Table of contents

3rd International Conference on Mathematical Methods & Computational Techniques in Science & Engineering (MMCTSE 2019)

This volume is from proceedings of the 3rd International Conference on Mathematical Methods & Computational Techniques in Science & Engineering. The main objective of the Conference was to provide a platform for researchers, engineers, academicians as well as industrial professionals from all over the world to present their research results and development activities in Mathematical Methods & Computational Techniques as well as their applications in Science & Engineering. Participants met to discuss topics such as Numerical Methods, Mathematical Models, ODEs and PDES, Linear and Multilinear Algebra, Ordinary Differential Equations, Partial Differential Equations, Numerical Behaviour of Optimization Algorithms, High Complexity Numerical Methods, Non-Linear Systems Theory, Evolutionary Computation, Linear Programming, Software Development, Heuristic Algorithms, Randomized Algorithms, Equational logic programming, Functional Languages, Parallel and Distributed Computation, Theoretical Computer Science, Coding and Information Theory, Classification Mathematical Physics, Image Compression, Complexity Theory and Neural Networks etc. The Conference was held February 23-25, 2019 in London, UK. All chapters are self-contained and peer reviewed.

The Editor

Nikolaos Bardis

This article contains list of International Scientific Committees.

All papers published in this volume of Journal of Physics: Conference Series have been peer reviewed through processes administered by the proceedings Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing.

The heat and mass transfer process in liquid desiccant dehumidification system (LDDS) is very complicated because it is affected by a combination of input parameters. In this paper, the numerical optimization method is applied to optimize the performance of LDDS depending on experiment data taken from previous study for authors. The input data includes air temperature (Ta), humidity ratio (Wa) and solution temperature (Ts) and ranged between (27 to 34.5 °C), (20.5 to 25 g/kg), and (27.5 to 38.5 °C) respectively. Whereas the responses are the rate of moisture removal (MRR) and effectiveness (ε). The statistical analysis shows that the (Wa) has the most considerable effect on MRR while the (Wa) and (Ts) have the most considerable effect on ε. The optimization analysis for MRR and ε are found to be 0.54 g/s and 0.50 with minimum desirability 0.92 and 1 respectively.

The increasing needs of clustering massive datasets and the high cost of running clustering algorithms poses difficult problems for users. In this context it is important to determine if a data set is clusterable, that is, it may be partitioned efficiently into well-differentiated groups containing similar objects. We approach data clusterability from an ultrametric-based perspective. A novel approach to determine the ultrametricity of a dataset is proposed via a special type of matrix product, which allows us to evaluate the clusterability of the dataset. Furthermore, we show that by applying our technique to a dissimilarity space will generate the sub-dominant ultrametric of the dissimilarity.

This paper studies a complex network formed as a directed graph in which nodes represent the companies traded on the NYSE or NASDAQ while directed edges represent a connectedness measure between the financial assets. The directed edge weight between any two nodes is calculated with use of the value of ΔCoVaR, one of the most popular systemic risk measures proposed by M. Brunnermeier and T. Adrian in 2011. The value of ΔCoVaR measures the relationship between any two assets and is based not only on the yields of the assets, but take into account the mutual effect of its performance. In contrast with correlation coefficient, ΔCoVaR is asymmetric. The analysis is focused on the static model of the ΔCoVaR estimation. Moreover, this paper uses statistical testing procedures to assess the significance of the findings and interpretations based on this co-risk measure. We examine the intrinsic properties and regularities of stock market analyzing the directed complex network with more than 3700 stocks as nodes which have been traded on the NYSE and NASDAQ in recent years. We connect any two stock with a directed edge if the value of the corresponding ΔCoVaR is statistically significant and its normalized value is greater than a given threshold. We discuss both out-degree and in-degree distributions and find essential vertices in the network, which represent the leading stocks. We demonstrate that the network follows the power-law distribution and behaves scale-free. Moreover, we address the problem of finding influential spreaders, i.e. companies which are more likely to spread negative shocks in a large part of the network. In this paper we use three different measures (closeness centrality, betweenness centrality, PageRank) to determine the most influential stocks in the directed market graph.

The knowledge of the structure and complexity of the food web is central for a better understanding of the ecosystem functioning. The food web analysis examines both species and energy flows among them, providing a natural basis for characterizing the ecological roles of the species and the mechanisms by which biodiversity influences the dynamics of the ecosystem. This paper, for the first time, presents the analysis of high-resolution food chain for the mixed forest ecosystem of Khvalynsky National Park.

The principle of the argument or the winding number is useful in finding the number of zeros of an analytic function in a given contour. A simple extension of this theorem yields relationships involving the locations of these zeros! The resulting equations can be solved very accurately for the zero locations, thus avoiding initial, guess values, which are required by many other techniques. Examples such as a 20th order polynomial, natural frequencies of a thin wire will be discussed.

Using advanced information technologies (IT) and Internet of things (IOT) methodologies, cloud-based control systems (CCS) emerge as natural paradigm of networked control systems (NCS) and it being under development by enforcing the concept of control as a service (CaaS). Day by day NCS methods strongly promoted for distributed closed-loop control and industrial automation systems and extremely developed to study the influence of bidirectional communication constraints. In this paper, we examine the basics of cloud-based control systems (CCS) where the controller methods and algorithms are remotely placed in cloud far from the physical system. This creates a two parts: cyber part and physical part. There are several issues arose including networking delays, data dropouts and vulnerability to attacks. Research activities into these and related directions are discussed.

As is known, there are some classes of numerical methods for solving of the initial-value problem for the Volterra integro-differential equations. Here, by comparison of the known methods have constructed the methods with the new properties which have applied to solve the initial-value problem for the ODE and for the Volterra integra-differential equations. By the construction of some relation between of these equations have established the direct connection among them which have called as the p-equivalents between the initial-value problem for ODE and for the Volterra integro-differential equations. Constructed here the stable methods with the high order of accuracy show some advantages of them. Some of them are applied to solving of the initial-value problem for the Volterra integro-differential equations. And also for the illustration of the received results here constructed have applied one of these methods to solve the model problem.

In this paper, we studied some consensus algorithms for the collective rotating motions of a team of agents, which has been widely studied in different disciplines ranging from physics, networks and engineering. Both discrete and continues consensus algorithm with processing delays are investigated. There are three motion patterns determined by the information exchange topology of systems and rotation angle of rotation matrices. The asymptotic consensus appears when 0 is an simple eigenvalue of Laplacian matrix and the rotation angle is less than the critical value, and the rotating consensus achieves when the rotation angle is equal to the critical value. At this point, all agents move on circular orbits and the relative radii of orbits are equal to the relative magnitudes of the components of a right eigenvector associated with 0 eigenvalue of the non-symmetric Laplacian matrix. Finally, all agents move along logarithmic spiral curves with a fixed center when the rotation angle is larger than the critical value.

Usually the derivation of conservation laws is analyzed using the Ostrogradsky-Gauss theorem for a fixed volume without moving. The theorem is a consequence of the application of the integration in parts at the spatial case. In reality, in mechanics and physics gas and liquid move and not only progressively, but also rotate. Discarding the term means ignoring the velocity circulation over the surface of the selected volume. When taking into account the motion of a gas, the extra-integral term is difficult to introduce into the differential equation. Therefore, to account for all components of the motion, it is proposed to use an integral formulation. Next question is the role of the discreteness of the description of the medium in the kinetic theory and the interaction of the discreteness and "continuity" of the media is investigated. The question of the relationship between the discreteness of a medium and its description with the help of continuum mechanics arises due to the fact that the distances between molecules in a rarefied gas are finite, the times between collisions are finite, but on definition under calculating derivatives on time and space we deal with infinitely small.

This paper studies delay-dependent robust stability analysis for uncertain linear systems with constant delay, time-varying delay and nonlinear perturbations. The restriction on the derivative of time-varying delay is removed, which means that the fast time-varying delays are allowed. Combined with Leibniz-Newton formula, integral inequalities, Wirtinger-based integral inequality, Peng-Park's integral inequality, utilization of zero equation and new Lyapunov-Krasovskii functional have been adopted to study. New delay-dependent robust stability criteria for uncertain time-delay systems are established in terms of linear matrix inequalities (LMIs). Numerical examples and simulation suggest that the results given to illustrate the effectiveness and improvement over some existing methods.

The formulation of a mathematical model that allows simulating the operation of chemisorption refrigeration systems that use medium and low temperature thermal sources such as waste heat and have the purpose of increasing the energy efficiency of industrial and/or residential applications, is a prerequisite to design and facilitate the use of these systems. Such model is described here and is intended to evaluate the heat transfer inside of chemisorption reactor for refrigeration system that can operate with a metallic salt (sorbent) mixed with expanded graphite and NH₃ as refrigerant fluid (adsorbate). The effectiveness of the model is validated through experimental results obtained for a SrCl₂/NH₃ pair allowing to postulate, a kinetic and thermal model that can be adapted to sorbent/adsorbate pairs of the same type operating under various conditions and thus quantify how operational variables affect the performance parameters of these systems. To this end, the impact of the cycle time and reactor radius on the specific cooling power (SCP) and the coefficient of performance (COP) is measured, concluding that these operational variables exert effects of an opposite nature on the SCP and COP of the system.

Lysine dendrimers could be used in many biomedical applications. For example these dendrimers could be used for delivery of short regulatory peptides consisting of several aminoacid residues. We investigated earlier interactions between lysine dendrigraft of 2^nd generation and molecules of KED peptide. In present paper we study interaction of lysine dendrimer and molecules of KED peptide. The system containing one dendrimer of 2^nd generation and 8 molecules of KED in water with explicit account of counterions was studied by computer simulation. The method of molecular dynamics was used for this goal. We obtained that formation of complex consisting of the dendrimer and all peptide molecules occurs during initial time (t < 40 ns) of simulation. The size, anisotropy of shape and radial density profile of stable complex (after t > 40ns) were studied also. We have shown that formation of complex occurs due to electrostatic interaction between oppositely charged dendrimer. At the same time other interactions, for example hydrogen bonds, also give their contribution to this process. Stable dendrimer-peptide complex has size close to 1,5nm and small shape anisotropy. Density of dendrimer atoms is highest in the centre of complex while density of peptides atoms has maximum at radial distance r=1nm. It total we have shown that lysine dendrimers is suitable carrier for molecules of KED peptide.

The paper deals with the problem of scaling under calculating the rate kernels in the Smoluchowski equation of binary aggregation. Compact overview and critical analysis of the practiced today approaches have been submitted. It is shown that the known methods do not take sufficient account of the role of cluster internal restructuring under self-organization phenomena and dependence of their outward aggregation activity on the prehistory of cluster formation and their fine structure. The novel considerations on these two classes of problems which can be useful for creating a more careful methodology have been submitted.

The Inverse Box-Cox (BC) transformation (see [1]) produces the Power Normal (PN) distribution family that includes the log-normal and the normal distributions, see [2], [4] and [3]. The Box-Cox power transformation aims to transform data to approximate normality (truncated normal (TN)) therefore the knowledge of the its inverse scale is of major importance. In this paper we consider the univariate PN distribution, and, because in many applications there are more than one variable and correlation we also consider the bivariate PN distribution (BPN), see [6] and [5]. We give some important results concerning both distributions. In [6] it is given a formula to approximate the ordinary moments of the PN distribution, herein, we give an exact formula for the ordinary integer moments of the PN distribution. Regarding the BPN distribution, we calculate the marginal probability density functions (pdf) and we note that they are not univariate PN distributed however the conditional pdf is PN univariate. This is also true for the corresponding functions of the transformed scale, the TN distribution. The correlation curve or curve of the conditional moments E(X₂|X₁ = x₁) that is useful to characterize the structure of the correlation is very well fitted by a power law model f(x) = ax^b + c.

A new method of equation formation for boundary layer series has been suggested. It enables to eliminate boundary condition singularity, to construct a complete asymptotics for problem solving and to substantiate it for the system of singularly perturbed elliptic equations given that Dirichlet boundary condition for one function and Neyman boundary condition for the other have been provided in the case of degenerate equation multiply root. Solution expansion was carried out on fractional degrees of a small parameter. Asymptotics contains two different types of boundary series. First type coefficients of boundary series exponentially decrease, coefficients of the second type have a different structure, a special benchmark function emerges in order to evaluate them. The boundary layer is divided into a few zones with different ways of proceeding for each zone. The constructed boundary layer series describe solution for all zones of boundary layer. The problem is solved without using splicing method.

The over the top service optimization in wireless networks is a challenging task due to the diversity of wireless terminals, variety of required quality of service requirements and diversity of wireless service access networks. In this paper, we propose an approach to optimize the allocation of users to wireless networks or even particular cells based on the wireless network exposed radio environment status. The radio cells collect the radio environment status based on terminal measurements and pre-calculated interference maps. It exposes the required information to the over the top service provider. The users are allocated applying the network/cell priority list or using a genetic optimization algorithm. The simulation results reveal that the pico-to-macro cell priority list deterministic approach outperforms macro-to-pico priority list approach, while the genetic based optimization approach slightly outperforms both deterministic approaches.

Rough set theory-based knowledge discovery systems have the good property of discovering rule sets solely based on data so that objectivity in the discovered rule sets is very good. But, sometimes this property may hinder to find better knowledge models, depending on the composition of target data sets, especially when the data sets have key-like attributes, or data sets have some attributes having poor dependency to class attributes. Key-like attributes have minutely detailed values and the correspondence between conditional attributes and class attributes is precise. This paper shows how we can surmount the problem thru experiment. Two data sets are adapted for the experiment. A data set that has attributes of key-like and poor dependency was selected. And the other data set that does not have such properties is used to present a counter case. The experiment showed very good results and we found precautions when we apply rough set-based knowledge discovery systems. In addition, the experiment confirmed the fact that adding more data for target data set may increase the accuracy of knowledge models using rough set.

A comparison of mathematical models applicable for hydrocarbons formation process by catalytic transformation of dimethyl ether is discussed in the article. Dimethyl transformation is complex catalytic process used for aliphatic, aromatic hydrocarbons and olefins formation. The process can be realized for transformation of renewable and waste organic resources into fuel range hydrocarbons. Proper mathematical modelling for this complex process can play a driver role for process productivity intensification. A mathematical models proposed by different authors are discussed and compared to self-developed Eley-Rideal based model. Advantages and models limitations are discussed in the article.

In this paper, we introduce a novel method to estimate fuzzy regression model when the center and spread have different pattern. We combine the least absolute deviations estimation for the center with discriminant analysis for spread. Also, we use the fuzzy partition to categorize spreads of the dependent variable into several classes. A numerical study is given to show that our method outperforms the estimation methods based on a fuzzy regression model with the assumption of equivalent functions for the center and spread.

Let G be a primitive strongly regular graph of order n and A its adjacency matrix. In this paper, we first associate an Euclidean Jordan algebra V to G considering the real Euclidean Jordan algebra spanned by the identity of order n and the natural powers of A. Next, by the analysis of the spectra of an Hadamard logarithmic series of V we establish new admissibility conditions on the parameters of the strongly regular graph G.