International Journal of Modeling, Simulation, and Scientific Computing

It is significantly important to have the knowledge of electromagnet situation of battlefield, because it contains a lot of useful information to understand the situation of the whole battlefield. Electromagnet situation visualization would be very useful to help people to understand the situation, and handle battlefield situation, such as the size and quality of the electromagnetic radiation source, target objects, and operational purposes by the two parties engaged in combat scale. This paper proposes two electromagnet situation visualization methods, one is an isoline tracking algorithm based on floating rectangle and the other is an isoline filling algorithm based on linear interpolation, which also solves the problem of low accuracy and low efficiency. This paper also implements and displays the visualization results using different ways of demonstration modules, which show that the visualization results are more intuitive.

The aim of this paper is to obtain the numerical solution of singular ordinary differential equations using the Haar-wavelet approach. The proposed method is mathematically simple and provides highly accurate solutions. In this method, we derive the Haar operational matrix using Haar function. Haar operational matrix is a basic tool and applied in system analysis to evaluate the numerical solution of differential equations. The convergence of the proposed method is discussed through its error analysis. To illustrate the efficiency of this method, solutions of four singular differential equations are obtained.

This study presents a novel fractional order grey model FGM ($\alpha$,1) obtained by extending the grey model (GM (1,1)). For this, we generalize the whitenization first-order differential equation to fractional order by using the Caputo fractional derivative of order $\alpha$. A real-world case study, scrutinize the economic growth influence on environmental degradation in Turkey, is performed to evaluate the significance of the projected model FGM ($\alpha$,1) in contrast to the current classical GM. We apply autoregressive distributed lags bounds testing co-integration approach to empirically examine the long-run and short-run relation among economic growth, agriculture, forestry and fishing (AFF), electricity utilization and CO₂ emissions. Using the new fractional order model, all the variables are forecasted in the forthcoming years until 2030. Findings disclose that electricity utilization and economic growth (GDP) accelerate emission of CO₂ though in the long run agriculture, forestry, and fishing reduce the environmental pollution in Turkey.

Cloud computing brings incipient transmutations in different fields of life and consists of different characteristics and virtualization is one of them. Virtual machine (VM) is one of the main elements of virtualization. VM is a process in which physical server changes into the virtual machine and works as a physical server. When a user sends data or request for data in cloud data center, a situation can occur that may cause the virtual machines to underload data or overload data. The aforementioned situation can lead to failure of the system or delay the user task. Therefore, appropriate load balancing techniques are required to surmount the above two mentioned problems. Load balancing is a technique utilized in cloud computing for management of the resource by a condition such that a maximum throughput is achieved with slightest reaction time and additionally dividing the traffic between different servers or VM so that it can get data without any delay. For the amelioration of load balancing technique in this study, a novel technique is used which is coalescence of BAT and ABC algorithms both of which are nature-inspired algorithms. When the ABC algorithm local search section changes with BAT algorithm local search section, a second modification takes place in the fitness function of BAT algorithm. The proposed technique is known as HBATAABC algorithm. The novel technique implemented by utilizing transfer strategy policy in VM improves the performance of data allocation system of VM in the cloud data center. To check the performance of the proposed algorithm, three main parameters are used which are network average time, network stability and throughput. The performance of the proposed novel technique is verified and tested with the help of cloudsim simulator. The result shows that the suggested modified algorithm increases performance by 1.30% of network average time, network stability and throughput as compared with BAT algorithm, ABC algorithm and RRA algorithm. Nevertheless, the proposed algorithm is more precise and expeditious as compared with the three models.

The problem of estimating quantization error in 2D images is an inherent problem in computer vision. The outcome of this problem is directly related to the error in reconstructed 3D position coordinates of an object. Thus estimation of quantization error has its own importance in stereo vision. Although the quantization error cannot be controlled fully, still statistical error analysis helps us to measure the performance of stereo systems that relies on the imaging parameters. Generally, it is assumed that the quantization error in 2D images is distributed uniformly that need not to be true from a practical aspect. In this paper, we have incorporated noise distributions (Triangular and Trapezoidal) for the stochastic error analysis of the quantization error in stereo imaging systems. For the validation of the theoretical analysis, the detailed simulation study is carried out by considering different cases.

When network users are intensively interacting during rush hour, avoiding data loss and latency is a concern in guaranteeing segment reliability. Implementing a leaky bucket could be needed to achieve flows effective monitoring by generating network of queues at given output link. Packets associated with different sessions and originated from different hosts may be mixed up and queuing delay may become longer, depending on network segment state, buffering strategy, users’ behavior among other factors. It is interesting to assimilate these stages of packets traveling through network segment to the concept of epidemic control. This paper proposes a SIR (Susceptible–Infected–Recovered) approach in modeling data packets transmission at a leaky bucket at peak hour. We focused our analysis on packets buffering and recovery strategy impact on segment forwarding performance in heavy load situation. Numerical results suggested adapting buffering strategy and packets recovery to enhance transmission and network overall performance.

In this paper, we study several asymptotic behaviors of the estimators of convex and coherent entropic risk measures. First, the moderate deviation principles of the estimators are given. Second, the central limit theorems of the estimators are given. Finally, several simulation results are given to support our main conclusions.

This study aims to apply the two-step Adomian decomposition method (TSADM) to find an analytical solution of integro-differential equations for fractional order without discretization/approximation with less number of iterations and reduce the computational efforts. Moreover, we have established the results for the existence and uniqueness of a solution with the help of some fixed point theorems and the Banach contraction principle. Furthermore, the method is demonstrated on different test examples arising in real life situations. It is concluded that the TSADM provides exact solution of the fractional integro-differential equations in one iteration. At the same time, the other existing methods furnish an approximate solution and require lots of computation to solve the problem applying discretization/approximation on fractional operators.

An artificial neural networks modeling, based on spin dynamics, has been proposed to predict the 2D ferromagnetic crystals. The purpose is to establish an artificial neural network model (ANNM), which is able to predict the ordered magnetic surfaces. In crystal surfaces, Wood’s notation is used to describe different configurations. From the magnons dispersion curves and group velocity data of 2D spins crystallographic waveguide, we deduce the data used as an up input–output set to feed the ANNM. The input data are being given by the magnons modes over high symmetry points, or, by the group velocity of the magnon modes. However, the basis vectors of matrix notation and the rotation angle that aligns the unit cell of the reconstructed surface are giving the output data.

The proposed neurocomputing model was capable of predicting the 2D crystals for the possible arrangements that the surface might take. In addition, the magnon excitations data were a useful approach for building a neural model which was able to predict and classify the magnetic surfaces of the crystals.

A mathematical model describing the epidemic interactions between humans and blackflies in the transmission of onchocerciasis is considered. In this model, the onchocerciasis infected human individuals are divided into two classes of infected humans with high and low microfilarial output incorporating saturated treatment function, which caters for high saturation of onchocerciasis disease. We analyze the model feasible region and obtain the basic reproduction number $(R_{hb})$ using the next generation matrix method. Also, we obtain the onchocerciasis-free and onchocerciasis endemic equilibrium solutions and show that if $R_{hb}$ is less than unity, the onchocerciasis-free equilibrium is locally and globally asymptotically stable. Furthermore, we employed a Lyapunov function to analyze the global asymptotic stability of the onchocerciasis — endemic equilibrium whenever $R_{hb}$ is greater than unity. In addition, data on mass drug distribution of ivermectin drug to combat onchocerciasis prevalence in Ekiti state of Nigeria were fitted to the model. Graphical results reveal that consistent annual or bi-annual distribution of ivermectin drug in the region is effective in reducing the disease menace. Further simulations also show that public health control measures are needed to minimize infectious contact between humans and blackflies which leads to onchocerciasis infections and visual blindness.

Cloud computing is attracting an increasing number of simulation applications running in the virtualized cloud data center. These applications are submitted to the cloud in the form of simulation jobs. Meanwhile, the management and scheduling of simulation jobs are playing an essential role to offer efficient and high productivity computational service. In this paper, we design a management and scheduling service framework for simulation jobs in two-tier virtualization-based private cloud data center, named simulation execution as a service (SimEaaS). It aims at releasing users from complex simulation running settings, while guaranteeing the QoS requirements adaptively. Furthermore, a novel job scheduling algorithm named adaptive deadline-aware job size adjustment (ADaSA) algorithm is designed to realize high job responsiveness under QoS requirement for SimEaaS. ADaSA tries to make full use of the idle fragmentation resources by tuning the number of requested processes of submitted jobs in the queue adaptively, while guaranteeing that jobs’ deadline requirements are not violated. Extensive experiments with trace-driven simulation are conducted to evaluate the performance of our ADaSA. The results show that ADaSA outperforms both cloud-based job scheduling algorithm KCEASY and traditional EASY in terms of response time (up to 90%) and bounded slow down (up to 95%), while obtains approximately equivalent deadline-missed rate. ADaSA also outperforms two representative moldable scheduling algorithms in terms of deadline-missed rate (up to 60%).

The Reynolds number of molten metal flowing mold cavity causes bulk turbulence and is the main cause of defects like shrinkage porosity and sand erosion. Machined housings with shrinkage porosity at critical bearing bores and surface made the casting useless. In old gating casting areas of perimeters 290mm and 264mm of transmission housing, Reynolds numbers were observed as 16307 and 13806, respectively using simulation software. Data were collected from experiments to change casting area perimeters from 785mm and 785mm along with the addition of overlap area. New Reynolds numbers at two locations were observed as 3705 and 3393, respectively. Molten metal pressure, velocity and temperature results were related for final shrinkage results of the components on full production. The purpose of the study is to reduce shrinkage and porosity defects in green sand casting part using MAGMAS simulation software. High outcome was the reduction of casting machining rejection in transmission housing casting from 5.8% 0.7% with savings of approximately 0.13 million USD over the period of 14 months. Implications of this work include casting defects study and reduction in different grades and weight range.

This paper gives an interpretation of the quasispecies concept for an experiment of growing the Lymantria dispar artificial population. The changes estimate in the population parameters for a significant change in environmental conditions at the stage of hatching caterpillars from the eggs was given. Three numerical and two random parameters were used for quantitative analysis. They characterize the development of individuals of the population at this stage. The parameter changes significance was verified with using statistical tests. The results obtained do not contradict the quasispecies model. The use of fuzzy sets describes the transition from one quasispecies to another at a significant change in environmental conditions.