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Sensitivity analysis is an important issue in large-scale mathematical modelling. We developed a novel 3-stage method for global sensitivity analysis of the Unified Danish Eulerian Model (UNI-DEM). This is a powerful large-scale air pollution model with an up-to-date high-performance software implementation. There is a number of uncertain internal parameters, especially in the chemistry–emission submodel, which are subject to our quantitative sensitivity study. Efficient Monte Carlo and quasi-Monte Carlo algorithms based on Sobol sequences are used in this study. A large number of numerical experiments with some special modifications of the model must be carried out in order to collect the necessary input data for the particular sensitivity study. For this purpose we created an efficient high performance implementation SA-DEM, based on the MPI version of the package UNI-DEM. A vast number of numerical experiments were carried out with SA-DEM on an IBM Blue Gene/P, the most powerful parallel supercomputer, at the time of the write-up of this paper, in Bulgaria. Even this powerful machine has some problems with the storage when SA-DEM is to be run with the refined (480×480) version of the mesh. The code was implemented with some enhancements on the IBM MareNostrum III at BSC — Barcelona, the most powerful parallel supercomputer in Spain. This implementation appears to be quite efficient for that challenging computational problem, as our experiments show. Some numerical results and performance analysis of these results will be presented in the paper. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a new method for the resolution of elliptic and parabolic equations in circular domains. It can be trivially extended to cylindrical domains. The algorithm uses a mixed Fourier-Compact Finite Difference method. The main advantage of the method is achieved by a new concept of mesh. The topology of the new grid keeps constant the aspect ratio of the cells, avoiding the typical clustering for radial structured meshes at the center. The reduction of the number of nodes has as a consequence the reduction in memory consumption. In the case of fluid mechanics problems, this technique also increases the time step for a constant Courant number. Several examples are given in the paper which show the potential of the method. [Copyright &y& Elsevier]

Abstract: This paper deals with the utilization of a symbolic regression tool, which is Analytic Programming (AP), together with two evolutionary algorithms, the Self-Organizing Migrating Algorithm (SOMA) and Differential Evolution (DE), for the synthesis of a new control law. This synthesized chaotic controller secures the stabilization of higher periodic orbits, which represent oscillations between several values of three selected discrete chaotic systems. Selected examples were: an artificially evolutionary synthesized system, logistic equation and Hénon map. The paper consists of the description of analytic programming as well as chaotic systems used, evolutionary techniques and the cost function. [Copyright &y& Elsevier]

Abstract: In this paper, the utilization of chaos pseudorandom number generators based on three different chaotic maps to alter the behavior and overall performance of PSO algorithm is proposed. This paper presents results of testing the performance and behavior of the proposed algorithm on typical benchmark functions that represent unimodal and multimodal problems. The promising results are analyzed and discussed. [Copyright &y& Elsevier]

Abstract: Designing a key distribution protocol with minimal computation and storage complexity is a challenging issue in secure multimedia multicast. In most of the multimedia multicast applications, the group membership requires secured dynamic key generation and updation operations that usually consume much of the computation time. In this paper, we propose a new GCD (Greatest Common Divisor) based Key Distribution Protocol which focuses on two dimensions. The first dimension deals with the reduction of computation complexity which is achieved in our protocol by performing fewer multiplication operations during the key updation process. To optimize the number of multiplication operations, the existing Karatsuba divide and conquer approach for multiplication is used in this proposed work. The second dimension aims at reducing the amount of information stored in the Group Center and group members while performing the update operation in the key content. The proposed algorithm which focuses on these two dimensions has been implemented and tested using a Cluster tree based key management scheme and has been found to produce promising results. Comparative analysis to illustrate the performance of various key distribution protocols is shown in this paper and it has been observed that this proposed algorithm reduces the computation and storage complexity significantly. [Copyright &y& Elsevier]

Abstract: This paper analyzes the fault-tolerance nature of Evolutionary Algorithms (EAs) when executed in a distributed environment subjected to malicious acts. More precisely, the inherent resilience of EAs against two types of failures is considered: (1) crash faults, typically due to resource volatility which lead to data loss and part of the computation loss; (2) cheating faults, a far more complex kind of fault that can be modeled as the alteration of output values produced by some or all tasks of the program being executed. This last type of failure is due to the presence of cheaters on the computing platform. Most often in Global Computing (GC) systems such as BOINC, cheaters are attracted by the various incentives provided to stimulate the volunteers to share their computing resources: cheaters typically seek to obtain rewards with little or no contribution to the system. In this paper, the Algorithm-Based Fault Tolerance (ABFT) aspects of EAs against the above types of faults is characterized. Whereas the inherent resilience of EAs has been previously observed in the literature, for the first time, a formal analysis of the impact of the considered faults over the executed EA including a proof of convergence is proposed in this article. By the variety of problems addressed by EAs, this study will hopefully promote their usage in the future developments around distributed computing platform such as Desktop Grids and Volunteer Computing Systems or Cloud systems where the resources cannot be fully trusted. [Copyright &y& Elsevier]

Abstract: Existing models from scheduling often over-simplify the problems appearing in real-world industrial situations. The original application is often reduced to a single-objective one, where the presence of uncertainty is neglected. In this paper, we focus on multi-objective optimization in uncertain environments. A bi-objective flowshop scheduling problem with uncertain processing times is considered. An indicator-based evolutionary algorithm is proposed to handle these two difficulties (multiple objectives and uncertain environment) at the same time. Four different strategies, based on uncertainty-handling quality indicators, are proposed in the paper. Computational experiments are performed on a large set of instances by considering different scenarios with respect to uncertainty. We show that an uncertainty-handling strategy is a key issue to obtain good-quality solutions, and that the algorithm performance is strongly related to the level of uncertainty over the environmental parameters. [Copyright &y& Elsevier]

Abstract: Electrical capacitance tomography (ECT) is considered as a promising tomography technology, and exactly reconstructing the original objects is highly desirable in real applications. In this paper, a generalized image reconstruction model that simultaneously considers the inaccurate property in the measured capacitance data and the linearization approximation error is presented. A generalized objective function, which has been developed using a combinational M-estimation and an extended stabilizing item, is proposed. The objective function unifies six estimation methods into a concise formula, where different estimation methods can be easily obtained by selecting different parameters. The homotopy method that integrates the beneficial advantages of the alternant iteration scheme is employed to solve the proposed objective function. Numerical simulations are implemented to evaluate the numerical performances and effectiveness of the proposed algorithm, and the numerical results reveal that the proposed algorithm is efficient and overcomes the numerical instability in the process of ECT image reconstruction. For the reconstructed objects in this paper, a dramatic improvement in accuracy and spatial resolution can be achieved, which indicates that the proposed algorithm is a promising candidate for solving ECT inverse problems. [Copyright &y& Elsevier]

Abstract: Value and ambiguity are two parameters which were introduced to represent fuzzy numbers. In this paper, we find the nearest trapezoidal approximation and the nearest symmetric trapezoidal approximation to a given fuzzy number, with respect to the average Euclidean distance, preserving the value and ambiguity. To avoid the laborious calculus associated with the Karush–Kuhn–Tucker theorem, the working tool in some recent papers, a less sophisticated method is proposed. Algorithms for computing the approximations, many examples, proofs of continuity and two applications to ranking of fuzzy numbers and estimations of the defect of additivity for approximations are given. [Copyright &y& Elsevier]

Abstract An algorithm on computing accurate finite element approximation to the Poisson equation on a polygonal domain with corner singularities was studied in Kim and Lee (2016, 2017) numerically. The algorithm requires several iterations depending on singularities of the solution. Each iteration requires a solution of the standard finite element approximation to the Poisson equation with possible different Dirichlet data and the corresponding stress intensity factors. This paper provides an error estimate of the finite element approximation given by the algorithm, and, hence, determine the number of iterations needed to achieve full rates of convergence in both the energy and the L 2 norms. [ABSTRACT FROM AUTHOR]

Abstract: This paper deals with the utilization of two evolutionary algorithms Self-Organizing Migrating Algorithm (SOMA) and Differential Evolution (DE) for the optimization of the control of chaos. This paper is aimed at an explanation on how to use evolutionary algorithms (EAs) and how to properly define the advanced targeting cost function (CF) securing fast, precise and mainly robust stabilization of selected chaotic system on a desired state for any initial conditions. The role of EA here is as a powerful tool for an optimal tuning of control technique input parameters. As a model of deterministic chaotic system, the one-dimensional discrete Logistic equation was used. The four canonical strategies of SOMA and six canonical strategies of DE were utilized. For each EA strategy, repeated simulations were conducted to outline the effectiveness and robustness of used method and targeting CF securing robust solution. Satisfactory results obtained by both heuristic and the two proposed cost functions are compared with previous research, given by different cost function designs. [ABSTRACT FROM AUTHOR]

Abstract: Integer factorization is known to be one of the most important and useful methods in number theory and arithmetic. It also has a very close relationship to some algorithms in cryptography such as RSA algorithm. The RSA cryptosystem is one of the most popular and attractive public-key cryptosystems in the world today. Its security is based on the difficulty of integer factorization. Solving a large and sparse linear system over GF(2) is one of the most time consuming steps in most modern integer factorization algorithms including the fastest one, GNFS algorithm. The Montgomery block Lanczos method from Linbox  is for solving large and sparse linear systems over finite fields and it can be integrated into the general number field sieve (GNFS) algorithm which is the best known algorithm for factoring large integers over 110 digits. This paper will present an improved Montgomery block Lanczos method integrated with parallel GNFS algorithm. The experimental results show that the improved Montgomery block Lanczos method has a better performance compared with the original method. It can find more solutions or dependencies than the original method with less time complexities. Implementation details and experimental results are provided in this paper as well. [Copyright &y& Elsevier]

Abstract: The file system becomes the usual target of malicious attacks because it contains lots of sensitive data, such as executable programs, configuration and authorization information. File integrity monitoring is an effective approach to discover aggressive behavior by detecting modification actions on these sensitive files. Traditional real-time integrity monitoring tools, which insert hooks into the OS kernel, are easily controlled and disabled by malicious software. Such existing methods, which insert kernel module into OS, are hard to be compatible because of the diversity of OS. In this paper, we present a non-intrusive real-time file integrity monitoring method in virtual machine-based computing environment, which is transparent to the monitored system. The monitor is isolated from the monitored system, since it observes the state of the monitored system from the outside. This method brings two benefits: detecting file operations in real time and being invisible to malicious attackers in the monitored system. Furthermore, a kind of file classification algorithm based on file security level is proposed to improve efficiency in this paper. The proposed file integrity monitoring method is implemented in the full-virtualization mode supported by the Xen platform. The experimental results show that the method is effective with acceptable overhead. [Copyright &y& Elsevier]

Abstract: Self-stabilizing systems of the Dolev type were first introduced by Dolev et al. in their famous paper in 1993. In contrast to self-stabilizing systems of the Dijkstra type, such self-stabilizing systems assume the read/write atomicity model instead of the composite atomicity model. In this paper, we introduce the notion of quasi-self-stabilizing systems of the Dolev type. A naturally-adapted version from Dijkstra’s -state mutual exclusion algorithm is employed to illustrate the new notion. The adapted algorithm is shown to be self-stabilizing if is greater than or equal to , quasi-self-stabilizing but not self-stabilizing if is less than but greater than or equal to , and not quasi-self-stabilizing if is less than . [Copyright &y& Elsevier]

Abstract: Duality properties have been investigated by many researchers in the recent literature. They are introduced in this paper for a fully fuzzified version of the minimal cost flow problem, which is a basic model in network flow theory. This model illustrates the least cost of the shipment of a commodity through a capacitated network in terms of the imprecisely known available supplies at certain nodes which should be transmitted to fulfil uncertain demands at other nodes. First, we review on the most valuable results on fuzzy duality concepts to facilitate the discussion of this paper. By applying Hukuhara’s difference, approximated and exact multiplication and Wu’s scalar production, we exhibit the flow in network models. Then, we use combinatorial algorithms on a reduced problem which is derived from fully fuzzified MCFP to acquire fuzzy optimal flows. To give duality theorems, we utilize a total order on fuzzy numbers due to the level of risk and realize optimality conditions for providing some efficient combinatorial algorithms. Finally, we compare our results with the previous worthwhile works to demonstrate the efficiency and power of our scheme and the reasonability of our solutions in actual decision-making problems. [Copyright &y& Elsevier]

Abstract: Shortest path finding has a variety of applications in transportation and communication.In this paper, we study a well-known self-stabilizing algorithm for the shortest path problem for the distributed systems. The previous works on this topic had two assumptions that can be relaxed in this paper. First, in the previous works, the systems were assumed to be integral-weighted, whereas in this paper, the systems are real-weighted. Second, and more importantly, the previous works have shown that the algorithm is self-stabilizing under the more restricted central demon model, whereas in this paper, we give a rigorous proof showing that the algorithm is actually self-stabilizing under the more general distributed demon model. The work in this paper is of significance because in the existing literature on self-stabilizing systems, most of the papers regarding the distributed demon are for the ring networks only; there are very few papers that discuss the self-stabilizing algorithms for the general distributed systems assuming the distributed demon model. [Copyright &y& Elsevier]

In this paper, high dimensional two-sided space fractional diffusion equations, derived from the fractional Fick’s law, and with monotonic variable diffusion coefficients, are solved by alternating direction implicit method. Each linear system corresponding to each spatial direction thus resulted is solved by Krylov subspace method. The method is accelerated by applying an approximate inverse preconditioner, where under certain conditions we showed that the normalized preconditioned matrix is equal to a sum of identity matrix, a matrix with small norm, and a matrix with low rank, such that the preconditioned Krylov subspace method converges superlinearly. We also briefly present some fast algorithms whose computational cost for solving the linear systems is O ( n log n ) , where n is the matrix size. The results are illustrated by some numerical examples. [ABSTRACT FROM AUTHOR]

This paper is concerned with the two-phase obstacle problem, a type of a variational free boundary problem. We recall the basic estimates of Repin and Valdman (2015) and verify them numerically on two examples in two space dimensions. A solution algorithm is proposed for the construction of the finite element approximation to the two-phase obstacle problem. The algorithm is not based on the primal (convex and nondifferentiable) energy minimization problem but on a dual maximization problem formulated for Lagrange multipliers. The dual problem is equivalent to a quadratic programming problem with box constraints. The quality of approximations is measured by a functional a posteriori error estimate which provides a guaranteed upper bound of the difference of approximated and exact energies of the primal minimization problem. The majorant functional in the upper bound contains auxiliary variables and it is optimized with respect to them to provide a sharp upper bound. A space density of the nonlinear related part of the majorant functional serves as an indicator of the free boundary. [ABSTRACT FROM AUTHOR]

In this paper, a fourth-order compact and energy conservative difference scheme for three-dimensional Rosenau-RLW equation is proposed. The scheme is a two-level and nonlinear-implicit scheme. It is proved by the discrete energy method that the compact scheme is solvable, the convergence and stability of the difference scheme is obtained, and its numerical convergence order is O ( τ 2 + h 4 ) in the L ∞ -norm. We discuss an iterative algorithm for solving the nonlinear algebraical system generated by the nonlinear compact scheme and prove its convergence. Numerical experiment results show that the theory is accurate and the method is efficient and reliable. [ABSTRACT FROM AUTHOR]

In this paper we propose a novel divide-and-conquer (DC) algorithm to compute the SVD of banded matrices, and further accelerate it by using rank-structured matrix techniques, especially the hierarchically semiseparable (HSS) matrix. The DC algorithm for the symmetric banded eigenvalue problem can also be accelerated similarly. For matrices with few deflations, the banded DC algorithms require more flops than the classical DC algorithm, and thus they are suitable for narrowly banded matrices. While, if there exist many deflations, the banded DC algorithms can be faster than the classical ones for matrices with relatively large bandwidths. Numerous experiments have been done to test the proposed algorithms. Some of the tested matrices are from construction and some are from real applications. Comparing with the DC algorithm in Intel MKL, our proposed algorithms can be hundreds times faster for matrices with narrow bandwidths or many deflations. [ABSTRACT FROM AUTHOR]

The latest developments in the domain of HPC have lead to the deployment of complex extreme-scale systems, based on diverse computing devices (CPU, GPU, accelerators) thus posing the question of scalability in the light not only of parallel efficiency, but also in terms of energy efficiency. In this paper we propose a new metrics for energy aware performance estimation based on our experience and the analysis of the existing metrics. We study the performance of computationally intensive Monte Carlo applications deployed on heterogeneous HPC systems with focus on energy efficiency and equipment costs. We compare the energy aware performance results of CPU and GPU variants of the tested algorithms with respect to the introduced measures and metrics. The results of our study demonstrate the importance of taking into account not only scalability of the HPC applications but also energy efficiency and equipment cost. They also show how to optimize the selection of CPU computing or computing with GPGPUs. The results can be used by application developers/users and also by resource providers. [ABSTRACT FROM AUTHOR]

An enhanced version of a stochastic SParse Approximate Inverse (SPAI) preconditioner for general matrices is presented in this paper. This method is used in contrast to the standard deterministic preconditioners computed by the Modified SParse Approximate Inverse Preconditioner (MSPAI). Thus we present a Monte Carlo preconditioner that relies on the use of Markov Chain Monte Carlo (MCMC) methods to compute a rough approximate matrix inverse first, which can further be optimized by an iterative filter process and a parallel refinement, to enhance the accuracy of the inverse and the preconditioner respectively. The advantage of the proposed approach is that finding the sparse Monte Carlo matrix inversion has a computational complexity linear of the size of the matrix, it is inherently parallel and thus can be obtained very efficiently for large matrices and can be used also as an efficient preconditioner while solving systems of linear algebraic equations. The behaviour of the proposed algorithm is studied and its performance measured, evaluated and compared with MSPAI. [ABSTRACT FROM AUTHOR]

Restoration of images degraded by blurring and impulse noise has received considerable attention recently. Guo et al. (2009) proposed a fast l 1 -total variation algorithm for grayscale image restoration with impulse noise. In this paper, we extend their idea for deblurring color images with impulse noise. An alternating iteration scheme is adopted for solving the corresponding problem. More importantly, we employ the five-point property to analyze the convergence of the proposed alternating algorithm. Numerical experiments demonstrate that the proposed method could deblur color images with good quality. [ABSTRACT FROM AUTHOR]

In this paper, two adaptive refinement techniques are presented for enhancing the capability of the new kind of finite volume method called meshless finite volume (MFV) method in which the moving least squares (MLS) approximation technique is employed. The proposed algorithms perform by inserting new nodes in large error regions identified using the Zienkiewicz–Zhu (Z–Z) error estimator and the T-Belytschko (TB) stress recovery scheme. In the first method referred as CV-based adaptive refinement, the new nodes are inserted at the vertices of control volumes with higher errors. The second method, referred as GA-based adaptive refinement, contains two schemes where an adaptive refinement procedure is proposed by applying a genetic algorithm (GA) based optimization technique. To make the adaptive refinement procedure work effectively, the optimal locations of the new required nodes and the values of those effective parameters involved in the adaptive refinement are found by using GA through minimization of the global error indicator. The effectiveness and versatility of the proposed methods are demonstrated by studying three benchmark problems. It is revealed that the suggested methods achieve a predefined level of accuracy by inserting new nodes at identified locations. [ABSTRACT FROM AUTHOR]

To better eliminate the staircase effect and simultaneously preserve edge details, this paper investigates a hybrid regularizers model for image denoising and deblurring. This technique closely incorporates the advantages of the classical total variation (TV) filter and the fourth-order filter. Computationally, we develop an extremely efficient relaxation scheme and alternating minimization algorithm, and give the rigorous convergence analyses there in detail. Provided experimental results distinctly illustrate the high efficiency of the addressed numerical algorithms. Also, in comparison with the recovered results by the state-of-the-art models, simulations manifestly demonstrate the competitive performance of our proposed scheme in reducing the blocky images and sharply maintaining the edge features. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we present a fast algorithm for MR image reconstruction based on the TV- /TGV- model. By utilizing the connection between the cut operator (called projection operator) and the shrink operator, the proposed algorithm adopts a semi-implicit scheme to get a compact iteration sequence so as to reduce computational cost. By combining the proximal point scheme, we can ensure that the system of linear equations to be solved at each iteration is well-conditioned. In addition, we also give convergence analysis of the proposed method. Numerical results with comparison to the split Bregman algorithm are supplied to demonstrate the efficiency of the proposed algorithm. [Copyright &y& Elsevier]

Abstract: Safety analysis is of ultimate importance for the operation of Nuclear Power Plants (NPP). Experiments even on a pilot scale are quite expensive and therefore numerical simulations play a major role in this analysis. They allow to predict the behaviour of NPP systems under different operational and accident conditions and to develop proper action plans for minimizing the risks of accidents, and minimizing the consequences of possible accidents. For a proper risk assessment and development of emergency plans, a very large number of scenarios have to be simulated, achieving acceptable accuracy for the critical parameters, such as radioactive pollution and temperature. The existing software tools are either very simplistic and therefore quite inaccurate or they use general CFD codes that makes them very slow. This paper presents a customized algorithm and corresponding software tools for simulation of non-isothermal flows in the containment pool of a NPP. It first summarizes the requirements for such simulation tools and then presents the new algorithm which, in the opinion of the authors, is a proper compromise between accuracy and efficiency for solving such problems. It uses a Fictitious Domain Method (FDM) to impose the boundary conditions, a Cartesian finite volume discretization combined with a Domain Decomposition (DD) technique, and a factorized perturbation of the incompressibility constraint proposed recently in Guermond and Minev (2010). Finally, results from numerical simulations in idealized and realistic geometries are presented and discussed. [Copyright &y& Elsevier]

Abstract: In our work we analyze computational aspects of the problem of numerical integration in finite element calculations and consider an OpenCL implementation of related algorithms for processors with wide vector registers. As a platform for testing the implementation we choose the PowerXCell processor, being an example of the Cell Broadband Engine (CellBE) architecture. Although the processor is considered old for today’s standards (its design dates back to year 2001), we investigate its performance due to two features that it shares with recent Xeon Phi family of co-processors: wide vector units and relatively slow connection of computing cores with main global memory. The performed analysis of parallelization options can also be used for designing numerical integration algorithms for other processors with vector registers, such as contemporary x86 microprocessors. We consider higher order finite element approximations and implement the standard algorithm of numerical integration for prismatic elements. Original contributions of the paper include the analysis of data movement and vector operations performed during code execution. Several versions of the implementation are developed and tested in practice. [Copyright &y& Elsevier]

Abstract: Several classification tasks in different application domains can be seen as hierarchical classification problems. In order to deal with hierarchical classification problems, the use of existing flat classification approaches is not appropriate. For these reason, there has been a growing number of studies focusing on the development of novel algorithms able to induce classification models for hierarchical classification problems. In this paper we study the performance of a novel algorithm called Hierarchical Classification using a Competitive Neural Network (HC-CNN) and compare its performance against the Global-Model Naïve Bayes (GMNB) on eight protein function prediction datasets. Interestingly enough, the comparison of two global-model hierarchical classification algorithms for single path of labels hierarchical classification problems has never been done before. [Copyright &y& Elsevier]

Abstract: The traveling salesman problem (TSP) is a well known NP-hard benchmark problem for discrete optimization. However, there is a lack of TSP test instances for multiobjective optimization and some current TSP instances are combined to form a multiobjective TSP (MOTSP). In this paper, we present a way to systematically design MOTSP instances based on current TSP test instances, of which the degree of conflict between the objectives is measurable. Furthermore, we propose an approach, named multiobjective estimation of distribution algorithm based on decomposition (MEDA/D), which utilizes decomposition based techniques and probabilistic model based methods, to tackle the newly designed MOTSP test suite. In MEDA/D, an MOTSP is decomposed into a set of scalar objective sub-problems and a probabilistic model, using both priori and learned information, is built to guide the search for each sub-problem. By the cooperation of neighbor sub-problems, MEDA/D could optimize all the sub-problems simultaneously and thus find an approximation to the original MOTSP in a single run. The experimental results show that MEDA/D outperforms MOACO and MOEA/D-ACO, two ant colony based methods, on most of the given test instances and MEDA/D is insensible to its control parameters. [Copyright &y& Elsevier]

Abstract: Design of an optimal controller for higher-order or time-delay systems often leads to complex control algorithms. One of the possibilities of control of such processes is their approximation by a lower-order model with a time-delay (dead time). These time-delay processes can be effectively handled by the Model-based Predictive Control (MPC) method. The paper deals with design of an algorithm for adaptive predictive control of higher-order processes, which are approximated by a second-order model of the process with a time-delay. Most processes in industrial practice are characterized by nonlinear behavior and contain uncertainties. The adaptive control strategy is one of the possible approaches to optimal control of such systems. The proposed adaptive predictive controller for control of the time-delay system was tested and verified by simulation of a model of a laboratory heat exchanger which was obtained from measured experimental data. [Copyright &y& Elsevier]

Abstract: Solving systems of nonlinear equations is a relatively complicated problem in which arise a diverse range of sciences. There are a number of different approaches that have been proposed. In this paper, we employ the imperialist competitive algorithm (ICA) for solving systems of nonlinear equations. Some well-known problems are presented to demonstrate the efficiency of this new robust optimization method in comparison to other known methods. [Copyright &y& Elsevier]

Abstract: This paper deals with the numerical solution of financial applications, more specifically the computation of American option derivatives modeled by nonlinear boundary values problems. In such applications we have to solve large-scale algebraic systems. We concentrate on synchronous and asynchronous parallel iterative algorithms carried out on CPU and GPU networks. The properties of the operators arising in the discretized problem ensure the convergence of the parallel iterative synchronous and asynchronous algorithms. Computational experiments performed on CPU and GPU networks are presented and analyzed. [Copyright &y& Elsevier]

Abstract: Catalysts are omnipresent in the chemical industry. Understanding of catalytic chemical reactions is crucial for a rational development of catalysts. The present paper describes approaches for simulating phenomena in heterogeneous catalysis on a molecular level. Modeling of porous structures and their fractal surfaces will be presented. Simulation of adsorption and diffusion of reactants and products inside the pores by means of various Monte Carlo and Molecular Dynamics algorithms is described, followed by quantum chemical methods for calculating reactions on the active centers. Optimization of pore structures and multiscaling procedures complete the picture. [Copyright &y& Elsevier]

Abstract: Advanced Encryption Standard (AES) has replaced its predecessor, Double Encryption Standard (DES), as the most widely used encryption algorithm in many security applications. Up to today, AES standard has key size variants of 128, 192, and 256-bit, where longer bit keys provide more secure ciphered text output. In the hardware perspective, bigger key size also means bigger area and power consumption due to more operations that need to be done. Some companies that employ ultra-high security in their systems may look for a key size bigger than 256-bit AES. In this paper, 128 and 256-bit AES hardware, as well as two variants of an AES encryption algorithm for 512-bit and 1024-bit key size, are implemented and compared in terms of power consumption and area. The experiment is done in 45 nm CMOS technology at 1.1 V using a Synopys DC Compiler and Modelsim and total power consumption and area results are presented and graphically compared. [Copyright &y& Elsevier]

Abstract: In MBarka and Stern (2011) [3], we proposed a new algorithm to validate certificates in the past with the goals of it being simple to present, simple to implement and to fall back on standard algorithms with current time settings. This paper improves our algorithm through a much cleaner handling of time issues by defining a new notion, called revocation freshness, yielding a better fall-back on the standard algorithm. It also takes into account a practical “caution period” to lessen some of our previous hypothesis. [Copyright &y& Elsevier]

Abstract: In this paper advanced variance-based algorithms for global sensitivity analysis are studied. We consider efficient algorithms, such as Monte Carlo, quasi-Monte Carlo (QMC) and scrambled quasi-Monte Carlo algorithms based on Sobol sequences. Low discrepancy Sobol sequences are considered as a basis. Two other approaches are also analyzed. The first one is an efficient Monte Carlo (MC) algorithm for multidimensional integration based on modified Sobol sequences (MCA-MSS) and proposed in an earlier work by some of the authors Dimov and Georgieva (2011) [28]. The second one is a randomized QMC algorithm proposed by Art Owen (1995) [20]. The procedure of randomization in the latter case is also known as Owen scrambling. The algorithms considered in this work are applied to sensitivity studies of air pollution levels calculated by the Unified Danish Eulerian Model (UNI-DEM) to some chemical reaction rates. UNI-DEM is chosen as a case study since it constitutes a typical large-scale mathematical model in which the chemical reactions are adequately presented. Extensive numerical experiments are performed to support the theoretical studies and to analyze applicability of algorithms under consideration to various classes of problems. Conclusions about the applicability and efficiency of the algorithms under consideration are drawn. [Copyright &y& Elsevier]

Abstract: This paper surveys recent research on the use of sensor networks, communications and computer systems to enhance the human outcome of emergency situations. Areas covered include sensing, communication with evacuees and emergency personnel, path finding algorithms for safe evacuation, simulation and prediction, and decision tools. The systems being considered are a special instance of real-time cyber-physical-human systems that have become a crucial component of all large scale physical infrastructures such as buildings, campuses, sports and entertainment venues, and transportation hubs. [Copyright &y& Elsevier]

Abstract: Detection of external defects on potatoes is the most important technology in the realization of automatic potato sorting stations. This paper presents a hierarchical grading method applied to the potatoes. In this work a potato defect detection combining with size sorting system using the machine vision will be proposed. This work also will focus on the mathematics methods used in automation with a particular emphasis on the issues associated with designing, implementing and using classification algorithms to solve equations. In the first step, a simple size sorting based on mathematical binarization is described, and the second step is to segment the defects; to do this, color based classifiers are used. All the detection standards for this work are referenced from the United States Agriculture Department, and Canadian Food Industries. Results show that we have a high accuracy in both size sorting and classification. Experimental results show that support vector machines have very high accuracy and speed between classifiers for defect detection. [Copyright &y& Elsevier]

Abstract: This paper considers the identification problems of Hammerstein finite impulse response moving average (FIR-MA) systems using the maximum likelihood principle and stochastic gradient method based on the key term separation technique. In order to improve the convergence rate, a maximum likelihood multi-innovation stochastic gradient algorithm is presented. The simulation results show that the proposed algorithms can effectively estimate the parameters of the Hammerstein FIR-MA systems. [Copyright &y& Elsevier]

Abstract: Cellular automata are used to model dynamical phenomena by focusing on their local behavior which depends on the neighboring cells in order to express their global behavior. The geometrical structure of the models suggests the algebraic structure of cellular automata. After modeling the dynamical phenomena, it is sometimes an important problem to be able to move backwards in order to understand it better. This is only possible if cellular automata is reversible. In this paper, 2D finite cellular automata defined by local rules based on hexagonal cell structure are studied. Rule matrix of the hexagonal finite cellular automaton is obtained. The rank of rule matrices representing the 2D hexagonal finite cellular automata via an algorithm is computed. It is a well known fact that determining the reversibility of a 2D cellular automata is a very difficult problem in general. Here, the reversibility problem of this family of 2D hexagonal cellular automata is also resolved completely. [Copyright &y& Elsevier]

Abstract: This paper deals with a posteriori error estimators for the non conforming Crouzeix–Raviart finite element approximations of the Steklov eigenvalue problem. First, we define an error estimator of the residual type which can be computed locally from the approximate eigenpair and we prove the equivalence between this estimator and the broken energy norm of the error with constants independent of the corresponding eigenvalue. Next, we prove that edge residuals dominate the volumetric part of the residual and that the volumetric part of the residual terms dominate the normal component of the jumps of the discrete fluxes across interior edges. Finally, based on these results, we introduce two simpler equivalent error estimators. The analysis shows that these a posteriori error estimates are optimal up to higher order terms and that may be used for the design of adaptive algorithms. [Copyright &y& Elsevier]

Abstract: Kong et al. [Kong, Z., Gao, L., Wang, L., and Li, S., The normal parameter reduction of soft sets and its algorithm, Computers and Mathematics with Applications 56 (12) (2008) 3029–3037] introduced the definition of normal parameter reduction in soft sets and presented a heuristic algorithm of normal parameter reduction. However, the algorithm is hard to understand and involves a great amount of computation. In this paper, firstly, we give some new related definitions and proved theorems of normal parameter reduction. Then we propose a new efficient normal parameter reduction algorithm of soft sets based on the oriented-parameter sum, which can be carried out without parameter important degree and decision partition. The comparison result on a dataset shows that the proposed algorithm involves relatively less computation and is easier to implement and understand as compared with the algorithm of normal parameter reduction proposed by Kong et al. [Copyright &y& Elsevier]

Abstract: The inexact generalized Newton method is an iterative method for solving systems of nonsmooth equations. In this paper, the iterative process with a relative residual control is presented and the conditions for local convergence to a solution are provided. These results can be applied to solve Lipschitz continuous equations under some mild assumptions. Moreover, a globally convergent version of the algorithm with a damped approach based on the Armijo rule is considered. [Copyright &y& Elsevier]

Abstract: This paper presents a numerical study of the system unbalance and the fault impedance effect on faulted power system analysis. Two short circuit techniques, the symmetrical components and phase components algorithms are implemented and analyzed based on numerical simulations of the IEEE 13 bus test feeder. Test cases include voltage unbalance effect and fault impedance effect on during-fault voltages and currents. The results show that the during-fault voltages and currents are greatly affected by both voltage unbalance and fault impedance. [ABSTRACT FROM AUTHOR]

Abstract: Accurate remaining useful life (RUL) prediction of machines is important for condition based maintenance (CBM) to improve the reliability and cost of maintenance. This paper proposes artificial neural network (ANN) as a method to improve accurate RUL prediction of bearing failure. For this purpose, ANN model uses time and fitted measurements Weibull hazard rates of root mean square (RMS) and kurtosis from its present and previous points as input. Meanwhile, the normalized life percentage is selected as output. By doing that, the noise of a degradation signal from a target bearing can be minimized and the accuracy of prognosis system can be improved. The ANN RUL prediction uses FeedForward Neural Network (FFNN) with Levenberg Marquardt of training algorithm. The results from the proposed method shows that better performance is achieved in order to predict bearing failure. [ABSTRACT FROM AUTHOR]

Abstract: Chaos driven Differential Evolution algorithm and Self-Organizing Migrating Algorithm are presented in this paper for the task of PID (Proportional–Integral–Derivative) controller optimization. The dissipative chaotic Lozi map is embedded as a number generator inside DE and SOMA in order to avoid local optima stagnation and embed a superior search strategy. Three unique PID controller problems are presented and successfully resolved using these new approaches. The obtained results compare favorably with published results. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a method for designing a centralized coordinated controller for several HVDC links. The controller increases the damping of the power oscillations by modulating the current through the HVDC links in a coordinated fashion. To design a centralized coordinated controller a reduced order open system model is estimated. The open system model of the power system is estimated using the Numerical Algorithms for Subspace State-Space System Identification (N4SID) algorithm which is a black-box system identification technique. The current set-point change through the HVDC links is the set of input signals and the speeds of the generators are the set of outputs. This controller design method increases the damping significantly, which is shown for a small power system. [ABSTRACT FROM AUTHOR]

Abstract: With the large-scale development in satellite and network communication technologies, there is a great demand for preserving the secure storage and transmission of satellite imagery over internet and shared network environment. This brings new challenges to protect sensitive and critical satellite images from unauthorized access and illegal usage. In this paper, we address the aforementioned issues and develop techniques to eliminate the associated problems. To achieve this, we propose a new chaos-based symmetric-key encryption technique for satellite imagery. This scheme utilizes multiple chaotic maps e.g. Logistic, Henon, Tent, Cubic, Sine and Chebyshev for enhancing the key space, robustness and security of satellite imagery. We perform key sensitivity, statistical and performance analysis experiments to determine the security, reliability, and speed of our algorithm for satellite imagery. The proposed algorithm presents several interesting features, such as a high level of security, large enough key space, pixel distributing uniformity and an acceptable encryption speed as compared to AES, 3-DES, and DES. [Copyright &y& Elsevier]

Abstract: IEEE 802.16 technology also well known as WiMax is poised to deliver the next step in the wireless evolution. This is further fostered by the 802.16e specification which, amongst other things, introduces support for mobility. The Multicast/Broadcast Service (MBS) is also an integral part of 802.16e destined to deliver next generation services to subscribers. In this paper we concentrate on the Multicast and Broadcast Rekeying Algorithm (MBRA) of 802.16e. This algorithm has been recently criticized for various vulnerabilities and security inefficiencies, as its designers are trying to balance wisely between performance and security. After surveying related work, we extensively discuss MBRA security issues and propose the use of a novel asymmetric group key agreement protocol based on the work in Wu et al. (2009) . Our scheme guarantees secure delivery of keys to all the members of a given group and mandates rekeying upon join and leave events. It can prevent insider attacks since only the Base Station possesses a secret encryption key while all other members in the network acquire the transmitted data by using their secret decryption keys. We compare our scheme with related work and demonstrate that although heavier in terms of computing costs, it compensates when scalability and security come to the foreground. [Copyright &y& Elsevier]