, on the other hand, by the disadvantages of laboratory diagnostics techniques. The article discusses the problem of clinical and morphological aspects of the differential colitis diagnostics with more detailed characteristics of fibrinous colitis of different etiology. The morphological differential diagnostics criteria, as well as a summary table of comparative characteristics of antibiotics-associated pseudomembranous colitis, dysentery, invasive candidiasis of bowel, ulcerative colitis and ischemic colitis have been presented. The importance of an integrated approach to the differential diagnostics of inflammatory bowel diseases, based on the analysis of anamnesis, clinical-laboratory and morphological data is stressed. The algorithm for optimizing of differential diagnostics of inflammatory bowel diseases with recommendations for qualitative morphological examination has been suggested.
Synchronization and anti-synchronization of chaotic systems: A differential and algebraic approach
Martinez-Guerra, Rafael [Departamento de Control Automatico, Cinvestav-IPN A. P. 14-740, Av. IPN 2508, 07360 Mexico, D.F. (Mexico)], E-mail: rguerra@ctrl.cinvestav.mx; Pasaye, Jose Juan Rincon [Departamento de Control Automatico, Cinvestav-IPN A. P. 14-740, Av. IPN 2508, 07360 Mexico, D.F. (Mexico)], E-mail: jrincon@ctrl.cinvestav.mx
2009-10-30
Chaotic systems synchronization and anti-synchronization problems are tackled by means of differential and algebraic techniques for nonlinear systems. An algebraic observer is proposed for systems satisfying an algebraic observability condition. This observer can be used as a slave system whose states are synchronized with the master (chaotic) system. This approach has the advantages of being independent of the chaotic nature of the master system, it uses a reduced set of measurable signal from the master system and it also solves the anti-synchronization problem as a straightforward extension of the synchronization one. A Colpitts oscillator is given to illustrate the effectiveness of the suggested approach.
一种基于精英云变异的差分演化算法%A Novel Differential Evolution Algorithm Based on Elite-Cloudy Mutation
郭肇禄; 吴志健; 汪靖; 汪慎文; 谢承旺
2013-01-01
针对传统差分演化算法在演化过程中存在少数个体出现停滞的现象,提出一种基于精英云变异的差分演化算法.该算法在演化过程中统计出每个个体的停滞代数,当一个个体的停滞代数达到指定的阈值时,对该个体执行精英云变异操作,使其向最优个体靠近,从而加快收敛速度；同时以一定的概率对所有个体执行一般反向学习操作,以增加种群的多样性.对比实验结果表明该算法在收敛速度和求解精度上均具有一定的优势.%Aiming at the disadvantage of traditional differential evolution, namely, existing some stagnating individuals in the evolutionary process, a novel differential evolution algorithm based on elite-cloudy mutation (ECMDE) is proposed in this study. In the proposed algorithm, stagnation generation of each individual is counted in the evolutionary process. Moreover, an individual is executed by the elite-cloudy mutation to approach the best individual when the stagnation generation of the individual is more than a pre-defined threshold value. Thus, it can accelerate the convergence speed. Additionally, in order to increase the population diversity, it executes the opposition-based learning operator with a certain probability. Experimental results indicate that the proposed algorithm obtains promising performance in both solution precision and convergence speed.
A conceptual approach to model co-evolution of urban structures
Schweitzer, Frank
2016-01-01
Urban structures encompass settlements, characterized by the spatial distribution of built-up areas, but also transportation structures, to connect these built-up areas. These two structures are very different in their origin and function, fulfilling complementary needs: (i) to access space, and (ii) to occupy space. Their evolution cannot be understood by looking at the dynamics of urban aggregations and transportation systems separately. Instead, existing built-up areas feed back on the further development of transportation structures, and the availability of the latter feeds back on the future growth of urban aggregations. To model this co-evolution, we propose an agent-based approach that builds on existing agent-based models for the evolution of trail systems and of urban settlements. The key element in these separate approaches is a generalized communication of agents by means of an adaptive landscape. This landscape is only generated by the agents, but once it exists, it feeds back on their further act...
Auler,P.A.; C.O. Gamba; R.S. Horta; G.E. Lavalle; G.D. Cassali
2014-01-01
This report describes a case of a well differentiated squamous cell carcinoma (SCC) of the foreskin of a dog, with metastasis in the regional lymph node. A six-year-old male intact Pit Bull dog presented a preputial ulcerated lesion with an evolution time of one year and enlarged left inguinal lymph node. Surgical resection of the preputial lesion and inguinal lymph nodes was made. The diagnosis of a well differentiated SCC was made following histopathological analysis and immunohistochemistr...
Alison Shaw
2014-05-01
Full Text Available Emerging sustainability challenges, such as food security, livelihood development and climate change, require innovative and experimental ways of linking science, policy and practice at all scales. This requires the development of processes that integrate diverse knowledge to generate adaptive development strategies into the future. Social learning is emerging as a promising way to make these linkages. If and how social learning approaches are being applied in practice among smallholder farming families—the bulk of the world’s food producers, requires specific attention. In this paper we use a case study approach to explore social learning among the agricultural poor. Five key evaluative factors: context assessment, inclusive design and management, facilitating learning, mobilizing knowledge and assessing outcomes, are used to analyze nine projects and programs in (or affiliated with the Consultative Group on International Agricultural Research (CGIAR. We explore three main questions: (1 in what contexts and in what ways are socially differentiated and marginalized groups enrolled in the learning process? (2 what, if any, are the additional benefits to social learning when explicitly using strategies to include socially differentiated groups? and (3 what are the benefits and trade-offs of applying these approaches for development outcomes? The findings suggest that, in the agricultural development context, social learning projects that include socially differentiated groups and create conditions for substantive two-way learning enhance the relevance and legitimacy of knowledge and governance outcomes, increasing the potential for accelerating sustainable development outcomes.
Differential evolution of MAGE genes based on expression pattern and selection pressure.
Qi Zhao
Full Text Available 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.
Probabilistic evolution approach for initial value problems over Fourier basis set
Tuna, Süha; Demiralp, Metin
2012-11-01
Initial Value Problems (IVPs) which are ordinary differential equations (ODEs) with an accompanying initial value are one of the most important subjects in science and engineering. They are encountered in many applications of scientific fields such as physics, quantum mechanics, statistical mechanics etc. Scientists have a large amount of knowledge about how to solve them even analytically or numerically. In this work, a new and novel approach to solve IVPs, especially having non-linear descriptive functions, based on linearization will be introduced.
Differential stepwise evolution of SARS coronavirus functional proteins in different host species
Tang Xianchun
2009-03-01
Full Text Available Abstract Background SARS coronavirus (SARS-CoV was identified as the etiological agent of SARS, and extensive investigations indicated that it originated from an animal source (probably bats and was recently introduced into the human population via wildlife animals from wet markets in southern China. Previous studies revealed that the spike (S protein of SARS had experienced adaptive evolution, but whether other functional proteins of SARS have undergone adaptive evolution is not known. Results We employed several methods to investigate selective pressure among different SARS-CoV groups representing different epidemic periods and hosts. Our results suggest that most functional proteins of SARS-CoV have experienced a stepwise adaptive evolutionary pathway. Similar to previous studies, the spike protein underwent strong positive selection in the early and middle phases, and became stabilized in the late phase. In addition, the replicase experienced positive selection only in human patients, whereas assembly proteins experienced positive selection mainly in the middle and late phases. No positive selection was found in any proteins of bat SARS-like-CoV. Furthermore, specific amino acid sites that may be the targets of positive selection in each group are identified. Conclusion This extensive evolutionary analysis revealed the stepwise evolution of different functional proteins of SARS-CoVs at different epidemic stages and different hosts. These results support the hypothesis that SARS-CoV originated from bats and that the spill over into civets and humans were more recent events.
Sachinidis, Agapios; Sotiriadou, Isaia; Seelig, Bianca; Berkessel, Albrecht; Hescheler, Jürgen
2008-01-01
Cell replacement therapy of severe degenerative diseases such as diabetes, myocardial infarction and Parkinson's disease through transplantation of somatic cells generated from embryonic stem (ES) cells is currently receiving considerable attention for the therapeutic applications. ES cells harvested from the inner cell mass (ICM) of the early embryo, can proliferate indefinitely in vitro while retaining the ability to differentiate into all somatic cells thereby providing an unlimited renewable source of somatic cells. In this context, identifying soluble factors, in particular chemically synthesized small molecules, and signal cascades involved in specific differentiation processes toward a defined tissue specific cell type are crucial for optimizing the generation of somatic cells in vitro for therapeutic approaches. However, experimental models are required allowing rapid and "easy-to-handle" parallel screening of chemical libraries to achieve this goal. Recently, the forward chemical genetic screening strategy has been postulated to screen small molecules in cellular systems for a specific desired phenotypic effect. The current review is focused on the progress of ES cell research in the context of the chemical genetics to identify small molecules promoting specific differentiation of ES cells to desired cell phenotype. Chemical genetics in the context of the cell ES-based cell replacement therapy remains a challenge for the near future for several scientific fields including chemistry, molecular biology, medicinal physics and robotic technologies.
Couvreur, T L P; Richardson, J E; Sosef, M S M; Erkens, R H J; Chatrou, L W
2008-04-01
The congenital fusion of carpels, or syncarpy, is considered a key innovation as it is found in more than 80% of angiosperms. Within the magnoliids however, syncarpy has rarely evolved. Two alternative evolutionary origins of syncarpy were suggested in order to explain the evolution of this feature: multiplication of a single carpel vs. fusion of a moderate number of carpels. The magnoliid family Annonaceae provides an ideal situation to test these hypotheses as two African genera, Isolona and Monodora, are syncarpous in an otherwise apocarpous family with multicarpellate and unicarpellate genera. In addition to syncarpy, the evolution of six other morphological characters was studied. Well-supported phylogenetic relationships of African Annonaceae and in particular those of Isolona and Monodora were reconstructed. Six plastid regions were sequenced and analyzed using maximum parsimony and Bayesian inference methods. The Bayesian posterior mapping approach to study character evolution was used as it accounts for both mapping and phylogenetic uncertainty, and also allows multiple state changes along the branches. Our phylogenetic analyses recovered a fully resolved clade comprising twelve genera endemic to Africa, including Isolona and Monodora, which was nested within the so-called long-branch clade. This is the largest and most species-rich clade of African genera identified to date within Annonaceae. The two syncarpous genera were inferred with maximum support to be sister to a clade characterized by genera with multicarpellate apocarpous gynoecia, supporting the hypothesis that syncarpy arose by fusion of a moderate number of carpels. This hypothesis was also favoured when studying the floral anatomy of both genera. Annonaceae provide the only case of a clear evolution of syncarpy within an otherwise apocarpous magnoliid family. The results presented here offer a better understanding of the evolution of syncarpy in Annonaceae and within angiosperms in general.
Tan Xiaodong; Qiu Jing; Liu Guanjun; Lv Kehong; Yang Shuming; Wang Chao
2013-01-01
Prognostics and health management (PHM) significantly improves system availability and reliability,and reduces the cost of system operations.Design for testability (DFT) developed concurrently with system design is an important way to improve PHM capability.Testability modeling and analysis are the foundation of DFT.This paper proposes a novel approach of testability modeling and analysis based on failure evolution mechanisms.At the component level,the fault progression-related information of each unit under test (UUT) in a system is obtained by means of failure modes,evolution mechanisms,effects and criticality analysis (FMEMECA),and then the failure-symptom dependency can be generated.At the system level,the dynamic attributes of UUTs are assigned by using the bond graph methodology,and then the symptom-test dependency can be obtained by means of the functional flow method.Based on the failure-symptom and symptom-test dependencies,testability analysis for PHM systems can be realized.A shunt motor is used to verify the application of the approach proposed in this paper.Experimental results show that this approach is able to be applied to testability modeling and analysis for PHM systems very well,and the analysis results can provide a guide for engineers to design for testability in order to improve PHM performance.
Gaurang Mahajan
Full Text Available High-throughput experiments such as microarrays and deep sequencing provide large scale information on the pattern of gene expression, which undergoes extensive remodeling as the cell dynamically responds to varying environmental cues or has its function disrupted under pathological conditions. An important initial step in the systematic analysis and interpretation of genome-scale expression alteration involves identification of a set of perturbed transcriptional regulators whose differential activity can provide a proximate hypothesis to account for these transcriptomic changes. In the present work, we propose an unbiased and logically natural approach to transcription factor enrichment. It involves overlaying a list of experimentally determined differentially expressed genes on a background regulatory network coming from e.g. literature curation or computational motif scanning, and identifying that subset of regulators whose aggregated target set best discriminates between the altered and the unaffected genes. In other words, our methodology entails testing of all possible regulatory subnetworks, rather than just the target sets of individual regulators as is followed in most standard approaches. We have proposed an iterative search method to efficiently find such a combination, and benchmarked it on E. coli microarray and regulatory network data available in the public domain. Comparative analysis carried out on artificially generated differential expression profiles, as well as empirical factor overexpression data for M. tuberculosis, shows that our methodology provides marked improvement in accuracy of regulatory inference relative to the standard method that involves evaluating factor enrichment in an individual manner.
Mahajan, Gaurang; Mande, Shekhar C
2015-01-01
High-throughput experiments such as microarrays and deep sequencing provide large scale information on the pattern of gene expression, which undergoes extensive remodeling as the cell dynamically responds to varying environmental cues or has its function disrupted under pathological conditions. An important initial step in the systematic analysis and interpretation of genome-scale expression alteration involves identification of a set of perturbed transcriptional regulators whose differential activity can provide a proximate hypothesis to account for these transcriptomic changes. In the present work, we propose an unbiased and logically natural approach to transcription factor enrichment. It involves overlaying a list of experimentally determined differentially expressed genes on a background regulatory network coming from e.g. literature curation or computational motif scanning, and identifying that subset of regulators whose aggregated target set best discriminates between the altered and the unaffected genes. In other words, our methodology entails testing of all possible regulatory subnetworks, rather than just the target sets of individual regulators as is followed in most standard approaches. We have proposed an iterative search method to efficiently find such a combination, and benchmarked it on E. coli microarray and regulatory network data available in the public domain. Comparative analysis carried out on artificially generated differential expression profiles, as well as empirical factor overexpression data for M. tuberculosis, shows that our methodology provides marked improvement in accuracy of regulatory inference relative to the standard method that involves evaluating factor enrichment in an individual manner.
Nitsch Daniela
2010-09-01
Full Text Available Abstract Background Discovering novel disease genes is still challenging for diseases for which no prior knowledge - such as known disease genes or disease-related pathways - is available. Performing genetic studies frequently results in large lists of candidate genes of which only few can be followed up for further investigation. We have recently developed a computational method for constitutional genetic disorders that identifies the most promising candidate genes by replacing prior knowledge by experimental data of differential gene expression between affected and healthy individuals. To improve the performance of our prioritization strategy, we have extended our previous work by applying different machine learning approaches that identify promising candidate genes by determining whether a gene is surrounded by highly differentially expressed genes in a functional association or protein-protein interaction network. Results We have proposed three strategies scoring disease candidate genes relying on network-based machine learning approaches, such as kernel ridge regression, heat kernel, and Arnoldi kernel approximation. For comparison purposes, a local measure based on the expression of the direct neighbors is also computed. We have benchmarked these strategies on 40 publicly available knockout experiments in mice, and performance was assessed against results obtained using a standard procedure in genetics that ranks candidate genes based solely on their differential expression levels (Simple Expression Ranking. Our results showed that our four strategies could outperform this standard procedure and that the best results were obtained using the Heat Kernel Diffusion Ranking leading to an average ranking position of 8 out of 100 genes, an AUC value of 92.3% and an error reduction of 52.8% relative to the standard procedure approach which ranked the knockout gene on average at position 17 with an AUC value of 83.7%. Conclusion In this study we
Application of recursive approaches to differential orbit correction of near Earth asteroids
Dmitriev, Vasily; Lupovka, Valery; Gritsevich, Maria
2016-10-01
Comparison of three approaches to the differential orbit correction of celestial bodies was performed: batch least squares fitting, Kalman filter, and recursive least squares filter. The first two techniques are well known and widely used (Montenbruck, O. & Gill, E., 2000). The most attention is paid to the algorithm and details of program realization of recursive least squares filter. The filter's algorithm was derived based on recursive least squares technique that are widely used in data processing applications (Simon, D, 2006). Usage recursive least squares filter, makes possible to process a new set of observational data, without reprocessing data, which has been processed before. Specific feature of such approach is that number of observation in data set may be variable. This feature makes recursive least squares filter more flexible approach compare to batch least squares (process complete set of observations in each iteration) and Kalman filtering (suppose updating state vector on each epoch with measurements).Advantages of proposed approach are demonstrated by processing of real astrometric observations of near Earth asteroids. The case of 2008 TC3 was studied. 2008 TC3 was discovered just before its impact with Earth. There are a many closely spaced observations of 2008 TC3 on the interval between discovering and impact, which creates favorable conditions for usage of recursive approaches. Each of approaches has very similar precision in case of 2008 TC3. At the same time, recursive least squares approaches have much higher performance. Thus, this approach more favorable for orbit fitting of a celestial body, which was detected shortly before the collision or close approach to the Earth.This work was carried out at MIIGAiK and supported by the Russian Science Foundation, Project no. 14-22-00197.References:O. Montenbruck and E. Gill, "Satellite Orbits, Models, Methods and Applications," Springer-Verlag, 2000, pp. 1-369.D. Simon, "Optimal State Estimation
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)
Zhabitskaya Evgeniya
2016-01-01
Full Text Available 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.
A Novel Discrete Differential Evolution Algorithm for the Vehicle Routing Problem in B2C E-Commerce
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.
S. U. Khan
2014-01-01
Full Text Available 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.
Li, Zhi; Chen, Weidong; Lian, Feiyu; Ge, Hongyi; Guan, Aihong
2017-01-01
Quantitative analysis of component mixtures is an important application of terahertz time-domain spectroscopy (THz-TDS) and has attracted broad interest in recent research. Although the accuracy of quantitative analysis using THz-TDS is affected by a host of factors, wavelength selection from the sample's THz absorption spectrum is the most crucial component. The raw spectrum consists of signals from the sample and scattering and other random disturbances that can critically influence the quantitative accuracy. For precise quantitative analysis using THz-TDS, the signal from the sample needs to be retained while the scattering and other noise sources are eliminated. In this paper, a novel wavelength selection method based on differential evolution (DE) is investigated. By performing quantitative experiments on a series of binary amino acid mixtures using THz-TDS, we demonstrate the efficacy of the DE-based wavelength selection method, which yields an error rate below 5%.
Guoliang Li
2017-01-01
Full Text Available We study the order acceptance and scheduling (OAS problem with time-dependent earliness-tardiness penalties in a single agile earth observation satellite environment where orders are defined by their release dates, available processing time windows ranging from earliest start date to deadline, processing times, due dates, sequence-dependent setup times, and revenues. The objective is to maximise total revenue, where the revenue from an order is a piecewise linear function of its earliness and tardiness with reference to its due date. We formulate this problem as a mixed integer linear programming model and develop a novel hybrid differential evolution (DE algorithm under self-adaptation framework to solve this problem. Compared with classical DE, hybrid DE employs two mutation operations, scaling factor adaptation and crossover probability adaptation. Computational tests indicate that the proposed algorithm outperforms classical DE in addition to two other variants of DE.
Sotirios K. Goudos
2015-01-01
Full Text Available This paper addresses the problem of designing shaped beam patterns with arbitrary arrays subject to constraints. The constraints could include the sidelobe level suppression in specified angular intervals, the mainlobe halfpower beamwidth, and the predefined number of elements. In this paper, we propose a new Differential Evolution algorithm, which combines Composite DE with an eigenvector-based crossover operator (CODE-EIG. This operator utilizes eigenvectors of covariance matrix of individual solutions, which makes the crossover rotationally invariant. We apply this novel design method to shaped beam pattern synthesis for linear and conformal arrays. We compare this algorithm with other popular algorithms and DE variants. The results show CODE-EIG outperforms the other DE algorithms in terms of statistical results and convergence speed.
Chen Kaiyan; Si Junhong; Zhou Fubao; Zhang Renwei; Shao He; Zhao Hongmei
2015-01-01
In mine ventilation networks, the reasonable airflow distribution is very important for the production safety and economy. Three basic problems of the natural, full-controlled and semi-controlled splitting were reviewed in the paper. Aiming at the high difficulty semi-controlled splitting problem, the general nonlinear multi-objectives optimization mathematical model with constraints was established based on the theory of mine ventilation networks. A new algorithm, which combined the improved differential evaluation and the critical path method (CPM) based on the multivariable separate solution strategy, was put forward to search for the global optimal solution more efficiently. In each step of evolution, the feasible solutions of air quantity distribution are firstly produced by the improved differential evolu-tion algorithm, and then the optimal solutions of regulator pressure drop are obtained by the CPM. Through finite steps iterations, the optimal solution can be given. In this new algorithm, the population of feasible solutions were sorted and grouped for enhancing the global search ability and the individuals in general group were randomly initialized for keeping diversity. Meanwhile, the individual neighbor-hood in the fine group which may be closely to the optimal solutions were searched locally and slightly for achieving a balance between global searching and local searching, thus improving the convergence rate. The computer program was developed based on this method. Finally, the two ventilation networks with single-fan and multi-fans were solved. The results show that this algorithm has advantages of high effectiveness, fast convergence, good robustness and flexibility. This computer program could be used to solve large-scale generalized ventilation networks optimization problem in the future.
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.
N. Manonmani
2015-01-01
Full Text Available 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.
Fei Gao
2013-01-01
Full Text Available In this paper, a non-Lyapunov novel approach is proposed to estimate the unknown parameters and orders together for noncommensurate and hyper fractional chaotic systems based on cuckoo search oriented statistically by the differential evolution (CSODE. Firstly, a novel Gaos’ mathematical model is proposed and analyzed in three submodels, not only for the unknown orders and parameters’ identification but also for systems’ reconstruction of fractional chaos systems with time delays or not. Then the problems of fractional-order chaos’ identification are converted into a multiple modal nonnegative functions’ minimization through a proper translation, which takes fractional-orders and parameters as its particular independent variables. And the objective is to find the best combinations of fractional-orders and systematic parameters of fractional order chaotic systems as special independent variables such that the objective function is minimized. Simulations are done to estimate a series of noncommensurate and hyper fractional chaotic systems with the new approaches based on CSODE, the cuckoo search, and Genetic Algorithm, respectively. The experiments’ results show that the proposed identification mechanism based on CSODE for fractional orders and parameters is a successful method for fractional-order chaotic systems, with the advantages of high precision and robustness.
Lozhkin, V.; Tarkhov, D.; Timofeev, V.; Lozhkina, O.; Vasilyev, A.
2016-11-01
The paper presents a novel differential neural network model estimating the dispersion of CO emissions from a peat fire near a highway. We have developed approaches for the optimization of the model on the base of simulated and experimental measurements of CO concentrations in the area of dispersion of the smoke cloud. The numerical solutions of the problem are presented in the form of neural network approximations by the Gaussian model and in the form of neural network approximate solutions of partial differential equations. The trained neural network model can be used for the prediction of emergency when wind speed and direction and other fire parameters are changing. The method is also recommended for the development of air quality monitoring and predicting information systems.
A. H. Bhrawy
2014-01-01
Full Text Available One of the most important advantages of collocation method is the possibility of dealing with nonlinear partial differential equations (PDEs as well as PDEs with variable coefficients. A numerical solution based on a Jacobi collocation method is extended to solve nonlinear coupled hyperbolic PDEs with variable coefficients subject to initial-boundary nonlocal conservation conditions. This approach, based on Jacobi polynomials and Gauss-Lobatto quadrature integration, reduces solving the nonlinear coupled hyperbolic PDEs with variable coefficients to a system of nonlinear ordinary differential equation which is far easier to solve. In fact, we deal with initial-boundary coupled hyperbolic PDEs with variable coefficients as well as initial-nonlocal conditions. Using triangular, soliton, and exponential-triangular solutions as exact solutions, the obtained results show that the proposed numerical algorithm is efficient and very accurate.
A matrix approach for partial differential equations with Riesz space fractional derivatives
Popolizio, M.
2013-09-01
Fractional partial differential equations are emerging in many scientific fields and their numerical solution is becoming a fundamental topic. In this paper we consider the Riesz fractional derivative operator and its discretization by fractional centered differences. The resulting matrix is studied, with an interesting result on a connection between the decay behavior of its entries and the short memory principle from fractional calculus. The Shift-and-Invert method is then applied to approximate the solution of the partial differential equation as the action of the matrix exponential on a suitable vector which mimics the given initial conditions. The numerical results confirm the good approximation quality and encourage the use of the proposed approach.
A Semi-parametric Bayesian Approach for Differential Expression Analysis of RNA-seq Data.
Liu, Fangfang; Wang, Chong; Liu, Peng
2015-12-01
RNA-sequencing (RNA-seq) technologies have revolutionized the way agricultural biologists study gene expression as well as generated a tremendous amount of data waiting for analysis. Detecting differentially expressed genes is one of the fundamental steps in RNA-seq data analysis. In this paper, we model the count data from RNA-seq experiments with a Poisson-Gamma hierarchical model, or equivalently, a negative binomial (NB) model. We derive a semi-parametric Bayesian approach with a Dirichlet process as the prior model for the distribution of fold changes between the two treatment means. An inference strategy using Gibbs algorithm is developed for differential expression analysis. The results of several simulation studies show that our proposed method outperforms other methods including the popularly applied edgeR and DESeq methods. We also discuss an application of our method to a dataset that compares gene expression between bundle sheath and mesophyll cells in maize leaves.
Differentiation in Online Retailing from a Consumer’s Perspective – A Repertory Grid Approach
Kellner, Julian; Wagner, Gerhard; Zielke, Stephan
2014-01-01
Due to the highly competitive nature of the online retail environment, differentiation strategies are import for online retailers to gain competitive advantages and achieve a unique positioning on the market. However, little is known about perceptual dimensions used by consumers to make a distinc......Due to the highly competitive nature of the online retail environment, differentiation strategies are import for online retailers to gain competitive advantages and achieve a unique positioning on the market. However, little is known about perceptual dimensions used by consumers to make...... a distinction between online retailers. The present study addresses this research gap by using a repertory grid approach to identify points of difference between online retailers from a consumer’s perspective. The results have important implications for positioning strategies of online retailers and research...
Lemes, N. H. T.; Borges, E.; Sousa, R. V.; Braga, J. P.
Important physical and chemical information can be extracted from scattering experiments data. This kind of problem is usually ill-posed in the sense that one of the three conditions, existence, uniqueness, and continuity, is not satisfied. For example, the inversion of intermolecular potential functions from scattering data, such as experimental cross section, is an ill-posed problem which can be modeled as a Fredholm integral equation. In this work, an inversion method based on recursive neural networks is proposed to solve this inverse quantum scattering problem within the Born approximation. As physical example, the repulsive component of the potential function for the interaction Ar-Ar is obtained from differential cross-section data. The sensitivity of the potential energy function to be inverted, in relation to the differential cross-section data, is also analyzed. The present approach is simple, general, and numerically stable.
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.
From lanosterol to cholesterol: Structural evolution and differential effects on lipid bilayers
Miao, Ling; Nielsen, Morten; Thewalt, J.
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......-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...
Differentiation Mechanism and Evolution of High-level Magma Chamber at Xiangshan,China
无
1992-01-01
The calc-alkaline volcanic magmas,which formed the Mesozoic uraniferous volcanic complex of Xiangshan,resulted from partial melting of the mixture of lower crust and enriched mantle with a high mixing proportion in a specific tectonic setting such as active continental margin or ocean-continent collision zone.The preliminary concentrations of Uand Th occur in low-degree par-tial melts.Only small part of these melts was rapidly extracted and erupted and most intruded into the high-level magma chamber(depth:12-13 km) of the compressed upper lithosphere ,in which occurred a strong differentiation which would resulted in strong preconcentrations of the high-hygromagmaphile elements U and Th associated with strong depletion of the 3-d transition ele-ments Ti,Sc,Co,Zr,etc.At the final stage of subduction of the West-Pacific-Kula plate towards the Asian continental plate,the regional tectonic environment was transformed from a compressive in-to a tensional setting.The strongly differentiated,U(and Th) enriched silicic alkalic magmas in high level magma chamber extensively erupted,extruded and intruded.The hydrothermal fluids released as a result of late volcano-degassing and dewatering during crystallization-solidification of magmas,re-sulted in the remobilization,leaching,migration and reconcentration of uranium ,which had been preconcentrated in volcanic rocks.Therefore,specific regional petrogeochemical criteria are expected for the uraniferous volcanic series.
Jans, Lennart B.O.; Huysse, Wouter C.; Verstraete, Koenraad L. [Ghent University Hospital, Department of Radiology and Medical Imaging, Ghent (Belgium); Jaremko, Jacob L.; Ditchfield, Michael [University of Melbourne Royal Children' s Hospital, Department of Medical Imaging, Melbourne, Vic (Australia)
2011-06-15
To determine if MRI (magnetic resonance imaging) of the femoral condyles in children can differentiate variations in ossification from osteochondritis dissecans (OCD). MRI studies of the knee of 315 patients demonstrated ossification defects of the femoral condyles involving the subchondral bone plate. MRI features categorized the defects as ossification variability (N = 150) or OCD (N = 165). Both groups were compared for age, residual physeal cartilage, site, configuration, 'lesion angle' and associated findings. (a) Ossification variability did not occur in girls >10 year. and boys >13 year., OCD did not occur in children younger than 8 year. (b) Ossification variability was not seen in patients with 10% or less residual physeal cartilage, OCD was rare in patients with 30% or greater residual physeal cartilage. (c) Ossification variability was located in the posterior third of the femoral condyle, OCD occurred most commonly in the middle third. (d) Intracondylar extension was seen in OCD and not in ossification variability. (e) Perilesional oedema was very common with OCD and absent with ossification variability. (f) Lesion angle <105 was a feature of ossification variability. MRI may help differentiate variations in ossification of the femoral condyles from OCD. (orig.)
Chen, Z; Lönnberg, T; Lahesmaa, R
2013-08-01
Current knowledge of helper T cell differentiation largely relies on data generated from mouse studies. To develop therapeutical strategies combating human diseases, understanding the molecular mechanisms how human naïve T cells differentiate to functionally distinct T helper (Th) subsets as well as studies on human differentiated Th cell subsets is particularly valuable. Systems biology approaches provide a holistic view of the processes of T helper differentiation, enable discovery of new factors and pathways involved and generation of new hypotheses to be tested to improve our understanding of human Th cell differentiation and immune-mediated diseases. Here, we summarize studies where high-throughput systems biology approaches have been exploited to human primary T cells. These studies reveal new factors and signalling pathways influencing T cell differentiation towards distinct subsets, important for immune regulation. Such information provides new insights into T cell biology and into targeting immune system for therapeutic interventions.
Effects of extrinsic mortality on the evolution of aging: a stochastic modeling approach.
Shokhirev, Maxim Nikolaievich; Johnson, Adiv Adam
2014-01-01
The evolutionary theories of aging are useful for gaining insights into the complex mechanisms underlying senescence. Classical theories argue that high levels of extrinsic mortality should select for the evolution of shorter lifespans and earlier peak fertility. Non-classical theories, in contrast, posit that an increase in extrinsic mortality could select for the evolution of longer lifespans. Although numerous studies support the classical paradigm, recent data challenge classical predictions, finding that high extrinsic mortality can select for the evolution of longer lifespans. To further elucidate the role of extrinsic mortality in the evolution of aging, we implemented a stochastic, agent-based, computational model. We used a simulated annealing optimization approach to predict which model parameters predispose populations to evolve longer or shorter lifespans in response to increased levels of predation. We report that longer lifespans evolved in the presence of rising predation if the cost of mating is relatively high and if energy is available in excess. Conversely, we found that dramatically shorter lifespans evolved when mating costs were relatively low and food was relatively scarce. We also analyzed the effects of increased predation on various parameters related to density dependence and energy allocation. Longer and shorter lifespans were accompanied by increased and decreased investments of energy into somatic maintenance, respectively. Similarly, earlier and later maturation ages were accompanied by increased and decreased energetic investments into early fecundity, respectively. Higher predation significantly decreased the total population size, enlarged the shared resource pool, and redistributed energy reserves for mature individuals. These results both corroborate and refine classical predictions, demonstrating a population-level trade-off between longevity and fecundity and identifying conditions that produce both classical and non
Effects of extrinsic mortality on the evolution of aging: a stochastic modeling approach.
Maxim Nikolaievich Shokhirev
Full Text Available The evolutionary theories of aging are useful for gaining insights into the complex mechanisms underlying senescence. Classical theories argue that high levels of extrinsic mortality should select for the evolution of shorter lifespans and earlier peak fertility. Non-classical theories, in contrast, posit that an increase in extrinsic mortality could select for the evolution of longer lifespans. Although numerous studies support the classical paradigm, recent data challenge classical predictions, finding that high extrinsic mortality can select for the evolution of longer lifespans. To further elucidate the role of extrinsic mortality in the evolution of aging, we implemented a stochastic, agent-based, computational model. We used a simulated annealing optimization approach to predict which model parameters predispose populations to evolve longer or shorter lifespans in response to increased levels of predation. We report that longer lifespans evolved in the presence of rising predation if the cost of mating is relatively high and if energy is available in excess. Conversely, we found that dramatically shorter lifespans evolved when mating costs were relatively low and food was relatively scarce. We also analyzed the effects of increased predation on various parameters related to density dependence and energy allocation. Longer and shorter lifespans were accompanied by increased and decreased investments of energy into somatic maintenance, respectively. Similarly, earlier and later maturation ages were accompanied by increased and decreased energetic investments into early fecundity, respectively. Higher predation significantly decreased the total population size, enlarged the shared resource pool, and redistributed energy reserves for mature individuals. These results both corroborate and refine classical predictions, demonstrating a population-level trade-off between longevity and fecundity and identifying conditions that produce both
Bedform genesis and evolution in bedrock substrates: a new experimental approach
Parsons, D. R.; Yin, N.; Peakall, J.
2014-12-01
Most previous studies on the genesis and evolution of bedforms have focused on aggradational bedforms within cohesionless sediments, with very few investigations that concern either erosive bedform genesis and evolution or bedrock channel abrasion processes. The study presented here details experiments that involve the genesis and formation of erosional bedform features within natural (soft clay) cohesive sediment beds and analogue bedrock substrates by modelling clay under the effect of both open-channel plain water flows, and sediment-laden flows. A new approach without using plaster-of-Paris or real bedrock developed provides a feasible method to simulate the genesis and evolution of the erosional bedforms in cohesive sediment beds and sculpted forms in bedrock channels on relatively short time-scales in the laboratory by using a realistic substrate substitute.A series of flume experiments are presented herein where the undrained shear strength of two different kinds of substrate material is systematically varied under constant flow conditions. Experiments using plain water flow indicated that erosive bedforms in cohesive sediment substrate cannot be produced only under the effect of sediment-free flow. Particulate-laden flows do form erosional bedforms in both kinds of clay beds and the shear strength of the bed material plays a key role in determining the diversity of erosional features forming on such substrates. Optimisation of modelling clay beds has enabled us to successfully replicate a suite of bedrock bedforms, including potholes, flutes, longitudinal furrows, etc., that have clear equivalents to those observed in bedrock rivers and contributed to investigate the genesis and evolution process of them and explore the flow structures within and above them in experimental analogue bedrock substrate for the first time.
Matrix approach to discrete fractional calculus II: Partial fractional differential equations
Podlubny, Igor; Chechkin, Aleksei; Skovranek, Tomas; Chen, YangQuan; Vinagre Jara, Blas M.
2009-05-01
A new method that enables easy and convenient discretization of partial differential equations with derivatives of arbitrary real order (so-called fractional derivatives) and delays is presented and illustrated on numerical solution of various types of fractional diffusion equation. The suggested method is the development of Podlubny's matrix approach [I. Podlubny, Matrix approach to discrete fractional calculus, Fractional Calculus and Applied Analysis 3 (4) (2000) 359-386]. Four examples of numerical solution of fractional diffusion equation with various combinations of time-/space-fractional derivatives (integer/integer, fractional/integer, integer/fractional, and fractional/fractional) with respect to time and to the spatial variable are provided in order to illustrate how simple and general is the suggested approach. The fifth example illustrates that the method can be equally simply used for fractional differential equations with delays. A set of MATLAB routines for the implementation of the method as well as sample code used to solve the examples have been developed.
A Matrix Approach to Numerical Solution of the DGLAP Evolution Equations
Ratcliffe, P G
2001-01-01
A matrix-based approach to numerical integration of the DGLAP evolution equations is presented. The method arises naturally on discretisation of the Bjorken x variable, a necessary procedure for numerical integration. Owing to peculiar properties of the matrices involved, the resulting equations take on a particularly simple form and may be solved in closed analytical form in the variable t=ln(alpha_0/alpha). Such an approach affords parametrisation via data x bins, rather than fixed functional forms. Thus, with the aid of the full correlation matrix, appraisal of the behaviour in different x regions is rendered more transparent and free of pollution from unphysical cross-correlations inherent to functional parametrisations. Computationally, the entire programme results in greater speed and stability; the matrix representation developed is extremely compact. Moreover, since the parameter dependence is linear, fitting is very stable and may be performed analytically in a single pass over the data values.
A convective-advective balance approach for solving some nonlinear evolution equations analytically
Abdel Hamid, B. [United Arab Emirates Univ. (United Arab Emirates). Dept. of Mathematics and Computer Science
1999-09-01
A symbolic computation-based approach of balancing the convective and advective effects in a nonlinear evolution equation leads to a transformation that maps the nonlinear equation onto either a linear one or to a system of linear and homogeneous equations. The method is demonstrated by mapping Burgers' equation and nonlinear heat equation onto the linear heat equation. It is shown that the transformation obtained by balancing the convective-advective effects are reducible to those obtained by the Cole and Hopf through Backlund transformation. The method is also used to transform the modified KdV equation into a system of linear and homogeneous functions in the partial derivatives which leads to an exact solution. Computations in the presented approach are carried out in a straightforward way.
孙成富; 张亚红; 陈剑洪; 陈礼青
2013-01-01
在差分进化算法的优化过程中,不断生成更优的解并采用达尔文的“适者生存”思想进行择优保留,这样的遗弃会导致个体有效成分缺失,并失去对新空间的探索开发能力,降低种群多样性,进而使算法早熟收敛并陷入局部最优,因此需要改进差分进化算法并权衡算法的空间探索和开发能力,提高解的精确度和算法收敛速度.为此,基于高斯扰动和免疫搜索策略的差分进化算法被提出.首先,通过生物免疫系统的信息处理机制实现自适应地修正差分进化算法中的缩放因子和交叉因子,以满足优化过程中对这两个参数的取值要求；然后,通过基于高斯扰动的交叉操作算子增加种群的多样性,扩展算法的探索空间,以避免陷入局部最优,进而提高算法的性能.实验结果表明,该优化算法具有良好的寻优性能.%During the evolution process of differential evolution algorithm, good solutions are generated and the 'survival of the fittest' theory of Darwin is employed to select the better solutions, which results in failures of the abandoned individual's effective component and the reduction of population diversity. Thus the differential evolution algorithm is not able to explore new space and traps in local optima. So the differential evolution algorithm has been shown to have certain weaknesses, especially if the global optimum should be located using a limited number of function evaluations. In order to remedy these defects of the differential evolution algorithm mentioned above, weighting space exploration and exploitation is employed for improving it to enhance the convergence speed and solution quality. In this paper,improved differential evolution algorithm based on Gaussian disturbance and immune search startegy is proposed to solve the global optimization problems. Our approach combines several features of previous evolution algorithms in a unique manner. In the novel approach
Ioannidis, P.; Schmitt, J. H. M. M.
2016-10-01
We use high accuracy photometric data obtained with the Kepler satellite to monitor the activity modulations of the Kepler-210 planet host star over a time span of more than four years. Following the phenomenology of the star's light curve in combination with a five spot model, we identify six different so-called spot seasons. A characteristic, which is common in the majority of the seasons, is the persistent appearance of spots in a specific range of longitudes on the stellar surface. The most prominent period of the observed activity modulations is different for each season and appears to evolve following a specific pattern, resembling the changes in the sunspot periods during the solar magnetic cycle. Under the hypothesis that the star exhibits solar-like differential rotation, we suggest differential rotation values of Kepler-210 that are similar to or smaller than that of the Sun. Finally, we estimate spot life times between ~60 days and ~90 days, taking into consideration the evolution of the total covered stellar surface computed from our model.
Ioannidis, P
2016-01-01
We use high accuracy photometric data obtained with the Kepler satellite to monitor the activity modulations of the Kepler-210 planet host star over a time span of more than four years. Following the phenomenology of the star's light curve in combination with a five spot model, we identify six different so-called spot seasons. A characteristic, which is common in the majority of the seasons, is the persistent appearance of spots in a specific range of longitudes on the stellar surface. The most prominent period of the observed activity modulations is different for each season and appears to evolve following a specific pattern, resembling the changes in the sunspot periods during the solar magnetic cycle. Under the hypothesis that the star exhibits solar-like differential rotation, we suggest differential rotation values of Kepler-210 that are similar to or smaller than that of the Sun. Finally, we estimate spot life times between 60 days and 90 days, taking into consideration the evolution of the total covere...
Proteotyping: A New Approach Studying Influenza Virus Evolution at the Protein Level
无
2007-01-01
Phylogenetic methods have been widely used to detect the evolution of influenza viruses.However, previous phylogenetic studies of influenza viruses do not make full use of the genetic information at the protein level and therefore cannot distinguish the subtle differences among viral genes. Proteotyping is a new approach to study influenza virus evolution. It aimed at mining the potential genetic information of the viral gene at the protein level by visualizing unique amino acid signatures (proteotypes). Neuraminidase gene fragments of some H5N1 avian influenza viruses were used as an example to illustrate how the proteotyping method worked. Bayesian analysis confirmed that the NA gene tree was mainly divided into three lineages. The NA proteotype analysis further suggested there might be multiple proteotypes within these three lineages and even within single genotypes. At the same time, some proteotypes might even involve more than one genotype. In particular, it also discovered some amino acids of viruses of some genotypes might co-reassort. All these results proved this approach could provide additional information in contrast to results from standard phylogenetic tree analysis.
The floral morphospace--a modern comparative approach to study angiosperm evolution.
Chartier, Marion; Jabbour, Florian; Gerber, Sylvain; Mitteroecker, Philipp; Sauquet, Hervé; von Balthazar, Maria; Staedler, Yannick; Crane, Peter R; Schönenberger, Jürg
2014-12-01
Morphospaces are mathematical representations used for studying the evolution of morphological diversity and for the evaluation of evolved shapes among theoretically possible ones. Although widely used in zoology, they--with few exceptions--have been disregarded in plant science and in particular in the study of broad-scale patterns of floral structure and evolution. Here we provide basic information on the morphospace approach; we review earlier morphospace applications in plant science; and as a practical example, we construct and analyze a floral morphospace. Morphospaces are usually visualized with the help of ordination methods such as principal component analysis (PCA) or nonmetric multidimensional scaling (NMDS). The results of these analyses are then coupled with disparity indices that describe the spread of taxa in the space. We discuss these methods and apply modern statistical tools to the first and only angiosperm-wide floral morphospace published by Stebbins in 1951. Despite the incompleteness of Stebbins’ original dataset, our analyses highlight major, angiosperm-wide trends in the diversity of flower morphology and thereby demonstrate the power of this previously neglected approach in plant science.
The floral morphospace – a modern comparative approach to study angiosperm evolution
Chartier, Marion; Jabbour, Florian; Gerber, Sylvain; Mitteroecker, Philipp; Sauquet, Hervé; von Balthazar, Maria; Staedler, Yannick; Crane, Peter R.; Schönenberger, Jürg
2017-01-01
Summary Morphospaces are mathematical representations used for studying the evolution of morphological diversity and for the evaluation of evolved shapes among theoretically possible ones. Although widely used in zoology, they – with few exceptions – have been disregarded in plant science and in particular in the study of broad-scale patterns of floral structure and evolution. Here we provide basic information on the morphospace approach; we review earlier morphospace applications in plant science; and as a practical example, we construct and analyze a floral morphospace. Morphospaces are usually visualized with the help of ordination methods such as principal component analysis (PCA) or nonmetric multidimensional scaling (NMDS). The results of these analyses are then coupled with disparity indices that describe the spread of taxa in the space. We discuss these methods and apply modern statistical tools to the first and only angiosperm-wide floral morphospace published by Stebbins in 1951. Despite the incompleteness of Stebbins’ original dataset, our analyses highlight major, angiosperm-wide trends in the diversity of flower morphology and thereby demonstrate the power of this previously neglected approach in plant science. PMID:25539005
Library selection and directed evolution approaches to engineering targeted viral vectors.
Jang, Jae-Hyung; Lim, Kwang-il; Schaffer, David V
2007-10-15
Gene therapy, to delivery of genetic material to a patient for therapeutic benefit, has significant promise for translating basic knowledge of disease mechanism into biomedical treatments. The clinical development of the field has been slowed, however, by the need for improvements in the properties and capabilities of gene delivery vehicles. Vehicles based on viruses offer the potential for efficient gene delivery, but because viruses did not evolve to serve human therapeutic needs, many of their properties require significant improvement, including their safety, efficiency, and capacity for targeted gene delivery. Since viruses are highly complex biological entities, engineering such properties at the molecular level can be challenging. However, there has been significant progress in developing approaches that mimic the mechanisms by which viruses arose in the first place. In particular, library-based selection, the generation of one diverse genetic library and selection for new properties, and directed evolution, based on the multiple rounds of library generation and selection for iterative improvement of function, have strong potential in engineering novel properties into these complex biomolecular assemblies. This review will discuss progress in the application of peptide display, library selection, and directed evolution technologies toward engineering vectors based on retrovirus, adeno-associated virus, and adenovirus that are capable of targeted delivery to specific cell types. In addition to creating biomedically useful products, these approaches have future potential to yield novel insights into viral structure-function relationships. Copyright 2007 Wiley Periodicals, Inc.
A differentiated approach in the sporting dance studies with teenagers of 13 years old
Demidova O.N.
2012-04-01
Full Text Available A differentiated approach to the sporting dance exercises on the stage of a preliminary basic training was grounded. The experiment involved 20 dancers (10 boys and 10 girls aged 13 years old. Morpho-functional status, physical development and physical preparedness levels of adolescents were determined. The criteria for the distribution of the children into groups to practice sporting dances in accordance with the levels of physical development and physical preparedness (including sensitive periods performance of physical performance development to ensure optimal loading and exercises efficiency.
A call for differentiated approaches to delivering HIV services to key populations
Virginia Macdonald
2017-07-01
Conclusions: The application of a differentiated service approach for KP could increase the number of people who know their status and receive effective and sustained prevention and treatment for HIV. However, while community-based and lay provider testing are effective and affordable, they are not implemented to scale. Furthermore regulatory barriers to legitimizing lay and peer providers as part of healthcare delivery systems need to be overcome in many settings. WHO recommendations on task shifting and decentralization of ART treatment and care are often not applied to KP settings.
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.
Brüne Martin
2012-04-01
Full Text Available Abstract 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. Please see related manuscript: http://www.biomedcentral.com/1741-7015/10/37
Optimum Synthesis of Mechanism for single- and hybrid-tasks using Differential Evolution
Penunuri, F; Villanueva, C; Pech-Oy, D
2011-01-01
In this document the optimal dimensional synthesis for planar mechanisms using differential evo- lution (DE) is shown. Four study cases are presented: in the first case, the synthesis of a mechanism for hybrid-tasks, considering path generation, function generation, and motion generation, is car- ried out. The second and third cases deal with path generation with and without prescribed timing. Finally, the synthesis of an Ackerman's mechanism is performed. The order defect problem is addressed by manipulating individuals instead of penalizing or discretizing the searching space for the parameters, as was proposed by other authors. A new technique which consists of applying a transformation in order to satisfy the Grashof and crank conditions to generate an initial elitist population is introduced. As a result, the evolutionary algorithm increases its efficiency.
Kratchmarova, Irina; Kalume, Dario E; Blagoev, Blagoy;
2002-01-01
We have undertaken a systematic proteomic approach to purify and identify secreted factors that are differentially expressed in preadipocytes versus adipocytes. Using one-dimensional gel electrophoresis combined with nanoelectrospray tandem mass spectrometry, proteins that were specifically secre...
Niang, Oumar; Thioune, Abdoulaye; El Gueirea, Mouhamed Cheikh; Deléchelle, Eric; Lemoine, Jacques
2012-09-01
The major problem with the empirical mode decomposition (EMD) algorithm is its lack of a theoretical framework. So, it is difficult to characterize and evaluate this approach. In this paper, we propose, in the 2-D case, the use of an alternative implementation to the algorithmic definition of the so-called "sifting process" used in the original Huang's EMD method. This approach, especially based on partial differential equations (PDEs), was presented by Niang in previous works, in 2005 and 2007, and relies on a nonlinear diffusion-based filtering process to solve the mean envelope estimation problem. In the 1-D case, the efficiency of the PDE-based method, compared to the original EMD algorithmic version, was also illustrated in a recent paper. Recently, several 2-D extensions of the EMD method have been proposed. Despite some effort, 2-D versions for EMD appear poorly performing and are very time consuming. So in this paper, an extension to the 2-D space of the PDE-based approach is extensively described. This approach has been applied in cases of both signal and image decomposition. The obtained results confirm the usefulness of the new PDE-based sifting process for the decomposition of various kinds of data. Some results have been provided in the case of image decomposition. The effectiveness of the approach encourages its use in a number of signal and image applications such as denoising, detrending, or texture analysis.
Ricardo D’Oliveira Albanus
2014-01-01
Full Text Available Chemoreception is among the most important sensory modalities in animals. Organisms use the ability to perceive chemical compounds in all major ecological activities. Recent studies have allowed the characterization of chemoreceptor gene families. These genes present strikingly high variability in copy numbers and pseudogenization degrees among different species, but the mechanisms underlying their evolution are not fully understood. We have analyzed the functional networks of these genes, their orthologs distribution, and performed phylogenetic analyses in order to investigate their evolutionary dynamics. We have modeled the chemosensory networks and compared the evolutionary constraints of their genes in Mus musculus, Homo sapiens, and Rattus norvegicus. We have observed significant differences regarding the constraints on the orthologous groups and network topologies of chemoreceptors and signal transduction machinery. Our findings suggest that chemosensory receptor genes are less constrained than their signal transducing machinery, resulting in greater receptor diversity and conservation of information processing pathways. More importantly, we have observed significant differences among the receptors themselves, suggesting that olfactory and bitter taste receptors are more conserved than vomeronasal receptors.
An abstract approach to some spectral problems of direct sum differential operators
Maksim S. Sokolov
2003-07-01
Full Text Available In this paper, we study the common spectral properties of abstract self-adjoint direct sum operators, considered in a direct sum Hilbert space. Applications of such operators arise in the modelling of processes of multi-particle quantum mechanics, quantum field theory and, specifically, in multi-interval boundary problems of differential equations. We show that a direct sum operator does not depend in a straightforward manner on the separate operators involved. That is, on having a set of self-adjoint operators giving a direct sum operator, we show how the spectral representation for this operator depends on the spectral representations for the individual operators (the coordinate operators involved in forming this sum operator. In particular it is shown that this problem is not immediately solved by taking a direct sum of the spectral properties of the coordinate operators. Primarily, these results are to be applied to operators generated by a multi-interval quasi-differential system studied, in the earlier works of Ashurov, Everitt, Gesztezy, Kirsch, Markus and Zettl. The abstract approach in this paper indicates the need for further development of spectral theory for direct sum differential operators.
Chen, G; de Figueiredo, R P
1993-01-01
The unified approach to optimal image interpolation problems presented provides a constructive procedure for finding explicit and closed-form optimal solutions to image interpolation problems when the type of interpolation can be either spatial or temporal-spatial. The unknown image is reconstructed from a finite set of sampled data in such a way that a mean-square error is minimized by first expressing the solution in terms of the reproducing kernel of a related Hilbert space, and then constructing this kernel using the fundamental solution of an induced linear partial differential equation, or the Green's function of the corresponding self-adjoint operator. It is proved that in most cases, closed-form fundamental solutions (or Green's functions) for the corresponding linear partial differential operators can be found in the general image reconstruction problem described by a first- or second-order linear partial differential operator. An efficient method for obtaining the corresponding closed-form fundamental solutions (or Green's functions) of the operators is presented. A computer simulation demonstrates the reconstruction procedure.
Xu, Mingdong; Wu, Fan; Leung, Henry
2009-09-01
Based on the stochastic delay differential equation (SDDE) modeling of neural networks, we propose an effective signal transmission approach along the neurons in such a network. Utilizing the linear relationship between the delay time and the variance of the SDDE system output, the transmitting side encodes a message as a modulation of the delay time and the receiving end decodes the message by tracking the delay time, which is equivalent to estimating the variance of the received signal. This signal transmission approach turns out to follow the principle of the spread spectrum technique used in wireless and wireline wideband communications but in the analog domain rather than digital. We hope the proposed method might help to explain some activities in biological systems. The idea can further be extended to engineering applications. The error performance of the communication scheme is also evaluated here.
De Salvo, Simona; Bramanti, Placido; Marino, Silvia
2012-01-01
Summary The neurophysiological approach to patients with disorders of consciousness allows recording of both central and peripheral nervous system electrical activities and provides a functional assessment. Data obtained using this approach can supplement information from clinical neurological examination, but also from the use of morphological neuroimaging techniques: computed tomography and magnetic resonance imaging. Neurophysiological techniques, such as electroencephalography (EEG), evoked potentials, transcranial magnetic stimulation, and EEG in association with functional magnetic resonance imaging, allow monitoring of clinical conditions and can help in the formulation of a prognosis. The aim of this review is to describe the main neurophysiological techniques used in disorders of consciousness to evaluate residual cerebral function, to provide information on the neuronal dysfunction for outcome evaluation, and to differentiate clinically between the vegetative and minimally conscious states. PMID:23402676
Xiao Xue
2015-01-01
Full Text Available Companies have been aware of the benefits of developing Cluster Supply Chains (CSCs, and they are spending a great deal of time and money attempting to develop the new business pattern. Yet, the traditional techniques for identifying CSCs have strong theoretical antecedents, but seem to have little traction in the field. We believe this is because the standard techniques fail to capture evolution over time, nor provide useful intervention measures to reach goals. To address these problems, we introduce an agent-based modeling approach to evaluate CSCs. Taking collaborative procurement as research object, our approach is composed of three parts: model construction, model instantiation, and computational experiment. We use the approach to explore the service charging policy problem in collaborative procurement. Three kinds of service charging polices are compared in the same experiment environment. Finally, “Fixed Cost” is identified as the optimal policy under the stable market environment. The case study can help us to understand the workflow of applying the approach, and provide valuable decision support applications to industry.
Constructive Approaches for Understanding the Origin of Self-Replication and Evolution
Norikazu Ichihashi
2016-07-01
Full Text Available The mystery of the origin of life can be divided into two parts. The first part is the origin of biomolecules: under what physicochemical conditions did biomolecules such as amino acids, nucleotides, and their polymers arise? The second part of the mystery is the origin of life-specific functions such as the replication of genetic information, the reproduction of cellular structures, metabolism, and evolution. These functions require the coordination of many different kinds of biological molecules. A direct strategy to approach the second part of the mystery is the constructive approach, in which life-specific functions are recreated in a test tube from specific biological molecules. Using this approach, we are able to employ design principles to reproduce life-specific functions, and the knowledge gained through the reproduction process provides clues as to their origins. In this mini-review, we introduce recent insights gained using this approach, and propose important future directions for advancing our understanding of the origins of life.
Constructive Approaches for Understanding the Origin of Self-Replication and Evolution.
Ichihashi, Norikazu; Yomo, Tetsuya
2016-07-13
The mystery of the origin of life can be divided into two parts. The first part is the origin of biomolecules: under what physicochemical conditions did biomolecules such as amino acids, nucleotides, and their polymers arise? The second part of the mystery is the origin of life-specific functions such as the replication of genetic information, the reproduction of cellular structures, metabolism, and evolution. These functions require the coordination of many different kinds of biological molecules. A direct strategy to approach the second part of the mystery is the constructive approach, in which life-specific functions are recreated in a test tube from specific biological molecules. Using this approach, we are able to employ design principles to reproduce life-specific functions, and the knowledge gained through the reproduction process provides clues as to their origins. In this mini-review, we introduce recent insights gained using this approach, and propose important future directions for advancing our understanding of the origins of life.
Pabel, Sven-Olav; Pabel, Anne-Kathrin; Schmickler, Jan; Schulz, Xenia; Wiegand, Annette
2017-09-01
The aim of this study was to evaluate if differential learning in a preclinical dental course impacted the performance of dental students in a practical exam (preparation of a gold partial crown) immediately after the training session and 20 weeks later compared to conventional learning. This controlled study was performed in a preclinical course in operative dentistry at a dental school in Germany. Third-year students were trained in preparing gold partial crowns by using either the conventional learning (n=41) or the differential learning approach (n=32). The differential learning approach consisted of 20 movement exercises with a continuous change of movement execution during the learning session, while the conventional learning approach was mainly based on repetition, a methodological series of exercises, and correction of preparations during the training phase. Practical exams were performed immediately after the training session (T1) and 20 weeks later (T2, retention test). Preparations were rated by four independent and blinded examiners. At T1, no significant difference between the performance (exam passed) of the two groups was detected (conventional learning: 54.3%, differential learning: 68.0%). At T2, significantly more students passed the exam when trained by the differential learning approach (68.8%) than by the conventional learning approach (18.9%). Interrater reliability was moderate (Kappa: 0.57, T1) or substantial (Kappa: 0.67, T2), respectively. These results suggest that a differential learning approach can increase the manual skills of dental students.
Kuiper, W.E.; Cozijnsen, A.J.
2011-01-01
We outline a new estimation method for the multinomial probit model (MNP). The method is a differential evolution Markov chain algorithm that employs a Metropolis-within-Gibbs sampler with data augmentation and the Geweke–Hajivassiliou–Keane (GHK) probability simulator. The method lifts the curse of
Rasim M. Alguliev
2011-01-01
Full Text Available Extractive multidocument summarization is modeled as a modified p-median problem. The problem is formulated with taking into account four basic requirements, namely, relevance, information coverage, diversity, and length limit that should satisfy summaries. To solve the optimization problem a self-adaptive differential evolution algorithm is created. Differential evolution has been proven to be an efficient and robust algorithm for many real optimization problems. However, it still may converge toward local optimum solutions, need to manually adjust the parameters, and finding the best values for the control parameters is a consuming task. In the paper is proposed a self-adaptive scaling factor in original DE to increase the exploration and exploitation ability. This paper has found that self-adaptive differential evolution can efficiently find the best solution in comparison with the canonical differential evolution. We implemented our model on multi-document summarization task. Experiments have shown that the proposed model is competitive on the DUC2006 dataset.
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.
A QoS Control Approach in Differentiated Web Caching Service
Ang Gao
2011-01-01
Full Text Available As the heterogeneity ofWeb clients increasing, the differentiated service becomes an important issue especially for e-commerce Web site. Web caching as a key accelerator on the Internet plays an important role in alleviating the client-perceived delay. To meet the Service Level Agreement (SLA for clients without excessively over-provisioning resources, this paper proposes and evaluates a novel framework for enforcing Proportional Hit Rate. The framework combines the implement of Isolated Cache Model and the usage of control-theoretical approach for storage control. With system identification, the linear model is identified as well as the controller. At every sampling time, by dynamically reallocating storage spaces for different Web classes, the controller operates to guarantee the relationship of QoS metric among classes constant. The experimental results demonstrate the proposed approach achieves differentiated caching service with the enforcement of Greedy Dual Size Frequency (GDSF, Latest Recently Used (LRU and Latest Frequently Used (LFU cache replacement policies.
Macagno, Anna L M; Beckers, Oliver M; Moczek, Armin P
2015-11-01
Fecundity is a fundamental determinant of fitness, yet the proximate developmental and physiological mechanisms that enable its often rapid evolution in natural populations are poorly understood. Here, we investigated two populations of the dung beetle Onthophagus taurus that were established in exotic ranges in the early 1970s. These populations are subject to drastically different levels of resource competition in the field, and have diverged dramatically in female fecundity. Specifically, Western Australian O. taurus experience high levels of resource competition, and exhibit greatly elevated reproductive output compared to beetles from the Eastern US, where resource competition is minimal and female fecundity is low. We compared patterns of ovarian maturation, relative investment into and timing of egg production, and potential trade-offs between ovarian investment and the duration of larval development and adult body size between populations representative of both exotic ranges. We found that the rapid divergence in fecundity between exotic populations is associated with striking differences in several aspects of ovarian development: (1) Western Australian females exhibit accelerated ovarian development, (2) produce more eggs, (3) bigger eggs, and (4) start laying eggs earlier compared to their Eastern US counterparts. At the same time, divergence in ovarian maturation patterns occurred alongside changes in (5) larval developmental time, and (6) adult body size, and (7) mass. Western Australian females take longer to complete larval development and, surprisingly, emerge into smaller yet heavier adults than size-matched Eastern US females. We discuss our results in the context of the evolutionary developmental biology of fecundity in exotic populations. © 2015 Wiley Periodicals, Inc.
The Lazarus project: A pragmatic approach to binary black hole evolutions
Baker, John; Campanelli, Manuela; Lousto, Carlos O.
2002-02-01
We present a detailed description of techniques developed to combine 3D numerical simulations and, subsequently, a single black hole close-limit approximation. This method has made it possible to compute the first complete waveforms covering the post-orbital dynamics of a binary-black-hole system with the numerical simulation covering the essential nonlinear interaction before the close limit becomes applicable for the late time dynamics. In order to couple full numerical and perturbative methods we must address several questions. To determine when close-limit perturbation theory is applicable we apply a combination of invariant a priori estimates and a posteriori consistency checks of the robustness of our results against exchange of linear and nonlinear treatments near the interface. Our method begins with a specialized application of standard numerical techniques adapted to the presently realistic goal of brief, but accurate simulations. Once the numerically modeled binary system reaches a regime that can be treated as perturbations of the Kerr spacetime, we must approximately relate the numerical coordinates to the perturbative background coordinates. We also perform a rotation of a numerically defined tetrad to asymptotically reproduce the tetrad required in the perturbative treatment. We can then produce numerical Cauchy data for the close-limit evolution in the form of the Weyl scalar ψ4 and its time derivative ∂tψ4 with both objects being first order coordinate and tetrad invariant. The Teukolsky equation in Boyer-Lindquist coordinates is adopted to further continue the evolution. To illustrate the application of these techniques we evolve a single Kerr hole and compute the spurious radiation as a measure of the error of the whole procedure. We also briefly discuss the extension of the project to make use of improved full numerical evolutions and outline the approach to a full understanding of astrophysical black-hole-binary systems which we can now
Lynch, A; Baker, A J
1993-04-01
We investigated cultural evolution in populations of common chaffinches (Fringilla coelebs) in the Atlantic islands (Azores, Madeira, Canaries) and neighboring continental regions (Morocco, Iberia) by employing a population memetics approach. To quantify variability within populations, we used the concept of a song meme, defined as a single syllable or a series of linked syllables capable of being transmitted. The frequency distribution of memes within populations generally fit a neutral model in which there is an equilibrium between mutation, migration, and drift, which suggests that memes are functionally equivalent. The diversity of memes of single syllables is significantly greater in the Azores compared to all other regions, consistent with higher population densities of chaffinches there. On the other hand, memes of two to five syllables have greater diversity in Atlantic island and Moroccan populations compared to their Iberian counterparts. This higher diversity emanates from a looser syntax and increased recombination in songs, presumably because of relaxed selection for distinctive songs in these peripheral and depauperate avifaunas. We urge comparative population memetic studies of other species of songbirds and predict that they will lead to a formulation of a general theory for the cultural evolution of bird song analogous to population genetics theory for biological traits.
Water Inrush Mode and Its Evolution Characteristics with Roadway Excavation Approaching to the Fault
Hu Wang
2012-07-01
Full Text Available Water inrush disaster is an important factor in restricting safe production of the coal mine. Taking the roadway in seam in Danhou Coal Mine, as the engineering background, according to the spatial relationship of the roadway, the impermeable layer, the fault and the loading conditions, the fault activation mechanical model under the roadway excavation disturbance was built, and the fault activation conditions, roadway water inrush criterion and water inrush three modes were put forward. A three-dimensional numerical calculation models were built by using FLAC3D. Through fluid-solid coupling calculation, the surrounding rock damage and failure, the water inrush channel formation, and the evolution process of water inrush of the roadway excavation approaching the fault were analyzed, moreover, the displacement field, the stress field and the surrounding rock plastic failure characteristics of the roadway were revealed. Furthermore, under the conditions of different water pressure, impermeable rock thickness, fault displacement, and fault dip angles, the roadway water inrush modes and their evolution characteristics were comparatively analyzed.
The chemodynamical evolution of the Milky Way disc -- A new modeling approach
Minchev, Ivan; Martig, Marie
2014-01-01
Despite the recent advancements in the field of galaxy formation and evolution, fully self-consistent simulations are still unable to make the detailed predictions necessary for the planned and ongoing large spectroscopic and photometry surveys of the Milky Way disc. These difficulties arise from the very uncertain nature of sub-grid physical energy feedback within models, affecting both star formation rates and chemical enrichment. To avoid these problems, we have introduced a new approach which consists of fusing disc chemical evolution models with compatible numerical simulations. We demonstrate the power of this method by showing that a range of observational results can be explained by our new model. We show that due to radial migration from mergers at high redshift and the central bar at later times, a sizable fraction of old metal-poor, high-[alpha/Fe] stars can reach the solar vicinity. This naturally accounts for a number of recent observations related to both the thin and thick discs, despite the fa...
Modeling of microstructure evolution in direct metal laser sintering: A phase field approach
Nandy, Jyotirmoy; Sarangi, Hrushikesh; Sahoo, Seshadev
2017-02-01
Direct Metal Laser Sintering (DMLS) is a new technology in the field of additive manufacturing, which builds metal parts in a layer by layer fashion directly from the powder bed. The process occurs within a very short time period with rapid solidification rate. Slight variations in the process parameters may cause enormous change in the final build parts. The physical and mechanical properties of the final build parts are dependent on the solidification rate which directly affects the microstructure of the material. Thus, the evolving of microstructure plays a vital role in the process parameters optimization. Nowadays, the increase in computational power allows for direct simulations of microstructures during materials processing for specific manufacturing conditions. In this study, modeling of microstructure evolution of Al-Si-10Mg powder in DMLS process was carried out by using a phase field approach. A MATLAB code was developed to solve the set of phase field equations, where simulation parameters include temperature gradient, laser scan speed and laser power. The effects of temperature gradient on microstructure evolution were studied and found that with increase in temperature gradient, the dendritic tip grows at a faster rate.
A Grammatical Evolution Approach for Content Extraction of Electronic Commerce Website
Wei Qing-jin
2013-03-01
Full Text Available Web content extraction, a problem of identifying and extracting interesting information from Web pages, plays an important role in integrating data from different sources for advanced information-based services. In this paper, an approach and techniques of extracting electronic commercial information from the Web pages without any given template is investigated in a way of Grammatical Evolution (GE method. Although a lot of research used the Xpath technique to extract the content of Web pages, but due to the complexity of the Xpath grammar, it is too difficult to perform the processing automatically for evolutional tools. Hence, a reduced language integrating Xpath and DOM techniques is given to generate the solution of parse in a BNF grammar form, which is used in the GE. Moreover, a fitness function evaluation method is also proposed on the fuzzy membership of the two parts in the chromosome. Finally, empirical results on several real Web pages show that the new proposed technique can segment data records and extract data from them accurately, automatically and flexibly.
El Mouden, C; André, J-B; Morin, O; Nettle, D
2014-02-01
Transmitted culture can be viewed as an inheritance system somewhat independent of genes that is subject to processes of descent with modification in its own right. Although many authors have conceptualized cultural change as a Darwinian process, there is no generally agreed formal framework for defining key concepts such as natural selection, fitness, relatedness and altruism for the cultural case. Here, we present and explore such a framework using the Price equation. Assuming an isolated, independently measurable culturally transmitted trait, we show that cultural natural selection maximizes cultural fitness, a distinct quantity from genetic fitness, and also that cultural relatedness and cultural altruism are not reducible to or necessarily related to their genetic counterparts. We show that antagonistic coevolution will occur between genes and culture whenever cultural fitness is not perfectly aligned with genetic fitness, as genetic selection will shape psychological mechanisms to avoid susceptibility to cultural traits that bear a genetic fitness cost. We discuss the difficulties with conceptualizing cultural change using the framework of evolutionary theory, the degree to which cultural evolution is autonomous from genetic evolution, and the extent to which cultural change should be seen as a Darwinian process. We argue that the nonselection components of evolutionary change are much more important for culture than for genes, and that this and other important differences from the genetic case mean that different approaches and emphases are needed for cultural than genetic processes.
多目标优化差分进化算法%Differential Evolution Algorithm for Multi-Objective Optimization
敖友云; 迟洪钦
2011-01-01
Fitness assignment of individuals and diversity maintenance of population are two key techniques of evolutionary algorithms. First, on the one hand, this paper introduces some related concepts of Pareto e~dom-inance which can determine the strength Pareto values of the individuals of population, according to the strength Pareto values of individuals, some better individuals are selected into the offspring population by the technique of Pareto ranking; on the other hand, in order to maintain the diversity of population, a crowded-density method is introduced to remove some individuals that are located in the crowed regions. Then, according to some characteristics of differential evolution (DE), through using the appropriate DE strategies and control parameters, this paper proposes a differential evolution algorithm for multi-objective optimization, which is called DEAMO. Finally, numerical experiments show that DEAMO can perform well when tested on several benchmark multi-objective optimization problems.%个体的适应度赋值和群体的多样性维护是进化算法的两个关键问题.首先,一方面,定义了Paretoε-支配关系的相关概念,通过Paretoε-支配关系确定个体的强度Pareto值,根据个体的强度Pareto值对群体进行Pareto分级排序,实现优胜劣汰；另一方面,使用拥挤距离估算个体的拥挤密度,淘汰位于拥挤区的一些个体,维持群体的多样性.然后,根据差分进化算法的特点,使用适当的进化策略和控制参数,给出了一种用于求解多目标优化问题的差分进化算法DEAMO.最后,数值实验表明,DEAMO在求解标准的多目标优化问题时性能表现优良.
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.
Lin Wang
2013-01-01
Full Text Available 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.
Li Hai-Long
2011-06-01
Full Text Available Abstract Liver flukes belonging to the genus Fasciola are among the causes of foodborne diseases of parasitic etiology. These parasites cause significant public health problems and substantial economic losses to the livestock industry. Therefore, it is important to definitively characterize the Fasciola species. Current phenotypic techniques fail to reflect the full extent of the diversity of Fasciola spp. In this respect, the use of molecular techniques to identify and differentiate Fasciola spp. offer considerable advantages. The advent of a variety of molecular genetic techniques also provides a powerful method to elucidate many aspects of Fasciola biology, epidemiology, and genetics. However, the discriminatory power of these molecular methods varies, as does the speed and ease of performance and cost. There is a need for the development of new methods to identify the mechanisms underpinning the origin and maintenance of genetic variation within and among Fasciola populations. The increasing application of the current and new methods will yield a much improved understanding of Fasciola epidemiology and evolution as well as more effective means of parasite control. Herein, we provide an overview of the molecular techniques that are being used for the genetic characterization, detection and genotyping of Fasciola spp..
Vermaak, Danielle; Bayes, Joshua J; Malik, Harmit S
2009-01-01
Comparative genomics provides a facile way to address issues of evolutionary constraint acting on different elements of the genome. However, several important DNA elements have not reaped the benefits of this new approach. Some have proved intractable to current day sequencing technology. These include centromeric and heterochromatic DNA, which are essential for chromosome segregation as well as gene regulation, but the highly repetitive nature of the DNA sequences in these regions make them difficult to assemble into longer contigs. Other sequences, like dosage compensation X chromosomal sites, origins of DNA replication, or heterochromatic sequences that encode piwi-associated RNAs, have proved difficult to study because they do not have recognizable DNA features that allow them to be described functionally or computationally. We have employed an alternate approach to the direct study of these DNA elements. By using proteins that specifically bind these noncoding DNAs as surrogates, we can indirectly assay the evolutionary constraints acting on these important DNA elements. We review the impact that such "surrogate strategies" have had on our understanding of the evolutionary constraints shaping centromeres, origins of DNA replication, and dosage compensation X chromosomal sites. These have begun to reveal that in contrast to the view that such structural DNA elements are either highly constrained (under purifying selection) or free to drift (under neutral evolution), some of them may instead be shaped by adaptive evolution and genetic conflicts (these are not mutually exclusive). These insights also help to explain why the same elements (e.g., centromeres and replication origins), which are so complex in some eukaryotic genomes, can be simple and well defined in other where similar conflicts do not exist.
Cruz-Roa, Angel; Arevalo, John; Basavanhally, Ajay; Madabhushi, Anant; González, Fabio
2015-01-01
Learning data representations directly from the data itself is an approach that has shown great success in different pattern recognition problems, outperforming state-of-the-art feature extraction schemes for different tasks in computer vision, speech recognition and natural language processing. Representation learning applies unsupervised and supervised machine learning methods to large amounts of data to find building-blocks that better represent the information in it. Digitized histopathology images represents a very good testbed for representation learning since it involves large amounts of high complex, visual data. This paper presents a comparative evaluation of different supervised and unsupervised representation learning architectures to specifically address open questions on what type of learning architectures (deep or shallow), type of learning (unsupervised or supervised) is optimal. In this paper we limit ourselves to addressing these questions in the context of distinguishing between anaplastic and non-anaplastic medulloblastomas from routine haematoxylin and eosin stained images. The unsupervised approaches evaluated were sparse autoencoders and topographic reconstruct independent component analysis, and the supervised approach was convolutional neural networks. Experimental results show that shallow architectures with more neurons are better than deeper architectures without taking into account local space invariances and that topographic constraints provide useful invariant features in scale and rotations for efficient tumor differentiation.
Liu, Xiao
2017-03-21
Privacy risks of recommender systems have caused increasing attention. Users’ private data is often collected by probably untrusted recommender system in order to provide high-quality recommendation. Meanwhile, malicious attackers may utilize recommendation results to make inferences about other users’ private data. Existing approaches focus either on keeping users’ private data protected during recommendation computation or on preventing the inference of any single user’s data from the recommendation result. However, none is designed for both hiding users’ private data and preventing privacy inference. To achieve this goal, we propose in this paper a hybrid approach for privacy-preserving recommender systems by combining differential privacy (DP) with randomized perturbation (RP). We theoretically show the noise added by RP has limited effect on recommendation accuracy and the noise added by DP can be well controlled based on the sensitivity analysis of functions on the perturbed data. Extensive experiments on three large-scale real world datasets show that the hybrid approach generally provides more privacy protection with acceptable recommendation accuracy loss, and surprisingly sometimes achieves better privacy without sacrificing accuracy, thus validating its feasibility in practice.
Qingxue Huang
2017-01-01
Full Text Available In this paper, a robust, effective, and accurate numerical approach is proposed to obtain the numerical solution of fractional differential equations. The principal characteristic of the approach is the new orthogonal functions based on shifted Legendre polynomials to the fractional calculus. Also the fractional differential operational matrix is driven. Then the matrix with the Tau method is utilized to transform this problem into a system of linear algebraic equations. By solving the linear algebraic equations, the numerical solution is obtained. The approach is tested via some examples. It is shown that the FLF yields better results. Finally, error analysis shows that the algorithm is convergent.
Abhijit Chandra
2012-10-01
Full Text Available In recent times, system designers are becoming very much apprehensive in reducing the structural complexity of digital systems with which they deal in practice. However, the uncontrolled minimization of any digital hardware always leads to significant deterioration of system performance making it incompatible for use in any practical system. As proper trade-off is inevitably essential between achievable performance and required hardware, researchers have sought a number of artificially intelligent optimization techniques to solve it out. Since such a technique generally involves variety of constructional alternatives, appropriate use of correct option demands justified attention. Numerous evolutionary computation techniques, being a branch of biologically inspired optimization process, are being increasingly used for a number of signal processing applications of late. This paper throws enough light to select the most suitable mutation strategy of Differential Evolution (DE algorithm for efficient design of multiplier-less low-pass finite duration impulse response (FIR filter. Computationally efficient mutation scheme has been identified by observing convergence behavior and error histogram plot for different alternatives. Performance of the designed filter has been compared in terms of its magnitude response and the requirement of various hardware blocks for four different lengths of the filter. Consequently the name of the most favorable mutation rule has been suggested upon analyzing all the factors. Finally the supremacy of our proposed design has been established by comparing its performance with that of other state-of-the-art multiplier-less low-pass FIR filters.
Xiao-lei DONG; Sui-qing LIU; Tao TAO; Shu-ping LI; Kun-lun XIN
2012-01-01
The differential evolution (DE) algorithm has been received increasing attention in terms of optimizing the design for the water distribution systems (WDSs).This paper aims to carry out a comprehensive performance comparison between the new emerged DE algorithm and the most popular algorithm-the genetic algorithm (GA).A total of six benchmark WDS case studies were used with the number of decision variables ranging from 8 to 454.A preliminary sensitivity analysis was performed to select the most effective parameter values for both algorithms to enable the fair comparison.It is observed from the results that the DE algorithm consistently outperforms the GA in terms of both efficiency and the solution quality for each case study.Additionally,the DE algorithm was also compared with the previously published optimization algorithms based on the results for those six case studies,indicating that the DE exhibits comparable performance with other algorithms.It can be concluded that the DE is a newly promising optimization algorithm in the design of WDSs.
Fernando Martín
2015-09-01
Full Text Available 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
2015-09-16
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.
Hesheng Tang; Yu Su; Jiao Wang
2015-08-01
The paper describes a procedure for the uncertainty quantification (UQ) using evidence theory in buckling analysis of semi-rigid jointed frame structures under mixed epistemic–aleatory uncertainty. The design uncertainties (geometrical, material, strength, and manufacturing) are often prevalent in engineering applications. Due to lack of knowledge or incomplete, inaccurate, unclear information in the modeling, simulation, measurement, and design, there are limitations in using only one framework (probability theory) to quantify uncertainty in a system because of the impreciseness of data or knowledge. Evidence theory provides an alternative to probability theory for the representation of epistemic uncertainty that derives from a lack of knowledge with respect to the appropriate values to use for various inputs to the model. Unfortunately, propagation of an evidence theory representation for uncertainty through a model is more computationally demanding than propagation of a probabilistic representation for uncertainty. In order to alleviate the computational difficulties in the evidence theory based UQ analysis, a differential evolution-based computational strategy for propagation of epistemic uncertainty in a system with evidence theory is presented here. A UQ analysis for the buckling load of steel-plane frames with semi-rigid connections is given herein to demonstrate accuracy and efficiency of the proposed method.
Xiangtao Li
2011-01-01
Full Text Available Multibeam antenna arrays have important applications in communications and radar. This paper presents a new method of designing a reconfigurable antenna with quantized phase excitations using a new hybrid algorithm, called DE/BBO. The reconfigurable design problem is to find the element excitation that will result in a sector pattern main beam with low sidelobes with additional requirement that the same excitation amplitudes applied to the array with zero-phase should be in a high directivity, low sidelobe pencil-shaped main beam. In order to reduce the effect of mutual coupling between the antenna-array elements, the dynamic range ratio is minimized. Additionally, compared with the continuous realization and subsequent quantization, experimental results indicate that the performance of the discrete realization of the phase excitation value can be improved. In order to test the performances of hybrid differential evolution with biogeography-based optimization, the results of some state-of-art algorithms are considered, for the purposed of comparison. Experiment results indicate the better performance of the DE/BBO.
An improved differential evolution algorithm for TSP%旅行商问题的改进差分进化方法
梅觅; 薛惠锋; 谷雨
2011-01-01
TSP(Traveling Salesman Problem)旅行商问题是一类典型的NP完全问题,目前大多采用遗传算法求解.差分进化算法(Differential Evolution Algorithm, DE)作为一种新型的进化算法,与遗传算法有很多相似之处.提出用改进的差分进化算法解决TSP问题.采用基于整数序规范的辅助算子解决变异问题,并引入刘海交叉算子.实验结果表明该方法有效地提高了算法的收敛速度与寻优质量,表现出了良好的特性.%TSP ( Traveling Salesman Problem) is a kind of typical NP problems, and currently is solved by genetic algorithm ( GA) generally. As a new kind of evolution algorithm, differential evolution algorithm ( DE) shares many common performances with GA. In order to solve TSP more conveniently,an improved differential evolution algorithm was proposed. The new method added an auxiliary operator for regulating integer sequence in the mutation process and used Liuhai crossover operator to replace the original crossover operator. Through several experiments, it could be concluded that this method can significantly improve the speed of convergence and the quality of optimal results, features well characteristic in TSP.
Differential diagnosis of nongap metabolic acidosis: value of a systematic approach.
Kraut, Jeffrey A; Madias, Nicolaos E
2012-04-01
Nongap metabolic acidosis is a common form of both acute and chronic metabolic acidosis. Because derangements in renal acid-base regulation are a common cause of nongap metabolic acidosis, studies to evaluate renal acidification often serve as the mainstay of differential diagnosis. However, in many cases, information obtained from the history and physical examination, evaluation of the electrolyte pattern (to determine if a nongap acidosis alone or a combined nongap and high anion gap metabolic acidosis is present), and examination of the serum potassium concentration (to characterize the disorder as hyperkalemic or hypokalemic in nature) is sufficient to make a presumptive diagnosis without more sophisticated studies. If this information proves insufficient, indirect estimates or direct measurement of urinary NH(4)(+) concentration, measurement of urine pH, and assessment of urinary HCO(3)(-) excretion can help in establishing the diagnosis. This review summarizes current information concerning the pathophysiology of this electrolyte pattern and the value and limitations of all of the diagnostic studies available. It also provides a systematic and cost-effective approach to the differential diagnosis of nongap metabolic acidosis.
Adjusting for differential-verification bias in diagnostic-accuracy studies: a Bayesian approach.
de Groot, Joris A H; Dendukuri, Nandini; Janssen, Kristel J M; Reitsma, Johannes B; Bossuyt, Patrick M M; Moons, Karel G M
2011-03-01
In studies of diagnostic accuracy, the performance of an index test is assessed by verifying its results against those of a reference standard. If verification of index-test results by the preferred reference standard can be performed only in a subset of subjects, an alternative reference test could be given to the remainder. The drawback of this so-called differential-verification design is that the second reference test is often of lesser quality, or defines the target condition in a different way. Incorrectly treating results of the 2 reference standards as equivalent will lead to differential-verification bias. The Bayesian methods presented in this paper use a single model to (1) acknowledge the different nature of the 2 reference standards and (2) make simultaneous inferences about the population prevalence and the sensitivity, specificity, and predictive values of the index test with respect to both reference tests, in relation to latent disease status. We illustrate this approach using data from a study on the accuracy of the elbow extension test for diagnosis of elbow fractures in patients with elbow injury, using either radiography or follow-up as reference standards.
Differential-Psychological and Psychophysiological Approaches to Learning in Modern School
Kabardov M. K.,
2017-01-01
Full Text Available The article shows the background and specifics of application of differential psychological and psychophysiological approach to learning in modern school. The revealed problems of the use of the process of individualization and differentiation of teaching, the necessity of taking into account the individual learning opportunities, individual style of pedagogical activity, as well as features the method used by the teacher during training at the modern stage of education development. Presents the advantages and disadvantages of the 3 paradigms of providing training and education of students in modern school in terms of educational and personal needs of the participants in the learning process. The study showed which may have the effect of environmental factors (primarily representations of teachers about the abilities of their wards, i.e., the performance assessment by teachers on the results of the exam. And that is not unimportant, skills and abilities of teachers play an important role in the learning process. According to the authors, the effectiveness of the learning process depends on "valence" the following three factors against each other and their variability: styles of teaching; learning styles (types of teaching techniques or learning technologies; individual strategies to acquire knowledge, skills, skills. This work was supported by grant RFH № 16-06-00887а
Differential Diagnosis of Nongap Metabolic Acidosis: Value of a Systematic Approach
Madias, Nicolaos E.
2012-01-01
Summary Nongap metabolic acidosis is a common form of both acute and chronic metabolic acidosis. Because derangements in renal acid-base regulation are a common cause of nongap metabolic acidosis, studies to evaluate renal acidification often serve as the mainstay of differential diagnosis. However, in many cases, information obtained from the history and physical examination, evaluation of the electrolyte pattern (to determine if a nongap acidosis alone or a combined nongap and high anion gap metabolic acidosis is present), and examination of the serum potassium concentration (to characterize the disorder as hyperkalemic or hypokalemic in nature) is sufficient to make a presumptive diagnosis without more sophisticated studies. If this information proves insufficient, indirect estimates or direct measurement of urinary NH4+ concentration, measurement of urine pH, and assessment of urinary HCO3− excretion can help in establishing the diagnosis. This review summarizes current information concerning the pathophysiology of this electrolyte pattern and the value and limitations of all of the diagnostic studies available. It also provides a systematic and cost-effective approach to the differential diagnosis of nongap metabolic acidosis. PMID:22403272
Decaris, Martin L; Leach, J Kent
2011-04-01
The presentation of extracellular matrix (ECM) proteins provides an opportunity to instruct the phenotype and behavior of responsive cells. Decellularized cell-secreted matrix coatings (DM) represent a biomimetic culture surface that retains the complexity of the natural ECM. Microenvironmental culture conditions alter the composition of these matrices and ultimately the ability of DMs to direct cell fate. We employed a design of experiments (DOE) multivariable analysis approach to determine the effects and interactions of four variables (culture duration, cell seeding density, oxygen tension, and media supplementation) on the capacity of DMs to direct the osteogenic differentiation of human mesenchymal stem cells (hMSCs). DOE analysis revealed that matrices created with extended culture duration, ascorbate-2-phosphate supplementation, and in ambient oxygen tension exhibited significant correlations with enhanced hMSC differentiation. We validated the DOE model results using DMs predicted to have superior (DM1) or lesser (DM2) osteogenic potential for naïve hMSCs. Compared to cells on DM2, hMSCs cultured on DM1 expressed 2-fold higher osterix levels and deposited 3-fold more calcium over 3 weeks. Cells on DM1 coatings also exhibited greater proliferation and viability compared to DM2-coated substrates. This study demonstrates that DOE-based analysis is a powerful tool for optimizing engineered systems by identifying significant variables that have the greatest contribution to the target output.
Wang, Jiao; Hu, Fuyan; Cheng, Hua; Zhao, Xing-Ming; Wen, Tieqiao
2012-11-01
Understanding the molecular mechanism that underlies the differentiation of neural stem cells (NSCs) is vital to develop regenerative medicines for neurological disorders. In our previous work, Rho-GDI-γ was found to be able to prompt neuronal differentiation when it was down regulated. However, it is unclear how Rho-GDI-γ regulates this differentiation process. Therefore, a novel systems biology approach is presented here to identify putative signalling pathways regulated by Rho-GDI-γ during NSC differentiation, and these pathways can provide insights into the NSC differentiation mechanisms. In particular, our proposed approach combines the predictive power of computational biology and molecular experiments. With different biological experiments, the genes in the computationally identified signalling network were validated to be indeed regulated by Rho-GDI-γ during the differentiation of NSCs. In particular, one randomly selected pathway involving Vcp, Mapk8, Ywhae and Ywhah was experimentally verified to be regulated by Rho-GDI-γ. These promising results demonstrate the effectiveness of our proposed systems biology approach, indicating the potential predictive power of integrating computational and experimental approaches.
A new approach in treating the ballistic coefficient in the differential correction fitting program
Barker, William N.; Eller, Thomas J.; Herder, Leland E.
This paper describes the results of study to improve the accuracy of tracking and impact prediction special perturbations software in use at the Space Surveillance Center. First, historical data are considered for a wide range of decayed satellites. In general, this data indicate that the satellite ballistic coefficient (a model parameter in the directional correction process) varies as a function of time in the last days prior to decay. At present, this effect is not modeled and the ballistic coefficient is held constant over the differential correction observation span. The new approach presented here is a parameterization of the ballistic coefficient in the form of a simple linear function with time. The slope of this function is the time derivative of the ballistic coefficient which is treated as a new model parameter. Numerical results obtained from processing two important historical satellite decay cases are presented.
Seo, Ni Eun [Dept. of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul (Korea, Republic of); Kim, So Yeon; Lee, Seung Soo; Byun, Jae Ho; Kim, Hyoung Jung; Kim, Jin Hee; Lee, Moon Gyu [Dept. of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul (Korea, Republic of)
2016-02-15
Sclerosing cholangitis is a spectrum of chronic progressive cholestatic liver disease characterized by inflammation, fibrosis, and stricture of the bile ducts, which can be classified as primary and secondary sclerosing cholangitis. Primary sclerosing cholangitis is a chronic progressive liver disease of unknown cause. On the other hand, secondary sclerosing cholangitis has identifiable causes that include immunoglobulin G4-related sclerosing disease, recurrent pyogenic cholangitis, ischemic cholangitis, acquired immunodeficiency syndrome-related cholangitis, and eosinophilic cholangitis. In this review, we suggest a systemic approach to the differential diagnosis of sclerosing cholangitis based on the clinical and laboratory findings, as well as the typical imaging features on computed tomography and magnetic resonance (MR) imaging with MR cholangiography. Familiarity with various etiologies of sclerosing cholangitis and awareness of their typical clinical and imaging findings are essential for an accurate diagnosis and appropriate management.
P. O. Rumyantsev
2013-01-01
Full Text Available Levothyroxine therapy with purpose to suppress thyroid stimulating hormone (TSH after surgery in patients with well-differentiated thyroid cancer is implemented since 1937. Accumulated results of levothyroxine suppressive therapy (LST application are attesting its heterogeneous efficacy in various risk groups of tumor recurrence: low, medium and high. Similar risk groups are emphasized towards adverse effect risk due to LST. The more intensivity and duration of TSH suppression the higher risk of adverse effects. First, they include osteopenia or osteoporosis and atrial fibrillation. Contemporary approaches to intensivity and duration of LTS are based on accounting of its potential efficiency into various clinical risk groups of tumor recurrence as well as adverse effects risk groups.
Subgrouping patients with low back pain: evolution of a classification approach to physical therapy.
Fritz, Julie M; Cleland, Joshua A; Childs, John D
2007-06-01
The development of valid classification methods to assist the physical therapy management of patients with low back pain has been recognized as a research priority. There is also growing evidence that the use of a classification approach to physical therapy results in better clinical outcomes than the use of alternative management approaches. In 1995 Delitto and colleagues proposed a classification system intended to inform and direct the physical therapy management of patients with low back pain. The system described 4 classifications of patients with low back pain (manipulation, stabilization, specific exercise, and traction). Each classification could be identified by a unique set of examination criteria, and was associated with an intervention strategy believed to result in the best outcomes for the patient. The system was based on expert opinion and research evidence available at the time. A substantial amount of research has emerged in the years since the introduction of this classification system, including the development of clinical prediction rules, providing new evidence for the examination criteria used to place a patient into a classification and for the optimal intervention strategies for each classification. New evidence should continually be incorporated into existing classification systems. The purpose of this clinical commentary is to review this classification system, its evolution and current status, and to discuss its implications for the classification of patients with low back pain.
Variational B-spline level-set: a linear filtering approach for fast deformable model evolution.
Bernard, Olivier; Friboulet, Denis; Thévenaz, Philippe; Unser, Michael
2009-06-01
In the field of image segmentation, most level-set-based active-contour approaches take advantage of a discrete representation of the associated implicit function. We present in this paper a different formulation where the implicit function is modeled as a continuous parametric function expressed on a B-spline basis. Starting from the active-contour energy functional, we show that this formulation allows us to compute the solution as a restriction of the variational problem on the space spanned by the B-splines. As a consequence, the minimization of the functional is directly obtained in terms of the B-spline coefficients. We also show that each step of this minimization may be expressed through a convolution operation. Because the B-spline functions are separable, this convolution may in turn be performed as a sequence of simple 1-D convolutions, which yields an efficient algorithm. As a further consequence, each step of the level-set evolution may be interpreted as a filtering operation with a B-spline kernel. Such filtering induces an intrinsic smoothing in the algorithm, which can be controlled explicitly via the degree and the scale of the chosen B-spline kernel. We illustrate the behavior of this approach on simulated as well as experimental images from various fields.
Wang, Chen; Zhao, Wu; Wang, Jie; Chen, Ling; Luo, Chun-Jing
2016-06-01
The printed circuit boards basis of electronic equipment have seen a rapid growth in recent years and played a significant role in modern life. Nowadays, the fact that electronic devices upgrade quickly necessitates a proper management of waste printed circuit boards. Non-destructive desoldering of waste printed circuit boards becomes the first and the most crucial step towards recycling electronic components. Owing to the diversity of materials and components, the separation process is difficult, which results in complex and expensive recovery of precious materials and electronic components from waste printed circuit boards. To cope with this problem, we proposed an innovative approach integrating Theory of Inventive Problem Solving (TRIZ) evolution theory and technology maturity mapping system to forecast the evolution trends of desoldering technology of waste printed circuit boards. This approach can be applied to analyse the technology evolution, as well as desoldering technology evolution, then research and development strategy and evolution laws can be recommended. As an example, the maturity of desoldering technology is analysed with a technology maturity mapping system model. What is more, desoldering methods in different stages are analysed and compared. According to the analysis, the technological evolution trends are predicted to be 'the law of energy conductivity' and 'increasing the degree of idealisation'. And the potential technology and evolutionary state of waste printed circuit boards are predicted, offering reference for future waste printed circuit boards recycling.