Parallel genetic approach for routing optimization in large ad hoc networks

<span>This article presents a new approach of integrating parallelism into the genetic algorithm (GA), to solve the problem of routing in a large ad hoc network, the goal is to find the shortest path routing. Firstly, we fix the source and destination, and we use the variable-length chromosomes (routes) and their genes (nodes), in our work we have answered the following question: what is the better solution to find the shortest path: the sequential or parallel method?. All modern systems support simultaneous processes and threads, processes are instances of programs that generally run independently, for example, if you start a program, the operating system spawns a new process that runs parallel elements to other programs, within these processes, we can use threads to execute code simultaneously. Therefore, we can make the most of the available central processing unit (CPU) cores. Furthermore, the obtained results showed that our algorithm gives a much better quality of solutions. Thereafter, we propose an example of a network with 40 nodes, to study the difference between the sequential and parallel methods, then we increased the number of sensors to 100 nodes, to solve the problem of the shortest path in a large ad hoc network.</span>

A Literature Review on the Relatiohship between Competition and Efficiency for Takaful and Conventional Insurance

When there are many players operate in a particular market, it encourages competition. Thus, healthy competition in the market leads to efficiency performance. Takaful and conventional insurance are the two parallel methods used to transact insurance business. The emergence of the Takaful market has contributed to competitiveness and conventional insurance is facing competition with Takaful. Therefore, this paper attempts to review the literatures relating to the relationship between competition and efficiency between Takaful and conventional and whether there exist a positive or a negative relationship.

Dual approach as empirical reliability for fractional differential equations

Computational methods for fractional differential equations exhibit essential instability. Even a minor modification of the coefficients or other entry data may switch good results to the divergent. The goal of this paper is to suggest the reliable dual approach which fixes this inconsistency. We suggest to use two parallel methods based on the transformation of fractional derivatives through integration by parts or by means of substitution. We introduce the method of substitution and choose the proper discretization scheme that fits the grid points for the by-parts method. The solution is reliable only if both methods produce the same results. As an additional control tool, the Taylor series expansion allows to estimate the approximation errors for fractional derivatives. In order to demonstrate the proposed dual approach, we apply it to linear, quasilinear and semilinear equations and obtain very good precision of the results. The provided examples and counterexamples support the necessity to use the dual approach because either method, used separately, may produce incorrect results. The order of the exactness is close to the exactness of fractional derivatives approximations.

Multienzymatic Processes Involving Baeyer–Villiger Monooxygenases

Baeyer–Villiger monooxygenases (BVMOs) are flavin-dependent oxidative enzymes capable of catalyzing the insertion of an oxygen atom between a carbonylic Csp2 and the Csp3 at the alpha position, therefore transforming linear and cyclic ketones into esters and lactones. These enzymes are dependent on nicotinamides (NAD(P)H) for the flavin reduction and subsequent reaction with molecular oxygen. BVMOs can be included in cascade reactions, coupled to other redox enzymes, such as alcohol dehydrogenases (ADHs) or ene-reductases (EREDs), so that the direct conversion of alcohols or α,β-unsaturated carbonylic compounds to the corresponding esters can be achieved. In the present review, the different synthetic methodologies that have been performed by employing multienzymatic strategies with BVMOs combining whole cells or isolated enzymes, through sequential or parallel methods, are described, with the aim of highlighting the advantages of performing multienzymatic systems, and show the recent advances for overcoming the drawbacks of using BVMOs in these techniques.

3-D CSEM inversion for the complex model with topography using an efficient dual parallel approach

&lt;p&gt;&amp;#160; &amp;#160; There is a significant interest in improving the efficiency of 3-D CSEM inversion and obtaining more reliable inversion results. A 3-D CSEM inversion code using unstructured tetrahedral elements has been developed in order to consider the topographic effect by directly incorporating it into computational grids. In the forward modeling, the electric dipole source is divided into a set of short electric dipoles to simulate its practical shape, size and attitude. We adopt the edge-based finite-element method to discretize the electric field equation. In the inversion, the inversion grids are entirely independent of the forward grids. The lower and upper bounding constraints on model parameters are used to improve the reliability of the inversion result further. We use the Gauss-Newton algorithm to minimize the inversion objective function and obtain the underground conductivity model. The calculation of the forward modeling and the sensitivity matrix spends most of the time in the inversion. At present, most inversion codes use frequency-based parallel methods to accelerate the inversion, to further improve the efficiency of 3D CSEM inversion, except for the frequency-based parallel methods, we use the open-source software METIS to divide the model into several parts and then use the MPI-based parallel toolkits (such as PETSc and MUMPS) to solve the forward linear equations. The same parallel scheme can also be used to calculate the sensitivity matrix. Finally, we can further improve the efficiency of 3-D CSEM inversion by the dual parallel strategy based on the frequency and domain decomposition.&lt;/p&gt;

Parallel iteration of two-step Runge-Kutta methods

In this paper, we introduce the Parallel iteration of two-step Runge-Kutta methods for solving non-stiff initial-value problems for systems of first-order differential equations (ODEs): y′(t) = f(t, y(t)), for use on parallel computers. Starting with an s−stage implicit two-step Runge-Kutta (TSRK) method of order p, we apply the highly parallel predictor-corrector iteration process in P (EC)mE mode. In this way, we obtain an explicit two-step Runge-Kutta method that has order p for all m, and that requires s(m+1) right-hand side evaluations per step of which each s evaluation can be computed parallelly. By a number of numerical experiments, we show the superiority of the parallel predictor-corrector methods proposed in this paper over both sequential and parallel methods available in the literature.

Elimination of Information Redundancy of Hyperspectral Raster Images Technology Enhancement

The work is a continuation of the authors' research on the problem of adaptive compression of raster hyperspectral images of Earth remote sensing. In the first part of the article, the authors give an overview of the current state of affairs in the processing of images of remote sensing of the Earth, the characteristic properties of raster hyperspectral images in the context of the prospects for lossy compression, the problems of the effectiveness of existing compression methods of this type of graphic documents are indicated. Further, the article highlights the issues of increasing the efficiency of methods for eliminating information redundancy of raster hyperspectral images of Earth remote sensing. The problems of designing and creating parallel methods and algorithms for the compression of raster hyperspectral ERS images are considered. A method for the development of a parallel algorithm for constructing a system of local homogeneous "well-adapted" basis functions for raster hyperspectral images, based on the Chebyshev approximation for systems using the CUDA graphics processor, is proposed.

Intelligent analysis of color preferences: search for associative rules vs. cluster analysis

The aim of the article is to present the experience of experimental implementation on the basis of modern software platforms and technologies of two different methods of data mining: (1) the method of associative rules, and (2) the method of clustering. The authors analyze potential and limitations of using both methods in socio-psychological research of color preferences. The material for the conducted experiment was the data of a socio-psychological study in the course of which the subjects (N = 50) were shown a color palette containing 27 different shades and were asked to select from it the colors which, in their opinion, best fit the interior of each of seven different room types: living room, entrance room, bedroom, bathroom, toilet, kitchen, and hallway. By means of the Apriori algorithm we obtained associative rules corresponding to the relationship between color preferences and room types. We discuss the potential of hierarchical clustering method application for obtaining conclusions, which cannot be achieved by calculating percentages. The choice of rules for combining clusters, which give the most objective and informative assessment of responses, is performed. It was proposed to calculate the distance between color choices of respondents using CIEDE2000 color difference metric. By means of concrete examples it is shown that both methods of intellectual analysis open wide possibilities for visualization of the revealed psychological mechanisms and regularities. Experiments were carried out, in the course of which it was established that the chosen methods allow to carry out an effective assessment of social and psychological research data. We can conclude that with a significant increase in the number of respondents and the range of possible variants of answers, the task of finding associations is effectively solved by parallel methods.

Soft-Body Simulation With CUDA Based on Mass-Spring Model and Verlet Integration Scheme

Soft-body simulation is widely used in animation, prostheses, organs, and so on. The most common way is to use 3D software. However, Their simulation models and the data processing speed are limited. Therefore, one model based on the mass-spring mechanism is proposed. To realize real-time rendering, a parallel computing architecture based on the CUDA architecture is introduced. Besides, to increase the accuracy of the simulation, the Verlet integration is employed. The work is to check whether the massively parallel computing method based on the CUDA architecture improves the rendering performance. To meet the minimum requirement to make the human eye comfortable, all the tests had at least a 60 Hz refreshing rate. Also, the soft body of mass-particles and springs has a uniform width and depth, but the height is much smaller. It was modeled to fall under the influence of gravity, and then, to impact on a rigid object. The serial and the parallel methods were not significantly different when the rendering nodes were less than 2,000, but it became apparent when the number of nodes reached 10,000. Therefore, the simulation efficiency of a soft body is improved by the proposed method.