International Journal of Modeling, Simulation, and Scientific Computing

Due to the successful applications in engineering, physics, biology, finance, etc., there has been substantial interest in fractional diffusion equations over the past few decades, and literatures on developing and analyzing efficient and accurate numerical methods for reliably simulating such equations are vast and fast growing. This paper gives a concise overview on finite element methods for these equations, which are divided into time fractional, space fractional and time-space fractional diffusion equations. Besides, we also involve some relevant topics on the regularity theory, the well-posedness, and the fast algorithm.

In this paper, a multi-product newsvendor problem is formulated as a random nonlinear integrated optimization model by taking into consideration the selling price, the producing and outsourcing quantities, and the nonlinear budget constraint. Different from the existing models, the demands of products depend on the prices, as well as being time-varying due to random market fluctuation. In addition, outsourcing strategy is adopted to deal with possible shortage caused by the limited capacity. Consequently, the constructed model is involved with joint optimization of the producing and outsourcing quantities, and the selling prices of all the products. For this model with continuous random demands, we first transform it into a nonlinear programming problem by expectation method. Then, an efficient algorithm, called the feasible-direction-based spectral conjugate gradient algorithm, is developed to find a robust solution of the model. By case study and sensitivity analysis, some interesting conclusions are drawn as follows: (a) Budget is a critical constraint for optimizing the decision-making of the retailer, and there exist different threshold values of the budget for the substitute and complementarity scenarios. (b) The price sensitivity matrix seriously affects the maximal expected profit mainly through affecting the optimal outsourcing quantity.

To decode the pilot’s behavioral awareness, an experiment is designed to use an aircraft simulator obtaining the pilot’s physiological behavior data. Existing pilot behavior studies such as behavior modeling methods based on domain experts and behavior modeling methods based on knowledge discovery do not proceed from the characteristics of the pilots themselves. The experiment starts directly from the multimodal physiological characteristics to explore pilots’ behavior. Electroencephalography, electrocardiogram, and eye movement were recorded simultaneously. Extracted multimodal features of ground missions, air missions, and cruise mission were trained to generate support vector machine behavior model based on supervised learning. The results showed that different behaviors affects different multiple rhythm features, which are power spectra of the $𝜃$ waves of EEG, standard deviation of normal to normal, root mean square of standard deviation and average gaze duration. The different physiological characteristics of the pilots could also be distinguished using an SVM model. Therefore, the multimodal physiological data can contribute to future research on the behavior activities of pilots. The result can be used to design and improve pilot training programs and automation interfaces.

The population-based efficient iterative evolutionary algorithm (EA) is differential evolution (DE). It has fewer control parameters but is useful when dealing with complex problems of optimization in the real world. A great deal of progress has already been made and implemented in various fields of engineering and science. Nevertheless, DE is prone to the setting of control parameters in its performance evaluation. Therefore, the appropriate adjustment of the time-consuming control parameters is necessary to achieve optimal DE efficiency. This research proposes a new version of the DE algorithm control parameters and mutation operator. For the justifiability of the suggested method, several benchmark functions are taken from the literature. The test results are contrasted with other literary algorithms.

The standard finite difference scheme (forward difference approximation for time derivative and central difference approximations for spatial derivatives) for fourth-order nonlinear diffusion filter allows very small time-step size to obtain stable results. The alternating directional implicit (ADI) splitting scheme such as Douglas method is highly stable but compromises accuracy for a relatively larger time-step size. In this paper, we develop $3\times 3$ stencils for the approximation of second-order spatial derivatives based on the finite pointset method. We then make use of these stencils for approximating the fourth-order partial differential equation. We show that the proposed scheme allows relatively bigger time-step size than the standard finite difference scheme, without compromising on the quality of the filtered image. Further, we demonstrate through numerical simulations that the proposed scheme is more efficient, in obtaining quality filtered image, than an ADI splitting scheme.

JShellLab is an easy to use MATLAB-like environment for the Java Virtual Machine (JVM). It implements scientific scripting based on the JShell Application Programming Interface (API) of modern Java.

The paper illustrates that JShellLab can significantly facilitate and simplify the development of complex computational demanding scientific software at the JVM. The novelty at the JShellLab is that it completely hides the complexity and the intricate dependencies of optimized scientific software. As an example, the demanded field of deep learning is exploited. Specifically, the implementation of effective practical deep learning-based systems using the JShellLab environment and the Deeplearning4j Java library is considered.

The technical advancement in information systems contributes towards the massive availability of the documents stored in the electronic databases such as e-mails, internet and web pages. Therefore, it becomes a complex task for arranging and browsing the required document. This paper proposes an approach for incremental clustering using the Bat-Grey Wolf Optimizer (BAGWO). The input documents are initially subjected to the pre-processing module to obtain useful keywords, and then the feature extraction is performed based on wordnet features. After feature extraction, feature selection is carried out using entropy function. Subsequently, the clustering is done using the proposed BAGWO algorithm. The BAGWO algorithm is designed by integrating the Bat Algorithm (BA) and Grey Wolf Optimizer (GWO) for generating the different clusters of text documents. Hence, the clustering is determined using the BAGWO algorithm, yielding the group of clusters. On the other side, upon the arrival of a new document, the same steps of pre-processing and feature extraction are performed. Based on the features of the test document, the mapping is done between the features of the test document, and the clusters obtained by the proposed BAGWO approach. The mapping is performed using the kernel-based deep point distance and once the mapping terminated, the representatives are updated based on the fuzzy-based representative update. The performance of the developed BAGWO outperformed the existing techniques in terms of clustering accuracy, Jaccard coefficient, and rand coefficient with maximal values 0.948, 0.968, and 0.969, respectively.

Influenza A-virus infection represents a global threat causing seasonal outbreaks and pandemics. Among the global threats creating seasonal outbreak, Influenza “A-virus” infection is a dominating theme nowadays. Using the theory of fractional calculus, this study considers an influenza model for quantitative study via the Laplace Adomian Decomposition Method (LADM). We proceed to explore the role of LADM on the proposed model. The method is described and explained for the proposed model while providing some plots to present the behavior of the disease.

The performance of task scheduling algorithm in cloud computing determines the performance of the cloud system. This study mainly analyzed the application of the artificial bee colony (ABC) algorithm in the cloud task scheduling. In order to solve the problem of cloud task scheduling, the ABC algorithm was discretized to get the discrete artificial bee colony (DABC) algorithm. Then the mathematical model of cloud task scheduling was established and solved by the DABC algorithm. Finally, the simulation experiment was carried out, and the performance of first-come-first-served (FCFS), MIN–MIN, ABC and DABC algorithms under different cloud tasks was compared to verify the performance of the proposed algorithm. The results showed that the user waiting time of the DABC algorithm was 1210s, the load balance degree was 0.01, and the user payment fee was 1688 yuan when the number of cloud tasks was 500; compared with other algorithms, the user waiting time of the DABC algorithm was shorter, the resource load balance degree was higher, and the overall performance was better. The research results verify the effectiveness of the DABC algorithm in solving the problem of cloud task optimal scheduling, and it can be further extended and applied in practice.

This paper presents the mathematical analysis of the dynamical system for avian influenza. The proposed model considers a nonlinear dynamical model of birds and human. The half-saturated incidence rate is used for the transmission of avian influenza infection. Rigorous mathematical results are presented for the proposed models. The local and global dynamics of each model are presented and proven that when ${{ℛ}}_0<1$, then the disease-free equilibrium of each model is stable both locally and globally, and when ${{ℛ}}_0>1$, then the endemic equilibrium is stable both locally and globally. The numerical results obtained for the proposed model shows that influenza could be eliminated from the community if the threshold is not greater than unity.

The ship energy efficiency management is an important topic in the field of the energy management onboard and the exhaust gases emission nowadays. The advanced model plays a vital role to improve the ship energy efficiency management when considering the variable factors. The establishment of the ship energy efficiency model through energy efficiency operational indicator (EEOI) index has been conducted through Monte Carlo simulation method along with using the operation data of a bulk carrier. A bulk carrier is chosen, namely, M/V NSU JUSTICE 250,000 DWT of VINIC Shipping Transportation Company in Vietnam. This research uses the real operational data to perform a statistical methodology which calculates the various factors used to calculate EEOI. This method is supported by Matlab program through the curve fitting tool. The normal distribution estimation and the kernel density estimation method are used for the parametric curve fitting and non-parametric curve fitting, respectively. The average weather condition (wind speed and wave height) and the fouling condition of hull have been investigated and compared with the research results. The validation of the proposed methods has been conducted through the study of the external factors influencing the research results. The research result shows the optimal operational data for the fuel consumption at each certain voyage. This paper is useful for the ship-owners and the ship-operators in the field of the ship energy efficiency management.

An application of differential inclusions in the epidemic spread models is presented. Some mostly used epidemic models are discussed here, and a brief survey of epidemic modeling is given. Most of the models are some modifications of the Susceptible–Infected–Recovered model. Simple simulations are carried out. Then, we consider the influence of some uncertain parameters. It is pointed out that the presence of some fluctuating model parameters can be treated by differential inclusions. The solution to such differential inclusion is given in the form of reachable sets for model variables. Here, we focus on the differential inclusion application rather than the model construction.