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In this paper the randomized Cauchy-Euler differential equation is studied. With this aim, from a statistical point of view, both the first and second probability density functions of the solution stochastic process are computed. Then, the main statistical functions, namely, the mean, the variance and the covariance functions are determined as well. The study includes the computation of the first and second probability density functions of the regular-singular infinite point via an adequate mapping transforming the problem about the origin. The study is strongly based upon the Random Variable Transformation technique along with some results that have been recently published by some of authors to the random homogeneous linear second-order differential equation. Finally, an illustrative example is shown.

We provide a method for fast and exact simulation of Gaussian random fields on the sphere having isotropic covariance functions. The method proposed is then extended to Gaussian random fields defined over the sphere cross time and having covariance functions that depend on geodesic distance in space and on temporal separation. The crux of the method is in the use of block circulant matrices obtained working on regular grids defined over longitude and latitude. [ABSTRACT FROM AUTHOR]

This paper presents an improved method for applying smoothed particle hydrodynamics within a nested Lagrangian domain of fluid particles. In our previous implementation, ghost particles, generated by Poisson-disk sampling to enclose the region occupied by fluid particles, transferred necessary physical quantities from the outer domain to the fluid. Using this technique, the local fluid motion agreed with results simulated by SPH over the entire domain. However, the cost of generating ghost particles was burdensome. We propose herein a much less expensive, patch-based sampling method to generate ghost particles. In this new approach, the ghost particles are generated locally around each fluid particle and corresponding physical quantities are determined using the local ghost particles. Furthermore, we introduce a new formulation that determines the physical quantities of ghost particles from the outer domain, and transfers them to the local fluid particles. [ABSTRACT FROM AUTHOR]

Controlled-source electromagnetic (CSEM) surveying becomes a widespread method for oil and gas exploration, which requires fast and efficient software for inverting large-scale EM datasets. In this context, one often needs to solve sparse systems of linear equations with a large number of sparse right-hand sides, each corresponding to a given transmitter position. Sparse direct solvers are very attractive for these problems, especially when combined with low-rank approximations which significantly reduce the complexity and the cost of the factorization. In the case of thousands of right-hand sides, the time spent in the sparse triangular solve tends to dominate the total simulation time, and here we propose several approaches to reduce it. A significant reduction is demonstrated for marine CSEM application by utilizing the sparsity of the right-hand sides (RHS) and of the solutions that results from the geometry of the problem. Large gains are achieved by restricting computations at the forward substitution stage to exploit the fact that the RHS matrix might have empty rows (vertical sparsity) and/or empty blocks of columns within a non-empty row (horizontal sparsity). We also adapt the parallel algorithms that were designed for the factorization to solve-oriented algorithms and describe performance optimizations particularly relevant for the very large numbers of right-hand sides of the CSEM application. We show that both the operation count and the elapsed time for the solution phase can be significantly reduced. The total time of CSEM simulation can be divided by approximately a factor of 3 on all the matrices from our set (from 3 to 30 million unknowns, and from 4 to 12 thousands RHSs). [ABSTRACT FROM AUTHOR]

Variable fractional order Riccati equation is solved to get the exact solution by mixing Sumudu transform and Adomain decomposition method. This procedure is powerful and takes short computations' as in the illustrative examples. [ABSTRACT FROM AUTHOR]

Using a result of Blanchet and Wallwater (2015) for exactly simulating the maximum of a negative drift random walk queue endowed with independent and identically distributed (i.i.d.) increments, we extend it to a multi-dimensional setting and then we give a new algorithm for simulating exactly the stationary distribution of a first-in–first-out (FIFO) multi-server queue in which the arrival process is a general renewal process and the service times are i.i.d.: the FIFO GI/GI/c queue with $ 2 \leq c \lt \infty$. Our method utilizes dominated coupling from the past (DCFP) as well as the random assignment (RA) discipline, and complements the earlier work in which Poisson arrivals were assumed, such as the recent work of Connor and Kendall (2015). We also consider the models in continuous time, and show that with mild further assumptions, the exact simulation of those stationary distributions can also be achieved. We also give, using our FIFO algorithm, a new exact simulation algorithm for the stationary distribution of the infinite server case, the GI/GI/ $\infty$ model. Finally, we even show how to handle fork–join queues, in which each arriving customer brings c jobs, one for each server. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to develop a detection algorithm for the first jump point in sampling trajectories of jump-diffusions which are described as solutions of stochastic differential equations driven by α-stable white noise. This is done by a multivariate Lagrange interpolation approach. To this end, we utilize computer simulation algorithm in MATLAB to visualize the sampling trajectories of the jump-diffusions for various combinations of parameters arising in the modeling structure of stochastic differential equations. [ABSTRACT FROM AUTHOR]

In the present paper, we discuss algorithms of record generation when records are taken from a normal population. We propose three new generation algorithms, compare their efficiency and find the most efficient algorithm (Algorithm 2.1). We then compare these algorithms with known generation algorithms presented in the work of Balakrishnan, So, and Zhu (2016). [ABSTRACT FROM AUTHOR]

In this paper we suggest a combination of exponential integrators and half-explicit Runge–Kutta methods for solving index-1 DAE systems with a stiff linear part in their differential equations. We discuss the behavior of the resulting half-explicit exponential Runge–Kutta (HEERK) methods for a simple numerical example and for a coupled rotor simulation. The coupled rotor simulation is based on a modular software design where all subsystems are modeled by ODEs in state-space form. By connecting the subsystems' inputs and outputs we obtain an index-1 DAE system. Large terms in the system can be expressed as a stiff linear part which includes strong damping or oscillation terms as well as coefficients for the discretization of the rotor blades (3d beam equations). We show that the proposed HEERK methods can solve the resulting system efficiently with a reasonable timestep size. [ABSTRACT FROM AUTHOR]

The Magnus effect is responsible for deflecting the trajectory of a spinning baseball. The deflection at the end of the trajectory can be estimated by simulating some similar trajectories or by clustering real paths; however, previous to this study, there are no reports for a detailed connection between the initial throw conditions and the resulting deflection by using. The only approximation about this is the PITCHf/x algorithm, which uses the kinematics equations. In this work, deflections from simulated spinning throws with random linear and angular velocities and spin axis parallel to the horizontal plane are analyzed in their polar representation. A cardioid function is proposed to express the vertical deflection as response of the angular velocity. This is based on both theoretical arguments from the ball movement equations and from the numerical solution of such equations. We found that the vertical deflection fits a cardioid model as function of the Magnus coefficient and the spin angle, for a set of trajectories with initial linear velocities symmetrically distributed around the direction of motion. A variation of the model can be applied to estimate the radial deflection whereas an extended model should be explored for trajectories with velocities asymmetrically distributed. The model is suitable for many applications: from video games to pitching machines. In addition, the model approaches to the results obatined with the kinematic equations, which serves as validation of the PITCHf/x algorithm. [ABSTRACT FROM AUTHOR]

Continuous inkjet systems are commonly used to print expiry date labels for food products. These systems are designed to print on flat surfaces; however, a lot of food products package have a cylindrical shape (e.g., bottled and canned products) which causes an enlargement in characters at the ends of the label. In this work, we present an algorithm to correct this defect by calculating the extra-distance that an ink drop travels when the printing surface approaches an elliptic cylinder. Each charged ink drop is modeling as a solid particle which is affected by the air drag, Earth's gravitation, and voltage due to the electrical field that causes the perturbation in the ink drop path. Numerical results show the correction of the enlargement mentioned above by varying the electric field along the width of the label. In addition, the equation and the values of a second electric field to correct the printing's inclination caused by the method of the system's operation are presented. [ABSTRACT FROM AUTHOR]

The article deals with numerical solution of the laminar-turbulent transition. A mathematical model consists of the Reynolds-averaged Navier-Stokes equations, which are completed by the explicit algebraic Reynolds stress model (EARSM) of turbulence. The algebraic model of laminar-turbulent transition, which is integrated to the EARSM, is based on the work of Kubacki and Dick (Int. J. Heat Fluid Flow 58, 68–83, 2016) where the turbulent kinetic energy is split in to the small-scale and large-scale parts. The algebraic model is simple and does not require geometry data such as wall-normal distance and all formulas are calculated using local variables. A numerical solution is obtained by the finite volume method based on the HLLC scheme and explicit Runge-Kutta method. [ABSTRACT FROM AUTHOR]

The article deals with the numerical simulation of unsteady flows through the turbine part of the turbocharger. The main focus of the article is the extension of the in-house CFD finite volume solver for the case of unsteady flows in radial turbines and the coupling to an external zero-dimensional model of the inlet and outlet parts. In the second part, brief description of a simplified one-dimensional model of the turbine is given. The final part presents a comparison of the results of numerical simulations using both the 3D CFD method and the 1D simplified model with the experimental data. The comparison shows that the properly calibrated 1D model gives accurate predictions of mass flow rate and turbine performance at much less computational time than the full 3D CFD method. On the other hand, the more expensive 3D CFD method does not need any specific calibration and allows detailed inspections of the flow fields. [ABSTRACT FROM AUTHOR]

To thoroughly test and validate algorithms and systems of autonomous vehicles, a large number of vehicles, many tests and a multitude of datasets are needed. This way of developing and testing is difficult, expensive and sometimes even dangerous. To combine the benefits of real world testing with the scalability and lower cost of simulation based testing, we present a novel methodology for a real-time hybrid simulator that is capable of handling real and simulated vehicles simultaneously with full interaction, in real time. We validated our methodology by assessing its overall performance and real-time capabilities using an F1/10 scale vehicle. The results effectively show the viability of this approach for validation of autonomous vehicles in a cost-efficient and safe manner. [ABSTRACT FROM AUTHOR]

The focus of this paper is to develop a new partial differential equation model for the pattern formation of the human pathogen Staphylococcus aureus, starting from a newly developed model of selected gene regulation mechanisms. In our model, we do not only account for the bacteria densities and nutrient concentrations, but also for the quorum sensing and biofilm components, since they enable bacteria to coordinate their behavior and provide the environment in which the colony grows. To this end, we model the relevant gene regulation systems using ordinary differential equations and therefrom derive our evolution equations for quorum sensing and biofilm environment by time-scale arguments. Furthermore, we compare and validate our model and the corresponding simulation results with biological real data observations of Staphylococcus aureus mutant colony growth in the laboratory. We show that we are able to adequately display the qualitative biological features of pattern formation in selected mutants, using the parameter changes indicated by the gene regulation mechanisms. [ABSTRACT FROM AUTHOR]

In this paper, we compare two numerical methods for approximating the probability that the sum of dependent regularly varying random variables exceeds a high threshold under Archimedean copula models. The first method is based on conditional Monte Carlo. We present four estimators and show that most of them have bounded relative errors. The second method is based on analytical expressions of the multivariate survival or cumulative distribution functions of the regularly varying random variables and provides sharp and deterministic bounds of the probability of exceedance. We discuss implementation issues and illustrate the accuracy of both procedures through numerical studies. [ABSTRACT FROM AUTHOR]

Cooperative knowledge creation among enterprises is becoming increasingly common in uncertain environments with rapidly changing technology and increasingly complex network relationships. The objective of this paper is to better understand why and how an uncertain network environment affects an enterprise's knowledge creation performance and knowledge creation decision-making based on a dynamic knowledge supernetwork. Regarding the methodology, we proposed a dynamic knowledge supernetwork model that contains an enterprise subnetwork and a knowledge subnetwork, constructed the knowledge creation and knowledge diffusion mechanisms, and simulated the process of knowledge creation and diffusion through a multi-agent simulation. Moreover, we utilized the empirical patent data of power technology to prove the rationality and effectiveness of our model and simulation results. The results involve three main aspects. First, knowledge performance shows an exponential growth pattern. Second, in the dynamic network, we obtain a U-shaped relationship only when the effort needed to establish new cooperation is relatively small, and the inverted U-shaped relationship disappears when the above parameter exceeds a certain threshold. Third, the knowledge-based cooperation strategy is superior to the network-based cooperation strategy, and knowledge performance increases linearly and decreases exponentially with increasing network dynamics when the effort needed to establish new cooperation does not exceed and exceeds the threshold, respectively. [ABSTRACT FROM AUTHOR]

We introduce a new approach to wildland fire spread modeling. We evolve a 3-D surface curve, which represents the fire perimeter on the topography, as a projection to a horizontal plane. Our mathematical model is based on the empirical laws of the fire spread influenced by the fuel, wind, terrain slope, and shape of the fire perimeter with respect to the topography (geodesic and normal curvatures). To obtain the numerical solution, we discretize the arising intrinsic partial differential equation by a semi-implicit scheme with respect to the curvature term. For the advection term discretization, we use the so-called inflow-implicit/outflow-explicit approach and an implicit upwind technique which guarantee the solvability of the corresponding linear systems by an efficient tridiagonal solver without any time step restriction and also the robustness with respect to singularities. A fast treatment of topological changes (splitting and merging of the curves) is described and shown on examples as well. We show the experimental order of convergence of the numerical scheme, we demonstrate the influence of the fire spread model parameters on a testing and real topography, and we reconstruct a simulated grassland fire as well. [ABSTRACT FROM AUTHOR]

Methane gas hydrates have increasingly become a topic of interest because of their potential as a future energy resource. There are significant economical and environmental risks associated with extraction from hydrate reservoirs, so a variety of multiphysics models have been developed to analyze prospective risks and benefits. These models generally have a large number of empirical parameters which are not known a priori. Traditional optimization-based parameter estimation frameworks may be ill-posed or computationally prohibitive. Bayesian inference methods have increasingly been found effective for estimating parameters in complex geophysical systems. These methods often are not viable in cases of computationally expensive models and high-dimensional parameter spaces. Recently, methods have been developed to effectively reduce the dimension of Bayesian inverse problems by identifying low-dimensional structures that are most informed by data. Active subspaces is one of the most generally applicable methods of performing this dimension reduction. In this paper, Bayesian inference of the parameters of a state-of-the-art mathematical model for methane hydrates based on experimental data from a triaxial compression test with gas hydrate-bearing sand is performed in an efficient way by utilizing active subspaces. Active subspaces are used to identify low-dimensional structure in the parameter space which is exploited by generating a cheap regression-based surrogate model and implementing a modified Markov chain Monte Carlo algorithm. Posterior densities having means that match the experimental data are approximated in a computationally efficient way. [ABSTRACT FROM AUTHOR]

In the present paper, we discuss algorithms of generation of weak records. These generation algorithms are based on two different methods. In the case, when the inverse function for the underlying distribution function can be obtained explicitly, the corresponding generation algorithms are built on the inverse-transform method. In the case, when the inverse function cannot be obtained explicitly, the algorithms are based on the rejection method. Generation algorithms of our paper are supplied with illustrative examples. [ABSTRACT FROM AUTHOR]

Stochastic simulations applied to black-box computer experiments are becoming more widely used to evaluate the reliability of systems. Yet, the reliability evaluation or computer experiments involving many replications of simulations can take significant computational resources as simulators become more realistic. To speed up, importance sampling coupled with near-optimal sampling allocation for these experiments is recently proposed to efficiently estimate the probability associated with the stochastic system output. In this study, we establish the central limit theorem for the probability estimator from such procedure and construct an asymptotically valid confidence interval to quantify estimation uncertainty. We apply the proposed approach to a numerical example and present a case study for evaluating the structural reliability of a wind turbine. [ABSTRACT FROM AUTHOR]

This article presents the development of an autonomous and distributed movement coordination algorithm for Unmanned Aerial Vehicles (UAVs) swarms used in communication relay networks and in exploratory area surveillance missions. This work studies the performance of a hybrid algorithm combining pheromone maps, market auction paradigms and proactive link maintenance mechanisms to create a self-organizing flying network capable of providing network support for the UAV nodes already engaged in exploration and targeting tasks in the surveillance missions. In order to validate the proposal, simulations were performed assessing the desired performance aspects related to the target allocation and network connectivity. The acquired results provide evidence that the proposed solution is able to maintain the balance between the performance goals. [ABSTRACT FROM AUTHOR]

In this note the issue of fluid flow computation in a Discrete Fracture-Matrix (DFM) model is addressed. In such a model, a network of percolative fractures delimits porous matrix blocks. Two frameworks are proposed for the coupling between the two media. First, a FEM-BEM technique is considered, in which finite elements on non-conforming grids are used on the fractures, whereas a boundary element method is used on the blocks; the coupling is pursued by a PDE-constrained optimization formulation of the problem. Second, a VEM-VEM technique is considered, in which a 2D and a 3D virtual element method are used on the fractures and on the blocks, respectively, taking advantage of the flexibility of VEM in using arbitrary meshes in order to ease the meshing process and the consequent enforcement of the matching conditions on fractures and blocks. [ABSTRACT FROM AUTHOR]

In this article, we consider a linear regression model with AR(p) error terms with the assumption that the error terms have a t distribution as a heavy-tailed alternative to the normal distribution.We obtain the estimators for the model parameters by using the conditional maximum likelihood (CML) method. We conduct an iteratively reweighting algorithm (IRA) to find the estimates for the parameters of interest. We provide a simulation study and three real data examples to illustrate the performance of the proposed robust estimators based on t distribution. [ABSTRACT FROM AUTHOR]

Computer simulation for pedestrian dynamics is at the same time an application area in which research has completed its cycle and an active and lively research context in which contributions from different disciplines still produce advancements on the state of the art. The study of effects of the presence of groups in the simulated population is object of growing interest in the community. While previous results have started to investigate the phenomenon, implying conflicting tendencies for pedestrians, mechanisms for the flexible management of cohesion among group members are still investigated both in continuous and discrete modelling efforts. The present effort is aimed at extending previous modelling efforts, preserving and improving the capability to generate overall plausible aggregated dynamics, while at the same time improving the precision in the microscopic group dynamics, with particular attention to the shape of dyads. The paper presents validated results based on empirical data from a controlled experiment, also discussing qualitative results in different common situations (bends and bottlenecks). [ABSTRACT FROM AUTHOR]

This paper deals with the numerical solution of Volterra-Fredholm integral equations. In this work, we approximate the unknown functions based on the shifted orthonormal Bernstein polynomials, in conjunction with the least-squares approximation method. The method is using a simple computational manner to obtain a quite acceptable approximate solution. The merits of this method lie in the fact that, on the one hand, the problem will be reduced to a system of algebraic equations. On the other hand, the efficiency and accuracy of the method for solving these equations are high. The convergence analysis of proposed method have been discussed through some theorems. Moreover, we will obtain an estimation of error bound for this algorithm. Finally, some examples are given to show the capability of presented method in comparison with four well-known algorithms in the literature namely the Legendre collocation method, Taylor collocation method, Taylor polynomial method and Lagrange collocation method. [ABSTRACT FROM AUTHOR]

Online advertising has become an important marketing instrument for many enterprises, and the impact of enterprises’ online advertising has been increasing rapidly. Significant long-term enterprise profits are dynamically determined by the continuous online advertisement investment strategies implemented. This paper formulates the investment cost function which reflects the characteristics and impacts of online advertising spillover effect to enterprises. Then the improved Lanchester model is used based on the investment cost function. According to the model, our research calculates the Nash equilibrium and does the numerical analysis under open-loop strategy and closed-loop strategy. (1) With the change of time and under the condition of open-loop and closed-loop, when the spillover effect level is higher, the investment amount on fixed-position online advertisement by the enterprises becomes opposite. (2) When the level of spillover effect is strong, the change in the market share of competitive enterprises is related to the initial market share under the condition of open-loop. Under the condition of closed-loop, the market share does not change in accordance with the level of spillover effect. (3) No matter under the open-loop strategy or the closed-loop strategy, the competitive enterprises with lower initial market share should increase the investment of the online advertising in order to attract new customers as early as possible. [ABSTRACT FROM AUTHOR]

Modern partial differential equation (PDE) models across scientific disciplines require sophisticated numerical methods resulting in complex codes as well as large numbers of simulations for analysis like parameter studies and uncertainty quantification. To evaluate the behavior of the model for sufficiently long times, for instance, to compare to laboratory time scales, often requires long-time simulations with small time steps and high mesh resolutions. This motivates the need for very efficient numerical methods and the use of parallel computing on the most recent modern architectures. We use complex code resulting from a PDE model of calcium dynamics in a heart cell to analyze the performance of the recently released Intel Xeon Phi Knights Landing (KNL). The KNL is a second-generation many-integrated-core (MIC) processor released in 2016 with a theoretical peak performance of over 3 TFLOP/s of double-precision floating-point operations for which complex codes can be easily ported because of the x86 compatibility of each KNL core. We demonstrate the benefit of hybrid MPI+OpenMP code when implemented effectively and run efficiently on the KNL including on multiple KNL nodes. For multi-KNL runs for our sample code, it is shown to be optimal to use all cores of each KNL, one MPI process on every other tile, and only two of the maximum of four threads per core. [ABSTRACT FROM AUTHOR]

Cloud computing brings new technologies and concepts that support communication services and data storage. Services like OneDrive, Google Drive and DropBox increase data availability and provide new features as synchronization and collaboration. These services require high availability and performance characteristics like high throughput and low probability that a timeout occurs, since it is fundamental to guarantee both business continuity and uninterrupted public services. In this research, we aim at evaluating availability and performance-related metrics for private cloud storage services. A hierarchical model-based strategy is proposed to evaluate distinct metrics by means of the composition of continuous-time Markov chains, reliability block diagrams and stochastic Petri nets. A case study is presented to illustrate the applicability of the proposed models through a cloud storage service hosted in the Eucalyptus platform. We also adopt availability importance index to identify the most critical components in relation to the system availability. Our numerical analyses indicate that, for instance, the adoption of redundant components reduces the probability that timeouts occur and the probability that users are attended due to failures. Furthermore, the results obtained from the stochastic models show that the proposed approach is indeed a good approximation to the measures obtained from the experiments conducted in a real cloud environment. [ABSTRACT FROM AUTHOR]

In this paper, we present a new hybrid binary version of dragony and enhanced particle swarm optimization algorithm in order to solve feature selection problems. The proposed algorithm is called Hybrid Binary Dragonfly Enhanced Particle Swarm Optimization Algorithm(HBDESPO). In the proposed HBDESPO algorithm, we combine the dragony algorithm with its ability to encourage diverse solutions with its formation of static swarms and the enhanced version of the particle swarm optimization exploiting the data with its ability to converge to the best global solution in the search space. In order to investigate the general performance of the proposed HBDESPO algorithm, the proposed algorithm is compared with the original optimizers and other optimizers that have been used for feature selection in the past. Further, we use a set of assessment indicators to evaluate and compare the different optimizers over 20 standard data sets obtained from the UCI repository. Results prove the ability of the proposed HBDESPO algorithm to search the feature space for optimal feature combinations. [ABSTRACT FROM AUTHOR]

This work approximates the unknown functions based on the two-dimensional shifted Legendre polynomials operational matrix method (2D-SLPOM) for the numerical solution of two-dimensional fractional integral equations. The present method reduces these equations to a system of algebraic equations and then this system will be solved numerically by Newton’s method. Moreover, an estimation of the error bound for this algorithm will be shown by preparing some theorems. Some examples are presented to demonstrate the validity and applicability of the proposed method with respect to the two-dimensional block pulse functions method (2D-BPFs) and two-dimensional Bernstein polynomials operational matrix method (2D-BPOM). [ABSTRACT FROM AUTHOR]

We consider the general problem of estimating probabilities which arise as a union of dependent events. We propose a flexible series of estimators for such probabilities, and describe variance reduction schemes applied to the proposed estimators. We derive efficiency results of the estimators in rare-event settings, in particular those associated with extremes. Finally, we examine the performance of our estimators in numerical examples. [ABSTRACT FROM AUTHOR]

We develop a Bayesian variable selection method for logistic regression models that can simultaneously accommodate qualitative covariates and interaction terms under various heredity constraints. We use expectation-maximization variable selection (EMVS) with a deterministic annealing variant as the platform for our method, due to its proven flexibility and efficiency. We propose a variance adjustment of the priors for the coefficients of qualitative covariates, which controls false-positive rates, and a flexible parameterization for interaction terms, which accommodates user-specified heredity constraints. This method can handle all pairwise interaction terms as well as a subset of specific interactions. Using simulation, we show that this method selects associated covariates better than the grouped LASSO and the LASSO with heredity constraints in various exploratory research scenarios encountered in epidemiological studies. We apply our method to identify genetic and non-genetic risk factors associated with smoking experimentation in a cohort of Mexican-heritage adolescents. [ABSTRACT FROM AUTHOR]

The aim of this paper is to produce a new hybrid algorithm to solve minimax problems by combining the bat algorithm with direct search methods. The proposed algorithm is called hybrid bat direct search algorithm (HBATDS). In HBATDS, the global exploration and the local exploitation process are balanced. The bat algorithm has a good ability to make exploration and exploitation search. The exploitation capability of the proposed algorithm is increased by invoking the pattern search method as a local search method instead of the random walk method in the standard bat algorithm. In the final stage of the algorithm, the Nelder-Mead method is applied in order to refine the best found solution from the bat and pattern search method instead of running the algorithm more iterations without any improvements in the fitness function value. The performance of the HBATDS algorithm is investigated by applying it on 10 minimax problems and comparing it against 8 benchmark algorithms. The experimental results indicate that HBATDS is a promising algorithm and outperforms the other algorithms in most cases. [ABSTRACT FROM AUTHOR]

The cloud data center is accommodated with many servers for cloud-based services which cause more consumption of energy and menace cost factors in computing tasks. Many existing scheduling techniques hinge on allocating task where scheduling algorithm is not based on assigning tasks through urgent and non-urgent task scheduling using dynamic voltage frequency scaling (DVFS) controller. In demand to reduce energy consumption and to maintain the quality of services, this paper proposes non-urgent and urgent task scheduling (NUTS) algorithm using DVFS, to restraint and scheduling of task in the more efficient way for minimizing the power consumption of the IT equipment. To increase the energy efficiency, we proposed scheduling queue and non-completed task queue for scheduling urgent, non-urgent and non-completed tasks to ally utilization of resources efficiently and to decrease the consumption of energy in the data center. In this paper, we compared proposed algorithm with two existing standard scheduling algorithms. The experimental results boast that NUTS algorithm performs better than the existing algorithms and can centrist energy efficiency in cloud data center. [ABSTRACT FROM AUTHOR]

Radial basis function-generated finite difference method (RBF-FD) has been a popular method for simulating the derivatives of a function and has been successfully applied for the partial differential equations (PDEs). In this paper we introduce an effective h -adaptive RBF-FD method to the convection-diffusion equation in high-dimension space including two dimensions and three dimensions. The derivative of the solution is represented on overlapping a new influence domain through RBF-FD by using Thin Plane Spline Radial Basis Functions (TPS) augmented with additional polynomial functions. The number of the nodes added in the domain by the h -adaptive RBF-FD method is triggered by an error indicator, which very simply depends on the local residual norm. Several numerical examples are given to demonstrate the validity of h -adaptive RBF-FD method for the convection-diffusion equation in high-dimension space. [ABSTRACT FROM AUTHOR]

The frequentist forward search yields a flexible and informative form of robust regression. The device of fictitious observations provides a natural way to include prior information in the search. However, this extension is not straightforward, requiring weighted regression. Bayesian versions of forward plots are used to exhibit the presence of multiple outliers in a data set from banking with 1903 observations and nine explanatory variables which shows, in this case, the clear advantages from including prior information in the forward search. Use of observation weights from frequentist robust regression is shown to provide a simple general method for robust Bayesian regression. [ABSTRACT FROM AUTHOR]

In this article, we propose mixtures of skew Laplace normal (SLN) distributions to model both skewness and heavy-tailedness in the neous data set as an alternative to mixtures of skew Student-t-normal (STN) distributions. We give the expectation–maximization (EM) algorithm to obtain the maximum likelihood (ML) estimators for the parameters of interest. We also analyze the mixture regression model based on the SLN distribution and provide the ML estimators of the parameters using the EM algorithm. The performance of the proposed mixture model is illustrated by a simulation study and two real data examples. [ABSTRACT FROM PUBLISHER]

The present work deals with the simulation of the flow in Discrete Fracture Networks (DFN), using the mixed formulation of the Virtual Element Method (VEM) on polygonal conforming meshes. The flexibility of the VEM in handling polygonal meshes is used to easily generate a conforming mesh even in the case of intricate DFNs. Mixed Virtual Elements of arbitrary polynomial accuracy are then used for the discretization of the velocity field. The well posedness of the resulting discrete problem is shown. Numerical results on simple problems are proposed to show convergence properties of the method with respect to known analytic solutions, whereas some tests on fairly complex networks are also reported showing its applicability and effectiveness. [ABSTRACT FROM AUTHOR]

Imatinib is a highly effective treatment for chronic myeloid leukemia, a common type of leukemia. Treatment efficacy of imatinib has been further improved by combination therapy with exogenic cytokine interferon- $$\alpha $$ . However, the prolonged administration of drug and immunotherapy exacts a significant cost to the patient's quality of life, due to the treatments side effects. We present a mathematical model for the scheduling of combined treatment with imatinib and interferon- $$\alpha $$ by finite-dimensional optimal control problems. The explicit formulas for the optimal controls minimizing the integral quality criterion are obtained, and the corresponding leukemia treatment process is then described by numerical simulation. The attained optimization of treatment holds clinical potential for improving patient's quality of life, as well as overall prognosis. [ABSTRACT FROM AUTHOR]

Cyclone separators are widely used in a variety of industrial applications. A low-mass loading gas cyclone is characterized by two performance parameters, namely the Euler and Stokes numbers. These parameters are highly sensitive to the geometrical design parameters defining the cyclone. Optimizing the cyclone geometry therefore is a complex problem. Testing a large number of cyclone geometries is impractical due to time constraints. Experimental data and even computational fluid dynamics simulations are time-consuming to perform, with a single simulation or experiment taking several weeks. Simpler analytical models are therefore often used to expedite the design process. However, this comes at the cost of model accuracy. Existing techniques used for cyclone shape optimization in literature do not take multiple fidelities into account. This work combines cheap-to-evaluate well-known mathematical models of cyclones, available data from computational fluid dynamics simulations and experimental data to build a triple-fidelity recursive co-Kriging model. This model can be used as a surrogate with a multi-objective optimization algorithm to identify a Pareto set of a finite number of solutions. The proposed scheme is applied to optimize the cyclone geometry, parametrized by seven design variables. [ABSTRACT FROM AUTHOR]

In this study, for two-dimensional Maxwell's equations, an efficient preconditioned generalized minimum residual method on the graphics processing unit (GPUPGMRES) is proposed to obtain numerical solutions of the equations that are discretized by a multisymplectic Preissmann scheme. In our proposed GPUPGMRES, a novel sparse matrix–vector multiplication (SpMV) kernel is suggested while keeping the compressed sparse row (CSR) intact. The proposed kernel dynamically assigns different number of rows to each thread block, and accesses the CSR arrays in a fully coalesced manner. This greatly alleviates the bottleneck of many existing CSR-based algorithms. Furthermore, the vector-operation and inner-product decision trees are automatically constructed. These kernels and their corresponding optimized compute unified device architecture parameter values can be automatically selected from the decision trees for vectors of any size. In addition, using the sparse approximate inverse technique, the preconditioner equation solving falls within the scope of SpMV. Numerical results show that our proposed kernels have high parallelism. GPUPGMRES outperforms a recently proposed preconditioned GMRES method, and a preconditioned GMRES implementation in the AmgX library. Moreover, GPUPGMRES is efficient in solving the two-dimensional Maxwell's equations. [ABSTRACT FROM AUTHOR]

Pore-scale modeling and simulation of reactive flow in porous media has a range of diverse applications, and poses a number of research challenges. It is known that the morphology of a porous medium has significant influence on the local flow rate, which can have a substantial impact on the rate of chemical reactions. While there are a large number of papers and software tools dedicated to simulating either fluid flow in 3D computerized tomography (CT) images or reactive flow using porenetwork models, little attention to date has been focused on the pore-scale simulation of sorptive transport in 3D CT images, which is the specific focus of this paper. Here we first present an algorithm for the simulation of such reactive flows directly on images, which is implemented in a sophisticated software package. We then use this software to present numerical results in two resolved geometries, illustrating the importance of pore-scale simulation and the flexibility of our software package. [ABSTRACT FROM PUBLISHER]

The main aim of this paper is to perform sensitivity analysis to the specification of prior distributions in a Bayesian analysis setting of STAR models. To achieve this aim, the joint posterior distribution of model order, coefficient, and implicit parameters in the logistic STAR model is first being presented. The conditional posterior distributions are then shown, followed by the design of a posterior simulator using a combination of Metropolis-Hastings, Gibbs Sampler, RJMCMC, and Multiple Try Metropolis algorithms, respectively. Following this, simulation studies and a case study on the prior sensitivity for the implicit parameters are being detailed at the end. [ABSTRACT FROM AUTHOR]