Journal of Modeling and Simulation
Volume:52 Issue: 2, Summer-Autumn 2020

The burst wave lithotripsy is a cutting-edge non-invasive treatment for kidney stones. Due to their paramount importance, efforts for elevating the effectiveness of non-invasive treatment have been always amongst researchers’ top priorities. The purpose of the current study is numerically investigating the details of exerted stress and the effects of some parameters such as pressure amplitude, frequency and stone’s material for burst wave lithotripsy, and feasibility study of the synthesis of shock and burst waves. In addition, thermal side effects are investigated on surrounding tissues for both methods. The COMSOL Multiphysics based on finite element method is utilized to couple and solve the governing non-linear equations of acoustic wave propagation, the elasticity of structure and biological heat transfer. The results for BWL show that increases in pressure amplitude do not change the distribution of von Mises stress. In addition, increasing frequency leads to a reduction of the focal region, which reduced damages to adjacent tissues. The result for the synthesis of the shock wave and burst wave shows that due to the return of the shock wave from the stone, a wall of compressive wave is created in the front of the stone, and it prevents the burst wave to reach the stone. Therefore, the location of the maximum pressure changes and side effects on the kidney tissue increases.

Association between neural oscillation and personality has been a topic of enhancing interest of neuroscience and psychology. It is believed that human personality reflects in the brain’s electrical neural oscillation. However, it’s exact relations and mechanisms are not fully understood. In the present work, we analyze electroencephalograms (EEG) signals recorded from 40 healthy subjects during the performance of electronic personality traits tasks, as measured by the NEO-FFI. Subsequently, we examine how these personality traits are related to patterns of different neural oscillations. We found that a personality trait of contrasting conscientiousness is significantly associated with engagement of frontal cortex theta and gamma oscillations. Likewise, extraversion is significantly associated with activity in the temporal and parietal regions. We realized that, the personality traits were significantly and consistently associated with the frequency rhythms in different brain areas. We then investigated the correlations between personality traits and psychophysical health factors. Our data indicated that the level of fatigue and social disturbance among participants are inappropriate in relation to other psychometric properties. We found a significant correlation between neuroticism and fatigue, Depressive reactions and Physical health. This study illustrates an approach to investigating personality traits and how it is related to patterns of brain activity.

In this paper, we investigate the trade-off between reliability and security in a device to device (D2D) network including a pair of D2D, a jammer, and an untrusted relay . The untrusted relay is used as aiding to D2D communications but due to untrusting the relay , data protection from eavesdropping by relay is very important. According to this, two protocols direct transmission (DT) and relay transmission (RT) are considered for transmission in network . In the DT protocol, D2D pair directly and without relay aid and in the RT protocol, D2D pair use from relay for communication. In this paper, first, secrecy outage probability (SOP) and intercept probability and then trade-off between reliability and security presenting the closed-form relations for two protocols are investigated. Simulation results show the reliability of analytic relationships and show that in a steady intercept probability, DT protocol into RT protocol has a higher reliability. Also, the simulation results show that in RT, intercept probability is lower into the DT protocol and consequently security increases in RT into the DT protocol. Also the results confirm that increasing security increases the likelihood of loss of communication, and increasing the likelihood of reliable communication reduces communications security.

Financial markets play an important role in the economy of modern societies. Therefore, many researchers have investigated to forecast these markets using various statistical and soft computing methods. Financial time series are essentially complex, dynamic, nonlinear, noisy, non parametric and chaotic in nature, so they cannot describe by analytical equations with few parameters, because their dynamics are too complex or unknown. In recent years, deep learning methods have attracted lots of attention, due to their exceptional performance compared to other existing approaches in many learning problems. The objective of this paper is long term prediction of price time series in Tehran Stock Exchange. For this purpose, a new architecture of two deep learning methods, Long Short Term Memory (LSTM) and Recurrent Neural Network (RNN), for ten steps ahead simultaneous prediction, are proposed. That is a multi variable structure with multi outputs. By using the output error feedbacks as the internal inputs, the network can learn error dynamics during the training phase. Experimental results show the high capability of our proposed structure for both methods in multi steps ahead stock price forecasting and the superiority of the Long Short Term Memory network compared to Recurrent Neural Network for long term predictions.

IIn this article, a new approach to find the Fourier expansion coefficients was carried out by a recursive algorithm, without computing their correspondent integral. Furthermore, in virtue of this new method, some partial differential equations were solved and compared with their exact solutions. After deriving the recursive relation, some differential equations along with the partial differentiation were solved and also compared with the numerical answers. The Fourier series coefficients were computed more accurately and swiftly with this method as compared to others and then cylinder potential in electrodynamics was calculated by this method. The results show that the algorithm proposed in this paper has achieved more optimal results. As we know, the cube problem, whose potential at its levels is defined as definite, also leads to a three-dimensional Fourier series, which can be used to obtain the potential inside it. Using these calculations, in the future we can further investigate the problems in the areas of ion capture inside the cylinder and the cube. Naturally, due to the use of the Fourier series in solving many physical issues, this technique can be used to improve computations in other sciences such as civil engineering and mechanics in such topics as vibrations and thermodynamics.

The quality of providing health care services is one of the factors used to compare countries’ development, and hospitals account for a major share of this. In this study, the discrete event simulation is employed to measure the performance of a health care center in terms of various Key Performance Indicators (i.e., the number of patients admitted, time spending in the system, waiting time and resource utilization) and to improve capacity management. For this purpose, different parts of a hospital, from the entry of the patients to the exit door including Specialized Clinics, General Clinic, Emergency Department, Cashier Desk, and Pharmacy are considered from a comprehensive perspective. Arena Simulation software is applied as the platform for building the simulation model. Real hospital data is used to validate the proposed model. The collected real data are analyzed using Input analyzer tool available in Arena to fed up the simulation model with the required data, information, and structural and parametric assumptions of the system as inputs. In order to improve hospital performance in its key performance indicators, eight scenarios are proposed in comparison with the current situation. After running the scenarios, the results and impact of the proposed scenarios are discussed and compared with each other.

In this paper, the problem of robust H∞ control for continuous-time affine nonlinear singular systems with norm-bounded time-varying uncertainties is addressed. The problem is solved locally by employing two different approaches. The first approach is an extended version of the guaranteed cost control method, adapted in order to conform to affine nonlinear singular systems. The second approach is an indirect method in which a known auxiliary system is presented and it is shown that any control law which solves the H∞ control problem for this auxiliary system, also solves the robust H∞ control problem for the original unknown system. In both approaches, a modified solution of the H∞ control problem is used which is founded on the nonlinear two-person zero-sum differential games theory. As a result, sufficient conditions for the solvability of the considered robust H∞ control problem are provided in terms of a generalized Hamilton-Jacobi-Isaacs inequality. In order to show the consistency of our results, solving the robust H∞ control problem for linear uncertain singular systems is considered, and, it is shown that the corresponding results in linear domain are the special cases of our results. Finally, a numerical example is given to illustrate the applicability of the presented approaches.

Telecommunication operators need to accurately predict the customer churn for surviving in the Telecom market. There is a huge volume of customer records such as calls, SMSs and the use of Internet. This data contains rich and valuable information about costumer behavior and his/her pattern consumption. Machine learning is a powerful tool for extraction of costumer information that can be useful for churn prediction. Although several researchers have studies some types of machine learning methods, but, there is not any work which assess different methods from various point of views. The aim of this work is to assess the performance of a wide range of machine learning methods for churn prediction in the form of a comparison study. In this paper, various machine learning methods consisting of 7 classifiers, 7 target detectors, 10 feature reduction methods containing 4 feature extraction algorithms and 6 feature selection ones are discussed. The performance of these methods are experimented on three Telecom datasets with 6 evaluation measures. The results show that the random forest and feed-forward neural network beside the genetic algorithm outperform other competitors. The superior methods achieve 97%, 62% and 93% prediction accuracy in BigML, kaggle and Telco customer churn datasets, respectively.

In the human gait modeling, it is common to employ 2D models that consist of a chain of rigid links joined together by frictionless hinge joints. Although Newton’s method is usually used to obtain equations of motion in the previous studies, in this research, the constrained Lagrange’s method was employed for this purpose. This method has some advantages over the previous one, such as the solution process is independent of the coordinate system and there is no necessity to know the ground reaction force beforehand. In this work, optimization was also performed by genetic algorithm so that the moment of each joint was estimated by tracking the kinematic data. Moreover, by solving the inverse dynamics and by applying Lagrange multipliers, the distribution of ground reaction force under both feet in the double support mode was calculated and compared with the experimental data to verify the effectiveness of the proposed method. Finally, as one of the applications of dynamic modeling of the human gait, the optimal value of passive stiffness in the ankle joint was obtained to provide a better design of the orthoses used for patients with motor impairment. The results show compatibility between the simulations and experiments for normalized joint moments as well as reaction forces. The optimal joint stiffness is also in the range reported by available experimental data. In conclusion, the methodology can be used for modelling human movements and can be considered as an optimal approach in designing assistive devices especially passive exsoskeletons.

Thermal therapy is a type of cancer treatment that uses heat to kill cancer cells, but it also may harm healthy tissue. Numerical simulations can help to accurately analyze the thermal damage of the tissue during heat exposure. The target of this study is to investigate the effect of time lags on the thermal response of the biological tissue during laser irradiation to the tumoral tissue. The classical Fourier, single phase lag (SPL) and dual phase lag (DPL) models of bio-heat transfer are implemented and compared. The numerical solution based on the finite volume method (FVM) is applied to solve the bio-heat transfer equations. Beer-Lambert’s law is applied to determine the heat source distribution caused by the laser irradiation. The thermal damage caused by the laser exposure for the three models is discussed. Results show that the DPL model predicts a significantly different thermal damage from the classical Fourier and the SPL models. It is observed that the DPL model predicts the maximum temperature 4.1℃ and 5.7℃ less than the Fourier and the SPL models, respectively. The deviation between the maximum temperatures obtained by the three models can be attributed to the finite speed of thermal wave propagation in the non-Fourier models.

In each RDF knowledge base, several errors must be corrected by correction methods. Correction methods can be divided into three classes for the correction of outliers, inconsistencies, and erroneous relations. RDF knowledge base outliers can be considered as two types of outlier entities and triples. Inconsistent triples are corrected by inconsistency correction methods and there are many erroneous relation correction methods that each of them is used for a special objective. The variety of these errors is so wide so that no correction method could be able to cover them all. Most of the correction methods have been focused only on some of these errors, so a comprehensive study is mandatory to cover all of these elements for different objectives. Nevertheless, a couple of survey articles on the RDF knowledge base correction exist, but they are out-dated and did not present different configurations of these errors for various objectives. Since there is no configuration in this field, a new general configuration of the RDF knowledge base correction for a different objective is proposed here that can cover these various errors. In this configuration, a new classification of the errors is presented in which they are divided into three classes. The correction of each class is performed in a separate step. Finally, the state-of-the-art approach of each step is identified for each objective and a different configuration of these methods will be proposed for various objectives.

Current research on quantitative pain measurement using the electroencephalogram (EEG) signals showed a promising result just on classifying pain from no-pain states. In this paper, we go one step further introducing pain-level dependent EEG features as well as proposing a physiologically-inspired hierarchical classifier to provide promising results for differentiating five classes of pain. In this research, forty four subjects were voluntarily enrolled, each of whom executed the Cold-Pressor Test (CPT), while their EEGs were simultaneously recorded. We filtered the EEGs through the alpha band and elicited meaningful features to reveal the behavior of signals in terms of distribution, spectrum and complexity at each pain state. To assess the susceptibility of the features in classifying one/group of classes against others, Kruscall-Walis test was applied to give a clue in order to construct the structure of our decision tree, where a Bayesian Optimized support vector machine (BSVM) was trained at each node. After arranging the tree, sequential forward selection (SFS) was applied to select a customized subset of features for each node. Our results provide 93.33% accuracy for the five classes and also generate 99.8% for pain and non-pain classes, which is statistically superior (P