International Journal of Mathematics and Computers in Simulation

  
E-ISSN: 1998-0159
Volume 15, 2021

Notice: As of 2014 and for the forthcoming years, the publication frequency/periodicity of NAUN Journals is adapted to the 'continuously updated' model. What this means is that instead of being separated into issues, new papers will be added on a continuous basis, allowing a more regular flow and shorter publication times. The papers will appear in reverse order, therefore the most recent one will be on top.

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Volume 15, 2021


Title of the Paper: Mathematical Tools to Understand the Field Theories of the Standard Model and Beyond

 

Authors: Philippe Durand

Pages: 54-61

DOI: 10.46300/9102.2021.15.10     XML

Abstract: Since Isaac Newton the understanding of the physical world is more and more complex. The Euclidean space of three dimensions , independent of time is replaced in Enstein’s vision by the Lorentzian space-time at first, then by four dimensions manifold to unify space and matter. String theorists add to space more dimensions to make their theory consistent. Complex topological invariants which characterize different kind of spaces are developed. Space is discretized at the quantum scale in the loop quantum gravity theory. A non-commutative and spectral geometry is defined from the theory of operator algebra by Alain Connes. In this review, our goal is to enumerate different approaches implementing algebra and topology in order to understand the standard model of particles and beyond


Title of the Paper: Energy Efficiency of LVDC Supplies Including PV Sources

 

Authors: Anis Ammous, Abdulrahman Alahdal, Kaiçar Ammous

Pages: 46-53

DOI: 10.46300/9102.2021.15.9     XML

Abstract: The Low Voltage Direct Current (LVDC) system concept has been growing in the recent times due to its characteristics and advantages like renewable energy source compatibility, more straightforward integration with storage utilities through power electronic converters and distributed loads. This paper presents the energy efficiency performances of a proposed LVDC supply concept and others classical PV chains architectures. A PV source was considered in the studied nanogrids. The notion of Relative Saved Energy (RSE) was introduced to compare the studied PV systems energy performances. The obtained results revealed that the employment of the LVDC chain supply concept is very interesting and the use of DC loads as an alternative to AC loads, when a PV power is generated locally, is more efficient. The installed PV power source in the building should be well sized regarding to the consumed power in order to register a high system RSE.


Title of the Paper: A Broadband High-Gain Printed Antenna Array using Dipole and Loop Patches for 5G Communication Systems

 

Authors: Yuanzhi Liu, Mustapha C. E. Yagoub

Pages: 42-45

DOI: 10.46300/9102.2021.15.8     XML

Abstract: A broadband and high-gain printed antenna array is presented in this paper. Its single antenna element consists of a loop and two symmetric dipole patches, making the element exhibiting broad impedance bandwidth and improved gain at the targeted frequency, which is 28 GHz, one of the 5G mm-wave band, for this design. An 8×3 antenna array fed by a microstrip line feed network was designed and simulated. With a compact size of 98×32.5 mm2, the array presents a broad -10 dB impedance bandwidth of 6.8 GHz (24.3%) and a high gain of 18 dBi at 28 GHz. Besides, the single-layered array also features low profile, simple geometry, and low cost, making it a good candidate for 5G communication systems.


Title of the Paper: m-ark-Support Vector Machine for Early Detection of Parkinson’s Disease from Speech Signals

 

Authors: Luca Parisi, Amir Zaernia, Renfei Ma, Mansour Youseffi

Pages: 34-41

DOI: 10.46300/9102.2021.15.7     XML

Abstract: Recent advances in the state-of-the-art open-source kernel functions for support vector machines (SVMs) have widened the choices of benchmark kernels for Machine Learning (ML)-based classification. However, it is still challenging to achieve margin maximisation in SVM, and further evidence is required to ensure such novel kernel functions can have translational applications with tangible impact. Noteworthily, m-arcsinh, freely available in scikit-learn, was preliminarily proven as a benchmark kernel function on 15 datasets in its seminal paper. Quantifying the benefit from leveraging this kernel in a specific application is essential to provide further evidence of its accuracy and reliability on real-life supervised ML-aided tasks. Thus, the predictive capability of SVM, including that with Lagrange multipliers for the first time coupled with m-arcsinh (m-ark-SVM with soft margin; m-arK-SVM with hard margin), is hereby assessed in aiding early detection of Parkinson’s Disease (PD) from speech data. This is important to leverage the m-arcsinh kernel ‘trick’ to maximise the margin width and, therefore, the linear separability of input speech features via automated pattern recognition. In this study, we demonstrate the accuracy and reliability of m-ark-SVM to aid early diagnosis of PD, evaluated against other gold standard kernel functions. Two benchmark datasets from the University of California-Irvine (UCI) database, pre-processed solely via min-max normalisation, were used to discriminate between speech patterns of 72 healthy subjects and 211 patients with PD. Overtraining was avoided via cross validation and the models were developed and tested in Python 3.7. The supervised model (m-ark-SVM) could detect early Parkinson’s Disease with 87.18% and 86.9% classification accuracy from the two datasets respectively (F1- scores: 85 and 86.2% correspondingly). Furthermore, the model achieved high precision (89.2% and 86.8%) and specificity (87% and 86.8%). Thus, this study validates the application of m-arcsinh to aid real-life supervised ML-based classification, in particular early diagnosis of Parkinson’s Disease from speech data.


Title of the Paper: Wind Turbine Control based on MRAS Methodology

 

Authors: A. G. Aissaoui, A. Tahour, I. Colak, N. Essounbouli, M. Abid

Pages: 27-33

DOI: 10.46300/9102.2021.15.6     XML

Abstract: The use of renewable energies has increased in these last decades. The wind energy attracts more attention of several research studies. The control of the power generated by the wind turbine is very complicated. It requires the application of new techniques of control. This paper presents an application of Model reference adaptive system (MRAS) in the control of wind turbine power. The structure of the proposed MRAS consists of Neuro fuzzy (NF) controller and an adaptive system based on sliding mode controller (SMC). The use of NF and SMC methodologies is very interest and it allows improving the performances of the system control. The NF has the advantages of expert knowledge of the fuzzy inference system and the learning capabilities of neural networks. The use of SMC gives more flexibility to the adaptive system. According to digital simulation results, the designed MRAS-NF-SMC controller provides a good dynamic behaviour, and an excellent tracking of the requested trajectory


Title of the Paper: On Symmetry, Lie Symmetry and Curved Path Particle Motion: A Case for Hydrogen

 

Authors: JM Manale

Pages: 24-26

DOI: 10.46300/9102.2021.15.5     XML

Abstract: We divert from popular practice by describing a motion of a macroscopic body, a hydrogen atom in this case, through quantum mechanics. What we realise is that a body can follow a curved path, without any external force acting on it, which is in contrast to Newtonian mechanics. To test the idea, we determine a formula for G, the universal gravitational constant.


Title of the Paper: The Notion of Stability of a Differential Equation and Delay Differential Equation Model of HIV Infection of CD4+ T-Cells

 

Authors: Normah Maan, Izaz Ullah Khan, Nor Atirah Izzah Zulkefli

Pages: 20-23

DOI: 10.46300/9102.2021.15.4     XML

Abstract: This research presents a deep insight to address the notion of stability of an epidemical model of the HIV infection of CD4+ T-Cells. Initially, the stability of an ordinary differential equation (ODE) model is studied. This is followed by studying a delay differential equation (DDE) model the HIV infection of CD4+ T-Cells. The available literature on the stability analysis of the ODE model and the DDE model of the CD4+ T-Cells shows that the stability of the models depends on the basic reproduction number “R0”. Accordingly, for the basic reproduction number R0 <1, the model is asymptotically stable, whereas, for R0 >1, the models are globally stable. This research further studies the stability of the models and address the lower possible stability limits for the infection rate of CD4+ T-Cells with virus and the reproduction rate of infectious CD4+ T-Cells, respectively. Accordingly, the results shows that the lower possible limits for the infection rate of CD4+ T-Cells with virus are 0.0000027 mm-3 and 0.000066 mm-3 for the ODE and DDE models, respectively. Again, the lower stability limits for the reproduction rate of infectious CD4+ T-Cells with virus are 12 mm3day-1 and 273.4 mm3day-1 for the ODE and DDE models, respectively. The research minutely studies the stability of the models and gives a deep insight of the stability of the ODE and DDE models of the HIV infection of CD4+ T-Cells with virus.


Title of the Paper: Numerical Investigation of Thermal Stability of Catalyst Granules with Internal Heat Generation in a Random Temperature Field

 

Authors: Igor Derevich, Daria Galdina

Pages: 14-19

DOI: 10.46300/9102.2021.15.3     XML

Abstract: Method for numerical simulation of temperature of granules with internal heat release in a medium with random temperature fluctuations it is proposed. The method utilized solution of a system of ordinary stochastic differential equations describing temperature fluctuations of surrounding and granules. Autocorrelation function of temperature fluctuations has a finite decay time. The suggested method is verified by the comparison with exact analytical results. Random temperature behavior of a granule with internal heat release qualitatively differs from the results obtained in the deterministic approach. Mean first passage time of granules temperature intersects critical temperature is estimated at different regime parameters.


Title of the Paper: New Convergence Theorems for Maximal Monotone Operators in Banach Spaces  

 

Authors: Siwaporn Saewan

Pages: 8-13

DOI: 10.46300/9102.2021.15.2     XML

Abstract: The purpose of this paper is to introduce a new hybrid iterative scheme for resolvents of maximal monotone operators in Banach spaces by using the notion of generalized f- projection. Next, we apply this result to the convex minimization and variational inequality problems in Banach spaces. The results presented in this paper improve and extend important recent results in the literature.


Title of the Paper: Web Intelligent for Forecasting Exchange Rate Currency using Clever Extraction Agent Combine with Financial Data Mining

 

Authors: Khammapun Khantanapoka

Pages: 1-7

DOI: 10.46300/9102.2021.15.1     XML

Abstract: From the current economic climate results in fluctuations of currency exchange rates in all countries. Since the most countries use USD as the reference exchange rate. The exchange rate will change from day to day so variety of factors which affect the exchange rate forecasting in the exchange rates in advance are critical to evaluate for the impact of the economic system of each country. It is important for investment decisions, exports, and profitability in the money market. It was reported on website (www) in the daily exchange rate changes. We use clever search agent (CSA) gather information from financial website generate to financial data mining. Kohonen Neural Networks is the method to determine similarity of internet documents using pattern index of financial document. And Ontology Structure of Sentence is the method to determine keyword using pattern index of financial content. Both are important components of Financial Data Mining. It is analyzed for exchange rate forecasting about USD/ Pounds. Our experimental forecast exchange rates for currency's USD / Great Britain Pounds by compare three algorithms as fallows GA, Meiosis Genetic Algorithms (MGA). This research propose new algorithm is called Dash Predator Swarm Optimization (DP2SO) which are accurate in prediction than other methods in generation of Genetic algorithm (GA) 35.83-41.52% which it depend on the accuracy of the information in each factor which are important finance dataset. It will present the future trends of exchange rate to the individual website.